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Improving Air Quality in Asian Developing Countries

AIT Reports

Asian Regional Research Programme on Environmental Technology (ARRPET)

Improving Air Quality in Asian Developing Countries

AIT Research Activities

Final Report Phase 1: 2001-2004

By

Dr. N. T. Kim Oanh

Urban Environmental Engineering and Management Program School of Environment, Resources and Development

Asian Institute of Technology Bangkok, Thailand

Project Coordinated by Asian Institute of Technology, Bangkok, Thailand

Project Funded by Swedish International Development Cooperation Agency (Sida)

June, 2004

AIT Final Report July 2004 ii

Abstract This report presents the research activities and major findings of the 3 issues (monitoring, dispersion modeling and control technology) in phase 1 (January 2001-June 2004) of AIRPET-AIT. 1) Monitoring: (i) Monitoring for toxic organic pollutants: levels and size distributions in particle and levels in gas phase of persistent organic pollutants were monitored since early 2001. A database of levels and phase distribution of these pollutants was obtained for BMR in dry and wet seasons 2001-2002. (ii) PM monitoring: emphasis was given to the monitoring of levels and composition (ions, elements, EC/OC) of fine and coarse particles (PM2.5 and PM10-2.5) to obtain the spatial and temporal trends of the pollutants. (iii) Receptor modeling: PM composition data was used for the source apportionment using CMB, COREM and PMF receptor models. Diesel traffic, secondary sulfate and biomass/refuse burning have been identified as major sources for PM2.5 while re-suspended soil, construction activities, and secondary NaNO3 as the contributors for PM10-2.5 in BMR. (iv) Source characterization was done for rice straw open burning and diesel exhaust (in the chasis testing laboratory) which are the major sources of PM2.5 in BMR found by receptor modeling. The study is intended to provide source profiles for the receptor modeling and emission inventory for dispersion modeling. Further, the results will be the basis for development of open crop residues burning management strategies. 2) Control technology: Experiments with upward extension of diesel exhaust pipes for a diesel powered bus were conducted. Effects of the extension on the concentration of air pollutants (CO, HC, and PM10) at the sitting breathing level (1 m height) and 3 m from the traffic lane were assessed both by monitoring and modeling methods for free flow highway and the street canyon conditions. Modeling results showed that the upward extension of the exhaust pipe could reduce the maximum ambient pollutant concentrations from a passing-by bus emission by around 3 times, while monitoring gave a reduction of 1.25-3 times. 3) Dispersion modeling: (i) Photochemical smog modeling started with the analysis of relationships between hourly ozone and its precursors and meteorological conditions in Bangkok for 1996-2000. Simulations were made by two photochemical smog model systems, UAM-V/SAIMM and CHIMERE/ECMWF. Results showed O3 formation in Bangkok is more VOC sensitive than NOx sensitive. (ii) A scheme to classify the meteorological conditions governing over the Bangkok Metropolitan Region was developed using synoptic climatological model. Conditions with high ozone were identified. iii) Mixing height monitoring data (by remote sounding systems) at the Mae Moh site in Lampang province and Maptaput site in Rayong province (outside BMR) were investigated for 1 year (2001). Detail final reports were prepared for each of the 3 issues. In addition, secondary data on natural (meteorology, geography) and socio-economic conditions as well as data related to fuel consumption, major air pollution sources, current air quality management practices in Thailand was collected and analyzed. AIT coordinated and followed-up the NRIs’ project related activities. AIT has involved intensively in the promotion of the AIRPET in the region and the world through the AIRPET website and numerous presentations at regional and international events. This is believed to create impacts of the research to policy maker community.

AIT Final Report July 2004 iii

Table of Contents

Page

Title Title page i Abstract ii Table of content iii 1. Period 1 2. Research activities 1 2.1 Comprehensive assessment of air quality status 1 2.2 Issue 1: Monitoring issue 1 2.3 Issue 2: Control technology 3 2.4 Issue 3: Dispersion modeling 4 3. Publications 4 4. Training 6 5. Scientific equipment and laboratory facilities 6 6. Progress 7 7. Plans 7 8. Others 7 Appendices Appendix I Final report on monitoring issue Appendix II Final report on control technology issue Appendix III Final report on dispersion modelling issue

Final Report-AIT July 2004 1

1. PERIOD This is the final progress report of the AIT team under the AIRPET project. The report includes completed activities and findings of phase 1 (January 2001-June 2004) of the AIT-AIRPET project. 2. RESEARCH ACTIVITIES All the planned activities of phase 1 have been completed to fulfill the established research objectives. Additional activities, such as source profile study, were also performed due to the need to get more realistic research results. Main activities include i) collecting and analyzing secondary data on air pollution, meteorology and air quality management practices in Thailand, ii) conducting monitoring for toxic organic pollutants and particulate matters (PM2.5 and PM10-2.5) in the Bangkok Metropolitan Region (BMR) and source apportionment by receptor modeling, iii) upward extension of diesel exhaust for reduction of exposure to toxic emission, iv) monitoring for emission characterization of open rice straw burning and diesel buses/trucks, and v) photochemical smog modeling for BMR and meteorological data analysis and data base preparation for dispersion modeling purpose. 2.1 Comprehensive assessment of air quality status Basic information on geography, meteorology, fuel consumption in different economic sectors, current air quality status and air quality management practices (air quality standards, emission standards, strategies, etc.) of Thailand have been collected and analyzed. The past evidences of health effects were also collated. The database is included in the AIRPET country database which will be edited for publication after phase 1 is completed. 2.2 Issue 1: Monitoring issue Monitoring for particulate matters and persistent organic pollutants (POPs) was conducted in the Bangkok Metropolitan Region. A comprehensive database of PM2.5 and PM10-2.5 and composition was developed and used for PM source apportionment using receptor modeling. Detail final report on monitoring issue and source apportionment is presented in Appendix 1. 2.2.1 Particulate matter monitoring and receptor modeling Equipment acquisition and preparation work started at the beginning of the project in 2001. Monitoring activities for PM2.5 and PM10-2.5 started early in 2002 and lasted till the end of 2003. In addition, samples are being collected during the extension period to provide a continuous record. In total, 235×2 pairs of 24-hour samples for PM2.5 and PM10-2.5 have been collected from five sites in the Bangkok metropolitan region (BMR). They represent different urban environment and include: Bang Na (BNG, urban-industrial mixed), Ban Somdej (BSD, urban residential), Dindang (DDN, traffic), Bangkok University (BKU, suburban upwind) and AIT campus (suburban upwind). Two dichots were used in parallel to provide enough samples for chemical analyses for receptor modeling. The latter include 8 water-soluble ions (NH4

+, Na+, K+, Ca++, Mg++, Cl-, NO3-, SO4

=) by ion chromatography (IC), 29 elements (Al, Si, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Ga, As, Se, Br, Rb, Sr, Zr, Mo, Ag, Cd, Sn, Sb,Ba, Pb and Bi) by the proton induced x-ray emission (PIXE), and black carbon (BC) by optical reflectometer. Forty pairs of dichot samples on quartz filters from AIT and 3 pairs from DDN sites were also analyzed for organic and elemental carbons


(OC/EC) by thermal optical/reflectance (TOR) to compare the data with those by the optical method. Regression relationships between OC/EC by TOM (NIOSH) and BC by reflectometer for the samples were used to estimate the EC/OC for all ambient PM samples. The database was used for the source apportionment. The results of monitoring activities are summarized below. i) Particulate matter concentration. The obtained PM levels are high in the dry season and frequently exceeded the Thai National Ambient Air Quality Standards (NAAQS) for PM10 (24-hour average value of 120 ug/m3) and US EPA standard for PM2.5 (24-hour average value of 65 ug/m3). The levels in the wet season were lower and only at the traffic site there were cases of standard exceedance. The ratio of PM2.5 to PM10 varied from 0.57-0.67 in dry season and from 0.33-0.66 in wet season. Strong correlations existed between PM2.5 and PM10 at all sites in both seasons. ii) Particulate matter compositions. OC, EC, SO4

=, NO3-, NH4

+ are the major constituents of PM2.5. At AIT in dry season, OC contributed the highest fraction in PM2.5 ranging from a minimum of 24% (12.0 µg/m3) at BSD to a maximum of 34% (12.1 µg/m3). But, the highest average loading of OC (24.5 µg/m3) was observed at the traffic site DDN. The typical crustal elements found in abundance in PM10-2.5 are Al, Si, Ca, Ti, Mn, and Fe with the most abundant contribution ranging from 12% (3.0 µg/m3) at BSD to 18% (6.4 µg/m3) at BKU. iii) Particle source apportionment. Source apportionment by CMB and PMF was carried for all five sites in wet and dry season. Diesel traffic, secondary particles and biomass/refuse burning have been identified as major sources/contribution of PM2.5 and, re-suspended soil, construction activities, road dust and NaNO3 are the major contributors to PM10-2.5 in BMR. Industrial source appeared prominently at mixed urban Bang Na site in the PMF output but not with a large contribution. The results of PMF and CMB are comparable at the AIT site. Results of COPREM for dry season 2002 PM data are comparable to CMB results, and the results for all sets of data in dry and wet seasons are under revision for publication. Quality assurance and control (QA QC) of the data was followed through sampling, analyses and data interpretation. Inter comparison of samplers (Dichot, FRM and MinVol) for PM2.5 mass and ion balance showed dichotomous sampling gave comparable results as FRM. A standard reference material (SRM) sample (same for all NRIs of AIRPET) was analyzed by PIXE and the results are comparable with the certified and average concentrations in the SRM analyzed by different analytical methods. Ion balance was performed for each site in every season to check the data quality. Monitoring for PM is continuously conducted in 2004. About 100 pairs of PM2.5 and PM10-2.5 samples in quartz, mixed cellulose and Teflon filters were collected from January to May 2004 at AIT. Samples are measured for BC, analyzed for ions, Teflon samples were analyzed at the Desert Research Institute (DRI), US for elements by X-ray fluorescence (XRF). The results will be compared to PIXE analysis on collocated mixed cellulose filters. Additionally, Teflon PM2.5 samples from the Dindang traffic site for the period of May to December 2003 have been acquired from the pollution control department (PCD), Thailand and being analyzed for elements by XRF.


2.2.2 Monitoring for source characterization Monitoring of Open Rice Straw Burning Emission: Monitoring was done to obtain the source profile and emission factors of open rice straw burning in Thailand. In total, 3 field samplings and 3 laboratory samplings were conducted. Samples were collected using dichots (PM10-2.5 and PM2.5), HiVol (PAH, pesticides/PCB in the gas and particle phases), personal pumps and sampling bags-adsorbent tubes (VOC) and charcoal tubes (BTX) or sepak (formaldehyde) were collected during late 2003 to February 2004. Samples are being chemically characterized for the inorganic (ions, EC/OC and elements in the particle samples) and organic compositions (VOC, BTX, PAHs, PCBs/pesticides, formaldehyde) for the emission factors (for laboratory samples) and source profiles (for field samples). Extensive sampling for ambient air at AIT with dichot samplers in parallel with field survey for paddy field burning (questionnaires to farmers) was conducted for 11 days in March 2004. The obtained data is used to revise the source profile for biomass burning for receptor modeling. Monitoring for Diesel Engine Emission Characterization: Samples were collected from chasis testing of diesel vehicles in BMR and are analyzed to obtain emission factors and source profiles. Particle samples (30 samples) on mixed cellulose and quartz filter (70 mm Φ) are analyzed for mass, EC and ions. Thirty gas samples on charcoal (400/200 mg and 100/50 mg) and XAD-2 tubes are analyzed for BTX and PAHs. The results will be used to update diesel source profile for receptor modeling. 2.2.3 Monitoring of persistent organic pollutants Levels and phase distributions of Persistent organic pollutants (POPs) in suspended particulate matters were also studied. POPs such as PAHs, PCBs and pesticides on particle and gas phase were monitored in both wet and dry seasons since March 2001. The level and phase distribution of airborne PCB, pesticides, and PAHs in the Bangkok urban area has been investigated at 5 sites: Bangkok University, Dingdeng, Bangplee (industrial site), Bangna, and AIT. Airborne organic compound samples were taken by both active and passive sampling methods. The active sampling uses an Anderson-type low volume cascade sampler which was modified by adding a PUF adsorbent tube to trap gas phase of the compounds, and a Hi-Vol PUF sampler. The samples from the active samplers were prepared and analyzed by GC-ECD, GC-MS and HPLC for respective compounds following USEPA Compendium Methods TO-10A (for pesticides and PCB) and Method TO13 (for PAHs). Semi-permeable membrane device (SPMD) was used as the passive samplers and analytical procedure was according to the SPMD guide from the website. Totally, above 40 particulate and 40 gas-phase samples were collected and analyzed by the active method. In parallel, 10 weekly SPMD samples (gas phase) were collected and results of the two methods were compared. More compounds were found in samples by the active sampling method while the passive sampling method gave higher concentrations but detected fewer compounds. Details are presented in Appendix I: Part 2 of the monitoring issue final report. 2.3 Issue 2: Control technology The exhaust pipe of an diesel-powered 6–wheel air conditioned bus was extended from the original level at 0.52 m height to the bus roof level, around 4 m. Effects of the extension on the street level of air pollutants (CO, HC, PM10) at the sitting breathing level (1 m height) and 3 m from the traffic lane were assessed both by monitoring and modeling methods. Two


dispersion conditions were studied, the free flow highway and the street canyon, at various bus passing speeds (20, 40, 60 and 80 km/h). Monitoring results showed that the upward extension of the exhaust pipe could reduce the maximum ambient pollutant concentrations from the passing bus emission by around 1.25-3 times. The model results also showed that the extension of exhaust pipe reduced the maximum ambient air pollution concentration by a factor of 3. The cost of the extension varies from US$ 35 to 50 for a simple external installation for a bus. A limited survey study showed a high acceptance rate of the technique by stakeholders though some subsidy may be required to promote its wide application. Detail on control technology activity by AIT is given in Appendix II. 2.4 Issue 3: Dispersion modeling The dispersion modeling activities at AIT consist of 3 main parts: i) photochemical smog modeling for Bangkok metropolitan region, ii) development of synoptic climatological models for prediction of air pollution, and iii) estimation of mixing heights. An in depth analysis of relationships between hourly ozone and its precursors, and meteorological conditions in Bangkok was made for 5 years (1996-2000). The simulation studies using two photochemical smog model systems, UAM-V/SAIMM and CHIMERE/ECMWF, showed O3 formation in Bangkok to be more VOC sensitive than NOx sensitive. To attain the Thailand ambient air quality standard for 1-hr O3 of 100ppb, VOC emission in BMR should be reduced by 50% - 60%. Management strategies considered in the scenario study consist of Stage I, Stage II vapor control, replacement of 2-stroke by 4-stroke motorcycles, 100% CNG bus, 100% NG-fired power plants, and replacement of MTBE by ethanol as additive for gasoline. (ii) Synoptic climatological model: A scheme to classify the meteorological conditions governing over the Bangkok Metropolitan Region was developed using which producing six distinct synoptic categories which exhibited significant relationships with the O3 monitoring data of 9 years (1992-2000) over high ozone months (November-May). Statistical models were developed to predict O3 for each synoptic category over BMR based on the 0700 LST meteorological conditions which showed satisfactory results. (iii) Mixing height estimation: monitoring data and model estimation for mixing height were compared at 2 sites, the MaeMoh site in Lampang province and Maptaput site in Rayong province for 1 year (2001). Other modeling activities include a back calculation of emission factors for on-the-road vehicles by a street canyon model, OSPM, with the inputs as monitoring results for BTX, NOx and CO on both sides of a busy road in Bangkok. More monitoring data are necessary to produce realistic emission factors. Detail on dispersion modeling activity by AIT is given in Appendix III. 3. PUBLICATIONS a) List of papers published/accepted for publication 1. Kim Oanh, N. T. and Zhang, B-N (2004). Photochemical smog modeling for air quality

management of Bangkok Metropolitan Region. J. Air & Waste Management Association. (In press).

2. Kim Oanh, N.T., Zhang, B.-N., (2003). Impacts of Different Air Quality Management Strategies on Photochemical Pollution in Bangkok, Thailand. Asian society for Environmental Protection (ASEP) Newsletter, Vol. 19, No. 2, June 2003.

3. Kim Oanh, N. T. and Prapat, P. (2003). Upward extended exhaust pipe for diesel-powered buses and associated changes in maximum concentrations at street level. Asian Journal of Energy and Environment, Volume 4, Issues 1-2, March-June 2003.


4. Kim Oanh, N. T. and Prapat, P. (2002). Upward Extended Exhaust Pipe of Diesel Powered vehicles and Its Effects on Pollutants Concentration at Street Level. Asian Society for Environmental Protection (ASEP) Newsletter, volume 18, no.4, June 2002.

4. Zhang, B-N. and Kim Oanh, N. T. (2002). Photochemical smog pollution in the Bangkok Metropolitan Area in relation to O3 precursor concentrations and meteorological conditions. Atmospheric Environment, 36: 4211-4222.

b) List of papers in manuscript form (communicated) 1. Zhang B-N., Vautax, R. and Kim Oanh, N. T. Comparative study of two photochemical

smog modeling systems for a tropical city. J. Air & Waste Management Association in 2004. (In review).

2. Kim Oanh, N. T., Chutimon, P., Supat, W. and Ekbordin, P. (2004). Meteorological classification and synoptic climatological approach to predict episode potential in a mountain-valley area. Journal of Atmospheric Environment, 2004. (In review).

c) List of papers in manuscript form (in preparation) 1. Kim Oanh, N. T., Liu S., Simpson, C., Raja B., and Danutawat, T. (2004).

Characterization of emission from rice straw open burning in Thailand. To be submitted to the journal of Environmental Science and Technolology.

2. Kim Oanh, N. T., Martel, M., Vanisa, S., and Ruwim, M. (2004). Evaluation of Pollutant Levels in a Selected Busy Street Canyon in Bangkok, Thailand. To be submitted to the journal of Atmospheric Environment.

3. Kim Oanh N. T. and Ekbordin W. Synoptic climatological model for prediction of photochemical smog potential in Bangkok Metropolitan region. To be submitted to the Atmospheric Environment.

4. Kim Oanh, N. T., Opal, P. and Nabin, U. (2004). Comparative study of PM mass and ionic composition monitoring methods. To be submitted to the J. Air & Waste Management Association.

5. Kim Oanh, N. T., Nabin, U., Paisarn, K., and Opal, P. (2004). CMB8 modeling for the source apportionment of fine and coarse PM fractions in Bangkok Metropolitan Region. To be submitted to the J. Air & Waste Management Association.

6. Kim Oanh, N. T., Prapat, P., Nabin, U., Paisarn, K. and Wahlin, P. (2004). A comparative study of receptor models applied for source apportionment of PM2.5 and PM10-2.5. To be submitted to the Journal of atmospheric environment.

7. Kim Oanh, N. T. and Duong Van Minh. A study on mixing height variation and mixing height modeling in Thailand. To be submitted to the Journal of Atmospheric Environment.

d) List of conference presentations

1. Kim Oanh, N.T., Chongrak, P. and Nabin, U. (2004). Improving Air Quality in Asian

Developing Countries (AIRPET). Presented in coordination meeting of regional programs and initiatives on air quality management (AQM) in Asia on 16 June 2004 organized by CAI-Asia, Bangkok, Thailand.

2. Kim Oanh, N.T. and Prapat, P. (2004). Preliminary findings of the Asian Air Pollution Research Network: PM levels, composition and source apportionment in the Bangkok Metropolitan Region. Presented at National Workshop on Stakeholders’ involvement in air quality management, cooperation between UN and Bangkok Metropolitan


Administration, 19-20 April, 2004, Bangkok, Thailand. 3. Kim Oanh, N.T., Nabin, U. and Wahlin, P. (2003). Receptor modeling for the source

apportionment of PM in Bangkok Metropolitan Region. Presented in BAQ03 in Manila, 17-19 December 2003.P

4. Prapat P., Kim Oanh, N. T. (2003). Estimation of air masses long-range transportation to Bangkok by using trajectory model. Presented in BAQ03 in Manila, 17-19 December 2003.

5. Kim Oanh, N. T., Nabin, U., Paisarn, K. and Wahlin, P. (2003). Source apportionment for PM2.5 and PM10-2.5 in the Bangkok Metropolitan Region in dry season. Proceedings of A&WMA annual meeting, Sandiego, June 24-27, 2003.

6. Kim Oanh, N. T. (2003). Receptor modeling for PM2.5 and PM10-2.5 in BMR during dry season. Paper presented in the A&WMA annual meeting, San Diego, USA, March 22-26, 2003.

7. Kim Oanh, N. T. and Zhang, B-N. (2002). Photochemical smog pollution in BMR and simulation results. Proceedings of A&WMA annual meeting, Baltimore, June 24-28, 2002.

8. Prapat, P. and Kim Oanh, N. T. (2002). Upward Extended Discharge Height of Diesel Exhaust and Its Effects on Pollutants Concentration at Street Level. Poster presentation at the 95th A&WMA Annual Conference & Exhibition, Baltimore, June 24-28, 2002.

9. Kim Oanh, N. T. (2002). Regional air pollution monitoring network (AIRPET). Presented in the Better Air Quality Conference in Hongkong, 14-18 Dec. 2002.

10. Kim Oanh, N. T. (2001). Status and Perspectives of the Asian Regional Air Pollution Monitoring Network. Proceedings of the international information exchange workshop organized by USEPA and Taiwan EPA, 5-7 Sept. 2001.

e) List of arranged national, regional and international workshops AIT organized and coordinated to organize the following workshops: 1. First methodology workshop of AIRPET, organized in Bandung, 28-30 December 2001. 2. Second AIRPET project workshop was organized in Chennai, India, 4-5 October 2002. 3. Third AIRPET project workshop was organized at Hanoi, Vietnam, January 8-10, 2003. 4. An AIRPET national result dissemination workshop was organized in Beijing, China in

April 1-2, 2004. 5. An AIRPET national result dissemination workshop was organized in Chennai, India in

March 22, 2004. 6. An AIRPET national result dissemination workshop was organized in Bandung,

Indonesia in April 12, 2004. 7. An AIRPET national result dissemination workshop was organized in Manila, Philippine

in April 21, 2004. 8. An AIRPET national result dissemination workshop was organized in Hanoi Vietnam, in

March 24-25, 2004. Other activities arranged within the programme Mutual scientists’ visits 1. Mr. Prapat P. from AIT visited India NRI’s result dissemination national workshop in

Chennai in March 22, 2004. 2. Mr. Nabin U. from AIT visited Philippines NRI’s result dissemination national workshop


in Manila in April 21, 2004. Dissemination of research findings 1. With discussion to NRIs, AIT prepared the guidelines for disseminating results of

AIRPET phase 1 based on which NRIs produced brochures in English and national language(s) and distributed in their national workshops.

2. Mr. Prapat Pongkiatkul was interviewed on ‘effect of upward extended discharge height of diesel exhaust’ by local Bangkok TV channel and was aired in the program ‘Thai People Today’ on 8 August 2002.

4. TRAINING

AIT recruited research staffs and technicians who subsequently got training through the project activities. Students involved in research on air pollution got technical training on monitoring and analyses of air samples through AIRPET project. Project staffs at AIT also attended the following training session: • Ms. Do Thanh Canh, RA at AIT got training on active and passive sampling for organic

pollutants and analytical methods. • 1 AIT staff got training on IC analysis, and 1 staff got training on PM monitoring for

receptor modeling. • Mr. Prapat P., RA (AIT) attended a seminar-cum-training on automotive air pollution

control strategies at pollution control department, Bangkok in January 2002 which was conducted USEPA.

• Mr. Ekbordin W., RA at AIT, got training on Air pollution modeling and inspection of particle control devices at Pollution Control Department, Bangkok in February/March 2003.

• Mr. Nabin U., RA at AIT, got training on Inspection of procedures and safety, and gas control devices at Pollution Control Department, Bangkok in March 2003.

5. SCIENTIFIC EQUIPMENT AND LABORATORY FACILITIES

Following are the list of equipment/laboratory items purchased by AIT during this period: (Brief description, equipment acquired during the report period) Equipment (>50,000 SEK): A cascade impactor was purchased ($ 6000.00) for particulate matter monitoring. Minor equipment (<50,000 SEK): Stack sampler repairing, ion chromatograph (IC) repairing (columns and loops), personal pumps, calibration kits, XRF/PIXE analyses of PM samples, office equipment (computer, monitor, printer, fax, etc.), office furniture, chemicals/filters, glassware for the laboratory use, books/Literature, tedlar bags, optical reflectometer, analytical costs


6. PROGRESS The progress of the phase 1 was satisfactory. We have completed all the planned activities as scheduled. Some activities are additional from the planned due to the need to get full and more realistic results of the current status of the air quality in the study area. We also took samples in Hanoi, Vietnam and made the analysis for pesticides, PCB and PAHs. In the extension period the monitoring was continued in order to produce a long term record of the data. Updating PM database with the EC/OC by NIOSH method enables us to have a better input for receptor modeling.

7. PLANS

In phase 2, AIT has plan to further update the source characterization for diesel emission and rice straw burning. Necessary equipment (fore example, Nano-MOUDI) will be purchased for monitoring of fine particles. Regional transport of air pollutants will be studied using modeling. Interaction of air pollution and climate will be included. We will extend cooperation with other regional research projects working in the field of air pollution. Phase 2 will witness more scientific findings and continuous interaction and result dissemination to policy makers.

8. OTHERS AIT, as a coordinating institution, has responsibilities of coordinating project activities with NRIs. AIT has edited the country database and will prepare and analyze the regional data for the publication. AIT coordinated and followed-up with the NRIs’ activities including organization of AIRPET regional and, coordination of NRIs national workshops, progress and final reports. Three AIRPET regional workshops (first: 27-28 December 2001, Bandung; second: 3-5 October 2002, Chennai, India; and third: 8-10 January 2004, Hanoi) were organized. Five national workshops were organized at Beijing (China NRI, April 1-2, 2004), Chennai (India NRI, March 22, 2004), Bandung (Indonesia NRI, April 12, 2004), Manila (Philippine NRI, April 21, 2004) and Hanoi (Vietnam NRI, March 24-25, 2004) in the phase 1 of the project. AIT prepared the AIRPET overview presentation for the NRIs to present at their national workshops in April 2004. AIT also sent representatives to attend 2 NRI workshops. AIT has involved intensively in the promotion of the AIRPET in the region and the world through the AIRPET website, CAI-Asia, and numerous presentations at regional and international events. AIT continuously updates the AIRPET website; more than 1300 visitors have accessed the website till the end of June 2004.This all is believed to create impacts of the research to policy maker community. Some mutual visits by scientists/experts were made to share and discuss the experiences on project activities. During the expert meeting at ADB in Manila, May 2002, Dr. Kim Oanh and Dr. Puji made a visit to the Philippines NRI lab and sampling sites. Dr. Kim Oanh has attended and gave inputs to several methodology workshops of the VN NRI in Hanoi. In October 2003, Mr. K. Jagannathan and Mr. K. RamaKrishna, India NRI’s projects associates, spent 3 days at AIT to learn receptor modeling. In March 2003, Mr. Dam Duy An from Vietnam NRI spent 1 week at AIT as a visiting scientist to work with the AIT team on receptor and dispersion modeling. In May 2004, Mr. Hoang Anh Le from Vietnam spent 1


week at AIT to get training on BC measurements to update PM database and application of PMF modeling. Four AIT students, Mr. Prapat P. and Ms. Zhang B. N. in 2001; and Mr. Ekbordin W. and Mr. Nabin U. in 2002, involved in the project activities, got the international student awards for the best student papers in the STUDENT PAPER COMPETITION organized by the West Coast Section of the A&WMA. Mr. Prapat P.’s interview on ‘effect of upward extended discharge height of diesel exhaust’ with local Bangkok TV channel was aired in the program ‘Thai People Today’ on 8 August 2002.

Details of Research Activities by AIT Appendix I


Improving Air Quality in Bangkok Metropolitan Region, Thailand AIT Report

Final Report for Monitoring Issue

Phase 1: 2001-2004

Prepared by

Dr. N. T. Kim Oanh

Asian Institute of Technology Environmental Engineering Program

School of Environment, Resources and Development PO Box 4, Klong Luang, Pathumthani 12120

Thailand

June 2004

Monitoring Report-AIT July 2004

ii

Abstract

Monitoring of particulate matters (fine and coarse PM) and persistent organic compounds (POPs) were conducted in Bangkok Metropolitan Region (BMR) as part of a research project on improving air quality in Asian developing countries namely AIRPET (2001-2003). Results of PM samples collected in dry and wet seasons during March 2002 to August 2003 have been reported. Sampling has also been conducted from January 2004 to May 2004 and the results are being processed. Two collocated dichotomous samplers were used to collect in total of 242×2 pairs of 24-hour fine (PM2.5) and coarse (PM10-2.5) particle samples from five locations of the city. Final analyses were based on 235×2 pairs of samples. Samples on mixed cellulose ester and quartz fiber filters were collected from Bang Na (urban industrial), Ban Somdej (urban residential), Dindang (traffic), Bangkok University (suburban) and AIT campus (semi urban) situated about 20-40 km distances from each other. Mixed cellulose filters were used to evaluate gravimetric mass and analyzed for elements by proton-induced x-ray emission (PIXE) and for black carbon by smoke stain reflectometer. Quartz samples were analyzed for ionic compositions by ion chromatography. Forty quartz samples from AIT and 3 from Dindang were analyzed for OC, EC by thermal optical methods. Mass and chemical concentrations were used in receptor models to identify the major sources and estimate their contributions to ambient particle levels. Quality assurance (QA) of the data was ensured through sampling, analyses and data interpretation. Intersampler comparison (Dichot, FRM and MinVol) for PM2.5 mass and ion balance showed dichotomous sampling as a reliable method. PIXE results were compared with the average standard reference material (SRM) results analyzed by different analytical methods; ion balance was performed to see the data quality from ion analyses. The average PM10 and PM2.5 in dry season were much higher than in wet season exceeding Thai National Ambient Air Quality Standards (NAAQS) for PM10 (24-hour average value of 120 ug/m3) and US EPA standard for PM2.5 (24-hour average value of 65 ug/m3) in many days. In both seasons, traffic site was found to be highly polluted. The ratio of PM2.5 to PM10 varied from 0.57-0.67 in dry season and from 0.33-0.66 in wet season. Besides, strong correlations existed between PM2.5 and PM10 in all sites, except Bangkok University in dry season, indicating that a fluctuation in PM10 is largely driven by PM2.5. In all three sites OC, EC and secondary sulfates in PM2.5, and crustal elements, OC, nitrate, Na+ and Cl- in PM10-2.5 were the major chemical species. The source apportionment by CMB, PMF and COPREM models shows that (NH4)2SO4, diesel and biomass/refuse burning are major sources of PM2.5. Re-suspended soil, biomass/refuse burning, construction and NaNO3 are the major sources of PM10-2.5. Persistent Organic Pollutants such as PAHs, PCBs and pesticides on particle and gas phase were monitored in wet and dry seasons during March-September in 2001. Levels and phase distributions of POPs in suspended particulate matters were also studied.


iii

Table of Content

Page

Title Cover page i Abstract ii Table of content iii Part 1: Monitoring and receptor modelling for particulate matters 1. Introduction 1 1.1 Background 1 1.2 Statement of problems 1 1.3 Objectives 2 1.4 Scope 2 2. Methodology 2 2.1 Study area 2 2.2 Sampling and analysis 4 2.3 Quality assurance/quality control 5 2.4 Receptor modeling 5 2.4.1 Chemical mass balance 5

2.4.2 Positive matrix factorization 6 2.5 Data used for source apportionment 7 5. Results and discussions 7 3.1 Concentrations and chemical compositions 7 3.2 Quality assurance and quality control 11 3.2.1 Sampler performance 11 3.2.2 Analysis of standard reference material 11 3.2.3 Internal consistency test 12 3.3 Receptor modeling 16 3.3.1 Results of CMB 16 3.3.2 Results of PMF 19 3.3.3 Discussion on CMB and PMF results 24 4. Conclusions and recommendations 25 5. References 25 6. Appendix 27


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Part 2: Monitoring for persistent organic pollutants 1. Introduction 40 1.1 Background 40 1.2 Statement of problems 40 1.3 Objectives 40 1.4 Scope 40 2. Methodology 41 2.1 Study area/sampling and analytical methods 41 3. Results and discussions 41 3.1 Size distribution of SPM in Bangkok air 41 3.2 Levels and phase distribution of airborne PCB in Bangkok air 41 3.3 Levels and phase distribution of airborne organochlorine pesticides in Bangkok air 42 4. Conclusions 42 5. References 42 6. Appendix 43


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Part 1: Monitoring and receptor modelling for particulate matters 1. Introduction 1.1 Background Urban sprawl with development and population growth in Asian developing countries in general and in Bangkok Metropolitan Region (BMR, currently ~ 10 millions) in particular in the recent decades have resulted in adverse air quality in the city that include elevated levels of atmospheric particulate matters. Measured concentrations of PM10 (particles with aerodynamic diameter ≤ 10 µm) in Bangkok for many years have often exceeded the Thai National Ambient Air Quality Standards (NAAQS) of 120 µg/m3 for 24-hour average and 50 µg/m3 for the annual average values (Supat, 1999a). Historical measurements of PM2.5 (particles with aerodynamic diameters ≤ 2.5 µm) in Bangkok is absent; however, some sparse studies show that about 50% of PM10 in Bangkok consists of PM2.5 which, in busy parts of the city, have been recorded as high as 100 µg/m3—much higher than US EPA 24-hour PM2.5 standard of 65 µg/m3 (Ostro et al., 1999). Ostro et al. (1999) have also shown that there exist statistically significant relationships between PM levels and health outcomes in Bangkok. The main reported anthropogenic PM sources in Bangkok are vehicular traffic, industries and the construction activities. In the year 2000, 4.5 million licensed vehicles travelled on 4076 km of traffic roads in Bangkok. The vehicle population in Bangkok soared by 113% from 1991 to 2000 (UNEP, 2001). The emission inventory in Bangkok in 1997 (in metric tons) was 19,672 of PM, 177,724 of NOX, 254,696 of CO and 179,936 of VOC (PCD, 2000). It was reported that 60% by weight of total suspended particles in Bangkok was respirable PM10 particles (Supat, 1999b). 1.2 Statement of problems High frequencies of exceeding the National Ambient Air Quality Standard (NAAQS) for PM in many populated areas of Bangkok are of concerns from both public health and environmental viewpoints. Adequate information is required on different physical/chemical characteristics and contributing sources of particulate matter to work out abatement policy for this ubiquitous harmful substance. In the absence of detail and up to date source emission inventory, it is difficult to focus on the sources to enforce their emission reductions for the better air quality management. Hence, this study has been designed to generate the information on particulate air pollution of BMR namely fine (PM2.5) and coarse (PM10-2.5) fractions of PM10 in different seasons of the year. The chemical composition data sets are then used in the receptor modelling to identify the major PM contributing sources for the air quality management in the absence of reliable emission inventories.


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1.3 Objectives The primary objectives of this particulate monitoring program in the Bangkok Metropolitan Region are: 1) To assess the levels of fine (PM2.5) and coarse (PM10-2.5) fractions of PM in different

seasons in the BMR. 2) To make chemical characterization of fine and coarse PM10 for chemical composition

evaluation. 3) To use receptor modeling to identify and quantify the major contributing sources to fine and

coarse fractions of PM. 1.4 Scope This study covers the monitoring of particulate matter (PM) in Bangkok Metropolitan Region (BMR). Fine and coarse fractions of PM10 were collected from five monitoring sites—Bang Na (BNG, urban mixed), Ban Somdej (BSD, urban residential), Dindang (DDN, traffic), Bangkok University (BKU, sub urban), and AIT (rural/suburban). The study focussed on the levels and spatial variation of fine and coarse PM10 in dry and wet seasons from March 2002 to August 2003. The mass concentrations and chemical analytical data of PM were used for such receptor models as chemical mass balance (CMB), constrained physical receptor model (COPREM), and positive matrix factorization (PMF) to estimate the contributions to fine and coarse fraction particles from the major sources in BMR. 2. Methodology 2.1 Study area The Bangkok City (latitude 13o44’ N, longitude 100o34’ E, 2 m above mean sea level, Fig. 1), located on the flat alluvial basin of Chaopraya river which enters the Gulf of Thailand about 40 km in the south, covers an area of 1565 km2. Bangkok and its five surrounding provinces (Samutprakarn, Nonthaburi, Pathumthani, Nakorn Pathom and Samut Sakorn) constitute the Bangkok Metropolitan Region (BMR) with a total area of 7,724 km2 and a population about 10 millions. They are closely related in terms of traffic and industrial activities. Bangkok has a tropical monsoon climate with two main seasons, wet or rainy and dry. The wet season extends from 16 May to 15 October during which mostly westerly to southerly winds prevail from the Gulf of Thailand and the Andaman Sea to Bangkok. The dry season can be classified into two periods. The first period (16 October-15 February) is characterized by a mild weather of the winter monsoon and is known as local winter. The second period (16 February-15 May) is known as local summer and is extremely hot (Ostro et al., 1999). During the dry season, the southerly sea breeze in the coastal area counteracts the northeasterly monsoon resulting in the low wind conditions over the city. This, in turn, reduces the mixing and enhances the pollutant levels in the ambient air (Zhang and Kim Oanh, 2002). The predominant northerly to northeasterly winds in the dry season also bring polluted air across the Asian Continent to Bangkok. The surface wind analysis of Bangkok shows the high percentage of calm wind conditions ranging from 30% in March to 61% in October (Zhang and Kim Oanh, 2002).


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Meteorological statistics from 1990-1999 show the annual average temperature of Bangkok 25-33 oC, average wind velocity 1.3 m/s and high relative humidity year-round (UNEP, 2001).

Figure 1. Bangkok Metropolitan Region showing sampling sites of AIRPET research program (map of Thailand in the inset). The selected sites are located at the existing air quality monitoring stations in BMR (Figure 1). The fifth site, AIT, is in the weather station of Asian Institute of Technology which is located in the rural area in the northern part of BMR. Bangkok University (BKU, Rangsit Campus), nearly 300m west of the Paholiyothin highway, is a suburb location about 40 km North of Bangkok. There are houses, roadways and farmlands surrounding the station and some industries (cosmetic and textile industries in the southeast) within 5 km radius, and international airport and railway station about 15 km in the south. Dindang (DDN) is the traffic station adjacent to the curb side (within 5m) of heavily travelled road in the middle of the city. Ban Somdej College (BSD) is located in the busy part of the city and is classified as the urban residential area. It is 300 m away from the main road and mixed types of houses surround the area within 5 km radius with high population density. There are some industries viz. metal, cement, rubber, etc. within 5-7 km in the south and southeast. Bang Na (BNG) station close to Samutprakarn industrial estate is located in the southern part of Bangkok. It is a mixed area, 200m east of the main road, with many industries and residential houses within 5 km radius. The types of industries include iron and steel, motor parts and electric goods, plastics and packaging, food processing, garments, etc. that are located in the south and southwest of the station. BSD and BNG are close to sea.

N


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2.2 Sampling and analysis Two collocated dichotomous samplers were used to collect 24-hour PM2.5 and PM10-2.5 samples on two types of 37 mm diameter filters: 1.2 µm pore size (Advantec, no. 91110308) quartz fiber filter and, 0.8 µm pore size (Advantec Mfs. Inc., no. 419IMA) mixed cellulose ester filter. Filters were conditioned for at least 24-hours in a controlled room (temperature 23±3 oC, relative humidity 40±5%) and weighed by a microbalance before pre and post sampling. The number of samples and sampling schedules for the whole study period are summarized in Table 1. Table 1. Summary of particulate (PM2.5 and PM10-2.5) sampling schemes in BMR. Sites Period Samples (pairs) Samples for analysis (pairs) Dry Season 2002 BNG

21 March-14 May

48×2

46×2

BSD 5-18 March 13×2 10×2 BKU 4-18 March 15×2 15×2 Wet Season 2002 BSD@

14 July-30 Sep

27×2

27×2

DDN@ 14 July-30 Sep 27×2 27×2 Dry Season 2002-2003 DDN

20 Dec-14 Jan

15×2

14×2

AIT 28 Jan-19 April 45×2 44×2 Wet Season 2003 BNG

10-24 June

10×2

10×2

BKU 4-13 July 10×2 10×2 AIT 17 July-30 August 32×2 32×2 Note: @ sampling every third day Mixed cellulose filters were used to evaluate gravimetric mass (except in dry season 2002 sampling when quartz fiber filters were used for mass measurement) and analyzed for 29 elements (Al, Si, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Ga, As, Se, Br, Rb, Sr, Zr, Mo, Ag, Cd, Sn, Sb,Ba, Pb and Bi) by proton-induced x-ray emission (PIXE). However, only 21 elements were detected in 2002 dry season Bang Na samples (Ti, Zr, Ga, Mo, Ag, Cd, Sn and Bi were below detection limit). PIXE analyses were conducted at National Environmental Research Institute (NERI), Denmark. The detection limit for the elements by PIXE are in the range of 2-20 ng/m3 for elements with 13>Z>20, and 0.1-1 ng/m3 for elements with Z>20, where Z is the atomic number of the elements. Organic and elemental carbons (OC/EC) on 1/4th of quartz filters for 40 pairs of samples at AIT and 3 pairs at DDN were analyzed by thermal optical method (TMO) following NIOSH protocol at Sunset laboratory, Desert Research Institute (DRI), USA. Parallel samples on mixed cellulose were measured for black carbon (BC) before sending to PIXE analysis. The regression relationships between OC/EC vs. BC for AIT samples were extended to calculate OC/EC along with their analytical uncertainties for all other sites for which BC on mixed cellulose filters had been measured with the optical reflectometer at AIT. One-half of each quartz samples were extracted in deionized distilled water (DDW) and analyzed for 8 water-soluble ions (NH4

+, Na+, K+, Ca++, Mg++, Cl-, NO3-, SO4

=) using ion chromatography (IC)


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at the pollution control department (PCD) laboratory, Bangkok. NH4+ was not analyzed in dry

season 2002 Bang Na samples. Details of analytical schemes are shown in Figure A1 (Annex 1). 2.3 Quality assurance and quality control (QA/QC) QA/QC of the monitoring program and monitored data were practiced to assure the data quality. Filters were prepared and weighed in standard conditions before transporting to the sites for PM collection. Various levels of data validation as parts of data quality checks followed in this study are summarized as follows. Samples when there was equipment malfunction, electric power failures were discarded. The samplers were periodically calibrated with standard flowmeter to maintain the desired flow rates to collect the particles of given size ranges. Internal consistency for dichot samplers were checked by parallel sampling with the standard federal reference samplers (PM2.5). Ion balance was also carried for PM2.5 samples by dichot and federal samplers. Other internal consistency checks for dichot samples were carried using sum of measured mass vs. sum of chemical species analyzed, chemical consistency (Sulfate vs. total S, soluble K+ vs. total K, soluble Cl- vs. total Cl), and cation vs. anion balance. Standard reference material (SRM) test by PIXE method which was used for elemental analysis in PM samples was performed to check the accuracy of PIXE analytical method. 2.4 Receptor modeling 2.4.1 Chemical mass balance model Chemical mass balance (CMB8) is a receptor model that has been widely used in the source apportionment studies of particulate matter air pollution. The basic of the receptor models is that the original receptor concentrations can adequately be explained by a linear combination of contributions from different sources with fixed compositions such that

kjk

ikij fax ∑≅ Eq. 1

where xij is the measured ambient concentration of the pollutant i in the sample j. The constant source profiles, aik (ith species mass fraction, ug/ug, from the kth source), from a number of major sources in the study area are used from the literature and/or from the studies in the area based on the local air pollution problems and the emission sources. The model solves Eq 1 to find the source strength fkj (by an iterative method to stagnant minimum values of Chi2 (χ2), which is the squared distance between the measured and predicted values.

∑∑∑

∑

=

+

−=

j ip

kkjax

kkjikij

f

fax

ikij1

222

2

2)(

σσχ Eq. 2


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CMB uses the ambient measurements and source profiles with the uncertainties associated in both (Eq 2) by a method known as effective variance least-squares (Henry, 1984). Chemical compositions with measurement uncertainties of particulate pollution at the receptor levels are the input to CMB model and it presumes source profiles are known and fixed. CMB outputs are examined with the performance indices such as χ2, R2 and the percent of mass accounted for along with TSTAT (for individual source), ratios C/M and R/U for individual fitting species. The values are considered good fit if they meet the following criteria: mass explained, 100±20%; R2, 0.80-1.0; χ2<4; T-statistic (TSTAT), >2; ratio calculated/measured (C/M), 0.5-2.0 and ratio residuals/uncertainties (R/U), -2.0 to +2.0 (Watson et al., 1997). The source profiles used in this study relied on the profiles from literature (Radiant International, 1988) study in BMR, measurements within this project and professional experience gained from past works in this field. However, the major source profiles were from US EPA profile library and literature, modifications were made using constrained physical receptor model (COPREM) (Wahlin, 2003). The ambient data set of dry season 2002 study was used in COPREM to test and modify the source profiles. COPREM is more flexible in that source profiles can be adjusted by changing elements (1=free/0=fixed) in the form matrix until we get best-fit model results. The profiles tested by COPREM were then applied to CMB model. All together 12 source profiles were used with different combinations for fine and coarse fraction particles in different sampling sites and seasons. Biomass burning (BIOMAS), and diesel traffic (DIESEL) source profiles were from the US EPA library in which the major components OC/EC were modified by AIT using the real values of OC/EC analyzed on samples collected from rice straw burning and diesel engine emissions. Oil fuel burning (OIL), Lead-rich source (LEAD), Zinc-rich source (ZINC), Tin-rich source (TIN), Sea profiles (SEAS) are from JICA (1991) study in Bangkok with some modifications by COPREM model. Re-suspended soil (SOIL-PMF) and Construction activities (CONST-PMF) are the source profiles obtained by PMF model on dry season AIT data. Three secondary source profiles used in CMB modeling are (NH4)2SO4, NaNO3 and NH4NO3 (Table A1, Annex 1). 2.4.2 Positive matrix factorization Positive matrix factorization (PMF) is an approach to factor analysis (Paatero, 1997). It has recently got wide application in the source apportionment of PM in many air pollution studies. We used PMF2 version in this study. PMF solves Eq 1 without prior knowledge of aik but with constraints on positive factors. PMF provides only one of an infinite number of possible solution to Eq 1, that is, only one of possible many combinations of the “a” and “f” matrices (Henry, 1997). PMF solution is the one that minimizes an object function, Q(E), based upon the value of each observation and its corresponding uncertainty. The solutions are constrained to be nonnegative through the use of a penalty function (Paatero, 1997). The function is specifically expressed as


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2

1 1

1)( ∑∑∑

= =

=

−

≡n

i

m

j ij

p

kkjikij

u

faxEQ Eq 3

Where uij is an error/uncertainty estimates for the matrix of ith species in jth sample. Details and application of PMF have been described in many publications (e.g. Paatero, 1997). The number of factors/sources as resolved by PMF should be examined by the statistical indices such as: • The sum of squares of the residual weighted inversely the variation of the data points, Q(E); • The maximum individual column mean (IM) and the maximum column standard deviation

(IS) of the residual matrix; • The largest number in the rotational matrix (LR), and • Sum of the mass fractions of the derived features. 2.5 Data used for source apportionment The ambient measurements of PM2.5 and PM10-2.5 mass and chemical compositions at five monitoring sites were used as inputs for source-receptor analysis. In all, 242×2 pairs of quartz and mixed cellulose filter samples were collected during the whole period. Final results on chemical data analyses and model analyses were based only on 235×2 pairs of valid particulate samples (those with sampling period of 24±1.5 hrs, not damaged during and before handling and analyses). Total 19 elements (Al, Si, S, Cl, K, Ca, Va, Mn, Fe, Ni, Cu, Zn, As, Se, Br, Rb, Sr, Sn, and Pb), 8 water-soluble ions (NH4

+, Na+, K+, Ca++, Mg++, Cl-, NO3-, SO4

=) and OC, EC were used in the compositional and source apportionment analyses. NH4

+ was not analyzed and Ti, Zr, Ga, Mo, Ag, Cd, Sn and Bi were not detected in 2002 dry season BNG samples. CMB source apportionment results presented in this paper are for site wise average PM2.5 and PM10-2.5 data. PMF was used for the fine and coarse particle data of Bang Na and AIT sites. 3. Results and discussion 3.1 Concentrations and chemical compositions A summary of the particle mass concentrations (PM2.5, PM10-2.5 and PM10) by site and season over the sampling period is presented in Table 2. The fine and coarse fraction masses were summed up to determine PM10 mass. The results show that the PM levels are significantly high in the dry seasons than in the wet seasons. The lower PM level in the wet season is obviously the effects of washout by rain. Strong correlations existed between PM2.5 and PM10 at all sites in both dry and rainy seasons except BKU in dry season. At BKU in the dry season, correlation was weak (R2=0.45) due to some outlying values of PM10 in that high contributions of PM10-2.5 most probably from the road cleaning and construction activities near the samplers. The arithmetic average PM2.5-to-PM10 ratios were higher (>60%) in the dry season than in the wet season (<50%, except in traffic site). However, it is not clearly understood why the ratio PM2.5/PM10 is low in the wet season. Yet it is conclusive that PM2.5 is dominant in Bangkok ambient particles and any fluctuation in PM10 is mainly driven by the change in PM2.5 level.


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Dry season compositions. The most abundant chemical components of PM2.5 and PM10-2.5 in this study are shown in Figure 2 and Table A2-a, Annex 1. OC, EC, SO4

=, NO3-, NH4+ (measured at

DDN and AIT only), Na+ and K+ are the major constituents of PM2.5. OC contained the highest fraction in PM2.5 ranging a minimum of 24% (12.0 ug/m3) at BSD to a maximum of 34% (12.1 ug/m3) at AIT in dry season. But, the highest average loading of OC (24.5 ug/m3) was observed at the traffic site DDN. EC follows OC in the fine fraction of PM at all sites with more or less uniform composition in the range of 6-8%. Average minimum and maximum EC in the dry season are 2 ug/m3 at BNG and 5.7 ug/m3 at DDN, respectively. Both OC and EC are the major components of PM2.5 which results from the incomplete combustion of fossil fuels in the urban atmosphere and from biomass/refuse burning in the background and rural environment. Secondary SO4

= was the second highest ranging in average 9% (7.4 ug/m3) of PM2.5 at DDN to 17% (4.2 ug/m3) at BNG. NO3

- was not a significant component of PM2.5 in the dry season which had a range of 1% (0.2 ug/m3) at BNG to 4% (1.5 ug/m3) at AIT. NH4

+ analyzed in dry season DDN and AIT samples was also low with 2% (1.7 ug/m3) at DDN to 4% (1.5 ug/m3) at AIT. Na+, K+, Cl-, crustal elements and the trace and combustion generated “others” group in the fine fraction were more or less uniform (each comprising of about 1-2% of PM2.5) across the sampling sites. The averages of trace and combustion generated elements in “others” group were in the range of 0.4% at DDN to 1.6% at BKU. The typical elements found in the fine fraction and produced by the combustion process are Mg2+, Va, Ni, Cu, Zn, Cr, Ga, As, Se, Br, Rb, Sr, Zr, Mo, Ag, Cd, Sn, Sb,Ba, Pb and Bi which are depicted as “others” in the tables. The typical crustal elements as found in abundances in PM10-2.5 are Al, Si, Ca, Ti, Mn, and Fe. The most abundant species in the coarse fraction particles were crustal elements ranging from 12% (3.0 ug/m3) at BSD to 18% (6.4 ug/m3) at BKU. The highest fraction of crustal components in BKU PM10-2.5 is as expected as the road repairing and construction activities were ongoing within 100m from the sampling station. OC was another single important component in the coarse PM with minimum average value of 0.76 ug/m3 (4.4%) at BNG to maximum of 6.5 ug/m3 (15%) at DDN. Similarly, sulfate (3% at DDN and 8% at BSD) and nitrate particles (4% at DDN and 8% at AIT) and Na+, Cl- and K+ (total of 3% at DDN to 12% at AIT) were the other major constituents of PM10-2.5. High concentrations of Na+ and Cl- at near sea sites BNG (Na+ and Cl-

: 4% each) and BSD(Na+: 4% and Cl-: 3%) are most probably influence of sea salt while high percentage of Na+ and Cl- (5 and 6% respectively) at the remote site AIT may be attributed to the burning of biomass and agro-waste. “Others” group in the coarse fraction has a less contribution, 1% in average, at all sites.


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Table 2. Summary of dichotomous particulate matter measurements (Avg±std and range) in µg/m3 in BMR in the years 2002-2003. Stations PM2.5 PM10-2.5 PM10 PM2.5/PM10 Av±std range Av±std range Av±std range *Slope **R2 Dry season AIT 35±22 14-111 21±9 10-50 56±30 30-161 0.63 0.98 Bangkok University 48±13 36-81 36±14 16-63 85±19 54-124 0.57 0.45 Dindaeng 86±28 45-146 35±10 19-53 120±38 68-199 0.71 0.97 Ban Somdej 51±24 26-107 25±14 14-38 75±16 49-146 0.67 0.99 Bang Na 25±13 5-68 17±5 8-29 43±16 17-92 0.59 0.95 Wet season AIT 4±2 1-10 8±3 2-16 12±5 4-23 0.33 0.79 Bangkok University 6±3 2-11 7±2 5-9 13±5 6-21 0.46 0.96 Dindaeng 48±15 24-/88 25±8 11-41 73±19 38-113 0.66 0.80 Ban Somdej 14±7 5-34 12±4 2-26 26±9 9-50 0.54 0.86 Bang Na 8±2 5-12 12±3 6-16 20±5 11-28 0.40 0.85 Note: * average arithmetic mean of PM2.5/PM10; ** from linear regression between PM2.5 and PM10. Wet Season compositions. Major chemical compositions of fine and coarse fractions of PM10 in the wet season are presented in Figure 2 and Table A2-b, Annex 1. Though the major constituents of fine and coarse fraction particles are the same in both dry and wet seasons, OC, EC and SO4

= in PM2.5, and crustal material, OC in PM10-2.5 comprised of significantly higher percentage mass at all sites in the wet season. OC ranged 4.6 ug/m3 (78.5%) at BKU to 13.2 ug/m3 (93.7%) at BSD; EC 19.2% (1.1 ug/m3) at BKU to 3.2 ug/m3 (6.6%) at DDN. However, there might be high uncertainties in the mass and its components concentrations arising from gravimetric and analytical measurements especially in the wet season samples when the PM loadings are relatively small (<15ug/m3). The highest average loadings of EC (3.2 ug/m3) were observed at BSD and DDN and showed a strong spatial variations across the five sampling sites. SO4

= in PM2.5 ranged from the lowest 6% (3 ug/m3) at DDN to the highest 41% (2.4 ug/m3) at AIT. High relative humidity (>75%) at coastal areas influences the aqueous-phase SO4

= chemistry yielding higher secondary sulfate concentrations (Kim et al., 2000). It may be speculated that higher SO4

= at near-sea stations (29% at BNG, 20% at BSD) is due to aqueous-phase SO4

= chemistry and in the down wind stations (41% at BKU, 39% at AIT) is due to gas-phase SO4

= chemistry showing strong spatial variations in PM2.5. NH4+ in PM2.5 ranged from a

minimum 1% (0.47 ug/m3) at DDN to a maximum of 11% (0.6 ug/m3) at BKU. All Na+, K+, crustal and “others” were high in the near-sea stations and down wind stations—BKU and AIT.


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a. PM composition in dry season

0

5

10

15

20

25

30

35

40

45

50

BNG BSD DDN BKU AIT BNG BSD DDN BKU AITPM2.5 PM10-2.5

Con

cent

ratio

n, u

g/m

3OthersK+Cl-Na+SO4=NO3-NH4+ECOCCrustal

b. PM composition in wet season

0

5

10

15

20

25

BNG BSD DDN BKU AIT BNG BSD DDN BKU AITPM2.5 PM10-2.5

Conc

entra

tion,

ug/

m3

OthersK+Cl-Na+SO4=NO3-NH4+ECOCCrustal

Figure 2. Average composition data for dichot PM2.5 and PM10-2.5 samples at five sampling sites in BMR during a) dry season, and b) wet season 2002-2003. (NH4

+ was not analyzed in dry season BNG, BSD and BKU samples). Concentrations of crustal components in the wet season were more or less uniform (16% at DDN to 40% at BKU) across all sites except BKU where crustal components had 29% of PM10-2.5. However, DDN had the highest loading (4.32 ug/m3) of crustal material. Though less in terms of mass concentrations, OC and EC had the significant fractions in wet season PM10-2.5. OC ranged 1.1 ug/m3 at BKU to 5.0 ug/m3 at DDN; EC ranged 0.2 ug/m3 at BKU and AIT to 1.0 ug/m3 at DDN. NO3

- and SO4= were also significant in the coarse fractions which comprised of

3% (DDN) to 9% (BKU) and 4% (AIT) to 10% (BSD), respectively. In the coarse fraction, Na+


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and Cl- were high in the near-sea stations as expected from the sea salt, and at BKU and AIT probably part due to burning of biomass/agro-waste, local soil and part due to sea salt from upwind side. 3.2 Quality assurance and quality control Data quality of the valid particulate samples was assured through testing of sampler performance; PIXE analytical method by analyzing standard reference material (SRM) and through internal consistency tests of the analyzed data. Brief discussion on achieving quality data for the particulate analyses follows below. 3.2.1 Sampler performance PM2.5 samples collected in July-September 2002 by collocated MinVol, Dichot and Federal Reference Method (FRM) were used for intersampler comparison in terms of mass and ion balance. Dichot sampler resembled closely to FRM with the study average mass difference of less than 17% whereas MinVol sampler collected as high as 70% of PM2.5 mass compared to FRM. Ion balance was conducted to check the data quality obtained from the three methods. The ion concentrations were expressed in terms of µeq/m3 (1 µeq of a substance is the amount of the substance which would react with or replace 1 µg of hydrogen) to enable testing of the ion charge balance. Table 1 shows the averages and ranges of cation-to-anion ratios by different PM2.5 samplers. High fluctuation observed in MinVol samples is apparently the reflection of its low performance.

Table 1. Ion balance ratio (ranges in µeq/m3) for different PM2.5 samplers. Σcation/Σanion Methods Dindang Bansomdej FRM 1.009 (0.749-1.148) 1.020 (0.967-1.146) Dichot 1.007 (0.921-1.128) 0.966 (0.749-1.276) MinVol 1.054 (0.686-1.750) 1.186 (0.581-2.481)

Regression plots were conducted for cation sums (Na+, NH4

+, K+, Mg2+ and Ca2+) vs. anion sums (Cl-, NO3

- and SO42-) for intersampler comparison between FRM and dichot and, FRM and

MinVol, respectively (Figures A2-a, A2-b, Annex-1). Among three methods, FRM and dichot samplers’ ion charge balances are moderately to well in agreement (FRM: R2=0.86-0.99; dichot: R2=0.80-0.94). They indicate that most of the points are located on or close to the diagonal 1:1 lines and cations are associated with anions. Poor correlations are observed in MinVol samples (R2<0.51). 3.2.2 Analysis of standard reference material SRM courtesy from China NRI was analyzed by PIXE and the results were compared with the certified and reference values by other methods (Table A3-a, Annex-1). As, Cu, Pb, V and Ba concentrations agreed more than 80% with the certified contents. Be was not detected by PIXE, Cd was overestimated by PIXE, and Cr was underestimated with a high standard deviation.


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Major oxides contents of SRM provided in the certificate were calculated for equivalent elemental weights and compared with the metals calculated from the PIXE results (Table A3-b, Annex-1). Si, Fe, Al, Ca, Ti and K by PIXE were in agreement with the equivalent elements of certified contents in the range of -30% to 4%. Fe by PIXE included both Fe (II) and Fe (III), K measured by PIXE was more than 100% of certified value. 3.2.3 Internal consistency test Following internal consistency tests were applied to validate the analyzed chemical data in this study: 1) sum of chemical species vs. measured PM mass, 2) physical consistency (e.g. sulfate vs. sulfur, chloride vs. chlorine and soluble potassium vs. total potassium), and 3) anion and cation balance. • Mass closure. One way to evaluate the chemical consistency of the data is the mass closure

or the difference between the gravimetric PM mass and the sum of the analyzed chemical species. The mass closure should be ≤100 percent. The sum of chemical species includes the elements quantified on mixed cellulose filter by PIXE and ions on quartz fiber filter by IC. Soluble Ca2+, Cl- and K+, and S are excluded from the sum to avoid double counting since Ca, SO4

=, Cl and K were included in the sum. In average, about 70% of fine mass and 50% of coarse mass were quantified by the chemical analyses in the dry season samples across sampling sites. In the wet season, about 200% of fine mass 73% of coarse mass were quantified by chemical species. Figure 3a show mass closure between measured PM vs. sum of chemical species at BNG (dry) and BSD (wet). Higher uncertainties in the mass closure values were observed in the wet season probably due to the lower PM concentrations. Unexplained parts of the measured mass include metal oxides in the crustal material, hydrogen and oxygen associated with organic carbon, organic fractions, water and unknowns. In all sites, the data points followed a consistent pattern.

Figure 3a is a scatter plot of the fine and coarse PM10 sum of species vs. mass at Bang Na (dry season) and Ban Somdej (wet season) sites respectively. At both sites, fine PM are chemically well quantified than the corresponding coarse particle samples. The regression relationships show that 60-70% of fine PM and 35-40% of coarse PM were explained by the chemical species measured at the two sites.

a. Dry season: calculated vs. measured PM2.5

y = 1.9125x - 5.1103R2 = 0.7197

0

50

100

150

0 50 100 150

Calculated PM2.5 (ug/m3)

Mea

sure

d PM

2.5

(ug/

m3)

b. Dry season: calculated vs. measured PM10-2.5

y = 2.0103x + 1.351R2 = 0.7687

0

20

40

60

80

0 20 40 60 80

Calculated PM10-2.5 (ug/m3)

Mea

sure

d PM

10-

2.5

(ug/

m3)


13

a. Wet season: calculated vs.

measured PM2.5

y = 1.6707x - 9.3452R2 = 0.3825

020406080

100

0 20 40 60 80 100

Calculated PM2.5 (ug/m3)

Mea

sure

d PM

2.5

(ug/

m3)

b. Wet season: calculated vs. measured PM10-2.5

y = 1.7931x - 1.9501R2 = 0.7329

01020304050

0 10 20 30 40 50

Calculated PM10-2.5 (ug/m3)

Mea

sure

d PM

10-

2.5

(ug/

m3)

Figure 3a. Scatter plots of sum of species vs. mass measurements at Bang Na (dry season) and Ban Somdej (wet season) in 2002.

• Physical consistency 1) SO4

= vs. total S. Figure 3b shows the correlation between SO4= by IC and S by PIXE. Poor

to strong correlations (R2=0.26-0.88 for fine, 0.00-0.66 for coarse PM in dry season; R2=0.06-0.81 for fine, and 0.00-0.64 for coarse in wet season) were observed between water soluble SO4

= ions and total S. Arithmetic average SO4=/S ranged from 2.80-4.41 for fine and

5.28-10.01 for coarse PM in dry season; 3.29-4.11 for fine and 3.18-6.22 for coarse PM in wet season. 2) Soluble K+ vs. total K. Figure 3c shows the correlation between water soluble K+ by IC and total K by PIXE. Poor to strong correlations (R2=0.04-0.94 for fine and 0.00-0.85 for coarse PM in dry season; R2=0.00-0.65 for fine and 0.00-0.23 for coarse PM in wet season) were observed between them. Arithmetic average K/K+ ratio ranged from 0.74 to 1.13 for fine and 0.85-1.57 for coarse PM in dry season; 0.52-1.08 for fine and 0.70-1.10 for coarse PM in wet season. 3) Soluble Cl- vs. total Cl. Figure 3d shows the correlation between water soluble Cl- by IC and total Cl by PIXE. Poor to strong correlations (R2=0.00-0.36 for fine and 0.04-0.93 for coarse PM in dry season; R2=0.00-0.66 for fine and 0.06-0.88 for coarse PM in wet season) were observed between them. Arithmetic average Cl/Cl- ratio ranged from 1.40 to 13.50 for fine and 1.29-6.39 for coarse PM in dry season; 2.29-6.54 for fine and 1.70-2.91 for coarse PM in wet season. The ratio tends to close to unity in some coarse fractions. 4) Cation vs. anion balance. Figure 3e shows the correlation between cation to anion sums. Poor to strong correlations (R2=0.20-0.81 for fine and 0.02-0.84 for coarse PM in dry season; R2=0.00-0.94 for fine and 0.01-0.86 for coarse PM in wet season) were observed between them. Arithmetic average cation sum/anion sum ratios ranged from 0.39 to 1.09 for fine and 0.92-2.52 for coarse PM in dry season; 0.96-1.81 for fine and 1.26-3.26 for coarse PM in wet season.


14

a. BNG-Fine: dry seasony = 0.3017x + 0.1748R2 = 0.8849n = 46

0

1

2

3

4

0 3 6 9 12SO4= (ug/m3)

S (u

g/m

3)

b. BNG-Coars: dry season

y = 0.0793x + 0.0949R2 = 0.1097n = 46

0

1

1

0 1 2 3SO4= (ug/m3)

S (u

g/m

3)

a. BSD-Fine: wet season

y = 0.0434x + 0.5595R2 = 0.0571n = 27

0

1

2

0 2 4 6SO4= (ug/m3)

S (u

g/m

3)

b. BSD-Coars: wet season

y = 0.014x + 0.2113R2 = 0.0072n = 27

0

1

0 1 2 3SO4= (ug/m3)

S (u

g/m

3)

Figure 3b. Scatter plots of SO4

= vs. S at Bang Na (dry season) and Ban Somdej (wet season) in 2002. SO4

= was measured by IC and S by PIXE.

a. Bang Na-Fine:dry season

y = 0.9703x - 0.0179R2 = 0.6477n = 46

0

1

2

0 1 2K+ (IC)

K (P

IXE)

b. Bang Na-Coars:dry season

y = 0.2809x + 0.1461R2 = 0.1646n = 46

0.00

0.25

0.50

0.00 0.25 0.50 0.75 1.00K+ (IC)

K (P

IXE)

a. DDN-Fine:wet season

y = 0.0275x + 0.8105R2 = 0.0006n = 27

0.0

0.5

1.0

1.5

0.0 0.5 1.0 1.5 2.0K+ (IC)

K (P

IXE)

b. DDN-Coars:wet season

y = 0.3406x + 0.156R2 = 0.1519n = 27

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00K+ (IC)

K (P

IXE)

Figure 3c. Scatter plots of soluble K+ vs. total K at Bang Na in dry season and Dindang in wet season 2002. K+ was measured by IC and K by PIXE.


15


y = 0.0303x + 0.0412R2 = 0.007n = 46

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00Cl-, ug/m3 (IC)

Cl,

ug/m

3 (P

IXE)

b. Bang Na-Coars:dry seasony = 0.8718x - 0.0681R2 = 0.9338n = 46

0

1

2

3

0 1 2 3Cl-, ug/m3 (IC)

Cl,

ug/m

3 (P

IXE)


y = 0.1465x + 0.0636R2 = 0.1636n = 27

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00Cl-, ug/m3 (IC)

Cl,

ug/m

3 (P

IXE)

b. DDN-Coars:wet seasony = 0.6716x + 0.0674R2 = 0.7522n = 27

0

1

2

3

0 1 2 3Cl-, ug/m3 (IC)

Cl,

ug/m

3 (P

IXE)

Figure 3d. Scatter plots of soluble Cl- vs. total Cl at Bang Na in dry season and Dindang in wet season 2002. Cl- was measured by IC and Cl by PIXE. Graphs plotted by excel program.


y = 1.4525x + 0.0432R2 = 0.2153n = 46

0.000.050.100.150.200.25

0.00 0.05 0.10 0.15 0.20 0.25

Cation equivalent (ueq/m3)

Anio

n eq

uiva

lent

(u

eq/m

3)

b. Bang Na-Coars:dry season

y = 0.6079x + 0.0223R2 = 0.4232n = 46

0.00

0.05

0.10

0.15

0.00 0.05 0.10 0.15 0.20 0.25Cation equivalent (ueq/m3)

Anio

n eq

uiva

lent

(u

eq/m

3)


y = 0.9948x + 0.002R2 = 0.9352n = 27

0.00

0.05

0.10

0.15

0.20


Anio

n eq

uiva

lent

(u

eq/m

3)

b. DDN-Coars:wet season

y = 0.8614x - 0.0151R2 = 0.8373n = 27

0.00

0.05

0.10

0.15

0.20


Anio

n eq

uiva

lent

(u

eq/m

3)

Figure 3e. Scatter plots of cation sum vs. anion sum at Bang Na in dry season and Dindang in wet season 2002. Cation sum included at Bang Na, and NH4

+, Na+, K+, Mg2+ , Ca2+ at

Dindang and anion sum included Cl-, NO3- and SO4

= at both sites.


16

3.3 Receptor modeling 3.3.1 Results of CMB Dry season CMB results. CMB model results on average PM2.5 and PM10-2.5 mass and chemical concentrations in the dry season at five sampling sites are presented in Table A4-a (Annex 1) and Figure 4. In most sites Chi2, R2 and TSTAT values were all within the target range for fine fraction particles as mentioned in methodology section. But, the mass explained was <60% for fine PM and 92-107% for the coarse PM. Of the CMB calculated results, major contributors are: NH4SO4 (26-38%), DIESEL (30-43%), BIOMAS (15-28%), SOIL-PMF (4-8%), NH4NO3 (1-7%) and OIL (1-4%). The four major sources NH4SO4, DIESEL, BIOMAS and SOIL accounted for, in average, about 90% of fine PM in Bangkok. Some minor sources such as ZINC, TIN and CONS-PMF contributed <3% of PM2.5 mass. For the coarse fraction, SOIL (41-44%), CONS-PMF (15-25%) and NaNO3 (5-15%) were the major sources accounting over 92% of model calculated PM10-2.5 masses. SEAS, OIL, NH4SO4, DIESEL, and ZINC were other minor sources contributing about 8% of calculated coarse mass. Wet season CMB results. CMB model results on average PM2.5 and PM10-2.5 mass and chemical concentrations in the wet season at five sampling sites are presented in Table A4-b (Annex 1) and Figure 5. In all sites the Chi2, R2 and TSTAT values were within the target ranges for both fine and coarse fraction particles. But, the percent mass explained by CMB for PM2.5 were above the range at BNG (136%), BKU (154%) and AIT (181%), below the range at DDN (39%) and within the range at BSD (99%). Explained PM10-2.5 mass were in the range of 120% at BSD to 212% at DDN. If measured mass is very low (<5 to 10 ug/m3), percent mass explained by CMB may be outside 100±20% because the precision of the mass measurement is on the order of 1 to 2 ug/m3 (Watson et al., 1997). DDN average fine particle concentration (48 ug/m3) was significantly higher than those in other sites (4-14 ug/m3). Similarly, the average coarse faction particle was the highest of 25 ug/m3 at DDN and ranged 7-12 ug/m3 at other four sites. This may be the reason that the calculated mass in most sites are outside the range.


17

a. PM2.5-dry season

38

30

16

27 26

3428

43

3430

15

2326

2328

7 8 6 7 44 2 1 3 317 6

37

0 0 0 0 11 1 1 2 00

10

20

30

40

50

60

BNG BSD DDN BKU AIT

Per

cent

con

tribu

tion

NH4SO4 DIESEL BIOMAS SOIL-PMF OIL NH4NO3 ZINC CONS-PMF

b. PM10-2.5-: dry season

42 4144 44 42

23 23

1217

2118 17

25 25

1510

13

58

15

4 2 0 1 02 1 1 1 11 1 1 2 10 1

11

1 3

0

10

20

30

40

50

60

BNG BSD DDN BKU AIT

Per

cent

con

tribu

tion

SOIL-PMF BIOMAS CONS-PMF NaNO3 SEAS OIL NH4SO4 DIESEL

Figure 4. Average CMB source contribution to a) PM2.5 and b) PM10-2.5 in Bangkok in dry season. The sampling was conducted in the dry seasons of 2002-2003.


18

a. PM2.5-wet season

2318 16

26 2420

29

38

19 21

3229 30

33

27

11 117 6

16

5 3 3 4 43 4 27

43 2 1 3 12 4 2 2 31 1 0 1 0

0

10

20

30

40

50

BNG BSD DDN BKU AIT

Per

cent

con

tribu

tion

NH4SO4 DIESEL BIOMAS SOIL-PMF OILNH4NO3 ZINC CONS-PMF TIN

b. PM10-2.5-: wet season

48

3437 37

42

21

30

20 18

26

1821

3134

17

6 63 6

9

2 3 2 031 1 1 1 12 2 1 2 21 3 5

2 11 0 0 1 00

10

20

30

40

50

60

BNG BSD DDN BKU AIT

Perc

ent c

ontri

butio

n

SOIL-PMF BIOMAS CONS-PMF NaNO3 SEASOIL NH4SO4 DIESEL ZINC

Figure 5. Average CMB source contribution to a) PM2.5 and b) PM10-2.5 in Bangkok in the wet season. The sampling was conducted in the wet seasons of 2002-2003.


19

3.3.2 Results of PMF Positive matrix factorization method was applied for the dry season PM samples from Bang Na and AIT sites. Since the number of samples required for PMF is large (>40) other sites were not included for PMF source identification. In Bang Na, PMF was run for a total of 56 samples (46 dry season samples and 10 wet season samples). In PMF model, we considered the optimum results for both fine and coarse fractions at Bang Na to have 5 factors and at AIT 7 factors for fine PM and 6 factors for coarse PM. The model results were compared with CMB results. For fine tuning of the results, a user-defined parameter, FPEAKs, from 0.5 to 1.5 were used to rotate the data to find the optimal PMF solution with the most physically reasonable results. Different FPEAK values and different number of sources were used until optimal solutions were obtained. PMF in our case was run the default robust mode. • Derived source profiles from the PMF. Figures 6a and 6b show the source profiles for Bang

Na and 7a and 7b show the profiles for AIT fine and coarse fractions derived from the PMF. The profiles were identified as follows: i. diesel traffic- EC; ii. Biomass burning- OC, K; iii. Secondary sulfate or sulfate rich (XSO4)- SO4

=; iv. Secondary nitrate (XNO3) - NO3-; v. soil-

Si, Ca, Al, Mn, Fe; vi. Fuel oil- Ni, V; vii. Marine profiles- Na and Cl; viii. Construction- Ca, Al, Si, BC; ix. Zinc-rich- Fe, Zn, Pb, and x. Lead rich- Pb; xi. Industry- Cu, Sr. AIT fine PM showed the mix of NH4+ and Na+ rich profiles which could not be clearly explained; and in some cases secondary SO4= and NO3- did not separate in the profiles.

• Source contribution estimates (SCE). The source contributions to fine and coarse fractions of

PM10 predicted by the PMF are shown in the Figure 8. Very good correlations were obtained between measured and modeled PM mass at AIT with R2=0.95 and 0.92 for fine and coarse PM respectively. Fair correlations between measured and observed PM were observed at BNG with R2=0.77 for fine and 0.54 for coarse PM.


20

Diesel traff ic

0.0010.010.1

1

OC EC

NO

3-

SO4

Na+ Al Si C

l K Ca Ti V Mn Fe Ni

Cu

Zn As Se Br Sr Zr Sn Pb

Oil burning

0.0010.010.1

1

OC EC

NO

3-

SO42

-

Na+ Al Si C


Cu


SO4= rich

0.0010.010.1

1

OC EC

NO

3-

SO4

Na+ Al Si C


Cu


NaNO3

0.0010.010.1

1

OC EC

NO

3-

SO4

Na+ Al Si C


Cu


Biomass burning

0.0010.010.1

1

OC EC

NO

3-

SO4

Na+ Al Si C


Cu


Figure 6a. Fine particulate matter source profiles in mass fraction derived from PMF (FPEAK=0.5) at Bang Na site. There were 5 factors that best explained the fine PM mass.


21

Construction

0.0010.010.1

1

OC EC

NO

3-

SO4

Na+

Mg+ Al Si C


Cu

Zn As Se Br Rb Sr Zr Sn Pb

Sea salt

0.0010.010.1

1

OC EC

NO

3-SO

42-

Na+

Mg+ Al Si C


Cu


Soil

0.0010.010.1

1

OC EC

NO

3-

SO4

Na+

Mg+ Al Si C


Cu


Industries

0.0010.010.1

1

OC EC

NO

3-

SO4

Na+

Mg+ Al Si C


Cu


Secondary

0.0010.010.1

1

OC EC

NO

3-

SO4

Na+

Mg+ Al Si C


Cu


Figure 6b. Coarse particulate matter source profiles in mass fraction derived from PMF (FPEAK=0.7) at Bang Na site. There were 5 factors that best explained the coarse mass.


22

Biomass burning

0.0010.010.1

1

OC EC

NH

4-

NO

3-

SO4

Na+ Al Si C


Cu

Zn As Br Sr Zr Sn Pb

SO4= rich

0.0010.010.1

1

OC EC

NH

4-

NO

3-

SO42

-

Na+ Al Si C


Cu


NH4+ and Na+ rich

0.0010.010.1

1

OC EC

NH

4-

NO

3-

SO4

Na+ Al Si C


Cu


Zinc rich

0.0010.010.1

1

OC EC

NH

4-

NO

3-

SO4

Na+ Al Si C


Cu


Diesel traff ic

0.0010.010.1

1

OC EC

NH

4-

NO

3-

SO4

Na+ Al Si C


Cu


Lead rich

0.0010.010.1

1

OC EC

NH

4-

NO

3-

SO4

Na+ Al Si C


Cu


NH4NO3

0.0010.010.1

1

OC EC

NH

4-

NO

3-

SO4

Na+ Al Si C


Cu


Figure 7a. PMF source profiles in mass fraction for fine particles at AIT (FPEAK=1.5). There were 7 factors that best explained the fine PM mass.


23

Construction

0.0010.010.1

1

OC EC

NH

4-

NO

3-

SO4

Na+ Al Si C


Cu


Sea salt

0.0010.010.1

1

OC EC

NH

4-

NO

3-

SO42

-

Na+ Al Si C


Cu


Biomass

0.0010.010.1

1

OC EC

NH

4-

NO

3-

SO4

Na+ Al Si C


Cu


Zinc rich

0.0010.010.1

1

OC EC

NH

4-

NO

3-

SO4

Na+ Al Si C


Cu


Secondary

0.0010.010.1

1

OC EC

NH

4-

NO

3-

SO4

Na+ Al Si C


Cu


Soil

0.0010.010.1

1

OC EC

NH

4-

NO

3-

SO4

Na+ Al Si C


Cu


Figure 7b. PMF source profiles in mass fraction for coarse particles at AIT (FPEAK=0.8). There were 6 factors that best explained the coarse PM mass.


24

3.3.3 Discussion on CMB and PMF model results Figure 8 shows the major contributors to fine and coarse PM by CMB and PMF models. In the fine fraction, diesel traffic, secondary sources (XNO3 and XSO4) and biomass burning were the major contributing sources which explained about 90 percent of the fine particle mass. Diesel and secondary source contributions by CMB at Bang Na were higher than by PMF whereas PMF showed very high contributions to fine PM from biomass burning. But, the contributions to major sources of fine PM at AIT site are more comparable. There are also some minor sources that appear only in one model and not in another as seen in Figures 8. It is interesting that PMF identified zinc-rich factor as an important source at AIT for both fine and coarse PM. This factor was overlooked by the CMB. In the coarse fraction, percent contributions given by CMB and PMF models were much variable especially at Bang Na. Industrial source (32%) in the coarse PM appeared prominent at Bang Na which did not show up in the CMB. In addition, the COPREM results on 2002 dry season PM data are comparable to CMB results (Upadhyay, 2002). COPREM results for complete data at all sites are under revision for publication.

a. CMB vs. PMF for fine fraction PM

3439

15

4 71

30 3328

3 4 1

2413

59

4

29 27 31

92 2

010203040506070

Diesel

SECONDARY

BIOMAS

OIL BURN

SOIL

CONST

Diesel

SECONDARY

Biomas

s

OIL BURN

SOIL

NH4+, N

a+ ric

hZIN

CLE

AD

Banga Na AIT

Perc

ent c

ontr

ibut

ion CMB PMF

b. CMB vs PMF for coarse fraction PM

18

42

23

114 2

15

42

21 18

1 1 1

14 1420 20

32

8

47

27

59

4

0

10

20

30

40

50

CONSTSOIL

BIOMAS

SECONDARYSEAS

OIL BURN

Indus

try

CONSTSOIL

BIOMAS

SECONDARYSEAS

OIL BURN

ZINC

Bang Na AIT

Perc

ent c

ontr

ibut

ion CMB PMF

Figure 8. Comparison of source contributions to fine and coarse fraction particles at Bang Na and AIT estimated by CMB and PMF receptor models. FPEAK for fine PM 1.5, coarse PM 0.8 at AIT; fine PM 0.5 and coarse PM 0.7 at Bang Na.


25

4. Conclusion and recommendations Sampling and analyses of a large number of dichot samples helped to characterize fine and coarse PM compositions and enhanced the understanding of important sources in BMR. The information acquired from this study is expected to be useful for the policy-making level to design a framework for the overall improvement of Bangkok air quality. Dichotomous sampling used for the particulate sample collection in this study shows consistent results as compared to FRM. This has been seen by the PM mass comparisons and ion balances for the three sampling methods (FRM, dichot and MinVol). The results of this study show a clear seasonal pattern of PM variations with higher PM concentrations in dry season and lower in wet season. The question of concern is not only the high levels of PM in the dry season than in the wet season, but also their higher frequencies of standard exceedances in the urban areas and the health and environmental consequences thereof. High correlations existed between PM2.5 and PM10 in Bangkok particulates and change in PM10 levels is largely driven by the change in PM2.5 particles. OC, EC, SO4

= and NO3- are major components of fine particles whereas crustal

elements, NO3-, SO4

= and Cl- are the major components of coarse particles in Bangkok. Rather than any secondary fine aerosols as found in common in other urban environment, OC and EC are found to be significant chemical components of Bangkok fine aerosol. This might have effects on, including others, atmospheric chemistry and climate. So, there is a need for the control/management to reduce total carbon levels in Bangkok ambient air. Besides, it is recommended to work out in policy for PM2.5 standards/regulation. Chemical mass balance model results revealed that major contributors to fine particles are traffic, secondary sulfate, biomass burning and re-suspended soil which together account for about 90% of PM2.5. The study identified biomass burning as an important source of fine PM in Bangkok which needs management strategies to reduce the pollution level. Major coarse PM contributors identified are re-suspended soil dust, construction, biomass burning and sodium nitrate that comprised about 92% of coarse mass. PMF results from one intensive site have not yielded reliable results which require further work and analysis of results. In order to better understand the seasonal characteristics of PM and the influences of important sources to ambient particulates in different seasons, routine sampling and chemical characterization of PM at different locations in the study area and the guidelines of using receptor modeling are necessary. 5. References Henry, R. C., Lewis, C. W., Hopke, P. K. and Williamson, H. J. Review of Receptor Model Fundamentals; Atmospheric Environment 1984, Vol. 18, No. 8, pp. 1507-1515. Japan International Cooperation Agency (JICA); The Study on the Air Quality Management Planning for the Samut Prakarn Industrial District in the Kingdom of Thailand, Final Report, 1991. Kim, Y.P., Moon, K.C., Lee, J.H. and Baik, N.J. Concentrations of carbonaceous species in particles a Seoul and Cheju in Korea. Atmospheric Environment 1999, 33:2751-2758.


26

Lin, J.J. and Tai, H.S. Concentrations and distributions of carbonaceous species in ambient particles in Kaohsiung City, Taiwan. Atmospheric Environment 2001, 35: 2627-2636. Ostro, N., Chestnui, L., Vichit-vadakan, N., Laixthai, A. The impact of particulate matters on daily mortality in Bangkok, Thailand; J. Air & Waste Manage. Assoc. 1999, 49 (special issue on PM, September), 100-107. PCD, 2000. Air Emission Source Database Update and ambient air quality impact assessment in Bangkok Metropolitan Region, PCD 03-034, ISBN 974-7880-16-4. Radiant International LIC, PM Abatement Strategy for the Bangkok Metropolitan Area, Final Report Volume I. Report prepared for Pollution Control Department, Ministry of Science, Technology, and Environment, Bangkok, Thailand, 1988. Supat, W., 1999a. Air Pollution Control Strategies in Thailand; Paper Presented at the International Urban Environmental Infrastructure Forum, AWMA 92nd Annual Meeting and Exhibition America Centre, St. Louis, Missouri, USA, June 20-24, 1999. Supat, W., 1999b. Ambient Air Quality monitoring network in Thailand; Air Quality and Noise Management Division, Pollution Control Department, Ministry of Science, Technology and Environment, Bangkok, Thailand. United Nations Environmental Program (UNEP): Bangkok State of the Environment, 2001. Wahlin, P. A. COPREM-- A Multivariate Receptor Model with a Physical Approach; Atmospheric Environment 2003, 37: 4861-4867. Upadhyay, N. (2002). Source Apportionment of Fine and Coarse Fractions of Particulate Matters in Bangkok Metropolitan Region by Receptor Modeling. AIT Thesis No. Ev-02-21. Wahlin, P. A Multivariate Receptor Model with a Physical Approach; Paper presented in Fifth International Symposium on Arctic Air Chemistry, September 8-10, 1992. Wall, S. M., John, W., Ondo, J. L. Measurement of Aerosol Size Distributions for Nitrate and Major Ionic Species, Atmospheric Environment 1988, 22, 1649-1656. Watson, J. G., Robinson, N. F., Lewis, C., Coulter, T. Chemical Mass Balance Receptor Model- Version 8 (CMB8) User’s Manual, Document No. 1808.1D1, Desert Research Institute, Reno, NV, 1997. Yoshizumi, K., Ishibashi, Y., Grivait, H., Paranamra, M., Sukomsunk, K., and Tabucanon, M. S. Size Distributions and Chemical Composition of Atmospheric Aerosols in a Suburb of Bangkok, Thailand; Environ. Technol. 1996, 17: 777-782. Zhang, B.-N. and Kim Oanh, N.T. Photochemical smog pollution in the Bangkok Metropolitan Region of Thailand in relation to O3 precursor concentrations and meteorological conditions; Atmospheric Environment 2002, 36, 4211-4222.


27

6. Appendix

Annex 1

Tables


28

Table A1. Source profiles (mass fraction) with uncertainties used in the Bangkok PM2.5 and PM10-2.5 CMB receptor modeling. Chem BIOM01 OIL001 LEAD01 SEAS01 ZINC01 TIN001 CONS01 NaNO3 NH4SO4 NH4NO3 DIES01 CONS-PMF SOIL-PMF OC 0.3863 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.2675 0.0003 0.0001 OC 0.2318 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0802 0.0001 0.0000 EC 0.0238 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.6489 0.0094 0.0000 EC 0.0143 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.1947 0.0019 0.0000 NH4+ 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.2730 0.2260 0.0010 0.0000 0.0000 NH4+ 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0000 0.0546 0.0452 0.0010 0.0000 0.0000 NO3- 0.0090 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.7111 0.0000 0.7750 0.0000 0.0099 0.0001 NO3- 0.0119 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.1422 0.0000 0.0775 0.0001 0.0020 0.0000 Na+ 0.0020 0.0000 0.0000 0.3244 0.0000 0.0000 0.0000 0.2258 0.0000 0.0000 0.0000 0.0001 0.0000 Na+ 0.0036 0.0001 0.0001 0.0973 0.0001 0.0001 0.0001 0.0452 0.0000 0.0000 0.0001 0.0000 0.0000 Mg2+ 0.0000 0.0000 0.0000 0.0391 0.0000 0.0000 0.0000 0.0272 0.0000 0.0000 0.0000 0.0000 0.0000 Mg2+ 0.0001 0.0001 0.0001 0.0117 0.0001 0.0001 0.0001 0.0054 0.0001 0.0001 0.0001 0.0010 0.0010 Al 0.0030 0.0000 0.0000 0.0000 0.0000 0.0000 0.0254 0.0000 0.0000 0.0000 0.0000 0.0001 0.0376 Al 0.0027 0.0001 0.0001 0.0001 0.0001 0.0001 0.0051 0.0001 0.0001 0.0001 0.0001 0.0000 0.0075 Si 0.0032 0.0000 0.0000 0.0000 0.0000 0.0000 0.1034 0.0000 0.0000 0.0000 0.0000 0.0002 0.1149 Si 0.0027 0.0001 0.0001 0.0001 0.0001 0.0001 0.0207 0.0001 0.0001 0.0001 0.0001 0.0000 0.0230 S 0.0150 0.0000 0.0000 0.0272 0.0000 0.0000 0.0002 0.0189 0.2420 0.0000 0.0030 0.0000 0.0000 S 0.0045 0.0010 0.0010 0.0082 0.0010 0.0010 0.0001 0.0038 0.0242 0.0000 0.0005 0.0000 0.0001 Cl 0.0310 0.0000 0.0000 0.5830 0.0000 0.0000 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000 0.0045 Cl 0.0046 0.0003 0.0003 0.1749 0.0060 0.0060 0.0060 0.0000 0.0000 0.0000 0.0060 0.0000 0.0009 K 0.0590 0.0000 0.0000 0.0117 0.0000 0.0000 0.0151 0.0000 0.0000 0.0000 0.0000 0.0000 0.0061 K 0.0354 0.0006 0.0006 0.0035 0.0006 0.0006 0.0030 0.0000 0.0000 0.0000 0.0005 0.0000 0.0012 Ca 0.0040 0.0000 0.0000 0.0123 0.0000 0.0000 0.2351 0.0086 0.0000 0.0000 0.0000 0.2443 0.0029 Ca 0.0040 0.0003 0.0003 0.0037 0.0001 0.0001 0.0470 0.0017 0.0000 0.0000 0.0001 0.0489 0.0006 Ti 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000 0.0028 0.0000 0.0000 0.0000 0.0000 0.0024 0.0031 Ti 0.0002 0.0001 0.0001 0.0001 0.0001 0.0001 0.0008 0.0000 0.0000 0.0000 0.0003 0.0005 0.0006 V 0.0000 0.0090 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 0.0002 0.0001 V 0.0001 0.0020 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0000 0.0001 0.0000 0.0000 Mn 0.0002 0.0000 0.0185 0.0000 0.0370 0.0000 0.0016 0.0000 0.0000 0.0000 0.0000 0.0001 0.0010 Mn 0.0003 0.0010 0.0185 0.0010 0.0111 0.0010 0.0010 0.0000 0.0000 0.0000 0.0010 0.0000 0.0002 Fe 0.0023 0.0000 0.3527 0.0000 0.2168 0.0000 0.0815 0.0000 0.0000 0.0000 0.0000 0.0197 0.0342 Fe 0.0042 0.0002 0.2469 0.0002 0.0650 0.0002 0.0163 0.0000 0.0000 0.0000 0.0008 0.0039 0.0068 Ni 0.0000 0.0062 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0002 0.0000 Ni 0.0010 0.0019 0.0010 0.0010 0.0010 0.0010 0.0010 0.0000 0.0000 0.0000 0.0010 0.0000 0.0000 Cu 0.0001 0.0000 0.0151 0.0000 0.0198 0.0255 0.0000 0.0000 0.0000 0.0000 0.0010 0.0017 0.0000


29

Cu 0.0005 0.0001 0.0060 0.0001 0.0059 0.0077 0.0002 0.0000 0.0000 0.0000 0.0004 0.0003 0.0000 Table A1. Source profiles (mass fraction) with uncertainties used in the Bangkok PM2.5 and PM10-2.5 CMB receptor modeling (contd.). ZN 0.0004 0.0004 0.0000 0.0000 0.7228 0.0000 0.0000 0.0000 0.0000 0.0000 0.0010 0.0064 0.0000 Zn 0.0001 0.0001 0.0000 0.0000 0.2168 0.0001 0.0001 0.0000 0.0000 0.0000 0.0001 0.0013 0.0000 As 0.0001 0.0001 0.0000 0.0000 0.0000 0.0164 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 As 0.0002 0.0002 0.0002 0.0002 0.0002 0.0049 0.0002 0.0000 0.0000 0.0000 0.0002 0.0000 0.0000 Se 0.0001 0.0006 0.0009 0.0000 0.0035 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Se 0.0001 0.0006 0.0004 0.0001 0.0011 0.0003 0.0003 0.0000 0.0000 0.0000 0.0003 0.0000 0.0000 Br 0.0005 0.0000 0.0000 0.0020 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Br 0.0008 0.0001 0.0001 0.0010 0.0001 0.0001 0.0001 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 Rb 0.0002 0.0000 0.0008 0.0000 0.0000 0.0104 0.0001 0.0000 0.0000 0.0000 0.0000 0.0001 0.0003 Rb 0.0001 0.0001 0.0003 0.0001 0.0001 0.0042 0.0001 0.0000 0.0000 0.0000 0.0001 0.0001 0.0003 Sr 0.0000 0.0000 0.0010 0.0004 0.0000 0.0022 0.0004 0.0003 0.0000 0.0000 0.0000 0.0004 0.0001 Sr 0.0010 0.0010 0.0010 0.0010 0.0010 0.0009 0.0010 0.0001 0.0010 0.0010 0.0010 0.0001 0.0000 Zr 0.0000 0.0000 0.0010 0.0000 0.0000 0.0042 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 Zr 0.0010 0.0010 0.0010 0.0010 0.0010 0.0021 0.0010 0.0010 0.0010 0.0010 0.0010 0.0000 0.0000 Sn 0.0000 0.0000 0.0000 0.0000 0.0000 0.1960 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Sn 0.0010 0.0010 0.0010 0.0010 0.0010 0.0392 0.0010 0.0010 0.0010 0.0010 0.0010 0.0000 0.0000 Pb 0.0000 0.0000 0.6099 0.0000 0.0000 0.7452 0.0000 0.0000 0.0000 0.0000 0.0000 0.0004 0.0001 Pb 0.0010 0.0010 0.1830 0.0010 0.0010 0.2236 0.0010 0.0010 0.0010 0.0010 0.0010 0.0001 0.0000


30

Table A2-a. Average, minimum and maximum 24-hour PM2.5 and PM10-2.5 compositions (µg/m3) at each sampling site in dry season 2002-2003. Bang Na Ban Somdej Dindang Bangkok U. AIT Avg Min Max Avg Min Max Avg Min Max Avg Min Max Avg Min Max PM2.5 mass 25 5 68 51 26 107 86 45 146 48 36 81 35 14 111 OC 8.22 2.62 30.85 11.99 6.3 18.79 24.51 18.18 50.38 15.20 10.70 19.25 12.11 6.06 45.54EC 2.00 0.64 7.53 2.92 1.54 4.58 5.70 4.43 12.53 3.71 2.61 4.69 2.73 1.48 6.26 NH4+ na na na na na na 1.72 0.42 3.26 na na na 1.47 0.01 2.52 NO3- 0.18 0.03 1.20 1.43 0.14 4.59 2.06 0.69 4.65 0.84 0.27 2.69 1.50 0.21 7.59 SO4= 4.17 1.30 9.94 5.67 2.83 10.02 7.38 2.51 13.14 5.84 3.42 10.25 4.98 0.10 12.07EC 4.88 0.02 17.46 8.29 3.56 19.70 32.94 19.05 44.25 11.30 7.31 18.54 11.41 5.65 23.16Na+ 0.32 0.07 0.79 0.59 0.23 0.95 1.25 0.01 4.13 0.50 0.19 1.04 0.80 0.01 2.29 K+ 0.57 0.21 1.31 1.03 0.51 2.91 1.58 0.98 2.52 0.80 0.49 1.51 0.83 0.03 2.82 Cl- 0.06 0.01 0.39 0.15 0.01 0.46 0.47 0.11 2.20 0.17 0.01 0.46 0.64 0.02 3.93 Crustal 0.34 0.15 0.77 0.54 0.25 1.25 0.72 0.40 1.14 0.77 0.29 1.44 0.27 0.13 0.81 Others 0.20 0.07 0.48 0.41 0.16 1.45 0.33 0.19 0.54 0.81 0.15 1.71 0.17 0.06 0.38

Avg Min Max Avg Min Max Avg Min Max Avg Min Max Avg Min Max PM10-2.5 mass 17 8 29 25 14 38 35 19 53 36 16 63 21 10 50 OC 0.76 0.05 2.63 1.87 0.70 3.21 6.49 2.54 7.75 2.46 1.60 3.30 2.96 1.47 5.63 EC 0.16 0.01 0.54 0.38 0.14 0.66 1.73 1.03 5.49 0.50 0.33 0.68 0.57 0.22 1.27 NH4+ na na na na na na 0.19 0.01 0.85 na na na 0.08 0.01 0.65 NO3- 1.16 0.31 1.98 1.93 1.15 3.41 1.48 0.01 3.64 2.20 0.62 4.19 1.74 0.24 3.08 SO4= 0.91 0.38 1.94 2.00 0.87 5.32 0.95 0.10 5.46 1.84 0.92 3.36 1.41 0.10 9.71 EC 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Na+ 0.71 0.06 1.96 0.98 0.21 2.36 0.45 0.01 1.26 0.90 0.14 1.90 1.04 0.01 2.04 K+ 0.14 0.02 0.45 0.25 0.08 1.23 0.19 0.01 1.14 0.24 0.01 0.63 0.22 0.01 1.24 Cl- 0.72 0.06 2.32 0.75 0.13 1.99 0.33 0.01 1.76 0.77 0.14 1.94 1.20 0.18 6.42 Crustal 2.24 1.17 4.78 2.95 1.84 5.35 4.85 2.88 7.38 7.09 2.57 14.43 2.92 1.64 8.82 Others 0.24 0.13 0.42 0.27 0.14 0.64 0.32 0.14 0.83 0.44 0.10 0.84 0.16 0.04 0.28


31

Table A2-b. Average, minimum and maximum 24-hour PM2.5 and PM10-2.5 compositions (µg/m3) at each sampling site in wet season. Bang Na Ban Somdej Dindang Bangkok U. AIT Avg Min Max Avg Min Max Avg Min Max Avg Min Max Avg Min Max PM2.5 mass 8 5 12 14 5 34 48 24 88 6 2 11 4 1 10 OC 6.88 4.89 9.42 13.15 5.41 21.44 13.01 11.50 14.73 4.60 3.08 5.87 4.74 1.43 9.69 EC 1.68 1.19 2.30 3.21 1.32 5.23 3.17 2.81 3.59 1.12 0.75 1.43 1.16 0.35 2.36 NH4+ 0.65 0.28 1.53 0.43 0.08 2.06 0.47 0.04 2.18 0.62 0.16 1.34 0.37 0.04 1.31 NO3- 0.26 0.09 0.74 0.46 0.22 0.96 0.43 0.24 0.74 0.58 0.19 1.68 0.24 0.05 0.66 SO4= 2.17 1.75 3.38 2.80 1.44 7.98 3.00 1.57 8.22 2.37 1.03 4.43 1.52 0.60 3.48 EC 4.57 2.92 6.64 8.44 2.59 14.53 25.30 14.50 35.85 3.20 0.98 6.36 1.84 0.00 5.18 Na+ 0.78 0.05 1.55 0.35 0.11 0.68 0.32 0.12 0.84 0.69 0.00 2.09 0.74 0.00 1.93 K+ 0.55 0.38 0.84 0.61 0.31 0.90 0.77 0.52 1.16 0.56 0.15 2.12 0.43 0.19 0.83 Cl- 0.33 0.07 0.67 0.27 0.07 0.57 0.22 0.10 0.50 0.29 0.11 0.61 0.20 0.03 1.96 Crustal 0.41 0.19 0.53 0.51 0.22 0.84 0.49 0.26 0.94 0.30 0.07 0.66 0.34 0.13 0.67 Others 0.40 0.18 0.83 0.45 0.14 0.88 0.34 0.16 0.87 0.64 0.10 1.83 0.14 0.04 0.35

Avg Min Max Avg Min Max Avg Min Max Avg Min Max Avg Min Max PM10-2.5 mass 12 6 16 12 2 26 25 11 41 7 5 9 8 2 16 OC 1.58 1.00 2.29 2.65 1.08 4.28 5.02 4.44 5.68 1.10 0.60 1.52 1.15 0.34 2.34 EC 0.32 0.21 0.47 0.54 0.22 0.88 1.03 0.91 1.17 0.23 0.12 0.31 0.24 0.07 0.48 NH4+ 0.35 0.00 2.54 0.04 0.00 0.33 0.04 0.00 0.23 0.04 0.00 0.32 0.04 0.00 0.28 NO3- 0.87 0.36 2.05 0.64 0.00 1.19 0.70 0.28 1.56 0.66 0.36 1.24 0.63 0.15 1.72 SO4= 0.54 0.32 0.82 1.15 0.56 2.53 1.25 0.59 2.20 0.44 0.15 0.98 0.33 0.10 0.76 EC 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Na+ 1.54 0.36 3.10 0.64 0.00 2.40 0.71 0.17 2.64 0.39 0.00 2.22 0.70 0.00 1.48 K+ 0.24 0.06 0.53 0.24 0.12 0.49 0.28 0.20 0.48 0.12 0.05 0.28 0.13 0.07 0.21 Cl- 0.94 0.22 4.05 0.91 0.17 3.96 0.96 0.19 4.17 0.32 0.02 0.65 0.41 0.09 1.10 Crustal 2.58 1.89 3.80 2.21 0.88 4.12 4.32 2.54 6.67 2.01 0.94 2.88 1.41 0.26 2.59 Others 0.45 0.19 0.88 0.28 0.13 0.45 0.34 0.22 0.52 0.27 0.09 0.68 0.14 0.06 0.25


32

Table A3-b. Comaparision of major constituents of the Reference Material GBW 08401 with PIXE contents. Equivalent Constituent % content Elements Certified content PIXE content Difference (%) SiO2 51.18% Si 23.92% 23.00% 3.9 Fe2O3 9.20% Fe III 3.22% FeO 1.73% Fe II 1.34%

5.90% (*Fe)

-29.4 (*Fe)

Al2O3 24.43% Al 12.93% 15.00% -16.0 CaO 3.90% Ca 2.79% 2.90% -4.0 MgO 0.90% Mg MnO 0.17% Mn 0.13% 0.08% 39.2 TiO2 0.97% Ti 0.58% 0.59% -1.5 K2O 1.22% K 0.51% 1.09% -115.2 P2O5 0.20% P 0.04% 0.03% 31.3 U 5.1 ug/g U 5.1 ug/g 7 ug/g -37.3 Th 25.0 ug/g Th 25.0 ug/g 20 ug/g 20.0 Note: * PIXE method yields total Fe i.e., Fe III + Fe II

Table A3-a. Certified Values of Elements in the Reference Material GBW 08401 by different methods.Element Certified content, µg/g Analytical Methods used PIXE Method, µg/g

As 11.4± 0.60 AAS, INAA, SP, POL, AFS 9.4±1.00 Be 10.7±0.90 AAS, AES, ICP, FS, GC nd Cd 0.16±0.04 AAS, POL 1±2.00 Co 33.2±2.80 AAS, INAA, ICP, SP 0±30.00 Cr 60±7.00 AAS, INAA, ICP, SP (-)10±80.00 Cu 53±4.00 AAS, AES, ICP, INAA, VOL 44±4.00 Fe 7.65±0.14% AAS, AES, ICP, INAA, VOL 5.9±0.50% Mn 1178±40.00 AAS, AE, ICP, INAA, SP 800±200.00 Pb 33.8±4.40 AAS, AES, POL 30±3.00 Se 1.13±0.16 AAS, FS, GC, AFS 0.7±0.30 V 95±9.00 ICP, SP 90±30.00 Zn 61±7.00 AAS, XRF, INAA 41±4.00

Ba* 1450.00 AES, ICP, INAA 1220±90.00 Hg* 0.04 AAS, MIP, AFS nd

Note: *Reference content


33

Table A4-a. Average CMB source contributions to PM2.5 and PM10-2.5 at the sampling sites in Bangkok in dry season 2002-2003. PM2.5 BNG BSD DDN BKU AIT ug/m3 % ug/m3 % ug/m3 % ug/m3 % ug/m3 % NH4SO4 5.71 ± 0.66 38 7.90 ± 0.96 30 5.94 ± 0.74 16 7.73 ± 0.95 27 4.43 ± 0.53 26DIES01 5.12 ± 1.69 34 7.33 ± 2.00 28 15.73 ± 4.02 43 9.82 ± 2.97 34 5.14 ± 1.42 30BIOM01 2.18 ± 1.00 15 5.90 ± 1.68 23 9.61 ± 3.01 26 6.73 ± 2.03 23 4.77 ± 1.12 28SOIL-PMF 1.04 ± 0.32 7 2.14 ± 0.47 8 2.23 ± 0.54 6 1.93 ± 0.55 7 0.71 ± 0.28 4 OIL001 0.54 ± 0.16 4 0.51 ± 0.20 2 0.36 ± 0.25 1 0.94 ± 0.28 3 0.45 ± 0.15 3 NH4NO3 0.20 ± 0.09 1 1.77 ± 0.53 7 2.27 ± 0.91 6 1.00 ± 0.49 3 1.20 ± 0.36 7 CONS-PMF 0.20 ± 0.07 1 0.36 ± 0.13 1 0.54 ± 0.21 1 0.56 ± 0.17 2 0.07 ± 0.10 0 TIN001 - - 0.05 ± 0.03 0 0.05 ± 0.06 0 0.03 ± 0.01 0 ZINC01 - - 0.16 ± 0.05 0 0.07 ± 0.32 0 0.10 ± 0.03 1 R2 0.85 0.87 0.83 0.85 0.85 Chi2 2.73 2.53 2.69 2.83 2.14 meas mass 25.3 ± 7.0 50.8 ± 12.8 85.9 ± 21.4 48.4 ± 12.5 28.3 ± 8.1 calc mass 15.0 ± 1.9 25.9 ± 2.5 36.9 ± 4.4 28.8 ± 2.7 16.9 ± 1.8 %mass 59 51 43 60 60

PM10-2.5 BNG BSD DDN BKU AIT ug/m3 % ug/m3 % ug/m3 % ug/m3 % ug/m3 % SOIL-PMF 7.31 ± 1.23 42 9.21 ± 1.09 41 15.82 ± 1.90 44 17.12 ± 2.84 44 7.82 ± 0.92 42BIOM01 3.91 ± 1.30 23 5.32 ± 1.67 23 4.32 ± 1.20 12 6.47 ± 2.08 17 3.97 ± 1.25 21CONS-PMF 3.08 ± 0.64 18 3.94 ± 0.70 17 8.92 ± 0.99 25 9.85 ± 1.69 25 2.86 ± 0.53 15NaNO3 1.66 ± 0.49 10 2.96 ± 0.74 13 1.88 ± 0.47 5 3.13 ± 0.80 8 2.81 ± 0.71 15SEAS01 0.71 ± 0.23 4 0.48 ± 0.20 2 ns - - 0.55 ± 0.24 1 0.08 ± 0.10 0 OIL001 0.26 ± 0.11 2 0.30 ± 0.13 1 0.41 ± 0.16 1 0.58 ± 0.20 1 0.25 ± 0.11 1 NH4SO4 0.24 ± 0.20 1 0.21 ± 0.22 1 0.28 ± 0.16 1 0.71 ± 0.29 2 0.24 ± 0.13 1 DIES01 0.03 ± 0.35 0 0.26 ± 0.41 1 4.01 ± 1.25 11 0.40 ± 0.45 1 0.55 ± 0.30 3 ZINC01 - - - - 0.03 ± 0.01 0 R2 0.96 0.97 0.92 0.98 0.97 Chi2 0.64 0.43 1.39 0.29 0.55 meas mass 17.3 ± 4.8 24.6 ± 5.7 34.7 ± 8.9 36.3 ± 9.4 18.0 ± 4.8 calc mass 17.2 ± 1.7 22.7 ± 2.1 35.6 ± 2.4 38.8 ± 3.6 18.6 ± 1.5 %mass 99 92 103 107 104


34

Table A4-b. Average CMB source contributions to PM2.5 and PM10-2.5 at the sampling sites in Bangkok in wet season 2002-2003 BNG BSD DDN BKU AIT ug/m3 % ug/m3 % ug/m3 % ug/m3 % ug/m3 %

NH4SO4 2.38 ± 0.30 23 2.52 ± 0.34 18 3.07 ± 0.42 16 2.31 ± 0.28 26 1.70 ± 0.22 24DIES01 2.04 ± 1.29 20 3.98 ± 1.80 29 7.19 ± 2.02 38 1.76 ± 1.12 19 1.46 ± 0.92 21BIOM01 3.31 ± 0.91 32 3.99 ± 1.13 29 5.56 ± 1.64 30 2.95 ± 0.72 33 1.88 ± 0.67 27SOIL-PMF 1.17 ± 0.26 11 1.50 ± 0.33 11 1.29 ± 0.35 7 0.55 ± 0.18 6 1.14 ± 0.22 16OIL001 0.50 ± 0.15 5 0.46 ± 0.15 3 0.52 ± 0.18 3 0.35 ± 0.12 4 0.27 ± 0.11 4 NH4NO3 0.29 ± 0.15 3 0.52 ± 0.17 4 0.46 ± 0.17 2 0.65 ± 0.26 7 0.26 ± 0.13 4 CONS-PMF 0.24 ± 0.08 2 0.51 ± 0.14 4 0.46 ± 0.15 2 0.14 ± 0.07 2 0.20 ± 0.07 3 TIN001 0.07 ± 0.02 1 0.13 ± 0.03 1 0.08 ± 0.02 0 0.06 ± 0.01 1 0.03 ± 0.01 0 ZINC01 0.30 ± 0.06 3 0.23 ± 0.05 2 0.19 ± 0.05 1 0.25 ± 0.06 3 0.09 ± 0.02 1 R2 0.94 0.93 0.89 0.96 0.92 Chi2 0.96 1.29 1.84 0.6 1.20 meas mass 7.6 ± 4.0 14.0 ± 7.0 47.9 ± 13.5 5.9 ± 3.1 3.9 ± 2.1 calc mass 10.3 ± 1.5 13.8 ± 2.1 18.8 ± 2.3 9.0 ± 1.3 7.0 ± 1.1 %mass 136 99 39 154 181 PM10-2.5 BNG BSD DDN BKU AIT ug/m3 % ug/m3 % ug/m3 % ug/m3 % ug/m3 % SOIL-PMF 8.47 ± 1.06 48 5.80 ± 0.79 34 11.10 ± 1.35 37 5.49 ± 0.80 37 4.16 ± 0.53 42BIOM01 3.74 ± 1.28 21 5.21 ± 1.51 30 5.99 ± 1.96 20 2.65 ± 0.90 18 2.56 ± 0.85 26CONS-PMF 3.22 ± 0.58 18 3.62 ± 0.65 21 9.19 ± 1.25 31 5.05 ± 0.76 34 1.68 ± 0.32 17NaNO3 1.11 ± 0.46 6 1.05 ± 0.32 6 0.99 ± 0.33 3 0.84 ± 0.34 6 0.84 ± 0.31 9 SEAS01 0.29 ± 0.14 2 0.47 ± 0.19 3 0.51 ± 0.21 2 0.04 ± 0.07 0 0.25 ± 0.11 3 OIL001 0.24 ± 0.12 1 0.24 ± 0.11 1 0.38 ± 0.15 1 0.16 ± 0.10 1 0.10 ± 0.08 1 NH4SO4 0.39 ± 0.17 2 0.36 ± 0.12 2 0.31 ± 0.21 1 0.26 ± 0.11 2 0.16 ± 0.09 2 DIES01 0.21 ± 0.45 1 0.51 ± 0.49 3 1.35 ± 0.69 5 0.31 ± 0.36 2 0.08 ± 0.42 1 ZINC01 0.11 ± 0.03 1 0.04 ± 0.01 0 0.01 ± 0.02 0 0.21 ± 0.05 1 0.03 ± 0.01 0 R2 0.97 0.96 0.97 0.94 0.96 Chi2 0.53 0.75 0.68 1.22 0.53 meas mass 11.9 ± 3.6 12.3 ± 3.4 25.0 ± 7.1 7.1 ± 1.8 7.7 ± 2.4 calc mass 17.8 ± 1.5 17.3 ± 1.5 29.8 ± 2.2 15.0 ± 1.1 9.8 ± 0.9 %mass 149 141 120 212 128


35

Annex 1

Figures

Figure A1. Analytical schemes used in this study. Note: PCD, Pollution Control Department of Thailand; NERI, National Environmental Research Institute, Denmark; DRI, Desert Research Institute, USA; TOM, Thermal Optical Method; IC, Ion Chromatography

¼ OC/EC ½ Ions

Sampler 1 Sampler 2

37 mm-Φ Quartz- fibre filters

BC

at A

IT la

b IC at PCD lab

Data collection and processing

Input to Receptor models (CMB8/PMF)

PM2.5 PM10-2.5

37 mm-Φ Cellulose esters

PIXE at NERI lab, Denmark.

Elem

ents

PM2.5 PM10-2.5

Elem

ents

+

TMO at DRI lab


36

Figure A2-a. Comparative ion balance in collocated PM2.5 samples by FRM vs. Dichot (all 4 cations and 3 anions are taken into account).

DDN ion balance: FRM vs. Dichot

FRMy = 1.07x - 0.00R2 = 0.89n = 10

Dichoty = 1.01x - 0.00R2 = 0.94n = 10

0.00

0.05

0.10

0.15

0.00 0.05 0.10 0.15Cation Equivalents (umol/m3)

Ani

on E

quiv

alen

ts (u

mol

/m3)

FRM Dichot Linear (FRM) Linear (Dichot)

BSD ion balance: FRM vs. Dichot

FRMy = 1.05x - 0.00R2 = 0.99n = 11

Dichoty = 0.88x + 0.01R2 = 0.80n = 11

0.00

0.05

0.10

0.15


Ani

on E

quiv

alen

ts (u

mol

/m3)

FRM Dichot Linear (FRM) Linear (Dichot)


37

Figure A2-b. Comparative ion balance in collocated PM2.5 samples by FRM vs. MinVol (all 4 cations and 3 anions are taken into account).

DDN ion balance: FRM vs. MinVol

MinVoly = 0.62x + 0.01R2 = 0.51n = 9

FRMy = 1.06x - 0.00R2 = 0.86n = 9

0

0.05

0.1

0.15


Ani

on E

quiv

alen

ts (u

mol

/m3)

FRM MinVol Linear (MinVol) Linear (FRM)

BSD ion balance: FRM vs. MinVol

FRMy = 1.05x - 0.00R2 = 0.99n = 12

MinVoly = 0.22x + 0.03R2 = 0.40n = 12

0.00

0.05

0.10

0.15

0.00 0.05 0.10 0.15

Cation Equivalents (umol/m3)

Ani

on E

quiv

alen

ts (u

mol

/m3)

FRM MinVol Linear (FRM) Linear (MinVol)


38

Annex 2

List of publications from this research/conference presentations/manuscript under preparation

a) Conference proceedings/abstracts/presentations 1. Kim Oanh, N. T., (2003). “The AIRPET project and AIT results” paper presented at the

local A&WMA chapter meeting in Bangkok, March 2003. 2. Kim Oanh, N. T., (2003). Receptor modeling for PM2.5 and PM10-2.5 in BMR during

dry season. Paper presented in the A&WMA annual meeting, San Diego, USA, March 22-26, 2003.

b) Papers/books/reports in manuscript form

1. Kim Oanh, N. T., Prapat, P., Nabin, U., Paisarn, K. and Wahren, P. A comparative study

of receptor models applied for source apportionment of PM2.5 and PM10-2.5. To be submitted to the Journal of Air & Waste Management Association.

2. Kim Oanh N. T., Nabin U., Paisarn K. and Wahlin P. Receptor models applied for source

apportionment of PM2.5 and PM10-2.5 in the Bangkok Metropolitan Region during dry season. To be submitted to the J. Air and Waste Management Association.

3. Kim Oanh, N. T. and Opal, P. Comparative study of PM mass and ionic composition

monitoring methods. To be submitted to the Journal of Air & Waste Management Association.


39

Annex 3

Devices and equipment purchased Following is the list of items purchased/spent for the AIRPET project activities in phase 1. Cascade impactor Optical reflectometer, Personal pumps, Calibration kits, Stack sampler repairing, Breeze ISC model, Literature, Papers/stationary, Publications, Chemicals Filters, Glassware for the laboratory use, Tedlar bags, IC repairing (columns and loops), IC analysis (at PCD, Thailand), PIXE/XRF analysis of PM samples (at NERI, Denmark and DRI, USA), Office equipment (computer, monitor, printer, fax, etc.), Office furniture


40

Part 2: Monitoring of persistent organic pollutants 1. Introduction 1.1 Background There is a strong causal link between air pollution and adverse health impacts (WHO, 2001), which triggers increased concern from scientists and publics. Exposure to the ambient air pollution poses a high risk to urban population. Exposure of public to toxic pollutants leads to several health related problems. Of several types of pollutants in the ambient air, persistent organic pollutant (POP) is a group of such pollutants such as pesticides, PCBs and PAHs that exist both in particle and gas phase, and their health impacts depends on the level of concentrations, period of exposure and the susceptibility of the receptors to these pollutants. Hence, it is important to study POPs to produce quantitative results for health effects. The results in turn are useful information for policy makers (Yakovleva et al, 1999 and Hopke et al, 2003) and also provide public awareness which help to take actions to reduce these pollutants from the ambient air. 1.2 Statement of the problems The ambient levels of small particulate matters and their health impacts have been correlated in several studies including that in Bangkok. Many of semi-volatile compounds such as PCBs, PAHs and pesticides are toxic and partly sorbed in particulate matters increasing potential health effects of the particulate matters. However, there is no systematic monitoring for fine PM in many Asian developing countries. Some fragmented monitoring activities now and then in some places have shown the levels far exceeding US or Western European standards. Besides, monitoring for trace organic compounds, also known as the persistent organic compounds (POPs) is normally complicated, and requires intensive technical resources and skills, which are not readily available in the region. Consequently, the database on these pollutants is scarce. Hence, monitoring of these pollutants in Bangkok has been conducted by AIT air pollution research team.

1.3 Objectives

The objectives of monitoring of the organic pollutants are to establish a preliminary database of some selected POPs in Bangkok and assess their exposure-impacts to health.

1.4 Scope

Gas and particle phase (suspended particulate matters (SPM)) samples were collected from four sites in Bangkok Metropolitan Region using 4-stage cascade impactor and a HiVol-PUF The levels and distribution of SPM, gas and particle bound PCBs and organochlorine pesticides were analyzed.


41

2 Methodology

2.1 Study area/sampling method

In this study, the level and phase distribution of airborne PCB in the Bangkok urban area has been investigated at 3 sites: Bangkok University (background), Dingdeng (traffic) and Bangplee (mixed site). Airborne PCB samples were taken by both active and passive sampling methods. The active sampling uses an Anderson-type low volume cascade sampler, which was modified by adding a PUF adsorbent tube to trap gas phase of the compounds. The samples were prepared and analyzed by GC-ECD following USEPA Compendium Methods TO-10A. The passive samplers are semi-permeable membrane device (SPMD) and analytical procedure was according to the SPMD Guide.

3 Results and discussion

3.1 Size distribution of SPM in Bangkok air

The size distributions of SPM based on mass in Bangkok urban area were studied using a cascade impactor for 4 sites both in dry (March-May 2001) and wet season (May-September 2001). The results show that suspended particulate matters (SPM) are distributed in a bimodal form- one peak in the fine particle, between 0.43-0.65 µm, and another peak in coarse particle range, between 4.7-7.0 µm. In dry season the level (270-333 µg/m3) is much higher than in the wet season (50-136 µg/m3). The highest level was found at the traffic site for both seasons. Coarse particle fraction was found to be 55-62% of collected SPM in dry season and 45-61% in wet season.

3.2 Levels and phase distribution of airborne PCB in Bangkok air

There are very few reports on airborne PCB from tropical climate and no systematic monitoring activities. In Thailand this would be one of the first attempts. Approximately 566 metric tons of PCB still exists in the in the country. In this study, the level and phase distribution of airborne PCB in the Bangkok urban area has been investigated at 3 sites: Bangkok University (background), Dingdeng (traffic) and Bangplee (mixed site). Airborne PCB samples were taken by both active and passive sampling methods. The active sampling uses an Anderson-type low volume cascade sampler, which was modified by adding a PUF adsorbent tube to trap gas phase of the compounds. The samples were prepared and analyzed by GC-ECD following USEPA Compendium Methods TO-10A. The passive samplers are semi-permeable membrane device (SPMD) and analytical procedure was according to the SPMD Guide. Totally 12 PCB were analyzed, separately for coarse, fine fractions of SPM, and for the gas phase, but only seven were detected by the method. The results showed that concentrations of PCBs are increased corresponding with SPM concentration in ambient air. The lower molecular weight species such as PCB28, 31, 52, 101, and 138 were predominantly found in the gas phases while the higher molecular weight species, PCB153 and 180, were mainly found in the particulate phase. The fine fraction of SPM contains higher PCB than the coarse fraction. The total 7 detected PCB in all phases ranged from 1.3 to 3.8 ng/m3. The levels of PCBs in Bangkok are in general one order higher than levels reported for US and Europe. Only 5 PCB detected by SPMD and the first lighter 4 were detected at higher level than the active sampling. The findings of the study is elaborated in Annex 1.


42

3.3 Levels and phase distribution of airborne organochlorine pesticides in Bangkok air

In Thailand, the use of persistent pesticides was already banned. However, the sampling conducted by PCD reveals its main rivers, soil and vegetable products still contain DDT, aldrin, dieldrin and BHC. Persistent organochlorine pesticides (POCP) in the air of BMR were investigated during the rainy season using the same sampling methods as for PCB. Samples were collected from 3 ambient air quality monitoring stations: Din Daeng, Bangkok University and Bang Na. Analysis was done using GC/ECD for 16 POCP following USEPA TO-4A method (for active) and SPMD Analytical Guide (for passive). 15 POCP were detected (Endosulfan sulfate was not detected in any samples). Pesticides mainly present in the gas phases. The fine fraction of SPM contains higher POCP than the coarse fraction. Total detected POCP in all phases of the samples ranged from 0.25 ng/m3, at the Bangkok University, to 8.3 ng/m3 at the traffic site (Dingdeng).

The active method trapped more POCP than SPMD. The potential of SPMD as an alternative method for remote areas, where electricity is lacking, should be considered especially if calibrated uptake rates specific for POCP could be established.

4 Conclusion The suspended particulate matters are found to have bimodal size distribution: fine particles in 0.43-0.65 µm, and coarse particles in 4.7-7.0 µm. The levels of particles are higher in the dry season than in the wet season at all sites, and especially in the traffic site. The active sampling method can trap larger numbers of OCPs compounds than the SPMD. However, most of OCPs detected by SPMD were at higher levels than the active sampler. The higher levels of OCPs in the wet season might possibly due to the higher amount of OCPs applied for pest control during this season. The results of this study can be used to build up a data base and guidelines for the suitable environmental management plan regarding pesticides as air pollutants. 5 References Yakoleva, E., Hopke, P. and Wallance, L., 1999. Receptor modeling assessment of particle total exposure assessment methodology data; Environ. Sci. Technol. 33: 3645-3652. Hopke P., Ramadan, Z., Paatero, P., Norris, G., Landis, M. S., Williams, R. and Lewis, C., 2003. Receptor modeling of ambient air and personal exposure samples: 1998 Baltimopre Particulate Matter Epidemiology-Exposure Study; Atmospheric environment 37: 3289-3302.

WHO, 2001. WHO Strategy on Air Quality and Health; Revised Final Draft, May 2001.


43

6 Appendix Appendix 1

Table A1. Size Distribution of Suspended Particulate Matter (SPM) in Selected locations in BMR

Mean Concentration of SPM, µg/m3 Aerodynamic

Cut-off Diameter (µm) Bangkok

University Bang Na

(1.a) Bang Na

(1.b) Din Daeng

>11 10.18 3.69 3.75 18.39 11–7.0 19.16 3.69 4.26 9.48 7.0–4.7 17.95 5.34 3.75 11.11 4.7–3.3 13.77 7.42 6.84 12.11 3.3–2.1 7.44 7.83 7.04 10.44 2.1–1.1 10.45 5.34 5.70 8.93 1.1–0.65 13.01 3.69 5.34 10.39 0.65–0.43 10.85 3.69 6.54 10.81

<0.43 (back-up) 8.74 8.24 10.91 44.70 No. of Samples 6 4 6 6 Total SPM 111 49 54 136 SPM Coarsea (%) 68 (61%) 28 (57%) 26 (48%) 61 (45%) SPM Fineb (%) 43 (39%) 21 (43%) 28 (52%) 75 (55%)

aParticulate matter collected from the impactor unit starting from stage 0 to 4 (size ≥ 2.1 µm). bParticulate matter collected from the impactor unit starting from stage 5 to 7 and back-up filter (size < 2.1 µm).

Total PCB and phase distribution, Bangkok

0

20

40

60

80

100

BKK U1 DinDeng Bangplee BKK U2Sampling stations

Dis

tribu

tion(

%) a

nd T

otal

PC

B (n

g/m

3)

Gas PM PCB*10, ng/m3

Figures A1-a. Levels and phase distributions of PCBs at the sampling sites in Bangkok.


44

Total POCP and phase distribution

0

20

40

60

80

100

120

BKK U2 Bangna1 Bangna2 DindengSampling stations

Dis

tribu

tion

(%) a

nd T

otal

PO

CP

(ng/

m3)

Gas PM POCP*10, ng/m3

Figure A1-b. Levels and phase distributions of POCP at the sampling sites in Bangkok.

Appendix II

Asian Regional Research Programme on Environmental Technology

(ARRPET)

Improving Air Quality in Asian Developing Countries AIT Report

Final Report for Control Issue

Upward extension of exhaust pipes for diesel powered vehicles

Phase 1: 2001-2004

Prepared by

Dr. N. T. Kim Oanh and AIT research team

Asian Institute of Technology Environmental Engineering Program

School of Environment, Resources and Development Pathumthani 12120, Thailand

December 2003

Control Technology Report-AIT July 2004

ii

Abstract This report highlights the activities and results obtained by the AIT team of the AIRPET project under the framework of the control issue “Upward extension of exhaust pipes for diesel powered vehicles”. The exhaust pipe of an diesel-powered 6–wheel air conditioned bus was extended from the original level at 0.52 m height to the bus roof level, around 4 m. Effects of the extension on the street level of air pollutants (CO, HC, PM10) at the sitting breathing level (1 m height) and 3 m from the traffic lane were assessed both by monitoring and modeling methods. Two dispersion conditions were studied, the free flow highway and the street canyon, at various bus passing speeds (20, 40, 60 and 80 km/h). Maximum ambient air pollutant concentrations measured at the sitting breathing level when the bus with the original exhaust passing by were compared to those for the bus with the extended exhaust. Monitoring results showed that the upward extension of the exhaust pipe could reduce the maximum ambient pollutant concentrations from the passing bus emission by around 1.25-3 times. Modeling was done only for the free flow highway using a Gaussian plume equation applied to the bus as a moving point source. The model results also showed that the extension of exhaust pipe reduced the maximum ambient air pollution concentration by a factor of 3. The cost of the extension varies from US$ 35 to 50 for a simple external installation for a bus. A limited survey study showed a high acceptance rate of the technique by stakeholders though some subsidy may be required to promote its wide application.


iii

Table of Contents

Contents Page Cover page Abstract Table of contents 1. Introduction

1.1 Background 1.2 Statement of Problems 1.3 Objectives 1.4 Scope

2. Methodology

2.1 Experimental Set up 2.2 Materials and Method

2.2.1 On-road emission measurement 2.2.2 Exhaust gas dispersion modeling 2.2.3 Ambient air quality measurement

3. Results and Discussion

3.1 Technical Performance of the device 3.1.1 Measurement data

3.2 Cost effectiveness of the upward extended exhaust pipe technique 3.3 Applicability of the upward extended exhaust pipe technique

4. Conclusions and Recommendations 5. References 6. Appendices

6.1 Details of results 6.2 List of publications, research/conference presentations/manuscript 6.3 List of technical training 6.4 Devices and equipment hired/purchased

i ii iii 1 1 1 1 2 2 4 5 5 5 8 9 9 9 16 16

17

17

18 18 27 27 27


1

1. Introduction 1.1 Background The numbers of trucks and buses in Bangkok increased by 62 and 21%, respectively in the period from 1990 to 1997. The number of trucks and buses registered in the Bangkok Metropolitan Area in 1999 was 99,072 and 24,928, respectively. Heavy-duty trucks and buses was only 2.8% of the total vehicles in Bangkok in 1994 but they were reported to contribute upto 61% of NOx and 41 % of total PM from traffic emission (Supat, 1999a). The emissions of major concern from diesel vehicles are particulate matters, unburned hydrocarbons and NOx. The diesel exhaust contains several harmful substances, which are recognized as human toxicants, carcinogens, reproductive hazards, or endocrine disrupters. Diesel exhaust has long been considered to be a probable human carcinogen by the U.S. National Institute of Occupational Safety and Health (NIOSH) and the International Agency for Research on Cancer (IARC). It was well recognized that exposure to diesel engine exhaust cause adverse health effects, which range from headaches to nausea to respiratory disease and finally to cancer (Springer and Patterson, 1973).

1.2 Statement of problems The dense and congested traffic with increasing growth in heavy-duty trucks and buses burning diesel fuel is one of the main causes of the air pollution problems in Asian developing cities. At present, most of diesel trucks and buses in these cities are designed to discharge their exhausts at the street level, 90 to 120 cm above the ground. Large population of old and poorly maintained diesel vehicles in the cities are of the foremost concern due to the high emission of toxic pollutants. Strong jet of low-level exhaust gas from these vehicles hits riders of two wheelers, car passengers and pedestrians or people in shop houses along streets leading to a high exposure levels (Kim Oanh, 2000). Upward extended exhaust pipes to an average discharge level of 3-4 m, which are used in some models of heavy duty diesel vehicles and commonly practiced in the North American and European cities would reduce the ground level concentrations of air pollutants (Weaver, 1986). Exposure to the emission in an immediate proximity of the exhaust would reduce substantially (Figure 1). This study was designed to investigate the impacts and feasibility of the exhaust pipe extension for diesel buses in Bangkok. It aims at promoting the technology as a short-term measure to cope with high exposure to diesel exhaust especially from a number of old diesel-powered buses, which are still on the road in Bangkok. 1.3 Objectives 1) To determine on-road emission of diesel powered vehicles 2) To assess the effect of upward extended exhaust pipe on ambient concentrations in the

street. 3) To evaluate socio-economical impacts of the extension of exhaust pipe.


2

Figure 1. Exhaust gas from original and upward extended exhaust pipe

1.4 Scope of the study The study was conducted in the Bangkok Metropolitan Region using an unloaded 6-wheel diesel powered bus. Considered pollutants included carbon monoxide (CO), hydrocarbon (HC) and opacity/PM10. Both monitoring and modeling (Gaussian plume) tools were used to assess the impacts of the original and upward extended exhaust pipe on the air pollution. Both free flow highway and street canyon dispersion conditions were considered. Experiment, field visits and interviews were conducted to assess socio-economic aspects of exhaust pipe extension. 2. Methodology Activities conducted in this study can be divided into three groups: 1) On-road exhaust emission measurement and dispersion modeling; 2) Ambient air quality measurement; and 3) Socio-economic study on acceptability of the technology. The overall methodology framework is presented in Figure 2. Measurement parameters are shown in Table A1, Appendix.


3

Figure 2. Framework of Methodology.

Prepare a study bus

Selection of a free flow highway

Testing the bus at various speeds

Field visits and interviews


Selection of a street canyon


Monitoring for ambient air in street


Upward extension of exhaust pipe

Monitoring for exhaust emission

Calculation of emission rate

Cost and applicability

Testing the bus with various speed

Testing the bus with various speed



Comparison

Data analysis

Pollutant dispersion in rural area

Effect of upward extended exhaust pipe

Promotion of the technology

Socio-economical aspects of exhaust pipe extension

Modeling


4

Chiang Rak Noi road was selected to study the free flow highway dispersion conditions while Pracha U-Thit road was selected for street canyon dispersion conditions. Chiang Rak Noi road is located at Klong Luang District, Prathum Thanee Province in the middle of Thailand. The width of Chiang Rak Noi road is around 30 meters. It is oriented from the west to east with 95 degree from the north with a few buildings along the road. Pracha U-Thit road is situated at Rat Burana District, Bangkok. The width of Pracha U-Thit road is about 15 meters with many high buildings (more than four-storeyed) on both sides of the road. It is oriented from the north to south with 15 degree from the north. Because of highly congested traffic conditions during daytime in this road, experiment was conducted in this road during nighttime only in order to assess contribution from the study bus alone. Ambient air quality concentration at the roadside (3 meters away from the lane and at 1 meter height) was measured when the bus passed through the road. 2.1 Experimental set up An unloaded 6-wheel four-stroke diesel powered bus was used in this study. The volume of the engine was 13.741 liters. The exhaust pipe was originally located at the back of the bus at 0.52-meter height. A steel pipe was used for the exhaust extension above the roof of the bus, 3.95m high above the ground (Figures 3 and Figure A1, Appendix). The end of the vertical exhaust pipe was cut in slope and bent such that it would emit the exhaust at 45-degree angle. Five-gas analyzers were installed in the bus to measure HC and CO. An opacimeter was installed at the end of exhaust pipe to simultaneously measure the opacity of exhaust gas. Similarly, a digital thermometer was placed at the end of exhaust pipe to record exhaust gas temperature.

Figure 3. Upward extended exhaust pipe for the study bus.


5

2.2 Materials and methods 2.2.1 On-road emission measurement The exhaust gas sample was taken before being mixed with the ambient air while running the bus at steady speeds of 20, 40, 60 and 80 km/h. The opacimeter and the digital thermometer set at the end of the exhaust pipe recorded the opacity and temperature of the exhaust gas respectively every 10 seconds when the bus was running. Concentration of HC and CO in the exhaust of the running bus was measured by five-gas analyzers. A schematic view of the exhaust gas measurement is shown in Figure 4. All data from the on-road exhaust emission measurement was used for input to dispersion model.

Figure 4. Sampling of exhaust gas. 2.2.2 Exhaust gas dispersion modeling The dispersion modeling was applied only for the free flow highway condition, i.e. for Chiang Rak Noi road, and is based on the Gaussian plume equation with 100% reflection from the ground for a point source. Concentrations of HC and CO were predicted by the model (Equation 1) at various distances behind the bus. The vehicle-induced turbulence behind the bus was accounted for by using increased dispersion coefficients, σy and σz (Figure A2, Appendix), presented in HIWAY2 model (Petersen, 1980). Blocking effect of the bus on wind immediately behind the bus was neglected.

+−+

−−

−= 2

2

2

2

2

2

2)(exp

2)(exp

2exp

2);,,(

zzyzy

HzHzyuQHzyxC

σσσσσπ Eq. 1

Where, C = Ambient air pollutant concentration, g/m3

Q = Source strength or emission rate, g/s u = Average plume transport velocity, m/s H = Effective discharge height, m σy = Cross wind dispersion coefficient, m σz = Vertical dispersion coefficient, m

To Five-Gas Analyzers

Sampling Tee Tube

Thermometer


6

Emission rate from bus exhaust Pollutant emission rate was obtained as the product of emission concentration (obtained directly from on-road emission measurement) and the exhaust gas flow rate. The latter was calculated following the method proposed by RADIAN (1996) as shown in Equation 2.

Cycle

EffVolRPMVV engair

××=

)( Eq. 2

Where, Vair = Volumetric flow rate of air through engine, L/min Veng = Swept volume of the engine, L RPM = Engine speed, rev/min Cycle = Number of times the engine turn over to displace its total swept volume (2 for four-

stroke engine) EffVol = Approximate volumetric efficiency EffVol was obtained from the graph of relationship between volumetric efficiency and speed range (Lilly, 1984), which is shown in Figure A3, Appendix.

Plume rise When a hot plume releases from a stack tip with a certain exit velocity it will rise to a certain height while dispersing downwind before spread horizontally. The effective height in Equation 1 is calculated as the sum of the plume rise and the physical height of the stack tip. In this study, as the exit velocity of the flue gas was high (16 to 38 m/s) and the gas was hot hence the plume rise should be important. However, for the original exhaust case, the exit direction was horizontal and the plume rise was neglected. For the case of extended exhaust, the plume rise was calculated using the Briggs' method presented in Wark et al. (1998) for unstable dispersion conditions. (Unstable dispersion conditions were assumed for both plume rise and plume dispersion modeling due to the increased turbulence on the road by the vehicle motion and high exhaust temperature). A gradual plume rise scheme was used and the rise was calculated for every second until the final rise was reached. As a matter of fact, the high exit velocity of the plume has resulted in only the momentum dominated cases. The high exit velocity also justifies the assumption of no plume downwash. Due to the inclined exhaust pipe (45o) the vertical component of the plume exit velocity (Rvertical = Vg sin 45°) was used as the Vs in the Briggs’ formula. Plume transport velocity When the bus is moving there are three vectors to be considered for plume transport, namely, vector of airflow pass the bus (VB), exhaust gas velocity vector (Vg), and average wind vector (U). The X axis was directed along the horizontal transport vector, RT. For the case of upward extended exhaust pipe, in the first stage when the plume was rising gradually before reaching the final rise its horizontal transport velocity is depicted in Figure 5 and calculated using Equation 3. After plume reaches its final/maximum rise the plume spreads horizontally with the transport velocity determined by Equation 4. For the case of the original exhaust pipe, no plume rise considered, hence the plume transport velocity was also


7

only determined by Equation 4 as seen in Figure 6. The transport speed was first calculated and substituted for u value into Equation 1. Resulting vector of plume horizontal transport during rising:

RT = VB + Vg cos 45° + U Eq. 3 Resulting vector affecting plume horizontal transport in plume spreading:

RT = VB + U Eq. 4

Figure 5. Plume transport vectors during rising phase (Equation 4).

Plume rise and transport for different bus speeds and hypothetical wind directions were calculated using Eq. 3 and 4. The details are shown in Figures A4-A7 (Appendix).

Figure 6. Plume transport vector during horizontal spreading phase (Equation 4).

Air flow from running bus

Y

Z

RT

X

Y

Z

RT X


8

The vehicle-induced turbulence behind the bus was accounted by using the σy and σz presented in HIWAY2 model (Petersen, 1980) which are higher than the Pasquill dispersion coefficents. The blocking effect of the bus on wind immediately behind the bus was neglected. This effect could be pronounced for the case of original exhaust pipe when the wind blows against the bus motion. In this case the plume transport in a short distance behind the bus would mainly follow the plume exit velocity from the exhaust. In addition, the airflow pass the bus was assumed to equal to the bus speed, i.e. no atmospheric and bus friction were accounted for. Only the short term modeling is used (30 minutes for sigma, Rao and Keenan, 1980) the reactivity of pollutants was neglected and both HC and CO were considered conservative. 2.2.3 Ambient air quality measurement Ambient CO and HC were measured by the respective automatic analyzer, which are built-in in a mobile ambient air quality monitoring station with the sampling point shown in Figure 7. Simultaneous measurement of PM10 was done at the same sampling point using a portable dust monitor (GRIMM dust monitor series-1100 v.5.10E). The data were recorded every second, well before the bus passed the sampling site to record the background concentration and continued until the pollutant levels reduced to the background concentration again after the bus passed. On average the recording periods were about 2 minutes for each test. At the free flow highway road the bus speeds were maintained at 20, 40, 60 and 80 km/h while at street canyon road the speeds were 20, 40 and 60 km/h. This was to reflect the actual speed limit at the busy streets inside the city. The measurements were conducted when the roads were not crowded to ensure as much as possible that the contribution to the concentration peaks when the bus passing-by was from the study bus only. The tests, which clearly did not meet this condition, were discarded. For the same reason the measurement at inner city street, Pracha U-Thit, was made during nighttime. Simultaneously, the wind speed and wind direction were also measured using the built-in meteorological equipment in the mobile station.

Figure 7. Ambient air monitoring equipment and location of the sampling site.

Flexible rubber tube

3 meters

1 meter


9

3. Results and discussions 3.1 Technical performance of the device Technical performance of the exhaust pipe extension is assessed using both monitoring and modeling. Thus it is presented in three sections: 1) Measurement data, 2) Modeling result, 3) Comparison of modeling results and measurement data. 3.1 .1 Measurement data The measurement data include both on-road exhaust emission, and ambient air quality data in the streets when the bus passing. On-road exhaust emission measurement The HC and CO concentrations in the exhaust did not change much but the opacity (a measure of particulate emission) fluctuated largely when the bus was maintained at desired speeds. Less black smoke was emitted when the bus was running at stable speeds. It was, however, quite challnging for the driver to maintain the bus stable at the desired speed for all the emission measurement periods of 5 minutes. There were many factors affecting the bus speed such as a bridge, roughness on the road, and traffic on the same road. Therefore, accelerations and decelerations were necessary to bring the speed back to the desired level, which were associated with high black smoke emission. The average on-road measurement results are presented in Table 1. The data were used to determine the emission rate using Equation 2 and the results are presented in Table 1. Details of emission rate, concentrations of CO, HC and PM10 before and after extended pipe are presented in Tables A2-A5 (Appendix). Parameters used for the calculations are given in Table A6. Table 1. Relationships between bus speed, engine speed, average emissions, and emission rate.

Bus speed (km/h)

Engine speed (rpm)

Hydrocarbon(ppm)

Carbon monoxide

(ppm)

Opacity (%)

Exhaust gas temperature

(°C)

Emission rate (m3/s)

20 800 16 312 0.13 86 0.073 40 1000 17 225 0.10 88 0.093 60 1500 20 145 0.31 90 0.144 80 1800 25 153 0.22 98 0.174

Effects of extension pipe: dispersion modeling The considered distances along the horizontal transport vector (RT) during plume spreading phase include x = 0, 0.5, 1, 2, 4, 8, 10, 15, 20, 25, 30, 40, 50, 60, 80, and 100 meters from the running bus for the free flow highway conditions. The concentrations at the plume centerline (y = 0) and at receptor height z = 1 meter were calculated. This simple model (Equation 1) does not consider the street canyon effects, therefore it was applied only for the free flow highway condition, i.e. the Chiang Rak Noi road. The calculation was made for all 4 considered bus speeds for HC, CO and opacity, which is closely related to PM10 (Wark et al., 1998). Due to the short term dispersion from the exhaust source, the assumption on


10

conservative pollutants was used for all 3 pollutants, hence these pollutants dispersion would change in a similar way when the exhaust is extended from the original exhaust pipe level. Therefore, only HC was modeled. The model results for HC for various hypothetical ambient wind directions are illustrated in Figure 8 for the Chiang Rak Noi road. The 30-year average wind speed in Bangkok (30-year average data in April from Don Muang airport station of Meteorological Department) of 3.2 m/s was used in the model. For all wind directions, the extension of the exhaust pipe resulted in the reduction of the simulated plume centerline pollutant concentration by around 3 times (Figure 8). Considering the variation of the pollutant with the wind direction, it is shown that the highest concentration was obtained for the 275°- wind direction (from the west). As metioned earlier, the Chiang Rak Noi road is oriented almost East-West at the radial of 95o from the North. In the calculation, it was supposed that the bus running direction was from the West to the East. Thus, the highest pollutant concentration was obtained when the wind blows in the same direction of the bus or opposite to VB, which resulted in the lowest plume transport velocity (u value in Equation 1). In this particular case, all 3 vectors (wind, VB and RT) are on the same line and the centerline of the plume was parallel to the road leading to higher exposure to people on the road, e.g. motorcycle riders. The minimum pollutant concentration was obtained when the wind blows against the bus movement or in the same direction as VB, around 95o, which resulted in the largest plume transport speed. The variation of pollutant concentrations with the distance along RT for the two cases is presented in Figure 9a and 9b, respectively. The larger difference in pollutant concentrations thus was observed at short distances from the running bus and no practical difference was observed for the distance larger than 80 m.

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 50 100 150 200 250 300 350

Wind direction, degrees

conc

entra

tion

of h

ydro

carb

on, p

pmV

Original

Extension

Figure 8. Plume centerline HC concentration for different wind directions at the bus speed of 20 km/h and wind speed of 3.2 m/s.


11

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 20 40 60 80 100 120

distance, meters

conc

entra

tion

of h

ydro

carb

on, p

pmV

origianlextension

Figure 9a. Simulated plume centerline HC concentrations for the original and upward extended exhaust pipe at various distances along RT, wind direction 275o (Bus speed of 20

km/h).

0

0.02

0.04

0.06

0.08

0.1

0.12

0 20 40 60 80 100 120

distance, meters

conc

entra

tion

of h

ydro

carb

on, p

pmV

origianlextension

Figure 9b. Simulated plume centerline HC concentrations for the original and upward extended exhaust pipe at various distances along RT, wind direction 95o (Bus speed of 20

km/h). In principle, the Gaussian modeling with reflection from the ground produces a maximum concentration of pollutants at the ground level (z = 0 m) in the down transport direction. In this study the maximum concentration at the sitting breathing level (z = 1 m) and the distance where it ocurrs were also simulated. The results showed that the maximum HC concentration at the sitting level, for the original exhaust pipe, occurred almost at the end of the exhaust pipe, i.e. x = 0. For the upward extended exhaust pipe to 3.95 m in this study, the maximum concentration occurred at x around 8 m (Figure 10). The maximum down transport concentration of original exhaust pipe (0.52 m above ground) is greater than the maximum


12

concentration of upward extended exhaust pipe (3.95 m) by a factor of 2.96-2.99 or around 3. Thus, in the original exhaust pipe case, due to the lower plume centerline the exhaust plume reaches the ground very fast producing the maximum concentration close to the exhaust release point. Figure 10 also shows the effect of extension height on maximum down transport concentration at 1 m above ground. For other bus speeds, the effect of extension height on maximum down transport concentration at 1 m above ground and distance where it occurs are similar but the maximum concentration value is different due to the different pollution emission rate.

0

0 . 0 2

0 . 0 4

0 . 0 6

0 . 0 8

0 . 1

0 . 1 2

0 1 2 3 4 5exhaust pipe height, meters

max

con

cent

ratio

n, p

pmV

Figure 10. Maximum downwind concentration and distance where it occurs for different exhaust pipe heights (wind direction of 95o and bus speed of 20 km/h). * Distance from the bus where the maximum concentration occurs The height of the extended exhaust pipe in this study (3.95 m) is marked by the arrow.

Results of ambient air quality measurement Ambient concentration depends on many variables including emission, meteorology, and terrain. Wind speed and wind direction, in particular, can affect the pollutant dispersion and resulting ambient concentrations from the bus exhaust. In this study the experiments were conducted in the air, i.e in uncontrolled conditions with a single bus. Hence, the simultaneous monitoring for both original and extended pipes were not possible. Monitoring for the ogininal and extended exhaust cases were conducted within 3-4 hour period for each street. The recorded wind data during the period showed substantial variations in speed and direction. In fact even small changes in wind direction may have a significant impact. For the 25 cm diameter of the conical sampling intake opening used in this study, which was mounted at the monitoring site, 3 m from the bus, a change in wind direction of 2.6° would be enough to fail the conical intake to catch the exhaust plume centerline concentration. Thus, strictly speaking, wind direction change by more than 2.6o would lead to incomparability of the results between the 2 sets of the experiments (original and extended exhaust pipe cases). Therefore, to assess the impact of the exhaust extension it was essential to pick up the

~ 0 meter*

8 meter* 10 meter*

15 meter*


13

experiemtns conducted when wind speed and wind directions between the 2 tests are similar. A few tests satisfied this condition and showed that the extension would reduce concentration at breathing level. The magnitude of reducions differs from test to test and they were in the range 1-3. For HC shown in Figure 11a the reduction in maximum concentration is around 3 times but for PM10 shown in Figure 11b it is around 1.5 times. Setlling of particles may partly contribute to this discrepancy. Not much reported data are available from literature. Weaver et al. (1986) estimated that the exhaust pipe extension above vehicle roof would reduce concentration of pollutants behind the vehicle by 65-87% or from 3 to 7.6 times. Another study, as cited by Weaver et al. (1986), found that the extension reduced pollutant concentration at breathing zone near a bus station by eight times.

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0 50 100 150

time after the bus passes, seconds

conc

entra

tion

of h

ydro

carb

on, p

pmV

OriginalExtension

Figure 11a. Ambient HC concentration at Chiang Rak Noi road when the bus passing at 40 km/h (background concentration was excluded).

0

2

4

6

8

10

0 5 10 15 20 25

time, seconds

PM10

con

cent

ratio

n, p

pmV

OriginalExtension

Figure 11b. Ambient PM10 concentration at Chiang Rak Noi road when the bus passing at 20 km/h (background concentration was excluded).


14

Comparison between monitoring and modeling results Since the measurements were done for 4 bus speeds for each of case, i.e. the original and extended pipe cases. Three tests were conducted for each speed resulting in 24 data points. The difference between the recorded peak value and the background (Figure 11a, b) was considered to represent the contribution of the passing bus emission to the ambient concentration at the monitoring location. The difference was used to compare with the modeled results. The latter were the maximum ambient concentrations resulted from the bus emission, which were calculated for the same mean wind direction and speed recorded during the tests. For comparison, first both monitoring and modeling results should be converted to the same averaging time basis. The dispersion coefficients used in this study were the average of 30-minutes sampling (Rao and Keenan, 1980), as mentioned earlier. The ambient air measurements were done for a 3 second sampling period. Conversion of the modeling results at 30-minutes sampling to the 3 second sampling concentration was made using Equation 5 (Beychok, 1994 and Wark et al., 1998).

q

ttCC

=

2

112 Eq. 5

where, C2 = Concentration at sampling period t2 (minute) C1 = Concentration at sampling period t1 (minute) q = Constant

The q value found in literature varies widely. There is strong solar radiation and the wind speed is 3-4 m/s during the experiment. Therefore, unstable condition, Class B, was taken. In this study q equals to 0.535 was used. This is the average between the value suggested by EPA (0.52) and the value suggested by the State of Texas (0.55) for stability class B as presented in Beychok (1994). The t1 value was of 30 minutes and t2 was 3 seconds. Simulated and measured 3-second averaged CO and HC concentrations for Chiang Rak Noi road were plotted in Figures 12 and 13, respectively. The agreement seemed better for CO in the extended exhaust and for HC in the original exhaust. The model overestimated CO for the original exhaust and underestimated HC for extended exhaust in some cases. There might be many factors contributing to the discepancies. Uncertainty in the model results may be related to 1) use simple Gaussian equation, 2) assumptions made in model formulation, 3) errors in etimation of dispersion coefficients and emisison rate, and 4) others. The monitoring could not be error free either. High fluctuations of wind during the measurements may produce errors as mentioned ealier. The short averaging time (3 seconds) used in monitoring, which was necessary to generate enough data points, would produce monitoring results that are more fluctuating due to wind fluctuations and atmopsheric turbulence. Nevertheless, in general the modeling and monitoring results are considered to be in a reasonable agreement, especially when the concentration range is considered for HC, for example. The ratio between modeled and measurement for HC varied from 0.2 to 2.4 with the average of 0.9. For CO, the ratio varied from 0.5 to 14.5 with the average of 3.3. The upper values of the ratio for CO were caused by 2 cases for orginal exhaust when modelled CO values were much higher than the monitoring. This would be interesting to further investigate but it is beyond the scope of this study.


15

Carbonmonoxide

0

0.5

1

1.5

2

2.5

0 0.5 1 1.5 2 2.5

Measurement (ppmV)

Mod

elin

g (p

pmV

)

OriginalExtension

Figure 12. CO concentration by measurement and modeling.

Hydrocarbon

0

0.05

0.1

0.15

0.2

0.25

0 0.05 0.1 0.15 0.2 0.25

Measurement (ppmV)

Mod

elin

g (p

pmV

)

originalextension

Figure 13. HC concentration by measurement and modeling.


16

3.2 Cost effectiveness of the upward extended exhaust pipe technique Cost of of the bus exhaust pipe extension was estimated for different pipe materials and installation options (outside or inside the bus body). The simple temporary upward extension done in the study had the material cost around US$ 35-50. The time for simple installation ranges from 2-4 man-hours. However, rerouting the exhaust pipe within the bus body/shell would give a good-look to the bus. In this case, heat insulation material should be used for the vertical exhaust pipe inside the bus and the interior design should be properly done. This would cost totally US$ 230 to 450 depending on the materials used and take 1-2 man-days for installation. For the truck case, the exhaust pipe should be rerouted to be placed to a gap between the cabin and truck body. The cost for this would be US$ 230-350 for stainless steel pipe. The benefit of the extension is seen through the reduction of the exposure to the exhaust emission in the immediate vicinity of the emission, i.e. in the streets. Cost of the health effects should be considered to analyse the cost-benefit of the technique. 3.3 Applicability of the upward extended exhaust pipe technique The social acceptance of the technique was studied through interview and questionnaires to stakeholders including 1) bus manufacturers, 2) bus drivers and 3) bus owners, and 4) general public. In all, 53 respondents were obtained among which three manufacturers, 25 drivers, 10 bus owners, and 15 public. Results of the interviews and questionnaires presented in Table 2 show that, in general, the technique was acceptable for most of the groups except for the bus owners who did not feel secured about the bus look. Some drivers shared the same concern. None of the groups would be willing to pay for the cost except the manufacturers who will eventually transfer the cost to the buyers. Thus, to promote the technique application initial subsidy should be considered to cover the cost. Besides, the potential reduction in maximum ambient concentrations and the associated benefit from the health effect reduction should be disseminated to all stakeholders. The awareness of the outweighing benefit to the small investment cost may promote the technique widely. Table 2. Social acceptance.

Parameters

Drivers (25 resp.)

Bus Owners (10 resp.)

Public people (15 resp.)

Manufacturers (3 resp.)

Accept 80% 0% 100% 100% Not accept 20% 100% 0% 0% Willing to pay 0% 0% 0% 100%*

* = The cost would be transferred to buyers Temporary upward extension of exhasut pipes for bus and diesel trucks may be easy to start with as short term remedy. However, rerouting the pipe, use of heat insultatin material and good designs would attract owners to adopt this technique. However, retrofiting vertical exhaust extension to trucks with original horizontal exhaust would be feasible in some cases and not feasible in other cases. This is due to limits imposed by truck design and use, such as garbage trucks or specialized construction vehicles. In Thailand, for example, most heavy-duty trucks are designed to emit their exhaust at the back. However, upward extended exhaust pipe at the back of trucks is not convenient because the vehicles are usually designed


17

with a movable back cover for unloading/loading materials or goods. The exhaust pipe should then be rerouted to be placed at the gap between the cabin and the truck body. It is speculated that some power loss may be associated with the exhaust upward extension. However, in urban areas buses do not normally run at maximum power output hence the power loss and subsequent fuel economy effect would not be substantial. It is necessary to note that this technology can only lead to reduction of immediate exposure by reducing the high concentration at the breathing level. It cannot reduce the total emission from the buses and trucks to the atmosphere. The reduction of exposure to high local concentrations would lead to reduction of both chronic and acute health effects from diesel exhaust. Considering the actual situation of many developing cities, where old and highly polluting fleet of diesel buses and trucks are still in streets, implementation of this low cost technique could be a workable shortterm measure to reduce health effects from the vehicle emission. The longer term solution to the problem should be the use of less but clean vehicles in cities. This in turn requires both technological measures such as exhaust cleaning or fuel alternatives and improved traffic management which lead to reduction of the pollution emision into the environment. 4. Conclusions and recommendations The upward extension of exhaust pipe to emit pollutants above the bus roof leads to reduction of the immediate exposure to the maximum ambient pollutants levels by a factor of around 3 as compared to the original horizontal exhaust pipe. Though the technique will not affect overall pollutant emission in to the atmosphere it should be used as a shortem retrofitting measure to the polluting old buses and trucks in cities. The material and installation cost is reasonable. A better design for the extension to give vehicle a good-look, subsidy and awareness raising would promote the technology wide application. The technique should be used in combination with other longterm techniques to eventually improve urban air quality. Further study on the effect of extension to the engine power and fuel economy should be made to gain better understanding of social-economical impact of the extension and to promote the technique. 5. References Beychok, M.R., 1994. Fundamentals of Stack gas Dispersion. Third edition, ISBN 0-9644588-0-2. Lilly, L C R, 1984. Diesel Engine Referent Book., Butterworths and Co. Ltd.: 6/10. Petersen, W.B., 1980. User's Guide for HIWAY-2 a Highway Air Pollution Model. (U.S.) Environmental Sciences Research Laboratory. EPA-600/8-80-018: 63. Radian, 1996. Revise Final Report Performance and Emissions Comparison of Three Diesel Buses in Bangkok.- Pre-emissions Control, Euro 1, and Euro 2 Technologies. Pollution Control Department (PCD): 33-42.


18

Rao S. T. and Keenan M. T. 1980. Suggestions for Improvement of the EPA-HIWAY Model. Journal of Air Pollution Control Association, Volume 30: 60-69. Springer, G.S. and Patterson, D.J., 1973. Engine Emissions Pollutant Formation and Measurement. A Division of Plenum Publishing Corporation, London: 4-7. Supat Wangwongwatana, 1999. Air Pollution Control Strategies in Thailand. Paper presented at International Urban Environmental Infrastructure Forum, A&WMA, 92nd Annual Meeting & Exhibition, St. Louis, Missouri, USA, June 20-24, 1999. Wark, K., Warner, C.F., and Davis, W.T., 1998. Air Pollution Its Origin and Control. Addison Wesley Longman Inc.: 1-55. Weaver, C.S., Klausmeler, R.J., and Erickson, L.M., 1986. Feasibility of Retrofit Technologies for Diesel Emissions Control. SAE Technical Paper Series, No. 860296: 231-250. 6. Appendices 6.1 Details of results

A. Figures

Figure A1. Upward extended exhaust pipe connection for temporary use


19

Source: Petersen (1980) Figure A2. The dispersion curves (scatter line) used in the original HIWAY model and the Pasquill-Gifford dispersion curves (solid line).

Source: Lilly (1984) Figure A3. Relationship between volumetric efficiency and speed range for common diesel engine.


20

0

5

10

15

20

25

0 50 100 150 200 250 300 350Wind direction, degrees

Plum

e sp

eed,

m/s

Resulting vector duringplume riseResulting vector duringplume dispersion

0

50

100

150

0 50 100 150 200 250 300 350


dire

ctio

ns, d

egre

es


Figure A4. Plume rise speed and directions at 20-km/hr bus speed for extended pipe (30 years average wind speed for April of 3.2 m/s is used).


21

05

101520253035

0 50 100 150 200 250 300 350


Plum

esp

eed,

m/s


0

50

100

150

0 50 100 150 200 250 300 350

wind direction, degrees

dire

ctio

ns, d

egre

es


Figure A5. Plume rise speed and direction at 40-km/hr bus speed for extended pipe (30 years average wind speed for April of 3.2 m/s is used).


22

0

10

20

30

40

50

0 50 100 150 200 250 300 350


Plum

e sp

eed,

m/s Resulting vector

during plume rise

Resulting vectorduring plume

0

50

100

150

0 50 100 150 200 250 300 350


dire

ctio

ns, d

egre

es Resulting vectorduring plume riseResulting vectorduring plume

Figure A6. Plume rise speed and direction at 60-km/hr bus speed for extended pipe (30 years average wind speed for April of 3.2 m/s is used).


23

0

10

20

30

40

50

60

0 100 200 300 400


Plum

e sp

eed,

m/s


0

50

100

150

0 50 100 150 200 250 300 350


dire

ctio

ns, d

egre

es


Figure A7. Plume rise speed and direction at 80-km/hr bus speed for extended pipe (30 year average wind speed for April of 3.2 m/s is used).

B. Tables

Table A1. Measurement parameters.

Parameters Equipment Used for Wind speed Mobile station Input in model Wind direction Mobile station Input in model Total hydrocarbon concentration in ambient air (THC)

Mobile station Compare with model output

Carbon monoxide concentration in ambient air (CO)

Mobile station Compare with model output

Particulate matter size less than 10 µm. concentration in ambient air (PM10)

Portable dust monitor Compare with model output

Engine speed Speedometer Input in model Temperature of exhaust gas Thermometer Input in model Total hydrocarbon concentration of exhaust gas (THC)

5-gas analyzers Input in model

Carbon monoxide concentration of exhaust gas (CO)

5-gas analyzers Input in model

Opacity of exhaust gas Opacimeter Input in model


24

Table A2. Emission rates from the study bus.

Bus speed

(km/hr)

Temperature

(°C)

Emission rate of CO

(ppm. m3/s)

Emission rate of CO (mg/s)

Emission rate of HC

(ppm. m3/s)

Emission rate of opacity (% m3/s)

20 86 22.841 21.704 1.186 0.00941 40 88 20.998 19.842 1.602 0.00918 60 90 20.871 19.614 2.922 0.04438 80 98 26.642 24.497 4.297 0.03881

Table A3. Maximum CO concentrations of pollutants in ambient air in Pracha U-Thit road (excluding background concentration).

Original exhaust pipe Upward extended exhaust pipe Bus speed (km/hr)

Test Wind speed and

Wind direction Concentration

(ppm) Wind speed and Wind direction

Concentration (ppm)

1 ws = 1.7 m/s wd = 231 deg

0.8 ws = 1.0 m/s wd = 204 deg

0.7

2 ws = 1.7 m/s wd = 212 deg

0.3 ws = 1.7 m/s wd = 219 deg

0.7

20

3 ws = 1.9 m/s wd = 150 deg

0.5 ws = 1.8 m/s wd = 230 deg

0.4

1 ws = 2.0 m/s wd = 261 deg

0.4 ws = 2.1 m/s wd = 155 deg

0.9

2 ws = 1.5 m/s wd = 247 deg

2.0 ws = 2.0 m/s wd = 251 deg

0.8

40

3 ws = 1.9 m/s wd = 186 deg

0.4 ws = 2.3 m/s wd = 231 deg

1.6

1 ws = 1.3 m/s wd = 140 deg

0.8 ws = 1.6 m/s wd = 177 deg

1.1

2 ws = 1.4 m/s wd = 131 deg

0.8 ws = 1.2 m/s wd = 167 deg

0.9

60

3 ws = 2.3 m/s wd = 215 deg

1.0 ws = 1.5 m/s wd = 251 deg

0.8


25

Table A4. Maximum HC concentrations of pollutants in ambient air in Pracha U-Thit road (excluding background concentration).


Test Wind speed and


(ppm) Wind speed and Wind direction

Concentration (ppm)

1 ws = 1.7 m/s wd = 231 deg

0.09 ws = 1.0 m/s wd = 204 deg

0.10

2 ws = 1.7 m/s wd = 212 deg

0.14 ws = 1.7 m/s wd = 219 deg

0.11

20

3 ws = 1.9 m/s wd = 150 deg

0.14 ws = 1.8 m/s wd = 230 deg

0.10

1 ws = 2.0 m/s wd = 261 deg

0.21 ws = 2.1 m/s wd = 155 deg

0.16

2 ws = 1.5 m/s wd = 247 deg

0.25 ws = 2.0 m/s wd = 251 deg

0.12

40

3 ws = 1.9 m/s wd = 186 deg

0.15 ws = 2.3 m/s wd = 231 deg

0.18

1 ws = 1.3 m/s wd = 140 deg

0.16 ws = 1.6 m/s wd = 177 deg

0.17

2 ws = 1.4 m/s wd = 131 deg

0.15 ws = 1.2 m/s wd = 167 deg

0.15

60

3 ws = 2.3 m/s wd = 215 deg

ws = 1.5 m/s wd = 251 deg


26

Table A5. Maximum PM10 concentrations of pollutants in ambient air in Pracha U-Thit road (excluding background concentration).


Test Wind speed and


(µg/m3) Wind speed and Wind direction

Concentration (µg/m3)

1 ws = 1.7 m/s wd = 231 deg

32 ws = 1.0 m/s wd = 204 deg

19

2 ws = 1.7 m/s wd = 212 deg

19 ws = 1.7 m/s wd = 219 deg

14

20

3 ws = 1.9 m/s wd = 150 deg

15 ws = 1.8 m/s wd = 230 deg

24

1 ws = 2.0 m/s wd = 261 deg

14 ws = 2.1 m/s wd = 155 deg

23

2 ws = 1.5 m/s wd = 247 deg

19 ws = 2.0 m/s wd = 251 deg

21

40

3 ws = 1.9 m/s wd = 186 deg

12 ws = 1.6 m/s wd = 261 deg

8

1 ws = 1.3 m/s wd = 140 deg

11 ws = 1.6 m/s wd = 177 deg

15

2 ws = 1.4 m/s wd = 131 deg

12 ws = 1.2 m/s wd = 167 deg

12

60

3 ws = 2.3 m/s wd = 215 deg

21 ws = 1.5 m/s wd = 251 deg

19


27

Table A6. Parameters used in input data preparation for modeling.

Type Symbol Definition Value Unit Source

Emission Information

RPM Ts

Veng

Eff Vol Cycle

VB

D hs

conc.

Engine speed Exhaust gas temperature Swept volume of the engine Volumetric efficiency Number of times the engine turn over Air flow through the bus (equal bus speed) Exhaust pipe diameter Emitted exhaust height Emission concentration

Table 3.2 Table 3.2

13.741

Fig. 2.5 2

Table 3.1

0.0762 0.52/3.95 Table 3.1

RPM °C

Liters

% Cycle

m/s

m. m.

ppm.

Measurements Measurements Secondary data

Secondary data Secondary data

Measurements

Measurements Measurements Measurements

Receptor Coordinates

x y z

Distance from source in x-axis Distance from source in y-axis Height from ground in z-axis

Sec 3.2.4

Sec 3.2.4 1

m.

m.

m.

Fix data

Fix data

Fix data

Meteorological

data

Ta u

Avg. ambient air temperature*

Avg. ambient wind speed*

29.7

3.2

°C

m/s

Secondary data

Secondary data

* 30-year average data in April from Don Muang airport Meteorological station. 6.2 List of publications from this research/conference presentations/manuscript 1. Kim Oanh, N. T., and Prapat, P., (2002). Upward Extended Exhaust Pipe of Diesel

Powered vehicles and Its Effects on Pollutants Concentration at Street Level. Asian Society for Environmental Protection (ASEP) Newsletter, Volume 18, No.4, June 2002.

2. Prapat, P. and Kim Oanh, N. T. (2002). Upward Extended Discharge Height of Diesel Exhaust and Its Effects on Pollutants Concentration at Street Level. Award-winner in 95th student paper competition organized by Air & Waste Manage. Assoc. West coast section in Baltimore, USA.

3. Result dissemination on local TV channel: Mr. Prapat P. gave a TV interview on the scoop of “Thai People Today” during the evening news on Bangkok Broadcasting Television Channel 7 (local TV channel) about Effect of Upward Extended Discharge Height of Diesel Exhaust. The interview was held on 2 August 2002 and broadcasted on 8 August 2002.

6.3 List of technical training: no technical training

6.4 Devices and equipment hired/purchased An air condition bus was rented for 3 weeks One mobile air quality monitoring unit was rented for 1 week Consumable items for exhaust pipe extension and sampling set-up Total cost (excluding field work): US$ 2500.

Dispersion Modeling Report-AIT July 2004 i

Appendix III


Improving Air Quality in Asian Developing Countries (AIRPET) AIT Report

Final Report for Dispersion Modeling Issue

Phase 1: 2001-2004

Prepared by

Dr. Nguyen Thi Kim Oanh

July 2004

Dispersion Modeling Report-AIT July 2004 ii

Abstract The dispersion modeling activities at AIT consist of 3 main parts: 1) photochemical smog modeling for Bangkok metropolitan region, 2) development of synoptic climatological models for prediction of air pollution, and 3) estimation of mixing heights. The summary of each part is given below. Photochemical smog modeling: An in depth analysis of relationships between hourly ozone and its precursors and meteorological conditions in Bangkok was made for 5 years (1996-2000). Both status and trend (unadjusted meteorologically) of photochemical smog pollution in the Bangkok Metropolitan Region were analyzed. Wind field was analyzed and monthly windrose was constructed for Bangkok based on 10-year data at the Bangkok Metropolis meteorological station. Simulation of photochemical smog for Bangkok Metropolitan Region was done for a 2 day episode using two photochemical smog model systems, UAM-V/SAIMM and CHIMERE/ECMWF. The simulations by the two models systems were compared with each other and with the observations. Multiple model runs with different precursor emission reduction scenarios which showed that the best model performance with the simulated 1-hr O3 meeting all the criteria was obtained when the VOC and NOx emission from mobile source reduced by about 50% and CO by 20% from the original database. O3 formation in Bangkok was found to be more VOC sensitive than NOx sensitive. To attain the Thailand ambient air quality standard for 1-hr O3 of 100ppb, VOC emission in BMR should be reduced by 50% - 60%. Management strategies considered in the scenario study consist of Stage I, Stage II vapor control, replacement of 2-stroke by 4-stroke motorcycles, 100% CNG bus, 100% NG-fired power plants, and replacement of MTBE by ethanol as additive for gasoline. Synoptic climatological model: A scheme to classify the meteorological conditions governing over the Bangkok Metropolitan Region was developed using i) meteorological over a single Bangkok weather station and ii) on the regional weather stations at 0700 LST. The first approach produced six distinct synoptic categories but could not describe clearly in terms of relationships between meteorological parameters in each synoptic cluster and ozone concentration levels in BMR. The second approach produced six distinct synoptic categories, which exhibited better relationships with the O3 monitoring data of 9 years (1992-2000) over high ozone months (November-May). Statistical models were developed to predict O3 for each synoptic category over BMR based on the 0700 LST meteorological conditions. Mixing height: Mixing height monitoring data (by remote sounding systems) at the Mae Moh site in Lampang province and Maptaput site in Rayong province were investigated for 1 year (2001). A modified zero-order mixed layer model BLES with adaptation of virtual temperature and kinematics virtual potential temperature flux to the existing set of equations was applied to determine the mixing height at these sites. In addition, graphical method was also applied to estimate the mixing height. The model outputs showed a reasonable agreement with monitoring data for most of the analyzed days.

Dispersion Modeling Report-AIT July 2004 iii

Table of Contents

Page Title Cover page i Abstract ii Table of contents iii Part 1: Photochemical Smog Modeling for Bangkok Metropolitan Region 1. Introduction 1 1.1 Background 1 1.2 Statement of problem 1 1.3 Objectives 1 1.4 Scope of the study 1 2. Methodology 2

2.1 Simulation Models and Model Domain 2 2.2 Modeling Domain 2 2.3 Ambient Air Quality and Meteorological Data 3 2.4 Selection of Photochemical Episode for Historical Simulation 4 2.5 Anthropogenic Emission Data 4 2.6 Biogenic Emission Data 5 2.7 Initial and Boundary Conditions 6

3. Results and discussion 7 3.1 Modelling results based on PCD anthropogenic emission database 7 3.2 Evaluation of PCD anthropogenic emission database 21 3.3 Emission scenario study 21 3.4 Scenarios simulation 24 4. Summary and conclusions 25 5. References 26 Part 2: Synoptic Climatological Modeling 1. Introduction 28 1.1 Background 28 1.2 Statement of the problem 28 1.3 Objectives 29 1.4 Scope of the study 29 2. Methodology 29 3. Results and discussion 32 4. Conclusions and recommendations 39

Dispersion Modeling Report-AIT July 2004 iv

Part 3: Mixing Height Calculation 1. Introduction 41

1.1 Background 41 1.2 Statement of the Problem 41 1.3 Objectives 42 1.4 Scope of the Study 42

2. Methodology 43

2.1 Description of Modeling Domain 44 2.2 Description of Selected Model 44 2.3 Input Data Processing and Analysis 45

3. Results and Discussion 46 3.1 Mixing Height Variations 46 3.2 Estimation of MH Using BLES Model and Model

Performance Evaluation 49

4. Conclusions and Recommendations 51

5. References 52

Dispersion Modeling Report-AIT July 2004 1

Part 1: Photochemical smog modeling for Bangkok Metropolitan Region 1. INTRODUCTION 1.1 Background Photochemical ozone is formed in the atmosphere as a result of complex chemical reactions of precursors under the strong influence of meteorology. The relationship between precursor pollutants and photochemical O3 is different from place to place due to the emission distribution and meteorology (NRC, 1991). Ozone formation can be described as either VOC (volatile organic compound) or NOx (nitrogen oxides, NO+NO2) sensitive, depending on VOC/NOx ratios, VOC reactivity, and other factors (Sillman, 1999). During the last three decades, significant progress has been made in the understanding of ozone-precursor relationships through laboratory, field, and modeling studies (Chang et al., 1997). Photochemical air quality models play a central role in scientific investigation of how pollutants evolve in the atmosphere as well as developing policies to manage air quality (Russell and Dennis, 2000). 1.2 Statement of the problem Applications of photochemical smog models for air quality management require systematic monitoring data of ozone and its precursors, and model input databases, which are not sufficiently available in most of Asian developing countries. Lack of a satisfactory emission database is common and in most of the cases only rough estimates are available with many uncertainties. These are the barriers to the scientific research for photochemical smog and modeling work in the countries. Similar to most of megacities in the world, Bangkok, the capital of Thailand, has also experienced photochemical smog pollution that is largely due to high precursor emissions from mobile sources. Highest O3 pollution was found in the period from January to April (winter and local summer) and lowest during mid-rainy season (Zhang and Kim Oanh, 2002). Meteorology unadjusted trend showed a slight increase in O3 from 1998 to 2000. In 2000 there was also a large number of hours (174 hr) exceeding 100ppb in Bangkok. 1.3 Objectives This research aims at applications of photochemical smog models to understand the formation and accumulation of the photochemical smog in the city, as well as to develop management strategies to minimize the pollution in Bangkok city. 1.4 Scope of the study Two modeling systems were used. Performance of the 2 model systems was compared on the input data for BMR. Impacts of various management scenarios on photochemical smog pollution in Bangkok were tested.


2. METHODOLOGY 2.1 Simulation models and model domain In this study two modeling systems, the Variable-Grid Urban Airshed Model (UAM-V), developed by the Systems Applications International (SAI) combined with the System Applications International Mesoscale Model (SAIMM) meteorological model and CHIMERE combined with ECMWF (European Center for Medium Range Weather Forecasts) meteorological model, were applied to study a selected episode of January 13-14 in 1997 which are representatives of meteorological conditions with high O3 observed in BMR. Emission database provided by Pollution Control Department (PCD) in 1997 were first evaluated through comparison of modeling results with observations in terms of O3, NOy (total reactive nitrogen, NOx plus all other species with N and O such as HNO3, HNO2, PAN, etc.), Ox (O3+NO2, an indicator of photochemical oxidation level), and CO (a less reactive species which can represent the atmospheric dispersion). Further, two modeling systems have been used to study the sensitivity of O3 formation to change in VOC and NOx emissions in Bangkok. An extended version of the Carbon Bond IV (CB-IV) mechanism (SAI, 1999) is used in UAM-V, in which model species represent different types of carbon bonds in hydrocarbons. MELCHIOR (ModèlE Lagrangien de la CHImie de l’Ozone à l’échelle Régionale) mechanism (Lattuati, 1997; Vautard et al., 2001) is used in CHIMERE model, which belongs to lumped species mechanism with fix parameters. The MELCHIOR mechanism can be regarded as an extended version of the European Monitoring and Evaluation Programme (EMEP) mechanism (Simpson, 1992). For both CHIMERE and UAM-V modeling systems, the horizontal grid resolution is 4km × 4km. There are 6 layers in vertical direction for both models, with the top height of 50m, 100m, 500m, 1500m, 2500m, and 4000m for UAM-V, and the top height of 50m, 300m, 600m, 1200m, 2000m, and 3000m approximately for CHIMERE. The horizontal grid size was also 4km × 4km for SAIMM. The vertical structure of the SAIMM model consisted of 16 levels with the top heights at 10, 35, 75, 150, 300, 600, 1000, 1500, 2100, 2900, 3700, 4500, 5500, 6500, 7500, 8500m. For CHIMERE, the data from ECMWF were used, which is known to be among the best global-scale forecast model (Vautard et al., 2001). 2.2 Modeling domain Bangkok and its surrounding five provinces Samut Prakarn, Nonthaburi, Pathumthani, Nakhon Pathom, and Samut Sakhon, which collectively known as the Bangkok Metropolitan Region (BMR) are closely linked in terms of traffic and industrial development. With consideration of the available emission inventory and distribution of air quality monitoring stations, most of the area of BMR (88km × 72km) covering the UTM (Universal Transverse Mercator) coordinate from the southwest corner (615km, 1488km) to the northeast corner (703km, 1560km), was selected as the modeling domain (Figure 1). The BMR lies on an open plain, not hemmed by any geographical barriers. The plain is the alluvial basin of the Chao Praya River that enters the Gulf of Thailand at Samut Prakarn to the south of Bangkok. The model simulation domain is given in Figure 1.


2.3 Ambient air quality and meteorological data Ambient air quality data. Hourly air quality and meteorological data were collected from 29 automatic ambient air monitoring stations in the domain for five consecutive years (1996 to 2000). Air pollution parameters collected include CO, NO, NO2, and O3. In addition, air quality data of 1997 from the Thailand Environmental Research and Training Center (station No.30) were also collected. Surface and upper air meteorological data input for SAIMM. Hourly surface meteorological parameters were collected from 29 air quality monitoring stations and 2 surface meteorological stations, the Bangkok Metropolis (M1, Figure 1) and the Donmuang Airport (M2). In addition, three-hourly surface meteorological data were also collected from two other meteorological stations, Bangkok Port (M3) and Pilot Station (M4). The data collected include wind speed, wind direction, temperature, relative humidity, pressure, globe radiation, and net radiation for the period from 1996 to 2000. The SAIMM input data include hourly surface and at least two time daily upper air data (RAWINSONDE at 0GMT and 12GMT) of wind, potential temperature and specific humidity. Therefore, the upper data at 12GMT were downloaded from the National Oceanic and Atmospheric Administration (NOAA) website (anonymous FTP archive.cdc.noaa.gov), including pressure levels, temperature, relative humidity, and corresponding heights for two points (12.5N, 100E) and (15N, 100E). The mean values of the corresponding data from these two points together with RAWIN/PIBAL wind data collected from the Thai Meteorological

Figure 1 Locations for air quality monitoring stations (circle and the number) and meteorological stations (square and number). Inside solid square shows simulation domain, UTM (615km, 1488km) to (703km, 1560km). Circles show the distances from Bangkok city center.


Department (TMD) were used as 12GMT upper air data input for the SAIMM model. Meteorological data for CHIMERE. Hourly meteorological data of wind, temperature, humidity, pressure, radiation, and mixing height are calculated by interpolation from raw ECMWF data, and data are averaged within the CHIMERE model layers.

2.4 Selection of photochemical episode for historical simulation A number of photochemical episodes in the domain during the study period of 1996-2000 have been identified. The episodes were characterized with ozone levels above the Thai National Ambient Air Quality Standards (NAAQS) of 100 ppb at a number of air quality monitoring stations (spatial) and for a large number of hours (temporal). Selection of photochemical episodes for simulation was made based on the following criteria: 1) the area with monitored ozone concentrations exceeding the Thai NAAQS of 100ppb across Bangkok was as large as possible, 2) high ozone concentrations at several stations in Bangkok should last at least two hours during one day and at least for two consecutive days, and 3) meteorology of episodes should be representative of the frequently occurring conditions for future applications of the modeling systems for air quality management purposes. A winter episode of January 13-14 in 1997 was finally selected for simulation. Analysis of meteorological conditions showed that the selected episode days were characterized by light winds (1-2 m/s), low cloud cover (3/10), and high temperatures (maximum of 32oC) in Bangkok. The surface synoptic charts (at 7:00 LST) of both days showed the typical winter synoptic situation with a presence of a high pressure over China, which extended a ridge to Southeast Asia. This synoptic pattern prevails in November to February, which governs weather conditions in BMR during this period of the year (Ekbordin, 2002). Highest O3 pollution in Bangkok was found in the period from January to April (Zhang and Kim Oanh, 2002). The meteorology on January 13 – 14, 1997 is thus representative of the high ozone conditions in BMR hence the episode was selected for the study. 2.5 Anthropogenic emission data The anthropogenic emission databases include area sources (residential area, petrol service station, airport, and refuse disposal), mobile sources, and point sources. There was only one set of average 24-hour mobile source data rather than a pattern for each day of a week (weekly pattern). Therefore, this 24-hour data was used for all simulation days. There are 7054 point sources listed in BMR including one municipal incinerator and the rest are industries and power plants (PCD, 2000). For the area and mobile sources, 4km × 4km gridded hourly data of CO, NOx, and VOC were obtained from the Airviro (an air quality management system currently used at PCD). The low-level point sources (stack heights less than 31m) were treated as area sources, and the pollutant emissions were directly inputted into the corresponding grid cells. The point sources with high stacks were treated as elevated sources. In the simulation domain, there were 47 such point sources, which have stack heights in the range of 31m to 109m. NOx emission data obtained from PCD as nitrogen dioxide (NO2) was converted to corresponding portions of NO and NO2, assuming NOx consisted of 90% NO and 10% NO2 by weight. The maximum NOx and VOC emissions were obtained at the city center, estimated at 600 and 1600kg /[(4km×4km)·h] at 13:00, respectively. The mobile sources contributed more than 92 % of the


total anthropogenic emissions for NOx and VOC in the city center (PCD, 2000). Maps showing distribution of NOx, VOC emission data are given in Figure 2.

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Figure 2. Anthropogenic NOx and VOC emission strength (kg (4km x 4km)-1h-1) for area and line sources at 13:00

Recently, a limited number of hydrocarbon emission profiles were made available for major sources in Bangkok. However, the unknown portions of NMHC are high, for example, unknown for petrol vehicle exhaust was 30%, for diesel vehicle exhaust was 58%, and for paint was 67% (PCD, 2001). Therefore, the well-developed volume VOC emission profile for the European boundary layer (Kuhn et al., 1998) was converted to mass VOC profile and then used in this study. 2.6 Biogenic emission data Firstly, land use classification in the BMR land use map was matched with ecosystem types described by Guenther et al., (1995), which resulted in 9 ecosystem types: city/water body, crop/woods-warm, farm/city-warm, grass/shrub-hot, irrigation crop-warm, paddy rice, tropical rain forest, tropical seasonal forest, and swamp. The equations described by Guenther et al. (1995; 1993) were first used to calculate isoprene and monoterpene emissions at the standard conditions for the photosynthetically active radiation flux (1000 µmol/ (m2 ·s) and leaf temperature (303K). The gridded emission was then calculated and presented in Figure 3.

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Figure 3. Biogenic isoprene and monoterpene emission strength (kg (4km x 4km)-1h-1) at standard condition


For CHIMERE model, calculated method is same as UAM-V, but land use data of 1km × 1km were downloaded from website (http://gaia.umiacs.umd.edu:8811/cgi-bin/prod/landcover/load.pl.) and gridded surface temperature data were produced by ECMWF; only isoprene was calculated because MELCHIOR mechanism neglects terpenes on the assumption that their net-effect on ozone is negligible. Actually they react not only with OH, but also with ozone itself, and probably form aerosols quickly (Hass et al., 1997). 2.7 Initial and boundary conditions Two start-up days (Jan 11-12, 1997) were used to minimize the effects of the initial concentration field on the episode day simulation (SAI, 1999). Actual meteorological conditions of the start-up days were applied for the start-up simulation while the same emission database was used for both start-up and episode day simulations. Examination of the synoptic charts of these two days and the episode days (13-14 Jan) showed that meteorologically the period was representative of the Northeast monsoon conditions prevailing in the dry season. For UAM-V, observed NO, NO2, CO, VOC and O3 at the start-up time were interpolated to obtain gridded concentrations and then served as the initial concentration field. The vertical top boundary conditions and the up air vertical lateral (other than the surface/first layer boundary conditions) were, however, time invariant and included concentrations of 23 species obtained from SAI (SAI, 2002). The value for O3 was 60 ppb, which was obtained by running the regional CHEMERE model (http://euler.lmd.polytechnique.fr/chimere) (Zhang, 2002). For CHIMERE, hourly boundary conditions were obtained through running a large scale CHIMERE model from latitude 8N to 22N and longitude 96E to 112E with the resolution of 0.5deg × 0.5deg. UAM-V/SAIMM was run on the Sun Workstation at Asian Institute of Technology (AIT), Thailand. CHIMERE model was run at Laboratoire de Meteorology Dynamique (LMD), Ecole Polytechnique, France. The model systems were first run on the PCD anthropogenic emission data and the estimated biogenic emission database. Multiple runs of the model systems with alternative reductions in anthropogenic emission of NOx, CO, and VOC were then made to assess the sensitivity of the model performance to the modifications in the emission database. In order to assess model performance for ozone, the ability of the models to predict the concentrations of key ozone precursors should also be considered. Since chemiluminescent analyzers used to measure NOx (NO and NO2) also respond to other nitrogen-containing air pollutants, NOx data would closely reflect the true NOy (total reactive nitrogen, NOx plus all other species with N and O such as HNO3, HNO2, PAN, etc.) rather than NOx (Marr et al., 2002). Ox (O3+NO2) is an indicator of photochemical oxidation level by the models while CO, a less reactive species, can be used to study the atmospheric dispersion. Therefore, simulated results of O3, NOy, Ox, and CO were analyzed against the observations at the Bangkok air quality stations both temporally and spatially. It is noted that due to lack of complete records of monitoring data at monitoring stations outside Bangkok during the episode, the model outputs were evaluated against the measurements in the city only.


3. RESULTS AND DISCUSSION 3.1 Modeling results based on PCD anthropogenic emission database Spatial Distribution. Available observed data at the 18 monitoring stations in BMR were used to draw observation contours. Wind observed at the stations was also plotted (Figure 4). The limited observed O3 data points do not guarantee the reliability of the drawn contours, especially outside the Bangkok city where data points are sparse. Therefore, the comparison between the simulated and observed patterns for all parameters has only limited meaning. Nevertheless, the observed O3 on both days, as expected, show a minimum close to the Bangkok city center (Figure 4) where NOx emission was maximum. The observed O3 patterns on Jan 13-14, as expected, show a minimum close the Bangkok city center (Figure 4) where NOx emission was maximum. Based on the PCD emission database, the UAM-V simulated O3 spatial distribution on Jan 13 shows a minimum in the North of Bangkok and a maximum O3 in downwind direction to the Southwest corner of the city; the CHIMERE also shows a minimum in the North of Bangkok but a maximum O3 in downwind direction to the Southwest corner outside the Bangkok city. On Jan 14, the simulated spatial pattern by both models shows a minimum at a North corner of the city and a maximum O3 in the southwest part of the city, which is close to the observed location. Similarly, for NOy (Figure 5), Ox (Figure 6) and CO (Figure 7), there is a better agreement between simulated and observed spatial distribution patterns in terms of locations of the maximum values on 14 January than 13 January. However, there are substantial deviations between the simulated results and observations in terms of the maximum of considered parameters in the domain as seen from Table 1. Simulated maximum O3 in the whole domain for the PCD database is higher than the observed by 60 ppb by UAM-V and 40ppb by CHIMERE on 13 Jan and by 90 ppb by UAM-V and 40ppb by CHIMERE on 14 Jan. The simulated and observed minimum O3 in the Bangkok city are in a better agreement on 14 Jan than 13 Jan.

There are simulated low O3 slots by both models in the south part of the domain, just outside the Bangkok city for both days, but they did not appear on the observed plot. The limited observed data points (from 18 monitoring stations) do not guarantee the reliability of the observed contours, especially outside the Bangkok city where data points are sparse. It is also noted that most of elevated point sources with high NOx emissions are located in this part including two power plants with stack height 109 m (PCD, 2000). In this study for simplification we did not use the plume-in-grid algorithm (SAI, 1999) for processing point source input for UAM-V (Kim Oanh and Zhang, 2003), and elevated point sources were summed up in different grid and then input to second layer for CHIMERE. The processing of point sources mentioned above may introduce some error to simulation results. Observed NOy on 13 Jan shows a maximum at the city center but the simulated NOy has several maximum locations with the highest value in the domain. Both observed and simulated distribution patterns by two models on 14 Jan show one maximum location at the city center and the other at outside the city, to the south of the domain. On 13 Jan, the simulated maximum NOy by UAM-V in the city center was lower than the observed by 30 ppb, and NOy by CHIMERE in the city center was higher than the observed by 10 ppb (Table 1). On Jan 14 simulated maximum NOy in the city center were higher than the observed by 25ppb for UAM-V and 65ppb for CHIMERE. The simulated maximum Ox values in the domain were also higher than the observed, by 40 ppb for UAM-V and 20 ppb for CHIMERE on Jan 13, and 105 ppb for UAM-V and 75 ppb for CHIMERE on Jan 14. On Jan 14, the


simulated CO by both models is higher than the observed, by 1.0ppm for UAM-V and 1.8ppm for CHIMERE.

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[Observed hourly O3 and wind (0-3.5m/s) at 13:00 Jan13, 1997] [Observed hourly O3 and wind (0-2.3m/s) at 13:00 Jan14, 1997]

[UAM-V/SAIMM simulated O3 and wind (0.1-1.6m/s) at 13:00 Jan14]

[UAM-V /SAIMM simulated O3 and wind (0.4-1.8m/s) at 13:00 Jan13]

[CHIMERE/ECMWF simulated O3 and wind (1.42-1.99m/s) at 13:00 Jan13]

[CHIMERE/ECMWF simulated O3 and wind (1.02-1.31 m/s) at 13:00 Jan14]

Figure 4 Comparison between the simulated based on PCD emissions and observed O3 (ppb) at 13:00 on Jan 13 and Jan 14.


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Figure 5 Comparison between the simulated based on PCD emissions and observed NOy (ppb) at 13:00 on Jan 13 and Jan 14.


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Figure 6 Comparison between the simulated based on PCD emissions and observed Ox (ppb) at 13:00 on Jan 13 and Jan 14.


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Figure 7 Comparison between the simulated based on PCD emissions and observed CO (ppm) at 13:00 on Jan 13 and Jan 14.


Table 1 Maximum and minimum observations and simulations based on PCD emissions and modified emissions at 13:00 on January 13-14, 1997


PCD emissions Modified emissions PCD emissions Modified emissions Cases Observation UAM-V CHIMERE UAM-V CHIMERE Observation UAM-V CHIMERE UAM-V CHIMERE O3 minimum in city center (ppb)

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Ox maximum in domain (ppb)

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CO maximum in city center (ppm)

2.6 1.8 3.0 1.6 2.2 1.6 2.6 3.4 2.2 2.6


Figure 8 shows scatter plots of O3, NOy, Ox, and CO concentrations modeled by UAM-V versus modeled by CHIMERE at all grids in the simulation domain based on the PCD emission at 13:00 on Jan 13-14. UAM-V generated comparable O3 and Ox concentrations relative to CHIMERE. For NOy and CO, the differences between the two models’ results are noticeable larger at lower concentration ranges, that means CHIMERE is higher than UAM-V that is consistent with the analysis mentioned above. In general, the agreement between the models in terms of maximum values and spatial distributions for all considered parameters is better than the agreement with observations. However, maximum O3 and Ox in the domain simulated by UAM-V are higher than CHIMERE while maximum precursors concentration of NOy and CO simulated by UAM-V are lower than CHIMERE. Based on the modified emission database, simulated spatial distributions of O3, NOy, Ox, and CO for Jan 14 (Figure 8) are similar to the corresponding distributions for the PCD database case. However, the corresponding maximum values in the domain in this case are much lower and in better agreements with the observations than the PCD database (Table 1). Two models produced similar results during the ozone period in terms of O3 minimum and CO maximum in city center, and O3 maximum and Ox maximum in domain except for NOy (Table 1). Temporal distribution. Figure 9 shows time series plots of ozone for four sites located along the path of wind flow (from NE to SW) across the domain during the episode days of Jan 13-14 based on the PCD emission database. The peak O3 values simulated with the PCD database

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were lower than observations at all the four stations on Jan 13 for both models especially for CHIMERE. Better agreements between simulated and observed O3 were obtained on Jan 14 for all stations except for No.10 at which the simulated value by UAM-V was higher than observation by 60ppb. UAM-V simulated O3 are higher than CHIMERE at all presented stations that maybe related with different meteorological model used. ECMWF is a global model, and it cannot simulate the small scale phenomena such as the heat island effects occurred in the urban areas. Therefore, the peak temperature simulated by ECMWF is 2-3°C lower than observations (Figure 10) which affect the O3 formation as a change of 40°F (about 4.4°C) would increase the overall rates of photochemical reactions by a factor of 2-4 (Wark et al., 1998). SAIMM simulated temperatures are nearly consistent with observations because of its consideration of total available surface meteorological measurements as model input. Temporal changes of observed and simulated NOy and CO at the city center (station No.8), and downwind of the city center (station No.3), for the PCD database, show a better agreement during afternoon hours when O3 is maximum (12:00-16:00) than other times of a day (Figure 11). NOy is low during this period due to photochemical reactions, and higher in early morning and later afternoon during traffic rush hours. The simulated NOy and CO by CHIMERE during 12:00-16:00 are slightly higher than simulated by UAM-V partially due to lower maximum mixing height, about 1500m by ECMWF versus about 1900m by SAIMM model (Figure 10) that maybe due to stronger convective turbulence produced by SAIMM. Ox is a representation of the overall chain of photooxidation which is an indication of the model capability of simulating the overall chain of photooxidation and in particular the rates of VOC oxidations at noon time when peak O3 is normally formed. Compared to observations, there are better agreements during noontime at both stations for both models on Jan 14 than Jan 13. Based on the modified emission database, the peak O3 on time series (Figure 12) are slightly lower or almost the same for UAM-V at stations No. 3, No. 7, No. 8 and No.10 as compared to the PCD database case while the O3 values for CHIMERE are slightly higher than the PCD case (Figure 9). There is a significant improvement of prediction by UAM-V for Station 10 on Jan 14. Both models produced consistent results with observations on Jan 14. Model performances statistics. Performance statistics for both models for PCD emissions in Table 2 show a better model performance for O3 on Jan14 with MNBE (mean normalized bias error) and MNGE (mean normalized gross error) met the EPA suggested criteria (USEPA, 1991), while for Jan 13 none of MNBE and MNGE was met. It is noted that two sets of values of bias, MNBE, and MNGE generated by UAM-V and by CHIMERE for all considered parameters (O3, CO and NOy) for both days are close to each other. Compared with observations, CO is slightly over predicted because of bias (predicted minus observed) with positive values of 0.5-0.9ppm for UAM-V and 0.1-0.5ppm for CHIMERE on Jan 13-14. NOy is over predicted much, and MNBE and MNGE for both models are more than 100% for Jan 13-14. As UAM-V, CHIMERE also suggested that PCD database for 1997 overestimated precursor emissions in BMR.


Table 2 Model performances statistics based on PCD emission and modified emission database for January 13-14 of 1997


PCD emissions Modified emissions PCD emissions Modified emissions Statistical measurea UAM-V CHIMERE UAM-V CHIMERE UAM-V CHIMERE UAM-V CHIMERE

Suggested criteria by US EPAb

O3 Bias, ppb Mean normalized bias error (MNBE), % Mean normalized gross error (MNGE), % Unpaired peak prediction (UPA), %

-26.4 -24 36 -22

-43.9 -43 47 19

-27.0 -24 32 5

-33.5 -33 37 19

9.5 10 24 -52

-9.9 -13 24 -20

0.5 -2 16 -20

-2.7 -3 18 -14

±15 35 ±20

CO Bias, ppm Mean normalized bias error (MNBE), % Mean normalized gross error (MNGE), %

0.9 41 70

0.5 35 74

0.2 15 56

-0.1 11 60

0.5 23 54

0.1 25 73

-0.2 1 45

-0.5 2 60

NOy Bias, ppb Mean normalized bias error (MNBE), % Mean normalized gross error (MNGE), %

62.9 103 135

47.6 104 126

-2.1 22 84

-8.5 22 69

67.7 123 144

40.2 107 135

-2.3 39 86

-19 24 72

a The statistical measures were calculated using all pairs of predicted and observed concentrations at all Bangkok stations where the observed value was greater than or equal to the cutoff of 60ppb for O3and 20ppb for NOy. All paired were calculated for CO. Bias is the average of the residual (predicted minus observed) concentrations. Normalized bias is computed by dividing each residual by the corresponding observed concentration and then averaging as for the bias. The gross error statistics are computed in the same manner as the bias statistics, except that the absolute values of the residuals are used throughout. Unpaired peak prediction accuracy focuses on the single site with the highest observed concentration and compared this value with the peak model prediction over all hours and all surface grid squares. b USEPA, 1991.


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Figure 10 Temperature and mixing height comparison at Bangkok Metropolis during January 11-14 in 1997 (PCRAMMET is USEPA preferred meteorological program for calculating hourly mixing height).


Figure 11 NOy, Ox, and CO time series at Bangkok city center station Huaikhwang and downwind of city center station Ratburana based on PCD emissions (◦ observed, •simulated by UAM-V, and ∆ simulated by CHIMERE)

Figure 12 Observed and simulated O3 concentrations at four sites located along wind flow path during episode based on modified emissions (◦ observed, • simulated by UAM-V, and ∆ simulated by CHIMERE)

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For modified emissions model performance statistics showed significant improvement (Table 2) for two episode days. In particular, the simulated O3 on January 14 met all the three criteria suggested by the US EPA, and MNBE, MNGE, and UPA (unpaired peak prediction) were -2%, 16%, and -20% for UAM-V and –3%, 18%, and –14% for CHIMERE, respectively. CO and NOy statistical results are reduced by the similar percent for two models for both days compared to the PCD database. MNBE and MNGE was reduced by about 20% and 10%, respectively, for CO; MNBE and MNGE reduced by about 80% and 60%, respectively, for NOy. CHIMERE produced comparable results with UAM-V for all considered parameters when 10% less VOC reduction used in modified emission for CHIMERE than used for UAM-V. There is a better agreement between simulations and observations on Jan 14 than Jan 13. The reasons were described in detail by Kim Oanh and Zhang (2003). The meteorology on Jan 14 was representative of high O3 condition in BMR and is used for further model simulation in this study.

Contributions of different emissions to O3 formation in Bangkok. To compare the performances of two modeling systems, the assessment of the contributions of different emissions to O3 formation in Bangkok was done by both models with only 1) Bangkok anthropogenic emission and BMR biogenic emission (type A), 2) BMR surrounding province anthropogenic emission without Bangkok and BMR biogenic emission (type B), 3) BMR biogenic emission (type C), 4) BMR anthropogenic emission (type D), and 5) BMR anthropogenic and biogenic emissions (type E). Both the PCD emission database and the modified emission database were used. The O3 contour lines at 13:00 (Jan 14) were constructed and the maximum simulated results for the Bangkok city are shown in Table 3. Table 3 Simulated maximum O3 values (ppb) in Bangkok for different emission scenarios

PCD emissions Modified emissions Emission scenarios UAM-V CHIMERE UAM-V CHIMERE

Location of O3 maximum

A: Bangkok anthropogenic +BMR biogenic 230 190 180 180 Southwest

B: Province anthropogenic +BMR biogenic 100 100 80 80 Northwest

C: BMR biogenic 56 60 56 60 Northeast D: BMR anthropogenic 230 170 170 170 Southwest E: BMR anthropogenic + BMR biogenic 240 190 180 180 Southwest

For all emission scenarios except for type C, the simulated O3 maxima by two modeling systems are identical for modified emissions while for PCD database CHIMERE generated lower O3 maxima than UAM-V due to lower temperature field produced by ECMWF compared to observation. Comparison of simulated results of type A and B with type E by both models apparently showed that the Bangkok photochemical pollution is mainly caused by the emission sources in the city itself. For UAM-V, Bangkok anthropogenic emission contributed 174ppb (type A-type C) out of 240 ppb (73%) of the highest 1-hr O3 in the city for the PCD database and 124 ppb (type A-type C) out of 180 ppb (69%) for the modified database case; for CHIMERE, Bangkok anthropogenic emission contributed 130ppb out of 190 ppb (68%) of the highest 1-hr O3 in the city for PCD database and 120 ppb out of 180 ppb (67%) for the modified database case. Although UAM-V results are slightly higher than


CHIMERE, it can be concluded that around 70% of O3 pollution in the Bangkok city comes from the anthropogenic emission in the city itself. There would be only VOC species from biogenic source in BMR for type C. The spatial distribution of O3 in this case was seen gradually reducing from upwind (60ppb for UAM-V and CHIMERE) to downwind area at the Bangkok city center (54ppb for UAM-V and 52ppb for CHIMERE), which is thought due to the O3 dry deposition in the domain. The O3 reduction amount from upwind to downwind shows that ozone dry deposition velocity for both models are similar. Simulation results of type D and type E showed that biogenic sources in BMR contribute only about 10ppb of O3 for UAM-V and 10-20ppb of O3 for CHIMERE to the Bangkok photochemical O3. This is partially due to different chemical mechanisms related with isoprene applied for two models. There are mainly three isoprene decomposition reactions for both models (SAI, 1999; http://euler.lmd.polytechnique.fr/chimere), C5H8+OH, C5H8 + O3, C5H8 + NO3. The rate constants for first two reactions are the same for two models, 1.0×10-10 cm3/(molecules ·s) and 1.3×10-17 cm3/(molecules ·s) at 298K, respectively. Rate constant for the reaction C5H8 + NO3 in MELCHIOR mechanism (7.8×10-13 cm3/(molecules ·s)) implemented in CHIMERE is higher than in CB-IV mechanism (6.7×10-

13 cm3/(molecules ·s) used in UAM-V. Sensitivity of ozone formation to precursor emission reduction. Of key importance in applications of air quality models is their response to changes in emissions (Jiang et al., 1998). It is of interest whether the models predict similar changes in ozone concentrations as emissions are changed. In the case of photochemical air pollution, the question of whether to reduce NOx or VOC emissions, or both, is greatly complicated by the fact that the impacts of the reductions might not be spatially uniform in the domain. In order to provide a basis for future control efforts, the isopleths of the highest simulated 1-hr ozone levels among all grids in the Bangkok city for the corresponding pairs of anthropogenic NOx and VOC reductions (in 20% reduction) were constructed leaving boundary and initial conditions unchanged (Figure 13). The figures were constructed based on 36 runs for each case, the PCD database and the modified database. Since the majority (>92%) of both CO and VOC in Bangkok come from mobile sources (PCD, 2000), CO emission reduction should be following the VOC reduction scale. The shapes of isopleths (Figure 13) between UAM-V and CHIMERE are very similar for both emission databases, a to b and c to d. But compared to CHIMERE, the isopleths produced by UAM-V are more densely those maybe mainly due to different meteorological models used thus producing different temperature fields that have important effect on O3 formation rate as mentioned before. After NOx emission reduced by around 80% for both database cases (Figure 13), O3 concentration across Bangkok would become sensitive to NOx emission. In the upper region (above 0.2 of NOx), for PCD emission case (Figure 13 a, b), on the basis of equivalent fractional reductions, VOC controls are predicted to be more effective than NOx controls for O3 formation in Bangkok. Modified emission database makes chemical regimes much more VOC sensitive for both models (Figure 13 c, d). For Bangkok, the morning (7:00-8:00) observed average VOC/NOx concentration ratio of 13 indicates that O3 in the city is sensitive both to NOx and VOC emission controls but more to VOC emission control (Kim Oanh and Zhang, 2003), which is consistent with the model results in Figure 13. In order to meet the Thailand national hourly standard of 100ppb for O3, VOC emission should be reduced by 45% (CHIMERE) - 50% (UAM-V) for the PCD database and 60% (CHIMERE and UAM-V)


for the modified database. Sensitivity of ozone formation to NOx and VOC emission. In order to meet the Thailand NAAQS for O3 of 100ppb, VOC emission should be reduced by at least 50% if based on the original database and 60% based on the modified database.

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Figure 13 Model predicted response of maximum peak ozone concentrations (ppb) over Bangkok a) for PCD emissions by UAM-V, b) for PCD emissions by CHIMERE, c) for modified emissions by UAM-V, d) for modified emissions by CHIMERE. The upper right-hand corners of diagrams correspond to the cases without emission reductions.


3.2 Evaluation of PCD anthropogenic emission database Both spatial and temporal distributions of O3, NOy, Ox, and CO in the domain suggested that there is a possibility that the PCD database for 1997 overestimated the precursors emissions. Likewise, overestimation of NOx and VOC emissions may be the reason for the simulated O3 concentrations that are higher (by around 50ppb) than observation at downwind of the Bangkok city center (station No.10 Singha, for example) as seen in Figure 9. The PCD 1997 database was also several times higher than the data provided by Streets et al. (2003) (gridded data at http://www.cgrer.uiowa.edu/EMISSION_DATA/index_16.htm). The website data for 1° × 1° area with the grid center of (100.5, 13.5) for NOx, CO, and NMHC are 57630 t/ yr, 272695 t/yr, and 235578 t/yr, respectively, for the year 2000. The total emission estimated by the PCD database for the model domain, which is less than 1° × 1°, from (100.07, 13.45) to (100.9, 14.07), for NOx, CO, and NMHC were about 2.5, 5.0, and 1.1 times, respectively, as much as the website data given above. Compared to the website data, the PCD database considered more detailed variables affecting local emissions such as vehicle types, road types, vehicle speeds, and vehicle model year for mobile sources. However, much uncertainty still exists. For example, the NOx emission from mobile sources in the PCD 1997 database may be overestimated due to the use of high NOx emission factors in the Mobile 5 model (Supat Wangwongwattana, 2002, personal communication). Generally speaking, the increase in NOx emission acts to raise ozone formation potential and increase in NMHC emissions acts to raise ozone formation speed (Wakamatsu et al., 1999). NOx emissions may suppress ozone formation in the immediate vicinity of large sources, due to the NO titration effect, but enhance downwind ozone formation. Overestimation of both NMHC and NOx emissions may produce some compensating errors, but may also lead to higher simulated peak O3 values in the domain as compared to the observations. In this study the downwind sites from the city center, where the NO titration effect is less pronounced such as station No.10 (Figure 9), showed higher simulated O3 than the observed. 3.3 Emission scenarios study Description of Emission Scenarios. Model system with the meteorological conditions of Jan 14 in 1997 was used to check the impacts of following emission scenarios on ozone pollution in Bangkok: 1) Stage I control (vapor recovery during the gasoline transfer), 2) Stage I and stage II control (vapor recovery during auto-refueling), 3) phasing out 2-stroke motorcycles, 4) 100% gas fired power plants in BMR, 5) 100% CNG (compressed natural gas) heavy duty buses, and 6) replacement of MTBE (methyl-tertiary-butyl-ether) with ethanol as additive in gasoline. Stage I and Stage II Control. Without vapor emission control at gasoline service stations (without Stage I control), the emission rate is 2900mg per liter of gasoline throughput (PCD, 2000). After Stage I control (Scenario 1), VOC emission rate reduces to 1556mg/L, and after Stage I and Stage II controls (scenario 2) it reduces to 346mg/L. It is noted that the stage I control was already implemented in BMR in 2001 while the stage II control started from 2002 and will be completed in the near future. Phasing Out 2-Stroke Motorcycles. As compared to the two-stroke engine the four-stroke engine has higher fuel efficiency and lower VOC and CO emissions, but higher emission of NOx. Exhaust emission levels obtained by the laboratory tests for motorcycles in Thailand were used in this study (PCD, 2000). In 1997, 2-stroke motorcycles accounted for more than


80% in BMR and Yamaha brand was predominant. The emission factors of 2-stroke Yamaha (HC - hydrocarbons: 8.552 g/km, NOx: 0.051 g/km and CO: 5.868 g/km) were used for all motorcycles in the original PCD database. For 4-stroke motorcycles, Yamaha and Honda brands are the most prevalent in Thailand. The scenarios for mobile source emission include scenario 3a: replacement of 2-stroke motorcycles by 50% 4-stroke Yamaha (HC: 0.677 g/km, NOx: 0.154 g/km, and CO: 5.234 g/km) and 50% 4-stroke Honda (HC: 1.183 g/km, NOx: 0.082 g/km, CO: 9.436 g/km) and scenario 3b: by 100% 4-stroke Yamaha. In fact, phasing out 2-stroke motorcycles in BMR started from 2000, and is expected to complete after a few years. Natural Gas for Power Plants. There are 11 power plants located in the simulation domain: two are natural gas (NG) fired, one is diesel fired, and the rest are heavy oil fired. The scenario is constructed for 100% NG-fired power plants in the domain. The NOx emission control is either flue gas recirculation (FGR) with selective catalytic reduction (SCR), which is referred to as scenario 4a, or FGR and selective noncatalytic reduction (SNCR), which is referred to as scenario 4b. Table 4 shows the pollutant emission factors for different power plants used for emission estimation. Table 4 Pollutants emission percents (of fuel amount combusted) for different power plants a

Fuel type NOx CO VOC

Heavy oil 0.85 % 0.06 % 0.0132 %

Diesel 6.65 % 1.44 % 0.001 %

0.03127 % b Natural gas

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a PCD(2000) b Natural gas combustion: steam injection, flue gas recirculation (FGR) and selective

catalytic reduction (SCR) technology for NOx control. According to AP-42 (compilation

of air pollutant emission factors), NOx emission factor for FGR control is 2105 kg/106m3.

Assuming NOx reduction efficiency for SCR control is 90%. The emission factor for FGR

+ SCR is 210.5 kg/106m3, so NOx emission amount is 0.03127% of total NG consumed

(NG density is 0.673kg/m3). c Natural gas combustion: steam injection, FGR and selective noncatalytic reduction

(SNCR) technology for NOx control. According to AP-42, FGR + SNCR has NOx

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consumed.


100% CNG Bus (scenario 5). Heavy duty buses accounted for 19% of the large diesel vehicles in the Bangkok city, 17% in Samut Prakan, 11% in Nontha Buri, 10% in Pathum Thani, 12% in Samut Sakhon, and 4% in Nakhon Pathom. The scenario is constructed based on 100% CNG-powered heavy buses in BMR. Exhaust emissions for large vehicles mix of diesel trucks and CNG-buses for BMR are given in Table 5. Table 5 Pollutants emission rates for large vehicles (diesel trucks and CNG buses)

Item THC NOx CO

Diesel vehiclesa (original case) (g/km) 3.074 28.478 11.887

CNG bus for lifetime average b (g/km) 0.98 3.84 0.93

Mixed vehicles for Bangkok (g/km) 2.676 23.797 9.805

Mixed vehicles for Samut Prakan (g/km) 2.713 24.29 9.787

Mixed vehicles for Nontha Buri (g/km) 2.844 25.767 10.682

Mixed vehicles for Pathum Thani (g/km) 2.865 26.014 10.791

Mixed vehicles for Samut Sakhon (g/km) 2.823 25.521 10.572

Mixed vehicles for Nakhon Pathom (g/km) 2.99 27.492 11.448

a PCD(2000)

b US EPA(2001).

Replacement of MTBE by Ethanol Additive for Gasoline (scenario 6). MTBE additive at about 7.0% by volume or 1-2% by weight oxygen has been used to replace lead as an octane enhancer for gasoline in Thailand since 1996. Due to the potential harmful effects of MTBE to the air and water resources there is an increasing interest in Thailand to substitute MTBE by ethanol (10% volume or about 3.5% by weight oxygen). Blending ethanol with gasoline at 4% to 20% by volume would increase the gasoline’s Reid Vapor Pressure (RVP) by about 1 pound per square inch (psi). When RVP is increased by 1 psi, exhaust emission of CO from motor vehicle would decrease 10%, while NOx and THC (total hydrocarbons) would increase 14% and 3%, respectively; also vehicle evaporative emission THC would increase by 52% (Daedalus LLC and ERM-Siam, Co. Ltd, 2002). Replacing the MTBE additive by ethanol would result in a reduction in the formaldehyde emission (a product of incomplete emission from MTBE), but increases in the acetaldehyde emission (a product of incomplete emission of ethanol) (Daedalus LLC and ERM-Siam, Co. Ltd, 2002). Thummarat et al. (1999). reported that laboratory tests in Thailand for catalyst-equipped vehicles which used ethanol blended gasoline showed the acetaldehyde emission increased more than 3 fold (3.48 times) in exhaust gas. Due to the lack of reliable data, changes in emission of other pollutants such as formaldehyde, benzene, ethanol etc. were not considered in this study. Thus, emission of this scenario was estimated based on the corresponding emission change due to 1 psi increased in RVP, and based on the increase of acetaldehyde in the VOC profile from 0.00117 to 0.00408g/(cm3 ⋅s) for the gasoline vehicle exhaust emission. It is noted that the emission estimate is most probably conservative which would be applicable for high degrees of incomplete combustion of the fuel.


3.4 Scenarios simulation Scenario 1 (Stage I control) and scenario 2 (Stage I and II control) will reduce VOC emission from petrol service stations. Scenario 3 will reduce VOC but increase NOx emissions slightly. Scenario 4 would reduce all 3 emissions but NOx reduction would be more pronounced. Scenario 5 would also result in more NOx reduction than VOC reduction. Scenario 6 will reduce CO but increase NOx and VOC emissions with a change in the VOC profile. For the O3 formulation in Bangkok it is shown in Figure 14 that any control strategy leading to a reduction in VOC will lead to reduction in O3, and a reduction in NOx will lead to increase in O3 (if the NOx reduction is not more than 80%). The scenario emissions were applied for both the original database and the modified database. The changes in maximum ozone level in Bangkok due to emission changes are shown in Figure 14.

-40.0%

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-10.0%

0.0%

10.0%

20.0%

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nge

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ent

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4-stroke 50% Honda 50% Yamaha4-stroke 100% Honda

4-stroke 100% Yamaha

NG fired power plants(SCR)NG fired power plants(SNCR)CNG bus

MTBE replaced by ethanol(CARB)MTBE replaced by ethanol(USEPA)

Figure 14 a. Simulated peak O3 change percent referred to PCD original emission database for different emission scenarios.

-25.0%

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-5.0%

0.0%

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4-stroke 100% Yamaha

NG fired power plants(SCR)NG fired power plants(SNCR)CNG bus

MTBE replaced by ethanol(CARB)MTBE replaced by ethanol(USEPA)

Figure 14 b. Simulated percentage change of peak O3 associated with different emission control scenarios using modified emission database


4. SUMMARY AND CONCLUSIONS

The relative performances of two modeling systems UAM-V/SAIMM and CHIMERE/ECMWF were compared by applying systems to Bangkok ozone episode Jan 13-14 of 1997. With the same emission database provided by PCD, the agreement between the models for all considered parameters in spatial and temporal distributions is better than the agreement with observations. The results of performance statistics for both models are similar: O3 produced by both models met EPA suggested criteria of MNBE and MNGE on Jan 14, but none on Jan 13; for CO on Jan 13-14, MNBE are 23-41% and 25-35% for UAM-V and CHIMERE, MNGE are 54-70% and 73-74% for UAM-V and CHIMERE, respectively; MNBE and MNGE for NOy are more than 100% for both models. Both models suggested that the PCD anthropogenic emission database for 1997 most likely overestimated precursors emissions. When VOC, NOx, and CO emission reduce by 50%, 50%, and 20% of the PCD mobile emissions for UAM-V and reduce by 50%, 40%, and 20% of the PCD mobile emission for CHIMERE, respectively, two models tended to produce comparable results for all considered parameters and simulated O3 met all the US EPA suggested criteria. O3 pollution in Bangkok is mostly caused by the anthropogenic emission sources within the city. Both model simulation results show that about 70% of O3 pollution in Bangkok comes from anthropogenic emission in the city itself; biogenic sources in BMR contribute about 10-20 ppb of O3 to the city; O3 formation in Bangkok is more VOC emission sensitive than NOx emission sensitive. VOC emission should be reduced by about 45% (CHIMERE)-50% (UAM-V) based on the PCD emission database and by about 60% based on modified emission database in order to attain the 1-hr O3 ambient air quality standard of 100ppb.

The control strategies leading to VOC emission reduction such as Stage I, Stage II vapor control and replacement of 2-stroke by 4-stroke motorcycles, would decrease O3. Scenarios leading to reduction of NOx emission such as CNG bus, NG-fired power plants would increase O3. Use of ethanol instead of methyl-tertiary-butyl-ether (MTBE) as an additive for gasoline will increase evaporative VOC emission and together with incomplete fuel combustion would lead to increase in O3. Development of strategies to reduce O3 pollution in Bangkok should take into account the complicated relationships of O3 and its precursors at the present situation of the city. It is noted that the conclusions presented here were based on the UAM-V/SAIMM model simulation for a 2-day photochemical smog episode in BMR only. Reliability of the existing emission database should be scrutinized by elaborate bottom-up and top-down techniques. In depth study on meteorology and SAIMM performance for the tropical conditions of the domain should be conducted. Further studies considering multiple episodes and covering other periods of the year would be necessary to understand the complex photochemical smog formation and dispersion in the domain. The conclusions presented here were based on the UAM-V/SAIMM and CHIMERE/ECMWF model simulations for a 2-day photochemical smog episode in BMR only. It is recommended that both modeling systems should be applied to multiple episodes for longer time periods in order to understand in depth the fundamentals of the model performances and provide sound basis for ozone control strategies conducted in Bangkok.


5. REFERENCES Chang, T.Y.; Chock, D.P.; Nance, B.I.; Winkler, S.L., 1997. A Photochemical Extent

Parameter to Aid Ozone Air Quality Management. Atmos. Environ. 31, 2787-2794. Daedalus LLC and ERM-Siam, Co. Ltd., 2002. A Study on Changes in Specifications for

Gasoline and Diesel Fuels in Thailand, final report. Prepared for National Energy Policy Office, Petroleum Division, Department of Industry, Science and Resource, Thailand. pp.178-184.

Ekbordin, W., 2002. Development of Synoptic Climatological Model for Forecasting Photochemical Smog Potential in Bangkok Metropolitan Region. AIT Thesis: EV 02-8. Asian Institute of Technology, Thailand.

Guenther, A.; Zimmerman, P.; Harley, P.; Monson, R.; Fall, R., 1993. Isoprene and Monoterpene Emission Rate Variability: Model Evaluation and Sensitivity Analysis. J. Geophys. Res. 98, 12609-12617.

Guenther, A.; Hewitt, C.; Erickson, D.; Fall, R.; Geron, C.; Graedel, T.; Harley, P.; Klinger, L.; Lerdau, M.; Mckay, W.; Pierce, T.; Scholes, B.; Steinbrecher, R. A., 1995. Globe Model of Natural Volatile Organic Compound Emissions. J. Geophys. Res. 100, 8873-8892.

Hass, H., Builtjes, P.J.H., Simpson, D., Stern, R., 1997. Comparison of Model Results Obtained with Several European Regional Air Quality Models. Atmospheric Environment 31, 3259-3279.

Jiang, W., Hedley, M., Singleton, D.L., 1998. Comparison of the MC2/CALGRID and SAIMM/UAM-V Photochemical Modeling Systems in the Lower Fraser Valley, British Columbia. Atmospheric Environment 32, 2969-2980.

Kim Oanh, N.T., Zhang, B.N., 2003. Photochemcial Smog Modeling for Air Quality Management of Bangkok Metropolitan Region, Thailand. Journal of the Air and Waste Management Association (submitted).

Kuhn, M.; Builtjes, P.J.H.; Poppe, D.; Simpson, D.; Stockwell, W.R.; Andersson-SkÖld, Y.; Barrt, A.; Das, M.; Fiedler, F.; Hov, ∅.; Kirchner, F.; Makar, P.A., Milford, J.B.; Roemer, M.G.M.; Ruhnke, R.; Strand, A.; Vogel, B.; Vogel H., 1998. Intercomparison of the Gas-Phase Chemistry in Several Chemistry and Transport Models. Atmos. Environ. 32, 693-709.

Lattuati, M., 1997. Contribution à l’étude du bilan de l’ozone troposphérique à l’interface de l’Europe et de l’Atlantique Nord: Modélisation lagrangienne et mesuresen en altitide. Ph.D. Thesis, Universit/’e Paris 6, Paris.

Marr, L.C.; Black, D.R.; Harley, R.A., 2002. Formation of Photochemical Air Pollution in Central California 1. Development of A Revised Motor Vehicle Emission Inventory. J. Geophys. Res. 107, 10.1029/2001JD000689, in press.

National Research Council (NRC), 1991. Rethinking the Ozone Problem in Urban and Regional Air Pollution. National Academy Press, Washington, District of Columbia. pp.50-65.

Pollution Control Department (PCD), 2000. Air Emission Source Database Update and Ambient Air Quality Impact Assessment in Bangkok Metropolitan Region. PCD 03-034. ISBN 974-7879-12-3.

Pollution Control Department (PCD), 2001. Investigation and Analysis of Ozone Precursors for the Mitigation of Photochemical Air Pollution in Bangkok. PCD 03-034, ISBN 974-7880-22-9.

Russell, A., Dennis, R., 2000. NARSTO Critical Review of Photochemical Models and Modeling. Atmospheric Environment 34, 2283-2324.


Sillman, S., 1999. The Relation Between Ozone, NOx and Hydrocarbons in Urban and Polluted Rural Environments. Atmos. Environ. 33, 1821-1845.

Simpson, D., 1992. Long Period Modelling of Photochemical Oxidants in Europe: Calculations for July 1985. Atmos. Environ. 26, 1609-1634.

Streets, D.G., Bond, T.C., Carmichael, G.R., Fernandes, S.D., Fu, Q., He,D., Klimont, Z., Nelson, S.M., Tsai, N.Y., Wang, M.Q., Woo, J.H., Yarber. K.F., 2003. An inventory of Gaseous and Primary Aerosol Emissions in Asia in the year 2000. Journal of Geophysical Research, TRACE-P special issue (in press).

Systems Applications International (SAI), 1999. User’s Guide to the Variable-Grid Urban Airshed Model (UAM-V). SYSAPP-99-95/27r3; Systems Applications International: San Rafael, CA.

Systems Applications International (SAI), 2002. Preprocessors for Preparation of UAM-V Initial and Boundary Concentration Data Files. http://uamv.saintl.com.

Thummarat, T.; Arunratt, W.; Suttipong, T.; and Kimura, T., 1999. Effect of Gasoline Compositions and Properties on Tailpipe Emissions of Currently Existing Vehicles in Thailand. Paper Presented at the International Fuels & Lubricants Meeting & Exposition, Tornoto, ON, Canda.

US EPA, 1991. Guideline for Regulatory Application of the Urban Airshed Model. EPA-450/4-91-013, United States Environmental Protection Agency, Research Triangle Park, NC 27711.

US EPA. Mobile 6 Emission Factors for Natural Gas Vehicles. EPA420-R-01-033, 2001. Available at http://www.epa.gov/otag/models/mobile6/r01033.pdf.

Vautard, R., Beekmann, M., Roux, J., Gombert, D., 2001. Validation of a Deterministic Forecasting System for the Ozone Concentrations over the Paris Area. Atmos. Environ. 35, 2449-2461.

Wakamatsu, S.; Uno, I.; Ohara, T.; Schere, L., 1999. A Study of the Relationship Between Photochemical Ozone and Its Precursor Emissions of Nitrogen Oxides and Hydrocarbons in Tokyo and Surrounding Areas. Atmos. Environ. 33, 3097-3108.

Wark, K.; Warner, C.F.; Davis, W.T., 1998. Air Pollution: Its Origin and Control. Addison Wesley Longman, Inc., USA.

Zhang, B.N. Ph.D. Dissertation, Asian Institute of Technology, Thailand, December 2002. Zhang, B.N., Kim Oanh, N.T., 2002. Photochemical Smog Pollution in the Bangkok

Metropolitan Region of Thailand in Relation to O3 Precursor Concentrations and Meteorological Conditions. Atmos. Environ. 36, 4211-4222.


Part 2: Synoptic climatological modeling 1. INTRODUCTION 1.1 Background Bangkok, the metropolitan of Thailand, is located in the middle part of the country. It occupies a total area of 1,568 km2. It is situated on the flat alluvial plain divided by the Chao Phraya River and has boundaries that attach other seven provinces (Samut Prakarn, Nonthaburi, Pathum Thani, Nakhon Pathom, Nakon Nayok, Samut Sakhon, and Chachoengsao). Bangkok and the five surrounding provinces (excluding Nakon Nayok and Chachoengsao) are called the Bangkok Metropolitan Region (BMR). The climate of Thailand is under the influence of monsoon. The southwest monsoon that starts in May brings warm moist air from the Indian Ocean towards Thailand causing abundant rain over the country, especially the windward side of the mountains. Rainfall during this period is not only caused by the southwest monsoon but also by the Inter Tropical Convergence Zone (ITCZ) and tropical cyclones. The northeast monsoon, which starts in October, brings the cold and dry air from the anticyclone in China to the Southeast Asia. The ridge often extends to major parts of Thailand, especially the Northern and Northeastern Part. In the Southern Part of the country, this monsoon causes mild weather and abundant rain along its eastern coast. The southwest monsoon usually starts in mid-May and ends in mid-October while the northeast monsoon normally starts in mid-October and ends in mid-February (TMD, 2000). Bangkok’s weather is hot and humid year round with temperatures ranging from 26 to 31 ºC (average 28.6 ºC). This is a highly populated city with the actual population close to nine million. The city currently faces air pollution problems, which is mainly the result of local emission from traffics, industry, construction, open burning ,though the role of the regional transport of pollutants could be significant. During the past decade the air quality in BMR has seen improvement and levels of many pollutants are declining. Ozone is an exceptional, its level during the past few years is somewhat increasing (Zhang and Kim Oanh, 2002). The 1-h average ozone concentration in BMR, especially downwind ambient stations, were reported to exceed the Thailand ambient air quality standard (100ppb) at 0.3% of total measurements (PCD, 2001). 1.2 Statement of the problem Ozone (O3) is a secondary air pollutant. It is the main pollutant of the photochemical smog, which is a mixture of hundreds of different chemicals that is formed in the complex atmospheric photochemical reactions. The ozone precursors, i.e. NOx and VOC, are directly emitted from fossil fuel combustion both in stationary and mobile sources as well as biogenic sources (VOC). The photochemical smog formation strongly depends on meteorological conditions, most obviously the presence of sunlight. The variability in ozone levels was found to be induced by meteorology in a much larger extent than due to changes in emission in the U.S. The meteorological variables explained as high as 40-60% of the variance in raw O3 data in the midwest and northwest of the country (Wolf et al., 2001). In Thailand, the tropical climate with high temperature and sunshine is favorable for ozone formation all year around, which in conjunction with high emission of precursors from mobile and stationary sources would lead to high O3 levels. The highest O3 levels in BMR, however, are observed in the dry season, from November to May (Zhang and Kim Oanh, 2002). Several mathematical models


have been used to simulate the ozone pollution in BMR including the California Photochemical Smog Grid Model (CALGRID) (PCD, 2001) and Urban Airshed Model (UAM-V) (Kim Oanh and Zhang, 2003). These models require intensive emission input data especially the detail VOC profile, which is not yet available for the BMR. As a matter of fact, most of the photochemical smog model applications for BMR are so far limited to the past episodes simulation and to conduct the scenario studies for air quality management (Kim Oanh and Zhang, 2003) rather than for real time O3 forecasting. The complexity of the photochemical reactions and strong influence of meteorological conditions on photochemical smog formation may be the reasons for many attempts of using the synoptic climatological approach for tropospheric O3 studies in many parts of the world (Davis, 1991; Lam and Cheng, 1998; Kartal and Ozer, 1998). The use of synoptic climatological approach to evaluate environmental problem underwent resurgence in the 1980s, with several objective procedures being developed. The resurgence of this methodology is attributed to the ability of the synoptic climatological approach to categorize a wide variety of complex meteorological variables as one cohesive unit, a synoptic weather-based approach to investigating air pollution episodes, air quality indicators, and the effect of meteorological variables (Scott and Diab, 2000). 1.3 Objectives This study aims at development of a meteorological classification scheme, which can be applied to classify the meteorological conditions governing the Bangkok Metropolitan Region (BMR). The relationships between hourly ozone levels in BMR and meteorological conditions will be analyzed to identify regional meteorological patterns that are associated with potentially high O3 levels for episode warning. Statistical models to predict ozone for meteorological pattern will be developed. 1.4 Scope of the study For meteorological classification both regional and local weather parameters for 9 year period are used (1992-2000). For ozone levels only the monitoring data within BMR are investigated. The study focuses on both highest hourly ozone level in BMR for each day and the average of highest ozone at all the monitoring stations in BMR. 2. METHODOLOGY

The synoptical climatological modeling was done in 3 steps, as shown in Figure 1. First step: the synoptic meteorological conditions governing weather over the region and Thailand were classified into a finite number of homogeneous patterns. The classification was done for the period of high ozone in BMR, i.e. November-May (Zhang and Kim Oanh, 2002). A selected set of weather elements recorded at 0700 LST (00 GMT) over the period of 1992-2000 was used. Two schemes were used in this study for the purpose of comparison. In scheme 1, only data from a single meteorological weather station in Bangkok (station 48455) were used for the meteorological classification. In scheme 2, the data from 13 regional weather stations were employed. The latter included five stations in Thailand: Chiang Mai (48327), Udorn Thani (48354), Ubon Rachathani (48407), Bangkok (48455), Hat Yai (48568); two stations in India: Imphal (42623), Port Blair (43333); four stations in China: Chengdu (56294), Wuhan (57494), Guiyang (57816), Shantou (59316), and Hanoi (48820) in and Kuching (96413) in Malaysia (Figure 2). Meteorological parameters were selected to


represent both thermal/moisture and dynamic characteristic of the air mass. They include air temperature, dew point temperature, sea-level pressure, cloud cover, solar radiation, rainfall column, east-west (u) and north-south (v) wind components, pressure tendency, and pressure differences between selected weather stations (for regional weather station scheme). The u and v components of the wind vector were computed by sine-cosine transformation of wind speed and direction (u = -VHsinΦ and v = -VHcosΦ, where, VH is horizontal wind speed and Φ is wind direction: northerly wind = 360˚, easterly wind = 90˚, etc. The minus sign is used in the formula to convert from meteorological wind, i.e. from where it blows, to the flow direction with positive direction of y to the north and x to the east). The best classification of synoptic patterns is often the one based on synoptic maps. This process however requires intensive knowledge on meteorology and may be subjective. Therefore, in this study the classification of synoptic meteorological conditions was done using the principal component analysis (PCA) by SPSS10.05 package and a two-stage clustering procedure. The purpose was to produce an objective identification of the synoptic categories, which should match the synoptic (mainly pressure) patterns identified in the synoptic charts. Before applying PCA, the meteorological data set was examined to detect significant outliers that may distort the results. The data first were converted to standard scores, which have a mean of 0 and a standard deviation of 1. Possible outliers were then examined using the statistical distribution of the meteorological observation data. For large sample size, which is the case of this study, the guidelines suggest that the threshold value of standard scores of 4. Thus the data points with the distance from the mean value beyond the threshold are considered as outliers. By these criteria, however, no outlier was detected in the meteorological data sets in this study. For the considered period (November-May, 1992-2000), a symmetric matrix of correlation coefficients of the original meteorological data (daily at 0700 LST) was formed and principal

Figure 1. Scheme for meteorological classifications

Meteorological Data set

PCA Average linkage

k-means

Clusters

Comparison with criteria

Synoptic Categories

YES

NO

Criteria for classification - Similarity of surface synoptic situation within each cluster - Distinct separation in of the surface synoptic situation

between clusters


Figure 2 Bangkok Metropolitan Region (BMR) and Ozone monitoring station in BMR

BMR

Nakhom Pathom

Pathum Thani

Nonthaburi

Samut Prakarn Samut Sakhon

Bangkok

STATIONS 3 - Ratburana 0 08 - Hui Khwang 13 - Din Dang 4 - Meteorological Department 09 - Non-tree Vitaya 15 - Bangkok University 5 - Junkasame 10 - Singharatpitayakom 20 - EGAT

7

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component analysis was performed on this matrix. The number of extracted principal components was selected based on a cut-off value of 1 for eigenvalue after performing a rotation and the total variance explained by the selected components should be greater than 70% of the total variance of the original data sets. The principal component scores, which represent the projection of the original meteorological data onto the extracted principal components, were then calculated. A two-stage clustering technique was employed to produce synoptic patterns based on the resulting component scores produced from PCA. Firstly, average linkage, a hierarchical agglomerative method, was performed to determine the initial number of clusters and the mean conditions within each cluster. Once the number of initial clusters and the starting “seed” values are known, a nonhierarchical technique called convergent k-means was applied. This method allows for the reclassification of meteorological conditions of each day (0700 LST) after they have been grouped into clusters, thus refining the final cluster solution. For both schemes, various trial sets of meteorological parameters were picked up for PCA-clustering analysis. Each set produced several possible ways of groupings of meteorological conditions. An optimal meteorological set, which produced a number of groups that best satisfy the predefined criteria was then selected. The criteria were: i) similarity of synoptic situations of the days within each identified pattern was made and ii) distinction between the typical synoptic situations of different patterns. In scheme 1, a total number of 14 meteorological variables at 0700 LST collected from the Bangkok weather station were considered for the meteorological classification. They included both the surface data and up air weather observation (wind at 850 mb). The selected set consists of 11 variables. In scheme 2, totally 117 daily regional meteorological variables from the 13 regional weather stations were considered (Fig. 1). They include surface data and wind at 850 mb from all stations in Thailand as well as the pressure gradient between the stations in Thailand and selected regional stations. Second step: average daily highest 1-h ozone level in Bangkok was determined for each identified synoptic pattern using ozone data from 15 monitoring stations in Bangkok in the period 1998-2000 (Fig. 1). Data of 1997 were excluded due to the abnormal meteorological conditions and the high ozone due to biomass burning in the region (Zhang and Kim Oanh, 2002). This helps to identify the synoptic patterns associated with potentially high ozone in Bangkok, thus allowing develop a warning system of high ozone in the city through identification of synoptic pattern identification. The larger differences in O3 levels between the synoptic patterns, the more efficiently the warning system for potentially high ozone would be. Third step: The stepwise linear regression procedure was performed to develop prediction models for the highest 1-h ozone for each meteorological pattern. 3. RESULTS AND DISCUSSION

Scheme 1: PCA, after rotation, has reduced the data set to four principal components explaining 71.4 % of the total variance in the original data set. The clustering procedure produced three possible ways of groupings: six, eight and nine synoptic patterns for the considered period of 9 years. Visual examination of the synoptic patterns presented on the synoptic maps at 0700 LST each day (against the criteria) was made for each 6, 8 and 9


possible groupings. Finally, 8-cluster solutions, which matched with 8 synoptic patterns on the synoptic maps was selected. The occurrence frequency of the clusters 1, 2, 3, 4, 5, 6, 7, and 8 is 11.3%, 30.2%, 3.6%, 22.9%, 6.4%, 12.6%, 3.6%, 9.5% for the 9-year period, respectively. The average of the highest 1-h ozone in BMR for all the days within each pattern is shown in Figure 3. The frequency of cases exceeding the National Ambient Air Quality Standard (NAAQS) of 100 ppb, listed from the highest to the lowest, is cluster 1 (26.2%), cluster 8 (23.5%), cluster 2 (20.3%), cluster 4 (19.2%), cluster 6 (17.7%), cluster 5 (15.4%), cluster 3 (14.3%), and cluster 7 (7.1%). The variations in the O3 levels between the pattern with maximum and that minimum O3 [(max-min)/min] is 27.8%, which is not considered large enough for a good O3 potential prediction based on synoptic patterns. Therefore scheme 2 using regional weather data and pressure differences between stations, was studied.

41133528681498672N =

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87654321

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pb) 210

2001901801701601501401301201101009080706050403020

Figure 3 Levels of maximum hourly O3 associated with each pattern

Scheme 2. 117 daily meteorological variables (0700 LST) from 13 regional stations were considered and finally 19 variables from seven stations were chosen for the classification. PCA reduced the data set to seven principal components explaining 81.1% of the total variance. The 8-cluster solution was selected, which corresponds to eight synoptic patterns for the study period. The average of highest 1-h ozone (among the 15 stations in Bangkok) for all days classified within each pattern was calculated and presented in Figure 3. The ozone level was the highest for cluster 5 to the lowest for cluster 8. The frequency of cases exceeding the NAAQS of 100 ppb, listed from the highest to the lowest, is cluster 5 (28.6%), cluster 2 (23.3%), cluster 1 (22.2%), cluster 3 (19.5%), cluster 7 (17.7%), cluster 4 (11.8%), cluster 6 (8.6%), and cluster 8 (0%). The maximum difference in ozone concentrations between the patterns (between pattern 5 and pattern 8) is 43.7%, which is considerably better than scheme 1. The patterns produced by scheme 2 were therefore used for O3 prediction and will be further discussed. The most prevalent pattern for the study period of 9 years is pattern 3 (26.8%) followed by pattern 7 (22.8%). The least prevalent is pattern 8 (0.6%) and then


pattern 6 (6%). The rest 4 patterns have the average occurrence frequency from 9.7% to 12.6%. The spatial distribution of the ozone levels in Bangkok was also examined. Average meteorological variables of each cluster are presented in Table 1. The typical weather maps at 0700 LST for the 8 identified patterns are presented in Figure 4a-h. Levels of maximum ozone associated with each pattern are presented in Figure 5a-h. The spatial distribution of the average of the daily highest 1-h ozone over 15 stations in BMR for each pattern was examined using ozone data were for 3 year period, from 1998-2000. Due to the limited number of points (15 stations) the shape of the isopleths may not be reliable especially outside the city where not much data available. The spatial patterns presented in Figure 5a-h show low ozone concentration in the Bangkok city center for all cases. This is due to the titration effects of NO emission from vehicles in the city center, which destroys ozone. Maximum ozone is observed at a downwind peripheral area, mostly on the southeast corner of the Bangkok. Spatial distribution of each pattern would reflect the predominant wind direction in the pattern. It is also noted that Bangkok is located close to the sea and the influence of sea-land breeze is substantial (Zhang and Kim Oanh, 2002). During daytime, usually when O3 is maximum the sea breeze is also most pronounced. Thus, if there are no strong large-scale systems present, there will be a south component of the wind due to the sea-breeze. Wind at 0700 is normally slight due to the reduced effects of sea-land breeze. However, the large-scale wind will be most clearly shown at this time and hence is useful for meteorological classification. However, this 0700 wind does not reflect the average wind of the day (normally strongest at 13-15:00, Zhang and Kim Oanh, 2002), especially when there are no strong synoptic scale systems present. For example, pattern 5, which has the highest ozone concentration and weak wind, shows minimum ozone at the center and high ozone on the surrounding of the city. Pattern 2, when the ozone is second highest and wind is strong (ENE), the high ozone should be expected to the west to southwest of the city. However, this could not be seen due to the lack of data points in the south. Thus, in order to fully interpret the ozone isopleths more data points should be used for the plots and wind data should be analyzed for the time when O3 is maximum. Prediction models Models for prediction of the daily highest 1-h average ozone level in Bangkok were developed using the stepwise linear regression procedure. Ozone and meteorological data during the period of November – May of 1998 – 2000 were used. Models were developed for all patterns when there were sufficient data points for the analysis (more than 20). The dependent variable was the highest hourly average ozone concentration occurred at a station out of 15 air monitoring stations in the BMR. Altogether 223,587 independent regional weather variables at 0700 LST were considered including meteorological parameters from five weather stations in Thailand and eight weather stations in region. The parameters are denoted using abbreviations and the last 3 digits of the station, for example CLD455 is cloud cover at Bangkok (station 455). At Bangkok the following surface parameters are considered: sea-level pressure (SLP), cloud cover (CLD), air temperature (TA), dew point (TD), rain (RAI), solar radiation (RAD), duration of sunshine (SUN), moisture content (MOI), visibility (VIS), dew point deficit (TF), east-west (U) and


Figure 4a-h Examples of regional surface synoptic maps for the second approach for the eight synoptic clusters

(a) (b)

(c) (d)

(e) (f)

(g) (h)


Figure 5a-h Isopleths of average highest 1-h ozone concentration for the second approach for the eight clusters

(a) (b) (c) (d)

(e) (f) (g) (h)


Table 1 Mean values of nineteen meteorological variables in each synoptic category (1992-2000)

Cluster SLP455 TA455 TD455 MOI455 RAI455 RAD455 U455 V455 VIS455 PGB333 PGB494 PGB294 PGB816 PGB413 PGB568 3PC455 24PC494 24PC294 24PC816 Mean 1009.4 26.4 23.6 24.0 1.15 2055 0.004 -0.088 9162 0.4 13.2 14.0 10.0 1.3 0.1 1.4 7.7 6.8 7.31

Std. Dev. 2.10 1.90 2.44 2.71 4.23 503.54 2.38 1.98 2302.63 1.59 6.02 6.29 6.25 1.76 1.33 0.60 4.57 4.59 4.29Mean 1012.2 23.8 19.0 19.2 0.16 1714 -6.287 -1.755 9783 -1.6 16.1 17.0 15.1 -2.0 -2.2 1.3 -1.0 -1.1 -0.82

Std. Dev. 2.34 2.29 3.05 2.88 1.39 404.57 2.85 2.43 2222.11 1.67 4.75 4.97 4.89 2.07 1.52 0.57 4.66 3.65 4.30Mean 1009.0 27.2 24.3 24.7 1.06 21747 -0.255 0.121 9453 0 3.1 4.5 2.1 1.8 0.4 1.5 -1.7 -1.7 -2.13

Std. Dev. 2.10 1.75 1.85 2.06 3.46 468.25 2.02 1.82 2192.05 1.53 4.44 4.14 4.44 1.73 1.20 0.60 3.56 3.36 3.79Mean 1012.3 23.8 19.3 19.9 0.8 1859 3.717 4.207 9312 -1.5 15.0 16.0 14.1 -1.9 -2.2 1.4 -1.8 -1.4 -1.44

Std. Dev. 2.49 2.19 3.38 3.45 3.13 682.15 3.94 3.37 2486.79 1.75 5.09 4.94 5.07 2.10 1.70 0.58 4.06 3.83 4.30Mean 1013.7 21.1 16.5 17.3 0.03 2509 -0.168 0.016 7016 -1.6 11.6 11.5 9.4 -2.4 -1.9 1.2 -0.2 -0.6 -0.95

Std. Dev. 2.27 2.42 3.38 3.15 0.40 993.98 3.10 1.77 2279.79 2.03 3.97 4.36 4.44 1.97 1.47 0.56 3.70 4.12 4.26Mean 1008.2 26.3 24.2 25.0 24.47 1559 0.872 0.107 8987 -0.1 5.4 6.4 4.8 2.4 0.8 1.0 -1.4 -0.7 -1.66

Std. Dev. 1.94 1.64 1.26 1.64 14.45 546.10 2.07 1.75 2638.25 1.31 5.47 5.23 5.44 1.56 1.32 0.74 4.14 3.69 3.43Mean 1010.7 25.5 23.4 23.9 0.43 1924 0.114 -0.156 5726 0.2 12.8 14.2 11.8 0.2 -0.4 1.3 -0.6 -0.2 0.07

Std. Dev. 2.02 1.97 1.95 2.34 2.75 551.96 2.34 1.48 2603.28 1.36 4.46 4.53 4.96 1.71 1.06 0.55 3.46 3.53 4.23Mean 1009.0 25.7 24.1 25.1 80.40 1060 1.987 1.066 7375 0.4 9.7 10.5 9.4 1.9 -0.1 1.5 -0.1 1.2 2.08

Std. Dev. 2.14 1.41 0.98 1.49 32.50 490.25 2.24 2.42 2199.84 1.06 6.62 7.73 5.59 2.63 1.81 0.54 3.26 3.20 4.50 Note: SLP455 = Sea-level pressure at station 48455 (mb), 3PC455 = 3-h pressure change at station 48455 (mb)

TA455 = Dry bulb temperature at station 48455 (°C), 24PC494 = 24-h pressure change at station 57494 (mb) TD455 = Dew point temperature at station 48455 (°C), 24PC294 = 24-h pressure change at station 56294 (mb) MOI 455 = Moisture content at station 48455, 24PC816 = 24-h pressure change at station 57816 (mb) RAI455 = Precipitation at station 48455 (mm) U455 = east-west surface wind component at station 48455 V455 = north-south surface wind component at station 48455 VIS455 = Visibility at station 48455 (m) PGB333 = Pressure gradient between station 43333 against station 48455 (mb) PGB494 = Pressure gradient between station 57494 against station 48455 (mb) PGB294 = Pressure gradient between station 56294 against station 48455 (mb) PGB816 = Pressure gradient between station 57816 against station 48455 (mb) PGB413 = Pressure gradient between station 96413 against station 48455 (mb) PGB568 = Pressure gradient between station 48568 against station 48455 (mb)


north-south (V wind component; and the up air temperature at 850 mb (TU). Also included are the wind U and V components at all the standard pressure levels (100, 200, 300, 400, 500, 600, 700 and 850 mb) at Bangkok. They are denoted by the pressure level followed by the component and the station number, e.g. 100U455 means east-west wind at 100 mb over Bangkok. At Chiang Mai station (U327, V327), Udorn Thani station (U568, V568), Ubon Rachathani station (U407, V407) and Hat Yai station (U568, V568). Similarly, wind components at the surface, at the standard pressure levels and the SLP at other four weather stations in Thailand are also included for regression: Chiang Mai (327), Udorn Thani (354), Ubon Rachathani (407), Hat Yai (568). Sea-level pressure at the regional stations include Imphal (623), Port Blair (333), Chendu (294), Wuhan (494), Guiyang (816), Shantou (316), Hanoi (820) and Kuching (431). Pressure differences between pressure at eight regional weather stations and five weather stations in Thailand, for example between Imphal station and Thailand stations: Imphal and Bangkok (PGB333), Imphal and Chiang Mai (PGC333), Imphal and Udorn Thani (PGD333), Imphal and Ubon Rachathani (PGU333), Imphal and Hat Yai (PGH333). Similarly, all pressure differences for the rest of 8 regional weather stations and five weather stations in Thailand were considered in the regression analysis. The pressure difference of during the last 3-h (3PC), 6-h (6PC) and 24-h (24PC) of the 5 Thailand stations are taken in to accounts. At the 8 regional stations only 24-h pressure tendency was included in the regression analysis. The resulting equations for the seven patterns are as follows: Pattern 1:

Max. O3 = 188.246-3.316(24PC816)-2.698(TD455)+1.579(300U407)-17.881(3PC407)

-0.363(200U327)-0.553(300V327)+0.3116(PGU316)-0.706(100V407)

-0.230(200V327)

Pattern 2:

Max. O3 = 47.68+12.685(PGH333)-4.514(24PC327)-0.7(400V327)+26.089(3PC407)

+1.494(100V407)+0.55(100U327)-3.386(24PC820)

Pattern 3:

Max. O3 = -1523.43-6.494(TD455)-8.349(3PC455)+3.919(PGC333)+0.687(200V407)

-0.0185(RAD455)-2.438(PGU623)-0.235(100U407)-7.661(24PC333)

+0.761(700U407)+1.786(SLP316)

Pattern 4:

Max. O3 = 54.748-2.627(24PC623)+0.233(500U327)+9.154(PGU333)+0.756(850U407)


+0.472(200V407)+3.664(PGH316)+2.014(V327)

Pattern 5:

Max. O3 = 221.566+21.129(PGC333)-4.190(PGB294)+11.684(6PC327)

+0.317(300V327)-0.639(700V327)

Pattern 6:

Max. O3 = 96.453-32.619(6PC407)-19.486(PGB327)+4.662(PGB623)+12.589(6PC327)

+2.277(SUN455)

Pattern 7:

Max. O3 = 2909.048-8.023(PGB327)-5.949(TD455)+1.186(850V407)-2.612(SLP316)

+0.484(100V407)-8.201(3PC568)+0.436(400V407)

The regression models are all significant at 0.01 significant levels. Each of the independent variables in the equations has a meaningful contribution to the variation in the ozone concentrations at 0.1 significant levels. Plots show reasonably good performance of the models in predicting ozone. The coefficients of determination (R2) which indicate the proportion of the total variance of the maximum hourly ozone concentrations explained by the models, are 81%, 69%, 62%, 87%, 98%, 97% and 50% for the first 7 synoptic patterns, respectively. 4. CONCLUSIONS AND RECOMMENDATIONS The PCA and 2 stage clustering procedure used meteorological data from regional weathers produced synoptic patterns with larger discrepancy in ozone level than that based on data from a single weather station. The regional stations and the pressure difference between the stations are thus better in term of describing the possible movement of the air masses to the study site. The synoptic pattern with high pressure, dry air, high solar radiation and stagnation air are found to associate with highest ozone in Bangkok. The lowest ozone concentration in Bangkok is observed in the patterns, which are associated with a low pressure system, high rainfall, and low solar radiation. The stepwise linear regression models produce reasonable agreement between observed and predicted values with coefficient of determination (R2) = 50-98%. A procedure was developed to produce warning signal of unfavorable meteorological conditions with potential high ozone concentrations during the day based on the regional 0700 LST meteorological conditions for BMR.


5. REFERENCES Davis, R.E., 1991. A Synoptic Climatological Analysis of Winter Visibility Trends in the Mid-

Eastern United States. Atmospheric Environment 25B,165-175. Kartal, S. and Ozer, U., 1998. Determination and Parameterization of Some Air Pollutants as

a Function of Meteorological Parameters in Kayseri, Turkey. Journal of the Air & Waste Management Association 48,853-859.

Kim Oanh, N.T., Zhang, B.N., 2003. Photochemcial Smog Modeling for Air Quality Management of Bangkok Metropolitan Region, Thailand. Journal of the Air and Waste Management Association (submitted).

Lam, K. C. and Cheng, S., 1998. A Synoptic Climatological Approach to Forecast Concentrations of Sulfur Dioxide and Nitrogen Dioxide in Hong Kong. Environmental Pollution 101, 183-191.

Pollution Control Department (PCD), 2001. Investigation and Analysis of Ozone Precusors for the Mitigation of Photochemical Air Pollution in Bangkok. Air&Waste Technology Co., Ltd., Bangkok, Thailand. ISBN 974-7880-22-9.

Scott, G. M. and Diab, R. D., 2000. Forecasting Air pollution Potential: A Synoptic Climatological Approach. Journal of Air and Waste Management Association 50, 1831-1842.

TMD, Meteorological Department of Thailand, Climatology Division, “Climate of Thailand”, August, 2000. Available online: http://www.tmd.go.th/en/entxt.html.

Wolff, G. T., Siak, J. S., Chan, T. L. and Korsog, P. E., 1986. Multivariate Statistical Analysis of Air Quality Data and Bacterial Mutagenicity Data from Ambient Aerosols. Atmospheric Environment 20, 2231-2241.

Zhang, B.N., Kim Oanh, N.T., 2002. Photochemical Smog Pollution in the Bangkok Metropolitan Region of Thailand in Relation to O3 Precursor Concentrations and Meteorological Conditions. Atmos. Environ. 36, 4211-4222.


Part 3: Mixing height calculation 1. INTRODUCTION 1.1 Background A key input to dispersion models is the meteorological data, which are required to compute the transport, dispersion and removal of pollutants. The concentrations of atmospheric trace constituents in the planetary boundary layer (PBL) are strongly affected by the meteorological conditions and dispersion characteristics (Gryning et al., 1999).

One of the most important parameters to characterize the dispersion potential of the PBL is the height of mixing layer or mixing height (MH). It determines the air volume available for the dispersion of pollutants and is involved in many predictive and diagnostic methods and models to assess pollutant concentrations, and it is also an important parameter in atmospheric flow models. MH is an important parameter governing the dispersion of atmospheric pollutants, and measured concentrations of trace gases and aerosols strongly depend on it. Data of MH is necessary for both operational air quality monitoring and as a direct input to numerical transport and dispersion models. In dispersion models, the MH is a key parameter needed to determine the turbulence domain in which dispersion takes place or as a scaling parameter to describe the vertical profiles of PBL variables. The MH is not measured by standard meteorological practices and moreover it is often a rather unspecific parameter whose definition and estimation is not straightforward. In principle, the MH can be inferred from vertical profiles of quantities such as wind speed and wind direction, temperature and humidity, directly influenced by turbulent mixing. However, the actual observed profiles of these atmospheric parameters generally contain complicated structures. As a result, the determination of the MH is often ambiguous under realistic atmospheric conditions, even over relatively homogeneous terrain. Beside that, MH values derived from measurements are available, if at all, at specific sites and partly also for limited time periods only.

Therefore, parameterizations are very widely used. They are called meteorological preprocessors. They have been developed to compute the MH as well as other PBL variables needed as input to dispersion models from routinely available meteorological data, vertical profile such as gradient pressure, temperature, wind categories and solar radiation (Beyrich and Gryning, 1999). 1.2 Statement of the problem

Most current dispersion models are developed by mid-latitude countries and as such many of the empirical parameters used were based on observations taken in the midlatitude boundary layer which is physically different from that of the tropical boundary layer.

The MH determination packages are also suitable firstly for midlatitude meteorology conditions. These meteorological preprocessors have been developed to compute the MH from vertical profiles of quantities such as wind speed and wind direction, temperature and normally without the consideration of atmospheric humidity.


There is a lack of appropriate models to estimate MH parameter in tropical regions. Actually, tropical meteorology conditions are very changeable and moisture plays a more important role in the control of stability and the surface energy balance. These large changes in moisture and temperature profile may cause the changes of MH, atmospheric turbulence, dispersion of pollutants and of course the predicted air pollutants concentration. 1.3 Objectives

This study has been carried out to determine the evolution of MHs in tropical meteorology conditions using graphical and computational methods with and without considerations on moisture contents. These determinations will be based on surface measurement and radiosonding data from Thailand. The results will also be compared to actual measurements given by lidar and sodar remote sounding system. The specific objectives of this study are as follows:

- To review and analyze the actual monitoring data of MHs in tropical locations for

diurnal, monthly and annual variations.

- To modify and propose MH calculation package in order to develop applicable models for tropical conditions.

- To conduct a comparative analysis of the MH obtained by existing and proposed

models against actual measurement. 1.4 Scope of the study

Available measurement data at a seaside (Maptaput, Rayong province) and mountain location (MaeMoh valley, Lampang province) in Thailand are the target areas of this study. Selected exiting meteorology preprocessor for MH determination is BLES preprocessor.


2. METHODOLOGY Overall framework of the methodology of this study is presented in Figure 1.

Figure 1: Overall framework of the methodology

Surface observatio

n data

Calculation method with

BLES

Remote sounding

data

Upper air observatio

n data

Data Pre-processing

Comparison of different methods

Graphical method with tephigram

Annual, monthly, daily MH variations

Meteorology data collection

Simulated MH

C i

With moisture content

considerati

Without moisture content

considerati

Graphical MH

C i

With condensation energy

considerati

Without condensation energy

considerati

Actual monitorin

g MH


2.1 Description of modeling domain Maptaput area Maptaphut Industrial Estate is a part of Eastern Seaboard Development Program, established in Rayong province, about 180 km from Bangkok since 1981. The number of heavy industries in the estate is increased up to 50 factories, with more than 200 stacks, because of good facilities and incentives that the Thai government provided. The Maptaphut Industrial Estate covers an area of 17 km by 30 km, with Universal Trans Mercator (UTM) Grid at 720-750 km East and 1401-1418 km North (70°E-155°E, 10°S-50°N). There are 6 air quality monitoring stations of PCD located in the Maptaphut Industrial Estate. The topography of the study area is flat to undulating terrain. The surrounding of Maptaput Industrial Estate is quite clear with some ranges of big trees and the land is covered with grass. There is a range of low moutain around 10 km far in the North. Mae Moh area The Mae Moh Valley is located approximately 25 km east of Lampang province in northern of Thailand and 630 km north of Bangkok. The valley is about 15 to 20 km wide, 50 km long, and aligned from the northeast to the southwest. The valley floor is relatively flat with an average elevation between 320 and 360 m above the mean sea level. There are two ridges that parallel the valley on the northwest and southeast sides. Hill elevations average 500 m to the northwest, and 600 m to the southeast. The valley is also enclosed by hilly terrain to the northeast, and to the southwest the valley opens to the Lampang province. Mae Moh valley covers an area of 35 km by 35 km, with Universal Trans Mercator (UTM) Grid at 560-595 km East and 2010-2045 km North (70°E-155°E, 10°S-50°N) covering the power plant locations and the 12 air quality monitoring stations located in the Mae Moh Valley was considered. The elevation of sounding system in environmental monitoring system is 326 m above the mean sea level and of the local meteorology station is 314 m above the mean sea level.

2.2 Description of selected model

BLES meteorology preprocessor is a validated model for estimation of the height of the daytime mixed layer has been reported by Batchvarova and Gryning (1991). The model can be used to study individual cases of mixing height evolution with and without subsidence. It has been developing by Sven-Erik Gryning, Riso national Laboratory, and Roskilde, Denmark in order to be widely used with varieties of atmospheric conditions. Small adaptations in tropical meteorology would be a new specific investigation with BLES. The used equations account for the effect of mechanical and convective turbulence. Momentum flux and Monin-Obukhov length can be determined iteratively for different classes of wind speed by using the wind profile and derived value of sensible heat flux. BLES preprocessor includes two modules: FLUX and BLES program. The FLUX program is used to calculate the turbulent fluxes of momentum and heat for every hour. Than the BLES program reads the results file generated by FLUX to calculate the development of mixing height from half an hour before and half an hour after the given time. They are in source code and written in the FORTRAN programming language. In this study, with the need arises to generate a new executable program with moisture content consideration BLES preprocessor and FORTRAN programming files must be compiled and linked using a FORTRAN compiler such as the Lahey F77L-EM/32 FORTRAN 77 compiler.


2.3 Input data processing and analysis MH determination from the remote sounding measurement

LAP-3000 profiler at Maptaput LAP-3000 profiler is a remote sensing system with instruments give information about a volume of the atmosphere at a distance, much the same way the eyes give information about objects at a distance without actually touching the objects. This is fundamentally different from instruments that give information about a specific point in the atmosphere, the same way the finger gives information when touching something. So, data obtained from a remote sensing instrument can not be compared to data from another instrument because one is measuring a volume while the other is measuring a single point. LAP-3000 is called a "profiler" because it gives air speed and direction data at several different elevations, thus provides a vertical profiles of the earth's atmosphere. The profiler transmits a waveleghth of 30cm in three or five orthogonal pointing directions. The profiler listens to the echoes from returned signals, which are extremely small, that bounce of the turbulence in the atmosphere. Then the profiler computes wind speed and direction for many heights above the ground. Typically, the profiler transmits about 6 percent of the time. Averaging time can vary from every 3 to 60 minutes, depending on how the parameters are configured. If a short averaging time is chosen the height range of the profiler and the quality of the data will be reduced. The profiler is a sensitive Doppler radar that is designed to respond to fluctuations of the refractive index in clear air. The reflectivity measured by the profiler is related to the turbulence intensity, gradients of temperature and humidity, and particulate matters. RASS, an acronym for Radio Acoustic Sounding System, is an option that is added to the LAP-3000 at Maptaput to provide virtual temperature profiles up to approximately two kilometers.

REMTECH sodar at MaeMoh This is an effective tool for atmospheric measurements. The first piece of information extracted from measurements is the intensity of effective backscattered signals. Sodar subtracts ambient noise from the information received by the antenna, retaining only the echo return. The power of the scattered echo is proportional to the temperature structure function CT2 which is a measure of the intensity of the small scale fluctuations of the air temperature. This is important because CT2 has large values, and repeatable patterns, especially during ground based radiation inversions, within elevated inversion layers, at the periphery of convective columns or thermals (with which pilots are familiar), in sea breeze/land breeze frontal surface and in a general way at the boundary between masses of air of different temperatures. Since the Remtech sodar electronically generates three beams, it can calculate the dimensional "wind speed components by a simple mathematical coordinate transformat addition, the resultant speed (V) and direction (theta) of the horizontal winds and vertical (W) winds are also determined. Meteorology data collection and preprocessing

Meteorology data in the year 2001 is applied in MH determination as input file for preprocessor as well as source data for tephigram method. Thus, two types of required data during this research are hourly surface observation and twice daily upper air (routine radiosonding) data from meteorology stations in Lampang and Rayong provinces. They were collected from Meteorology Department, Bangkok Thailand. When data points are connected


with line segments, vertical profiles of several physical quantities are obtained. Other complex package of regional meteorology data includes hourly surface and twice daily upper air observation of the year 2001 is ordered online from NOAA, USA. MH determination in this study should be based on most cases on profile measurements of mean meteorological variables such as wind, temperature, humidity, and reflective index. These profiles are collected from meteorological stations in Thailand and would satisfy the conditions:

- They should cover the layer between the earth’s surface and about 2-3 km above ground,

considering the typical height range over which the MH varies during its annual and diurnal cycles in defined climatic regions in Thailand.

- The collected profile measurements should be available with a time resolution of about 1h

or less in order to properly describe the evolution of the MH, especially during the morning and evening transition phases.

- The measured profiles must have a vertical resolution of about 10-30m in order to avoid

relative uncertainties of more than 10-20%, especially for low MHs (<250m). This issue will be discussed with the specified Thai meteorological stations.

3. RESULTS AND DISCUSSION 3.1 Mixing height variations Variations of mixing heights for selected periods at the 2 sites are presented in Figure 2 and 3, respectively. Diurnal variations of the MH for each month at the Mae Moh site are shown in Figure 4, a & b.

0

100

200

300

400

500

600

700

800

900

1000

21 22 23 24 25 26 27 28 29 30

Date in January 2001

Altit

ude

(m, a

gl)

Figure 2 Diurnal evolution of mixing height at Maptaput for 8 days in December 2001


0

100

200

300

400

500

600

700

800

900

1000

21 22 23 24 25 26 27 28 29 30

Date in January 2001

Altit

ude

(m, a

gl)

Figure 3 Diurnal evolution of mixing height at Mae Moh for 10 days in January 2001

Year 2001

0

100

200

300

400

500

600

700

800

900

1000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Local time (hour)

Altit

ude

(m, a

gl)

Jan Feb Mar Apr May Jun

Figure 4a Monthly average evolution of diurnal mixing height at Mae Moh in the year 2001


Year 2001

0

100

200

300

400

500

600

700

800

900

1000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Local time (hour)

Altit

ude

(m, a

gl)

Jul Aug Sep Oct Nov Dec

Figure 4b Monthly average evolution of diurnal mixing height at MaeMoh in the year 2001


3.2 Estimation of MH using BLES model and model performance evaluation Figure 5 and 6 present examples of MH evolution at Mae Moh and Maptaphud, respectively, given by models as compared to the measurements. Analysis shows that simulations with moisture content consideration give slightly higher MH than those without it. The average difference between obtained results in two cases is around 80m. MH derived from simulation method produced the pattern which is in good agreement with observed MH for time 10:00÷12:00. The differences occurred around 13:00 and 14:00 when observed MH values are higher and after 15:00 when observed value started decreasing gradually while the simulated kept increasing. Simulation results were increasing all the fines from 9:00 to 16:00. The beginning values are differed from observations because the actual observations start at level 50m height. The ending values need further studies.

Scatter plot comparisons of MH observation and estimation at Mae Moh in two cases with and without moisture content consideration are illustrated in Figure 7. The number of chosen data points (N) is 248. They are also plotted as height above the ground level, (m). Consideration of tropical moisture content in BLES preprocessor includes virtual temperature and mixing ratio equal to 0.02 g/kg as described earlier. As a result, fair agreements are found with obtained correlation coefficient, R2 is equal to 0.4385 in case of MH estimated without moisture content consideration, and a better agreement in case of simulation with moisture content consideration, R2 (0.4517).

23rd January 2001

0

100

200

300

400

500

600

700

800

900

1000

9.00 10.00 11.00 12.00 13.00 14.00 15.00 16.00

Local time (hour)

Altit

ude

(m, a

gl).

Simulated w ithout MC Simulated w ith MC Sodar measurement

Figure 5 Average evolution of mixing height at Mae Moh in January 2001 given by different methods


29th Dec 2001

0

100

200

300

400

500

600

700

800

900

1000

9.00 10.00 11.00 12.00 13.00 14.00

Local time (hour)

Altit

ude

(m, a

gl).

Simulated w ithout MC Simulated w ith MC Profiler measurement

January 2001

R2 = 0.43580

300

600

900

1200

1500

0 300 600 900 1200 1500

MH Observation (m)

MH

Est

imat

ed w

ithou

t MC

(m, a

gl)

January 2001

R2 = 0.45170

300

600

900

1200

1500

0 300 600 900 1200 1500

MH Observation (m)

MH

Est

imat

ed w

ith M

C (m

, agl

)

Figure 7 Scatter plot comparison of maximum MH observation and estimation with and without moisture content (MC) consideration at Mae Moh in January 2001

Figure 6 Evolution of mixing height at Maptaput on 29th December 2001 by different methods


4. CONCLUSIONS AND RECOMMENDATIONS Diurnal, monthly, and annual mixing height variation from remote sounding systems at both sites in Thailand in 2001 showed a good agreement with the theoretical evolution of MH. Daily minimum mixing height values occurred in early morning (around 7:00) and the daily maximum values occurred in the afternoon (around 14:00÷15:00). The monthly and annual averages of hourly mixing height were highest in April corresponding, which is the hottest month. The values were also high in February and March and became lower in July and August corresponding to the rainy season in Thailand. The Holzworth’s graphical method usually gave higher mixing height values than those obtained from observation at MaeMoh in January and December 2001. The graphical method can account only for convective mixing height therefore it cannot be used for early morning hours when radiation inversion present. Scatter plots show poor agreement between the results from Holzworth’s graphical method and the measurements. Holzworth’s graphical method use only 1 sounding data set per day thus provided only a rough estimation of the minimum and maximum mixing height values.

Cloud formation with condensation energy made dramatic and significant changes in mixing height determination in tropical conditions as shown by the Holzworth method. Daily maximum mixing heights determined by the method with consideration of condensation energy were higher than those without it. Averaged difference was significant (414m) i.e. mixing height increase by 23.8%. The role of cloud formation with condensation energy was not accounted in BLES simulation but moisture air density increased the mixing height slightly. Averaged difference between results in two cases at both sites was not significant (10.8m at MaeMoh and 2.8m at Maptaput) corresponding to an increase in mixing height of 3.2% and 1.5% respectively. Good agreements with measurements were obtained but the simulation results were increasing all the fines from 9:00 to 16:00. Mixing heights derived from graphical Holzworth’s method with consideration of condensation energy were the highest. Graphical determination without condensation energy consideration gave lower results but they are still much higher than those from measurements and numerical simulation by the BLES preprocessor. Mixing heights obtained by the later two methods were in reasonable agreement.

Actual diurnal, monthly, and annual mixing height observed values derived from remote sounding system in long-term should be done and used as a good MH database for further atmospheric research as well as air dispersion models application in the area.


5. REFERENCES

Batchvarova, E., Gryning, S.E., 1991. Applied Model for the Growth of the Daytime Mixed Layer. Boundary Layer Meteorology 56, 261-274. Gryning, S.E., Nyren, K., 1999. Nomogram for the Mixing Height of the Daytime Mixed Layer. Boundary Layer Meteorology 91, 307-322.

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