Journal of Cleaner Production - SSEnv · 2019-10-06 · Evaluation of control strategies for industrial air pollution sources using American Meteorological Society/Environmental Protection

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Journal of Cleaner Production 116 (2016) 110e117

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Journal of Cleaner Production

journal homepage: www.elsevier .com/locate/ jc lepro

Evaluation of control strategies for industrial air pollution sourcesusing American Meteorological Society/Environmental ProtectionAgency Regulatory Model with simulated meteorology by WeatherResearch and Forecasting Model

Awkash Kumar a, *, Rashmi S. Patil a, Anil Kumar Dikshit a, Sahidul Islam b, Rakesh Kumar c

a Centre for Environmental Science and Engineering, Indian Institute of Technology, Bombay, Mumbai 400 076, Indiab Department of Information Technology, Centre for Development of Advanced Computing, Pune, Indiac Council of Scientific & Industrial Research-National Environmental Engineering Research Institute, Mumbai 400 018, India

a r t i c l e i n f o

Article history:Received 24 April 2015Received in revised form21 December 2015Accepted 22 December 2015Available online 31 December 2015

Keywords:AERMODWRFIndustrial sourcesControl scenarios

* Corresponding author. Tel.: þ7208246617 (mobileE-mail address: [email protected] (A. K

http://dx.doi.org/10.1016/j.jclepro.2015.12.0790959-6526/© 2016 Elsevier Ltd. All rights reserved.

a b s t r a c t

Industrial air pollution creates severe problems and evaluation of control scenarios rationally which canbe carried out using air quality models. In this study, an industrial region Chembur in Mumbai city wasselected to estimate air quality change for various control scenarios. Weather Research and Forecasting(WRF) and AMS/EPA Regulatory Model (AERMOD) were used for this study. Since, the industries ofChembur region were already existing and residential and commercial zones were pre-set in the domain,so scenarios like increment of stack height and fuel change of industries can help to make better andhealthier air quality. Scenarios selected were the 10% (SH1), 25% (SH2) and 50% (SH3) increment of heightof stacks of industry and use of low emission fuel (F1 and F2) in the industries. Industrial air pollutionmodelling was done for current scenario as well as proposed control scenarios. The reduction of airquality level for each control scenario was calculated. Overall scenario SH3 as expected had more or lessmaximum reduction at ground level concentration for various temporal cases for all pollutants. However,it would cause more residence time of pollutants in atmosphere. Also this scenario was not based onsource emission reduction strategy. Scenario F1 considers fuel with lowest emission and source emissionreduction strategy. Therefore, scenario F1 seems to be the most preferred option for all pollutants amongall scenarios. This study can help in taking objective and rational decisions for control scenarios forindustrial sources.

© 2016 Elsevier Ltd. All rights reserved.

1. Introduction

Concentration of air pollutants is high in urban areas and islargely derived from vehicular and industrial sources as well aspopulation congestion. This causes climate change and a spectrumof health effects ranging from eye irritation to death (Bamniya et al.,2012a; Ilyas et al., 2007, 2010; Panepinto et al., 2014; Sharma, 2010).The control strategies are easy to implement for industries as theyare within regulatory requirements. Therefore, the study of controlscenarios is needed for air quality management framework of in-dustrial regions.

); fax: þ91 22 2576 4650.umar).

Many air quality monitoring stations have been installed tocontrol and regulate environmental pollution. Also, monitoringdata can help to identify the source type and locationwith the helpof meteorology (Clarke et al., 2014). But monitoring technique doesnot help to evaluate the efficiency of control strategies of airpollution sources. Few case studies have been analysed to controlair pollution for industries using control devices and specific road-side trees in industrial region of India (Bamniya et al., 2012b;Bandyopadhyay, 2011; Furlan et al., 2007). Various air qualitystudies and some control options have been carried out to mitigateair pollution based on emission control strategies (Moraes et al.,2002; Sivacoumar et al., 2001; Vafa-arani et al., 2014; Vieira deMelo et al., 2012; Wei et al., 2015). The achievements of the co-benefits of climate change and air pollution reduction have beencarried out in different sectors through policymeasures (Jiang et al.,2013; Kanada et al., 2013). Five factors such as coal pollution

A. Kumar et al. / Journal of Cleaner Production 116 (2016) 110e117 111

intensity, end-of-pipe treatment, energy mix, productive efficiencychange, and production scale changes were considered to controlemission from industrial sectors and this reduced 65% dust emis-sion over 10 years (Fujii et al., 2013). Policies and regulations werefocused on prevention and control PM2.5 pollution level and currentstatus of PM2.5 were illustrated (Zhang et al., 2015). Many otherstudies have been carried out for air pollution control for variousperspectives like energy system and management (Liu et al., 2003;Zhu et al., 2013; Zhou et al., 2014). In these studies, evaluations ofcontrol strategies and modelling approach have been attempted.All these studies have been performed on estimated emission butdispersion modelling has not been carried out to include atmo-spheric dynamic. Modelling techniques can help to evaluate theefficiency of control strategies and provide concentration profile ofair pollutants in spatial and temporal scale. Various control sce-narios and their efficiency can be studied using modelling toimprove ambient air quality before applying management plans(Bandyopadhyay, 2009; Mukherjee, 2011; Sappurd and Al-damkhi,2011). This methodology is useful to make environmental policy forrational air quality management by regulatory authorities(Jim�enez-guerrero et al., 2007; Mumovic et al., 2006; Sax andIsakov, 2003; Thunis et al., 2012a, 2012b).

Modelling requires emission inventory, geographical charac-teristics and meteorological parameters. In emission data, emissionfrom sources such as vehicles, industries, bakeries, open eatburning and domestic sector are needed. Geographical data such asterrain data and base map are needed. In meteorological data, nineparameters such as wind speed, wind direction, rainfall, tempera-ture, humidity, pressure, ceiling height, global horizontal radiationand cloud cover are needed. Data availability of the region for thestudy is a crucial issue in modelling. The estimation of emission ofindustrial sources can be done for each stack as point source.Generally meteorological data are taken from nearby recognizedmeteorological station and it is interpolated with time and space tothe study site. This leads to major inaccuracy. Number of meteo-rological stations in India is comparatively less, thereby gettingaccurate meteorological data valid for the site under study for airquality modelling is a severe problem. Hourly onsitemeteorologicaldata can be provided by WRF model and this can increase the ac-curacy with saving time and resources. A recognized air qualitymodel AERMOD has been applied in study because it has been usedin many case studies for air quality modelling for various per-spectives (Khare and Sharma, 1999; Kumar et al., 2015; Mohan,2011; Mokhtar et al., 2014; O'Shaughnessy and Altmaier, 2011;Rood, 2014; Seangkiatiyuth et al., 2011; Tartakovsky et al., 2013;Zou et al., 2010). Coupling of AERMOD with WRF has been re-ported for existing air quality but control scenarios have not beenconsidered by Kesarkar et al. (2007).

The aim of this study is to formulate an air quality managementframework for industrial air pollution based on impact of differentcontrol scenarios using air quality models. Many studies have beendone for air pollution reduction. Since, the industries of Chemburregion are already existing and residential and commercial zonesare pre-set in the domain, thus, in the present study, not all controlstrategies can be applied. Moreover the data needed to apply thesecontrol strategies cannot be obtained. Hence, two control strategiescould be tested. These are based on increment of stack height andfuel change. Some control strategies for industrial sources based onabatement of emissions which included cleaner fuel substitution,change in basic production processes, and pollution abatementthrough flue gas treatment modifications of exit gas characteristicsbesides shifting of industries outside the city premises have beenevaluated (NEERI, 2010).

In the present study, air quality modelling has been done forindustrial sources of Chembur region of Mumbai city using

simulated meteorology by WRF model. Various control scenarioshave been applied for industrial sources and impacts have beenquantified for each control scenario. Further, ranking has beenmade for the control scenarios. This would be useful for makingdecisions and policy for air quality management of an urban region.

2. Study area

The industrial area of Chembur in Mumbai was one of the mostpolluted areas about 20 years back (Gupta and Kumar, 2006).However, sustained efforts and public pressure led to series ofcontrol measures by authorities and industries. But even now,Chembur is one of the most densely populated and highly indus-trialized areas of Mumbai city. In the last two decades, the regionhas changed in terms of closure of many industries, high-densityresidential development and increased vehicular density. Fourmajor industries such as two refineries, one fertilizers and onepower plant still exist in this region. The Chembur region has ascore 69.19 in the Comprehensive Environmental Pollution Index(CEPI) score published by Central Pollution Control Board, Gov-ernment of India, for various industrial regions in the country,which indicates that this region should be rated as severelypolluted area (CEPI, 2010). Hence, this region should be kept undersurveillance and pollution control measures should be efficientlyplanned and implemented for this region. The latitude and longi-tude of Chembur are 19.05� N and 72.89� E respectively. The studyarea is 6.5 km east-to-west and 8.45 km north-to-south as shown inFig. 1. Major industries in this area are Bharat Petrochemical Cor-poration Limited (BPCL), Hindustan Petroleum Corporation Limited(HPCL), Tata Power Corporation Limited (TPCL) and RashtriyaChemical Fertilizers Limited (RCFL). The residential block mainlycovers Anushakti Nagar Colony, Tata Colony, BPCL, RCF Colony,Mhada Colony; and Chembur Colony area. North boundary of studyarea is Sindhu Society and Shivnery nagar. South boundary is TPCL,west boundary is RCFL and Mahul and east boundary is ShahyadriNagar and Prayag Nagar. Emission data were collected for fourmajor industries namely BPCL, HPCL, RCFL and TPCL for air qualitymodelling. The annual average of stack emission data (g/sec) wasused for each stack in air quality modelling. Terrain data and basemap are needed for modelling. Hourly nine meteorological pa-rameters were generated using WRF model at 25 km resolution.

3. Methodology

The methodology of the study has been given in Fig. 2. Theemissions for NOx, SO2 and PM were estimated for each stack offour major industries of Chembur region for the year 2011 forexisting scenario. These emission inventories were fed based onactual spatial locations of stacks in the base map. Onsite hourlymeteorological datawere generated byWRFmodel version 3.2. Themodel domain extends between 71�E to 81�E zone and 11�N to21�Nmeridian consisting of 100 by 100 grid points with 25 km gridspacing. NCEP FNL (Final) Operational Global Analysis data with1.0 � 1.0� grid and six hours temporal resolutionwere been used asan input of WRF. This product was from the Global Data Assimila-tion System (GDAS), which continuously collects observational datafrom the Global Telecommunications System (GTS) and othersources. The model run was performed from 1st January to 31stDecember of the year 2011. The details of WRF model descriptionare given in NCAR (2011). The output of WRF model was fed asinput of AERMOD in pre-processor AERMET of the model. Theobserved meteorological data were collected from RCFL andcomparedwithWRF results. Thesemeteorological parameters fromWRF model (wind speed, wind direction, rain fall, temperature,humidity, pressure, ceiling height, global horizontal radiation and

Fig. 1. Study area “Chembur” of Mumbai city.

A. Kumar et al. / Journal of Cleaner Production 116 (2016) 110e117112

cloud cover) were prepared in columns and temporal resolutionwas prepared in rows of a spread sheet. This spread sheet wasprocessed in AERMET which is a pre-processor of AERMOD. Theterrain data at 90 m resolution of Shuttle Radar TopographyMission (SRTM) was used in AERMAP which is also the pre-processor of AERMOD. It provides a physical relationship betweenterrain features and the behaviour of air pollution plumes. It alsogenerates location and height data for each receptor location.AERMOD version 7.6 was used tomodel air quality in this study andfunctional details are given in Cimorelli et al. (2004). AERMODmodel was used for prediction of NOx, SO2 and PM concentrationfrom industrial emissions for existing and validationwas donewithobserved values for existing scenario for all pollutants.

Fig. 2. Methodology for evaluation of control strategies for industrial sources.

The modelling was been done for hourly annual average,maximum daily average and maximum hourly average for pollut-ants NOx, SO2 and PM. Subsequently, model was applied for pre-diction of future proposed control scenarios to improve air qualityfor four industries of Chembur. These scenarios were based onincrement of height of stack and cleaner fuel. The maximum con-centration of each scenario was estimated using model and changein concentration for each control scenario. These strategies mayhelp to make rational air quality management framework for in-dustrial regions.

4. Results and discussions

Existing scenario of air pollution levels from industrial sourceshas beenmodelled. Subsequently, the samemodel has been appliedfor various control scenarios to estimate change in air quality levelfor air quality management purpose. This present method can helpto prioritize the decision to implement various control strategies bypolicy maker. It can be applied for an industrial region for fixingstack height of industries and investigate the risk zone, site selec-tion for design of monitoring program, residential and commercialzones.

4.1. Air quality modelling for existing scenario

Air pollution control strategies in India has been limited to useof SO2 concentration to compute stack height as 14 � (Q)0.3, where,Q is the emission rate of SO2 in kg/hr (CPCB, 1998). However, cur-rent ambient air pollution data across the country does not showany significant issues related to SO2 values. PM has become a majorair pollutant showing exceedance in many cities. In view of theconcern about high PM concentration, in this study control stra-tegies have been evaluated through modelling of emission inChembur region. Thus, emissions from all industries (BPCL, HPCL,RCFL and TPCL) in the study domainwere considered for modellingSO2, NOx and PM for the year 2011. The model evaluation wascarried out as shown in Table A1 (in Appendix) where modellingwas done for all sources and modelled concentrations have been

Fig. 3. Temperature profile for the year 2011.


compared with observed concentration because observed con-centration accounts for all sources). Strategies to control industrialair pollution were considered as stack height changes and fueltypes and model runs were undertaken for these strategies. Themodelling was done for hourly annual average, maximum dailyaverage andmaximum hourly average for the entire duration of themodel run.

The meteorological output of WRF model was compared withobserved values in the study domain. Since, vertical profiles oftemperature and wind are more significant for air modellingtherefore, comparison of temperature and wind has been made forevery hour. The observed meteorological data have been collectedat 6 m height from RCFL industry, Chembur. The comparison ofhourly simulated temperature has been done in Fig. 3 withobserved temperature. Temperature has good agreement betweensimulated and observed values because topographical variabilitydoes not affect much on temperature. WRF model is able toreproduce this weather phenomenon reasonably well. Fig. 4 showswind roses which have been plotted from WRF outputs and

Fig. 4. Annual Wind Rose Simulated by (a

observed wind data. Wind roses have some differentiation in theterms of wind speeds as well as wind directions. The variation intopography and land usemay be responsible for differentiation. Thewind was blowing from west and south-west corners during theentire study period. Therefore, pollutant concentration was domi-nating in mainly east and north part of the study domain. Contourplots of hourly annual average concentration for SO2 NOx and PMfor Chembur area for the year 2011 is shown in Fig. 5. SO2 hasmaximum emission from these industries. Thus, maximum con-centration for SO2 is observed amongst SO2 NOx and PM in thestudy domain. The maximum concentration of SO2 is 25.6 mg/m3 atBPCL and HPCL. NOx and PM emission was less in the region fromindustries, thereby the ambient concentration of NOx and PM wereobserved to be less. Maximum concentration of NOx and PM are8.6 mg/m3 and 4.7 mg/m3 seen at HPCL respectively. Also NOx con-centration at BPCL is 6.8 mg/m3. The Northern part of study domainhas maximum NOx emission from vehicles which has not beenconsidered in this study. The concentration of PM is mainly causedby resuspension of particulate matter where congestion density of

) WRF and (b) RCF Limited observed.

Fig. 5. Contour plots of hourly annual average concentration for (a) SO2, (b) NOx and (c) PM from industries of Chembur.


vehicles and people are the main factors. The middle part of thestudy area is more dominated by industrial sources for PM. Also, thecontour plot of PM has two hotspots for the study area where assingle hotspot has been found for SO2 and NOx.

4.2. Control scenarios for industrial sources

Control scenarios for industrial sources can be considered forrational air quality management for the industrial region. In thisstudy, two types of control scenarios have been made based onincrement of height of stack and fuel change. The first type ofscenario considers 10%, 25% and 50% increment of stack height andsecond type of scenario considers change from high emission fuelto low emission fuel. More height decreases ground level concen-tration but it may cause regional impact on climate. Taller stackneeds larger area than shorter stack on the ground. The makingtaller stack has more cost and risk. So, the control scenario based onchanging of fuel was taken. The SO2, NOx and PM emissions inChembur region are mainly from industries but emission changestrategies are available only for NOx and PM not for SO2. Thus,emission based control strategies have been applied for NOx andPM and increment of stack height has been applied for these allthree pollutants.

There are four major industries in Chembur where RCFL is fullyoperated by natural gas. Therefore, it has full control for fuel basedemission. BPCL and HPCL, both industries are operated by LowSulphur Heavy Stock (LSHS) and natural gas. The quantitativeportion of LSHS and natural gas vary based on limitation of emissionand production rate. Therefore, 50% LSHS and 50% natural gas havebeen assumed for quantitative portion of fuel input in industry.Similarly, Tata Power has four unit (each unit has one stack) fromUnit-5 to Unit-8. Unit-5 and Unit-6 which are multi fuel operatorsystem where coal, LSHS and natural gas are used. It has beenassumed that all fuel types are used in equal proportion. Unit-7 usesnatural gas as fuel. Thus, there isno fuel change in thisunit forcontrolscenarios. Unit-8 operates on coal which can be changed to LSHS ornatural gas. The control scenarios examined are given below:

Scenario SH1 ¼ 10% increment of stack heightScenario SH2 ¼ 25% increment of stack height

Scenario SH3 ¼ 50% increment of stack heightScenario F1 ¼ Low Sulphur Heavy Stock (LSHS) to Gas, Coal toGas and Gas to Gas (no Change for gas)Scenario F2 ¼ Low Sulphur Heavy Stock (LSHS) to Light DieselOil (LDO), Coal to Gas (Coal to 50% coal and 50% Gas for TPCL,Unit-8) and Gas to Gas (no Change for gas)

4.3. Predicted hourly annual average concentration for controlscenarios

The above five scenarios have been applied for NOx and PM.Only scenario SH1, SH2 and SH3 have been applied for SO2 andscenario F1 and F2 cannot be applied for pollutant SO2 because theSO2 emission data were not available for fuel change in literaturefor Mumbai city. All control strategies have been applied for in-dustrial sources and efficiency have been calculated for hourlyannual average, highest daily average and highest hourly average.Modelling has been carried out for hourly annual average for SO2,NOx and PM for the year 2011. Then control strategies have beenapplied for hourly annual average. Figs. A1eA3 (in Appendix) areshowing concentration contour maps for current and control sce-narios for NOx, PM and SO2 respectively. The comparison of variousscenarios has been done with maximum concentration for the re-gion in the terms of health risk. Maximum concentration andpercentage change in concentration for various control scenariosfor NOx, PM and SO2 is shown in Table 1. The efficiency of scenarioF2 was poor in other scenarios for NOx, PM and SO2. As expected,scenario SH3 was most preferred among all scenarios as it has 54%,50% and 43% concentration reduction for NOx, PM and SO2respectively. Scenario SH3 has 50% increment in stack height sopollutants can disperse in atmosphere but it may cause regionalimpact on climate. Increment of stack height was also tested forfurther 75% and 100% but it was not giving more reduction forground level concentration. Percentage concentration reductionwith percentage increment of stack height for NOx, PM and SO2 isshown in Fig. 6. After 50% increment of stack height, the rate ofdecrease of concentration was low and therefore, it was not costeffective. Increment of stack height cannot be exceeded more than50% of present stack height because it requires more horizontal

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Fig. 6. Percentage concentration reduction vs. percentage increment of stack heightfor a) NOx, b) PM and c) SO2.

Table 1Maximum concentration and % change in concentration for various controlscenarios.

Pollutant Scenario Maximumconcentration(mg/m3)

% Change inConcentration

NOx Current 8.57 0SH1 6.81 �20.54SH2 5.06 �40.96SH3 3.94 �54.03F1 6.37 �25.67F2 8.54 �0.35

PM Current 4.68 0SH1 3.98 �14.96SH2 3.21 �31.41SH3 2.36 �49.57F1 2.99 �36.11F2 4.36 �6.84

SO2 Current 25.66 0SH1 20.87 �18.67SH2 17.83 �30.51SH3 14.65 �42.91


space on ground, more expensive, contains higher risk as it maycause climatic impact. In other scenario type, scenario F1 can beselected in case of source mitigation plan where F1 fuel change has26% and 36% reduction for NOx and PM. Hence, scenario SH3 wasfound to be most optimum (54% efficiency) for increase of stackheight whereas scenario F1 was found to be most efficient for fuelchange approach. Also, F1 scenario would have high SO2 reductionfor industrial pollution as well, because this scenario has lowemission fuel and it reduces SO2 emission at source. Overall, F1 ismost preferred in fuel changes option for all three pollutants.

4.4. Predicted daily (24 h) maximum average concentration forcontrol scenarios

Modelling has been also carried out for prediction of all controlscenarios. This scenario has been considered to assess the impact ofcontrol scenario in worst meteorology condition which occursduring winter. These control strategies have been applied for in-dustrial sources in same way but 24 h average concentration hasbeen calculated for each scenario, each pollutant and each receptor.The highest daily average concentration has been plotted for NOx,PM and SO2 for the year 2011 over the region. Figs. B1eB3 (inAppendix) show contour maps for current and control scenariosfor NOx, PM and SO2 respectively. The maximum concentrations ofcontrol scenarios have been compared with maximum concentra-tion of existing scenario to compute percentage reductions forconsidered control scenarios. Maximum concentration and per-centage change in concentration for various control scenarios forNOx, PM and SO2is shown in Table 2. The evaluated scenario F2 hasless reduction than other scenarios for NOx, PM and SO2. As ex-pected, scenario SH3 is most optimum amongst all scenarios as ithas 32, 40 and 44% for NOx, PM and SO2, respectively. Scenario SH3has more dispersion in atmosphere as it has 50% increment in stackheight but also it may cause regional impact on climate. Scenario F1is based on fuel change and it gives 27 and 28% reduction for NOx

and PM, respectively. Hence, scenario SH3 will be most preferred(44% efficiency) for stack height increment and scenario F1 will bemost preferred for fuel change consideration.

4.5. Predicted hourly maximum annual concentration for controlscenarios

Again, the above control scenarios have been applied fordifferent temporal cases. These control strategies have been applied




NOx Current 29.12 0SH1 26.38 �9.41SH2 23.74 �18.48SH3 19.81 �31.97F1 21.38 �26.58F2 29.11 �0.034

PM Current 18.35 0SH1 16.60 �9.54SH2 14.19 �22.67SH3 10.93 �40.44F1 13.19 �28.12F2 18.21 �0.76

SO2 Current 134 0SH1 118 �11.94SH2 95 �29.10SH3 75 �44.03


for industrial sources in sameway but every hour concentration hasbeen calculated for each receptor, each pollutant and each scenario.The hourly maximum concentration at each receptor has beenplotted for NOx, PM and SO2 for the year 2011 over the region. Thismay help for consideringworst scenario of the period and generallyit happens in the winter season. Figs. C1eC3 (in Appendix) areshowing maximum hourly concentration contour maps for currentand control scenarios for NOx, PM and SO2 respectively. Themaximum concentration of various control scenarios have beencompared with existing scenario in terms of percentage reductionof concentration level. Maximum concentration and percentagechange in concentration for various control scenarios for NOx, PMand SO2is shown in Table 3. As expected, scenario SH3 is mostpreferred among all scenarios as it has 72, 69 and 79% reduction ofconcentration for NOx, PM and SO2 respectively. Scenario SH3 maycause regional impact on climate as it has 50% increment in stackheight. The evaluated scenario F2 has less reduction than otherscenarios for NOx, PM and SO2. Scenario F1 was based on fuelchange and it has 27% reduction for NOx and 11% reduction for PM.The reduction of PM was very less in scenarios F1. So, scenario SH3will be most preferred for NOx, PM and SO2. Scenario F1 would bebetter if stack increment was constrained. Hence, scenario SH3would be most preferred (79% reduction) for SO2 and (69% reduc-tion) for PM. Natural gas fuel would be better for long term plan forfuture scenario.

5. Summary and conclusions

In the present study, air quality model was used to estimatechange in ambient air quality levels for current and future controlscenarios for industrial sources of SO2, NOx and PM for the year2011. These control scenarios were made based on increment ofstack height and change in low emission fuel of industries. Sce-narios were the 10% (SH1), 25% (SH2) and 50% (SH3) increment ofstack height and use of low emission fuel (F1 and F2) in the in-dustries. Modelling was carried out for hourly annual average,maximum daily average and maximum hourly average for entireduration of the study. According to results obtained, as expectedscenario SH3 seems to be most preferred as increasing stack heightcaused a huge reduction of pollution at ground level for all pol-lutants for all cases. After increment of 50% stack height, thereduction rates of pollution level are low so, it was not cost effectiveto have further increment in stack height. This scenario was not




NOx Current 435 0SH1 348 �20SH2 209 �51.95SH3 122 �71.95F1 319 �26.67F2 435 0

PM Current 179 0SH1 110 �38.55SH2 67 �62.57SH3 55 �69.27F1 159 �11.17F2 178 �0.56

SO2 Current 1550 0SH1 1054 �32SH2 704 �54.58SH3 326 �78.97

based on source emission reduction strategy. In this case, emissionswere same but pollutants will disperse in atmosphere. There wasless difference between reduction of concentration in scenariosSH3 and scenario F1, except in the case of 1 h maximum concen-tration. Since, the fuel based emission data for SO2 was not availablebut its emission reduction strategies will be applied for scenario F1then it will havemaximum reduction of SO2 and also, as scenario F1had natural gas fuel which contains less emission of SO2. Thus,overall scenario F1 seems to be the most preferred option for allpollutants.

The results of this study can help to assess the efficiency ofvarious control strategies for air quality management framework byregulatory authorities. Stack height computation is based onsulphur dioxide emission from stack by Indian regulatory authority,Central Pollution Control Board (CPCB). However, in current sce-nario, where SO2 alone cannot be considered the most importantpollutant as PM has become more prevalent pollutant in manycities. This implies that SO2 is not a good criterion for fixing thestack height. The stack height also can be optimized and fixed basedon ground level concentration of pollutants like PM. Additionalbackground concentration can be includedwhich comes from othersources. This study also helps to investigate the risk zone of studyarea and site selection for design of monitoring program based onthe predicted concentration for various control scenarios.

Acknowledgements

We extend our sincere thanks to Dr. Ajay Deshpande andMaharashtra Pollution Control Board (MPCB) for providing relevantdata.

Appendix A. Supplementary data

Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.jclepro.2015.12.079

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Journal of Cleaner Production - SSEnv · 2019-10-06 · Evaluation of control strategies for industrial air pollution sources using American Meteorological Society/Environmental Protection