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Best Practices in Air Quality Monitoring

Prof. Shun‐cheng LEEDr. Wing‐tat HUNG

Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University

1. URBAN AIR QUALITY MANAGEMENT STRATEGY IN ASIA GUIDEBOOK By Steinar Larssen, Knut Erik Grønskei, Norwegian Institute for Air Research

2. Kjeller, Norway and M. C. Hanegraaf, Huib Jansen, O. J. Kuik, F. H. Oosterhuis , Xander A. Olsthoorn, Institute for Environmental Studies, The Free Unversity

3. Amsterdam, the Netherlands.4 Air Quality Monitoring Programme Design By Bjarne Sivertsen Norwegian

KEY REFERENCES 

4. Air Quality Monitoring Programme Design By Bjarne Sivertsen, Norwegian Institute for Air Research, Kjeller, Norway. NILU: F  2/2002 REFERENCE:

5. O‐101128 DATE: FEBRUARY 20026. Guidelines for Ambient Air Quality Monitoring By CENTRAL POLLUTION CONTROL 

BOARD (Ministry of Environment & Forests, Govt. of India), NATIONAL AMBIENT AIR QUALITY MONITORING SERIES : NAAQMS/ ... /2003‐04

7. GUIDELINES FOR DEVELOPING NATIONAL STRATEGIES TO USE AIR QUALITY8. MONITORING AS AN ENVIRONMENTAL POLICY TOOL, United Nations Economic 

and Social Council, ECE/CEP/2009/10, 14 October 2009and Social Council, ECE/CEP/2009/10, 14 October 20099. National Air Pollution Surveillance Network Quality Assurance and Quality 

Control Guidelines, Environment Canada, Environmental Protection Service, Environmental Technology Advancement Directorate, Analysis and Air Quality Division, Environmental Technology Centre, Report No. AAQD 2004‐ 1

10. GUIDANCE FOR NETWORK DESIGN AND OPTIMUM SITE EXPOSURE FOR PM2,5 AND PM10, PREPARED BY John G. Watson, Judith C. Chow, David DuBois, Mark Green, Neil Frank, Marc Pitchford, Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency

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Essential Elements of Good Air Quality Monitoring System

ATTRIBUTE/CATEGORY INDICATORS SUPPORTING DOCUMENTS

A Ability to properly plan and implement a AQM network to a compatible international standard

Evidence of proper planning documents (for example guidelines) and planning process for AQM system (in particular, number and locations of AQM stations

Evidence of a proper equipment sourcing and procurement procedure (for example, tendering documents)

Evidence of qualification requirements for personnel (including responsible officers and technicians)

Guidelines for siting and planning of AQM station

Lists of AQ monitoring equipment, equipment specifications

B Ability to plan and implement a QA/QC process

Evidence of a QA/QC process (for example, a published requirements and guides for data quality, i.e., acceptable accuracy and

lid d t t i l l )

QA/QC guidelines for AQM system

Summary of Essential Attributes of Good Practices in implementing  AQM t valid data capturing levels)

Evidence of remedial measures in case of un-reliable / uncertain data records (for example, a model to rectify the faulty data)

Evidence of qualification requirements for independent QA/QC agent

C Ability to disseminate AQM data and analytical results to stakeholders

Evidence of published data and reports (hardcopy of electronic forms)

Evidence of formal established channels to disseminate data and results to stakeholders

Evidence of channels receiving feedbacks from stakeholders

Samples of air quality data record sheets

D Ability to utilize the AQM results to improve AQ control policy

Evidence of changes in policy resulting from AQM results (for example, changes in legislation and implementation of clean air programme)

Evidence of stakeholders using the AQM

Local air quality standards

AQM system

data records and resultsE Ability to provide

manpower and financial resources to sustain the AQM system

Evidence of staff development and training programmes for personnel in AQM system

Evidence of funding commitment for sustaining the expenses of the AQM system

Evidence of continuous review exercise to improve AQM system

Manpower structure, personnel qualification requirements

Costs : i) initial site capital cost; ii) costs of spare parts and consumables; iii) Operating costs ,electricity, calibration gases, filter papers and iv) maintenance cost

Monthly/ Annual running costs for AQM system

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World Health Organization Guidelines

The Role of Monitoring in Air Quality Management

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AQM NETWORK SIZE DESIGN

Good practice to check the necessity of station, the types of pollutant monitoring, the appropriate equipment to use

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US ENVIRONMENTAL PROTECTION AGENCYSummarized below is the minimum number of stations per metropolitan statistical area (MSA) for PM10, PM2.5 and O3 under the US EPA monitoring guidelines.PM10 Minimum Monitoring RequirementsMSA Population HIGH 

CONCENTRATION (exceeds NAAQS by 20% or more)

MEDIUM CONCENTRATION(exceeds NAAQS 

by 80%)

LOW CONCENTRATION(less than 80% of 

NAAQS)

>1,000,000 6 ‐ 10 4 ‐ 8 2 ‐ 4500,000 ‐ 1,000,000 4 ‐ 8 2 ‐ 4 1 ‐ 2250,000 ‐ 500,000 3 ‐ 4 3 ‐ 4 0 ‐ 1100 000 250 000 1 2 1 2 0100,000 ‐ 250,000 1‐ 2 1 ‐ 2 0Source: Federal Register / Vol. 71, No. 200 / October 17, 2006 / Rules and Regulations

PM2.5 Minimum Monitoring Requirements MSA Population Most recent 3‐

year design value 85% of any PM2.5 NAAQS

Most recent 3‐year design value 

<85% of any PM2.5 NAAQS

>1,000,000 3 2

500,000 ‐ 1,000,000 2 1

50,000 ‐ 500,000 1 0

Source: Federal Register / Vol. 71, No. 200 / October 17, 2006 / Rules and Regulations

O3 Minimum Monitoring Requirements 

MSA Population Most recent 3 Most recent 3MSA Population Most recent 3‐year design value 85% of any PM2.5 NAAQS

Most recent 3‐year design value 

<85% of any PM2.5 NAAQS

>10 million 4 2

4 ‐ 10 million 3 1

350,000 ‐ 4 million 2 1

50,000 ‐ 350,000 1 0

Source: Federal Register / Vol. 71, No. 200 / October 17, 2006 / Rules and Regulations

Australian Specification

• The Peer Review Committee (PRC), Australian National Environment Protection Council (NEPC) – National Environment Protection (Ambient Air Quality) Measure Technical Paper No. 4 on Screening Procedures, May 

2001 – “Fewer performance monitoring stations may be needed where it can be demonstrated that pollutant levels are reasonably expected to be consistently lower than the standards mentioned in this Measure.”

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Equipment Considerations

The case of California Air Resources Board (CARB) 

AQM Management and manpower training

TRAINING ‐ The ARB has recruitment and screening procedures to ensure that station operators are experienced and qualified instrument technicians. On‐the‐jobtraining is completed by all new station operators before they are allowed to independently operate field stations.

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The primary purpose of the QAPP is to provide an overview of the project, describe the need for the measurements, and define QA/QC activities to be applied to the project, all within a single document.

Common Issues and Problems in Asian Cities

1. Inadequate political drive – deficiency of public awareness and no champion in government senior management

2. Insufficient funds to sustain the recurrent and replacement costs of air monitoring system

3. Deficiency in  data analysis and especially in  QA/QC  requirement and processing –insufficient fund and technical ability

4 Di i i d di i i4. Discrepancies in data dissemination –stakeholders have no access to raw data

5. Insufficient use of data to make policy changes to improve air quality

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THE CASE OF HONGTHE CASE OF HONG KONG AND PEARL RIVER DELTA AQM NETWORK

Problems/ Issues in early 1980s 

1. Serious air pollution from factories2. Inadequate public awareness on air pollution3. Inadequate funds to set up AQM system4. No political leader(s) to advocate abatement of air pollution.

How the problems were tackled ?

1. Air pollution control work started in late 1980s when the British Hong Kong Government implemented the international obligation on Hong Kong; the political drive came from the UK government  and expatriates working in HK

2 The AQM network started with a few stations and gradually expanded2. The AQM network started with a few stations and gradually expanded3. The planning, operation, QA/QC systems were borrowed directly from UK

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Air quality monitoring network in Hong Kong

• Hong Kong has 14 air quality monitoring stations for measuring concentrations of 

Land Use Type

Land Use Characteristics

Air Monitoring Stations

Urban Densely populated residential areas mixed with some commercial

1. Central/ western2. Eastern3 K i Chmajor air pollutants. 

• It consists of 11 general stations for monitoring ambient air quality and 3 roadside stations for measuring street level air quality since 1999

with some commercial and/ or industrial area

3. Kwai Chung4. Kwun Tong5. Sham Shui Po6. Tsuen Wan

New Town Mainly residential area 7. Sha Tin8. Tai Po9. Tung Chung10. Yuen Long

Rural Rural area 11. Tap Mum (background station)q y (background station)

Roadside Urban roadside in mixed residential/ commercial area with heavy traffic

and surrounded by many tall buildings

12. Causeway Bay13. Central14. Mong Kok

Air quality monitoring network in Hong Kong

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General stations and Roadside stations

Contents General station Roadside station

Purposes Represent an area of mixed residential and commercial/industrial activities or only residential activities

Monitor street-level emissions from nearby vehicle exhaust and road dustonly residential activities road dust

Stations cost US$ 385000 for capital and US$ 220000 for Annual operation/ development

US$ 510000 for capital and US$ 220000 for Annual operation/ development

Height above ground level

From 11 meters (Tap Mun) to 27.5 meter (Tung Chung)

Quite low, at 2 to 3 meters

Distance to nearest major roadway

From 20 meter (Sham Shui Po) to 200 meters (Sha Tin)

All less than 5 meters

roadway

Parameter SO2, NOx, NO, NO2, CO, O3, RSP, TSP

SO2, NOx, NO, NO2, CO, RSP

Maintenance Schedule

Less frequent More frequent

API Comes from measurements at 11 general AQM stations

Comes from measurements at 3 roadside AQM stations

Hok Tsui Station

Hok Tsui was established by the Civil& Structural Engineering Departmentof Hong Kong Polytechnic Universityin 1993. It is the first long‐termbackground site

Equipment

• CO analyze; 

• O3 analyzer;

• Multi‐gas calibrator; Standard gas;

UV h t t i O lib t Parameter: ozone, solar ultraviolet

radiation, aerosol, CO and NOx, PM2.5,Rn

• UV photometric O3 calibrator;

• Brewer Spectrophotometer; external UVB lamp; 

• Data logger; temperature and Humidity data logger; 

• Three fine particle collecting instruments (belong to EPD);

• Radon analyzer; Radon calibration unit; ;

• Telephone lines; 

• Pump; dehumidifier control equipment; air condition….

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With the rapid economic development in the Mainland, air quality in HK is With the rapid economic development in the Mainland, air quality in HK is severely affected, there is a great problem to identify responsibilities, Pearl severely affected, there is a great problem to identify responsibilities, Pearl River Delta AQM network was set up ten years ago to identify our source River Delta AQM network was set up ten years ago to identify our source apportioningapportioning

Mobile stationMobile station

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How to use the data?1.Determine air‐pollution levels and trend2 Determine source region for measured2.Determine source region for measured plume using wind and back trajectories

3.Improving emission inventories4.Determine what precursors control photochemical production of ozone

5.Modelling – Emission Based models and Observation Based models

10‐day back trajectories arriving HK for days with PM2.5

samples(November 2000‐October 2001)

(Wang, Final Report to EPD, 2003)

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Local vs. regional contribution to PM2.5 in coastal air mass  

(PM2.5 data courtesy of 

HKEPD)

35

40) Rural (Hok Tsui)

15

20

25

30

35

Com

posi

tion

(μg/

m3) Rural (Hok Tsui)

Urban (Tsuen Wan)

0

5

10

15

Sulfate Nitrate Ammonium EC OC PM 2.5

Che

mic

al C

Source apportionments of NMHCs

36.4%19.4%

5.2%0.1%

Use of solvent

Vehicle emission

LPG or natural gas leakage

Industrial sources

Biogenic emissions

3%

17%

9%

8% 2%

Gasoline evaporation

Biomass burning

Natural gas leakage

Industrial sources

Biogenic emissions

Urban Hong Kong Rural Lin’an

38.9%

Biogenic emissions 61% LPG leakage

Guo et al., EP, 2003, AE 2004

Vehicle and solvent Biofuel/biomass 

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Contribution of different pollutants to light extinction (Bext)

Bext (Mm‐1) = 3f(rh) [Sulfate]+Hygroscopic species growth function

7( ) ( )

3f(rh)[Nitrate]+

4 [Organic] +

1[Soil]+

0.6[Coarse Mass]+  0123456

0 20 40 60 80 100

f(rh

)

10[EC] + 

0.175[NO2] + 

10

Relative Humidity (%)

[Sulfate] = (NH4)2SO4

[Nitrate] = NH4NO3

[Organic] = 1.4[OC][Soil] = 2.2[Al] + 2.19[Si] + 1.63[Ca] + 2.42[Fe] + 1.94[Ti][Coarse Mass] = [RSP]-[FSP]f(rh)= hygroscopic species growth function

Inverse model result

(Y. X. Wang et al., JGR, 2004)

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Roadside emissions problems:  unclear characteristics and factors influences the roadside pollution concerntrationsSolution: carried out a detailed study and analaysis with a Roadside Supersite at PolyU

Supersite Objectives• Evaluate measurement methods (Inter‐comparisons, practicality, operating procedures)

• Understand atmospheric processes and source contributions (Chemical/size characteristics, Influencing factors, effects of meteorology)

• Establish PM/health relationships (epidemiology, human exposure, toxicological responses, practical 

d f h l h ff )PM indicators of health effects)

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Traffic Patterns at Supersite

2030

3030

4030

5030

6030

c co

unts

(# h

our-1

)

30

1030

2030

Tra

ffi

Diesel fueled vehicle Gasoline fueled vehicle Taxis Total

Sun Mon Tue Wed Thu Fri Sat

Sun Mon Tue Wed Thu Fri Sat

Research‐Grade Supersite Measurements

• Continuous, high‐time‐resolution measurements of , gparticle mass, number, and chemical components

• Ultrafine, fine, and coarse particle size distributions

• Standard and advanced chemical and morphological characterization

• Precursor gases, co‐pollutants, and meteorology

• Collocated instrument testing

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Observation and Method Avg Time Frequency Filter Filter Mass and Chemistry PM2.5 mass, ions, carbon (RP sequential /Quartz filter)

24-hr Daily Quartz (47mm)

PM1 mass, ions, carbon (RP sequential /Quartz filter)

24-hr Daily Quzrts (47mm)

PM1, PM2.5, PM10 Mass, ions, Elements, carbon including water soluble organic carbon (URG sampler with Quartz/Teflon filters)

24-hr Every six days Teflon (47mm) Quartz (47mm)

Positive OC artifact for PM1 and PM2.5 (URG sampler with Quartz/Teflon filters)

24-hr Intensive Teflon (47mm) Quartz (47mm)

Diurnal variation of PM2.5 and major chemical composition (RP sequential /Quartz filter)

2-hr Intensive Quartz (47mm)

Particle Sizes Mass, ion and elemental size distribution (MOUDI 0.056-18 m in 11 fraction with Teflon & IC, AC, XRF; and Aluminum & OC/EC analyser)

48-hr to 120-hr

intensive

Teflon (47mm and 37 mm) Quartz (47mm and 37 mm)

Mass, ion and elemental size distribution (MOUDI 0.01-2.5 m in 11 fraction with Aluminum foil & IC, OC/EC analyzer)

48-hr to 72-hr Intensive

Aluminium foil (47mm) Quartz (37mm) Teflon (47 mm)

Carbon size distribution (Anderson Impactor 0.43-10 m in 8 fraction with Quartz & IC, OC/EC analyzer)

24-hr Weekly Quartz (81mm)

Continuous Particle Mass and Chemistry PM (TEOM) 30 i E d C dPM2.5 mass (TEOM) 30-min Every day Connected to a computer EC (Aethalometer Model AE-30) 5-min Every day Kimoto PMcoarse/fine/OBC 1-hr Every day Total Particulate PAH 1-min Every day Particle number Concentration 0.007-0.217 μm size distribution (TSI 3936 SMPS)

30-min Every day Connected to a computer

0.1-2 μm size distribution (PMS 1003) 30-min Every day Morphology Particles on Nucleapore filter (SEM) 30-min - Nuclepore (47 mm) Gases NO-NO2-NOx 1-min Every day Connected to a computer NH3 15-min Every day

PM Measurements‐filterBGI frmOMNITM Ambient Air Sampler

Airmetrics Mini‐Vol portable samplers 

URG‐3000ABC multi‐channel samplers 

Partisol‐Plus Sequential Air Sampler (2025)

Roadside Medium‐Vol Air Sampler (PM2.5)MSP MOUDI model 110 

and model 115 (Nano‐MOUDI)

High‐Vol Air Sampler (PM2.5)

MOUDI)

High‐Vol PUF sampler (PAHs)

Eight Stage Non‐Viable Impacor(0.4‐10 m)

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Aerosol Physical Properties

• Near‐real‐time automated measurements

Sizing sensitivities from 0.1 ‐2.0 microns (0.1, 0.2, 0.3, 0.4, 0.5, 0.7,

OPC = optical particle counter, SMPS = scanning mobility particle spectrometer

SMPS ‐ 3080 Electrostatic Classifiers and 3022A Condensation Particle Counters (CPCs), measuring high‐resolution size distributions of ultrafine particles. display data using 162 size channels from 10 to 1000 nm

microns (0.1, 0.2, 0.3, 0.4, 0.5, 0.7, 1.0 and 2.0 µm)

Continuous Measurements

Li‐Cor CO2 and H2ONOx and NH3

BC      Single‐ and seven‐wavelength aethalometer

BC Kimoto SPM‐613D Dichotomous Monitor

PAH EcoChem R&P TEOM

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i fConsistency Tests for PM Measurements

Chemical Analyses 

• consistency tests include: (1) comparisons between mass concentrations and the weighted sum of chemical species; (2) comparisons between concentrations of the same species measured by different analysis methods (e.g., sulfate by IC versus total sulfur by XRF; soluble potassium by IC versus total potassium by XRF; and chloride by IC versus chlorine by XRF); (3) charge balances between anions and cations; and (4) comparisons between mass and chemical concentrations in different size fractions (e.g., PM2 5 concentrations must always ( g 2.5 ybe less than or equal to PM10 concentrations).

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Mass Concentrations versus the Sum of Chemical Species

60

90

120

y=0.80(0.03)+10.50(1.67)

R2=0.96n=40y/x=1.09(0.13)

es o

f P

M1.

0 (ug

m-3

)

1:1 line

0 30 60 90 1200

30

Sum

of

spec

ie

Teflon particulate mass (ug m-3)

Different Analytical Methods

24

32

40

3

4

5

y=2.67(0.06)x-0.21(0.32)

R2=0.95n=120

g m

-3)

y=0.89(0.02)x-0.06(0.03)

R2=0.93n=120

um (

ug m

-3)

0 2 4 6 8 10 12 140

8

16

0 1 2 3 4 50

1

2

4

5

25

30

Sul

fate

(ug

Sulfur (ug m-3)

Sol

uble

pot

assi

u

Total potassium (ug m-3)

y=0.80(0.03)x+0.25(0.03)2

Ammonium sulfate y=1 87(0 06)x+0 44(0 18)

g m

-3)

0 1 2 3 4 50

1

2

3

0 5 10 15 20 25 300

5

10

15

20R2=0.90n=86

Chl

orid

e (u

g m

-3)

Chlorine (ug m-3)

Ammonium bisullfatey=1.07(0.04)x+0.32(0.16)

R2=0.87, n=120

y=1.87(0.06)x+0.44(0.18)

R2=0.90, n=120

Cal

cula

ted

amm

oniu

m (

ug

Measured ammonium (ug m-3)

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Different Size Fractions

60

90

120

s co

ncen

trat

ion

(ug

m-3

)

y=1.30(0.05)-1.53(1.77)2

0 30 60 90 1200

30

PM

2.5 m

ass

PM1.0

mass concentration (ug m-3)

R2=0.96PM

1.0/PM

2.5=0.81(0.18)

Inter‐comparisons of Different Instruments

• Measurement methods developed by different organizations may give different results when sampling the same atmosphere even though the techniques appear to be similar (e g Hitzenberger et al 2004)similar (e.g., Hitzenberger, et al., 2004). 

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Comparison of different PM2.5 samplers

Instrument BGI High Volume

Mini Volume

URG Y-Shape Kimoto Rel. Ref.*

Mass Concentration, μgm-3

56.0 54.4 56.7 56.4 59.0 51.5 56.5

Diff. to Rel. Ref.*

-0.9 -3.7 0.3 -0.2 4.5 -8.8

*Rel Ref represents the Relative Reference Value Relative Reference Value is the average mass*Rel. Ref. represents the Relative Reference Value, Relative Reference Value is the average mass

concentrations obtained by different filter-based samplers.

Time Series Analysis

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Inter‐comparisons of hourly PM2.5 between Kimoto (beta‐gauge) and TEOM (weighing) 

60

90

120o

hour

ly P

M2

.5 (μ

g m

-3)

y = 0.96(0.01)x + 11.40(0.51)

R2 = 0.72

P<0.001, n=2658

0

30

0 30 60 90 120

TEOM hourly PM2.5 (μg m-3

)

Kim

ot

Results:

The Characteristics of Fine (PM2.5) and 

Coarse (PM2.5‐10) Particles

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Seasonal Variations

10

20

30

40

50

con

cent

rati

on (u

g m

-3)

PMcoarse at PU roadside

20

40

60

80

100

120

con

cent

rati

on (u

g m

-3)

PM2.5 at PU roadside

0

Jan

Feb

Mar

Apr

May Jun

Jul

Aug Se

p

Oct

Nov Dec

MonthPM0

Jan

Feb

Mar

Apr

May Jun

Jul

Aug Se

p

Oct

Nov Dec

Month

PM

The PM data was determined by  Kimoto SPM‐613D Dichotomous Monitor

30

50

70

90

110

S M T W d Th F i S

PM c

once

ntra

tion

(ug

m-3

)

PM2.5

PM10Weekly cycle

Sun Mon Tue Wed Thu Fri Sat

5

13

21

29

37

Sun Mon Tue Wed Thu Fri Sat

Con

cent

rati

on (u

g m-3

) PMcoarse

BCR PM2.5 PMcoarse BC

Diesel-fueled vehicle 0.80 0.48 0.94

Gasoline-fueled vehicle 0.73 0.46 0.65

30

1030

2030

3030

4030

Sun Mon Tue Wed Thu Fri Sat

Tra

ffic

cou

nts

(# h

our-1

)

Diesel fueled vehicle

Gasoline fueled vehicle

Taxis

Taxis -0.30 0.05 -0.39

The PM data were determined by  Kimoto SPM‐613D Dichotomous Monitor

12/17/2012

25

24

Median concentrations of PMcoarse for each 0.4 m s‐1 wind speed bin .

16

18

20

22

PMcoarse

PM

coar

se

0 2 4 6 812

14coarse

R=0.98, P<0.0001

Wind speed (m s-1)

The PM data was determined by  Kimoto SPM‐613D Dichotomous Monitor

Chemical Composition

PM2.5 (55.5±25.6 μg m‐3) PMcoarse (25.9±15.5 μg m‐3)

27%

25%

6%

6%5% 2%

8%

8%

17%

32%11%

13%

29% 11%

2 7 %

4 2 %

1 9 %

2 %7 %3 %0 %

OM( OC*1. 4)ECAmmoni um sul f at eAmmoni um ni t r at eSea- sal tMi ner al mat er i al and t r ace el ementUni dent i f i ed

URG‐3000ABC multi‐channel samplers 

12/17/2012

26

The diurnal patterns of OC/EC ratios

0 5

1.0

1.5

2.0

2.5

3.0

OC

/EC

Rat

io

1:00-4:00

12:0020:00 6:00 14:0022:00 8:00 16:00 0:00 10:0018:00 4:00 12:0020:00 6:00 14:0022:000.0

0.5O

Time (h)

8:00-10:00

The samples were collected by RP2025

Average OC/EC ratio: ~0.6 during 8:00‐10:00; ~1.6 during 1:00‐4:00

Results:

Pollution Episodes

the air pollution in Hong Kong has close relations to synoptic 

systems, especially continental high pressure in cold season 

(Pathak et al., 2003; Wang et al., 1997; 2003; Louie et al., 

2005b) and tropical storms in the warm season (Cheng et al., 

2006; Wang et al., 2006) 

12/17/2012

27

Aerosols during Pollution EpisodesJan 21 – May 31, 2004

0

50

100

150

PM1.0 at PU roadside station

50

100

150

PM2.5 at PU roadside station

Episode days: Jan 30 Feb 14 15 23 26 Apr 19 20

The samples were collected by RP2025

0

On episode days, the average OC concentration in PM1.0 and PM2.5 increased  70% and 100%, respectively, compared to average values. EC showed only a 20–30% increase.

1000

2000

3000

4000Case I Case II Case III

ffic

cou

nts

(# h

-1) Diesel vehicleLPG Taxi Gasoline vehicle

Aerosols during Pollution EpisodesJul 20 – Jul 28, 2005

0

30

60

90

120

1500

1000

10

15

20

25

entr

atio

n (u

g m

-3)

PM2.5

EC OC

(b)

Tra

f

(a)

Sulfate Ammnonium Nitrate

(c)

0

5

10

14:00 0:00 12:00 22:00 10:00 20:00 8:00 18:00 6:00 16:00 4:00 14:00 0:000

5

10

15Mas

s co

nce

Time (hour)

SOC

(d)

The samples were collected by RP2025

12/17/2012

28

Results: Size distributions

SMPS                                                                     OPC

MSP MOUDI model 110 and model 115 (Nano‐MOUDI)

Size Distributions of EC and Ions

15

20

25

dlog

Dp

Sample 6

Sample 7

Sample 8

EC

30

40

50

60

dlog

Dp

Sample 6

Sample 7

Sample 8

SO42-

0

5

10

0.001 0.01 0.1 1 10

Diameter (µm)

dM/d

0

10

20

30

0.001 0.01 0.1 1 10

Diameter (µm)

dM/d

8

10Sample 6

Sample 7

NH4+

8

10Sample 6

Sample 7

NO3-

0

2

4

6

0.001 0.01 0.1 1 10

Diameter (µm)

dM/d

logD

p Sample 7

Sample 8

0

2

4

6

0.001 0.01 0.1 1 10

Diameter (µm)

dM/d

logD

p Sample 7

Sample 8

12/17/2012

29

Size Distributions of Metals

0

1

2

3

4

5

0.001 0.1 10

Si

0

1

2

3

0.001 0.1 10

K

0.002

0.004 Ni

0 2

0.4

0.6

0.8Cl

0

1

2

3

0.001 0.1 10

Al

0.0

0.3

0.6

0.9

0.001 0.1 10

dM/d

logD

p Na

2

3

4

5Fe

0.01

0.02

M/d

logD

p V

0.000

0.001 0.1 10

0.00

0.05

0.10

0.15

0.20

0.001 0.1 10

dM/d

logD

p Cu

0.0

0.5

1.0

1.5

0.001 0.1 10

Zn

0.00

0.02

0.04

0.06

dM/d

logD

p Br

0.00

0.05

0.10

0.15

0.20Ba

0.000

0.008

0.016 Rb

0.00

0.08

0.16

0.24Pb

0.0

0.2

0.001 0.1 10

0.00

0.02

0.04

0.06

0.08

0.001 0.1 10

Sn

0.00

0.01

0.02

0.03

0.001 0.1 10

Sb

0

1

0.001 0.1 10

0.00

0.001 0.1 10

dM

0.001 0.1 10

Diameter (µm)

0.001 0.1 10

Diameter (µm)

0.001 0.1 10

Diameter (µm)

0.001 0.1 10

Diameter (µm)

Elements with similar size distributions:

1. Al, Si… ‐ Crustal elements

2. V and Ni ‐ ship emissions (Yu et al., 2003)

3. Cu, Zn, and Ba – brake dust

4. Br, Rb, and Pb ‐ incinerator (Louie et al., 2005a) 

Typical size distribution of particle numbers (7‐2000 

nm) in roadside environment in winter70000

(a) winter

30000

40000

50000

60000

SMPS

/log

Dp

(# c

m-3)

( )

Lasair OPC

1 10 100 10000

10000

20000dN/

Midpoint diameter (nm)

12/17/2012

30

The relationship between ultrafine/accumulation mode particle numbers and PM mass

0 40 80 120 160100000

3 )

R2=0 26

R2 0 27

0

20000

40000

60000

80000

8000

Ultr

afin

e pa

rtic

le (

# cm

-3R =0.26

R2=0.73

R =0.27

R2=0.74

(# c

m-3

)

0 40 80 120 1600

2000

4000

6000

PM10

(µg m-3)

Acc

umul

atio

n m

ode

part

icle

PM2.5

(µg m-3)

Evolution of ultrafine particle size distribution in a typical day 

217

rib u

tion

dN/d

logD

p Si

z e d

i st

7

Time of day (hour)

12/17/2012

31

Diurnal patterns of ultrafine particle, BC, total traffic number and meteorological parameters

20

24

120000

140000

BC Ultrafine particle )# cm

-3)

4

8

12

16

0

20000

40000

60000

80000

100000

500

1000

1500

2

4

6

B

C (

µg c

m-3)

Ultr

afin

e pa

rtic

le (

#

Jan 6 Jan 11 Jan 20 Jan 21

Mix

ing

heig

ht (

m)

MixingHeight

Win

d sp

eed

(m s

-1)

Wind speed

R2=0.73

0

10

20

30

40

50

6000

4:00 9:00 14:00 19:00 0:00 5:00 10:00 15:00 20:00 1:00 6:00 11:00 16:00 21:00 2:00 7:00 12:00 17:00 22:00

2000

4000

6000

Sol

ar r

adia

tion

(m

W c

m-2)

Solar radiation

MW

Tot

oal v

ehic

le n

umbe

r Total vehicle number

Summary

• Continuous mass and chemical monitors are available and practical for long term measurement programspractical for long‐term measurement programs

• Fine particles were mainly from vehicle exhausts, while coarse particles were mainly from the resuspension of road dust by wind and vehicle‐generated turbulence

• Continuous particle number measurements indicate ultrafine concentrations are fresh emissions from vehicles 

• The seasonal variations of PM is influenced by Asian monsoon

• For day‐to‐day variations, regional or long‐range transport have significant impact on the loading of some chemical species during pollution episode days 

• Besides traffic, wind speed and mixing height influence the loading of hourly PM