Chemical composition and source apportionment of PM1.0 , PM2.5 and PM10-2.5 in the roadside environmentof Hong Kong

Dr. Cheng YanDepartment of Environmental Science and Technology

School of Human Settlements and Civil Engineering

Xi’an Jiaotong University

Background information

� Effects of aerosol on human health, environment, and global climate change

� Hong Kong has been facing two air pollution issues (HKEPD website)

� local street-level pollution (285 vehicle per kilometer of road)

� transport of polluted air from an upwind area

NORT H

SOUT H

WEST EAST

5%

10%

15%

20%

25%

WIND SPEED

(m/s)

>= 11.1

8.8 - 11.1

5.7 - 8.8

3.6 - 5.7

2.1 - 3.6

0.5 - 2.1

Calms: 3.72%

The climate scheme of Hong Kong

Wind rose in summer Wind rose in winter, spring, and autumn

Data source: 2004 Waglon station

NORTH

SOUTH

WEST EAST

3%

6%

9%

12%

15%

WIND SPEED

(m/s)

>= 11.1

8.8 - 11.1

5.7 - 8.8

3.6 - 5.7

2.1 - 3.6

0.5 - 2.1

Calms: 4.51%

(Km h-1) (Km h-1)(Km h-1)

Objective

1. To characterize the chemical properties of particulate matters at PU Supersite;

- Fine particle PM1.0, PM2.5

- Coarse particle PMcoarse (PM10-2.5)

2. To quantify source contributions to fine (PM2.5) and coarse (PM10-2.5) particles at PU Supersite by using PMF and CMB receptor model.

Sampling Location

PolyU (PU) Supersite

PM1.0, PM2.5, and PM10 mass and chemistry by URG Sampler

URG-3000ABC multi-channel samplers

•Two channels for PM1.0, two channels for PM2.5and four channels for PM10

•Collect 24-h PM1.0, PM2.5, and PM10 on quart and Teflon filters simultaneously

•One sample set every seventh day from Oct 2004 to Sept 2005

•Flow rate: 8.3 lpm for each channel

PU Supersite

Continuous PM2.5 and PM10 mass and BC by Kimoto SPM-613D

Kimoto SPM-613D Dichotomous Monitor

•Particle mass was quantified by beta gauge method

•BC was quantified by optical method

•Collect hourly PM2.5, PM10, and BC simultaneously Jan 05 – Dec 05

•Flow rate: 16.7 lpm

PU Supersite

Chemical analysis

� Carbonaceous aerosols (e.g., OC, EC)DRI Model 2001 OC/EC analyzer with flame ionization detectorHKPU

� Water-soluble inorganic ions (e.g., sulfate, nitrate, ammonium, potassium, sodium)

Ion Chromatography (DIONEX 600) with an electrochemical detectorHKPU

� Elements (e.g., 40 elements from Na to U) X-Ray Fluorescence analyzer (XRF, PANalytical Epsilon 5) with an electrochemical detector Desert Research Institute (DRI)

Receptor Models – the Positive matrix factorization (PMF) & the Chemical Mass Balance (CMB)

PMF (Paatero and Tapper, 1994) uses a least squares approach to solve the factor analysis problem by integrating non-negativity constraints into the optimization process and utilizing the error estimates for each data value as point-by-point weights.

CMB (Friedlander, 1973; Watson et al., 2004) quantifies contributions from chemically distinct source types by using a variance weighted least squares solution.

PMF was used to make source apportionment for PM2.5 and PMcoarse at PU Supersite

CMB was used to make source apportionment for PM2.5 at PU Supersite

1. Chemical characteristics of fine and

coarse particles at PU Supersite

- Diurnal variation

- Chemical composition

Results and discussion

Mean mass concentrations of PM1.0, PM2.5, and PMcoarse

R = 0.95

n=356

0

50

100

150

200

0 50 100 150

PM2.5 (µg m-3

)

PM

10 (

µg

m-3

)

PM2.5/PM10 ~70%

PMcoarse/PM10 ~30%

PM1.0/PM2.5 ~80%

N=40

N=56

N=56

N=56

R = 0.46

n=356

0

10

20

30

40

50

60

0 50 100 150

PM2.5 (µg m-3

)

PM

co

arse

(µ

g m

-3)

Data source: Kimoto

Site Method PM1.0 PM2.5 PMcoarse

PUa roadside gravimetrical mass 44.4±6.7 55.5±25.5 25.9±12.2

MKb roadside gravimetrical mass 58.1±18.5

TWb ambient gravimetrical mass 33.9±19.4

HTb suburban gravimetrical mass 23.7±14.8a this study;

b Louie et al., 2005 STE

Mean±sd (µg m-3

)

Diurnal variation of PM2.5 and PMcoarse

35

40

45

50

55

60

65

70

0:0

0

1:0

0

2:0

0

3:0

0

4:0

0

5:0

0

6:0

0

7:0

0

8:0

0

9:0

0

10

:00

11

:00

12

:00

13

:00

14

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15

:00

16

:00

17

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19

:00

20

:00

21

:00

22

:00

23

:00

Co

nce

ntr

atio

n (µ

g m

-3)

Series1

15

17

19

21

23

25

0:0

0

1:0

0

2:0

0

3:0

0

4:0

0

5:0

0

6:0

0

7:0

0

8:0

0

9:0

0

10

:00

11

:00

12

:00

13

:00

14

:00

15

:00

16

:00

17

:00

18

:00

19

:00

20

:00

21

:00

22

:00

23

:00

Time of day (hr)

Co

nce

ntr

atio

n (µ

g m

-3)

Series1

PM2.5

PMcoarse

30

35

40

45

50

55

0 1 2 3 4 5 612

14

16

18

20

22

24

PM

2.5 (µ

g m

-3)

PM2.5

R=0.51

PMcoarse

R=0.98P

Mco

arse

(µg

m-3

)

Wind speed (m s-1)

the median concentrations of PM2.5 and PMcoarse

for each 0.4 m s-1 wind speed bin was usedData source: Kimoto

R PM2.5 PMcoarse

Taxis -0.30 -0.06

Gasoline-fueled vehicles 0.85 0.80

Diesel-fueled vehicles 0.85 0.64

Good relationship between hourly BC and diesel-fueled vehicles

0

8

16

24

32

40

Co

ncen

trati

on

(µ

g m

-3)

BC

0

1000

2000

3000

4000

Tra

ffic

co

un

ts (

# h

ou

r-1)

Diesel fueled vehicle

Sun Mon Tue Wed Thu Fri Sat

R=0.94

Data source: Kimoto

Elemental carbon was mainly emitted from diesel-fueled vehicles (Norbeck et al. 1998; Allen et al. 2001, Gertler et al. 2002).

30%

21%

Sea-salt

6%Ammonium

5%

Nitrate

4%

27%

Unidentified

2%

5%

PM2.5 55.5± 25.5 µg m-3

Mineral dust and trace element

EC

8%

Sulfate

7%

12%

Ammonium

1%

Nitrate

9%

14%17%

32%

PMcoarse 25.9±12.2 µg m-3

Average chemical compositions of fine and coarse particles

Sulfate

OM (OC××××1.4)

EC

OM (OC××××1.4)

Unidentified

Mineral dust

and trace

element Sea-salt 12%

Data source: URG sampler

Other studies also get a large percentage of unidentified materials for PMcoarse

10/29/2004 11/24/2004 1/22/2005 4/12/2005 6/7/2005 7/13/2005 8/9/2005 9/23/2005

5

10

15

20

25

30

35

40

5

10

15

20

25

30

10/29/2004 11/24/2004 1/22/2005 4/12/2005 6/7/2005 7/13/2005 8/9/2005 9/23/2005

-1

0

1

2

3

4

5

6

7

8

0

2

4

6

8

10

12

14

OC

co

nce

ntr

atio

ns

(µg m

-3)

OC

EC

conce

ntr

atio

ns

(µg m

-3)

EC

conce

ntr

atio

ns

(µg m

-3)

PM2.5

EC

OC

co

nce

ntr

atio

ns

(µg

m-3)

Date

OC

EC

PMcoarse

OC/EC ratio: 0.7±0.3

OC/EC ratio: 7.8±14.2

Time series of carbonaceous aerosol in fine and coarse particles

Major sources for particles

� PM2.5

- low OC/EC ratio, high carbon content, good correlation with vehicle number- Vehicle emissions 机动车尾气

� PMcoarse

- high OC/EC ratio, low carbon content, moderate correlation with vehicle number- local sources (tire dust, paved soil dust, and vehicle)

2. Source apportionment by using PMF and CMB receptor models

20%

13%

Tire dust

3%

Paved soil dust

7%

26%

Residual oil

combustion

8%

Unidentified

10%

13%

Source contributions to PM2.5 by PMF receptor model

Diesel-fueled vehicle

Gasoline-fueled vehicle

Secondary aerosol

Coal combustion

Nearby local sources

Transported sources

Yuan et al. (2006)’s study claimed that secondary sulfate and local vehicle emissions gave the largest contribution to PM10 in HK (25% each), followed by secondary nitrate (12%). Contributions from other source types were below 10%.

Unidentified=PM2.5measured – PM2.5predicted

Predicted PM2.5 49.4 µg m-3

Measured PM2.5 55.5 µg m-3

Tire dust

20%

Field

burning+second

ary aerosol

13%

Paved soil dust

17%

Vehicle

11%

Marine aerosol

17%

Unidentified

22%

Source contributions to PMcoarse by PMF receptor model

Nearby Local sources

Unidentified=PMc measured – PMc predicted

Predicted PMcoarse 14.4 µg m-3

Measured PMcoarse 25.9 µg m-3

Transported sources

Comparison of source contributions to PM2.5 between CMB and PMF

Model

µg m-3

% µg m-3

%

Local sourcesa

31.1 56 37.2 67

Transported sourcesb

18.3 33 21.9 39

Over/under estimation 5.5 10 -3.7 -7

Predicted PM2.5 mass 49.4 59.1

Measured PM2.5 mass 55.5 55.5

Annual

PMF CMB

a Local sources include vehicle exhaust, paved road dust, brake lining,

tire dust, and residual oil combustion.b Regional sources include secondary aerosol, field burning, and coal

combustion.

Conclusion

� Overall, ~60% of fine particulate mass is from the nearby local sources and ~30% is from transported sources at PU Supersite.

� The majority (~60%) of coarse particulate mass is from the nearby local sources (tire dust, paved soil dust, and vehicle) and marine aerosol.

Thanks !

Acknowledgment

We would like to acknowledge DRI for the elemental analyses by XRF. This research was supported by Hong Kong Polytechnic University and Research Grants Council of Hong Kong (PolyU 5197/05E and PolyU 5145/03E) and the Area of Strategic Development on Atmospheric and Urban Air Pollution (A516) funded by the Hong Kong Polytechnic University.