Chemical composition and source apportionment of PM 1.0, PM 2.5 and PM in the roadside environment of Hong Kong

Transcription

1 Chemical composition and source apportionment of PM 1.0, PM 2.5 and PM in the roadside environment of Hong Kong Dr. Cheng Yan Department of Environmental Science and Technology School of Human Settlements and Civil Engineering Xi an Jiaotong University

2 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

3 The climate scheme of Hong Kong NORTH NORT H 15% 25% 12% 20% 9% 15% 6% 10% 3% 5% WEST EAST WEST EAST SOUTH Wind rose in summer Data source: 2004 Waglon station WIND SPEED WIND SPEED (Km (m/s) h -1 ) (Km (m/s) h -1 h) -1 ) >= Calms: 4.51% SOUT H Wind rose in winter, spring, and autumn >= Calms: 3.72%

4 Objective 1. To characterize the chemical properties of particulate matters at PU Supersite; - Fine particle PM 1.0, PM Coarse particle PM coarse (PM ) 2. To quantify source contributions to fine (PM 2.5 ) and coarse (PM ) particles at PU Supersite by using PMF and CMB receptor model.

5 Sampling Location PolyU (PU) Supersite

6 PU Supersite PM 1.0, PM 2.5, and PM 10 mass and chemistry by URG Sampler URG-3000ABC multi-channel samplers Two channels for PM 1.0, two channels for PM 2.5 and four channels for PM 10 Collect 24-h PM 1.0, PM 2.5, and PM 10 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

7 PU Supersite Continuous PM 2.5 and PM 10 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 PM 2.5, PM 10, and BC simultaneously Jan 05 Dec 05 Flow rate: 16.7 lpm

8 Chemical analysis Carbonaceous aerosols (e.g., OC, EC) DRI Model 2001 OC/EC analyzer with flame ionization detector HKPU Water-soluble inorganic ions (e.g., sulfate, nitrate, ammonium, potassium, sodium) Ion Chromatography (DIONEX 600) with an electrochemical detector HKPU 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)

9 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 PM 2.5 and PM coarse at PU Supersite CMB was used to make source apportionment for PM 2.5 at PU Supersite

10 Results and discussion 1. Chemical characteristics of fine and coarse particles at PU Supersite - Diurnal variation - Chemical composition

11 Mean mass concentrations of PM 1.0, PM 2.5, and PM coarse Site Method PM 1.0 PM 2.5 PM coarse Mean±sd (µg m -3 ) PU a roadside gravimetrical mass 44.4± ± ±12.2 MK b roadside gravimetrical mass 58.1±18.5 TW b ambient gravimetrical mass 33.9±19.4 HT b suburban gravimetrical mass 23.7±14.8 N=40 N=56 N=56 N=56 a this study; b Louie et al., 2005 STE PM 2.5 /PM 10 ~70% PM coarse /PM 10 ~30% PM10 (µg m -3 ) R = 0.95 n=356 PMcoarse (µg m -3 ) R = 0.46 n=356 PM 1.0 /PM 2.5 ~80% PM2.5 (µg m -3 ) PM2.5 (µg m -3 ) Data source: Kimoto

12 Diurnal variation of PM 2.5 and PM coarse Series1 PM 2.5 R PM 2.5 PM coarse Taxis Gasoline-fueled vehicles Diesel-fueled vehicles :00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 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 Concentration (µg m -3 ) Series1 PM coarse 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 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 Concentration (µg m -3 ) Time of day (hr) PM2.5 (µg m-3 ) PM 2.5 R=0.51 PM coarse R=0.98 PMcoarse (µg m-3 ) Data source: Kimoto Wind speed (m s -1 ) the median concentrations of PM 2.5 and PM coarse for each 0.4 m s -1 wind speed bin was used

13 Good relationship between hourly BC and diesel-fueled vehicles Concentration (µg m -3 ) Traffic counts (# hour -1 ) BC 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).

14 Average chemical compositions of fine and coarse particles PM ± 25.5 µg m -3 Sea-salt 6% Ammonium 5% Nitrate 4% 21% Sulfate 5% Mineral dust and trace element Unidentified 2% 30% EC Data source: URG sampler 27% OM (OC 1.4) PM coarse 25.9±12.2 µg m -3 Unidentified 17% 14% OM (OC 1.4) EC 8% Sulfate 7% 32% Nitrate Mineral dust 9% Ammonium and trace 12% 1% element Sea-salt 12% Other studies also get a large percentage of unidentified materials for PM coarse

15 Time series of carbonaceous aerosol in fine and coarse particles OC concentrations (µg m -3 ) PM 2.5 OC EC OC/EC ratio: 0.7± EC concentrations (µg m -3 ) OC concentrations (µg m -3 ) /29/ /24/2004 1/22/2005 4/12/2005 6/7/2005 7/13/2005 8/9/2005 9/23/ PM coarse OC EC OC/EC ratio: 7.8± /29/ /24/2004 1/22/2005 4/12/2005 6/7/2005 7/13/2005 8/9/2005 9/23/2005 Date EC concentrations (µg m -3 )

16 Major sources for particles PM low OC/EC ratio, high carbon content, good correlation with vehicle number - Vehicle emissions 机动车尾气 PM coarse - high OC/EC ratio, low carbon content, moderate correlation with vehicle number - local sources (tire dust, paved soil dust, and vehicle)

17 2. Source apportionment by using PMF and CMB receptor models

18 Source contributions to PM 2.5 by PMF receptor model Nearby local sources Transported sources 20% Secondary aerosol Unidentified 10% 26% Diesel-fueled vehicle 13% Coal combustion Residual oil combustion 8% 13% Paved soil dust Tire dust 7% 3% Gasoline-fueled vehicle Unidentified=PM 2.5measured PM 2.5predicted Predicted PM µg m -3 Measured PM µg m -3 Yuan et al. (2006) s study claimed that secondary sulfate and local vehicle emissions gave the largest contribution to PM 10 in HK (25% each), followed by secondary nitrate (12%). Contributions from other source types were below 10%.

19 Source contributions to PM coarse by PMF receptor model Nearby Local sources Unidentified 22% Vehicle 11% Tire dust 20% Marine aerosol 17% Field burning+second ary aerosol 13% Transported sources Paved soil dust 17% Unidentified=PM c measured PM c predicted Predicted PM coarse 14.4 µg m -3 Measured PM coarse 25.9 µg m -3

20 Comparison of source contributions to PM 2.5 between CMB and PMF Model PMF Annual µg m -3 % µg m -3 % Local sources a Transported sources b Over/under estimation Predicted PM 2.5 mass Measured PM 2.5 mass 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.

21 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.

22 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.