Source Apportionment of PM 2.5 in the Southeastern US Sangil Lee 1, Yongtao Hu 1, Michael Chang 2, Karsten Baumann 2, Armistead (Ted) Russell 1 1 School.

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Presentation transcript:

Source Apportionment of PM 2.5 in the Southeastern US Sangil Lee 1, Yongtao Hu 1, Michael Chang 2, Karsten Baumann 2, Armistead (Ted) Russell 1 1 School of Civil and Environmental Engineering 2 School of Earth and Atmospheric Sciences Georgia Institute of Technology, Atlanta, GA

Concerns Adverse Health Effects PM mass, chemical composition (sulfate, EC, OC etc), size Identify PM sources Understand a relationship between sources and adverse health effects Develop control strategies of PM

EPA STN sites PM2.5 chemical composition (ionic species, OC & EC, trace elements) − covers from January 2002 to November 2003 every 3 day every 6 day

Primary and Secondary OC Atlanta, GA SOC = OC - (OC/EC)primary x EC SOC = OC - POC Significant Uncertainty ! POC + SOC Minimum OC/EC ratio approach (Castro et al., 1999)

SOC and ( Unidentified Mass+OC)/OC Atlanta, GA 44 % SOC (20 % ~ 75 %) at Atlanta, August, 1999 (Lim and Turpin, 2002) seasonal variability of SOC positive relationship between SOC and (Uni Mass + OC)/OC max & min average std

Primary OC/EC Ratios  Lower: major cities (more diesel vehicles)  Higher: others (more biomass burning)

Source Apportionment - CMB Receptor Model - C i : ambient concentration of species i f i,j : fraction of species i in source j S j : source contribution of source j Wood burning: Fine et al. (2002) Motor vehicles: Schauer et al. (1999, 2002) Coal power plant: Chow et al. (2004) Dust, Pulp & Paper, Oil combustion, Metal, Mineral production: EPA SPECIATE 3.2

Source Apportionments

Interpolation - Inverse Distance Weighted -  g/m 3 PM2.5 mass

Interpolation - Inverse Distance Weighted - NH4HSO4 (NH4)2SO4 NH4NO3 SOC  g/m 3

Interpolation - Inverse Distance Weighted - Wood Burning Motor Vehicles Coal Power Plant Pulp & Paper  g/m 3

Interpolation - Inverse Distance Weighted - Dust Mineral Production Oil CombustionMetal Production  g/m 3 Port Shipping (?)

Comparisons with Emission Inventories Source apportionment Emission  g/m 3 PM2.5 Motor Vehicles Max: 628 tons/yr Max: 12,465 tons/yr

 g/m 3 Mineral production Pulp & Paper production Comparisons with Emission Inventories Source apportionment Emission Max: 1,843 tons/yr Max: 1,431 tons/yr

Spatial Correlations of Sources Which sources are/are not correlated in the region? Source correlation calculations –Pearson numbers between two sites were calculated for each source based on daily source apportionment results –how daily source correlations are changed with distance

Spatial Correlations PM2.5 mass  2 = 4.29 NH4HSO4 + (NH4)2SO4  2 = 6.31

Spatial Source Correlations NH4HSO4(NH4)2SO4 NH4NO3SOC  2 = 8.00  2 = 7.72  2 = 6.27  2 = 7.83

Spatial Source Correlations Wood BurningMotor Vehicles Dust Pulp & Paper production  2 = 9.64  2 =  2 =  2 = 9.72

Spatial Source Correlations Coal Power Plant Mineral Production Oil Combustion Metal Production  2 = 9.32  2 =  2 = 7.95  2 = 7.02

Summary SOC : 40 ~ 60 % of OC, Seasonal difference Secondary PM : more than 50 % of PM Significant spatial variability of source contributions Agreement or disagreement with emission inventories Significant regional correlation; secondary PM, wood burning, motor vehicles, dust Little regional correlation; industrial sources Can identify port shipping impacts?

Acknowledgements Funding Agencies –U.S. EPA (RD , RD , and RD ) –GA DNR –Georgia Power