The Use of Source Apportionment for Air Quality Management and Health Assessments

1. The Use of Source Apportionment for Air Quality Management and Health Assessments Philip K. Hopke Clarkson University Center for Air Resources Engineering and Science hopkepk@clarkson.edu

2. Monitoring PM2.5 and its Constituents • In order to assess the sources of fine airborne particulate matter (PM2.5), it is necessary to have compositional data that can then be analyzed by a number of data analysis tools known as receptor models. • In this presentation, the data bases, types of models and typical results of recent source apportionment studies will be presented.

3. Monitoring PM2.5 • The US EPA monitors PM2.5 mass at about 1100 locations nationwide. • In December 2004, non-attainment of the PM2.5 NAAQS were made. In most cases, it is the annual arithmetic mean standard that was exceeded over a 3 year period.

4. PM2.5 Designations - 2004

5. Monitoring PM2.5 Constituents • To provide data for source identification and apportionment, the US EPA established the Speciation Trends Network in urban areas in 2001 as well as expanding the existing rural IMPROVE network particularly in the eastern and midwestern US.

6. Urban & Rural PM2.5 Speciation Networks 2006

7. Urban PM2.5 Composition

8. SOx/NOx dominate Eastern Regional ‘Background’ ParticlesIMPROVE/CASTNet Data (1997 - 1999)

9. Average Spatial Patterns Combined CAPMoN CASTNet (USEPA) filter pack (2000-2001)

10. Receptor Modeling Receptor models are focused on the behavior of the ambient environment at the point of impact as opposed to the source-oriented models that focus on the transport, dilution, and transformations that begin at the source and follow the pollutants to the sampling or receptor site.

11. Receptor Modeling • SOURCES PROFILES KNOWN • Chemical Mass Balance • Multivariate Calibration Methods • Partial Least Squares • Artificial Neural Networks • Simulated Annealing • Genetic Algorithm

12. Receptor Modeling • SOURCES PROFILES UNKNOWN • Factor Analysis • Principal Components Analysis • Absolute Principal Components Analysis • SAFER/UNMIX • Positive Matrix Factorization

13. Receptor Modeling • Positive Matrix Factorization (PMF) has a number of features that make it effective for analyzing STN and IMPROVE data. • It has now been applied in a number of locations around the United States • Illustrative example from St. Louis, MO

14. MISSOURI ILLINOIS Site Location

15. Sources Summary • Ten sources identified from PMF & CPF: • Secondary sulfate, secondary nitrate • Carbon-rich sulfate • Gasolineexhaust, diesel/railroad • Airborne soil (incl. Saharan dust soil) • Steel processing, zinc smelting, lead smelting, copper production

16. Secondary Sulfate

17. Secondary Sulfate

18. Secondary Nitrate

19. SecondaryNitrate Adapted from Lee et al. (2005) 2005 AAAR Supersites Conference.

20. Steel Processing

21. Steel Processing ILLINOIS MISSOURI

22. Zinc Smelting

23. Zinc Smelting ILLINOIS MISSOURI

24. Lead Smelting

25. Lead Smelting ILLINOIS MISSOURI

26. Copper Production

27. Copper Production MISSOURI ILLINOIS

28. Carbon-rich Sulfate 19.6% Secondary Sulfate 32.6% Gasoline Exhaust 16.4% Steel Processing 6.8% Secondary Nitrate 15.3% Soil 4.2% Zinc Smelting, 1.3% Lead Smelting, 1.3% Copper Production 0.5% Diesel/Railroad, 2.1%

29. Receptor Modeling

30. EPA PM & Health Centers Retreat on Source Apportionment in Health Effect Modeling • In May 2003, a retreat was held • Two data sets, Washington, DC and Phoenix, AZ, were distributed prior to the workshop • Source apportionment methods applied to both sets • Health effects models were calculated for each city

31. EPA PM & Health Centers Retreat on Source Apportionment in Health Effect Modeling ANOVA Analysis of Submitted Source Apportionments Washington, DC Phoenix, Az

32. EPA PM & Health Centers Retreat on Source Apportionment in Health Effect Modeling • The inter-comparison among results from some of the leading source apportionment research groups indicate that the same major source types (i.e., that contribute most of the PM2.5 mass at each site) are consistently identified by the different groups in each city, with similar elemental make-ups (i.e., key tracers).

33. EPA PM & Health Centers Retreat on Source Apportionment in Health Effect Modeling • Methods were generally found to yield the most consistent results (i.e., the highest correlations across groups over time) for sources with the most definable (i.e., most unique) tracers or combinations of tracers in each city. • Source mass impacts predicted for the various source categories were generally not significantly different from one another across the research groups

34. Health Effects Modeling

35. Estimated total cardiovascular mortality relative risk per 5th-to-95th percentile increment in source-apportioned PM2.5 by source type and investigators/methods for Washington, DC

36. Estimated total cardiovascular mortality relative risk per 5th-to-95th percentile increment in source-apportioned PM2.5 by source type and investigators/methods for Phoenix, AZ

37. EPA PM & Health Centers Retreat on Source Apportionment in Health Effect Modeling • Overall, the results of this inter-comparison of the health effects apportionments found that variations in PM source apportionment research group or method introduced relatively little uncertainty into the evaluation of differences in PM toxicity on a source-specific basis, adding an average of only approximately 15% to the overall source-specific mortality relative risk uncertainties.

38. Conclusions • Good tools are available to help with the source identification and apportionment • Method development continues and better tools can be expected in the near future • Apportionment can assist in SIP development, and • Potentially can be used to assist in health effects epidemiology

39. Thanks for the Invitation QUESTIONS?