Fine particulate matter source apportionment for the Chemical Speciation Trends Network site at Birmingham, Alabama, using Positive Matrix Factorization.

Model runs with fewer (8 and 9) and more (11) factors yielded unsatisfactory results with significantly worse model performance as indicated by larger deviations of [Q.sub.r] from [Q.sub.t] with [Q.sub.r]/[Q.sub.o] > 2 and increased number of scaled residuals outside the tolerable range. In addition to the introduction of POC, the uniform increase of input data uncertainty and exclusion of a few outlier samples identified by spikes in K further improved the model results, explaining 97% of the measured total [PM.sub.2.5] mass at a minimal intercept of 0.25 [micro]g x [m.sup.-3] and an [r.sup.2] of 0.96.

Secondary particles including S[O.sub.4.sup.2-] and SOC (SOC determined from the difference OC - POC) combined contribute the majority of ambient [PM.sub.2.5] in summer with 55 [+ or -] 16% compared with 37 [+ or -] 10% in winter. SOC associated with the sS[O.sub.4] from the model run with the original OC data compares well with the average SOC contribution estimated by the EC tracer method. A small primary S[O.sub.4.sup.2-] source was found associated with the major coking plants in the area, contributing less than 3% of the average total mass apportioned to the regional sS[O.sub.4] and only approximately 0.9% to the total average apportioned [PM.sub.2.5] mass.

Motor vehicle emissions constitute the largest primary [PM.sub.2.5] mass contribution with a long-term average of almost 25 [+ or -] 2% and a winter maximum of 29 [+ or -] 11%, likely due to the combined effects from increased cold-start emissions and meteorological conditions. The annual [PM.sub.2.5] NAAQS of 15 [micro]g x [m.sup.-3] is already slightly exceeded by the regional secondary plus local primary traffic sources alone at a combined average of 15.5 [+ or -] 0.85 [micro]g x [m.sup.-3], so that SIP development efforts should focus on traffic and certain industrial sources, although as indicated by the wind roses, a coal-fired power plant located approximately 25 km east seems to add a maximum of 6-8% to the regional [PM.sub.2.5] arriving with air masses from the east. [PM.sub.2.5] contributions from the five identified industrial factors vary little with season and average 14 [+ or -] 1.3%, led by mineral processing with 4.6 [+ or -] 0.9% and coking with 3.6 [+ or -] 0.8%. The combined evaluation with wind direction adds value in building plausible evidence toward the identification of critical source contributions.

The described approach can provide guidance on how to utilize the STN database for source apportionment and how to effectively support air quality management efforts by identifying emissions contributions of the most important sources at different seasons. Examples are provided for estimating point-wise uncertainty terms suitable for PMF modeling from information contained in the STN dataset posted on EPA's AQS.

ACKNOWLEDGMENTS

Contributions and helpful suggestions by the following people are gratefully acknowledged: Shelly Eberly and Paul Solomon (EPA), Eric Edgerton (Atmospheric Research and Analysis), Ed Rickman (RTI International), Sam Bell (Jefferson County Department of Health, Birmingham, AL), Sangil Lee (Georgia Institute of Technology), Amit Marmur (Georgia Environmental Protection Division), Phil Hopke (Clarkson University), and three diligent reviewers of the manuscript, one of which was particularly constructive and detailed.

REFERENCES

1. Dockery, D.W.; Pope, C.A.; Xu, X.P.; Spengler, J.D.; Ware, J.H.; Fay, M.E.; Ferris, B.G.; Speizer, F.E. An Association between Air-Pollution and Mortality in 6 United States Cities; New Engl. J. Med. 1993, 329, 1753-1759.

2. Pope, C.A.; Thun, M.J.; Namboodiri, M.M.; Dockery, D.W.; Evans, J.S.; Speizer, F.E.; Heath, C.W. Particulate Air-Pollution as a Predictor of Mortality in a Prospective-Study of U.S. Adults; Am. J. Respir. Crit. Care Med. 1995, 151, 669-674.

3. Schwartz, J.; Dockery, D.W.; Neas, L.M.; Wypij, D.; Ware, J.H.; Spengler, J.D.; Koutrakis, P.; Speizer, F.E.; Ferris, B.G. Acute Effects of Summer Air-Pollution on Respiratory Symptom Reporting in Children; Am. J. Respir. Crit. Care Med. 1994, 150, 1234-1242.

4. Schwartz, J.; Dockery, D.W.; Neas, L.M. Is Daily Mortality Associated Specifically with Fine Particles?; J. Air & Waste Manage. Assoc. 1996, 46, 927-939.

5. Thurston, G.D.; Ito, K.; Hayes, C.G.; Bates, D.V.; Lippmann, M. Respiratory Hospital Admissions and Summertime Haze Air Pollution in Toronto, Ontario: Consideration of the Role of Acid Aerosols; Environ. Res. 1994, 65, 271-290.

6. Laden, F.; Neas, L.M.; Dockery, D.W.; Schwartz, J. Association of Fine Particulate Matter from Different Sources with Daily Mortality in Six U.S. Cities; Environ. Health Perspect. 2000, 108, 941-947.

7. Mar, T.F.; Norris, G.A.; Koenig, J.Q.; Larson, T.V. Associations between Air Pollution and Mortality in Phoenix, 1995-1997; Environ. Health Perspect. 2000, 108, 347-353.

8. Tsai, F.C.; Apte, M.G.; Daisey, J.M.; An Exploratory Analysis of the Relationship between Mortality and the Chemical Composition of Airborne Particulate Matter; Inhal. Toxicol. 2000, 12(Suppl. 2), 121-135.

9. Sarnat, J.A.; Klein, M.; Tolber, P.E.; Marmur, A.; Russell, A.G.; Kim, E.; Hopke, P.K.; Examining the Cardiovascular Health Effects of Atlanta Aerosol Using Three Source Apportionment Techniques. In: Proceedings of 7th International Aerosol Conference, American Association of Aerosol Research: St. Paul, MN, 2006.

10. EPA Needs to Direct More Attention, Efforts, and Funding to Enhance Its Speciation Monitoring Program for Measuring Fine Particulate Matter; Evaluation Report No. 2005-P-00004; U.S. Environmental Protection Agency; Office of Inspector General: Washington, DC, 2005; available on U.S. Environmental Protection Agency Web site, http://www.epa.gov/oig/reports/2005/20050207-2005-P-00004.pdf (accessed November 2007).

11. Jayanty, R.K.M.; Flanagan, J.B.; Rickman, E.E. An Overview of [PM.sub.2.5] Chemical Speciation Nationwide Network Program in the United States. Presented at the 13th World Clean Air Congress, London, U.K. August 2004.

12. Kim, E.; Hopke, P.K.; Edgerton, E.S. Source Identification of Atlanta Aerosol by Positive Matrix Factorization; J. Air & Waste Manage. Assoc. 2003, 53, 731-739.

13. Kim, E.; Hopke, P.K.; Edgerton, E.S. Improving Source Identification of Atlanta Aerosol Using Temperature Resolved Carbon Fractions in Positive Matrix Factorization; Atmos. Environ. 2004, 38, 3349-3362.

14. Kim, E.; Hopke, P.K. Characterization of Fine Particle Sources in the Great Smoky Mountains Area; Sci. Tot. Environ. 2006, 368, 781-794.

15. Liu, W.; Wang, Y.; Russell, A.; Edgerton, E.S. Atmospheric Aerosol over Two Urban-Rural Pairs in the Southeastern United States: Chemical Composition and Possible Sources; Atmos. Environ. 2005, 39, 4453-4470.

16. Marmur, A.; Unal, A.; Mulholland, J.A.; Russell, A.G. Optimization-Based Source Apportionment of [PM.sub.2.5] Incorporating Gas-to-Particle Ratios; Environ. Sci. Technol. 2005, 39, 3245-3254.

17. Marmur, A.; Park, S.K.; Mulholland, J.A.; Tolbert, P.E.; Russell, A.G. Source Apportionment of [PM.sub.2.5] in the Southeastern United States Using Receptor and Emissions-Based Models: Conceptual Differences and Implications for Time-Series Health Studies; Atmos. Environ. 2006, 40, 2533-2551.

18. Park, S.K.; Cobb, C.E.; Wade, K.; Mulholland, J.; Hu, Y.; Russell, A.G. Uncertainty in Air Quality Model Evaluation for Particulate Matter Due to Spatial Variations in Pollutant Concentrations; Atmos. Environ. 2006, 40:S563-S573.

19. Zheng, M.; Cass, G.R.; Schauer, J.J.; Edgerton, E.S. Source Apportionment of [PM.sub.2.5] in the Southeastern United States Using Solvent-Extractable Organic Compounds as Tracers; Environ. Sci. Technol. 2002, 36, 2361-2371.

20. Zheng, M.; Ke, L.; Edgerton, E.S.; Schauer, J.J.; Dong, M.; Russell, A.G. Spatial Distribution of Carbonaceous Aerosol in the Southeastern United States Using Molecular Markers and Carbon Isotope Data; J. Geophys. Res. 2006, 111, D10S06.

21. Pekney, N.J.; Davidson, C.I.; Robinson, A.; Zhou, L.M.; Hopke, P.K.; Eatough, D.J.; Rogge, W.F. Major Source Categories for [PM.sub.2.5] in Pittsburgh Using PMF and UNMIX; Aerosol Sci. Technol. 2006, 40, 910-924.

22. Hopke, P.K.; Ito, K.; Mar, T.; Christensen, W.F.; Eatough, D.J.; Henry, R.C.; Kim, E.; Laden, F.; Lall, R.; Larson, T.V.; Liu, H.; Neas, L.; Pinto, J.; Stolzel, M.; Suh, H.; Paatero, P.; Thurston, G.D. PM Source Apportionment and Health Effects: 1. Intercomparison of Source Apportionment Results; J. Expo. Sci. Environ. Epidemiol. 2006, 16, 275-286.

23. Chen, L.-W.A.; Watson, J.G.; Chow, J.C.; Magliano, K.L. Quantifying [PM.sub.2.5] Source Contributions for the San Joaquin Valley with Multivariate Receptor Models; Environ. Sci. Technol. 2007, 41, 2818-2826.

24. Reff, A.; Eberly, S.I.; Bhave, P.V. Receptor Modeling of Ambient Particulate Matter Data Using Positive Matrix Factorization: Review of Existing Methods; J. Air & Waste Manage. Assoc. 2007, 57, 146-154.

25. Blanchard, C.; Hidy, G.; Tanenbaum, S.; Edgerton, E. Particulate Matter Sources in Birmingham, Alabama; Final Report to the Alabama Department of Environmental Management and the Jefferson County Department of Health, 2006; available on Jefferson County Department of Health Web site at http://www.jcdh.org/pdf/Final_v3.pdf (accessed 2007).

26. Eberly, S. EPA PMF 1.1 User's Guide Unofficially Distributed Non-Peer-Reviewed Release for Beta Testing; available with PMF software (eberly.shelly@epa.gov), 2005.