New England Statistics Symposium 2016
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My talk on particulate matter source apportionment methods at the New England Statistics Symposium 2016 at Yale University.
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New England Statistics Symposium 2016
- 1. Bayesian hierarchical models for estimating the health effects of air pollution sources Roger D. Peng, PhD Department of Biostatistics Johns Hopkins Bloomberg School of Public Health @rdpeng, simplystatistics.org (joint work with Jenna Krall and Amber Hackstadt) New England Statistics Symposium April 2016
- 2. Not So Standard Deviations (with Hilary Parker of Stitch Fix) Subscribe in iTunes: https://goo.gl/ZhWYbd https://soundcloud.com/nssd-podcast
- 3. Particulate Matter and Health • PM has been linked with health outcomes: hospitalization, mortality, decreased lung function, cardiac events • WHO estimates ~800,000 premature deaths per year • Evidence of both short-term (acute) and long-term (chronic) effects of exposure to ambient PM • Much recent work has examined ambient PM mass (PM10, PM2.5, PM10-2.5) indicator
- 4. • There is strong evidence that ambient PM is associated with mortality and morbidity • What should we do about it? How can we intervene to improve health? • Target sources of PM that are most harmful to human health • How do we identify sources of ambient PM? What’s Next?
- 5. • There is strong evidence that ambient PM is associated with mortality and morbidity • What should we do about it? How can we intervene to improve health? • Target sources of PM that are most harmful to human health • How do we identify sources of ambient PM? What’s Next?
- 6. Pollution Source Apportionment 9% 10% 12% 13% 36% 19% Particulate matter Power plant Car Oil heating 7%8% 10% 11% 29% 35% 12% 15% 15% 20% 24% 15% 5% 17% 20% 23% 5% 30% Source-specific concentrations Elemental carbon Organic carbon Sulfate Nitrate Nickel Vanadium
- 9. Pollution Source Apportionment Methods Y = F Λ o ε Source profiles Observed chemical constituents (n x p) Error (n x p) Source concentrations (n x k) (k x p)
- 10. Problems • Current source apportionment methods are applied on an ad hoc, highly tweaked, basis and are difficult to scale to a region or nation • Source apportionment models are typically informed by investigator’s local knowledge • No reproducible way to combine information across locations to gain power when estimating health effects (multi-site studies)
- 11. Incorporating New Data Sources on Pollution Sources Component Data Source Particulate Matter EPA Air Quality System (AQS) PM Chemical Constituents EPA Chemical Speciation Network PM Source Profiles EPA SPECIATE Database PM Source Emissions EPA National Emissions Inventory
- 13. 2002 2003 2004 2005 0 5 10 15 20 date SULFATE 2002 2003 2004 2005 0 5 10 15 date NITRATE 2002 2003 2004 2005 0.5 1.0 1.5 2.0 2.5 date Elemental_Carbon 2002 2003 2004 2005 0 2 4 6 8 10 OC_K14 Sulfate Nitrate Elemental carbon Organic carbon
- 18. Agricultural Crop/Livestock Dust (1999-2008)
- 20. Model Fitting • We use Markov chain Monte Carlo to simulate from the posterior distribution of the unknown parameters • Adaptive MCMC approach of Haario et al (2001) • Use data from SPECIATE and NEI to calibrate the prior distributions • Constraints placed on profile matrix based on what is known about composition of specific sources
- 23. Estimating Health Effects • For individual cities, estimated source time series can be plugged into regression models with health outcomes • For multi-site studies, source determination cannot be a manual process (not reproducible) • Need automatic method to combine information across a region • Current approaches assume pollution sources are the same everywhere
- 24. US EPA Chemical Speciation Network • 85 monitors, 24 constituents Medicare cohort (1999—2010) • CVD hospitalizations for 63 counties
- 25. SHARE • A method for estimating health effects of sources SHared Across a REgion • Sources are estimated at individual locations and health effects estimated • Sources are matched across locations via population value decomposition (Crainiceanu et al. 2011) • Health effects combined for common sources via hierarchical modeling
- 26. SHARE for Source ID
- 28. Summary • Bayesian source apportionment model can integrate information from 3 national databases • Data on sources and profiles can be used to constrain the problem and to construct informative prior distributions • SHARE method can be used to automatically combine health effects of estimated sources across a region