Advertisement

Improved Spatiotemporal Source-Based Air Pollutant Mixture Characterization for Health Studies

  • Heather A. HolmesEmail author
  • Xinxin Zhai
  • Jeremiah Redman
  • Kyle Digby
  • Cesunica Ivey
  • Sivaraman Balachandran
  • Sheila A. Sororian
  • Mariel Friberg
  • Wenxian Zhang
  • Marissa L. Maier
  • Yongtao Hu
  • Armistead G. Russell
  • James A. Mulholland
  • Howard H. Chang
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

The growing availability of spatially resolved health data sets (i.e., resident and county level patient records) requires spatially resolved exposure or air quality metrics to investigate the impact of air pollution on health outcomes. While daily air quality data are essential in time-series epidemiologic analysis, the spatial distribution of the observations is limited. Air pollution modeling (i.e., chemical transport modeling (CTM)) addresses this by producing spatially resolved air quality predictions using terrain, emissions and meteorology inputs. However, predicted concentrations may be biased. This work incorporates unique data fusion approaches to combine air quality observations from regulatory monitoring networks (OBS) with the output from a CTM (CMAQ) to generate spatially and temporally resolved gaseous and PM species concentrations. Species concentrations alone cannot directly identify emission sources or characterize pollutant mixtures, therefore source apportionment (SA) models are required to estimate source impacts. The focus of this work is a comparison of SA results for three U.S. regions with differing air pollution sources, St. Louis, Missouri; Atlanta, Georgia; and Dallas-Fort Worth, Texas.

Keywords

Ordinary Kriging Species Concentration Source Apportionment Positive Matrix Factorization Mobile Source 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This research was supported, in part, by USEPA grant R834799. Its contents are solely the responsibility of the grantee and do not necessarily represent the official views of the USEPA. Further, USEPA does not endorse the purchase of any commercial products or services mentioned in the publication. Additional support was made possible by grants from Georgia Power and the Southern Company.

References

  1. 1.
    Balachandran S, Chang HH, Pachon JE, Holmes HA, Mulholland JA, Russell AG (2013) A Bayesian-based ensemble technique for source apportionment of PM2.5. Environ Sci Technol 47:13511–13518Google Scholar
  2. 2.
    Balachandran S, Pachon JE, Hu Y, Lee D, Mulholland JA, Russell AG (2012) Ensemble trained source apportionment of fine particulate matter and method uncertainty analysis. Atmos Environ 61:387–394CrossRefGoogle Scholar
  3. 3.
    Bell ML (2006) The use of ambient air quality modeling to estimate individual and population exposure for human health research: a case study of ozone in the northern Georgia region of the United States. Environ Int 32(5):586CrossRefGoogle Scholar
  4. 4.
    Cohan DS, Hakami A, Hu Y, Russell AG (2005) Nonlinear response of ozone to emissions: source apportionment and sensitivity analysis. Environ Sci Technol 39(17):6739–6748CrossRefGoogle Scholar
  5. 5.
    Ivey C, Holmes H, Hu Y, Russell A, Mulholland J (2013) Spatial and temporal extension of a novel hybrid source apportionment model. In: International technical meeting on air pollution modelling and its application, Miami, FLGoogle Scholar
  6. 6.
    Ivy D, Mulholland JA, Russell AG (2008) Development of ambient air quality population-weighted metrics for use in time-series health studies. J Air Waste Manage Assoc 58(5):711–720CrossRefGoogle Scholar
  7. 7.
    Koo B, Dunker AM, Yarwood G (2007) Implementing the decoupled direct method for sensitivity analysis in a particulate matter air quality model. Environ Sci Technol 41(8):2847–2854CrossRefGoogle Scholar
  8. 8.
    Maier ML, Balachandran S, Mulholland JA, Russell AG, Ebelt S, Turner JR (2013) Application of an ensemble-trained source apportionment approach at a site impacted by multiple point sources. Environ Sci Technol 47:3743–3751CrossRefGoogle Scholar
  9. 9.
    Pachon JE, Balachandran S, Hu Y, Mulholland JA, Darrow LA, Sarnat JA, Tolbert PE, Russell AG (2012) Development of outcome-based, multipollutant mobile source indicators. J Air Waste Manage Assoc 62(4):431–442CrossRefGoogle Scholar
  10. 10.
    Reff A, Eberly SI, Bhave PV (2007) Receptor modeling of ambient particulate matter data using positive matrix factorization: review of existing methods. J Air Waste Manage Assoc 57(2):146–154CrossRefGoogle Scholar
  11. 11.
    Russell A, Holmes H, Friberg M, Ivey C, Hu Y, Balachandran S, Mulholland J, Tolbert P, Sarnat J, Sarnat S, Strickland M, Chang H, Liu Y (2013) Use of air quality modeling results in health effects research. In: International technical meeting on air pollution modelling and its application, Miami, FLGoogle Scholar
  12. 12.
    Sarnat JA, Marmur A, Klein M, Kim E, Russell AG, Sarnat SE, Mulholland JA, Hopke PK, Tolbert PE (2008) Fine particle sources and cardiorespiratory morbidity: an application of chemical mass balance and factor analytical source-apportionment methods. Environ Health Perspect 116(4):459Google Scholar
  13. 13.
    Sororian S, Holmes H, Friberg M, Ivey C, Hu Y, Mulholland J, Russell A, Strickland M, Chang H (2013) Temporally and spatially resolved air pollution in Georgia using fused ambient monitoring data and chemical transport model results. In: International technical meeting on air pollution modelling and its application, Miami, FLGoogle Scholar
  14. 14.
    Stanek LW, Sacks JD, Dutton SJ, Dubois JJB (2011) Attributing health effects to apportioned components and sources of particulate matter: an evaluation of collective results. Atmos Environ 45(32):5655–5663CrossRefGoogle Scholar
  15. 15.
    Watson JG, Zhu T, Chow JC, Engelbrecht J, Fujita EM, Wilson WE (2002) Receptor modeling application framework for particle source apportionment. Chemosphere 49(9):1093–1136CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Heather A. Holmes
    • 1
    Email author
  • Xinxin Zhai
    • 1
  • Jeremiah Redman
    • 1
  • Kyle Digby
    • 1
  • Cesunica Ivey
    • 1
  • Sivaraman Balachandran
    • 1
  • Sheila A. Sororian
    • 1
  • Mariel Friberg
    • 1
  • Wenxian Zhang
    • 1
  • Marissa L. Maier
    • 1
  • Yongtao Hu
    • 1
  • Armistead G. Russell
    • 1
  • James A. Mulholland
    • 1
  • Howard H. Chang
    • 2
  1. 1.School of Civil and Environmental EngineeringGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Department of Biostatistics and BioinformaticsEmory UniversityAtlantaUSA

Personalised recommendations