Characterizing the spatial distribution of multiple pollutants and populations at risk in Atlanta, Georgia

John L Pearce; Lance A Waller; Stefanie E Sarnat; Howard H Chang; Mitch Klein; James A Mulholland; Paige E Tolbert

doi:10.1016/j.sste.2016.02.002

Characterizing the spatial distribution of multiple pollutants and populations at risk in Atlanta, Georgia

Spat Spatiotemporal Epidemiol. 2016 Aug:18:13-23. doi: 10.1016/j.sste.2016.02.002. Epub 2016 Mar 24.

Authors

John L Pearce¹, Lance A Waller², Stefanie E Sarnat³, Howard H Chang², Mitch Klein³, James A Mulholland⁴, Paige E Tolbert³

Affiliations

¹ Department of Public Health Sciences, College of Medicine, Medical University of South Carolina, 135 Cannon Street, Charleston, SC 29422, United States. Electronic address: pearcejo@musc.edu.
² Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, United States.
³ Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States.
⁴ School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, United States.

Abstract

Background: Exposure metrics that identify spatial contrasts in multipollutant air quality are needed to better understand multipollutant geographies and health effects from air pollution. Our aim is to improve understanding of: (1) long-term spatial distributions of multiple pollutants; and (2) demographic characteristics of populations residing within areas of differing air quality.

Methods: We obtained average concentrations for ten air pollutants (p=10) across a 12 km grid (n=253) covering Atlanta, Georgia for 2002-2008. We apply a self-organizing map (SOM) to our data to derive multipollutant patterns observed across our grid and classify locations under their most similar pattern (i.e, multipollutant spatial type (MST)). Finally, we geographically map classifications to delineate regions of similar multipollutant characteristics and characterize associated demographics.

Results: We found six MSTs well describe our data, with profiles highlighting a range of combinations, from locations experiencing generally clean air to locations experiencing conditions that were relatively dirty. Mapping MSTs highlighted that downtown areas were dominated by primary pollution and that suburban areas experienced relatively higher levels of secondary pollution. Demographics show the largest proportion of the overall population resided in downtown locations experiencing higher levels of primary pollution. Moreover, higher proportions of nonwhites and children in poverty reside in these areas when compared to suburban populations that resided in areas exhibiting relatively lower pollution.

Conclusion: Our approach reveals the nature and spatial distribution of differential pollutant combinations across urban environments and provides helpful insights for identifying spatial exposure and demographic contrasts for future health studies.

Keywords: Air pollution; Classification; Cluster analysis; Geographic information systems (GIS); Kohonen map; Multipollutant.

MeSH terms

Air Pollutants / analysis*
Air Pollution*
Cities*
Demography*
Georgia
Humans

Substances

Air Pollutants

Abstract

MeSH terms

Substances

Grants and funding