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Geospatial Analysis of Environmental Health pp 395–409Cite as

Merging Satellite Measurement with Ground-Based Air Quality Monitoring Data to Assess Health Effects of Fine Particulate Matter Pollution

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Part of the book series: Geotechnologies and the Environment ((GEOTECH,volume 4))

Abstract

Geospatial technologies have been widely used in environmental health research, including air pollution and human health. This chapter demonstrates the potential of integrating satellite air quality measurement with ground-based PM2.5 data to explore health effects of fine particulate air pollution. This study assesses the association of estimated PM2.5 concentration with chronic coronary heart disease (CCHD) mortality. Years 2003 and 2004 daily MODIS (Moderate Resolution Imaging Spectrometer) Level 2 AOD images were collated with US EPA PM2.5 data covering the conterminous USA. Pearson’s correlation analysis and geographically weighted regression (GWR) found that the relationship between PM2.5 and AOD is not spatially consistent across the conterminous states. GWR predicts well in the east and poorly in the west. The GWR model was used to derive a PM2.5 grid surface for the eastern US (RMSE = 1.67 μg/m3). A Bayesian hierarchical model found that areas with higher values of PM2.5 show high rates of CCHD mortality: \(\beta _{{\textrm{PM}}_{2.5} } \)= 0.802, posterior 95% Bayesian credible interval (CI) = (0.386, 1.225). Aerosol remote sensing and GIS spatial analyses and modelling could help fill pervasive data gaps in ground-based air quality monitoring that impede efforts to study air pollution and protect public health.

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Abbreviations

AIC:

Akaike Information Criterion

AOD:

Aerosol optical depth

AQS:

Air Quality System

BUGS :

Bayesian inference using Gibbs sampling

CAR:

Conditional auto-regression

CCHD:

Chronic coronary heart disease

EPA:

Environmental Protection Agency

GOES:

Geostationary Operational Environmental Satellite

GWR:

Geographically weighted regression

ICD-10:

International Classification of Disease, 10th Revision

LIDAR:

Light detection and ranging

MCMC:

Markov chain Monte Carlo

MODIS:

Moderate Resolution Imaging Spectrometer

PM:

Particulate matter

SMR:

Standardized morbidity/mortality rate

USGS:

US Geological Survey

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Acknowledgments

The studies reported in this chapter have been partially supported by US EPA Cooperative Agreement Award X-9745002 to the University of West Florida. The content of this report are solely the responsibility of the authors and do not necessarily represent the official views of the US EPA.

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Authors and Affiliations

  1. Department of Environmental Studies, University of West Florida, Pensacola, FL, 32514, USA

    Zhiyong Hu & Johan Liebens

  2. Center for Environmental Diagnostics and Bioremediation, University of West Florida, Pensacola, FL, 32514, USA

    K. Ranga Rao

Authors
  1. Zhiyong Hu
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  2. Johan Liebens
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  3. K. Ranga Rao
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Corresponding author

Correspondence to Zhiyong Hu .

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Editors and Affiliations

  1. Lehman College, Dept. Environmental, Geographic &, City University of New York, Bedford Park Blvd. W. 250, Bronx, 10468, New York, USA

    Juliana A. Maantay

  2. Dept. Geography, University of Illinois, Urbana-Champaign, S. Mathews Ave. 607, Urbana, 61801, Illinois, USA

    Sara McLafferty

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Hu, Z., Liebens, J., Rao, K.R. (2011). Merging Satellite Measurement with Ground-Based Air Quality Monitoring Data to Assess Health Effects of Fine Particulate Matter Pollution. In: Maantay, J., McLafferty, S. (eds) Geospatial Analysis of Environmental Health. Geotechnologies and the Environment, vol 4. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0329-2_20

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