A novel calibration approach of MODIS AOD data to predict PM<sub>2.5</sub> concentrations
Epidemiological studies investigating the human health effects of PM<sub>2.5</sub> are susceptible to exposure measurement errors, a form of bias in exposure estimates, since they rely on data from a limited number of PM<sub>2.5</sub> monitors within their study area. Satelli...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2011-08-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | http://www.atmos-chem-phys.net/11/7991/2011/acp-11-7991-2011.pdf |
| Summary: | Epidemiological studies investigating the human health effects of PM<sub>2.5</sub> are susceptible to exposure measurement errors, a form of bias in exposure estimates, since they rely on data from a limited number of PM<sub>2.5</sub> monitors within their study area. Satellite data can be used to expand spatial coverage, potentially enhancing our ability to estimate location- or subject-specific exposures to PM<sub>2.5</sub>, but some have reported poor predictive power. A new methodology was developed to calibrate aerosol optical depth (AOD) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). Subsequently, this method was used to predict ground daily PM<sub>2.5</sub> concentrations in the New England region. 2003 MODIS AOD data corresponding to the New England region were retrieved, and PM<sub>2.5</sub> concentrations measured at 26 US Environmental Protection Agency (EPA) PM<sub>2.5</sub> monitoring sites were used to calibrate the AOD data. A mixed effects model which allows day-to-day variability in daily PM<sub>2.5</sub>-AOD relationships was used to predict location-specific PM<sub>2.5</sub> levels. PM<sub>2.5</sub> concentrations measured at the monitoring sites were compared to those predicted for the corresponding grid cells. Both cross-sectional and longitudinal comparisons between the observed and predicted concentrations suggested that the proposed new calibration approach renders MODIS AOD data a potentially useful predictor of PM<sub>2.5</sub> concentrations. Furthermore, the estimated PM<sub>2.5</sub> levels within the study domain were examined in relation to air pollution sources. Our approach made it possible to investigate the spatial patterns of PM<sub>2.5</sub> concentrations within the study domain. |
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| ISSN: | 1680-7316 1680-7324 |