A novel calibration approach of MODIS AOD data to predict PM<sub>2.5</sub> concentrations

Abstract

Abstract. Epidemiological studies investigating the human health effects of PM 2.5 are susceptible to exposure measurement errors, a form of bias in exposure estimates, since they rely on data from a limited number of PM 2.5 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 2.5 , 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 2.5 concentrations in the New England region. 2003 MODIS AOD data corresponding to the New England region were retrieved, and PM 2.5 concentrations measured at 26 US Environmental Protection Agency (EPA) PM 2.5 monitoring sites were used to calibrate the AOD data. A mixed effects model which allows day-today variability in daily PM 2.5 -AOD relationships was used to predict location-specific PM 2.5 levels. PM 2.5 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 2.5 concentrations. Furthermore, the estimated PM 2.5 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 2.5 concentrations within the study domain.

Air Pollution Monitoring Using Earth Observation & GIS

“…The use of earth observation is based on the monitoring and determination of AOT either direct or indirect as tool for assessing and measure air pollution. Measurements on PM10 and PM2.5 are found to be related with the AOT values as shown by Lee et al (2011), Hadjimitsis et al (2010), Nisantzi et al (2011).…”

“…MODIS data have been extended correlated with PM10 over several study areas Nisanzti et al, 2011) with improved correlation coefficients but with several limitations. PM2.5 are also found to be correlated against MODIS data using new novel improved algorithms (e.g Lee et al, 2011) that considers the low spatial resolution of MODIS with future positive premises. Themistocleous et al (2010) used Landsat TM/ETM+ and MODIS AOT data to investigate the air pollution near to cultural heritage sites in the centre of Limassol area in Cyprus.…”

“…It is expected that future satellite technologies will provide data with finer spatial and temporal resolutions and more accurate data retrievals (Lee et al, 2011). Moreover, the advanced capability of discriminating by aerosol species in satellite technologies will further contribute to health effect studies investigating species-specific health implications (Lee et al, 2011).…”

“…Moreover, the advanced capability of discriminating by aerosol species in satellite technologies will further contribute to health effect studies investigating species-specific health implications (Lee et al, 2011).…”

A robust calibration approach for PM<sub>10</sub> prediction from MODIS aerosol optical depth

“…However, these models generally predict < 60 % of the PM variability (Hoff and Christopher, 2009). The latest model developed by Lee et al (2011) uses a mixed effect model to establish daily specific AOD-PM 2.5 relationship and predicts the site mean PM 2.5 concentrations with R 2 = 0.62. Furthermore, Lee et al (2011) also hypothesized that the relationship between PM 2.5 and AOD varies daily due to time-varying parameters influencing the AOD-PM relationship, such as PM vertical and diurnal concentration profiles, PM optical properties, and others.…”