Spatiotemporal Associations between GOES Aerosol Optical Depth Retrievals and Ground-Level PM_2.5

Abstract

We assess the strength of association between aerosol optical depth (AOD) retrievals from the GOES Aerosol/Smoke Product (GASP) and ground-level fine particulate matter (PM2.5) to assess AOD as a proxy for PM2.5 in the United States. GASP AOD is retrieved from a geostationary platform and therefore provides dense temporal coverage with half-hourly observations every day, in contrast to once per day snapshots from polar-orbiting satellites. However, GASP AOD is based on a less-sophisticated instrument and retrieval algorithm. We find that correlations between GASP AOD and PM2.5 over time at fixed locations are reasonably high, except in the winter and in the western U.S. Correlations over space at fixed times are lower. Simple averaging over time actually reduces correlations over space dramatically, but statistical calibration allows averaging over time that produces strong correlations. These results and the data density of GASP AOD highlight its potential to help improve exposure estimates for epidemiological analyses. On average 40% of days in a month have a GASP AOD retrieval compared to 14% for MODIS and 4% for MISR. Furthermore, GASP AOD has been retrieved since November 1994, providing the possibility of a long-term record that pre-dates the availability of most PM2.5 monitoring data and other satellite instruments. We assess the strength of association between aerosol optical depth (AOD) retrievals from the GOES 2 Aerosol/Smoke Product (GASP) and ground-level fine particulate matter (PM 2.5 ) to assess AOD as a 3 proxy for PM 2.5 in the United States. GASP AOD is retrieved from a geostationary platform and there-4 fore provides dense temporal coverage with half-hourly observations every day, in contrast to once per 5 day snapshots from polar-orbiting satellites. However, GASP AOD is based on a less-sophisticated in-6 strument and retrieval algorithm. We find that correlations between GASP AOD and PM 2.5 over time 7 at fixed locations are reasonably high, except in the winter and in the western U.S. Correlations over 8 space at fixed times are lower. Simple averaging over time actually reduces correlations over space dra-9 matically, but statistical calibration allows averaging over time that produces strong correlations. These 10 results and the data density of GASP AOD highlight its potential to help improve exposure estimates for 11 epidemiological analyses. On average 40% of days in a month have a GASP AOD retrieval compared to 12

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

“…This explains Table 4. Comparisons of CV R 2 and % CV Precision (µg m −3 for CV precision) between the measured and predicted PM 2.5 concentrations using mixed effects model and regression model (Wang and Christopher, 2003) why previous investigations have not demonstrated that AOD can be a robust predictor of PM 2.5 Paciorek et al, 2008). The predictive ability of our model was also compared to that of the regression model in terms of percent precision (% Precision) (Table 4 and Fig.…”

Impact of data quality and surface-to-column representativeness on the PM<sub>2.5</sub> / satellite AOD relationship for the contiguous United States

“…This is consistent with several studies that have shown similar regional effects. For example, Hu (2009) reports average PM 2.5 / AOD correlations of 0.67 (eastern US) and 0.22 (western US), with Engel-Cox et al (2004) and Paciorek et al (2008) reporting similar correlations of 0.6-0.8 (eastern US) and 0.2-0.4 (western US). It has been suggested that this regional variability in the PM 2.5 / AOD relationship is due to differences in topography, surface albedo, and boundary layer depth between the eastern and western US (Engel-Cox et al, 2006).…”

Fusing Observational, Satellite Remote Sensing and Air Quality Model Simulated Data to Estimate Spatiotemporal Variations of PM2.5 Exposure in China

“…Satellite-derived aerosol optical depth (AOD) has been successfully associated with ground PM 2.5 [24] and has thus been used to generate spatiotemporal estimators of PM 2.5 by acting as a primary predictor in statistical models such as LUR [25][26][27] or being calibrated by ratios (PM 2.5 /AOD) simulated by a chemical transport model (e.g., GEOS-Chem) [28,29]. However, due to meteorological or geographical conditions, non-randomly missing values in satellite-derived AOD caused absent estimates of PM 2.5 in specific periods (e.g., winter [26]) or areas (e.g., deserts [28]).…”