Enhancing the Applicability of Satellite Remote Sensing for PM_(2.5) Estimation Using MODIS Deep Blue AOD and Land Use Regression in California, United States

摘要

We estimated daily ground-level PM_(2.5) concentrations combining Collection 6 deep blue (DB) Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) data (10 km resolution) with land use regression in California, United States, for the period 2006-2012. The Collection 6 DB method for AOD provided more reliable data retrievals over California's bright surface areas than previous data sets. Our DB AOD and PM_(2.5) data suggested that the PM_(2.5) predictability could be enhanced by temporally varying PM_(2.5) and AOD relations at least at a seasonal scale. In this study, we used a mixed effects model that allowed daily variations in DB AOD-PM_(2.5) relations. Because DB AOD might less effectively represent local source emissions compared to regional ones, we added geographic information system (GIS) predictors into the mixed effects model to further explain PM_(2.5) concentrations influenced by local sources. A cross validation (CV) mixed effects model revealed reasonably high predictive power for PM_(2.5) concentrations with R~2 = 0.66. The relations between DB AOD and PM_(2.5) considerably varied by day, and seasonally varying effects of GIS predictors on PM_(2.5) suggest season-specific source emissions and atmospheric conditions. This study indicates that DB AOD in combination with land use regression can be particularly useful to generate spatially resolved PM_(2.5) estimates. This may reduce exposure errors for health effect studies in California. We expect that more detailed PM_(2.5) concentration patterns can help air quality management plan to meet air quality standards more effectively.