Correcting Measurement Error in Satellite Aerosol Optical Depth with Machine Learning for Modeling PM_2.5 in the Northeastern USA.

ABSTRACT: Satellite-derived estimates of aerosol optical depth (AOD) are key predictors in particulate air pollution models. The multi-step retrieval algorithms that estimate AOD also produce quality control variables but these have not been systematically used to address the measurement error in AOD. We compare three machine-learning methods: random forests, gradient boosting, and extreme gradient boosting (XGBoost) to characterize and correct measurement error in the Multi-Angle Implementation of Atmospheric Correction (MAIAC) 1 × 1 km AOD product for Aqua and Terra satellites across the Northeastern/Mid-Atlantic USA versus collocated measures from 79 ground-based AERONET stations over 14 years. Models included 52 quality control, land use, meteorology, and spatially-derived features. Variable imp

SUBMITTER: Just AC 

PROVIDER: S-EPMC6497138 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature