Ground PM2.5 prediction using imputed MAIAC AOD with uncertainty quantification

Highlights: • Imputation of missing satellite AOD and quantification of imputation uncertainty. • AOD-based daily PM_2.5 predictions using multiple machine learning algorithms. • Uncertainty analysis in PM_2.5 predictions propagated from imputation. Satellite-derived aerosol optical depth (AOD) has been widely used to predict ground-level fine particulate matter (PM_2.5) concentrations, although its utility can be limited due to missing values. Despite recent attempts to address this issue by imputing missing satellite AOD values, the uncertainty associated with the AOD imputation and its impacts on PM_2.5 predictions have been understudied. To fill this gap, we developed a missing data imputation model for the AOD derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) and PM_2.5 prediction models using several machine learning methods. We also examined how the uncertainty associated with the imputed AOD and a choice of machine learning algorithm were propagated to PM_2.5 predictions. The application of the proposed imputation model to the data from New York State in the U.S. achieved a superior performance than those related studies, with a cross-validated R² of 0.94 and a Root Mean Square Error of 0.017. We also found that there was considerable uncertainty in PM_2.5 predictions associated with the use of imputed AOD values, although it was not as high as the uncertainty from the machine learning algorithms used in PM_2.5 prediction models. We concluded that the quantification of uncertainties for both AOD imputation and its propagation to AOD-based PM_2.5 prediction is necessary for accurate and reliable PM_2.5 predictions.