Impacts of snow and cloud covers on satellite-derived PM_2.5 levels

Satellite aerosol optical depth (AOD) has been widely employed to evaluate ground fine particle (PM_2.5) levels, whereas snow/cloud covers often lead to a large proportion of non-random missing AOD values. As a result, the fully covered and unbiased PM_2.5 estimates will be hard to generate. Among the current approaches to deal with the data gap issue, few have considered the cloud-AOD relationship and none of them have considered the snow-AOD relationship. This study examined the impacts of snow and cloud covers on AOD and PM_2.5 and made full- coverage PM_2.5 predictions by considering these impacts. To estimate missing AOD values, daily gap-filling models with snow/cloud fractions and meteorological covariates were developed using the random forest algorithm. By using these models in New York State, a daily AOD data set with a 1-km resolution was generated with a complete coverage. The "out-of-bag" R² of the gap-filling models averaged 0.93 with an interquartile range from 0.90 to 0.95. Subsequently, a random forest-based PM_2.5 prediction model with the gap-filled AOD and covariates was built to predict fully covered PM_2.5 estimates. A ten-fold cross-validation for the prediction model showed a good performance with an R² of 0.82. In the gap-filling models, the snow fraction was of higher significance to the snow season compared with the rest of the year. The prediction models fitted with/without the snow fraction also suggested the discernible changes in PM_2.5 patterns, further confirming the significance of this parameter. Compared with the methods without considering snow and cloud covers, our PM_2.5 prediction surfaces showed more spatial details and reflected small-scale terrain-driven PM_2.5 patterns. The proposed methods can be generalized to the areas with extensive snow/cloud covers and large proportions of missing satellite AOD data for predicting PM_2.5 levels with high resolutions and complete coverage.