Abstract
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" R(2) 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 R(2) 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.