Impacts of snow and cloud covers on satellite-derived PM2.5 levels

Abstract Satellite aerosol optical depth (AOD) has been widely employed to evaluate ground fine particle (PM 2.5 ) levels, whereas snow/ cloud coversoften lead to a large proportion of non-random missing AOD. 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 snowand cloud coverson AOD and PM 2.5 and made full-coverage PM 2.5 predictions with the consideration of these impacts. To estimate the missing AOD, daily gap-filling models with snow/ cloud fractionsand meteorological covariates were developed using the random forestalgorithm. 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 snowfraction was of higher significance in the snowseason compared with the rest of the year. The prediction models fitted with/without the snowfraction also suggested the discernible changes in PM 2.5 patterns, further confirming the significance of this parameter. Compared with the methods without considering snowand 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 coversand large proportions of missing satellite AOD for predicting PM 2.5 levels with high resolutions and complete coverage.