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 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.

Comparison of multiple PM2.5 exposure products for estimating health benefits of emission controls over New York State, USA

“…PM 2.5_IDW ). For the statistical approach, the contribution from satellite AOD is small, less important than land use and meteorological variables (Bi et al 2019). Bi et al (2019) suggest larger enhancement of PM 2.5 over roads after incorporating satellite AOD, but the difference is generally small (<0.2 μg m −3 ).…”

“…For the statistical approach, the contribution from satellite AOD is small, less important than land use and meteorological variables (Bi et al 2019). Bi et al (2019) suggest larger enhancement of PM 2.5 over roads after incorporating satellite AOD, but the difference is generally small (<0.2 μg m −3 ). Other studies that use statistical models to predict PM 2.5 find that models with satellite-based AOD better predict PM 2.5 than without (Beckerman et al 2013, Ma et al 2014 .…”

“…Although the Dalhousie products are designed for regional domains or larger, we evaluate their performance at the smaller spatial scale of a single state. The Emory product incorporates satellite AOD as a predictor along with other land use and meteorological variables to a machine learning model (random forest) (Bi et al 2019). The CDC WONDER product builds a linear regression model between satellite AOD and ground-based PM 2.5 , and then merges satellite-derived PM 2.5 with spatially interpolated ground-based PM 2.5 (Al-Hamdan et al 2014).…”

Filling the gaps of in situ hourly PM&lt;sub&gt;2.5&lt;/sub&gt; concentration data with the aid of empirical orthogonal function analysis constrained by diurnal cycles

“…Yang et al, 2019), is oftentimes unclear. Since ignoring missing values would undoubtedly introduce biases into the final results (Bondon, 2005;Larose et al, 2019), some studies have attempted to perform data analysis on a relatively long timescale by integrating hourly records into a monthly resolution so as to mitigate the impacts of data gaps (e.g., Bai et al, 2019b;Zhang et al, 2019). On the other hand, many previous studies preferred to exclude records on days subject to a certain degree of missing values (e.g., no more than six missing values within 24 h) in their analysis (e.g., van Donkelaar et al, 2016;L.…”

A Full-Coverage Daily Average PM2.5 Retrieval Method with Two-Stage IVW Fused MODIS C6 AOD and Two-Stage GAM Model

“…Fortunately, some researchers have tried to fill missing MODIS AOD data and estimate full-coverage daily PM 2.5 concentrations [46]. Bi et al [47] examined the impacts of snow and cloud cover on AOD and PM 2.5 and made full coverage PM 2.5 predictions by considering the relationship of snow-AOD. Daily gap filling models with snow/cloud fractions and meteorological covariates were developed to estimate the missing AOD, using the random forest algorithm.…”