To estimate PM concentrations, many parametric regression models have been developed, while nonparametric machine learning algorithms are used less often and national-scale models are rare. In this paper, we develop a random forest model incorporating aerosol optical depth (AOD) data, meteorological fields, and land use variables to estimate daily 24 h averaged ground-level PM concentrations over the conterminous United States in 2011. Random forests are an ensemble learning method that provides predictions with high accuracy and interpretability. Our results achieve an overall cross-validation (CV) R value of 0.80. Mean prediction error (MPE) and root mean squared prediction error (RMSPE) for daily predictions are 1.78 and 2.83 μg/m, respectively, indicating a good agreement between CV predictions and observations. The prediction accuracy of our model is similar to those reported in previous studies using neural networks or regression models on both national and regional scales. In addition, the incorporation of convolutional layers for land use terms and nearby PM measurements increase CV R by ∼0.02 and ∼0.06, respectively, indicating their significant contributions to prediction accuracy. A pair of different variable importance measures both indicate that the convolutional layer for nearby PM measurements and AOD values are among the most-important predictor variables for the training process.

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.

Satellite-retrieved aerosol optical depth (AOD) has become an important predictor of ground-level particulate matter (PM) and greatly empowered air pollution research worldwide. We evaluated the AOD parameters included in the Collection 6 aerosol product of the Moderate Resolution Imaging Spectroradiometer (MODIS) for two key factors affecting their applications in air quality research-coverage and accuracy-over the continental US. For the high confidence retrievals (QAC 3), the 10 km DB-DT combined AOD has the best coverage nationwide (29.7% of the days in a year in any given 12 km grid cell). While the Eastern US generally had more successful AOD retrievals, the highest spatial coverage of AOD parameters were found in California (>55%) and other vegetated parts of the Western US. If lower QAC retrievals were included, the coverage of the 10 km DB AOD was dramatically increased to 49.6%. In the Eastern US, the QAC 3 retrievals of all four AOD parameters are highly correlated with AERONET observations (correlation coefficients between 0.80 and 0.92). In the Western US, positive retrieval errors existed in all MODIS AOD parameters, resulting in lower correlations with AERONET. AOD retrieval errors showed significant dependence on flight geometry, land cover type, and weather conditions. To ensure appropriate use of these AOD values, air quality researchers should carefully balance the needs for coverage and accuracy, and develop additional data screening criteria based on their study design.

Despite China’s rapid progress improving water, sanitation and hygiene (WSH) access, in 2011, 471 million people lacked access to improved sanitation and 401 million to household piped water. Because certain infectious diseases are sensitive to changes in both climate and WSH conditions, we projected impacts of climate change on WSH-attributable diseases in China in 2020 and 2030 by coupling estimates of the temperature sensitivity of diarrheal diseases and three vector-borne diseases, temperature projections from global climate models, WSH-infrastructure development scenarios, and projected demographic changes. By 2030, climate change is projected to delay China’s rapid progress toward reducing WSH-attributable infectious disease burden by 8–85 months. This development delay summarizes the adverse impact of climate change on WSH-attributable infectious diseases in China, and can be used in other settings where a significant health burden may accompany future changes in climate even as the total burden of disease falls due to non-climate reasons.

Satellite-retrieved aerosol optical properties have been extensively used to estimate ground-level fine particulate matter (PM2.5) concentrations in support of air pollution health effects research and air quality assessment at the urban to global scales. However, a large proportion, ~70%, of satellite observations of aerosols are missing as a result of cloud-cover, surface brightness, and snow-cover. The resulting PM2.5 estimates could therefore be biased due to this non-random data missingness. Cloud-cover in particular has the potential to impact ground-level PM2.5 concentrations through complex chemical and physical processes. We developed a series of statistical models using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product at 1 km resolution with information from the MODIS cloud product and meteorological information to investigate the extent to which cloud parameters and associated meteorological conditions impact ground-level aerosols at two urban sites in the US: Atlanta and San Francisco. We find that changes in temperature, wind speed, relative humidity, planetary boundary layer height, convective available potential energy, precipitation, cloud effective radius, cloud optical depth, and cloud emissivity are associated with changes in PM2.5 concentration and composition, and the changes differ by overpass time and cloud phase as well as between the San Francisco and Atlanta sites. A case-study at the San Francisco site confirmed that accounting for cloud-cover and associated meteorological conditions could substantially alter the spatial distribution of monthly ground-level PM2.5 concentrations.

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With many coal-fired power plants scheduled to close across the United States in the coming years, there is interest in the potential impact on regional PM concentrations. In southwestern Pennsylvania, recent coal-fired power plant closings were coupled with a boom in unconventional natural gas extraction. Natural gas is currently seen as an economically viable bridge fuel between coal and renewable energy. This study provides policymakers with more information on the potential ambient concentration changes associated with coal-fired power plant closings as the nation's energy reliance shifts toward natural gas.

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