Predicting monthly high-resolution PM2.5 concentrations with random forest model in the North China Plain.

Exposure to fine particulate matter (PM2.5) remains a worldwide public health issue. However, epidemiological studies on the chronic health impacts of PM2.5 in the developing countries are hindered by the lack of monitoring data. Despite the recent development of using satellite remote sensing to predict ground-level PM2.5 concentrations in China, methods for generating reliable historical PM2.5 exposure, especially prior to the construction of PM2.5 monitoring network in 2013, are still very rare. In this study, a high-performance machine-learning model was developed directly at monthly level to estimate PM2.5 levels in North China Plain. We developed a random forest model using the latest Multi-angle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD), meteorological parameters, land cover and ground PM2.5 measurements from 2013 to 2015. A multiple imputation method was applied to fill the missing values of AOD. We used 10-fold cross-validation (CV) to evaluate model performance and a separate time period, January 2016 to December 2016, was used to validate our model's capability of predicting historical PM2.5 concentrations. The overall model CV R2 and relative prediction error (RPE) were 0.88 and 18.7%, respectively. Validation results beyond the modeling period (2013-2015) shown that this model can accurately predict historical PM2.5 concentrations at the monthly (R2 = 0.74, RPE = 27.6%), seasonal (R2 = 0.78, RPE = 21.2%) and annual (R2 = 0.76, RPE = 16.9%) level. The annual mean predicted PM2.5 concentration from 2013 to 2016 in our study domain was 67.7 μg/m3 and Southern Hebei, Western Shandong and Northern Henan were the most polluted areas. Using this computationally efficient, monthly and high-resolution model, we can provide reliable historical PM2.5 concentrations for epidemiological studies on PM2.5 health effects in China.

[1]  L. Waller,et al.  Improving satellite‐driven PM2.5 models with Moderate Resolution Imaging Spectroradiometer fire counts in the southeastern U.S. , 2014, Journal of geophysical research. Atmospheres : JGR.

[2]  Qiang Zhang,et al.  The 2013 severe haze over southern Hebei, China: model evaluation, source apportionment, and policy implications , 2013 .

[3]  Yang Liu,et al.  Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting , 2017, Scientific Reports.

[4]  Armistead G Russell,et al.  Improving the Accuracy of Daily PM2.5 Distributions Derived from the Fusion of Ground-Level Measurements with Aerosol Optical Depth Observations, a Case Study in North China. , 2016, Environmental science & technology.

[5]  L. Sheppard,et al.  Long-term exposure to air pollution and incidence of cardiovascular events in women. , 2007, The New England journal of medicine.

[6]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[7]  Yu Zhan,et al.  Spatiotemporal prediction of continuous daily PM2.5 concentrations across China using a spatially explicit machine learning algorithm , 2017 .

[8]  A. D. da Silva,et al.  Evaluation of PM2.5 surface concentration simulated by Version 1 of the NASA's MERRA Aerosol Reanalysis over Israel and Taiwan. , 2017, Aerosol and air quality research.

[9]  Bin Zhao,et al.  The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). , 2017, Journal of climate.

[10]  Yang Liu,et al.  Satellite-Based Spatiotemporal Trends in PM2.5 Concentrations: China, 2004–2013 , 2015, Environmental health perspectives.

[11]  T. Wallington,et al.  Attribution of PM2.5 exposure in Beijing-Tianjin-Hebei region to emissions: implication to control strategies. , 2017, Science bulletin.

[12]  Jun Wang,et al.  Intercomparison between satellite‐derived aerosol optical thickness and PM2.5 mass: Implications for air quality studies , 2003 .

[13]  A. Just,et al.  A New Hybrid Spatio-Temporal Model For Estimating Daily Multi-Year PM2.5 Concentrations Across Northeastern USA Using High Resolution Aerosol Optical Depth Data. , 2014, Atmospheric environment.

[14]  F. Laden,et al.  Long-Term Exposure to Particulate Matter and Self-Reported Hypertension: A Prospective Analysis in the Nurses’ Health Study , 2016, Environmental health perspectives.

[15]  Yujie Wang,et al.  Multiangle implementation of atmospheric correction (MAIAC): 2. Aerosol algorithm , 2011 .

[16]  Qiuhong Tang,et al.  Estimating ground-level PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model , 2017 .

[17]  J. H. Belle,et al.  Estimating PM2.5 Concentrations in the Conterminous United States Using the Random Forest Approach. , 2017, Environmental science & technology.

[18]  R. Burnett,et al.  Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. , 2002, JAMA.

[19]  Yan Wang,et al.  Air Pollution and Mortality in the Medicare Population , 2017, The New England journal of medicine.

[20]  Yuqi Bai,et al.  Daily Estimation of Ground-Level PM2.5 Concentrations over Beijing Using 3 km Resolution MODIS AOD. , 2015, Environmental science & technology.

[21]  William L. Crosson,et al.  Estimating Ground-Level PM(sub 2.5) Concentrations in the Southeastern United States Using MAIAC AOD Retrievals and a Two-Stage Model , 2014 .

[22]  Matthew L. Thomas,et al.  Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015 , 2017, The Lancet.

[23]  Yujie Wang,et al.  Assessing PM2.5 Exposures with High Spatiotemporal Resolution across the Continental United States. , 2016, Environmental science & technology.

[24]  Henrik Andersson,et al.  Environmental Research and Public Health Air Pollution Control Policies in China: a Retrospective and Prospects , 2022 .

[25]  Qingyang Xiao,et al.  MAIAC-based long-term spatiotemporal trends of PM2.5 in Beijing, China. , 2018, The Science of the total environment.

[26]  Duanping Liao,et al.  Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors , 2014, Environmental Health.

[27]  Qingyang Xiao,et al.  Full-coverage high-resolution daily PM 2.5 estimation using MAIAC AOD in the Yangtze River Delta of China , 2017 .

[28]  Yang Liu,et al.  Estimating Regional Spatial and Temporal Variability of PM2.5 Concentrations Using Satellite Data, Meteorology, and Land Use Information , 2009, Environmental health perspectives.

[29]  Yang Liu,et al.  Estimating ground-level PM2.5 in China using satellite remote sensing. , 2014, Environmental science & technology.

[30]  Christopher J. Paciorek,et al.  Predicting Chronic Fine and Coarse Particulate Exposures Using Spatiotemporal Models for the Northeastern and Midwestern United States , 2008, Environmental health perspectives.

[31]  M. Brauer,et al.  Global Estimates of Ambient Fine Particulate Matter Concentrations from Satellite-Based Aerosol Optical Depth: Development and Application , 2010, Environmental health perspectives.

[32]  M. Brauer,et al.  Risk of Nonaccidental and Cardiovascular Mortality in Relation to Long-term Exposure to Low Concentrations of Fine Particulate Matter: A Canadian National-Level Cohort Study , 2012, Environmental health perspectives.

[33]  Source attribution of particulate matter pollution over North China with the adjoint method , 2015 .

[34]  Bo Huang,et al.  Satellite-based high-resolution PM2.5 estimation over the Beijing-Tianjin-Hebei region of China using an improved geographically and temporally weighted regression model. , 2018, Environmental pollution.

[35]  Yang Liu,et al.  Estimating ground-level PM 2.5 concentrations over three megalopolises in China using satellite-derived aerosol optical depth measurements , 2016 .