Spatiotemporally continuous estimates of daily 1-km PM2.5 concentrations and their long-term exposure in China from 2000 to 2020

Bai, 2022, LGHAP: the Long-term Gap-free High-resolution Air Pollutant concentration dataset, derived via tensor-flow-based multimodal data fusion, Earth Syst. Sci. Data, 14, 907, 10.5194/essd-14-907-2022 Bi, 2019, Impacts of snow and cloud covers on satellite-derived PM2.5 levels, Rem. Sens. Environ., 221, 665, 10.1016/j.rse.2018.12.002 Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324 Brokamp, 2018, Predicting daily urban fine particulate matter concentrations using a random forest model, Environ. Sci. Technol., 52, 4173, 10.1021/acs.est.7b05381 Buchard, 2017, The MERRA-2 aerosol reanalysis, 1980 onward. Part II: evaluation and case studies, J. Clim., 30, 6851, 10.1175/JCLI-D-16-0613.1 Burnett, 2018, Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter, Proc. Natl. Acad. Sci. USA, 115, 9592, 10.1073/pnas.1803222115 Cao, 2012, Fine particulate matter constituents and cardiopulmonary mortality in a heavily polluted Chinese city, Environ. Health Perspect., 120, 373, 10.1289/ehp.1103671 Chen, 2021, Using lidar and historical similar meteorological fields to evaluate the impact of anthropogenic control on dust weather during COVID-19, Front. Environ. Sci., 9, 10.3389/fenvs.2021.806094 Chen, 2022, Estimation of atmospheric PM10 concentration in China using an interpretable deep learning model and top-of-the-atmosphere reflectance data from China's new generation geostationary meteorological satellite, FY-4A, J. Geophys. Res. Atmos., 127 Chen, 2022, Obtaining vertical distribution of PM2.5 from CALIOP data and machine learning algorithms, Sci. Total Environ., 805, 10.1016/j.scitotenv.2021.150338 Chen, 2023, Causes of the unexpected slowness in reducing winter PM2.5 for 2014–2018 in Henan Province, Environ. Pollut., 319, 10.1016/j.envpol.2022.120928 Chudnovsky, 2013, Spatial scales of pollution from variable resolution satellite imaging, Environ. Pollut., 172, 131, 10.1016/j.envpol.2012.08.016 Dominici, 2006, Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases, JAMA, 295, 1127, 10.1001/jama.295.10.1127 Dong, 2022, Decomposing PM2.5 air pollution rebounds in Northern China before COVID-19, Environ. Sci. Pollut. Control Ser., 29, 28688, 10.1007/s11356-021-17889-2 Fang, 2016, Satellite-based ground PM2.5 estimation using timely structure adaptive modeling, Rem. Sens. Environ., 186, 152, 10.1016/j.rse.2016.08.027 Geng, 2021, Tracking air pollution in China: near real-time PM2.5 retrievals from multisource data fusion, Environ. Sci. Technol., 55, 12106, 10.1021/acs.est.1c01863 Guo, 2017, Impact of diurnal variability and meteorological factors on the PM2.5-AOD relationship: implications for PM2.5 remote sensing, Environ. Pollut., 221, 94, 10.1016/j.envpol.2016.11.043 Habre, 2014, The effects of PM2.5 and its components from indoor and outdoor sources on cough and wheeze symptoms in asthmatic children, J. Expo. Sci. Environ. Epidemiol., 24, 380, 10.1038/jes.2014.21 He, 2021, Satellite-derived 1-km estimates and long-term trends of PM2.5 concentrations in China from 2000 to 2018, Environ. Int., 156, 10.1016/j.envint.2021.106726 He, 2021, The spatiotemporal relationship between PM2.5 and aerosol optical depth in China: influencing factors and implications for satellite PM2.5 estimations using MAIAC aerosol optical depth, Atmos. Chem. Phys., 21, 18375, 10.5194/acp-21-18375-2021 He, 2023, Spatiotemporal high-resolution imputation modeling of aerosol optical depth for investigating its full-coverage variation in China from 2003 to 2020, Atmos. Res., 281, 10.1016/j.atmosres.2022.106481 He, 2021, Spatiotemporal assessment of PM2.5 concentrations and exposure in China from 2013 to 2017 using satellite-derived data, J. Clean. Prod., 286, 10.1016/j.jclepro.2020.124965 Hough, 2021, Gaussian Markov random fields improve ensemble predictions of daily 1 km PM2.5 and PM10 across France, Atmos. Environ., 264, 10.1016/j.atmosenv.2021.118693 Hu, 2017, Estimating PM2.5 concentrations in the conterminous United States using the random forest approach, Environ. Sci. Technol., 51, 6936, 10.1021/acs.est.7b01210 Huang, 2018, Predicting monthly high-resolution PM2.5 concentrations with random forest model in the North China Plain, Environ. Pollut., 242, 675, 10.1016/j.envpol.2018.07.016 Jiang, 2021, Estimation of hourly full-coverage PM2.5 concentrations at 1-km resolution in China using a two-stage random forest model, Atmos. Res., 248, 10.1016/j.atmosres.2020.105146 Levy, 2013, The Collection 6 MODIS aerosol products over land and ocean, Atmos. Meas. Tech. Discuss, 6, 159 Li, 2017, India is overtaking China as the world's largest emitter of anthropogenic sulfur dioxide, Sci. Rep., 7, 1 Li, 2017, Point-surface fusion of station measurements and satellite observations for mapping PM2.5 distribution in China: methods and assessment, Atmos. Environ., 152, 477, 10.1016/j.atmosenv.2017.01.004 Liang, 2020, The 17-y spatiotemporal trend of PM2.5 and its mortality burden in China, Proc. Natl. Acad. Sci. USA, 117, 25601, 10.1073/pnas.1919641117 Liang, 2018, MAIAC-based long-term spatiotemporal trends of PM2.5 in Beijing, China, Sci. Total Environ., 616–617, 1589, 10.1016/j.scitotenv.2017.10.155 Lin, 2016, Estimation of long-term population exposure to PM2.5 for dense urban areas using 1-km MODIS data, Rem. Sens. Environ., 179, 13, 10.1016/j.rse.2016.03.023 Liu, 2022, Criteria air pollutants and hospitalizations of a wide spectrum of cardiovascular diseases: a nationwide case-crossover study in China, Eco-Environ. Health, 1, 204, 10.1016/j.eehl.2022.10.002 Liu, 2019, Evaluation and comparison of multiangle implementation of the atmospheric correction algorithm, Dark Target, and Deep Blue aerosol products over China, Atmos. Chem. Phys., 19, 8243, 10.5194/acp-19-8243-2019 Lv, 2017, Daily estimation of ground-level PM2.5 concentrations at 4km resolution over Beijing-Tianjin-Hebei by fusing MODIS AOD and ground observations, Sci. Total Environ., 580, 235, 10.1016/j.scitotenv.2016.12.049 Lyapustin, 2018, MODIS Collection 6 MAIAC algorithm, Atmos. Meas. Tech., 11, 10.5194/amt-11-5741-2018 Ma, 2022, A review of statistical methods used for developing large-scale and long-term PM2.5 models from satellite data, Rem. Sens. Environ., 269, 10.1016/j.rse.2021.112827 Ma, 2016, Satellite-based spatiotemporal trends in PM2.5 concentrations: China, 2004–2013, Environ. Health Perspect., 124, 184, 10.1289/ehp.1409481 Martins, 2017, Validation of high‐resolution MAIAC aerosol product over South America, J. Geophys. Res. Atmos., 10.1002/2016JD026301 Meng, 2021, Estimating PM2.5 concentrations in Northeastern China with full spatiotemporal coverage, 2005-2016, Rem. Sens. Environ., 253, 10.1016/j.rse.2020.112203 Peng, 2009, Emergency admissions for cardiovascular and respiratory diseases and the chemical composition of fine particle air pollution, Environ. Health Perspect., 117, 957, 10.1289/ehp.0800185 Pope, 2006, Health effects of fine particulate air pollution: lines that connect, J. Air Waste Manag. Assoc., 56, 709, 10.1080/10473289.2006.10464485 Provençal, 2017, Evaluation of PM2.5 surface concentration simulated by version 1 of the NASA's MERRA aerosol reanalysis over Israel and taiwan, Aerosol Air Qual. Res., 17, 253, 10.4209/aaqr.2016.04.0145 Randles, 2017, The MERRA-2 aerosol reanalysis, 1980 onward. Part I: System description and data assimilation evaluation, J. Clim., 30, 6823, 10.1175/JCLI-D-16-0609.1 Rose, 2021 Song, 2022, Combining Himawari-8 AOD and deep forest model to obtain city-level distribution of PM2.5 in China, Environ. Pollut., 297, 10.1016/j.envpol.2022.118826 Stafoggia, 2019, Estimation of daily PM10 and PM2.5 concentrations in Italy, 2013–2015, using a spatiotemporal land-use random-forest model, Environ. Int., 124, 170, 10.1016/j.envint.2019.01.016 van Donkelaar, 2021, Monthly global estimates of fine particulate matter and their uncertainty, Environ. Sci. Technol., 55, 15287, 10.1021/acs.est.1c05309 Wang, 2019, Aggravating O3 pollution due to NOx emission control in eastern China, Sci. Total Environ., 677, 732, 10.1016/j.scitotenv.2019.04.388 Wei, 2021, Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications, Rem. Sens. Environ., 252, 10.1016/j.rse.2020.112136 Xiao, 2017, Full-coverage high-resolution daily PM2.5 estimation using MAIAC AOD in the Yangtze River Delta of China, Rem. Sens. Environ., 199, 437, 10.1016/j.rse.2017.07.023 Xiao, 2021, Separating emission and meteorological contributions to long-term PM2.5 trends over eastern China during 2000–2018, Atmos. Chem. Phys., 21, 9475, 10.5194/acp-21-9475-2021 Xue, 2019, Spatiotemporal continuous estimates of PM2.5 concentrations in China, 2000-2016: a machine learning method with inputs from satellites, chemical transport model, and ground observations, Environ. Int., 123, 345, 10.1016/j.envint.2018.11.075 Yang, 2022, Geographical and temporal encoding for improving the estimation of PM2.5 concentrations in China using end-to-end gradient boosting, Rem. Sens. Environ., 269, 10.1016/j.rse.2021.112828 Zhong, 2021, PM2.5 reductions in Chinese cities from 2013 to 2019 remain significant despite the inflating effects of meteorological conditions, One Earth, 4, 448, 10.1016/j.oneear.2021.02.003 Zhang, 2018, Factor analysis for aerosol optical depth and its prediction from the perspective of land-use change, Ecol. Indicat., 93, 458, 10.1016/j.ecolind.2018.05.026