Extending the EOS Long-Term PM2.5 Data Records Since 2013 in China: Application to the VIIRS Deep Blue Aerosol Products

PM<sub>2.5</sub> is hazardous to human health, and high-quality data are thus needed on a routine basis. An attempt is made here to improve the accuracy of near-surface PM<sub>2.5</sub> estimates using the newly released aerosol product derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite with the Deep Blue retrieval algorithm. A high-quality PM<sub>2.5</sub> data set is generated at a spatial resolution of 6 km from 2013 to 2018 by applying the space-time extremely randomized trees (STET) model, which also aims to extend the Earth Observing System (EOS) long-term PM<sub>2.5</sub> data records in China. The PM<sub>2.5</sub> estimates are highly consistent with ground-based measurements, with an out-of-sample cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.88, a root-mean-square error (RMSE) of <inline-formula> <tex-math notation="LaTeX">$16.52~\mu \text{g}/\text{m}^{3}$ </tex-math></inline-formula>, and a mean absolute error of <inline-formula> <tex-math notation="LaTeX">$10~\mu \text{g}/\text{m}^{3}$ </tex-math></inline-formula> at the national scale. Spatiotemporal PM<sub>2.5</sub> variations at monthly scales are also well captured (e.g., <inline-formula> <tex-math notation="LaTeX">$R^{2} =0.91$ </tex-math></inline-formula>–0.94, RMSE = 5.8–<inline-formula> <tex-math notation="LaTeX">$11.6~\mu \text{g}/\text{m}^{3})$ </tex-math></inline-formula>. PM<sub>2.5</sub> varied greatly at regional and seasonal scales across China. Benefiting from emission reduction and air pollution controls, PM<sub>2.5</sub> pollution has reduced dramatically in China with an average of <inline-formula> <tex-math notation="LaTeX">$- 5.6~\mu \text{g}/\text{m}^{3}$ </tex-math></inline-formula>/yr<sup>−1</sup> during 2013–2018. Significant regional reductions are also seen, in particular, in the Beijing–Tianjin–Hebei region (<inline-formula> <tex-math notation="LaTeX">$- 6.6~\mu \text{g}/\text{m}^{3}$ </tex-math></inline-formula>/yr<sup>−1</sup>, <inline-formula> <tex-math notation="LaTeX">$p < 0.001$ </tex-math></inline-formula>), and the Deltas of Yangtze River (<inline-formula> <tex-math notation="LaTeX">$- 6.3~\mu \text{g}/\text{m}^{3}$ </tex-math></inline-formula>/yr<sup>−1</sup>, <inline-formula> <tex-math notation="LaTeX">$p < 0.001$ </tex-math></inline-formula>) and Pearl River Delta (<inline-formula> <tex-math notation="LaTeX">$- 4.5~\mu \text{g}/\text{m}^{3}$ </tex-math></inline-formula>/yr<sup>−1</sup>, <inline-formula> <tex-math notation="LaTeX">$p < 0.001$ </tex-math></inline-formula>). Our study improved the accuracy of near-surface PM<sub>2.5</sub> estimates in terms of their spatiotemporal variations at a relatively long-term record, which is important for future air pollution and health studies in China.

[1]  Jiansheng Wu,et al.  A multidimensional comparison between MODIS and VIIRS AOD in estimating ground-level PM2.5 concentrations over a heavily polluted region in China. , 2018, The Science of the total environment.

[2]  Yiran Peng,et al.  Intercomparison in spatial distributions and temporal trends derived from multi-source satellite aerosol products , 2019, Atmospheric Chemistry and Physics.

[3]  Jiming Hao,et al.  Drivers of improved PM2.5 air quality in China from 2013 to 2017 , 2019, Proceedings of the National Academy of Sciences.

[4]  Anu W. Turunen,et al.  Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project , 2014, The Lancet.

[5]  José Antonio Lozano,et al.  Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Liangpei Zhang,et al.  Estimating Ground‐Level PM2.5 by Fusing Satellite and Station Observations: A Geo‐Intelligent Deep Learning Approach , 2017, 1707.03558.

[7]  Lunche Wang,et al.  Aerosol radiative effects from observations and modelling over the Yangtze River Basin, China from 2001 to 2015 , 2019, International Journal of Climatology.

[8]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[9]  Xiaoping Liu,et al.  Satellite-based ground PM 2.5 estimation using timely structure adaptive modeling , 2016 .

[10]  Zhanqing Li,et al.  Relationships between the planetary boundary layer height and surface pollutants derived from lidar observations over China: regional pattern and influencing factors , 2018, Atmospheric Chemistry and Physics.

[11]  Liang-pei Zhang,et al.  Point-surface fusion of station measurements and satellite observations for mapping PM 2.5 distribution in China: Methods and assessment , 2016, 1607.02976.

[12]  Suresh Jain,et al.  Impact of air pollutants from surface transport sources on human health: A modeling and epidemiological approach. , 2015, Environment international.

[13]  Ting Yang,et al.  Investigation of the sources and evolution processes of severe haze pollution in Beijing in January 2013 , 2014 .

[14]  F. Ballester.  [Air pollution, climate change and health]. , 2005, Revista espanola de salud publica.

[15]  Tong Zhu,et al.  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. , 2019, Environment international.

[16]  Yan Feng,et al.  Air Pollution, Greenhouse Gases and Climate Change: Global and Regional Perspectives , 2009 .

[17]  Jun Yang,et al.  Extreme gradient boosting model to estimate PM2.5 concentrations with missing-filled satellite data in China , 2019, Atmospheric Environment.

[18]  Michael Brauer,et al.  Addressing Global Mortality from Ambient PM2.5. , 2015, Environmental science & technology.

[19]  Jing Li,et al.  An Intercomparison of AOD-converted PM 2.5 Concentrations Using Different Approaches for Estimating Aerosol Vertical Distribution , 2017 .

[20]  Zhanqing Li,et al.  The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China. , 2021, Environment international.

[21]  Jing Wei,et al.  Impact of Land-Use and Land-Cover Change on urban air quality in representative cities of China , 2016 .

[22]  Qingqing He,et al.  Satellite-based mapping of daily high-resolution ground PM 2.5 in China via space-time regression modeling , 2018 .

[23]  Yang Liu,et al.  Effects of air pollution control policies on PM2.5 pollution improvement in China from 2005 to 2017: a satellite-based perspective , 2018, Atmospheric Chemistry and Physics.

[24]  Zhanqing Li,et al.  Aerosol and boundary-layer interactions and impact on air quality , 2017 .

[25]  Lin Sun,et al.  Verification, improvement and application of aerosol optical depths in China Part 1: Inter-comparison of NPP-VIIRS and Aqua-MODIS , 2018 .

[26]  Zhengqiang Li,et al.  Remote sensing of atmospheric fine particulate matter (PM2.5) mass concentration near the ground from satellite observation , 2015 .

[27]  Yi Li,et al.  National-Scale Estimates of Ground-Level PM2.5 Concentration in China Using Geographically Weighted Regression Based on 3 km Resolution MODIS AOD , 2016, Remote. Sens..

[28]  Yan Yin,et al.  Aerosol and monsoon climate interactions over Asia , 2016 .

[29]  Zhanqing Li,et al.  Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach , 2019, Remote Sensing of Environment.

[30]  I. Tang,et al.  Water activities, densities, and refractive indices of aqueous sulfates and sodium nitrate droplets of atmospheric importance , 1994 .

[31]  J. Manson,et al.  Particulate matter and episodic memory decline mediated by early neuroanatomic biomarkers of Alzheimer's disease. , 2019, Brain : a journal of neurology.

[32]  Jin Huang,et al.  Enhanced Deep Blue aerosol retrieval algorithm: The second generation , 2013 .

[33]  C. Sioutas,et al.  Particulate Air Pollution, Ambulatory Heart Rate Variability, and Cardiac Arrhythmia in Retirement Community Residents with Coronary Artery Disease , 2013, Environmental health perspectives.

[34]  W. You,et al.  Estimating national-scale ground-level PM25 concentration in China using geographically weighted regression based on MODIS and MISR AOD , 2016, Environmental Science and Pollution Research.

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

[36]  T. Behrens,et al.  Spatial modelling with Euclidean distance fields and machine learning , 2018, European Journal of Soil Science.

[37]  G. Carmichael,et al.  Asian emissions in 2006 for the NASA INTEX-B mission , 2009 .

[38]  Dan Chen,et al.  Assimilating AOD retrievals from GOCI and VIIRS to forecast surface PM2.5 episodes over Eastern China , 2018 .

[39]  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.

[40]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[41]  W. Xia,et al.  Exposure to ambient fine particulate matter during pregnancy and gestational weight gain. , 2018, Environment international.

[42]  G. Carmichael,et al.  MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP , 2017 .

[43]  J. Lelieveld,et al.  The contribution of outdoor air pollution sources to premature mortality on a global scale , 2015, Nature.

[44]  Lin Sun,et al.  Improved 1 km resolution PM2.5 estimates across China using enhanced space–time extremely randomized trees , 2020 .

[45]  Yiran Peng,et al.  MODIS Collection 6.1 aerosol optical depth products over land and ocean: validation and comparison , 2019, Atmospheric Environment.

[46]  Claudia Twum,et al.  Exposure to ambient PM2.5 during pregnancy and preterm birth in metropolitan areas of the state of Georgia , 2018, Environmental Science and Pollution Research.

[47]  Yuan Cheng,et al.  Exploring the severe winter haze in Beijing: the impact of synoptic weather, regional transport and heterogeneous reactions , 2015 .

[48]  Jian Peng,et al.  A spatially structured adaptive two-stage model for retrieving ground-level PM2.5 concentrations from VIIRS AOD in China , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[49]  F. Dominici,et al.  Emergency Admissions for Cardiovascular and Respiratory Diseases and the Chemical Composition of Fine Particle Air Pollution , 2009, Environmental health perspectives.

[50]  Zhanqing Li,et al.  MODIS Collection 6.1 3 km resolution aerosol optical depth product: global evaluation and uncertainty analysis , 2020, Atmospheric Environment.

[51]  Kebin He,et al.  URBAN AIR POLLUTION IN CHINA: Current Status, Characteristics, and Progress , 2002 .

[52]  Bin Chen,et al.  Dynamic assessment of PM2.5 exposure and health risk using remote sensing and geo-spatial big data. , 2019, Environmental pollution.

[53]  Lin Sun,et al.  An Improved High‐Spatial‐Resolution Aerosol Retrieval Algorithm for MODIS Images Over Land , 2018, Journal of Geophysical Research: Atmospheres.

[54]  Jing Wei,et al.  Enhanced Aerosol Estimations From Suomi-NPP VIIRS Images Over Heterogeneous Surfaces , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[55]  N. C. Hsu,et al.  VIIRS Deep Blue Aerosol Products Over Land: Extending the EOS Long‐Term Aerosol Data Records , 2019, Journal of Geophysical Research: Atmospheres.

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

[57]  Rui Jiang,et al.  A random forest approach to the detection of epistatic interactions in case-control studies , 2009, BMC Bioinformatics.

[58]  Jiansheng Wu,et al.  VIIRS-based remote sensing estimation of ground-level PM2.5 concentrations in Beijing–Tianjin–Hebei: A spatiotemporal statistical model , 2016 .

[59]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.

[60]  K. Hubacek,et al.  Clean air for some: Unintended spillover effects of regional air pollution policies , 2019, Science Advances.

[61]  Maureen Cribb,et al.  Significant contribution of organics to aerosol liquid water content in winter in Beijing, China , 2019, Atmospheric Chemistry and Physics.

[62]  Robert E. Dickinson,et al.  PM2.5 Pollution in China and How It Has Been Exacerbated by Terrain and Meteorological Conditions , 2017 .

[63]  N. C. Hsu,et al.  Satellite Ocean Aerosol Retrieval (SOAR) Algorithm Extension to S‐NPP VIIRS as Part of the “Deep Blue” Aerosol Project , 2018, Journal of geophysical research. Atmospheres : JGR.

[64]  Jen-Ping Chen,et al.  Analysis of the relationship between MODIS aerosol optical depth and particulate matter from 2006 to 2008 , 2011 .

[65]  Lorraine A. Remer,et al.  Suomi‐NPP VIIRS aerosol algorithms and data products , 2013 .