Estimating Ground‐Level PM2.5 by Fusing Satellite and Station Observations: A Geo‐Intelligent Deep Learning Approach

Fusing satellite observations and station measurements to estimate ground‐level PM2.5 is promising for monitoring PM2.5 pollution. A geo‐intelligent approach, which incorporates geographical correlation into an intelligent deep learning architecture, is developed to estimate PM2.5. Specifically, it considers geographical distance and spatiotemporally correlated PM2.5 in a deep belief network (denoted as Geoi‐DBN). Geoi‐DBN can capture the essential features associated with PM2.5 from latent factors. It was trained and tested with data from China in 2015. The results show that Geoi‐DBN performs significantly better than the traditional neural network. The out‐of‐sample cross‐validation R2 increases from 0.42 to 0.88, and RMSE decreases from 29.96 to 13.03 μg/m3. On the basis of the derived PM2.5 distribution, it is predicted that over 80% of the Chinese population live in areas with an annual mean PM2.5 of greater than 35 μg/m3. This study provides a new perspective for air pollution monitoring in large geographic regions.

[1]  W. Tobler.  A Computer Movie Simulating Urban Growth in the Detroit Region , 1970 .

[2]  Marian Fierro.  Particulate Matter: What is it? A complex mixture of extremely small particles and liquid droplets. , 2001 .

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

[4]  P. Zhai,et al.  Relationship between vegetation coverage and spring dust storms over northern China , 2004 .

[5]  Raymond M Hoff,et al.  Recommendations on the Use of Satellite Remote-Sensing Data for Urban Air Quality , 2004, Journal of the Air & Waste Management Association.

[6]  D. Jacob,et al.  Mapping annual mean ground‐level PM2.5 concentrations using Multiangle Imaging Spectroradiometer aerosol optical thickness over the contiguous United States , 2004 .

[7]  Basil W. Coutant,et al.  Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality , 2004 .

[8]  R. Martin,et al.  Estimating ground-level PM2.5 using aerosol optical depth determined from satellite remote sensing , 2006 .

[9]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[10]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[11]  Who Europe.  Air Quality Guidelines Global Update 2005: Particulate Matter, ozone, nitrogen dioxide and sulfur dioxide , 2006 .

[12]  Yang Liu,et al.  Using aerosol optical thickness to predict ground-level PM2.5 concentrations in the St. Louis area: A comparison between MISR and MODIS , 2007 .

[13]  Yang Liu,et al.  Spatiotemporal associations between GOES aerosol optical depth retrievals and ground-level PM2.5. , 2008, Environmental science & technology.

[14]  Z. H. Chen,et al.  Relationship between atmospheric pollution processes and synoptic pressure patterns in northern China , 2008 .

[15]  R. Martin.  Satellite remote sensing of surface air quality , 2008 .

[16]  S. Christopher,et al.  Remote Sensing of Particulate Pollution from Space: Have We Reached the Promised Land? , 2009, Journal of the Air & Waste Management Association.

[17]  P. Gupta,et al.  Particulate Matter Air Quality Assessment using Integrated Surface, Satellite, and Meteorological Products , 2009 .

[18]  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.

[19]  P. Gupta,et al.  Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: Multiple regression approach , 2009 .

[20]  Yang Liu,et al.  Review of the applications of Multiangle Imaging SpectroRadiometer to air quality research , 2009 .

[21]  R. Hoff,et al.  The Relation between Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth and PM2.5 over the United States: A Geographical Comparison by U.S. Environmental Protection Agency Regions , 2009, Journal of the Air & Waste Management Association.

[22]  R. Hoff,et al.  An Improved Method for Estimating Surface Fine Particle Concentrations Using Seasonally Adjusted Satellite Aerosol Optical Depth , 2010, Journal of the Air & Waste Management Association.

[23]  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.

[24]  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.

[25]  J. Schwartz,et al.  A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations , 2011 .

[26]  M. Brauer,et al.  Creating National Air Pollution Models for Population Exposure Assessment in Canada , 2011, Environmental health perspectives.

[27]  J. Schwartz,et al.  Assessing temporally and spatially resolved PM2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements , 2011 .

[28]  Yang Liu,et al.  A statistical model to evaluate the effectiveness of PM2.5 emissions control during the Beijing 2008 Olympic Games. , 2012, Environment international.

[29]  A. Cohen,et al.  Exposure assessment for estimation of the global burden of disease attributable to outdoor air pollution. , 2012, Environmental science & technology.

[30]  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.

[31]  Jiahua Zhang,et al.  Synergy of satellite and ground based observations in estimation of particulate matter in eastern China. , 2012, The Science of the total environment.

[32]  Liangpei Zhang,et al.  Hyperspectral Image Denoising Employing a Spectral–Spatial Adaptive Total Variation Model , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[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]  M. G. Estes,et al.  Estimating ground-level PM(2.5) concentrations in the southeastern U.S. using geographically weighted regression. , 2013, Environmental research.

[35]  M. Greenstone,et al.  Evidence on the impact of sustained exposure to air pollution on life expectancy from China’s Huai River policy , 2013, Proceedings of the National Academy of Sciences.

[36]  R. Martin,et al.  Toward the next generation of air quality monitoring: Particulate Matter , 2013 .

[37]  L. Remer,et al.  The Collection 6 MODIS aerosol products over land and ocean , 2013 .

[38]  N. Lu,et al.  Spatiotemporal distribution and short-term trends of particulate matter concentration over China, 2006–2010 , 2014, Environmental Science and Pollution Research.

[39]  David John Lary,et al.  Estimating the global abundance of ground level presence of particulate matter (PM2.5). , 2014, Geospatial health.

[40]  Jiansheng Wu,et al.  Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China , 2015, Environmental Science and Pollution Research.

[41]  Jingfeng Huang,et al.  A satellite-based geographically weighted regression model for regional PM2.5 estimation over the Pearl River Delta region in China , 2014 .

[42]  Koji Zettsu,et al.  Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting PM2.5 , 2015, Neural Computing and Applications.

[43]  J. Fung,et al.  Using satellite remote sensing data to estimate the high-resolution distribution of ground-level PM2.5 , 2015 .

[44]  Zhanqing Li,et al.  The climatology of planetary boundary layer height in China derived fromradiosonde and reanalysis data , 2016 .

[45]  M. Brauer,et al.  Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors. , 2016, Environmental science & technology.

[46]  Zhanqing Li,et al.  The climatology of planetary boundary layer height in China derived from radiosonde and reanalysis data , 2016 .

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

[48]  Xiang Li,et al.  Deep learning architecture for air quality predictions , 2016, Environmental Science and Pollution Research.

[49]  Jiansheng Wu,et al.  Spatiotemporal patterns of remotely sensed PM2.5 concentration in China from 1999 to 2011 , 2016 .

[50]  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..

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

[52]  Liang-pei Zhang,et al.  High-quality seamless DEM generation blending SRTM-1, ASTER GDEM v2 and ICESat/GLAS observations , 2017 .

[53]  Fanghua Wu,et al.  Analysis of influential factors for the relationship between PM 2.5 and AOD in Beijing , 2017 .

[54]  Yi Li,et al.  Estimating ground-level PM2.5 concentrations in Beijing, China using aerosol optical depth and parameters of the temperature inversion layer. , 2017, The Science of the total environment.

[55]  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.

[56]  Huang Zheng,et al.  One year monitoring of volatile organic compounds (VOCs) from an oil-gas station in northwest China , 2017 .