Estimating ground-level PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model

Abstract Most time-sequenced ambient air pollution data in China is published through daily Air Quality Index (AQI). However, few studies have used the AQI data to calibrate satellite-based estimates of fine particulate matter (PM 2.5 , particles no greater than 2.5 μm in aerodynamic diameter) concentrations, partly because the AQI-derived PM 2.5 is not continuously obtained each day. Taking Beijing as an example, we developed a geographically and temporally weighted regression (GTWR) model that can account for spatial and temporal variability in the relationship between the non-continuous AQI-derived PM 2.5 and satellite-derived aerosol optical depth (AOD). The GTWR model, which uses AOD values with a 3-km spatial resolution obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS), meteorological fields, and land-use variables as predictors, was fitted seasonally from April 2013 to March 2015. After being cross-validated against ground observations, the coefficient of determination (R 2 ) of PM 2.5 ranged from 0.36 to 0.75, with a mean value of 0.58. The GTWR model outperforms several conventional models, such as the multiple linear regression (MLR) model, geographically weighted regression (GWR) model, temporally weighted regression (TWR) model, and linear mixed-effects (LME) model. Compared to a previous spatiotemporal model, the two-stage (LME + GWR) model, the GTWR model may be more feasible. When the number of daily records is ≥ 5, there is no obvious difference in prediction accuracy (cross-validated R 2 both valued at 0.68). However, when the number of daily records is 2 of 0.45 and 0.08). Our estimates indicate that the gridded annual mean PM 2.5 values range from 62 to 110 μg/m 3 , denoting strong spatial variation. We find that when available, continuous daily PM 2.5 observations can significantly improve model performance and therefore facilitate the estimation of surface PM 2.5 concentrations at urban scales. The GTWR model may serve as a reference for studying regions where continuous air pollution data are limited.

[1]  A. Lyapustin,et al.  10-year spatial and temporal trends of PM2.5 concentrations in the southeastern US estimated using high-resolution satellite data , 2013, Atmospheric chemistry and physics.

[2]  Can Li,et al.  A study on the potential applications of satellite data in air quality monitoring and forecasting , 2011 .

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

[4]  G. Pfister,et al.  Spatiotemporal prediction of fine particulate matter during the 2008 northern California wildfires using machine learning. , 2015, Environmental science & technology.

[5]  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.

[6]  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 .

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

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

[9]  A. Stewart Fotheringham,et al.  Geographical and Temporal Weighted Regression (GTWR) , 2015 .

[10]  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.

[11]  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.

[12]  Jinyuan Xin,et al.  The empirical relationship between the PM2.5 concentration and aerosol optical depth over the background of North China from 2009 to 2011 , 2014 .

[13]  Jie Tian,et al.  A semi-empirical model for predicting hourly ground-level fine particulate matter (PM2.5) concentration in southern Ontario from satellite remote sensing and ground-based meteorological measurements , 2010 .

[14]  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 .

[15]  Dan Chen,et al.  Improving the accuracy of daily satellite-derived ground-level fine aerosol concentration estimates for North America. , 2012, Environmental science & technology.

[16]  Ying Zhang,et al.  Satellite-based estimation of regional particulate matter (PM) in Beijing using vertical-and-RH correcting method , 2010 .

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

[18]  R. Koelemeijer,et al.  Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe , 2006 .

[19]  Liangfu Chen,et al.  Estimating Ground-Level PM2.5 Using Fine-Resolution Satellite Data in the Megacity of Beijing, China , 2015 .

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

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

[22]  D. Jacob,et al.  Estimating ground-level PM2.5 in the eastern United States using satellite remote sensing. , 2005, Environmental science & technology.

[23]  Y. Q. Wang,et al.  Spatial distribution and interannual variation of surface PM 10 concentrations over eighty-six Chinese cities , 2010 .

[24]  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.

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

[26]  B. Holben,et al.  MODIS 3 km aerosol product: applications over land in an urban/suburban region , 2013 .

[27]  Colette L. Heald,et al.  Aerosol loading in the Southeastern United States: reconciling surface and satellite observations , 2013 .

[28]  Sundar A. Christopher,et al.  Seven year particulate matter air quality assessment from surface and satellite measurements , 2008 .

[29]  R. Martin,et al.  Fifteen-year global time series of satellite-derived fine particulate matter. , 2014, Environmental science & technology.

[30]  Chang‐Hoi Ho,et al.  Weekly cycle of aerosol-meteorology interaction over China , 2007 .

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

[32]  Itai Kloog,et al.  Assessment of PM2.5 concentrations over bright surfaces using MODIS satellite observations , 2015 .

[33]  Sundar A. Christopher,et al.  Satellite remote sensing of fine particulate matter (PM2.5) air quality over Beijing using MODIS , 2014 .

[34]  F. Dominici,et al.  Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. , 2006, JAMA.

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

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

[37]  Lorraine A. Remer,et al.  MODIS 3 km aerosol product: algorithm and global perspective , 2013 .

[38]  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.

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

[40]  G. Leeuw,et al.  Exploring the relation between aerosol optical depth and PM 2.5 at Cabauw, the Netherlands , 2008 .

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

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

[43]  W. You,et al.  Estimating PM2.5 in Xi'an, China using aerosol optical depth: a comparison between the MODIS and MISR retrieval models. , 2015, The Science of the total environment.

[44]  Timothy S. Moore,et al.  Physical and chemical properties of surface and column aerosols at a rural New England site during MODIS overpass , 2004 .

[45]  Bo Wu,et al.  Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices , 2010, Int. J. Geogr. Inf. Sci..

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

[47]  J. Schwartz,et al.  Incorporating local land use regression and satellite aerosol optical depth in a hybrid model of spatiotemporal PM2.5 exposures in the Mid-Atlantic states. , 2012, Environmental science & technology.

[48]  Chang‐Hoi Ho,et al.  Spatial and Seasonal Variations of Surface PM10 Concentration and MODIS Aerosol Optical Depth over China , 2009 .

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

[50]  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.

[51]  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 .