Comparison of Four Ground-Level PM2.5 Estimation Models Using PARASOL Aerosol Optical Depth Data from China

Satellite remote sensing is of considerable importance for estimating ground-level PM2.5 concentrations to support environmental agencies monitoring air quality. However, most current studies have focused mainly on the application of MODIS aerosol optical depth (AOD) to predict PM2.5 concentrations, while PARASOL AOD, which is sensitive to fine-mode aerosols over land surfaces, has received little attention. In this study, we compared a linear regression model, a quadratic regression model, a power regression model and a logarithmic regression model, which were developed using PARASOL level 2 AOD collected in China from 18 January 2013 to 10 October 2013. We obtained R (correlation coefficient) values of 0.64, 0.63, 0.62, and 0.57 for the four models when they were cross validated with the observed values. Furthermore, after all the data were classified into six levels according to the Air Quality Index (AQI), a low level of statistical significance between the four empirical models was found when the ground-level PM2.5 concentrations were greater than 75 μg/m3. The maximum R value was 0.44 (for the logarithmic regression model and the power model), and the minimum R value was 0.28 (for the logarithmic regression model and the power model) when the PM2.5 concentrations were less than 75 μg/m3. We also discussed uncertainty sources and possible improvements.

[1]  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.

[2]  B. Coull,et al.  Use of satellite-based aerosol optical depth and spatial clustering to predict ambient PM2.5 concentrations. , 2012, Environmental research.

[3]  F. Bréon,et al.  An evaluation of satellite aerosol products against sunphotometer measurements , 2011 .

[4]  Jun Wang,et al.  Satellite remote sensing of particulate matter and air quality assessment over global cities , 2006 .

[5]  Florence Nadal,et al.  Parameterization of surface polarized reflectance derived from POLDER spaceborne measurements , 1999, IEEE Trans. Geosci. Remote. Sens..

[6]  F. Maignan,et al.  Remote sensing of aerosols over land surfaces from POLDER‐ADEOS‐1 polarized measurements , 2001 .

[7]  Andrew D. Foster,et al.  Remote sensing of ambient particles in Delhi and its environs: estimation and validation , 2008, International journal of remote sensing.

[8]  Zhengqiang Li,et al.  Aerosol variability over East Asia as seen by POLDER space-borne sensors , 2010 .

[9]  Alexei Lyapustin,et al.  Fine Particulate Matter Predictions Using High Resolution Aerosol Optical Depth (AOD) Retrievals , 2014 .

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

[11]  Qilong Min,et al.  A polarized Radiative Transfer model based on successive order of scattering , 2010 .

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

[13]  Didier Tanré,et al.  Characterization of aerosol pollution events in France using ground-based and POLDER-2 satellite data , 2006 .

[14]  M. Hashim,et al.  A robust calibration approach for PM 10 prediction from MODIS aerosol optical depth , 2012 .

[15]  D. Dockery,et al.  An association between air pollution and mortality in six U.S. cities. , 1993, The New England journal of medicine.

[16]  Xing-Fa Gu,et al.  [A review of atmospheric aerosol research by using polarization remote sensing]. , 2014, Guang pu xue yu guang pu fen xi = Guang pu.

[17]  Xingfa Gu,et al.  The reflection and polarization properties of non-spherical aerosol particles , 2010 .

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

[19]  D. Tanré,et al.  Evaluation of PARASOL aerosol retrieval over North East Asia , 2008 .

[20]  Nektarios Chrysoulakis,et al.  Estimation of urban PM10 concentration, based on MODIS and MERIS/AATSR synergistic observations , 2013 .

[21]  Xingfa Gu,et al.  Simultaneous retrieval of aerosol optical properties over the Pearl River Delta, China using multi-angular, multi-spectral, and polarized measurements , 2011 .

[22]  Jie Guang,et al.  Correlation between PM concentrations and aerosol optical depth in eastern China , 2009 .

[23]  F. Bréon,et al.  Global observation of anthropogenic aerosols from satellite , 2001 .

[24]  Annick Bricaud,et al.  The POLDER mission: instrument characteristics and scientific objectives , 1994, IEEE Trans. Geosci. Remote. Sens..

[25]  Kebin He,et al.  Acute health impacts of airborne particles estimated from satellite remote sensing. , 2013, Environment international.

[26]  Q. Min,et al.  Impact of vertical stratification of inherent optical properties on radiative transfer in a plane-parallel turbid medium. , 2010, Optics Express.

[27]  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 .

[28]  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 .

[29]  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.

[30]  Yang Liu,et al.  The effect of aerosol vertical profiles on satellite-estimated surface particle sulfate concentrations , 2011 .

[31]  Jonathan D. W. Kahl,et al.  Improved estimation of PM2.5 using Lagrangian satellite-measured aerosol optical depth , 2014 .

[32]  W. Che,et al.  Analysis of Spatial and Temporal Variability of PM10 Concentrations Using MODIS Aerosol Optical Thickness in the Pearl River Delta Region, China , 2013 .

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

[34]  Jinji Ma,et al.  Air quality evaluation on an urban scale based on MODIS satellite images , 2013 .

[35]  Didier Tanré,et al.  Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations , 2010 .

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

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

[38]  F. Bréon,et al.  Remote sensing of aerosols by using polarized, directional and spectral measurements within the A-Train: the PARASOL mission , 2011 .

[39]  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.

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

[41]  S. Christopher,et al.  Multi year satellite remote sensing of particulate matter air quality over Sydney, Australia , 2007 .

[42]  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 .

[43]  Zhengqiang Li,et al.  Aerosol optical depth and fine-mode fraction retrieval over East Asia using multi-angular total and polarized remote sensing , 2011 .

[44]  Huili Gong,et al.  Aerosol type over east Asian retrieval using total and polarized remote Sensing , 2013 .

[45]  M. Brauer,et al.  Use of Satellite Observations for Long-Term Exposure Assessment of Global Concentrations of Fine Particulate Matter , 2014, Environmental health perspectives.

[46]  Michal Krzyzanowski,et al.  Satellite-based estimates of ground-level fine particulate matter during extreme events: A case study of the Moscow fires in 2010 , 2011 .

[47]  Zhengqiang Li,et al.  Analysis of surface and aerosol polarized reflectance for aerosol retrievals from polarized remote sensing in PRD urban region , 2011 .

[48]  T. Eck,et al.  Variability of Absorption and Optical Properties of Key Aerosol Types Observed in Worldwide Locations , 2002 .

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

[50]  Nan Li,et al.  Assessment of human exposure level to PM10 in China , 2013 .