Enhancing the Applicability of Satellite Remote Sensing for PM2.5 Estimation Using MODIS Deep Blue AOD and Land Use Regression in California, United States.

We estimated daily ground-level PM2.5 concentrations combining Collection 6 deep blue (DB) Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) data (10 km resolution) with land use regression in California, United States, for the period 2006-2012. The Collection 6 DB method for AOD provided more reliable data retrievals over California's bright surface areas than previous data sets. Our DB AOD and PM2.5 data suggested that the PM2.5 predictability could be enhanced by temporally varying PM2.5 and AOD relations at least at a seasonal scale. In this study, we used a mixed effects model that allowed daily variations in DB AOD-PM2.5 relations. Because DB AOD might less effectively represent local source emissions compared to regional ones, we added geographic information system (GIS) predictors into the mixed effects model to further explain PM2.5 concentrations influenced by local sources. A cross validation (CV) mixed effects model revealed reasonably high predictive power for PM2.5 concentrations with R(2) = 0.66. The relations between DB AOD and PM2.5 considerably varied by day, and seasonally varying effects of GIS predictors on PM2.5 suggest season-specific source emissions and atmospheric conditions. This study indicates that DB AOD in combination with land use regression can be particularly useful to generate spatially resolved PM2.5 estimates. This may reduce exposure errors for health effect studies in California. We expect that more detailed PM2.5 concentration patterns can help air quality management plan to meet air quality standards more effectively.

[1]  Michael D. King,et al.  Aerosol properties over bright-reflecting source regions , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[2]  P. Koutrakis,et al.  Spatial and temporal variability of fine particle composition and source types in five cities of Connecticut and Massachusetts. , 2011, The Science of the total environment.

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

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

[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]  Petros Koutrakis,et al.  Prediction of daily fine particulate matter concentrations using aerosol optical depth retrievals from the Geostationary Operational Environmental Satellite (GOES) , 2012, Journal of the Air & Waste Management Association.

[7]  A. Lacis,et al.  How well do satellite AOD observations represent the spatial and temporal variability of PM2.5 concentration for the United States , 2015 .

[8]  Brent N. Holben,et al.  Regional characteristics of the relationship between columnar AOD and surface PM2.5: Application of lidar aerosol extinction profiles over Baltimore–Washington Corridor during DISCOVER-AQ , 2015 .

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

[10]  M. Bell,et al.  PM2.5 Exposure and Birth Outcomes: Use of Satellite- and Monitor-Based Data , 2014, Epidemiology.

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

[12]  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 .

[13]  Zev Ross,et al.  A hybrid approach to estimating national scale spatiotemporal variability of PM2.5 in the contiguous United States. , 2013, Environmental science & technology.

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

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

[16]  Michael D. King,et al.  Deep Blue Retrievals of Asian Aerosol Properties During ACE-Asia , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[17]  L. Chen,et al.  Quantifying PM2.5 source contributions for the San Joaquin Valley with multivariate receptor models. , 2007, Environmental science & technology.

[18]  J. Schwartz,et al.  Long-term Exposure to Black Carbon and Carotid Intima-Media Thickness: The Normative Aging Study , 2013, Environmental health perspectives.

[19]  M. Brauer,et al.  Spatiotemporal land use regression models of fine, ultrafine, and black carbon particulate matter in New Delhi, India. , 2013, Environmental science & technology.

[20]  Richard T Burnett,et al.  High-Resolution Satellite-Derived PM2.5 from Optimal Estimation and Geographically Weighted Regression over North America. , 2015, Environmental science & technology.

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

[22]  Andrew M. Sayer,et al.  Validation and uncertainty estimates for MODIS Collection 6 “Deep Blue” aerosol data , 2013 .

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

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

[25]  Petros Koutrakis,et al.  Daily ambient NO2 concentration predictions using satellite ozone monitoring instrument NO2 data and land use regression. , 2014, Environmental science & technology.

[26]  Philip K. Hopke,et al.  The concentrations and sources of PM2.5 in metropolitan New York City , 2006 .

[27]  Geert Wets,et al.  Modeling temporal and spatial variability of traffic-related air pollution: Hourly land use regression models for black carbon , 2013 .

[28]  T. Chai,et al.  Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature , 2014 .

[29]  A. Strawa,et al.  Improving retrievals of regional fine particulate matter concentrations from Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI) multisatellite observations , 2013, Journal of the Air & Waste Management Association.

[30]  Judith C. Chow,et al.  PM2.5 chemical composition and spatiotemporal variability during the California Regional PM10/PM2.5 Air Quality Study (CRPAQS) , 2006 .

[31]  Kees de Hoogh,et al.  Western European land use regression incorporating satellite- and ground-based measurements of NO2 and PM10. , 2013, Environmental science & technology.

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

[33]  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.

[34]  B. Coull,et al.  Assessment of primary and secondary ambient particle trends using satellite aerosol optical depth and ground speciation data in the New England region, United States. , 2014, Environmental Research.

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

[36]  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 .

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

[38]  C. Willmott,et al.  Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance , 2005 .

[39]  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.

[40]  J. Gulliver,et al.  A review of land-use regression models to assess spatial variation of outdoor air pollution , 2008 .