TY - JOUR
T1 - Fine particulate matter predictions using high resolution Aerosol Optical Depth (AOD) retrievals
AU - Chudnovsky, Alexandra A.
AU - Koutrakis, Petros
AU - Kloog, Itai
AU - Melly, Steven
AU - Nordio, Francesco
AU - Lyapustin, Alexei
AU - Wang, Yujie
AU - Schwartz, Joel
N1 - Funding Information:
This work was made possible by USEPA grant RD 83479801 . Its contents are solely the responsibility of the grantee and do not necessarily represent the official views of the USEPA. Further, USEPA does not endorse the purchase of any commercial products or services mentioned in the publication. The support for A. Lyapustin and Y. Wang is provided by the NASA Terra and Aqua Science Program . The authors wish to thank the anonymous reviewers for their constructive comments. Inspiring discussions with Prof. Alex Kostinski from Michigan Technological University and Dr. Eran Ben Elia from Tel-Aviv University are greatly acknowledged.
PY - 2014/6/1
Y1 - 2014/6/1
N2 - To date, spatial-temporal patterns of particulate matter (PM) within urban areas have primarily been examined using models. On the other hand, satellites extend spatial coverage but their spatial resolution is too coarse. In order to address this issue, here we report on spatial variability in PM levels derived from high 1km resolution AOD product of Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm developed for MODIS satellite. We apply day-specific calibrations of AOD data to predict PM2.5 concentrations within the New England area of the United States. To improve the accuracy of our model, land use and meteorological variables were incorporated. We used inverse probability weighting (IPW) to account for nonrandom missingness of AOD and nested regions within days to capture spatial variation. With this approach we can control for the inherent day-to-day variability in the AOD-PM2.5 relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles and ground surface reflectance among others. Out-of-sample "ten-fold" cross-validation was used to quantify the accuracy of model predictions. Our results show that the model-predicted PM2.5 mass concentrations are highly correlated with the actual observations, with out-of-sample R2 of 0.89. Furthermore, our study shows that the model captures the pollution levels along highways and many urban locations thereby extending our ability to investigate the spatial patterns of urban air quality, such as examining exposures in areas with high traffic. Our results also show high accuracy within the cities of Boston and New Haven thereby indicating that MAIAC data can be used to examine intra-urban exposure contrasts in PM2.5 levels.
AB - To date, spatial-temporal patterns of particulate matter (PM) within urban areas have primarily been examined using models. On the other hand, satellites extend spatial coverage but their spatial resolution is too coarse. In order to address this issue, here we report on spatial variability in PM levels derived from high 1km resolution AOD product of Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm developed for MODIS satellite. We apply day-specific calibrations of AOD data to predict PM2.5 concentrations within the New England area of the United States. To improve the accuracy of our model, land use and meteorological variables were incorporated. We used inverse probability weighting (IPW) to account for nonrandom missingness of AOD and nested regions within days to capture spatial variation. With this approach we can control for the inherent day-to-day variability in the AOD-PM2.5 relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles and ground surface reflectance among others. Out-of-sample "ten-fold" cross-validation was used to quantify the accuracy of model predictions. Our results show that the model-predicted PM2.5 mass concentrations are highly correlated with the actual observations, with out-of-sample R2 of 0.89. Furthermore, our study shows that the model captures the pollution levels along highways and many urban locations thereby extending our ability to investigate the spatial patterns of urban air quality, such as examining exposures in areas with high traffic. Our results also show high accuracy within the cities of Boston and New Haven thereby indicating that MAIAC data can be used to examine intra-urban exposure contrasts in PM2.5 levels.
KW - Aerosol Optical Depth (AOD)
KW - High resolution aerosol retrieval
KW - Intra-urban pollution
KW - MAIAC
KW - PM
KW - Particulate matter
KW - Scales of pollution
KW - Variability in PM levels
UR - http://www.scopus.com/inward/record.url?scp=84896858453&partnerID=8YFLogxK
U2 - 10.1016/j.atmosenv.2014.02.019
DO - 10.1016/j.atmosenv.2014.02.019
M3 - Article
AN - SCOPUS:84896858453
SN - 1352-2310
VL - 89
SP - 189
EP - 198
JO - Atmospheric Environment
JF - Atmospheric Environment
ER -