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Fine particulate matter predictions using high resolution Aerosol Optical Depth (AOD) retrievals
Journal article   Open access   Peer reviewed

Fine particulate matter predictions using high resolution Aerosol Optical Depth (AOD) retrievals

Alexandra A. Chudnovsky, Petros Koutrakis, Itai Kloog, Steven Melly, Francesco Nordio, Alexei Lyapustin, Yujie Wang and Joel Schwartz
Atmospheric environment (1994), v 89, pp 189-198
01 Jun 2014
DOI: https://doi.org/10.1016/j.atmosenv.2014.02.019
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
http://hdl.handle.net/2060/20150006835View
Published, Version of Record (VoR)Open Access (License Unspecified),  Open

Abstract

Aerosol Optical Depth (AOD) High resolution aerosol retrieval Intra-urban pollution MAIAC Particulate matter PM2.5 Scales of pollution Variability in PM2.5 levels
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 1 km 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. •We investigate the spatial variability of the AOD-PM2.5 relationship.•The model-predicted PM2.5 mass concentrations are highly correlated with the actual observations (R2 = 0.89).•The model captures the pollution levels along highways.•High accuracy of PM2.5 estimates enables to examine PM2.5 levels within cities.

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106 citations in Web of Science
112 citations in Scopus

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#13 Climate Action
#3 Good Health and Well-Being
#11 Sustainable Cities and Communities

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Environmental Sciences
Meteorology & Atmospheric Sciences
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