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Benefits of High Resolution PM2.5 Prediction using Satellite MAIAC AOD and Land Use Regression for Exposure Assessment: California Examples
journal contribution
posted on 2019-10-17, 02:03 authored by Hyung Joo LeeThis
study estimated annual average ambient fine particulate matter
(PM2.5) concentrations at 1 km resolution using satellite
Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol
optical depth (AOD), land use parameters, and meteorology in California
for the year 2016 (cross-validation R2 = 0.73 (site-based) and 0.81 (observation-based)). Using these high-resolution
PM2.5 estimates, regionally varying urban enhancements
of PM2.5 concentrations, 1.43–2.77 μg/m3 (23.9–36.2%), were identified in the densely populated
air basins of San Francisco Bay Area, San Joaquin Valley, and South
Coast. On the other hand, within-urban PM2.5 variability
was found to be 31.4–35.6% of between-urban variability across
California. However, this pattern was not consistent from region to
region, even showing higher within-urban variability (e.g., San Francisco
Bay Area). In addition, satellite-based PM2.5 concentrations
were statistically significantly associated with demographic factors
(i.e., % people of color, % poverty, and % low education) with the
strongest positive association with % people of color (1.05 and 2.72
μg/m3 increases per interquartile range (IQR) and
range increases, respectively). The fine-scale PM2.5 estimates
enabled the assessment of long-term PM2.5 exposures for
all populations particularly benefiting rural populations and socially
vulnerable populations widely distributed in each urban area. This
study provided evidence of regionally varying exposure misclassification
that would arise without accounting for rural and within-urban exposure
variabilities.
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Atmospheric CorrectionPM 2.5 estimateswithin-urban PM 2.5 variabilityPM 2.5 exposuresExposure Assessmentrange increasesSan Francisco Bay AreaSouth Coastwithin-urban exposure variabilitiesbetween-urban variabilityHigh Resolution PM 2.5 PredictionSan Joaquin ValleySatellite MAIAC AODcross-validation R 2fine-scale PM 2.5 estimatesCalifornia Exampleswithin-urban variability1 km resolutionPM 2.5IQRsatellite-based PM 2.5 concentrationsair basinsPM 2.5 concentrationssatellite Multi-Angle Implementationinterquartile rangeexposure misclassificationyear 2016
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