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Activity Number:
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162
- Recent Development in Data Fusion
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 4, 2020 : 10:00 AM to 11:50 AM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #312575
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Title:
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Multivariate Spectral Downscaling for PM2.5 Species
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Author(s):
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Yawen Guan* and Brian Reich and James Mulholland and Howard Chang
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Companies:
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University of Nebraska-Lincoln and North Carolina State University and Georgia Tech and Emory University
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Keywords:
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Statistical downscaling;
Multiresolution;
Spectral analysis;
Multivariate spatial;
PM2.5 species
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Abstract:
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Fine particulate matter (PM2.5) is a mixture of air pollutants that has adverse effects on human health. Understanding the health effects of PM2.5 mixture and its individual species has been a research priority over the past two decades. However, the limited availability of speciated PM2.5 measurements continues to be a major challenge in exposure assessment for conducting large-scale population-based epidemiology studies. The PM2.5 species have complex spatial-temporal and cross dependence structures that should be accounted for in estimating the spatiotemporal distribution of each component. Two major sources of air quality data are commonly used for deriving exposure estimates: point-level monitoring data and gridded numerical computer model simulation, such as the Community Multiscale Air Quality (CMAQ) model. We propose a statistical method to combine these two data sources for estimating speciated PM2.5 concentration. Our method models the complex relationships between monitoring measurements and the numerical model output at different spatial resolutions, and we model the spatial dependence and cross dependence among PM2.5 species.
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Authors who are presenting talks have a * after their name.
