Recently, due to accelerations in urban and industrial development, the health impact of air pollution has become a topic of key concern. Of the various forms of air pollution, fine atmospheric particulate matter (PM2.5; particles less than 2.5 micrometers in diameter) appears to pose the greatest risk to human health. While even moderate levels of PM2.5can be detrimental to health, spikes in PM2.5to atypically high levels are even more dangerous. These spikes are believed to be associated with regionally specific meteorological factors. To quantify these associations, we develop a Bayesian spatiotemporal quantile regression model to estimate the spatially varying effects of meteorological variables purported to be related to PM2.5levels. By adopting a quantile regression model, we are able to examine the entire distribution of PM2.5levels; for example, we are able to identify which meteorological drivers are related to abnormally high PM2.5levels. Our approach uses penalized splines to model the spatially varying meteorological effects and to account for spatiotemporal dependence. The performance of the methodology is evaluated through extensive numerical studies. We apply our modeling techniques to 5 years of daily PM2.5data collected throughout the eastern United States to reveal the effects of various meteorological drivers.
The health risks associated with particulate matter pollution (PM2.5) highlight the importance of comprehending key drivers of its spatial and temporal variability. While explanatory modeling is widely used for statistical inference in such applications, the role of wind variables (speed, direction) and their interplay with built environment are often overlooked. This study addresses this gap through a mobile air quality campaign in a suburban area over 10 days for each of three distinct seasons. We developed four innovative wind-based buffers to account for the wind factors and assess the impact of LiDAR-derived 3D built environment structure on PM2.5variability at local and regional scales. Our second objective is to assess the predictive capabilities of wind-based variables. Results indicate that in our low-elevation, low-rise building study area, the built environment does not emerge as a significant factor, contrary to findings in larger, denser urban areas. Wind direction proves more effective in capturing concentration fluctuations than wind speed, and its interaction with pollution source orientation can change pollution dynamics. This study introduces new insights and methodologies for incorporating wind-related factors into the analysis of air pollution dynamics, offering opportunities for further investigation in various urban settings.
more » « less- Award ID(s):
- 2117505
- NSF-PAR ID:
- 10506324
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Transactions in Urban Data, Science, and Technology
- Volume:
- 3
- Issue:
- 1-2
- ISSN:
- 2754-1231
- Format(s):
- Medium: X Size: p. 61-79
- Size(s):
- p. 61-79
- Sponsoring Org:
- National Science Foundation
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