In an era where air pollution poses a significant threat to both the environment and public health, we present a network-based approach to unravel the dynamics of extreme pollution events. Leveraging data from 741 monitoring stations in the contiguous United States, we have created dynamic networks using time-lagged correlations of hourly particulate matter (PM2.5) data. The established spatial correlation networks reveal significant PM2.5anomalies during the 2020 and 2021 wildfire seasons, demonstrating the approach’s sensitivity to detecting regional pollution phenomena. The methodology also provides insights into smoke transport and network response, highlighting the persistence of air quality issues beyond visible smoke periods. Additionally, we explored meteorological variables’ impacts on network connectivity. This study enhances understanding of spatiotemporal pollution patterns, positioning spatial correlation networks as valuable tools for environmental monitoring and public health surveillance.
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.
more » « less- Award ID(s):
- 1826715
- NSF-PAR ID:
- 10450851
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Environmetrics
- Volume:
- 32
- Issue:
- 5
- ISSN:
- 1180-4009
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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