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Identifying meteorological drivers of PM2.5 levels via a Bayesian spatial quantile regression

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  • Stella W. Self
  • Christopher S. McMahan
  • Brook T. Russell

Abstract

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 PM2.5 can be detrimental to health, spikes in PM2.5 to 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 PM2.5 levels. By adopting a quantile regression model, we are able to examine the entire distribution of PM2.5 levels; for example, we are able to identify which meteorological drivers are related to abnormally high PM2.5 levels. 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 PM2.5 data collected throughout the eastern United States to reveal the effects of various meteorological drivers.

Suggested Citation

  • Stella W. Self & Christopher S. McMahan & Brook T. Russell, 2021. "Identifying meteorological drivers of PM2.5 levels via a Bayesian spatial quantile regression," Environmetrics, John Wiley & Sons, Ltd., vol. 32(5), August.
    • Handle: RePEc:wly:envmet:v:32:y:2021:i:5:n:e2669
    DOI: 10.1002/env.2669
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    References listed on IDEAS

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    1. Brook T. Russell & Dewei Wang & Christopher S. McMahan, 2017. "Spatially modeling the effects of meteorological drivers of PM2.5 in the Eastern United States via a local linear penalized quantile regression estimator," Environmetrics, John Wiley & Sons, Ltd., vol. 28(5), August.
    2. Howard D. Bondell & Brian J. Reich & Huixia Wang, 2010. "Noncrossing quantile regression curve estimation," Biometrika, Biometrika Trust, vol. 97(4), pages 825-838.
    3. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    4. Korobilis, Dimitris, 2017. "Quantile regression forecasts of inflation under model uncertainty," International Journal of Forecasting, Elsevier, vol. 33(1), pages 11-20.
    5. Yu, Keming & Moyeed, Rana A., 2001. "Bayesian quantile regression," Statistics & Probability Letters, Elsevier, vol. 54(4), pages 437-447, October.
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    Cited by:

    1. Eric Yanchenko & Howard D. Bondell & Brian J. Reich, 2024. "Spatial regression modeling via the R2D2 framework," Environmetrics, John Wiley & Sons, Ltd., vol. 35(2), March.
    2. Silius M. Vandeskog & Thordis L. Thorarinsdottir & Ingelin Steinsland & Finn Lindgren, 2022. "Quantile based modeling of diurnal temperature range with the five‐parameter lambda distribution," Environmetrics, John Wiley & Sons, Ltd., vol. 33(4), June.

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