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Pollution state modelling for Mexico City

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  • Philip A. White
  • Alan E. Gelfand
  • Eliane R. Rodrigues
  • Guadalupe Tzintzun

Abstract

Ground level ozone and particulate matter pollutants are associated with a variety of health issues and increased mortality. For this reason, Mexican environmental agencies regulate pollutant levels. In addition, Mexico City defines pollution emergencies by using thresholds that rely on regional maxima for ozone and for particulate matter with diameter less than 10 μm, PM10. To predict local pollution emergencies and to assess compliance to Mexican ambient air quality standards, we analyse hourly ozone and PM10‐measurements from 24 stations across Mexico City from 2017 by using a bivariate spatiotemporal model. With this model, we predict future pollutant levels by using current weather conditions and recent pollutant concentrations. Employing hourly pollutant projections, we predict regional maxima needed to estimate the probability of future pollution emergencies. We discuss how predicted compliance to legislated pollution limits varies across regions within Mexico City in 2017. We find that the predicted probability of pollution emergencies is limited to a few time periods. In contrast, we show that predicted exceedance of Mexican ambient air quality standards is a common, nearly daily occurrence.

Suggested Citation

  • Philip A. White & Alan E. Gelfand & Eliane R. Rodrigues & Guadalupe Tzintzun, 2019. "Pollution state modelling for Mexico City," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(3), pages 1039-1060, June.
    • Handle: RePEc:bla:jorssa:v:182:y:2019:i:3:p:1039-1060
    DOI: 10.1111/rssa.12444
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    Cited by:

    1. Galatia Cleanthous & Emilio Porcu & Philip White, 2021. "Regularity and approximation of Gaussian random fields evolving temporally over compact two-point homogeneous spaces," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(4), pages 836-860, December.
    2. Philip A. White & Alan E. Gelfand, 2021. "Multivariate functional data modeling with time-varying clustering," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 586-602, September.
    3. Fabian Krüger & Sebastian Lerch & Thordis Thorarinsdottir & Tilmann Gneiting, 2021. "Predictive Inference Based on Markov Chain Monte Carlo Output," International Statistical Review, International Statistical Institute, vol. 89(2), pages 274-301, August.
    4. Emilio Porcu & Philip A. White, 2022. "Random fields on the hypertorus: Covariance modeling and applications," Environmetrics, John Wiley & Sons, Ltd., vol. 33(1), February.

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