Developing a Hierarchical Model for the Spatial Analysis of PM10 Pollution Extremes in the Mexico City Metropolitan Area

Int J Environ Res Public Health. 2017 Jul 6;14(7):734. doi: 10.3390/ijerph14070734.

Abstract

We implemented a spatial model for analysing PM 10 maxima across the Mexico City metropolitan area during the period 1995-2016. We assumed that these maxima follow a non-identical generalized extreme value (GEV) distribution and modeled the trend by introducing multivariate smoothing spline functions into the probability GEV distribution. A flexible, three-stage hierarchical Bayesian approach was developed to analyse the distribution of the PM 10 maxima in space and time. We evaluated the statistical model's performance by using a simulation study. The results showed strong evidence of a positive correlation between the PM 10 maxima and the longitude and latitude. The relationship between time and the PM 10 maxima was negative, indicating a decreasing trend over time. Finally, a high risk of PM 10 maxima presenting levels above 1000 μ g/m 3 (return period: 25 yr) was observed in the northwestern region of the study area.

Keywords: Markov Chain Monte Carlo (MCMC); air pollution; extreme value theory; nonstationary; particulate matter.

MeSH terms

  • Air Pollutants / analysis*
  • Air Pollution / analysis
  • Bayes Theorem
  • Cities
  • Environmental Monitoring / statistics & numerical data*
  • Mexico
  • Models, Statistical*
  • Particulate Matter / analysis*
  • Spatial Analysis

Substances

  • Air Pollutants
  • Particulate Matter