Abstract

We implemented a spatial model for analysing ${\mathrm{PM}}_{10}$ 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 ${\mathrm{PM}}_{10}$ 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 ${\mathrm{PM}}_{10}$ maxima and the longitude and latitude. The relationship between time and the ${\mathrm{PM}}_{10}$ maxima was negative, indicating a decreasing trend over time. Finally, a high risk of ${\mathrm{PM}}_{10}$ maxima presenting levels above 1000 $\mathsf{\mu }$ g/m ${}^3$ (return period: 25 yr) was observed in the northwestern region of the study area. View Full-Text