Next Article in Journal
Place of Birth and Sleep Duration: Analysis of the National Health Interview Survey (NHIS)
Previous Article in Journal
Spatio-Temporal Analysis of Suicide-Related Emergency Calls
Open AccessArticle

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

1
Department of Physics and Mathematics, Universidad Tecnológica de la Mixteca, 69000 Huajuapan de León, Oax., Mexico
2
Department of Statistics, Colegio de Postgraduados, Campus Montecillo, Texcoco, 56230 Montecillo, Mex., Mexico
3
Faculty of Engineering, Universidad Autónoma de San Luis Potosí, 78280 San Luis Potosí, S.L.P., Mexico
*
Author to whom correspondence should be addressed.
Academic Editor: Kim Natasha Dirks
Int. J. Environ. Res. Public Health 2017, 14(7), 734; https://doi.org/10.3390/ijerph14070734
Received: 22 May 2017 / Revised: 23 June 2017 / Accepted: 3 July 2017 / Published: 6 July 2017
(This article belongs to the Section Environmental Science and Engineering)
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. View Full-Text
Keywords: air pollution; particulate matter; extreme value theory; Markov Chain Monte Carlo (MCMC); nonstationary air pollution; particulate matter; extreme value theory; Markov Chain Monte Carlo (MCMC); nonstationary
Show Figures

Figure 1

MDPI and ACS Style

Aguirre-Salado, A.I.; Vaquera-Huerta, H.; Aguirre-Salado, C.A.; Reyes-Mora, S.; Olvera-Cervantes, A.D.; Lancho-Romero, G.A.; Soubervielle-Montalvo, C. 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, 14, 734. https://doi.org/10.3390/ijerph14070734

AMA Style

Aguirre-Salado AI, Vaquera-Huerta H, Aguirre-Salado CA, Reyes-Mora S, Olvera-Cervantes AD, Lancho-Romero GA, Soubervielle-Montalvo C. Developing a Hierarchical Model for the Spatial Analysis of PM10 Pollution Extremes in the Mexico City Metropolitan Area. International Journal of Environmental Research and Public Health. 2017; 14(7):734. https://doi.org/10.3390/ijerph14070734

Chicago/Turabian Style

Aguirre-Salado, Alejandro I.; Vaquera-Huerta, Humberto; Aguirre-Salado, Carlos A.; Reyes-Mora, Silvia; Olvera-Cervantes, Ana D.; Lancho-Romero, Guillermo A.; Soubervielle-Montalvo, Carlos. 2017. "Developing a Hierarchical Model for the Spatial Analysis of PM10 Pollution Extremes in the Mexico City Metropolitan Area" Int. J. Environ. Res. Public Health 14, no. 7: 734. https://doi.org/10.3390/ijerph14070734

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop