Achcar JA, Fernández-Bremauntz AA, Rodrigues ER, Tzintzun G (2008) Estimating the number of ozone peaks in Mexico City using a non-homogeneous Poisson model. Environmetrics 19:469–485. https://doi.org/10.1002/env.890
Achcar JA, Rodrigues ER, Paulino CD, Soares P (2010) Non-homogeneous Poisson processes with a change-point: an application to ozone exceedances in Mexico City. Environ Ecol Stat 17:521–541
Achcar JA, Rodrigues ER, Tzintzun G (2011a) Using non-homogeneous Poisson models with multiple change-points to estimate the number of ozone exceedances in Mexico City. Environmetrics 22:1–12. https://doi.org/10.1002/env.1029
Achcar JA, Rodrigues ER, Tzintzun G (2011b) Using stochastic volatility models to analyse weekly ozone averages in Mexico City. Environ Ecol Stat 18:271–290. https://doi.org/10.1007/s10651-010-0132-1
Álvarez LJ, Fernández-Bremauntz AA, Rodrigues ER, Tzintzun G (2005) Maximum a posteriori estimation of the daily ozone peaks in Mexico City. J Agric Biol Environ Stat 10:276–290. https://doi.org/10.1198/108571105X5917
Barrios JM, Rodrigues ER (2015) A queueing model to study the occurrence and duration of ozone exceedances in Mexico City. J Appl Stat 42:214–230
Bell ML, McDermont A, Zeger SL, Samet JM, Dominici F (2004) Ozone and short-term mortality in 95 US urban communities, 1987–2000. J Am Med Soc 292:2372–2378
Bell ML, Peng R, Dominici F (2005) The exposure-response curve for ozone and risk of mortality and the adequacy of current ozone regulations. Environ Health Perspect 114:532–536
Box GEP (1980) Sampling and Bayes’ inference in scientific modelling and robustness. J R Stat Soc Ser A 143:383–430
Castro-Morales FE, Gamerman D, Paez MS (2013) State space models with spatial deformation. Environ Ecol Stat 20:191–214. https://doi.org/10.1007/s10651-012-0215-2
Cox DR, Lewis PA (1966) Statistical analysis of series events. Methuen
Cressie NA (1991) Statistics for spatial data. Wiley, Hoboken
Cruz-Juárez JA, Reyes-Cervantes H, Rodrigues ER (2016) Analysis of ozone behaviour in the city of Puebla–Mexico using non-homogeneous Poisson models with multiple change-points. J Environ Prot 7:1886–1903
de Jesús-Romo V, Rodrigues ER, Tzintzun G (2012) A Gibbs sampling algorithm to estimate the parameters of a volatility model: an application to ozone data. Spec Issue Air Pollut Appl Math 12A:2178–2190
Dias CTdS, Samaranayaka A, Manly B (2008) On the use of correlated beta random variables with animal population modelling. Ecol Model 215:293–300
Diggle PJ, Ribeiro PJ Jr (2007) Model-based geostatistics. Springer, New York
Galizia A, Kinney PL (1999) Long-term residence in areas of high ozone: association with respiratory health in a nationwide sample of nonsmoking adults. Environ Health 99:675–679
Gamerman D, Lopes HF (2006) Markov chain Monte Carlo: stochastic simulation for Bayesian inference, 2nd edn. Chapman and Hall, Boca Raton
Gauderman WJ, Avol E, Gililand F, Vora H, Thomas D, Berhane K, McConnel R, Kuenzli N, Lurmman F, Rappaport E, Margolis H, Bates D, Peter J (2004) The effects of air pollution on lung development from 10 to 18 years of age. New Engl J Med 351:1057–1067. https://doi.org/10.1056/NEJMoa040610
Gelfand AE, Smith AFM (1990) Sampling-based approaches to calculating marginal densities. J Am Stat Assoc 85:398–409. https://doi.org/10.1080/01621459.1990.10476213
Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB (2013) Bayesian data analysis, 3rd edn. Chapman and Hall/CRCP, Boca Raton
Gouveia N, Fletcher T (2000) Time series analysis of air pollution and mortality: effects by cause, age and socio-economics status. J Epidemiol Community Health 54:750–755
Guardani R, Aguiar JL, Nascimento CAO, Lacava CIV, Yanagi Y (2003) Ground-level ozone mapping in large urban areas using multivariate analysis: application to the São Paulo Metropolitan Area. J Air Waste Manage Assoc 53:553–559
Gyarmati-Szabó J, Bogachev LV, Chen H (2011) Modelling threshold exceedances of air pollution concentrations via non-homogeneous Poisson process with multiple change-points. Atmos Environ 45:5493–5503
Huerta G, Sansó B (2007) Time-varying models for extreme values. Environ Ecol Stat 14:285–299. https://doi.org/10.1007/s10651-007-0014-3
Huerta G, Sansó B, Stroud JR (2004) A spatiotemporal model for Mexico City ozone levels. Appl Stat 53:231–248
Javits JS (1980) Statistical interdependencies in the ozone national ambient air quality standard. J Air Pollut Control Assoc 30:58–59. https://doi.org/10.1080/00022470.1980.10465918
Koop G, Potter SM (2009) Prior elicitation in multiple change-points models. Int Econ Rev 50:751–772
Lagona F, Maruotti A, Picone M (2011) A non-homogeneous hidden Markov model for analysis of multi-pollutant exceedances data. In: Dymarski P (ed) Hidden Markov models: theory and applications. InTech, Croatia, pp 207–222
Larsen LC, Bradley RA, Honcoop GL (1990) A new method of characterizing the variability of air quality-related indicators. In: Air and waste management association, international specialty conference, tropospheric ozone and the environment. Los Angeles, California Air and Waste Management Series, Pittsburgh, Penn., USA
Lawless JF (1982) Statistical models and methods for lifetime data. Wiley, Hoboken
Loomis D, Borja-Arbuto VH, Bangdiwala SI, Shy CM (1996) Ozone exposure and daily mortality in Mexico City: a time series analysis. Health Effects Inst Res Rep 75:1–46
Majumdar A, Gelfand AE, Banerjee S (2005) Spatio-temporal change-point modeling. J Stat Plan Inference 130:149–166
Martins LC, de Oliveira Latorre MRD, Saldiva PHN, Braga ALF (2002) Air pollution and emergency rooms visit due to chronic lower respiratory diseases in the elderly: an ecological time series study in São Paulo, Brazil. J Occup Environ Med 44:622–627
NOM (2002) Modificación a la Norma Oficial Mexicana NOM-020-SSA1-1993. Diario Oficial de la Federación. 30 October 2002. Mexico. (in Spanish)
NOM (2014) Norma Oficial Mexicana NOM-020-SSA1-2014, Diario Oficial de la Unión. 19 de agosto de 2014. Segunda Edición. (in Spanish)
Paez MS, Gamerman D (2003) Study of the space-time effects in the concentration of airborne pollutants in the Metropolitan Region of Rio de Janeiro. Environmetrics 14:387–408
Paroli R, Pistollato Rosa M, Spezia L (2005) Non-homogeneous Markov mixture of periodic autoregressions for the analysis of air pollution in the Lagoon of Venice. In: Proceedings of applied stochastic models and data analysis. Janseen J, Lenca P (eds). Brest. France. May 16–20: pp 1124–1132
Raftery AE (1989) Are ozone exceedance rate decreasing?, Comment of the paper “Extreme value analysis of environmental time series: an application to trend detection in ground-level ozone” by R. L. Smith”. Statistical Sciences 4:378–381
Raftery AE (1996) Hypothesis testing and model selection. In: Gilks W, Richardson S, Speigelhalter DJ (eds) Markov chain Monte Carlo in practice. Chapman and Hall, Boca Raton, pp 163–187
Robert CP, Casella G (1999) Monte Carlo statistical methods. Springer, New York
Rodrigues E, Achcar JA (2013) Applications of discrete-time Markov chains and Poisson processes to air pollution modeling and studies. Springer Briefs in Mathematics. Springer. New York
Rodrigues ER, Gamerman D, Tarumoto MH, Tzintzun G (2015a) A non-homogeneous Poisson model with spatial anisotropy applied to ozone data from Mexico City. Environ Ecol Stat 22:393–422
Rodrigues ER, Tarumoto MH, Tzintzun G (2015b) A non-homogeneous Markov chain model to study ozone exceedances in Mexico City. In: Nejadkoorki F (ed) Current air quality issues. InTech, Croatia, pp 375–394
Sahu SK, Gelfand AE, Holland DM (2007) High resolution space-time ozone modeling for assessing trends. J Am Stat Assoc 120:1221–1234
Sang H, Gelfand AE (2009) Hierarchical modeling for exteme values observed over space and time. Environ Ecol Stat 16:407–426
Schliep EM, Gelfand AE, Holland DM (2018) Alternating Gaussian process modulated renewal processes for modeling threshold exceedances and duration. Stoch Environ Res Risk Assess 32:401–417
Schmidt AM, Rodríguez MA (2010) Modelling multivariate counts varying continuously in space. In: Bernardo JM, Bayani MJ, Berger JO, David AP, Heckerman D, Smith AFM, West M (eds) Bayesian inference 9. Oxford University Press, Oxford, pp 1–20
Shaddick G, Yan H, Salway R, Vienneau D, Kounall D, Briggs D (2013) Large-scale Bayesian spatial modelling of air pollution for policy support. J Appl Stat 40:777–794
Smith RL (1989) Extreme value analysis of environmental time series: an application to trend detection in ground-level ozone. Stat Sci 4:367–393
Smith AFM, Roberts GO (1993) Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods (with discussion). J R Stat Soc Ser B 55:3–23
Spiegelhalter DJ, Best NG, Carlin BP, Van der Linde A (2002) Bayesian measures of model complexity and fit (with discussion and rejoinder). J R Stat Soc Ser B 64:583–639
Szpiro AA, Sampson PD, Sheppard L, Lumley T, Adar SD, Kaufman JD (2010) Predicting intra-urban variation in air pollution concentrations with complex spatio-temporal dependencies. Environmetrics 21:606–631
Villaseñor-Alva JA, González-Estrada E (2010) On modelling cluster maxima with applications to ozone data from Mexico City. Environmetrics 21:528–540
WHO (2006) Air Quality Guidelines-2005. Particulate matter, ozone, nitrogen dioxide and sulfur dioxide. World Health Organization Regional Office for Europe, EU
Yang TE, Kuo L (2001) Bayesian binary segmentation procedure for a Poisson process with multiple change-points. J Comput Gr Stat 10:772–785
Zozolotto HC (2010) Aplicação de modelos de volatilidade estocástica em dados de poluição do ar de duas grandes cidades: Cidade do México e São Paulo. Master’s Dissertation, Universidade de São Paulo, Ribeirão Preto, Brazil. (in Portuguese)