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When Space and Time Matter in Environmental Injustice: A Bayesian Analysis of the Association between Socio-economic Disadvantage and Air Pollution in Greater Mexico City

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Abstract

Environmental injustice refers to the unequal burden of pollutants on groups with lower socioeconomic status. An increasing number of studies have identified associations between high levels of pollution and socioeconomic disadvantage. However, few studies have controlled adequately for spatio-temporal variations in pollution. This study uses a Bayesian approach to explore the association between socioeconomic disadvantage and pollution in Mexico City Metropolitan Area. We quantify the association of socioeconomic disadvantage with PM10 and ozone and evaluate the impact of accounting for spatio-temporal structure of the pollution data. We find a significant positive association between socio-economic disadvantage and pollution for levels of PM10, but not ozone. The inclusion of the spatio-temporal element in the modeling results in improved weaker estimates of this association but this does not alter results substantially. These findings confirm the robustness of previous studies that found signs of environmental injustice where spatio-temporal variations have not been explicitly considered, confirming that targeted policies to reduce pollution in socio-economically disadvantaged areas are required.

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Data availability

The obtained raw datasets during the current study are available in the cited references in the data section.

Notes

  1. An additional municipality was excluded from this analysis, belonging to the State of Hidalgo.

  2. After having burned in the first 3000 iterations, 37,000 were left for making inferences for model 1. Similarly, models 2 and 3 were run for 90,000 and 100,000 MCMC chains, and were left 70,000 and 85,000 for making inferences, respectively. To measure the convergence of the MCMC chains in each model, the history plots and the Gelman-Rubin diagnostic (Gelman and Rubin, 1992) were used. The first one was examined by visual inspection of the history plots, which is a common practice in Bayesian models. The values from the Gelman-Rubin diagnostic were obtained and they remained lower than 1.025 for every single model parameter, showing that the chains achieved convergence after the burn-in period.

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Author contributions

In terms of the specific contributions of the different authors, I, AL-H designed and conducted the study, carried out the analysis and wrote the first draft of the paper. JT and PW provided supervision for the research. They provided new content for the revised paper, especially around the justification and rationale for the research, interpretation and contextualization of the findings, and re-drafted the paper, as well as providing final editing. CMC contributed to the discussion of the methods, interpretation of the modeling results, and reviewed the manuscript before submission.

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Authors and Affiliations

  1. Facultad de Ciencias Económicas y Empresariales, Universidad Panamericana, Álvaro del Portillo 49, Zapopan, Jalisco, 45010, Mexico

    Alejandro Lome-Hurtado

  2. Department of Environment and Geography & York Environmental Sustainability Institute, University of York, Wentworth Way, University of York, York, YO10 5NG, United Kingdom

    Julia Touza, Piran C. L. White & Colin J. McClean

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  1. Alejandro Lome-Hurtado
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Correspondence to Alejandro Lome-Hurtado.

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Lome-Hurtado, A., Touza, J., White, P.C.L. et al. When Space and Time Matter in Environmental Injustice: A Bayesian Analysis of the Association between Socio-economic Disadvantage and Air Pollution in Greater Mexico City. Environmental Management 73, 657–667 (2024). https://doi.org/10.1007/s00267-023-01905-x

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