Estimation of neighborhood scale PM_2.5 impacts in rural towns in the Purepecha region of Mexico

Environmental Science: Atmospheres 2025 Vol. 5(2) 171-180

Yucheng He, Sanika R. Nishandar, Rufus D. Edwards, Belén Olaya-García, Montserrat Serrano-Medrano, Víctor M. Ruiz-García, Víctor Berrueta, Marko Princevaca & Omar Masera

DOI: https://doi.org/10.1039/D4EA00082J

Abstract

The impact of cooking with solid fuels on neighborhood-scale PM2.5 concentrations in rural towns and communities is poorly quantified due to the lack of credible ground-level monitoring sites and spatial heterogeneity at a scale that is below the resolution of remote sensing GEOS-Chem hybrid models. Emissions of PM2.5 from use of open fires for cooking in rural Mexico are known to cause poor indoor air quality. The effectiveness of different intervention strategies to reduce such pollution exposures also varies because of different local building densities and source intensities. In this study, the effectiveness of stove intervention strategies on the neighborhood-scale PM2.5 concentrations were evaluated in a village Cucuchucho, located in the Purepecha highlands of Mexico. The Quick Urban & Industrial Complex (QUIC) is deployed in the assessment. The model’s performance in simulating interactions between pollutants and flow around building structures is validated through comparison with a water channel experiment, which shows good quantitative agreement. The case study simulation results demonstrate that upstream households contributed ∼30% of concentrations, and current trends will not meet WHO air quality guidelines or interim targets. The magnitude of neighborhood-scale PM2.5 concentrations depends on the intervention and community structure. Based on these simulations, a statistical model is presented to estimate ambient neighborhood PM2.5 pollution concentrations for more communities at a regional level. The statistical model allows neighborhood PM2.5 pollution to be included in estimates of health burdens from household pollution in Mexico using readily accessible community parameters.

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