Assessment of long-term exposure to fine particulate matter (PM2.5) in MEDELLIN cohort study.
DOI:
https://doi.org/10.17533/udea.rfnsp.e357114Keywords:
air quality, air pollution, exposure to environmental risks, particulate matter, land use regression modelsAbstract
Objective: to evaluate the variability of pm2.5 during the period 2018-2019 in the urban area of Medellín at different geographic scales.
Methods: the land use regression (lur) model was applied, considering as dependent variable the mean annual concentration of pm2.5 of Medellin and nearby monitoring stations, buffers were defined with radii of 100, 150, 200, 300 and 500 m and center in the coordinates of the monitoring sites of the dependent variable, with each buffer a model was built.
Results: The selected models for the years 2018 and 2019 explain between 40 and 46% of the variability of pm2.5 with errors of the predicted concentrations of 1.64 µg/m3 and 2.18 µg/m3, respectively. The distribution of air pollutant was heterogeneous at neighborhood and block level, with the highest annual concentrations located towards the central strip of the city in the areas surrounding the Medellin River with marked areas towards the south and center. While for 2018, concentrations above 15 µg/m3 were estimated in 21% of the blocks, for 2019 100% of the estimates were above this concentration.
Conclusions: lur models were developed to assess pm2.5 exposure at different spatial scales in which land use and traffic explanatory variables predominated. Exposure levels below 25 µg/m3 were estimated at the different scales, a low variability that allowed the assignment of individual long-term exposures by place of residence in the participants of the medellín cohort project.
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