Land use regression modelling of NO2 in São Paulo, Brazil

Luminati, Ornella; Ledebur de Antas de Campos, Bartolomeu; Flückiger, Benjamin; Brentani, Alexandra; Röösli, Martin; Fink, Günther; de Hoogh, Kees

Land use regression modelling of NO2 in São Paulo, Brazil

2021

Air pollution is a major global public health problem. The situation is most severe in low- and middle-income countries, where pollution control measures and monitoring systems are largely lacking. Data to quantify the exposure to air pollution in low-income settings are scarce.In this study, land use regression models (LUR) were developed to predict the outdoor nitrogen dioxide (NO₂) concentration in the study area of the Western Region Birth Cohort in São Paulo. NO₂ measurements were performed for one week in winter and summer at eighty locations. Additionally, weekly measurements at one regional background location were performed over a full one-year period to create an annual prediction.Three LUR models were developed (annual, summer, winter) by using a supervised stepwise linear regression method. The winter, summer and annual models explained 52 %, 75 % and 66 % of the variance (R²) respectively. Cross-holdout validation tests suggest robust models. NO₂ levels ranged from 43.2 μg/m³ to 93.4 μg/m³ in the winter and between 28.1 μg/m³ and 72.8 μg/m³ in summer. Based on our annual prediction, about 67 % of the population living in the study area is exposed to NO₂ values over the WHO suggested annual guideline of 40 μg/m³ annual average.In this study we were able to develop robust models to predict NO₂ residential exposure. We could show that average measures, and therefore the predictions of NO₂, in such a complex urban area are substantially high and that a major variability within the area and especially within the season is present. These findings also suggest that in general a high proportion of the population is exposed to high NO₂ levels.

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