Effectiveness of public policies related to traffic emissions in improving air quality in Brazil: A causal inference study using Bayesian structural time-series models

Abstract

Air pollution is a major environmental health risk, particularly in low- and middle-income countries. In Brazil, the government has implemented progressive stages of traffic emission controls since 1986 through a program called “Proconve”. In this study, we evaluated the effectiveness of two of these stages, Proconve stages L-5 and P-7, in reducing air pollution in the 10 most populated cities in Brazil. We used Bayesian structural time-series models to estimate the causal effect of the vehicle emission control measures on the air pollution time series for each city and air pollutant (PM2.5, O3, NO2, and CO), accounting for potential confounding factors such as temperature and humidity. While previous studies have shown positive effects of control measures on air quality, our study suggests that the effects may not always be statistically significant, which has implications for the design and interpretation of future studies. Our study provides valuable information for policymakers in Brazil and adds to the international literature on the effectiveness of air pollution control measures. Despite the lack of statistically significant results, our study highlights the importance of considering statistical power and unmeasured confounding factors when evaluating the effectiveness of air pollution control measures.