Keywords
pollution
Krakow
random forest
interrupted time series
Bayesian STM
How to Cite
Abstract
We evaluate the impact of Krakow’s Anti-Smog Resolution, which was passed on January 15, 2016, and prohibits the use of coal and wood within the city. We use random forest, interrupted time series, and Bayesian structural time series models to assess air quality gains in terms of PM10, PM2.5, and benzo(a)pyrene concentrations, predicting pollution levels if the legislation had not been implemented. The results show significant reductions in pollutant concentrations: PM10 fell by 23% to 39%, PM2.5 by 23% to 36% and benzo(a)pyrene in PM10 by 39% to 41%, with the highest declines occurring during the heating season. These findings indicate the efficacy of Krakow's legislative strategy, offering evidence-based benchmarks for policymakers and public health officials in other cities considering similar residential heating restrictions to achieve measurable air quality improvements.
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