Assessing the causal impact of the Chinese Spring Festival on PM_2.5 air quality in Beijing-Tianjin-Hebei and surrounding region using a machine learning counterfactual modeling approach 

Abstract. Acute short-term exposure to extremely high PM_2.5 levels posed serious health risks. Human culture-based festival activities can significantly alter emission patterns, often leading to sharp yet understudied fluctuations in air quality. The Chinese Spring Festival (CSF), marked by large-scale family reunions and widespread use of fireworks, raises air pollution concerns. Commonly, this effect is quantified using receptor models or chemical transport models, but the relevant chemical component data and emission inventories are often lacking. This study presents a machine learning counterfactual approach to causally quantify PM_2.5 changes associated with holiday activities. The results align well with traditional chemical composition-based estimates of fireworks contributions, highlighting the strong potential of using widely accessible routine monitoring data to quantify source contributions driven by specific interventions. Applied to the twenty-eight major cities in Beijing-Tianjin-Hebei and surrounding area, one of the most polluted regions in China, the approach revealed an average PM_2.5 reduction of 19.0 ± 17.5 μg/m³ during the CSF holiday period in 2025, with fireworks accounting for ≥35 % of first-day severe deteriorated PM_2.5 air quality and up to 89 % in Baoding. The approach offers a robust tool for evaluating holiday emissions and guiding air quality interventions.