Abstract. Acute short-term exposure to extremely high PM2.5 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 quantify PM2.5 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 28 major cities in the Beijing-Tianjin-Hebei and surrounding region, one of the most polluted areas in China, the approach revealed an average PM2.5 reduction of 19.0 ± 17.5 µg m−3 during the full 2025 CSF holiday period. Despite this regional mean decrease, short-lived but extremely high PM2.5 peaks were observed in several cities during the peak fireworks window, with fireworks contributing ≥ 35 % of first-day severe PM2.5 deterioration and up to 89 % in the city of Baoding. This approach offers a robust tool for evaluating holiday emissions and guiding air quality interventions.
Li et al. (Thu,) studied this question.