Evaluating Policy Interventions for Air Quality During a National Sports Event with Machine Learning and Causal Framework

Short-term control measures are often implemented during major events to improve air quality and protect public health. In preparation for the 11th National Traditional Games of Ethnic Minorities of China (denoted as “NMG”), held from 8 to 16 September 2019 in Zhengzhou, China, the authorities introduced several air pollution control measures, including traffic restrictions and dust control. In the study presented herein, we applied automated machine learning-based weather normalisation combined with an augmented synthetic control method (ASCM) to evaluate the effectiveness of these interventions. Our results show that the impacts of the NMG control measures were not uniform, varying significantly across pollutants and monitoring stations. On average, nitrogen dioxide (NO₂) concentrations decreased by 8.6% and those of coarse particles (PM₁₀) decreased by 3.0%. However, the interventions had little overall effect on fine particles (PM_2.5), despite clear reductions observed at the traffic site, where NO₂ and PM_2.5 levels decreased by 7.2 and 5.2 μg m⁻³, respectively. These reductions accounted for 56.3% of the NMG policy’s effect on NO₂ concentration and 73.2% of its effect on PM_2.5 concentration at the traffic site. Notably, the control measures led to an increase in ozone (O₃) concentrations. Our results demonstrate the moderate effect of the short-term NMG intervention, emphasising the necessity for holistic strategies that address pollutant interactions, such as nitrogen oxides (NO_X) and volatile organic compounds (VOCs), as well as location-specific variability to achieve sustained air quality improvements.