Urban–Rural Disparities in Air Quality Responses to Traffic Changes in a Megacity of China Revealed Using Machine Learning
Yifan Wen, Zihang Zhou, Shaojun Zhang, Timothy J. Wallington, Wei Shen, Qinwen Tan, Ye Deng, Ye Wu
Abstract
Assessing the disparities of urban–rural air quality response to changes in emissions is essential for the development of effective air pollution mitigation strategies in megacities. However, meteorology and nonlinear atmospheric chemistry complicate the determination of emission–air quality responses. Here, we established a machine learning (ML)-based air quality simulator based on hourly air quality, meteorology, traffic activity, and other relevant indicators for Chengdu, a megacity in Southwest China. The ML-based simulator exhibits high fidelity in reproducing hourly pollutant concentrations (with cross validation R2 > 0.6 for NO2, O3, and PM2.5). The results indicated similar trends of meteorological impacts but various effects from traffic activities on air quality between urban and rural areas. Truck restriction policies have significantly reduced the impacts of truck traffic on air quality in the urban area. Repartitioning between NO2 and O3 is observed in both urban and rural areas, indicating a VOC-limited regime in winter across Chengdu. Total gaseous oxidant (i.e., OX = NO2 + O3) and PM2.5 concentrations are more sensitive to changes in nontruck (which emit more VOC) traffic than truck (which emit more NOX) traffic. We suggest that effective mitigation policies of OX should be developed according to local features to improve and alleviate winter haze simultaneously.