Urban–Rural Disparities in Air Quality Responses to Traffic Changes in a Megacity of China Revealed Using Machine Learning

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.

Agrimonia: a dataset on livestock, meteorology and air quality in the Lombardy region, Italy

“…The Agrimonia dataset could be useful to other researchers, for example, in comparing urban air pollution and rural air quality 7 , 8 . Other uses of the dataset may move toward the study of different livestock management techniques and organic products 9 or for epidemiological studies, which aim to assess the impact of agricultural emissions on the mortality attributable to air pollution 10 .…”

Dynamic Traffic Data in Machine-Learning Air Quality Mapping Improves Environmental Justice Assessment

“…However, most of these methods are insufficient to address the dynamic and fine-scale traffic-induced air pollution patterns as they typically consider static geo-features (e.g., road length) in their frameworks . On-road traffic is recognized as one of the most important contributors to urban air pollution such as NO 2 and PM 2.5 , and a major source of exposure disparities in global megacities. ,, Racial/ethnic minorities and lower-income groups were reported to bear a higher risk of exposure to traffic-related air pollution in the U.S., , especially for those living close to busy freight corridors or ports . Moreover, the spatial heterogeneity and temporal dynamics of on-road traffic activities significantly contribute to the complexity of multiscale and time-varying air quality patterns in megacities.…”

“…2 Air pollution in metropolitan areas varies in complex patterns with multiple gradients, including city-level, urban-rural, and neighborhood-level disparities, as well as highly localized and dynamic spikes near major sources. 9 Fine-scale and continuous air quality mapping is thus required to characterize air pollution exposure patterns among residents, identify highly polluted areas and periods, and take targeted actions to combat inequalities through policies. 8 However, the full complexity of multiscale and time-varying air quality patterns in megacities is hard to track despite great advances in air quality measurements and modeling over the past few decades.…”

Revealing the Covariation of Atmospheric O2 and Pollutants in an Industrial Metropolis by Explainable Machine Learning

“…Machine learning (ML) models have been demonstrated to be a powerful tool for reconstructing, simulating, and predicting atmospheric pollution, including PM 2.5 , − O 3 , , NO x , , etc., outperforming finely designed chemical transport models . The use of ML models provides greater flexibility and efficiency when utilizing real-world data and is especially adept at revealing complex and hidden nonlinear correlations , that might not be easily identified using traditional physical models, providing new insights into the underlying mechanisms of the studied phenomena .…”

“…The SHapley Additive exPlanations (SHAP) framework, for instance, offers insights into the impact of a feature on model outcomes . ML models combined with explainable tools have been extensively used in various aspects of air pollution modeling, ,,, yet their application in reproducing variations in O 2 concentrations, particularly in urban settings, has been rather scant.…”