Air pollution threatens worldwide human health, ecosystems, and climate change. Transportation is a major contributor to air pollution. However, the link between transportation and air pollution is intricate and influenced by multiple elements. This study employs spatiotemporal causal convolutional networks to predict air pollutants by utilizing traffic and meteorological data as inputs. Using Seoul, South Korea as a case study, a dataset of 25 regional air monitoring stations is used for prediction. The results confirm that wind speed and direction significantly impact PM_2.5 dispersion, while humidity positively correlates with PM_2.5 concentrations and temperature shows an inverse relationship. Additionally, vehicular traffic and subway passenger numbers exhibit positive associations, attributed to automotive emissions, road dust resuspension, and heightened human activity near subway stations, respectively. The model can potentially be utilized in a real-time air pollution tracking system to facilitate prompt interventions and reduce the harmful effects of air contamination on public health.

空气污染威胁着全世界人类健康、生态系统和气候变化。交通运输是造成空气污染的主要原因。然而，交通运输与空气污染之间的联系错综复杂，并受到多种因素的影响。本研究采用时空因果卷积网络，利用交通和气象数据作为输入来预测空气污染物。以韩国首尔为例，使用 25 个区域空气监测站的数据集进行预测。结果证实，风速和风向显着影响 PM _2.5扩散，而湿度与 PM _2.5浓度呈正相关，而温度则呈反比关系。此外，车辆交通和地铁乘客数量呈现出正相关关系，分别归因于汽车排放、道路灰尘再悬浮和地铁站附近人类活动的增加。该模型有可能用于实时空气污染跟踪系统，以促进及时干预并减少空气污染对公众健康的有害影响。