Air pollution is a chronic problem in large cities worldwide and awareness is rising as the long-term health implications become clearer. Vehicular traffic has been identified as a major contributor to poor air quality. In a lot of cities the publicly available air quality measurements and forecasts are coarse-grained both in space and time. However, in general, real-time traffic intensity data is openly available in various forms and is fine-grained. In this paper, we present an in-depth study of pollution sensor measurements combined with traffic data from Mexico City. We analyse and model the relationship between traffic intensity and air quality with the aim to provide hyper-local, dynamic air quality forecasts. We developed an innovative method to represent traffic intensities by transforming simple colour-coded traffic maps into concentric ring-based descriptions, enabling improved characterisation of traffic conditions. Using Partial Least Squares Regression, we predict pollution levels based on these newly defined traffic intensities. The model was optimised with various training samples to achieve the best predictive performance and gain insights into the relationship between pollutants and traffic. The workflow we have designed is straightforward and adaptable to other contexts, like other cities beyond the specifics of our dataset.

翻译：空气污染是全球大城市的长期问题，随着长期健康影响日益明确，公众意识也在不断提高。机动车交通已被确定为空气质量恶化的主要因素。在许多城市，公开可用的空气质量测量与预报在空间和时间维度上均较为粗粒度。然而，实时交通强度数据通常以多种形式公开可用，且具有细粒度特征。本文结合墨西哥城的交通数据，对污染传感器测量值进行了深入研究。我们通过分析与建模交通强度与空气质量之间的关系，旨在提供超本地化的动态空气质量预报。我们开发了一种创新方法，通过将简单的彩色编码交通图转换为基于同心环的描述来表征交通强度，从而实现对交通状况的改进刻画。利用偏最小二乘回归方法，我们基于这些新定义的交通强度预测污染水平。该模型通过多样化的训练样本进行优化，以获得最佳预测性能并深入理解污染物与交通之间的关联。我们设计的工作流程简洁明了，可适应其他场景（如不同城市），无需受限于本数据集的具体特性。