A Proxy Model for Traffic Related Air Pollution Indicators Based on Traffic Count

Abstract

Understanding how traffic contributes to air pollution, especially in urban areas, is essential for designing effective strategies to reduce air pollution emissions. This study examines the hourly association between traffic volume and concentrations of two air pollution indicators (NO2 and PM10) using high-resolution data from two monitoring stations in Helsinki. A Prophet time series model was applied to forecast hourly traffic trends for 2024, which were then compared to yearly average NO2 and PM10 concentrations. Polynomial regression and cross-correlation analyses were used to capture temporal patterns and assess the strength and timing of the relationship. The results show a strong alignment between traffic and NO2 and PM10 concentrations, particularly at the traffic-heavy measuring site (Mäkelänkatu supersite), with minimal time lag observed. Root mean square error (RMSE) and polynomial fit comparisons confirmed the predictive value of traffic trends in estimating the behavior of NO2 and PM10 concentrations. These findings support the use of traffic-based proxy models as practical tools for real-time air pollution assessment and for informing targeted urban air quality interventions.