Toward a Causal PM2.5 Concentration Forecasting by Inferencing from Local Vehicle Tracking with a Low-Cost End-to-End Sensor System

This paper introduces a pioneering, end-to-end system designed for the accurate estimation of PM2.5 concentrations, which uniquely integrates custom-designed hardware with advanced computational algorithms. A central innovation of this work is the development of a seamless data pipeline that connects real-time vehicle tracking with sophisticated air quality prediction, offering a novel and comprehensive solution for urban environmental monitoring. The system employs a cost-effective, custom-built sensor package, including PMS7003 and BME280 sensors alongside a 5MP camera, with a bespoke hardware design specifically engineered to enhance operational stability. For the estimation component, a rigorous comparative analysis was conducted, demonstrating that the Cubist regression model significantly outperforms other contemporary machine learning and traditional mathematical models; this success is attributed to its superior ability to model the complex, nonlinear relationship between observed traffic density and ambient PM2.5 levels. Furthermore, a streamlined vehicle counting algorithm, leveraging a fine-tuned YOLOv7 model, ensures robust and accurate traffic detection performance across various lighting and environmental conditions. This research successfully establishes an optimal PM2.5 estimation pipeline based on real-world vehicle counts, presenting an integrated framework for dynamic urban air quality analysis. Received: 30 August 2024 | Revised: 30 September 2025 | Accepted: 14 January 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in GitHub at https://github.com/comrang-altf4/PM2.5. Author Contribution Statement Chuong Dinh Le: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Hoang Viet Pham: Conceptualization, Methodology, Software, Investigation, Data curation, Writing – original draft, Visualization. Thinh Gia Tran: Conceptualization, Methodology, Software, Validation, Investigation, Writing – original draft, Visualization. An Dinh Le: Conceptualization, Methodology, Resources, Writing – review & editing, Supervision, Project administration. Anh-Duy Pham: Conceptualization, Methodology, Resources, Supervision, Project administration. Dat Thanh Vo: Software, Writing – original draft, Visualization. Hien Bich Vo: Resources, Supervision. Dzung Huy Han: Supervision.