Daily estimation of NO2 concentrations using digital tachograph data

• Published in: Environmental monitoring and assessment 2024-11, Vol.196 (11), p.1109, Article 1109
• Main Authors: Joo, Yoohyung, Joo, Minsoo, Nguyen, Minh Hieu, Hong, Jiwan, Kim, Changsoo, Wong, Man Sing, Heo, Joon
• Format: Article
• Language: English
• Subjects: Air pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Big Data; Commercial vehicles; Earth and Environmental Science; Ecology; Ecotoxicology; Environment; Environmental Management; Estimation; Fluctuations; Land use; Monitoring/Environmental Analysis; Nitrogen dioxide; Predictions; Real time; Regression models; Spatiotemporal data; Tachographs; Traffic
• ISSN: 0167-6369; 1573-2959; 1573-2959
• DOI: 10.1007/s10661-024-13190-0

Traffic information is crucial for estimating NO 2 concentrations, but it is static and limited in predicting constantly changing NO 2 levels. To overcome these challenges, this study utilized real-time spatial big data to capture both the spatial and temporal fluctuations in traffic. Digital tachograph (DTG) data, sourced from digital devices in all commercial vehicles, are employed to construct a DTG land use regression (LUR) model, and its performance is compared with that of a non-DTG-LUR model. The DTG-LUR model exhibits superior performance, with an explanatory power of 0.46, in contrast to the 0.36 of the non-DTG model. This significant improvement stems from the spatially and temporally dynamic DTG variables such as cargo traffic. This study introduces a novel approach for incorporating DTG data in correlating with NO 2 concentrations. It underscores the advantage of DTG data in predicting daily NO 2 fluctuations at a precise 200-m grid, which is not feasible with conventional data. The findings of the study highlight the immense potential of spatial big data for fine-grained analyses, which could enable hourly predictions of air pollution.