Daily estimation of NO2 concentrations using digital tachograph data

Environ Monit Assess. 2024 Oct 28;196(11):1109. doi: 10.1007/s10661-024-13190-0.

Abstract

Traffic information is crucial for estimating NO2 concentrations, but it is static and limited in predicting constantly changing NO2 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 NO2 concentrations. It underscores the advantage of DTG data in predicting daily NO2 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.

Keywords: DTG data; Daily estimation; Land use regression (LUR); NO2 concentrations; Spatial–temporal variation.

MeSH terms

  • Air Pollutants* / analysis
  • Air Pollution* / statistics & numerical data
  • Environmental Monitoring* / methods
  • Nitrogen Dioxide* / analysis
  • Vehicle Emissions / analysis

Substances

  • Nitrogen Dioxide
  • Air Pollutants
  • Vehicle Emissions