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Gross polluters and vehicle emissions reduction

Nature Sustainability volume 5, pages 699–707 (2022)Cite this article

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Abstract

Vehicle emissions produce an important share of a city’s air pollution, with a substantial impact on the environment and human health. Traditional emission estimation methods use remote sensing stations, missing the full driving cycle of vehicles, or focus on a few vehicles. We have used GPS traces and a microscopic model to analyse the emissions of four air pollutants from thousands of private vehicles in three European cities. We found that the emissions across the vehicles and roads are well approximated by heavy-tailed distributions and thus discovered the existence of gross polluters, vehicles responsible for the greatest quantity of emissions, and grossly polluted roads, which suffer the greatest amount of emissions. Our simulations show that emissions reduction policies targeting gross polluters are far more effective than those limiting circulation based on an uninformed choice of vehicles. Our study contributes to shaping the discussion on how to measure emissions with digital data.

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Fig. 1: Computation of emissions from GPS trajectories.
Fig. 2: Distributions of emissions.
Fig. 3: Simulation of vehicle electrification.

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Data availability

The data that support the findings of this study are not publicly available due to privacy restrictions and were used under licence for the current study. Aggregated source data for figures are available from the authors upon reasonable request.

Code availability

The Python code to reproduce the analyses in the study from public GPS data (taxi trips) is publicly available in GitHub (https://github.com/matteoboh/mobility_emissions) and the Zenodo repository66.

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Acknowledgements

This work was partially funded by the Horizon 2020 projects Track & Know (grant agreement no. 780754, M.N.), SoBigData++ (grant agreement no. 871042, M.B.) and HumanE-AI-Net (grant agreement no. 952026, L.P.). We thank G. Cornacchia, V. Voukelatou, M. Luca and G. Mauro for their useful suggestions, D. Fadda for his precious support in data visualization and F. Totti for his inspiration.

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Authors and Affiliations

  1. Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy

    Matteo Böhm

  2. Institute of Information Science and Technologies, National Research Council, Pisa, Italy

    Matteo Böhm, Mirco Nanni & Luca Pappalardo

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  1. Matteo Böhm
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  2. Mirco Nanni
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  3. Luca Pappalardo
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Contributions

M.B. designed the study, performed the data pre-processing and cleaning, developed the code for and performed the statistical analyses, experiments and simulations, created the plots, contributed to the interpretation of the results and wrote the paper. M.N. provided the data, contributed to the work methodology and interpretation of the results, and revised the paper. L.P. developed the concept and designed the study, contributed to the work methodology and interpretation of the results, wrote the paper and coordinated the study. All authors read, edited and approved the final version of the paper.

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Correspondence to Matteo Böhm or Luca Pappalardo.

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The authors declare no competing interests.

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Nature Sustainability thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Step-by-step procedure for the computation of emissions and results analyses.

The left column describes the steps followed starting from the data, passing through the data processing, and ending with the analyses performed. The central column shows a schema of what happens in each step. The right column shows some numbers and results in support of the central column. The heatmap in step 1 is plotted with the Python library scikit-mobility51. The small road network in step 7 is plotted with the Python library OSMnx63. Car icons from the Noun Project (thenounproject.com).

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Supplementary Figs. 1–32, Tables 1–13, Notes 1–8 and references.

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Böhm, M., Nanni, M. & Pappalardo, L. Gross polluters and vehicle emissions reduction. Nat Sustain 5, 699–707 (2022). https://doi.org/10.1038/s41893-022-00903-x

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