Vehicles' emissions produce a significant share of cities' air pollution, with a substantial impact on the environment and human health. Traditional emission estimation methods use remote sensing stations, missing vehicles' full driving cycle, or focus on a few vehicles. This study uses GPS traces and a microscopic model to analyse the emissions of four air pollutants from thousands of vehicles in three European cities. We discover 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 way more effective than those limiting circulation based on a non-informed choice of vehicles. Our study applies to any city and may contribute to shaping the discussion on how to measure emissions with digital data.

Improving vehicles' emissions reduction policies by targeting gross polluters

Nanni M;Pappalardo L
2021

Abstract

Vehicles' emissions produce a significant share of cities' air pollution, with a substantial impact on the environment and human health. Traditional emission estimation methods use remote sensing stations, missing vehicles' full driving cycle, or focus on a few vehicles. This study uses GPS traces and a microscopic model to analyse the emissions of four air pollutants from thousands of vehicles in three European cities. We discover 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 way more effective than those limiting circulation based on a non-informed choice of vehicles. Our study applies to any city and may contribute to shaping the discussion on how to measure emissions with digital data.
2021
Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" - ISTI
Human mobility
Computational social science
AI for social good
Applied data science
Data science
Sustainable development goals
File in questo prodotto:
File Dimensione Formato  
prod_456583-doc_176745.pdf

accesso aperto

Descrizione: Improving vehicles' emissions reduction policies by targeting gross polluters
Dimensione 11.26 MB
Formato Adobe PDF
11.26 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/397466
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact