Gross polluters and vehicles' emissions reduction

TL;DR

This study uses GPS data and microscopic modeling to identify gross polluters among vehicles, revealing that targeted policies on these vehicles significantly reduce emissions more effectively than broad restrictions.

Contribution

Introduces a novel approach combining GPS traces and microscopic modeling to identify gross polluters and grossly polluted roads, informing more effective emission reduction policies.

Findings

Emissions follow heavy-tailed distributions among vehicles and roads.

Gross polluters contribute disproportionately to total emissions.

Targeted policies on gross polluters outperform broad circulation restrictions.

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. We use GPS traces and a microscopic model to analyse the emissions of four air pollutants from thousands of private vehicles in three European cities. We find that the emissions across the vehicles and roads are well approximated by heavy-tailed distributions and thus 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 contributes to shaping the discussion on how to measure emissions with digital data.