The (un)bearable heaviness of vehicle emissions: emergence of scaling and reduction policies
Among the Sustainable Development Goals to be reached by 2030, the United Nations pose an urgent call for action to reduce “the adverse per capita environmental impact of cities, paying particular attention to air quality”. This post shows that we can succeed only if we make informed decisions.
With 55% of the world population residing in urban settlements (and a predicted 68% by 2050), about 100 of the highest emitting urban areas accounting for 18% of the global carbon footprint , and urban transport being responsible for 8% of the global carbon dioxide (CO2) emissions , urban sustainability and transport emissions are a major concern for all cities around the world.
Besides GHG emissions, the transport sector emits severely dangerous pollutants to human health. While viruses such as Covid-19 are hopefully related to temporary emergencies, ozone or particulate matter (PM) are our lungs' permanent hosts, causing 7.8 million years of life lost globally in 2015 . The quantity and nature of what citizens breathe every day are related to factors that vary in time and space.
In a recent paper published in Nature Sustainability, we use GPS data describing the trips of private vehicles and a microscopic emissions model to estimate the emissions coming from private urban transport in London, Rome, and Florence. We find that considerable heterogeneity characterises their distribution across both the vehicles and roads.
In all three cities, 10% of the streets host nearly 60% of the vehicles' emissions, and 10% of the vehicles are responsible for more than half the overall vehicle emissions. Translating these results into a well-known inequality measure, we find that the distributions of emissions across the roads and vehicles are associated with Gini coefficients higher than 0.64 and 0.55, respectively. This heterogeneity in the vehicles' emissions' distributions, well described by heavy-tailed models such as truncated power laws (Fig. 1), is fundamental to designing effective solutions to avoid the FFP2 face mask to stay in our daily urban routine.
Given the observed emissions distribution, if only 1% of the most polluting vehicles (the gross polluters) shifted to electric engines, the CO2 emissions reduction would be the same as the reduction obtained by the electrification of 10% of the vehicles chosen at random. We find similar results simulating a home working scenario aimed at eliminating the commuting trips between work and home of a particular share of the vehicles' fleet: while the CO2 reduction grows linearly with the share of home working vehicles if chosen at random, it grows much faster when these vehicles are selected starting from the gross polluters.
Our results show the importance of making informed decisions: for example, emissions reduction policies based on the vehicles' number plates, still adopted in many cities, are way less effective than reduction policies that take focused choices, e.g., banning the circulation of old highly-polluting vehicles or incentives to electric vehicles conceived for those who emit the most.
But can we identify any mobility behaviour adopted with our vehicles that causes more emissions? Our study shows that yes, we can. People who move more predictably - for example, those travelling each day with their car from home to work and back - are more likely to be gross polluters than people moving more erratically.
In conclusion, we need to know the phenomenon in depth if we want vehicle emissions reduction policies to be effective and, thus, to have a tangible positive impact on our cities: indeed, only through informed choices we could know the target to hit, thus obtaining the best outcomes. We hope that studies like ours could help in this. In the meantime, we better save our face masks.
This study was supported/funded by the European project SoBigData++, Sapienza University of Rome, and the ISTI-CNR of Pisa. More details can be found in our article “Gross polluters and vehicle emissions reduction”, Nature Sustainability (2022), https://www.nature.com/
 Moran, D. et al., 2018: Carbon footprints of 13 000 cities. Environ. Res. Lett., 13(6), 064041, 27 doi:10.1088/1748-9326/aac72a
 Lwasa, S., K.C. Seto, X. Bai, H. Blanco, K.R. Gurney, S. Kilkiş, O. Lucon, J. Murakami, J. Pan, A. Sharifi, Y. Yamagata, 2022: Urban systems and other settlements. In IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA. doi: 10.1017/9781009157926.010
 Anenberg, S., J. Miller, D. Henze, and R. Minjares, 2019: A global snapshot of the air pollution-related health impacts of transportation sector emissions in 2010 and 2015. International Council on Clean Transportation, 55. Available here.