New plume regression technique expands insight into real-world vehicle emissions
Tested in York and Milan, this newly developed methodology provides a more detailed picture of vehicle exhaust pollutants and offers cities the opportunity to make data-driven decisions to improve air quality and reduce pollution.
In a recently published paper in Environmental Science and Technology, researchers at the University of York and the International Council on Clean Transportation (ICCT), a TRUE partner, have developed a promising new emissions measurement technique to help cities understand real-world vehicle pollutants and improve local air quality.
Known as plume regression, this new adapted analysis methodology advances the traditional point sampling technique to deliver clearer and more precise data on the contribution of specific vehicle types on urban air pollution. The new technique not only greatly simplifies point sampling, but also provides valuable new information.
Traditional point sampling uses roadside equipment to capture vehicle exhaust plumes as they pass fast-response instruments. The technique has already demonstrated its versatility in various cities and helped to measure a broader spectrum of pollutants which traditional remote sensing may not provide. For example, in the study researchers used point sampling in Milan, capturing emission factors for pollutants such as nitrogen oxides (NOx), and also particulate number (PN), black carbon (BC), and several volatile organic compounds (VOCs). However, point sampling faces practical limitations, particularly at busier locations where multiple plumes overlap, making it difficult to isolate emissions from individual vehicles.
To address the overlapping plume issue, researchers developed plume regression, an approach which applies regression analysis to disaggregate data from overlapping plumes, identifying and quantifying pollutant contributions from individual vehicle types.
When tested in York and Milan, plume regression showed strong alignment with remote sensing data, validating its reliability. By breaking down roadside pollutant concentrations by vehicle type, cities can better understand which vehicles—and fuel types—are the largest contributors to pollutants like NOx.
This new approach could significantly enhance the functionality of the many existing roadside stations that are used to monitor air quality in cities around the world, providing regulators with new capabilities to measure one of the most important sources, vehicle emissions. The method would require a low-invasive upgrade that integrates well with the technology already deployed across cities, utilizing fast-response instruments and adding CO₂ measurements to derive emission factor estimates.
As evidenced in many TRUE studies, diesel vehicles, particularly older models, emerged as the primary source of roadside NOx emissions as researchers tested the technique, highlighting once more the issue of excess emissions from these vehicles across Europe. As investigations reignite into the lasting effects of the Dieselgate scandal, which served as a key impetus for the founding of the TRUE Initiative, many vehicles may yet face recalls, particularly in the UK.
By providing near-real-time data on major pollution sources from on-road vehicles, plume regression would enable cities to enhance existing air quality stations to obtain a comprehensive picture of on-road emissions, identify high-emitters, and develop more effective air quality policies for their residents.
The methodology behind plume regression is described in more detail in the paper, “An Ambient Measurement Technique for Vehicle Emission Quantification and Concentration Source Apportionment,” and simplified in a complementary blog by the ICCT.
