Air pollution is one of the world’s biggest environmental and health challenges, and road transport is a major contributor. Nitrogen oxides from vehicle emissions are particularly harmful, affecting both air quality and public health. Understanding the sources of these pollutants is essential for developing better policies and technologies to improve urban air quality.

Now, researchers at the National Centre for Atmospheric Science (NCAS) and the University of York have developed a groundbreaking new technique – plume regression – to more accurately measure vehicle emissions. This advancement is expected to provide policymakers with better information, leading to improved air quality measures in cities across the UK and beyond.

Traditionally, vehicle emissions are measured using curbside sensors and point sampling, where scientific instruments placed at the roadside capture exhaust pollutants as vehicles pass. While effective, these methods face challenges in busy areas where exhaust plumes from multiple vehicles mix together, making it difficult to attribute emissions to specific vehicles.

The importance of accurate emissions measurement became evident after the Volkswagen emissions scandal, known as “Dieselgate,” a decade ago. It revealed that some vehicles emitted far higher levels of nitrogen oxides in real-world conditions than during laboratory testing.

Since 2015, measurement technologies like curbside sensors and point sampling have been developed further to ensure accurate vehicle emissions monitoring outside of controlled laboratory environments. While useful for tracking common pollutants, these methods provide limited information about specific vehicles.

Plume regression builds on these existing techniques, offering a more comprehensive understanding of vehicle emissions.

Plume regression involves locating highly accurate, portable and fast response instruments at the roadside that measure ammonia, nitrogen oxides, and carbon dioxide emissions, and then calculating the quantity and concentration of the gases.

The plume regression approach starts with identifying when a vehicle passes the instruments and tracks how the gases begin to peak and then subside. There tends to be a rapid rise in concentration followed by a slower falloff, and this can be seen in the profile – or shape – of the plume on the instruments’ data graphs.

An average plume profile can be generated for different vehicle types, and during this process 11 different vehicle categories were identified and applied to the emissions data being collected – enabling the research team to identify pollution emitted from multiple vehicles at once.

Dr Marvin Shaw outlines the key advantage of the technique is its ability to pinpoint emissions sources that spotlights the vehicle types that contribute the most:

“During our initial tests of the plume regression method, in the cities of York and Milan, we found strong alignment with existing remote sensing data, validating its accuracy. We found that emissions of ammonia are generally very well controlled from diesel vehicles, including those with selective catalytic reduction systems that use ammonia to reduce emissions of nitrogen oxides. Petrol passenger cars are shown to be the dominant contributor of ammonia emissions, which increase with vehicle mileage.”

As cities worldwide look for ways to combat air pollution, more precise data and advancements like plume regression will play a vital role in shaping air quality regulations that aim to create cleaner, healthier urban environments.

The plume regression technique is detailed in a recent publication in Environmental Science & Technology, with credits to the University of York, the International Council on Clean Transportation, and TRUE partnership.