RTCA Prof David Carslaw - A new approach to making ambient measurements to quantify both vehicle emissions and concentration source apportionment

Transcript

1. A new approach to making ambient measurements to quantify both vehicle emissions and concentration source apportionment Naomi Farren1, Samuel Wilson1, Marvin Shaw1, Yoann Bernard3, Kaylin Lee3 and Mallery Crowe3 and David Carslaw1,2 ROUTES TO CLEAN AIR, BIRMINGHAM, 21–22 OCTOBER 2026 1 Wolfson Atmospheric Chemistry Laboratories, University of York 2 WSP 3 The International Council on Clean Transportation
2. 1. Introduction 1.1. Vehicle Emissions 1.2. Remote Sensing 2. Point Sampling 2.1. Emission Factors 2.2. Source Apportionment 3. Further Insights 3.1. Influence of Vehicle Form 3.2. EVs 4. Concluding remarks
3. Vehicle exhaust emissions • Tricky to quantify • Millions of sources that move in space and time • Different fuels, types, manufacturers, after-treatment technologies, ages … • Environmental factors • Ways to measure emissions from individual vehicles • Rolling road - laboratory • Instrument individual vehicles – PEMS • Remote sensing Hard to get away from the need to measure many vehicles
4. Vehicle emission remote sensing • Cross-road or top-down spectroscopic technique • Measures individual vehicle plumes and pollutant ratios e.g. NOx/CO2 • Large sample sizes – 1000s a day • Fuel-based emission factors • ANPR camera - highly detailed vehicle information • Some limitations • Short sampling time ~ 0.5s • Best for species such as NO, NO2, CO2 but not best – or even possible for particles and many other species
5. Point Sampling The concept where continuous fast response measurements are made of dispersing vehicle plumes at kerbside • Could be a useful approach, but some challenges • Extend what can be measured beyond remote sensing e.g. PN, BC, individual VOCs – best instrument for the job • Aim to extract individual vehicle plumes – but small gaps between vehicles can be troublesome • ‘Goldilocks’ location – not too busy but busy enough to measure a useful number of vehicles • Dispersion of plumes – might not measure much, or anything at all! What are the wider benefits of measuring at high time resolution?
6. 15 minutes of 1-second data Trying to isolate plumes results in ‘throwing away’ at least 70% of the data
7. A different reference frame • We always know when a vehicle passes • Don’t try and isolate individual plumes • Look at average plume shape when there is at least a 30 second gap between vehicles • Essentially provides a weighting / probability on the chances of detecting a plume and how much of it – on average
8. Construct a plume time series Method Overview • • • Requires individual vehicle information Add a normalised plume each time a vehicle of a particular type passes Robust regression: what’s the optimum contribution by each vehicle type that best explains concentrations?
9. Comparison with remote sensing • Can provide emissions ratios to CO2 and hence fuel-based emission factors • Plot shows good agreement with remote sensing • Regression also provides an estimate of the standard error / confidence interval, which is lower than remote sensing for the same sample size (±13 vs. ±23 %) • More information value: remote sensing is 0.5 s and a plume is ~ 10 to 20 s • Use all the data – not possible with remote sensing • Much simpler than trying to isolate individual plumes
10. Ambient concentration source apportionment • New capability is a direct measure of concentration source apportionment • For example, how much roadside NO2 concentration can be attributed to Euro 5 diesel cars? • This information would normally require an air quality model to predict concentrations
11. Some other questions about roadside concentrations • Close to roads, near-field dispersion will be important • How do the physical characteristics of a vehicle affect near-road concentrations? • Proximity of source and location of exhaust • Left – right – centre – split • Vehicle-induced turbulence
12. Exhaust location – use CO2 concentration as a tracer Key Results Broadly in line with expectations — bigger vehicles are associated with higher roadside CO2, but … • • HGVs tend to be associated with less CO2 than buses despite higher emissions on average • Diesel vans are associated with less CO2 than diesel cars despite higher emissions • Gasoline hybrids reflect time spent on batteries and emitting no CO2 • EVs have negative CO2?
13. Passenger cars in detail Key Results • Systematic differences in impact of exhaust location • Right-hand exhausts associated with ≈ 40% less CO2 at kerbside than left-hand exhausts for diesel, gasoline and hybrid • Most diesel cars have left-hand exhaust • Most gasoline cars have left-hand exhaust • Nearly all gasoline hybrid cars have right-hand exhausts Implications for concentrations of pollutants such as NO2
14. Influence of vehicle-induced turbulence (VIT) • Two competing effects • Higher levels of VIT associated with higher engine loads and higher emissions (concentrations) in g s−1 • … but higher VIT associated with increased plume dilution and lower concentrations • Fossil fuelled cars superpose these two effects • Electric vehicles help reveal VIT effect
15. Final thoughts • There are benefits to making fast-response measurements • Especially when coupled with individual vehicle information • More flexibility (than remote sensing) in instruments that can be used • Applied so far to CO2, NOx, NO2, NH3, particle number, black carbon and ~10 individual VOCs • Other problems such as non-exhaust vehicle emissions? • Potentially could adapt existing ambient measurement sites to provide disaggregated emissions and concentration source apportionment – a different sort of super site • Doing more research into near-field influences such as vehicle-generated turbulence
16. Thank you for listening And thanks to: Thanks to Dr Naomi Farren, Sam Wilson and Dr Marvin Shaw at the University of York The International Council on Clean Transportation (ICCT) for funding