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A novel method for comparing passenger car fleets and identifying high-chance gross emitting vehicles using kerbside remote sensing data
- Christopher E. Rushton, James E. Tate, Simon P. Shepherd
- Science of the total environment 2021 v.750 pp. 142088
- automobiles, data collection, environment, gasoline, markets, pollutants
- The quantification and comparison of NOX emission from in-situ car fleets, and identification of the highest emitters is an ongoing challenge. This challenge will become more important as new and increasingly complex emissions removal systems penetrate the market. We combine real-world data with new-to-the-field statistical methods to describe fleet-scale emissions behaviours and identify candidate gross-emitter vehicles.19,605 passenger cars were observed using a Remote Sensing Device across Aberdeen in 2015. Of these, 736 were Euro 6 Passenger Cars. The distribution of observed pollutant per unit of fuel burnt ratios for most fuel type and Euro standards followed an asymmetrical shape best characterised by the Gumbel distribution. The Gumbel distribution approach was not able to fully replicate the distribution of measurements of petrol or Euro 6 diesel cars due to the presence of a subset of high-emitting outliers, ranging from the 13ᵗʰ percentile for Euro 3 petrol to the 2ⁿᵈ percentile for Euro 6 petrol, with Euro 6 diesel having a 5ᵗʰ percentile outlier value. No outlier fraction was observed for pre-Euro 6 diesels.The off-model fractions resembled Gumbel distributed data and in some cases could be modelled as a separate distribution with the fleet behaving as a superposition of them. It is shown that VSP was not directly linked to this behaviour and it is hypothesised that it is caused by the emissions control systems operating sub-optimally. The reasons for sub-optimal operation are beyond the scope of this paper but may be linked to air-fuel mixture sensors, cold-start running and deterioration of the catalytic converter. Larger data-sets with more Euro 6 passenger cars are required to fully test this. Application of this methodology to larger data sets from more widely deployed remote sensing devices will allow observers to identify potentially problematic vehicles for further investigation into their emission control systems.