Mobile monitoring reveals congestion penalty for vehicle emissions in London

TY - JOUR

T1 - Mobile monitoring reveals congestion penalty for vehicle emissions in London

AU - Wilde, Shona E.

AU - Padilla, Lauren E.

AU - Farren, Naomi J.

AU - Alvarez, Ramón A.

AU - Wilson, Samuel

AU - Lee, James D.

AU - Wagner, Rebecca L.

AU - Slater, Greg

AU - Peters, Daniel

AU - Carslaw, David C.

N1 - Funding Information: We acknowledge funding from Environmental Defense Fund, whose work is supported by gifts from Signe Ostby, Scott Cook, Valhalla Foundation, and VoLo Foundation. The authors thank Alkesh Solanki, Danny Vickers and Elizabeth Fonseca for help arranging and coordinating the logistics of the mobile monitoring platform. The authors acknowledge help from Katie Read at the University of York regarding instrument calibration and maintenance. Rebecca Wagner was supported by the NERC Panorama Doctoral Training Partnership (grant no. NE/S007458/1 ). Publisher Copyright: © 2024 The Authors

PY - 2024/2/14

Y1 - 2024/2/14

N2 - Mobile air pollution measurements have the potential to provide a wide range of insights into emission sources and air pollution exposure. The analysis of mobile data is, however, highly challenging. In this work we develop a new regression-based framework for the analysis of mobile data with the aim of improving the potential to draw inferences from such measurements. A quantile regression approach is adopted to provide new insight into the distribution of NOx and CO emissions in Central and Outer London. We quantify the emissions intensity of NOx and CO (ΔNOx/ΔCO2 and ΔCO/ΔCO2) at different quantile levels (τ) to demonstrate how transient high-emission events can be examined in parallel to the average emission characteristics. We observed a clear difference in the emissions behaviour between both locations. On average, the median (τ = 0.5) ΔNOx/ΔCO2 in Central London was 2x higher than Outer London, despite the stringent emission standards imposed throughout the Ultra Low Emissions Zone. A comprehensive vehicle emission remote sensing data set (n ≈ 700,000) is used to put the results into context, providing evidence of vehicle behaviour which is indicative of poorly controlled emissions, equivalent to high-emitting classes of older vehicles. Our analysis suggests the coupling of a diesel-dominated fleet with persistently congested conditions, under which the operation of emissions after-treatment technology is non-optimal, leads to increased NOx emissions.

AB - Mobile air pollution measurements have the potential to provide a wide range of insights into emission sources and air pollution exposure. The analysis of mobile data is, however, highly challenging. In this work we develop a new regression-based framework for the analysis of mobile data with the aim of improving the potential to draw inferences from such measurements. A quantile regression approach is adopted to provide new insight into the distribution of NOx and CO emissions in Central and Outer London. We quantify the emissions intensity of NOx and CO (ΔNOx/ΔCO2 and ΔCO/ΔCO2) at different quantile levels (τ) to demonstrate how transient high-emission events can be examined in parallel to the average emission characteristics. We observed a clear difference in the emissions behaviour between both locations. On average, the median (τ = 0.5) ΔNOx/ΔCO2 in Central London was 2x higher than Outer London, despite the stringent emission standards imposed throughout the Ultra Low Emissions Zone. A comprehensive vehicle emission remote sensing data set (n ≈ 700,000) is used to put the results into context, providing evidence of vehicle behaviour which is indicative of poorly controlled emissions, equivalent to high-emitting classes of older vehicles. Our analysis suggests the coupling of a diesel-dominated fleet with persistently congested conditions, under which the operation of emissions after-treatment technology is non-optimal, leads to increased NOx emissions.

KW - Congestion

KW - Emissions ratio

KW - Mobile monitoring

KW - Quantile regression

KW - Vehicle emissions

UR - http://www.scopus.com/inward/record.url?scp=85185300913&partnerID=8YFLogxK

U2 - 10.1016/j.aeaoa.2024.100241

DO - 10.1016/j.aeaoa.2024.100241

M3 - Article

AN - SCOPUS:85185300913

SN - 2590-1621

VL - 21

JO - Atmospheric Environment: X

JF - Atmospheric Environment: X

M1 - 100241

ER -