Investigating the background and local contribution of the oxidants in London and Bangkok.

M Anwar H Khan,Rayne Holland,Michael E Jenkin,Douglas Lowe,Amy Foulds,Carl J Percival,Sanjee Panditharatne,James C Matthews,Panida Navasumrit,Dudley E Shallcross

doi:10.1039/d0fd00086h

Abstract

The contribution of NOx emissions and background O3 to the sources and partitioning of the oxidants [OX (= O3 + NO2)] at the Marylebone Road site in London during the 2000s and 2010s has been investigated to see the impact of the control measures or technology changes inline with the London Mayor's Air Quality Strategy. The abatement of the pollution emissions has an impact on the trends of local and background oxidants, [OX]L and [OX]B, decreasing by 1.4% per year and 0.4% per year, respectively from 2000 to 2019. We also extend our study to three roadside sites (Din Daeng, Thonburi and Chokchai) in another megacity, Bangkok, to compare [OX]L and [OX]B and their behavioural changes with respect to the Marylebone Road site. [OX]L and [OX]B at the Marylebone Road site (0.21[NOx] and 32 ppbv) are comparable with the roadside sites of Thailand (0.12[NOx] to 0.26[NOx] and 29 to 32 ppbv). The seasonal variation of [OX]B levels displays a spring maximum for London, which is due to the higher northern hemispheric ozone baseline, but a maximum during the dry season is found for Bangkok which is likely due to regional-scale long-range transport from the Asian continent. The diurnal variations of [OX]L for both London and Bangkok roadside sites confirm the dominance of the oxidants from road transport emissions, which are found to be higher throughout the daytime. WRF-Chem-CRI model simulations of the distribution of [OX] showed that the model performed well for London background sites when predicting [OX] levels compared with the measured [OX] levels suggesting that the model is treating the chemistry of the oxidants correctly. However, there are large discrepancies for the model-measurement [OX] levels at the traffic site because of the difficulties in the modelling of [OX] at large road networks in megacities for the complex sub grid-scale dynamics that are taking place, both in terms of atmospheric processes and time-varying sources, such as traffic volumes. For roadside sites in Bangkok, the trend in changes of [OX] is predicted by the model correctly but overestimated in absolute magnitude. We suggest that this large deviation is likely to be due to discrepancies in the EDGAR emission inventory (emission overestimates) beyond the resolution of the model.

Highlights

Nitrogen dioxide (NO2) and ozone (O3) are key urban air pollutants with welldocumented public health impacts.[1]
[OX]L is believed to be mainly derived from primary emissions of NO2, at roadside and kerbside locations, such that the slope of the [OX] vs. [NOx] relationship provides an estimate of the volumetric fraction of NOx emitted as NO2.6,7 [OX]B provides a quanti cation of the background [O3] which would exist at the given location in the notional absence of NOx
In response to the Euro III control in the U.K., we found an increase of 5.2% per year NOx emitted as NO2 in the Marylebone Road from 2001 to 2005

Summary

Introduction

Nitrogen dioxide (NO2) and ozone (O3) are key urban air pollutants with welldocumented public health impacts.[1]. [OX]L is believed to be mainly derived from primary emissions of NO2, at roadside and kerbside locations, such that the slope of the [OX] vs [NOx] relationship provides an estimate of the volumetric fraction of NOx emitted as NO2.6,7 [OX]B provides a quanti cation of the background [O3] which would exist at the given location in the notional absence of NOx It can be regarded as the global (hemispheric) baseline O3 level, modi ed by regional-scale processes (i.e. deposition and chemistry) that can either remove or produce OX. Statistics for the linear relationship were calculated using the ‘least squares’ method to give exact values from gradient (local NOx-dependent contribution), intercept (background NOx-independent contribution) and standard errors for both values This data was compiled and averaged to give insights into the monthly and hourly dependencies of [OX]B and [OX]L and how their contributions and trends vary over time.

Methods

Results

Conclusion