Preprints
https://doi.org/10.5194/acp-2022-161
https://doi.org/10.5194/acp-2022-161
 
09 Mar 2022
09 Mar 2022
Status: a revised version of this preprint is currently under review for the journal ACP.

Improving NOX emissions in Beijing using network observations and a novel perturbed emissions ensemble

Le Yuan1, Olalekan Popoola1, Christina Hood2, David Carruthers2, Roderic L. Jones1, Haitong Zhe Sun1, Huan Liu3, Qiang Zhang4, and Alexander T. Archibald1,5 Le Yuan et al.
  • 1Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
  • 2Cambridge Environmental Research Consultants, Cambridge, CB2 1SJ, UK
  • 3State Key Joint Laboratory of ESPC, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, International Joint Laboratory on Low Carbon Clean Energy Innovation, School of the Environment, Tsinghua University, Beijing, 100084, China
  • 4Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
  • 5National Centre for Atmospheric Science, Cambridge, CB2 1EW, UK

Abstract. Emissions inventories are crucial inputs to air quality simulations and represent a major source of uncertainty. Various methods have been adopted to optimise emissions inventories, yet in most cases the methods were only applied to total anthropogenic emissions. We have developed a new approach that updates a priori emission estimates by source sector, which are particularly relevant for policy interventions. At its core is a perturbed emissions ensemble (PEE), constructed by perturbing parameters in an a priori emissions inventory within their respective uncertainty ranges. This PEE is then input to an air quality model to generate an ensemble of forward simulations. By comparing the simulation outputs with observations from a dense network, the initial uncertainty ranges are constrained and a posteriori emission estimates are derived. Using this approach, we were able to derive the transport sector NOX emissions for a study area centred around Beijing in 2016 based on a priori emission estimates for 2013. The absolute emissions were found to be 1.5–9 × 104 Mg, corresponding to a 57–93 % reduction from the 2013 levels, yet the night-time fraction of the emissions was 67–178 % higher. These results provide robust and independent evidence of the trends of traffic emission in the study area between 2013 and 2016 reported by previous studies. We also highlighted the impacts of the chemical mechanisms in the underlying model on the emission estimates derived, which is often neglected in emission optimisation studies. This work paves forward the route for rapid analysis and update of emissions inventories using air quality models and routine in situ observations, underscoring the utility of dense observational networks. It also highlights some gaps in the current distribution of monitoring sites in Beijing which result in an underrepresentation of large point sources of NOX.

Le Yuan et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-161', Anonymous Referee #1, 07 Apr 2022
  • RC2: 'Comment on acp-2022-161', Anonymous Referee #2, 11 Apr 2022
  • AC1: 'Response to reviewers' comments on acp-2022-161', Le Yuan, 18 May 2022

Le Yuan et al.

Model code and software

construct_PEE.R Le Yuan https://github.com/yuanle731/PEE/blob/main/construct_PEE.R

Le Yuan et al.

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Short summary
Emission estimates represent a major source of uncertainty in air quality modelling. We developed a novel approach to improve emission estimates from existing emissions inventories using air quality models and routine in situ observations. Using this approach, we derived estimates of NOX emissions from the transport sector in Beijing in 2016. This approach has great potential in deriving timely updates of emissions for other pollutants, particularly in regions undergoing rapid emission changes.
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