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Improving NOxemission estimates in Beijing using network observations and a perturbed emissions ensemble

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cam.depositDate2022-06-15
cam.issuedOnline2022-07-05
cam.orpheus.counter4
cam.orpheus.success2022/07/29
datacite.issourceof.doi10.17863/CAM.85111
dc.contributor.authorYuan, L
dc.contributor.authorPopoola, OAM
dc.contributor.authorHood, C
dc.contributor.authorCarruthers, D
dc.contributor.authorJones, RL
dc.contributor.authorSun, HZ
dc.contributor.authorLiu, H
dc.contributor.authorZhang, Q
dc.contributor.authorArchibald, AT
dc.contributor.orcidYuan, L [0000-0002-4282-0459]
dc.contributor.orcidPopoola, OAM [0000-0003-2390-8436]
dc.contributor.orcidHood, C [0000-0001-9244-5696]
dc.contributor.orcidArchibald, AT [0000-0001-9302-4180]
dc.date.accessioned2022-06-15T23:30:54Z
dc.date.available2022-06-15T23:30:54Z
dc.date.updated2022-06-15T14:05:00Z
dc.description.abstractEmissions 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.
dc.identifier.doi10.17863/CAM.85555
dc.identifier.eissn1680-7324
dc.identifier.issn1680-7316
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/338146
dc.language.isoeng
dc.publisherCopernicus Publications
dc.publisher.departmentYusuf Hamied Department of Chemistry
dc.publisher.departmentDepartment of Chemistry
dc.rightsAll Rights Reserved
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserved
dc.titleImproving NO<inf>x</inf>emission estimates in Beijing using network observations and a perturbed emissions ensemble
dc.typeArticle
dcterms.dateAccepted2022-06-13
prism.publicationNameAtmospheric Chemistry and Physics
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
pubs.licence-identifierapollo-deposit-licence-2-1
rioxxterms.typeJournal Article/Review
rioxxterms.versionAM
rioxxterms.versionofrecord10.5194/acp-22-8617-2022

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