气候变化领域集成服务门户(CCPortal): Improving NOx emission estimates in Beijing using network observations and a perturbed emissions ensemble

CCPortal

DOI	10.5194/acp-22-8617-2022
	Improving NOx emission estimates in Beijing using network observations and a perturbed emissions ensemble
	Yuan, Le; Popoola, Olalekan A. M.; Hood, Christina; Carruthers, David; Jones, Roderic L.; Sun, Haitong Zhe; Liu, Huan; Zhang, Qiang; Archibald, Alexander T.
发表日期	2022
ISSN	1680-7316
EISSN	1680-7324
起始页码	8617
结束页码	8637
卷号	22 期号:13 页码:21
英文摘要	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 x 10(4) 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.
学科领域	Environmental Sciences; Meteorology & Atmospheric Sciences
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:000820743500001
来源期刊	ATMOSPHERIC CHEMISTRY AND PHYSICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/273854
作者单位	University of Cambridge; Tsinghua University; Tsinghua University; UK Research & Innovation (UKRI); Natural Environment Research Council (NERC); NERC National Centre for Atmospheric Science; University of Cambridge
推荐引用方式 GB/T 7714	Yuan, Le,Popoola, Olalekan A. M.,Hood, Christina,et al. Improving NOx emission estimates in Beijing using network observations and a perturbed emissions ensemble[J],2022,22(13):21.
APA	Yuan, Le.,Popoola, Olalekan A. M..,Hood, Christina.,Carruthers, David.,Jones, Roderic L..,...&Archibald, Alexander T..(2022).Improving NOx emission estimates in Beijing using network observations and a perturbed emissions ensemble.ATMOSPHERIC CHEMISTRY AND PHYSICS,22(13),21.
MLA	Yuan, Le,et al."Improving NOx emission estimates in Beijing using network observations and a perturbed emissions ensemble".ATMOSPHERIC CHEMISTRY AND PHYSICS 22.13(2022):21.