1
|
Jia G, Huang Z, Tang X, Ou J, Lu M, Xu Y, Zhong Z, Sha Q, Wu H, Zheng C, Deng T, Chen D, He M, Zheng J. A meteorologically adjusted ensemble Kalman filter approach for inversing daily emissions: A case study in the Pearl River Delta, China. J Environ Sci (China) 2022; 114:233-248. [PMID: 35459489 DOI: 10.1016/j.jes.2021.08.048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 08/22/2021] [Accepted: 08/23/2021] [Indexed: 11/29/2022]
Abstract
The conventional Ensemble Kalman filter (EnKF), which is now widely used to calibrate emission inventories and to improve air quality simulations, is susceptible to simulation errors of meteorological inputs, making accurate updates of high temporal-resolution emission inventories challenging. In this study, we developed a novel meteorologically adjusted inversion method (MAEInv) based on the EnKF to improve daily emission estimations. The new method combines sensitivity analysis and bias correction to alleviate the inversion biases caused by errors of meteorological inputs. For demonstration, we used the MAEInv to inverse daily carbon monoxide (CO) emissions in the Pearl River Delta (PRD) region, China. In the case study, 60% of the total CO simulation biases were associated with sensitive meteorological inputs, which would lead to the overestimation of daily variations of posterior emissions. Using the new inversion method, daily variations of emissions shrank dramatically, with the percentage change decreased by 30%. Also, the total amount of posterior CO emissions estimated by the MAEInv decreased by 14%, indicating that posterior CO emissions might be overestimated using the conventional EnKF. Model evaluations using independent observations revealed that daily CO emissions estimated by MAEInv better reproduce the magnitude and temporal patterns of ambient CO concentration, with a higher correlation coefficient (R, +37.0%) and lower normalized mean bias (NMB, -17.9%). Since errors of meteorological inputs are major sources of simulation biases for both low-reactive and reactive pollutants, the MAEInv is also applicable to improve the daily emission inversions of reactive pollutants.
Collapse
Affiliation(s)
- Guanglin Jia
- School of Environment and Energy, South China University of Technology, University Town Campus, Guangzhou 510006, China
| | - Zhijiong Huang
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511486, China.
| | - Xiao Tang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Jiamin Ou
- Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht, CH 3584, the Netherlands
| | - Menghua Lu
- School of Environment and Energy, South China University of Technology, University Town Campus, Guangzhou 510006, China
| | - Yuanqian Xu
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511486, China
| | - Zhuangmin Zhong
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511486, China
| | - Qing'e Sha
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511486, China
| | - Huangjian Wu
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Chuanzeng Zheng
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511486, China
| | - Tao Deng
- Guangzhou Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou 510640, China
| | - Duohong Chen
- State Environmental Protection Key Laboratory of Regional Air Quality Monitoring, Guangdong Environmental Monitoring Center, Guangzhou 510308, China
| | - Min He
- Department of Environmental Science and Engineering, College of Architecture and Environment, Sichuan University, Chengdu 610065, China.
| | - Junyu Zheng
- School of Environment and Energy, South China University of Technology, University Town Campus, Guangzhou 510006, China; Institute for Environmental and Climate Research, Jinan University, Guangzhou 511486, China
| |
Collapse
|
2
|
Broquet G, Chevallier F, Rayner P, Aulagnier C, Pison I, Ramonet M, Schmidt M, Vermeulen AT, Ciais P. A European summertime CO2biogenic flux inversion at mesoscale from continuous in situ mixing ratio measurements. ACTA ACUST UNITED AC 2011. [DOI: 10.1029/2011jd016202] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
|