Changes in air pollutant emissions in China during two clean-air action periods derived from the newly developed Inversed Emission Inventory for Chinese Air Quality (CAQIEI)

Lei Kong; Xiao Tang; Zifa Wang; Jiang Zhu; Jianjun Li; Huangjian Wu; Qizhong Wu; Huansheng Chen; Lili Zhu; Wei Wang; Bing Liu; Qian Wang; Duohong Chen; Yuepeng Pan; Jie Li; Lin Wu; Gregory Carmichael

doi:10.5194/essd-16-4351-2024

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Changes in air pollutant emissions in China during two clean-air action periods derived from the newly developed Inversed Emission Inventory for Chinese Air Quality (CAQIEI)

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Changes in air pollutant emissions in China during two clean-air action periods derived from the newly developed Inversed Emission Inventory for Chinese Air Quality (CAQIEI)

Lei Kong, Xiao Tang, Zifa Wang, Jiang Zhu, Jianjun Li, Huangjian Wu, Qizhong Wu, Huansheng Chen, Lili Zhu, Wei Wang, …

Earth system science data, Vol.16(9), pp.4351-4387

09/26/2024

DOI: 10.5194/essd-16-4351-2024

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Abstract

A new long-term emission inventory called the Inversed Emission Inventory for Chinese Air Quality (CAQIEI) was developed in this study by assimilating surface observations from the China National Environmental Monitoring Centre (CNEMC) using an ensemble Kalman filter (EnKF) and the Nested Air Quality Prediction Modeling System. This inventory contains the constrained monthly emissions of NOx, SO2, CO, primary PM2.5, primary PM10, and non-methane volatile organic compounds (NMVOCs) in China from 2013 to 2020, with a horizontal resolution of 15 km × 15 km. This paper documents detailed descriptions of the assimilation system and the evaluation results for the emission inventory. The results suggest that CAQIEI can effectively reduce the biases in the a priori emission inventory, with the normalized mean biases ranging from -9.1 % to 9.5 % in the a posteriori simulation, which are significantly reduced from the biases in the a priori simulations (-45.6 % to 93.8 %). The calculated root-mean-square errors (RMSEs) (0.3 mg m-3 for CO and 9.4–21.1 µgm3 for other species, on the monthly scale) and correlation coefficients (0.76–0.94) were also improved from the a priori simulations, demonstrating good performance of the data assimilation system. Based on CAQIEI, we estimated China's total emissions (including both natural and anthropogenic emissions) of the six species in 2015 to be as follows: 25.2 Tg of NOx, 17.8 Tg of SO2, 465.4 Tg of CO, 15.0 Tg of PM2.5, 40.1 Tg of PM10, and 46.0 Tg of NMVOCs. From 2015 to 2020, the total emissions decreased by 54.1 % for SO2, 44.4 % for PM2.5, 33.6 % for PM10, 35.7 % for CO, and 15.1 % for NOx but increased by 21.0 % for NMVOCs. It is also estimated that the emission reductions were larger during 2018–2020 (from -26.6 % to -4.5 %) than during 2015–2017 (from -23.8 % to 27.6 %) for most of the species. In particular, the total Chinese NOx and NMVOC emissions were shown to increase during 2015–2017, especially over the Fenwei Plain area (FW), where the emissions of particulate matter (PM) also increased. The situation changed during 2018–2020, when the upward trends were contained and reversed to downward trends for the total emissions of both NOx and NMVOCs and the PM emissions over FW. This suggests that the emission control policies may be improved in the 2018–2020 action plan. We also compared CAQIEI with other air pollutant emission inventories in China, which verified our inversion results in terms of the total emissions of NOx, SO2, and NMVOCs and more importantly identified the potential uncertainties in current emission inventories. Firstly, CAQIEI suggested higher CO emissions in China, with CO emissions estimated by CAQIEI (426.8 Tg) being more than twice the amounts in previous inventories (120.7–237.7 Tg). Significantly higher emissions were also suggested over western and northeastern China for the other air pollutants. Secondly, CAQIEI suggested higher NMVOC emissions than previous emission inventories by about 30.4 %–81.4 % over the North China Plain (NCP) but suggested lower NMVOC emissions by about 27.6 %–0.0 % over southeastern China (SE). Thirdly, CAQIEI suggested lower emission reduction rates during 2015–2018 than previous emission inventories for most species, except for CO. In particular, China's NMVOC emissions were shown to have increased by 26.6 % from 2015 to 2018, especially over NCP (by 38.0 %), northeastern China (by 38.3 %), and central China (60.0 %). These results provide us with new insights into the complex variations in air pollutant emissions in China during two recent clean-air actions, which has the potential to improve our understanding of air pollutant emissions in China and their impacts on air quality. All of the datasets are available at 10.57760/sciencedb.13151 (Kong et al., 2023a).

Air Pollution

Air Quality

Environmental Monitoring

Aerosols

Air

Air monitoring

Air quality models

Anthropogenic factors

Bias

Carbon

Carbon monoxide emissions

Correlation coefficient

Correlation coefficients

Data assimilation

Data collection

Emission analysis

Emission inventories

Emissions control

Human influences

Kalman filters

Nitrates

Nitrogen compounds

Nitrogen oxides

Organic compounds

Outdoor air quality

Particulate emissions

Particulate matter

Particulate matter emissions

Pollutants

Prediction models

Sulfur dioxide

Suspended particulate matter

Trends

VOCs

Volatile organic compounds

Details

Title: Subtitle: Changes in air pollutant emissions in China during two clean-air action periods derived from the newly developed Inversed Emission Inventory for Chinese Air Quality (CAQIEI)
Creators: Lei Kong - State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry
Xiao Tang - State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry
Zifa Wang - State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry
Jiang Zhu - State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry
Jianjun Li - China National Environmental Monitoring Center
Huangjian Wu - State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry
Qizhong Wu - Beijing Normal University
Huansheng Chen - State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry
Lili Zhu - China National Environmental Monitoring Center
Wei Wang - China National Environmental Monitoring Center
Bing Liu - China National Environmental Monitoring Center
Qian Wang
Duohong Chen
Yuepeng Pan - State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry
Jie Li - State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry
Lin Wu - State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry
Gregory Carmichael
Resource Type: Journal article
Publication Details: Earth system science data, Vol.16(9), pp.4351-4387
Publisher: Copernicus GmbH; GOTTINGEN
DOI: 10.5194/essd-16-4351-2024
ISSN: 1866-3508
eISSN: 1866-3516
Number of pages: 37
Grant note: National Natural Science Foundation of China: 42175132, 92044303, 42205119 National Key RD Program: 2020YFA0607802 CAS Information Technology Program: CAS-WX2021SF-0107-02
This research has been sponsored by the National Natural Science Foundation of China (grant nos. 42175132, 92044303, and 42205119), the National Key R&D Program (grant no. 2020YFA0607802), and the CAS Information Technology Program (grant no. CAS-WX2021SF-0107-02).
Language: English
Date published: 09/26/2024
Academic Unit: Nursing; Chemical and Biochemical Engineering; Civil and Environmental Engineering
Record Identifier: 9984721144402771

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