Preprints
https://doi.org/10.5194/egusphere-2025-3667
https://doi.org/10.5194/egusphere-2025-3667
03 Nov 2025
 | 03 Nov 2025
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

High-resolution Emission Inventory Development and Co-emission Hotspot Identification of Air Pollutants and Greenhouse Gases in Central Plains Region, China

Jie Li, Shasha Yin, Conghui Su, Jiaxin Wei, Mingyue Guan, and Chong Yu

Abstract. A high-resolution inventory provides scientific basis for numerical simulations and control strategies. Under the background of synergistic carbon reduction and pollution control, constructing a carbon-pollutant co-emission inventory is of great significance for regional air quality improvement. Taken Henan Province in the Central Plains region as an example, the most polluted regions in China, an update emission inventory was developed. The study presents results showing that in 2022, the total emissions of SO2, NOX, CO, PM10, PM2.5, VOCs, NH3, BC, OC, CO2, CH4, and N2O in Central China, particularly Henan Province, were 408.7, 1336.2, 4647.3, 901.1, 440.0, 759.3, 672.7, 47.4, 90.3, 540462.0, 12462.0 and 42.9 kt respectively. The emissions were predominantly attributed to industrial combustion, electricity generation, motor vehicles, and agricultural activities. Significant spatial heterogeneity was observed. Northern heavy industrial cities exhibited high carbon and pollution intensities with carbon emission 1.75–3.7 times higher than the provincial average. In contrast, central transportation hubs were primarily characterized by elevated emissions of NOX and VOCs. Southern agricultural areas showed low carbon but high NH3 emissions. Temporally, emissions of SO2 and PM2.5 peaked during winter, whereas NH3 increased during the summer agricultural season. High-emission grids were predominantly concentrated in urban agglomerations of the north-central region, especially around Zhengzhou, Jiaozuo, and Anyang. Hotspot analysis revealed that 5 % of high-emission grids accounted for more than 50 % of total emissions, indicating a highly uneven spatial distribution. These results highlight that understanding the region-specific emission characteristics of different regions is critical for developing mitigation strategies in future.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
Share
Jie Li, Shasha Yin, Conghui Su, Jiaxin Wei, Mingyue Guan, and Chong Yu

Status: open (until 15 Dec 2025)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Jie Li, Shasha Yin, Conghui Su, Jiaxin Wei, Mingyue Guan, and Chong Yu
Jie Li, Shasha Yin, Conghui Su, Jiaxin Wei, Mingyue Guan, and Chong Yu

Viewed

Total article views: 80 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
69 8 3 80 13 0 2
  • HTML: 69
  • PDF: 8
  • XML: 3
  • Total: 80
  • Supplement: 13
  • BibTeX: 0
  • EndNote: 2
Views and downloads (calculated since 03 Nov 2025)
Cumulative views and downloads (calculated since 03 Nov 2025)

Viewed (geographical distribution)

Total article views: 65 (including HTML, PDF, and XML) Thereof 65 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Nov 2025
Download
Short summary
Atmospheric pollution control and climate change response are mutually reinforcing. We developed a newest high-resolution integrated emision inventory for air pollutants and greenhouse gases in China's Central Plain. It provided valuable emission dataresource and revealed emission characteristics and spatial patterns across different sources, cities, and co-hotspot. Interesting variations have been discovered in megacity cluster, industrial agglomeration areas and agricultural dominant regions.
Share