High-resolution emission inventory development and co-emission hotspot identification of air pollutants and greenhouse gases in central plains region, China

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 SO 2 , NO x , CO, PM 10 , PM 2.5 , VOCs, NH 3 , BC, OC, CO 2 , CH 4 , and N 2 O 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 NO x and VOCs. Southern agricultural areas showed low carbon but high NH 3 emissions. Temporally, emissions of SO 2 , CO, PM 10 and PM 2.5 peaked during winter, whereas NH 3 , CH 4 and N 2 O 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. Co-hotspot analysis identified the central-northern industrial zone, especially around Zhengzhou, as the key region for coordinated control, with winter offering the most significant synergistic potential. This inventory provides a critical scientific basis for targeted, region-specific mitigation strategies.