Development of a High-Resolution Integrated Emission Inventory of Air Pollutants for China

排放清单 污染物 中国 环境科学 空气污染物 高分辨率 分辨率(逻辑) 空气污染 计算机科学 遥感 地理 化学 考古 人工智能 有机化学
作者
Nanping Wu,Guannan Geng,Ruibo Xu,Shigan Liu,Xiaodong Liu,Qinren Shi,Ying Zhou,Yu Zhao,Huan Liu,Yu Shi,Junyu Zheng,Qiang Zhang
标识
DOI:10.5194/essd-2024-3
摘要

Abstract. Constructing a highly-resolved comprehensive emission dataset for China is challenging due to limited availability of refined information for parameters in a unified bottom-up framework. Here, by developing an integrated modeling framework, we harmonized multi-source heterogeneous data including several up-to-date emission inventories at national and regional scale, and for key species and sources in China, to generate a 0.1° resolution inventory for 2017. By source mapping, species mapping, temporal disaggregation, spatial allocation and spatial-temporal coupling, different emission inventories are normalized in terms of source categories, chemical species, and spatiotemporal resolutions. This achieves the coupling of multi-scale, high-resolution emission inventories with the MEIC (Multi-resolution Emission Inventory for China), forming a high-resolution INTegrated emission inventory of Air pollutants for China (i.e., INTAC). We find that the INTAC provides more accurate representations for emission magnitudes and spatiotemporal patterns. In 2017, China’s emissions of SO2, NOx, CO, NMVOC, NH3, PM10, PM2.5, BC, and OC are 12.3, 24.5, 141.0, 27.9, 9.2, 11.1, 8.4, 1.3 and 2.2 Tg, respectively. The proportion of point source emissions for SO2, PM10, NOx, PM2.5 increases from 7–19 % in MEIC to 48–66 % in INTAC, resulting in improved spatial accuracy, especially mitigating overestimations in densely populated areas. Compared to MEIC, INTAC reduced mean biases in simulated concentrations of major air pollutants by 2–14 μg/m³ across 74 cities against ground observations. The enhanced model performance by INTAC was particularly evident at finer grid resolutions. Our new dataset is accessible at https://doi.org/10.5281/zenodo.10459198 (Wu et al., 2024), and it will provide a solid data foundation for fine-scale atmospheric research and air quality improvement.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
zzzzz完成签到,获得积分10
1秒前
子衿完成签到,获得积分10
1秒前
a_hu发布了新的文献求助10
1秒前
qaz完成签到,获得积分10
1秒前
1秒前
脑袋尖尖的完成签到,获得积分10
1秒前
1秒前
罂粟完成签到,获得积分10
3秒前
3秒前
发sci发布了新的文献求助10
4秒前
4秒前
summer发布了新的文献求助10
4秒前
wenming发布了新的文献求助10
4秒前
情怀应助Search瞬间采纳,获得10
4秒前
WYQ完成签到 ,获得积分10
5秒前
板栗小狗发布了新的文献求助10
5秒前
Roy完成签到,获得积分10
6秒前
NexusExplorer应助脑袋尖尖的采纳,获得10
6秒前
hong应助soini采纳,获得10
6秒前
7秒前
8秒前
8秒前
8秒前
jennifer_zhuang完成签到,获得积分10
9秒前
维稳十年发布了新的文献求助10
9秒前
完美世界应助Liquor采纳,获得10
9秒前
Howie完成签到,获得积分10
10秒前
黎明完成签到,获得积分10
10秒前
10秒前
CodeCraft应助平安顺遂采纳,获得10
10秒前
帅气紫易完成签到,获得积分10
12秒前
summer完成签到,获得积分10
12秒前
醋灯笼完成签到,获得积分10
12秒前
珊啊是珊珊啊完成签到 ,获得积分10
12秒前
12秒前
13秒前
wanci应助light采纳,获得10
13秒前
13秒前
悲凉的孤菱完成签到,获得积分10
13秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Aerospace Standards Index - 2026 ASIN2026 3000
Polymorphism and polytypism in crystals 1000
Signals, Systems, and Signal Processing 610
Discrete-Time Signals and Systems 610
Research Methods for Business: A Skill Building Approach, 9th Edition 500
Social Work and Social Welfare: An Invitation(7th Edition) 410
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 纳米技术 有机化学 物理 生物化学 化学工程 计算机科学 复合材料 内科学 催化作用 光电子学 物理化学 电极 冶金 遗传学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 6052824
求助须知:如何正确求助?哪些是违规求助? 7868760
关于积分的说明 16276128
捐赠科研通 5198265
什么是DOI,文献DOI怎么找? 2781353
邀请新用户注册赠送积分活动 1764315
关于科研通互助平台的介绍 1646013