Development of a high-resolution integrated emission inventory of air pollutants for China

IF 11.2 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Nana Wu, Guannan Geng, Ruochong Xu, Shigan Liu, Xiaodong Liu, Qinren Shi, Ying Zhou, Yu Zhao, Huan Liu, Yu Song, Junyu Zheng, Qiang Zhang, Kebin He
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引用次数: 0

Abstract

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 scales 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 Multi-resolution Emission Inventory for China (MEIC), forming the high-resolution INTegrated emission inventory of Air pollutants for China (INTAC). We find that INTAC provides more accurate representations for emission magnitudes and spatiotemporal patterns. In 2017, China's emissions of sulfur dioxide (SO2), nitrous oxides (NOx), carbon monoxide (CO), non-methane volatile organic compounds (NMVOCs), ammonia (NH3), PM10 and PM2.5 (particulate matter), black carbon (BC), and organic carbon (OC) were 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, and 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 with MEIC, INTAC reduces mean biases in simulated concentrations of major air pollutants by 2–14 µg m−3 across 74 cities, compared against ground observations. The enhanced model performance by INTAC is particularly evident at finer-grid resolutions. Our new dataset is accessible at http://meicmodel.org.cn/intac (last access: 15 April 2024) and 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.
编制中国大气污染物高分辨率综合排放清单
摘要由于在统一的自下而上框架中,参数的细化信息有限,为中国构建一个高分辨率的综合排放数据集具有挑战性。在此,通过开发一个综合建模框架,我们协调了多源异构数据,包括多个国家和区域尺度上的最新排放清单以及中国的关键物种和排放源,生成了一个 0.1° 分辨率的 2017 年清单。通过来源映射、物种映射、时间分解、空间分配和时空耦合,不同排放清单在来源类别、化学物种和时空分辨率方面实现了标准化。这就实现了多尺度、高分辨率排放清单与中国多分辨率排放清单(MEIC)的耦合,形成了高分辨率的中国大气污染物综合排放清单(INTAC)。我们发现,INTAC 能更准确地反映排放规模和时空模式。2017年,中国二氧化硫(SO2)、氧化亚氮(NOx)、一氧化碳(CO)、非甲烷挥发性有机物(NMVOCs)、氨(NH3)、可吸入颗粒物(PM10和PM2.5)、黑碳(BC)和有机碳(OC)的排放量分别为12.3、24.5、141.0、27.9、9.2、11.1、8.4、1.3和2.2 Tg。SO2、PM10、NOx和PM2.5的点源排放比例从MEIC的7%-19%增加到INTAC的48%-66%,从而提高了空间精度,特别是减少了人口稠密地区的高估。与 MEIC 相比,INTAC 可将 74 个城市主要空气污染物模拟浓度的平均偏差减少 2-14 µg m-3(与地面观测数据相比)。在更精细的网格分辨率下,INTAC模型性能的提升尤为明显。我们的新数据集可在 http://meicmodel.org.cn/intac(最后访问日期:2024 年 4 月 15 日)和 https://doi.org/10.5281/zenodo.10459198(Wu 等人,2024 年)上访问,它将为精细尺度大气研究和空气质量改善提供坚实的数据基础。
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来源期刊
Earth System Science Data
Earth System Science Data GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
18.00
自引率
5.30%
发文量
231
审稿时长
35 weeks
期刊介绍: Earth System Science Data (ESSD) is an international, interdisciplinary journal that publishes articles on original research data in order to promote the reuse of high-quality data in the field of Earth system sciences. The journal welcomes submissions of original data or data collections that meet the required quality standards and have the potential to contribute to the goals of the journal. It includes sections dedicated to regular-length articles, brief communications (such as updates to existing data sets), commentaries, review articles, and special issues. ESSD is abstracted and indexed in several databases, including Science Citation Index Expanded, Current Contents/PCE, Scopus, ADS, CLOCKSS, CNKI, DOAJ, EBSCO, Gale/Cengage, GoOA (CAS), and Google Scholar, among others.
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