Bai Shengxi, Zhang Yongguang, Li Fei, Yan Yingqi, Chen Huilin, Feng Shuzhuang, Jiang Fei, Sun Shiwei, Wang Zhongting, Zhou Chunyan, Zhou Wei, Zhao Shaohua
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, International Institute for Earth System Sciences, Nanjing University, Nanjing, Jiangsu 210023, China; Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China; International Joint Carbon Neutrality Laboratory, Nanjing University, Nanjing, Jiangsu 210023, China.
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, International Institute for Earth System Sciences, Nanjing University, Nanjing, Jiangsu 210023, China; Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China; International Joint Carbon Neutrality Laboratory, Nanjing University, Nanjing, Jiangsu 210023, China.
Sci Total Environ. 2024 Nov 10;950:175446. doi: 10.1016/j.scitotenv.2024.175446. Epub 2024 Aug 10.
Coal mines are significant anthropogenic sources of methane emissions, detectable and traceable from high spatial resolution satellites. Nevertheless, estimating local or regional-scale coal mine methane emission intensities based on high-resolution satellite observations remains challenging. In this study, we devise a novel interpolation algorithm based on high-resolution satellite observations (including Gaofen5-01A/02, Ziyuan-1 02D, PRISMA, GHGSat-C1 to C5, EnMAP, and EMIT) and conduct assessments of annual mean coal mine methane emissions in Shanxi Province, China, one of the world's largest coal-producing regions, spanning the period 2019 to 2023 across various scales: point-source, local, and regional. We use high-resolution satellite observations to perform interpolation-based estimations of methane emissions from three typical coal-mining areas. This approach, known as IPLT (Interpolation based on Satellite Observations), provides spatially explicit maps of methane emission intensities in these areas, thereby providing a novel local-scale coal mine methane emission inventory derived from high-resolution top-down observations. For regional-scale estimation and mapping, we utilize high-resolution satellite data to complement and substitute facility-level emission inventories for interpolation (IPLT, Interpolation based on Satellite Observations and Global Coal Mine Tracker). We evaluate our IPLT and IPLT estimation with emission inventories, top-down methane emission estimates from TROPOMI observations, and TROPOMI's methane concentration enhancements. The results suggest a notable right-skewed distribution of methane emission flux rates from coal mine point sources. Our IPLT estimates the annual average coal mine methane emission in Shanxi Province from 2019 to 2023 at 8.9 ± 0.5 Tg/yr, marginally surpassing top-down inversion results from TROPOMI (8.5 ± 0.6 Tg/yr in 2019 and 8.6 ± 0.6 Tg/yr in 2020). Furthermore, the spatial patterns of methane emission intensity delineated by IPLT and IPLT closely mirror those observed in TROPOMI's methane enhancements. Our comparative assessment underscores the superior performance and substantial potential of the developed interpolation algorithm based on high-resolution satellite observations for multi-scale estimation of coal mine methane emissions.
煤矿是甲烷排放的重要人为源，可从高空间分辨率卫星上检测和追踪到。然而，基于高分辨率卫星观测来估算局部或区域尺度的煤矿甲烷排放强度仍然具有挑战性。在本研究中，我们设计了一种基于高分辨率卫星观测（包括高分五号01A/02、资源一号02D、PRISMA、GHGSat-C1至C5、EnMAP和EMIT）的新型插值算法，并对中国山西省（世界最大产煤地区之一）2019年至2023年不同尺度（点源、局部和区域）的煤矿甲烷年平均排放量进行了评估。我们利用高分辨率卫星观测对三个典型煤矿区的甲烷排放进行基于插值的估算。这种方法称为IPLT（基于卫星观测的插值法），提供了这些区域甲烷排放强度的空间明确地图，从而提供了一种从高分辨率自上而下观测得出的新型局部尺度煤矿甲烷排放清单。对于区域尺度的估算和制图，我们利用高分辨率卫星数据来补充和替代设施层面的排放清单进行插值（IPLT，基于卫星观测和全球煤矿追踪器的插值法）。我们用排放清单、TROPOMI观测的自上而下甲烷排放估算值以及TROPOMI的甲烷浓度增强值来评估我们的IPLT和IPLT估算。结果表明，煤矿点源的甲烷排放通量率呈现出明显的右偏分布。我们的IPLT估算出2019年至2023年山西省煤矿甲烷年平均排放量为8.9±0.5太克/年，略高于TROPOMI的自上而下反演结果（2019年为8.5±0.6太克/年，2020年为8.6±0.6太克/年）。此外，IPLT和IPLT描绘的甲烷排放强度空间模式与TROPOMI的甲烷增强观测结果密切相似。我们的比较评估强调了所开发的基于高分辨率卫星观测的插值算法在多尺度估算煤矿甲烷排放方面的卓越性能和巨大潜力。
