Zhang Yucong, Du Shanshan, Guan Linlin, Chen Xiaoyu, Lei Liping, Liu Liangyun
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China.
Sci Total Environ. 2024 Nov 1;949:175177. doi: 10.1016/j.scitotenv.2024.175177. Epub 2024 Jul 31.
Satellite remote sensing is a promising approach for monitoring global CO emissions. However, existing satellite-based CO observations are too coarse to meet the requirements of fine-scale global mapping. We propose a novel data-driven method to estimate global anthropogenic CO emissions at a 0.1° scale, which integrates emissions inventories and satellite data while bypassing the inadequate accuracy of CO observations. Due to the co-emitted anthropogenic emissions of nitrogen oxides (NO = NO + NO) and CO, high-resolution NO measurements from the TROPOspheric Monitoring Instrument (TROPOMI) are employed to map the global anthropogenic emissions at a global 0.1° scale. We construct the driving features from NO data and also incorporate gridded CO/NO emission ratios and NO/NO conversion ratios as driving data to describe co-emissions. Both ratios are predicted using a long short-term memory (LSTM) neural network (with an R of 0.984 for the CO/NO emission ratio and an R of 0.980 for the NO/NO conversion ratio). The data-driven model for estimating anthropogenic CO emissions is implemented by random forest regression (RFR) and trained using the Emissions Database for Global Atmospheric Research (EDGAR). The satellite-based anthropogenic CO emission dataset at a global 0.1° scale agrees well with the national CO emission inventories (an R of 0.998 with Global Carbon Budget (GCB) and an R of 0.996 with EDGAR) and consistent with city-level emission estimates from Carbon Monitor Cities (CMC) with the R of 0.824. This data-driven method based on satellite-observed NO provides a new perspective for fine-resolution anthropogenic CO emissions estimation.
卫星遥感是监测全球一氧化碳（CO）排放的一种很有前景的方法。然而，现有的基于卫星的一氧化碳观测数据过于粗糙，无法满足精细尺度全球制图的要求。我们提出了一种新的数据驱动方法，以0.1°的尺度估算全球人为一氧化碳排放量，该方法整合了排放清单和卫星数据，同时绕过了一氧化碳观测精度不足的问题。由于氮氧化物（NOₓ = NO + NO₂）和一氧化碳的人为排放是共同产生的，因此利用对流层监测仪器（TROPOMI）的高分辨率一氧化氮测量数据，以全球0.1°的尺度绘制全球人为排放量地图。我们从一氧化氮数据中构建驱动特征，并纳入网格化的一氧化碳/一氧化氮排放比率和一氧化氮/一氧化氮ₓ转化率作为驱动数据来描述共同排放。这两个比率均使用长短期记忆（LSTM）神经网络进行预测（一氧化碳/一氧化氮排放比率的R值为0.984，一氧化氮/一氧化氮ₓ转化率的R值为0.980）。估算人为一氧化碳排放量的数据驱动模型通过随机森林回归（RFR）实现，并使用全球大气研究排放数据库（EDGAR）进行训练。全球0.1°尺度的基于卫星的人为一氧化碳排放数据集与国家一氧化碳排放清单高度吻合（与全球碳预算（GCB）的R值为0.998，与EDGAR的R值为0.996），并且与碳监测城市（CMC）的城市层面排放估计结果一致，R值为0.824。这种基于卫星观测一氧化氮的数据驱动方法为精细分辨率的人为一氧化碳排放量估算提供了新的视角。
