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引用本文:

DOI:

10.11834/jrs.20244018

收稿日期:

2024-01-11

修改日期:

2024-04-04

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TROPOMI NO2间接估算化石能源CO2日排放量
陆凌霄1, 秦凯1, 科恩杰森1, 李晓璐1, 周春艳2
1.中国矿业大学 环境与测绘学院;2.生态环境部卫星环境应用中心
摘要:

为实现“双碳”目标,利用卫星遥感技术估算化石能源消费二氧化碳(CO2)排放量至关重要。然而,由于CO2在大气中的存活寿命长,且现有CO2卫星传感器的空间覆盖度有限,直接采用卫星观测反演的CO2柱浓度数据估算其排放量难度较大。鉴于化石能源消费同时排放CO2和氮氧化物(NOx),NOx存活寿命短且利用卫星遥感估算其排放量具有较好的可行性。本文选择28个东部城市和1个西部能源金三角地区为研究对象,开展了基于TROPOMI NO2柱浓度间接估算化石能源CO2日排放量研究。首先,本文使用2019年的TROPOMI NO2对流层柱浓度数据产品和质量守恒模型估算得到NOx日排放量及不确定度。其次,分析多尺度排放清单模型构建的排放清单数据库(MEIC)中CO2与NOx的排放量关系。最后估算获得化石能源消费的CO2日排放量。结果表明:估算结果与MEIC清单中的CO2排放空间分布一致,但其更高的空间分辨率和时间频次能够揭示由于统计资料缺失的新兴及小型的排放源。东部城市以北京为例,在市中心周围的城郊地区遥感估算结果高于MEIC清单约104%,这表明随着东部城市的快速扩张,出现较多的新兴排放源。能源金三角以榆林为例,在该地区的电厂、钢铁厂以及煤矿区,存在部分排放源的排放量在MEIC清单中仅占整体排放量的10%,而在估算结果中该比例为37%,表明一些未纳入排放清单的小型电厂和工业源能够被卫星遥感捕捉到。研究结果可为我国化石能源碳排放核算提供技术支持。

Indirect estimation of daily CO2 emissions from fossil fuel consumption based on TROPOMI NO2
Abstract:

Objective: Anthropogenic emissions, primarily resulting from the combustion of fossil fuel, have led to a rapid and accelerating rise in atmospheric carbon dioxide (CO2) concentration in recent decades. Utilizing satellite remote sensing technology is crucial in estimating CO2 emissions from fossil fuel consumption, which is essential for achieving the "dual carbon" targets. However, the long lifetime of CO2 makes it challenging to directly estimate CO2 emissions from satellite measurements, and the existing CO2 satellite sensors have insufficient spatial resolution. Considering that fossil fuel combustion emits both CO2 and nitrogen oxides (NOx), the lifespan of NOx is much shorter and the feasibility of estimating its emissions through satellite remote sensing is more favorable. Therefore, this study aims to indirectly estimate daily CO2 emissions based on TROPOMI NO2 observations. Method: In this work, we focus on 28 eastern cities and one western energy-intensive region, known as the "Energy Golden Triangle", to indirectly estimate daily CO2 emissions from fossil fuel consumption based on TROPOMI nitrogen dioxide (NO2) column concentrations. There are three steps in our methodology: First, we utilize TROPOMI observations of NO2 tropospheric column concentrations, and inverts the daily NOx emissions in 2019 based on a MCMFE (Mass-Conserving Model Free approximation of Emissions) approach. This approach is proposed by the authors" team based on the principle of atmospheric component mass conservation. Secondly, the MEIC inventory (Multi-resolution Emission Inventory for China) is employed to compute and analyze the emission relationships between CO2 and NOx. Finally, this work estimates the daily CO2 emissions from fossil fuel consumption in these regions by using the NOx emission (MCMFE-NOx) and CO2-to-NOx ratio computed. Result: This work analyzes the estimation results of 28 eastern cities and three type of sources (power plants, iron and steel factories and coal mines) in the Energy Golden Triangle separately. The findings indicate that the estimations align with the spatial distribution of CO2 emissions in the MEIC inventory, yet offer higher spatial resolution and temporal frequency, revealing emerging and smaller emission sources missed in the inventories. In 28 eastern cities, significant emerging emission sources have surfaced in suburban areas due to recent urbanization expansion and economic development, exhibiting substantial emission volumes. For instance, in eastern cities like Beijing, remote sensing estimations in suburban areas exceeded the MEIC inventory by approximately 104%, indicating the emergence of numerous new emission sources accompanying the rapid expansion of these urban centers. In the Energy Golden Triangle, it was discovered that there are small-scale power plants and industrial sources overlooked in the MEIC inventory in places like Baotou, Yulin, Yinchuan, and Wuzhong. Exemplified by Yulin, estimations from grids containing power plants, steel factories, and coal mines surpassed the MEIC inventory by around 41%, indicating that some smaller power plants and industrial sources not included in the emission inventory were captured through satellite remote sensing. Conclusion: This paper combines the NOx emission and uncertainty results with the "bottom-up" CO2-to-NOx emission ratio, and derives the daily CO2 emission estimation results of the study areas in 2019 (whole year). It performs a statistical analysis of emissions in 28 large cities, and sources in the Energy Golden Triangle. The findings indicate that the estimations align with the spatial distribution of CO2 emissions in the MEIC inventory, yet offer higher spatial resolution and temporal frequency, revealing emerging and smaller emission sources missed in the inventories. This study provides technical support for carbon emission accounting related to fossil fuel consumption in China.

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