{"title":"OCO-2 卫星测量捕捉到的印度上空化石燃料二氧化碳排放特征","authors":"Vigneshkumar Balamurugan, Jia Chen","doi":"10.1029/2023EF004411","DOIUrl":null,"url":null,"abstract":"<p>Monitoring greenhouse gas (GHG) emissions is crucial for developing effective mitigation strategies. Recent advances in satellite remote-sensing measurements allow us to track greenhouse gas emissions globally. This study assessed <span></span><math>\n <semantics>\n <mrow>\n <msub>\n <mtext>CO</mtext>\n <mn>2</mn>\n </msub>\n </mrow>\n <annotation> ${\\text{CO}}_{2}$</annotation>\n </semantics></math> emissions from various point or local sources, particularly power plants in India, using 8 years of concurrent high-spatial resolution OCO-2 satellite measurements. A Gaussian plume (GP) model was used to evaluate the power plant emissions reported in the Carbon Brief (CB) database. In total (39 cases), 42 different power plant <span></span><math>\n <semantics>\n <mrow>\n <msub>\n <mtext>CO</mtext>\n <mn>2</mn>\n </msub>\n </mrow>\n <annotation> ${\\text{CO}}_{2}$</annotation>\n </semantics></math> emissions were assessed, with 26 of them being assessed more than once. The estimated power plant <span></span><math>\n <semantics>\n <mrow>\n <msub>\n <mtext>CO</mtext>\n <mn>2</mn>\n </msub>\n </mrow>\n <annotation> ${\\text{CO}}_{2}$</annotation>\n </semantics></math> emissions were within <span></span><math>\n <semantics>\n <mrow>\n <mo>±</mo>\n </mrow>\n <annotation> $\\pm $</annotation>\n </semantics></math> 25% of the emissions reported in the CB database in 11 out of 39 cases and within <span></span><math>\n <semantics>\n <mrow>\n <mo>±</mo>\n </mrow>\n <annotation> $\\pm $</annotation>\n </semantics></math> 50% in 18 cases. To evaluate the EDGAR and ODIAC <span></span><math>\n <semantics>\n <mrow>\n <msub>\n <mtext>CO</mtext>\n <mn>2</mn>\n </msub>\n </mrow>\n <annotation> ${\\text{CO}}_{2}$</annotation>\n </semantics></math> emission inventories in terms of missing and highly underestimated sources, we estimated the cross-sectional (CS) <span></span><math>\n <semantics>\n <mrow>\n <msub>\n <mtext>CO</mtext>\n <mn>2</mn>\n </msub>\n </mrow>\n <annotation> ${\\text{CO}}_{2}$</annotation>\n </semantics></math> emission flux for 45 cases. We identified the possible omission of power plant emissions in three cases for both inventories. Furthermore, we also showed 17 cases in which <span></span><math>\n <semantics>\n <mrow>\n <msub>\n <mtext>CO</mtext>\n <mn>2</mn>\n </msub>\n </mrow>\n <annotation> ${\\text{CO}}_{2}$</annotation>\n </semantics></math> emissions from unknown (non-power plant) sources were highly underestimated in the EDGAR and ODIAC <span></span><math>\n <semantics>\n <mrow>\n <msub>\n <mtext>CO</mtext>\n <mn>2</mn>\n </msub>\n </mrow>\n <annotation> ${\\text{CO}}_{2}$</annotation>\n </semantics></math> emission inventories. Due to the simplicity of the employed approaches and their lower computational requirements compared to other methods, they can be applied to large data sets over extended time periods. This enables the acquisition of initial emission estimates for various sources, including those that are unknown and underestimated.</p>","PeriodicalId":48748,"journal":{"name":"Earths Future","volume":"12 11","pages":""},"PeriodicalIF":7.3000,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023EF004411","citationCount":"0","resultStr":"{\"title\":\"Fossil Fuel CO2 Emission Signatures Over India Captured by OCO-2 Satellite Measurements\",\"authors\":\"Vigneshkumar Balamurugan, Jia Chen\",\"doi\":\"10.1029/2023EF004411\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Monitoring greenhouse gas (GHG) emissions is crucial for developing effective mitigation strategies. Recent advances in satellite remote-sensing measurements allow us to track greenhouse gas emissions globally. This study assessed <span></span><math>\\n <semantics>\\n <mrow>\\n <msub>\\n <mtext>CO</mtext>\\n <mn>2</mn>\\n </msub>\\n </mrow>\\n <annotation> ${\\\\text{CO}}_{2}$</annotation>\\n </semantics></math> emissions from various point or local sources, particularly power plants in India, using 8 years of concurrent high-spatial resolution OCO-2 satellite measurements. A Gaussian plume (GP) model was used to evaluate the power plant emissions reported in the Carbon Brief (CB) database. In total (39 cases), 42 different power plant <span></span><math>\\n <semantics>\\n <mrow>\\n <msub>\\n <mtext>CO</mtext>\\n <mn>2</mn>\\n </msub>\\n </mrow>\\n <annotation> ${\\\\text{CO}}_{2}$</annotation>\\n </semantics></math> emissions were assessed, with 26 of them being assessed more than once. The estimated power plant <span></span><math>\\n <semantics>\\n <mrow>\\n <msub>\\n <mtext>CO</mtext>\\n <mn>2</mn>\\n </msub>\\n </mrow>\\n <annotation> ${\\\\text{CO}}_{2}$</annotation>\\n </semantics></math> emissions were within <span></span><math>\\n <semantics>\\n <mrow>\\n <mo>±</mo>\\n </mrow>\\n <annotation> $\\\\pm $</annotation>\\n </semantics></math> 25% of the emissions reported in the CB database in 11 out of 39 cases and within <span></span><math>\\n <semantics>\\n <mrow>\\n <mo>±</mo>\\n </mrow>\\n <annotation> $\\\\pm $</annotation>\\n </semantics></math> 50% in 18 cases. To evaluate the EDGAR and ODIAC <span></span><math>\\n <semantics>\\n <mrow>\\n <msub>\\n <mtext>CO</mtext>\\n <mn>2</mn>\\n </msub>\\n </mrow>\\n <annotation> ${\\\\text{CO}}_{2}$</annotation>\\n </semantics></math> emission inventories in terms of missing and highly underestimated sources, we estimated the cross-sectional (CS) <span></span><math>\\n <semantics>\\n <mrow>\\n <msub>\\n <mtext>CO</mtext>\\n <mn>2</mn>\\n </msub>\\n </mrow>\\n <annotation> ${\\\\text{CO}}_{2}$</annotation>\\n </semantics></math> emission flux for 45 cases. We identified the possible omission of power plant emissions in three cases for both inventories. Furthermore, we also showed 17 cases in which <span></span><math>\\n <semantics>\\n <mrow>\\n <msub>\\n <mtext>CO</mtext>\\n <mn>2</mn>\\n </msub>\\n </mrow>\\n <annotation> ${\\\\text{CO}}_{2}$</annotation>\\n </semantics></math> emissions from unknown (non-power plant) sources were highly underestimated in the EDGAR and ODIAC <span></span><math>\\n <semantics>\\n <mrow>\\n <msub>\\n <mtext>CO</mtext>\\n <mn>2</mn>\\n </msub>\\n </mrow>\\n <annotation> ${\\\\text{CO}}_{2}$</annotation>\\n </semantics></math> emission inventories. Due to the simplicity of the employed approaches and their lower computational requirements compared to other methods, they can be applied to large data sets over extended time periods. This enables the acquisition of initial emission estimates for various sources, including those that are unknown and underestimated.</p>\",\"PeriodicalId\":48748,\"journal\":{\"name\":\"Earths Future\",\"volume\":\"12 11\",\"pages\":\"\"},\"PeriodicalIF\":7.3000,\"publicationDate\":\"2024-11-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023EF004411\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Earths Future\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1029/2023EF004411\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earths Future","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1029/2023EF004411","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
摘要
监测温室气体(GHG)排放对制定有效的减排战略至关重要。卫星遥感测量的最新进展使我们能够跟踪全球温室气体排放情况。本研究利用 8 年的同期高空间分辨率 OCO-2 卫星测量数据,评估了各种点源或本地源(尤其是印度的发电厂)的 CO 2 ${text{CO}}_{2}$ 排放情况。使用高斯烟羽(GP)模型对 Carbon Brief(CB)数据库中报告的发电厂排放量进行了评估。共评估了(39 个案例)42 家不同发电厂的二氧化碳排放量,其中 26 家发电厂的二氧化碳排放量被评估了不止一次。在 39 个案例中,有 11 个案例的估计发电厂二氧化碳排放量与 CB 数据库中报告的排放量相差在 ± $\pm $ 25% 以内,有 18 个案例的估计排放量与 CB 数据库中报告的排放量相差在 ± $\pm $ 50% 以内。为了评估 EDGAR 和 ODIAC CO 2 ${text{CO}}_{2}$ 排放清单中的遗漏源和高度低估源,我们估算了 45 个案例的横截面 (CS) CO 2 ${text{CO}}_{2}$ 排放通量。我们发现两个清单中都有三个案例可能遗漏了发电厂的排放。此外,我们还发现了 17 个案例,在 EDGAR 和 ODIAC CO 2 ${text{CO}}_{2}$ 排放清单中,来自未知(非发电厂)来源的 CO 2 ${text{CO}}_{2}$ 排放被严重低估。与其他方法相比,所采用的方法简单且计算要求较低,因此可应用于长时间的大型数据集。这样就可以获得各种排放源的初始排放估计值,包括未知和低估的排放源。
Fossil Fuel CO2 Emission Signatures Over India Captured by OCO-2 Satellite Measurements
Monitoring greenhouse gas (GHG) emissions is crucial for developing effective mitigation strategies. Recent advances in satellite remote-sensing measurements allow us to track greenhouse gas emissions globally. This study assessed emissions from various point or local sources, particularly power plants in India, using 8 years of concurrent high-spatial resolution OCO-2 satellite measurements. A Gaussian plume (GP) model was used to evaluate the power plant emissions reported in the Carbon Brief (CB) database. In total (39 cases), 42 different power plant emissions were assessed, with 26 of them being assessed more than once. The estimated power plant emissions were within 25% of the emissions reported in the CB database in 11 out of 39 cases and within 50% in 18 cases. To evaluate the EDGAR and ODIAC emission inventories in terms of missing and highly underestimated sources, we estimated the cross-sectional (CS) emission flux for 45 cases. We identified the possible omission of power plant emissions in three cases for both inventories. Furthermore, we also showed 17 cases in which emissions from unknown (non-power plant) sources were highly underestimated in the EDGAR and ODIAC emission inventories. Due to the simplicity of the employed approaches and their lower computational requirements compared to other methods, they can be applied to large data sets over extended time periods. This enables the acquisition of initial emission estimates for various sources, including those that are unknown and underestimated.
期刊介绍:
Earth’s Future: A transdisciplinary open access journal, Earth’s Future focuses on the state of the Earth and the prediction of the planet’s future. By publishing peer-reviewed articles as well as editorials, essays, reviews, and commentaries, this journal will be the preeminent scholarly resource on the Anthropocene. It will also help assess the risks and opportunities associated with environmental changes and challenges.