Ke Che, Thomas Lauvaux, Noemie Taquet, Wolfgang Stremme, Yang Xu, Carlos Alberti, Morgan Lopez, Agustín García-Reynoso, Philippe Ciais, Yi Liu, Michel Ramonet, Michel Grutter
{"title":"利用地面和空间遥感估算墨西哥城的二氧化碳排放量","authors":"Ke Che, Thomas Lauvaux, Noemie Taquet, Wolfgang Stremme, Yang Xu, Carlos Alberti, Morgan Lopez, Agustín García-Reynoso, Philippe Ciais, Yi Liu, Michel Ramonet, Michel Grutter","doi":"10.1029/2024JD041297","DOIUrl":null,"url":null,"abstract":"<p>The Mexico City Metropolitan Area (MCMA) stands as one of the most densely populated urban regions globally. To quantify the urban <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 in the MCMA, we independently assimilated observations from a dense column-integrated Fourier transform infrared (FTIR) network and OCO-3 Snapshot Area Map observations between October 2020 and May 2021. Applying a computationally efficient analytical Bayesian inversion technique, we inverted for surface fluxes at high spatio-temporal resolutions (1-km and 1-hr). The fossil fuel (FF) emission estimates of 5.08 and 6.77 Gg<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>/hr reported by the global and local emission inventories were optimized to 4.85 and 5.51 Gg<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>/hr based on FTIR observations over this 7 month period, highlighting a convergence of posterior estimates. The modeled biogenic flux estimate of −0.14 Gg<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>/hr was improved to −0.33 to −0.27 Gg<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>/hr, respectively. It is worth noting that utilizing observations from three primary sites significantly enhanced the accuracy of estimates (13.6 <span></span><math>\n <semantics>\n <mrow>\n <mo>∼</mo>\n </mrow>\n <annotation> ${\\sim} $</annotation>\n </semantics></math> 29.2%) around the other four. Using FTIR posterior estimates can improve simulation with the OCO-3 data set. OCO-3 shows a similar decreasing trend in FF emissions (from 6.37 Gg<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>/hr to 6.36 and 5.04 Gg<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>/hr) as FTIR, but its correction trends for biogenic sources differ, changing from 0.37 to 0.48 Gg<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>/hr. The primary reason is OCO-3's lower temporal sampling density. Aligning the FTIR inversion timing with that of OCO-3 yielded comparable corrections for FF emissions, yet discrepancies in biogenic emissions persisted, which can be attributed to their different sampling locations in the rural region and discrepancy in X<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> observations. Our findings mark a significant step toward validating OCO-3 and FTIR inversion results in metropolitan region.</p>","PeriodicalId":15986,"journal":{"name":"Journal of Geophysical Research: Atmospheres","volume":"129 20","pages":""},"PeriodicalIF":3.8000,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024JD041297","citationCount":"0","resultStr":"{\"title\":\"CO2 Emissions Estimate From Mexico City Using Ground- and Space-Based Remote Sensing\",\"authors\":\"Ke Che, Thomas Lauvaux, Noemie Taquet, Wolfgang Stremme, Yang Xu, Carlos Alberti, Morgan Lopez, Agustín García-Reynoso, Philippe Ciais, Yi Liu, Michel Ramonet, Michel Grutter\",\"doi\":\"10.1029/2024JD041297\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The Mexico City Metropolitan Area (MCMA) stands as one of the most densely populated urban regions globally. To quantify the urban <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 in the MCMA, we independently assimilated observations from a dense column-integrated Fourier transform infrared (FTIR) network and OCO-3 Snapshot Area Map observations between October 2020 and May 2021. Applying a computationally efficient analytical Bayesian inversion technique, we inverted for surface fluxes at high spatio-temporal resolutions (1-km and 1-hr). The fossil fuel (FF) emission estimates of 5.08 and 6.77 Gg<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>/hr reported by the global and local emission inventories were optimized to 4.85 and 5.51 Gg<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>/hr based on FTIR observations over this 7 month period, highlighting a convergence of posterior estimates. The modeled biogenic flux estimate of −0.14 Gg<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>/hr was improved to −0.33 to −0.27 Gg<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>/hr, respectively. It is worth noting that utilizing observations from three primary sites significantly enhanced the accuracy of estimates (13.6 <span></span><math>\\n <semantics>\\n <mrow>\\n <mo>∼</mo>\\n </mrow>\\n <annotation> ${\\\\sim} $</annotation>\\n </semantics></math> 29.2%) around the other four. Using FTIR posterior estimates can improve simulation with the OCO-3 data set. OCO-3 shows a similar decreasing trend in FF emissions (from 6.37 Gg<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>/hr to 6.36 and 5.04 Gg<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>/hr) as FTIR, but its correction trends for biogenic sources differ, changing from 0.37 to 0.48 Gg<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>/hr. The primary reason is OCO-3's lower temporal sampling density. Aligning the FTIR inversion timing with that of OCO-3 yielded comparable corrections for FF emissions, yet discrepancies in biogenic emissions persisted, which can be attributed to their different sampling locations in the rural region and discrepancy in X<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> observations. 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引用次数: 0
摘要
墨西哥城大都市区(MCMA)是全球人口最稠密的城市地区之一。为了量化墨西哥城都会区的城市 CO 2 ${text{CO}}_{2}$ 排放量,我们独立同化了 2020 年 10 月至 2021 年 5 月期间密集的列积分傅立叶变换红外(FTIR)网络观测数据和 OCO-3 快照区域图观测数据。我们采用计算高效的贝叶斯分析反演技术,对高时空分辨率(1 公里和 1 小时)的地表通量进行了反演。根据这7个月期间的傅立叶变换红外观测数据,全球和地方排放清单报告的化石燃料(FF)排放量估计值分别为5.08和6.77千兆克CO 2 ${text{CO}}_{2}$ /小时,优化后分别为4.85和5.51千兆克CO 2 ${text{CO}}_{2}$ /小时,凸显了后验估计值的趋同。模拟的生物通量估计值-0.14 千兆克 CO 2 ${text{CO}}_{2}$ /小时分别提高到-0.33 到-0.27 千兆克 CO 2 ${text{CO}}_{2}$ /小时。值得注意的是,利用三个主要观测点的观测数据大大提高了其他四个观测点的估计精度(13.6 ∼ ${\sim} $ 29.2%)。使用傅立叶变换红外后验估计值可以改进对 OCO-3 数据集的模拟。OCO-3 显示了与 FTIR 相似的 FF 排放下降趋势(从 6.37 千兆克 CO 2 ${text{CO}}_{2}$ /小时下降到 6.36 和 5.04 千兆克 CO 2 ${text{CO}}_{2}$ /小时),但其生物源修正趋势不同,从 0.37 千兆克 CO 2 ${text{CO}}_{2}$ /小时变化到 0.48 千兆克 CO 2 ${text{CO}}_{2}$ /小时。主要原因是 OCO-3 的时间采样密度较低。将傅立叶变换红外反演时间与 OCO-3 的反演时间相一致,可以得到类似的傅立叶排放修正,但生物源排放的差异仍然存在,这可能是由于它们在农村地区的采样位置不同以及 X CO 2 ${\text{CO}}_{2}$ 观测结果的差异造成的。我们的发现标志着向验证大都市地区的 OCO-3 和傅立叶变换反演结果迈出了重要一步。
CO2 Emissions Estimate From Mexico City Using Ground- and Space-Based Remote Sensing
The Mexico City Metropolitan Area (MCMA) stands as one of the most densely populated urban regions globally. To quantify the urban emissions in the MCMA, we independently assimilated observations from a dense column-integrated Fourier transform infrared (FTIR) network and OCO-3 Snapshot Area Map observations between October 2020 and May 2021. Applying a computationally efficient analytical Bayesian inversion technique, we inverted for surface fluxes at high spatio-temporal resolutions (1-km and 1-hr). The fossil fuel (FF) emission estimates of 5.08 and 6.77 Gg/hr reported by the global and local emission inventories were optimized to 4.85 and 5.51 Gg/hr based on FTIR observations over this 7 month period, highlighting a convergence of posterior estimates. The modeled biogenic flux estimate of −0.14 Gg/hr was improved to −0.33 to −0.27 Gg/hr, respectively. It is worth noting that utilizing observations from three primary sites significantly enhanced the accuracy of estimates (13.6 29.2%) around the other four. Using FTIR posterior estimates can improve simulation with the OCO-3 data set. OCO-3 shows a similar decreasing trend in FF emissions (from 6.37 Gg/hr to 6.36 and 5.04 Gg/hr) as FTIR, but its correction trends for biogenic sources differ, changing from 0.37 to 0.48 Gg/hr. The primary reason is OCO-3's lower temporal sampling density. Aligning the FTIR inversion timing with that of OCO-3 yielded comparable corrections for FF emissions, yet discrepancies in biogenic emissions persisted, which can be attributed to their different sampling locations in the rural region and discrepancy in X observations. Our findings mark a significant step toward validating OCO-3 and FTIR inversion results in metropolitan region.
期刊介绍:
JGR: Atmospheres publishes articles that advance and improve understanding of atmospheric properties and processes, including the interaction of the atmosphere with other components of the Earth system.