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Source decomposition of eddy-covariance CO2 flux measurements for evaluating a high-resolution urban CO2 emissions inventory

Abstract : We present the comparison of source-partitioned CO2 flux measurements with a high-resolution urban CO2 emissions inventory (Hestia). Tower-based measurements of CO and 14C are used to partition net CO2 flux measurements into fossil and biogenic components. A flux footprint model is used to quantify spatial variation in flux measurements. We compare the daily cycle and spatial structure of Hestia and eddy-covariance derived fossil fuel CO2 emissions on a seasonal basis. Hestia inventory emissions exceed the eddy-covariance measured emissions by 0.36 µmol m-2 s-1 (3.2%) in the cold season and 0.62 µmol m-2 s-1 (9.1%) in the warm season. The daily cycle of fluxes in both products matches closely, with correlations in the hourly mean fluxes of 0.86 (cold season) and 0.93 (warm season). The spatially averaged fluxes also agree in each season and a persistent spatial pattern in the differences during both seasons that may suggest a bias related to residential heating emissions. In addition, in the cold season, the magnitudes of average daytime biological uptake and nighttime respiration at this flux site are approximately 15% and 27% of the mean fossil fuel CO2 emissions over the same time period, contradicting common assumptions of no significant biological CO2 exchange in northern cities during winter. This work demonstrates the effectiveness of using trace gas ratios to adapt eddy-covariance flux measurements in urban environments for disaggregating anthropogenic CO2 emissions and urban ecosystem fluxes at high spatial and temporal resolution.
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Submitted on : Thursday, September 15, 2022 - 1:27:30 PM
Last modification on : Saturday, September 17, 2022 - 3:24:49 AM

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Kai Wu, Kenneth J. Davis, Natasha L. Miles, Scott J. Richardson, Thomas Lauvaux, et al.. Source decomposition of eddy-covariance CO2 flux measurements for evaluating a high-resolution urban CO2 emissions inventory. Environmental Research Letters, 2022, 17, ⟨10.1088/1748-9326/ac7c29⟩. ⟨insu-03778108⟩

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