Experimental Estimates of Integral Anthropogenic CO2 Emissions in the City of St. Petersburg

Timofeyev, Yu. M.; Nerobelov, G. M.; Поберовский, А. В.

doi:10.1134/s0001433822030100

Cited by 5 publications

(3 citation statements)

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“…Estimating anthropogenic emissions of CO 2 using the described observation-based method is an illposed (in sense of Hadamard) inverse problem [5][6][7]. Examples of using this method for CO 2 emission estimation (also known as inverse modelling of atmospheric transport) for specific city areas are presented in many modern studies [5][6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…Box and particle dispersion models, together with spectroscopic differential observations of CO 2 total columns (TC), were used in studies [5,6,12,13] to estimate the anthropogenic emissions of St. Petersburg. St. Petersburg's megapolis is one of the largest Russian industrial cities, with a population and area of ~5 mln people and ~1400 km 2 , respectively [16].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, St. Petersburg is a large anthropogenic source of CO 2 . The differences between the anthropogenic CO 2 emissions estimates obtained in studies [5,6,12,13] reach up to 30% and are ~1.5-2 times higher than the city's emissions according to the inventory of anthropogenic CO 2 emissions, ODIAC (Open-source Data Inventory for Anthropogenic CO 2 ) [12,13]. Hence, it is important to set up and validate a model of atmospheric composition appropriate to the territory of interest before using it to estimate anthropogenic CO 2 emissions.…”

Section: Introductionmentioning

confidence: 99%

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Complex Validation of Weather Research and Forecasting—Chemistry Modelling of Atmospheric CO2 in the Coastal Cities of the Gulf of Finland

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The increase of the CO2 content in the atmosphere caused by anthropogenic emissions from the territories of large cities (~70%) is the critical factor in determining the accuracy of emission estimations. Advanced experiment-based methods of anthropogenic CO2 emission estimation are based on the solution of an inverse problem, using accurate measurements of CO2 content and numerical models of atmospheric transport and chemistry. The accuracy of such models decreases the errors of the emission estimations. The aim of the current study is to adapt numerical weather prediction and atmospheric chemistry model WRF-Chem and validate its capability to simulate atmospheric CO2 for the territories of the two large coastal cities of the Gulf of Finland—St. Petersburg (Russia) and Helsinki (Finland). The research has demonstrated that the WRF-Chem model is able to simulate annual variation, as well as the mean seasonal and diurnal variations of the near-surface CO2 mixing ratio, in Helsinki, at a high spatial resolution (2 km). Correlation between the modelled and measured CO2 mixing ratio is relatively high, at ~0.73, with a mean difference and its standard deviation of 0.15 ± 0.04 and 1.7%, respectively. The differences between the WRF-Chem data and the measurements might be caused by errors in the modelling of atmospheric transport and in a priori CO2 emissions and biogenic fluxes. The WRF-Chem model simulates well the column-averaged CO2 mixing ratio (XCO2) in St. Petersburg (January 2019–March 2020), with a correlation of ~0.95 relative to ground-based spectroscopic measurements by the IR–Fourier spectrometer Bruker EM27/SUN. The error of the XCO2 modelling constitutes ~0.3%, and most likely is related to inaccuracies in chemical boundary conditions and a priori anthropogenic CO2 emissions. The XCO2 time series in St. Petersburg by the WRF-Chem model fits well with global CAMS reanalysis and CarbonTracker-modelled data (the differences are less than ~1%). However, due to much higher spatial resolution (2 vs. over 100 km), the WRF-Chem data are in the best agreement with the ground-based remote measurements of XCO2. According to the study, the modelling errors of XCO2 in St. Petersburg during the whole simulated period are sufficiently minimal to fit the requirement of “Error ≤ 0.2%” in 60% of cases. This requirement should be satisfied to evaluate properly the anthropogenic CO2 emissions of St. Petersburg on a city-scale.

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