The state of the atmosphere in the city of Krasnoyarsk (Russia) in indicators of sustainable development

Shaparev, N. Ya.; Tokarev, A V; Yakubailik, Oleg

doi:10.1080/13504509.2019.1699879

Cited by 12 publications

(3 citation statements)

References 12 publications

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“…The orographic features of an area can considerably influence its natural climatic characteristics. In particular, they can be a cause of increased concentrations of pollutants [20], degradation of arable lands and desertification of river valleys [21]. Within this context, it is of interest to estimate the in-fluence of the area relief on fog formation, thus, their possible interconnections are considered in this study.…”

Section: Influence Of the Relief And Formation Of Turbulent Structuresmentioning

confidence: 99%

Fogs on the Yenisei River (Krasnoyarsk, Russia)

2022

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This work presents a study of the formation of cooling and steam fogs on the Yenisei River during 2020. The formation of the above types of fogs resulted from changes in the hydrothermal regime downstream the Krasnoyarsk hydroelectric power station after its construction and it is also associated with a combination of weather conditions and water temperature in the river. Advective cooling fogs form in summer when moist air cools over the surface of cold moving water. The formation of steam fogs in winter, autumn and spring is associated with the cooling of water vapor above the river surface by cold lower layers of air. The conditions for the formation of fogs are analyzed depending on the air temperature and humidity, water temperature, and wind speed. Based on the remote sensing data, the spatial distribution of steam fogs along the river is determined.

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Section: Influence Of the Relief And Formation Of Turbulent Structuresmentioning

confidence: 99%

Fogs on the Yenisei River (Krasnoyarsk, Russia)

2022

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“…The absence of wind covers the city by a smog-shroud just in several hours (Figure 1). Regulatory authorities and ecologists often record the excess of pollutants indicators, such as Benzo(a)pyrene, NO 2 , CO, SO 2 , and PM10 [17,18]; the city is often included in the top global air quality index anti-rating [19], and this problem seriously escalated in the society (the activists widely use "the black sky" ideologeme attracting attention to the problem) [20]. The major management decisions are made based on the air quality monitoring network and a regulatory document on the gross emissions limits [21].…”

Section: Applicationmentioning

confidence: 99%

Graz Lagrangian Model (GRAL) for Pollutants Tracking and Estimating Sources Partial Contributions to Atmospheric Pollution in Highly Urbanized Areas

et al. 2020

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Computational modeling allows studying the air quality problems in depth and provides the best solution reducing the population risks. This research demonstrates the Graz Lagrangian model effectiveness for assessing emission sources contributions to the air pollution: particles tracking and accumulation estimate. The article describes model setting up parameters and datasets preparation for the analysis. The experiment simulated the dispersion from the main groups of emission sources for real weather conditions during 96 h of December 2018, when significant excess of NO2, CO, SO2, PM10, and benzo(a)pyrene concentrations were observed in the Krasnoyarsk surface atmospheric layer. The computational domain was a parallelepiped of 40 × 30 × 2.5 km, which was located deep inside the Eurasian continent on a heterogeneous landscape exaggerated by high-rise buildings, with various pollutions sources and the ice-free Yenisei River. The results demonstrated an excellent applicability of the Lagrange model for hourly tracking of particle trajectories, taking into account the urban landscape. For values <1 MPC (maximum permissible concentration) of peak pollutants concentrations, the coincidences were 93 cases, and for values < 0.1 shares of MPC, there were 36 cases out of the total number of 97. The same was found for the average daily concentration for values <1 MPC—31, and for values <0.1 MPC—5 matches out of 44. Wind speeds COR—65.3%, wind directions COR—68.6%. The Graz Lagrangian model showed the ability to simulate air quality problems in the Krasnoyarsk greater area conditions.

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“…The most polluted cities include Krasnoyarsk. It is possible to assess the impact of various types of emission sources on the formation of unfavorable conditions by use of modern calculation models for the transport of impurities in the atmosphere, analysis of ground and satellite measurements and data re-analysis [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Parametrization of temperature inversion over Krasnoyarsk city

Belolipetskii

Genova

2023

E3S Web Conf.

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Atmospheric pollution is associated with weather conditions. Meteorological processes make a significant contribution to the creation of unfavorable meteorological conditions (NMU). NMUs are a special shortterm combination of meteorological factors contributing to the accumulation of pollutants in the surface layer of atmospheric air. We draw a particular attention to the temperature stratification of the lower layers of the atmosphere which can cause a temperature inversion – an increase in air temperature with altitude. Temperature inversion limits the vertical dispersion of harmful impurities in the atmosphere. Observational data show that the increased level of air pollution in the city of Krasnoyarsk is most pronounced in winter with temperature inversion and weak winds (no more than 1-2 m/s). To predict periods of increased concentration of pollutants in the boundary layer of the Krasnoyarsk atmosphere we consider a numerical algorithm for determining the time intervals of formation and destruction of temperature inversion based on a onedimensional vertical model.

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The state of the atmosphere in the city of Krasnoyarsk (Russia) in indicators of sustainable development

Cited by 12 publications

References 12 publications

Fogs on the Yenisei River (Krasnoyarsk, Russia)

Fogs on the Yenisei River (Krasnoyarsk, Russia)

Graz Lagrangian Model (GRAL) for Pollutants Tracking and Estimating Sources Partial Contributions to Atmospheric Pollution in Highly Urbanized Areas

Parametrization of temperature inversion over Krasnoyarsk city

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