Russian permafrost regions are unparalleled in extent, history of development, population presence, and the scale of economic activities. This special issue, «Permafrost Regions in Transition», provides a timely opportunity to (a) examine major issues associated with changing permafrost conditions in natural environments and areas of economic development; (b) present insights into new methods of permafrost investigations; and (c) describe new opportunities and risks threatening sustainable development of Arctic populations and industrial centers in Russia. The issue begins with papers focused on methods of permafrost research, followed by papers focused on examining changes in permafrost under natural conditions, and in Arctic settlements. The last two papers examine potential impacts of permafrost degradation on the Russian economy and potential health implications.

The massive ice (MI) bodies are widespread phenomena on Chukotka coastal plains. Although they have been studying since 1930s, stable isotope method was applied for the ice beds quite recently. In this study cryostratigraphy and stable oxygen and hydrogen isotope composition of MI bodies on the extreme North-Eastern Chukotka (near Lavrentiya settlement and Koolen’ lake) have been studied in detail. It was concluded that studied MI bodies have intrasedimental origin and most likely are dated back to the Late Pleistocene age. Mean δ¹⁸O values range from –18.5 ‰ to –15 ‰ whereas mean δ² H values range from –146‰ to –128 ‰ that is higher than expected for the Late Pleistocene ice bodies in this region, which most likely resulted from isotopic fractionation during freezing of water-saturated sediments in a closed system when forming ice became isotopically enriched compared with initial water. The analysis of co-isotope ratios for MI shows that initial water is mainly of meteoric origin (precipitation, water of lakes and taliks).

The advantages and limitations of the petrography method and the relevance of its use for the study of natural ice are reviewed in the present work. The petrographic method of ground ice study is often used for solving paleogeographic issues. The petrofabric analysis of ground ice is not only useful for descriptive purposes but, like the study of cryostructures, helps to infer growth processes and conditions. Different types of natural ice have specific features that can help us to determine ice genesis. Surface ice, such as glacier ice is often presented by foliation formed by large crystals (50-60 mm); lake ice is characterised by the upper zone of small (6 mm x 3 mm) dendritic and equigranular crystals, which change with increasing depth to large (may exceed 200 mm) columnar and prismatic crystals; segregated ice is composed by crystals forming foliation. Ground ice, such as ice wedge is presented by vertical-band appearance and small crystals (2-2.5 mm); closed-cavity ice is often distinguished by radial-ray appearance produced by elongated ice crystals; injection ice is composed by anhedral crystals, showing the movement of water; snowbank ice is presented by a high concentration of circular bubbles and small (0.1-1 mm) equigranular crystals; icing is described by foliation and mostly columnar crystals. Identification of the origin of ground ice is a complicated task for geocryology because it is difficult to distinguish different types of ground ice based on only visual explorations. The simplest way to get an ice texture pattern is by using polarized light. Distinctions between genetic types of ground ice are not always made in studies, and that can produce erroneous inferences. Petrography studies of an ice object are helpful to clarify the data interpretation, e.g., of isotopic analyses. It is particularly relevant for heterogeneous ice wedges’ study.

In this paper, the features of landscape indication of permafrost characteristics required for assessing the environmental state at various research scales are discussed. A number of permafrost characteristics affect the geoecological state and stability of natural landscapes, especially in the context of climate warming and technogenic surface disturbances. These include the distribution, temperature regime, thickness and cryogenic structure of permafrost, seasonal freezing and thawing, as well as the development of cryogenic processes. Their determination through the landscape view, however, is ambiguous. The choice of certain permafrost characteristics for geoecological assessment is based on many years of experience in creating cryo-ecological maps on a landscape basis by the school of Faculty of Geography, Moscow State University. The recent studies on the identification of regional cryoindicators are analyzed, including the issues of cryogenic landscapes classification and clarification of the boundaries of geocryological zones using the landscape structural method. The content of the two maps, «Permafrost Landscape Differentiation Map of the Russia Cryolithozone» at a scale of 1: 15,000,000 and «Permafrost Landscape Map of the Republic of Sakha (Yakutia)» at a scale of 1: 1,500,000, is presented, as well as their use as a basis for environmental planning and geoecological assessment.

The lakes of the Arctic lowlands are both the unique indicator and the result of climatic and permafrost changes. Remote sensing methods and field measurements were used to consider the patterns and features of the morphometric indicators dynamics of the Anadyr lowland lakes over 65 years. We analyzed the parameters of 36 lakes with an area of 0.02–0.3 km2 located in the bottoms of drained lake basins, in river floodplains, on sea-shore terraces. Field studies were conducted on 22 typical lakes. The considered dynamics of seasonal thawing are based on the monitoring of the active layer for 1994–2020. Due to an increase of mean annual air temperature by 1.8 °C, as well as an increase and then a decrease in the mean annual precipitation by 135 mm, the average share of a lake area in the study area decreased by 24%. It is shown for the first time that cryogenic processes of the lacustrine coastal zone affect the change in the area of lakes simultaneously with the influence of precipitation and air temperature. Based on field observations, we considered two causes of natural drainage: discharge of the lakes through newly formed thermokarst and thermoerosional surface flow channels and decrease in suprapermafrost groundwater recharge as a result of changing depth of seasonally thawed active layer in the coastal zone.

This paper provides information on active layer thickness (ALT) dynamics, or seasonal thawing above permafrost, from a Circumpolar Active Layer Monitoring (CALM) site near the city of Norilsk on the Taimyr Peninsula (north-central Siberia) and the influences of meteorological and landscape properties on these dynamics under a warming climate, from 2005 to 2020. The average ALT in loamy soils at this 1 ha CALM site over the past 16 years was 96 cm, higher than previous studies from 1980s conducted at the same location, which estimated ALT to be 80 cm. Increasing mean annual air temperatures in Norilsk correspond with the average ALT increasing trend of 1 cm/year for the observation period. Active layer development depends on summer thermal and precipitation regimes, time of snowmelt, micro-landscape conditions, the cryogenic structure (ice content) of soils, soil water content leading up to the freezing period, drainage, and other factors. Differences in ALT, within various micro landscape conditions can reach 200% in each of the observation periods.

The Infrastructure stability on permafrost is currently an important topic as the Arctic countries are developing climate change adaptation and mitigation programs. Assessing the sustainability of infrastructure facilities (especially in urban environments) is a difficult task as it depends on many parameters. This article discusses the city of Vorkuta, which is located in the northwest of Russia. This city differs from many others built on permafrost because most of buildings were built according to Principle II (The Active Method) of construction on permafrost with thawing soil prior to construction. Assessments of the engineering and geocryological conditions, basic principles of construction in the city, and reasons for building failures, were carried out within this study. The research is based on publications, open data about buildings, and visual observations in Vorkuta. About 800 buildings are in use in Vorkuta in 2020 (43% of what it was 50 years ago). According to the analysis, about 800 houses have been demolished or disconnected from utility lines over the past 50 years (about 250 of these are still standing, pending demolition). Since 1994, the construction of new residential buildings has almost stopped. Therefore, buildings that have been in use for over 50 years will account for 90% of the total residential housing stock by 2040. The effects of climate change in the city will depend primarily on the principle of construction employed and on the geocryological conditions of the district. Buildings constructed according to Principle I (The Passive Method) were found to be more vulnerable due to a decrease in permafrost bearing capacity. The impact of increasing air temperature on some of the buildings built on bedrock (the central part of the city) and some built on thawing soil will be minimal, as other factors are more significant.

In the paper, we consider a method of ground temperature monitoring using the thermometric boreholes and computer modeling the residential buildings with the pile foundation in the city of Salekhard; note that it is located in the permafrost zone. Construction of the residential buildings and industrial structures in the permafrost zone and their operation is carried out according to the principle of preserving the frozen state of foundations. For ground temperature monitoring, thermometric boreholes are used. In a given time period, the measured temperatures are transferred to a server for further processing. Information about the temperature is an important factor for the safety of the buildings and it can be used to evaluate the piles bearing capacity. It allows to propose options for the soil thermal stabilization or to eliminate the detected technogenic heat sources. An approach of mathematical modeling to reconstruct the temperature fields in the pile foundation base of a building is discussed taking into account the data of temperature monitoring. 24 boreholes were equipped with more than 400 in-borehole thermal sensors for testing the method under the residential building I. The preliminary modeling is carried out for December and January 2020 for the contact thermal conductivity model with phase transition with the upper part of the geological section typical for Salekhard (the sandy soils). The modeling describes the freezing processes during the months in detail. The thermal monitoring allows to say that the ground in the base of the Residential building I is stable. But there are detected heat transfers near the borehole T1 at the depth of 12–14 m. The combination of monitoring and computer modeling makes it possible to assess the safety of the operation of the residential buildings in cities located in the permafrost zones.

The relevance of the study lay in the need to obtain reliable information on the possible economic consequences of changing geocryological conditions in the Russian Arctic, to find methods for preventing (reducing) potential damage, increasing the safety of the population and economy in the areas of the highest geocryological risks, and ensuring balanced socio-economic development in the Russian Arctic permafrost zone for the long term. The study aimed to assess the cost of fixed assets, including their most vulnerable part – buildings and structures (case study: municipalities of the Russian Arctic Asian sector). Economic sectoral structure was evaluated in accordance with the Russian Standard Industrial Classification of Economic Activities using primary statistical data – closed data from companies accounting reports. The work used statistical, cartographic, and visual-graphic methods, as well as methods for analyzing spatial information and microeconomic data. According to calculations, the Russian Arctic Asian sector concentrates the fixed assets of commercial companies with a total value of about 14.8 trillion rubles, including buildings and structures worth 10.7 trillion rubles. The obtained calculated data can be used in modeling the directions of state policy in the field of climate change adaptation and territory protection from natural hazards.

In this paper, we review both practical and theoretical assessments for evaluating radon geohazards from permafrost landforms in northern environments (>60º N). Here, we show that polar amplification (i.e. climate change) leads to the development of thawing permafrost, ground subsidence, and thawed conduits (i.e. Taliks), which allow radon migration from the subsurface to near surface environment. Based on these survey results, we conjecture that abruptly thawing permafrost soils will allow radon migration to the near surface, and likely impacting human settlements located here. We analyze potential geohazards associated with elevated ground concentrations of natural radionuclides. From these results, we apply the main existing legislation governing the control of radon parameters in the design, construction and use of buildings, as well as existing technologies for assessing the radon hazard. We found that at present, these laws do not consider our findings, namely, that increasing supply of radon to the surface during thawing of permafrost will enhance radon exposure, thereby, changing prior assumptions from which the initial legislation was determined. Hence, the legislation will likely need to respond and reconsider risk assessments of public health in relation to radon exposure. We discuss the prospects for developing radon geohazard monitoring, methodical approaches, and share recommendations based on the current state of research in permafrost effected environments. 

The COVID-19 pandemic has severely affected all countries and the global scientific agenda, particularly that of health, economy, environment, geography and geosciences in general. This Special Issue is also a contribution to the global efforts of the scientific community in the analysis of the geography of the COVID-19 pandemic with public health, economic and environmental consequences. Two blocks of papers are considered: (1) the socio-spatial, statistical and geographical analysis of COVID-19 distributions; and (2) the impacts of the pandemic lockdown on the environment, air pollution, and the quality of water.

The natural and socio-economic characteristics of the territory play a decisive role in the spread of the pandemic of COVID-19. It provoked a restructuring process in practically all fields of the social life. Its main areas were laid before the pandemic, but the changes were sharply accelerated by the pandemic. In analyzing a number of Russian and foreign publications, the authors discuss the main areas and methods of human-geographical study of the development and consequences of the pandemic. The constantly growing flow of publications in this field can be divided into three major parts: studies of the spatial spread of infection on the different stages; analysis of demographic, (geo) political and economic implications of the pandemic, and attempts to forecast the impact of social and technological changes accelerated by it on territorial structures. The authors note in particular that the geopolitical picture of the world with the division of countries into developed and developing, rich and poor, authoritarian and democratic, Eastern and Western, became much less clear. The most obvious geopolitical consequence of the pandemic is the further fragmentation of the political and socio-economic space. Not only state, but often also administrative boundaries have turned into almost insurmountable barriers for people and trade. The COVID crisis has opened new opportunities for a reasonable combination of the concentration of social life in the «archipelago» of large cities and the development of other territories.

The spread of the 2019 novel coronavirus disease (COVID-19) has engulfed the world with a rapid, unexpected, and far-reaching global crisis. In the study of COVID-19, Geographic Information Systems (GIS) and Remote Sensing (RS) have played an important role in many aspects, especially in the fight against COVID-19. This review summarises 102 scientific papers on applications of GIS and RS on studies of the COVID-19 pandemic. In this study, two themes of GIS and RS-related applications are grouped into the six categories of studies of the COVID-19 including spatio-temporal changes, WebGISbased mapping, the correlation between the COVID-19 and natural, socio-economic factors, and the environmental impacts. The findings of this study provide insight into how to apply new techniques (GIS and RS) to better understand, better manage the evolution of the COVID-19 pandemic and effectively assess its impacts.

 As the COVID-19 outbreak spread worldwide, multidisciplinary researches on COVID-19 are vastly developed, not merely focusing on the medical sciences like epidemiology and virology. One of the studies that have developed is to understand the spread of the disease. This study aims to assess the contribution of crowdsourcing-based data from social media in understanding locations and the distribution patterns of COVID-19 in Indonesia. In this study, Twitter was used as the main source to retrieve location-based active cases of COVID-19 in Indonesia. We used Netlytic (www.netlytic.org) and Phyton’s script namely GetOldTweets3 to retrieve the relevant online content about COVID-19 cases including audiences’ information such as username, time of publication, and locations from January 2020 to August 2020 when COVID-19 active cases significantly increased in Indonesia. Subsequently, the accuracy of resulted data and visualization maps was assessed by comparing the results with the official data from the Ministry of Health of Indonesia. The results show that the number of active cases and locations are only promising during the early period of the disease spread on March – April 2020, while in the subsequent periods from April to August 2020, the error was continuously exaggerated. Although the accuracy of crowdsourcing data remains a challenge, we argue that crowdsourcing platforms can be a potential data source for an early assessment of the disease spread especially for countries lacking the capital and technical knowledge to build a systematic data structure to monitor the disease spread.

The coronavirus (COVID-19) outbreak has created havoc all across the States and Union Territories (UTs) of India since its beginning on 30th January 2020. As of 1st January 2021, India has recorded 10,305,788 cases and 149,218 deaths from this deadly pandemic. It has been observed through the data; across states and UTs, the trend and pattern of this disease are not similar at all. There are many reasons for these dissimilarities which are categorized into indicators to assess the vulnerability in this study. We have examined vulnerabilities in 28 states and 8 UTs of India. Livelihood Vulnerability Index (LVI) has been applied with certain modifications to calculate the Vulnerability Index (VI). The figure resulting from the vulnerability assessment corresponds that the factors involved in the three-section exposure, sensitivity, and adaptive capacity had a significant impact on deciding the vulnerability of the population. The result identified the states and UTs which are more vulnerable and need more attention from the government and policymakers. The proposed method of study is unique in its sense as vulnerability index calculation is purely based on a secondary source of data and therefore has an expectation of a higher degree of practical application.

An outbreak of the 2019 Novel Coronavirus Disease (COVID-19) in China caused by the emergence of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARSCoV2) spreads rapidly across the world and has negatively affected almost all countries including such the developing country as Vietnam. This study aimed to analyze the spatial clustering of the COVID-19 pandemic using spatial auto-correlation analysis. The spatial clustering including spatial clusters (high-high and low-low), spatial outliers (low-high and high-low), and hotspots of the COVID-19 pandemic were explored using the local Moran’s I and Getis-Ord’s G* i statistics. The local Moran’s I and Moran scatterplot were first employed to identify spatial clusters and spatial outliers of COVID-19. The Getis-Ord’s G* i statistic was then used to detect hotspots of COVID-19. The method has been illustrated using a dataset of 86,277 locally transmitted cases confirmed in two phases of the fourth COVID-19 wave in Vietnam. It was shown that significant low-high spatial outliers and hotspots of COVID-19 were first detected in the NorthEastern region in the first phase, whereas, high-high clusters and low-high outliers and hotspots were then detected in the Southern region of Vietnam. The present findings confirm the effectiveness of spatial auto-correlation in the fight against the COVID-19 pandemic, especially in the study of spatial clustering of COVID-19. The insights gained from this study may be of assistance to mitigate the health, economic, environmental, and social impacts of the COVID-19 pandemic.

The world was shocked by an unprecedented outbreak caused by coronavirus disease 2019 (COVID-19). In Malaysia, it started with the largest number of COVID-19 cases with the first wave of infection on 25 January 2020. The objectives of this paper are to obtain the perspective of the respondents about the need for web-mapping in the form of mapping the geospatial data in Malaysia and to visualize the current online datasets of COVID-19 disease case clusters. The study area would cover the entire Malaysia since a rapidly increasing number of citizens were affected by this virus. To be specific, this study focused on the active clusters of COVID-19 in Malaysia. The data were freely shared in real-time by referring to the Ministry of Health (MOH) channel. The hotspots map were explored using the Map Editor by Cloud GIS. The approach has been illustrated using a dataset of whole Malaysia which are locally transmitted confirmed cases in four phases of COVID-19 wave in Malaysia. This study is significant to raise public awareness of the virus, especially among Malaysian citizens. It can provide an accurate estimation of the cluster tracking of the COVID-19 system by using geospatial technology. Therefore, people are more concerned and followed all the Standard Operating Procedure (SOP) provided by the government to prevent the spread of COVID-19.

Spatial distribution and spreading patterns of COVID-19 in Thailand were investigated in this study for the 1 April – 23 July 2021 period by analyzing COVID-19 incidence’s spatial autocorrelation and clustering patterns in connection to population density, adult population, mean income, hospital beds, doctors and nurses. Clustering analysis indicated that Bangkok is a significant hotspot for incidence rates, whereas other cities across the region have been less affected. Bivariate Moran’s I showed a low relationship between COVID-19 incidences and the number of adults (Moran’s I = 0.1023- 0.1985), whereas a strong positive relationship was found between COVID-19 incidences and population density (Moran’s I = 0.2776-0.6022). Moreover, the difference Moran’s I value in each parameter demonstrated the transmission level of infectious COVID-19, particularly in the Early (first phase) and Spreading stages (second and third phases). Spatial association in the early stage of the COVID-19 outbreak in Thailand was measured in this study, which is described as a spatio-temporal pattern. The results showed that all of the models indicate a significant positive spatial association of COVID-19 infections from around 10 April 2021. To avoid an exponential spread over Thailand, it was important to detect the spatial spread in the early stages. Finally, these findings could be used to create monitoring tools and policy prevention planning in future.

The relationship between the dynamics of the atmospheric pollutants and meteorological conditions has been analyzed during the COVID-19 pandemic in Moscow in spring, 2020. The decrease in traffic emissions during the lockdown periods from March 30^th until June 8^th played an important role in the decrease (up to 70%) of many gaseous species and aerosol PM₁₀ concentrations and in the increase of surface ozone (up to 18%). The analysis of the pollutant concentrations during the lockdown showed much smoother diurnal cycle for most of the species due to the reduced intensity of traffic, especially during rush hours, compared with that before and after the lockdown. The specific meteorological conditions with low temperatures during the lockdown periods as well as the observed smoke air advection have made a considerable contribution to the air quality. After removing the cases with smoke air advection the decrease in concentration of many pollutants was observed, especially in NO_x  and PM₁₀. The analysis of Pearson partial correlation coefficients with fixed temperature factor has revealed a statistically significant negative correlation between the Yandex self-isolation indices (SII), which can be used as a proxy of traffic intensity, and daily concentrations of all pollutants, except surface ozone, which has a statistically significant positive correlation with SII caused by specific photochemical reactions. In situations with SII>2.5 more favorable conditions for surface ozone generation were observed due to smaller NOx and the higher O₃ /NOx ratios at the same ratio of VOC/NOx. In addition, this may also happen, since during the Arctic air advection, which was often observed during the lockdown period, the growth of ozone could be observed due to the downward flux of the ozone-rich air from the higher layers of the atmosphere.

The COVID-19 pandemic has had a major impact on various sectors. Iran is one of the countries most affected by this pandemic. After considering the huge impact, the government imposed strict rules prohibiting social gatherings and restricting travel for the entire population following the large number of victims in the country. These restrictions lead to changes in the environment, especially air quality. The purpose of this study was to find out how the COVID-19 pandemic affected air quality in Iran following the activity restrictions in the region. The method used in this research was based on the use of multitemporal Sentinel-5P data processing with scripts available on the Google Earth Engine applied on the images, acquired in the period before and after the COVID-19 pandemic. The data used included the image collection of Sentinel-5P NRTI CO: Near Real-Time Carbon Monoxide, Sentinel-5P NRTI NO₂: Near Real-Time Nitrogen Dioxide and Sentinel-5P NRTI SO₂: Near Real-Time Sulphur Dioxide. The results showed, that for Iran in general, changes in the concentration of CO are clearly visible in urban areas with high population activity such as Tehran, where there was a decrease from 0.05 to 0.0286 mol/m², while for other areas it is also influenced by the varying climate conditions, which affect the level of pollution. For the NO₂ pollutant, there was a significant decrease in pollution levels in big cities such as Tehran, Qom, Isfahan and Mashhad from 0.0002 to 0.000114 mol/m². For the SO₂ pollutant, there was a decrease in pollution levels in Iran’s big cities from 0.0005 to 0.0000714 mol/m². For Tehran province, which is the most populous and busiest province in Iran, it can be observed that there was also a decrease in the concentration of pollutants after the lockdown compared to the pre-lockdown period. The CO concentration decreased from 0.043 to 0.036 mol/m², while for the NO₂ pollutant there was a decrease from 0.0002 to 0.000142 mol/m² and for the SO₂ pollutant, there was a decrease from 0.0005 to 0.000143 mol/m².

The study aimed to examine the change in the concentration of nitrogen dioxide due to the lockdown amid the COVID-19 pandemic in India at the district level using Sentinel-5P TROPOMI. The spatio-temporal characteristics of the tropospheric column NO₂  concentration during 45 days of the lockdown were compared with the same days of 2019. Further, to model spatially varying relationships of NO₂ during the lockdown period, it was given as a dependent variable whereas NO₂ during the pre-lockdown period was considered as an independent variable. Results show that the mean NO₂ concentration was reduced from 0.00406 mol/m² before the lockdown (2019-03-25 to 2019-05-10) to 0.0036 mol/m² during the lockdown period (2020-03-25 to 2020-05-10). The maximum decline of NO₂ concentration was observed in Gautam Buddha Nagar and Delhi. This indicates the high level of atmospheric pollution due to the excess use of fuel in human activities. The results of the Ordinary Least Squares (OLS) method show a strong positive relationship between both variables. Positive standard residuals indicate that the concentration of NO₂ has reduced more than expected as per the OLS model. The z-score (24.11) was obtained from spatial autocorrelation. It indicates that residuals are highly clustered and there is less than a 1% likelihood that this clustered pattern could be a result of a random chance. The highest decrease was observed in districts/urban agglomerations of Gautam Buddha Nagar (-40%), Delhi (-37%), Greater Bombay (-31%), Hyderabad (-29%), Faridabad (-29%), Bangalore Urban (-28%), Gandhinagar (-27%), Chennai (-27%) and Gurgaon (-26%) respectively.

The Black Sea is one of the main recreational facilities in Russia subject to a high annual anthropogenic stress. Anthropogenic activity led to high coastal sea waters pollution, eutrophy, and endangered the sea’s self-purification capabilities. The total quarantine introduced on the Black Sea coast of the Krasnodar territory associated with the new coronavirus infection COVID-19 pandemic led to a decrease in anthropogenic pressure on coastal ecosystems and provided a unique opportunity to trace the dynamics of the most important hydrochemical indicators of coastal waters in the Tuapse district. The study aimed to characterize the impact of quarantine measures against the coronavirus on the state of coastal waters in the eastern part of the Russian Black Sea. For this, we identified and characterized the hydrochemical indicators and determined the effect of quarantine measures on their dynamics. The study used the standardized methods. The results obtained showed that a decrease in the recreational stress led to a proportional decrease in the pollutants supply to coastal sea waters; with the recreational stress resumption the concentrations of mobile pollutants tended to increase; a proportional relationship was established between biochemical oxygen demand (BOD5 ) and the ammonium nitrogen (NH4+) concentration; the nitrates’ (NO3–) concentration, in the seawater did not depend on the recreational stress degree. In particular, a proportional increase in NH4+ concentration and BOD5 in seawater was detected: in the third quarter of 2019 the concentration of NH4+ and BOD5 amounted to 3.0 mg/dm3 and 8.5 mg/dm3 , and 3.8 mg/dm3 and 7.5 mg/dm3 in the fourth quarter, respectively; in the 2020 samples, a decrease in the NH4+ concentration to 0.8 mg/dm3 in the third and to 1.2 mg/dm3 in the fourth quarter led to a proportional decrease in BOD5 4.5 mg/dm3 and 3.9 mg/dm3 , respectively. Thus, it was shown that the quarantine measures were shown to have a positive effect on the processes of self-purification of coastal sea waters in recreational zones.