GIS-Analysis Of The Ural Power Grid Vulnerability To The Impact Of Sleet And Wind

In this paper, we describe an experiment of complex power grid structure and wind and sleet mapping of territory using two different network indices: standard edge betweenness centrality and new author’s index – electrical grid centrality. Such analysis of the network allows to identify power lines with high load which could be vulnerable elements of the power grid. It is very important for strategic planning of power grids to reduce the risk of accidents by distributing loads across several lines so that they will be able to reserve each other. As a case territory for this research, we took the Ural united power system in Russia which is greatly exposed to different sleet and wind according to the statistics of the power grid operator. The degree of natural hazard consequences could be compensated by the network structure through alternative paths of energy supply or vice versa – increased if they are absent. At the same time, in this paper we consider that power grids have their own features from the graph theory point of view, for example multiple (parallel) edges, branches, different types of vertices. The existing index of edge betweenness centrality does not perfectly cope with them. We compare two indices characterizing power line importance within the system – betweenness centrality and electrical grid centrality and analyze the network structure features together with the spatial distribution of sleet and wind. As a result, we could identify bottlenecks in the study network. According to this study the most vulnerable power lines were detected, for example 500 kV Iriklinskaya CHP – Gazovaya and 500 kV Yuzhnouralskaya CHP-2 – Shagol power lines, that supply big cities such as Chelyabinsk and Orenburg and a bunch of industries around them.

Formation Conditions And Debris Flow Regime In Jiangjia Ravine, Yunnan, China – Applicability Of Russian Methodology

The requirements of the debris flows’ parameters assessments vary from country to country. They are based on different theoretical and empirical constructions and are validated by data from different regions. This makes difficult comparison of the reported results on estimated debris flows activity and extent. The Russian normative documents for the debris flows’ parameters calculations are based on empirically-measured parameters in wide range of geological and climatic conditions at the territory of former USSR, but still not cover all the possible conditions of debris flow formation. An attempt was made to check applicability of the Russian empirical constructions for the conditions of the debris flows formation in Yunnan, China, where unique long-term dataset of debris flows characteristics is collected by the Dongchuan Debris Flow Observation and Research Station. The results show, that in general the accepted in Russia methodology of calculation of the parameters of debris flows of certain probability corresponded well to the observed in Dongchuan debris flows characteristics. Some discrepancies (in the average debris flow depth) can be explained by unknown exact return period of the actually observed debris flows. This allowed to conclude that the presently adopted empirical dependencies based on country-wide (USSR) empirical data can be extrapolated up to the monsoon climate and geological conditions of Yunnan province.

Waste Management Reform In Regions Of The Russian Federation: Implementation Issues On The Way To Sustainable Development

Disposal of production and consumption waste is a worldwide problem. Despite the experience of foreign countries, waste disposal practice in the Russian Federation remains at the level of the 1970s. The method of waste burial at landfill sites prevails, leading to a loss of secondary resources and the appearance of sites of accumulated environmental damage, which is connected with the lack of a clear legal framework for waste management activities. Analysis of waste accumulation standards for apartment buildings in 20 regions of the Russian Federation showed that the difference in accumulation standards can vary by 2.32 times (from 0.125 m3 in the Kursk region to 0.279 m3 in the Voronezh region). At the same time, the difference in the cost of solid waste removal services can be varied by 2.74 times from 51.55 rubles in the Altai Territory (on average in the region) to 141.45 rubles in the Tyumen region. At the same time, the share of the population with incomes below the subsistence minimum in different regions reaches 7 - 36%. This is largely due to the critically low recovery of secondary materials (about 7%). The capacity of landfills in the regions of the European part of Russia (where more than 2/3 of the population lives) is almost exhausted. Many landfills of solid waste are objects of accumulated environmental damage. The decision to introduce the «institute» of “regional environmental operators”, which was adopted at the level of the Russian Federation to implement the waste management reform, has not, yet had any positive effect. Given the constant deficit of the consolidated budgets of most regions, the high level of poverty and the lack of state support, the prospects for waste management reform indicate the need for additional efforts on the part of the state, business and society

Assessing The Vulnerability Index Of Covid-19 Pandemic In India

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.

Using Moran’s I For Detection And Monitoring Of The Covid-19 Spreading Stage In Thailand During The Third Wave Of The Pandemic

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.

Change In Nitrogen Dioxide (No2 ) Concentration Due To The Lockdown Amid The Covid-19 Pandemic In India

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.

Towards A Strategic Approach Of Covid-19 Cluster Web Mapping In Malaysia

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.

Gis And Remote Sensing: A Review Of Applications To The Study Of The Covid-19 Pandemic

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.

Spatial Clustering Analysis of the COVID-19 Pandemic: A Case Study of the Fourth Wave in Vietnam

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.

Influence Of Quarantine Measures Against The New Coronavirus Infection Covid-19 On The State Of Black Sea Coastal Waters

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.