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The quality of groundwater in the Jaunpur district of Uttar Pradesh is poorly studied despite the fact that it is the only supply of water for both drinking and irrigation and people use it without any pre-treatment. The evaluation of groundwater quality and suitability for drinking and irrigation is presented in this study. Groundwater samples were collected and analysed by standard neutralisation and atomic emission spectrophotometry for major anions (HCO

The COVID-19 pandemic has a negative impact on the environment. Waste generation and improper management during the COVID-19 pandemic posed a major threat to human health and the environment. Irregular and improper waste collection, handling, suspension of waste recycling and unsanitary disposal were all important issues in the processing and management of generated waste. This study emphasised a systematic review and content analysis to categorise all types of waste management (WM) during the COVID-19 pandemic to accomplish a well understanding of the relation between the COVID-19 pandemic and its impacts on WM within the literature. In this systematic review, a number of published papers on different aspects of WM during March 2020 to February 2021 were considered in order to identify major challenges in handling WM during the pandemic time and highlight multi-strategic approaches suggested. A content analysis of the 58 relevant papers was carried out by incorporating different types of WM at local as well as global scales. The present review results revealed that the COVID-19 has impacted the quantity and composition of waste, and the crisis caused by the pandemic has also altered the nature of global WM system. A comprehensive analysis on how the systems of WM were affected through the advancement of COVID-19 and what would be the healthier solutions was also highlighted in this systematic review. The results of this systematic review would be beneficial for better policymakers to holistically address potential future pandemics, if any.

The present study aims to enrich the specialized literature by proposing and calculating a new flash-flood propagation susceptibility index (FFPSI). Thus, firstly the Flash-Flood Potential Index (F...

This article is intended to assess the flood-induced landslide susceptibility in the Indian state of Assam. This study area has high frequency and severity of landslides that are triggered by heavy...

Hazards and disasters have always negative impacts on the way of life. Landslide is an overwhelming natural as well as man-made disaster that causes loss of natural resources and human properties throughout the world. The present study aimed to assess and compare the prediction efficiency of different models in landslide susceptibility in the Kysuca river basin, Slovakia. In this regard, the fuzzy decision-making trial and evaluation laboratory combining with the analytic network process (FDEMATEL-ANP), Naive Bayes (NB) classifier, and random forest (RF) classifier were considered. Initially, a landslide inventory map was produced with 2000 landslide and non-landslide points by randomly divided with a ratio of 70%:30% for training and testing, respectively. The geospatial database for assessing the landslide susceptibility was generated with the help of 16 landslide conditioning factors by allowing for topographical, hydrological, lithological, and land cover factors. The ReliefF method was considered for determining the significance of selected conditioning factors and inclusion in the model building. Consequently, the landslide susceptibility maps (LSMs) were generated using the FDEMATEL-ANP, Naive Bayes (NB) classifier, and random forest (RF) classifier models. Finally, the area under curve (AUC) and different arithmetic evaluation were used for validating and comparing the results and models. The results revealed that random forest (RF) classifier is a promising and optimum model for landslide susceptibility in the study area with a very high value of area under curve (AUC = 0.954), lower value of mean absolute error (MAE = 0.1238) and root mean square error (RMSE = 0.2555), and higher value of Kappa index (K = 0.8435) and overall accuracy (OAC = 92.2%).

Sanitary landfill is still considered as one of the most significant and least expensive methods of waste disposal. It is essential to consider environmental impacts while selecting a suitable landfill site. Thus, the site selection for sanitary landfill is a complex and time-consuming task needing an assessment of multiple criteria. In the present study, a decision support system (DSS) was prepared for selecting a landfill site in a growing urban region. This study involved two steps of analysis. The first step of analysis involved the application of spatial data to prepare the thematic maps and derive their weight. The second step employed a fuzzy multicriteria decision-making (FMCDM) technique for prioritizing the identified landfill sites. Thus, initially, the analytic hierarchy process (AHP) was used for weighting the selected criteria, while the fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) was applied for addressing the uncertainty associated with decision-making and prioritizing the most suitable site. A case study was conducted in the city of Memari Municipality. The main goal of this study was the initial evaluation and acquisition of landfill candidate sites by utilizing GIS and the following decision criteria: (1) environmental criteria consisting of surface water, groundwater, land elevation, land use land cover, distance from urban residence and buildup, and distance from sensitive places; and (2) socioeconomic criteria including distance from the road, population density, and land value. For preparing the final suitability map, the integration of GIS layers and AHP was used. On output, 7 suitable landfill sites were identified which were further ranked using FTOPSIS based on expert’s views. Finally, candidate site-7 and site-2 were selected as the most suitable for proposing new landfill sites in Memari Municipality. The results from this study showed that the integration of GIS with the MCDM technique can be highly applied for site suitability. The present study will be helpful to local planners and municipal authorities for proposing a planning protocol and suitable sites for sanitary landfill in the near future. Validerad;2021;Nivå 2;2021-02-02 (alebob)

The world has now facing a health crisis due to outbreak of novel coronavirus 2019 (COVID-19). The numbers of infection and death have been rapidly increasing which result in a serious threat to the social and economic crisis. India as the second most populous nation of the world has also running with a serious health crisis, where more than 8,300,500 people have been infected and 123,500 deaths due to this deadly pandemic. Therefore, it is urgent to highlight the spatial vulnerability to identify the area under risk. Taking India as a study area, a geospatial analysis was conducted to identify the hotspot areas of the COVID-19. In the present study, four factors naming total population, population density, foreign tourist arrivals to India and reported confirmed cases of the COVID-19 were taken as responsible factors for detecting hotspot of the novel coronavirus. The result of spatial autocorrelation showed that all four factors considered for hotspot analysis were clustered and the results were statistically significant (p value 3 and > 0.7295 respectively). The present analysis reveals that the reported cases of COVID-19 are higher in Maharashtra, followed by Tamil Nadu, Gujarat, Delhi, Uttar Pradesh, and West Bengal. The spatial result and geospatial methodology adopted for detecting COVID-19 hotspot in the Indian subcontinent can help implement strategies both at the macro and micro level. In this regard, social distancing, avoiding social meet, staying at home, avoiding public transport, self-quarantine and isolation are suggested in hotspot zones; together with, the international support is also required in the country to work jointly for mitigating the spread of COVID-19. Electronic supplementary material The online version of this article (10.1007/s41324-020-00375-1) contains supplementary material, which is available to authorized users.

The government authorities in developing countries are awfully concerned with coping out the problems of the rising issues related to the disposal of solid wastes. Most of the Indian cities still dispose of solid waste unscientifically causing to severe environmental as well as public health problems. Geographic information systems (GIS) and analytic hierarchy process (AHP) have emerged as efficient tools for multiple criteria decision analysis (MCDA) in effective solutions of solid waste management. The present study focuses on the integration of GIS and AHP in identifying potential sanitary landfill areas for solid waste disposal in Durgapur city, West Bengal, India. Eleven criteria were selected viz. land elevation, slope, soil, geology, land use land cover, distance to surface water, distance to tube wells, distance to roads, distance to industrial belts, distance to sensitive places, and land cost. All the criteria were aggregated using weighted overlay analysis in GIS environment. The study identified three potential landfill areas for MSW disposal covering the areas of 13.83854, 33.80678, and 27.20085 ha, respectively, in Durgapur city. The result found that land cost value was the most significant criterion in the model with a weight of 0.25258. Followed by land cost value, sensitive places and roads were the second and third most important criteria with a weight value of 0.1409 and 0.1233, respectively.

The present study aimed to assess the impacts of land cover on groundwater quality by integrating physico-chemical data and satellite imageries. Initially, fourteen groundwater parameters of both pre-monsoon and post-monsoon were collected from nineteen sampled stations and water quality index (WQI) was calculated. Consequently, Google earth, Landsat-8, and Sentinel-2A imageries were considered for land cover mapping including the concentration of settlement and urban built-up, greenery coverage, and micro-level land use. Two machine learning models such as artificial neural network (ANN) and random forest (RF) were used for pixel-based classification and establish spatial relation between water quality and land cover. This study trained and tested the models for the whole study area as well as 500m buffers from each sampled station. The result of model’s validation including mean absolute error (MAE), root mean squared error (RMSE), Kappa statistic (K), Overall accuracy of model (OAC), and receiver operating characteristic (ROC), indicated that random forest classifier has better performance than the artificial neural network. The results show that the testing dataset of pre-monsoon season has higher accuracy with MAE 0.343, RMSE 0.397, K-value 0.55, and ROC value 0.838 to envisage the impact of land cover on groundwater quality in comparison to post-monsoon season. The results also reveal that the classification accuracy is greater within 500m buffer areas in comparison to the whole study area with a close to 0 value of MAE and RMSE, and absolutely 1 value of K and ROC. Based on the above findings, the present study suggested to consider a large scale for determining the controlling factors of groundwater degradation.

The water quality of rivers is deteriorating due to human interference. It is essential to understand the relationship between human activities and land use types to assess the water quality of a region. GIS is the latest tool for analyzing this spatial correlation. Land use land cover, and change detection are the best illustration for showing the human interactions with land features. This study assessed water quality index of the upper Ganges River near Haridwar, Uttarakhand, and spatially correlated it with changing land use to reach a logical conclusion. In the upper course of Ganges, along a 78-km stretch from Kaudiyala to Bhogpur, water samples were collected from five stations. For water quality index, physicochemical parameters like pH, EC, DO, TDS, CaCO3−, CaCO3, Cl−, Ca2+, Mg2+, Na+, K+, F−, Fe2+ were considered. The results of the spatial analysis were evaluated through error estimation and spatial correlation. The root mean square error between spatial land use and water quality index at the selected sampling sites was estimated to be 0.1443. The spatial correlation between land use change and site-wise differences in water quality index also showed a high positive correlation, with R2 = 0.8455. The degree of positive correlation and root mean square error strongly indicated that the water quality of the river in the upper course of the Ganges is highly impacted by human activities.

The water quality of the river is becoming deteriorated due to human interference. It is essential to understand the relationship between human activities and land-use types to assess the water quality of a region. GIS has the latest tool for analyzing the spatial correlation. Land use land cover and change detection is the best illustration to show the human interactions on land features. The study assessed water quality index of upper Ganga River near Haridwar, Uttarakhand and spatially correlated them with changing land use to reach a logical conclusion. At the upper course of Ganga along 78 Km long from Kaudiyala to Bhogpur, water samples were collected from five stations. For water quality index the physicochemical parameters like pH, EC, DO, TDS, CaCO3-, CaCO3, Cl¯, Ca++, Mg++, Na+, K+, F-, Fe2+ were considered. The result of the spatial analysis was evaluated through error estimation and spatial correlation. The root mean square error between spatial land use and water quality index of selected sampling sites was estimated as 0.1443. The spatial correlation between land-use change and site-wise differences in water quality index has also shown a high positive correlation with R² = 0.8455. The degree of positive correlation and root mean square error has strongly indicated that the water quality of the river at the upper course of Ganga is highly impacted through human activities.

Cyclones are one of the devastating natural hazards in the earth that have extensive consequences on both life and livelihood. Assessment of cyclone risk is important not only for the survival of people but also for designing adaptation strategies in major cyclone-prone areas. Risk can be defined as the integrated function of vulnerability, hazard, and mitigation capacity. Risk assessment is a very protracted task needing analysis of multiple factors which are also changes with changing geographical location. Thus, we applied the multi-criteria decision-making technique to assess the block-level risk to cyclone in the Sundarban region, India. The weight-based analysis reveals that nearness to coastline and distance from cyclone tract have the greatest vulnerability exposure; windspeed has the highest hazard score; and nearness to cyclone shelter and cyclone awareness, on the other hand, has utmost mitigation capacity to cyclone risk analysis. The risk analysis shows that out of the total blocks (19) in the Sundarban, nearly half of the blocks are with very high to moderate cyclone risk with the least mitigation capacity to cope with such natural hazards. The blocks like Gosaba and Kultali to be found in the southern portions and nearby to the coast exposed more risk to cyclone while those situated farther to the coast in the central and northern parts exposed low risk. The findings of the present study may have high implications for developing more mitigation capacity in a very-high- to a moderate-cyclone-risk area. Therefore, the applied approach can help the local authorities in identifying vulnerable and hazard areas and building actionable strategies for mitigation and reworked copy of cyclone hazards in the Sundarban Biosphere Reserve.

The varieties of water-borne disease are caused by pathogens transmitted and spread by different routes. The spread of such infection increases while the water quality affected by external sources. Changes in the quality of water can influence the dynamics of microbial pathogens which can also influence the prevalence and transmission dynamics of water-borne diseases. The present study highlighted on selected chemical parameters of ground water to signify the water quality index and mapping of water-borne diseases susceptibility areas in Kolkata Municipal Corporation, India. The main objective of present study was to demonstrate the significance of geographical information system based Geostatistical technique for identifying water-borne diseases susceptibility by taking different ground water parameters, water quality index and reported water-borne diseases into consideration. Using Kriging interpolation, the spatial map of water parameters, water quality index and reported disease cases were prepared and reclassified by putting risk rank to generate susceptibility map of water-borne diseases. The accuracy of the result was assessed through error estimation and spatial autocorrelation. The result from error estimation, it was found that the root mean square error between diseases susceptibility and selected chemical parameters, water quality index and reported water-borne diseases are 0.0847, 0.1182 and 0.0640 respectively which is always

The prevalence of vector-borne diseases (VBDs) like malaria and dengue claims many parts of the capital city Kolkata. Although the frequency of affects has been declining, still several cases are still reported from different parts of Kolkata Municipal Corporation. The present study aimed to apply multi-criteria decision making (MCDM) approach along with geospatial technique to map susceptible areas of vector-borne diseases. For growing vectors and transmitting diseases, there are always many factors responsible instead of a single factor. Hence, the present work was carried out in multiple stages. Initially different susceptible factors to vector-borne diseases like environment, demography, epidemic and related to suitable breeding sites were selected. Analytic hierarchy process as a technique of MCDM was considered and pair-wise comparison matrix (PCM) was established for each selected factor. Synergistically, weight-based single layer of susceptible zonation was developed and finally, GIS integration was performed for susceptible map of VBDs. The decision-making process was judged by consistency measurement and result shows that the consistency ratio of each selected factor ranged between 0.02 and 0.07, i.e.

Flood is one of the most devastating natural calamities with environmental and socio-economic impacts. Comprehensive flood management is essential to reduce the flood effects on human lives and livelihoods. The main objective of this study was to examine the capability of geographic information system (GIS) in coupling with analytic hierarchy process (AHP) and frequency ratio (FR) model for flood vulnerability mapping. The study was carried out in two stages of analysis. First of all, a flood inventory map was prepared. Consequently, seven flood contributing factors viz. land elevation, slope angle, topographic wetness index, rainfall deviation, land use land cover, clay content in soil and distance from rivers were prepared for spatial analysis. Looking toward the flood records, a total of 270 flood points were marked from field study area, out of which 200 (75%) flood points were randomly selected for training data and the remaining 70 (25%) flood points were used for testing or validation purposes using prediction accuracy and success rate. The result revealed that distance from river, rainfall deviation and land use land cover have the great role for flood occurring in the study area with selected factor weight value (SFWV) of 0.33, 0.21 and 0.14, respectively. The validation result showed that prediction accuracy was 0.8142 and success rate was 0.8450 which may consider for validating the frequency ratio model that applied in present study. The application of AHP and frequency ratio model helps in identifying flood vulnerable zonation and potential risk area estimation. The findings from present study will be helpful for planner in flood mitigation strategies as a part of flood preparedness and will appear as a source for further research in the study area.

Disposal of collected waste is the least preferable way of sustainable solid waste management. But most of the cities in developing nations prefer to use open dumping in an inappropriate and non-scientific way, causing negative impacts on the environment as well as human health. This study offers a novel approach for scientific landfill site selection and sustainable waste management in Aligarh city, India. This could be possible through relevant data collection, selection of suitable models for criterion weighting, and model validation. In order to prepare a suitable landfill site selection map, a GIS-based ensemble FAHP-SVM and FAHP-RF model was implemented. Considering the previous studies and the characteristics and the study area, a total of eighteen thematic layers (decision criteria) were selected. The result reveals that land value, nearness to residential roads, nearness to hospitals and clinics, distance from waste bins, and NDBI having a fuzzy weight of > 0.10, indicates significant factors; whereas land elevation, land slope, surface temperature, soil moisture index, NDVI and urban classification having a fuzzy weight of 0, indicates these criteria have no importance for the present study. The result further reveals that FAHP-RF with an AUC value of 0.9182 is the more accurate model in comparison to FAHP-SVM. According to the final result of weight-based overlay, a total of seven potential landfill sites were identified, out of which three sites were determined as most suitable by considering current land cover, environmental and economic concerns, and public opinions. This study proposed a zonal division model based on the location of suitable landfill sites for sustainable waste management in the study area. The findings of this study may provide a guideline to the decision-makers and planners for optimal landfill site selection in other cities of developing countries.

Disposal of waste without treatment is the least preferable way of sustainable solid waste management (SWM). But most cities in developing nations still use open dumps, causing negative impacts on the environment and human health. This study offered a novel approach for selecting landfill sites and sustainable SWM in Aligarh city, India. This was done through data collection, selecting models for criterion weighting, and validation. In order to prepare a landfill site suitability map, a geographic information system (GIS)-based ensemble fuzzy analytic hierarchy process-support vector machine (FAHP-SVM) and fuzzy analytic hierarchy process-random forest (FAHP-RF) models were implemented. Considering the previous studies and the study area characteristics, eighteen thematic layers were selected. The result revealed that land value; distance from residential roads, hospitals and clinics, and waste bins; and normalized difference built-up index (NDBI) have a fuzzy weight greater than 0.10, indicating significant factors. In contrast, land elevation, land slope, surface temperature, soil moisture index, normalized difference vegetation index (NDVI), and urban classification have a zero fuzzy weight, indicating these criteria have no importance. The result further revealed that FAHP-RF with an area under curve (AUC) value of 0.91 is the more accurate model than FAHP-SVM. According to the final weight-based overlay result, seven potential landfill sites were identified, out of which three were determined as most suitable by considering current land cover, public opinions, and environmental and economic concerns. This research proposed a zonal division model based on landfill sites location for sustainable SWM in Aligarh city. However, the findings may provide a guideline to the decision-makers and planners for optimal landfill site selection in other cities of developing countries.

One of the most important challenges in solid waste management throughout the world is selection of suitable landfill sites. It is essential for solid waste management, because unscientific dumping and landfilling of waste could negatively impact the environment and human health. This research presents a GIS-based multi-criteria decision support approach for assessing the suitability analysis for landfill site selection in Kolkata Municipal Corporation, India. In highly urbanized areas of Kolkata, there is stress regarding the location of potential landfill sites, because existing waste dumping ground, i.e., Dhapa have no longer space to hold waste after 2 or 3 years. The availability of land is also a major tension than financial assistance in the study area. Therefore, the present study developed a methodological framework for locating suitable landfill candidate sites and selecting the best alternatives. Thus, 20 relevant alternatives were selected and relative weight calculated using the fuzzy analytic hierarchy process. The geographic information system was considered to arrange the spatial and aspatial data. The alternatives of all criteria were rated from 1–5 scale indicating lowest to highest suitability for landfill. The weighted linear combination was performed between different criteria for modeling suitable candidate sites. The results revealed that throughout the study area, initially, 16 candidate sites were identified based on geospatial analysis, but out of them, 13 sites cannot be accepted for a landfill site due to current land use, public acceptance, transportation, and local environmental issues. Only three suitable sites can be considered for landfilling, although these are also not completely satisfying the environmental concern. However, landfill site is also essential; otherwise, it will appear as more adverse impact. Therefore, with little compromising with the environmental component, the engineered plan can be designed for the sanitary landfill on specific sites that recommended in this suitability analysis.

Mosquito-borne diseases are those which transmitted through the bite of an infected mosquito. Stagnant water bodies are often preferable for breeding sites of mosquitos. But from breeding eggs to final stage, there are many factors responsible for its incubation, maturation and growth enough to bite and transmit diseases. The main aim of present study is to focus on associated environmental factors that provide suitable breeding sites and susceptibility mapping of mosquito-borne-diseases through geospatial technique and decision making approach. Analytic hierarchy process as a decision making approach was integrated with geographic information system to map of mosquito-borne diseases in Kolkata Municipal Corporation. Choice based various ranking was used to decide the weights of selected factors through establishing pairwise comparison matrix. Initially, 10 relevant environmental factors were chosen to determine their weight through pairwise comparison matrix. Concomitantly, weight of each causative factor was used as geo-database to support overlay analysis. Consistency ratio was calculated to check the decision process and significance measurement. The consistency ratio of decision factors was calculated as 0.0470, which is

Forecasting the quantity of municipal solid waste generation is an essential task for sustainable solid waste management and strategy implementation. The estimation of future waste generation rates can help to motivate for analyzing gaps in existing waste management and better planning strategies. Improper management and unsafe disposal of solid waste create a threat to the environment and human health. Hence, a sound forecasting of solid waste generation is very crucial for planning and management accordingly. Artificial intelligence is an excellent and new application of soft computing which is used as a forecasting tool. The main objective of this study is to apply ANN time series model along with autoregressive technique to forecast the monthly solid waste generation in Kolkata. For the same, data related to the monthly solid waste generation was gathered from 2010 to 2017. Total data of 96 months were divided into three categories, i.e., 70%, 15%, and 15% for training, validation, and testing, respectively. The model was evaluated based on performance value of mean square error, root mean square error, and regression coefficient. The ANN structure of 1-19-1 was considered as optimized model for solid waste forecasting because it has the lowest mean square error and the highest regression coefficient. The applied time series model forecasts that Kolkata will generate about 5205 MT/day municipal solid waste in 2030 which will add more than 1000 MT/day waste with the existing rate of generation. The present study helps in estimating and allocating essential resources that need in future for sound solid waste management and preparing alternative strategies to reach the sustainable goals.

Flood is a devastating natural hazard that may cause damage to the environment infrastructure, and society. Hence, identifying the susceptible areas to flood is an important task for every country to prevent such dangerous consequences. The present study developed a framework for identifying flood-prone areas of the Topľa river basin, Slovakia using geographic information system (GIS), multi-criteria decision making approach (MCDMA), bivariate statistics (Frequency Ratio (FR), Statistical Index (SI)) and machine learning (Naive Bayes Tree (NBT), Logistic Regression (LR)). To reach such a goal, different physical-geographical factors (criteria) were integrated and mapped. To access the relationship and interdependences among the criteria, decision-making trial and evaluation laboratory (DEMATEL) and analytic network process (ANP) were used. Based on the experts’ decisions, the DEMATEL-ANP model was used to compute the relative weights of different criteria and a GIS-based linear combination was performed to derive the susceptibility index. Separately, the flood susceptibility index computation through NBT-FR and NBT-SI hybrid models assumed, in the first stage, the estimation of the weight of each class/category of conditioning factor through SI and FR and the integration of these values in NBT algorithm. The application of LR stand-alone required the calculation of the weights of conditioning factors by analysing their spatial relation with the location of the historical flood events. The study revealed that very high and high flood susceptibility classes covered between 20% and 47% of the study area, respectively. The validation of results, using the past flood points, highlighted that the hybrid DEMATEL-ANP model was the most performant with an Area Under ROC curve higher than 0.97, an accuracy of 0.922 and a value of HSS of 0.844. The presented methodological approach used for the identification of flood susceptible areas can serve as an alternative for the updating of preliminary flood risk assessment based on the EU Floods Directive.