The study implemented Geographic Information System (GIS) techniques and multivariate hydrogeochemical analysis to evaluate the spatial-temporal and seasonal variation in the groundwater quality of Patna, India. For this purpose, sixty groundwater samples were collected and analyzed for major anions and cations during the pre-monsoon, monsoon, and post-monsoon seasons of 2019-2020. The physicochemical parameters such as pH, EC (Electrical Conductivity), TDS (Total Dissolved Solids), TH (Total Hardness), Ca2+, Mg2+, Na+, K+, HCO3-, Cl-, SO42- were considered to evaluate the water quality index. The result revealed degradation in groundwater quality from pre-monsoon (49.21) to post-monsoon (74.48). EC, TDS, TH, Mg2+, Na+, Ca2+, K+, and HCO3- ions were found accountable for high WQI values at various sampling sites during different seasons. Spatial maps showed that 45 % of the sampling stations exhibited poor quality in all three seasons, where the eastern part of the studied region was revealed to be the most affected area. The application of multivariate statistical methods and hydrogeochemical investigation has clearly defined the dominant role of the weathering process, and reverse ion exchange mechanism in controlling the aquifer’s ionic chemistry. Moreover, poor seepage system, and waste leachate from the surface have been found as the main cause of high levels of Na+, K+, and Cl- in the eastern part of Patna.

Global warming is one of the primary causes contributing to melting glaciers and shrinking of glaciers moth. Because of the glacier retreat, more lakes increase the risk of flooding in people’s homes and lives. Several studies on the surging glaciers have been conducted by researchers using various techniques, as well as with the aid of multiple models like the Normalized Differential Water Index (NDWI). The Number of glacial lakes is increasing in the Himalayan region due to climate change (rise of the temperature). Some glacial lakes are potentially dangerous so monitoring is very necessary. It is necessary to evaluate such vulnerable lakes. Therefore, current work is carried out to identify such glacial lakes present in the Teesta River Basin (Eastern Himalaya). Spatiotemporal Landsat data for the last four decades at intervals of ten years from 1990 to 2020 has been considered which was cloud-free and spatial resolution of 30 meters. The dataset mentioned above was used for lake identification and delineation. The findings indicate the presence of lakes with respective areas of 275 (18.90 km2), 337 (24.92 km2), 295 (22.96 km2), and 419 (31.44 km2). It has also been observed that the growth rate is increasing with approximate water spread from 1990 to 2000 (+129%), 2000 to 2010 (+106%), and 2010 to 2020 (+136%). The present study aimed to identify such glacial lakes based on their water spreading area, which is an essential step followed in the study of GLOF (Glacial Lake Outburst Flood) as it will be helpful in the identification of hazardous lakes. In that study, we found that eleven glacial lakes are in the potentially dangerous category situated in the upper Teesta Basin due to the presence of glaciers, which gives a clear reason for the time-to-time assessment of such lakes. By the conducted study it has been observed that the number of glacial lakes has increased, due to which water spread has also increased in the area. It can also be demonstrated that GIS (Geographical Information System), along with remote sensing, is one of the best tools for assessing and monitoring such change detection and differentiation of hazardous glacial lakes in the cryosphere, along with the supporting data.

The impact of global warming is climate change which affects elements of society. This condition causes a decrease in the level of community welfare and increases the level of community vulnerability. Some climate change impacts are floods, droughts, landslides, and shoreline changes. In this study, we will focus on landslides. Landslides are among the most dangerous natural disasters that often occur in mountainous areas, especially during the rainy season. Various factors influence events involving landslides. This study aims to utilize GIS to identify landslide-prone areas in Padang. The method used in this study is the Zuidam and Concelado criteria overlay method for the level of landslide hazard and the broken method (jenks). The natural break (jenks) classification method reduces within-class variation and maximizes between-class variation. This study shows that the level of landslide vulnerability in Padang City is low, with a total area of 288854.38173 ha with a percentage of 42.21%. We need to consider more factors and experiment with training and validating data in more detail to gain insight into the physical contributions of the factors to landslide occurrences.

The goal of this study was to examine the water quality for drinking and domestic purposes in the Korba coalfield region of Chhattisgarh, India. The Korba Coalfield region has seen the collection of fifteen groundwater samples from different places. The content of eight metals was determined using ICP-MS instruments: aluminum (Al), barium (Ba), cadmium (Cd), iron (Fe), magnesium (Mn), lead (Pb), nickel (Ni), and zinc (Zn). Spatial distribution maps were produced using GIS software to make it simple to understand the groundwater’s quality. The groundwater samples were collected during the pre-monsoon season and the amount of Al, Ba, Cd, Fe, Mn, Pb, Ni, and Zn exceeded the ideal drinking water standards in a few sites. The elevated metal concentrations in the study region’s groundwater could be hazardous to the quality of water. The HPI value based on mean concentration was calculated to be 21.64, which is significantly lower than the reference pollutant index score of 100. The HPI calculation revealed that 73.33% of groundwater samples had low HPI values, 6.67% had medium HPI values, and the remaining 20% had high HPI values. The correlation between heavy metals and HPI was calculated; HPI is positively correlated with Fe (r > 0.9471), Pb (r > 0.9666), and Zn (r > 0.9634), indicating that these elements contribute significantly more to heavy metal concentration in the various samples examined than the other selected elements. The box plot seems to be a graphical representation of the outcomes of the different parameter concentrations which show the mean, maximum, and minimum metal values. The cluster analysis was performed and it was classified into two clusters. Cluster-1 comprises 14 members (93.33%) of the water samples examined and is distinguished by relatively low Ba (<700 μg.L-1), pH, TDS, Al, Fe, Cd, Mn, Pb, and Zn concentrations. Cluster-II is made up of 1 member (6.67%), which is primarily made up of groundwater samples (GW-10) taken in the KCF region, India. High values of HPI are found in the eastern portion of Chhattisgarh’s KCF region, reflecting the spatial distribution of metals. Heavy metal leaching from open-pit mining and transit routes was observed to have contaminated groundwater in the eastern section of the research region.

The objective of this article is to present comprehensive findings and analysis of studies performed on air pollutant dispersion in urban environments. It captures India’s rising environmental pollution due to urbanization, industrialization, and population growth. Dispersion of pollutants due to the wind in the lower Atmospheric Boundary Layer (ABL) is a major concern nowadays. The dispersion field around the buildings is a critical parameter to analyze and it primarily depends on the correct simulation of the wind flow structure. Therefore, studies performed on this in past years are being reviewed. Additionally, a brief review of different air dispersion models that are integrated with the Geographic Information System (GIS) has been studied in this article to assess the exposure. The results of these studies provide the urban air dispersion model aligning to three sub-models i.e., Emission, Weather Prediction, and Dispersion models. Various factors like wind speed, wind direction, cloud cover, traffic emission, disposal of waste, transportation, and others are considered. This study also captures the problems and risks being faced while creating a model, and its possible mitigation approaches.

Soil erosion is one of the major environmental problems facing the world. The multi-scale characteristics of soil erosion and the complexity of its influencing factors put forward higher requirements for soil erosion prevention and control. Based on GIS technology and the RUSLE model, this paper quantitatively studies the temporal and spatial variation characteristics of soil erosion intensity in Chengde City(CC) from 2003 to 2018 and analyzes the temporal and spatial characteristics of R, K, LS, C, P factors according to the model calculation results, and analyzes the formation mechanism of key units of soil erosion in CC. The results show that: The area of tolerable erosion in CC in 2018 was 35152.19 km2 (accounting for 90.22% of the total area), which was at the level of tolerable erosion on the whole. The average soil erosion modulus of CC in 2003, 2006, 2009, 2012, 2015, and 2018 were 41.38, 45.06, 46.58, 83.66, 27.67, and 73.34 t.km-2.y-1, reaching the maximum value of 83.66 t.km-2.y-1 in 2012, showing a rising trend and then declining trend in the research period. Soil erosion deteriorated in some areas of CC and regional differences increased, which caused serious environmental problems. Fitting results showed that the R factor was one of the important factors for the increase of regional differences and average erosion modulus. According to the characteristics of the problem, a precise governance model of soil erosion prevention based on the intensity and causes of soil erosion was put forward, and a “landing” scheme of soil erosion prevention and control measures was put forward. Furthermore, the control of soil and water loss in key areas should be strengthened in the future.

Noise is an underestimated threat that can cause several short- and long-term health problems. It is increasingly becoming a potential hazard to health, physically and psychologically, and affects the general well-being of an individual. The objective of the current study was to examine noise levels at ten different locations in the city of Bhubaneswar, Odisha State, India based on the land use pattern in urban and rural setup. The paper focuses on deploying geospatial techniques using ArcGIS desktop to perform better sampling and further interpolate the statistical data using the Kriging technique to generate a surface representing the distribution of noise levels in various areas. In addition, a health impact survey enabled us to understand the perspectives of the people in and around the monitoring location where health issues like stress, headache, hypertension, and sleeping disorders emerged as some of the most common issues faced. Noise levels were in the range of 43.0 to 74.5 (A) Leq. in rural areas and 61 to 96.5 dB (A) Leq in urban areas. In the current study, noise levels in rural and urban areas exceeded the recommended noise limits as per The Noise Pollution (Regulation and Control) Rules, 2000.

Flood occurs when the water inundates normally dry ground, which could happen in a variety of ways like excessive rainfall, overflowing of embankments, dams, rivers, snowmelt, and other factors. Floods are one form of a natural hazard which are difficult to contain and control. A flood susceptibility mapping using Geographic Information System (GIS) and Analytical Hierarchy Process (AHP) techniques were carried out at Sairang village in Aizawl, Mizoram in Northeast India. The study area Sairang is situated on the banks of the Tlawng river, the longest river in Mizoram. Floods have wreaked havoc in Sairang frequently resulting in huge losses and damage to property with numerous loss of life over the years. The total study area is 131.27 sq km and the resulting flood hazard potential zonation map shows that 1/3 of the watershed area falls in Vey High and High Potential Flood Hazard Zonation areas.

The assessment of groundwater quality is essential for the conservation of natural resources. Hence, this study aims to assess the hydrochemistry of groundwater in and around the Nagalapura Taluk in Bellary district, Karnataka, India. The groundwater quality variables are mapped using a Geographic Information System (GIS). For the hypothesis, the mean value of ten groundwater quality variables was obtained from 50 bore well samples (2016-2018). To assess the lead ions and type of water, the USSL, SAR, and Na% were measured. Ionic ratio and Gibbs graphs were used to demonstrate the chemical reactions in the water samples. ArcGIS was used for spatial analysis of the quality variables. The results showed the order of Cl- > SO42- > HCO3- with water types Na+-Cl- and Cl-, and the order of Na+ > Mg++ > Ca++ > K+ with Na+ and Mg++ as the dominant anion and cation, respectively. The hydrochemistry of groundwater is determined by the geological structure in 64 percent of the water samples examined. The Wilcox diagram shows that no-alkali exposure to the crops is expected. Forty one samples (82%) fit within the C3-S1 group; this category is fit for irrigational needs. Only 01 and 03 samples showed maximum SAR during two seasons like pre-monsoon and post-monsoon periods. The maps showed that groundwater in the selected sites is usually of higher quality, whereas the presence of dolomite indicates a reduction in water quality.

This study investigates the physical characteristics of red clay using the IDW approach and linear regression modeling in an area of 268.12 km2, focusing on Kirkuk, Bor, and Jambor structures. Through the analysis of 52 soil samples and the integration of laboratory data with IDW and regression results, several significant findings have emerged. The IDW method combined with linear regression proves to be a cost-effective and efficient approach for obtaining soil property data and generating accurate digital maps of red clay’s physical features. The Silt concentration exhibits a wide range, while the gravel content remains relatively low, indicating the predominance of silt in the soil composition. Analysis of Atterberg limits reveals the soil’s behavior and consistency in response to moisture, with the plasticity index generally falling within the low to medium range due to the considerable silt content in most soil samples. The linear regression model highlights positive correlations between the liquid limit, plastic limit, and plasticity index. Moderately positive relationships exist between the liquid limit and clay content, as well as a weak positive association between the liquid limit and specific gravity. Dry density, on the other hand, shows no significant correlation with other physical variables, suggesting its independence from the measured parameters. The plastic limit demonstrates a stronger relationship with the clay content compared to the liquid limit. Additionally, weak positive correlations are found between the liquid limit, plastic limit, and specific gravity and water content, indicating the influence of moisture on these parameters. Furthermore, gravel exhibits a moderate positive correlation with sand and silt concentrations, while a strong positive correlation is observed between sand and silt contents, underscoring their close association with the soil composition.