Climate uncertainty and vulnerability of urban flooding associated with regional risk using multi-criteria analysis in Mumbai, India.

The study made a comprehensive effort to examine climatic uncertainties at both yearly and monthly scales, along with mapping flood risks based on different land use categories. Recent studies have progressively been engrossed in demonstrating regional climate variations and associated flood probability to maintain the geo-ecological balance at micro to macro-regions. To carry out this investigation, various historical remote sensing record, reanalyzed and in-situ data sets were acquired with a high level of spatial precision using the Google Earth Engine (GEE) web-based remote sensing platform. Non-parametric techniques and multi-layer integration methods were then employed to illustrate the fluctuations in climate factors alongside creating maps indicating the susceptibility to floods. The study reveals an increased pattern in LST (Land Surface Temperature) (0.03 °C/year), albeit marginal declined in southern coastal regions (-0.15 °C/year) along with uneven rainfall patterns (1.42 mm/year). Moreover, long-term LULC change estimation divulges increased trends of urbanization (16.4 km2/year) together with vegetation growth (8.7 km2/year) from 2002 to 2022. Furthermore, this inquiry involves numerous environmental factors that influence the situation (elevation data, topographic wetness index, drainage density, proximity to water bodies, slope, and soil properties) as well as socio-economic attributes (population) to assess flood risk areas through the utilization of Analytical Hierarchy Process and overlay methods with assigned weights. The outcomes reveal nearly 55 percent of urban land is susceptible to flood in 2022, which were 45 and 37 percent in 2012 and 2002 separately. Additionally, 106 km2 of urban area is highly susceptible to inundation, whereas vegetation also occupies a significant proportion (52 km2). This thorough exploration offers a significant chance to formulate flood management and mitigation strategies tailored to specific regions during the era of climate change.

Emerging pollutant in surface water bodies: a review on monitoring, analysis, mitigation measures and removal technologies of micro-plastics.

Water bodies play a crucial role in supporting life, maintaining the environment, and preserving the ecology for the people of India. However, in recent decades, human activities have led to various alterations in aquatic environments, resulting in environmental degradation through pollution. The safety of utilizing surface water sources for drinking and other purposes has come under intense scrutiny due to rapid population growth and industrial expansion. Surface water pollution due to micro-plastics (MPs) (plastics < 5 mm in size) is one of the emerging pollutants in metropolitan cities of developing countries because of its utmost resilience and synthetic nature. Recent studies on the surface water bodies (river, pond, Lake etc.) portrait the correlation between the MPs level with different parameters of pollution such as specific conductivity, total phosphate, and biological oxygen demand. Fibers represent the predominant form of MPs discovered in surface water bodies, exhibiting fluctuations across seasons. Consequently, present study prioritizes understanding the adaptation, prevalence, attributes, fluctuations, and spatial dispersion of MPs in both sediment and surface water environments. Furthermore, the study aims to identify existing gaps in the current understanding and underscore opportunities for future investigation. From the present study, it has been reported that, the concentration of MPs in the range of 0.2-45.2 items/L at the Xisha Islands in the south China sea, whereas in India it was found in the range of 96 items/L in water samples and 259 items/kg in sediment samples. This would certainly assist the urban planners in achieving sustainable development goals to mitigate the increasing amount of emergent pollutant load.

Evaluating hotspots for stormwater harvesting through participatory sensing.

Geographic Information Systems have been widely accepted to manage and manipulate spatial data associated with the hydrologic response of a watershed. Due to climate change and drought impacts, there is a need to conserve freshwater resources, which can be accomplished by introducing the concept of stormwater harvesting. Apart from hotspot identification and site screening, several economic, social, cultural, environmental aspects need to be considered before finalizing the suitable site for stormwater harvesting. The shortlisted sites are commonly ranked by considering various parameters, i.e. water demand, availability of stormwater and distance to end-use locations, which relate to economic aspects. In the present study, socio-environmental considerations are also constituted by adopting a web-GIS based approach. The geospatial datasets and metadata associated with the study area are organized as a repository in the open source database server (PostgreSQL/PostGIS), which is further assessed and analyzed by using GeoServer. This technique publishes the geospatial datasets to the public domain websites that can be accessed and visualized around the clock and across the world. This will help stakeholders gather and store responses from water planners and inhabitants, while minimizing the time and cost associated with field visits for collecting individual responses. In this respect, a questionnaire is prepared that includes queries associated with site selection and the responses are gathered from various institutions, water professionals, stakeholders and residents. Once the responses are collected, the Analytic Hierarchy Process has been implemented to compute the relative weights of each criterion with respect to the responses collected. The weights thus obtained assisted the planners in deciding the suitable stormwater harvesting site for Dehradun city in India. In context to responses gathered the sites 'B' and 'D' are given the maximum weightage to be the suitable sites in the study region. Also, the socio-environmental criteria such as 'community acceptance', 'recreational activities' and 'need for water reuse' have gathered the maximum weightage from the responses for the specific sites. Hence, the proposed methodology demonstrated how water professionals, civilians, planners, stakeholders and public can be included as participants in water-related decision making processes.

Groundwater quality index development using the ANN model of Delhi Metropolitan City, India.

Groundwater is widely recognized as a vital source of fresh drinking water worldwide. However, the rapid, unregulated population growth and increased industrialization, coupled with a rise in human activities, have significantly harmed the quality of groundwater. Changes in the local topography and drainage systems in an area have negative impacts on both the quality and quantity of groundwater. This underscores the critical need to assess the susceptibility of groundwater to pollution and implement measures to mitigate these risks. The water quality index (WQI) is an approach that simulates the water quality at peculiar locations for a particular period of time. The artificial neural network (ANN) model approach is such an idealistic methodology that can be utilized for WQI development and provides better results for specific locations in optimum time. Therefore, the goal of the current study is to provide a unique way for using artificial neural networks (ANN) to characterize the groundwater quality of Delhi Metropolitan City, India. In order to make the water fit for residential and drinking use, the research also pinpoints the geographical variability and spots where the contaminated region has to be sufficiently cleaned. A minimum WQI of 41.51 was obtained at the Jagatpur location while a maximum value of 779.01 was at the Peeragarhi location. During the training phase, the results obtained using the ANN model were highly favorable, demonstrating a strong association with an R-value of 98.10%, thus highlighting the program's exceptional efficiency. However, in accordance with the correlation regression findings, the prediction outcomes of the ANN model in testing are observed to be an R-value of 99.99-100%. This study confirms the promise and advantages of employing advanced artificial intelligence in managing groundwater quality in the studied area.

Water Quality Index Assessment of River Ganga at Haridwar Stretch Using Multivariate Statistical Technique.

The Ganges (Ganga) river contributes significant water resources for the ecology and economy, but it frequently encounters severe deterioration due to cumulative impact from upstream natural and anthropogenic variables. Knowledge and understanding of the dynamic behavior of such networks remain a significant challenge, particularly in the context of rising environmental pressures, such as climate change and industrialization, as well as constraints in both process and data understanding across geographies. An interdisciplinary approach is required to be developed to investigate the hydrogeochemical dynamics and anthropogenic sources influencing water quality in major river systems. The present study has been carried out to evaluate the characterization of river water quality in terms of the physico-chemical & bacteriological parameters. Also, the development of a water quality index (WQI) for Domestic (drinking) and Spiritual (bathing) usage is a part of the study. The water quality index has been developed using the Canadian Council of Ministers of the Environmental Water Quality Index (CCME WQI). The river's water quality index score in the present study lies in the range of 38.32 to 79.82, indicating the quality of water from fair to poor for drinking purposes. The highest water quality index value of 79.82 has been observed at Guru Kashnik Ghat, while the lowest WQI value of 38.32 has been observed at Har ki Pauri for drinking purposes. However, the water quality score for bathing purposes ranged from 71.04 to 91.22 thus signifying the quality of the water from fair to good for bathing purposes. The highest water quality index value of 91.22 has been assessed at Guru Kashnik Ghat, while the lowest WQI value of 71.04 has been assessed at Bhimgoda Barrage. The developed water indices assessment in the present study will be beneficial for society to provide a benchmark for the control of water pollution in River Ganga. These findings will support policymakers and stakeholders in addressing water quality issues in a more efficient and effective manner. The study also emphasizes the requirement for ongoing water quality monitoring and evaluation in order to guarantee the long-term well-being of the river and its ecosystems.