Sustainable Technologies for Water and Environment Under Climate Change Scenario

Analysis of morphometric parameters of a watershed involves the quantitative study of its physical characteristics and features. This analysis provides valuable insights into the geomorphologic properties and hydrological behavior of the watershed. Morphometric parameter analysis helps in understanding the drainage patterns, sediment transport, erosion processes, and flood behavior of the watershed. The study has been conducted in Koramangala valley watershed using geospatial analysis tools of Arc GIS software, watershed having area of 285 Km2 has delineated using SRTM DEM. Various aspects have been analyzed results shows that it has drainage pattern of four order streams with drainage density of 0.92 km/Km2 and it has slope varies from 00–280 means there’s gentle to moderate slope and the mean bifurcation ratio 18.77 suggest that watershed area is elongated more prominent to flood. Elongation ratio value 0.79 indicates the watershed is less elongated in shape.

Existing roads often feature pavements with uneven characteristics. The thickness of each layer and the materials used can vary significantly throughout the road, creating inconsistencies that complicate the assessment of overall structural health. Traditionally, the back-calculation method was used to evaluate the structural health of pavements, but this approach is cumbersome and time-consuming. This research proposes a novel prediction model that simplifies the process by directly evaluating critical strains within the pavement. The study applies prediction models based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) to estimate critical strains. These models utilize deflection bowl parameters, including the surface curvature index (SCI) and base curvature index (BCI), derived from falling weight deflectometer (FWD) deflection data, pavement surface temperature, and existing layer thickness data. The proposed ANFIS model demonstrates a high coefficient of determination (R2) of 0.96 and a very low root mean square error (RMSE) of 0.04, indicating excellent predictive accuracy. By eliminating the need for time-intensive back-calculation methods, this model provides a more straightforward and efficient approach to assessing pavement conditions.

Indian economy gained fifth position in the world with the rapid growth since post- globalization. It is the result of technological developments and modernization of various industries such as manufacturing and services. This led to people moving from rural to urban areas seeking jobs and modern amenities. Such a rapid shift in the population has altered the Land Use and Land Cover (LULC). Assessment of LULC changes is essential for understanding landscape transformations. Thakurdwara, a Tehsil in Moradabad District, Uttar Pradesh, was chosen for this study. The population growth in Thakurdwara has significantly driven the city's rapid urbanization in the last two decades. In this study, Landsat 7 ETM + and Landsat 8 OLI/TIRS data of the same season were used for the years 2000 and 2023 to analyze the changes in LULC of the study area. ArcGIS Pro 2.9.0 software was employed for layer- stacking and pre-processing of the raw Landsat Data. A supervised classification using the Maximum Likelihood Classification (MLC) algorithm was performed to configure the changes. The results revealed rapid changes from pervious to impervious areas in Thakurdwara. Additionally, for the study period, the mean temperature increased by 1.87 °C. These findings will be valuable for future regional and urban planning, as well as agricultural management.

Accurate prediction of the dynamic response of simply supported bridges under varying loading conditions (particularly earthquake) is crucial for ensuring their structural safety and longevity. This paper discusses the response of simply supported bridges with moving loads and investigates the application of machine learning (ML) techniques, specifically boosting algorithms, to predict bridge responses to both moving loads and seismic events. A finite element method (FEM) model is used to conduct dynamic analysis simulating train moving loads and seismic events on a simply supported bridge. The resultant responses were meticulously compiled into a dataset, serving as the foundation for constructing a surrogate model employing boosting algorithms. Subsequently, the performance of this boosting model was compared with that of the conventional linear regression approach. Results showed that the R2 values for XGBoost are close to 1, making it powerful for structured data and predictive modeling tasks. Additionally, the feature importance analysis reveals that PGA is the dominant feature for both displacement and acceleration predictions.

Monopile-supported offshore wind turbines (MSOWT) are constantly subjected to various cycles of forces, including wind, waves, currents, and seismic activity. To avoid resonance problems, it is crucial to ensure that the structure's natural frequencies are distinct from those of these external forces. However, calculating these natural frequencies can be computationally expensive, especially considering the dynamic interactions between fluids, soil, and structure. To address this, an artificial neural networks (ANN) based surrogate model is proposed to estimate the eigen frequencies of the MSOWT. The comprehensive procedure for generating the training dataset using the Latin hypercube sampling (LHS) method is detailed. The output and input parameters are related using a rectified linear unit (ReLU) activation function based on Pearson correlations. The results show that this model can predict natural frequencies with over 90% accuracy. The surrogate model is applicable for multiple evaluations having high level of precision, combined with a significant reduction in computational costs.

Rainfall is the primary means by which the earth is supplied with water. In a country like India, food production and economy depend on the timely accessibility of water, study of rainfall trends are critically important. In the present study, statistical analysis is being conducted to determine the potential trend in seasonal and annual rainfall time series in the Bhadar river basin in Saurashtra, Gujarat, between the years 2000 and 2020. A detailed statistical analysis is executed using commonly applied methods like Mann–Kendall's test and Sen's slope. Study reveals very high seasonal rainfall variability in an entire study area. The data confirmed that rainfall is predominantly concentrated in the monsoon season. Significant increases in trend can be seen in post-monsoon season which indicate shifting of rainfall pattern toward post-monsoon season. This study suggests the importance of a regional approach to water resource management for the Bhadar river basin.

Effective watershed management and sustainable development hinge on unraveling the intricate interplay between land use, climate, and hydrological processes—a cornerstone of contemporary environmental research. This study addresses this knowledge gap by developing a SWAT-based rainfall-runoff hydrological model. This model will be instrumental in elucidating the various components of the hydrological cycle within the specific study area. SWAT being a very potent tool for hydrological modeling which uses a wide variety of data to simulate watershed and can also accommodate changing climatic variable hence employed in this study. Damoh district is selected as study area which is situated on the Vindhyachal plateau in central India. The analysis revealed that out of total flow, 64% is converted into surface runoff and remaining 36% is converted as base flow. The average Curve Number value estimated for the catchment is worked out to be 86.41, indicating the surface as more impervious. Out of total precipitation, 58% flow in streams and 38% of total precipitation as loss in the form of evapotranspiration. A total of 24% of precipitation percolated in ground and 1% of total precipitation goes into deep percolation. The obtained results are indicating the need of further investigations and appropriate planning for better management of water resources.

Depletion of groundwater (GW) means a scarcity of water which means that there is a lack of water. Water scarcity is a shortage of clean water sources to meet the water demand. Many regions of India are facing a decrease in levels of groundwater which just so happens to be one of the most important water sources in India. Our motive is to analyze Nashik, as it is one of the cities reaching Day 0 condition cities since 5 medium-sized dams out of the total 24 medium and large dams in Nashik district have gone dry. The aim is to determine there charge rate of groundwater using data of annual rainfall and annual groundwater at Nashik, since groundwater recharge is dependent on the mean annual GW and mean annual sum of precipitation. By using these datasets, a prediction can be made to determine at what rate the groundwater of Nashik is being recharged and will analyze the scarcity of water that residents of Nashik will have to face in the future. For this, time-series analysis and forecasting have been applied to rainfall and groundwater datasets using SARIMA models. The metric used for the optimal choice of parameters for SARIMA is AIC. A minimum value of the Akaike Information Criterion shows the most optimal parameters for a particular model. R square score is used for model evaluation.

In the recent years, climate variation and urbanization can be considered as the major factors affecting the runoff in rivers, which could eventually lead to increased flood risks, reduced ground water potential, availability of drinking water and so on. The study focuses upon the quantitative analysis of impact contributed by major climatic variations (rainfall and Potential evapotranspiration) and urbanization (Land Use and Land Cover changes) on the runoff for the study area. The Karamana River Basin (KRB) has been chosen as the study area. To quantify the impact of climatic variations and urbanization, combination of statistical impact analysis methods like Pettit’s test, Double Mass Curve (DMC) method and hydrological benefit calculations have been applied. The study utilized the rainfall, Potential Evapo-Transpiration (PET) and runoff data for the study area collected over a period of 2010–2021, to carry out the combined statistical impact analysis. The PET values can be calculated from the collected temperature data using Hargreaves equation. From the Pettit’s test, the year 2016 has been identified as the change point year for (a) rainfall—runoff and (b) PET—runoff. From the identified change point year, DMC has been established for (a) cumulative rainfall—cumulative runoff and (b) cumulative PET—cumulative runoff. Then hydrological benefit calculations have been carried out, from the entities calculated using DMC. The results of the study indicate that the changes in land use patterns have a more significant impact on runoff than the climate variations.

The design and optimization of rain gauge networks are pivotal for accurate hydrologic modelling and effective flood management. This study explores the application of the Analytic Hierarchy Process (AHP) to rank and select key rain gauges, forming an optimized network that enhances hydrologic model performance. Initially, rain gauges were ranked using AHP based on various criteria, including their importance in capturing spatial rainfall variability and their statistical relationship with the areal average rainfall. The selection of key rain gauges was guided by statistical analyses that identified stations significantly contributing to the overall rainfall pattern representation. The designed network, comprising these key rain gauges, was subsequently integrated into a hydrologic model to simulate discharge. The performance of the hydrologic model was evaluated by comparing observed and simulated discharge data. Performance metrics, including the correlation coefficient, Nash–Sutcliffe Efficiency (NSE), Normalized Root Mean Square Error (NRMSE), and Index of Agreement (IOA), were computed to assess the model's accuracy and reliability. Results indicated that the AHP-based selection method significantly improved the efficiency and accuracy of the hydrologic model. Model-AHP exhibits higher NSE values of 0.727 and IOA values of 0.904 compared to the model with all twenty-six rain gauges, which have NSE of 0.563 and IOA of 0.821. This difference is attributed to the overlapping representative areas for each rain gauge station in the basin and the inadequate spatial distribution in the latter model. This optimized network is particularly beneficial in scenarios requiring rapid runoff predictions, such as flash floods, where time-efficient modelling is crucial for effective emergency response. The study demonstrates that AHP is a powerful tool for designing rain gauge networks, enabling more efficient and accurate hydrologic modelling. The key rain gauge network developed through this approach can be effectively used in flood management and emergency scenarios, providing timely and reliable runoff predictions.

The present study uses a semi-distributed hydrological model to predict streamflow within Lower Godavari River Basin. The Soil and Water Assessment Tool (SWAT) has been utilized to understand and replicate the basin’s intricate hydrological mechanisms by incorporating meteorological data, land use information, and soil properties into the ArcSWAT interface within ArcGIS. Sensitivity analysis using the SWAT-CUP software and SUFI-2 methodology identifies the model parameters that have the most effects on catchment flow. The model is calibrated and validated using a comprehensive dataset covering 38 years, from 1982 to 2020. The results indicate that the SWAT model exhibits strong and consistent performance in effectively replicating the channel runoff. The correlation between the predicted and observed values for the calibration period spanning from 1984 to 2014 is 89%, and for the validation period from 2015 to 2020, it is 85%. The model’s effectiveness is supported by the Nash–Sutcliffe Efficiency (NSE) values of 0.86 during calibration and 0.82 for the validation. The present study’s findings provide valuable insights into water resource management and hydrologic modeling involving complex hydrological processes. The presented results can help in decision-making for sustainable water resource planning and utilization.

This study assesses drought in Rajasthan, one of India's most drought-affected states. The study region's agriculture drought condition is assessed using GIS and remote sensing (RS) techniques. The drought condition is analyzed using both spatial and non-spatial datasets. To maximize efficiency, temporal pictures from MODIS and NOAA-AVHRR satellites are used as alternate sources for the study. Drought is measured using the Normalized Difference Vegetation Index (NDVI), the Vegetation Condition Index (VCI), and the meteorological-based Standardized Precipitation Index. From the satellite images, NDVI values are obtained through GIS processing, and then they are used to derive the values of VCI and generate drought maps. The SPI index is also derived utilizing observed precipitation data to classify the drought regions based on precipitation variation. The obtained results prove and justify the usefulness of RS and GIS techniques for drought assessment and identification of drought conditions.

Understanding turbulent flow patterns over surfaces with varying roughness heights is crucial for numerous engineering and environmental applications. Numerical studies have been performed to investigate how bed roughness elements influence characteristics of turbulent flow in an open channel. Experiments are performed with various types of bed roughness elements with different roughness height to examine their impact on the velocity profile downstream of these elements under fixed water flow discharge. The numerical analysis utilized Computational Fluid Dynamics (CFD) methods. Hexahedral meshing techniques were employed to discretize the computational domain, with mesh sizes of 0.01 m and 0.05 m investigated for comparison. Validation of the CFD model against experimental data was conducted for fixed water depth and flow discharge. Findings revealed a strong correlation between the velocity distribution profiles from the numerical simulations and from experimental results downstream of the bed roughness elements. A 3D ADV (Acoustic Doppler Velocimeter) is used for finding turbulence characteristics of flow in non-uniform open-channel. In comparison to sand roughness’s, Chopan sand bed (with less roughness height) exhibits the strongest turbulence intensities in streamwise direction just next to the bed when away from the channel boundary. This study contributes to a better understanding of turbulent flow behavior over rough surfaces and provides valuable data for hydraulic engineering design and analysis.

Piers in water environments are susceptible to local scour, particularly when there are two piers arranged eccentrically. The flow of water causes erosion of the sediment surrounding the piers, resulting in distinct flow patterns near the eccentric pier. Researchers are now using data-driven soft computing algorithms to more correctly compute local scour at isolated piers. However, there is limited study on reliable formulas for determining maximum scour depth (sdm), and even little experimental data exists for eccentric arrangements of piers. For this study, 50 data sets are collected from previous experimental studies conducted during the last decade. To determine the sdm in clear water, ensemble frameworks such as the Light Gradient Boosting Machine (LGBM) and Random Forest Regression (RFR) are used. The seven parameters that are considered independent variables include flow shallowness, flow intensity, sediment gradation, sediment coarseness, time, flow skew angle, and spacing between piers. Three indicators of performance are used to evaluate machine learning models (MLMs) such as Coefficient of Determination (CD), Mean Absolute Error (MAE), and Mean Squared Error (MSE). Two equations from the literature were compared to the current MLM based on the stated performance indicators. The study’s findings show that LGBM outperforms RFR in both training and testing. In addition, it is used LGBM and RFR to assess the relevance of features. The Taylor skill score compared the newly developed model to the literature model, demonstrating that LGBM is superior to computing the scour depth.

In this study, an attempt is made to maximize the availability of the rain water in a crop period by altering the date of sowing (DOS) of crop such that crop period requires the least amount of irrigation. The study is focussed on Rice farming in Tropical regions of India, for Kharif-season. The seasonally aggregated values of precipitation (PA), evapotranspiration (ETA) and crop water balance (CWBA) are used to determine optimal sowing dates, which are varied in a range of 40 days from the existing DOS (− 10 to + 30 days). The hydro-meteorological data (HMD: P, Tmax and Tmin) are made available by IMD, Pune for a span of 7 decades (year 1951–2020) at daily resolution. Three Koppen climate zones in India are considered in this study: Tropical Dry (As), Tropical Savannah (Aw) and Tropical monsoon (Am). The results show that for the As region, PA initially increases till 4th of the June starts falling afterwards for all DOS, but ETA decreasing continuously, eventually CWBA increases continuously till mid-June thus sowing dates are ideal. For Aw, PA initially rises (for early DOS) and thereafter it falls slightly (for late DOS), whereas ETA decreases continuously for all progressing DOS. CWBA is increasing initially till the mid-June and falls afterwards till June end, therefore results reveal that the best time to sow crops in this climate zone is around 13 days after monsoon starts. For Am, PA initially rises (for early DOS) and thereafter it falls continuously (for late DOS), whereas ETA stays same for all progressing DOS. CWBA initially rises till 5th June after that it falls till end of June, so results reveal to sow in early days of monsoon. This study shows that combining PA, ETA, and CWBA data efficiently analyses patterns and optimizes DOS. The CWBA could be improved by modifying DOS in response to the ET. Using this, a decision support system may be developed to help farmers make well-informed decisions that will increase agricultural productivity.

Rain gardens minimize flood peaks, aid groundwater recharging, and enhance biodiversity. The flora in rain gardens serves as a filter media for stormwater treatment. Scutch grass (Cynodon dactylon), Chandani flower (candytuft), marigold, and daisy flower plants were employed in the study for investigating rain garden infiltration rate. According to the findings, bare soil has the lowest average infiltration rate, whereas scutch grass plants infiltration rate is higher. According to the results, the rain garden with the denser scutch grass plant cover infiltrates the water more rapidly than others. This study examines the infiltration capacities of rain gardens by focusing on various plant species, including marigolds, daisy, scutch grass, and candytuft flowers. The experimental data were used to model the infiltration characteristics using a Support Vector Machine (SVM). This model was employed to predict the infiltration rate. The Pearson VII kernel (SVM_PUK) acquired higher values of C.C (0.944), and RMSE (0.6361) for training and testing values of C.C (0.7463), and RMSE (1.1728). The performance criteria suggest that the SVM regression-based Radial basis kernel function has a very good and satisfactory performance.

For the gauge of land suitability for crops, runoff plays a vital role. Runoff is an important parameter for estimating irrigation management as it effects the water scarcity index and irrigation water requirement. Interpretation of rainfall and runoff is very important to assay the water availability. In the present work, the surface runoff has been estimated for the watershed under Nagamangala taluk in Hemavathi command area. The daily runoff from the watershed for 12 years, i.e., 2011–2022 has been estimated. The maximum rainfall was 1919.7 mm in 2022 for Basaralu and minimum rainfall was 207.9 mm in 2012 in Devalapura. It has been ascertained that that maximum runoff is 690.30 mm in 2022 year for Basaralu station, while the minimum runoff is 4.34 mm in 2012 year for Devalapura due to the change in the intensity of rainfall. Curve Number methods is widely used for determining the runoff and SCS–CN method is helpful for the estimation of runoff for better management of irrigation projects.

Saltwater intrusion is a serious environmental problem that affect coastal areas. This phenomenon happens when seawater seeps into rivers, wetlands, and freshwater aquifers. The main reason of the phenomenon is increased groundwater usage, tidal influence, and rising sea levels. Gujarat state is surrounded by oceans and seas. This study investigates saltwater intrusion in the Mahi River Basin, specifically in the Vasad district of Gujarat, India. Mahi river water meets Arabian sea. The study analyzed water samples, Total Dissolved Solids (TDS), Electrical Conductivity (EC), and pH values, and conducted a community survey to understand the health, ecological, and socio-economic effects of saltwater intrusion. The study highlights need for sustainable water management measures like desalination and increased water quality monitoring. Villages near the Padara and Sindhrot regions of the Mahi River face problems due to seawater intrusion. Lachanpur Village has the greatest electrical conductivity (EC) and total dissolved solids (TDS) values, reaching 2393 μS/cm and 1195 ppm, respectively.

Groundwater quality is vital to human health and the health of the ecosystem, it is dynamic and susceptible to many natural and man-made influences. This study looks at how groundwater quality has changed over the year 2020 in different geographic regions. An analysis was carried out using Ground Water Quality Index (GWQI) values and related ratings in order to identify patterns, trends, and drivers of change in groundwater quality. The findings show varied dynamics, with some sites showing steady decline and others showing steady improvement or variation in quality over time. Seven water quality parameters’ spatial distribution layers are used to generate weighted overlay maps. Stress zones in the Rohtak district were identified by drinking water quality analysis from the Water Quality Index, overlay maps, and spatial distribution maps of particular water quality metrics. The groundwater in the Rohtak District area was classified into categories that were appropriate and unsuitable for irrigation utilizing Wilcox, salinity diagrams, and overlay maps. Groundwater quality trends are significantly shaped by both natural processes, including geological formations and hydrological cycles, and human activities, like industrialization, urbanization, and agriculture. Also, the result shows that the groundwater is unfit for drinking as well as for irrigation purposes.

The excessive use of groundwater for the household, agriculture, and commercial uses increases the probability of droughts on a global scale. Therefore, it is essential to precisely estimate groundwater in order to ensure its sustainable utilization. In India, the utilization of groundwater is the highest worldwide. Groundwater consumes 83% of the country's water supply for residential and irrigation purposes. This scenario is particularly severe in India. Also, in Haryana, the extraction of groundwater has caused significant problems in addressing the water requirements. Approximately, 60% of the area is identified as over-exploited. The Kurukshetra district (Haryana) depends primarily on groundwater resources. Estimating groundwater resources usually involves using numerical models, geospatial analysis, and regular monitoring of water levels. Conventional techniques that do not involve geospatial analysis are not as efficient when applied to wide regions. The integration of remote sensing and GIS in modern techniques provides improved accuracy in assessments. This study presents a thorough evaluation of groundwater depletion in the Kurukshetra region. It utilizes geospatial analysis to assess changes that have occurred in the past ten years. The study reveals a significant decrease in the yearly groundwater capacity, resulting in a classification of the area as “over-exploited.” The research highlights the dependence on groundwater resources in the regions of Thaneshar and Pehowa. The results suggest a significant rise in water extraction, and it is projected that the current availability will not be sufficient for the future.

Groundwater is a crucial resource, serving as a primary source of drinking water in various regions worldwide, such as India. Consequently, maintaining high groundwater quality is essential. Because of the inevitable advantages of data-driven models, Machine Learning (ML) techniques can contribute to predicting and analyzing water quality. This study employed four ML methods to assess water quality in Haryana state, India: Multiple Linear Regression (MLR), Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Random Forest Regression (RFR). The Pollution Index of Groundwater (PIG) results indicated that 56.72% of groundwater samples exhibited insignificant pollution levels. Additionally, 14.92% of samples showed low pollution, 9.88% moderate pollution, 7.56% high pollution, and 10.92% very high pollution. Among the models tested, Random Forest Regression (RFR) demonstrated superior performance across all datasets compared to the other three models. These findings could assist decision-makers in formulating effective water management and quality initiatives in the future.

In this study, the utilization of chemically modified activated carbon (AC) was investigated for malachite green (MG) dye extraction at various dye solution concentrations (50 mg/L, 100 mg/L, 150 mg/L, and 200 mg/L). The experimental variables were temperature (298 K, 308 K, and 318 K), adsorbent dose (10 g/L to 20 g/L), contact time, and pH of the starting solution (ranging from 2 to 7). The outcomes demonstrated that adsorption worked best at lower temperatures and higher pH values. Other models, though, might have been applied. The optimal adsorbent dose was found to be 20 g/L, and the maximum adsorption efficacy was recorded at pH 7.0.

The gradual contamination resulting from heavy metals poses a substantial challenge to global endeavors in environmental sustainability. Chromium (Cr) and Arsenic (As) stand out as particularly hazardous to human well-being. The utilization of plants for the removal of such contaminants from the environment, known as phytoremediation, emerges as a promising technique to existing remediation techniques. In the current experimental investigation, the application of atomic absorption spectrometry (AAS) facilitated the assessment through mass balance of Chromium (Cr) and Arsenic (As) levels in both the substrate and various plant species, namely Phragmites karka, Canna indica, Iris, Sagittaria, and Phragmites australis. The findings show mean Cr concentration of 172 mg/kg in the substrate, while the As concentration was measured at 157 mg/kg. Chromium (Cr) and Arsenic (As) exhibited an average highest concentration in the roots and shoots by P. australis (109.9 mg/kg and 91.9 mg/kg for Cr and 131.4 mg/kg and 34.1 mg/kg for As) and P. karka (108 mg/kg and 90.6 mg/kg for Cr and 132.2 mg/kg and 34.3 mg/kg for As), respectively, compared to the substrate and other plants. For both Cr and As, plants removal and tolerance capacities expressed and quantified as bioconcentration factor values (BCF > 1) and translocation factor values (TF < 1) with (p < 0.05). These findings suggest that Phragmites australis and Phragmites karka are a competent species with promising results for the phytoremediation of Cr and As laden wastewater.

Menstrual waste disposal is a necessary element in household solid waste management. The reasons behind this are the material used (up to 90% plastic) in the sanitary products and the biological composition of menstrual waste. If not correctly handled, these two factors devastate the health and the environment in the long run. Therefore, factors that govern the disposal become essential in managing the waste. The paper intended to identify the behavioral patterns responsible for and influence the disposal process of menstrual waste in a certain way. A comparative study of multiple scientific studies has been conducted to conduct this research, and results have been analyzed based on a survey. The study identifies that most women use sanitary napkins more than other products due to ease and comfort in using and disposing of them, and most educated women also need to gain knowledge of the product and its adverse effects because it is hardly discussed. Myths are the most important governing factor. It is necessary to create awareness of all kinds of information related to menstrual products and waste disposal. Social media and television, as widely accessible platforms, can become effective mediators for educating people about the same, especially in reaching a larger audience and dispelling myths.

Aerosol particles with an aerodynamic diameter of less than 2.5 μm (PM2.5) can deeply enter into human respiratory system, which is a common cause of air pollution in India. Water-soluble inorganic ions (WSII) found in PM2.5 have the property of absorbing and condensing water and leads to new particle formation. These ions also indirectly affect ecosystems and environmental materials through deposition hence research on these ions can elucidate on their primary sources and methods of formation. In the present study, the characteristics of water-soluble inorganic ions in fine particulate matter at Calicut, a city on the Malabar Coast in the state of Kerala, India, are presented. Ambient PM2.5 concentrations were monitored and further the particles were chemically characterized for its water-soluble ions. It is found that the PM2.5 concentrations exceeded the National Air Quality Standard by about 10% of the time and the maximum value of PM2.5 was found to be 98 μg/m3. Chemical analysis of the collected samples indicated that 39% of PM2.5 mass is accounting for water-soluble inorganic ions. Among the total WSIIs in ambient PM2.5, SO42– (25.8%) was the predominant component, followed by Na+ (13.6%) Cl− (13.4%), and K+ (13.1%). Presence of Cl− and Na+ in particles indicated that it is sea salts origin. These findings imply that inorganic aerosol pollution is very significant in coastal cities.

Fixed speed pumps (FSP) exhibit optimal efficiency within their designated operating range. Nevertheless, the total water consumption during the day and the maximum flow rate could be two to three times higher than the average flow rate. The scheduling of pump operations is done with the aim of minimizing the cost of operating. However, it demands frequent on–off of pumps which compromises on the efficiency of pumps and results in increased operational costs. Variable-speed pumps (VSP) are becoming increasingly popular as a method to improve the efficiency of a pumping unit by expanding the optimal operating range for the VSP pump. A VSP adjusts its speed in real-time to accommodate changing demand, resulting in reduced on–off operations. The EPANET 2.0 and its latest version EPANET 2.2 allow for the modelling of a water distribution network (WDN) with pumps. The VSP can be indirectly simulated in EPANET using relative speed factors, which are calculated using the affinity laws. However, it is observed that the high variations between peak and lean flows and their respective head does not allows the FSP to mimic as VSP. In this paper, a new methodology is developed which does not depend on a single curve as it is required in modelling VSP using affinity laws. However, it uses EPANET for modelling multiple pump curves without using any relative speed factors with EPANET-PYTHON toolkit in a non-iterative fashion.

The effects of the changing climate on human societies are significant. The unprecedented advancement in population, number, and size of our cities over the past few years is embodied in the sharp shortage of residence units which resulted in overcrowding, increased rents, inadequate urban living circumstances, inadequate infrastructure services, and increased crime rates. Will human settlements be able to offer a safe place to live and protection from severe weather phenomena like cyclones and heat waves. Numerous health concerns can also be caused by the design and quality of housing buildings, including exposure to temperature extremes. The purpose of this study is to determine Madhya Pradesh, India’s Sustainable Residual Housing Indicator. We must first identify the issues surrounding Madhya Pradesh, India's inadequate housing infrastructure, particularly for the urban poor, to describe the current situation regarding sustainable, resilient housing. To improve the resilience and sustainability of housing, we are also investigating the different housing policies and potential offered by locally made sustainable building materials and technologies in Madhya Pradesh. This research study aims to identify the issues of climate change and its adverse effects on housing. This study examines and categorizes housing units that mitigate the social implications of climate change-related crises brought on by severe weather. Comprehensive solutions that integrate sustainable and climate-resilient design with everyday routine and the larger socio-environmental context.

Linear programming (LP)-based algorithms are the one amongst the various techniques used for the optimization of WDNs. They are quick to produce results; however, most of them, when used for design of looped WDNs, cannot move further from a local optimum solution to a global optimal solution. Further, they result in split pipe solutions which require a special connector that increases the cost of network. A solution considering a single size for each link in the network is desirable and proposed herein to obtain using an integer linear programming (ILP) approach, specifically zero–one ILP. This proposed methodology selects the most suitable size for each link, satisfying all constraints of the problem. The application of ILP is extended to obtain a solution with a single-pipe for each link using the replacement-elimination method in which an initially selected branched network is iteratively improved. The algorithm moves from one local optimum to another in search of a global optimum tree solution. The application of the methodology is shown herein with two benchmark networks. The proposed methodology is generic and can be applied to any single source gravity network.

This paper examines the advantages and difficulties linked to the incorporation of 3D-PT (3D printing technology) in the construction sector. There are several advantages to consider, such as improved constructability, sustainability, and design flexibility. 3D printing enables the production of complex architectural components with great accuracy and minimal material wastage, effectively tackling concerns like scarcity of labour and expensive materials. In addition, the incorporation of novel printable materials such as geopolymer concrete and recycled plastics plays a role in promoting sustainable building practice. Nevertheless, there are several challenges that arise with the use of 3D printing in the construction industry, several problems that need to be overcome, including material limitations, regulatory hurdles, and the substantial initial investment needed for equipment and training. It is necessary to address technical issues regarding structural integrity, process parameters, and the development of appropriate construction materials. Despite the obstacles, ongoing progress in technology and regulatory frameworks might enable wider acceptance of 3D printing in construction. This, in turn, could encourage inventive and environmental friendly building methods.

Cr (VI), a heavy metal found in aquatic environments, causes cancer, lung tumors, and allergies, among other problems in people. In order to obtain the maximum adsorption capacity for Cr (VI) adsorption, this review evaluates the use of various adsorbents, which include biosorbents and activated carbon made from biomass, while keeping an eye on the operating parameters (beginning pH, adsorbent dosage, temperature, Cr (VI) concentration, and contact time). The study concludes that the highest possible capacity for adsorption of Cr (VI) adsorption can be achieved through the use of activated carbons and biosorbents. The pH of the solution has a major impact on Cr (VI) adsorption; in an alkaline pH environment, Cr (VI) adsorbed little to none at all, but in an acidic pH environment, it adsorbed heavily. Using kinetic, isotherm, and thermodynamic mechanisms, an analysis has been conducted on the Cr (VI) adsorption data. To fit adsorption data, the Langmuir equation and a pseudo-second-order (PS2) kinetic model are commonly employed. Generally, Cr (VI) is adsorbed by the outer-sphere Cr (VI) compound at pH values over 6 and by the internal zone Cr (VI) compound at pH levels under 6. This work is important since it provides several insights into the Cr (VI) adsorption process.

IDF curves and rainfall equations are crucial tools for the planning, design, and management of water resource projects. This study emphasizes the creation and comparison of rainfall intensity–duration–frequency (IDF) relationships using Gumbel and Log Pearson type III distributions specifically for the Srinagar Station in Jammu and Kashmir, India. Accurate IDF curves are essential for effective hydrological planning and infrastructure design, particularly in regions prone to extreme weather events. By analyzing historical rainfall data, IDF curves were generated for various return periods and durations using both statistical distributions. For this purpose, a dataset comprising 20 years of hourly rainfall data sourced from the ECMWF Web site was utilized. Through screening the data via R Programming, relevant data for various durations up to 18 h was extracted. For the Srinagar station, frequency analysis techniques such as Gumbel and Log Pearson type III methods are employed for analyzing rainfall corresponding to different return periods spanning between 2 and 100 years. Statistical analyses including chi-square, Kolmogorov–Smirnov, and Anderson–Darling tests are conducted for identification of best-fit distribution. This involved comparing calculated values with chi-square critical values to determine the most appropriate statistical distribution, whether Gumbel or Log Pearson type III. The derived intensity equations were then compared with existing equations to assess improvements and discrepancies. Results indicate that the Gumbel distribution generally fits best for rainfall intensities compared to the Log Pearson distribution. This comparative analysis enhances our understanding of rainfall patterns in this region and provides a more reliable foundation for managing water resources and mitigating flood risks.

Land surface temperature (LST) is the temperature of the Earth’s surface, typically measured in degrees Celsius (°C). It is the temperature of the surface of the Earth, including the soil, vegetation, and buildings. This study investigates the temporal variations of land surface temperature (LST) using MODIS data and analyzes its correlation with air temperature using ERA5 data in the Google Earth Engine. The results show a clear trend of increasing LST over the years, with the highest temperatures observed in May and June. The spatial distribution of LST reveals an expansion of the highest temperature values, indicating a general increase in LST across the region. The correlation between LST and air temperature was strong, with a coefficient of 0.99. The results suggest that LST can be used as a proxy for air temperature in various applications, such as climate modeling and environmental monitoring. The predictive modeling of air temperature using an artificial neural network (ANN) model, which incorporates LST, NDVI, and elevation data, showed promising results. The model accurately estimated air temperature with a mean squared error of 0.4950 in 2013 and 0.6889 in 2023. The study emphasizes the importance of considering LST’s temporal and spatial variations and its correlation with air temperature in climate modeling and environmental monitoring applications. The results can be used to inform policy decisions and develop strategies to mitigate the effects of rising temperatures and climate change.

The rise in the salinity of groundwater resources due to the intrusion of seawater in the freshwater bodies has been a vital issue in these contemporary times. Saltwater intrusion is the phenomenon, where seawater intrudes into freshwater bodies, increasing their salinity. The intensification of global warming leading to a rise in sea levels, excessive usage of groundwater, yearly variation in climatic patterns, and tidal influences have equally contributed to exacerbating this phenomenon globally. The scope of this study specifically focuses on the Mahi River basin of the Sindhrot region in Gujarat, India. This phenomenon has been observed in this region with mild as well as severe impacts on this region of the basin. Through the course of this study, a very systematic analysis of water samples collected in and along the Sindhrot region encompassing not only the water bodies but also water from households has been carried out. The water samples were tested and analyzed based on two major parameters, total dissolved solids (TDS) and electrical conductivity (EC). The severity of the phenomenon was also gauged by a community survey that collectively addressed the major health, environmental, and economic effects of saltwater intrusion. In the current study, in-situ testing of water specimens was performed at Anklav, Amrapura, Gangapura, Kotna Village, Sindhrot, Umeta, Angadh, Chaukari, and Dajipura villages. The results showed high values of electrical conductivity (EC) and total dissolved solids (TDS) in these villages. The Sindhrot region near the Mahi River basin exhibited an average value of TDS and EC as 955.78 ppm and EC as 2001.05 µS/cm, respectively. Both in-situ testing and community survey results revealed that the maximum TDS values and EC values were significantly higher than the acceptable limit ranges, indicating a substantial amount of saltwater intrusion and the corresponding need for mitigation measures.