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This study investigates the impact of temperature variations on the performance of an aerobic membrane bioreactor (MBR) as it transitions from mesophilic (30 °C) to hyper-thermophilic (65 °C) conditions. The microbial community structure was analyzed using 16S rRNA gene sequencing to assess how temperature influences microbial diversity and composition. In mesophilic conditions, the system exhibited high alpha diversity with a Shannon index of 5.92 and 224 observed species. As the temperature increased to 45 °C and 65 °C, diversity decreased significantly, with Shannon indices of 2.54 and 2.82, and 96 and 77 observed species, respectively. Additionally, nutrient removal efficiency, particularly for ammonia and phosphorus, declined at higher temperatures. COD removal efficiency reached 96.5% at 30 °C but decreased to 57% at 45 °C before recovering to 94% at 65 °C. Notably, biomass yield at hyper-thermophilic conditions was 37% lower than at mesophilic conditions, with a yield of 0.06 gVSS/gCOD removed . These findings highlight the potential advantages of operating under hyper-thermophilic conditions, including reduced sludge production, lower nutrient requirements, and increased organic loading capacity. The results provide valuable insights into optimizing high-temperature wastewater treatment processes for more efficient and sustainable industrial applications., Competing Interests: Declarations. Ethical approval: Not applicable. Consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Water is a fundamental necessity for humans and a critical resource in agriculture. However, water scarcity poses a significant challenge, especially considering that agriculture accounts for a substantial portion of freshwater usage. The inadequate monitoring resources in agriculture lead to unnecessary wastage of water, affecting crop growth and the water supply. This challenge has spurred us to focus on the agricultural domain, aiming to conserve water by imparting intelligence into irrigation systems by converging IoT and machine learning technologies. Our study has proposed a Smart Irrigation System (SIS), designed to operate remotely, leveraging open-source IoT platforms. Utilizing IoT and machine learning methodologies can effectively optimize water usage in irrigation practices. The designed system comprises hardware and software tools, where comprehensive data is monitored through an application interface. The sensor's interface with a 32-bit microcontroller is used to gather data such as environmental temperature, humidity, and soil moisture and temperature. The user (farmer) continuously receives real-time updates about the condition of the farmland through the Blynk app. The app triggers a notification advising the user to start or stop watering based on pre-set threshold values of soil moisture. Additionally, users can control the water pump remotely through the app. The data collected from the deployed smart irrigation system was used to train a machine learning algorithm incorporating weather parameters and soil temperature to predict soil moisture content. This trained model aims to offer guidance for future assessments of unknown soil samples based on the weather conditions., Competing Interests: Declarations. All authors have read, understood, and have complied as applicable with the statement on "Ethical responsibilities of Authors" as found in the Instructions for Authors and are aware that with minor exceptions, no changes can be made to authorship once the paper is submitted. Conflict of interest: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Change in flow regime is one of the major reasons which influence the services offered by rivers and integrity of their aquatic ecosystems requiring certain amount of flow in the river known as environmental flow. In this study, the environmental flows described by Tennant's method are correlated with a very versatile index defined in terms of Curve Number (CN) that incorporates hydrometeorological and geomorphological characteristics of catchment. Parameter CN is commonly known to be closely related with catchment characteristics (viz., land use, soil type, etc.). The rainfall-runoff data of 17 catchments from five major Indian river basins (i.e., Brahmani-Baitarani, Godavari, Mahanadi, Mahi, and Narmada) of low flow season (October-June) are used to derive the relationship between percentage of average annual flow (%AAF) and CN. The %AAF is seen to increase linearly with increasing CN, and vice versa, for all catchments and the correlation (R) is found to be greater than 0.7 for 16 (out of 17) catchments. The correlation between %AAF and CN is maximum for Dhariawad (R 2  = 0.899) and Baronda (R 2  = 0.814) catchments, indicating excellent relationship between %AAF and CN, which can be useful for the environmental flow prediction based on catchment characteristics. The existence of such a relationship is further strengthened by the relation established between the standard normal deviate of CN and the Standard Flow Index (SQI), a variation of %AAF, using the same field data., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Irrigation is crucial for sustainable agriculture and improving farm yields, but a significant gap exists between the irrigation potential created and its actual utilization. This gap is due to losses in canal conveyance and the inefficiency of conventional irrigation methods within canal command areas. Most modernization efforts in India focus on implementing micro-irrigation for tube well systems, addressing the problem of water table decline experienced in many districts. With this context, the present study examines the command area of Gadarjudda minor of the Upper Ganga Canal in Haridwar district, Uttarakhand, India, to assess the present state of the canal by conducting on-site surveys and feasibility of micro-irrigation by evaluating the viability of replacing minor canals with a gravity flow piped irrigation network. The evaluation involves assessing the current canal status, designing the gravity flow piped irrigation network, and conducting a social survey to determine farmers' willingness, awareness, and purchasing capacity toward adoptin of micro-irrigation systems on their farms. The study identifies high conveyance loss and poor maintenance in conventional irrigation methods, highlighting the importance of micro-irrigation in the study area. The profile of the minor canal is adequate to support gravity flow in the pipe network, with velocity and pressure head within permissible limits. A social survey revealed that 85% of farmers are willing to adopt micro-irrigation, but low purchasing capacity (36%) hampers its adoption. The study concludes that micro-irrigation is viable in the Gadarjudda minor canal command area as long as a piped irrigation network is implemented and farmers receive government subsidies and proper training., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Optimal irrigation water depth is a crucial parameter in irrigation engineering, often referred to as root zone depth. It is typically assumed to lie between 1 and 1.5 m below the ground surface, depending on the crop and soil types as well as the practitioner's skill and experience. This approach can lead to inefficient irrigation scheduling. Coupling Richards' equation with the Soil Conservation Service Curve Number (SCS-CN) concept and using the three-phase diagram of soil column widely used in geotechnical engineering, this paper suggests an analytical expression for optimal irrigation water depth providing the maximum storage capacity of a soil depending on its hydraulic/storage properties. The results for winter wheat crop in different hydrologic soil groups show that the use of the proposed concept can lead to savings of 71.79% and 57.69% of irrigation water in sandy soils (HSG-A) compared to that used in traditional irrigation considering lump-sum 1.5 m and 1 m optimal irrigation water depths, respectively. In the case of silty loam soils (HSG-C), these savings can assume 52.42% and 28.62%, respectively. The proposed relation can also be of great help in volumetric assessment of field capacity, moisture content, maximum water storage capacity (of different agricultural soils), and avoiding the issue of waterlogging that may arise from over-irrigation and thus is useful in efficient irrigation scheduling as well as in sustainable agricultural water management., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Floods cause substantial losses to life and property, especially in flood-prone regions like northwestern Bangladesh. Timely and precise evaluation of flood impacts is critical for effective flood management and decision-making. This research demonstrates an integrated approach utilizing machine learning and Google Earth Engine to enable real-time flood assessment. Synthetic aperture radar (SAR) data from Sentinel-1 and the Google Earth Engine platform were employed to generate near real-time flood maps of the 2020 flood in Kurigram and Lalmonirhat. An automatic thresholding technique quantified flooded areas. For land use/land cover (LULC) analysis, Sentinel-2's high resolution and machine learning models like artificial neural networks (ANN), random forests (RF) and support vector machines (SVM) were leveraged. ANN delivered the best LULC mapping with 0.94 accuracy based on metrics like accuracy, kappa, mean F1 score, mean sensitivity, mean specificity, mean positive predictive value, mean negative value, mean precision, mean recall, mean detection rate and mean balanced accuracy. Results showed over 600,000 people exposed at peak inundation in July-about 17% of the population. The machine learning-enabled LULC maps reliably identified vulnerable areas to prioritize flood management. Over half of croplands flooded in July. This research demonstrates the potential of integrating SAR, machine learning and cloud computing to empower authorities through real-time monitoring and accurate LULC mapping essential for effective flood response. The proposed comprehensive methodology can assist stakeholders in developing data-driven flood management strategies to reduce impacts., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Hydrological droughts severely affect the demand of water for domestic water supply, irrigation, hydropower generation, and several other purposes. The pervasiveness and consequences of hydrological droughts necessitate a thorough investigation of their characteristics, which is hindered due to unavailability of continuous streamflow records at desirable resolutions. This study aims to assess the hydrological drought characteristics and their spatial distribution using high-resolution Global Flood Awareness System (GloFAS) v3.1 streamflow data for the period 1980 to 2020. Streamflow Drought Index (SDI) was used to characterize droughts at 3-, 6-, 9-, and 12-monthly timescales starting from June, i.e., the start of water year in India. GloFAS is found to capture the spatial distribution of streamflow and its seasonal characteristics. The number of hydrological drought years over the basin varied from 5 to 11 during the study duration, implying that the basin is prone to frequent abnormal water deficits. Interestingly, the hydrological droughts are more frequent in the eastern portion of the basin, i.e., the Upper Narmada Basin. The trend analysis of multi-scalar SDI series using non-parametric Spearman's Rho test exhibited increasing drying trends in the easternmost portions. The results were not similar for the middle and western portions of the basin, which may be due to presence of a large number of reservoirs in these regions and their systematic operations. This study highlights the importance of open-access global products that can be used for monitoring hydrological droughts, especially over ungauged catchments., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Lentic small water bodies (LSWBs) deteriorate owing to anthropogenic activities, such as untreated domestic and agricultural waste disposal. Moreover, different turnover mechanisms occur during different seasons, contributing to nutrient enrichment and consequent degradation of LSWBs. However, understanding their spatial, temporal, and vertical variations during different seasons is understudied. In addition, studies on the variation in water quality under varying rainfall and land-use conditions are limited. Therefore, in this study, three LSWBs located in Northern India were studied during the pre-monsoon and monsoon seasons (December 2022 to October 2023). Total nitrogen (TN), chlorophyll-a (Chl-a), total phosphorus (TP), temperature, pH, dissolved oxygen (DO), total dissolved solids (TDS), chemical oxygen demand (COD), secchi disk depth (SDD), and water level (WL) were measured monthly. Sentinel-2 and CHIRPS pentad data were used for land use, land cover classification, and rainfall analysis. The spatial analysis indicates that the seasonal shift affects the water quality distribution, especially near the inlets and at the edges. The overall concentrations of TN and TP decreased during the monsoon season; however, they increased significantly at the inlets of the LSWBs. On the other hand, the Chl-a concentration shifted towards the edges due to the inflow during the monsoon. Temporal analysis also suggests that the arrival of the monsoon lowers pH, DO, and TDS. However, the concentrations of TN and TP increased because of agricultural runoff. Chl-a and COD show distinct variations due to the individual LSWBs' local conditions. Vertical variability analysis demonstrated pH, temperature, and TN stratification during the pre-monsoon period. However, during the monsoon, stratification is less significant due to intermixing. Redundancy analysis (RDA) showed that land use and rainfall patterns affected the water quality of LSWB 1, 2, and 3 by 53.49%, 81.62%, and 92.64%, respectively. This shows that land use, land cover, and rainfall changes affect the water quality of LSWBs. This study highlights the negative impact of runoff from agricultural land use as the main factor responsible for increased nutrient levels in the LSWBs., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study aims to assess the effectiveness of PCB-based capacitive soil moisture sensors for local field conditions. The electrical scheme of designed sensors has been presented in this study. The PCB-based capacitive soil moisture sensors are calibrated using a linear equation developed between analog values of capacitive sensors and soil moisture content measured from the gravimetric method. The performance of the designed soil moisture sensors was assessed at five different locations at varying depths (i.e., 15 cm, 30 cm, and 45 cm). The calibration results indicated a positive correlation between the soil moisture content and measurement frequency of the sensor for wheat crop, with R 2 values of 0.72, 0.83, and 0.83 for 15 cm, 30 cm, and 45 cm depths, respectively. Results reveal that 85% of the sensors accurately detected the patterns in soil moisture fluctuations during the cropping period. The designed capacitive sensors demonstrated a maximum relative error of 5.87% for 45 cm depth. However, the relative error remained below 5% for the 15 cm and 30 cm soil depths. For the sugarcane crop, R 2 values vary from 0.66 to 0.82, with the highest relative error of 5.22% at a 15 cm depth. These sensors offer a highly cost-effective solution for farmers, with the entire wireless sensor network system including one sensor node, three soil moisture sensors, and one soil temperature sensor, which is priced at approximately $150, making it a practical and affordable option for widespread adoption., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Microplastic (MP) load in urban sewage sludge could vary annually in wastewater treatment plants (WWTPs) depending on seasonal precipitation and human activities. We investigated the seasonal dynamics in abundance and characterization of MP loading in WWTPs located in two cities across River Ganga, India's ecologically sensitive upper stretch. During a 12-month seasonal sampling (pre-monsoon, monsoon, and post-monsoon), sludge samples (n = 36) (primary sludge, PS; drying bed sludge, DBS) were collected and analyzed for load, polymer types, shape, colour, and size (20-1000 µm). Across the three seasons, MP concentrations (particles/kg) were found to be in the ranges of 93.4 ± 5.0 × 10 3 -189.4 ± 11 × 10 3 in the PS and 39.6 ± 4.0 × 10 3 -152.0 ± 7 × 10 3 in the DBS. The trend of MP loading was in the following order: monsoon > post-monsoon > pre-monsoon. The dominant MP size was 50-200 µm (36.22%) followed by 20-50 µm (27.65%), 200-500 µm (24.55%) and 500-1000 µm (11.58%). ATR-FTIR results revealed polypropylene, polyethylene terephthalate, polyvinyl chloride, and nylon dominating MP in sludge. This study highlights the importance of long-term monitoring of MP loading in sewage sludge to offer a more accurate estimate of MP contamination in sludge from WWTPs and develop a possible mechanism for its elimination to safeguard the environment., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study investigates the performance of CMIP6 models in reproducing historical temperature and precipitation data for Iran and neighboring countries (Afghanistan, Pakistan, Turkmenistan, Azerbaijan, Armenia, Turkey, and Iraq) from 1980 to 2014. Reanalysis data from the ECMWF database (ERA5) for temperature and precipitation were utilized as a reference for the period 1980-2014. Additionally, ten Atmosphere-Ocean General Circulation Models (AOGCMs) from CMIP6 were employed to simulate temperature and precipitation data for the study region based on the IPCC Sixth Assessment Report databases. The Kling-Gupta Efficiency (KGE) index was used to evaluate the accuracy of CMIP6 models in replicating daily temperature and precipitation. The results indicate that different CMIP6 models exhibit varying degrees of accuracy in simulating historical temperatures and precipitation, depending on the month and the country. For instance, the IPSL-CM6A-LR model demonstrated the best annual performance in estimating temperature in Azerbaijan (KGE = 0.5), while the HadGEM3-GC31-LL model showed the lowest annual performance in Pakistan (KGE = -1.4). Interestingly, the models were found to be more accurate in simulating temperatures during warm months compared to cold ones. Furthermore, the accuracy of different models in estimating annual precipitation varied significantly, ranging from -0.64 (MRI-EMS2-0 model in Afghanistan) to 0.05 (CMCC-ESM2 model in Armenia). Similar to temperature, the study found that models were generally more accurate in simulating precipitation during cold months compared to warm ones., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Rivers are vital and complex natural systems that provide a wide range of ecosystem services. This study presents a methodology for assessing the riverine provisioning and supporting ecosystem services, whose applicability has been demonstrated over the Budhabalanga River Basin of India. The Soil and Water Assessment Tool (SWAT) is used to generate streamflow time series at various ungauged sites, and then the streamflow is characterized for the evaluation of provisioning services. Further, the diversity and abundance of macroinvertebrates, along with the Lotic-invertebrate Index for Flow Evaluation (LIFE), is used to study the riverine supporting ecosystem services. The streams show intermittent behavior and strong seasonality for low flows, which limits the water availability, particularly during pre-monsoon season. The Baseflow Index (BFI) is greater than 0.6, indicating that groundwater contributes more than 60% of the total streamflow. Interestingly, despite the high BFI, the streams did not conform to the prevailing opinion that a greater baseflow contribution results in a later commencement of the low-flow period in the hydrological year. Furthermore, the study depicts significant variations in the diversity and abundance of the macroinvertebrates across the various sampling sites. However, the LIFE score across the sites remained consistent within a narrow range, i.e., 8 to 9, suggesting a steady supply of supporting ecosystem services. The results of the study can help the policymakers towards an informed decision making and the simplistic methodology proposed in this study can be replicated in other river basins for identifying vulnerable watersheds and prioritizing management actions., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Effectively managing water resources in karst systems requires a thorough understanding of their general conduit network along with their seasonal dynamics. Their investigation has involved well construction or several advanced natural tracer data, most of which are not always available. Hence, this work showcases a pragmatic approach that makes use of basic hydrochemical variables of springs with coarse temporal resolution in characterising a karst system. In this study's example, physicochemical variables like major ion concentrations/ratios, Electrical Conductivity (EC), pH and water temperature (T w ) were measured on 20-day basis for a hydrological year at the Louros Catchment, Greece. We further performed the frequency distribution and variation analysis of EC and T w , principal component analysis (PCA), scatter plots of carbonate ions vs sulphate and hydrochemographs to determine relevant hydrochemical processes and hydrogeological features. PCA and the scatter plots showed that the simple-type upper karst level is entirely dominated by carbonate dissolution, whereas the complex-type middle and lower levels also involve gypsum and dolomite dissolution. Presence of mixing between karst units was also detected. EC and T w analyses revealed the degree of karstification of different units and relative depths of flow systems. Hydrochemographs reflected the seasonality of limestone and gypsum dissolution's contributions linked to the dominant flow type (conduit vs diffuse). This study thus was able to demonstrate the usefulness of such holistic hydrochemical analyses to better understand karst systems. Given their cost-effectiveness, they can be easily applied to any understudied karst system worldwide., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Satellite-based precipitation estimates are a critical source of information for understanding and predicting hydrological processes at regional or global scales. Given the potential variability in the accuracy and reliability of these estimates, comprehensive performance assessments are essential before their application in specific hydrological contexts. In this study, six satellite-based precipitation products (SPPs), namely, CHIRPS, CMORPH, GSMaP, IMERG, MSWEP, and PERSIANN, were evaluated for their utility in hydrological modeling, specifically in simulating streamflow using the Variable Infiltration Capacity (VIC) model. The performance of the VIC model under varying flow conditions and timescales was assessed using statistical indicators, viz., R 2 , KGE, PBias, RMSE, and RSR. The findings of the study demonstrate the effectiveness of VIC model in simulating hydrological components and its applicability in evaluating the accuracy and reliability of SPPs. The SPPs were shown to be valuable for streamflow simulation at monthly and daily timescales, as confirmed by various performance measures. Moreover, the performance of SPPs for simulating extreme flow events (streamflow above 75%, 90%, and 95%) using the VIC model was assessed and a significant decrease in the performance was observed for high-flow events. Comparative analysis revealed the superiority of IMERG and CMORPH for streamflow simulation at daily timescale and high-flow conditions. In contrast, the performances of CHIRPS and PERSIANN were found to be poor. This study highlights the importance of thoroughly assessing the SPPs in modeling diverse flow conditions., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Groundwater quality in Wadi Fatimah is evaluated and demarcated for agriculture utilities using comprehensive approaches namely, international standards, agricultural water quality (AWQ) indices, irrigation water quality index (IWQI), and trace metals. Groundwater samples were collected (n = 59) and analysed for EC, pH, major and minor ions and trace metals. According to FAO recommendations, 42% of samples (EC > 3000 µS/cm) are inappropriate for agricultural uses. AWQ indices including salinity hazard, Kelly's ratio and Na% show that 50%, 19% and 37% of samples, respectively, are unsuitable for agricultural uses. USSL classification reveals that groundwater is preferable only for high-permeability soils and salt-tolerant crops. IWQI suggests that 88% of samples are moderately usable for agriculture. The interrelationship between water salinity and crop yield justified that 73%, 59%, 51% and 25% of samples are desirable to yield 90% in date palm trees, sorghum, rice and citrus fruits, respectively. Groundwater is appropriate for date palm trees except in downstream regions. Boron concentration suggests that 52%, 81% and 92% of samples are suitable for sensitive, semi-tolerant and tolerant crops, respectively. Groundwater in the central part (suitable for sensitive crops), central and upstream regions (semi-tolerant crops) and all regions except downstream (tolerant crops) are suitable for cultivation. Trace metals contents illustrate that 36%, 34%, 22%, 8%, 5% and 100% of samples are inappropriate for agriculture due to high concentrations of Cr, Cu, Ni, V, Mn and Mo, respectively in the groundwater. Further, AWQ indices, IWQI, USSL classifications and trace metals ensure that groundwater in the downstream, and a few pockets in the upstream are unfit for agricultural uses. This study recommends that groundwater in this basin is more suitable for tolerant crops (ie. date palm, sorghum) followed by semi-tolerant and sensitive crops., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The water source reservoirs are the important urban water source in northern China. Although external pollution has been greatly improved, the internal pollutants in reservoirs continue to accumulate with the complex deposition and release processes, resulting in potential risks to water supply safety. To address the aforementioned issue, this paper proposed a simulation model of water quality named ECOlab EU1-WSR to simulate the spatio-temporal changes of water quality under the influence of internal pollution for the water source reservoirs. Based on the analysis of the water quality characteristics and the distribution of benthic vegetation in the reservoir, a three-dimensional hydrodynamic model was established based on MIKE3, the corresponding parameters and the related state variables were set, the ECOlab EU1-WSR model was established by secondly developing the original ECOlab EU1 template, and the real-time dynamic outputs of pollutant content in sediment were added to link the water quality index with sediment nutrition index for better revealing the impact of the internal pollution on the water quality. The performance of the model was evaluated by the case application on the water quality simulation of Daye reservoir and the optimization of the connection project between Daye reservoir and Xueye reservoir in Shandong Province China. The results showed that the model can accurately and simultaneously simulate the pollution in water and sediment by the comparative verification of hydrodynamics, water temperature, and water quality. Moreover, the model can effectively reflect the influence of the accumulation, deposition, and release of internal pollution on water quality by analyzing the correlation between the content of various pollution in water body and those in sediment, such as the total nitrogen and total phosphorus in the water body at the bottom of the water intake, were negatively correlated with the total nitrogen and total phosphorus in the sediments with correlation coefficients of 0.538 and 0.917, respectively. In addition, the optimal water inlet position and water flow rate of the connection project can be optimized and determined by using the model to effectively control water quality. The established model will be a useful tool for the design and management of a reservoir, the interconnection projects, and other water bodies by adaptively recoded., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Landfill leachate, which is a complicated organic sewage water, presents substantial dangers to human health and the environment if not properly handled. Electrochemical technology has arisen as a promising strategy for effectively mitigating contaminants in landfill leachate. In this comprehensive review, we explore various theoretical and practical aspects of methods for treating landfill leachate. This exploration includes examining their performance, mechanisms, applications, associated challenges, existing issues, and potential strategies for enhancement, particularly in terms of cost-effectiveness. In addition, this critique provides a comparative investigation between these treatment approaches and the utilization of diverse kinds of microbial fuel cells (MFCs) in terms of their effectiveness in treating landfill leachate and generating power. The examination of these technologies also extends to their use in diverse global contexts, providing insights into operational parameters and regional variations. This extensive assessment serves the primary goal of assisting researchers in understanding the optimal methods for treating landfill leachate and comparing them to different types of MFCs. It offers a valuable resource for the large-scale design and implementation of processes that ensure both the safe treatment of landfill leachate and the generation of electricity. The review not only provides an overview of the current state of landfill leachate treatment but also identifies key challenges and sets the stage for future research directions, ultimately contributing to more sustainable and effective solutions in the management of this critical environmental issue., (© 2023. Crown.)

Anthropogenic activities have drastically transformed natural landscapes, profoundly impacting land use and land cover (LULC) and, consequently, the provision and functionality of ecosystem service values (ESVs). Evaluating the changes in LULC and their influence on ESVs is imperative to protect ecologically fragile ecosystems from degradation. This study focuses on a highly sensitive Upper Ganga riverine wetland in India, covering Hapur, Amroha, Bulandshahr, and Sambhal districts, which is well-known for its significant endemic flora and fauna. The study analyzes the subtle variability in ecosystem services offered by the various LULC biomes, including riverine wetland, built-up, cropland, forest, sandbar, and unused land. LULC classification is carried out using Landsat satellite imagery 5 and 8 for the years 2000, 2010, and 2020, using the random forest method. The spatiotemporal changing pattern of ESVs is assessed utilizing the value transfer method with two distinct value coefficients: global value coefficients (C14) for a worldwide perspective and modified local value coefficients X08 for a more specific local context. The results show a significant increase in built-up and unused land, with a corresponding decrease in wetlands and forests from 2000 to 2020. The combined ESVs for all the districts are worth US $5072 million (C14) and US $2139 million (X08) in the year 2000, which declined to US $4510 million (C14) and US $1770 million (X08) in the year 2020. The sensitivity analysis reveals that the coefficient of sensitivity (CS) is below one for all biomes, suggesting the robustness of the employed value coefficients in estimating ESVs. Moreover, the analysis identifies cropland, followed by forests and wetlands, as the LULC biomes most responsive to changes. This research provides crucial insights to stakeholders and policymakers for developing sustainable land management practices aimed at enhancing the ecological worth of the Upper Ganga Riverine Wetland., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Bangladesh, a third-world country with the seventh highest population density in the world, has always struggled to ensure its residents' basic needs. But in recent years, the country is going through a serious humanitarian and financial crisis that has been imposed by the neighboring country Myanmar which has forced the government to shelter almost a million Rohingya refugees in less than 3 years (2017-2020). The government had no other option but to acquire almost 24.1 km 2 of forest areas only to construct refugee camps for the Rohingyas which has led to catastrophic environmental outcomes. This study has analyzed the land use and land surface temperature pattern change of the Rohingya camp area for the course of 1997 to 2022 with a 5-year interval rate. Future prediction of the land use and temperature of Teknaf and Ukhiya was also done in this process using a machine learning algorithm for the years 2028 and 2034. The analysis says that in the camp area, from 1997 to 2017, percentage of settlements increased from 5.28 to 11.91% but in 2022, it reached 70.09%. The same drastically changing trend has also been observed in the land surface temperature analysis. In the month of January, the average temperature increased from 18.86 to 21.31 °C between 1997 and 2017. But in 2022. it was found that the average temperature had increased up to 25.94 °C in only a blink of an eye. The future prediction of land use also does not have anything pleasing in store., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Quantifying flood risks through a cascade of hydraulic-cum-hydrodynamic modelling is data-intensive and computationally demanding- a major constraint for economically struggling and data-scarce low and middle-income nations. Under such circumstances, geomorphic flood descriptors (GFDs), that encompass the hidden characteristics of flood propensity may assist in developing a nuanced understanding of flood risk management. In line with this, the present study proposes a novel framework for estimating flood hazard and population exposure by leveraging GFDs and Machine Learning (ML) models over severely flood-prone Ganga basin. The study incorporates SHapley Additive exPlanations (SHAP) values in flood hazard modeling to justify the degree of influence of each GFD on the simulated floodplain maps. A set of 15 relevant GFDs derived from high-resolution CartoDEM are forced to five state-of-the-art ML models; AdaBoost, Random Forest, GBDT, XGBoost, and CatBoost, for predicting flood extents and depths. To enumerate the performance of ML models, a set of twelve statistical metrics are considered. Our result indicates a superior performance of XGBoost (κ = 0.72 and KGE = 82%) over other ML models in flood extent and flood depth prediction, resulting in about 47% of the population exposure to high-flood risks. The SHAP summary plots reveal a pre-dominance of Height Above Nearest Drainage during flood depth prediction. The study contributes significantly in comprehending our understanding of catchment characteristics and its influence in the process of sustainable disaster risk reduction. The results obtained from the study provide valuable recommendations for efficient flood management and mitigation strategies, especially over global data-scarce flood-prone basins., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Reducing emissions from deforestation and forest degradation (REDD) is a specific strategy for combating deforestation and forest degradation to alleviate the effects of climate change. In this study, the potential greenhouse gas (GHG) emission reduction resulting from the implementation of a REDD project is estimated. Changes in forest cover throughout the years 1985, 1990, 1995, 2000, 2010, 2015, and 2020 were analyzed using time-series Landsat imagery (TM, ETM + , and OLI) and a random forest algorithm. Multilayer perceptron neural networks were used to model the transition potential of the forest cover, which were then predicted via Markov chain analysis. The change detection analysis revealed two discernible patterns in forest cover dynamics. Between 1985 and 2000, a notable decrease in forest cover was seen, whereas from 2000 to 2020, it significantly increased. The results suggested that the absence of REDD implementation would result in the deforestation of approximately 199,569 hectares of forest cover between 2020 and 2050, leading to the release of 1,995,695 tCO2e of emissions into the atmosphere. However, with the implementation of REDD, these emissions would be reduced to 405,512 tCO2e, effectively preventing the release of 1,590,183 tCO2e of emissions into the upper atmosphere. This study demonstrates that the implementation of REDD projects can be an effective strategy for reducing GHG emissions and mitigating climate change in the Hyrcanian forests., (© 2024. The Author(s).)

This study evaluates models for predicting volatile fatty acid (VFA) concentrations in sludge processing, ranging from classical statistical methods (Gaussian and Surge) to diverse machine learning algorithms (MLAs) such as Decision Tree, XGBoost, CatBoost, LightGBM, Multiple linear regression (MLR), Support vector regression (SVR), AdaBoost, and GradientBoosting. Anaerobic bio-methane potential tests were carried out using domestic wastewater treatment primary and secondary sludge. The tests were monitored over 40 days for variations in pH and VFA concentrations under different experimental conditions. The data observed was compared to predictions from the Gaussian and Surge models, and the MLAs. Based on correlation analysis using basic statistics and regression, the Gaussian model appears to be a consistent performer, with high R 2 values and low RMSE, favoring precision in forecasting VFA concentrations. The Surge model, on the other hand, albeit having a high R 2 , has high prediction errors, especially in dynamic VFA concentration settings. Among the MLAs, Decision Tree and XGBoost excel at predicting complicated patterns, albeit with overfitting issues. This study provides insights underlining the need for context-specific considerations when selecting models for accurate VFA forecasts. Real-time data monitoring and collaborative data sharing are required to improve the reliability of VFA prediction models in AD processes, opening the way for breakthroughs in environmental sustainability and bioprocessing applications., (© 2024. The Author(s).)

Land use and land cover (LULC) analysis gives important information on how the region has evolved over time. Kerala, a land with an extensive and dynamic history of land-use changes, has, until now, lacked comprehensive investigations into this history. So the current study focuses on Kerala, one of the ecologically diverse states in India with complex topography, through Landsat images taken from 1990 to 2020 using two different machine learning classifications, random forest (RF) and classification and regression trees (CART) on Google Earth Engine (GEE) platform. RF and CART are versatile machine learning algorithms frequently employed for classification and regression, offering effective tools for predictive modelling across diverse domains due to their flexibility and data-handling capabilities. Normalised Difference Vegetation Index (NDVI), Normalised Differences Built-up Index (NDBI), Modified Normalised Difference Water Index (MNDWI), and Bare soil index (BSI) are integral indices utilised to enhance the precision of land use and land cover classification in satellite imagery, playing a crucial role by providing valuable insights into specific landscape attributes that may be challenging to identify using individual spectral bands alone. The results showed that the performance of RF is better than that of CART in all the years. Thus, RF algorithm outputs are used to infer the change in the LULC for three decades. The changes in the NDVI values point out the loss of vegetation for the urban area expansion during the study period. The increasing value of NDBI and BSI in the state indicates growth in high-density built-up areas and barren land. The slight reduction in the value of MNDWI indicates the shrinking water bodies in the state. The results of LULC showed the urban expansion (158.2%) and loss of agricultural area (15.52%) in the region during the study period. It was noted the area of the barren class, as well as the water class, decreased steadily from 1990 to 2020. The results of the current study will provide insight into the land-use planners, government, and non-governmental organizations (NGOs) for the necessary sustainable land-use practices., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Coastal aquifer vulnerability assessment (CAVA) studies are essential for mitigating the effects of seawater intrusion (SWI) worldwide. In this research, the vulnerability of the coastal aquifer in the Lahijan region of northwest Iran was investigated. A vulnerability map (VM) was created applying hydrogeological parameters derived from the original GALDIT model (OGM). The significance of OGM parameters was assessed using the mean decrease accuracy (MDA) method, with the current state of SWI emerging as the most crucial factor for evaluating vulnerability. To optimize GALDIT weights, we introduced the biogeography-based optimization (BBO) and gray wolf optimization (GWO) techniques to obtain to hybrid OGM-BBO and OGM-GWO models, respectively. Despite considerable research focused on enhancing CAVA models, efforts to modify the weights and rates of OGM parameters by incorporating deep learning algorithms remain scarce. Hence, a convolutional neural network (CNN) algorithm was applied to produce the VM. The area under the receiver-operating characteristic curves for OGM-BBO, OGM-GWO, and VM CNN were 0.794, 0.835, and 0.982, respectively. According to the CNN-based VM, 41% of the aquifer displayed very high and high vulnerability to SWI, concentrated primarily along the coastline. Additionally, 32% of the aquifer exhibited very low and low vulnerability to SWI, predominantly in the southern and southwestern regions. The proposed model can be extended to evaluate the vulnerability of various coastal aquifers to SWI, thereby assisting land use planers and policymakers in identifying at-risk areas. Moreover, deep-learning-based approaches can help clarify the associations between aquifer vulnerability and contamination resulting from SWI., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Dewatered sludges from twenty-two sewage treatment facilities were characterized to develop the quality control indices in India. This study used fertilizer index (FI) and clean index (CI) as a tool for categorizing sludge utilization into different classes (A, B, C and limited use classes LU-1, LU-2, LU-3) by their fertilizing potential, toxicity level, pathogen presence, and vector attraction reduction criteria. The findings revealed that total P (as P 2 O 5 ), total N, and K (as K 2 O) in the sludge samples ranged from 0.9 to 5.7%, 1.2 to 3.8%, and 0.1 to 1.5%, respectively. Moreover, all sludges belong to Class C and lower category due to the presence of toxic metals and pathogens. The helminth eggs were found in the range of 25-1450 Numbers/4gTS in sludge samples. The highest number of helminth eggs of 1450/4 g of TS was found in the fecal sludge. The specific oxygen uptake rate (SOUR) values in the sludge samples varied from 0.3 to 4.9, with a median value of 1.3 and an RSD of 86% is way too high. It indicates that the sludge samples need further treatment to be less attractive as a food source for vectors and rodents. However, sludge samples have fertilizing potential and FI values ranges from 4.1 to 4.9, and CI value ranges from 2.5 to 5.0. It indicates that compost is best in quality and has high-fertilizer potential and low heavy-metal content, which is suitable for high-value crops such as organic farming. Further sludge treatment using typical composting, aerobic or anaerobic digestion, and solar or thermal drying could bring the sludges into the Class A and B categories., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Choosing a suitable gridded climate dataset is a significant challenge in hydro-climatic research, particularly in areas lacking long-term, reliable, and dense records. This study used the most common method (Perkins skill score (PSS)) with two advanced time series similarity algorithms, short time series distance (STS), and cross-correlation distance (CCD), for the first time to evaluate, compare, and rank five gridded climate datasets, namely, Climate Research Unit (CRU), TERRA Climate (TERRA), Climate Prediction Center (CPC), European Reanalysis V.5 (ERA5), and Climatologies at high resolution for Earth's land surface areas (CHELSA), according to their ability to replicate the in situ rainfall and temperature data in Nigeria. The performance of the methods was evaluated by comparing the ranking obtained using compromise programming (CP) based on four statistical criteria in replicating in situ rainfall, maximum temperature, and minimum temperature at 26 locations distributed over Nigeria. Both methods identified CRU as Nigeria's best-gridded climate dataset, followed by CHELSA, TERRA, ERA5, and CPC. The integrated STS values using the group decision-making method for CRU rainfall, maximum and minimum temperatures were 17, 10.1, and 20.8, respectively, while CDD values for those variables were 17.7, 11, and 12.2, respectively. The CP based on conventional statistical metrics supported the results obtained using STS and CCD. CRU's Pbias was between 0.5 and 1; KGE ranged from 0.5 to 0.9; NSE ranged from 0.3 to 0.8; and NRMSE between - 30 and 68.2, which were much better than the other products. The findings establish STS and CCD's ability to evaluate the performance of climate data by avoiding the complex and time-consuming multi-criteria decision algorithms based on multiple statistical metrics., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

There is growing evidence of negative impacts of antidepressants on behavior of aquatic non-target organisms. Accurate environmental risk assessment requires an understanding of whether antidepressants with similar modes of action have consistent negative impacts. Here, we tested the effect of acute exposure to two antidepressants, fluoxetine and venlafaxine (0-50 µg/L), on the behavior of non-target organism, i.e., freshwater pond snail, Lymnaea stagnalis. As compounds interact with chemical cues in the aquatic ecosystems, we also tested whether the effects altered in the presence of bile extract containing 5α-cyprinol sulfate (5α-CPS), a characterized kairomone of a natural predator, common carp (Cyprinus carpio). Behavior was studied using automated tracking and analysis of various locomotion parameters of L. stagnalis. Our results suggest that there are differences in the effects on locomotion upon exposure to venlafaxine and fluoxetine. We found strong evidence for a non-monotonic dose response on venlafaxine exposure, whereas fluoxetine only showed weak evidence of altered locomotion for a specific concentration. Combined exposure to compounds and 5α-CPS reduced the intensity of effects observed in the absence of 5α-CPS, possibly due to reduced bioavailability of the compounds. The results highlight the need for acknowledging different mechanisms of action among antidepressants while investigating their environmental risks. In addition, our results underline the importance of reporting non-significant effects and acknowledging individual variation in behavior for environmental risk assessment., (© 2024. The Author(s).)

Microbial fuel cells (MFCs), hailed as a promising technology, hold the potential to combat various wastewater pollutants while simultaneously converting their chemical energy into electricity through biocatalysts. This study explores the applicability of a dual compartment MFC (DC-MFC) under varying conditions, targeting the removal of chemical oxygen demand (COD) from landfill leachate and electricity generation. In this setup, anaerobic sludge from a wastewater treatment plant serves as the inoculum in the anode compartment of the MFC, with a Nafion117 membrane acting as the separator between MFC units. The cathode compartments are filled with distilled water and continually aerated for 24 h to enhance air supply. The study assesses the MFC's performance across different COD concentrations, focusing on COD removal, power generation, and Coulombic efficiency. The findings reveal that COD removal efficiency is notably enhanced at higher concentrations of organic matter. Specifically, at a COD concentration of 3325.0 mg L -1 , the MFC exhibited the highest COD removal efficiency (89%) and maximum power density (339.41 mWm -2 ), accompanied by a Coulombic efficiency of 25.5%. However, as the initial substrate concentration increased to 3825 mg L -1 , the efficiency decreased to 72%, with a Coulombic efficiency of 13.56% and a power density of 262.34 mWm -2 . Optical density levels increased due to bacterial growth at ambient temperature and neutral pH, reflecting the dynamic microbial response within the system., (© 2024. Crown.)

Microbial fuel cells (MFCs) have garnered attention in bio-electrochemical leachate treatment systems. The most common forms of inorganic ammonia nitrogen are ammonium ([Formula: see text]) and free ammonia. Anaerobic digestion can be inhibited in both direct (changes in environmental conditions, such as fluctuations in temperature or pH, can indirectly hinder microbial activity and the efficiency of the digestion process) and indirect (inadequate nutrient levels, or other conditions that indirectly compromise the microbial community's ability to carry out anaerobic digestion effectively) ways by both kinds. The performance of a double-chamber MFC system-composed of an anodic chamber, a cathode chamber with fixed biofilm carriers (carbon felt material), and a Nafion 117 exchange membrane is examined in this work to determine the impact of ammonium nitrogen ([Formula: see text]) inhibition. MFCs may hold up to 100 mL of fluid. Therefore, the bacteria involved were analysed using 16S rRNA. At room temperature, with a concentration of 800 mg L -1 of ammonium nitrogen and 13,225 mg L -1 of chemical oxygen demand (COD), the study produced a considerable power density of 234 mWm -3 . It was found that [Formula: see text] concentrations above 800 mg L -1 have an inhibitory influence on power output and treatment effectiveness. Multiple routes removed the most nitrogen ([Formula: see text]-N: 87.11 ± 0.7%, NO 2 N: 93.17 ± 0.2% and TN: 75.24 ± 0.3%). Results from sequencing indicate that the anode is home to a rich microbial community, with anammox (6%), denitrifying (6.4%), and electrogenic bacteria (18.2%) making up the bulk of the population. Microbial fuel cells can efficiently and cost-effectively execute anammox, a green nitrogen removal process, in landfill leachate.- N: 93.17 ± 0.2% and TN: 75.24 ± 0.3%). Results from sequencing indicate that the anode is home to a rich microbial community, with anammox (6%), denitrifying (6.4%), and electrogenic bacteria (18.2%) making up the bulk of the population. Microbial fuel cells can efficiently and cost-effectively execute anammox, a green nitrogen removal process, in landfill leachate., (© 2023. Crown.)

While the availability of "big data" on biophysical parameters through citizen science and/or from public/private sources is expected to help in addressing data scarcity issues, there is little understanding of whether and/or how such data will improve watershed simulations. This research aimed to evaluate whether improvements in resolutions of Digital Elevation Model (DEM) and soil data will enhance streamflow and sediment yield simulations and thereby improve soil and water management decisions. The study was conducted in two different-sized watersheds (Anjeni and Gilgel Abay with ~ 1 km 2 and ~ 1655 km 2 area, respectively) in the Upper Blue Nile basin in Ethiopia. Effects of DEM and soil data resolutions on streamflow and sediment yield were evaluated using the Soil and Water Assessment Tool (SWAT). The results showed that the effect of DEM and soil data resolution on streamflow and sediment yield simulation was scale dependent finer resolution DEM and soil datasets improved streamflow and sediment yield simulations in the smaller Anjeni watershed, whereas DEM resolution had no effect in the bigger Gilgel Abay watershed. Small watersheds are often used to understand watershed processes, and thus the use of finer-resolution spatial data for watershed simulations could result in better results. Findings from the smaller Anjeni watershed suggested that the combined use of finer resolution DEM and soil data could potentially improve sediment yield simulations although the lack of observed sediment yield data did not allow verification of this at the larger Gilgel Abay watershed., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The application of restoration plans for the Iraqi marshlands is encountering significant challenges due to water scarcity and the impacts of climate change. This paper assesses the impact of water scarcity on the possibility of continuing the application of restoration and sustainable management plans for the main marshlands in Iraq. This assessment was conducted based on the available data and expected situation of available water resources under climate change conditions until the year 2035. Additionally, a satellite image-based index model was prepared and applied for the period 2009-2020 to obtain the spatiotemporal distribution of the restored marshlands. The results show that the shortage in water resources and insufficient inundation rates prevented the adequate application of the restoration plans. Also, applying the scenarios of distributing the deficit equally over all water demand sectors (S1) and according to the percentage of demand for each sector (S2) shows that the expected deficit in available water for the three marshes by the years 2025 and 2035 will be approximately 25% and 32% for S1 and 9% for S2. Consequently, the considered marshes are expected to lose approximately 20 to 33% of their eligible restoration areas. Accordingly, looking for suitable alternatives to support the water resources of these marshes became a very urgent matter and/or recourse to reduce the areas targeted by inundation and being satisfied with the areas that can be sustainable and maintain the current status of the rest of the regions as an emerging ecosystem characterized by lands that are inundated every few years. Accordingly, steps must be urged to develop plans and programs to maintain the sustainability of these emerging ecosystems within the frameworks of climate change and the conditions of scarcity of water resources and water and air pollution to ensure that they are not lost in the future., (© 2023. The Author(s).)

Evaluation of land use and land cover (LULC) change is among vital tools used for tracking environmental health and proper resource management. Remote sensing data was used to determine LULC change in Bahi (Manyoni) Catchment (BMC) in central Tanzania. Landsat satellite images from Landsat 5 TM and Landsat 8 OLI/TIRS were used, and support vector machine (SVM) algorithm was applied to classify the features of BMC. The obtained kappa values were 0.74, 0.83 and 0.84 for LULC maps of 1985, 2005 and 2021, respectively, which indicates the degree of accuracy from produced being substantial to almost perfect. Classified maps along with geospatial, socio-economic and climatic drivers with sufficient explanatory power were incorporated into MLP-NN to produce transition potential maps. Transition maps were subsequently used in cellular automata (CA)-Markov chain model to predict future LULC for BMC in immediate-future (2035), mid-future (2055) and far-future (2085). The findings indicate BMC is expected to experience significant expansion of agricultural lands and built land from 31.89 to 50.16% and 1.48 to 9.1% from 2021 to 2085 at the expense of open woodland, shrubland and savanna grassland. Low-yield crop production, water scarcity and population growth were major driving forces for rapid expansion of agricultural lands and overall LULC in BMC. The findings are essential for understanding the impact of LULC on hydrological processes and offer insights for the internal drainage basin (IDB) board to make necessary measures to lessen the expected dramatic changes in LULC in the future while sustaining harmonious balance with livelihood activities., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Water quality variables, including chlorophyll-a (Chl-a), play a pivotal role in comprehending and evaluating the condition of aquatic ecosystems. Chl-a, a pigment present in diverse aquatic organisms, notably algae and cyanobacteria, serves as a valuable indicator of water quality. Thus, the objectives of this study encompass: (1) the assessment of the predictive capabilities of four deep learning (DL) models - namely, recurrent neural network (RNN), long short-term memory (LSTM), gated recurrence unit (GRU), and temporal convolutional network (TCN) - in forecasting Chl-a concentrations; (2) the incorporation of these DL models into ensemble models (EMs) employing genetic algorithm (GA) and non-dominated sorting genetic algorithm (NSGA-II) to harness the strengths of each standalone model; and (3) the evaluation of the efficacy of the developed EMs. Utilizing data collected at 15-min intervals from Small Prespa Lake (SPL) in Greece, the models employed hourly Chl-a concentration lag times, extending up to 6 h, as models' inputs to forecast Chla (t+1). The proposed models underwent training on 70% of the dataset and were subsequently validated on the remaining 30%. Among the standalone DL models, the GRU model exhibited superior performance in Chl-a forecasting, surpassing the RNN, LSTM, and TCN models by 8%, 2%, and 2%, respectively. Furthermore, the integration of DL models through single-objective GA and multi-objective NSGA-II optimization algorithms yielded hybrid models adept at effectively forecasting both low and high Chl-a concentrations. The ensemble model based on NSGA-II outperformed standalone DL models as well as the GA-based model across a range of evaluation indices. For instance, considering the R-squared metric, the study's findings demonstrated that the EM-NSGA-II stands out with exceptional effectiveness compared to DL and EM-GA models, showcasing improvements of 14% (RNN), 8% (LSTM), 6% (GRU), 8% (TCN), and 3% (EM-GA) during the testing phase., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Studying spatiotemporal water quality characteristics and their correlation with land use/land cover (LULC) patterns is essential for discerning the origins of various pollution sources and for informing strategic land use planning, which, in turn, requires a comprehensive analysis of spatiotemporal water quality data to comprehend how surface water quality evolves across different time and space dimensions. In this study, we compared catchment, riparian, and reach scale models to assess the effect of LULC on WQ. Using various multivariate techniques, a 14-year dataset of 20 WQ variables from 20 monitoring stations (67,200 observations) is studied along the Middle Ganga Basin (MGB). Based on the similarity and dissimilarity of WQPs, the K-means clustering algorithm classified the 20 monitoring stations into four clusters. Seasonally, the three PCs chosen explained 75.69% and 75% of the variance in the data. With PCs > 0.70, the variables EC, pH, Temp, TDS, NO 2  + NO 3 , P-Tot, BOD, COD, and DO have been identified as dominant pollution sources. The applied RDA analysis revealed that LULC has a moderate to strong contribution to WQPs during the wet season but not during the dry season. Furthermore, dense vegetation is critical for keeping water clean, whereas agriculture, barren land, and built-up area degrade WQ. Besides that, the findings suggest that the relationship between WQPs and LULC differs at different scales. The stacked ensemble regression (SER) model is applied to understand the model's predictive power across different clusters and scales. Overall, the results indicate that the riparian scale is more predictive than the watershed and reach scales., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Suboptimal management of healthcare waste poses a significant concern that can be effectively tackled by implementing Internet of Things (IoT) solutions to enhance trash monitoring and disposal processes. The potential utilisation of the Internet of Things (IoT) in addressing the requirements associated with biomedical waste management within the Kaduna area was examined. The study included a selection of ten hospitals, chosen based on the criterion of having access to wireless Internet connectivity. The issue of biomedical waste is significant within the healthcare sector since it accounts for a considerable amount of overall waste generation, with estimates ranging from 43.62 to 52.47% across various facilities. Utilisation of (IoT) sensors resulted in the activation of alarms and messages to facilitate the prompt collection of waste. Data collected from these sensors was subjected to analysis to discover patterns and enhance the overall efficiency of waste management practices. The study revealed a positive correlation between the quantity of hospital beds and the daily garbage generated. Notably, hospitals with a higher number of beds were observed to generate a much greater amount of waste per bed. Hazardous waste generated varies by hospital, with one hospital leading in sharps waste (10.98 kgd -1 ) and chemical waste (21.06 kgd -1 ). Other hospitals generate considerable amounts of radioactive waste (0.60 kgd -1 and 0.50 kgd -1 ), pharmaceuticals, and genotoxic waste (16.19 kgd -1 ), indicating the need for specialised waste management approaches. The study sheds light on the significance of IoT in efficient waste collection and the need for tailored management of hazardous waste., (© 2023. Crown.)

Remediation of arsenic-contaminated soil by the technique of electro-kinetic using various enhanced conditions is the thrust of this study. Many tests were performed with different operating conditions, such as the electrodes solution (tap water) with a variable pH at (2, 5, and 8). Cyclodextrin (10% hydroxypropyl-beta-cyclodextrin, or HPCD) has been used as an activating agent that helps enhance this process of soil arsenic clean-up. Agricultural wastes (pomegranate peels) were used after treating them as an adsorbent to impede the return of pollutants to the soil as a result of reverse osmosis. The results obtained after the remediation's completion indicated that the best elimination capacity was 72% at a pH of 2. This indicates that the elimination capacity increases with the decreasing pH of the electrodes solution. Mixing the HPCD solution with soil as an enhanced solution increased the solubility and desorption of arsenic from the soil. Then, arsenic-containing HPCD micelles readily moved to the cathode electrode by electro-osmotic flow, and the elimination capacity increased (84%). It was concluded that pomegranate peels are a good adsorbent for arsenic returning to the soil during the treatment process as a result of reverse osmosis. Hence, the application of this material in this research gave a perfect impression of its use as an inexpensive, environmentally friendly adsorbent and an alternative to commercial absorbents., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Knowledge of dietary intakes of essential elements in groundwater is important for proper assessment of the actual contribution of drinking water to daily nutrient requirements. This study assessed the daily intake of nutritional elements in drinking groundwater of Lagos and Ogun States of Nigeria. One hundred and seventy (170) water samples were collected and analyzed for thirteen (13) nutritional elements (K, Mg, Ca, Co, Fe, Cu, Zn, Mo, Se, Na, Mn, Cr, and I) by Inductively Coupled Plasma-Mass Spectrometry (ICP/MS). Chronic daily intake CDI of the nutritional elements was higher for children and infants than adults in all cases. Percentages of tolerable daily intake reference value (TIRV) obtained for Lagos State groundwater were Mg 0.425%, Ca 0.309%, Fe 14.0%, Cu 1.84%, Zn 6.25%, Mo 0.057%, and Mn 1.08%; for Ogun State groundwater, % TIRV were Mg 1.99%, Ca 0.586%, Fe 81.1%, Cu 2.12%, Zn 9.21%, Mo 1.16%, and Mn 0.023%. The order of dermal absorption by an adult in Lagos when the water is used for bathing daily was Cr > Mn > Fe > Zn > Cu while the order in Ogun was Fe > Mn > Zn > Cu > Cr. The research concluded that groundwater alone cannot provide enough essential elements for human dietary needs in the study area., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The prime challenges limiting efficient flood management, especially over large regions, are concurrently related to limited hydro-meteorological observations and exorbitant economics with computational modeling. Reanalysis datasets are a valuable alternative, as they furnish relevant variables at high spatiotemporal resolutions. In recent times, ERA5 has gained significant recognition for its applications in hydrological modeling; however, its efficacy at the inundation scale needs to be understood. The advent of "global flood models" has ensured flood inundation and hazard modeling over large regions, otherwise obscure with regional models. For the first time, the present study explores the fidelity of ERA5 reanalysis at the inundation scale over the Mahanadi River basin, a severely flood-prone region in India. The biases in the discharges within ERA5 are ascertained by comparing them with station-level data at the nascent and extreme levels (i.e., 95 th and 99 th percentiles). Later, ERA5 is fed to LISFLOOD-FP, an acclaimed global flood model, to reenact the 2006, 2008, 2011, and 2014 flood events. Hit rates exceeding 0.8 compared to MODIS satellite imageries affirm the suitability of ERA5 in accurately capturing flood inundation. Distributed design discharges for 50 yr and 100 yr are derived using a set of extreme value distributions and fed to LISFLOOD-FP to derive design flood inundation and hazards in terms of both "depth" and "product of depth and velocity" of flood waters. Results derived from the study provide vital lessons for efficient land-use planning and adaptation strategies linked to flood protection and resilience., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Climate change is arguably the most alarming global concern of the twenty-first century, particularly due to the increased frequency of meteorological extremes, e.g., heatwaves, droughts, and floods. Heatwaves are considered a potential health risk and urge further study, robust preparedness, and policy framing. This study presents an analysis of heatwave characteristics for historical (1980-2014), near-future (2021-2055), and far-future (2056-2090) scenarios over three highly populated cities of South India, i.e., Bangalore, Chennai, and Hyderabad. Two different approaches, i.e., the India Meteorological Department (IMD) criterion and the percentile-based criterion, are considered for defining the threshold of a heatwave day. Nine general circulation models (GCMs) from the Coupled Model Inter-comparison Project phase 6 (CMIP6) experiment are selected, evaluated after bias correction, and the best performer was utilized to obtain the temperature projections corresponding to two shared socioeconomic pathways (SSP 2-4.5 and 5-8.5) for the future periods. The results reveal a high frequency of heatwave days over the cities in recent years from both approaches, which may further exacerbate in the future, thereby putting a large population at risk. The number of heatwave days is much higher for SSP5-8.5 than that for SSP2-4.5, depicting the direct effects of anthropogenic activities on the frequency of heatwaves. The detailed analysis of heatwave projections will help develop equitable heat resilient mitigation and adaptation strategies for the future, thereby alleviating their pernicious impacts., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Soil erosion caused by water refers to the removal of topsoil by rainfall and runoff. Proper selection of an assessment method is crucial for quantifying the spatial variance of soil erosion. The Universal Soil Loss Equation (USLE) and its revised version (RUSLE) are widely used for modelling soil erosion. This study aimed to evaluate the effectiveness of the USLE-based soil erosion modelling in different agroecological regions of India, identify potential issues, and provide suggestions for future applications. The review revealed that little attention has been given to estimate soil erosion in high-priority land degradation regions of India. Additionally, many studies failed to thoroughly verify the authenticity of stated soil loss rates in their research regions either by overestimating or underestimating at least one of the five soil loss parameters. Furthermore, flaws in the application of methods to calculate these parameters leading to erroneous values were identified and suggestions for improvement were made. The USLE-based soil erosion modelling is an effective tool for quantifying soil erosion risk, but researchers should put emphasis on thoroughly verifying the methodologies adopted, unit conversions, and data availability for the estimation of soil loss parameters to improve the accuracy of their final results. This paper provides valuable insights to assist researchers in implementing USLE-based erosion models in diverse agroecological regions in India and elsewhere. However, for effective soil conservation and sustainable agriculture, further research is necessary to develop efficient techniques for using USLE-based soil erosion modelling to achieve a comprehensive understanding of erosion risk across different agroecological regions., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The high mountain ecosystem of the Indian Himalayas has frequently been experiencing primary hazards (like earthquakes, avalanches, and landslides). Often, these events are followed by the triggering of secondary hazards (like landslide dams, debris flows, and flooding), thereby posing massive risks to infrastructure and residents in the region. This study was taken up to understand the dynamics of an extraordinary debris flood disaster in the Rishiganga River valley, Chamoli district of Uttarakhand on 7th February 2021. Rapid mass movements (RAMMS)-debris flow software was employed to recreate the entire sequence of the hazard consisting of a rock-ice slide, mass deposition and erosion along the channel, and subsequent debris flood. Forty-nine scenarios were analyzed for accurate calibration of dry-Coulomb type friction coefficient (µ) and viscous-turbulent friction coefficient (ξ). Consequently, the geomorphologic characteristics of the debris flow were validated using high-resolution satellite image interpretation and field photographs. The volume of detached rock-ice mass was estimated to be 26.42 × 10 6 m 3 . At the same time, the RAMMS-derived model outputs for velocity, flow depth, and momentum were found in good agreement with the extent and height of actual debris on the ground. The study highlights an urgent need to identify the glaciers with a high risk of ice avalanches in the Indian Himalayas. The presented modeling approach may be applied in dynamic mountain ecosystems to simulate potential flash floods due to avalanches. Moreover, the information reported in this study can be vital input for improving the district-level disaster management plan., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Water quality is one of the essential parameters of environmental monitoring; even a slight variation in its characteristics may significantly influence the ecosystem. The water quality of Vembanad Lake is affected by anthropogenic effects such as industrial effluents and tourism. The optical parameters representing water quality, such as diffuse attenuation (K d ), turbidity, suspended particulate matter (SPM), and chlorophyll-a (Chl-a), are considered in this study to evaluate the water quality of Vembanad Lake, Kerala, India. As this lake is regarded as of ecological importance by the Ramsar Convention and has faced severe concerns over recent years, there was a substantial change in the water quality during the lockdowns of the COVID-19 pandemic. This research is aimed at examining the change in water quality using optical data from Sentinel-2 satellites in the ACOLITE processing software from 2016 to 2021. The analyses showed a 2.5% decrease in the values of K d , whereas SPM and turbidity show a reduction of about 4.3% from the year 2016 to 2021. The flood and the COVID lockdown had an impact on the improvement in the quality of water from 2018 to 2021. The findings indicated that the reduction in industrial activities and tourism had a more significant effect on the improvement in the water quality of the lake. There was no substantial change in the Chl-a until 2020, whereas an average decrease of 12% in Chl-a values was observed throughout 2021. This decrease can be attributed to the reduction in the lake's hydrological residence time (HRT). Thus, these findings will be a valuable reference to help the government and non-government organizations (NGO) during strategic planning., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)