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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.)

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.)

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.)

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.)

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.)

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.)

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).)

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.)

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.)

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.)

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.)

Untreated landfill leachate can harm the environment and human health due to its organic debris, heavy metals, and nitrogen molecules like ammonia. Microbial fuel cells (MFCs) have emerged as a promising technology for treating landfill leachate and generating energy. However, high concentrations of total ammonia-nitrogen (TAN), which includes both ammonia and the ammonium ion, can impede MFC performance. Therefore, maintaining an adequate TAN concentration is crucial, as both excess and insufficient levels can reduce power generation. To evaluate the worldwide research on MFCs using landfill leachate as a substrate, bibliometric analysis was conducted to assess publication output, author-country co-authorship, and author keyword co-occurrence. Scopus and Web of Science retrieved 98 journal articles on this topic during 2011-2022; 18 were specifically evaluated and analysed for MFC ammonia inhibition. The results showed that research on MFC using landfill leachate as a substrate began in 2011, and the number of related papers has consistently increased every 2 years, totaling 4060 references. China, India, and the USA accounted for approximately 60% of all global publications, while the remaining 40% was contributed by 70 other countries/territories. Chongqing University emerged as one of the top contributors among this subject's ten most productive universities. Most studies found that maintaining TAN concentrations in the 400-800 mg L -1 in MFC operation produced good power density, pollution elimination, and microbial acclimatization. However, the database has few articles on MFC and landfill leachate; MFC ammonia inhibition remains the main factor impacting system performance. This bibliographic analysis provides excellent references and future research directions, highlighting the current limitations of MFC research in this area., (© 2023. Crown.)

A coastal region is a section of land that borders a significant body of water, often the sea or ocean. Despite their productivity, they are sensitive to even little alterations in the outside environment. This study aims to develop a spatial coastal vulnerability index (CVI) map for the Tamil Nadu coast of India, which has diverse coastal and marine environments that are ecologically fragile zones. Climate change is expected to increase the intensity and frequency of severe coastal hazards, such as rising sea levels, cyclones, storm surges, tsunamis, erosion, and accretion, severely impacting local environmental and socio-economic conditions. This research employed expert knowledge, weights, and scores from the analytical hierarchy process (AHP) to create vulnerability maps. The process includes the integration of various parameters such as geomorphology, Land use and land cover (LULC), significant wave height (SWH), rate of sea level rise (SLR), shoreline change (SLC), bathymetry, elevation, and coastal inundation. Based on the results, the very low, low, and moderate vulnerability regions comprise 17.26%, 30.77%, and 23.46%, respectively, whereas the high and very high vulnerability regions comprise 18.20% and 10.28%, respectively. The several locations tend to be high and very high due to land-use patterns and coastal structures, but very few are contributed by geomorphological features. The results are validated by conducting a field survey in a few locations along the coast. Thus, this study establishes a framework for decision-makers to implement climate change adaptation and mitigation actions in coastal zones., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Urban floods are more concerned in recent days due to their substantial effect in loss of human lives and properties. Due to climate change, urban floods are frequently observed in many parts of the world. Flood events in Chennai city are a frequent scenario due to rapid increase in the density of population. Adyar river watershed and surrounding urban cover are focused in the present study. The present study aims at mapping flooded region using Sentinel 1A datasets over Adyar watershed. Series of Sentinel 1A image is collected before, during and after floods for mapping the extent of flood and mapping risk zones in Adyar watershed. Methodologies such as ISODATA Technique, Multi-Temporal Analysis, Thresholding Method, PCA and ICA Analysis and Grey Level Co-Occurrence Matrix are adopted for the extraction of flooded extent from the SAR datasets. Analysis performed over the Adyar watershed provided promising results in the extraction of flooded extent with Thresholding Method and Grey Level Co-Occurrence Matrix being the dominant of all the methods. Though higher accuracy is obtained in the extraction of flooded extent, limitation of layover, foreshortening and shadow is experienced in the built up region for the extraction of flooded pixels., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Glaciers in the Karakoram region are widely recognized for their historical surging phenomenon. Accurate field-based glacier monitoring is challenging in the Karakoram due to the presence of mixed-nature glaciers that are advancing, receding, and surging. Many geographers came to the opinion that surging is a thermally controlled activity in the Karakoram as opposed to a hydrologically controlled activity as a result of characteristics including high-altitude warmth, precipitation, and accumulation patterns of these glaciers. But the main surge mechanism is still a mystery. The current study used Landsat multispectral satellite datasets to examine and investigate the glaciers' vulnerability to surging activity in the Hunza basin based on the annual surface ice flow rate and frontal snout advancement of the glaciers from 1990 to 2021. Around 80 glaciers in the Hunza basin have been researched, and based on interannual surface flow rates, it has been determined that Batura, Hassanabad II, Barpu, Gharesa, Hispar, Khurdopin, Minapin, Virjerab, Yazgil, and Ghulkin glaciers are more vulnerable to surging. The findings show that during the research period, these glaciers had surged and advanced along their snouts. The frontal snout of these glaciers advances, and moraines are deposited closer to the glacier terminus as a consequence of active surge points over the ablation region. The Hunza basin's topography, precipitation, and thermal regimes regulate the glaciers' surging phenomena causing successive acceleration in the glaciers. Field-based measurements made with a differential global positioning system are used to corroborate the obtained results., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The accurate estimation of dissolved oxygen (DO) as an important water quality indicator can provide a basis for ensuring the preservation of the riverine ecosystem and designing proper water quality development plans. Therefore, this study aimed to propose a novel hybrid model based on long short-term memory (LSTM) networks with Satin Bowerbird optimizer (SBO) algorithm for the estimation of the DO concentration based on multiple water quality parameters. Furthermore, to compare the supreme performance of proposed hybrid model, standalone LSTM, support vector machine (SVM) and Gaussian process regression (GPR) were employed. The models were prepared using the datasets collected from three successive gauging stations along the Savannah River, USA, for the period 2015-2021. The modeling process was performed through local and cross-station scenarios to assess the interrelations between the DO values of upstream/downstream stations. The comparison of estimation accuracies of different employed models revealed that the proposed SBO-LSTM yields a correlation coefficient (R) of 0.981, Nash-Sutcliffe efficiency (NSE) of 0.957, and root mean square error (RMSE) of 0.034 for a test series of dissolved oxygen series which was the most accurate model through both local and cross-station scenarios. Also, the proposed SBO-LSTM model showed better performance by 0.52% and 1.26% than employed SVM and GPR models, respectively. The obtained results showed the essential role of the water temperature parameter in the DO modeling of all three studied stations., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The high level of reliability of water resources is always an advantage for consumers, but in arid and semi-arid regions where the inflow to the reservoir is faced with severe fluctuations, it makes sense to decrease the percentage of reliability of the system and allocate less water to consumption zones to prevent critical conditions such as emptying of the reservoir. In this research, the employed operation model is based on the simulation-optimization combination by considering the objectives of minimizing the violation of the allowed capacity of the reservoir and maximizing the percentage of supplying the demands. The optimal hedging variables are specified by linking the WEAP (Water Evaluation and Planning System) to the MOPSO multi-objective optimization algorithm. According to the available data, the duration of the simulation and optimization period in the model is 360 months. After 1000 iterations, the optimal reservoir volume values are obtained at the hedging level and hedging coefficient in different months. Finally, the model results are compared with the results obtained from the standard operation policy (SOP). The results show that the proposed model is able to manage the allocation to needs in the dry months and prevent the reservoir from emptying. Also, by storing a part of the flow in the reservoir in watery months and consuming it in low water months, it increases the supply of needs by 20 to 35% and reduces the failure rate in dry months., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Glacier-associated hazards are becoming a common and serious challenge to the high mountainous regions of the world. Glacial lake outburst floods (GLOFs) are one of the most serious unanticipated glacier hazards, with the potential to release a huge amount of water and debris in a short span of time, resulting in the loss of lives, property, and severe damage to downstream valleys. The present study used multi-temporal Landsat and Google earth imageries to analyze the spatio-temporal dynamism of the selected glacial lake (moraine-dammed) in the Satluj basin of Western Himalayas. Furthermore, GLOF susceptibility of the lake was assessed using a multi-criteria decision-based method. The results show that the lake area has increased from 0.11 to 0.26 km 2 over the past 28 years from 1990 to 2018. The susceptibility index value for the lake was calculated as 0.75, which indicates that the lake is highly susceptible to the GLOF. The depth and volume of the lake were estimated to be 16 m and 57 × 10 5 m 3 , respectively, using an empirical formula. HEC-RAS, HECGeo-RAS, and Arc-GIS software were utilized in this study to perform unsteady flow analysis and to determine the GLOF impact on the downstream area. The worst-case GLOF scenario (breach width of 75 m) was revealed during an overtopping failure of the moraine dam, resulted in a peak discharge of 4060 m 3 /s and releasing a total water volume of 57 × 10 5 m 3 . The breach hydrograph has been routed to calculate the spatial and temporal distribution of peak flood, inundation depth, velocity, water surface elevation, and flood peak arrival time along the river channel. The analysis further reveals that the routed flood waves reach the nearest settlement, i.e., Rajpur town, situated at a distance of 102 km in the downstream valley of the lake at 6 h after the beginning of the lake breach event with a peak discharge/flood of 1757 m 3 /s and maximum flow velocity of 1.5 m/s. With the ongoing climate warming and glacier retreat, moraine-dammed lakes are becoming more hazardous and thus increasing the total threat. Therefore, it is mandatory to monitor and assess such lakes at regular intervals of time to lessen the disastrous impacts of GLOFs on the livelihood and infrastructure in the downstream valleys. The findings of this study will aid in the creation of risk management plans, preparatory tactics, and risk reduction techniques for GLOF hazards in the region., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The Hindukush-Karakoram-Himalaya (HKH) mountain ranges are the sources of Asia's most important river systems, which provide fresh water to 1.4 billion inhabitants in the region. Environmental and socioeconomic conditions are affected in many ways by climate change. Globally, climate change has received widespread attention, especially regarding seasonal and annual temperatures. Snow cover is vulnerable to climate warming, particularly temperature variations. By employing Moderate Resolution Imaging Spectroradiometer (MODIS) datasets and observed data, this study investigated the seasonal and interannual variability using snow cover, vegetation and land surface temperature (LST), and their spatial and temporal trend on different elevations from 2001 to 2020 in these variables in Gilgit Baltistan (GB), northern Pakistan. The study region was categorized into five elevation zones extending from < 2000 to > 7000 masl. Non-parametric Mann-Kendall trend tests and Sen's slope estimates indicate snow cover increases throughout the winter, but decreases significantly between June and July. In contrast, GB has an overall increasing annual LST trend. Pearson correlation coefficient (PCC) reveals a significant positive relationship between vegetation and LST (PCC = 0.73) and a significant negative relationship between LST and snow cover (PCC =  - 0.74), and vegetation and snow cover (PCC =  - 0.78). Observed temperature data and MODIS LST have a coefficient of determination greater than 0.59. Snow cover decreases at 3000-2000 masl elevations while increases at higher  5000 masl elevations.The vegetation in low and mid-elevation < 4000 masl zones decreases significantly annually. The temperature shows a sharply increasing trend at lower 2000-3000 masl elevations in the autumn, indicating the shifting of the winter seasons at this elevation zone. These findings better explain the spatiotemporal variations in snow cover, vegetation, and LST at various elevation zones and the interactions between these parameters at various elevations across the HKH region., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Due to its heterogeneous and complex nature, groundwater modeling needs great effort to quantify the aquifer, a crucial tool for policymakers and hydrogeologists to understand the variations in groundwater levels (GWL). This study proposed a set of supervised machine learning (ML) models to delineate the GWL changes in the Zarand-Saveh complex aquifer in Iran using 15-year (2005-2020) monthly dataset. The wavelet transform (WT) procedure was also used to improve the GWL prediction ability of ML models for 3-month horizons using input datasets of precipitation, evapotranspiration, temperature, and GWL. The four well-accepted standalone ML methods, i.e., artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), group method of data handling (GMDH), and least square support vector machine (LSSVM), were implemented and compared with the hybrid wavelet conjunction models. The methods were compared based on root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R), and Nash-Sutcliffe efficiency (NSE). Comparison outcomes showed that the hybrid wavelet-ML considerably improved the standalone model results. The wavelet transform-least square support vector machine (WT-LSSVM) model was superior to other standalone and hybrid wavelet-ML methods to predict GWL. The best GWL predictions were acquired from the WT-LSSVM model with input scenario 5 involving all influential variables, and this model produced RMSE, MAE, R, and NSE as 0.05, 0.04, 0.99, and 0.99 for 1 month ahead of GWL prediction, while the corresponding values were obtained as 0.18, 0.14, 0.95, and 0.90 for 3 months ahead of GWL prediction, respectively., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Microplastic (MP) pollution in marine environments has been a major global concern in recent years. Microplastic particles pose a threat in aquatic animals by accumulating in their digestive system, acting like a pollution vector, and they can also transfer to upper trophic levels. For that reason, commercially important deep-water rose shrimp Parapenaeus longirostris were employed in this study to examine the MP pollution status of two different regions (Samandağ and Mersin offshore waters) of the northeastern Mediterranean Sea. MPs were detected in all examined specimens (average of 18.8 MPs ind -1 ), and fiber tangle-shaped like balls were observed by 22% and 9% at Samandağ and Mersin, respectively. P. longirostris individuals from Samandağ showed higher occurrence (100%) and higher accumulation (29.7 ± 24.4 MPs ind -1 ). MP abundance extracted from the shrimp individuals from Samandağ region was higher than that of previously reported shrimp species. The majority of extracted microplastics were fiber (100%), black (46%) in color and 1-2.5 mm in size. Polyethylene was identified as the most common polymer type by Fourier transform infrared spectroscopy (FTIR). This study is the first report to evaluate microplastic occurrence and fiber tangles in P. longirostris from northeastern Mediterranean Sea. Results obtained in this study will enhance the understanding of MP pollution among different trophic levels., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Land use and land cover (LULC) change has become a critical issue for decision planners and conservationists due to inappropriate growth and its effect on natural ecosystems. As a result, the goal of this study is to identify the LULC for the Vembanad Lake system (VLS), Kerala, in the short term, i.e., within a decade, utilizing three standard machine learning approaches, random forest (RF), classification and regression trees (CART), and support vector machines (SVM), on the Google Earth Engine (GEE) platform. When comparing the three techniques, SVM performed poor at an average accuracy of around 82.5%, CART being the next at accuracy of 87.5%, and the RF model being good at the average of 89.5%. The RF outperformed the SVM and CART in almost identical spectral classes such as barren land and built-up areas. As a result, RF-classified LULC is considered to predict the spatio-temporal distribution of LULC transition analysis for 2035 and 2050. The study was conducted in Idrisi TerrSet software using the cellular automata (CA)-Markov chain analysis. The model's efficiency is evaluated by comparing the projected 2019 image to the actual 2019 classified image. The efficiency was good with more than 94.5% accuracy for the classes except for barren land, which might have resulted from the recent natural calamities and the accelerated anthropogenic activity in the area., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

To achieve the sustainable and effective removal efficiency of nutrients in black odorous water, light source, inter-species microalgae mixed culture, and the harvesting effect were all explored. The results showed that under a LED light source, the addition of interspecific soluble algal products (SAP) promoted the growth of Haematococcus pluvialis (H. pluvialis) M1, and its maximum specific growth rate was 1.76 times that of H. pluvialis cultivated alone. That was due to the hormesis effect between the two kinds of microalgae, the SAP produced by Scenedesmus could stimulate the growth of H. pluvialis. The algae and bacteria symbiotic system with black odorous water as the medium showed excellent performance to treat nutrients, where the concentration of ammonia nitrogen (NH 3 -N) and total phosphorus (TP) (0.84, 0.23 mg/L) met the requirements of landscape water. The microbial diversity analysis revealed that the introduction of microalgae changed the dominant species of the bacterial community from Bacteroidota to Proteobacteria. Furthermore, timely microalgae harvesting could prevent water quality from deteriorating and was conducive to microalgae growth and resource recycling. The higher harvest efficiency (98.1%) of H. pluvialis was obtained when an inoculation size of 20% and 0.16 g/L FeCl 3 were provided., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The detailed analyses of the water balance components (WBCs) of the catchment help assess the available water resources, especially in the arid climate regions for their sustainable management and development. This paper mainly used the Soil and Water Assessment Tool (SWAT) model to analyze the variation in the WBCs considering the change in the Land Use and Land Cover (LULC) and meteorological variables. For this purpose, the model used the inputs of LULC and meteorological variables between 2001 and 2020 at 5 years and daily time intervals, respectively, from the Chittar river catchment. The developed models were calibrated using SWAT-CUP split-up procedure (pre-calibration and post-calibration). The model was found to be good in calibration and validation, yielding the coefficient of determination (R 2 ) of 0.94 and 0.81, respectively. Furthermore, WBCs of the catchment were estimated for the near future (2021-2030) at the monthly and annual scales. For this endeavor, LULC was forecasted for the years 2021 and 2026 using Cellular Automata (CA)-Artificial Neural Network (ANN), and for the same period, meteorological variables were also forecasted using the smoothing moving average method from the historical data., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Understanding the dynamics of water quality in any water body is vital for the sustainability of our water resources. Thus, investigating spatio-temporal changes of dominant water quality parameters (WQPs) in any study is indeed critical for proposing the appropriate treatment for the water bodies. Traditionally, concentrations of WQPs have been measured through intensive fieldwork. Additionally, many studies have attempted to retrieve concentrations of WQPs from satellite images using regression-based methods. However, the relationship between WQPs and satellite data is complex to be modeled accurately by using simple regression-based methods. Our study attempts to develop a machine learning model for mapping the concentrations of dominant optical and non-optical WQPs such as electrical conductivity (EC), pH, temperature (Temp), total dissolved solids (TDS), silicon dioxide (SiO 2 ), and dissolved oxygen (DO). In this context, a remote sensing framework based on the extreme gradient boosting (XGBoost) and multi-layer perceptron (MLP) regressor with optimized hyper parameters (HPs) to quantify concentrations of different WQPs from the Landsat-8 satellite imagery is developed. We evaluated six years of satellite data stretching spatially from upstream to downstream Ankinghat to Chopan (20 stations under Central Water Commission (CWC), Middle Ganga Basin) for characterizing the trends of dominant physico-chemical WQPs across the four clusters identified in our previous study. Through the developed XGBoost and MLP regression models between measured WQPs and the reflectance of the pixels corresponding to the sampling stations, a significant coefficient of determination (R 2 ) in the range of 0.88-0.98 for XGBoost and 0.72-0.97 for MLP were generated, with bands B1-B4 and their ratios more consistent. Indeed, these findings indicate that from a small number of in-situ measurements, we can develop reliable models to estimate the spatio-temporal variations of physico-chemical and biological WQPs. Therefore, models generated from Landsat-8 could facilitate the environmental, economic, and social management of any waterbody., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Natural streams longitudinal dispersion coefficient (Kx) is an essential indicator for pollutants transport and its determination is very important. Kx is influenced by several parameters, including river hydraulic geometry, sediment properties, and other morphological characteristics, and thus its calculation is a highly complex engineering problem. In this research, three relatively explored machine learning (ML) models, including Random Forest (RF), Gradient Boosting Decision Tree (GTB), and XGboost-Grid, were proposed for the Kx determination. The modeling scheme on building the prediction matrix was adopted from the well-established literature. Several input combinations were tested for better predictability performance for the Kx. The modeling performance was tested based on the data division for the training and testing (70-30% and 80-20%). Based on the attained modeling results, XGboost-Grid reported the best prediction results over the training and testing phase compared to RF and GTB models. The development of the newly established machine learning model revealed an excellent computed-aided technology for the Kx simulation., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Reclaimed water (RW) has been widely used as an alternative water resource to recharge rivers in mega-city Beijing. At the same time, the RW also recharges the ambient aquifers through riverbank filtration and modifies the subsurface hydrodynamic system and hydrochemical characteristics. To assess the impact of RW recharge on the unconfined groundwater system, we conducted a 3D groundwater flow and solute transport model based on 10 years of sequenced groundwater monitoring data to analyze the changes of the groundwater table, Cl - loads, and NO 3 -N loads in the shallow aquifer after RW recharge to the river channel. The results show that the groundwater table around the river channel elevated by about 3-4 m quickly after RW recharge from Dec. 2007 to Dec. 2009, and then remained stable due to the continuous RW infiltration. However, the unconfined groundwater storage still declined overall from 2007 to 2014 due to groundwater exploitation. The storage began to recover after groundwater extraction reduction, rising from 3.76 × 10 8 m 3 at the end of 2014 to 3.85 × 10 8 m 3 at the end of 2017. Cl - concentrations varied from 5-75 mg/L before RW recharge to 50-130 mg/L in 2 years (2007-2009), and then remained stable. The zones of the unconfined groundwater quality affected by RW infiltration increased from 11.7 km 2 in 2008 to 26.7 km 2 in 2017. Cl - loads in the zone increased from 1.8 × 10 3  t in 2008 to 3.8 × 10 3 t in 2017, while NO 3 -N loads decreased from 29.8 t in 2008 to 11.9 t in 2017 annually. We determined the maximum area of the unconfined groundwater quality affected by RW, and groundwater outside this area not affected by RW recharge keeps its original state. The RW recharge to the river channel in the study area is beneficial to increase the groundwater table and unconfined groundwater storage with lesser environmental impacts., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

CO 2 can be injected into deep saline aquifers for storage, thereby reducing the concentration of CO 2 in the atmosphere. When CO 2 is injected into the aquifer, salt precipitation may occur, which may impair the injectivity and affect storage safety. In this study, numerical simulation was used to study salt precipitation in heterogeneous anisotropic sandstone aquifers, the feedback effect of salt precipitation on the flow was considered, and the additional pressure increase caused by salt precipitation was evaluated. The results showed that the maximum decrease in formation permeability and the maximum additional pressure buildup caused by salt precipitation reached 88% and 4.91 MPa, respectively. The salinity of the formation water and the maximum additional pressure buildup is approximately proportional when the salinity is low. Once the salinity exceeds a certain value (approximately 20% in this study), the maximum additional pressure buildup increases sharply. As the permeability increases, the additional pressure buildup decreases. When permeability reaches a certain threshold (approximately 5×10 -14 m 2 in this study), the maximum additional pressure buildup decreases rapidly and changes only slightly as permeability increases. The CO 2 injection rate is basically proportional to the maximum additional pressure buildup. When the vertical permeability increases, the additional pressure buildup due to salt precipitation shows a downward trend. The low-permeability interbeds above the CO 2 injection well will cause more local salt precipitation near it, which will further cause a greater and wider pressure buildup. The heterogeneity of the formation will greatly enhance salt precipitation, thereby promoting the formation pressure buildup. The formation heterogeneity must be considered in the study of the salt precipitation and its effect on CO 2 injection, especially when the formation permeability is low, the CO 2 injection rate is high, and the salinity of the formation water is high., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Metals can be mobilized from contaminated sediments under variable environmental conditions. This paper discusses the effects of specific ions of the water column in conjunction with natural attenuation processes on the leaching of metals from marine sediments. In particular, the effect of the salinity and the presence of ions in the seawater, especially the chlorides of the water column, in leaching of metals was examined. Sediment samples were collected from sampling stations in the inner port of Piraeus, Greece. Due to the fact that natural attenuation is a slow procedure which consists of natural, chemical, and biological processes and is influenced by many factors, it was approached with experiments taking place under quite aggressive conditions. Sequential leaching tests in cycles of seven repetitions were performed. The results of these experiments showed that leaching of metals from contaminated sediments to the water column was influenced by the concentration of dissolved constituents. Initially leaching was significant with maximum concentration of leachable copper (Cu) 0.25 mg/kg, lead (Pb) 0.0048 mg/kg, and zinc (Zn) 0.28 mg/kg, and then fell in the last repetitions. The leaching of Cu and Zn from contaminated sediments to the water column was positively correlated to the concentration of chlorides., (© 2020. Springer-Verlag GmbH Germany, part of Springer Nature.)

Analytical data of fluoride concentration in groundwater collected from various geological formations in Gujarat, India, have been studied to assess their spatial distribution characteristics and related potential chronic health risks. Decadal analysis of groundwater was attempted for precise quantification and a realistic ground representation of fluoride concentration in the entire state. This exercise involved collection of 6407 samples over a period of 10 years (2009-2018), from 641 representative locations, distributed evenly throughout the state (6407 = 641 locations × 10 years). The analytical results indicate that 19% of the sample locations have fluoride concentration higher than the permissible limit and 42% of the locations have fluoride concentration much below the desirable limit, thereby exposing children to a higher risk of associated dental morbidities. Monte Carlo simulation integrated with sensitivity and uncertainty analysis was applied for an accurate and realistic assessment of the non-carcinogenic health risk. Model results indicated that groundwater fluoride exposure through consumption is way higher than the exposure due to dermal absorption pathway. It is inferred that 94 locations (15%) have total hazard index greater than the unity value for all population groups, thereby increasing the vulnerability of the local populace to dental and skeletal fluorosis. Total hazard index in 210 locations (36%) and 188 locations (29%) are beyond the permissible limit for the population comprising infants and children. Populaces living in these locations are susceptible to health hazards that stems from high fluoride concentration. Children and infants are at greater risk due to groundwater fluoride toxicity when compared to the adult populace. The order of the geographic stratification of vulnerability is Mainland regions > Saurashtra region > Kachchh region. Ingestion rate and fluoride concentration are the sensitive parameters with high impact and residents of these vulnerable locations should be advised to abstain from direct intake of groundwater and resort to defluoridised groundwater., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The purpose of this study was to analyze the effects of the application of multivariate criteria of principal components and hierarchical clustering as a mechanism for monitoring the performance of Tifton 85 grass (Cynodon spp.) planted in horizontal subsurface flow constructed wetland reactor (HSSF-CW) under different organic (OLR), nutritional and sodium loads of swine wastewater (SW). The HSSF-CW planted with Tifton 85 grass was used as a swine wastewater after treatment applying organic loading rates between 26.1 (1st cut) and 360.6 kg ha -1 day -1 COD (8th cut). The maximum performances of HSSF-CW consisted of 52.0 t ha -1 of productivity and 24.0% of crude protein, with the application of 59.7, 64.2, and 31.2 kg ha -1 day -1 of TKN, P T , and K + , respectively. The eleven original variables generated four new components, with PC 4 accounting for 94.0% of total variance, a condition strengthened with four data groupings greater than 48% similarity and three data groupings greater than 95% similarity between the variables. There was a strong association between of nitrogen, phosphorus, and potassium concentration by the hierarchical grouping and the intermediate cuts and lower temperatures.

Desalination to increase irrigation water supply for agricultural production is becoming important in water-scarce regions. While desalination has positive effects on the potential irrigation water quantity and quality, the technique may also be a considered potential source of groundwater pollution. The present study investigated the effects of desalination wastewater discharge on groundwater quality in an arid area in southern Iran for the 2012-2017 period. The chemical composition of the groundwater samples was evaluated considering pH, EC, Na + , Ca 2+ , Mg 2+ , SO 4 2+ , Cl - . The suitability of groundwater for drinking and irrigation purposes as well as spatial pattern of groundwater pollution was analyzed. The results showed that mean concentration of Na 3 - . The suitability of groundwater for drinking and irrigation purposes as well as spatial pattern of groundwater pollution was analyzed. The results showed that mean concentration of Na + , Ca 2+ , Mg 2+ in all investigated wells increased from 148, 94, 46, 247, and 257 mg/L in 2012 to 282, 146, 71, 319, and 582 mg/L in 2017, respectively. Using Gibb's diagram, it was shown that the groundwater quality is slightly alkaline and primarily controlled by evaporation. Based on our findings, about 78% of the study aquifer displayed groundwater with good to excellent water quality that can be used for drinking and irrigation purposes. However, the eastern part of the aquifer was classified as unsuitable for use due to the disposal of desalination plant wastewater. The spatial distribution of WQI and other indices such as SAR, TDS, and TH showed that groundwater in the eastern part of the aquifer has deteriorated since the establishment of the desalination plants. To reverse this trend, it is important to implement regulations against wastewater discharge from desalination plants.4 2- , and Cl - in all investigated wells increased from 148, 94, 46, 247, and 257 mg/L in 2012 to 282, 146, 71, 319, and 582 mg/L in 2017, respectively. Using Gibb's diagram, it was shown that the groundwater quality is slightly alkaline and primarily controlled by evaporation. Based on our findings, about 78% of the study aquifer displayed groundwater with good to excellent water quality that can be used for drinking and irrigation purposes. However, the eastern part of the aquifer was classified as unsuitable for use due to the disposal of desalination plant wastewater. The spatial distribution of WQI and other indices such as SAR, TDS, and TH showed that groundwater in the eastern part of the aquifer has deteriorated since the establishment of the desalination plants. To reverse this trend, it is important to implement regulations against wastewater discharge from desalination plants.

Drought is a major natural disaster that significantly impacts the susceptibility and flexibility of the ecosystem by changing vegetation phenology and productivity. This study aimed to investigate the impact of extreme climatic variation on vegetation phenology and productivity over the four sub-regions of China from 2000 to 2017. Daily rain gauge precipitation and air temperature datasets were used to estimate the trends, and to compute the standardized precipitation-evapotranspiration index (SPEI). Remote sensing-based Enhanced Vegetation Index (EVI) data from a moderate resolution imaging spectroradiometer (MODIS) was used to characterize vegetation phenology. The results revealed that (1) air temperature had significant increasing trends (P < 0.05) in all sub-regions. Precipitation showed a non-significant increasing trend in Northwest China (NWC) and insignificant decreasing trends in North China (NC), Qinghai Tibet area (QTA), and South China (SC). (2) Integrated enhanced vegetation index (iEVI) and SPEI variations depicted that 2011 and 2016 were the extremely driest and wettest years during 2000-2017. (3) Rapid changes were observed in the vegetation phenology and productivity between 2011 and 2016. In 2011, changes in the vegetation phenology with the length of the growing season (ΔLGS) = was - 14 ± 36 days. In 2016, the overall net effect changed at the onset and end of the growing season with ΔLGS of 34 ± 71 days. The change in iEVI per SPEI increased rapidly with a changing rate of 0.16 from arid (NWC, and QTA) to semi-arid (NWC, QTA and NC) and declined with a rate of - 0.04 from semi-humid (QTA, NC, and SC) to humid (SC) region. A higher association was observed between iEVI and SPEI as compared to iEVI and precipitation. Our finding exposed that north China is more sensitive to climatic variation.

Membrane distillation (MD) has a high heat requirement. Integrating MD with thermophilic bioreactors could remedy this problem. A laboratory-scale thermophilic anaerobic granular sludge membrane distillation bioreactor (ThAGS-MDBR) was used to treat wastewater with a high organic loading rate (OLR). Waste heat from ThAGS was used directly for the MD process to reduce energy consumption. The result demonstrated that the ThAGS-MDBR system achieved a high-efficiency removal of chemical oxygen demand (more 99.5%) and NH 4 + at OLR of 16 kg COD/m removed /day. Specifically, an aggregate of densely packed diverse microbial communities in anaerobic granular sludge was the main mechanism for the enhancement of bioreactor tolerance with environmental changes. High-quality distillate water from ThAGS-MDBR was reclaimed in one step with total organic carbon less than 1.7 mg/L and electrical conductivity less than 120 μS/cm. Furthermore, the result of the DNA extraction kit recorded that Methanosaeta thermophila was a critical archaea for high COD removal and bioreactor stability.3 /day. Specifically, an aggregate of densely packed diverse microbial communities in anaerobic granular sludge was the main mechanism for the enhancement of bioreactor tolerance with environmental changes. High-quality distillate water from ThAGS-MDBR was reclaimed in one step with total organic carbon less than 1.7 mg/L and electrical conductivity less than 120 μS/cm. Furthermore, the result of the DNA extraction kit recorded that Methanosaeta thermophila was a critical archaea for high COD removal and bioreactor stability.

Supplying adequate water to individuals and maintaining water supplies to support human life, particularly to rapidly urbanizing communities, are of paramount importance in the development of urban areas in each country worldwide. In turn, maintaining water resource quality and avoiding permanent damage as a consequence of environmental pollution and unsustainable off-take from sources such as rivers and aquifers should be considered as important as the water supply quantity. In this study, random forest (RF) and M5 model tree (M5) models were used to predict water biochemical oxygen demand (BOD). Having decomposed the input variables by wavelet transform, based on the feature selection algorithms (FS) (relief (RA), correlation (CA), principal component analysis (PCA), and ant colony optimization (ACO) algorithms), the important components were recognized and inserted into the RF and M5 models. The proposed approach was applied to Karun River in Ahvaz station on a monthly basis from 2006 to 2018. The results showed that the RF model had better performance with R = 0.872, MAE = 0.0312, and RMSE = 0.0332 values for the variable of BOD compared with the M5 model with R = 0.751, MAE = 0.0377, and RMSE = 0.0468 values. In addition, comparing RF and hybrid models, the purposed hybrid models were considered as viable options to improve the prediction accuracy of BOD. The findings also showed that, among the hybrid models, the WRF-PCA model with R = 0.927, MAE = 0.0198, and RMSE = 0.0241 values was the best model for the prediction of BOD values.

The fate of carbaryl in the Seto Inland Sea (west Japan) was predicted using a mass distribution model using carbaryl concentrations in river and sea water samples, degradation data, and published data. The predicted carbaryl concentrations in water in Kurose River and the Seto Inland Sea were 4.320 and 0.2134 μg/L, respectively, and the predicted concentrations in plankton, fish, and sediment were 0.4140, 2.436, and 1.851 μg/g dry weight, respectively. The carbaryl photodegradation and biodegradation rates were higher for river water (0.330 and 0.029 day -1 , respectively) than sea water (0.23 and 0.001 day -1 , respectively). The carbaryl photodegradation rates for river and sea water (0.33 and 0.23 day -1 , respectively) were higher than the biodegradation rates (0.029 and 0.001 day -1 , respectively). The hydrolysis degradation rate for carbaryl in sea water was 0.003 day -1 , and the half-life was 231 days. Land (via rivers) was the main source of carbaryl to the Seto Inland Sea. The model confirmed carbaryl is distributed between sediment, plankton, and fish in the Seto Inland Sea. Degradation, loss to the Open Ocean, and sedimentation are the main carbaryl sinks in the Seto Inland Sea, accounting for 43.81, 27.90, and 17.68%, respectively, of total carbaryl inputs. Carbaryl source and sink data produced by the model could help in the management of the negative impacts of carbaryl on aquatic systems and human health.

Contamination of drinking water with arsenic causes severe health problems in various world regions. Arsenic exists predominantly as As(III) and As(V) depending on the prevailing redox conditions of the environment. Most of the techniques developed for treating As(V) are not very effective for As(III), which is more toxic and mobile than As(V). In this study, novel cerium-loaded pumice (Ce-Pu) and red scoria (Ce-Rs) adsorbents were developed to remove both As(III) and As(V) ions from water. The Ce-Pu and Ce-Rs adsorbents were characterized using ICP-OES, EDX, and SEM. The experimental equilibrium sorption data fitted well Freundlich and Dubinin-Radushkevich (D-R) isotherms. The adsorption was very fast and reached an equilibrium within 2 h. Both Ce-Rs and Ce-Pu showed high As(III) and As(V) removal efficiency in a wide pH range between 3 and 9, which is an important asset for practical applications. The Ce-Pu and Ce-Rs adsorbents can be recycled and used up to three adsorption cycles without significant loss of their original efficiency. Accordingly, Ce-Pu and Ce-Rs seem to be suitable for removal of arsenic from aqueous systems.

Non-point source pollution is a significant environmental issue in small watersheds in China. To study the effects of rainfall on pollutants transported by runoff, rainfall was monitored in Xueyan town in the Taihu Lake Basin (TLB) for over 12 consecutive months. The concentrations of different forms of nitrogen (N) and phosphorus (P), and chemical oxygen demand, were monitored in runoff and river water across different land use types. The results indicated that pollutant loads were highly variable. Most N losses due to runoff were found around industrial areas (printing factories), while residential areas exhibited the lowest nitrogen losses through runoff. Nitrate nitrogen (NO3-N) and ammonia nitrogen (NH4-N) were the dominant forms of soluble N around printing factories and hotels, respectively. The levels of N in river water were stable prior to the generation of runoff from a rainfall event, after which they were positively correlated to rainfall intensity. In addition, three sites with different areas were selected for a case study to analyze trends in pollutant levels during two rainfall events, using the AnnAGNPS model. The modeled results generally agreed with the observed data, which suggests that AnnAGNPS can be used successfully for modeling runoff nutrient loading in this region. The conclusions of this study provide important information on controlling non-point source pollution in TLB.

Due to the inherent complexity, uncertainty, and posterity in operating a biological wastewater treatment process, it is difficult to control nitrogen removal in the biological wastewater treatment process. In order to cope with this problem and perform a cost-effective operation, an integrated neural-fuzzy control system including a fuzzy neural network (FNN) predicted model for forecasting the nitrate concentration of the last anoxic zone and a FNN controller were developed to control the nitrate recirculation flow and realize nitrogen removal in an anoxic/oxic (A/O) process. In order to improve the network performance, a self-learning ability embedded in the FNN model was emphasized for improving the rule extraction performance. The results indicate that reasonable forecasting and control performances had been achieved through the developed control system. The effluent COD, TN, and the operation cost were reduced by about 14, 10.5, and 17 %, respectively.

This study deals with the evaluation of water quality of the Three Gorges Reservoir (TGR) in order to assess its suitability as a raw water source for drinking water production. Therefore, water samples from (1) surface water, (2) tap water, and (3) wastewater treatment plant effluents were taken randomly by 2011-2012 in the area of the TGR and were analyzed for seven different organic contaminant groups (207 substances in total), applying nine different analytical methods. In the three sampled water sources, typical contaminant patterns were found, i.e., pesticides and polycyclic aromatic hydrocarbons (PAH) in surface water with concentrations of 0.020-3.5 μg/L and 0.004-0.12 μg/L, disinfection by-products in tap water with concentrations of 0.050-79 μg/L, and pharmaceuticals in wastewater treatment plant effluents with concentrations of 0.020-0.76 μg/L, respectively. The most frequently detected organic compounds in surface water (45 positives out of 57 samples) were the pyridine pesticides clopyralid and picloram. The concentrations might indicate that they are used on a regular basis and in conjunction in the area of the TGR. Three- and four-ring PAH were ubiquitously distributed, while the poorly soluble five- and six-ring members, perfluorinated compounds, polychlorinated biphenyls, and polybrominated diphenyl ethers, were below the detection limit. In general, the detected concentrations in TGR are in the same range or even lower compared to surface waters in western industrialized countries, although contaminant loads can still be high due to a high discharge. With the exception of the two pesticides, clopyralid and picloram, concentrations of the investigated organic pollutants in TGR meet the limits of the Chinese Standards for Drinking Water Quality GB 5749 (Ministry of Health of China and Standardization Administration of China 2006) and the European Union (EU) Council Directive 98/83/EC on the quality of water intended for human consumption (The Council of the European Union 1998), or rather, the EU Directive on environmental quality standards in the field of water policy (The European Parliament and The Council of the European Union 2008). Therefore, the suggested use of surface water from TGR for drinking water purposes is a valid option. Current treatment methods, however, do not seem to be efficient since organic pollutants were detected in significant concentrations in purified tap water.