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Surface soil moisture (SSM) refers to the capacity of the top layer of soil to hold moisture. It is an essential part of the budget for surface water. Soil moisture monitoring is crucial to reduce the effects of precipitation deficits and determine the best ways to manage natural ecosystems in the face of climate change. The current study collected daily SSW data from MERRA-2 for the Tel River Basin in Odisha, India, from 2001 to 2020 with a spatial resolution of 0.5° × 0.625°. To forecast SSW time series (SSWTS) one step ahead, this study examines the reliability of three deep learning (DL) models: gated recurrent unit (GRU), long short-term memory (LSTM), and simple recurrent neural network (simpleRNN). This study aims to address the following research questions: (1) How accurately can DL models predict SSWTS? (2) Which DL model—GRU, LSTM, or simpleRNN—is the most reliable for SSW forecasting? (3) How can the uncertainty in the predicted SSW be quantified and analyzed? Further, in an uncertainty investigation on SSW projected values, a Wilson score technique was employed to evaluate the uncertainty of the DL methods. GRU has outdone the other two models in forecasting monthly SSW with a 12-lookback timestep with a lower error for all the stations. The model appeared more accurate as it declined in gradient on larger sequencing samples. GRU's ability to remember significant prior knowledge, whereas discarding irrelevant data may assist in finding a novel, dependable solution for SSWTS forecasting.

Acid mine drainage (AMD) has a low pH and contains harmful metals, making it a severe problem in the mining industry. Neutralization treatment using slaked lime is widely applied to remove potentially toxic metals and adjust the pH. However, this generates neutralized sludge containing large amounts of harmful metals. Therefore, in this study, we propose a bacterial bioprocess for removing metals before neutralization. Acid- and metal-tolerant bacteria were isolated from neutral soils and utilized as As removers from AMD. The Paenathrobacter sp. strain H1 removed As (43.6%) and Fe (10.6%) from AMD in a single-batch test (pH 1.95; initial concentrations were 6.13 and 283 mg L⁻¹, respectively). Repeated batch tests using fresh cells enhanced the As removal ratio, achieving successful removal of As (95.3%) and Fe (75.5%). Although further research is required, this study has substantial implications for the development of a sustainable AMD treatment to suppress harmful waste generation.

Timely and reliable estimates of reference evapotranspiration (ET₀) are imperative for robust water resources planning and management. Applying machine learning (ML) algorithms for estimating ET₀ has been evolving, and their applicability in different sectors is still a compelling field of research. In this study, four Gaussian process regression (GPR) algorithms—polynomial kernel (PK), polynomial universal function kernel (PUK), normalized poly kernel (NPK), and radial basis function (RBF)—were compared against widely used random forest (RF) and a simpler locally weighted linear regression (LWLR) algorithm at a humid subtropical region in India. The sensitivity analysis of the input variables was followed by application of the best combination of variables in algorithm testing and training for generating ET₀. The results were then compared against the Penman–Monteith method at both daily and monthly time steps. The results indicated that ET₀ is least sensitive to wind speed at 2 m height. Additionally, at a daily time step, RF, followed by PUK, generated the best results during both training and testing phases. In contrast, at a monthly time step, using multiple model evaluation matrices, PUK followed by RF performed best. These results demonstrate the application of the ML algorithms is subjected to user-required time steps. Although this study focused on Northwest India, the findings are relevant to all humid subtropical regions across the world.

Drought is an abnormal condition characterized by dry weather which can continue for days, months, and years. Drought often has major effects on the ecosystems and agriculture of vulnerable regions leading to catastrophe on the local economies. Deep learning was employed in this study to forecast hydrological drought in the Wardha River basin in Maharashtra, Vidarbha region, India. Monthly streamflow data from 1971 to 2020 for the Wardha River serve as the basis for analysis. The study calculates the standardized streamflow index (SSI) at several timescales (3, 6, 9, 12, and 24 months). Deep learning models, specifically the long short-term memory (LSTM) model and the multilayer perceptron (MLP) model, are employed for drought prediction within the study region. The models are trained with data spanning from 1971 to 2005 and tested against data from 2006 to 2020. Predictions are made for lead time scales of 6 and 12 months by considering lagged SSI values. Drought event lead time scale forecasts will serve as an early warning strategy. The 6- and 12-month lead times of the SSI forecast could be used as a warning for anticipated drought conditions. The study assesses model efficiency by comparing the root mean square error (RMSE) and mean absolute error (MAE) between the LSTM and MLP models. The results indicate that the LSTM model performs better for higher time scales in predicting hydrological drought, whereas the MLP model demonstrates superior predictive capabilities for lower time scales of drought index.

Arsenic is considered one of the most hazardous trace metals in groundwater researched to date because of the hazardous impacts like cancer, skin irritation, and other skin-related diseases. The present study involved collecting 60 water samples from Bhagalpur district, Bihar, India, to estimate the arsenic concentration. The human health risk assessment of the samples concerning children and adults was also performed, and the maximum concentration of arsenic was found to be relatively high in some sample sites. Prolonged exposure to arsenic could be fatal to the local population. The current study also focuses on developing a low-cost paper-based arsenic detection kit. The paper-based test kit was tested for parameters like color development for different forms and concentrations of arsenic, storage conditions for the test strips, the effect of different interfering agents on color development, and optimization of the AgNO₃ solution. The cost analysis was carried out, and it was found that the kit would cost 0.046 USD per sample, which is 70–100 times lower than the cost of current methods.

Sediment from the Serbian Great Bačka Canal (GBC), which has long been classified as toxic waste due to high pollutant concentrations, exemplifies the sediment management challenges in Europe, where regulations vary by country. Serbian legislation primarily focuses on total metal concentrations relative to prescribed limits. Our study addresses this limitation by using an integrated approach to assess sediment pollution's detrimental effects at the ecosystem level. This approach is particularly relevant for the GBC, an environmental hotspot historically impacted by severe pollution from untreated industrial wastewater and population growth. Although previous research on the GBC has predominantly focused on chemical analyses, often overlooking broader environmental and health impacts, our study aims to evaluate whether ecotoxicological tests provide a more comprehensive assessment of sediment quality compared to traditional methods. Although only copper concentrations surpassed national limits, multiple metals and polycyclic aromatic hydrocarbons (PAHs) exceeded international sediment quality guidelines (SQGs). Sequential extraction revealed that 50% of copper was immobilized in the residual fraction, and ecotoxicological tests with Myriophyllum aquaticum indicated potential toxicity. Human health risk assessments showed a low risk of carcinogenic effects from PAHs, but a higher risk associated with zinc and copper. These findings highlight the urgent need for pollution reduction and ecological restoration in the GBC and similar river systems.

Water, a crucial input in agricultural production, is distributed based on geographical and topographical patterns. However, anthropogenic climate change has intensified water scarcity in semi-arid regions. This research aims to precisely estimate crop evapotranspiration (ET) and examine the diurnal and seasonal patterns of surface energy fluxes in maize (Zea mays) crops cultivated in a semi-arid region. The precision of our methodology is underscored by the use of a large-aperture scintillometer (LAS), which measured surface energy fluxes at 5-min intervals over two crop-growing seasons. The results, a testament to the accuracy of the LAS, indicated that during the rainy (Kharif) season of 2015–2016, the seasonal sensible heat flux (H) and latent heat flux (LE) values were 185.91 and 242.14 mm, respectively. In the rainy (Kharif) season of 2017–2018, these values were 151.57 mm for H and 373.63 mm for LE. LE values ranged from 0.40 to 6.83 MJ m⁻² day⁻¹ throughout the growing season. The findings, which highlight the LAS's ability to accurately estimate surface energy fluxes, provide a deeper understanding of their interactions with microclimatic factors, such as weather, soil, and crop management. These insights, with their significant implications for ecophysiological studies and improving agricultural practices in semi-arid regions, underscore the importance of our research.