Applied Water Science最新文献

Slaughterhouse wastewater (SWW) is considered an industrial wastewater, which seriously harms the environment due to the high concentration of contaminants such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), and total suspended solids (TSS). Additionally, the wastewater from slaughterhouses contains harmful bacteria. This study used a lap-scale model to treat SWW from a local private slaughterhouse. The treatment process involves three stages: adsorption using activated carbon, which is derived from sawdust, followed by sedimentation, and finally, a slow sand filter with a modified layer of woven textile cotton. The first two steps were tested to obtain the ideal operation condition of the treatment system. After the final step of treatment, we evaluated the overall process using a modified slow sand filter (MSSF). We used a Jar test to determine the optimal dosage of activated carbon from sawdust (ACS). The monitored parameters were physicochemical, such as turbidity, total suspended solids (TSS), total dissolved solids (TDS), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total phosphorus (TP), and total nitrogen (TN). The bacteriological examination included both total coliform count (TCC) and fecal coliform count (FCC). The results of the jar test revealed that the optimal ACS dose was 2.0 g/l. After adjusting the contact time and pH levels for the adsorption process, we discovered that the ideal contact time was 100 min and the ideal pH level was 4.0. Finally, we evaluated the entire treatment system by applying the MSSF after the sedimentation process, and found that the removal efficiencies of turbidity, BOD, COD, TSS, TDS, TP, and TN were 97.14, 94.80, 91.80, 98.96, 81.17, 81.12, and 82.50%, respectively. This is in addition to the filter's ability to remove bacteria counts at a rate of up to 98.93 and 99.13% of TCC and FCC, respectively.

This study investigates the effectiveness of flow splitters in reducing scour downstream of trapezoidal Piano Key Weirs through a comprehensive experimental study. Three distinct geometries of flow splitters—square, rectangular, and circular—are examined under various hydraulic conditions to assess their impact on local scouring. The experiments were conducted in a dedicated channel measuring 10 m in length, 0.75 m in width, and 0.80 m in height. The results indicate that flow splitters facilitate flow separation by linking trapped air beneath the flow to the free surface, thereby mitigating nappe oscillation. Additionally, the geometric variations of flow splitters did not significantly influence the upstream water head, with rectangular-shaped flow splitters proving more effective than square and circular splitters. On average, the maximum scour depth for the weir with rectangular, square, and circular splitters is reduced by approximately 13, 11, and 10%, respectively, compared to the weir without splitters. Furthermore, the volume of scour holes in tests with rectangular, square, and circular splitters showed reductions of 18.53, 17.77, and 14.92%, respectively, compared to tests without splitters. As discharge decreases, the effectiveness of these flow splitters in reducing scour depth becomes more pronounced. Due to the existence of splitters, the location of maximum scour depth approaches the weir. New equations were developed for predicting scour hole parameters with and without flow splitters, incorporating various splitter geometries. These equations were formulated using non-linear regression, achieving high accuracy with a correction factor, yielding R² values between 0.78 and 0.94, and RMSE values ranging from 0.09 to 0.54. Overall, the findings underscore the significance of flow splitter geometry in mitigating scour effects, providing valuable insights for future engineering applications.

Effective monitoring of groundwater contamination is crucial to protect human livelihoods and ecosystems. This paper presents a machine learning-based approach to improve groundwater monitoring networks by providing predictions of groundwater contamination in space. The method is demonstrated through a practical application in Central Spain, where nitrate was used as a proxy for groundwater contamination. Predictive mapping identifies the spatial markers for groundwater contamination based on twenty-four predictor variables and a dataset of 213 existing monitoring boreholes. Tree-based algorithms found meaningful associations between the explanatory variables and known nitrate concentrations. Comparing the outcomes of the algorithms with the areas officially delineated as vulnerable to nitrate suggests that machine learning algorithms are able to predict groundwater contamination. The extra trees algorithm outperformed decision trees, random forest, gradient boosting, and AdaBoost classifiers, with an area under the curve score in excess of 0.88. Major predictors for groundwater contamination were depth to the water table, lithology, distance to rivers, and distance to livestock farms. Predictive mapping suggests that there are unmonitored regions to the northeast and to the southwest of Madrid’s metropolitan area that present similar markers to monitored regions known to be contaminated. These unmonitored areas should be prioritized in future attempts to improve the network. From a research perspective, the main conclusion of this work is that machine learning techniques can be used as a technique to automate the siting of monitoring boreholes. Practical applications should nevertheless be overseen by an expert eye to guarantee the quality of the outcomes.

In temperate regions, snow and its meltwater constitute primary freshwater resources and snowmelt isotopes offer valuable insights into understanding the snowmelt processes including the timing and contribution of snowmelt to the soil and watershed in spring. Assessing the storage and movement of liquid water within natural snowpacks, a previously unquantified aspect, holds significance for predicting natural hazards and managing water resources for agricultural purposes and ecosystem health. The escalating occurrence of rain-on-snow (ROS) events, attributed to winter warming, has the potential to trigger natural hazards and surface runoff into major river systems in temperate climate regions. End member mixing calculations (EMMC) based on isotopic and chemical tracers were employed to quantify the proportions of rainwater, meltwater, and pore water within the snowpack discharge. In this study, artificial rain-on-snow experiments involving conservative anions and stable water isotopes were conducted at the surface of snowpack to differentiate each component (rainwater, pore water, and snowmelt) within the discharge collected at the bottom of the snowpack. Pore water content exhibited a shift from 1.1 ± 1.1% (± 1σ, N = 23) after the initial artificial ROS event to 2.8 ± 1.2% (± 1σ, N = 19) following the spray in our experiment. Based on the EMMC, the contributions of rainfall, pore water, and snowmelt to the meltwater discharge were 2,620.2 L (63.3%), 829.0 L (20.0%), and 687.4 L (16.6%), respectively. Notably, contrary to prior studies, our experimental results suggest that rainwater reached the bottom through multiple rapid flow channels before matrix flow occurred. This experimental approach provides additional insights into the dynamics of water percolation in snowpacks during rain-on-snow events.

Acid mine drainage (AMD), characterized by its acidity and high content of heavy metals, is a significant global environmental problem that harms human health through its impact on rivers. Therefore, this study aims to identify heavy metals in both surface and underground AMD-polluted karst rivers, focusing on the Zhijin River area which is severely affected by AMD, and assess their health risks to residents. Through the collection of 30 surface water samples and 16 groundwater samples from both wet and dry seasons, the study examines the concentration, sources of pollution, and health implications of six heavy metals (Fe, Mn, Cr, Cd, As, and Hg). The results showed that Fe and Mn levels in surface water were highly polluted during both seasons, especially during the wet season, with Fe levels reaching 20.0 mg/L and Mn levels reaching 1.9 mg/L. Further correlation and principal component analyses revealed that mining activities are the primary contributors to the contamination in this region. Health risk assessments and Monte Carlo simulation, including both deterministic and probabilistic, showed that the noncarcinogenic health risk indices for surface water and groundwater were within acceptable limits for both seasons. However, groundwater poses a higher carcinogenic risk to children, with As levels during the wet season and Cr levels during the dry season warranting close monitoring. Factors such as body weight and intake rate played a crucial role in health risk evaluations. This study underscores the need for further attention to groundwater risk, temporal heterogeneity in the Zhijin River.

Rapid industrial growth in Bangladesh, especially in the textile industry, has led to water pollution from toxic azo dyes like orange-II, which are harmful to ecosystems and enter the food chain via irrigation. This study examined the use of chemical coagulation (using ({text{C}}_{6} {text{H}}_{11} {text{NO}}_{4} {text{X}}_{2}) and ({text{FeCl}}_{3} cdot 6{text{H}}_{2} {text{O}})) and advanced oxidation process (using ({text{NaOCl}})) to treat orange-II dye for irrigation. However, ({text{C}}_{6} {text{H}}_{11} {text{NO}}_{4} {text{X}}_{2}) and ({text{FeCl}}_{3} cdot 6{text{H}}_{2} {text{O}}) showed limited effectiveness in removing orange-II dye across various pH (3, 6, 9, and 12) levels. In contrast, ({text{NaOCl}}) achieved significant dye removal rates of over 90–99%. The study focused on high color removal, limited ({text{ClO}}_{2}) and neutral pH after the test. Variables included ({text{NaOCl}}) doses (0.5 ml, 2.5 ml, and 5 ml), orange II dye doses (50 mg, 100 mg, and 150 mg) under different pH (3, 6, 8, 9 and 12) conditions. The manuscript is the first to assess orange-II dye using higher doses (2.5 ml and 5 ml) of ({text{NaOCl}}) compared to other studies, as an alkaline chemical to neutralize pH levels post-test. The highest dye removal occurred at pH 9 with similar results at other pH levels except pH 12. However, despite effective color removal, the pH remained alkaline at pH 8, 9, and 12 after the test. Hence, optimal experimental conditions of operational parameters were pH = 3 or 6, 2.5 ml ({text{NaOCl}}) dose with 100 mg/L or 150 mg/L dye doses. Further research is recommended on the degradation process, toxicological analysis of the final product, and cost-effectiveness for safe irrigation water.

An integrated membrane filtration system was developed to make water purity suitable for microbiology/molecular biology research. Water samples were collected from outlets in different buildings of the National Research Center and analyzed for their composition before filtration. An integrated membrane system was developed based on mathematical modeling. Flat sheet membranes were produced, including microfiltration, ultrafiltration, and nanofiltration membranes. The flat sheet membranes were converted to a spiral wound module filter to simulate the local market filters and applied in the integrated membrane system that was designed and installed. The produced water was analyzed and compared to molecular-grade water used in different molecular biology/microbiology applications. Both PCR, agarose gel electrophoresis, bacterial liquid cultures, and viral propagation indicated that treated water using the herein-developed system exhibited comparable performance to the molecular grade water provided with imported reagent kits. So, this research can offer a promising solution for producing high-quality water suitable for sensitive laboratory applications.

Groundwater resources are essential for ensuring a consistent water supply in many regions. Groundwater potential maps (GPMs) can be utilized in many ways to estimate the quantity, quality, and distribution of subsurface water, supporting the decision-making processes of numerous stakeholders. This study contributes to improving the accuracy of GPMs, focusing on implementing Geospatial Artificial Intelligence (GeoAI) models. For this purpose, the accuracy performance of the Extreme Gradient Boosting (XGBoost) algorithm is improved in this study. To do this, two such popular metaheuristic algorithms, i.e., invasive weed optimization (IWO) and biogeography-based optimization (BBO), are integrated into the XGBoost algorithm for modeling and spatial prediction of the areas prone to groundwater. Three models—XGBoost, XGBoost-IWO, and XGBoost-BBO—are implemented within the Python programming environments to execute spatial modeling and generate predictive maps. The evaluation of results unfolds in two stages: model validation and GPM validation. For the training data, the root mean square error (RMSE) and mean absolute error (MAE) indices were 0.165 and 0.121 for XGBoost, 0.13 and 0.087 for XGBoost-IWO, and 0.114 and 0.082 for XGBoost-BBO, respectively. The test data showed similar trends, with XGBoost yielding RMSE and MAE values of 0.424 and 0.295, XGBoost-IWO at 0.416 and 0.287, and XGBoost-BBO at 0.39 and 0.28. XGBoost-BBO, XGBoost-IWO, and XGBoost had a prediction accuracy higher than other models. The respective area under the curve (AUC) of GMPs using receiver operating characteristic (ROC) curves for XGBoost, XGBoost-IWO, and XGBoost-BBO were 81.8 %, 83.1 %, and 83.7 %. Using bio-inspired metaheuristic algorithms, the GPM accuracy rate has improved further. The study of groundwater resources demonstrated how geological feature extraction by GeoAI may help employ advanced techniques.

This study evaluated the efficiency of an on-site household greywater treatment system for indirect human reuse and for domestic lawn irrigation. This helps in the reduction in the disparity between water demand and supply that is facing the rapidly increasing global populace. Natural household greywater was settled and then conventionally filtered by using two types of non-woven geotextile media; thermally bonded and needle punched. A third woven cotton textile media was also experimented and all the non-woven geotextile media were tested in single and double layers and combined with the woven cotton textile layer. The different filter media configurations were tested for a period of one year operation (six runs) with two filtration rates of 15.00 and 25.00 m³/m²/day for each run. For all runs, the final treated effluent was disinfected using calcium hypochlorite prior to reuse. The double layer needle punched non-woven geotextile media together with the woven cotton textile media gave the best removal efficiencies; 96.34 ± 1.85% for turbidity 81.87 ± 6.43% for BOD₅, 97.49 ± 1.68% for TSS, 75.35 ± 3.99% for CODt, 99.59% for E.coli. The soluble CODs removal efficiencies were negligible (below 3%) in the first four runs with non-woven geotextile media and increased to 28.05 ± 4.29% when the woven cotton textile media was added. In general, the system was found to save about 63% of the daily water consumption reflecting a net 22.50% reduction in the daily water billing costs for the whole household.

In South Korea, the snowy season spans from October to April, and the annual average snowfall varies significantly depending on specific regions, latitudes, and elevations, ranging from 0 to 260 cm. The average annual snowfall in South Korea is 25.1 cm. Despite of the relatively shallow snowfall depth, over the past decade, South Korea has experienced approximately 120 million dollars in damages attributed to snow-related incidents. In this study, the DPSIR (Driver-Pressure-State-Impact-Response) framework was employed to consider the meteorological and socioeconomic factors to calculate the snow damage vulnerability. A total of 17 indicators were taken into account to comprehend meteorological conditions, socioeconomic factors, and historical damage records from 1994 to 2020. However, due to the limited availability of meteorological observatories and changes in greenhouse design standards, accurately estimating the snow damage amount poses challenges. Therefore, based on the vulnerability, the risk levels were classified into four categories and estimated snow damage generated by the categorized models was compared with those of the model constructed using the entire dataset. The categorized models offer improved estimation results, as the meteorological and socioeconomic characteristics within each category differ and should be addressed separately in modeling. Among the categorized models, the Green zone exhibited the best results, primarily because it did not include outlier snow damage incidents. The developed model in this study could be utilized to mitigate the impact of heavy snowfall and prioritize snow removal regions.