Applied Water Science最新文献

Water quality monitoring is critical for maintaining safe water and conserving ecosystem diversity. However, data and information on riverine water quality are sparse in India’s river systems. Remote sensing analytics have huge potential to enhance the ecological state of water resources by monitoring the evolution of water contamination over time. The principal aim of the study is to use empirical modelling approaches in developing models for estimating water quality parameters (WQPs) such as total suspended solids (TSS), dissolved oxygen (DO), Calcium, Chloride, and pH using Landsat-8 and PlanetScope satellite data and laboratory analysis. Surface reflectance and band ratios are mainly utilized as input data to develop linear regression with measured water quality data. Regression-based results with PlanetScope generated significantly higher R² for all WQPs (0.65–0.78) except pH (0.41) as compared to Landsat-8. Results also showed that the regression models of TSS, DO, Calcium, Chloride, and pH are highly significant to visible (B, G and R) and near-infrared (NIR) bands of PlanetScope which can be attributed to finer spatial resolution. The water quality is mainly very poor around densely populated areas which crosses the permissible limit. Furthermore, the findings of this study illustrated the considerable capacity of water quality models based on remote sensing for conducting periodic monitoring and assessment. The applied empirical approach demonstrates the potential applicability of remote sensing analytics for the formulation of water management strategies, policies, and decision-making.

Conserving water saves energy. Energy is needed to filter, heat, and pump water to our homes. So, reducing your water use also reduces our carbon footprint. Using less water keeps more water in our ecosystems and helps to keep wetland habitats topped up for animals like otters, water voles, herons, and fish. In this regard, the practice of water efficiency plays an important role. Water conservation is the practice of reducing water consumption by measuring the amount of water required for a particular purpose and is proportionate to the amount of essential water used. Water efficiency differs from water conservation in that it focuses on reducing waste, not restricting use. The washing machine has become one of the most essential household appliances in every modern household. It not only conserves time and energy but also increases the longevity of your clothing along with making it cleaner clothes than hand washing. On average, washing machines consume around 140–200 L of water per wash cycle and around the same amount for rising. Here, I am proposing a simple water efficiency strategy that can be practiced in every household without restricting water usage.

Groundwater is an essential source for drinking purposes; hence, a qualitative analysis is necessary for groundwater resources. This study aims to assess the impacts of dynamic land use land cover (LULC) and climatic conditions on groundwater quality for drinking purposes. The investigative analysis of research used the water quality index (WQI) to analyze the groundwater quality and the source identified using the LULC map and climatic conditions. It extends an integrated and combined approach of different aspects. It provides a comprehensive understanding of how the various factors influence groundwater quality. The total area is classified as excellent, good, poor, very poor, and unfit for consumption based on the WQI. The results concluded that only 10.17% of the area has excellent drinking water quality, 19.97% has good water quality, 9.013% and 5.73% have poor and very poor water quality, respectively, and 55% of the water is unfit for consumption. The results indicated that the areas with agricultural expansion, urban development, and some natural conditions such as topographic features and high soil erodibility led to high total dissolved solids, electrical conductivity levels, and heavy metals. The main factors of LULC that lead to groundwater contamination include agricultural expansion and urban development. On the other hand, climatic conditions, such as variations in temperature and precipitation, also influenced groundwater quality. The research aids in examining different perspectives, which will lend a hand to water and land managers to make suitable decisions for sustainable development plans to conserve an economically important region.

In the present study, the optimum design of an abnormal stilling basin which may not to be categorized in the available standards, is investigated downstream of bottom outlet of a dam, using a physical model and three-dimension (3D) numerical simulation. The length of basin, shape and location of blocks and sills are changed and behavior of flow in stilling basin is investigated. RNG (Renormalization Group) is used for turbulence modeling scheme along with the Volume Of Fluid (VOF) for distinguishing the free water surface and Fraction Area/Volume Obstacle Representation (FAVOR) for detecting geometry components of the basin. According to the performed verification with the experimental data, it is concluded that the total deviation between numerical results and experimental data for velocity is below 15%. Results showed that increasing the length of basin is the most reason of decreasing flow velocity at the tail water where by increasing about 133% of the basin length, the velocity decreased about 34% at the tail water. Moreover, stepped sill at the end of basin had considerable effect on controlling hydraulic jump. Finally, it is concluded that the optimum design dissipated about 65% of the energy more in comparison with the initial design.

Increased fluoride and nitrate concentration in water resources can affect consumers' health adversely. The objective of this study is to health risk assessment of fluoride and nitrate in the drinking water of municipal and rural areas of Zahedan using probabilistic approaches. For this purpose, 347 water samples were collected from both urban and rural areas of this province. After the chemical analysis of the samples, a health risk assessment was conducted using the USEPA model, and a sensitivity analysis was performed by Monte Carlo software. The average concentration of nitrate in rural and municipal areas drinking water was 31.89 mg/L and 40.87 mg/L, respectively. Fluoride concentration in rural samples was 2.13 mg/L while municipal samples had 1.28 mg/L. 14.53% and 24.12% of rural and urban areas exceeded NO₃⁻ limits, respectively. Rural samples had higher F- concentrations than WHO standards. CDI values for fluoride and nitrate in municipal areas were 0.04 and 1.15 mg/kg/day, for adults and 0.09 and 2.82 mg/kg/day, for children. The corresponding values for rural areas were 0.06, 0.9, 0.15, and 2.2 mg/kg/day. The HQ for nitrate in children was between 0 and 5.2 in children, with an average of 1.71. These values were registered to be 0–3.85 and 1.26, respectively, in the adult group. Also, the average value of HQ fluoride in children is much higher than that of adults, with values of 2.45 and 1.47 in rural and urban areas, respectively, both exceeding 1. The results indicate a possibility non-carcinogenic risk of nitrate and fluoride, particularly for children in these areas, is significant. Therefore, it is necessary to pay special attention to improving the quality of drinking water in this province.

Identifying the primary pollution source poses a challenge in river watersheds characterized by diverse land-cover types and mixed pollution sources. We addressed this challenge by focusing on the major tributaries influencing the water quality of the Mankyung River’s mainstream, successfully identifying the primary pollution source. Additionally, it identified the limiting nutrient for algal growth in the Mankyung River, proposing an alternative strategy to enhance water quality and mitigate algal growth. Positive matrix factorization (PMF) was employed to discern pollution sources in major tributaries, namely Jeonju-cheon and Iksan-cheon, impacting mainstream water quality. For Jeonju-cheon, pollution from urban and agricultural areas, including wastewater treatment plants, emerged as the primary source. For Iksan-cheon, pollution from urban and agricultural areas predominated. The nitrogen-to-phosphorus ratio and correlation analysis revealed that total phosphorus is the limiting factor for algal growth. Furthermore, scenarios to improve water quality and reduce algal growth were developed, and the Environmental Fluid Dynamic Code (EFDC) was used in the simulation, while the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) was used in water quality assessment. The findings demonstrated improved water quality and decreased algal blooms in the downstream Mankyung River region. This research provides a foundation for applying PMF, the EFDC, and the WQI in tracking pollution sources and enhancing water quality in rivers.

This study assesses the impact of land-use and climate change on hydrological regime of three dam watersheds (Karaj, Latian and Mamlu) in Alborz and Tehran Provinces of Iran. Daily precipitation and temperature data from CMIP6 are transiently downscaled to ten climatic stations using the LARS-WG under global warming scenarios SSP1-1.9, SSP2-4.5, and SSP5-8.5. The Cellular Automata-Markov-chain machine learning is used to simulate future land-use maps (2021–2080) by training and testing its multilayer perceptron neural network with observed land-use change during the period 1995–2015. The study area will experience warming by 0.78, 2.1, and 2.4 °C under SSP1-1.9, SSP2-4.5, and SSP5-8.5, respectively, and precipitation anomalies by +129.3, −95.6, and −54.2 mm, respectively, compared to the baseline period 1991–2014. Extreme precipitation depth will increase at all stations under SSP1-1.9. However, precipitation change depends on the storm’s return period, station, and scenario under warmer scenarios. The SWAT-predicted river flow over three watersheds will increase, compared to the baseline period, under SSP1-1.9 but decrease under SSP2-4.5 and SSP5-8.5. Among combinations of land-use and climate change scenarios, land-use scenario High under SSP1-1.9 leads to greatest annual streamflow, while no change in land-use under SSP2-4.5 results in maximum reduction in streamflow over three watersheds. Extreme flows over Karaj and Latian watersheds show no sensitivity to different land-use scenarios due to negligible land-use development in future but they still are sensitive to climate change scenarios. Meanwhile, extreme flows over Mamlu watershed show significant sensitivity to both land-use and climate change scenarios due to significant land-use change in future.

In this study, Fe₃O₄ magnetite nanoparticles were coated on the granular activated carbon (GAC) surface and packed in stainless steel (SS) mesh, which was named MGACSS. It was developed as a novel three-dimensional cathode in the heterogeneous electro-Fenton (HEF) process for direct dye removal from colored wastewater. According to the Box–Behnken design, the Direct Blue 80 (DB80) dye removal efficiency and chemical oxygen demand (COD) degradation under the optimal conditions of initial dye concentration of 35 mg/L, current of 0.09 A and reaction time of 18  min reached 98.97% and 66.66%, respectively. Furthermore, scavenger studies confirmed that the surface-bounded ·OH was the major oxidant responsible for the degradation of DB80 dye. The MGACSS electrode exhibited a dye removal efficiency of 98.56% even after six consecutive operations, indicating excellent stability and reusability. The efficiency of the MGACSS cathode demonstrated an excellent treatment performance in removing other direct dyes of Direct Brown 103 (DB 103), Direct Red 23 (DR23) and a real sample with 97.34%, 98.15% and 93.1% efficiency, respectively. This study suggested a highly efficient and stable novel electrode for removing direct dye from wastewater through the HEF process, over a wide pH range.

Water is a limited natural resource that is essential for both the survival of the environment and all forms of life. Nowadays, heavy metal pollution containing Cr has put serious threat to our environment. It can enter into soil, water, and even particulate matter in air, and can be harmful to human health and wild life. In this work, the removal of Cr from synthetic wastewater by electrocoagulation supported by natural coagulant (eggshell powder) with aluminum electrodes was investigated. The central composite design of the response surface methodology was employed to estimate and optimize process variables, such as initial Cr concentration (225–475 mg/L), solution pH (5–9), and current density (0.35–045 A/m²), and treatment time (30–40 min) with an electrode distance (ED) of 0.5 and 1 cm, respectively. 99.90% and 99.74% of removal efficiencies were observed at initial Cr concentration of 456.11 mg/L, a solution pH of 5.45, with current density of 0.47 A/m², and treatment time of 36.84 min. The analysis of variance (ANOVA) was performed, and the multiple correlation coefficients (R²) of both ED were found to be 0.9996 and 0.9955, which confirms the significance of the predicted model. Furthermore, X-ray diffraction, Fourier transform infrared spectroscopy, Brunauer–Emmett–Teller analysis, and thermogravimetric analysis were used to characterize the crystal structure, functional groups, specific surface area, and thermal stability of the coagulants (eggshell powder). The findings of this study suggest that using this natural coagulant, synthetic wastewater can be treated in a more cost-effective and simple way than other existing method.

Drought assessment is inherently complex, particularly under the influences of climate change, which complicates long-term forecasting. This study introduces a novel hybrid deep learning model, Deep Feedforward Natural Networks (DFFNN), enhanced by War Strategy Optimization (WSO), aimed at forecasting the Standardized Precipitation Evapotranspiration Index (SPEI) for lead times of one, three, six, nine, and twelve months. Key parameters of the DFFNN, including the number of neurons and layers, learning rate, training function, and weight initialization, were optimized using the WSO algorithm. The model’s performance was validated against two established optimizers: Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Evaluations were conducted at two synoptic stations with distinct climatic conditions in Iran. Results demonstrated that the WSO-DFFNN model achieved superior performance for SPEI 12 (t + 1) with a correlation coefficient (r) of 0.9961 and Normalized Root Mean Square Error (NRMSE) of 0.1028; for SPEI 12 (t + 3) with r = 0.8856 and NRMSE = 0.1833; for SPEI 12 (t + 6) with r = 0.8573 and NRMSE = 0.2203; for SPEI 12 (t + 9) with r = 0.7951 and NRMSE = 0.2479; and for SPEI 12 (t + 12) with r = 0.7840 and NRMSE = 0.3279 at the Chabahar station. Additionally, the WSO-DFFNN model outperformed for SPEI 12 (t + 1) with r = 0.9118 and NRMSE = 0.1704; for SPEI 12 (t + 3) with r = 0.8386 and NRMSE = 0.2048; for SPEI 12 (t + 6) with r = 0.7602 and NRMSE = 0.2919; for SPEI 12 (t + 9) with r = 0.6379 and NRMSE = 0.2843; and for SPEI 12 (t + 12) with r = 0.6044 and NRMSE = 0.3463 at the Anzali station. The results obtained from this study have the potential to improve drought management strategies.