In recent years, algal blooms break out frequently and often accumulate in nearshore zones of eutrophic lakes and reservoirs, which seriously threaten regional water supply security. It is of great significance to grasp the status of algal blooms in key nearshore zones timely for the emergency prevention and control of algal blooms. A video surveillance system provides a new method for achieving this goal. The results of algal-bloom monitoring in current research, however, are usually interfered by onshore vegetation for their similar textural features. Accordingly, there are great limitations in current works in terms of decision support for emergency prevention and control of algal blooms. To solve this problem, a binocular video surveillance system based an accurate monitoring method of algal blooms is proposed in this paper. Binocular images of monitoring areas are obtained periodically by exploiting the binocular video surveillance system, which is performed by a stereoscopic 3D reconstruction method to obtain the 3D point cloud data of monitoring areas. Afterward, water regions and non-water regions are intelligently discriminated according to the elevation characteristics of point clouds, and only the image data of the water regions are finally adopted for algal-bloom extraction. Thus, the influence of onshore vegetation on the extraction of algal blooms can be eliminated. The system was implemented and applied, and the experimental results show that the proposed method can eliminate effectively the interference of onshore vegetation on the extraction of algal blooms and improve significantly the accuracy of existing methods for algal-bloom monitoring based on video surveillance system. Full article

After years of treatment, the water pollution situation in the Huaihe River Basin (HRB) is still grim, and agricultural nonpoint source pollution has become the leading cause of the problem. However, agricultural nonpoint source pollution in the HRB is complicated due to the compounding effects of multiple factors. In this study, we first applied the export coefficient model to estimate the total nitrogen (TN) and total phosphorus (TP) loads used as two pollution source indicators in HRB. Then we constructed an index evaluation system of nonpoint source pollution risk by coupling the two source indicators with five additional indicators: rainfall erosion, river network distribution, soil erodibility, slope length, and land use. The primary source of TN and TP loads is fertilizer application (81.96%), followed by livestock and poultry breeding (16.3%) and rural domestic wastes (1.74%). The risk assessment results indicate that 66.43% of the HRB is at medium to high risk of nonpoint source pollution, 12.37% is at high risk, and 11.20% is at low risk. Moreover, the medium-to-high-risk areas are mainly concentrated in the Henan and Anhui provinces. In contrast, the medium-risk regions are mainly distributed along the mainstream of the Huaihe River. Finally, the observed water quality categories were used to verify our findings. The controlling areas of nonpoint source pollution in HRB are identified. This study could provide a scientific basis for effectively preventing and treating water pollution in the HRB. Full article

Diffuse pollution loads are crucial information for water resource management, and yet field data are often scarce, implying questionable accuracy in load estimates made from low-frequency water quality monitoring. This paper aimed to characterize diffuse pollution in a stream of a mixed-land-cover watershed with a significant portion of urbanized areas through intensive monitoring and to perform a comparative analysis between the loads estimated by pollutant rating curves obtained by regression and the estimates using monthly water quality data, which is the method currently used. Continuous rainfall and flow monitoring was conducted between 2019 and 2021, and samples were collected during flood events and the dry period for water quality analysis. Flood events were found to induce an increase in suspended solids (TSS) and COD concentrations, while inorganic nitrogen (Inorg-N) concentrations were higher in the dry season. Flood characteristics showed a positive correlation with solids and COD event mean concentrations (EMCs) and negative with Inorg-N EMCs, while rainfall characteristics, such as antecedent dry days and intensity, correlate positively with all these pollutants. The rating curves performed well for total load estimation in low discharge events (R² and NSE > 0.8), except for total phosphorus (TP) loads. Estimated annual unit loads found for the watershed were 2 ton TSS/ha.year, 300 kg COD/ha.year, 5 kg Inorg-N/ha.year, and 0.5 kg TP/ha.year, showing high pollution generated in the watershed. Finally, a comparison with estimates based on monthly monitoring data indicated that this method is sufficient for accurate nutrient loads, but not for TSS and COD loads, which require continuous monitoring to improve the accuracy of estimation. Full article

Cephalexin (CFX) residues in the environment represent a major threat to human health worldwide. Herein we investigate the use of novel approaches in deep learning in order to understand the mechanisms and optimal conditions for the sorption of cephalexin in water onto an acidic pretreated jackfruit peel adsorbent (APJPA). The interaction between the initial concentration of CFX (10–50 mg/100 mL), APJAP dosage (3–10 mg/100 mL), time (10–60 min), and the pH (4–9), was simulated using the one-factor-at-a-time method. APJPA was characterized by FESEM images showing that APJPA exhibits a smooth surface devoid of pores. FTIR spectra confirmed the presence of -C-O, C–H, C=C, and -COOH bonds within the APJPA. Maximum removal was recorded with 6.5 mg/100 mL of APJAP dosage, pH 6.5, after 35 min and with 25 mg/100 mL of CFX, at which the predicted and actual adsorption were 96.08 and 98.25%, respectively. The simulation results show that the dosage of APJAP exhibits a high degree of influence on the maximum adsorption of CFX removal (100%) between 2 and 8 mg dose/100 mL. The highest adsorption capacity of APJAP was 384.62 mg CFX/g. The simulation for the effect of pH determined that the best pH for the CFX adsorption lies between pH 5 and 8. Full article

Bioretention facilities with different inflow concentrations, growing media and plants were examined to determine whether the soil in these facilities was polluted with heavy metals and whether runoff had obvious toxic effects on plants. Using Beijing soil background value as the standard, the soils were evaluated by bioaccumulation index and single factor index. The results show that stormwater runoff containing Cu caused slight pollution in soils, and stormwater runoff containing Zn and Pb was not polluted. Nemerow comprehensive index evaluation revealed that the heavy metals content in the facilities containing vermiculite (a yellow or brown mineral found as an alteration product of mica and other minerals, used for insulation or as a moisture-retentive medium for growing plants) and perlite (a form of obsidian characterized by spherulites formed by cracking of the volcanic glass during cooling, used as insulation or in plant growth media) were higher than the standard. High influent concentration caused significantly higher heavy metals content in plants. While Pb accumulation in the two studied plants was the highest, Cu and Zn accumulation, which are essential for plant growth, was relatively low. The contents of the three heavy metals in the studied plants also exceeded their corresponding critical values. Full article

Nitrogen (N) losses through runoff from cropland and atmospheric deposition contributed by agricultural NH₃ volatilization are important contributors to lake eutrophication and receive wide attention. Studies on the N runoff and atmospheric N deposition from the paddy ecosystem and how the agriculture-derived N deposition was related to NH₃ volatilization were conducted in the paddy ecosystem in the Erhai Lake Watershed in southwest China. The critical period (CP) with a relatively high total N (TN) and NH₄⁺-N deposition occurred in the fertilization period and continued one week after the completion of fertilizer application, and the CP period for N loss through surface runoff was one week longer than that for deposition. Especially, the mean depositions of NH₄⁺-N in the CP period were substantially higher than those in the subsequent period (p < 0.01). Moreover, agriculture-derived NH₄⁺ contributed more than 54% of the total NH₄⁺-N deposition in the CP period, being positively related to NH₃ volatilization from cropland soil (p < 0.05). The N concentrations were higher in the outlet water of ditches and runoff in May than in other months due to fertilization and irrigation. Therefore, to reduce the agricultural N losses and improve lake water quality, it is important to both reduce agricultural NH₄⁺-N deposition from NH₃ volatilization and intercept water flow from the paddy fields into drainage ditches during the CP. Full article

The transport of agricultural nonpoint source (NPS) pollutants in water pathways is affected by various factors such as precipitation, terrain, soil erosion, surface and subsurface flows, soil texture, land management, and vegetation coverage. In this study, based on the transmission mechanism of NPS pollutants, we constructed a five-factor model for predicting the path-through rate of NPS pollutants. The five indices of the hydrological processes, namely the precipitation index (α), terrain index (β), runoff index (TI), subsurface runoff index (LI), and buffer strip retention index (RI), are integrated with the pollution source data, including the rural living, livestock and farmland data, obtained from the national pollution source census. The proposed model was applied to the headwater of the Miyun Reservoir watershed for identifying the areas with high path-through rates of agricultural NPS pollutants. The results demonstrated the following. (1) The simulation accuracy of the model is acceptable in mesoscale watersheds. The total nitrogen (TN) and total phosphorus (TP) agriculture loads were determined as 705.11 t and 3.16 t in 2014, with the relative errors of the simulations being 19.62% and 24.45%, respectively. (2) From the spatial distribution of the agricultural NPS, the TN and TP resource loads were mainly distributed among the upstream of Dage and downstream of Taishitun, as well as the towns of Bakshiying and Gaoling. The major source of TN was found to be farmland, accounting for 47.6%, followed by livestock, accounting for 37.4%. However, the path-through rates of TP were different from those of TN; rural living was the main TP source (65%). (3) The path-through rates of agricultural NPS were the highest for the towns of Wudaoying, Dage, Tuchengzi, Anchungoumen, and Huodoushan, where the path-through rate of TN ranged from 0.17 to 0.26. As for TP, it was highest in Wudaoying, Kulongshan, Dage, and Tuchengzi, with values ranging from 0.012 to 0.019. (4) A comprehensive analysis of the distribution of the NPS pollution load and the path-through rate revealed the towns of Dage, Wudaoying, and Tuchengzi as the critical source areas of agricultural NPS pollutants. Therefore, these towns should be seriously considered for effective watershed management. In addition, compared with field monitoring, the export coefficient model, and the physical-based model, the proposed five-factor model, which is based on the path-through rate and the mechanism of agricultural NPS pollutant transfer, cannot only obtain the spatial distribution characteristics of the path-through rate on a field scale but also be applicable to large-scale watersheds for estimating the path-through rates of NPS pollutants. Full article

Modelling tools are commonly used for predicting non-point source (NPS) pollutants and it is timely to review progress that has been made in terms of the development of NPS models. This paper: (1) proposes a systematic description of model framework and generalizes some commonly used models; (2) identifies the common challenges in model structure and applications; (3) summarizes the future directions of NPS models. Challenges in model construction and application are based on the following: (1) limitations in understanding specific NPS pollution processes; (2) model expansion to different scales; (3) data scarcity and its impacts on model performance; (4) prediction uncertainty due to model input, parameter and model structure; (5) insufficient accuracy for decision-making. Finally, this paper proposes future directions for model development, including: (1) a source–flow–sink framework for model development; (2) standardization for model input and parameter; (3) reliable decision support for environmental management. The findings of this review provide helps in the accurate prediction and management of NPS pollution around the world. Full article

Watershed management must take into account both the quantity and quality of water. Therefore, many hydrological models have been developed for hydrological and water quality prediction for various purposes. The Spreadsheet Tool for Estimating Pollutant Loads (STEPL), which was developed in the United States for water quality regulation, can predict both the quantity and quality of water, and has the advantage of including information on livestock. However, complex characteristics of the watershed must be generated by users for use as input data, and simulations only yield annual average values. Therefore, in this study, we developed a model that overcomes these limitations using geographic information data and enabling monthly predictions. The model developed in the study estimates monthly direct runoff and baseflow using daily rainfall data, while the STEPL model employs average annual approaches that are limited to consider seasonal variances of hydrological behaviors. It was developed for use within the QGIS software, and was applied to a watershed covering an area of 128.71 km², considering information on livestock, soil, and land use. The model exhibited good predictive accuracy for four nonpoint source (NPS) pollutant loads and river flow, displaying acceptable criteria greater than 0.83 for river flow rates and 0.71 for all NPS pollutant load rates during calibration and validation. Full article