This paper deals with the study of a water quality forecast model through application of BP neural network technique and GUI (Graphical User Interfaces) function of MATLAB at Yuqiao reservoir in Tianjin. To overcome the shortcomings of traditional BP algorithm as being slow to converge and easy to reach extreme minimum value, the model adopts LM (Levenberg-Marquardt) algorithm to achieve a higher speed and a lower error rate. When factors affecting the study object are identified, the reservoir’s 2005 measured values are used as sample data to test the model. The number of neurons and the type of transfer functions in the hidden layer of the neural network are changed from time to time to achieve the best forecast results. Through simulation testing the model shows high efficiency in forecasting the water quality of the reservoir.

For recovering the water quality of a river, it is a key factor to improve purifying capacity of wastewater in wastewater treatment plants (WTPs). The relational model for some key parameters of WTP processes is important for it can reveal the current situation and handling ability of the WTP and offer managers more useful information to design the processes for the optimized operation. The seasonal artificial neural network (ANN) models were designed for improving purifying ability of wastewater in a WTP of Harbin, northeast of China. The ANN models revealed the relationship of raw water quality, energy consumption, and effluent water quality. The effluent water quality could be predicted by the models. The clustering analysis method, an important data mining method, was used to classify the WTP data for building seasonal models. Meanwhile, an annual model was built by the whole data. It indicates that the prediction accuracy of seasonal models is better than the annual model by contrasting the errors. Seasonal models should be a more effective tool to reveal the relationship of WTP data. So it can offer managers more precise information to control and design the processes of WTPs, which result in better purifying ability of wastewater.

With the development and industrial applications of nanotechnology, increasing productions of engineered nanomaterials enter the aqueous environments by several routes (i.e. remediation purpose and unintentional release). Due to the potential risk deriving from their higher surface activity and greater transferability, engineered nanoparticles bring a new challenge for the environmental systems and human beings. Under favorable particle–particle interactions, nanoparticles aggregate, form large clusters, and settle out from the environmental solutions. The settling process can reduce the mobility of nanoparticles in environmental systems. In the current study, sedimentation experiments were conducted under different environmental conditions to investigate the settling process of nanoparticles. The results indicated that the A/A₀ ratios of titanium dioxide (TiO₂) nanoparticles increased with increasing ionic strength. They also increased near the pH_pzc (point zero of charge). However, the increasing concentrations of humic acids in aqueous solutions reduced the A/A₀ ratios even near the pH_pzc. The measured zeta potentials revealed that the TiO₂ nanoparticles became less charged in the range of pH_pzc and at higher ionic strength. It was due to the double electric layers of TiO₂ nanoparticles compressed with increasing in ionic strength and charge screening effect. Therefore, the electrostatic repulsive force between nanoparticles significantly declined, which lead to favorable aggregation. The interaction energies calculated using the DLVO theory under different pH and ionic strength conditions were consistent with the experimental results from the sedimentation experiments. In addition, the steric interactions between humic acid absorbed on the surface of TiO₂ nanoparticles were responsible for the reduction in the A/A₀ ratios.

In this study, the remote sensing images of the 30 km buffer zone from Zhaoyuan to Baidu of the Songhua River, which is rich in land use types and frequent in human activities, were selected as the research object to analyze land use change and driving factors. The objective of this research is to evaluate the ecological risk of watershed landscapes and provide a basis for watershed ecological environment protection and planning. On this basis, the landscape pattern index data were extracted, and a three-dimensional comprehensive index system of the natural, social and landscape pattern was constructed. In addition, based on the spatial principal component analysis (SPCA), data fusion was carried out to improve the accuracy and comprehensiveness of landscape ecological risk assessment results. The risk level of watershed landscape ecology was divided into low ecological risk, medium-low ecological risk, medium ecological risk, medium-high ecological risk, and high ecological risk by the Natural Breaks method. According to the results of the landscape ecological risk assessment and the characteristics of risk sources in each risk level area, the ecological protection and planning enlightenment suitable for each risk level area were obtained. The research content can provide ideas and evidence for environmental managers to formulate ecological risk protection strategies and reduce the impact of ecological risk threats.

Surface water is a vital resource for human survival. However, economic and social development has resulted in significant pollutants from human activities, causing environmental pollution in watersheds. This pollution has had a profound impact on the surface water environment. However, limited studies have been conducted on the environmental risk evaluation of the watershed. In this study, we accounted for agricultural, industrial, and domestic source discharges in the districts and counties of the Harbin section of the Songhua River Basin for 2021. Data were collected from Statistical Yearbooks and governmental departments, and the characteristics of pollutant discharges in Harbin’s districts and counties were analyzed. Subsequently, we employed the Back Propagation neural network optimization method, combining remote sensing data, accounting data, pollution discharge data from each district and county, and economic and social data from the Statistical Yearbook and literature. This fusion of multiple data sources facilitated the construction of a watershed environmental risk evaluation system. The analysis considered four levels: economic and social, resource load, environmental infrastructure, and pollution discharge. Via this comprehensive evaluation, we identified the reasons for environmental risks in the water environment of the Harbin section of the Songhua River Basin. The evaluation results indicate that Nangang District, Xiangfang District, and Pingfang District face a higher risk to the water environment. Consequently, recommendations for mitigating water environment risks in these areas and across Harbin City are presented. The research methods and findings in this paper contribute valuable insights for developing control strategies to manage water quality in critically polluted areas of the Harbin section of the Songhua River Basin, providing a scientific foundation for regional river water quality management studies.