Identification of pollution source and prediction of water quality based on deep learning techniques.

Semi-arid rivers are particularly vulnerable and responsive to the impacts of industrial contamination. Prompt identification and projection of pollutant dynamics are crucial in the accidental pollution incidents, therefore required the timely informed and effective management strategies. In this study, we collected water quality monitoring data from a typical semi-arid river. By water quality inter-correlation mapping, we identified the regularity and abnormal fluctuations of pollutant discharges. Combining the association rule method (Apriori) and characterized pollutants of different industries, we tracked major industrial pollution sources in the Dahei River Basin. Meanwhile, we deployed the integrated multivariate long and short-term memory network (LSTM) to forecast principal contaminants. Our findings revealed that (1) biological oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen, total phosphorus, and ammonia nitrogen exhibited high inter-correlations in water quality mapping, with lead and cadmium also demonstrating a strong association; (2) The main point sources of contaminant were coking, metal mining, and smelting industries. The government should strengthen the regulation and control of these industries and prevent further pollution of the river; (3) We confirmed 4 key pollutants: COD, ammonia nitrogen, total nitrogen, and total phosphorus. Our study accurately predicted the future changes in this water quality index. The best results were obtained when the prediction period was 1 day. The prediction accuracies reached 85.85%, 47.15%, 85.66%, and 89.07%, respectively. In essence, this research developed effective water quality traceability and predictive analysis methods in semi-arid river basins. It provided an effective tool for water quality surveillance in semi-arid river basins and imparts a scientific scaffold for the environmental stewardship endeavors of pertinent authorities.

Study on remote sensing inversion and temporal-spatial variation of Hulun lake water quality based on machine learning.

Hulun Lake is facing significant water quality degradation, necessitating effective monitoring for safety. Traditional methods lack the necessary spatial and temporal coverage, underscoring the need for a remote sensing model. In this study, we utilized the Landsat 8 OLI dataset, incorporating cross-section monitoring and field sampling data comprehensively. Employing the random forest algorithm, we constructed a remote sensing inversion model for six water quality parameters in Hulun Lake: chlorophyll-a (Chl-a), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH3-N), chemical oxygen demand (COD), and dissolved oxygen (DO). The model was applied to the non-freezing period of Hulun Lake from 2016 to 2021, exhibiting commendable performance and generating high-resolution maps. Time series analysis revealed that during the study period, the pollution levels of TN, TP, and COD in Hulun Lake were extremely serious, exceeding the Class V water standard of China's surface water environmental quality standard. Regional analysis indicated lower pollutant concentrations in the central lake area compared to the lake inlet. The inflowing rivers with high pollution adversely impacted Hulun Lake's water quality. To ensure the continued health of Hulun Lake's water quality, it is imperative to monitor lake water quality attentively and implement necessary measures to prevent further deterioration. This study holds crucial importance for shaping and executing ecological protection and restoration strategies for Hulun Lake.

Contribution of nonpoint source pollution from baseflow of a typical agriculture-intensive basin in northern China.

Baseflow is an essential component of total surface runoff that is widely considered one of the most influential factors regarding water quality via nonpoint source (NPS) pollution. Previously, many researchers and policy makers have directed their efforts toward surface runoff pollution, largely ignoring nutrient delivery via baseflow. Taking a typical agriculture-intensive basin of northern China as an example, this study explored the spatiotemporal characteristics of baseflow and pollution load in relation to NPS pollution. Baseflow was quantified using digital filtering techniques, and the results together with observed pollution data were used to validate a physically based hydrological model, i.e., the Soil and Water Assessment Tool. Then, the spatial and temporal distribution characteristics of NPS and baseflow pollution were investigated using the modeling results. Results indicated that baseflow contribution to total runoff accounted for more than 70% during the studied years (2016-2018), and 84.15% of the basin area showed non-point source pollution dominated by baseflow pollution; both baseflow and its pollution load were greater in the nonflood seasons (spring, autumn, and winter) than in the flood season (summer); the spatial distribution of baseflow total nitrogen and total phosphorus pollution intensity showed higher values in the east and lower values in the west; the scaling effects of baseflow and its pollution load was found with increasing basin area. The results of our study highlighted the necessity for management of pollution load via baseflow in the river basin and provided reference information for improvement of NPS pollution management in other similar basins.

Non-point source pollution risks in a drinking water protection zone based on remote sensing data embedded within a nutrient budget model.

A simple, transparent and reliable method for evaluating non-point source pollution (NPSP) risks to drinking water source areas lacking observational data is proposed herein. The NPSP risks are assessed by using nutrient budget models for total nitrogen and total phosphorus, making the best use of remote sensing and field survey data. We demonstrate its potential using a case study of the Chaihe Reservoir in northeastern China. Fertilizer inputs and crop-uptake outputs were estimated based on normalized difference vegetation index, which is derived from remote sensing as indicators of crop growth and production. The nutrient balances for this area showed surpluses of both N and P within the soil system. Estimated imbalances per unit area were consistent with statistical relationships derived from all Chinese counties, demonstrating that the proposed method is reliable. The surplus P amounts were higher than the standard threshold for NPSP risks, indicating the existence of a potential contamination risk of P to this drinking water source.