Pollution source identification and abatement for water quality sections in Huangshui River basin, China

Novel knowledge for identifying point pollution sources in watershed environmental management

Identifying point pollution sources (PPSs) is essential for enforcing penalties against illegal discharge behaviours that violate acceptable water quality (WQ) standards. However, there are existing knowledge gaps in understanding the association between the pollutants in water bodies and the pollutants emitted by PPSs, as well as how to utilize the knowledge to identify PPSs in water pollution accidents. This study developed a novel framework for identifying PPSs based on the conventional chemical pollutants and matrix calculations model (CCI-MCM). A two-step statistical analysis and correlation analysis extracted pollutant information in sewage wastewater from 256,025 PPSs and further developed the similarity matrix of industrial sewage wastewater indicators (SM-ISWI) and the correlation matrix of industrial sewage wastewater indicators (CM-ISWI). The SM-ISWI and CM-ISWI comprised 820 and 7790 pollution units, which could distinguish 41 industries and further identify the PPSs in these industries. Single factor index analysis and Pearson correlation analysis developed the WQ concentration matrix (WQ-CM) and WQ concentration correlation matrix (WQ-CCM), highlighting concentration anomalies of conventional chemical pollutants in natural water bodies and supply data for matrix calculation model to identify PPSs. The matrix calculation model with the Zf, Zc and Zf-c scores indicated the relative probability of each PPS responsible for the water pollution. Four publicly reported water pollution incidents in China were selected as case studies to validate the effectiveness of the CCI-MCM in PPSs identification. The TE values in four case areas ranged from 25.0 % to 53.9 %, demonstrating a practical enhancement in identifying PPSs relative to random sampling identifying PPSs methods. The proposed CCI-MCM method provided specialized knowledge in understanding the association between the pollutants in water bodies and the pollutants emitted by PPSs, as well as how to utilize the knowledge to identify PPSs in water pollution accidents.

SWAT model application for calculating ecological flow in sub-basins of the Huangshui River Basin

Ecological flow denotes the minimum volume of water required to sustain the integrity and health of watershed ecosystems, ensuring their stability and functioning over time. However, research on estimating ecological flow at the sub-basin level and developing strategies for ecological water replenishment is still limited. The study begins by utilizing measured runoff data from 1952 to 2020. The SWAT model is then employed to reconstruct the natural runoff, adjusting for hydrological anomalies, and to simulate the runoff for each sub-basin. The ecological flow and water shortage conditions for each sub-basin are calculated using the Tenant method under three standards: minimum, suitable, and ideal. Finally, specific water supplementation strategies are proposed for each sub-basin based on these standards. The study reveals that the Huangshui River Basin has experienced significant development and utilization of its water resources. The reconstructed natural runoff, derived from the SWAT model, is 16% higher than the actual runoff. Significant variations in ecological flow are found across the 28 sub-basins, with sub-basins 19, 20, 22, 25, and 26 identified as facing ecological water shortage. To address the water shortages in these sub-basins, a combination of in-basin water storage and inter-basin water transfer is recommended for sub-basins 20, 22, and 25. And sub-basins 19 and 26 can meet their ecological flow needs using only in-basin water storage. The study's findings establish a robust scientific foundation and offer actionable recommendations for the preservation of the ecological integrity of the Huangshui River Basin.

Optimization method of collaborative ecological water supplement and water purification plants strategies for improving water quality of urban river network

Climate change and urbanization pose serious challenges to the hydrodynamic processes and water quality needed to support the aquatic ecological environment. As an important scheme to cope with the deterioration of urban river ecosystems worldwide, ecological water supplement (EWS) can directly bring water quality improvement, but it also produces a lot of water resources and economic pressure, especially in water-scarce regions. In order to save water resources, water purification plants (WPP) are important means to eliminate pollutants in river network and enhance the hydrodynamic and water quality. Taking the southern Haihe river network in Tianjin as an example, this study established the evaluation framework of EWS and WPP collaborative schemes, which includes the hydrodynamic and water quality simulation module, the multi-level indicator module based on hydrodynamic, water quality and economic cost, and the decision-making module. For the current 200 thousand tons/day of WPP, 56 million tons of EWS is the best scheme. In this scenario, the cost of 70 million yuan and 56 million tons of water resources are saved under the condition of ensuring good hydrodynamic and water quality per year. The optimal scheme is proposed to increase the WPP scale to 500 thousand tons/day together with a small amount EWS for promoting water circulation and purifying water quality for severe water-scarce mega Tianjin. Compared with only relying on EWS, the optimized scheme saves 82 million Chinese yuan and 85 million tons of water resources per year. At the same time, the proportion of chemical oxygen demand (COD) concentration that meets the standard is increased by 13.3% and the cross-sectional compliance rates of ammonia nitrogen and total phosphorus are both above 95%. The selection method of optimal EWS and WPP collaborative scheme and the assessment framework can be extended to other city river networks to advance sustainable urban river management.

Farmland's silent threat: Comprehensive multimedia assessment of micropollutants through non-targeted screening and targeted analysis in agricultural systems

Intricate agricultural ecosystems markedly influence the dynamics of organic micropollutants, posing substantial threats to aquatic organisms and human health. This study examined the occurrence and distribution of organic micropollutants across soils, ditch sediment, and water within highly intensified farming setups. Using a non-targeted screening method, we identified 405 micropollutants across 10 sampling sites, which mainly included pesticides, pharmaceuticals, industrial chemicals, and personal care products. This inventory comprised emerging contaminants, banned pesticides, and controlled pharmaceuticals that had eluded detection via conventional monitoring. Targeted analysis showed concentrations of 3.99-1021 ng/g in soils, 4.67-2488 ng/g in sediment, and 12.5-9373 ng/L in water, respectively, for Σ40pesticides, Σ8pharmaceuticals, and Σ3industrial chemicals, indicating notable spatial variability. Soil organic carbon content and wastewater discharge were likely responsible for their spatial distribution. Principal component analysis and correlation analysis revealed a potential transfer of micropollutants across the three media. Particularly, a heightened correlation was decerned between soil and sediment micropollutant levels, highlighting the role of sorption processes. Risk quotients surpassed the threshold of 1 for 13-23 micropollutants across the three media, indicating high environmental risks. This study highlights the importance of employing non-targeted and targeted screening in assessing and managing environmental risks associated with micropollutants.

Reliable water quality prediction and parametric analysis using explainable AI models

The consumption of water constitutes the physical health of most of the living species and hence management of its purity and quality is extremely essential as contaminated water has to potential to create adverse health and environmental consequences. This creates the dire necessity to measure, control and monitor the quality of water. The primary contaminant present in water is Total Dissolved Solids (TDS), which is hard to filter out. There are various substances apart from mere solids such as potassium, sodium, chlorides, lead, nitrate, cadmium, arsenic and other pollutants. The proposed work aims to provide the automation of water quality estimation through Artificial Intelligence and uses Explainable Artificial Intelligence (XAI) for the explanation of the most significant parameters contributing towards the potability of water and the estimation of the impurities. XAI has the transparency and justifiability as a white-box model since the Machine Learning (ML) model is black-box and unable to describe the reasoning behind the ML classification. The proposed work uses various ML models such as Logistic Regression, Support Vector Machine (SVM), Gaussian Naive Bayes, Decision Tree (DT) and Random Forest (RF) to classify whether the water is drinkable. The various representations of XAI such as force plot, test patch, summary plot, dependency plot and decision plot generated in SHAPELY explainer explain the significant features, prediction score, feature importance and justification behind the water quality estimation. The RF classifier is selected for the explanation and yields optimum Accuracy and F1-Score of 0.9999, with Precision and Re-call of 0.9997 and 0.998 respectively. Thus, the work is an exploratory analysis of the estimation and management of water quality with indicators associated with their significance. This work is an emerging research at present with a vision of addressing the water quality for the future as well.

Effects of landscape pattern on water quality at multi-spatial scales in Wuding River Basin, China

Urbanization and agricultural land use have led to water quality deterioration. Studies have been conducted on the relationship between landscape patterns and river water quality; however, the Wuding River Basin (WDRB), which is a complex ecosystem structure, is facing resource problems in river basins. Thus, the multi-scale effects of landscape patterns on river water quality in the WDRB must be quantified. This study explored the spatial and seasonal effects of land use distribution on river water quality. Using the data of 22 samples and land use images from the WDRB for 2022, we quantitatively described the correlation between river water quality and land use at spatial and seasonal scales. Stepwise multiple linear regression (SMLR) and redundancy analyses (RDA) were used to quantitatively screen and compare the relationships between land use structure, landscape patterns, and water quality at different spatial scales. The results showed that the sub-watershed scale is the best spatial scale model that explains the relationship between land use and water quality. With the gradual narrowing of the spatial scale range, cultivated land, grassland, and construction land had strong water quality interpretation abilities. The influence of land use type on water quality parameter variables was more distinct in rainy season than in the dry season. Therefore, in the layout of watershed management, reasonably adjusting the proportion relationship of vegetation and artificial building land in the sub-basin scale and basin scope can realize the effective control of water quality optimization.