Using EEM-PARAFAC to identify and trace the pollution sources of surface water with receptor models in Taihu Lake Basin, China

Compositions of suspended particulates in typical urban river of Shanghai, China and its significance for ecological restoration

Although the water quality of urban rivers in Shanghai, China has been improved significantly in the past decades, their transparency is still unsatisfactory. To clarify the turbidity and its possible mechanisms, the characteristics of suspended particulate matters (SPM) are analyzed carefully, which reveals that suspended microbes dominate the component in urban rivers with high turbidity. Based on the principal component analysis and random forest analysis, nutrients and organic pollutants is revealed to promote the turbidity by promoting the growth of suspended algae and microbes. Furthermore, high-throughput sequencing is used to analyze the microbes in bulk water of urban rivers and iris rhizosphere of ecological floating bed. It reveals that there are significant differences between the microbial communities in bulk water and iris rhizosphere, suggesting that microbes immobilized in iris roots are not derived from bulk water. The metabolic function enrichment analysis based on PICRUSt shows that rhizosphere microbes mainly concentrate on the metabolism of plant secretions, while suspended microbes in bulk water mainly concentrate on the metabolism of pollutants. Since microbial diversity, metabolic richness, and interactions of rhizosphere microbes are much higher than those microbes in bulk water, it suggests that rhizosphere microbes may reduce suspended microbes in water via their competitive effects, thus purify pollutants and reduce turbidity in bulk water (improve transparency). These findings reveal the theoretical basis of water ecological restoration, thus might be helpful to technological innovation in the ecological restoration of urban rivers with high turbidity.

New insights into pollution source analysis using receptor models: Effects of interaction between heavy metals and DOM on source identification and apportionment in rivers across industrial city

To effectively control pollution and protect the ecosystem, it is essential to accurately analyze the potential pollution sources of heavy metals (HMs) in rivers. However, the traditional source apportionment methods based on HMs disregard the interaction between HMs and dissolved organic matter (DOM). In this study, data of HMs and DOM was combined for tracing sources and assessing the effect of interaction between HMs and DOM on source apportionment in urbanized rivers that cross urban (URR), industrial (INR), and rural (RUR) regions. Four types of fluorescent substances were extracted from DOM: tryptophan-like (TRLF), microbial byproduct (MB), fulvic-like (FLF), and humic-like (HLF) fluorescence substances. Anthropogenic activities (42.3 %), microbial products (21.5 %), and geogenic origin (23.7 %) were respectively recognized as the dominant source in URR, INR, and RUR. Additionally, significant correlations were obtained between HMs and high molecular mass DOM. There was no direct effect pathway obtained between HMs and sources and distributional characteristics of HMs are influenced by both economic and social factors. HMs have been found to indirectly affect source apportionment through interaction with DOM. This work provided a comprehensive understanding of the effects mechanism of the interaction of HMs and DOM on source identification and offered an effective method for tracing pollution sources.

A new view into the characterization of dissolved organic matter composition in lakes and traceability studies

With the booming socio-economic development and accelerated urbanisation, the problem of water pollution is becoming more and more prominent, which not only puts great pressure on nature, but also poses a serious threat to the production and life of human beings. Therefore, the study of dissolved organic matter fractions in lakes and their accurate traceability is the key to alleviate the ecological pressure. In this study, the three-dimensional fluorescence spectral properties, characteristic parameters and fluorescence characteristics of dissolved organic matter in water bodies were analyzed in depth using Changdang Lake in China as an example. Three methods, peak-finding method, Tucker coefficient and self-organised neural network, were prominently used for the analysis. Combined with conventional water quality parameters, Combined with conventional water quality parameters, this method further reveals the correlation between DOM composition and surrounding pollution sources in Changdang Lake. The results showed that there were four main components of dissolved organic matter in the lake body of Changdang Lake, of which C1, C2 and C4 were humic substances and C3 was protein. In addition, the fluorescence characteristic parameters of Changdang Lake, FI, ranged from 1.64 to 1.75, BIX, ranged from 0.95 to 1.05, and HIX, ranged from 0.5 to 0.65, which indicated that the increment of dissolved organic matter in Changdang Lake was mainly a mixture of endogenous and exogenous inputs. Through the joint interpretation of peak discovery, data presentation and result visualisation, it was found that these fluorescence fractions were extremely similar to those of the surrounding aquaculture and textile printing and dyeing. The results of this study not only provide effective data support for the local environmental protection department of Changdang Lake, but also provide a useful reference for pollution traceability in other lake basins.

Source apportionment and influencing factors of surface water pollution through a combination of multiple receptor models and geodetector

In line with sustainable development goals (SDGs), precise quantification of water pollution and analysis of environmental interactions are crucial for effectively safeguarding water resources. In this study, Nemerow's pollution index was used to evaluate water quality, three receptor models were used to identify pollution sources, and Geodetector analysis was applied to explore environmental interactions in the North Shangyu Plain, Southeast China. Using 5207 surface water samples from September 2023 with 11 physicochemical parameters, the results showed that surface rivers in the North Shangyu Plain exhibited varying degrees of pollution: slight pollution upstream, moderate pollution in midstream and downstream, and concentrated high pollution in certain areas, with TN, CODCr, and TP as the primary pollutants. Multimethod source apportionment significantly improved the accuracy of pollution source attribution and identified five main sources: domestic sewage (1.42%-3.54%) characterized by NO3-N, phytoplankton source (38.43%-50.05%) indicated by chl and PC, agricultural cultivation (16.1%-17.63%) marked by TP and CODMn, industrial wastewater (17.64%-25.1%) primarily associated with TN, and natural source (10.32%-13.26%) characterized by DO, NH3-N, and CODCr. Influencing factor analysis validated the source identification. Natural factors had minor impacts on water parameters, while pollution control from agricultural activities was suggested to diversify fertilizer types rather than merely reduce quantities. The combined effects of industrial and aquaculture activities intensified pollution from TN, chl, and PC, underscoring the need for targeted management practices. This study showed the objectivity and reliability of using a combined approach of multiple receptor models and Geodetector to evaluate the river water quality status, which helps assist decision-makers in formulating more effective water resource protection strategies.

Seasonal responses of microbial communities to water quality variations and interaction of eutrophication risk in Gehu Lake

Gehu Lake, as a key upstream reservoir of Taihu Lake, China, plays a crucial role in improving the water quality, and eutrophication control of the Taihu Lake Basin. Although the microbial communities are significantly important in maintaining the ecological health of lake, the microbial response to water quality, especially for eutrophication has been rarely reported in Gehu Lake. In this study, the water quality parameters and the corresponding effects on the structure and function of microbial communities were determined seasonally. It was found that the poorest water quality in summer (Water Quality Index = 116.52) with severe eutrophication (Trophic Level Index >70), was primarily driven by agricultural non-point sources (33.4%) and seasonal pollution (23.8%). The chemical oxygen demand (COD) was the most important indicator of water quality that affected the concentration of Chlorophyll-a (Chla) according to Pearson correlation analysis (p < 0.001), random forest modeling (p < 0.01), and structural equation modeling (path coefficient = 0.926). Redundancy analysis revealed that total nitrogen, total phosphorus, Chla, and COD significantly influenced the microbial community (p < 0.05). Microbial co-occurrence networks demonstrated significantly seasonal variations, and winter exhibited a more complex structure under lower temperature and limited nutrients compared to the other seasons. In addition, the Chla-sensitive microbial species that involved in nitrogen and phosphorus metabolism were identified as the biological indicators of eutrophication in response to the changes of seasonal water quality. These findings have taken insights into the interactions between water quality and microbial communities, and might provide the basis for improvement of the ecological and environmental management of Gehu Lake, as well as the control of eutrophication in Taihu Lake.

Riverine seasonal rainfall event tracing of organic pollution sources using fluorescence fingerprint difference spectrum

Elucidating the dynamics of dissolved organic matter (DOM) transport and transformation under seasonal rainfall events is essential for the conservation of riverine ecosystems, for mitigating the effects of climate change, and for crafting informed water management strategies. Therefore, this study aimed to investigate the evolutionary characteristics of organic pollution sources during consecutive rainfall events in early spring and to quantify their relative contributions to the process of surface water pollution. The results showed seasonal rainfall induces water quality exceedances in rivers due to the combined impacts of terrestrial inputs and endogenous releases. Humic acid (HA) (region V) and fulvic acid (FA) (region III) emerged as the predominant organic matter in the water column, with their fluorescence intensity altering as rainwater flushed the riverbed. Sources of pollution include agricultural and urban domestic sources (AS + DS) (72.29 %), industrial and urban domestic and microbial sources (IS + DS + MS) (37.71 %), and agricultural and industrial sources (AS + IS) (63.32 %), indicating that agricultural surface pollution discharges contribute significantly. The gas-chromatography-mass spectrometry (GC-MS) further confirmed that exogenous inputs were predominantly comprised of particulate pollutants. This study underscores the efficacy of fluorescence difference spectrometry in delineating the migration and transformation of river pollution sources during seasonal rainfall and facilitating the implementation of targeted management strategies for river ecosystems.

Source Apportionment and Human Health Risks of Potentially Toxic Elements in the Surface Water of Coal Mining Areas

Contamination with potentially toxic elements (PTEs) frequently occurs in surface water in coal mining areas. This study analyzed 34 surface water samples collected from the Yunnan-Guizhou Plateau for their hydrochemical characteristics, spatial distribution, source apportionment, and human health risks. Our statistical analysis showed that the average concentrations of PTEs in the surface water ranked as follows: Fe > Al > Zn > Mn > Ba > B> Ni > Li > Cd > Mo > Cu > Co > Hg > Se > As > Pb > Sb. The spatial analysis revealed that samples with high concentrations of Fe, Al, and Mn were predominantly distributed in the main stream, Xichong River, and Yangchang River. Positive matrix factorization (PMF) identified four sources of PTEs in the surface water. Hg, As, and Se originated from wastewater discharged by coal preparation plants and coal mines. Mo, Li, and B originated from the dissolution of clay minerals in coal seams. Elevated concentrations of Cu, Fe, Al, Mn, Co, and Ni were attributed to the dissolution of kaolinite, illite, chalcopyrite, pyrite, and minerals associated with Co and Ni in coal seams. Cd, Zn, and Pb were derived from coal melting and traffic release. The deterministic health risks assessment showed that 94.12% of the surface water samples presented non-carcinogenic risks below the health limit of 1. Meanwhile, 73.56% of the surface water samples with elevated As posed level III carcinogenic risk to the local populations. Special attention to drinking water safety for children is warranted due to their lower metabolic capacity for detoxifying PTEs. This study provides insight for PTE management in sustainable water environments.

Water quality assessment and its pollution source analysis from spatial and temporal perspectives in small watershed of Sichuan Province, China

Rapid socio-economic development has led to many water environmental issues in small watersheds such as non-compliance with water quality standards, complex pollution sources, and difficulties in water environment management. To achieve a quantitative evaluation of water quality, identify pollution sources, and implement refined management in small watersheds, this study collected monthly seven water quality indexes of four monitoring points from 2010 to 2023, and ten water quality indexes of 23 sampling points in the Shiting River and Mianyuan River which are tributaries of the Tuojiang River Basin. Then, water quality evaluation and pollution source analysis were conducted from both temporal and spatial perspectives using the Water Quality Index (WQI) method, the Absolute Principal Component Scores/Multiple Linear Regression (APCS-MLR) method, and the Positive Matrix Factorization (PMF) receptor modeling technique. The results indicated that except for total nitrogen (TN), the concentrations of other water quality indexes exhibited a decreasing trend, and all were divided into two obvious stages before and after 2016. Furthermore, the proportion of water quality grade of Good and above increased from 73.96 to 84.94% from 2010-2015 to 2016-2023, and the water quality grade of Good and above from upstream to downstream dropped from 100 to 23.33%. From the temporal scale, four and five pollution sources were identified in the first and second stages, respectively. The distinct TN pollutant is mainly affected by agricultural non-point sources (NPS), whose impact is enhanced from 17.76 to 78.31%. Total phosphorus (TP) was affected by the phosphorus chemical industry, whose contribution gradually weakened from 50.8 to 24.9%. From a spatial perspective, four and five pollution sources were identified in the upstream and downstream, respectively. Therefore, even though there are some limitations due to the data availability of water monitory and hydrology data, the proposed research framework of this study can be applied to the water environmental management of other similar watersheds.

Source apportionment based on EEM-PARAFAC combined with microbial tracing model and its implication in complex pollution area, Wujin District, China

Further improving the quality of surface water is becoming more difficult after the control of main point-sources, especially in the complex pollution area with mixed industrial and agricultural productions, whereas the pollution source apportionment might be the key to quantify different pollution sources and developing some effective measures. In this study, a technical framework for source apportionment based on three-dimensional fluorescence and microbial traceability model is developed. Based on screening of the main environmental factors and their spatiotemporal characteristics, potential pollution sources have been tentatively identified. Then, the pollution sources are further tested based on the analysis of fluorescence excitation-emission matrix (EEM) and the similarity of fluorescence components in surface water and potential pollution sources. At the same time, the correlation between microbial species and pollution sources is constructed by analyzing the spatiotemporal characteristics of microbial composition and the response of main species to environmental factors. Therefore, pollution source apportionment is quantified using PCA-APCS-MLR, Fast Expectation-maximization for Microbial Source Tracking (FEAST), and Bayesian community-wide culture-independent microbial source tracking (SourceTracker). PCA-APCS-MLR could not effectively distinguish the contributions of different industrial sources in the complex environment of this study, and the contribution of unknown sources was high (average 39.60%). In contrast, the microbial traceability model can accurately identify the contribution of 7 pollution sources and natural sources, effectively reduce the proportion of unknown sources (average of FEAST is 19.81%, SourceTracker is 16.72%), and show better pollution identification and distribution capabilities. FEAST exhibits a more sensitive potential in source apportionment and shorter calculation time than SourceTracker, thus might be used to guide the precise regional pollution control, especially in the complex pollution environments.

Identification of agricultural surface source pollution in plain river network areas based on 3D-EEMs and convolutional neural networks

Agricultural non-point sources, as major sources of organic pollution, continue to flow into the river network area of the Jiangnan Plain, posing a serious threat to the quality of water bodies, the ecological environment, and human health. Therefore, there is an urgent need for a method that can accurately identify various types of agricultural organic pollution to prevent the water ecosystems in the region from significant organic pollution. In this study, a network model called RA-GoogLeNet is proposed for accurately identifying agricultural organic pollution in the river network area of the Jiangnan Plain. RA-GoogLeNet uses fluorescence spectral data of agricultural non-point source water quality in Changzhou Changdang Lake Basin, based on GoogLeNet architecture, and adds an efficient channel attention (ECA) mechanism to its A-Inception module, which enables the model to automatically learn the importance of independent channel features. ResNet are used to connect each A-Reception module. The experimental results show that RA-GoogLeNet performs well in fluorescence spectral classification of water quality, with an accuracy of 96.3%, which is 1.2% higher than the baseline model, and has good recall and F1 score. This study provides powerful technical support for the traceability of agricultural organic pollution.

Using machine learning to trace the pollution sources of disinfection by-products precursors compared to receptor models

To increase the efficiency of managing backup water resources, it is critical to identify and allocate pollution sources. Source apportionment of dissolved organic matter (DOM) was investigated in our work. Parallel factor analysis (PARAFAC) and the Spearman correlation analysis were used for source identification. After that, a newly hybrid model applying the fuzzy c-means and support vector regression (FCM-SVR) was employed for source apportionment compared to receptor models. The results demonstrated that the FCM-SVR model exhibited excellent generalization, and only required standardization and normalization as pre-processing steps for dataset. According to the results, microbial sources played a key role (28.1 %) in the formation potential of disinfection byproducts (DBPFPs). Additionally, shipping marine sources exhibited a substantial contribution (21.2 %) to DBPFPs. The prediction accuracy of DBPFPs was matched or exceeded receptor models, and the R2 of DOC (0.884) was significantly high. Therefore, we recommend the FCM-SVR model combined with PARAFAC to trace the source of DBPFPs as its significant effectiveness in source identification, source apportionment, and prediction accuracy, possessing the potential for further applicability in tracking more organic compounds. ENVIRONMENTAL IMPLICATION: The disinfection byproducts precursors in water sources, which were thought to be hazardous materials in this study, are proved to be chlorinated into carcinogenic disinfection byproducts (DBPs) during drinking water treatment, However, the source apportionment methods of DBPs are not well developed compared to other inorganic matter, e.g., heavy metals and ammonia nitrogen. We proposed a new FCM-SVR model to trace the source of DBPs, which required easier pre-treatment and resulted a better source apportionment and prediction accuracy. As a result, it could provide a different prospect and useful management advices to trace the source of DBPs.

Short-term photodegradation of autochthonous and allochthonous dissolved organic matter in Lake Taihu, China

Photochemistry is one of the key processes that shape the quality of dissolved organic matter (DOM) in aquatic systems, yet the photoreactivity of DOM from different sources remains largely unclear. In this study, DOM from 10 typical autochthonous and allochthonous sources in Lake Taihu basin were exposed to simulated sunlight, and quantitative and compositional changes of the DOM were explored by measuring its UV-Visable absorption and fluorescence spectroscopy. Photochemical release of nutrients was also explored during the incubations. Results showed that, although DOM from most sources experienced photobleaching effects with decreased absorption coefficients at 254 nm (a(254)) and fluorescence component intensities after light exposure, photochemical alterations of DOM linked to their original composition. Macrophyte-derived (Potamogeton malaianus) DOM, with the largest molecular size, showed increased a(254), humic- and protein-like fluorescence component (C1 and C2) abundances, and inorganic nutrient concentrations relative to dark controls, indicating photo-release of labile components. However, DOM with relatively higher aromaticity, e.g., from agricultural water and the lake, showed photobleaching effects and increased humification degree, probably due to the loss of aromatic components. Allochthonous anthropogenic DOM, e.g., from sewage, showed stronger photo-ammonification, likely relating to the fresh labile N-containing compositions. The form of inorganic nutrient releases during the DOM photolysis also varied with the original DOM sources. Macrophyte-derived DOM incubations showed larger photo-releases of NO3- and PO43-, while NO2- dominated inorganic nutrient releases during groundwater DOM light incubations. Thus, this study concludes that the photoreactivity of DOM closely relates to its original composition and sources.

Water quality pollution assessment and source apportionment of lake wetlands: A case study of Xianghai Lake in the Northeast China Plain

Surface water pollution has always posed a serious challenge to water quality management. Improving water quality management requires figuring out how to comprehend water quality conditions scientifically and effectively as well as quantitatively identify regional pollution sources. In this study, Xianghai Lake, a typical lake-type wetland on the Northeast China Plain, was taken as the research area. Based on a geographic information system (GIS) method and 11 water quality parameters, the single-factor evaluation and comprehensive water quality index (WQI) methods were used to comprehensively evaluate the water quality of the lake-type wetland in the level period. Four key water quality parameters were determined by the principal component analysis (PCA) method, and more convenient comprehensive water quality evaluation models, the minimum WQI considering weights (WQImin-w) and the minimum WQI without considering weights (WQImin-nw) were established. The multiple statistical method and the absolute principal component score-multiple liner regression (APCS-MLR) model were combined to analyse the lake pollution sources based on the spatial changes in pollutants. The findings demonstrated that the WQImin-nw model's water quality evaluation outcome was more accurate when weights were not taken into account. The WQImin-nw model can be used as a simple and convenient way to comprehend the variations in water quality in wetlands of lakes and reservoirs. It was concluded that the comprehensive water quality in the study area was at a "medium" level, and CODMn was the main limiting factor. Nonpoint source pollution (such as agricultural planting and livestock breeding) was the most important factor affecting the water quality of Xianghai Lake (with a comprehensive contribution rate of 31.65%). The comprehensive contribution rates of sediment endogenous and geological sources, phytoplankton and other plants, and water diversion and other hydrodynamic impacts accounted for 25.12%, 19.65%, and 23.58% of the total impact, respectively. This study can provide a scientific method for water quality assessment and management of lake wetlands, and an effective support for migration of migratory birds, habitat protection and grain production security.

Interaction between the hydrochemical environment, dissolved organic matter, and microbial communities in groundwater: A case study of a vegetable cultivation area in Huaibei Plain, China

Intensive vegetable planting has a profound impact on the surrounding aquatic environment. The self-purification ability of groundwater is poor, and it is difficult to return groundwater to its original state once polluted. Therefore, it is necessary to clarify the impact of intensive vegetable planting on groundwater. This study selected the groundwater of a typical intensive vegetable planting base in the Huaibei Plain of China as the research object. This work analyzed the content of major ions, the dissolved organic matter (DOM) composition, and the bacterial community structure in groundwater. Redundancy analysis was used to explore the interactions between the major ions, the DOM composition, and the microbial community. The results showed that under the influence of intensive vegetable planting, the F- and NO3--N contents in groundwater were significantly increased; the excitation-emission matrix combined with parallel factor analysis identified four fluorescent components (C1 and C2 were humus-like components, while C3 and C4 were protein-like components), which mainly consisted of protein-like components. Proteobacteria was the dominant phylum (mean = 69.27 %), followed by Actinobacteriota (mean = 7.25 %) and Firmicutes (mean = 4.02 %), which together explained over 80 % of the total abundance; and TDS, pH, K+, and C3 were the main influencing factors affecting the microbial community structure. This study provides a better understanding of the impact of intensive vegetable cultivation on groundwater.

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

Accurately obtaining the pollution sources and their contribution rates is the basis for refining watershed management. Although many source analysis methods have been proposed, a systematic framework for watershed management is still lacking, including the complete process of pollution source identification to control. We proposed a framework for identification and abatement of pollutants and applied in the Huangshui River Basin. A newer contaminant flux variation method based on a one-dimensional river water quality model was used to calculate the contribution of pollutants. The contributions of various factors to the over-standard parameters of water quality sections at different spatial and temporal scales were calculated. Based on the calculation results, corresponding pollution abatement projects were developed, and the effectiveness of the projects was evaluated through scenario simulation. Our results showed that the large scale livestock and poultry farms and sewage treatment plants were the largest sources of total nitrogen (TP) in Xiaoxia bridge section, with contribution rates of 46.02% and 36.74%, respectively. Additionally, the largest contribution sources of ammonia nitrogen (NH3-N) were sewage treatment plants (36.17%) and industrial sewage (26.33%). Three towns that contributed the most to TP were Lejiawan Town (14.4%), Ganhetan Town (7.3%) and Handong Hui Nationality town (6.6%), while NH3-N mainly from the Lejiawan Town (15.9%), Xinghai Road Sub-district (12.4%) and Mafang Sub-district (9.5%). Further analysis found that point sources in these towns were the main contributor to TP and NH3-N. Accordingly, we developed abatement projects for point sources. Scenario simulation indicated that the TP and NH3-N could be significantly improved by closing down and upgrading relevant sewage treatment plants and building facilities for large scale livestock and poultry farms. The framework adopted in this study can accurately identify pollution sources and evaluate the effectiveness of pollution abatement projects, which is conducive to the refined water environment management.

Occurrence of per- and polyfluoroalkyl substances in aquatic environments and their removal by advanced oxidation processes

Per- and polyfluoroalkyl substances (PFAS), one of the main categories of emerging contaminants, are a family of fluorinated organic compounds of anthropogenic origin. PFAS can endanger the environment and human health because of their wide application in industries, long-term persistence, unique properties, and bioaccumulation potential. This study sought to explain the accumulation of different PFAS in water bodies. In aquatic environments, PFAS concentrations range extensively from <0.03 (groundwater; Melbourne, Australia) to 51,000 ng/L (Groundwater, Sweden). Additionally, bioaccumulation of PFAS in fish and water biota has been stated to range from 0.2 (Burbot, Lake Vättern, Sweden) to 13,900 ng/g (Bluegill samples, U.S.). Recently, studies have focused on PFAS removal from aqueous solutions; one promising technique is advanced oxidation processes (AOPs), including microwaves, ultrasound, ozonation, photocatalysis, UV, electrochemical oxidation, the Fenton process, and hydrogen peroxide-based and sulfate radical-based systems. The removal efficiency of PFAS ranges from 3% (for MW) to 100% for UV/sulfate radical as a hybrid reactor. Therefore, a hybrid reactor can be used to efficiently degrade and remove PFAS. Developing novel, efficient, cost-effective, and sustainable AOPs for PFAS degradation in water treatment systems is a critical area of research.

Effects of land use and slope on water quality at multi-spatial scales: a case study of the Weihe River Basin

Exploring the impact of land use and slope on basin water quality can effectively contribute to the protection of the latter at the landscape level. This research concentrates on the Weihe River Basin (WRB). Water samples were collected from 40 sites within the WRB in April and October 2021. A quantitative analysis of the relationship between integrated landscape pattern (land use type, landscape configuration, slope) and basin water quality at the sub-basin, riparian zone, and river scales was conducted based on multiple linear regression analysis (MLR) and redundancy analysis (RDA). The correlation between water quality variables and land use was higher in the dry season than in the wet season. The riparian scale was the best spatial scale model to explain the relationship between land use and water quality. Agricultural and urban lands had a strong correlation with water quality, which was most affected by land use area and morphological indicators. In addition, the greater the area and aggregation of forest land and grassland, the better the water quality, while urban land presented larger areas with poorer water quality. The influence of steeper slopes on water quality was more remarkable than that of plains at the sub-basin scale, while the impact of flatter areas was greater at the riparian zone scale. The results indicated the importance of multiple time-space scales to reveal the complex relationship between land use and water quality. We suggest that watershed water quality management should focus on multi-scale landscape planning measures.

Design and Application of an Early Warning and Emergency Response System in the Transboundary Area of the Taihu Lake Basin

The inter-provincial transboundary area of the Taihu Lake Basin is characterized by a complex river network and reciprocating flow. Frequent environmental pollution events in recent years have become a major safety hazard for the water quality in the Taihu Lake Basin. There are few early warning systems for environmental pollution events in China, the ability to simulate risk is insufficient, and systematic research on technology, development, and application is lacking. Thus, water management requirements are not met in the inter-provincial transboundary area of the Taihu Lake Basin. This paper proposes a cross-border risk management plan for pollution sources in the transboundary areas of the Taihu Lake Basin and an early warning and emergency response system for water pollution events using modern information technology. We used this system to assess and classify 2713 risk sources for nitrogen and phosphorus pollution into 5 categories. We simulated the discharge of a pollutant into a tributary and the early warning and emergency response for the transboundary region. The results indicate that the proposed early warning and emergency response system substantially improved the transboundary water environment and lowered the risk of pollution in the Taihu Lake watershed.

Removal of Dye from Aquatic Environments: State-of-the-Art and Future Perspectives

Surface water sources play a vital role in numerous aspects of societal demand, including as sources of drinking water and water used for agricultural and industrial purposes [...]

Health Risk Assessment of Heavy Metals in Shallow Groundwater of Coal-Poultry Farming Districts

This study aimed to assess the heavy metal (Mn, Ni, Cu, Zn, Sr, Cd, Pb, and Cr) pollution characteristics, sources, and human health risks in shallow groundwater in the impact zones of urban and rural semi-intensive poultry farms in Suzhou City. Ordinary kriging interpolation showed that poultry farming contributed substantially to the pollution of shallow groundwater by Mn, Zn, and Cu. Positive matrix factorization was applied to identify the sources of heavy metals, and the health risks were assessed based on the hazard index and carcinogenic risks of the various sources. Heavy metal enrichment was closely related to anthropogenic activities. In addition, four sources were identified: poultry manure (29.33%), natural source (27.94%), industrial activities (22.29%), and poultry wastewater (20.48%). The main exposure route of carcinogenic and non-carcinogenic risks to adults and children was oral ingestion. The non-carcinogenic risk of oral ingestion in children was higher than that in adults; the carcinogenic risk was higher in adults than in children. Poultry manure (42.0%) was considered the largest contributor to non-carcinogenic risk, followed by poultry wastewater (21%), industrial activities (20%), and natural sources (17%). Industrial activity (44%) was the primary contributor to carcinogenic risk, followed by poultry wastewater (25%), poultry manure (19%), and natural sources (12%).