Evaluation of water quality using a Takagi-Sugeno fuzzy neural network and determination of heavy metal pollution index in a typical site upstream of the Yellow River

Evaluation and source identification of water pollution

Maintaining good surface water quality is essential for protecting ecosystems and human health. Henan Province has long faced challenges related to water scarcity and severe water pollution. To support effective management of water pollution in Henan Province and provide insights for regional water pollution management, we collected extensive water quality monitoring data and applied spatial autocorrelation along with random forest to analyze the sources of heavily polluted areas. Results indicate that the spatial pollution pattern of surface water quality in Henan Province can be generally classified as insignificant pollution in the north, heavy pollution in the central regions, and light pollution in the south. Heavily polluted areas are mainly located in Zhengzhou, Luoyang, and Kaifeng. Key indicators affecting water quality in these regions are chemical oxygen demand (CODMn), dissolved oxygen (DO), ammonia nitrogen (NH3-N), and total phosphorus (TP), with urban sewage and industrial wastewater identified as the main causes of deterioration. These results not only provide a scientific basis for the systematic management of surface water quality pollution in Henan Province but also provide a reference for regional water pollution management.

Deep optimization of water quality index and positive matrix factorization models for water quality evaluation and pollution source apportionment using a random forest model

Effective evaluation of water quality and accurate quantification of pollution sources are essential for the sustainable use of water resources. Although water quality index (WQI) and positive matrix factorization (PMF) models have been proven to be applicable for surface water quality assessments and pollution source apportionments, these models still have potential for further development in today's data-driven, rapidly evolving technological era. This study coupled a machine learning technique, the random forest model, with WQI and PMF models to enhance their ability to analyze water pollution issues. Monitoring data of 12 water quality indicators from six sites along the Minjiang River from 2015 to 2020 were used to build a WQI model for determining the spatiotemporal water quality characteristics. Then, coupled with the random forest model, the importance of 12 indicators relative to the WQI was assessed. The total phosphorus (TP), total nitrogen (TN), chemical oxygen demand (CODCr), dissolved oxygen (DO), and five-day biochemical oxygen demand (BOD5) were identified as the top five significant parameters influencing water quality in the region. The improved WQI model constructed based on key parameters enabled high-precision (R2 = 0.9696) water quality prediction. Furthermore, the feature importance of the indicators was used as weights to adjust the results of the PMF model, allowing for a more reasonable pollutant source apportionment and revealing potential driving factors of variations in water quality. The final contributions of pollution sources in descending order were agricultural activities (30.26%), domestic sewage (29.07%), industrial wastewater (26.25%), seasonal factors (6.45%), soil erosion (6.19%), and unidentified sources (1.78%). This study provides a new perspective for a comprehensive understanding of the water pollution characteristics of rivers, and offers valuable references for the development of targeted strategies for water quality improvement.

Evaluating river health through respirogram metrics: Insights from the Weihe River basin, China

Human activities pose a significant threat to rivers, requiring robust assessment methods for effective river management. This study focuses on the Weihe River Basin in Shaanxi province and introduces the respirogram as an innovative assessment technique. The respirogram allows the simultaneous assessment of river health from two important aspects: pollution levels and microbial status. Specifically, the in-situ respiration ratio (Rs/t) serves as an indicator of pollution, with higher Rs/t values correlating with increased pollution levels. Conversely, the recovery index (RI) measures microbial vitality, with values below 0.15 indicating greater microbial activity and recovery potential. Using predefined thresholds of Rs/t = 0.3 and RI = 0.15, water bodies were categorized into four types. For example, rivers with Rs/t > 0.3 and RI > 0.15 were identified as receiving sewage, characterized by high pollution and low microbial vitality. Similarly, different assessment criteria delineated urban rivers, natural rivers, and wastewater treatment plants. Based on these classifications, targeted engineering measures were proposed to enhance the self-purification capabilities of rivers of different statuses.

Agricultural nonpoint source pollutant loads into water bodies in a typical basin in the middle reach of the Yangtze River

Phosphorus and nitrogen pollution from agricultural nonpoint sources heavily burden the water environment, and a scientific calculating system is needed to calculate the pollutant loads under the water pollution treatment. This study established a system to calculate the coefficients of agricultural nonpoint source pollutants into water bodies in the subregion in Poyang Lake basin in the middle reach of the Yangtze River combining with multiple driving factors. Validation results showed that the errors of the typical unit were 30.58% for total phosphorus (TP), 13.43% for total nitrogen (TN) and 33.93% for ammonia nitrogen (NH3-N), respectively. The errors of the subregion were 26.92% for TP, 31.83% for TN and 29.15% for NH3-N, respectively. Besides, there were higher TP and TN loads in the east area of subregion in both units and county scales, which indicated the heavy phosphorus and nitrogen burden on water environment. In contrast, higher NH3-N loads occurred in the north area of subregion. The establishment of coefficient system for agricultural pollutants into water bodies and the pollutant loads calculation would provide enlightenment for water pollution treatment and agricultural nonpoint source pollution controlling.

Alternative states in microbial communities during artificial aeration: Proof of incubation experiment and development of recurrent neural network models

Artificial aeration, a widely used method of restoring the aquatic ecological environment by enhancing the re-oxygenation capacity, typically relies upon empirical models to predict ecological dynamics and determine the operating scheme of the aeration equipment. Restoration through artificial aeration is involved in oxic-anoxic transitions, whether these transitions occurred in the form of a regime shift, making the development of predictive models challenging. Here, we confirmed the existence of alternative states in microbial communities during artificial aeration through aeration incubation experiment for the first time and considered its existence in neural network modeling in order to improve model performance. By aeration incubation experiment, it was confirmed that the alternative states exist in microbial communities during artificial aeration by two independent approaches, potential analysis and "enterotyping" approach. Comparing neural network models with and without considering the existence of alternative states, it was found that considering the existence of alternative states in modeling could improve the performance of neural network model. Our study provides a reference for the prediction of systems containing time series data where the current state will have an impact on later states. The developed model could be used for optimizing the operating scheme of the artificial aeration.

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.

Water quality assessment and pollution source apportionment using multivariate statistical and PMF receptor modeling techniques in a sub-watershed of the upper Yangtze River, Southwest China

Rapid industrial and agricultural development as well as urbanization affect the water environment significantly, especially in sub-watersheds where the contaminants/constituents present in the pollution sources are complex, and the flow is unstable. Water quality assessment and quantitative identification of pollution sources are the primary prerequisites for improving water management and quality. In this work, 168 water samples were collected from seven stations throughout 2018-2019 along the Laixi River, a vital pollution control unit in the upper reaches of the Yangtze River. Multivariate statistics and positive matrix factorization (PMF) receptor modeling techniques were used to evaluate the characteristics of the river-water quality and reveal the pollution sources. Principal component analysis was employed to screen the crucial parameters and establish an optimized water quality assessment procedure to reduce the analysis cost and improve the assessment efficiency. Cluster analysis further illustrates the spatiotemporal distribution characteristics of river-water quality. Results indicated that high-pollution areas are concentrated in the tributaries, and the high-pollution periods are the spring and winter, which verifies the reliability of the evaluation system. The PMF model identified five and six potential pollution sources in the cold and warm seasons, respectively. Among them, pollution from agricultural activities and domestic wastewater shows the highest contributions (33.2% and 30.3%, respectively) during the cold and warm seasons, respectively. The study can provide theoretical support for pollutant control and water quality improvement in the sub-watershed, avoiding the ecological and health risks caused by the deterioration of water quality.

Water quality characteristics and evaluation of Qilian Mountain National Park section in Heihe River Basin based on water quality indices and 3D fluorescence technology

The water quality of the Heihe River Basin affects the life quality and health of tens of thousands of residents along it. However, there are relatively few studies that evaluate its water quality. In this study, we used principal component analysis (PCA), an improved comprehensive water quality index (WQI), and three-dimensional (3D) fluorescence technology to identify pollutants and evaluate water quality at nine monitoring sites in the Qilian Mountain National Park in Heihe River Basin. PCA was applied to concentrate the water quality indices into nine items. The analysis shows that the water quality in the study area is mainly polluted by organic matter, nitrogen, and phosphorus. According to the revised WQI model, the water quality of the study area is from moderate to good, while the water quality of Qinghai section is worse than that of Gansu section. According to the 3D fluorescence spectrum analysis of the monitoring sites, the organic pollution of water comes from vegetation decay, animal feces, and some human activities. This study can not only provide support and basis for water environment protection and management in the Heihe River Basin, but also promote the healthy development of the water environment in the Qilian Mountains.

Heavy metal toxicity in poultry: a comprehensive review

Arsenic (As), lead (Pb), cadmium (Cd), and mercury (Hg) have been recognized as most toxic heavy metals that are continuously released into the environment, both from natural sources and from anthropogenic production of fertilizers, industrial activities, and waste disposal. Therefore, As, Cd, Hg, and Pb are found in increasing concentrations in bodies of water, fodder, feed, and in the tissues of livestock, including poultry, in the surroundings of industrial areas, leading to metabolic, structural, and functional abnormalities in various organs in all animals. In poultry, bioaccumulation of As, Pb, Cd, and Hg occurs in many organs (mainly in the kidneys, liver, reproductive organs, and lungs) as a result of continuous exposure to heavy metals. Consumption of Cd lowers the efficiency of feed conversion, egg production, and growth in poultry. Chronic exposure to As, Pb, Cd, and Hg at low doses can change the microscopic structure of tissues (mainly in the brain, liver, kidneys, and reproductive organs) as a result of the increased content of these heavy metals in these tissues. Histopathological changes occurring in the kidneys, liver, and reproductive organs are reflected in their negative impact on enzyme activity and serum biochemical parameters. Metal toxicity is determined by route of exposure, length of exposure, and absorbed dosage, whether chronic and acute. This review presents a discussion of bioaccumulation of As, Cd, Pb, and Hg in poultry and the associated histopathological changes and toxic concentrations in different tissues.

New insights into pollution source analysis using receptor models in the upper Yangtze river basin: Effects of land use on source identification and apportionment

To effectively control pollution and improve water quality, it is essential to accurately analyze the potential pollution sources in rivers. The study proposes a hypothesis that land use can influence the identification and apportionment of pollution sources and tested it in two areas with different types of water pollution and land use. The redundancy analysis (RDA) results showed that the response mechanisms of water quality to land use differed among regions. In both regions, the results indicated that the water quality response relationship to land use provided important objective evidence for pollution source identification, and the RDA tool optimized the procedure of source analysis for receptor models. Positive matrix decomposition (PMF) and absolute principal component score-multiple linear regression (APCS-MLR) receptor models identified five and four pollution sources along with their corresponding characteristic parameters. PMF attributed agricultural nonpoint sources (23.8%) and domestic wastewater (32.7%) as the major sources in regions 1 and 2, respectively, while APCS-MLR identified mixed sources in both regions. In terms of model performance parameters, PMF demonstrated better-fit coefficients (R²) than APCS-MLR and had a lower error rate and proportion of unidentified sources. The results show that considering the effect of land use in the source analysis can overcome the subjectivity of the receptor model and improve the accuracy of pollution source identification and apportionment. The results of the study can help managers clarify the priorities of pollution prevention and control, and provide a new methodology for water environment management in similar watersheds.

Grade evaluation of black-odorous urban rivers in the Greater Bay Area of China using an improved back propagation (BP) neural network

With the rapid development of urbanization, the urban water environment is receiving continuous attention. It is necessary to understand water quality in a timely manner and make a reasonable comprehensive evaluation. However, existing black-odorous water grade evaluation guidelines are not sufficient. Understanding the changing situation of black-odorous water in urban rivers is a growing concern, especially in real-world scenarios. In this study, a BP neural network combined with the fuzzy membership degree was applied to evaluate the black-odorous grade of urban rivers in Foshan City, which is within the Greater Bay Area of China. The optimal 4 × 11 × 1 topology structure of the BP model was constructed by taking the dissolved oxygen (DO), ammonia nitrogen (NH₃-N), chemical oxygen demand (COD), and total phosphorus (TP) concentrations as input water quality indicators. There was almost no occurrence of black-odorous water in the two public rivers outside the region in 2021. Black-odorous water was most significant in 10 urban rivers, with grade IV and grade V occurring over 50% of the time in 2021. These rivers had three features, i.e., parallel with a public river, beheaded, and close proximity to Guangzhou City, the provincial capital of Guangdong. The results of the grade evaluation of the black-odorous water found basically matched those of the water quality assessment. The existence of some inconsistencies between the two systems justified the necessity to expand and extend the number of employed indicators and grades in the present guidelines. The results confirm the capability of the BP neural network combined with the fuzzy-based membership degree in the quantitative grade evaluation of black-odorous water in urban rivers. This study makes a step forward in understanding the grading of black-odorous urban rivers. The findings can provide a reference for local policy-makers regarding the priority of practical engineering projects in prevailing water environment treatment programs.

Contamination, sources and health risks of potentially toxic elements in the coastal multimedia environment of South China

Coastal ecosystems are vulnerable to the accumulation of potentially toxic elements (PTEs), which pose a threat to marine ecosystems and human health. In this study, the concentrations of eight PTEs in a typical area of South China were analysed, and their distributions, seasonal variations, pollution degrees, potential health risks and sources in seawater, sediment and organisms were evaluated. The comprehensive pollution index (CPI), pollution load index (PLI), potential ecological risk index (PERI) and target hazard quotient (THQ) were applied to assess seawater, sediment and organism quality, respectively. The annual mean concentrations of Zn, Hg, Cr and As in the bottom seawater were higher than those in the surface water while those of Pb, Mn and Cu were higher in the surface seawater. The mean content of Hg was higher than the corresponding background value of that in China Shelf Sea sediment. Marine organisms have a high enrichment capacity for Cu, Zn, Cr, Hg, As and Mn in seawater. Based on CPI, the seawater was generally not polluted by PTEs. The PLI and PERI results demonstrated that Hg was the main contamination element in surface sediment. The total target hazard quotient (TTHQ) analysis illustrated that long-term consumption of some fish by children poses a noncarcinogenic health risk, while that risk to adults is negligible. Natural sources, agricultural activity sources, coal burning and industrial emission sources were the main sources of the PTEs in surface sediments according to positive matrix factorization (PMF) model.

Impacts of land uses on spatio-temporal variations of seasonal water quality in a regulated river basin, Huai River, China

Land use impacts from agriculture, industrialization, and human population should be considered in surface water quality management. In this study, we utilized an integrated statistical analysis approach mainly including a seasonal Mann-Kendall test, clustering analysis, self-organizing map, Boruta algorithm, and positive matrix factorization to the assessment of the interactions between land use types and water quality in a typical catchment in the Huai River Basin, China, over seven years (2012-2019). Spatially, water quality was clustered into three groups: upstream, midstream, and downstream/mainstream areas. The water quality of upstream sites was better than of mid-, down-, and mainstream. Temporally, water quality did not change significantly during the study period. However, the temporal variation in water quality of up-, down-, and mainstream areas was more stable than in the midstream. The interactions between land use types and water quality parameters at the sub-basin scale varied with seasons. Increasing forest/grassland areas could substantially improve the water quality during the wet season, while nutrients such as phosphorus from cropland and developed land was a driver for water quality deterioration in the dry season. Water area was not a significant factor influencing the variations of ammonia nitrogen (NH₃-N) and total phosphorus (TP) in the wet or dry season, due to the intensive dams and sluices in study area. The parameters TP, and total nitrogen (TN) were principally linked with agricultural sources in the wet and dry seasons. The parameters NH₃-N in the dry season, and chemical oxygen demand (COD_Cr) in the wet season were mainly associated with point source discharges. Agricultural source, and urban point source discharges were the main causes of water quality deterioration in the study area. Collectively, these results highlighted the impacts of land use types on variations of water quality parameters in the regulated basin.

Analysis of water quality indexes and their relationships with vegetation using self-organizing map and geographically and temporally weighted regression

Natural vegetation has been proved to promote water purification in previous studies, while the relevant laws has not been excavated systematically. This research explored the relationships between vegetation cover and water quality indexes in Liaohe River Basin in China combined with self-organizing map (SOM) and geographically and temporally weighted regression (GTWR) innovatively and systematically based on the distributing heterogeneity of water quality conditions. Results showed that the central and northeast regions of the study area had serious organic and nutrient pollution, which needed targeted treatment. And SOM verified that high vegetation coverage with retention potential of organic and inorganic pollutants as well as nutrients improved water quality to some degree, while the excessive discharges of pollutants still had serious threats to nearby water environment despite the purification function of vegetation. GTWR indicated that the waterside vegetation was beneficial for dissolved oxygen increasing and contributed to the decreasing of organic pollutants and inorganic pollutants with reducibility. Natural vegetation also obsorbed nutrients like TN and TP to some degree. However, the retential potential of nitrogen and organic pollutants became not obvious when there were heavy pollution, which demonstrated that pollution sources should be controlled despite the purification function of vegetation. This study implied that natural vegetation purified water quality to some degree, while this function could not be revealed when there was too heavy pollution. These findings underscore that the pollutant discharge should be controlled though the natural vegetation in ecosystem promoted the purification of water bodies.

A holistic assessment of spatiotemporal variation, driving factors, and risks influencing river water quality in the northeastern Qinghai-Tibet Plateau

The Qinghai-Tibet Plateau (QTP) is the source for many of the most important rivers in Asia. It is also an essential ecological barrier in China and has the characteristic of regional water conservation. Given this importance, we analyzed the spatiotemporal distribution patterns and trends of 10 water quality parameters. These measurements were taken monthly from 67 monitoring stations in the northeastern QTP from 2015 to 2019. To evaluate water quality trends, major factors influencing water quality, and water quality risks, we used a series of analytical approaches including Mann-Kendall test, Boruta algorithm, and interval fuzzy number-based set-pair analysis (IFN-SPA). The results revealed that almost all water monitoring stations in the northeastern QTP were alkaline. From 2015 to 2019, the water temperature and dissolved oxygen of most monitoring stations were significantly reduced. Chemical oxygen demand, permanganate index, five-day biochemical oxygen demand, total phosphorus, and fluoride all showed a downward trend across this same time frame. The annual average total nitrogen (TN) concentration fluctuation did not significantly decrease across the measured time frame. Water quality index (WQI-DET) indicated bad or poor water quality in the study area; however, water quality index without TN (WQI-DET') reversed the water quality value. The difference between the two indexes suggested that TN was a significant parameter affecting river water quality in the northeastern QTP. Both Spearman correlation and Boruta algorithm show that elevation, urban land, cropland, temperature, and precipitation influence the overall water quality status in the northeastern QTP. The results showed that between 2015 and 2019, most rivers monitored had a relatively low risk of degradation in water quality. This study provides a new perspective on river water quality management, pollutant control, and risk assessment in an area like the QTP that has sensitive and fragile ecology.

Comprehensive efficiency evaluation of wastewater treatment plants in northeast Qinghai-Tibet Plateau using slack-based data envelopment analysis

Comprehensive efficiency analysis of wastewater treatment plants (WWPTs) in the alpine region with harsh environment and poor techniques as well as managing experience could provide targeted and effective improvement evidences for local wastewater treatment industry and help to improve the water quality of downstream reaches. In this paper, slack-based data envelopment analysis (SBM-DEA) was adopted to assess the operating efficiencies of WWPTs in northeast Qinghai-Tibet Plateau (QTP). Results showed that the average efficiency score for all WWPTs was 0.608, and 32.5% of WWPTs were efficient. Some WWPTs had large improvement potentials in operating costs and pollutant removal rates. Lowering expenditures and promoting facility construction for WWPTs to overcome the climate difficulties and improve management level was necessary according to their improvement potentials. In addition, the relative importance of the quantitative influential factors to efficiencies scores calculated by random forest regression (RFR) indicated that design capacity and temperature were important quantitative factors affecting the performance of WWPTs. Furthermore, geographical location and design capacity also had significant influence on the comprehensive efficiency of WWPTs verified by Kruskal-Wallis test. Our results highlight the importance of facilities upgrading, scientific management for WWPTs. And the relative improvement suggestions on overcoming the high and cold environment should also be considered for the efficient operations of WWTPs as well as the protection the aquatic environment.

An Application of Artificial Neural Network to Evaluate the Influence of Weather Conditions on the Variation of PM2.5-Bound Carbonaceous Compositions and Water-Soluble Ionic Species

Previous studies have determined biomass burning as a major source of air pollutants in the ambient air in Thailand. To analyse the impacts of meteorological parameters on the variation of carbonaceous aerosols and water-soluble ionic species (WSIS), numerous statistical models, including a source apportionment analysis with the assistance of principal component analysis (PCA), hierarchical cluster analysis (HCA), and artificial neural networks (ANNs), were employed in this study. A total of 191 sets of PM2.5 samples were collected from the three monitoring stations in Chiang-Mai, Bangkok, and Phuket from July 2020 to June 2021. Hotspot numbers and other meteorological parameters were obtained using NOAA-20 weather satellites coupled with the Global Land Data Assimilation System. Although PCA revealed that crop residue burning and wildfires are the two main sources of PM2.5, ANNs highlighted the importance of wet deposition as the main depletion mechanism of particulate WSIS and carbonaceous aerosols. Additionally, Mg2+ and Ca2+ were deeply connected with albedo, plausibly owing to their strong hygroscopicity as the CCNs responsible for cloud formation.