Water Quality Grade Research Articles

An Evidential Reasoning Method of Comprehensive Evaluation of Water Quality Based on Gaussian Distribution

This study provides an evidential reasoning method for water quality evaluation based on Gaussian distribution to handle the problem of comprehensive water quality evaluation for a region across a period (multiple sections and multiple time points). The method turns the collection of observed water quality indicator values into a probability distribution of water quality grades by using the Gaussian distribution to compute the confidence assessment of water quality grades over one period. It eliminates the subjectivity involved in determining confidence levels and the problem of information loss during data fusion that arises with conventional approaches. The probability distribution of each assessment grade is then determined by repeatedly synthesizing evidence of the same water quality grade using the improved evidential reasoning synthesis rule. To avoid the subjectivity included in experience-based weight settings, principal component analysis (PCA) is utilized to calculate the weights of water quality indicators based on contribution rates and load coefficients. In the end, utility theory is presented to modify the discrete probability distribution of precise numerical expressions, offering thorough results for the evaluation of water quality and facilitating the comparison of various water quality grades. Using the Xiangjiang River Basin as a case study, the proposed evaluation method is contrasted with popular techniques for assessing water quality, including the Single-Factor Evaluation Method, the Fuzzy Comprehensive Evaluation Method, and the Evidential Reasoning Comprehensive Evaluation Method. The findings suggest that the evidence reasoning approach for evaluating water quality that is based on Gaussian distribution is more rational, accurate, and scientific. Additionally, empirical studies on the annual water quality trends in various regions, the upstream, midstream, and downstream trends, and the water quality trends during wet and dry periods are conducted using this method to assess and analyze changes in water quality in the Xiangjiang River Basin during the “11th Five-Year Plan” and “12th Five-Year Plan” periods. The analysis findings demonstrate that, even if the rate of progress has slowed, the Xiangjiang River Basin’s overall water quality has been steadily improving since management and protection measures were put in place. This shows that the preventive and control efforts implemented in the “11th Five-Year Plan” and “12th Five-Year Plan” periods were successful; nevertheless, carrying out the current tactics might only have a limited impact. As a result, more advanced and creative approaches are required to encourage the ongoing enhancement of the water quality in the Xiangjiang River Basin.

Remote Sensing Inversion of Water Quality Grades Using a Stacked Generalization Approach

Understanding water quality is crucial for environmental management and policy formulation. However, existing methods for assessing water quality are often unable to fully integrate with multi-source remote sensing data. This study introduces a method that employs a stacking algorithm within the Google Earth Engine (GEE) for classifying water quality grades in the Songhua River Basin (SHRB). By leveraging the strengths of multiple machine learning models, the Stacked Generalization (SG) model achieved an accuracy of 91.67%, significantly enhancing classification performance compared to traditional approaches. Additionally, the analysis revealed substantial correlations between the normalized difference vegetation index (NDVI) and precipitation with water quality grades. These findings underscore the efficacy of this method for effective water quality monitoring and its implications for understanding the influence of natural factors on water pollution.

Assessing water quality environmental grades using hyperspectral images and a deep learning model: A case study in Jiangsu, China

Water quality assessment is essential for effective environmental management, yet traditional methods such as chemical sampling are often labor-intensive and inefficient for large-scale, continual monitoring. This study addresses these limitations by leveraging hyperspectral images (HSIs) analysis and introducing a capsule network (CapsNet) model enhanced with a multidimensional integration attention (MDIA) mechanism. The model is specifically designed to integrate both channel and spatial information, enabling precise water quality grade assessment by detecting subtle features within HSIs data. To validate the performance of the model, spectral data from 5 water quality regions are collected and processed via a UAV-carried spectrometer, with 4503 water quality data samples. Rigorous classification experiments demonstrated that the model achieves 98.73 % accuracy, with an average improvement of 4.89 % compared with the other models. This approach significantly improves decision support systems for water resource management, facilitating the sustainable use of water resources.

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.

Water quality analysis, treatment, and economic feasibility of water services of the Neora River in the fringe area of Neora-Valley National Park, India

ABSTRACT Surface water assessment is essential for an accurate water management plan. The present investigation was focused on quality analysis, treatment, and economic feasibility of the River Neora, a raw water source in the fringe area of the Neora-Valley National Park, India. Pearson's correlations showed a positive correlation (0.629) between temperature and total solids (TS), whereas a negative correlation (−0.23) for dissolved oxygen with other parameters. In ANOVA, F-value (>1) was obtained for all parameters except BOD5. The water quality index (WQI) was determined to find out whether the river water meets the drinking water requirements for local inhabitants in the lower fringe. A high coliform count (approximately 4.5/100 mL) downstream further warranted the design of an advanced treatment, the electrochemical oxidation batch reactor. To maintain the water quality grade, the estimation of water benefits was mandatory. The water consumption for domestic and non-domestic purposes was calculated at 5,314,400 and 1,470,000,000 L/yr, respectively. Obtainability and no tax coverage might have led to the overexploitation of water, which paved the way for monetary evaluation (US$695.29). This study provided an overview of the potential of River Neora, which can be restored in the long run by adopting the water development policies by the government.

Analysis of Spatiotemporal Variation and Driving Factors of Water Quality in the Xiangjiang River Basin from 1990 to 2016

In order to explore the spatiotemporal variation characteristics and driving mechanism of water quality in the Xiangjiang River Basin, the data of 16 water quality parameters at 113 stations over 26 years from 1990 to 2016 in the Xiangjiang River Basin were collected for synthetically assessing the water quality and identifying its main pollutants through the water quality index and other methods. The causal mechanism of water quality, especially the driving effect of water level and land use pattern, was analyzed. The results showed that： ① The overall water quality grade of the Xiangjiang River Basin during the study period was "good." However, the water quality deteriorated first （from 1990 to 2003） and then improved （from 2004 to 2016）. The season variation in water quality was not obvious, but the water quality fluctuation of the wet season was larger. The water pollution load of the main stream decreased successively from the middle reaches, downstream reaches, and upstream reaches. The upstream tributaries had the best water quality, whereas areas with poor water quality were mainly distributed at the mouth of the middle and downstream tributaries, especially in areas where multiple tributaries converged. ② Toxic heavy metals had the characteristics of toxicity, persistence, and bioaccumulation. If they exceeded a certain concentration in water, they were difficult to purify, posing great harm to the natural environment and human health. The toxic metal index （CI1） was the leading factor affecting water quality, in which Hg and Cd were the main parameters affecting the overall water quality of the Xiangjiang River Basin. ③ The water level had a positive impact on the water quality of the Xiangjiang River by diluting environmental parameters. Land type had little effect on heavy metal concentration, whereas forest land could improve water quality. Grassland had a negative correlation with permanganate index over a large scale range （≥ 5 km）. The increase in water bodies, arable land, and impermeable surface areas within the watershed increased the probability of high fecal coliform concentration in the water body. ④ With the increase in buffer distance, the water quality explained by the land use pattern increased. On the scale of 10 km buffer zone in the riparian zone, the explanation degree by land use pattern on water quality was the highest, which was an effective scale for water quality control of the Xiangjiang River. This research showed that the driving factors of heavy metal pollution and other pollution were different, but their regional differences were all obvious in the Xiangjiang River Basin. Therefore, pollution control should be classified and taken according to local conditions.

Assessing and predicting water quality index with key water parameters by machine learning models in coastal cities, China

The water quality index (WQI) is a widely used tool for comprehensive assessment of river environments. However, its calculation involves numerous water quality parameters, making sample collection and laboratory analysis time-consuming and costly. This study aimed to identify key water parameters and the most reliable prediction models that could provide maximum accuracy using minimal indicators. Water quality from 2020 to 2023 were collected including nine biophysical and chemical indicators in seventeen rivers in Yancheng and Nantong, two coastal cities in Jiangsu Province, China, adjacent to the Yellow Sea. Linear regression and seven machine learning models (Artificial Neural Network (ANN), Self-Organizing Maps (SOM), K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF), Extreme Gradient Boosting (XGB) and Stochastic Gradient Boosting (SGB)) were developed to predict WQI using different groups of input variables based on correlation analysis. The results indicated that water quality improved from 2020 to 2022 but deteriorated in 2023, with inland stations exhibiting better conditions than coastal ones, particularly in terms of turbidity and nutrients. The water environment was comparatively better in Nantong than in Yancheng, with mean WQI values of approximately 55.3–72.0 and 56.4–67.3, respectively. The classifications "Good" and "Medium" accounted for 80 % of the records, with no instances of "Excellent" and 2 % classified as "Bad". The performance of all prediction models, except for SOM, improved with the addition of input variables, achieving R2 values higher than 0.99 in models such as SVM, RF, XGB, and SGB. The most reliable models were RF and XGB with key parameters of total phosphorus (TP), ammonia nitrogen (AN), and dissolved oxygen (DO) (R2 = 0.98 and 0.91 for training and testing phase) for predicting WQI values, and RF using TP and AN (accuracy higher than 85 %) for WQI grades. The prediction accuracy for "Medium" and "Low" water quality grades was highest at 90 %, followed by the "Good" level at 70 %. The model results could contribute to efficient water quality evaluation by identifying key water parameters and facilitating effective water quality management in river basins.

Study on quality of treated wastewater for urban agriculture use in Addis Ababa, Ethiopia

Non-conventional water supplied from Bole Lemi Industrial Park (BLIP) in Addis Ababa, Ethiopia is treated to remove contaminants, but never has been analyzed for its quality for urban agriculture (UA) use. The objective of this study is to analyze the quality of treated wastewater (TWW) using treated wastewater quality index (TWWQI). Treated wastewater samples were taken at the influent and effluent of the wastewater treatment plant located within BLIP. The physico-chemical properties of the water samples from BLIP were analyzed at the third-party laboratory. The result showed that the aggregate TWWQI value falls under the category of very poor water for urban irrigation agriculture (UIA) use. This very poor water quality grade attributes 85 % to the presence of heavy metals, 4 % to nutrients load, 8 % to saline condition contributors and 4 % to miscellaneous contaminants. Discrete analyses of the indices for heavy metals, nutrient loads, saline condition contributors and miscellaneous contaminants shifted the water quality from very poor to very polluted, excellent, poor and good for UIA use respectively. Though the aggregate TWWQI is of very poor category for UIA use, the treatment plants of BLIP exhibited contaminants removal efficiencies of between 30 and 100 % with aggregate removal efficiency of 58 %. Results also revealed that there were contaminants that exhibited higher effluent values than influent after treatment indicating the failure of BLIP’s treatment plants to remove these contaminants. In conclusion, treated wastewater from BLIP, falls under very poor water category for use in irrigation agriculture where Cr+3, Cl− and TDS have contributed most in that order. Therefore, BLIP should upgrade and optimize its treatment plants to increase the removal efficiency for the respective contaminants. Moreover, BLIP should enhance the capacity of the experts through training and continually monitor the quality of the water to protect the environment and ensure its contribution to the building of a resilient city.

Multidimensional water level and water quality response to severe drought in Xingyun Lake

Drought stress has a significant impact on the quality and quantity of lake water. Understanding this impact is crucial for preventing water security risks and pollution recovery. However, there is a lack of systemic understanding of how drought affects water quality and quantity, and how they change in multiple dimensions. This manuscript established a synthesized methodology with the principles to judge the applicability and three steps of application to detect the change in water quality and water level under severe drought in Xingyun Lake, China. Results show that (1) The water level and water quality of Xingyun Lake have a synchronous and evident response to drought during 2009–2014. The rainfall during 2008–2015 declined by 22.9 % to normal, and the inundated area and lake water depth in 2012 decreased by 10.50 % from 2002 to 1.38 m to the average depth, respectively. The pollution index climbed above 1.21 after 2008, fluctuating around 1.42. (2) Under drought, the water quality indicators significantly changed in the terms of the overall feature, trend, eigenvalue, and morphological characteristics. The water quality indicators of Set2008-2015 are significantly different from set2000-2007 and not in the groups of set1994-2000. The morphological characteristics of water quality indicators in set2008-2015 differs significantly from that in set2000-2007 shown by the minimum, maximum, median, quartiles, and extreme values. (3) Although NH3–N showed no significant change, the water quality deteriorated in the physical, chemical, and biological aspects. The TP, IMN, and BOD5 changed more evidently than DO and NH3–N. (4) Water quality grade and indicator concentration deteriorated significantly and sharply under severe drought and are threatened deeply by TP and TN. The synthesized methodology is scientifically constructed and canbe employed in the characteristics cognition of water quality and water level to severe drought in and out of this research. And the intervention time and various regulating measures for pollution degradation and water quality recovery canbe constructed based on the multi-dimensional analysis of water quality change under drought evolution.

Integrated assessment of groundwater contamination: A multi-marker approach for comprehensive water quality monitoring

Abstract This study investigated the presence of adenoviral markers (Library-Independent microbial source tracking tool) in the groundwater of Ludhiana, Punjab, India, a hitherto unexplored area. While deep aquifers post-chlorination were adenovirus free, shallow aquifers near farm wastewater pits exhibited human adenovirus and bovine adenovirus. Coliform-negative samples also harboured pathogens, highlighting the limitations of conventional indicators. Surface water displayed higher viral contamination, potentially impacting groundwater. The use of farm wastewater for irrigation and open pit disposal emerged as key contributors, advocating for sustainable wastewater management. Physiochemical and microbial analyses revealed variations across sites, emphasising regional and temporal variations. The weighted arithmetic water quality index ranged from good to very poor, with deep aquifers showing better quality than shallow ones. A novel approach incorporating graphical representations of adenovirus estimations alongside water quality index provided a more comprehensive understanding. Intriguingly, the study revealed the presence of coliforms irrespective of water quality grade, questioning its reliability as a sole indicator. Correlations between specific water quality grades and adenovirus types suggested targeted control measures. The lack of significant correlations between viral markers and conventional parameters in groundwater compared to surface water studies highlighted the unique dynamics of groundwater contamination.

무등산국립공원의 부착규조류 분포와 생물학적 수질평가

This survey was conducted to evaluate the distribution and biological water quality of epilithic diatoms from April to September 2022 by selecting a total of 17sites in Mudeungsan National Park, Korea. Environmental factors were analyzed such as water temperature, pH, electrical conductivity, BOD, TN and TP. A total of 111 species of epilithic diatoms appeared, and the dominant species were 13 species including Achnanthes convergens, Cocconeis placentula var. lineata, and Gomphonema clevei. Among dominant species, C. placentula var. lineata appeared as the dominant species 13times, showing the highest number of appearances. Among the ecological taxa, the relative frequencies of saproxenous group ranged from 20.63 to 99.57%, saprophilous group ranged from 0 to 50.08%. In evaluation of biological water quality, TDI was 12.5 ~ 99.7, water quality grade was E ~ A, DAIpo was 38.22 ~ 99.79, and water quality was D ~ A grade. In conclusion, the most representative epilithic diatom of Mudeungsan National Park was C. placentula var. lineata in terms of appearance number and dominance frequency. Biological water quality assessments are mostly in good condition, but some points appear to require continuous monitoring of the aquatic environment.

Evaluation of surface water quality in Heilongjiang Province, China: Based on different quantities of water quality indicators

Accurately evaluating surface water quality grade (WQG) is of great significance in improving the efficiency of regional water quality management. Based on seven main surface water quality indicators (including dissolved oxygen (DO), chemical oxygen demand (COD), permanganate index (CODMn), ammonia nitrogen (NH4+-N), fluoride (FL), total phosphorus (TP) and total nitrogen (TN)) data from 910 measuring points in Heilongjiang Province from May to August 2017, an improved Nemerow index method (IN method) based on combined weighting was constructed, and the WQG was evaluated. On this basis, the multiple linear or non-linear regression models (MLR or MNR) under different quantities of water quality indicators were established, and the prediction results were evaluated. Results show that: 1) WQG in Heilongjiang Province above grade III accounts for more than 92.6%, evaluated by the IN method; 2) The accuracy of WQG evaluated by MLR based on different quantities of water quality indicators are maintained at 77%-90% and decreases with the increase of the number of water quality indicators (N), which may be related to the selected combination of water quality indicators and key water pollutants. The accuracy is improved by 3.9%-17.4% for MNR as compared to MLR, and especially the accuracy can reach as high as 95.6% when N is 5; 3) Compare to the MLR, the MNR is less affected by the data fluctuation characteristics, and performs best when N is 5 under both the great and small data fluctuations, with an accuracy of 85% and 90%, respectively. 4) TN (0.72), NH4+-N (0.41), and CODMn (0.33) are significantly related to the WQG. COD is a key parameter affecting the stability and uncertainty of MLR and MNR. These results provide a simple and reliable method for accurately evaluating the surface WQG, which have important guiding significance for decision-makers and researchers in water environment protection and agricultural water fertilizer management in Heilongjiang Province.

A historical overview of water quality in the coastal seas of China

Using historical data, long-term variations in pollutant sources and water quality in China’s coastal waters over the last three decades are reviewed. The results show that the total area of non-clean water, which reflects state of total water quality, increased rapidly before 2000, but then underwent two stages of decline, with a modest decline by one-quarter between 2001 and 2015, followed by a sharp decline of more than half of that in 2015 since then. Consequently, water quality at present is better than it was at the beginning of the 1990s. The total area of polluted water fluctuated without any trend from the end of the 1990s until 2015, but has declined sharply by nearly two-thirds since 2015, indicating that the water quality in China’s coastal seas has improved substantially. Geographically, the Bohai Sea was the first to see a turning point in water quality, followed by the Yellow Sea and East China Sea, while the South China Sea was the last. The main pollutants that govern the water quality grade and area are dissolved inorganic nitrogen and phosphate as well as petroleum hydrocarbons. As a response to variations in water quality, changes in both the frequency and total area affected by harmful algal blooms were similar to those of water quality over the last three decades, albeit with a slight lag. Analysis showed that variations in water quality were closely related to the land- and sea-sourced pollutant inputs. The combination of shift in the mode of economic growth from high-speed growth to high-quality development and the enforcement of the new, strictest ever Environmental Protection Law resulted in a significant decline of pollutant emissions, inducing a turning point in the water quality in the coastal seas of China in the mid-2010s.

Stream Water Quality Control and Odor Reduction through a Multistage Vortex Aerator: A Novel In Situ Remediation Technology

In this work, we report the restoration of a polluted urban stream by employing the multistage vortex aerator (MVA), an in-line mixer device that improves the dissolved oxygen concentration of polluted streams and accelerates the water purification rate. It was observed during the field experiment that the dissolved oxygen was enhanced up to 7.05 mg/L and the water quality was improved to a good grade. As a result, the complex odor was successfully eliminated and reduced by up to 71.9%, while the water quality grade was also improved by more than two grades on average. Stream water quality indicators monitored for twelve months revealed high removal rates of total phosphorous (56.4%) and suspended solids (61%). The study demonstrated MVA as a promising eco-friendly technology for significant improvement in urban stream water quality. Moreover, the MVA process creates no secondary pollution and is believed to be a sustainable treatment option for odorous water bodies. Overall, the MVA process is technically feasible for implementation, and this study provides a specific reference as a basis for the treatment of polluted water bodies in urban settings.

Estimation of a suitable NDVI oriented for ecological water savings and phytoremediation in Baiyangdian Lake, North China

To maintain the ecosystem services of wetlands, this study developed an estimation method for suitable normalized difference vegetation index (NDVI) values by theintegration of ecological water savings and phytoremediation requirements based on experiments and literature surveys in Baiyangdian Lake, North China. In terms of ecological water savings (namely, reducing wetland water consumption by reducing vegetation evapotranspiration), the upper limit of the reed community area was determined by the water replenishment plan of the lake to achieve water balance. In terms of phytoremediation, Grade III of the Chinese water quality grade scale, the target water quality of the lake, was considered to calculate the lower limit of the reed community area for the purpose of good water quality. Considering the two aspects of the requirements of ecological water savings and phytoremediation comprehensively, the suitable reed planning area and NDVI threshold were determined. The results indicated that the suitable reed planting area was 138.05 ∼ 146.43 km2, and the NDVI threshold was 0.155 ∼ 0.316 in the high-water-lever situation (6.8 ∼ 7.0 m). Concurrently, the appropriate reed planting area was 122.39 ∼ 128.24 km2, and the NDVI threshold was 0.137 ∼ 0.277 in the low-water-lever situation (6.5 ∼ 6.8 m). These findings expanded the use of NDVI in the relationship research between wetland vegetation patterns and water resource utilisations, providing support for making vegetation pattern adjustment decisions for wetland protection and restoration in water-scarce regions.

The impact of climate change on the water quality of Baiyangdian Lake (China) in the past 30 years (1991–2020)

Climate change significantly influenced the water quality of lakes in recent decades. This study investigated the effects of climate change on the water quality of Baiyangdian Lake (China) in the past 30 years (1991–2020) using correlation analysis, regression analysis, and the generalized additive model (GAM). The results show that water quality grade, chemical oxygen demand (COD), total phosphorus (TP) concentrations, and annual average and minimum air temperatures of the lake showed significant differences (p < 0.05) in the one-way ANOVA during the studied period. The concentration of dissolved oxygen (DO) and TP, annual average and minimum air temperatures, and annual precipitation decreased, while the COD and total nitrogen (TN) concentration, annual maximum temperature, and monthly maximum precipitation increased. The annual average and minimum air temperature affected all water quality variables and explained 12.3 %–54.5 % of variation deviation in correlation and GAM analyses, indicating that the changes of air temperature influenced the water temperature, which then affected the biochemical reaction rates leading to changes in water quality. The precipitation factors explained 10.5 % (TN) to 54.8 % (TP) of variation deviation, implying that the increase in precipitation improved water quality by diluting the COD concentration. However, excessive precipitation also accelerated the endogenous release of phosphorus in sediments by increasing the TP concentration. Additionally, extreme climate factors correlated with some water quality variables and explained 57.7 %–95.9 % of the total variances in correlation and regression analyses, suggesting that the extreme temperatures changed the nitrogen and DO concentration to aggravate lake pollution. However, the extreme precipitation purified the water through dilution. This study will facilitate to understand the impacts of climate change on water quality and find appropriate adaptation measures for ecosystem management of shallow lakes.

River water quality variation and grade characteristic values analysis method based on “hydrological frequency-water quality” fitting curves

River water quality variation and grade characteristic values analysis method based on “hydrological frequency-water quality” fitting curves

Water Quality Grade Identification for Lakes in Middle Reaches of Yangtze River Using Landsat-8 Data with Deep Neural Networks (DNN) Model

Water quality grade is an intuitive element for people to understand the condition of water quality. However, in situ water quality grade measurements are often labor intensive, which makes measurement over large areas very costly and laborious. In recent years, numerous studies have demonstrated the effectiveness of remote sensing techniques in monitoring water quality. In order to automatically extract the water quality information, machine learning technologies have been widely applied in remote sensing data interoperation. In this study, Landsat-8 data and deep neural networks (DNN) were employed to identify the water quality grades of lakes in two cities, Wuhan and Huangshi, in the middle reach of the Yangtze River, central China. Additionally, linear support vector machine (L-SVM), random forest (RF), decision tree (DT), and multi-layer perceptron (MLP) were selected as comparative methods. The experimental results showed that DNN achieved the most promising performance compared to the other approaches. For the lakes in Wuhan, DNN gave water quality results with overall accuracy (OA) of 93.37% and Kappa of 0.9028. For the lakes in Huangshi, OA and kappa given by DNN were 96.39% and 0.951, respectively. The results show that the use of remote sensing images for water quality grade monitoring is effective. In the future, our method can be used for water quality monitoring of lakes in large areas at a low cost.

Impacts of Land Use on Surface Water Quality Using Self-Organizing Map in Middle Region of the Yellow River Basin, China.

The Yellow River is one of the most important water sources in China, and its surrounding land use affected by human activities is an important factor in water quality pollution. To understand the impact of land use types on water quality in the Sanmenxia section of the Yellow River, the water quality index (WQI) was used to evaluate the water quality. A self-organizing map (SOM) was used for clustering analysis of water quality indicators, and the relationship between surface water quality and land use types was further analyzed by redundancy analysis (RDA). The results showed that WQI values ranged from 82.60 to 507.27, and the highest value was the sampling site S3, whose water quality grade was “Likely not suitable for drinking”, mainly polluted by agricultural non-point sources ammonia nitrogen pollution. SOM clustered the sampling sites into 4 groups according to the water quality indicators, the main influencing factors for different groups were analyzed and explored in more depth in relation to land use types, suggesting that surface water quality was significantly connected with the proportion of land use types at the watershed scale in the interpretation of water quality change. The negative impact of cropland on surface water quality was greater than that of other land use types, and vegetation showed a greater positive impact on surface water quality than other land uses. The results provide evidence for water environment conservation based on land use in the watershed.

Water quality index assessment methods for surface water: A case study of the Citarum River in Indonesia

Water quality index (WQI) can express overall water quality status in a single term. As such, the application of daily WQI assessment should help the general public be more aware of the condition of the surface water around them. As the longest and biggest river in the West Java Province, the Citarum River plays an important role in the life of the community and ecosystem around it. Therefore, this research evaluated which WQI assessment method was best suited for determining the Citarum River's water quality. We utilized West Java Province monitoring data collected from four monitoring stations along the Upstream Citarum. The WQI was calculated using the National Sanitation Foundation WQI (NSF WQI), Canadian Council of Ministers of the Environment WQI (CCME WQI), and Oregon Water Quality Index (OWQI) assessment methods. Nine years of monitoring data were grouped and analyzed according to wet vs. dry months, wet vs. dry years, monitoring station, and year. Using the NSF WQI assessment method, the Citarum River obtained a ‘Fair’ and ‘Bad’ water quality grade with WQI ranging between 38.212 and 60.903 during dry months, 49.089 and 62.348 during wet months, 42.935 and 65.696 during dry years, and 39.002 and 58.898 during wet years. The data ranged from 41.458 and 61.206 from each monitoring station, and between 35.920 and 58.713 for the data from each monitoring year. The CCME WQI assessment method showed that the Citarum River had ‘Fair’, ‘Marginal’, and ‘Bad’ water quality with WQI ranging between 12.683 and 31.503 during dry months, 21.231 and 33.127 during wet months, 12.683 and 31.503 during dry years, 12.134 and 28.748 during wet years, 13.621 and 30.569 for the data from each monitoring station, and 13.219 and 68.808 for the data from each monitoring year. The OWQI assessment method gave the Citarum River a ‘Very Bad’ water quality rating with WQI ranging between 11.528 and 18.827 during dry months, 13.898 and 24.563 during wet months, 11.528 and 25.782 during dry years, 11.528 and 15.997 during wet years, 11.528 and 18.842 for each monitoring station, and 11.523 and 16.528 for the data from each monitoring year. Based on these results and the collated advantages and disadvantages of each method, the NSF WQI assessment method was deemed to be the best for determining the Citarum River's water quality.