Water Quality Sampling Frequency Analysis of Surface Freshwater: A Case Study on Bristol Floating Harbour

The Himalayan rivers are vulnerable to devastating flooding caused by landslides and outbreak of glacial lakes. On 7 February 2021, a deadly disaster occurred near the Rishi Ganga Hydropower Plant in the Rishi Ganga River, killing more than 100 people. During the event, a large volume of debris and broken glacial fragments flooded the Rishi Ganga River and washed away the Rishi Ganga Hydropower plant ongoing project. This study presents the impact of the Chamoli disaster on the water quality of Rishi Ganga River in upstream near Tapovan and Ganga River in downstream near Haridwar through remote sensing data. Five points have been used at different locations across the two study areas and three different indices were used such as Normalized difference water index (NDWI), Normalized difference turbidity Index (NDTI), and Normalized difference chlorophyll index (NDCI), to analyze changes in water quality. Spectral signatures and backscattering coefficients derived from Sentinel-2 Optical and Sentinel-1 Synthetic-aperture radar (SAR) data were also compared to study the changes in water quality. It was evident from the water quality indices and spectral signatures that the flood plains changed significantly. Using spectral signatures and different indices, the water level in the Chilla dam canal near Haridwar was found to decreased after the Chamoli disaster event as the flood gates were closed to stop the deposit of sediments in the canal. Results suggest changes in water quality parameters (turbidity, chlorophyll concentration, NDWI) at the five locations near the deadly site and far away at Haridwar along the Ganga River. This study is a preliminary qualitative analysis showing changes in river flood plain and water quality after the Chamoli disaster.

Does river restoration affect diurnal and seasonal changes to surface water quality? A study along the Thur River, Switzerland

We employed statistical techniques to assess the quality of surface water measured before and after filling the reservoirs of four important hydroelectric power plants (HPPs) established in the Brazilian Amazon (Belo Monte, Jirau, Teles Pires, and Santo Antonio do Jari). A cluster analysis efficiently identified distinctions between the dendrograms obtained for the pre- and post-filling phases in the four projects and indicated changes in water quality after the reservoirs were filled. The Mann-Whitney U test (at the 5% significance level) allowed for the identification of parameters that exhibited significant changes after filling the reservoirs. Across the four HPPs, a total of 45 monitoring points and 54 water quality parameters were analyzed. The results revealed 14 parameters that changed significantly at a greater number of monitoring points in more than one HPP, namely water temperature, pH, dissolved oxygen, electrical conductivity, oxidation-reduction potential, transparency, total dissolved solids, chlorophyll-α, thermotolerant coliforms, alkalinity, total phosphorus, and calcium, sulfate, and sulfide ion concentrations. These parameters were identified as the most important for explaining the changes in water quality after filling the reservoirs. All monitoring points located within the reservoirs (41 points) and immediately downstream of the dams (4 points) revealed significant changes in water quality. Thus, changes in the quality of surface water were observed along the entire lengths of the reservoirs, including the initial stretches. This study contributes to the understanding of the temporal and spatial effects of the formation of reservoirs on surface water quality parameters.

Evaluation of water quality and potential scaling of corrosion in the water supply using water quality and stability indices: A case study of Juja water distribution network, Kenya

A hybrid water-quality-index and grey water footprint assessment approach for comprehensively evaluating water resources utilization considering multiple pollutants

The study was conducted to spatiotemporally analyze the quality, location and critical water variables influencing water quality using water monitoring data from the Department of Environment and Natural Resources, Dong Thap province in 2019. The water quality parameters including turbidity, pH, temperature, dissolved oxygen (DO), total suspended solids (TSS), biological oxygen demand (BOD), chemical oxygen demand (COD), nitrite (N-NO2−), nitrate (N-NO3−), ammonium (N-NH4+), total nitrogen (TN), orthophosphate (P-PO43−), chloride (Cl−), oil and grease, sulfate (SO42−), coliforms, and Escherichia coli (E. coli) were collected at 58 locations with the frequency of four times per year (February, May, August, and November). These parameters were compared with national technical regulation on surface water quality—QCVN 08-MT: 2015/BTNMT. Water quality index (WQI) was calculated and spatially presented by geographical information system (GIS) tool. Pearson correlation analysis, cluster analysis (CA), and principal component analysis (PCA) were used to evaluate the correlation among water quality parameters, group and reduce the sampling sites, and identify key parameters and potential water pollution sources. The results showed that TSS, BOD, COD, N-NH4+, P-PO43−, coliforms, and E. coli were the significant concerns impairing the water quality. Water quality was assessed from poor to medium levels by WQI analysis. CA suggested that the current monitoring locations could be reduced from 58 sites to 43 sites which can be saved the total monitoring budget up to 25.85%. PCA showed that temperature, pH, TSS, DO, BOD, COD, N-NH4+, N-NO2−, TN, P-PO43−, coliforms, and E. coli were the key water parameters influencing water quality in Dong Thap province’s canals and rivers; thus, these parameters should be monitored annually. The water pollution sources were possibly hydrological conditions, water runoff, riverbank erosion, domestic and urban activities, and industrial and agricultural discharges. Significantly, the municipal and agricultural wastes could be decisive factors to the change of surface water quality in the study area. Further studies need to focus on identifying sources of water pollution for implementing appropriate water management strategies.

Daily changes in household water access and quality in urban slums undermine global safe water monitoring programmes.

Traditional Water Quality Indices (WQIs) often fail to capture the significant impact of flow velocity on water quality, especially under varying hydrological conditions. In this study, an Integrated Water Quality Index (IWQI) was developed by combining water quality parameters and flow rate, providing a more comprehensive assessment under various flow conditions. Compared to traditional indices, the IWQI showed slightly lower correlations in individual parameter performance, but it performed well in evaluating water quality changes associated with flow variations. Parameters such as Total Phosphorus (TP), Total Coliforms (TC), and Fecal Coliforms (FC), which are prevalent pollutants in the Cheongmi River, significantly influenced IWQI scores. River water quality was evaluated using input data simulated under a climate change scenario. When precipitation was abundant, the IWQI score remained relatively stable even with reduced flow rates. However, during periods of insufficient rainfall, water quality deteriorated sharply. While general water quality parameters exhibited approximately a 10% change as flow decreased, TC and FC showed rapid deterioration, with change rates ranging from 20% to 60%. These findings underscore the importance of managing TC and FC, particularly when insufficient rainfall is predicted, as they are major sources of pollution.

Surface water quality is an important factor affecting the ecological environment and human living environment. The monitoring of surface water quality by remote sensing monitoring technology can provide important research significance for water resources protection and water quality evaluation. Finding the optimal spectral index sensitive to water quality for remote sensing monitoring of water quality is extremely important for surface water quality analysis and treatment in the Ebinur Lake Basin in arid areas. This study used Sentinel-2MSI data at 10 m resolution to quickly monitor the water quality of the watershed. Through laboratory experiments and measurement data from the Ebinur Lake Basin, 22 water quality parameters (WQPs) were obtained. Through Z-score and redundancy analysis, 9 WQPs with significant contributions were extracted. Based on the remote sensing spectral band, four water indexes (NDWI, NWI, EWI, AWEI-nsh) and 2D modeling spectral index (DI, RI, NDI), the correlation analysis between WQPs and two kinds of spectral band indexes is carried out, and it is concluded that the overall correlation between WQP and 2D spectral modeling is more relevant. This paper calculates the evaluation and models the 2D spectrum of the Water Quality Index (WQI). The WQI is predicted and modeled through four machine learning algorithms (RF, SVM, PLSR, PLSR-SVM).The results show that the inversion effect of the two-dimensional spectral modeling index on water quality parameters (WQPs) is superior to that of the water index, and the correlation coefficient of the DI (R12-R1) SWIR-2 and BLUE band interpolation index reaches 0.787. On this basis, three kinds of two-dimensional spectral modeling indexes are used to inversely synthesize the WQI, and the correlation coefficient of the ratio index of the RI (R11/R8) SWIR-1 and near-infrared (NIR) bands is preferably 0.69. In the WQI prediction, the partial least squares regression support vector machine (PLSR-SVM) model in machine learning algorithms has good modeling and prediction effects (R2c = 0.873, R2v = 0.87), which can provide a good basis. The research results provide references for remote monitoring of surface water in arid areas, and provide a basis for water quality prediction and safety evaluation.

Optimizing the land use structure is one of the most effective means of improving the surface water aquatic environment. The relationship between land use patterns and water quality is complex due to the influence of dams and sluices. To further investigate the impact of land use patterns on water quality in different basins, we use the Shaying River as an example, which is a typical tributary of the Huai River Basin. Utilizing 2020 land use data and surface water quality monitoring data from two periods, this study employs GIS spatial analysis, the Random Forest Model, redundancy analysis, and Partial Least-Squares Regression to quantitatively explore how different-scale buffer zone land use patterns impact surface water quality. The key findings include: (1) notable seasonal differences in water quality indicators within the basin. The Water Quality Index (WQI) is significantly better in the non-flood season compared to the flood season, with water quality deteriorating towards the lower reaches. Key indicators affecting water quality include dissolved oxygen (DO), ammonia nitrogen (NH3-N), total phosphorus (TP), and turbidity (Tur) in the flood season and NH3-N, permanganate index (CODMn), and electrical conductivity (EC) in the non-flood season. (2) Cultivated land and construction land are the main land uses in the basin. The sub-basin buffer zone was identified as the most effective scale for land use impact on water quality indicators in the Shaying River. (3) Partial Least-Squares Regression (PLSR) analysis revealed that cultivated land, construction land, and grass are the primary land use types influencing surface water quality changes, and the PLSR model is better during the non-flood season. Cultivated and construction lands show a positive correlation with most water quality indicators, while forest land, water bodies, and grasslands correlate positively with DO and negatively with other indicators. The study underscores that rational land use planning in the sub-basin is crucial for enhancing the quality of the surface water environment.

The Yihe River is an important river in Shandong Province, China. It is a catchment river for the South-to-North Water Diversion Project (SNWDP-ER), providing a variety of benefits and ecosystem services, such as flood and drought regulation, fishery and aquaculture, drinking water sources, and biodiversity conservation. In order to objectively reflect the status and changing trend of water environmental quality of the Yihe River, reduce the cost of detection, and improve the efficiency of water quality evaluation, samples were collected at 8 sampling sites in the 220km main stream of the Yihe River from 2009 to 2019. The spatiotemporal variations of 10 water quality indicators were analyzed, including pH, water temperature (WT), dissolved oxygen (DO), 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), total phosphorus (TP), ammonia nitrogen (NH3-N), nitrate (NO3-N), fluoride (F-), and sulphate (SO42-). The water quality index (WQI) was used to evaluate the spatiotemporal water quality changes, and the minimum WQI (WQImin) model consisting of five key indicators, i.e., NH3-N, BOD5, DO, SO42-, and WT, was built by using stepwise multiple linear regression analysis. The results indicated that the water quality indicators in the Yihe River showed significant spatiotemporal variations. With the exception of the COD and TP, the other water quality indicators conformed to the Class I or II standards of China, indicating that the water quality of the Yihe River was better than most natural water bodies. Seasonally, the WQI was better in the autumn and higher in the upstream area compared to the downstream. The water quality remained at the "good" level. The weighted WQImin model performed well in evaluating water quality, with coefficient of determination (R2), mean square error (MSE), and percentage error (PE) values of 0.903, 3.05, and 1.70%, respectively.

A novel method for assessing water quality status using MODIS images: A case study of large lakes and reservoirs in China

Unraveling the impacts of multiscale landscape patterns and socioeconomic development on water quality: A case study of the National Sustainable Development Agenda Innovation Demonstration Zone in Lincang City, Southwest China

Taiwan hot spring is rich in resources, in addition to the sodium bicarbonate spring, but also includes cold spring. Cold spring Including low temperature and rich in free carbon dioxide. Because the hot springs and cold spring characteristics are different and need for heating or cooling before soaking. Heating and cooling action of hot spring water quality of the situation is also different. Therefore, this study explores the relationship between water quality and temperature. In this study, four kind of cold springs were selected to temperature test. After heated the spring water, then let the springs cool down naturally. the test process at the same time measuring water quality changes. Draw relationship curves to observe the correlation between water temperature and water quality changes. The results show that the total dissolved solids (TDS) show a continuous increase during the test, which is the main factor affecting the cooling rate of hot spring. Conductivity is positively correlated with temperature, pH increased, free carbon dioxide decreases, oxidation reduction potential is higher with the reduction of the test process. Suao cold spring heating temperature exceed 50 °C, the free carbon dioxide value will drop below 500 ppm. Nantong cold spring temperature exceed 46 °C, the pH will be acidic to alkaline phenomenon.

Different water quality sampling practices such as location selection or frequency can inform future watershed management strategies. The objective of this work was to compare water quality sampling strategies based on different weighted criteria to determine the optimal sampling frequency and sampling location for an urbanized, eutrophic, freshwater system. Weekly water sampling was conducted over a 2-year period at five locations for six water quality parameters. This high frequency (HF) dataset was then deconstructed into a lower frequency (LF) dataset to simulate a monthly sampling strategy. Statistical analyses conducted showed that for all sampling locations the LF datasets were not significantly different from the HF datasets, suggesting monthly sampling is sufficient to capture the overall water quality conditions in this system. A multi-criteria decision analysis was constructed for statistical and operational criteria to determine the optimal sampling locations given different criteria weights. Results showed that the optimal sampling location changed depending on the criteria weighting, suggesting that statistical analyses alone would not be sufficient to determine optimal sampling locations in this system. This analysis was then used if optimal sampling location depended on specific water quality monitoring goals. Results showed that the optimal location depends on the particular water quality monitoring goals and that this effect should also be considered in the design of future sampling programs.