Variations In Water Quality Parameters Research Articles

Revisiting seasonal dynamics of total nitrogen in reservoirs with a systematic framework for mining data from existing publications

Investigation of seasonal variations of water quality parameters is essential for understanding the mechanisms of structural changes in aquatic ecosystems and their pollution control. Despite the ongoing rise in scientific production on spatiotemporal distribution characteristics of water quality parameters, such as total nitrogen (TN) in reservoirs, attempts to use published data and incorporate them into a large-scale comparison and trends analyses are lacking. Here, we propose a framework of Data extraction, Data grouping and Statistical analysis (DDS) and illustrate application of this DDS framework with the example of TN in reservoirs. Among 1722 publications related to TN in reservoirs, 58 TN time-series data from 19 reservoirs met the analysis requirements and were extracted using the DDS framework. We performed statistical analysis on these time-series data using Dynamic Time Warping (DTW) combined with agglomerative hierarchical clustering as well as Generalized Additive Models for Location, Scale, and Shape (GAMLSS). Three patterns of seasonal TN dynamics were identified. In Pattern V-Sum, TN concentrations change in a "V" shape, dropping to its lowest value in summer; in Pattern P-Sum, TN increases in late summer/early fall before decreasing again; and in Pattern P-Spr, TN peaks in spring. Identified patterns were driven by phytoplankton growth and precipitation (Pattern V-Sum), nitrate wet deposition and agricultural runoff (Pattern P-Sum), and anthropogenic discharges (Pattern P-Spr). Application of the DDS framework has identified a key bottleneck in assessing the dynamics of TN — low data accessibility and availability. Providing an easily accessible data sharing platform and increasing the accessibility and availability of raw data for research will facilitate improvements and expand the applicability of the DDS framework. Identification of additional spatiotemporal patterns of water quality parameters can provide new insights for more comprehensive pollution control and management of aquatic ecosystems.

Assessing the spatiotemporal variability of lake water quality using A novel multidimensional shape – Position similarity cloud model

Cloud model theory provides a reliable method to effectively solve the problem of uncertainty associated with lake water quality assessments. To accurately match water quality parameters obtained from water samples and water quality standards, water quality parameters from water samples and water quality class levels were used as inputs to a reverse cloud generator algorithm to derive corresponding sample and level clouds. A multidimensional shape-position similarity cloud model (MSPSCM) was then developed to accurately evaluate lake water quality by considering shape and position similarities between the sample and level clouds. Using monthly water quality monitoring data from 2017 to 2019, spatiotemporal variability of water quality parameters of Nansi Lake in Shandong Province was analyzed, and the MSPSCM was used to further study the spatiotemporal variability of water pollution in Nansi Lake. Results showed that total nitrogen and total phosphorus were the main sources of pollution in Nansi Lake. Except for the severe pollution of the upper lake and its inflow waters (Class V water quality standard) in November 2017 and September 2019, Nansi Lake waters meet Class III water quality standard, and are suitable for drinking after being treated by a sewage treatment plant. Concentration of residential areas and industries around the upper lake is relatively high; large quantities of pollutants are discharged into the upper lake, resulting in considerably severe pollution of the upper lake (Class IV). Difference between water quality of Nansi Lake and that of its inflow water indicates that the purification ability of Nansi Lake should not be underestimated. In addition, compared with the existing cloud model used to evaluate lake water quality, the MSPSCM can more accurately reflect lake water quality, and provides a more flexible and effective method for lake water quality evaluation.

Spatial analysis of water quality parameters in Hilo Bay, Hawai'i, using a combination of interpolated surfaces and hot spot analysis.

Hilo Bay estuary, located on the northeastern side of Hawai'i Island, experiences variability in water quality parameters due to its numerous water inputs. This estuary experiences influxes of water from three sources: groundwater to the east, marine water from the north, and surface water from the Wailuku River to the west. High rainfall and river flow impacts Hilo Bay's water quality including salinity, turbidity, and chlorophyll a concentration. Here, maps of Hilo Bay water quality were examined to assess spatial patterns of these important parameters. Exploring the patterns of these water quality parameters by creating inverse distance weighted (IDW) interpolation surfaces of survey points and clusters based on hot spot analyses during low- and high-flow conditions showed statistically significant differences in spatial water quality in Hilo Bay. Water quality maps after a storm show (1) an overall decrease in salinity, (2) a river plume from the Wailuku River associated with a turbidity hot spot, and (3) a chlorophyll a hot spot offset from the river plume in the center of the bay. Using spatial analysis to analyze water quality throughout the entirety of Hilo Bay before and after storm events can lead to a better understanding of how this ecosystem is affected during these types of events, and furthermore, adopting this method of sampling and analysis allows for a greater representation of water quality all over the bay and can improve the monitoring of water quality in this important ecosystem.

Land–Lake Linkage and Remote Sensing Application in Water Quality Monitoring in Lake Okeechobee, Florida, USA

The state of water quality of lakes is highly related to watershed processes which will be responsible for the delivery of sediment, nutrients, and other pollutants to receiving water bodies. The spatiotemporal variability of water quality parameters along with the seasonal changes were studied for Lake Okeechobee, South Florida. The dynamics of selected four water quality parameters: total phosphate (TP), total Kjeldahl nitrogen (TKN), total suspended solid (TSS), and chlorophyll-a (chl-a) were analyzed using data from satellites and water quality monitoring stations. Statistical approaches were used to establish correlation between reflectance and observed water quality records. Landsat Thematic Mapper (TM) data (2000 and 2007) and Landsat Operational Land Imager (OLI) in 2015 in dry and wet seasons were used in the analysis of water quality variability in Lake Okeechobee. Water quality parameters were collected from twenty-six (26) monitoring stations for model development and validation. In the regression model developed, individual bands, band ratios and various combination of bands were used to establish correlation, and hence generate the models. A stepwise multiple linear regression (MLR) approach was employed and the results showed that for the dry season, higher coefficient of determination (R2) were found (R2 = 0.84 for chl-a and R2 = 0.67 for TSS) between observed water quality data and the reflectance data from the remotely-sensed data. For the wet season, the R2 values were moderate (R2 = 0.48 for chl-a and R2 = 0.60 for TSS). It was also found that strong correlation was found for TP and TKN with chl-a, TSS, and selected band ratios. Total phosphate and TKN were estimated using best-fit multiple linear regression models as a function of reflectance data from Landsat TM and OLI, and ground data. This analysis showed a high coefficient of determination in dry season (R2 = 0.92 for TP and R2 = 0.94 for TKN) and in wet season (R2 = 0.89 for TP and R2 = 0.93 for TKN). Based on the findings, the Multiple linear regression (MLR) model can be a useful tool for monitoring large lakes like Lake Okeechobee and also predict the spatiotemporal variability of both optically active (Chl-a and TSS) and inactive water (nutrients) quality parameters.

Multivariate Statistical Analysis of Water Quality and Trophic State in an Artificial Dam Reservoir

Paldang Reservoir, located in the Han River basin in South Korea, is used for drinking water, fishing, irrigation, recreation, and hydroelectric power. Therefore, the water quality of the reservoir is of great importance. The main objectives of this study were to evaluate spatial and seasonal variations of surface water quality in the reservoir using multivariate statistical techniques (MSTs) along with the Trophic State Index (TSI) and Trophic State Index deviation (TSID). The empirical relationships among nutrients (total phosphorus, TP; total nitrogen, TN), chlorophyll-a (CHL-a), and annual variations of water quality parameters were also determined. To this end, 12 water quality parameters were monitored monthly at five sites along the reservoir from 1996 to 2019. Most of the parameters (all except pH, dissolved oxygen (DO), and total coliform bacteria (TCB)) showed significant spatial variations, indicating an influence of anthropogenic activities. Principal component analysis combined with factor analysis (PCA/FA) suggested that the parameters responsible for water quality variations were primarily correlated with nutrients and organic matter (anthropogenic), suspended solids (both natural and anthropogenic), and ionic concentrations (both natural and anthropogenic). Stepwise spatial discriminant analysis (DA) identified water temperature (WT), DO, electrical conductivity (EC), chemical oxygen demand (COD), the ratio of biological oxygen demand (BOD) to COD (BOD/COD), TN, TN:TP, and TCB as the parameters responsible for variations among sites, and seasonal stepwise DA identified WT, BOD, and total suspended solids (TSS) as the parameters responsible for variations among seasons. COD has increased (R2 = 0.63, p < 0.01) in the reservoir since 1996, suggesting that nonbiodegradable organic loading to the water body is rising. The empirical regression models of CHL-a-TP (R2 = 0.45) and CHL-a-TN (R2 = 0.27) indicated that TP better explained algal growth than TN. The mean TSI values for TP, CHL-a, and Secchi depth (SD) indicated a eutrophic state of the reservoir for all seasons and sites. Analysis of TSID suggested that blue-green algae dominated the algal community in the reservoir. The present results show that a significant increase in algal chlorophyll occurs during spring in the reservoir. Our findings may facilitate the management of Paldang Reservoir.

Understanding Seasonal and Spatial Variation of Water Quality Parameters in Mangrove Estuary of the Nyong River Using Multivariate Analysis (Cameroon Southern Atlantic Coast)

To evaluate the actual status of water quality and conclude on the mains source of pollution in the Nyong estuary River, seasonal and spatial variation of water quality parameters was interpreted by multivariate statistical techniques (Principal Component analysis). Nine (09) environmental variables were monitored at four surface stations in the estuary for two seasonal cycles. The fieldwork was conducted from 2018 to 2019 during high tide and low tide for each survey. In situ physical parameters were measured for a total of 64 samples (32 samples for each tide). The laboratory works consisted of some physicochemical analyses and processing of these data by descriptive and multidimensional statistical analyses. Temperature, suspended particle matter, nitrate, nitrite and phosphate change significantly in the estuary with season (p p > 0.05). Principal Component analysis found temperature, salinity, pH, ammonium to be the most important parameters contributing to the fluctuations of surface water quality in the Nyong estuary during the dry seasons whereas suspended particle matter, nitrate, and phosphate are the most important parameters contributing to the fluctuation of surface water quality in the Nyong estuary during the rainy seasons. Based on spatial variation, the Principal Component analysis found that, suspended particle matter, nitrate and phosphate contribute to the fluctuation of surface water quality parameters upstream of the estuary while downstream salinity, pH, and ammonium contribute the most to the fluctuation of surface water quality. This study shows us the usefulness of multivariate statistical techniques used in assessing water quality data sets that would help us in understanding seasonal and spatial variations of water quality parameters to manage estuarine systems.

Seasonal variation in water quality parameters of Gudlavalleru Engineering College pond

The present work paying attention on the regular variations in physico-chemical parameters of the Gudlavalleru engineering college pond at a Krishna district, Andhra Pradesh during the summer and winter season. Pond is found outside the college and college management imposes restrictions over misuse of the pond, therefore they remain comparatively clean. The total nineteen parameters are measured and analysis in the research work. The correspondence matrix of different physico-chemical parameters are measured and analyzed. There is a relationship among the ecologists and micro planners about the importance for conservation of ponds as sustainable source of water in rural communities. The inorganic toxic substances may be observed in the pond is especially low or trace quantity. The present investigation indicated that the mean values of temperature, pH and total alkalinity are peak in summer period and lowest in winter period and signify standards of dissolved oxygen was maximum in winter season and lowest in summer season. The certain standards of total hardness, turbidity, chloride and trace metals are peak in the summer period and lowest in the monsoon period, in pond water sampled. The poor water quality can produce in small earnings, low down product value and probable human health risks. Manufacturing is declined when the water has contaminants that can reduce growth, reproduction or still reason mortality to the cultured species. The water quality is significant for intake, farming, aquaculture and manufacturing use.

Water quality characteristics and assessment of Yongding New River by improved comprehensive water quality identification index based on game theory

The Yongding New River is essential for the water supplies of Tianjin. To date, there is no comprehensive report that assesses the year-round water quality of the Yongding New River Main stream. Moreover, little attention has been given to determining a combined weight for improving the traditional comprehensive water quality identification index (ICWQII) by the game theory. Seven water quality parameters were investigated monthly along the main stream of the Yongding New River from May 2018 to April 2019. Organic contaminants and nitrogen pollution were mainly caused by point sources pollution, and the total phosphorus mainly by non-point source pollution. Dramatic spatio-temporal variations of water quality parameters were jointly caused by different pollutant sources and hydrometeorological factors. In terms of this study, an improved comprehensive water quality identification index (ICWQII) based on entropy weight or variation coefficient and traditional CWQII underestimated the water qualities, and an ICWQII based on the superstandard multiple method overvalued the assessments. By contrast, water qualities assessments done with an ICWQII based on the game theory matched perfectly with the practical situation. The ICWQII based on game theory proposed in this study takes into account not only the degree of disorder and variation of water quality data, but also the influence of standard-exceeded pollution indicators, whose results are relatively reasonable. All findings and the ICWQII based on game theory can provide scientific support for decisions related to the water environment management of the Yongding New River and other waters.

Water quality evaluation of two urban streams in Northwest Uruguay: are national regulations for urban stream quality sufficient?

Poor water quality in urban streams places at risk the health of urban residents and the integrity of urban environments, ultimately leading to the "urban stream syndrome." In response to growing concerns regarding urban streams in Uruguay, we evaluated the spatiotemporal variation in water quality parameters in two urban streams, the Ceibal and La Curtiembre streams, over 12-18months. A proposal for an Urban Water Quality Index (UWQI) was developed based on national water quality standards for two stream classes to assess variability in overall stream condition over time. Both streams displayed extremely high levels of fecal coliform concentrations in mid-downstream sites and relatively high levels of total dissolved phosphorus and ammonia well above the national standards of 0.025mg/L and 0.02mg/L, respectively. Nitrate was consistently below the national maximum of 44.3mg/L, calling to question the adequacy of this standard for designated uses. Over 40% of samples had oxygen levels below the national standard of 5mg/L, and a dead zone (average 1.5mg/L) was detected in the downstream reach of the La Curtiembre stream. Despite differences in land use and urban context, monthly observations of nutrients and coliforms indicated high levels of contamination in mid-downstream reaches, which could present a health risk for the populations in Paysandú and Salto. This study highlights the degradation of urban streams in two major cities in Uruguay and the need for a comparative diagnosis of stream condition as a basis for decision-making regarding urban development and water resources.

Evaluation of physicochemical water parameters in watersheds of southern Brazil

Water-quality monitoring is one of the main instruments for water-resource management. This work therefore evaluated the water quality of the contribution basin of the Sinnott water treatment plant as well as the relationship between physicochemical water parameters, and analyzed the seasonal variation of water quality parameters as a function of rainfall. The study area encompassed the contribution basin of the Sinnott water treatment plant, formed mainly by the drainage areas of the Pelotas stream and its tributary, the Quilombo Stream, located in the city of Pelotas - Rio Grande do Sul, Brazil. A set of 118,368 data points for each stream was analyzed between 2007 and 2012. The following water quality parameters were evaluated: turbidity, temperature, color, pH, hardness, dissolved oxygen, organic matter, and alkalinity. Results showed that dissolved oxygen and water pH values conformed with Brazilian legislation in the 6 years evaluated. However, water color was the parameter that remained the greatest number of days above the set limits, mainly in the Pelotas Stream. Result indicates the need for conservation actions in the catchment, especially considering the importance of color for the assessment of water quality for public supply, in terms of both treatment costs and of public health. Highest values for water color, turbidity, and organic matter coincided with the occurrence of the highest rainfall values. Agricultural activities may potentiate sedimentation in the contribution basin of the Sinnott water treatment plant.

Comparative assessment of surface and ground water quality using geoinformatics

Water quality demonstrates physical, chemical and biological characteristics of water. The quality of surface and groundwater is currently an important concern with population growth and industrialization. Over exploitation of water resources due to demand is causing the deterioration of surface water and ground water. Periodic water quality testing must be carried out to protect our water resources. The present research analyses the spatial variation of surface water and groundwater in and around the lakes of Hyderabad. Twenty-Seven lakes and their neighboring bore water samples are obtained for water quality monitoring. Samples are evaluated for specific physico-chemical parameters such as pH, Total Dissolved Solids (TDS), Cl, SO4, Na, K, Ca, Mg, and Total Hardness (TH). The spatial variation of water quality parameters for the 27 lakes and groundwater were analysed. Correlation and multiple regression analysis were carried out to determine comparative study of lake and ground water. The study found that most of the lakes were polluted and this had an impact on surrounding ground water.

Seasonal variation of water quality parameters of river Dikhow in Nagaland and Assam

Dikhow River is one of the largest tributaries of the Brahmaputra river basin originating from Yezami village near Zunheboto town of Nagaland. It debouches into the mighty river Brahmaputra river at Dikhowmukh, Sivasagar district, Asam covering a length of 255.8 km contributing 0.7% runoff. The geographical area of the Dikhow catchment is approximately 3100 km2, covering 85% of Nagaland, 10% of Assam and 5% of Arunachal Pradesh. The study area of the entire river stretch was divided into 6 stations whose elevation ranged from 90.83 msl to 669.9 msl (3 in Nagaland and 3 in Assam). These stations were selected to study the hydro-biological profile of the river stretch, its pollution status and fish diversity from January 2019 to February 2020. The important physico-chemical water quality parameters of the river in these stations like Dissolved Oxygen (5.2-10.1 mg/L), pH (7.0-8.2), Turbidity (3.8-143.8 NTU), Total Hardness (24.02-121.01 mg/L), Total alkalinity (33-89 mg/L), etc. were found to be ambient for the survival of aquatic fauna in some parts in some period of the year.

Satellite-Based Study of Seasonal Variability in Water Quality Parameters in a Tropical Estuary along the Southwest Coast of India

To sustain a healthy ecosystem, continuous monitoring of the water quality is vital. Ocean colour remote sensing is an important tool to carry out such studies. Thus, chlorophyll-a (Chl-a), chromophoric dissolved organic matter (aCDOM440), turbidity (T) and sea surface temperature (SST), among others, have been considered in the present study to evaluate the water quality of the Cochin Backwaters, a tropical estuary along the southwest coast of India. Landsat 8/OLI data have been used to derive the water quality parameters (WQP), and seasonal averages have been estimated to study the variations occurring in the area during 2014–2018. These were then plotted against the river runoff occurring in the area to find out its impact upon the said parameters. The results showed that the aCDOM440 and Chl-a concentrations were related to the runoff of the area as both the parameters were at their peaks during the southwest monsoon season with values approximately more than 1.6 m−1 and 5.6 mg/m3, respectively. However, in the other seasons, aCDOM440 was not seen to directly correspond with the runoff in the area. Turbidity was observed to be the highest during the spring inter-monsoon season (SIM) with values reaching more than 60 FNU. As far as the surface temperature of the estuary is concerned, it was found to be around ~ 25 °C for most parts of the year, except for the southwest monsoon season when the temperature dropped below 25 °C. Interannual variability of the WQP showed that they were at their highest in 2014 followed by 2016, 2017 and 2018, while the year 2015 had the lowest concentration. The study also brings out the utility of ocean colour remote sensing products as an alternative to study the Indian coastal and estuarine waters.

Spatial and Temporal Water Quality Assessment in the Lower St. Johns River, Florida

The St. Johns River is Florida's longest river and a valued resource. The river is important for the region's ecology and socioeconomics. Human disturbances, flooding from hurricanes, and runoff from industrial and wastewater treatment facilities and agricultural fields in recent years has prompted the need for an increased frequency in monitoring of the water quality conditions in the St. Johns River. The objective of this study was to measure various water chemistry parameters and metal (cadmium, copper, lead, nickel, silver, and zinc) concentrations at 8 sites on the Lower St. Johns River associated with a variety of anthropogenic sources from 2017 to 2019. Water chemistry and metal concentrations varied to some degree across sites. Seasonal variation and variation due to episodic storm events were much more pronounced than the spatial variability. For example, salinity was significantly different on every sampling date. This parameter, among others, can directly influence aquatic life, as well as the bioavailability and toxicity of metals to aquatic organisms. All metals tested except zinc fluctuated at levels above EPA class III water quality criterion values, thus raising concerns about the future health of flora and fauna. This research provides reference data to improve the understanding of spatial and temporal variability of water quality parameters in the Lower St. Johns River.

Monsoonal Influence and Variability of Water Quality, Phytoplankton Biomass in the Tropical Coastal Waters – A Multivariate Statistical Approach

Rushikulya estuary is rich in biodiversity facing significant changes in recent periods due to pollution/anthropogenic impacts from the industries and growing urbanization along the banks of the river. This estuary caters mass nesting of Olive Ridley sea turtles and one of the world's largest rookery in India. In view of the above the present study examined, the seasonal variability of water quality parameters (water temperature, pH, salinity, dissolved oxygen, total suspended matter, inorganic nutrients (NO2-N, NO3-N, NH4-N, PO4-P and SiO4-Si) and the phytopigment i.e. Chlorophyll-a (chl-a) from the seawater samples of three different seasons pre-monsoon, monsoon and post-monsoon.Time-series observations were made at five locations off Rushikulya estuary, Bay of Bengal from March 2011 to February 2013. A wide range of nutrient concentrations except for NO2-N varied from 0.89 - 3.62 µmol/l in the NO3-N, from 1.36 - 6.81 µmol/l in the NH4-N, from 0.66 - 3.45 µmol/l in the PO4- P and from 0.89 -7.97 µmol/l in the SiO4-Si. The highest chl-a (3.72 mg/m3) was recorded during pre-monsoon than monsoon and post monsoon. Factor analysis (FA), showed that three underlying factors, each during pre-monsoon, monsoon and post-monsoon influencing the water quality to the extent of 75.02 %, 67.33 % and 66.37 % respectively. The significant result from a statistical view of non-metric multidimensional scaling (nm-MDS) and cluster analysis (CA) revealed that the chl-a variability was due to the direct influence of nutrients than the physical parameters.Correlation analysis revealed that chl-a was positive correlation with DO, NO2, NO3, PO4 and SiO4, while negative with salinity in pre-monsoon and monsoon. The composite results indicated that the study area is well oxygenated, rich in nutrients and chl-a distribution represents typical upper ocean dynamics and food chain linked to the pristine coastal and ecologically rich ecosystem.

Monitoring of water quality variation trends in a tropical urban wetland system located within a Ramsar wetland city: A GIS and phytoplankton based assessment

The present study, was conducted in an urban wetland system within a Ramsar wetland city in Sri Lanka to study the effectiveness of using the Geostatitical and multivariate statistical techniques together with phytoplankton based biological indices to assess the wetland ecological status. Seven sites were selected from the wetland and the water quality parameters and the diversity and abundance of phytoplankton at each site was assessed. Thirteen species of phytoplankton from Class Cyanophyceae, 18 species from Class Chlorophyceae and six species from class Bacillorphyceae were recorded during the study. There were significant spatial variation of water quality parameters and phytoplankton composition among the study sites. Principal component analysis for water quality and phytoplankton distribution was performed. Palmers index, percentage biological index and the limnological condition index was calculated based on water quality and phytoplankton data. According to the classification based on Palmers index the sites located in the northern portion of the wetland indicated high organic pollution, while other sites indicated no or less less organic pollution. According to the limnological categorization based on pollution index, %BI and LICOI, the northern sites were categorized as sites with poor ecological quality and as severely contaminated sites. The sites in the southern portion of the wetland were categorized as moderately polluted sites with acceptable limnological conditions for the ecological functions to occur within tolerable limits. Water quality prediction maps for the wetland was developed using geostatistical methods and the water quality trends also agreed with the results of the biological indices and the results of the multivariate statistics. The results indicated that the geostatisical and multivariate techniques together with the Palmers index, percentage biological index and the limnological condition index can serve as important tools for monitoring the water quality trends in tropical urban wetland ecosystems.

Innovative Water Quality and Ecology Monitoring Using Underwater Unmanned Vehicles: Field Applications, Challenges and Feedback from Water Managers

With climate change and urban development, water systems are changing faster than ever. Currently, the ecological status of water systems is still judged based on single point measurements, without taking into account the spatial and temporal variability of water quality and ecology. There is a need for better and more dynamic monitoring methods and technologies. Aquatic drones are becoming accessible and intuitive tools that may have an important role in water management. This paper describes the outcomes, field experiences and feedback gathered from the use of underwater drones equipped with sensors and video cameras in various pilot applications in The Netherlands, in collaboration with local water managers. It was observed that, in many situations, the use of underwater drones allows one to obtain information that would be costly and even impossible to obtain with other methods and provides a unique combination of three-dimensional data and underwater footage/images. From data collected with drones, it was possible to map different areas with contrasting vegetation, to establish connections between fauna/flora species and local water quality conditions, or to observe variations of water quality parameters with water depth. This study identifies opportunities for the application of this technology, discusses their limitations and obstacles, and proposes recommendation guidelines for new technical designs.

Assessment of water quality conditions in the Upper Johor River Basin

The water quality data from 2 sampling stations in the Upper Johor River Basin (Upper Johor River and Sayong River) were analysed to evaluate the water quality conditions. The variation in water quality parameters between base and storm flow events were compared to identify the pollutant sources. The results indicated that Biochemical Oxygen Demand (BOD) was the main cause for non-point source pollution of the study area. The overall water quality conditions were good in terms of dissolved oxygen and total nutrients (Nitrogen and Phosphorus). The dominant sources of BOD could be from the fertiliser timing and application. Different from BOD, the majority of the water quality samples were from baseflow events for dissolved oxygen, total nitrogen and total phosphorus. The major differences between four water quality parameters are failing septic system, oil palm mills, wastewater treatment plants and domestic waste. The results from this research would provide useful information for total maximum daily load calculation, establishment of watershed and nutrient model, and development of Best Management Practices for the Upper Johor River Basin and comparable study area.

Evaluation of Temporal and Spatial Variations of Water Quality Parameters in Zohreh River, Iran

Discriminant analysis (DA) and principal component analysis (PCA), as multivariate statistical techniques, are used to interpret large complex water quality data and assess their temporal and spatial variation in the basin of the Zohreh river. In this study, data sets of 16 water quality parameters collected from 1966 to 2013) in 4 stations (1554 observations for each parameter) were analyzed. PCA for data sets of Kheirabad, Poleflour, Chambostan and Dehmolla stations resulted in 4, 4, 4, and 3 latent factors accounting for 88.985%, 93.828%, 88.648%, and 88.68% of the total variance in water quality parameters, respectively. It is indicated that total dissolved solids (TDS), electrical conductivity (EC), chlorides (Cl−), sodium (Na), sodium absorption ratio (SAR), and %Na were responsible for water quality variations which are mainly related to natural and anthropogenic pollution sources including climate effects, gypsum, and salt crystals in the supratidal of Zohreh river delta, fault zones of Chamshir I and II, drainage of sugarcane fields, and domestic and industrial wastewaters discharge into the river. DA reduced the data set to only seven parameters (discharge, temperature, electrical conductivity, HCO3-, Cl-, %Na, and T-Hardness), affording more than 58.5% correct assignations in temporal evaluations and describing responsible parameters for large variations in the quality of the Zohreh river.

Statistical and geostatistical spatial and temporal variability of physico-chemical parameters, nutrients, and contaminants in the Tenango Dam, Puebla, Mexico

Human activities such as farming have contributed to the contamination of water bodies. Studying the spatial and temporal variations of parameters that influence water quality is essential for determining whether the water is suitable for human consumption and the ecosystem. This work presents the results from a study of the Tenango Dam, Mexico, which was aimed at determining the spatial and temporal variability of water quality parameters. To this end, five field visits were conducted during 2015. A total of 34 georeferenced water collection stations were installed in order to analyze physico-chemical parameters, nutrients, and metals, namely: temperature, pH, dissolved oxygen, and hardness; nitrates (NO3−), nitrites (NO2−), and phosphorus (P); and cadmium (Cd), chromium (Cr), copper (Cu), and lead (Pb). Statistical analyses were performed in order to determine seasonal variations in these parameters and geostatistical methods (ordinary [OK] and universal kriging [UK]) were applied to define their spatial variability. The functioning of these prediction methods were evaluated with leave-one-out cross validation. The results indicate that the physico-chemical parameters do not present a problem for the dam's water system. Nevertheless, NO3−, NO2−, and metal levels indicate poorer water quality, making it unsuitable for human consumption. The geostatistical analysis shows that seasonality and the anthropogenic activities occurring in the area contributed to increasing or decreasing the values of the variables studied, as well as the geostatistical models' ability to capture their spatial variability patterns.