Historical Water Quality Data Research Articles

EstuarySAT Database Development of Harmonized Remote Sensing and Water Quality Data for Tidal and Estuarine Systems.

Researchers and environmental managers need big datasets spanning long time periods to accurately assess current and historical water quality conditions in fresh and estuarine waters. Using remote sensing data, we can survey many water bodies simultaneously and evaluate water quality conditions with greater frequency. The combination of existing and historical water quality data with remote sensing imagery into a unified database allows researchers to improve remote sensing algorithms and improves understanding of mechanisms causing blooms. We report on the development of a water quality database "EstuarySAT" which combines data from the Sentinel-2 multi-spectral instrument (MSI) remote sensing platform and water quality data throughout the coastal USA. EstuarySAT builds upon an existing database and set of methods developed by the creators of AquaSat, whose region of interest is primarily larger freshwater lakes in the USA. Following the same basic methods, EstuarySAT utilizes open-source tools: R v. 3.24+ (statistical software), Python (dynamic programming environment), and Google Earth Engine (GEE) to develop a combined water quality data and remote sensing imagery database (EstuarySAT) for smaller coastal estuarine and freshwater tidal riverine systems. EstuarySAT fills a data gap that exists between freshwater and estuarine water bodies. We are able to evaluate smaller systems due to the higher spatial resolution of Sentinel-2 (10 m pixel image resolution) vs. the Landsat platform used by AquaSat (30 m pixel resolution). Sentinel-2 also has a more frequent revisit (overpass) schedule of every 5 to 10 days vs. Landsat 7 which is every 17 days. EstuarySAT incorporates publicly available water quality data from 23 individual water quality data sources spanning 1984-2021 and spatially matches them with Sentinel-2 imagery from 2015-2021. EstuarySAT currently contains 299,851 matched observations distributed across the coastal USA. EstuarySAT's primary focus is on collecting chlorophyll data; however, it also contains other ancillary water quality data, including temperature, salinity, pH, dissolved oxygen, dissolved organic carbon, and turbidity (where available). As compared to other ocean color databases used for developing predictive chlorophyll algorithms, this coastal database contains spectral profiles more typical of CDOM-dominated systems. This database can assist researchers and managers in evaluating algal bloom causes and predicting the occurrence of future blooms.

WATER QUALITY PREDICTION USING MACHINE LEARNING TECHNIQUE

The quality of water is a critical parameter that affects human health, aquatic ecosystems, and environmental sustainability. The prediction of water quality using machine learning techniques has emerged as a promising solution for early detection and management of water pollution. This project focuses on developing a predictive model that leverages historical water quality data to forecast future water quality indices. Various machine learning algorithms, including regression and classification techniques, will be employed to analyze parameters such as pH, turbidity, dissolved oxygen, and contaminant levels. By training the model on a comprehensive dataset, the system aims to provide accurate and timely predictions, enabling proactive measures to be taken to ensure safe water supplies. The implementation of this model can significantly aid regulatory bodies and water management authorities in monitoring and maintaining water quality standards, ultimately contributing to public health and environmental conservation.

Multi-decadal geochemical evolution of drainage from underground coal mines in the Appalachian basin, USA

Coal mine drainage (CMD) in Appalachia is a widespread source of dissolved metals, SO4, and acidity that can degrade aquatic habitats and water supplies for decades following mine closure and flooding. In the bituminous coalfield of Pennsylvania, the Irwin Coal Basin (ICB) contains a series of partly to completely flooded, abandoned underground mines separated by leaky barriers within the Pittsburgh coal seam. CMD originated throughout the basin from minepool aquifers that formed after mine closures dating from 1910 to 1957. Historical and recent water quality data for eight CMD sites across the ICB, plus mineralogy and cation-exchange capacity of overburden lithologies, were analyzed to quantify important reactants and evaluate spatial and temporal water-quality trends. As overburden thickness and residence time increase along a ~ 50-km flowpath northeast to southwest in the basin, CMD becomes more alkaline, and Na concentrations increase. Since the 1970s, all eight ICB discharges have become less acidic, with exponential decreases in acidity, SO4, and Fe concentrations; only two CMD remain net-acidic (acidic pH at equilibrium). Exponential decay models that include a steady-state asymptote consistent with background groundwater chemistry and siderite equilibrium describe the early-stage, rapid contaminant concentration decay immediately after the “first flush” (initial flooding) and the progressive evolution toward late-stage background conditions. A geochemical evolution PHREEQC model indicates that spatial and temporal trends in pH, net-acidity, SO4, Fe, and major cations could be explained by the continuous dilution of first flush water by ambient groundwater combined with sustained water-mineral reactions involving pyrite and carbonates (calcite, dolomite, siderite) plus cation-exchange by clays (illite, chlorite, mixed-layer illite/smectite). These data and model results indicate that 1) cation-exchange reactions enhance calcite dissolution and alkalinity production, resulting in the evolution of CMD to Na-SO4-HCO3 type waters, and 2) siderite equilibrium could maintain dissolved Fe >16 mg/L over the next 40 years.

Historic water quality patterns in endemic schistosomiasis regions of southern Africa: Implications for disease vector distribution

Alterations in atmospheric temperature and rainfall have a major influence on the distribution patterns of vector-borne diseases, including schistosomiasis, by changing the optimum habitat of the intermediate hosts (vectors) that transmit these diseases. Water quality degradation and the creation of artificial freshwater habitats may widen or restrict the distribution of disease vectors. Understanding the influence of water quality on ecosystems is essential in determining the environments that vectors thrive in and where potential outbreaks of the disease they carry will occur. This study aims to analyse the changes in various water quality variables between 1977 and 2009 in the Mbombela river systems and how this may have altered the presence of schistosomiasis snail vectors in these areas. Historical water quality data for the Sabie, Kaap, Komati and Crocodile rivers were obtained from the Department of Water and Sanitation monitoring stations and separated into three decades (1977–1987; 1988–1998 and 1999–2009). The Physicochemical Driver Assessment Index (PAI) was used to determine the ecological category of the rivers in each decade. A Principal Component Analysis was also created for the different river systems to identify possible changes in water quality over the 3 decades. According to the PAI, the Sabie River remained in a relatively good state while the Crocodile, Komati and Kaap rivers had a low water quality to begin with and became further degraded over the three decades. The PCAs indicated the Sabie and Crocodile rivers had increased levels of salts, nutrients and pH between 1988-1998 and 1999–2009 while the Kaap River had high salt content and pH between 1977 and 1998 and high nutrients during the decade of 1999–2009. These results indicate that there has been a change in the water quality of Mbombela river systems with notable increases in salt, pH and nutrient levels. As a result, the aquatic biota have likely been negatively affected and this poses severe impacts on the presence and abundance of schistosomiasis vectors as the snails are sensitive to changes in salinity, nutrients and pH.

Enhanced Water Quality Prediction in the Yellow River Basin: The Application of the HHO-LSTM Model

In the pivotal water resource region of the Yellow River Basin in China, precise prediction of water resources is essential for their effective and rational management. This study introduces a novel approach to water resource prediction by employing the Harris Hawks Optimization-Long Short-Term Memory (HHO-LSTM) model. This method overcomes the constraints faced by traditional techniques in processing time series data and various variable factors. It encompasses a comprehensive description of the multi-source hydrological data collection process within the Yellow River Basin, followed by meticulous data preprocessing. The data set for this study includes estimates of four critical water quality parameters, and the efficacy of the model is gauged through the mean squared error (MSE) and root mean squared error (RMSE) metrics. This facilitates the projection of future water quality trends in specific areas by leveraging historical water quality data. The HHO-LSTM model has demonstrated outstanding accuracy and robustness in predicting water quality across diverse temporal scales and water resource variables, marking a significant advancement in water resource management within the Yellow River Basin. This approach not only enhances current management strategies but also contributes valuable insights for ongoing water resource research and decision-making processes.

Evaluation of the impact of coal mining on surface water in the Boesmanspruit, Mpumalanga, South Africa

Surface water quality has major environmental and socioeconomic consequences, notably in terms of the country’s long-term fresh water supply. This study aimed at assessing the current state of water quality and status of the Boesmanspruit in a coal mining environment. The study used historical water quality data for a period of five years from 2017 to 2021. Aluminum, calcium, iron, manganese, magnesium, sodium, sulfate, electrical conductivity, pH, and total dissolved solids were the water quality variables selected for the study; the chosen variables were chosen based on the available secondary data. The water quality was evaluated against South African resource quality objectives, the South African water quality guidelines, and the Canadian Council of Ministers of the Environment water quality index (CCME-WQI). The data were analyzed using such as the CCME-WQI, the comprehensive pollution index (CPI), and multivariate statistics. The following parameters were above the prescribed thresholds: pH, total dissolved solids, electrical conductivity, sulfate, manganese, and iron. The CCME-WQI results showed that monitoring locations GR S26 and GR S21 near mining activities had poor water quality (40–44), whereas comprehensive pollution index (CPI) also had similar category results for the monitoring points, indicating that they were heavily polluted (2.4–4.8). The WQI showed that if certain variables, such as aluminum, iron, magnesium, sulfate, electrical conductivity, and total dissolved solids, exceed the permissible range, the water quality would deteriorate in accordance with the CPI classification. Therefore, the CPI was the best way to categorize the water quality. The principal component analysis and cluster analysis identified two primary sources of pollution which are anthropogenic and natural. The utilization of statistical analysis proved to be effective in determining the ideal quantity of significant variables within the study area. The study recommends low-cost options for reducing the effects of acid mine drainage, which includes passive mine water treatment methods using artificial wetlands.

A perceptual approach to address complex water management issues in lowland permeable catchments

Water quality management is a pressing global concern, and an increasingly complex issue due to climate and land-use change, legacy pollution, and the persistent release of well-known and emerging contaminants from diffuse and point sources. The increasing availability of high-frequency monitoring data is leading existing, often heuristic approaches, to be judged inadequate. Water managers frequently rely on simple qualitative and/or quantitative approaches for decision-making, but a lack of tangible improvement in freshwater quality outcomes is demanding new and innovative approaches that rely more on physical process understanding, rather than precedent. In this study, we drew upon local geological, hydrogeological, and hydrological data to infer a high-level perceptual model of surface/groundwater interactions in a chalk stream in Dorset, UK. We used the perceptual model to interrogate spatial and temporal trends in historical water quality data and to construct reach-scale nutrient mass balances. Through novel representation with Sankey diagrams, the perceptual model highlighted the relative importance of different hydrological features. Surface/groundwater interactions were found to occur predominantly by spring flow. We demonstrate that river flow accretion was dominated by the Chalk aquifer despite only occupying ca. 15 % of the surface bedrock area, and that spring sources, whilst vital to dilute treated sewage inputs in baseflow conditions, were also major sources of legacy nitrate. Nutrient mass balances showed that sewage treatment works contributed ca. 13 % to soluble reactive phosphorus load, with groundwater accounting for ca. 48 %. Thus, a determinand often associated with point-source pollution was shown to be diffuse dominated in this river. The study demonstrated how a multi-disciplinary approach to water management, based on a comprehensive perceptual modelling approach, could identify hitherto unknown sources and relative contributors to freshwater pollution and allow flow and load apportionment studies to provide useful decision-support to manage nutrient pollution. The novel application of perceptual modelling tools, such as the Sankey diagram, allows different source attributions to be presented in an accessible manner, and can be readily transferred to other study areas.

Monitoring network optimization and impact of fish farming upon water quality in the Três Marias Hydroelectric Reservoir, Brazil.

Hydroelectric power is the main source of electrical energy in Brazil. Electrical energy providers have the duty to monitor water quality in reservoirs to preserve water quality and support best management practices that enable multiple wateruses, including fish production. In this context, the objectives of this study were (i) to perform a historical evaluation of water quality in Três Marias Reservoir, (ii) to present an optimization of the water quality monitoring network, and (iii) to evaluate the evolution and impact of fish farming upon surface water quality by using secondary data measured in situ and remote sensing. A systematic approach was applied to analyze historical water quality data. Principal component analysis (PCA) and cluster analysis (CA) were applied to identify the most important parameters and monitoring points. Images obtained from Sentinel 2 were treated by contrast to quantify simple and weighted densities of fish farming activities in the region while regression analysis was performed to verify correlations between these densities and water quality parameters. Results showed that the pH and total suspended solids were the most important parameters for characterizing water quality, especially near tributaries, and that monitoring points could be grouped into three clusters (upstream, central, and downstream regions) with distinct water quality conditions. The PCA indicated that there is no redundance among parameters nor monitoring stations and that areas near tributaries must be prioritized for monitoring as these are important sources of suspended solids. Remote sensing images showed thatthearea occupied by fish farms has increased in the reservoir from 2016 to 2022and the methodology used for this purpose in this study may be applied to other bodies of water. Chlorophyll-a showed a direct relationship with the density of fish farms indicating a possible influence of nutrient input to the reservoir by this activity. These results provide valuable information to support decision-making related to water management in the reservoir.

Historical trends of heavy metal contamination and eutrophication in an aquatic system from Kashmir Himalaya, India

Long-term trends of heavy metal pollution in aquatic systems are significant for assessing the influence of human activities and adopting relevant management strategies. In this study, we present analyses of heavy metals (iron, manganese, copper, zinc, cobalt, nickel, and lead) in 210Pb/137Cs dated sediment cores from Ahansar lake (Kashmir Himalaya, India) covering a time span of the last 200 years. Additionally, the relative water quality analyses and compilation of historical water quality data (since 1983 CE) were performed to understand the long-term eutrophication status of Ahansar lake system. The heavy metal concentrations varied very little in pre-anthropogenic sediments (before 1980 CE) and represent geochemical background concentrations. Since the 1980s, the heavy metals (Pb, Zn and Cu) began to increase gradually corresponding to the population expansion in the region and the beginning of industrialization. Similarly, the ecological risk index (ERI) values for all heavy metals lies below 40, indicating less toxic impact of Pb, Cu, Zn and Ni. However, the increasing trend of ERI observed in Pb and Zn since 1980, indicates deterioration of the lake ecosystem. Further, the water quality parameters revealed deteriorating trend from ∼2000 CE, with an increase of total phosphorus, ammonia, nitrate and a decrease of dissolved oxygen. The present day (∼2020 CE) quantitative assessment of Ahansar lake water quality using trophic level index (TLI) reflected the eutrophic condition of the lake waters. The long-term baseline information from this study will aid policy makers and environmentalists for the development, management and restoration of water resources.

Analysis of Groundwater Quality in Ghazni City, Ghazni, Afghanistan

Historical groundwater level and water quality data in Ghazni province poorly were reviewed and compared with the data collected in the past than recently. The results suggest that the groundwater quality and water level have been improved progressively with urban development, land use, climate change, social development and frequent drought events. The main impact of these events include; 1- most of the springs and flumes have dried up; 2- decreased annual atmospheric precipitation; 3- increased serious deterioration of water quality; 4- increased water logging and irregulation salinization; 5-declining of water level in excess of recharge trend; 7- increased evaporation and discharge; 6- marshes dried up in several areas of the Basin, leaving salt crust at the surface. The above impacts have resulted in the replacement of surface water with groundwater resources to support socio-economic development. This, however, is basically not possible because of the low thickness and productivity of the aquifers. We have done very little to advertency water quality deterioration and serious lowering of the groundwater level due to fragmented institutional arrangements and poor formulation of effective water policies, strategies and regulations for integrated groundwater resources management, development, protection and sustainability. Groundwater natural reserves have been depleted, and water quality has deteriorated due to over-exploitation. Overall Afghanistan, Arsenic (total as) contamination are an issue of current drinking water supply systems where users have been using groundwater sources. Arsenic contamination is a major environmental health management concern, especially in Ghazni province in the WASH sector. Increasing human activities and haphazard urbanization have modified the cycle of heavy metal, non-metal and metalloids. The arsenic contaminated groundwater used for drinking can cause an adverse effect on the human health of the study area. The water quality study with 96 samples from drinking water points (DWPs) was carried out in the center of Ghazni province results show that the arsenic concentration values in the study area varied between 0.00-0.99 mg/L and 38% of DWPs samples exceeded the value of the WHO guideline of 0.01 mg/L of As Owever, 62% of analyzed water samples exceeded the National drinking water quality standard (NDWQS) of 0.05 mg/L of As.

Decoupling of Estuarine Hypoxia and Acidification as Revealed by Historical Water Quality Data.

Hypoxia and acidification are commonly coupled in eutrophic aquatic environments because aerobic respiration is usually dominant in bottom waters and can lower dissolved oxygen (DO) and pH simultaneously. However, the degree of coupling, which can be weakened by non-aerobic respiration and CaCO3 cycling, has not been adequately assessed. In this study, we applied a box model to 20 years of water quality monitoring data to explore the relationship between hypoxia and acidification along the mainstem of Chesapeake Bay. In the early summer, dissolved inorganic carbon (DIC) production in mid-bay bottom waters was dominated by aerobic respiration, contributing to DO and pH declines. In contrast, late-summer DIC production was higher than that expected from aerobic respiration, suggesting potential buffering processes, such as calcium carbonate dissolution, which would elevate pH in hypoxic waters. These findings are consistent with contrasting seasonal relationships between riverine nitrogen (N) loads and hypoxic and acidified volumes. The N loads were associated with increased hypoxic and acidified volumes in June, but only increased hypoxic volumes in August, when acidified volume declines instead. Our study reveals that the magnitude of this decoupling varies interannually with watershed nutrient inputs, which has implications for the management of co-stressors in estuarine systems.

Investigating water quality sensitivity to climate variability and its influencing factors in four Lake Erie watersheds

Climate change alters weather patterns and hydrological cycle, thus potentially aggravating water quality impairment. However, the direct relationships between climate variability and water quality are complicated by a multitude of hydrological and biochemical mechanisms dominate the process. Thus, little is known regarding how water quality responds to climate variability in the context of changing meteorological conditions and human activities. Here, a longitudinal study was conducted using trend, correlation, and redundancy analyses to explore stream water quality sensitivity to temperature, precipitation, streamflow, and how the sensitivity was affected by watershed climate, land cover percentage, landscape configuration, fertilizer application, and tillage types. Specifically, daily pollutant concentration data of suspended solid (SS), total phosphorus (TP), soluble reactive phosphorus (SRP), total Kjeldahl nitrogen (TKN), nitrate and nitrite (NOx), and chloride (Cl) were used as water quality indicators in four Lake Erie watersheds from 1985 to 2017, during which the average temperature has increased 0.5 °C and the total precipitation has increased 9%. Results show that precipitation and flow were positively associated with SRP, NOx, TKN, TP, and SS, except for SRP and NOx in the urban basin. The rising temperatures led to increasing concentrations of SS, TKN, and TP in the urban basin. SRP and NOx sensitivity to precipitation was higher in the years with more precipitation and higher precipitation seasonality, and the basins with more spatially aggregated cropland. No-tillage and reduced tillage management could decrease both precipitation and temperature sensitivity for most pollutants. As one of the first studies leveraging multiple watershed environmental variables with long-term historical climate and water quality data, this study can assist target land use planning and management policy to mitigate future climate change effects on surface water quality.

Mixing zone delimitation for submarine outfall in tropical marine water using the uranine dilution measurement approach

Submarine outfalls are standard wastewater management systems implemented in coastal areas. This manuscript presents a methodology for mixing zone delimitation of wastewater plume discharged through a submarine outfall in tropical marine waters. Sodium fluorescein (Uranine) was used as a tracer to track wastewater mixing and dilution factor in the coastal water of Cartagena de Indias (Colombia). Historical marine water quality, marine currents, and meteorology data were obtained, and analyzed. Two field campaigns were carried out to monitor coliforms, enterococci, and uranine concentrations in the studied area. The results showed that the water column was well mixed, and stratification was negligible due to the low vertical variations in temperature (29.0–29.35 °C), salinity (35.8–36.2), and density (1022.6–1023.0 kg ⋅ m−3). The initial dilution during the dry to the rainy (April) and from the rainy to the dry (December) seasons reached similar dilution factors up to 1:43000 in less than 150 m. The regulatory mixing zone was delimited to an area of 6.6 km2. Uranine is suitable for delimiting the wastewater mixing zone in tropical marine waters.

A nonparametric statistical approach for the estimation of water quality characteristics in ungauged streams/watersheds

Water quality (WQ) data are essential for water resources as well as WQ management. However, due to limited fiscal resources, in many cases, historical WQ data may not be available for the watershed(s) of interest. Recently, few artificial intelligent and parametric statistical approaches were proposed for the estimation of WQ characteristics at ungauged watersheds. In this study, the main goal was to develop nonparametric statistical approaches for the estimation of the three main quartiles of different WQ indicators in ungauged watersheds using watershed attributes as predictors. Four nonparametric approaches were based on the region of influence (ROI) and Theil-Sen nonparametric multiple regression (TSMR). The ROI was used to identify watersheds similar to the ungauged watershed, and TSMR was calibrated and validated to estimate WQ quartiles in ungauged watersheds. The main proposed approach combined the ROI and TSMR. In the second approach, the Spearman correlation analysis was introduced as a filter, to identify watershed attributes that are highly correlated with the WQ quartile, to improve the ROI performance, (CO)ROI-TSMR. In the third approach, a step forward selection technique was integrated with the TSMR to facilitate the selection of the model predictors (watershed attributes), ROI-(SF)TSMR. In the fourth approach, both the (CO)ROI and (SF)TSMR were used, (CO)ROI-(SF)TSMR. The four nonparametric statistical approaches were calibrated and validated, to estimate the three main quartiles for three different WQ indicators, using data from 50 watersheds in the Nile Delta, Egypt. A Jackknife validation procedure was applied to evaluate the performance of the four approaches. The results indicated that the (CO)ROI-(SF)TSMR approach outperformed the three other approaches.

Research on water quality prediction model based on echo state network

Using artificial neural network (ANN) to solve the problem of time series water quality prediction has become increasingly mature. In this paper, through the study of leaky-integral echo state neural network (Leaky ESN), combined with the historical water quality data of Dongzhen Reservoir in Fujian Province, a single-day water quality prediction model was constructed, and the Bayesian optimization algorithm was used to realize the automatic optimization of hyper-parameters in the network. On this basis, multi-day prediction models were constructed by further improving the network, which used the historical water quality data of the previous 7 days to predict the water quality of the next 3 days, 5 days and 7 days. Then the prediction models were applied to the water quality prediction of the study. The experimental results show that the single-day prediction model with Bayesian optimization has high accuracy. The multi-day prediction models can also achieve good prediction effect, and have more practical application value. They are more suitable for early warning of water quality.

Adapting sanitary inspections for the monitoring of small drinking water supplies in Iceland

Sanitary inspections (SIs) are checklists of questions used to identify actual and potential sources and pathways of drinking water contamination. Though the importance of SI adaptation to local contexts is widely acknowledged, there is currently limited guidance on how this should be undertaken in practice. During this research, World Health Organization (WHO) draft template SI forms for spring and borehole supplies were adapted for use in Iceland based on a series of desk reviews and field tests, an approach which may guide other future SI adaptation processes. SI results were collected from 25 spring supplies and nine borehole supplies in three regions of Iceland using adapted SI forms. These results were combined with 10-year historical water quality data from the same supplies to explore potential relationships between both data sets. Binary logistic regression test results indicated a statistically significant association (P = 0.025; odds ratio (OR) 1.864, 95% CI 1.080-3.220) between SI Question 3 (Does ponding from surface water occur around the spring/borehole?) receiving a 'High' risk level assignment and at least one historical incidence of water quality noncompliance for the parameters heterotrophic plate count 22 °C, total coliforms, Escherichia coli, and turbidity at the same supply. The significant modifications applied to the starting template during the testing and development of the Icelandic SI form emphasises the importance of a robust adaptation process to ensure SI forms are appropriate for the local context. Results from the analysis of SI and water quality test results demonstrated the potential for these data sets to identify the primary risks at a supply. This information may then be used to direct remedial actions, especially when the amount of relevant data increases over time.

Water quality and ecology of Lake Kanyaboli, Kenya: Current status and historical changes

AbstractSmall waterbodies are the most threatened freshwater habitats because of the large ratio between their size and the catchment they drain. The present study assessed the current and historical changes in the physical, chemical and biological variables of Lake Kanyaboli, a satellite lake on the northern shores of Lake Victoria in western Kenya. Primary and secondary data on pH, electrical conductivity (EC), dissolved oxygen (DO) concentration, temperature, Secchi depth (SD), and nitrate (), nitrite (), ammonium (), soluble reactive phosphorus (SRP), total nitrogen (TN), and total phosphorus (TP) and chlorophyll‐a (Chl‐a) concentrations were utilized in the present study. The results indicated Secchi depth and chlorophyll‐a were the most erratic of all the analyzed environmental variables studied, exhibiting a range of 0.69 ± 0.29–0.87 ± 0.34 m and 9.03 ± 0.81–34.97 ± 3.36 µg/L respectively. Two‐way ANOVA yielded no significant interactions between sampling sites and seasons for all the variables. Except chlorophyll‐a, there also were no significant differences among the sampling sites for the studied variables. Seasonality yielded significant differences for Secchi depth, dissolved oxygen and chlorophyll‐a. The Carlson Trophic Index for Chl‐a and SD indicated Lake Kanyaboli is currently eutrophic, while the TP concentration indicated hypereutrophic conditions. The lake, however, has fluctuated between eutrophic and hypereutrophic conditions over the past years. Although historical water quality data for the lake is scanty and infrequent, most physical and chemical variables reflected anthropogenic effects on a temporal scale. Interestingly, despite its eutrophic status, the general lake condition is still relatively good, attributable to the buffering effect from the extensive macrophytes fringing it. The present study identified nutrient loading, wetland reclamation and connectivity with the Yala River through a feeder canal as the management issues of critical concern. Accordingly continuous monitoring of the lake's water quality to detect anthropogenic effects is recommended for management intervention purposes.

Spatial and Temporal Variation of Selected Water Quality Parameters in the Tanzanian Side of Lake Victoria

Lake Victoria’s water quality is increasingly becoming under heavy pressure mainly due to land based activities and aerial inputs which are taking place within the basin. This study was carried out to assess the spatial, including vertical and temporal, variation of the water quality of Lake Victoria on the Tanzanian side. Historical water quality data for the period from 2000 to 2016 was collected. Temperature, pH, Dissolved Oxygen (DO) and Turbidity were selected for study since they are the mostly measured and monitored water quality parameters. Land use-land cover changes were analysed using ArcGIS. ILWIS 3.7 software was used to classify the land use and land cover for the years 2000, 2010 and 2014. MERIS was used to analyse the spatial variation. One-way ANOVA was employed to test the significant variation between different parameters. The results showed that, for the pelagic zone, the range of temperature, pH, DO, and turbidity were 22.8 oC-28.68 oC, 6.3-10.52, 3.42-10.21 mg/l, 1.0 NTU-15.8 NTU respectively. The corresponding values for the littoral zones were 22.3 oC-26.8 oC, 6.47-10.16, 3.99-8.6 mg/l, 1.3-347 NTU respectively. The ANOVA analysis results show that there was a significant variation of NO3(p<0.01). Temperature, pH and DO decreased with the lake depth to the bottom for both zones. For the littoral zone, a strong correlation was observed between temperature and depth, temperature and DO, and between temperature and pH with R2=0.6, p<0.03, R2=-0.78, p<0.01 and R2=0.96, p<0.01, respectively. The bare soil, urban settlements and farm land increased by 38.9%, 8.4% and 10.7% respectively from the year 2000 to 2014 on the Tanzanian side. This could have led to water quality changes. Water quality parameters varied significantly between pelagic and littoral zones. Littoral zones are mostly polluted and thus should be the priority pollution control intervention areas.

Predictive Modeling Approach for Surface Water Quality: Development and Comparison of Machine Learning Models

Water pollution is an increasing global issue that societies are facing and is threating human health, ecosystem functions and agriculture production. The distinguished features of artificial intelligence (AI) based modeling can deliver a deep insight pertaining to rising water quality concerns. The current study investigates the predictive performance of gene expression programming (GEP), artificial neural network (ANN) and linear regression model (LRM) for modeling monthly total dissolved solids (TDS) and specific conductivity (EC) in the upper Indus River at two outlet stations. In total, 30 years of historical water quality data, comprising 360 TDS and EC monthly records, were used for models training and testing. Based on a significant correlation, the TDS and EC modeling were correlated with seven input parameters. Results were evaluated using various performance measure indicators, error assessment and external criteria. The simulated outcome of the models indicated a strong association with actual data where the correlation coefficient above 0.9 was observed for both TDS and EC. Both the GEP and ANN models remained the reliable techniques in predicting TDS and EC. The formulated GEP mathematical equations depict its novelty as compared to ANN and LRM. The results of sensitivity analysis indicated the increasing trend of input variables affecting TDS as HCO3− (22.33%) > Cl− (21.66%) > Mg2+ (16.98%) > Na+ (14.55%) > Ca2+ (12.92%) > SO42− (11.55%) > pH (0%), while, in the case of EC, it followed the trend as HCO3− (42.36%) > SO42−(25.63%) > Ca2+ (13.59%) > Cl− (12.8%) > Na+ (5.01%) > pH (0.61%) > Mg2+ (0%). The parametric analysis revealed that models have incorporated the effect of all the input parameters in the modeling process. The external assessment criteria confirmed the generalized outcome and robustness of the proposed approaches. Conclusively, the outcomes of this study demonstrated that the formulation of AI based models are cost effective and helpful for river water quality assessment, management and policy making.

Impact of Land Cover Changes on the Soil and Water Quality of Greens Bayou Watershed

Rapid land use and land cover changes have a significant effect on ecology, environment, and human health in urban watersheds. With increase in frequency and intensity of the urban flooding events and repeated inundation of the neighborhoods, it is important to monitor the water and soil chemical characteristics in Greens Bayou Watershed (GBW) region. The objectives of this study were to (1) analyze the nutrient and heavy metal concentrations in water and soil samples along the Greens Bayou, (2) monitor the historical water quality data, and (3) identify and analyze land cover changes in the watershed using Landsat imagery. A total of 12 water and 12 soil samples, from four different sampling locations along the Greens Bayou, were collected during two seasons and processed for chemical analysis. Our water sample analysis revealed that the As, Cd, Pb, and Hg concentrations were higher in the fall compared with the summer and exceeded the critical limit. The soil analysis indicated that the Zn and Pb concentrations were higher in the fall over summer season and exceeded the background concentrations. Remote sensing analysis revealed that the impervious surface in the GBW increased by about 62.2% while the vegetative surface decreased by 30.6% during the period of 1984 to 2018. Environmental chemical analysis coupled with geospatial data demonstrated the impact of land cover changes on water and soil quality along the Greens Bayou by identifying areas vulnerable to change, which can be better managed to preserve the health of this urban watershed ecosystem.