Surface Water Quality Parameters Research Articles

A preliminary study of direct observation and selected water quality monitoring in Putrajaya Lake: the status between October-December 2022

The objective of this study was to provide the status of water quality based on a direct observation and selected water quality parameters (nitrite, ammonia, and pH) monitoring study between October-December 2022. Between October and November 2022, the levels of the above three surface water quality parameters were measured in 6 different sites throughout Putrajaya Lake. Observational pictures of the sites were also taken, and water samples were collected. In addition to the above observation and water quality monitoring study, the rainfall data was collected from 17th October 2022 to 11th December 2022, cited from the official website of Public Infobanjir (https://publicinfobanjir.water.gov.my/?lang=en) at stations Sg. Anak Gajah and Sg. Limau Manis, was also used to understand the possible correlation. However, further studies are needed. It was found that the water quality status was in good condition, making Putrajaya Lake a safe and suitable lake for aquatic life and recreational activities between October -December 2022. This could be due to the sustainable practices and rainwater harvesting system already implemented at Putrajaya Lake. Putrajaya Lake's water quality monitoring and sustainability practices were well implemented. However, regular maintenance to provide acute monitoring of water quality data at Putrajaya Lakes is necessary in the future.

Feasibility study of multisensor systems for the assessment of water pollution index induced by heavy metal contamination

The development of the analytical instruments for rapid in-field evaluation of integral surface water quality parameters is an urgent analytical task. Using three different sensor devices (optical sensor for cadmium, voltammetric sensor for lead and potentiometric multisensor system), we have explored the possibility of direct quantification of water pollution index (WPI) for contaminations induced by heavy metals. We have applied linear (partial least squares, PLS) and non-linear (kernel regularized least squares, KRLS) multivariate regression tools to construct predictive models evaluating the content of individual metals and WPI in complex aqueous media simulating surface water composition. We have also explored the potential of data fusion at different levels combining the signals from all three sensor devices. The results indicate that all the instruments retain the sensitivity towards target analytes in complex aqueous samples containing humic substances and that the direct quantification of WPI in the range from 1 to 4 is possible using the employed instruments with RMSE values around 0.14.

Biogeochemistry of upland to wetland soils, sediments, and surface waters across Mid-Atlantic and Great Lakes coastal interfaces

Transferable and mechanistic understanding of cross-scale interactions is necessary to predict how coastal systems respond to global change. Cohesive datasets across geographically distributed sites can be used to examine how transferable a mechanistic understanding of coastal ecosystem control points is. To address the above research objectives, data were collected by the EXploration of Coastal Hydrobiogeochemistry Across a Network of Gradients and Experiments (EXCHANGE) Consortium – a regionally distributed network of researchers that collaborated on experimental design, methodology, collection, analysis, and publication. The EXCHANGE Consortium collected samples from 52 coastal terrestrial-aquatic interfaces (TAIs) during Fall of 2021. At each TAI, samples collected include soils from across a transverse elevation gradient (i.e., coastal upland forest, transitional forest, and wetland soils), surface waters, and nearshore sediments across research sites in the Great Lakes and Mid-Atlantic regions (Chesapeake and Delaware Bays) of the continental USA. The first campaign measures surface water quality parameters, bulk geochemical parameters on water, soil, and sediment samples, and physicochemical parameters of sediment and soil.

Assessment of Regression Models for Surface Water Quality Modeling via Remote Sensing of a Water Body in the Mexican Highlands

Remote sensing plays a crucial role in modeling surface water quality parameters (WQPs), which aids spatial and temporal variation assessment. However, existing models are often developed independently, leading to uncertainty regarding their applicability. This study focused on two primary objectives. First, it aimed to evaluate different models for chemical oxygen demand (COD), total phosphorus (TP), total nitrogen (TN), and total suspended solids (TSS) in a surface water body, the J. A. Alzate dam, in the Mexican highland region (R2 ≥ 0.78 and RMSE ≤ 16.1 mg/L). The models were estimated using multivariate regressions, with a focus on identifying dilution and dragging effects in inter-annual flow rate estimations, including runoff from precipitation and municipal discharges. Second, the study sought to analyze the potential scope of application for these models in other water bodies by comparing mean WQP values. Several models exhibited similarities, with minimal differences in mean values (ranging from −9.5 to 0.57 mg/L) for TSS, TN, and TP. These findings suggest that certain water bodies may be compatible enough to warrant the exploration of joint modeling in future research endeavors. By addressing these objectives, this research contributes to a better understanding of the suitability of remote sensing-based models for characterizing surface water quality, both within specific locations and across different water bodies.

Assessment of the Effect of West African Ebony (Diospyros mespiliformis) Seed Extracts on Selected Bacteria of Upper River Benue Surface Water

This study was conducted to investigate the effects of D. mespiliformis; crude seed, mucilage and seed extracts on some surface water quality parameters and total bacteria count. The experiment was design as a factorial experiment in Complete Randomized Design (CRD). Sample water was collected from Upper River Benue in Jimeta Bridge. D. mespiliformis purchased at Jimeta Market, Yola Adamawa State. The water sample was collected in Jerry cans (50 litters) transported to wet laboratory, Department of Fisheries, School Agriculture, Modibbo Adama University of Technology Yola where the following physico-chemical parameters were analyzed Electrical Conductivity, Total Dissolve Solid, Ionic Concentration, Temperature, Biological Oxygen Demand and Turbidity. However, Total Bacterial Count was analyzed from microbiology laboratory of same University. Data obtained were analyzed using Analysis of Variance (ANOVA). Result obtained shows the effect of D. mespiliformis treated surface water on the Physicochemical Parameters and effect of D. mespiliformis Crude Seed, Mucilage and Seed Extracts on Total Bacteria Count. The following bacteria Escherichia coli, Bacillus subtilis and Staphylococcus aureus were used during the studies with E. coli has highest total bacteria count of 233 and 22 recorded lowest in seed extract, B. subtilis recorded it highest of 164 and 42 reveal as lowest in the seed mucilage while S. aureus has highest of 100 with it lowest of 32 recorded in mucilage. Further study on the efficacy of D. mespiliformis crude seed, mucilage and seed extracts on contaminant water treatment should be done.

Effect of Abattoir Waste on Surface Water Quality Parameters of Iwofe River, Port-Harcourt, Rivers State, Nigeria

Indiscriminate discharge of abattoir wastewater has been a major cause of concern globally due to its negative effect on the environment. This study analyzed the effect of abattoir waste on surface water quality parameters the of Iwofe River, Port-Harcourt, and the River’s state using standard methods. The water samples were collected at three different points (upstream, midstream, and downstream) and the results were analyzed using Oneway ANOVA at p = 0.05. From the results; temperature, pH, salinity, conductivity, oxidation-reduction potential, and turbidity were in the range of 28.50 – 29.70 oC, 7.19 – 7, 3.00 – 6.35 PSU, 5059 – 11208 µS/cm, 91.70 – 164.80 mV, and 59.60 – 78.10 NTU respectively. Although the temperature was slightly above WHO standards, pH, Electrical conductivity, and Turbidity were all within WHO acceptable limits. TDS, TSS, DO, BOD5, and COD were in the range of 2542 - 5604 mg/L, 2.90 – 7.75 mg/L, 5.90 – 12.90 mg/L, 0.42 – 3.08 mg/L, and 5.90 – 18.50 mg/L respectively. DO and TS were above WHO permissible limit while BOD5, COD, TSS, COD, and ORQ were within WHO permissible limit in all samples. Total alkalinity, hardness, Cl-, CO32-, and NO3- were 25 – 113 mg/L, 990.00 – 1256.00 mg/L, 3669.08 – 7887.63 mg/L, 12.50 to 66.00 mg/L, 0.85 – 1.08 mg/L. SO42-, PO42-, oil and grease, and total coliform were in the range of 133.56 – 283.17 mg/L, 90.00 – 100.00 mg/L, 0.10 – 0.15 mg/L, 1.34 × 104 to 2.98 × 105 cfu/mL. Total Alkalinity was within limit only in upstream, while hardness, Cl-, NO3, SO42-, and total coliform were all above WHO permissible limit in drinking water. Heavy/trace metals results ranged as follow; As (<0.001 – 0.009 mg/L), Pb (<0.001 – 0.002 mg/L), Zn (<0.001), Fe (0.019 – 0.285 mg/L), K (8.245 – 8.540 mg/L), Mn (<0.001 – 0.005 mg/L), Mg (3.345 – 4.076 mg/L), Ca (2.452 – 4.085 mg/L), Ni (0.002 – 0.010 mg/L), Cu (<0.001 – 0.006 mg/L), Cr (0.001 – 0.003 mg/L), Cd (0.006 – 0.013 mg/L). As, Pb, Zn, K, Mn, and Ca were not above WHO permissible limit, but were present in significant amount indicating that the river was gradually being polluted with metals. Most of the water quality parameters did not meet WHO permissible limits for drinking water. Also, the study indicates that the meat processing industry can potentially reduce water portability, thereby adversely affecting the range of its uses. Hence the activities of the abattoir should be monitored closely by relevant agencies in order to prevent full-blown environmental problems and health hazards in the near future.

GIS-BASED LANDSCAPE MANAGEMENT OF THE UZH RIVER BASIN: A STRATEGY TO ENHANCE RIVER WATER QUALITY

The article includes the findings of a study on the Uzh River basin’s surface water’s quality. This region is extremely important because a fraction of it is radioactively contaminated because of the Chornobyl nuclear power plant accident. Considering this, the goal of this work was to conduct a geo-informational analysis of the state and structure of the landscapes of the Uzh River basin, to identify the surface water quality parameters of this region, and to identify potential correlations between these parameters. For this purpose, we spatially divided the territory of the basin into separate massifs, in each of which, using the method of automatic decoding, we determined the ratio of the structural components of landscapes, their disturbance by erosion processes, and compared them with ground data on the quality of surface water of the massifs. According to the salt composition, all the investigated massifs have good quality conditions. The Uzh River and its tributaries should be categorized as 4 (slightly polluted) based on the average values of the trophosaprobological state, but the extreme values in some places shifted to 5 (moderately polluted). Most of the listed toxic substances were present at average levels and, for the most part, did not exceed the permitted concentrations for category 4-slightly polluted. Extreme concentrations frequently reached category 6 (polluted), which was primarily brought on by anthropogenic activity and the buildup of radioactive materials. It was found that the degree of erosive landscape destruction and the degree of landscape stabilization significantly influence the value of the integrated water quality index. Since the collected data, a three-dimensional model was developed, allowing for accurate evaluations and projections of changes in the quality of surface water based on the structural characteristics of the land shafts. Considering that some of these territories are currently in the zone of radioactive contamination, the planning of changes in the landscape structure should be coordinated with the potential risks of surface water quality deterioration, and the model proposed in this article will have significant applied value in addressing this issue.

Application of artificial intelligence for forecasting surface quality index of irrigation systems in the Red River Delta, Vietnam

Water sources for irrigation systems in the Red River Delta are crucial to the socioeconomic growth of the region's communities. Human activities (discharge) have polluted the water source in recent years, and the water source from upstream is limited. Currently, the surface water quality index (WQI), which is calculated from numerous surface water quality parameters (physical, chemical, microbiological, heavy metals, etc.) is frequently used to evaluate the surface water quality of irrigation systems. However, the calculation of the WQI from water quality monitoring parameters remains constrained due to the need for a large number of monitoring parameters and the relative complexity of the calculation. To better serve the assessment of surface water quality in the study area, it is crucial and essential to conduct research to identify an efficient and accurate method of calculating the WQI. This study used machine learning and deep learning algorithms to calculate the WQI with minimal input data (water quality parameters) to reduce the cost of monitoring surface water quality. The study used the Bayes method (BMA) to select important parameters (BOD5, NH4+, PO43−, turbidity, TSS, coliform, and DO). The results indicate that the machine learning model is more effective than the deep learning model, with the gradient boosting model having the most accurate prediction results because it has the highest coefficient of determination R2 (0.96). This is a solid scientific basis and an important result for the application of machine learning and deep learning algorithms to calculate WQI for the research area. The study also demonstrated the potential of artificial intelligence algorithms to improve water quality forecasting compared to traditional methods with minimal cost and time.

Applicability of Electron Beam Technology for the Degradation of Microcystin-LR in Surface Waters.

Studies were performed to investigate the effects of surface water quality parameters on the degradation of microcystin-LR (MC-LR) using high-energy electron beam (eBeam) technology. Surface water samples were collected across different geographic locations in the United States. Water quality parameters including pH, alkalinity, TDS, and dissolved oxygen were measured in all samples. Degradation of MC-LR in all samples, regardless of parameter concentrations, was above 99%. The effect of natural organic matter (NOM) on MC-LR degradation was also investigated in the presence of fulvic acid. Similarly, the degradation efficiency of MC-LR exceeded 99% for all concentrations of fulvic acid at 5 kGy. This study suggests that surface water quality has a negligible effect on the degradation of MC-LR via eBeam treatment. The results indicate that eBeam technology is a promising technique for the treatment of water contaminated with microcystins.

Agricultural Pressures on the Quality of Ground and Surface Waters in Catchments of Artificial Reservoirs

Changes in nutrient concentrations in groundwater were investigated to determine the impact of agriculture on water quality in artificial lakes. The study covered three reservoirs in an agricultural area of Wielkopolska (central-western part of Poland). Assessing the agricultural impact required a network of piezometers around the reservoirs, used to determine the degree of groundwater pollution from nutrients supplied to the reservoirs. Moreover, the analysis covered the quality of water in streams flowing into the reservoirs, and in the reservoirs themselves. Field research was conducted every month of the growing season from March to November. The analysis covered land inclination, ground permeability, and soil type. Detailed objectives included: (1) assessment of the agricultural impact on the chemical parameters of ground and surface water quality, and (2) evaluation of the impact of different sources of agricultural pollution on water quality in artificial reservoirs in regards to their management. This study revealed high dynamics of nutrient concentrations, particularly for nitrates and phosphates, in groundwater and surface water. A significant effect of maize cultivation on an increase in nitrate concentrations in the groundwater of the catchment of the Przebędowo Reservoir was evidenced, as well as a substantial effect of the functioning of an animal farm on the quality of groundwaters in the catchment of the Lachotka Reservoir. The nutrient load of agricultural origin in the catchment of the Miedzichowo Reservoir was relatively low. Our study revealed elevated amounts of nutrients such as nitrogen and phosphorus, detected more frequently in groundwater than in running waters, which confirms our hypothesis of the importance of groundwater analysis in detecting the source of pollution of the studied lakes. High nutrient concentrations recorded in groundwater leached from arable fields were also observed in the summer period, failing to confirm the hypothesis that the greatest effect of factors would be related to the spring effect of arable fields on water quality (lack of compact vegetation cover, high water level, etc.). Our study also indicated a greater importance of the type of land use than lithology or permeability of deposits.

Integration of band regression empirical water quality (BREWQ) model with deep learning algorithm in spatiotemporal modeling and prediction of surface water quality parameters

Integration of band regression empirical water quality (BREWQ) model with deep learning algorithm in spatiotemporal modeling and prediction of surface water quality parameters

Water quality predictions through linear regression - A brute force algorithm approach

Linear regression is one of the oldest statistical modeling approaches. Still, it is a valuable tool, particularly when it is necessary to create forecast models with low sample sizes. When researchers use this method and have numerous potential regressors, choosing the group of regressors for a model that fulfills all the required assumptions can be challenging. In this sense, the authors developed an open-source Python script that automatically tests all the combinations of regressors under a brute-force approach. The output displays the best linear regression models, regarding the thresholds set by users for the required assumptions: statistical significance of the estimations, multicollinearity, error normality, and homoscedasticity. Further, the script allows the selection of linear regressions with regression coefficients according to the user’s expectations. This script was tested with an environmental dataset to predict surface water quality parameters based on landscape metrics and contaminant loads. Among millions of possible combinations, less than 0.1 % of the regressor combinations fulfilled the requirements. The resulting combinations were also tested in geographically weighted regression, with similar results to linear regression. The model's performance was higher for pH and total nitrate and lower for total alkalinity and electrical conductivity.•A Python script was developed to find the best linear regressions within a dataset.•Output regressions are automatically selected based on regression coefficient expectations set by the user and the linear regression assumptions.•The algorithm was successfully validated through an environmental dataset.

Predicting Coastal Dissolved Oxygen Values with the Use of Artificial Neural Networks: A Case Study for Cyprus

Coastal hypoxia is a serious environmental problem that needs to be addressed at a global level. The phenomenon of hypoxia is characterized by low Dissolved Oxygen (DO) levels in the water column that causes detrimental effects on aquatic organisms. Anthropogenic activities such as intensive agriculture practices and point-source nutrient loading are considered the main causes of coastal hypoxia. This study utilizes data-driven modelling based on Artificial Neural Networks (ANNs), and specifically Feed-Forward ANNs, to predict surface DO levels. Several surface water quality parameters such as water temperature, nitrogen species (ammonium, nitrite and nitrate), phosphorus, pH, salinity, electrical conductivity, and chlorophyll-a served as the ANN’s input parameters. These parameters were measured at several sampling sites in the coastal waters of Cyprus and some of the sites were located near areas with anthropogenic activities, during the period 2000-2021. An ANN with a 9-5-1 topology was developed and ANN managed to predict with good accuracy the DO levels, with the Coefficient of determination (r 2) as high as r 2=0.991 for the test set. Additionally, sensitivity analysis was performed to measure the impact of each input parameter on the DO level, and it was estimated that the water temperature is the most influential factor. The “Weights” sensitivity analysis algorithm was used for this purpose. The ANN-based method proposed can be used as a management tool for predicting the DO levels and prevention of hypoxia. Therefore, this work has a positive impact on marine sciences and marine information technology.

Assessment of Surface Water Quality in the Hau Giang Province Using Geographical Information System and Statistical Approaches

The study aims to assess the surface water quality in Hau Giang province in 2021 at 44 locations (with a frequency of 5 times per year) using Principal Component Analysis (PCA), Cluster Analysis (CA) and GIS. Surface water quality is compared with the national technical regulation on surface water quality (QCVN 08-MT:2015/BTNMT, column A1) and Water Quality Index (WQI). The results showed that the surface water quality parameters of total suspended solids, organic matters, nutrients, coliform, and Fe far exceeded the allowable limits while the parameter of Cl-, color, and CN- were within the allowable limits of QCVN 08-MT:2015/BTNMT, column A1. The values of WQI showed that the water quality of Hau Giang province ranged from poor to excellent. The water quality at Vam Cai Dau and Hau River areas could only be exploited for water supply, but appropriate treatment is needed. CA divided the monitoring months into three distinct clusters and reduced the sampling sites from 44 to 33 locations, reducing 25% of monitoring cost per year. PCA revealed three main factors which could explain 69.0% of the variation in water quality. The water pollution sources were mainly industrial and agricultural discharges, domestic and urban activities, transportation activities, salinity, hydrological conditions and water runoff. The current findings provide useful information which support local environmental managers and water supply companies for safe and sustainable.

Using Multivariate Statistical Methods to Identify Key Surface Water Pollutants in the Dry Season in a Coastal Province, Vietnam

This study was conducted to analyze water quality in a coastal province of Vietnam. Multivariate statistical methods, namely cluster analysis (CA) and principal component analysis (PCA) were utilized. Twelve parameters, namely pH, dissolved oxygen (DO), total suspended solids (TSS), biological oxygen demand (BOD), chemical oxygen demand (COD), nitrite (N-NO2−), nitrate (N-NO3−), ammonium (N-NH4+), orthophosphate (P-PO43−), chloride (Cl−), iron (Fe), and coliforms were collected from ten locations in the 2020 dry season. The results showed that surface water was polluted by TSS, organic matters, nutrients, salinity, and coliforms compared to the national technical regulations on surface water quality (QCVN 08-MT: 2015/BTNMT). Cluster analysis results classified the original ten sampling locations into three groups due to BOD, COD, TSS, N-NH4+, N-NO2−, coliforms, and salinity. Principal component analysis (PCA) revealed that three principal components (PCs) could explain 84.5% of the variance of surface water quality parameters in the study area. Moreover, pH, TSS, DO, BOD, COD, N-NH4+, N-NO2−, N-NO3-, P-PO43−, coliforms, and Cl‑ were the key variables that influenced surface water quality in the dry season. The findings in this study can provide useful information for policymakers in developing programs of surface water quality management and protection.

Effectiveness of Monitoring the Cumulative Impact on Environment due to Development: A Case Study on Southern Expressway Extension Project-Section 4

Environmental Impact Assessment (EIA) is a tool used to identify the environmental, social and economic impacts of a development project prior to decision-making. In this study, an assessment of the effectiveness of implementation of the Environmental Monitoring Program (EMOP) introduced in the EIA Report for Southern Expressway Extension Project-Section 4 was carried out. During the study, quantitative and qualitative data were gathered for before and during the project implementation on three main variables, namely Pollution Factors (physical and chemical), Ecological Factors and Management Factors. The measured parameters were evaluated against the national standards and for the baseline data (before project). An eight surface water quality parameters were analyzed, and fluctuations were found against the baseline data. Some parameters were exceeded the national standards and observed that the selected sample locations were not effective and not served the purpose of monitoring. All measured air quality parameters were fluctuated against the baseline during the project period without violating the national standards and revealed that there is a significant increase in NOx pollution with time series. Both daytime noise levels and residual noise levels were oscillated against the baseline without exceeding the national standards. All baseline night time noise levels were on or above the national standards and all night time residual noise levels were increased against the baseline. The reported vibration levels were well within the national standards. It was noted that an effectiveness of EMOP was not significant due to lack of proper baseline data in the EIA. The natural pristine habitats have been fragmented and degraded; more than 80% of its original habitats were affected. Spreading of invasive plant species is significantly higher and the number of mammal species were declined compared to the baseline. Changes of movement pattern of animals were observed, and 1,266 timber value trees were removed with the removal of vegetation. However, management factors including cost of monitoring and enforcement of project approving authority for environment conservation were significantly improved by the implementation of EMOP. According to the results, even though the pollution factors have not exceeded the national standards, the cumulative impact has increased significantly compared to the year 2009. Implementation of EMOP has positively affected on almost all factors and revealed that measured variables have a direct impact on the effectiveness of environmental monitoring. Monitoring of environmental parameters by isolating the development initiatives, will not be captured the real impact on the environment. Only a holistic approach constitutes overall/cumulative environmental impact due to development initiatives in the area and analysis of strong set of historical monitoring data needed to see the real and cumulative impact created in the area before arriving at strong recommendations.
 Keywords: Development, Environmental Impact Assessment, Environmental monitoring

Assessment of Surface Water Quality of an Urban Wetland: Thalangama Wetland, Colombo, Sri Lanka

Thalangama wetland is situated within the Kelani river basin, Colombo District, Sri Lanka. It is one of the most attractive landscapes in Colombo that provides a wide range of ecological, social and economic functions. Wetland water supports aquatic life and is being prominently used for agriculture and bathing and contact recreational activities without knowing if the water quality is appropriate for such uses. This study was therefore conducted to assess the surface water quality of Thalangama wetland using key-physicochemical parameters and to examine the suitability of wetland waters for bathing and contact recreational water, aquatic life, irrigation and other agricultural activities. A total of 10 water quality parameters covering temperature, pH, salinity, TDS, EC, DO, BOD, COD, nitrate, and total phosphate were measured at eight sampling sites of the wetland. The study was conducted in August 2020. The spatial variation of water quality parameters was assessed using one-way ANOVA followed by Tukey’s pairwise comparison. Results were compared with ambient water quality standards of Sri Lanka. The study revealed that the surface water quality parameters including temperature, EC, TDS, DO, BOD, COD, nitrate and total phosphate show a significant spatial variation among the sampling sites. Surface water pH and BOD at all the sampling sites were within the ambient water quality standards for water suitable for bathing and contact recreational water and water suitable for aquatic life, irrigation and agricultural activities. EC at all the sampling sites were within the limits of water suitable for irrigation and agricultural activities. Nitrate and total phosphate concentrations at all the sampling sites were within the limits of water suitable as bathing and contact recreational water and water suitable for aquatic life. DO at six out of eight sampling sites were within the limits of water suitable as bathing and contact recreational waters and water suitable for aquatic life, irrigation and agricultural activities. COD at all the sampling sites were not within the limits of water suitable as bathing and contact recreational water and water suitable for aquatic life. The study identifies agricultural practices, livestock rearing, construction activities and urban discharges as major anthropogenic influences that degrade the surface water quality of the wetland. Hence, the study determines the necessity for effective management of the surface water of Thalangama wetland to prevent pollution and to ensure the water quality of the wetland is within the recommended standards.
 Keywords: Urban wetlands, Surface water quality, Spatial variation, Physicochemical parameters, Ambient water quality standards

Groundwater discharge drives water quality and greenhouse gas emissions in a tidal wetland

Wetlands play an important role in the global carbon cycle as they can be sources or sinks for greenhouse gases. Groundwater discharge into wetlands can affect the water chemistry and act as a source of dissolved greenhouse gases, including CO2 and CH4. In this study, surface water quality parameters and CO2 and CH4 concentrations were evaluated in a tidal wetland (Hunter Wetlands National Park, Australia) using time series measurements. Radon (222Rn), a natural groundwater tracer, was used to investigate the role of groundwater as a pathway for transporting dissolved CO2 and CH4 into the wetland. In addition, water-to-air CO2 and CH4 fluxes from the wetland were also estimated. The results showed a high concentration of radon in wetland surface water, indicating the occurrence of groundwater discharge. Radon concentration had a strong negative relationship with water depth with a determination coefficient (R2) of 0.7, indicating that tidal pumping was the main driver of groundwater discharge to the wetland. Radon concentration also showed a positive relationship with CO2 and CH4 concentrations (R2 = 0.4 and 0.5, respectively), while the time series data revealed that radon, CO2, and CH4 concentrations peaked concurrently during low tides. This implied that groundwater discharge was a source of CO2 and CH4 to the wetland. The wetland had an average water-to-air CO2 flux of 99.1 mmol/(m2·d), twice higher than the global average CO2 flux from wetlands. The average CH4 flux from the wetland was estimated to be 0.3 mmol/(m2·d), which is at the higher end of the global CH4 flux range for wetlands. The results showed that groundwater discharge could be an important, yet unaccounted source of CO2 and CH4 to tidal wetlands. This work has implications for tidal wetland carbon budgets and emphasizes the role of groundwater as a subsurface pathway for carbon transport.

Assessment of Physicochemical Water Quality using Principal Component Analysis: A Case Study Wadi Hanifa, Riyadh

In the current study variable statistical approaches are used; understanding of huge and complicated data matrix obtained throughout an observation of the Wadi Hanifa, Riyadh, Kingdom of Saudi Arabia. Twenty-three chemistry factors are investigated in water samples collected all month for one year from eight investigation sites wherever the Wadi influenced anthropogenic influences. The physicochemical dataset was handled using Principal Component Analysis (PCA) to extract the foremost vital parameters in assessing variation in water quality. 2 Principal issue were known as liable for the info structure explaining 87.6 percent of the whole variance of the dataset, in which season of the year (temperature) with share of variance equals to 71.3 percent and with a lesser impact the discharge of waste material treatment plants into the wadi (Conductivity) with percentage of variance of 16.3 % that represents total variance of water quality in wadi Hanifa. this study suggests that PCA techniques are helpful tools for identification of vital surface water quality parameters.

Evaluating Surface Water Quality Using Multivariate Statistical Methods: A Case Study in a Province of the Mekong Delta, Vietnam

This study aims to assess the quality, location and monitoring parameters of surface water quality in the water bodies of Vinh Long province using multivariate statistical methods. Water quality parameters including pH, temperature, turbidity, TSS, EC, DO, BOD, COD, NH4+-N, NO3--N, PO43--P, Cl-, E. coli, coliform and Fe were used in the analysis. Cluster analysis (CA) was employed to analyze spatial variations of water quality, while principal component analysis (PCA) was used to identify key indicators affecting water quality. The findings showed that surface water quality in Vinh Long province was contaminated with organic matters (high BOD, COD and low DO), nutrients (NH4+-N, PO43--P), and microorganisms (E. coli, coliforms). The value of water quality health comprehensive was mainly evaluated at a “poor” to “medium” level. The CA results revealed that the water monitoring locations could be reduced from 63 to 48 locations, saving 23.8% of the total monitoring cost. PCA identified seven parameters that considerably influenced surface water due to four polluting water sources: runoff, residential areas, industrial and agricultural activities. Further studies need to identify specific sources and scales of water pollution for appropriate water management strategies.