Surface Water Quality Monitoring Network Research Articles

Multi-Tier Validation of a Macroscale Nitrogen Model for Groundwater Management in Watersheds Using Data from Different Monitoring Networks

For the Hessian river basins, an area-differentiated modeling of the nitrogen input to the groundwater and surface waters was carried out for six diffuse input pathways and six point source input pathways on the basis of the geodata available at the state level. In this context, extensive plausibility checks of the model results were carried out using the data from several official monitoring networks at the state level. These include the comparison of modeled runoff components and input pathways for nitrogen using the data from the network of discharge monitoring stations. For the validation of the modeled nitrate concentrations in the leachate, the data from groundwater monitoring wells for controlling the chemical status of groundwater were used. The validation of the modeled nitrate inputs to the groundwater and denitrification in the groundwater was carried out using the data from a special monitoring network of groundwater monitoring wells that include N2/Ar measurements. The data from the Surface Water Quality Monitoring Network were used to verify the plausibility of the modeled total N inputs to the surface waters from diffuse sources and from point sources. All of the model results evaluated by the plausibility checks prove that the nitrate pollution situation in Hesse is adequately represented by the model. This is a prerequisite for accepting the model results at the state level as a basis for developing and implementing regionally appropriate mitigation measures. The Hessian State Agency for Nature Conservation, Environment and Geology uses the model results in the broader context of the work on implementing the EU Water Framework Directive and the EU Nitrate Directive.

A simulation-based approach to assess the power of trend detection in high- and low-frequency water quality records.

To provide more precise understanding of water quality changes, continuous sampling is being used more in surface water quality monitoring networks. However, it remains unclear how much improvement continuous monitoring provides over spot sampling, in identifying water quality changes over time. This study aims (1) to assess our ability to detect trends using water quality data of both high and low frequencies and (2) to assess the value of using high-frequency data as a surrogate to help detect trends in other constituents. Statistical regression models were used to identify temporal trends and then to assess the trend detection power of high-frequency (15min) and low-frequency (monthly) data for turbidity and electrical conductivity (EC) data collected across Victoria, Australia. In addition, we developed surrogate models to simulate five sediment and nutrients constituents from runoff, turbidity and EC. A simulation-based statistical approach was then used to the compare the power to detect trends between the low- and high-frequency water quality records. Results show that high-frequency sampling shows clear benefits in trend detection power for turbidity, EC, as well as simulated sediment and nutrients, especially over short data periods. For detecting a 1% annual trend with 5years of data, up to 97% and 94% improvements on the trend detection probability are offered by high-frequency data compared with monthly data, for turbidity and EC, respectively. Our results highlight the benefits of upgrading monitoring networks with wider application of high-frequency sampling.

Evaluating Current Water Quality Monitoring System on Hau River, Mekong Delta, Vietnam Using Multivariate Statistical Techniques

This study aims to assess the sampling sites and frequencies of sampling of the existing surface water quality monitoring on Hau River using multivariate analysis techniques. Principal Component Analysis (PCA) and Cluster Analysis (CA) were used to analyze the water quality monitoring data collected every month in 2018 from 8 sampling stations. Surface water quality parameters including pH, temperature, dissolved oxygen (DO), total suspended solids (TSS), nitrate (N-NO3-), phosphate (P-PO43-), chemical oxygen demand (COD) and coliforms were used in the PCA and CA analyses. The findings indicated that the Hau River water quality was polluted by TSS, COD and coliforms in which COD was high in dry season, TSS was high in wet season and coliforms were high all year round. The PCA revealed that pH, temperature, DO, TSS, N-NO3-, P-PO43-, COD and coliforms influenced on the water quality, therefore, relevant for examination in the water samples. These water quality variables were affected by various polluting sources, for examples, runoff, human activities, and hydrological influence. Cluster analysis suggested that the current monitoring program could be reduced from 8 to 3-4 points and 12 to 3-4 times per year. This monitoring program could save the total budget for up to 42%. The findings of the present study could be useful to the policy maker especially to those who are dealing with surface water monitoring systems. The multivariate statistical techniques could be used to assess the surface water quality monitoring network.

A novel approach for selecting sampling points locations to river water quality monitoring in data-scarce regions

In order to rationalize a surface water quality monitoring network (WQMN), it is critical to appropriately design surface water quality sampling locations. This is due to high installation, operational, and maintenance costs for each sampling representative of the whole water system conditions. The main objective of this study was to propose an integrated method to determine the most appropriate sampling points in the Khoy watershed northwest of Iran, where financial resources and water quality data are limited. Multi criteria evaluation method including analytic network process (ANP) and Fuzzy logic were incorporated in River Mixing Length (RML) procedure in order to identify exact locations of sampling points. Based on RML procedure, 15 candidate sampling points were identified to suitably select sampling points based on budget deficiency. Relative weights for 12 criteria and 10 sub-criteria related to non-point sources and surficial rocks as well as criteria of topography were then calculated by the ANP method. According to the obtained results, a new total potential pollution score (TPPS) was presented to prioritize 15 candidate sampling points. Then, the values of TPPS were classified and fuzzified to distinguish real differences between scores. Based on current monitoring stations and budget deficiency, the hierarchy value, and Fuzzy rank, six points are proposed as the most appropriate locations for surface water quality monitoring. Furthermore, four points are identified as the second most appropriate for enhancing a robust WQMN in the study area in order for an expansion plan in the future. The results of this study should be valuable for water quality monitoring agencies looking for an efficient approach for selecting exact sampling locations.

Using multivariate techniques as a strategy to guide optimization projects for the surface water quality network monitoring in the Velhas river basin, Brazil.

Surface water quality monitoring networks are usually deployed and rarely re-evaluated with regard to their effectiveness. In this sense, this work sought to evaluate and to guide optimization projects for the water quality monitoring network of the Velhas river basin, using multivariate statistical methods. The cluster, principal components, and factorial analyses, associated with non-parametric tests and the analysis of violation to the standards set recommended by legislation, identified the most relevant water quality parameters and monitoring sites, and evaluated the sampling frequency. Thermotolerant coliforms, total arsenic, and total phosphorus were considered the most relevant parameters for characterization of water quality in the river basin. The monitoring sites BV156, BV141, BV142, BV150, BV137, and BV153 were considered priorities for maintenance of the network. The multivariate statistical analysis showed the importance of a monthly sampling frequency, specifically the parameters considered most important.

Statistical Assessment of the Surface Water Quality Monitoring Network in Saskatchewan

Surface water quality monitoring is one of the responsibilities of a number of provincial and federal environmental departments in Canada. In Saskatchewan, the Ministry of Environment is responsible for the province water quality monitoring network. The sampling effort was initiated 40 years ago and has been ongoing since, with varying degrees of spatial and temporal coverage. The main objective of the Saskatchewan monitoring network is the assessment of ambient water quality status. In addition, one of the main uses of the generated water quality data is the calculation of a Water Quality Index. The adequacy of the monitoring network to perform these tasks needs to be validated. The objective of this study is to provide a statistical assessment of two of the monitoring network main aspects, the water quality variables and their sampling frequency. A new rationalization approach is applied for the assessment and reselection of water quality variables. The proposed approach provides, in a systematic way, the optimal combinations of variables to continue measuring, variables that may be redundant and could be considered for discontinuance, and variables that may need to be added to the list of variables being measured. The confidence interval around the mean is used as the main criterion for the sampling frequency assessment. A design chart is provided for the sampling frequency assessment, which is easy to use, and provides an initial assessment of the number of samples required to provide a mean value with a predefined error percentage.

Assessment and rationalization of water quality monitoring network: a multivariate statistical approach to the Kabbini River (India).

The establishment of an efficient surface water quality monitoring (WQM) network is a critical component in the assessment, restoration and protection of river water quality. A periodic evaluation of monitoring network is mandatory to ensure effective data collection and possible redesigning of existing network in a river catchment. In this study, the efficacy and appropriateness of existing water quality monitoring network in the Kabbini River basin of Kerala, India is presented. Significant multivariate statistical techniques like principal component analysis (PCA) and principal factor analysis (PFA) have been employed to evaluate the efficiency of the surface water quality monitoring network with monitoring stations as the evaluated variables for the interpretation of complex data matrix of the river basin. The main objective is to identify significant monitoring stations that must essentially be included in assessing annual and seasonal variations of river water quality. Moreover, the significance of seasonal redesign of the monitoring network was also investigated to capture valuable information on water quality from the network. Results identified few monitoring stations as insignificant in explaining the annual variance of the dataset. Moreover, the seasonal redesign of the monitoring network through a multivariate statistical framework was found to capture valuable information from the system, thus making the network more efficient. Cluster analysis (CA) classified the sampling sites into different groups based on similarity in water quality characteristics. The PCA/PFA identified significant latent factors standing for different pollution sources such as organic pollution, industrial pollution, diffuse pollution and faecal contamination. Thus, the present study illustrates that various multivariate statistical techniques can be effectively employed in sustainable management of water resources. The effectiveness of existing river water quality monitoring network is assessed. Significance of seasonal redesign of the monitoring network is demonstrated. Rationalization of water quality parameters is performed in a statistical framework.

Quantifying saline groundwater seepage to surface waters in the Athabasca oil sands region

Western Canadian oil sands contain over 170 billion barrels of proven unconventional petroleum reserves currently extracted at 1.8 million barrels per day by either surface mining, or by in situ techniques that require subsurface injection of steam and hydrocarbon solvents. Natural high-salinity springs are known to add water and entrained inorganic and organic constituents to the Athabasca River and its tributaries in the region of ongoing bitumen production. However, the magnitude and synoptic distribution of these saline inputs has remained unquantified. Here, a chloride mass balance is used to estimate saline groundwater discharge to the Athabasca River from 1987 to 2010. Results show that the highest saline water discharge rate to the Athabasca River occurs between Ft. McMurray and the Peace-Athabasca Delta, supported by subcrop exposure of lower Cretaceous- and Devonian-aged formations bearing saline waters. Further, the input of saline groundwater is found to be an important control on the chemistry of the lower Athabasca River, despite comprising 10 � 1 to 3% of the Athabasca River’s discharge. The flux of natural saline groundwater entering the Athabasca does not appear to have increased or decreased from 1987 to 2010. The origin of seep salinity is interpreted as relict subglacial meltwater that has dissolved Devonian-aged evaporites, supported by saline Na-Cl type waters with low 18 O/ 16 O and 2 H/ 1 H ratios relative to modern precipitation. The magnitude of groundwater discharge and its impact on the Athabasca River’s chemistry in the area of ongoing bitumen development warrants the incorporation of natural groundwater seepages into surface water quality monitoring networks.

Application of water quality indices and analysis of the surface water quality monitoring network in semiarid North-Central Chile

Surface water quality has increasing importance worldwide and is particularly relevant in the semiarid North-Central Chile, where agriculture and mining activities are imposing heavy pressure on limited water resources. The current study presents the application of a water quality index in four watersheds of the 29°-33°S realm for the period 1999-2008, based on the Canadian Council of Ministers for the Environment approach and the Chilean regulation for irrigation water quality. In addition, two modifications to the index are tested and a comprehensive characterization of the existing monitoring network is performed through cluster analysis. The basins studied show fairly good water quality in the overall, specially the Limarí basin. On the other hand, the lower index values were obtained for the headwaters of Elqui, associated with the El Indio mining district. The first modification of the indicator (i.e., to consider parameters differentially according to their effect on human health or the environment) did not produce major differences with respect to the original index, given the generally good water quality. The second modification (i.e., to consider as threshold values the more restrictive figures derived from a set of regulations) yielded important differences in the indicator values. Finally, an adequate characterization of the monitoring network was obtained. The results presented spatial coherence and the information can be used as a basis for the optimization of the monitoring network if required.

A statistical approach for the rationalization of water quality indicators in surface water quality monitoring networks

Summary Despite several decades of operation and the increasing importance of water quality monitoring (WQM) networks, authorities still rely on subjective or semi-subjective decision processes to identify the optimal combination of water quality variables to measure. For this purpose, a statistical approach is developed for assessment and selection of the optimal combination of water quality variables. The proposed approach overcomes deficiencies in the conventional correlation–regression approach used to assess and eventually reduce the number of water quality variables in WQM networks. For the reduction of water quality variables, criteria developed from record-augmentation procedures are integrated with correlation analysis and cluster analysis to identify highly associated water quality variables. This step is followed by the application of an information performance index to systematically identify the optimal combination of variables to be continuously measured and those to be discontinued. The linear regression and maintenance of variance (MOVE) record-extension techniques are employed to reconstitute information about discontinued variables. The proposed approach is applied for rationalization of the water quality variables in the Nile Delta surface WQM network in Egypt. Results indicate that the proposed approach represents a useful decision support tool for the optimized selection of water quality variables. The MOVE record-extension technique is shown to result in better performance than regression for the estimation of discontinued variables.

Monitoring of diffuse pollution from agriculture to support implementation of the WFD and the Nitrate Directive in Estonia

Abstract This paper discusses the monitoring network for diffuse pollution from agriculture in Estonia in the context of implementation of the EU Water Framework Directive (WFD) and the Nitrate Directive (ND). Seven surface water monitoring stations in agricultural catchments represent two out of three river basin districts designated in Estonia according to the WFD criteria. The national monitoring programme of ground water quality involves 516 stations of which about half were monitored in 2005. The monitoring sites cover all main ground water bodies in Estonia but are largely concentrated in the Nitrate Vulnerable Zone (NVZ). Analyses did not reveal any significant trends in total nitrogen (TN) and total phosphorus (TP) concentrations in studied rivers during the last 15 years except in one site. The ground water quality stabilised after decrease of nitrate concentrations in the early 1990s, especially in the south part of the NVZ, but even in 2005 the nitrate concentration exceeded 50 mg l −1 in 42 out of 145 ground water samples in this region. The existing surface water quality monitoring network provides only restricted information to select between different management options when implementing action programmes for the NVZ and the river basin management plans (RBMP) under the WFD.

NEW ZEALAND'S NATIONAL WATER QUALITY MONITORING NETWORK ‐ DESIGN AND FIRST YEAR'S OPERATION1

ABSTRACT: The design and implementation of a national surface water quality monitoring network for New Zealand are described. Some of the lessons learned from the first year of operation are also addressed. Underpinning the design, and specified in advance, are the goal and objectives, the data quality assurance system, and the mechanism for data interpretation and reporting. Because of the difficulties associated with the use of a multitude of different agencies, only one agency is involved in field work and one laboratory undertakes the analysis. Staff training has been given a high priority. The network has been designed to give good trend detectability for regular sampling over a 5–10 year period.