Interpret Water Quality Data Research Articles

Groundwater and surface water quality characterization through positive matrix factorization combined with GIS approach

This study aims at testing the effectiveness of Positive Matrix Factorization in characterizing groundwater and surface water quality, in terms of identifying main hydrochemical features and processes (natural and anthropogenic) that govern them. This method is applied in a hydro-system featured by a strong interrelation between groundwater and surface water and highly impacted by agricultural activities. Therefore, a holistic approach considering groundwater together with the surface water bodies, consisting in lake, several rivers and springs, was used.Multivariate statistical analysis, in particular Factor Analysis, has been proved to be effective in elaborating and interpreting water quality data highlighting the information carried within them, but it presents some limitations: it does not consider data uncertainty and it groups variables which are correlated positively and negatively. Moreover, in some cases the resulting factors are not clearly interpretable, describing each one various overlapping features/processes.Here, Positive Matrix Factorization is applied to groundwater and surface water quality data, and the results are compared to those obtained through a Factor Analysis in terms of both factor profiles and their spatial distribution through a GIS approach. Results of isotopes analysis are used to validate PMF output and support interpretation. Positive Matrix Factorization allows to consider data uncertainty and the solution respects two positivity constraints, based on the concept of chemical mass balance, which leads to a more environmentally interpretable solution.Results show that Positive Matrix Factorization identifies five different factors reflecting main features and natural and anthropogenic processes affecting the study area: 1) surface water used for irrigation, 2) groundwater subjected to reducing processes at advanced stages, 3) groundwater subjected to reducing processes at early stages, 4) groundwater residence time and 5) the effects of the agricultural land use on both groundwater and surface water.Positive Matrix Factorization leads to a more detailed understanding of the studied system as compared to Factor Analysis which identifies only three factors with overlapping information. Based on the results of this study, Positive Matrix Factorization could be a useful technique to perform groundwater and surface water quality characterization and to reach a deeper understanding of the phenomena that govern water chemistry.

Identifying cross-well fracture connections using the dissolved oxygen alteration method

Determining how wells in fractured crystalline bedrock are interconnected is essential in defining contaminant flow paths and in interpreting water quality data from wells. Although various tracers and geophysical techniques have been used with success, the high cost or logistics may restrict their implementation. This study demonstrates a low-cost technique for identifying fracture connections between wells by combining the dissolved oxygen alteration method (DOAM) with cross borehole hydraulic pumping tests. Following the alteration of the dissolved oxygen (DO) profile in the observation well through bubbler aeration, an interconnected well is pumped, which results in changes in borehole flow in the aerated observation well. The DO profile in the observation well is monitored over time to characterize the changes in borehole flow and identify fractures that hydraulically connect to the pumping well. The method was demonstrated at three test sites in Storrs, Connecticut. Each site has two or three interconnecting wells in fractured crystalline bedrock and varying flow conditions. The DOAM successfully identified interconnecting fractures in wells 15–84 m apart.

Discriminant analysis as a decision-making tool for geochemically fingerprinting sources of groundwater salinity

Concern over contamination of groundwater resources in areas impacted by anthropogenic activities has led to an increasing number of baseline groundwater quality surveys intended to provide context for interpreting water quality data. Flexible screening tools that can parse through these large, regional datasets to identify spatial or temporal changes in water quality are becoming more important to groundwater scientists. One such tool, developed from previous work by the authors, makes use of linear discriminant analysis (LDA) to identify the most probable source of chloride salinity in groundwater samples based on their geochemical fingerprints. Here, we applied the model to a dataset of shallow groundwater with known sources of contamination compiled from two studies of groundwater quality in Illinois: Panno et al. (2005) and Hwang et al. (2015). By predicting the source of salinity in groundwater samples for which the sources of contamination are known, we validated model prediction-accuracy. Results show high classification accuracy for groundwater samples impacted by basin brines (e.g. deep saline groundwater) and road salt (>80%), with diminishing success for those impacted by organic sources of chloride, such as septic effluent and animal waste. Posterior probabilities, a statistic inherent to LDA, provide a proxy for prediction confidence that enables the model to be used for assessment and accountability measures, such as identifying parties responsible for contamination. LDA is complementary to fingerprinting using halogen ratios (e.g. Cl/Br) because it implicitly relies on halogen ratios for classification decisions while providing a clearer, more quantitative classification of contamination sources. Our model is ideal for regional assessment or initial screening of salinity sources in groundwater because it makes use of commonly measured solute concentrations in publicly available water quality databases.

The Importance of Clinicians and Community Members Receiving Timely and Accurate Information about Waterborne Hazards.

It is important for clinicians and community members to receive up-to-date information about the microbiological and elemental composition of local water supplies. Clinicians play an important role in helping their patients to interpret water quality data and understand the potential impact of water quality on their health. Expanding the medical school curriculum to include environmental health, public health, and health disparities-including disparities related to environmental quality and waterborne hazards-is key to clinicians' fulfilling this role.

Assessment of water quality parameters using multivariate analysis for Klang River basin, Malaysia.

This case study uses several univariate and multivariate statistical techniques to evaluate and interpret a water quality data set obtained from the Klang River basin located within the state of Selangor and the Federal Territory of Kuala Lumpur, Malaysia. The river drains an area of 1,288 km(2), from the steep mountain rainforests of the main Central Range along Peninsular Malaysia to the river mouth in Port Klang, into the Straits of Malacca. Water quality was monitored at 20 stations, nine of which are situated along the main river and 11 along six tributaries. Data was collected from 1997 to 2007 for seven parameters used to evaluate the status of the water quality, namely dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, suspended solids, ammoniacal nitrogen, pH, and temperature. The data were first investigated using descriptive statistical tools, followed by two practical multivariate analyses that reduced the data dimensions for better interpretation. The analyses employed were factor analysis and principal component analysis, which explain 60 and 81.6% of the total variation in the data, respectively. We found that the resulting latent variables from the factor analysis are interpretable and beneficial for describing the water quality in the Klang River. This study presents the usefulness of several statistical methods in evaluating and interpreting water quality data for the purpose of monitoring the effectiveness of water resource management. The results should provide more straightforward data interpretation as well as valuable insight for managers to conceive optimum action plans for controlling pollution in river water.

Water Quality Assessment of Gufu River in Three Gorges Reservoir (China) Using Multivariable Statistical Methods

To provide the reasonable basis for scientific management of water resources and certain directive significance for sustaining health of Gufu River and even maintaining the stability of water ecosystem of the Three-Gorge Reservoir of Yangtze River, central China, multiple statistical methods including Cluster Analysis (CA), Discriminant Analysis (DA) and Principal Component Analysis (PCA) were performed to assess the spatial-temporal variations and interpret water quality data. The data were obtained during one year (2010~2011) of monitoring of 13 parameters at 21 different sites (3003 observations), Hierarchical CA classified 11 months into 2 periods (the first and second periods) and 21 sampling sites into 2 clusters, namely, respectively upper reaches with little anthropogenic interference (UR) and lower reaches running through the farming areas and towns that are subjected to some human interference (LR) of the sites, based on similarities in the water quality characteristics. Eight significant parameters (total phosphorus, total nitrogen, temperature, nitrate nitrogen, total organic carbon, total hardness, total alkalinity and silicon dioxide) were identified by DA, affording 100% correct assignations for temporal variation analysis, and five significant parameters (total phosphorus, total nitrogen, ammonia nitrogen, electrical conductivity and total organic carbon) were confirmed with 88% correct assignations for spatial variation analysis. PCA (varimax functionality) was applied to identify potential pollution sources based on the two clustered regions. Four Principal Components (PCs) with 91.19 and 80.57% total variances were obtained for the Upper Reaches (UR) and Lower Reaches (LR) regions, respectively. For the UR region, the rainfall runoff, soil erosion, scouring weathering of crustal materials and forest areas are the main sources of pollution. The pollution sources for the LR region are anthropogenic sources (domestic and agricultural runoff, hydropower exploitation and municipal waste). The study demonstrates the utility of multivariate statistical techniques for river water quality assessment, identification of pollution sources, and exploring spatial and temporal variations of water quality.

Current Issues Regarding Gathering, Screening, and Interpreting Water Quality Data and Related Information in the Context of Section 303(d) of the Clean Water Act

Current Issues Regarding Gathering, Screening, and Interpreting Water Quality Data and Related Information in the Context of Section 303(d) of the Clean Water ActThough included in the original 1972 federal Clean Water Act, the Section 303(d) listing program lay dormant for nearly two decades. Since the early 1990s, EPA and the states have been struggling, despite having significantly less than ideal amounts of human and financial resources, to carry out this challenging program in a manner consistent with the letter and spirit of the law. (For instance,...Author(s)Bill PainterSourceProceedings of the Water Environment FederationSubjectSession 13: Current Issues in Integrated 303(d)/305(b) ReportingDocument typeConference PaperPublisherWater Environment FederationPrint publication date Jan, 2011ISSN1938-6478SICI1938-6478(20110101)2011:2L.649;1-DOI10.2175/193864711802864633Volume / Issue2011 / 2Content sourceImpaired Waters SymposiumFirst / last page(s)649 - 669Copyright2011Word count469Subject keywordsSection 303(d)303(d) listingassessment methodswater quality standardswater quality standards attainment determination

Towards an Integrated Ecosystem-Based Bioaccumulation and Metal Speciation Model

Heavy metal bioaccumulation models are important for interpreting water quality data, predicting bioaccumulation in organisms, and investigating the provenance of contaminants. To date they have been predominantly used as single-issue models, under steady-state conditions and in isolation of the biogeochemical processes that control metal bioaccumulation. Models that incorporate these processes would allow a more holistic approach to bioaccumulation modeling and contaminant assessment; however, this has been rarely undertaken, probably because it requires the integration of inter-disciplinary areas. In this study, we have developed such a model that integrates three key multi-disciplinary areas (biological, metal speciation, and bioaccumulation processes) and responds to variations in temporal external and internal forcing. Furthermore, spatial context is provided by developing the model within a simple hydrodynamic box-modeling framework. The calibrated model was able to predict with reasonable accuracy the temporal and spatial trends of soft-tissue copper bioaccumulation in a coastal oyster. This exploratory model was also used to highlight the importance of phytoplankton as an important vector of copper uptake dynamics by an oyster, therefore reinforcing the importance of the integrated approach. Finally, our model provides a framework for greater application beyond this specific example such as in the areas of waterway restoration, which has been shown to be an important area of ecological and environmental research.

Assessment of anthropogenic sources of water pollution using multivariate statistical techniques: a case study of the Alqueva’s reservoir, Portugal

Multivariate statistical techniques were applied to evaluate spatial/temporal variations, and to interpret water quality data set obtained at Alqueva reservoir (south of Portugal). The water quality was monitored at nine different sites, along the water line, over a period of 18 months (from January 2006 to May 2007) using 26 water quality parameters. The cluster analysis allowed the formation of five different similarity groups between sampling sites, reflecting differences on the water quality at different locations of the Alqueva reservoir system. The PCA/FA identified six varifactors, which were responsible for 64% of total variance in water quality data set. The principal parameters, which explained the variability of quality water, were total phosphorus, oxidability, iron, parameters that at high concentrations indicate pollution from anthropogenic sources, and herbicides indicative of an intensive agricultural activity. The spatial analysis showed that the water quality was worse in the north of the reservoir.

Interpreting drinking water quality in the distribution system using Dempster–Shafer theory of evidence

Interpreting water quality data routinely generated for control and monitoring purposes in water distribution systems is a complicated task for utility managers. In fact, data for diverse water quality indicators (physico-chemical and microbiological) are generated at different times and at different locations in the distribution system. To simplify and improve the understanding and the interpretation of water quality, methodologies for aggregation and fusion of data must be developed. In this paper, the Dempster–Shafer theory also called theory of evidence is introduced as a potential methodology for interpreting water quality data. The conceptual basis of this methodology and the process for its implementation are presented by two applications. The first application deals with the interpretation of spatial water quality data fusion, while the second application deals with the development of water quality index based on key monitored indicators. Based on the obtained results, the authors discuss the potential contribution of theory of evidence as a decision-making tool for water quality management.

Water Quality Laboratory for Large Civil Engineering Classes

In New Zealand, civil engineering classes are getting larger every year, which means that innovative ways must be sought to enable an increasing number of students to fulfill the laboratory requirements of the degree. With respect to water quality laboratories in introductory environmental engineering courses, it is advocated that it is more important that a civil engineering student be able to interpret water quality data, rather than to be acquainted in detail with the actual mechanics of water quality tests. As such, this paper describes a water quality laboratory that allows the student to gain an appreciation of a water quality data set without necessarily becoming fully conversant with the routine water quality tests. The laboratory’s emphasis on data interpretation instructs students on the importance of examining data sets for patterns, correlations and/or potential errors, while at the same time providing a stimulating and active hands-on learning environment for both students and staff.

Ground-water quality of the Nile west bank related to soil characteristics and geological setting

The objective of this study is to outline how the ground-water quality is affected by geological factors, soil characteristics and subsurface structure. The results of 25 ground-water samples, and 30 soil samples of which 10 soil samples analysed chemically provide the basis of this study. Procedures to interpret water quality data are based on a combined use of the traditional graphical methods, ratios of Na+/Cl−and Ca2+/Mg2+, multivariate geostatistical methods and computation of the saturation indices (SI) of minerals. The results of soil analyses show that the grain size ranges from very coarse sand to clayey loams, and from well sorted and extremely poorly sorted. The soil salinity increases in old alluvial plains and decreases in the dissected wadis of limestone plateaux, while vertical distribution decreases with depth. It is found that the ground-water is supersaturated with respect to calcite and dolomite, and undersaturated with respect to gypsum. Calculated values of PCO2for the ground-water samples range from 2×10−4to 7×10−3with a mean value of 3×10−3(atm.). This indicates that the ground-water becomes charged with CO2during infiltration processes.

A water quality index for contact recreation in New Zealand

We surveyed the opinions of 16 water quality experts in order to develop a water quality index for contact recreation in freshwaters in New Zealand. The index was developed by postal surveys using the Delphi method, involving feedback of information to the panel members at each iteration. Determinands selected for use in the index were as follows: faecal bacterial indicators (faecal coliforms or E. coli), pH, Munsell colour, visual clarity indicators (black disc visibility or turbidity), and nutrients promoting nuisance growths (filtered BOD5, and dissolved forms of phosphorus and nitrogen). "Sub-index" curves relating suitability-for-use to these water quality determinands have been developed. The mean ("consensus") sub-index curves can be used to interpret water quality data in terms of suitability-for-use scores. We advocate using the lowest suitability-for-use score for a water as its overall index value for contact recreation. Thus the water body's suitability-for-use is determined by its "poorest" characteristic. The index is now ready to be tested by water managers for its utility in state-of-environment reporting.

Measuring Water Quality and Predicting Well Impairment

The economic performance of a waterflood or a water disposal project can be significantly affected by suspended solids in the injection water. Here are methods and a theory that can be used to interpret water quality data obtained with membrane filters or cores and to predict well impairment caused by suspended solids. predict well impairment caused by suspended solids. Introduction In a waterflood or a water disposal project the possibility exists that suspended solids will cause the possibility exists that suspended solids will cause the injection wells to become impaired. Filtration can usually reduce the concentration of suspended solids; however, the cost of water treating should be balanced against the cost of other alternatives, such as periodic stimulation or replacement of injection wells. In some cases extensive water treating can be justified, but under other circumstances it will be more profitable to inject untreated water. Water quality is affected by several types of contaminants, including suspended silts, clays, scale, oil and bacteria. Any of these may be the predominant source of improvement in a particular injection water and environment. Formation cores, artificial cores. and membrane filters have been used in the industry to monitor suspended solids and to evaluate water quality. Some studies have defined water quality in terms of filtration rates or other experimental data. The disadvantage of these empirical definitions is that they cannot be directly related to well impairment. This paper proposes a measure of water quality that is defined as the ratio of the concentration of suspended solids to the permeability of the filter cake formed by those solids. The water quality ratio can be obtained directly from membrane or core filtration data and can be used to calculate the rate of formation impairment. Formation Impairment from Suspended Solids In considering the effects of suspended solids, some measure of the rate of impairment is needed. A convenient way to estimate how long an injector can be used before stimulation is required is to calculate its half-life. The half-life is defined as the time required for the injection rate to decrease to 50 percent of its initial value. The time required to reach some other fractional reduction in rate can also be calculated. Impairment from suspended solids is thought to occur by one of the following mechanisms (see Fig. 1): The solids form a filter cake on the face of the wellbore (wellbore narrowing); The solids invade the formation, bridge, and form an internal filter cake (invasion); The solids become lodged in the perforations (perforation plugging); and The solids settle to the bottom of the well by gravity and decrease the net zone height (wellbore fillup). Each of the four basic impairment mechanisms is modeled in Appendix A for a constant-pressure-drop process, Equations are derived that express the time process, Equations are derived that express the time required for the injection rate to decline to some fraction a of its initial value. For each mechanism, this time can be expressed as the product of the two function, F and G. P. 865