Review of aquatic monitoring program design

Dixon, W. V.; Chiswell, B.

doi:10.1016/0043-1354(96)00087-5

Cited by 220 publications

(115 citation statements)

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“…Regular monitoring by DOE provides the available spatial variation data. However, interpreting the huge amount of data is a challenge requiring one to use correct methods of data interpretation (Chapman 1992;Dixon and Chiswell 1996). Environmetrics can be considered as a branch of environmental analytical chemistry that uses multivariate statistical modeling and data treatment also known as chemometrics; (Simeonov et al 2000).…”

Section: Introductionmentioning

confidence: 99%

Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques

Juahir

Zain

Yusoff

et al. 2010

Environ Monit Assess

285

196

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This study investigates the spatial water quality pattern of seven stations located along the main Langat River. Environmetric methods, namely, the hierarchical agglomerative cluster analysis (HACA), the discriminant analysis (DA), the principal component analysis (PCA), and the factor analysis (FA), were used to study the spatial variations of the most significant water quality variables and to determine the origin of pollution sources. Twenty-three water quality parameters were initially selected and analyzed. Three spatial clusters were formed based on HACA. These clusters are designated as downstream of Langat river, middle stream of Langat river, and upstream of Langat River regions. Forward and backward stepwise DA managed to discriminate six and seven water quality variables, respectively, from the original 23 variables. PCA and FA (varimax functionality) were used to investigate the origin of each water quality variable due to land use activities based on the three clustered regions. Seven principal components (PCs) were obtained with 81% total variation for the high-pollution source (HPS) region, while six PCs with 71% and 79% total variances were obtained for the moderatepollution source (MPS) and low-pollution source (LPS) regions, respectively. The pollution sources for the HPS and MPS are of anthropogenic sources (industrial, municipal waste, and agricultural runoff). For the LPS region, the domestic and agricultural runoffs are the main sources of pollution. From this study, we can conclude that the application of environmetric methods can reveal meaningful information on the spatial variability of a large and complex river water quality data.

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Section: Introductionmentioning

confidence: 99%

Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques

Juahir

Zain

Yusoff

et al. 2010

Environ Monit Assess

285

196

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“…Therefore, the effective, long-term management of rivers requires a fundamental understanding of hydro-morphological, chemical and biological characteristics (Shrestha and Kazama, 2007). However, due to spatial and temporal variations in water quality (which are often difficult to interpret), a monitoring program, providing a representative and reliable estimation of the quality of surface waters is necessary (Dixon and Chiswell, 1996). The application of different multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA) helps in the interpretation of complex data matrices to better understand the water quality and ecological status of the studied systems, allows the identification of possible factors sources that influence water systems and offers a valuable tool for reliable management of water resources, as well as rapid solution to pollution problems (Lee et al, 2001;Reghunath et al, 2002;Vega et al, 1998;Wunderlin et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of spatial and seasonal variations in surface water quality using multivariate statistical techniques

Pejman

Bidhendi

Karbassi

et al. 2009

Int. J. Environ. Sci. Technol.

199

101

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ABSTRACT:In this study, spatial and seasonal variations of water quality in Haraz River Basin were evaluated using multivariate statistical techniques, such as cluster analysis, principal component analysis and factor analysis. Water quality data collected from 8 sampling stations in river during 4 seasons (Summer and Autumn of 2007, Winter and Spring of 2008) were analyzed for 10 parameters (dissolved oxygen, Fecal Coliform, pH, water temperature, biochemical oxygen demand, nitrate, total phosphate, turbidity, total solid and discharge). Cluster analysis grouped eight sampling stations into three clusters of similar water quality features and thereupon the whole river basin may be categorized into three zones, i.e. low, moderate and high pollution. The principle component analysis/factor analysis assisted to extract and recognize the factors or origins responsible for water quality variations in four seasons of the year. The natural parameters (temperature and discharge), the inorganic parameter (total solid) and the organic nutrients (nitrate) were the most significant parameters contributing to water quality variations for all seasons. Result of principal component analysis and factor analysis evinced that, a parameter that can be significant in contribution to water quality variations in river for one season, may less or not be significant for another one.

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“…Later on, in an attempt to assess systematic issues relevant to network design, more studies applied the techniques of integer programming (Hudak et al, 1995), statistical assessment (Hussain et al, 1995), multi-objective programming (Harmancioglu and Alpaslan, 1992;Cieniawski et al, 1995), and Kriging theory (Lo et al, 1996). A broader sense of applications was gained from the discussions of design principles of monitoring network (Dixon and Chiswell, 1996) and the guidelines related to biological impact assessment in the rivers (Timmerman et al, 1997). On the other hand, ground water monitoring network design was viewed as an intimate issue linked with surface water monitoring network design.…”

Section: Introductionmentioning

confidence: 99%

Optimal Expansion of Water Quality Monitoring Network by Fuzzy Optimization Approach

Ning¹,

Chang²

2004

Environ Monit Assess

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Abstract. River reaches are frequently classified with respect to various mode of water utilization depending on the quantity and quality of water resources available at different location. Monitoring of water quality in a river system must collect both temporal and spatial information for comparison with respect to the preferred situation of a water body based on different scenarios. Designing a technically sound monitoring network, however, needs to identify a suite of significant planning objectives and consider a series of inherent limitations simultaneously. It would rely on applying an advanced systems analysis technique via an integrated simulation-optimization approach to meet the ultimate goal. This article presents an optimal expansion strategy of water quality monitoring stations for fulfilling a long-term monitoring mission under an uncertain environment. The planning objectives considered in this analysis are to increase the protection degree in the proximity of the river system with higher population density, to enhance the detection capability for lower compliance areas, to promote the detection sensitivity by better deployment and installation of monitoring stations, to reflect the levels of utilization potential of water body at different locations, and to monitor the essential water quality in the upper stream areas of all water intakes. The constraint set contains the limitations of budget, equity implication, and the detection sensitivity in the water environment. A fuzzy multi-objective evaluation framework that reflects the uncertainty embedded in decision making is designed for postulating and analyzing the underlying principles of optimal expansion strategy of monitoring network. The case study being organized in South Taiwan demonstrates a set of more robust and flexible expansion alternatives in terms of spatial priority. Such an approach uniquely indicates the preference order of each candidate site to be expanded step-wise whenever the budget limitation is sensitive in the government agencies.Keywords: environmental systems analysis, fuzzy multi-objective programming, monitoring network, river basin planning, water quality management NotationThe following symbols are used in this article: SHU-KUANG NING AND NI-BIN CHANGE ij = the distance between the j th monitoring station located at the ith tributary and the nearest potable water intake in the upper stream area (km); L c = allowable overlapped half-life distance in this systems analysis (km); L i = the lower bound of tolerance interval in the ith fuzzy membership function; L ij k = the distance required for a decay of half concentration of the kth pollutant where the j th monitoring station in the ith tributary is located (km); M = the upper bound of the number of monitoring stations (no unit); p = the number of tributary in Kao-Ping River basin; P ij = the population covered within the 10 km in radius where the j th monitoring station in the ith tributary is located (capita); q i = the total number of candidate station in the ith ...

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Review of aquatic monitoring program design

Cited by 220 publications

References 91 publications

Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques

Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques

Evaluation of spatial and seasonal variations in surface water quality using multivariate statistical techniques

Optimal Expansion of Water Quality Monitoring Network by Fuzzy Optimization Approach

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