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This study assessed groundwater quality in Alaçam, where irrigations are performed solely with groundwaters and samples were taken from 35 groundwater wells at pre and post irrigation seasons in 2014. Samples were analyzed for 18 water quality parameters. SAR, RSC and %Na values were calculated to examine the suitability of groundwater for irrigation. Hierarchical cluster analysis and principal component analysis were used to assess the groundwater quality parameters. The average EC value of groundwater in the pre-irrigation period was 1.21 dS/m and 1.30 dS/m after irrigation in the study area. It was determined that there were problems in two wells pre-irrigation and one well post-irrigation in terms of RSC, while there was no problem in the wells in terms of SAR. Piper diagram and cluster analysis showed that most groundwaters had CaHCO3 type water characteristics and only 3% had NaCl- as the predominant type. Seawater intrusion was identified as the primary factor influencing groundwater quality. Multivariate statistical analyses to evaluate polluting sources revealed that ground-water quality is affected by seawater intrusion, ion exchange, mineral dissolution and anthropogenic factors. The use of multivariate statistical methods and geographic information systems to manage water resources will be beneficial for both planners and decision-makers. [ABSTRACT FROM AUTHOR]

In intermittent drinking water distribution systems, large volumes of the water are wasted due to leaks in the distribution networks. Similarly, user service is not always satisfied in the time required to fill the storage, nor with sufficient pressure. Hence the importance of this study. Measuring the variability of pressure in the distribution network and determining the factors that influence the definition of a sufficient minimum hours of service, is a first step to change to a continuous service 24/7, in order to minimize the volumes of lost water and meet demand. In total, 347 pressure sensors were placed in a network to detect changes in pressure and obtain data for 3 years. This study presents a new approach to determine the operating policy of the operating agency that provides the service intermittently. Two objectives are pursued: pressure variability – to minimize leaks – and define the minimum hours of service. The analysis was performed using multivariate statistical techniques, including principal component analysis, correlation matrix and ANOVAs, to explore the association between objectives. The results obtained show that the pressure distribution has a Gaussian behavior and that the hours of service have a Poisson distribution [ABSTRACT FROM AUTHOR]

This study evaluated temporal and spatial variations in water quality to understand the characteristics of the Jialing River watershed in Guangyuan City, China. Data on 17 parameters obtained from seven sites from 2012 to 2015, with a total of 329 samples for each parameter, were analyzed using multivariate statistical techniques. Observation months were grouped into two periods (Period A, May-November; Period B, December-April) according to similarities in water quality characteristics through time analysis and cluster analysis (CA). Water temperature (TEMP), flow rate (Q), dissolved oxygen (DO), oils, fluoride (F) and cadmium (Cd) were the most significant parameters for discriminating between the two periods. Through a spatial analysis, the sites were classified into two groups (Groups 1 and 2). Q, total phosphorus (TP), oils, F and fecal coliform bacteria (F. coli) were the most significant parameters for discriminating between the two groups. Results suggested that TEMP, DO and Cd as functions of time, TP and F. coli as functions of space, and Q, oils and F as functions of both time and space should be monitored closely. The main sources of water pollutant were surface runoff and industrial wastewater, of time, and wastewater from agricultural irrigation, industrial wastewater, and municipal sewage, of space. [ABSTRACT FROM AUTHOR]

Water quality in urban rivers is a product of the interactions of human activities and natural processes. To explore water quality characteristics and to assess the impacts of natural and anthropogenic processes on urban river systems, we used multivariate statistical techniques to analyse water quality of a typical urban river in eastern China. Cluster analysis grouped the sites into four clusters which were affected by wastewater treatment plant effluent, untreated domestic sewage, tributaries and shipping, respectively. Cluster analysis provided scientific basis for optimizing the monitoring scheme. Three latent factors obtained from principal component analysis/ factor analysis were interpreted as wastewater treatment plant effluent, untreated domestic sewage and surface runoff. Absolute principal component analysis indicated that most of the total dissolved phosphorus, nitrite, total dissolved nitrogen, and total nitrogen, Na, K and Cl resulted from the wastewater treatment plant effluent, most of the ammonia, dissolved organic carbon, sulfate and Mg resulted from the surface runoff. The pollution control measures for nitrogen and phosphorus were proposed based on the source apportionment results. The present study showed that the multivariate statistical methods are effective to identify the main pollution sources, quantify their relative contributions and provide useful water management suggesitions in urban rivers. [ABSTRACT FROM AUTHOR]

The present research deals with the important issue of assessing surface water quality ranking by combining the use of two advanced multivariate statistical techniques: Kohonen's self-organizing maps (SOM) and the Hasse diagram technique (HDT). The object of the study is the Mudan River of Mudanjiang city region, China. Samples were collected on a regular monthly basis in 2007-2011 from all sampling sites along the river, involving six major water quality parameters. The grouping of water parameters and the clustering of sampling events by the use of SOM has helped in their preprocessing for application of the HDT. The HDT orders clusters according to the pre-clustered water sampling events. The water quality was ranked against norms established by the Ministry of Environmental Protection of the People's Republic of China in order to assess in detail the water quality of the whole river system. The resulting map of the spatial and temporal changes in the water quality at each sampling site was specifically described by ArcGIS. [ABSTRACT FROM AUTHOR]