Water Pollution Analysis

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Introduction
Surface water (rivers, streams, and lakes) covers nearly 70% of the Earth’s surface are the main sources of water for industrial, domestic, and irrigation uses making it one of the most important players in the hydrologic and biogeochemical cycles. As important as these surface waters are, only a small number are maintained and found in their natural condition due to intensive anthropogenic activities such as urbanization and surface water pollution, thereby, making it a great environmental problem in the world at large (Zhao et al., 2011). Most often than not, these rivers and streams are highly vulnerable water bodies because of the role they play in carrying off and taking in point (domestic wastewater and industrial discharge)
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In an urban setting like Greensboro, the predominant factors determining the water quality characteristics of rivers and streams are the (pollutants washing off Greensboro’s urban landscape homes, yards, cars, roads, office buildings, etc.) rather than from just one identifiable source every time it rains (Greensboro Water Resources Dept., 2012). However, majority of the pollution comes from factories or Wastewater Treatment Plant (WWTP) where treated or untreated wastewater is indirectly discharged (spilled or leaked) into the creek or river (Greensboro Water Resources Dept., 2012). In 2012, some serious pollution events were recorded in the Buffalo Creek and Reedy Fork Creek river basin where sewage spills from the collection system exceeding 1, 000 Gallons were recorded (Greensboro Sewer Report, 2012). The growing municipal and industrial wastewater discharges due to rapid urbanization and industrialization, harmful agricultural practices, along with limited wastewater treatment facility and capacity, are the principal drivers of water pollution events. A larger percentage of total wastewater discharged into the rivers and lakes comes from industry and …show more content…
In general, the sample size is based on the power of a statistical test of the hypothesis. In descriptive studies, this approach cannot be used, and it is usually the range of the confidence interval of a given parameter which determines sample size. This is probably the case in internal validity studies of measurement scales in which, traditionally, two types of parameters are of interest, the Cronbach’s alpha coefficient (α) which assesses reliability, and factor analysis loadings which explore the dimensional structure of the scale. In practice, these loadings are estimated either by Principal Component Analysis (PCA) or by Exploratory Factor Analysis (EFA or FA) (Cattell, 1978; Gorsuch). Lots of proposed rules, with regards to sample size in factor analysis, have been made, but none are founded on a strict theoretical or empirical basis. Among all the rules, the most widely used one is the ratio of the number of subjects (N) to the number of items (p), and this varies from 3 to 10 depending on authors (Cattell, 1978; Gorsuch, 1983; Nunnaly, 1978). Others have also suggested an absolute minimum sample size of 50 to 500 to enable Factor Analysis (FA) to be performed on the samples (Aleamoni, 1973; Comrey et al., 1992; Loo, 1983). Taking into account all these suggestions about sample size and their lack of documented explanation, some

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