Water quality assessment and source identification of Daliao river basin using multivariate statistical methods

Abstract

Multivariate statistical methods, such as cluster analysis (CA), discriminant analysis (DA) and principal component analysis (PCA), were used to analyze the water quality dataset including 13 parameters at 18 sites of the Daliao River Basin from 2003-2005 (8424 observations) to obtain temporal and spatial variations and to identify potential pollution sources. Using Hierarchical CA it is classified 12 months into three periods (first, second and third period) and the 18 sampling sites into three groups (groups A, B and C). Six significant parameters (temperature, pH, DO, BOD(5), volatile phenol and E. coli) were identified by DA for distinguishing temporal or spatial groups, with close to 84.5% correct assignment for temporal variation analysis, while five parameters (DO, NH(4)(+)-N, Hg, volatile phenol and E. coli) were discovered to correctly assign about 73.61% for the spatial variation analysis. PCA is useful in identifying five latent pollution sources for group B and C (oxygen consuming organic pollution, toxic organic pollution, heavy metal pollution, fecal pollution and oil pollution). During the first period, sites received more oxygen consuming organic pollution, toxic organic pollution and heavy metal pollution than those in the other two periods. For group B, sites were mainly affected by oxygen consuming organic pollution and toxic organic pollution during the first period. The level of pollution in the second period was between the other two periods. For group C, sites were mainly affected by oil pollution during the first period and oxygen consuming organic pollution during the third period. Furthermore, source identification of each period for group B and group C provided useful information about seasonal pollution. Sites were mainly affected by fecal pollution in the third period for group B, indicating the character of non-point source pollution. In addition, all the sites were also affected by physical-chemistry pollution. In the second and third period for group B and second period for group C sites were also affected by natural pollution.

Spatial and temporal variations of nitrogen pollution in Wen-Rui Tang River watershed, Zhejiang, China

“…During most of the time period covered by this study, the sluice gates were closed and water bodies were relatively stagnant, effectively eliminating any effects from upstream flushing and pollution sources. As the opening and closing of floodgates can alter pollutant concentrations, the change of pollutant concentrations between months observed in this study was not as predictable as in other studies unaffected by floodgate operations (Girija et al 2007;Zhang et al 2009). …”

“…Because the distribution of DO diverged so far from normality, this parameter was excluded from the analysis. For PCA all selected parameters were z-scale standardized (mean = 0 and variance = 1), which renders the data dimensionless and minimizes the effects of differences in measurement units and variance (Zhou et al 2007;Zhang et al 2009). Before the PCA analysis, the Kaiser-Meyer-Olkin (KMO) and Bartlett's Sphericity tests were performed on the parameter correlation matrix in order to examine the validity of the subsequent PCA.…”

“…Zhang et al (2009) used CA, DA, and PCA to analyze water quality and identify pollution sources in the Daliao River basin, China. However, comprehensive applications of different multivariable statistical methods, GIS analysis and concrete studies of each pollutant have not been fully explored in river studies in China.…”

“…Based on the monitoring program, the government has established a set of water quality standards to protect waterbodies and guide remediation activities. To fully understand water quality issues, many researchers have successfully investigated the application of multivariate and GIS analyses to address the spatial and temporal variations of water quality in various regions (Grande et al 2003;Mingoti and Lima 2006;Kallioinen et al 2006;Holbrook et al 2006;Singh et al 2006;Astel et al 2007;Shreshtha and Kazama 2007;Zhang et al 2009). Many of these studies have been able to geographically link water quality deterioration with specific human activities, which can then be used to guide remediation efforts.…”

Contamination assessment of arsenic and heavy metals in a typical abandoned estuary wetland—a case study of the Yellow River Delta Natural Reserve

“…Heavy metal pollutants have been considered to be one of the most critical global pollutants and pose a serious threat to human health and natural ecosystems (Lotze et al 2006;Li et al 2007;Li et al 2009;Fang et al 2009;Arik and Yaldiz 2010;Deng et al 2010;Naser 2013;Zhang et al 2009b). The first national soil pollution status survey bulletin released on April 17, 2014 launched by the Ministry of Environmental Protection and Ministry of Land and Resources of the People's Republic of China demonstrated that the ratio of exceeding soil standards in all of the samples is 16.1 % and the rate of arable land reaches 19.4 %, while the ratio of Cd is 7.0 % in all of the samples.…”