Application of multivariate statistical techniques in the assessment of water quality in the Southwest New Territories and Kowloon, Hong Kong

Zhang, Xuan; Wang, Qishan; Liu, Yanfang; Wu, Jing; Yu, Miao

doi:10.1007/s10661-010-1366-y

Cited by 91 publications

(49 citation statements)

References 13 publications

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“…This factor explains 40.72% of the total variance and represents organic pollution mainly from domestic wastewater, industrial effluents, and discharges from wastewater treatment plants. This result is supported by the studies of Shrestha and Kazama [46] and Zhang et al [58]. VF2 explains 19.30% of the total variance, and consists of parameters EC, SO 2À 4 , and Cl À .…”

Section: Pollution Source Induced Changes Of Water Quality Between Drsupporting

confidence: 73%

Water Quality Management Based on Division of Dry and Wet Seasons in Pearl River Delta, China

Fan

Cui

Zhang

et al. 2012

CLEAN Soil Air Water

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In the Pearl River Delta (PRD), river water quality deteriorates continually due to the population increase and ongoing industrialization and urbanization. In this study, a water quality management paradigm based on the seasonal variation is proposed. For better exploring the seasonal change of water quality, wavelet analysis was used to analyze the division of dry and wet seasons in the PRD during 1952PRD during -2009. Then water quality seasonal variation in 2008 and relevant impact factors were analyzed by multivariate statistic methods as a case to make some management measures. The results show that there are some differences of dry and wet seasons division among different years. Wet season mainly appear from April to September, which occupy the largest proportion among the 58 years (about 70%) and then followed by the wet season from May to October (about 13.8% of the total years). As to the water quality of 2008, significant differences exist between dry and wet seasons for 17 water quality parameters except TP, NO À 3 , Fe 2þ , and Zn 2þ . Levels of parameters pH, EC, COD Mn , BOD 5 , NH þ 4 , SO 2À 4 , and Cl À in dry season are much higher than those in wet season. In dry season the variations of river water quality are mainly influenced by domestic sewage, industrial effluents, and salt water intrusion. While in wet season, except the aforementioned pollution sources, drainages from cultivated land and livestock farm are also the main factors influencing water pollution. Thus, water quality management measures are proposed in dry and wet seasons, respectively. The results obtained from this study would further facilitate water quality protection and water resources management in the PRD.

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Section: Pollution Source Induced Changes Of Water Quality Between Drsupporting

confidence: 73%

Water Quality Management Based on Division of Dry and Wet Seasons in Pearl River Delta, China

Fan

Cui

Zhang

et al. 2012

CLEAN Soil Air Water

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“…Thus, in recent years, there has been an increasing interest by researchers in analyzing such complex data using robust mathematics and statistical techniques, such as fuzzy comprehensive evaluation method (FCA), cluster analysis (CA), discriminant analysis (DA), and principal component analysis/factor analysis (PCA/FA), and absolute principal component score-multiple linear regression (APCS-MLR) [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. A literature review of these methods is described below.…”

Section: Introductionmentioning

confidence: 99%

“…A study conducted by Singh et al [18] showed that DA offers an important data reduction by using only six variables discriminating spatial pattern and two variables for temporal variation in Gomti River, India. Similarly, Zhang et al [19] applied the method to evaluate spatial-temporal variation of water quality in southwest new territories and Kowloon, Hong Kong, and revealed that four and eight parameters could afford 84.2% and 96.1% correct assignation in temporal and spatial analysis, respectively. Furthermore, they also suggested that the number of monitoring variables and the associated cost can be reduced, as the method afforded a considerable data reduction in the dimensionality of the large data set.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Variations and Source Apportionment of Water Pollution in Danjiangkou Reservoir Basin, Central China

Chen

Lan

Zhang

2015

Water

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Understanding the spatio-temporal variation and the potential source of water pollution could greatly improve our knowledge of human impacts on the environment. In this work, data of 11 water quality indices were collected during 2012-2014 at 10 monitoring sites in the mainstream and major tributaries of the Danjiangkou Reservoir Basin, Central China. The fuzzy comprehensive assessment (FCA), the cluster analysis (CA) and the discriminant analysis (DA) were used to assess the water pollution status and analyze its spatio-temporal variation. Ten sites were classified by the high pollution (HP) region and the low pollution (LP) region, while 12 months were divided into the wet season and the dry season. It was found that the HP region was mainly in the small tributaries with small drainage areas and low average annual discharges, and it was also found that most of these rivers went through urban areas with industrial and domestic sewages input into the water body. Principal component analysis/factor analysis (PCA/FA) was applied to reveal potential pollution sources, whereas absolute principal component score-multiple linear regression (APCS-MLR) was used to identify their contributions to each water quality variable. The study area was found as being generally affected by OPEN ACCESSWater 2015, 7 2592 industrial and domestic sewage. Furthermore, the HP region was polluted by chemical industries, and the LP region was influenced by agricultural and livestock sewage.

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“…It therefore becomes necessary to use tools which allow the reduction and grouping of the large amount of information (historical data) arising from studies of the varying quality and quantity of water resources, involving a multivariate function of several aspects in such a way as to allow the interpretation and recognition of trends over time and in space (Alexandre et al, 2010;Li et al, 2011;Zhang et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Assessment of the water quality in a large reservoir in semiarid region of Brazil

Lopes

Andrade

Meireles

et al. 2014

Rev. bras. eng. agríc. ambient.

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A B S T R A C TThe aim of this study was to identify spatial and temporal variations in water quality of Orós reservoir, Ceará, Brazil, as well as the sources of contamination. To get this information the Principal Component Analysis (PCA) and Cluster Analysis (CA) was used. Water samples were collected at seven (geo-referenced) points, from April 2008 to March 2011, totalling 4,032 samples. The following attributes of the waters were analysed: temperature, pH, CE, Ca , turbidity, colour, Sechi transparency, TS, TVS, TFS, TSS, VSS, FSS, TDS, DO, BO 5D , total phosphorus, soluble orthophosphate, EC, TTC, total ammonia, TKN, nitrate, SAR and chlorophyll-a. The PCA promoted the reduction from the 32 initial variables to 14, accounting for 84.39% of the total variance. The major factors responsible for water quality composition are: the natural weathering of geological soil components; the entrainment of suspended solids through surface runoff from agricultural areas; and anthropogenic action in the Upper Jaguaribe basin in Ceará. The similarity of the water of the Orós reservoir allows a reduction in the number of sampling points, which may result in significant cost savings without sacrificing the water quality monitoring. The similarity of the waters was influenced by anthropic activities being carried out near the reservoir and all along the watershed.Investigação da qualidade das águas de um grande reservatório na região semiárida do Brasil R E S U M O Objetivou-se estudar, com esta pesquisa, a variabilidade espaço temporal da qualidade das águas do açude Orós, Ceará, além das variáveis determinantes desta qualidade razão pela qual foram empregadas a análise de componentes principais (ACP) e a análise de agrupamento hierárquico (AAH). As amostras de água foram coletadas em sete pontos (georreferenciados), no período de abril de 2008 a março de 2011, totalizando 4032 amostras. Nas águas foram analisados os seguintes atributos: temperatura, pH, CE, Ca , turbidez, cor, transparência de sechi, ST, STV, STF, SST, SSV, SSF, SDT, OD, DBO 5 , fósforo total, ortofosfato solúvel, EC, CTT, amônia total, NKT, nitrato, RAS e clorofila-a. A ACP promoveu redução das 32 variáveis iniciais para 14 variáveis explicando 84,39% da variância total. Os principais fatores responsáveis pela qualidade das águas do Orós foram: processo natural de intemperismo dos componentes geológicos do solo; carreamentos dos sólidos suspensos através do escoamento superficial das águas oriundas de áreas agrícolas e ações humanas ocorridas nas partes altas da bacia do Alto Jaguaribe. A similaridade das águas do Orós permite uma redução nos pontos de amostragem, o que pode resultar em menor custo sem comprometer o monitoramento da qualidade das águas. A similaridade das águas foi influenciada pelas atividades antrópicas exercidas nas proximidades do reservatório e ao longo da bacia.

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Application of multivariate statistical techniques in the assessment of water quality in the Southwest New Territories and Kowloon, Hong Kong

Cited by 91 publications

References 13 publications

Water Quality Management Based on Division of Dry and Wet Seasons in Pearl River Delta, China

Water Quality Management Based on Division of Dry and Wet Seasons in Pearl River Delta, China

Spatio-Temporal Variations and Source Apportionment of Water Pollution in Danjiangkou Reservoir Basin, Central China

Assessment of the water quality in a large reservoir in semiarid region of Brazil

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