Article

Assessment of surface water quality using multivariate statistical techniques: a case study of Behrimaz Stream, Turkey.

Ministry of Agriculture and Rural Affairs, Province Control Laboratory, 21010, Diyarbakir, Turkey.
Environmental Monitoring and Assessment (impact factor: 1.4). 01/2009; 159(1-4):543-53. DOI:10.1007/s10661-008-0650-6 pp.543-53
Source: PubMed

ABSTRACT Multivariate statistical techniques, such as cluster analysis (CA), principal component analysis, and factor analysis, were applied for the evaluation of temporal/spatial variations and for the interpretation of a water quality data set of the Behrimaz Stream, obtained during 1 year of monitoring of 20 parameters at four different sites. Hierarchical CA grouped 12 months into two periods (the first and second periods) and classified four monitoring sites into two groups (group A and group B), i.e., relatively less polluted (LP) and medium polluted (MP) sites, based on similarities of water quality characteristics. Factor analysis/principal component analysis, applied to the data sets of the two different groups obtained from cluster analysis, resulted in five latent factors amounting to 88.32% and 88.93% of the total variance in water quality data sets of LP and MP areas, respectively. Varifactors obtained from factor analysis indicate that the parameters responsible for water quality variations are mainly related to discharge, temperature, and soluble minerals (natural) and nutrients (nonpoint sources: agricultural activities) in relatively less polluted areas; and organic pollution (point source: domestic wastewater) and nutrients (nonpoint sources: agricultural activities and surface runoff from villages) in medium polluted areas in the basin. Thus, this study illustrates the utility of multivariate statistical techniques for analysis and interpretation of data sets and, in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in water quality for effective stream water quality management.

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    Article: Spatial-temporal variation of surface water quality in the downstream region of the Jakara River, north-western Nigeria: A statistical approach.
    Adamu Mustapha, Ahmad Zaharin Aris, Mohammad Firuz Ramli, Hafizan Juahir
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    ABSTRACT: The pollution status of the downstream section of the Jakara River was investigated. Dissolved oxygen (DO), 5-day biochemical oxygen demand (BOD(5)), chemical oxygen demand (COD), suspended solids (SS), pH, conductivity, salinity, temperature, nitrogen in the form of ammonia (NH(3)), turbidity, dissolved solids (DS), total solids (TS), nitrates (NO(3)), chloride (Cl) and phosphates (PO(3-)(4)) were evaluated, using both dry and wet season samples, as a measure of variation in surface water quality in the area. The results obtained from the analyses were correlated using Pearson's correlation matrix, principal component analysis (PCA) and paired sample t-tests. Positive correlations were observed for BOD(5), NH(3), COD, and SS, turbidity, conductivity, salinity, DS, TS for dry and wet seasons, respectively. PCA was used to investigate the origin of each water quality parameter, and yielded 5 varimax factors for each of dry and wet seasons, with 70.7 % and 83.1 % total variance, respectively. A paired sample t-test confirmed that the surface water quality varies significantly between dry and wet season samples (P < 0.01). The source of pollution in the area was concluded to be of anthropogenic origin in the dry season and natural origins in the wet season.
    Journal of Environmental Science and Health Part A Toxic/Hazardous Substances & Environmental Engineering 09/2012; 47(11):1551-60.
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Keywords

1 year
 
effective stream water quality management
 
Factor analysis/principal component analysis
 
latent factors amounting
 
medium polluted
 
Multivariate statistical techniques
 
nonpoint sources
 
pollution sources/factors
 
principal component analysis
 
second periods
 
soluble minerals
 
surface runoff
 
temporal/spatial variations
 
two different groups
 
understanding temporal/spatial variations
 
Varifactors
 
water quality assessment
 
water quality data
 
water quality data sets
 
water quality variations