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

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.     

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

The application of different multivariate statistical techniques for the interpretation of a complex data matrix obtained during 2000?C2007 from the watercourses in the Southwest New Territories and Kowloon, Hong Kong was presented in this study. The data set consisted of the analytical results of 23 parameters measured monthly at 16 different sampling sites. Hierarchical cluster analysis grouped the 12 months into two periods and the 16 sampling sites into three groups based on similarity in water quality characteristics. Discriminant analysis (DA) provided better results both temporally and spatially. DA also offered an important data reduction as it only used four parameters for temporal analysis, affording 84.2% correct assignations, and eight parameters for spatial analysis, affording 96.1% correct assignations. Principal component analysis/factor analysis identified four latent factors standing for organic pollution, industrial pollution, nonpoint pollution, and fecal pollution, respectively. KN1, KN4, KN5, and KN7 were greatly affected by organic pollution, industrial pollution, and nonpoint pollution. The main pollution sources of TN1 and TN2 were organic pollution and nonpoint pollution, respectively. Industrial pollution had high effect on TN3, TN4, TN5, and TN6.     

Spatial distribution and source identification of persistent pollutants in marine sediments of Hong Kong

A data matrix, obtained during a 3-year monitoring period (2007–2009) from 45 sampling sites in Hong Kong marine, was subjected to determine the spatial characterization and identify the sources of main pollutants. Indicator analyses indicated that polycyclic aromatic hydrocarbons (PAHs), nickel, manganese, and arsenic (As) were at safe levels. Five heavy metals (zinc, lead, cupper, cadmium, chromium (Cr)) were moderate to severe enrichment at some sites. Inner Deep Bay and Victoria Harbor were considered as hot spots for PAHs and the heavy metals, while Tolo Harbor was highly polluted by the heavy metals. Cluster analysis classified the 45 sampling sites into three groups, representing different pollution levels. Principal component analysis/factor analysis identified four principal components (PCs) and explained 84.9 % of the total variances, standing for persistent pollution, N factor, P and Cr factor, and As factor, respectively. Group A was highly polluted by persistent pollution, group B was the less polluted group, and subgroup B1 was less affected by PC3 and PC4 than subgroup B2. Group C, considered as the moderately polluted group, was greatly affected by N factor or persistent pollution, while subgroup C2 received more N pollution than subgroup C1.     

Groundwater levels time series sensitivity to pluviometry and air temperature: a geostatistical approach to Sfax region,Tunisia

In this paper, the pattern of groundwater level fluctuations is investigated by statistical techniques for 24 monitoring wells located in an unconfined coastal aquifer in Sfax (Tunisia) for a time period from 1997 to 2006. Firstly, a geostatistical study is performed to characterize the temporal behaviors of data sets in terms of variograms and to make predictions about the value of the groundwater level at unsampled times. Secondly, multivariate statistical methods, i.e., principal component analysis (PCA) and cluster analysis (CA) of time series of groundwater levels are used to classify groundwater hydrographs regard to identical fluctuation pattern. Three groundwater groups (A, B, and C) were identified. In group “A,” water level decreases continuously throughout the study periods with rapid annual cyclic variation, whereas in group “B,” the water level contains much less high-frequency variation. The wells of group “C” represents a steady and gradual increase of groundwater levels caused by the aquifer artificial recharge. Furthermore, a cross-correlation analysis is used to investigate the aquifer response to local rainfall and temperature records. The result revealed that the temperature is more affecting the variation of the groundwater level of group A wells than the rainfall. However, the second and the third groups are less affected by rainfall or temperature.     

南京市地表水溶解氧分布特征及低氧成因初探——以外秦淮河七桥瓮为例

于2018—2021年对南京市及国考断面七桥瓮进行水质调查,分析其溶解氧变化特征,采用水质水量联合评价及皮尔逊相关分析法,并结合水文气象等相关信息,对南京市地表水溶解氧分布特征及国考七桥瓮断面低氧成因进行研究分析。结果表明,南京市地表水溶解氧浓度夏季最低,中心主城区及附近区域溶解氧浓度均相对较低。七桥瓮断面溶解氧浓度在2.25~11.07 mg/L,其中5—9月溶解氧易出现超标波动。溶解氧浓度昼间高于夜间,与pH值呈正相关关系,与水温、高锰酸盐指数、氨氮、总磷均呈负相关关系。水温和上游来水带入的耗氧污染物是七桥瓮断面溶解氧偏低的主要成因,其中,溶解氧浓度与水温相关性最为显著。研究结论可为七桥瓮断面稳定达标提供基础支撑,为秦淮河流域精准治污提供技术依据,为南京市水环境多源同治提供治理思路。     

The impact of traditional coffee processing on river water quality in Ethiopia and the urgency of adopting sound environmental practices

Although waste from coffee processing is a valuable resource to make biogas, compost, and nutrient-rich animal food, it is usually dumped into nearby water courses. We carried out water quality assessment at 44 sampling sites along 18 rivers that receive untreated waste from 23 coffee pulping and processing plants in Jimma Zone, Ethiopia. Twenty upstream sampling sites free from coffee waste impact served as control, and 24 downstream sampling sites affected by coffee waste were selected for comparison. Physicochemical and biological results revealed a significant river water quality deterioration as a result of disposing untreated coffee waste into running water courses. During coffee-processing (wet) season, the highest organic load (1,900?mg/l), measured as biochemical oxygen demand, depleted dissolved oxygen (DO) to a level less than 0.01?mg/l, and thus curtailed nitrification. During off season, oxygen started to recuperate and augmented nitrification. The shift from significantly elevated organic load and reduced DO in the wet season to increased nitrate in the off season was found to be the determining factor for the difference in macroinvertebrate community structure as verified by ordination analysis. Macroinvertebrate diversity was significantly reduced in impacted sites during the wet season contrary to the off season. However, there was a significant difference in the ratio of sensitive to pollution-tolerant taxa in the off season, which remained depreciated in the longer term. This study highlights the urgency of research exploring on the feasibility of adopting appropriate pollution abatement technologies to implement ecologically sound coffee-processing systems in coffee-growing regions of Ethiopia.     

杭州市钱塘江干支流水质多元统计分析

运用多元统计方法分析了杭州市钱塘江干支流上26个断面的水质监测指标。利用系统聚类分析方法将断面所在河流分为3组,与钱塘江流域污染空间分布现状基本一致。对各组水质的主成分分析表明,第1组河流水质以有机污染为主,水体中氮、磷营养盐浓度较高,水体污染程度较轻,污染来源相对单一;第2组河流水体受有机物、重金属、石油类等多个污染指标的影响,水体水质较第1组差,污染来源相对复杂;第3组河流水体既有一般有机污染,也有重金属、有毒有害物质的污染,水体水质污染严重。     

Near-field loading dynamics of total phosphorus and short-term water quality variations at a rainbow trout cage farm in Lake Huron

Aquatic total phosphorus (Tot-P) is measured at fish-cages in Lake Huron for environmental regulatory compliance. An improved understanding of how Tot-P is manifested in the near-field (相似文献   

Characterization and source apportionment of water pollution in Jinjiang River, China

Characterizing water quality and identifying potential pollution sources could greatly improve our knowledge about human impacts on the river ecosystem. In this study, fuzzy comprehensive assessment (FCA), pollution index (PI), principal component analysis (PCA), and absolute principal component score–multiple linear regression (APCS–MLR) were combined to obtain a deeper understanding of temporal–spatial characterization and sources of water pollution with a case study of the Jinjiang River, China. Measurement data were obtained with 17 water quality variables from 20 sampling sites in the December 2010 (withered water period) and June 2011 (high flow period). FCA and PI were used to comprehensively estimate the water quality variables and compare temporal–spatial variations, respectively. Rotated PCA and receptor model (APCS–MLR) revealed potential pollution sources and their corresponding contributions. Application results showed that comprehensive application of various multivariate methods were effective for water quality assessment and management. In the withered water period, most sampling sites were assessed as low or moderate pollution with characteristics pollutants of permanganate index and total nitrogen (TN), whereas 90 % sites were classified as high pollution in the high flow period with higher TN and total phosphorus. Agricultural non-point sources, industrial wastewater discharge, and domestic sewage were identified as major pollution sources. Apportionment results revealed that most variables were complicatedly influenced by industrial wastewater discharge and agricultural activities in withered water period and primarily dominated by agricultural runoff in high flow period.     

Analysis of hypoxia in the western interior parts of the Ariake Sea, Japan, using a box model

To clarify the mechanism of hypoxia in the western interior parts of the Ariake Sea (WIAS), field observation data collected in the period of 1972-2004 were analyzed using a two-layer box model. Monthly averages of advection velocity, vertical diffusion coefficient (K(z)), and biochemical oxygen consumption rate (R) in WIAS were evaluated quantitatively during the above period. The estimated advection velocity comparatively corresponded to the observed residual flow pattern of bay head in summer and winter. The estimated K(z) was relatively high (0.6-5.3 cm(2) s(?-1)) from September to March but lower (0.2-0.4 cm(2) s(?-1)) from April to August. The estimated R ranged from 0.30 to 0.46 mg L(?-1) day(?-1) during May to August. In summer, the temporal variation of dissolved oxygen (DO) concentration in the lower layer was controlled largely by K ( z ) and R. Monthly variations of K(z), R, and degree of density stratification (P) in the 1970s, the 1980s, and the 1990s-early 2000s were analyzed. P, K ( z ), and R were not significantly different among the calculated periods (p = 0.93, 0.23, and 0.49). However, the variations of R in summer between the 1970s and the other calculated periods changed. DO consumption period was longer in the 1980s and the 1990s-early 2000s than in the 1970s. R in the 1980s was highest among the calculated periods. The increase in R in the 1980s was caused by the increase in organic matter load originating from red tide phytoplankton due to a decrease in the suspension feeders.