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1.
In this study, a methodology for clustering 18 lakes in Alberta, Canada using the data of 19 water quality parameters for a period of 11 years (1988–2002) is presented. The methods consist of (i) principal component analysis (PCA) to determine the dominant water quality parameters, (ii) cluster analysis techniques to develop the characteristics of the clusters, and (iii) pattern‐match lakes to determine the appropriate cluster for each of the lakes. The PCA revealed that three principal components (PCs) were able to explain ～88% of the variability and the dominant water quality parameters were total dissolved solids, total phosphorus, and chlorophyll‐a. We obtained five clusters for the period 1994–1997 by using the dominant parameters with water quality deteriorating as the cluster number increased from 1 to 5. Upon matching cluster patterns with the entire dataset, it was observed that some of the lakes belonged to the same cluster all the time (e.g., cluster 1 for lakes Elkwater, Gregg, and Jarvis; cluster 3 for Sturgeon; cluster 4 for Moonshine; and cluster 5 for Saskatoon), while others changed with time. This methodology could be applied in other regions of the world to identify the most suitable source waters and prioritize their management. It could be helpful to analyze the natural controlling processes, pollution types, impact of seasonal changes and overall quality of source waters. This methodology could be used for monitoring water bodies in a cost effective and efficient way by sampling only less number of dominant parameters instead of using a large set of parameters. 相似文献
2.
In the study, multivariate statistical methods including principal component analysis (PCA)/factor analysis (FA) and cluster analysis (CA) were applied to analyze surface water quality data sets obtained from the Huaihe River segment of Bengbu (HRSB) and generated during 2 years (2011–2012) monitoring of 19 parameters at 7 sampling sites. The results of PCA for 7 sampling sites revealed that the first four components of PCA showed 94.89% of the total variance in the data sets of HRSB. The Principal components (Factors) obtained from FA indicated that the parameters for water quality variations were mainly related to heavy metals (Pb, Mn, Zn and Fe) and organic related parameters (COD, PI and DO). The results revealed that the major causes of water quality deterioration were related to inflow of industrial, domestic and agricultural effluents into the Huaihe River. Three significant sampling locations—(sites 2, 3 and 4), (sites 1 and 5) and (sites 6 and 7)—were detected on the basis of similarity of their water quality. Thus, these methods were believed to be valuable to help water resources managers understand complex nature of water quality issues and determine the priorities to improve water quality. 相似文献
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《Limnologica》2017
Eutrophication has become a crucial issue for water resource management in recent years. In addition, reservoir trophic states are varied with environmental and water quality variables. The objectives of this study were to apply the DFA model to examine which water quality variables significantly affect variations of trophic state index (TSI) factors (i.e. total phosphorus (TP), chlorophyll-a (Chl-a), and Secchi disk transparency (SD)) and use classification and regression tree (CART) to determine the trophic states of the Shinmen Reservoir based on the levels of TSI factors during spring 2001–winter 2009. Results showed that the optimal DFA model contained one common trend (the underlying processes influencing trophic states, which can be rainfall intensity or runoff volume) and 7 explanatory variables. Turbidity (TB), pH, and dissolved oxygen (DO) influence concentrations of TP, while ammonium nitrogen (NH3-N), organic nitrogen (O-N), and nitrate nitrogen (NO3-N) control variations of Chl-a, and TB is related to SD. The CART model can specify trophic states only using two dominant driving factors, i.e. TP and Chl-a. The results of the CART illustrated that eutrophication could be occurred in the Shihmen Reservoir if TP is greater than 31.65 μg/L or if Chl-a is greater than 5.95 μg/L while TP concentration is less than 31.65 μg/L. Runoff nonpoint source pollution resulted from heavy storms may be the important factor affecting reservoir trophic states. Establishing vegetative filter strips along the riparian zone may able to effectively reduce this pollution in a reservoir. The integrated DFA and CART serves as good-fit relationships among trophic states, TSI factors, and water quality variables and provide control strategies for managing water quality in the Shihmen Reservoir. 相似文献
5.
Assessment of temporal and spatial variation of coastal water quality and source identification along Macau peninsula 总被引：11，自引：5，他引：6
Jinliang Huang Manhim Ho Pengfei Du 《Stochastic Environmental Research and Risk Assessment (SERRA)》2011,25(3):353-361
Cluster analysis (CA), discriminant analysis (DA) and principal component analysis (PCA) were comprehensively coupled to explore
and identify the spatial and temporal variation and potential pollution sources in coastal water quality along Macau peninsula.
The results show that the 12 months could be grouped into two periods, June–September and the remaining months, and the entire
area divided into two clusters, one located at the western sides, and the other on the southeast and southern sides of the
Macau peninsula. Through backward stepwise DA, pH, Cl−, TSS, Color and TP, Chloride, Color, NH4
+, DO, COD were discriminant variables of spatial and temporal variation, with 84.82 and 76.54% correct assignments, respectively.
Fecal pollution, organic pollution and soil weathering are among the major sources for coastal water quality deterioration
along Macau peninsula. This study illustrates that application of multivariate statistical techniques was beneficial to gain
knowledge for further optimizing the monitoring network and controlling coastal water quality along Macau peninsula. 相似文献
6.
辽宁省大伙房水库及入库河流水质空间特征与河库水质关系 总被引：4，自引：1，他引：3
入库河流与水库存在空间上的连续性,河流污染物输入是水库水质恶化的主要原因,对大伙房水库及其入库支流61个采样点的水质状况进行调查,并运用聚类分析和主成分分析对大伙房水库及入库支流的水质空间特性和主要污染物进行分析.聚类分析显示,按照水质相似性将大伙房水库及入库支流水质可分为上游区、下游区和库区3个典型空间区域.分别对3个区域进行主成分分析,结果显示:入库支流上游区和下游区水质主要影响因素为氨氮、总氮和化学需氧量,库区影响水质的主要因素为温度、p H值、浊度、溶解氧、电导率、氨氮和总氮.对上游、下游和库区水质均有显著影响的因子为氨氮和总氮,上游区、下游区和库区氨氮浓度均值分别为0.06、0.10和0.19 mg/L,总氮浓度均值分别为0.13、0.16和0.26 mg/L.入库河流下游区对水库水质影响较大,受社河和浑河污染物输入的影响,大伙房水库水质在空间上呈现社河入库区水质优于浑河入库区水质.并且库区氨氮和总氮浓度均与距岸边距离呈负相关,溶解氧和p H值均与距入库口距离呈负相关,表明入库河流污染物输入和环库区面源污染均对大伙房水库水质产生一定影响. 相似文献
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太湖流域上游平原河网区水质空间差异与季节变化特征 总被引：4，自引：2，他引：2
在太湖流域上游的宜溧—洮滆水系主要河道设置67个监测点,分别于2014年1月(冬季)、4月(春季)、8月(夏季)、11月(秋季)进行水质监测,采用多元统计方法分析了水质的空间差异性和季节性变化,并利用水质标识指数法对水环境质量进行评价.结果表明,宜溧—洮滆水系污染程度较严重,总氮(TN)、总磷(TP)和高锰酸盐指数(CODMn)浓度年均值分别为4.93、0.26和7.63 mg/L;单因素多元方差分析和聚类分析显示污染物浓度具有显著时空差异性,时间上冬、春季污染程度较高而夏、秋季较低,空间上无锡和常州氮、磷污染较为严重,宜兴和溧阳市有机污染程度较高;水质标识评价结果显示流域内水质基本为IV类或V类,其中TN、TP及CODMn是关键污染指标. 相似文献
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A methodology for characterizing the hydrogeology of watersheds using hydrochemical data that combine statistical, geochemical, and spatial techniques is presented. Surface water and ground water base flow and spring runoff samples (180 total) from a single watershed are first classified using hierarchical cluster analysis. The statistical clusters are analyzed for spatial coherence confirming that the clusters have a geological basis corresponding to topographic flowpaths and showing that the fractured rock aquifer behaves as an equivalent porous medium on the watershed scale. Then principal component analysis (PCA) is used to determine the sources of variation between parameters. PCA analysis shows that the variations within the dataset are related to variations in calcium, magnesium, SO4, and HCO3, which are derived from natural weathering reactions, and pH, NO3, and chlorine, which indicate anthropogenic impact. PHREEQC modeling is used to quantitatively describe the natural hydrochemical evolution for the watershed and aid in discrimination of samples that have an anthropogenic component. Finally, the seasonal changes in the water chemistry of individual sites were analyzed to better characterize the spatial variability of vertical hydraulic conductivity. The integrated result provides a method to characterize the hydrogeology of the watershed that fully utilizes traditional data. 相似文献
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The main objective in this study was to compare the physico-chemical characteristics and biota of a river (Mukuvisi) passing through an urban area to that of a non-urbanised river (Gwebi). Five sites in the Mukuvisi River and five sites in the Gwebi River were sampled for water physico-chemical parameters (pH, conductivity, DO, BOD, TDS, ammonia, Cl, SO42−, PO42−, NO33−, F−, Pb, Cu, Fe, Mn, Zn and Cr) once every month between August, 2012–August, 2013. Cluster analysis based on the physico-chemical parameters grouped the sites into two groups. Mukuvisi River sites formed their own grouping except for one site which was grouped with Gwebi River sites. Principal Component Analysis (PCA) was used to extract the physico-chemical parameters that account for most variations in water quality in the Mukuvisi and Gwebi Rivers. PCA identified sulphate, chloride, fluoride, iron, manganese and zinc as the major factors contributing to the variability of Mukuvisi River water quality. In the Gwebi river, sulphate, nitrate, fluoride and copper accounted for most of the variation in water quality. Canonical Correspondence Analysis (CCA) was used to explore the relationship between physico-chemical parameters and macroinvertebrate communities. CCA plots in both Mukuvisi and Gwebi Rivers showed significant relationships between macroinvertebrate communities and water quality variables. Phosphate, ammonia and nitrates were correlated with Chironomidae and Simulidae. Gwebi River had higher (P < 0.05, ANOVA) macroinvertebrates and fish diversity than Mukuvisi River. Clarias gariepinus from the Mukuvisi River had high liver histological lesions and low AChE activity and this led to lower growth rates in this river. 相似文献
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Chemometrics data analysis of marine water quality and source identification in Southern Hong Kong 总被引：3，自引：0，他引：3
Various chemometric methods were used to analyze data sets of marine water quality for 19 parameters measured at 16 different sites of southern Hong Kong from 2000 to 2004 (18,240 observations), to determine temporal and spatial variations in marine water quality and identify pollution sources. Hierarchical cluster analysis (CA) grouped the 12 months into three periods (January-April, May-August and September-December) and the 16 sampling sites into two groups (A and B) based on similarities in marine water-quality characteristics. Discriminant analysis (DA) was important in data reduction because it used only eight parameters (TEMP, TURB, Si, NO(3)(-)-N, NH(4)(+)-N, NO(2)(-)-N, DO, and Chl-a) to correctly assign about 86% of the cases, and five parameters (SD, NH(4)(+)-N, TP, NO(2)(-)-N, and BOD(5)) to correctly assign >81.15% of the cases. In addition, principal component analysis (PCA) identified four latent pollution sources for groups A and B: organic/eutrophication pollution, natural pollution, mineral pollution, and nutrient/fecal pollution. Furthermore, during the second and third periods, all sites received more organic/eutrophication pollution and natural pollution than in the first period. SM5, SM6, SM17, SM10, SM11, SM12, and SM13 (second period) were affected by organic and eutrophication pollution, whereas SM3 (third period) and SM9 (second period) were influenced by natural pollution. However, differences between mineral pollution and nutrient/fecal pollution were not significant among the three periods. SM17 and SM10 were affected by mineral pollution, whereas SM4 and SM9 were highly polluted by nitrogenous nutrient/fecal pollution. 相似文献
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Water Resources - The present study is an attempt to apply principal component analysis (PCA) for spatial assessment of water quality parameters that are responsible for water quality deterioration... 相似文献
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Forecasting of Air Quality Index in Delhi Using Neural Network Based on Principal Component Analysis
Forecasting of the air quality index (AQI) is one of the topics of air quality research today as it is useful to assess the effects of air pollutants on human health in urban areas. It has been learned in the last decade that airborne pollution has been a serious and will be a major problem in Delhi in the next few years. The air quality index is a number, based on the comprehensive effect of concentrations of major air pollutants, used by Government agencies to characterize the quality of the air at different locations, which is also used for local and regional air quality management in many metro cities of the world. Thus, the main objective of the present study is to forecast the daily AQI through a neural network based on principal component analysis (PCA). The AQI of criteria air pollutants has been forecasted using the previous day’s AQI and meteorological variables, which have been found to be nearly same for weekends and weekdays. The principal components of a neural network based on PCA (PCA-neural network) have been computed using a correlation matrix of input data. The evaluation of the PCA-neural network model has been made by comparing its results with the results of the neural network and observed values during 2000–2006 in four different seasons through statistical parameters, which reveal that the PCA-neural network is performing better than the neural network in all of the four seasons. 相似文献
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云南星云湖水质变化及其人文因素驱动力分析 总被引：1，自引：0，他引：1
星云湖目前存在水污染加重、富营养化进程加快、水体功能受损等问题.以星云湖为研究对象,根据星云湖2005-2015年的水质数据、社会经济统计数据和遥感影像图,运用目视解译、叠加分析、污染足迹模型及主成分分析法,分析了星云湖流域近10年以来水质变化趋势、入湖河流污染物污染足迹及其人文因素驱动力.结果表明:(1)水质数据趋势表明,从月变化看,3月份水质最好,9月份水质最差;从年变化看,2005-2015年间,2008年水质状况最好,2014年的水质状况最差,从2008-2014年水质持续变差,到2015年好转.(2)2015年有机物、氮和磷的污染足迹分别为583.26、705.88和494.11 km~2.污染足迹前4位的入湖河流依次为:大街河东西大河东河渔村河东西大河西河,占星云湖流域总污染足迹的66.21%.污染程度大的大街河、东西大河和渔村河周边土地利用类型为水田、旱地和村庄.(3)星云湖水质影响因素第1主成分(总人口、播种面积、农村人口、化肥使用量、农膜使用量、大牲畜存栏量)与农村生活和农业面源污染有关;第2主成分(人均GDP、第一产业产值、第二产业产值、第三产业产值)与社会经济发展有关.因此,星云湖流域水质变化的人文因素驱动力为农村生活和农业面源污染类和社会经济发展类,其中第1主成分的贡献率是84.389%,农村生活和农业面源污染是水质变化的主要驱动力. 相似文献
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非点源污染对太湖上游西苕溪流域水环境的影响 总被引：14，自引：3，他引：11
水资源短缺是全球性关注的问题,水质恶化更加剧了这一问题的严重性,定量研究水质变化及其影响凶素可为治理水环境提供基础依据．与实测数据及综合污染指数的对比表明,水质指数能够合理反映水质的变化程度和时空变化趋势．利用西苕溪流域1996-2000年水质监测数据的研究结果如下：西苕溪流域水质的空间变化趋势是自上游至下游逐渐恶化,时间变化的总体趋势是逐年转好;点污染源得到有效控制年份(1999年)的水质指数比以前年份(1996-1998年)有所提高但幅度不大,说明非点源污染是影响西苕溪流域水质的重要因素;流域水污染的主要形式是氮、磷污染,其主要非点源是农田、经济竹林和城镇径流及居民生活污水等． 相似文献
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An understanding of groundwater flow and chemistry is important to operate underground storage caverns. Groundwater flow is mainly affected by cavern operating conditions. Groundwater chemistry is modified by disinfection activities for removing possible biological clogging and by mixing with cement pore water. It is important to discern these two effects, because wells affected by the disinfection activities may have hydrological connections with water curtains used to inject the disinfectant. However, it is difficult to separate these two effects using graphical methods because of their similar chemical characteristics. Instead, multivariate statistical analysis, such as principal component analysis (PCA) and factor analysis (FA), can be used. Groundwater samples for chemical analysis were obtained from four surveys in 1999–2000. Based on the results from PCA and FA, it appears that there were temporal variations of seepage water into the propane area when the cavern operation fluctuated, but we could not observe such variation in the butane area. These changes may occur mainly at depth, where water flow is slow and water renewal in the cavern surrounding is limited. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
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River water quality response under hypothetical climate change scenarios in Tunga‐Bhadra river,India
Analysis of climate change impacts on streamflow by perturbing the climate inputs has been a concern for many authors in the past few years, but there are few analyses for the impacts on water quality. To examine the impact of change in climate variables on the water quality parameters, the water quality input variables have to be perturbed. The primary input variables that can be considered for such an analysis are streamflow and water temperature, which are affected by changes in precipitation and air temperature, respectively. Using hypothetical scenarios to represent both greenhouse warming and streamflow changes, the sensitivity of the water quality parameters has been evaluated under conditions of altered river flow and river temperature in this article. Historical data analysis of hydroclimatic variables is carried out, which includes flow duration exceedance percentage (e.g. Q90), single low‐flow indices (e.g. 7Q10, 30Q10) and relationships between climatic variables and surface variables. For the study region of Tunga‐Bhadra river in India, low flows are found to be decreasing and water temperatures are found to be increasing. As a result, there is a reduction in dissolved oxygen (DO) levels found in recent years. Water quality responses of six hypothetical climate change scenarios were simulated by the water quality model, QUAL2K. A simple linear regression relation between air and water temperature is used to generate the scenarios for river water temperature. The results suggest that all the hypothetical climate change scenarios would cause impairment in water quality. It was found that there is a significant decrease in DO levels due to the impact of climate change on temperature and flows, even when the discharges were at safe permissible levels set by pollution control agencies (PCAs). The necessity to improve the standards of PCA and develop adaptation policies for the dischargers to account for climate change is examined through a fuzzy waste load allocation model developed earlier. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
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An assessment of water quality measurements during a spring flood in the Elbe River is presented. Daily samples were taken at a site in the middle Elbe, which is part of the network of the International Commission for the Protection of the Elbe River (IKSE/MKOL). Cluster analysis (CA), principal components analysis (PCA), and source apportionment (APCS apportioning) were used to assess the flood‐dependent matter transport. As a result, three main components could be extracted as important to the matter transport in the Elbe River basin during flood events: (i) re‐suspended contaminated sediments, which led to temporarily increased concentrations of suspended matter and of most of the investigated heavy metals; (ii) water discharge related concentrations of pedogenic dissolved organic matter (DOM) as well as preliminary diluted concentrations of uranium and chloride, parameters with stable pollution background in the river basin; and (iii) abandoned mines, i.e., their dewatering systems, with particular influence on nickel, manganese, and zinc concentrations. 相似文献
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A deterministic method for sensitivity analysis is developed and applied to a mathematical model for the simulation of flow in porous media. The method is based on the singular value decomposition (SVD) of the Jacobian matrix of the model. It is a local approach to sensitivity analysis providing a hierarchical classification of the directions in both the input space and of those in the output space reflecting the degree of sensitiveness of the latter to the former. Its low computational cost, in comparison with that of statistical approaches, allows the study of the variability of the results of the sensitivity analysis due to the variations of the input parameters of the model, and thus it can provide a quality criterion for the validity of more classical probabilistic global approaches. For the example treated here, however, this variability is weak, and deterministic and statistical methods yield similar sensitivity results. 相似文献
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This research explains the background processes responsible for the spatial distribution of hydrochemical properties of the picturesque eutrophic Himalayan Lake, Dal, located in Kashmir valley, India. Univariate and multivariate statistical analyses were used to understand the spatiotemporal variability of 18 hydrochemical parameters comprising of 12,960 observations collected from 30 sampling sites well distributed within the lake at a grid spacing of 1 km2 from March 2014 to February 2016. Hierarchical cluster analysis (HCA) grouped all the sampled data into three clusters based on the hydrochemical similarities, Discriminant analysis also revealed the same clusters and patterns in the data, validating the results of HCA. Wilk’s λ quotient distribution revealed the contribution of ions, nutrients, secchi disk transparency, dissolved oxygen and pH in the formation of clusters. The results are in consonance with the Principal Component Analysis of the whole lake data and individual clusters, which showed that the variance is maximally explained by the ionic component (46.82%) followed by dissolved oxygen and pH (9.36%), nitrates and phosphates (7.33%) and Secchi disk transparency (5.98%). Spatial variability of the hydrochemistry of the lake is due to the variations in water depth, lake water dynamics, flushing rate of water, organic matter decomposition, and anthropogenic pressures within and around the Dal lake ecosystem. Overall, the water quality of the lake is unfit for drinking due to the presence of coliform bacteria in the lake waters. 相似文献