Application of Surface Water Quality Classification Models Using Principal Components Analysis and Cluster Analysis

Water quality monitoring has one of the highest priorities in surface water protection policy. Many variety approaches are being used to interpret and analyze the concealed variables that determine the variance of observed water quality of various source points. A considerable proportion of these approaches are mainly based on statistical methods, multivariate statistical techniques in particular. In the present study, the use of multivariate techniques is required to reduce the large variables number of Nile River water quality upstream Cairo Drinking Water Plants (CDWPs) and determination of relationships among them for easy and robust evaluation. By means of multivariate statistics of principal components analysis (PCA), Fuzzy C-Means (FCM) and K-means algorithm for clustering analysis, this study attempted to determine the major dominant factors responsible for the variations of Nile River water quality upstream Cairo Drinking Water Plants (CDWPs). Furthermore, cluster analysis classified 21 sampling stations into three clusters based on similarities of water quality features. The result of PCA shows that 6 principal components contain the key variables and account for 75.82% of total variance of the study area surface water quality and the dominant water quality parameters were: Conductivity, Iron, Biological Oxygen Demand (BOD), Total Coliform (TC), Ammonia (NH3), and pH. However, the results from both of FCM clustering and K-means algorithm, based on the dominant parameters concentrations, determined 3 cluster groups and produced cluster centers (prototypes). Based on clustering classification, a noted water quality deteriorating as the cluster number increased from 1 to 3. However the cluster grouping can be used to identify the physical, chemical and biological processes creating the variations in the water quality parameters. This study revealed that multivariate analysis techniques, as the extracted water quality dominant parameters and clustered information can be used in reducing the number of sampling parameters on the Nile River in a cost effective and efficient way instead of using a large set of parameters without missing much information. These techniques can be helpful for decision makers to obtain a global view on the water quality in any surface water or other water bodies when analyzing large data sets especially without a priori knowledge about relationships between them.

The Water Quality Evaluation in Balihe Lake Based on Principal Component Analysis

The water pollution situation in Balihe Lake, the biggest tributary of Shaying River Basin in Anhui Province, China, has brought a huge pressure on the improvement of water quality in Huai River. On October 16th, 2017, 11 major pollution indexes were observed at 15 sampling points in Balihe Lake. Based on the data experimentally measured, the water quality in Balihe Lake was analyzed utilizing the Principal Component Analysis (PCA) of SPSS. The result suggested that the major components were oxygenated pollutants, water eutrophication pollutants and ammonia nitrogen, in which oxygenated pollutants played a dominant role. In addition, the upper part of Balihe Lake suffered serious situation and needed a focus on oxygenated pollutants.

Olive Mill Waste Water Management Study by Using Principal Component Analysis

Olive mill waste water (OMWW) is a by-product issued after triturating olives. In Sfax, its management is different from urban to farming area. In this paper we treat it through a statistical analysis study during the season 2005-2006. Principal Component Analysis (PCA) and Hierarchical Classification (HC) methods are carried out on this work. Applied to variables issued from an exhaustive questionnaire including 274 mills, four Principal Components (PCs) are found to be significant, explaining 67% of the total variance. The coordinates of the 13 active variables retained by PCA were used to create a typology relative to the OMWW management and offered 7 groups of individuals which have the same characteristics, explaining 70% of the total inter-variance. This study showed that OMWW management in farming area could causes environmental problems because oleifactors haven’t controlled tanks and could evacuated OMWW on soil (causing oil deposit, waterproofing and possible asphyxia) or on public sewage network (causing corrosion, flow reduction). So, mills transfer from urban to farming areas in the form of agro-industrial complex is needed in the Sfax region.

Application of Principal Component Analysis with SPSS in Water Quality Assessment of Shenzhen Eastern Beach

Survey and analysis were conducted on water quality of offshore seas in eastern region of Shenzhen by principal component analysis with SPSS. Then, 8 pollutants indices were then reduced to 5. Based on weighted analysis of principal component weights, comprehensive scores of different monitored stations were com- puted and sequenced in order to make evaluation on sea quality of eastern region of Shenzhen.

Water Quality Evaluation of Chapurson Valley in Hunza Nagar, Gilgit Baltistan, Pakistan, Based on Statistical Analysis and Water Quality Index

Water borne ailments are of serious public health concern in Gilgit Baltistan’s (GB) region of Pakistan. The pollution load on the glacio-fluvial streams and surface water resources of the Chapurson Valley in the Hunza Nagar area of the GB is increasing as a result of anthropogenic activities and tourism. The present study focuses on the public health quality of drinking water of Chapurson valley. The study addressed the fundamental drinking water quality criteria in order to understand the state of the public health in the valley. To ascertain the current status of physico-chemical, metals, and bacteriological parameters, 25 water samples were collected through deterministic sampling strategy and examined accordingly. The physico-chemical parameters of the water samples collected from the valley were found to meet the World Health Organization (WHO) guidelines of drinking water. The water samples showed a pattern of mean metal concentrations in order of Arsenic (As) > Lead (Pb) > Iron (Fe) > Zinc (Zn) > Copper (Cu) > Magnesium (Mg) > Calcium (Ca). As, Cu, Zn, Ca and Mg concentration were under the WHO guidelines range. However, results showed that Pb and Fe are present at much higher concentrations than recommended WHO guidelines. Similarly, the results of the bacteriological analysis indicate that the water samples are heavily contaminated with the organisms of public health importance (including total coliforms (TCC), total faecal coliforms (TFC) and total fecal streptococci (TFS) are more than 3 MPN/100mL). Three principal components, accounting for 48.44% of the total variance, were revealed using principal component analysis (PCA). Bacteriological parameters were shown to be the main determinants of the water quality as depicted by the PCA analysis. The dendrogram of Cluster analysis using the Ward’s method validated the same traits of the sampling locations that were found to be contaminated during geospatial analysis using the Inverse Distance Weight (IDW) method. Based on these findings, it is most likely that those anthropogenic activities and essentially the tourism results in pollution load from upstream channels. Metals may be released into surface and groundwater from a few underlying sources as a result of weathering and erosion. This study suggests that the valley water resources are more susceptible to bacteriological contamination and as such no water treatment facilities or protective measure have been taken to encounter the pollution load. People are drinking the contaminated water without questioning about the quality. It is recommended that the water resources of the valley should be monitored using standard protocol so as to protect not only the public health but to safe guard sustainable tourism in the valley.

Regulation effects of water and nitrogen on yield,water,and nitrogen use efficiency of wolfberry

Wolfberry(Lycium barbarum L.)is important for health care and ecological protection.However,it faces problems of low productivity and resource utilization during planting.Exploring reasonable models for water and nitrogen management is important for solving these problems.Based on field trials in 2021 and 2022,this study analyzed the effects of controlling soil water and nitrogen application levels on wolfberry height,stem diameter,crown width,yield,and water(WUE)and nitrogen use efficiency(NUE).The upper and lower limits of soil water were controlled by the percentage of soil water content to field water capacity(θ_(f)),and four water levels,i.e.,adequate irrigation(W0,75%-85%θ_(f)),mild water deficit(W1,65%-75%θ_(f)),moderate water deficit(W2,55%-65%θ_(f)),and severe water deficit(W3,45%-55%θ_(f))were used,and three nitrogen application levels,i.e.,no nitrogen(N0,0 kg/hm^(2)),low nitrogen(N1,150 kg/hm^(2)),medium nitrogen(N2,300 kg/hm^(2)),and high nitrogen(N3,450 kg/hm^(2))were implied.The results showed that irrigation and nitrogen application significantly affected plant height,stem diameter,and crown width of wolfberry at different growth stages(P<0.01),and their maximum values were observed in W1N2,W0N2,and W1N3 treatments.Dry weight per plant and yield of wolfberry first increased and then decreased with increasing nitrogen application under the same water treatment.Dry weight per hundred grains and dry weight percentage increased with increasing nitrogen application under W0 treatment.However,under other water treatments,the values first increased and then decreased with increasing nitrogen application.Yield and its component of wolfberry first increased and then decreased as water deficit increased under the same nitrogen treatment.Irrigation water use efficiency(IWUE,8.46 kg/(hm^(2)·mm)),WUE(6.83 kg/(hm^(2)·mm)),partial factor productivity of nitrogen(PFPN,2.56 kg/kg),and NUE(14.29 kg/kg)reached their highest values in W2N2,W1N2,W1N2,and W1N1 treatments.Results of principal component analysis(PCA)showed that yield,WUE,and NUE were better in W1N2 treatment,making it a suitable water and nitrogen management mode for the irrigation area of the Yellow River in the Gansu Province,China and similar planting areas.

DYNAMIC VARIATIONS OF WATER QUALITY IN TAIHU LAKE AND MULTIVARIATE ANALYSIS OF ITS INFLUENTIAL FACTORS

Dynamic variation of water quality in Meiliang Bay and part of West Taihu Lake has been analysed based on data from 1991 to 1992. Principal Component Analysis is used to reveal the mutual relationships of various factors. It is shown that there existis an obvious spatial and temporal variation in the main factors of water quality. Annual values of TP, CON, TN, Chl-a and conductivity decrease evidently from inner Meiliang Bay to the outer from north to south. TP and TN fluctuate seasonally with much higher value in winter. This is particularly true for the mouth of Liangxi River. In addition, the Chl-1 has a synchronous variation with water temperature, although being lagged a little, and closely relates to TP and TN. Finally, the results from Principal Component Analysis show that TP, TN, SS (or SD), water temperature and Chl-a are the most influential factors to water qualuty in this area, and both suspensions and algae can contribute to transparency to Taihu Lake.

Statistical Analysis of Leaf Water Use Efficiency and Physiology Traits of Winter Wheat Under Drought Condition

Five statistical methods including simple correlation, multiple linear regression, stepwise regression, principal components, and path analysis were used to explore the relationship between leaf water use efficiency （WUE） and physiological traits （photosynthesis rate, stomatal conductance, transpiration rate, intercellular CO2 concentration, etc.） of 29 wheat cultivars. The results showed that photosynthesis rate, stomatal conductance, and transpiration rate were the most important leaf WUE parameters under drought condition. Based on the results of statistical analyses, principal component analysis could be the most suitable method to ascertain the relationship between leaf WUE and relative physiological traits. It is reasonable to assume that high leaf WUE wheat could be obtained by selecting breeding materials with high photosynthesis rate, low transpiration rate, and stomatal conductance under dry area.

DYNAMIC VARIATIONS OF WATER QUALITY IN TAIHU LAKE AND MULTIVARIATE ANALYSIS OF ITS INFLUENTIAL FACTORS

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Environmental Characteristics of Groundwater:an Application of PCA to Water Chemistry Analysis in Yulin

For our investigation into the water quality in Yulin city, we collected 76 typical water samples to be tested for particle quality. By applying a Romani type classification method the groundwater of Yulin city was classified into nine categories by type, i.e., Ca-HCO3, Na-HCO3, Na-HCO3-SO4-Cl, Na-HCO3-SO4, Na-Cl, Na-Cl-HCO3, Na-Ca- HCO3, Ca-Cl-HCO3 and Ca-HCO3-SO4-Cl. A principal component analysis was carried out in order to analyze the groundwater environment. From this analysis we considered that the information collected could be represented by 21 indices from which we extracted seven principal components, which, respectively, accounted for 37.4%, 13.0%, 8.1%, 7.2%, 6.3%, 5.9% and 4.6% of the total variation. The results show that the groundwater environment of this region is largely determined by characteristic components of the natural groundwater background. One part of the water was polluted by leaching/eluviation of solid waste generated from coal mining. Another part of the ground water was contaminated by acid mine water from the coal layer and from improper irrigation. In addition, geological and hydrogeological conditions also cause changes in the water environment.

Interpretation of water quality parameters for Villages of Sanganer Tehsil, by using Multivariate Statistical analysis

In this study, the factor analysis techniques is applied to water quality data sets obtained from the Sanganer Tehsil, Jaipur District, Rajasthan (India). The data obtained were standardized and subjected to principal components analysis (PCA) extraction to simplifying its interpretation and to define the parameters responsible for the main variability in water quality for Sanganer Tehsil in Jaipur District. The PCA analysis resulted in two factors explaining more than 94.5% of the total variation in water quality data set. The first factor indicates the variation in water quality is due to anthropogenic sources and second factor shows variation in water quality due to organic sources that are taking place in the system. Finally the results of PCA reflect a good look on the water quality monitoring and interpretation of the surface water.

Water Quality Assessment in the Bamoun Plateau, Western-Cameroon: Hydrogeochemical Modelling and Multivariate Statistical Analysis Approach

This study focuses on the geochemical and bacteriological investigation of surface and ground water in the Bamoun plateau (Western-Cameroon). During the period from September 2013 to August 2014, 71 samples were collected from two springs, one borehole, four wells and the Nchi stream for analysis of major elements. In order to obtain the characteristics of the various species of bacteria, 7 samples were selected. The analytical method adopted for this study is the conventional hydrochemical technic and multivariate statistical analysis, coupled with the hydrogeochemical modelling. The results revealed that, water from the zone under study are acidic to basic, very weakly to weakly mineralized. Four types of water were identified: 1) CaMg-HCO3;2) CaMg-Cl-SO4;3) NaCl-SO4 and 4) NaK-HCO3. The major elements were all listed in the World Health Organization guidelines for drinking water quality, except for nitrates which was found at a concentration > 50 mg /l NO-3 in the borehole F401. As for the hydrobiological aspect, the entire sample contained all the bacteriological species except for spring S301 and well P401. According to the hydrogeochemical modelling, the Gibbs model and multivariate statistical tests, the quality of surface and ground water of the Foumban locality is influenced by two important factors: 1) the natural factors characterized by the water-rock interaction, evapotranspiration/crystallization, 2) the anthropogenic factors such as: uncontrolled discharges of liquid and solid effluents of all kinds and without any prior treatment within the ground and the strong urbanization accompanied by lack of sanitation and insufficient care.

Understanding Seasonal and Spatial Variation of Water Quality Parameters in Mangrove Estuary of the Nyong River Using Multivariate Analysis (Cameroon Southern Atlantic Coast)

To evaluate the actual status of water quality and conclude on the mains source of pollution in the Nyong estuary River, seasonal and spatial variation of water quality parameters was interpreted by multivariate statistical techniques (Principal Component analysis). Nine (09) environmental variables were monitored at four surface stations in the estuary for two seasonal cycles. The fieldwork was conducted from 2018 to 2019 during high tide and low tide for each survey. In situ physical parameters were measured for a total of 64 samples (32 samples for each tide). The laboratory works consisted of some physicochemical analyses and processing of these data by descriptive and multidimensional statistical analyses. Temperature, suspended particle matter, nitrate, nitrite and phosphate change significantly in the estuary with season (p < 0.05), while salinity, dissolved oxygen, pH, and ammonium do not vary significantly with season (p > 0.05). Principal Component analysis found temperature, salinity, pH, ammonium to be the most important parameters contributing to the fluctuations of surface water quality in the Nyong estuary during the dry seasons whereas suspended particle matter, nitrate, and phosphate are the most important parameters contributing to the fluctuation of surface water quality in the Nyong estuary during the rainy seasons. Based on spatial variation, the Principal Component analysis found that, suspended particle matter, nitrate and phosphate contribute to the fluctuation of surface water quality parameters upstream of the estuary while downstream salinity, pH, and ammonium contribute the most to the fluctuation of surface water quality. This study shows us the usefulness of multivariate statistical techniques used in assessing water quality data sets that would help us in understanding seasonal and spatial variations of water quality parameters to manage estuarine systems.

WATER QUALITY ANALYSIS AND ASSESSMENT OF RIVERS IN JILIN PROVINCE OF CHINA

By means of principal comPOnent analysis, based on 6 principal components chosen of computer, cluster analysis of the river water quality in Jinn Province was made objectively. Based on chosen principal components, the river water quality in Jinn Provinc

Water Quality Analysis of Fuping Section of the Shichuan River Based on Different Evaluation Methods

In order to study the water quality of the Shichuan River basin in Fuping,Shaanxi Province,based on improved Nemerow index method,comprehensive pollution index method and principal component analysis method,eight water quality indexes such as pH,dissolved oxygen(DO),total dissolved solids(TDS),COD,total hardness,total phosphorus,total nitrogen and Zn in three monitoring sections of Fuping section of the Shichuan River in Shaanxi Province were detected and analyzed.The results show that the water quality of the surface water in the Shichuan River basin is gradeⅢorⅣwater,that is,the water is slightly polluted and moderately polluted.It is necessary to monitor the water quality after regulation and clarify the main factors causing the water pollution.

Statistical Analysis of Bursa Nilufer Creek’s Water Quality Parameters for Period of 2002-2010

Increasing contamination of water resources in the world and our country and decreasing water quality over time, not having met the objectives of utilization of water resources;it has increased the importance of water management. The monitoring of the water resources and evaluation of these monitoring results have given direction to the studies’ outcome in order to control factors that pollute water resources and reduce water quality. Nilüfer Creek is very important for both being a source of drinking and potable water and a discharge area for wastewaters for the city of Bursa. In this study, the results of the analysis belonging to the period between 2002-2010 which are taken from 15 points by General Directorate of Bursa Water and Sewerage Administration (BUWSA) were evaluated in relation to water quality of the Nilüfer Creek. Non-parametric methods were used in the evaluation of the water quality data due to the lack of normally distributed data. The identification of the best represented parameters of the water quality was provided by applying Principal Component Analysis. According to results of the analysis, the best representative 9 parameters from the 19 water quality parameters were defined as parameters of BOD5, COD, TSS, T.Fe, Zn, conductivity, NO2-N, Ni and NO3-N that taking part of the first two components.

Assessment of the Water Quality of the Benin River, Southern Nigeria, Prior to the Seaport Development Project

Water resources in the form of rivers, oceans and seas are prime natural resources that man has either explored or exploited. The need for clean water is on the increase and water degradation due to industrialization and development has further exacerbated the state of water bodies’ degradation. The need to assess the quality status of the Benin River prior to the seaport development was inherent to document the baseline of the physicochemical parameters of the study stretch. Four stations were studied from Ajoki to opposite Young Town between January 2019 and December 2020. Physicochemical parameters and heavy metals for water were collected and analyzed adhering to quality assurance/control measures and standard procedures. Significant spatial variations (P < 0.001) were observed in water physicochemical parameters, except pH across the four stations. Principal Component Analysis (PCA), Pollution Load Index and Water Quality Index (WQI) were used to establish a relationship among water quality parameters and determine the water quality status. The first six components of PCA accounted for 87.77% of observed variations. WQI for sampling Station 2 was very poor for drinking (90.46) and Stations 1, 3 and 4 were unsuitable (113.13 - 188.21) for human consumption. PLI showed turbidity as the major pollutant across stations. The concentrations of heavy metals in the Benin River stretch are within background concentration level, except Fe and Cd. The mean dissolved oxygen was below the recommended level of 7 mg/l for aquatic life. The continuous monitoring of this stretch of the River during the seaport development activities and during operational stage is very paramount to prevent further degradation of the environment.

淡水养殖池塘水质评价及与底质相关性分析

在哈尔滨地区面积6 600 m^(2)、平均水深1.2 m、多年未清淤的泥沙底质典型淡水精养池溏中混养鲤(Cyprinus carpio)、鲇(Silurus asotus)、草鱼(Ctenopharyngodon idella)和鲢(Hypophthalmichthys molitrix),春季(4—5月)、夏季(6—8月)和秋季(9月)不同时间采集池塘水体和表层底泥样品,监测养殖周期内水质总氮(TN)、总磷(TP)、氨氮(NH_(4)^(+)-N)、亚硝酸盐氮(NO_(2)^(-)-N)、硝酸盐氮(NO_(3)^(-)-N)和总有机碳(TOC)含量及高锰酸盐指数(COD_(Mn))、p H的季节性变化,利用单项污染指数法、综合污染指数法评价水体污染程度,利用主成分分析法解析水质污染的主要驱动因子,并结合底质指标绘制网络关系图。结果表明:池塘水质整体处于严重污染水平,春季优于夏季和秋季。参与评价时池塘水质为Ⅳ类、Ⅴ类和劣Ⅴ类,水质超标数随着养殖时间延长而增加,TN在全养殖周期内均超出警戒水平;TN、TP、NH_(4)^(+)-N和COD_(Mn)是池塘水体的主要污染物。因子分析结果表明,池塘水质主要受鱼类的生长代谢、溶解氧和投饵的影响,污染物以N、P营养盐为主。水体TP、TN、NH_(4)^(+)-N和底泥NO_(2)^(-)-N呈极显著正相关关系(P<0.01),与底泥TOC呈显著正相关关系(P<0.05)。池塘水体和底泥中的有机物污染具有共现性,水体TOC和底泥TOC呈极显著正相关关系(P<0.01)。

Identification of Mine Water Inrush Source Based on PCA-BP Neural Network

It is of great significance to analyze the chemical indexes of mine water and develop a rapid identification system of water source, which can quickly and accurately distinguish the causes of water inrush and identify the source of water inrush, so as to reduce casualties and economic losses and prevent and control water inrush disasters. Taking Ca2+, Mg2+, Na+ + K+, , , Cl-, pH value and TDS as discriminant indexes, the principal component analysis method was used to reduce the dimension of data, and the identification model of mine water inrush source based on PCA-BP neural network was established. 96 sets of data of different aquifers in Panxie mining area were selected for prediction analysis, and 20 sets of randomly selected data were tested, with an accuracy rate of 95%. The model can effectively reduce data redundancy, has a high recognition rate, and can accurately and quickly identify the water source of mine water inrush.

西北煤电集聚区不同水体水化学特征及氟成因

为探明西北煤电集聚区不同水体水化学特征以及氟离子分布成因,以鄂尔多斯市补连塔矿区为研究对象,采用Piper三线图、主成分分析、相关性分析等方法,对采集的55个水样进行测试,结合非致癌健康评价模型对地下水中氟离子进行健康风险评估.结果表明:所有水样均呈现出弱碱性;Na^(+)、SO_(4)^(2-)是地表水以及深层地下水的优势离子,浅层地下水的优势离子为Ca2+和HCO_(3)-;地表水的水化学类型主要以SO_(4)·Cl-Na和HCO_(3)·SO_(4)-Na型为主,浅层地下水的化学类型较多,主要以SO_(4)·Cl-Na·Ca、HCO_(3)-Na·Mg、HCO_(3)-Mg和SO_(4)·Cl-Ca型为主,深层地下水则以HCO_(3)·SO_(4)-Ca和SO_(4)-Na型为主;其水化学特征主要受硅酸盐风化溶解作用影响,同时采煤活动促进了不同水体之间水化学转化;地表水、浅层地下水与深层地下水中F变化范围分别在0.16~4.82,0~0.69,0.08~6.26mg/L,地下水中F有19.35%的风险指数超过HQ=1,推测该地区地下水的高氟现象对人类健康产生一定的影响.