Construction of Inorganic Elemental Fingerprint and Multivariate Statistical Analysis of Marine Traditional Chinese Medicine Meretricis concha from Rushan Bay

Meretricis concha is a kind of marine traditional Chinese medicine(TCM), and has been commonly used for the treatment of asthma and scald burns. In order to investigate the relationship between the inorganic elemental fingerprint and the geographical origin identification of Meretricis concha, the elemental contents of M. concha from five sampling points in Rushan Bay have been determined by means of inductively coupled plasma optical emission spectrometry(ICP-OES). Based on the contents of 14 inorganic elements(Al, As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Se, and Zn), the inorganic elemental fingerprint which well reflects the elemental characteristics was constructed. All the data from the five sampling points were discriminated with accuracy through hierarchical cluster analysis(HCA) and principle component analysis(PCA), indicating that a four-factor model which could explain approximately 80% of the detection data was established, and the elements Al, As, Cd, Cu, Ni and Pb could be viewed as the characteristic elements. This investigation suggests that the inorganic elemental fingerprint combined with multivariate statistical analysis is a promising method for verifying the geographical origin of M. concha, and this strategy should be valuable for the authenticity discrimination of some marine TCM.

Quality assessment of Jinhongtang Granule using UFLC-MS/MS and multivariate statistical analysis

Jinhongtang is a traditional Chinese medicine formula composed of Rheum palmatum L.stem,Sargentodoxa cuneata stem,and Taraxacum mongolicum and is used for the treatment of sepsis.However,quality assessment method for Jinhongtang is not available.In present study,we developed a UFLC-MS/MS method to determine 16 analytes in 20 batches of home-made and commercial Jinhongtang.Multivariate statistical analysis revealed the significant differences in the quality of home-made and commercial Jinhongtang and the difference in the quality of home-made samples was more significant.The integrated strategy based on UFLC-MS/MS and multivariate statistical analysis provided a new basis for the overall quality assessment of Jinhongtang.

Study on the Relationship between Soil and Environment Based on Multivariate Statistical Analysis

[Objective] The study aimed to study the relationship between soil and environment on the basis of multivariate statistical analysis. [ Method] Through field investigation, sampling and laboratory analysis, we discussed the relationship between soil properties and environmental factors in Mizhi County, North Shaanxi by using Canoco multivariate statistical analysis. [ Result]According to the effects of various environmental factors on soil properties, the influencing order of environmental factors was land use way 〉 vegetation type 〉 vegetation restoration years 〉 vegeta- tion coverage 〉 slope aspect 〉 gradient 〉 elevation. In a word, soil properties were significantly affected by land use way and vegetation type which were the most important environmental factors of soil properties in spatial variation, while vegetation restoration years were closely related to the ac- cumulation of soil nutrients. [ Condusion]The research could provide theoretical references for the construction of ecological environment in Loess Plateau of China.

Multivariate Statistical Analysis of Large Datasets: Single Particle Electron Microscopy

Biology is a challenging and complicated mess. Understanding this challenging complexity is the realm of the biological sciences: Trying to make sense of the massive, messy data in terms of discovering patterns and revealing its underlying general rules. Among the most powerful mathematical tools for organizing and helping to structure complex, heterogeneous and noisy data are the tools provided by multivariate statistical analysis (MSA) approaches. These eigenvector/eigenvalue data-compression approaches were first introduced to electron microscopy (EM) in 1980 to help sort out different views of macromolecules in a micrograph. After 35 years of continuous use and developments, new MSA applications are still being proposed regularly. The speed of computing has increased dramatically in the decades since their first use in electron microscopy. However, we have also seen a possibly even more rapid increase in the size and complexity of the EM data sets to be studied. MSA computations had thus become a very serious bottleneck limiting its general use. The parallelization of our programs—speeding up the process by orders of magnitude—has opened whole new avenues of research. The speed of the automatic classification in the compressed eigenvector space had also become a bottleneck which needed to be removed. In this paper we explain the basic principles of multivariate statistical eigenvector-eigenvalue data compression;we provide practical tips and application examples for those working in structural biology, and we provide the more experienced researcher in this and other fields with the formulas associated with these powerful MSA approaches.

Multivariate Statistical Analysis of Dominating Groundwater Mineralization and Hydrochemical Evolution in Gao,Northern Mali

Population growth and expanding urbanization have caused persistent shortages and contamination of groundwater resources in Mali,Africa.The increase in groundwater salinity makes it more difficult for residents to obtain drinking water,it is necessary to clarify the causes and control factors of groundwater mineralization in Gao region,northern Mali.Based on the analysis of the hydrochemical composition of groundwater in 24 boreholes,Piper and Sch?eller diagrams,principal component analysis(PCA)and hierarchical cluster analysis(HCA)are used to carry out multivariate statistical analysis on the main ions.The results show that the groundwater samples are weakly alkaline,with pH values ranging from 5.83 to 8.40,and the average values of boreholes are 7.50,respectively.The average electrical conductivity(EC)value is 354.4(μS/cm),and the extreme value is between 124.0 and 1247(μS/cm).Water is usually mineralized and presents nine types of water phase.The three principal components explain 84.42%of the total variance for 13 parameters.The factor F1(58.85%),the factor F2(16.88%)and the factor F3(8.69%)present for the majority of the total data set.In addition,multivariate statistical analysis confirmed the genetic relationship among aquifers and identified three main clusters.Clustering related to groundwater mineralization(F1),clustering related to oxide reduction and iron enrichment(F2),and clustering of groundwater pollution caused by nitrate and magnesium(F3).We found that agriculture,weathering activities and dissolution of geological materials promote the mineralization of groundwater.Groundwater quality in the Gao region is becoming less and less potable because of increasing salinity.

Prediction of Coastal Fecal Indicator Bacteria Concentrations Using Multivariate Data Analysis

The application of multivariate data analysis, a method for coping with multi-colinearity among independent variables in analyzing coastal water quality data, is presented. This study investigates the statistical regression modeling of FIB （fecal indicator bacteria） concentrations at the outlet of Talbert Marsh in Orange County, California. The multivariate data modeling utilized FIB and physical variables measurements （n = 5,580） collected during a series of longitudinal study of the Talbert Marsh. For the statistical prediction modeling in predicting the FIB concentrations at the outlet of the Talbert Marsh, multivariate analysis techniques such as PCR （principal components regression）, PLS （partial least-squares） regression and SVM （support vector machine） regression were adopted. Statistical modeling results suggest that the statistical modeling predictions are all fell within the reasonable range of actual measurement data. In addition, it is indicated that the accuracy of SVM regression for predicting FIB concentrations at the Talbert Marsh outlet is better than that of other models.

Water Quality Assessment of a Tropical Mexican Lake Using Multivariate Statistical Techniques

Water quality of Mexican tropical lake Chapala was assessed through multivariate statistical techniques, cluster analysis (CA) and principal component analysis (PCA) at ten different monitoring sites for ten physicochemical variables and six metals. This study evaluated and interpreted complex water quality data sets and apportioned of pollution sources to get better information about water quality. From descriptive statistics results, the highest concentrations of metals occurred during the dry season, and this trend was explained by the fact that an unusual rainy event occurred during the month of February 2009 and brought metals into the lake by runoffs from nearby mountains. According to international criteria for water consumption by aquatic organisms [USEPA], only Zn concentration values were below these criteria whereas the values of Ni, Pb, Cd and Fe were above the corresponding values set in these criteria (Ni: 52 μg·L-1, Pb: 2.5 μg·L-1, Cd: 0.25 μg·L-1, and Fe: 1000 μg·L-1). The correlations were observed by PCA, which were used to classify the samples by CA, based on the PCA scores. Seven significant cluster groups of sampling locations—(sites 4 and 5), (sites 3 and 9), (site 7), (site 10), (sites 2 and 6), (site 8) and (site 1)— were detected on the basis of similarity of their water quality. The results revealed that the stress exerted on the lake caused by waste sources follows the order: domestic > agricultural > industrial.

Geochemical Anomalies Identified by Multifractal Modeling: Implications for Mineral Exploration in the Ziyoutun Cu-Au District, Jilin Province, China

The Ziyoutun Cu-Au district is located in the Jizhong–Yanbian Metallogenic Belt and possesses excellent prospects. However, the thick regolith and complex tectonic settings present challenges in terms of detecting and decomposition of weak geochemical anomalies. To address this challenge, we initially conducted a comprehensive analysis of 1:10,000-scale soil geochemical data. This analysis included multivariate statistical techniques, such as correlation analysis, R-mode cluster analysis, Q–Q plots and factor analysis. Subsequently, we decomposed the geochemical anomalies, identifying weak anomalies using spectrum-area modeling and local singularity analysis. The results indicate that the assemblage of Au-Cu-Bi-As-Sb represents the mineralization at Ziyoutun. In comparison to conventional methods, spectrumarea modeling and local singularity analysis outperform in terms of identification of anomalies. Ultimately, we considered four specific target areas(AP01, AP02, AP03 and AP04) for future exploration, based on geochemical anomalies and favorable geological factors. Within AP01 and AP02, the geochemical anomalies suggest potential mineralization at depth, whereas in AP03 and AP04 the surface anomalies require additional geological investigation. Consequently, we recommend conducting drilling, following more extensive surface fieldwork, at the first two targets and verifying surface anomalies in the last two targets. We anticipate these findings will significantly enhance future exploration in Ziyoutun.

Diatoms as indicators of environmental change in coastal areas:a case study in Lianjiang coast of East China Sea

Owing to the significant differences in environmental characteristics and explanatory factors among estuarine and coastal regions,research on diatom transfer functions and database establishment remains incomplete.This study analysed diatoms in surface sediment samples and a sediment core from the Lianjiang coast of the East China Sea,together with environmental variables.Principal component analysis of the environmental variables showed that sea surface salinity(SSS)and sea surface temperature were the most important factors controlling hydrological conditions in the Lianjiang coastal area,whereas canonical correspondence analysis indicated that SSS and pH were the main environmental factors affecting diatom distribution.Based on the modern diatom species–environmental variable database,we developed a diatom-based SSS transfer function to quantitatively reconstruct the variability in SSS between 1984 and 2021 for sediment core HK3 from the Lianjiang coastal area.The agreement between the reconstructed SSS and instrument SSS data from 1984 to 2021 suggests that diatombased SSS reconstruction is reliable for studying past SSS variability in the Lianjiang coastal area.Three low SSS events in AD 2019,2013,and 1999,together with an increased relative concentration of freshwater diatom species and coarser sediment grain sizes,corresponded to two super-typhoon events and a catastrophic flooding event in Lianjiang County.Thus,a diatom-based SSS transfer function for reconstructing past SSS variability in the estuarine and coastal areas of the East China Sea can be further used to reflect the paleoenvironmental events in this region.

Characteristics and genesis of groundwater salinization in coastal areas of the Lower Reaches of Oujiang Basin

The coastal areas of the lower reaches of Oujiang River Basin are rich in groundwater resources.However,the unsustainable exploitation and utilization of groundwater have led to significant changes in the groundwater environment.Understanding the characteristics and genesis of groundwater salinization is crucial for preventing its deterioration and ensuring sustainable utilization.In this study,a comprehensive approach combining the ion ratio method,mineral saturation index method and multivariate statistical analysis was employed to investigate the hydrochemical characteristics and main controlling factors in the study area.The findings reveal that:(1)Groundwater samples in study area exhibit a neutral to slightly alkaline pH.The predominant chemical types of unconfined water are HCO_(3)-Ca·Na,HCO_(3)·Cl-Na·Ca and HCO_(3)·SO_(4)-Ca·Na,while confined water mainly exhibits Cl·HCO_(3)-Na and Cl-Na types.(2)Salinity coefficients indicate an increase in salinity from unconfined to confined water.TDS,Na^(+)and Cl^(–)concentrations show an increasing trend from mountainous to coastal areas in unconfined water,while confined water displays variability in TDS,Na^(+)and Cl^(–)concentrations.(3)Groundwater salinity is mainly influenced by water-rock interactions,including the dissolution of halite and gypsum,cation exchange,and seawater intrusion etc.Additionally,human activities and carbonate dissolution contribute to salinity in unconfined water.Seawater intrusion is identified as the primary factor leading to higher salinity in confined water compared to unconfined water,with increasing cation exchange and seawater interaction observed from unconfined to confined water.

Metabolomics-based comparative analysis of the effects of host and environment on Viscum coloratum metabolites and antioxidative activities

Viscum coloratum(Kom.)Nakai is a well-known medicinal hemiparasite widely distributed in Asia.The synthesis and accumulation of its metabolites are affected by both environmental factors and the host plants,while the latter of which is usually overlooked.The purpose of this study was to comprehensively evaluate the effects of host and habitat on the metabolites in V.coloratum through multiple chemical and biological approaches.The metabolite profile of V.coloratum harvested from three different host plants in two habitats were determined by multiple chemical methods including high-performance liquid chromatography-ultraviolet(HPLC-UV),gas chromatography-flame ionization detector(GC-FID)and ultra-performance liquid chromatography quadrupole time of flight mass spectrometry(UPLC-QTOF/MS).The differences in antioxidant efficacy of V.coloratum were determined based on multiple in vitro models.The multivariate statistical analysis and data fusion strategy were applied to analyze the differences in metabolite profile and antioxidant activity of V.coloratum.Results indicated that the metabolite profile obtained by various chemical approaches was simultaneously affected by host and environment factors,and the environment plays a key role.Meanwhile,three main differential metabolites between two environment groups were identified.The results of antioxidant assay indicated that the environment has greater effects on the biological activity of V.coloratum than the host.Therefore,we conclude that the integration of various chemical and biological approaches combined with multivariate statistical and data fusion analysis,which can determine the influences of host plant and habitat on the metabolites,is a powerful strategy to control the quality of semi-parasitic herbal medicine.

Statistical evaluation of fluoride contamination in groundwater resources of Santiago del Estero Province, Argentina

This study investigates the suitability of statistical techniques for evaluating the fluoride content and the groundwater quality from Robles Department(RD)and Banda Department(BD)in Santiago del Estero(Argentina).For the original statistical study,evaluation of nine parameters(fluoride,pH,conductivity,atmospheric and water temperature,total dissolved solids,chloride,hardness,and alkalinity)of 110 collected underground water samples from 23 dispersed rural areas was proposed.Groundwater samples were obtained by sampling taken from wells at different depths.Fluoride levels were determined by a standard colorimetric method in two seasonal periods,the dry(from April to September)and rainy(from October to March)period.The analytical results obtained for physicochemical parameters such as pH,total dissolved solids(TDS),and temperature does not reveal any notable difference between the rainy and dry seasons studied.In both seasons,the atmospheric temperature average was 22℃.With respect to fluoride content,approximately 50%of the analysed groundwater samples exceeded the limit established by current legislation(1.0 mg/L),obtaining concentration levels in the range of 0.01-2.80 mg/L.This study demonstrates the usefulness of the univariate statistical method(quartiles calculation,interquartile range IQR),multivariate principal component analysis(PCA),and cluster analysis to establish a better understanding of the state of the contamination of the waters in the region studied.

Feasibility of using reflectance spectroscopy for the analysis of bio-element concentrations in Antarctic ornithogenic sediments

Compared with traditional chemical analysis methods,reflectance spectroscopy has the advantages of speed,minimal or no sample preparation,non-destruction,and low cost.The present study explored the application of the reflectance spectroscopy within near ultraviolet-visible-near infrared region to predict bio-element compositions in the ornithogenic sediments from the maritime Antarctic.A total of 106 samples were taken from four ornithogenic sediment cores on the Ardley Island of Antarctica,68 samples were used for building calibration equation,and 38 for prediction of nine bio-elements including P,Ca,Cu,Zn,Se, Sr,Ba,F and S.Three multivariate statistical analysis techniques,including stepwise multiple linear regression（Stepwise-MLR）,principal component regression（PCR） and partial least squares regression（PLS） were used to develop mathematical relationships between the spectral data and the chemical reference data.The results showed that the regression models constructed by PCR and PLS models have no significant differences,and obviously supervisor to Stepwise-MLR.The correlations between spectra-predicted and chemically analyzed concentrations of nine bio-elements are statistically significant,and the concentration-versusdepth profiles predicted from reflectance spectra using PLS calibration model are consistent with those from actual chemical analysis.These results demonstrated the feasibility of using reflectance spectroscopy to infer bio-element concentrations in the ornithogenic sediments,and thus it is suggested that the reflectance spectroscopy could provide a rapid and valuable technique to indirectly identify whether the sediments were influenced by penguin droppings in the Antarctic region.

Evaluation of hydro-chemistry in a phreatic aquifer in the Vindhyan Region, India, using entropy weighted approach and geochemical modelling

Groundwater quality monitoring and geochemical characterization in the phreatic aquifer are critical for ensuring universal and equitable access to clean,reliable,and inexpensive drinking water for all.This research was intended to investigate the hydrogeochemical attributes and mechanisms regulating the chemistry of groundwater as well as to assess spatial variation in groundwater quality in Satna district,India.To accomplish this,the groundwater data comprising 13 physio-chemical parameters from thirty-eight phreatic aquifer locations were analysed for May 2020 by combining entropy-weighted water quality index(EWQI),multivariate statistics,geochemical modelling,and geographical information system.The findings revealed that the groundwater is fresh and slightly alkaline.Hardness was a significant concern as 57.89% of samples were beyond the permissible limit of the World Health Organisation.The dominance of ions were in the order of Ca^(2+)> Na^(+)> Mg^(2+)> K^(+) and HCO_(3)^(-)> SO_(4)^(2-)> Cl^-> NO_(3)^(-)> F^(-).Higher concentration of these ions is mainly concentrated in the northeast and eastern regions.Pearson correlation analysis and principal component analysis(PCA) demonstrated that both natural and human factors regulate groundwater chemistry in the region.The analysis of Q-mode agglomerative hierarchical clustering highlighted three significant water clusters.Ca-HCO_3 was the most prevalent hydro-chemical facies in all three clusters.Geochemical modelling through various conventional plots indicated that groundwater chemistry in the research region is influenced by the dissolution of calcite/dolomite,reverse ion exchange,and by silicate and halite weathering.EWQI data of the study area disclosed that 73.69% of the samples were appropriate for drinking.Due to high salinity,Magnesium(Mg^(2+)),Nitrate(NO_(3)^(-)),and Bicarbonate(HCO_(3)^(-)) concentrations,the north-central and north-eastern regions are particularly susceptible.The findings of the study may be accomplished by policymakers and groundwater managers to achieve sustainable groundwater development at the regional scale.

Rapid Analysis and Identification of Absorbed Components and Their Metabolites of Yuanhu Zhitong Dropping Pill in Rat Plasma and Brain Tissue Using UPLC-Q-TOF/MS with Multivariate Statistical Analysis

Objective To establish an effective approach for rapid and comprehensive analysis on the absorbed and metabolic components in rats after ig administration of Yuanhu Zhitong Dropping Pill（YHZT）. Methods Based on the combination of UPLC-Q-TOF/MS and multivariate statistical analysis, the absorbed prototype constituents and their metabolites in rat plasma were rapidly analyzed and identified, and the components absorbed into brain were further identified by comparing the extracted ion chromatograms（EICs） of control and brain tissue samples of dosed rats. Results A total of 38 YHZT-related xenobiotic compounds were detected and identified as the potential bioactive constituents in rat plasma, including 24 absorbed prototype constituents and 14 metabolites. In particular, of all prototype constituents, 14 were also detected in rat brain tissue, indicating that they could penetrate the blood-brain barrier and enter into brain. Conclusion An effective method is established and applied to analyze the potential bioactive constituents in YHZT, which provides a pathway to further investigate the pharmacological pattern and mechanism of YHZT.

Chemical comparison of dried rehmannia root and prepared rehmannia root by UPLC-TOF MS and HPLC-ELSD with multivariate statistical analysis

To identify the chemical differences which lead to the different therapeutic effects of dried rehmannia root(DRR)and prepared rehmannia root(PRR),we compared the chemical composition of decoctions of randomly purchased DRR and PRR using ultra performance liquid chromatography(UPLC)coupled with time-of-fight mass spectrometry and high performance liquid chromatography(HPLC)coupled with evaporative light scattering detection(ELSD)with the aid of multivariate statistical analysis.Both approaches clearly revealed compositional and quantitative differences between DRR and PRR.UPLC-MS data indicated stachyose,rehmaiono-side A(or rehmaionoside B),acteoside(or forsythiaside,or isoacteoside),6-O-coumaroylajugol(or 6-O-E-feruloylajugol,or 6-O-Z-feruloylajugol)as important discriminators between DRR and PRR decoctions.HPLC-ELSD analysis showed that the content of fructose in the decoctions of PRR was about four times greater than that of DRR(P<10^(-5)),while sucrose content in the decoctions of PRR was only about one seventh of that in DRR(P<0.01).Our results suggest that some compounds,such as fructose,stachyose and rehmaionoside,may be responsible for the differing therapeutic effects of DRR and PRR.Furthermore,improvements in quality control for PRR,which is currently lacking in the Chinese Pharmacopoeia,are recommended.

Combined Use of Multivariate Statistical Analysis and Hydrochemical Analysis for Groundwater Quality Evolution: A Case Study in North Chain Plain

Understanding the controlling factor of groundwater quality can enhance promoting sustainable development of groundwater resources. To this end, multivariate statistical analysis（MA） and hydrochemical analysis were introduced in this work. The results indicate that the canonical discriminant function with 7 parameters was established using the discriminant analysis（DA） method, which can afford 100% correct assignation according to the 3 different clusters（good water（GW）, poor water（PW）, and very poor water（VPW）） obtained from cluster analysis（CA）. According to factor analysis（FA）, 8 factors were extracted from 25 hydrochemical elements and account for 80.897% of the total data variance, suggesting that groundwater with higher concentrations of sodium, calcium, magnesium, chloride, and sulfate in southeastern study area are mainly affected by the natural process; the higher level of arsenic and chromium in groundwater extracted from northwestern part of study area are derived by industrial activities; domestic and agriculture sewage have important contribution to copper, iron, iodine, and phosphate in the northern study area. Therefore, this work can help identify the main controlling factor of groundwater quality in North China plain so as to make better and more informed decisions about how to achieve groundwater resources sustainable development.

Effects of Vegetation Succession on Soil Fertility Within Farming-Plantation Ecotone in Ziwuling Mountains of the Loess Plateau in China

To further understand the relationship between vegetation succession and soil fertility within farming-plantation ecotone in Ziwuling Mountains of the Loess Plateau, nine kinds of widely distributed communities at different succession stages were selected, and the effects of vegetation succession on soil fertility were studied through the methods of comparing two hierarchical clustering （similarity index： B） and other multivariate analysis. The results showed that： （i） the similarity in clustering pattern of nine communities which classified by plant species and soil nutrients respectively showed a trend of B ^-overall plant-soil0-10cn〉B^-overall plant-soil 10-20 cm 〉B^- overall plant-soil 20-40 cm, and for the top soil, it showed a trend of B^- grass-soil 0-10 cm 〉 B^-shrub-soil 0-10 cm 〉 Btree-soil0-10 cm; （ii） soil fertility increased during the succession process from abandoned land to forest community, and the soil fertility of forest community showed an increased order of coniferous forest →mixed forest →broadleaf forest; （iii） during the process of vegetation succession, the variation of topsoil fertility was higher than that of the subsurface soil （coefficient of variation： CV0-10 cm 〉CV 10-20 cm 〉 CV20-40 cm）, and when the succession developed into the stages of shrub and forest communities, the top soil fertility had been improved significantly; and （iv） for the subsurface soil of the communities at the advanced succession stages, the soil fertility also increased to some extent. Our results suggested that the method of comparing two hierarchical clustering reflected the similarity level of different cluster patterns, therefore, it was helpful to study the relationship between vegetation succession and soil fertility. There was a corresponding relationship between the change process of soil fertility from the top soil to subsurface soil and the process of vegetation succession from the early stages to the advanced stage. The differentiations of soil fertility in vertical space and horizontal space were both caused by vegetation succession, which was significant for both the shrub and forest communities. The improved level of forest soil fertility was related to forest vegetation types and the improved fertility level of broad-leaved forest-soil community was higher than that of the coniferous forest soil. In the practice on soil fertility ecological restoration of the loess plateau, it is important to carry out reasonably artificial forestation so as to enhance the restoration and improvement of soil fertility.

Water quality of reclaimed water from treated urban wastewater in Chaobai River Basin,North China

The utilization of reclaimed water could be an efficient tool to alleviate water scarcity,especially for dry river augmentation.However,it is crucial to monitor water quality to ensure safety to human health and to avoid negative effects on the environment.Reclaimed water samples were collected bimonthly from May to November in 2010 in Chaobai River,and the physiochemical parameters were determined.The main results are as follows:The parameters exceeding the threshold value of the water guidelines are mainly nutrition related to nitrogen and phosphorus,which are known to increase the risk of eutrophication in surface waters.Additionally,nitrite and nitrate can be detrimental to human health.The majority of the parameters have a peaking concentration in May,whereas others either show significant temporal variation over the entire period or remain relatively constant in all four months.Correlation analysis shows that some parameters(pH,T and B) have no significant correlation with others,whereas significant positive correlation was found for Sr with EC and TDS,for CI with TDS,for Si02 with TP and for NO3-N with TN and a significant negative correlation between SO4 and Ba.According to principal component analysis,60.108%of the total data is represented by dominant solutes,and the second principal component with a percentage of 31.876 comprises parameters related to nitrogen.Subsequent cluster analysis of parameters identified four groups,which represent different compositions,and samples in May differ from others.

Assessment of temporal and spatial variations in water quality using multivariate statistical methods： a case study of the Xin＇anjiang River, China

This study evaluated the temporal and spatial variations of water quality data sets for the Xin＇anjiang River through the use of multivariate statistical techniques, including cluster analysis （CA）, discriminant analysis （DA）, correlation analysis, and principal component analysis （PCA）. The water samples, measured by ten parameters, were collected every month for three years （2008-2010） from eight sampling stations located along the river. The hierarchical CA classified the 12 months into three periods （First, Second and Third Period） and the eight sampling sites into three groups （Groups 1, 2 and 3） based on seasonal differences and various pollution levels caused by physicochemical properties and anthropogenic activ- ities. DA identified three significant parameters （tempera- ture, pH and E.coli） to distinguish temporal groups with close to 76% correct assignment. The DA also discovered five parameters （temperature, electricity conductivity, total nitrogen, chemical oxygen demand and total phosphorus） for spatial variation analysis, with 80.56% correct assignment. The non-parametric correlation coefficient （Spear- man R） explained the relationship between the water quality parameters and the basin characteristics, and the GIS made the results visual and direct. The PCA identified four PCs for Groups 1 and 2, and three PCs for Group 3. These PCs captured 68.94%, 67.48% and 70.35% of the total variance of Groups 1, 2 and 3, respectively. Although natural pollution affects the Xin＇anjiang River, the main sources of pollution included agricultural activities, industrial waste, and domestic wastewater.