Applying the scores of multivariate statistical analyses to characterize the relationships between the hydrochemical properties and groundwater conditions in respect of the monsoon variation in Kapas Island,Terengganu, Malaysia

The different factors (seasonal changes) and variables (physicochemical) controlling the groundwater hydrochemistry of Kapas Island were identified using multivariate techniques principal component analysis (PCA), discriminant analysis (DA) and hierarchy cluster analysis (HCA). In the present study, the hydrochemistry of 216 groundwater samples, consisting of information concerning the in situ parameters and major ions in six monitoring boreholes, was studied and compared in two different monsoon seasons. The dominant variables derived from four components by PCA in the pre-monsoon indicated the influence of the salinity process, while the dominant variables derived from three components in the post-monsoon mostly indicated on the mineralization process. The DA gave the final variables after discriminating the insignificant variables based on the pre- and post-monsoon classifications. This provided important data reduction in terms of the mineralization process, as it only discriminated physical variables (TDS, EC, salinity, DO and temperature). Based on the HCA result, samples belonging to stations KW 3 and KW 4 were under Ca-rich water, while the remaining boreholes were grouped in Na-rich water.     

Application of the chemometric approach to evaluate the spatial variation of water chemistry and the identification of the sources of pollution in Langat River, Malaysia

The current study presents the application of selected chemometric techniques—hierarchical cluster analysis (HCA) and principal component analysis (PCA)—to evaluate the spatial variation of the water chemistry and to classify the pollution sources in the Langat River. The HCA rendered the sampling stations into two clusters (group 1 and group 2) and identified the vulnerable stations that are under threat. Group1 (LY 1 to LY 14) is associated with seawater intrusion, while group 2 (LY 15 to LY 30) is associated with agricultural and industrial pollution. PCA analysis was applied to the water datasets for group 1 resulting in four components, which explained 85 % of the total variance while group 2 extracted six components, explaining 88 % of the variance. The components obtained from PCA indicated that seawater intrusion, agricultural and industrial pollution, and geological weathering were potential sources of pollution to the study area. This study demonstrated the usefulness of the chemometric techniques on the interpretation of large complex datasets for the effective management of water resources.     

Assessment of natural and anthropogenic impacts in groundwater,utilizing multivariate statistical analysis and inverse distance weighted interpolation modeling: the case of a Scopia basin (Central Greece)

In Scopia basin, central Greece, a hydrochemical investigation was completed. Groundwater samples from 41 sites were used to assess the natural and anthropogenic impacts in groundwater, utilizing the principal component analysis (PCA) involved with the inverse distance weighted (IDW) interpolation modeling and hierarchical cluster analysis (HCA). Best fit model to explain the spatial distribution of both hydrochemical parameters and PCA was chosen by optimizing the IDW interpolator’s parameters. Precision of the model was picked based on less root-mean-squared prediction error (RMSPE) amongst predicted and actual values measured at the same locations. Groundwater exhibit Ca–Mg–HCO₃ as the dominant hydrochemical type and their greater part are mixed waters with non-dominant ion. Interpolation models demonstrate high estimations of nitrates in zones with agricultural activities and high estimations of nickel and chromium in regions with the strong presence of ultrabasic rocks. Dominant part of the groundwater samples surpasses in many cases the European Community (EC) drinking water permissible limits. Thus, they are unsuitable for human consumption. PCA illustrated four factors, which clarified 80.62% of the aggregate variance of data and HCA classified two statistically significant clusters of sampling sites. Results show natural procedures ascribed to the weathering of the minerals contained in the ultrabasic rocks and anthropogenic influences related to the use of fertilizers and wastewater leak. In light of FAO standards and Richards’s classification, the groundwaters are reasonable for irrigation purposes, featuring waters with low sodium hazard and moderate salinity hazard.     

Identifying key hydrochemical processes in a confined aquifer of an arid basin using multivariate statistical analysis and inverse modeling

Tertiary fractured permeable confined aquifer, which covered about 70 % of the studying area, played an important role in alleviating drinking water shortages. However, about 58 and 79 % of the groundwater samples exceeded the desirable limits for fluoride (1.5 mg/L) and TDS (1,000 mg/L). Two multivariate statistical methods, hierarchical cluster analysis (HCA) and principal components analysis (PCA), were applied to a subgroup of the dataset in terms of their usefulness for groundwater classification, as well as to identify the key processes controlling groundwater geochemistry. In the PCA, two principal factors have been extracted, which could explain 73 % of the total data variability. Among them, factor 1 revealed the source of groundwater salinity and factor 2 explained the elevated fluoride. Two major groups were classified by HCA and Group 1 was near the groundwater recharge zone and Group 2 was mainly distributed over the groundwater discharge zone. Inverse modeling (NETPATH) results indicated that the hydrochemical evolution was primarily controlled by (1) the dissolution of mirabilite, gypsum and halite for the source of groundwater salinity; (2) the release of the adsorbed fluoride through desorption or through competition with HCO₃ ^? under alkalinity condition for the elevated fluoride in the groundwater.     

Principal component analysis and hierarchical cluster analyses of arsenic groundwater geochemistry in the Hetao basin,Inner Mongolia

Although high As groundwater has been observed in shallow groundwater of the Hetao basin, little is known about As distribution in deep groundwater. Quantitative investigations into relationships among chemical properties and among samples in different areas were carried out. Ninety groundwater samples were collected from deep aquifers of the northwest of the basin. Twenty-two physicochemical parameters were obtained for each sample. Statistical methods, including principal component analysis (PCA) and hierarchical cluster analysis (HCA), were used to analyze those data. Results show that As species were highly correlated with Fe species, NH₄-N and pH. Furthermore, result of PCA indicates that high As groundwater was controlled by geological, reducing and oxic factors. The samples are classified into three clusters in HCA, which corresponded to the alluvial fans, the distal zone and the flat plain. Moreover, the combination of PCA with HCA shows the different dominant factors in different areas. In the alluvial fans, groundwater is influenced by oxic factors, and low As concentrations are observed. In the distal zone, groundwater is under suboxic conditions, which is dominated by reducing and geological factors. In the flat plain, groundwater is characterized by reducing conditions and high As concentrations, which is dominated by the reducing factor. This investigations indicate that deep groundwater in the alluvial fans mostly contains low As concentrations but high NO₃ and U concentrations, and needs to be carefully checked prior to being used for drinking water sources.     

Source Apportionment of PAHs Using Two Mathematical Models for Mangrove Sediments in Shantou Coastal Zone,China

Thirty-five surface sediment samples collected from three mangroves in Shantou coastal zone, China in 2007 were analyzed for a suite of polycyclic aromatic hydrocarbons (PAHs). Two mathematical models were used to determine the profiles and relative contributions of PAH sources to the mangroves. The two models are principal component analyses (PCA) with multiple linear regression analysis (MLR) and positive matrix factorization (PMF). Both models identified five factors and gave excellent correlation coefficients between predicted and measured levels of 16 PAH compounds, but the results had some differences. The PAH contribution rate attributed to vehicular emission sources identified by PCA-MLR was 37.20%, but the rate identified by PMF was only 12.37%. The main sources identified by PCA-MLR were combination of biomass/coal combustion and vehicular emissions, whereas the main source identified by PMF was only biomass/coal combustion. The PMF analysis was the preferred model for the paper data set.     

The simulation of snowmelt runoff in the ungauged Kaidu River Basin of TianShan Mountains,China

Snowmelt runoff is an important source of water resources in the arid mountain area. Modelling snowmelt runoff for cold regions remains a problematic aspect because of the lack of data by gauges in large basins. In order to overcome the shortage of measured data in the snowmelt runoff modelling, the temperature interpolation method would greatly help in improving the simulation accuracy and describing the snow-hydrological behaviours of the study catchments. In this study, the temperature is the principal variable used to estimate the importance of the melting of snow cover using the snowmelt runoff model. Five different temperature interpolation attempts were performed over the Kaidu River Basin for the snowmelt season of the year 2000. Three temperature inputs were taken directly from the individual weather stations in or near the study area, and the other two temperature inputs were interpolated from the three weather stations. The results indicated that the temperature estimated from different methods could result in quite a difference in runoffs in comparison with the observed ones. The simulation results using average temperature from the three stations showed good results; the simulation run with the weighted average temperature generated a lower R ² than the average temperature of three stations and using temperature directly adopted from three individual stations gave various results. The weather stations used to perform the snowmelt runoff simulation should be located in the place which is most representative of the mountain weather conditions, and the land cover and topography that those stations represented also play an important role in the snowmelt runoff simulation.     

Hydrochemical characterization of groundwater using multivariate statistical analysis: the Maritime Djeffara shallow aquifer (Southeastern Tunisia)

Worldwide, groundwater resources have been considered as the main sources of drinking, domestic uses, industrial and agriculture water demands, especially in arid and semiarid regions. Accordingly, the monitoring of the groundwater quality based on different tools and methods becomes a necessity. The aim of this study was to apply several approaches to assess the water quality and to define the main hydrochemical process which affect groundwater of the Maritime Djeffara shallow aquifer. In addition to the hydrochemical approach, two multivariate statistical analyses, hierarchical clusters analysis (HCA) and principal component analysis (PCA), were carried out to identify the natural and the anthropogenic processes affecting groundwater chemistry. Hydrochemical approach, based on 47 analyzed groundwater samples, shows that most of samples present a sulfate to mixed chloride, with sodi-potassic tendency facies. According to their chemically composition, the HCA revealed three different groups (C1, C2 and C3) according to their electrical conductivity (EC) values: C1 (average EC = 4500 µS/cm), C2 (average EC = 7040 µS/cm) and C3 (average EC = 9767 µS/cm). Furthermore, PCA results show two principal factors account 84.05% of the total variance: (1) F1 represents the natural component, and (2) F2 symbolizes the anthropic component. Moreover, the groundwater quality map of the Maritime Djeffara shows three categories: suitable, doubtful and unsuitable water for irrigation. These different results should be taken to protect water resources in arid and semiarid regions, especially at the alluvial coastal regions. Also, they help to make a suitable planning to manage and protect the groundwater resources.     

Assessment of regional drought vulnerability and risk using principal component analysis and a Gaussian mixture model

Due to the complex characteristics of drought, drought risk needs to be quantified by combining drought vulnerability and drought hazard. Recently, the major focus in drought vulnerability has been on how to calculate the weights of indicators to comprehensively quantify drought risk. In this study, principal component analysis (PCA), a Gaussian mixture model (GMM), and the equal-weighting method (EWM) were applied to objectively determine the weights for drought vulnerability assessment in Chungcheong Province, located in the west-central part of South Korea. The PCA provided larger weights for agricultural and industrial factors, whereas the GMM computed larger weights for agricultural factors than did the EWM. The drought risk was assessed by combining the drought vulnerability index (DVI) and the drought hazard index (DHI). Based on the DVI, the most vulnerable region was CCN9 in the northwestern part of the province, whereas the most drought-prone region based on the DHI was CCN12 in the southwest. Considering both DVI and DHI, the regions with the highest risk were CCN12 and CCN10 in the southern part of the province. Using the proposed PCA and GMM, we validated drought vulnerability using objective weighting methods and assessed comprehensive drought risk considering both meteorological hazard and socioeconomic vulnerability.

     

Assessment of water quality and identification of pollution sources of three lakes in Kashmir, India, using multivariate analysis

Multivariate statistical techniques, such as cluster analysis, principal component analysis (PCA) and factor analysis (FA) were applied to evaluate and interpret the water quality data set for 13 parameters at 10 different sites of the three lakes in Kashmir, India. Physicochemical parameters varied significantly (p?<?0.05) among the sampling sites. Hierarchical cluster analysis grouped 10 sampling sites into three clusters of less polluted, moderately polluted and highly polluted sites, based on similarity of water quality characteristics. FA/PCA applied to data sets resulted in three principal components accounting for a cumulative variance of 69.84, 65.05 and 71.76% for Anchar Lake, Khushalsar Lake and Dal Lake, respectively. Factor analysis obtained from principal components (PCs) indicated that factors responsible for accelerated eutrophication of the three lakes are domestic waste waters, agricultural runoff and to some extent catchment geology. This study assesses water quality of three lakes through multivariate statistical analysis of data sets for effective management of these lakes.     

Water quality assessment and hydrochemical characteristics of groundwater on the aspect of metals in an old town,Foshan, south China

The present study is aimed at assessing the water quality and discussing the hydrochemical characteristics and seasonal variation of shallow groundwater on the aspect of metals in the eastern Chancheng district of Foshan city, south China. Multivariate analytical methods such as principal components analysis (PCA) and hierarchical cluster analysis (HCA) were used in this study. The results show that 45% of groundwater in the east-central of study area is not suitable for drinking purpose due to high concentrations of Fe, Pb and Mn. The mean concentrations of Fe, Hg, Cu, Pb, and Mn in dry season are higher than that in wet season. On the contrary, the mean concentrations of Cd, Co, Zn, Ba, Cr, Mo, Ni and Al in wet season are higher than that in dry season. PCA results show that four PCs are responsible for the 78.6% of the total hydrochemical variables in groundwater. Three groups were generated from HCA method. Group 1 reflects the characteristic of wet season and the low ion exchange capacity; group 2 is mainly influenced by the dry season. Reducing environment and high ion exchange capacity are responsible for group 3. The results are useful in addressing future measures in groundwater resource management for local government.     

Evaluation of spatial and seasonal variations in surface water quality using multivariate statistical techniques

In this study, spatial and seasonal variations of water quality in Haraz River Basin were evaluated using multivariate statistical techniques, such as cluster analysis, principal component analysis and factor analysis. Water quality data collected from 8 sampling stations in river during 4 seasons (Summer and Autumn of 2007, Winter and Spring of 2008) were analyzed for 10 parameters (dissolved oxygen, Fecal Coliform, pH, water temperature, biochemical oxygen demand, nitrate, total phosphate, turbidity, total solid and discharge). Cluster analysis grouped eight sampling stations into three clusters of similar water quality features and thereupon the whole river basin may be categorized into three zones, i.e. low, moderate and high pollution. The principle component analysis/factor analysis assisted to extract and recognize the factors or origins responsible for water quality variations in four seasons of the year. The natural parameters (temperature and discharge), the inorganic parameter (total solid) and the organic nutrients (nitrate) were the most significant parameters contributing to water quality variations for all seasons. Result of principal component analysis and factor analysis evinced that, a parameter that can be significant in contribution to water quality variations in river for one season, may less or not be significant for another one.     

Application of multivariate geostatistics in the investigation of heavy metal contamination of roadside dusts from selected highways of the Greater Toronto Area, Canada

A good understanding of roadside soil contamination and the location of pollution sources is important for addressing many environmental problems. The results are reported here of an analysis of the content of metals in roadside dust samples of four major highways in the Greater Toronto area (GTA) in Ontario, Canada. The metals analyzed are Pb, Zn, Cd, Ni, Cr, Cu, Mn, and Fe. Multivariate geostatistical analysis [correlation analysis (CA), principal component analysis (PCA), and hierarchical cluster analysis (HCA)] were used to estimate soil chemical content variability. The correlation coefficient shows a positive correlation between Cr–Cd, Mn–Fe, and Fe–Cu, while negatively between Zn–Cd, Mn–Cd, Zn-Cr, Pb–Zn, and Ni–Zn. PCA shows that the three eigenvalues are less than one, and suggests that the contamination sources are processing industries and traffic. HCA classifies heavy metals in two major groups. The cluster has two larger subgroups: the first contains only the variables Fe, Mn, Cu, Cr, Ni, and Pb, and the second includes Cd and Zn. The geostatistical analysis allows geological and anthropogenic causes of variations in the contents of roadside dust heavy metals to be separated and common pollution sources to be identified. The study shows that the high concentration of traffic flows, the parent material mineralogical and chemical composition, and land use are the main sources for the heavy metal concentration in the analyzed samples.     

Application of factor analysis in defining drought prone areas in Lake Urmia Basin

Data reduction methods such as principal components analysis and factor analysis can be used to define drought prone areas of a basin. In this study, factor analysis method applied for the purpose of projecting the information space on the few dominant axes. The main aim of this study is regionalization of Lake Urmia Basin from the view of drought using factor analysis. For this purpose, monthly precipitation data of 30 weather stations in the period 1972–2009 were used. For each of the selected stations, 3- and 12-month Standardized Precipitation Index (SPI) values were calculated. Factor analysis conducted on SPI values to delineate the study area with respect to drought characteristics. Homogeneity of obtained regions tested using the S statistics proposed by Wiltshire. Results of factor analysis of 3- and 12-month SPI values showed that 5 (6) factors having eigenvalues >1 accounted for 68.08 (78.88) % of total variance. The Lake Urmia Basin was delineated into the five distinct homogeneous regions using the 3-month SPI time series. This was six in the case of the 12-month SPI time series. It can be concluded that there are different distinct regions in Lake Urmia Basin according to drought characteristics. The map of regions defined using the 3- and 6-month SPI time series presented in this paper for Lake Urmia Basin.     

Spatial variability analysis of precipitation in northwest Iran

The spatial variability of precipitation was investigated in the northwestern corner of Iran using data collected at 24 synoptic stations from 1986 to 2015. Principal component analysis (PCA) and cluster analysis (CA) were used to regionalize precipitation in the study area. Eleven precipitation variables were averaged and arranged as an input matrix for the R-mode PCA to identify the precipitation patterns. Results suggest that the study area can be divided into four spatially homogeneous sub-zones. In addition, the spatial patterns of annual precipitation were identified by applying the T-mode PCA and CA to the annual precipitation data. The delineated spatial patterns revealed three distinct sub-regions. The resultant maps were compared with the spatial distribution of the rotated principal components (PCs). Results pointed out that the delineated clusters are characterized by different precipitation variability; and using different precipitation parameters can lead to different spatial patterns of precipitation over northwest Iran.     

Modelling chronic winter hazards as a function of precipitation and temperature

In previous research, winter weather conditions like precipitation and cold were recognized as factors affecting the change in traffic volume, and a simulation has been conducted to model the effect of weather factor on variations in traffic flow. A winter weather hazard model considering natural chronic hazards in the winter season has been proposed. To achieve this purpose, this research formulated a dummy variable winter weather traffic model while considering explanatory variables such as expected daily volume, snowfall, and temperature. This model was derived using six-year traffic data that were collected on a weigh-in-motion site on Highway 44, in Alberta, Canada. Precipitation and cold data collected from a weather station were linked with traffic data to model traffic variations caused by weather factors. The performance of the model, on the ground of temporal transferability and model specification, was tested using data from different years. The temporal transferability test confirmed that the model can be successfully applied regardless of the year. It was also revealed that each vehicle class prefers a different model specification in estimating correct traffic volume.

     

Spatial and statistical assessment of nitrate contamination in groundwater:Case of Sais Basin,Morocco

The objective of this study is to evaluate the nitrate contamination in the plioquaternary aquifer of Sais Basin based on a statistical approach. A total of 98 samples were collected in the cultivated area during the spring and autumn period of 2018. The results show that 55% and 57% of the samples in spring and autumn respectively exceed the threshold fixed by WHO(50 mg/L). However, nitrate concentrations do not show seasonal and spatial variation(p0.05). The results of the correlation matrix, principal component analysis(PCA), and hierarchical cluster analysis(HCA) suggest that nitrate pollution is related to anthropogenic source. Moreover, multiple linear regression results show that NO_3 is more positively explained in the spring period by Ca and SO_4 and negatively explained by pH and HCO_3. Regarding the autumn period, nitrate pollution is positively explained by Ca and negatively by pH. This study proposes a useful statistical platform for assessing nitrate pollution in groundwater.     

Hydro-geochemical processes in an area with saline groundwater in lower Shire River valley, Malawi: An integrated application of hierarchical cluster and principal component analyses

Our ability to adapt to changes in groundwater quality, arising from a changing climate and/or local pressures, is dependent on comprehension of the governing controls of spatial variation in groundwater chemistry. This paper presents results of an assessment of dominant hydro-geochemical processes controlling groundwater chemical composition, using an integrated application of hierarchical cluster analysis (HCA) and principal component analysis (PCA) of a major ion dataset of groundwater from lower Shire River valley, Malawi. The area is in the southernmost part of the western section of the East African Rift System (EARS) and has localised occurrence of saline groundwater. HCA classified samples into three main clusters (C1-C3) according to their dominant chemical composition: C1 (dominant composition: Na-Cl; median TDS: 3436 mg L⁻¹), C2 (dominant composition: Na-HCO₃; median TDS: 966 mg L⁻¹) and C3 (dominant composition: alkali earths-HCO₃; median TDS: 528 mg L⁻¹). These clusters were in turn described by the principal components PC1, PC3 and PC2, respectively, resulting from the PCA. The results of the PCA and geochemical interpretation suggest that the spatial variation of groundwater quality in the area is influenced by the following processes: C3 samples result mainly from H₂CO₃ weathering of aluminosilicate minerals by percolating water supersaturated with CO₂. In addition to aluminosilicate weathering, C2 samples are influenced by the processes of cation exchange of Ca²⁺ and Mg²⁺ in the water for Na⁺ on clay minerals, and carbonate precipitation. The increase in ionic strength of C2 samples is attributed to mixing with high TDS groundwater in proximity with C2 samples. The saline/brackish C1 groundwater results from the processes of evaporation (for samples with high water table close to the Shire marshes) and dissolution of Cl⁻ and SO₄-evaporative salts followed by mineralised seep from sedimentary Karoo and Cretaceous Lupata sandstones.     

Compositional multivariate statistical analysis of thermal groundwater provenance: A hydrogeochemical case study from Ireland

Thermal groundwater is currently being exploited for district-scale heating in many locations world-wide. The chemical compositions of these thermal waters reflect the provenance and circulation patterns of the groundwater, which are controlled by recharge, rock type and geological structure. Exploring the provenance of these waters using multivariate statistical analysis (MSA) techniques increases our understanding of the hydrothermal circulation systems, and provides a reliable tool for assessing these resources.Hydrochemical data from thermal springs situated in the Carboniferous Dublin Basin in east-central Ireland were explored using MSA, including hierarchical cluster analysis (HCA) and principal component analysis (PCA), to investigate the source aquifers of the thermal groundwaters. To take into account the compositional nature of the hydrochemical data, compositional data analysis (CoDa) techniques were used to process the data prior to the MSA.The results of the MSA were examined alongside detailed time-lapse temperature measurements from several of the springs, and indicate the influence of three important hydrogeological processes on the hydrochemistry of the thermal waters: 1) salinity and increased water-rock interaction; 2) dissolution of carbonates; and 3) dissolution of sulfides, sulfates and oxides associated with mineral deposits. The use of MSA within the CoDa framework identified subtle temporal variations in the hydrochemistry of the thermal springs, which could not be identified with more traditional graphing methods, or with a standard statistical approach. The MSA was successful in distinguishing different geological settings and different annual behaviours within the group of springs. This study demonstrates the usefulness of the application of MSA within the CoDa framework in order to better understand the underlying controlling processes governing the hydrochemistry of a group of thermal springs in a low-enthalpy setting.     

PGA distributions and seismic hazard evaluations in three cities in Taiwan

This study first presents the series of peak ground acceleration (PGA) in the three major cities in Taiwan. The PGAs are back-calculated from an earthquake catalog with the use of ground motion models. The maximums of the 84th percentile (mean?+?one standard deviation) PGA since 1900 are 1.03, 0.36, and 0.10?g, in Taipei, Taichung, and Kaohsiung, respectively. Statistical goodness-of-fit testing shows that the series of PGA follow a double-lognormal distribution. Using the verified probability distribution, a probabilistic analysis was developed in this paper, and used to evaluate probability-based seismic hazard. Accordingly, given a PGA equal to 0.5?g, the annual exceedance probabilities are 0.56, 0.46, and 0.23?% in Taipei, Taichung, and Kaohsiung, respectively; for PGA equal to 1.0?g, the probabilities become 0.18, 0.14, and 0.09?%. As a result, this analysis indicates the city in South Taiwan is associated with relatively lower seismic hazard, compared with those in Central and North Taiwan.