Chemometric Analysis of Surface Water Quality Data: Case Study of the Gorganrud River Basin, Iran

Canonical correlation analysis (CCA), principal component analysis (PCA), and principal factor analysis (PFA) have been adopted to provide ease of understanding: interpretation of a large complex data set in the Gorganrud River monitoring networks, evaluation of the temporal and spatial variations of water quality, and finally identification of monitoring stations and parameters which are most important in assessing annual variations of water quality in the river. In accomplishing the research, 11 surface water quality data related to both of physical and chemical parameters have been collected from seven monitoring stations from 1996 to 2002. In general, our results from CCA method indicated strong relationship between physical and chemical parameters in the Gorganrud River. In addition, analyzing data through the PCA and PFA techniques revealed that all monitoring stations are important in explaining the annual variation of data set. From the point of view of the degree of importance of parameters contributing to water quality variations, further investigations by running two scenarios (rotated factor correlation coefficient value equal to 0.95 and 0.90 for the first and second scenarios, respectively) showed that the important parameters in one season may not be important for another season. For example, unlike in summer, water temperature, total suspended solids, total phosphorous, and nitrate parameters were important, electrical conductivity, and turbidity parameters had been realized as important parameters in spring through the first scenario.     

Statistical analysis of aerosol species, trace gasses, and meteorology in Chicago

Both canonical correlation analysis (CCA) and principal component analysis (PCA) were applied to atmospheric aerosol and trace gas concentrations and meteorological data collected in Chicago during the summer months of 2002, 2003, and 2004. Concentrations of ammonium, calcium, nitrate, sulfate, and oxalate particulate matter, as well as, meteorological parameters temperature, wind speed, wind direction, and humidity were subjected to CCA and PCA. Ozone and nitrogen oxide mixing ratios were also included in the data set. The purpose of statistical analysis was to determine the extent of existing linear relationship(s), or lack thereof, between meteorological parameters and pollutant concentrations in addition to reducing dimensionality of the original data to determine sources of pollutants. In CCA, the first three canonical variate pairs derived were statistically significant at the 0.05 level. Canonical correlation between the first canonical variate pair was 0.821, while correlations of the second and third canonical variate pairs were 0.562 and 0.461, respectively. The first canonical variate pair indicated that increasing temperatures resulted in high ozone mixing ratios, while the second canonical variate pair showed wind speed and humidity’s influence on local ammonium concentrations. No new information was uncovered in the third variate pair. Canonical loadings were also interpreted for information regarding relationships between data sets. Four principal components (PCs), expressing 77.0 % of original data variance, were derived in PCA. Interpretation of PCs suggested significant production and/or transport of secondary aerosols in the region (PC1). Furthermore, photochemical production of ozone and wind speed’s influence on pollutants were expressed (PC2) along with overall measure of local meteorology (PC3). In summary, CCA and PCA results combined were successful in uncovering linear relationships between meteorology and air pollutants in Chicago and aided in determining possible pollutant sources.     

Statistical analysis of the physico-chemical data on the coastal waters of Cochin

Measurements of temperature, salinity, dissolved oxygen, nitrogen as ammonia, nitrate and nitrite, and phosphate along with chlorophyll were carried out at three stations on the coastal waters of Cochin, south west India, at two-levels of the water column over a period of five years. The data set has been factorised using principal component analysis (PCA) for extracting linear relationships existing among a set of variables. A graphical display of the scores generated from the PCA was done by means of boxplots and biplots, which helped in the interpretation of the data. The major factors conditioning the system are related to the input of fresh water from the estuary of the Periyar river and the high organic load of the bottom sediment in the coastal area which results in a reducing environment, as reflected in the parameters of dissolved oxygen, ammoniacal-nitrogen and nitrite-nitrogen. Another factor which contributes to the variation in the system is related to the unloading activity in the port area. The present approach presents a logical way to interpret the complex data of the physico-chemical measurements.     

Environmetric approaches for lake pollution assessment

The application of multivariate statistical methods to high mountain lake monitoring data has offered some important conclusions about the importance of environmetric approaches in lake water pollution assessment. Various methods like cluster analysis and principal components analysis were used for classification and projection of the data set from a large number of lakes from Rila Mountain in Bulgaria. Additionally, self-organizing maps of Kohonen were constructed in order to solve some classification tasks. An effort was made to relate the maps with the input data in order to detect classification patterns in the data set. Thus, discrimination chemical parameters for each pattern (cluster) identified were found, which enables better interpretation of the pollution situation. A methodology for application of a combination of different environmetric methods is suggested as a pathway to interpret high mountain lake water monitoring data.     

Assessment of anthropogenic sources of water pollution using multivariate statistical techniques: a case study of the Alqueva’s reservoir, Portugal

Multivariate statistical techniques were applied to evaluate spatial/temporal variations, and to interpret water quality data set obtained at Alqueva reservoir (south of Portugal). The water quality was monitored at nine different sites, along the water line, over a period of 18 months (from January 2006 to May 2007) using 26 water quality parameters. The cluster analysis allowed the formation of five different similarity groups between sampling sites, reflecting differences on the water quality at different locations of the Alqueva reservoir system. The PCA/FA identified six varifactors, which were responsible for 64% of total variance in water quality data set. The principal parameters, which explained the variability of quality water, were total phosphorus, oxidability, iron, parameters that at high concentrations indicate pollution from anthropogenic sources, and herbicides indicative of an intensive agricultural activity. The spatial analysis showed that the water quality was worse in the north of the reservoir.     

Water quality of a tributary of the Pearl River, the Beijiang, Southern China: implications from multivariate statistical analyses

Water quality information of Beijiang River, a tributary of Pearl River in Guangdong, China, was analyzed to provide an overview of the hydrochemical functioning of a major agricultural/rural area and an industrial/urban area. Eighteen water quality parameters were surveyed at 13 sites from 2005 to 2006 on a monthly basis. A bivariate correlation analysis was carried out to evaluate the regional correlations of the water quality parameters, while the principal component analysis (PCA) technique was used to extract the most influential variables for regional variations of river water quality. Six principal components were extracted in PCA which explained more than 78% and 84% of the total variance for agricultural/rural and industrial/urban areas, respectively. Physicochemical factor, organic pollution, sewage pollution, geogenic factor, agricultural nonpoint source pollution, and accumulated pesticide usage were identified as potential pollution sources for agricultural/rural area, whereas industrial wastewaters pollution, mineral pollution, geogenic factor, urban sewage pollution, chemical industrial pollution, and water traffic pollution were the latent pollution sources for industrial/urban area. A multivariate linear regression of absolute principal component scores (MLR-APCS) technique was used to estimate contributions of all identified pollution sources to each water quality parameter. High coefficients of determination of the regression equations suggested that the MLR-APCS model was applicable for estimation of sources of most water quality parameters in the Beijiang River Basin.     

Geospatial Analysis of Climatic and Geomorphic Interactions Influencing Stream Discharge, Appalachian Mountains, USA

Streamflow values are commonly synthesized for locations where flow measurement stations are lacking or where only intermittent measurements are available. In an Appalachian Mountains dataset comprised of 29 watersheds, the most appropriate among geomorphic, geologic, and hydrogeologic datasets were selected for use in prediction of streamflow at watershed scale. A statistical model was developed using principal components analysis (PCA) and cluster analysis (CA) for. Using CA on variables derived from the PCA, an optimum set of variables was derived for predicting streamflow. Results indicate there are two categories of watersheds in the study area. The first is strongly correlated with climatic variables (precipitation, temperature, elevation, and groundwater recharge). The second is strongly correlated with two geomorphic variables (watershed slope and percentage of forested area). The spatial distribution of cluster classifications shows that watersheds dominated by the climatic component are located along the Allegheny Front while watersheds dominated by the geomorphic component are located in the Allegheny Plateau and Valley and Ridge physiographic provinces. These variations between the Allegheny Plateau and Valley and Ridge physiographic provinces suggest that, to accurately model streamflow, modeling needs be done based on natural physiographic boundaries rather than political boundaries. In this physiographic setting, elevation seems to be a major control.     

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

Multivariate statistical techniques, such as cluster analysis (CA), principal component analysis, and factor analysis, were applied for the evaluation of temporal/spatial variations and for the interpretation of a water quality data set of the Behrimaz Stream, obtained during 1 year of monitoring of 20 parameters at four different sites. Hierarchical CA grouped 12 months into two periods (the first and second periods) and classified four monitoring sites into two groups (group A and group B), i.e., relatively less polluted (LP) and medium polluted (MP) sites, based on similarities of water quality characteristics. Factor analysis/principal component analysis, applied to the data sets of the two different groups obtained from cluster analysis, resulted in five latent factors amounting to 88.32% and 88.93% of the total variance in water quality data sets of LP and MP areas, respectively. Varifactors obtained from factor analysis indicate that the parameters responsible for water quality variations are mainly related to discharge, temperature, and soluble minerals (natural) and nutrients (nonpoint sources: agricultural activities) in relatively less polluted areas; and organic pollution (point source: domestic wastewater) and nutrients (nonpoint sources: agricultural activities and surface runoff from villages) in medium polluted areas in the basin. Thus, this study illustrates the utility of multivariate statistical techniques for analysis and interpretation of data sets and, in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in water quality for effective stream water quality management.     

Application of multivariate statistical techniques to evaluation of water quality in the Mała Wełna River (Western Poland)

The paper presents the results of determinations of physico-chemical parameters of the Ma?a We?na waters, a river situated in Wielkopolska voivodeship (Western Poland). Samples for the physico-chemical analysis were taken in eight gauging cross-sections once a month between May and November 2006. To assess the physico-chemical composition of surface water, use was made of multivariate statistical methods of data analysis, viz. cluster analysis (CA), factor analysis (FA), principal components analysis (PCA), and discriminant analysis (DA). They made it possible to observe similarities and differences in the physico-chemical composition of water in the gauging cross-sections, to identify water quality indicators suitable for characterising its temporal and spatial variability, to uncover hidden factors accounting for the structure of the data, and to assess the impact of man-made sources of water pollution.     

Multivariate Patterns of Antioxidative and Photoprotective Defence Compounds in Spruce Needles at Two Central European Forest Sites of Different Elevation

In this work we measured a set of antioxidative and photoprotective compounds (chlorophylls, carotenoids, tocopherol, ascorbate and glutathione), which were suggested previously as stress markers in conifer needles, at two spruce forest sites at different elevation in Saxony, Germany. Most variables differed significantly between current and 1-year-old needles, but only the content of the xanthophyll cycle per mg total chlorophyll and the oxidation state of glutathione were significantly different between the sites. We applied principal component analysis (PCA) to address the question if underlying accumulated variables are similar to the ones found in spruce needles across Alpine elevation profiles and/or for pines in Mediterranean ecosystems. Four principal components (accumulated variables, PC) representing 68% of the total variance of the dataset were extracted. PC 1 encompassed total chlorophyll, lutein, and β-carotene contents, PC 2 combined the epoxidation state of xanthophylls, ascorbate content and redox state, and glutathione content, PC 3 represented the content of xanthophylls and the redox state of glutathione, and PC 4 encompassed the content of α-carotene and the epoxidation state of xanthophylls. Only PC 3 was significantly different between sites. The PCA structure shows many similarities to corresponding findings in studies on spruce in mountain forests in the Alps and pines in Mediterranean systems. This corroborates the interpretation of PCs as indicative for underlying physiological processes. However, separation of the two investigated sites by PCs was in the present case study not superior to the separation by single input variables.     

Monitoring Studies of Augusta Bay Marine Waters

The monitoring of water quality today provides a great quantity of data consisting of the values of the parameters measured as a tunction of bme or as spatial function.In the marine environment, and especially in the suspended material, increasing importance is being given to the presence of particular pollution indices. With the increase in the number of sampling points, the amount of data increases and examining the results and their consequent interpretation becomes more difficult. To overcome such difficulties, numerous chemometric techniques have been introduced in environmental chemistry, such as Principal Component Analysis (PCA).The use of the PCA in this work has been applied to the analysis of twenty three different sampling points in three seasonal sampling cruises in the same year. This led to recognition of the influence and the localisation of wastewaters in the Augusta bay after measuring the water pollution parameters.The PCA made evident the difference between some sampling sites whose data were initially thought to be similar where the presence of hot industrial water discharge or urban wastewater determines the permanent water quality.Furthermore, it has allowed a choice of more significant parameters for monitoring programs and more representative sampling site locations.     

Evaluation of the effect of data pre-treatment procedures on classical pattern recognition and principal components analysis: a case study for the geographical classification of tea

A simple transformation that uses the half-range and central value has been used as a data pre-treatment procedure for principal component analysis (PCA) and pattern recognition techniques. The results obtained have been compared with the results from classical normalisation of data (mean normalisation, maximum normalisation and range normalisation), autoscaling and the minimum-maximum transformation. Three data sets were used in the study. The first was formed by determining 17 elements in 53 tea samples (901 pieces of data). The second and third data sets arose from two long-term drift studies performed to examine instrumental stability at standard and robust conditions. The instruments used were an inductively coupled plasma atomic emission spectrometer and an inductively coupled plasma mass spectrometer. Each drift diagnosis experiment consisted of replicate determinations of a test solution containing 15 analytes at 10 mg l-1 over 8 h without recalibration. Twenty-nine emission lines were determined 99 times, thus, each data set was formed by 2881 pieces of data. Data pre-treatment was applied to the three data sets prior to the use of principal component analysis, cluster analysis, linear discrimination analysis and soft independent modelling of class analogy. The study revealed that the half-range and central value transformation resulted in a better classification of the tea samples than that achieved using the classical normalisation. The loadings in the PCA for the long-term stability study, under both standard and robust conditions, were found to be similar to the drift trends only when the minimum-maximum transformation and the mean or maximum normalizations were used as data pre-treatments.     

Trend, seasonal and multivariate modelling study of wet precipitation data from the Austrian Monitoring Network (1990-1997)

The aim of the present study was to analyse the data structure of a large data set from rainwater samples collected during a long-term interval (1990-1997) by the Austrian Precipitation Monitoring Network. Eleven sampling sites from the network were chosen as data sources (chemical concentrations of major ions only) covering various location characteristics (height above sea level, rural and urban sampling positions, Alpine rim and Alpine valley disposition, etc.). The analytical results were treated by the application of already classical environmetric approaches, such as linear regression analysis, time-series analysis and principal components analysis (PCA). For most of the sampling sites, a distinct trend of acidity decrease of the wet precipitation was observed. An overall decrease in sulfate concentration for the whole period and all sites of 3.9% year(-1) (2.0 muequiv. L(-1) year(-1)) was found. The free acidity decrease for most of the sites was between 3.5 and 10.9% year(-1). No significant linear trends were found for nitrate. Base cations either decreased (mean percentage decrease for calcium was 5.4% year(-1) and for magnesium 4.4% year(-1)) or did not show any significant change (sodium, potassium). The overall decrease in ammonium concentration was 2.3% year(-1). Further, some typical "rural" (summer minima and winter maxima) and "urban" (winter minima and spring maxima) seasonal behaviour for the majority of the sites in consideration could be defined, indicating the influence of local emission sources. Several latent factors, named "anthropogenic", "crustal" and "mixed salt", were revealed by the multivariate modelling procedure (PCA) possessing a similar structure for most of the sites. The unavoidable exceptions observed were indications of the influence of sporadic local events (construction and agricultural activities, secondary emission sources, etc.), and an effort was made to explain these exceptions.     

Impact of intensive horticulture practices on groundwater content of nitrates, sodium, potassium, and pesticides

A monitoring program of nitrate, nitrite, potassium, sodium, and pesticides was carried out in water samples from an intensive horticulture area in a vulnerable zone from north of Portugal. Eight collecting points were selected and water-analyzed in five sampling campaigns, during 1 year. Chemometric techniques, such as cluster analysis, principal component analysis (PCA), and discriminant analysis, were used in order to understand the impact of intensive horticulture practices on dug and drilled wells groundwater and to study variations in the hydrochemistry of groundwater. PCA performed on pesticide data matrix yielded seven significant PCs explaining 77.67% of the data variance. Although PCA rendered considerable data reduction, it could not clearly group and distinguish the sample types. However, a visible differentiation between the water samples was obtained. Cluster and discriminant analysis grouped the eight collecting points into three clusters of similar characteristics pertaining to water contamination, indicating that it is necessary to improve the use of water, fertilizers, and pesticides. Inorganic fertilizers such as potassium nitrate were suspected to be the most important factors for nitrate contamination since highly significant Pearson correlation (r = 0.691, P < 0.01) was obtained between groundwater nitrate and potassium contents. Water from dug wells is especially prone to contamination from the grower and their closer neighbor's practices. Water from drilled wells is also contaminated from distant practices.     

Hierarchy of factors affecting the social perception of dam reservoirs

This study presents an assessment of factors that influence how people who live in the vicinity of dams view such projects. The usefulness of the principal component analysis (PCA) method for identifying variables that determine individuals' opinion about large dam projects was reviewed. The study focuses on people affected by the construction of the Mucharski Reservoir in the Polish Carpathians. The construction took over 30 years and took place at a time when Poland transitioned from a planned economy to a free market one.We used in-depth interviews (N = 96) and a set of 18 factors classified as personal, emotional and economic. Our results indicate that the variables that significantly affect social perception of dam projects by the local population include their opinion regarding the viability of the project, sense of security, personal benefits, the extent to which they have accepted the structure, respecting the local community's interests when drafting the development plans and new opportunities. The results allow for the future optimization of research tools used to comprehensively examine social perception of hydraulic structures. Using PCA allowed us to take semi-structured data from interviews and extract meaningful relationships between the various inputs, show correlations between seemingly unrelated data, as well as explain the variances within the studied population. It also shows that PCA can be a useful tool for analyzing data that is not formally structured.     

Hydrographic and sediment characteristics of seagrass meadows of the Gulf of Mannar Marine Biosphere Reserve, South India

The aim of the study was to examine the spatial and temporal variations in the physicochemical parameters of seagrass meadows of the Gulf of Mannar, South India using multivariate statistical techniques, namely, cluster analysis (CA) and principal component analysis (PCA), to explore the relationship. There were clear spatial and temporal variations in physicochemical variables of the seagrass meadow of the Gulf of Mannar, but such changes were subjected to seasonal variations especially during monsoon and post-monsoon seasons. The multivariate statistical techniques, viz., CA and PCA helped in the discrimination of islands according to the physicochemical parameters of the seagrass meadows. It was inferred that electrical conductivity, nitrate, particulate organic carbon, and phosphate strongly determined the discrimination of 19 islands, respectively, upon physicochemical characteristics of their seagrass meadows. These results highlight the important role of seagrasses in the Gulf of Mannar Marine Biosphere Reserve.     

The Seasonal Variation of the Concentrations of Ozone, Sulfur Dioxide, and Nitrogen Oxides in Two Nigerian Cities

A combination of multivariate statistical methods including factor analysis, principal component analysis, principal component regression, and multiple linear regression (MLR) were employed to evaluate the influence of seasons on the concentrations of ozone, sulfur (IV) oxide, and oxides of nitrogen in ambient air of Nigerian cities of Lagos and Ilorin. The former city is located in the coastal area, and it is highly congested with a high intensity of marine, vehicular, and industrial activities, and the latter city is a medium size town, located in the central guinea savannah zone of Nigeria. Samples were collected using a high-volume sampler from near the ground at various sites of diverse human and industrial activities, during wet and dry seasons from 2003 to 2006. The PCA reveals three distinct groupings during the day for all data, which is a reflection of different factors contributing to the atmospheric chemistry of these cities. The predicted ozone concentration values by MLR agree fairly well with the measured data. The dependence of ozone on meteorological parameters including relative humidity, air temperature, and sun exposure and the precursor pollutants depends on weather and the anthropogenic activities. The results for the two cities indicate that reduction in the level of NO₂ is accompanied by an increase in the level of ozone, suggesting the interconversion between the two via photochemical activity.     

Water security assessment in Haihe River Basin using principal component analysis based on Kendall τ

Kendall τ has reasonable theoretic background than Pearson correlation. It can be applied more widely in all aspects. Instead of using widely adopted Pearson correlation or its extensions in a large number of principal component analysis (PCA) instances, we introduce the Kendall τ into the PCA method. PCA is a well-known statistical data analysis algorithm and is aimed to extract feature from high-dimensional data. It is designed to reduce the number of variables to a small number of indices while attempting to preserve the relationships present in the original data. This paper uses PCA based on Kendall τ in water security assessment of Haihe River Basin.