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

Jiang, Yunfeng; Guo, Huaming; Jia, Yongfeng; Cao, Yingjie; Hu, Chenchen

doi:10.1016/j.chemer.2014.12.002

Cited by 84 publications

(26 citation statements)

References 44 publications

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“…For instance, the factors controlling arsenic mobilization in groundwater chemistry were investigated and groundwater areas were classifi ed with these methods [13]. In another study, HCA and PCA were applied using 10 chemical variables in 247 samples to classify groundwater [14].…”

Section: Application Of Multivariate Statistical Methodsmentioning

confidence: 99%

Application of Multivariate Statistical Methods for Water Quality Assessment of Karasu-Sarmisakli Creeks and Kizilirmak River in Kayseri, Turkey

Özdemir¹

2016

Pol. J. Environ. Stud.

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Monitoring water quality of rivers has particular ecological, economic, and environmental importance for a region. However, long-term monitoring of a river network requires a large amount of analysis with high work load and cost. In this study, multivariate analysis was performed to decrease the number of parameters and sampling points for monitoring water quality in the Kızılırmak River and its southern ranches of Karasu and Sarmısaklı creeks. On the study area, water quality parameters were collected from seven sampling points for 2004-07 and 2010-14. Multivariate statistical analysis was performed by hierarchical cluster analysis and principal component analysis for the evaluation of parameter variations and dataset interpretation. The dataset was divided into periods A and B to evaluate the effects of seasonal changes on sampling points and parameters.

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Section: Application Of Multivariate Statistical Methodsmentioning

confidence: 99%

Application of Multivariate Statistical Methods for Water Quality Assessment of Karasu-Sarmisakli Creeks and Kizilirmak River in Kayseri, Turkey

Özdemir¹

2016

Pol. J. Environ. Stud.

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“…DA was used in backward stepwise mode to confirm the groups found by CA and to evaluate the spatio-temporal variations of the discriminant variables. In DA, the monitoring period or site variables were the clustering variables, while the parameters from originally-measured datasets were independent [11,31]. Principal component analysis (PCA) is based on the assumption that there exists a bilinear model, which could explain the variance of observed water quality data by using less orthogonal variables, known as principal components [32].…”

Section: Multivariate Statistical Analysismentioning

confidence: 99%

“…Over the last decade, multivariate approaches have significantly advanced our understanding of the spatio-temporal patterns and pollution sources of river systems on the basis of water quality observations [8][9][10][11]. For instance, cluster analysis (CA), discriminant analysis (DA) and principal component analysis (PCA) have been widely used to assess spatial or temporal variations of groundwater and surface water quality [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Patterns and Source Identification of Water Pollution in Lake Taihu (China)

Chen

Zhao

et al. 2016

Water

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Abstract:Various multivariate methods were used to analyze datasets of river water quality for 11 variables measured at 20 different sites surrounding Lake Taihu from 2006 to 2010 (13,200 observations), to determine temporal and spatial variations in river water quality and to identify potential pollution sources. Hierarchical cluster analysis (CA) grouped the 12 months into two periods (May to November, December to the next April) and the 20 sampling sites into two groups (A and B) based on similarities in river water quality characteristics. Discriminant analysis (DA) was important in data reduction because it used only three variables (water temperature, dissolved oxygen (DO) and five-day biochemical oxygen demand (BOD 5 )) to correctly assign about 94% of the cases and five variables (petroleum, volatile phenol, dissolved oxygen, ammonium nitrogen and total phosphorus) to correctly assign >88.6% of the cases. In addition, principal component analysis (PCA) identified four potential pollution sources for Clusters A and B: industrial source (chemical-related, petroleum-related or N-related), domestic source, combination of point and non-point sources and natural source. The Cluster A area received more industrial and domestic pollution-related agricultural runoff, whereas Cluster B was mainly influenced by the combination of point and non-point sources. The results imply that comprehensive analysis by using multiple methods could be more effective for facilitating effective management for the Lake Taihu Watershed in the future.

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“…Before cluster analysis, the variables were standardized using the methods of Jiang et al . [37]. Based on the results of the cluster analysis, linear regression between R s and optimal VI was used to detect the possible relationship between R s and the photosynthesis proxy factor derived from remote sensing data in each cluster.…”

Section: Methodsmentioning

confidence: 99%

“…Cluster analysis was performed to identify possible groups of sites where soil CO 2 concentration could be affected by different factors [34]. Compared with the structural equation modeling approach, cluster analysis is simple because it does not require significant correlation among the analyzed variables and does not depend on the subjective experience and prior knowledge of the analyst [35], [36], [37]. …”

Section: Introductionmentioning

confidence: 99%

Spatial Variation of Soil Respiration in a Cropland under Winter Wheat and Summer Maize Rotation in the North China Plain

Huang

Wang

et al. 2016

PLoS ONE

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Spatial variation of soil respiration (Rs) in cropland ecosystems must be assessed to evaluate the global terrestrial carbon budget. This study aims to explore the spatial characteristics and controlling factors of Rs in a cropland under winter wheat and summer maize rotation in the North China Plain. We collected Rs data from 23 sample plots in the cropland. At the late jointing stage, the daily mean Rs of summer maize (4.74 μmol CO2 m-2 s-1) was significantly higher than that of winter wheat (3.77μmol CO2 m-2 s-1). However, the spatial variation of Rs in summer maize (coefficient of variation, CV = 12.2%) was lower than that in winter wheat (CV = 18.5%). A similar trend in CV was also observed for environmental factors but not for biotic factors, such as leaf area index, aboveground biomass, and canopy chlorophyll content. Pearson’s correlation analyses based on the sampling data revealed that the spatial variation of Rs was poorly explained by the spatial variations of biotic factors, environmental factors, or soil properties alone for winter wheat and summer maize. The similarly non-significant relationship was observed between Rs and the enhanced vegetation index (EVI), which was used as surrogate for plant photosynthesis. EVI was better correlated with field-measured leaf area index than the normalized difference vegetation index and red edge chlorophyll index. All the data from the 23 sample plots were categorized into three clusters based on the cluster analysis of soil carbon/nitrogen and soil organic carbon content. An apparent improvement was observed in the relationship between Rs and EVI in each cluster for both winter wheat and summer maize. The spatial variation of Rs in the cropland under winter wheat and summer maize rotation could be attributed to the differences in spatial variations of soil properties and biotic factors. The results indicate that applying cluster analysis to minimize differences in soil properties among different clusters can improve the role of remote sensing data as a proxy of plant photosynthesis in semi-empirical Rs models and benefit the acquisition of Rs in cropland ecosystems at large scales.

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

Cited by 84 publications

References 44 publications

Application of Multivariate Statistical Methods for Water Quality Assessment of Karasu-Sarmisakli Creeks and Kizilirmak River in Kayseri, Turkey

Application of Multivariate Statistical Methods for Water Quality Assessment of Karasu-Sarmisakli Creeks and Kizilirmak River in Kayseri, Turkey

Spatio-Temporal Patterns and Source Identification of Water Pollution in Lake Taihu (China)

Spatial Variation of Soil Respiration in a Cropland under Winter Wheat and Summer Maize Rotation in the North China Plain

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