Environmental conditions in the Donggi Cona lake catchment, NE Tibetan Plateau, based on factor analysis of geochemical data

IJmker, Janneke; Stauch, Georg; Hartmann, Kai; Diekmann, Bernhard; Dietze, Elisabeth; Opitz, Stephan; Wünnemann, Bernd; Lehmkuhl, Frank

doi:10.1016/j.jseaes.2011.04.021

Cited by 36 publications

(17 citation statements)

References 61 publications

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“…After this transformation, distributions with skewnesses varied between -0.8 and 0.8, or with kurtosis between -3 and 3 for all of the geochemical and most of the biochemical parameters. Such datasets more or less behave like normally distributed (Ijmker et al 2012). However, the water content at both levels (L1, L3) and Hg content at level L1 did not fulfill this condition, and this fact was taken into consideration when interpreting the results.…”

Section: Methodsmentioning

confidence: 99%

“…In addition to basic statistics, factor analysis was used to identify underlying variables or factors that explain the pattern of correlations within and between the biochemical and geochemical sets of the variables described above. Factor analysis was previously successfully employed using environmental data and was particularly tested for the purposes of mineral exploration (Harraz et al 2012) and to interpret diverse geochemical datasets (Tripathi 1979;Ijmker et al 2012) as well as water or soil chemical data (Fitzpatrick et al 2007). The authors assume that this method is also well suited to studying relationships and chemical/biochemical interactions in the soil/plant system and, to their knowledge, has not yet been used for this purpose.…”

Section: Introductionmentioning

confidence: 99%

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Assessing forest health via linking the geochemical properties of a soil profile with the biochemical parameters of vegetation

Kopačková

Lhotáková

Oulehle

et al. 2014

Int. J. Environ. Sci. Technol.

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The transfer of chemical elements/compounds within the soil-plant chain is a part of the biochemical cycling, and this system is controlled by biotic and abiotic factors which determine the final mobility and availability of chemical variables. Heavy metal contamination and low pH are stress factors that lead to changes in the contents of important foliage compounds, which can be used as nonspecific indicators of plant stress. In this study, Norway spruce forests in the Sokolov region, being a part of the ''Black Triangle,'' were selected to assess geochemical and biochemical interactions in the natural soil/plant system. The authors studied the relationship between soil and spruce needle contents of macronutrients and potentially toxic elements and tested whether the soil parameters and their vertical distribution within a soil profile (two organic and two mineral horizons) affect foliage biochemical parameters (contents of photosynthetic pigments, phenolic compounds and lignin). Factor analysis was used to identify underlying variables that explained the pattern of correlations within and between the biochemical and geochemical datasets. Aluminum (Al) and arsenic (As) were identified as toxic elements with high bio-availability for spruce trees, and both were taken up by trees and translocated to the foliage. The correlations between two toxic element contents in needles (Al and As) and the contents of soluble phenolic compounds and total carotenoid to chlorophyll ratio suggest that these latter two biochemical parameters, which both proved to be sensitive to the soil geochemical conditions, can serve as suitable non-specific stress markers.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing forest health via linking the geochemical properties of a soil profile with the biochemical parameters of vegetation

Kopačková

Lhotáková

Oulehle

et al. 2014

Int. J. Environ. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…These methods (Pinto & Maheshwari ; Ijmker et al . ) are briefly outlined in the following sections.…”

Section: Methodsmentioning

confidence: 99%

“…In this study, the principal component analysis (PCA) extraction method and Varimax rotation were employed to analyse the flood discharge variables in subwatersheds. These methods (Pinto & Maheshwari 2011;Ijmker et al 2012) are briefly outlined in the following sections.…”

Section: Factor Analysismentioning

confidence: 99%

Prioritization of subwatersheds based on flooding conditions using hydrological model, multivariate analysis and remote sensing technique

Darabi

Shahedi

Solaimani

et al. 2013

Water & Environment J

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Prioritization of watersheds has gained more importance in natural resources management, particularly in the context of watershed management. Flooding potential is one of the main indices used to prioritize watersheds. The objective of this study is to investigate the Pol‐Doab Shazand Watershed flooding conditions as a result of land use change and prioritization of its subwatersheds using rainfall – runoff data. For this purpose, after computation of design flow with different return periods, factor analysis technique was applied to determine the best flow with proper return period towards evaluation and prioritization of subwatersheds. The results showed that the design flow with 25‐year return period (Q25) is suitable. The results indicate that subwatershed 22 is the highest priority because of large changes in peak flow, and subwatershed 2 given the lowest priority since changes in peak flow is the lowest.

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“…At present, there were a large number of methods to establish the multi-factors evaluation model, such as analytic hierarchy process [4], comprehensive index method [5], artificial neural network [6], grey correlation analysis method [7], factor analysis (FA) [8], etc. In these methods, FA not only can reduce the numbers of evaluation indicators through principal component analysis to realize the comprehensive evaluation, but also can maintain the vast majority of information of evaluation indicators [9,10]. Furthermore, FA can avoid the interference of anthropic factor in the decision process of the weights of comprehensive factors owing to the reason that the weights of comprehensive factors are dependent on their contribution rate in FA [11].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the development level of construction industry based on factor analysis in Sichuan province, China

Fan¹,

Chen²,

Shen³

2016

Proceedings of the 2016 International Forum on Management, Education and Information Technology Application

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Abstract. To understand the development level status of construction industry in Sichuan province, China, this study selected 11 economical indicators of construction industry to evaluate the development level of construction industry in 21 cities (prefectures) of Sichuan province by using factors analysis. The results show that 11 economic indicators can be induced to 2 comprehensive factors (i.e., scale factor and efficiency factor). The development level status of construction industry of 21 cities (prefectures) can be divided into 4 categories. The development level of Chengdu was the highest, and the development level of Aba, Yaan and Ganzi were the lowest. The study indicates that the overall development level of construction industry in Sichuan province is dependent on scale factor. The difference of the development level of construction industry over the province is obvious.

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Environmental conditions in the Donggi Cona lake catchment, NE Tibetan Plateau, based on factor analysis of geochemical data

Cited by 36 publications

References 61 publications

Assessing forest health via linking the geochemical properties of a soil profile with the biochemical parameters of vegetation

Assessing forest health via linking the geochemical properties of a soil profile with the biochemical parameters of vegetation

Prioritization of subwatersheds based on flooding conditions using hydrological model, multivariate analysis and remote sensing technique

Evaluation of the development level of construction industry based on factor analysis in Sichuan province, China

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