scholarly journals Heavy Metals and Sulphur in Needles of Pinus sylvestris L. and Soil in the Forests of City Agglomeration

Forests ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1310
Author(s):  
Anna Figas ◽  
Anetta Siwik-Ziomek ◽  
Mirosław Kobierski
Keyword(s):  
Heavy Metals ◽  
Principal Component Analysis ◽  
Surface Layer ◽  
Organic Carbon ◽  
Pinus Sylvestris ◽  
Scots Pine ◽  
Principal Components ◽  
Principal Component ◽  
Total Change ◽  
Phytotoxic Effect

The content of sulphur and copper (Cu), iron (Fe), manganese (Mn), zinc (Zn) and of assimilation pigments in the needles of Scots pine (Pinus sylvestris L.) in the forests of Bydgoszcz, Poland was determined. The content of those metals and the activity of dehydrogenases (DHA) in the rhizosphere of the trees was assayed. The average total sulphur (TS) content in 2-year-old pine needles was 832.4 mg kg−1 d.w. No significant correlation was found between TS and Cu, Fe, Mn and Zn in needles and the content of assimilation pigments indicating no phytotoxic effect of sulphur dioxide (SO2) and metals on Scots pine. The content of metals in the needles pointed to an inconsiderable degree of human impact. The soils in the surface layer were not contaminated with heavy metals. With the principal component analysis (PCA) two principal components were identified which accounted for 68% of the total change in variation. The variables that determined the principal components were the soil content of organic carbon (TOC), total nitrogen (TN), TS and sulphates (SO42−), the soil content of Mn, Zn, available forms to plants of Cu, Mn, and the content of Cu, Fe in needles.

scholarly journals Analysis of the accumulation and distribution of heavy metals in the soil cover of Samara and Syzran

2019 ◽  
Vol 8 (4) ◽  
pp. 28-33
Author(s):  
Sergey Vyacheslavovich Bugrov ◽  
Yulia Vladimirovna Makarova ◽  
Nataliya Vladimirovna Prokhorova ◽  
Igor Artemyevich Platonov ◽  
Maksim Glebovich Goryunov
Keyword(s):  
Heavy Metals ◽  
Principal Component Analysis ◽  
Organic Carbon ◽  
Analysis Of Variance ◽  
Soil Cover ◽  
Total Concentration ◽  
Principal Component ◽  
Component Analysis ◽  
Organic Carbon Content ◽  
Industrial Enterprises

The paper presents the results of a statistical analysis of the data on the accumulation and distribution of a number of heavy metals (Cr, Ni, Cu, Pb, Cd, Zn) in the soils of the cities of Samara and Syzran in the Samara Region, carried out using the Spearmans rank correlation coefficient, the Kruskal-Wallis one-way analysis of variance and principal component analysis. The authors show that the soils of Samara are more susceptible to the accumulation of Cr, Ni, Cu and Cd than the soils of Syzran. Higher gross concentrations of metals are observed in the functional zones of the cities characterized by increased technogenic load - near industrial enterprises and along railways. The fixation of metals in the upper humus horizon is facilitated by the organic matter of the soil. The exception is Pb, whose total concentration does not depend on the type of functional zone and the content of organic carbon. The Kruskal-Wallis one-way analysis of variance made it possible to establish the presence of a statistically significant relationship between the mechanical composition of the soil and the gross concentrations of Cr and Ni, the content of which increases in soils of heavier texture. According to the principal component analysis, natural and technogenic sources of metals have a decisive influence on the content and distribution of heavy metals in the soil cover of the cities; soil characteristics (mechanical composition, actual acidity of the soil solution, organic carbon content) and the intensity of exposure to sources of heavy metals play a less significant role.

scholarly journals Principal Component Analysis on Soil Fertility Parameters in Vegetable Growing Locations of Kottayam District of Kerala

2021 ◽  
pp. 64-72
Author(s):  
A. Muhsina ◽  
Brigit Joseph ◽  
Vijayaraghava Kumar
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Organic Carbon ◽  
Soil Fertility ◽  
Principal Components ◽  
Principal Component ◽  
Component Analysis ◽  
Soil Testing ◽  
Score Plot

Present study utilizes Principal Component Analysis (PCA) of 13 soil testing variables obtained from 28 vegetable growing locations of Kottayam district and there were a total of 718 samples for analysis. Thirteen Principal Components (PCs) were generated and five PCs could explain the major share of variance (80%). Score plot was drawn based on PCA and the results indicated that none of the variables was predominant in Bharananganam, Kadanadu, Kozhuvanal, Kidangoor and Pallikkathode and also these panchayats had positive scores on both F1 and F2 when factor analysis was conducted. Boron (B), Copper (Cu) and Zinc (Zn) were predominant in Akalakkunnam, Kadalpalamattom, Meeaachil, Melukavu, Poonjar and Ramapuram panchayats. Elikulam, Erumeli, Karoor, Mundakkayam, Mutholi, Poonjar south, Thalapalm and Vakathanom were those panchayats where the contribution of Magnesium (Mg), Potassium (K) and pH was more. All other elements viz, Oxidisable Organic Carbon (OC), Sulphur (S), Phosphorus (P), Calcium (Ca), Manganese (Mn) and Iron (Fe) had significant importance in Ayarkkunnam, Aymanam, Chempu, Kaduthuruthy, Kurichi, Manjoor, Maravanthuruth, Puthuppally and Thalayazham panchayats.

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

Evaluation functions for integral mapping

2017 ◽  
Vol 921 (3) ◽  
pp. 24-29 ◽  
Author(s):  
S.I. Lesnykh ◽  
A.K. Cherkashin
Keyword(s):  
Principal Component Analysis ◽  
Environmental Factors ◽  
Principal Components ◽  
Principal Component ◽  
Evaluation Function ◽  
Final Value ◽  
Evaluation Functions ◽  
Geographical Environment ◽  
Environmental Background ◽  
Integral Mapping

The proposed procedure of integral mapping is based on calculation of evaluation functions on the integral indicators (II) taking into account the feature of the local geographical environment, when geosystems in the same states in the different environs have various estimates. Calculation of II is realized with application of a Principal Component Analysis for processing of the forest database, allowing to consider in II the weight of each indicator (attribute). The final value of II is equal to a difference of the first (condition of geosystem) and the second (condition of environmental background) principal components. The evaluation functions are calculated on this value for various problems of integral mapping. The environmental factors of variability is excluded from final value of II, therefore there is an opportunity to find the invariant evaluation function and to determine coefficients of this function. Concepts and functions of the theory of reliability for making the evaluation maps of the hazard of functioning and stability of geosystems are used.

scholarly journals Ecological Risk Assessment and Contamination History of Heavy Metals in the Sediments of Chagan Lake, Northeast China

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 894
Author(s):  
Panfeng Liu ◽  
Chaojie Zheng ◽  
Meilan Wen ◽  
Xianrong Luo ◽  
Zhiqiang Wu ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Principal Component Analysis ◽  
Ecological Risk ◽  
Surface Sediment ◽  
Northeast China ◽  
Principal Component ◽  
Potential Ecological Risk ◽  
210Pb Dating ◽  
History Of ◽  
Chagan Lake

The study deals with the spatio-temporal distribution of heavy metals in the sediments of Chagan lake, Northeast China. The pollution history of heavy metals is studied simultaneously through the 210Pb dating method by analyzing the characteristic of As, Hg, Cd, Cr, Ni, Cu, Pb, and Zn concentration-depth profiles. The potential ecological risk index (RI) and geo-accumulation index (Igeo) were used to evaluate the contamination degree. Principal component analysis (PCA), based on the logarithmic transformation and isometric log-ratio (ilr) transformed data, was applied with the aim of identifying the sources of heavy metals. The element concentrations show that the heavy metals are enriched in the surface sediment and sediment core with a varying degree, which is higher in the surficial residue. The results of Igeo indicate that the Cd and Hg in the surface sediment have reached a slightly contaminated level while other elements, uncontaminated. The results of RI show that the study area can be classified as an area with moderate ecological risk in which Cd and Hg mostly contribute to the overall risk. For the sediment core, the 210Pb dating results accurately reflect the sedimentary history over 153 years. From two evaluation indices (RI and Igeo) calculated by element concentration, there is no contamination, and the potential ecological risk is low during this period. The comparative study between raw and ilr transformed data shows that the closure effect of the raw data can be eliminated by ilr transformation. After that, the components obtained by robust principal component analysis (RPCA) are more representative than those obtained by PCA, both based on ilr transformed dataset, after eliminating the influence of outliers. Based on ilr transformed data with RPCA, three primary sources could be inferred: Cr, Ni, As, Zn, and Cu are mainly derived from natural sources; the main source of Cd and Hg are associated with agricultural activities and energy development; as for Pb, it originated from traffic and coal-burning activities, which is consistent with the fact that the development of tourism, fishery, and agriculture industries has led to the continuous increasing levels of anthropogenic Pb in Chagan Lake. The summarized results and conclusions will undoubtedly enhance the governmental awareness of heavy metal pollution and facilitate appropriate pollution control measures in Chagan Lake.

scholarly journals Effect of Prunus serotina Ehrh. Volatile Compounds on Germination and Seedling Growth of Pinus sylvestris L.

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 846
Author(s):  
Aleksandra Halarewicz ◽  
Antoni Szumny ◽  
Paulina Bączek
Keyword(s):  
Pinus Sylvestris ◽  
Volatile Compounds ◽  
Scots Pine ◽  
Prunus Serotina ◽  
Gas Chromatography Mass Spectrometry ◽  
Volatile Fraction ◽  
Black Cherry ◽  
Allelopathic Potential ◽  
Pine Seedlings ◽  
Phytotoxic Effect

In temperate European forests invaded by Prunus serotina Ehrh. (black cherry), a reduction in the spontaneous regeneration capacity of Pinus sylvestris L. (Scots pine) is observed. It could be caused by various factors, including allelopathic properties of this invasive plant. In this study the phytotoxic effect of P. serotina volatile compounds on P. sylvestris and the seasonal variation in this effect were assessed. Simple assays showed that volatiles emitted from P. serotina leaves significantly inhibited root growth of P. sylvestris seedlings. Their negative effect on stem growth was much weaker. The strongest phytotoxic effect on Scots pine seedlings was caused by the volatiles emitted from the youngest black cherry leaves. In fresh foliage of P. serotina, nineteen volatile organic compounds were identified by gas chromatography–mass spectrometry (GC–MS). The dominant compound was benzaldehyde. On the basis of tests of linalool alone, it was found that this monoterpene present in the volatile fraction has a strong allelopathic potential and inhibits germination, root elongation and shoot elongation of pine seedlings. The results of our research suggest that volatile compounds from P. serotina leaves could limited survival of P. sylvestris individuals in the seedling phase.

scholarly journals Assessment of heavy metals in soils of a vineyard region with the use of principal component analysis

2009 ◽  
Vol 66 (3) ◽  
pp. 361-367 ◽  
Author(s):  
Gustavo Souza Valladares ◽  
Otávio Antônio de Camargo ◽  
José Ruy Porto de Carvalho ◽  
Alessandra Maria Cia Silva
Keyword(s):  
Heavy Metals ◽  
Principal Component Analysis ◽  
Agricultural Soils ◽  
Principal Component ◽  
Component Analysis ◽  
Soil Samples ◽  
Metal Concentrations ◽  
Zn Content ◽  
Heavy Metals In Soils ◽  
Cu And Zn

Agricultural management with chemicals may contaminate the soil with heavy metals. The objective of this study was to apply Principal Component Analysis and geoprocessing techniques to identify the origin of the metals Cu, Fe, Mn, Zn, Ni, Pb, Cr and Cd as potential contaminants of agricultural soils. The study was developed in an area of vineyard cultivation in the State of São Paulo, Brazil. Soil samples were collected and GPS located under different uses and coverings. The metal concentrations in the soils were determined using the DTPA method. The Cu and Zn content was considered high in most of the samples, and was larger in the areas cultivated with vineyards that had been under the application of fungicides for several decades. The concentrations of Cu and Zn were correlated. The geoprocessing techniques and the Principal Component Analysis confirmed the enrichment of the soil with Cu and Zn because of the use and management of the vineyards with chemicals in the preceding decades.

The Design of Index about Corporate Governance Based on PCA Method

2014 ◽  
Vol 926-930 ◽  
pp. 4085-4088
Author(s):  
Chuan Jun Li
Keyword(s):  
Principal Component Analysis ◽  
Corporate Governance ◽  
Principal Components ◽  
Principal Component ◽  
Component Analysis ◽  
Contribution Rate ◽  
Variance Contribution ◽  
Pca Method

This article uses the PCA method (Principal component analysis) to evaluate the level of corporate governance. PCA is used to analyze the correlation among 10 original indicators, and extract some principal components so that most of the information of the original indicators is extracted. The formulation of the index of corporate governance can be got by calculating the weight based on the variance contribution rate of the principal component, which can comprehensively evaluate corporate governance.

Identification of Liquor Brands Based on near Infrared Spectroscopy

2013 ◽  
Vol 834-836 ◽  
pp. 935-938
Author(s):  
Lian Shun Zhang ◽  
Chao Guo ◽  
Bao Quan Wang
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Principal Components ◽  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component ◽  
Component Analysis ◽  
Correction Method ◽  
Variance Contribution

In this paper, the liquor brands were identified based on the near infrared spectroscopy method and the principal component analysis. 60 samples of 6 different brands liquor were measured by the spectrometer of USB4000. Then, in order to eliminate the noise caused by the external factors, the smoothing method and the multiplicative scatter correction method were used. After the preprocessing, we got the revised spectra of the 60 samples. The difference of the spectrum shape of different brands is not much enough to classify them. So the principal component analysis was applied for further analysis. The results showed that the first two principal components variance contribution rate had reached 99.06%, which can effectively represent the information of the spectrums after preprocessing. From the scatter plot of the two principal components, the 6 different brands of liquor were identified more accurate and easier than the spectra curves.

scholarly journals Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot

2015 ◽  
Vol 50 (8) ◽  
pp. 649-657 ◽  
Author(s):  
Regina Maria Villas Bôas de Campos Leite ◽  
Maria Cristina Neves de Oliveira
Keyword(s):  
Principal Component Analysis ◽  
Disease Severity ◽  
Principal Components ◽  
Leaf Spot ◽  
Oil Content ◽  
Principal Component ◽  
Component Analysis ◽  
High Yield ◽  
Gge Biplot ◽  
Alternaria Leaf Spot

Abstract:The objective of this work was to evaluate the suitability of the multivariate method of principal component analysis (PCA) using the GGE biplot software for grouping sunflower genotypes for their reaction to Alternaria leaf spot disease (Alternariaster helianthi), and for their yield and oil content. Sixty-nine genotypes were evaluated for disease severity in the field, at the R3 growth stage, in seven growing seasons, in Londrina, in the state of Paraná, Brazil, using a diagrammatic scale developed for this disease. Yield and oil content were also evaluated. Data were standardized using the software Statistica, and GGE biplot was used for PCA and graphical display of data. The first two principal components explained 77.9% of the total variation. According to the polygonal biplot using the first two principal components and three response variables, the genotypes were divided into seven sectors. Genotypes located on sectors 1 and 2 showed high yield and high oil content, respectively, and those located on sector 7 showed tolerance to the disease and high yield, despite the high disease severity. The principal component analysis using GGE biplot is an efficient method for grouping sunflower genotypes based on the studied variables.

Sign in / Sign up

Export Citation Format

Share Document