Geographically weighted principal component analysis for characterising the spatial heterogeneity and connectivity of soil heavy metals in Kumasi, Ghana

Aidoo, Eric; Appiah, Simon Kojo; Awashie, Gaston Edem; Boateng, Alexander; Darko, Godfred

doi:10.1016/j.heliyon.2021.e08039

Cited by 32 publications

(18 citation statements)

References 30 publications

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“…As presented at all three PCA plots, AS and ANS (anthropogenic HMs contamination) are placed at the same position and separated from both SS (naturally HMs rich) and HRS (uncontaminated). All analysed HMs mainly contributed to localities separation indicating that differences between localities depend on HMs content in soil and plants, as also been reported in several studies (Aidoo et al, 2021, Gergen at Hermanenscu, 2012. This is particularly interesting considering the fact that localities where contamination was caused by anthropogenic activity were grouped together at PCA plot and separated from site naturally enriched with HMs, while the HRS site (uncontaminated) was completely separated from the other three.…”

Section: Statistical Analysis Of Heavy Metal Concentration In Analyse...supporting

confidence: 82%

Geranium robertianum L. tolerates various soil types burdened with heavy metals

Hasanović,

Čakar,

Ahatović Hajro

et al. 2023

Environ Sci Pollut Res

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Heavy metals (HMs) are crucial micronutrients for the growth and development of plants, however, human activities such as mining, smelting, and fertilizers have led to toxic levels of HMs in soil. Fortunately, many plant species have developed incredible adaptive mechanisms to survive and thrive in such harsh environments. As a widespread and ruderal species, Geranium robertianum L. inhabits versatile soil types, both polluted and unpolluted. Considering the ubiquity of G. robertianum, the study aimed to determine whether geographically distant populations can tolerate HMs.We collected soil and plant samples from serpentine, an anthropogenic heavy metal contaminated, and a nonmetalliferous site to study the physiological state of G. robertianum. HMs in soil and plants were determined using ame atomic absorption spectrometry. Spectrophotometric methods were used to measure the total content of chlorophylls a and b, total phenolics, phenolic acids, avonoids, and proline. Principal component analysis (PCA) was used to investigate the potential correlation between HMs concentrations gathered from various soil types and plant samples and biochemical data acquired for plant material. A statistically signi cant difference was observed for all localities regarding secondary metabolite parameters. A positive correlation between Ni and Zn in soil and Ni and Zn in plant matter was observed (p<0.0005) indicating higher absorption. Regardless of high concentrations of heavy metals in investigated soils, G. robertianum displayed resilience and was capable of thriving. These results may be ascribed to several protective mechanisms that allow G. robertianum to express normal growth and development and act as a pioneer species.

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Section: Statistical Analysis Of Heavy Metal Concentration In Analyse...supporting

confidence: 82%

Geranium robertianum L. tolerates various soil types burdened with heavy metals

Hasanović,

Čakar,

Ahatović Hajro

et al. 2023

Environ Sci Pollut Res

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“…PCA is a multivariate statistical analysis method that can reflect most of the original multivariate information with fewer variables [ 32 ]. The method assumes that a smaller set of principal components will be comprised of linear combinations of the original variables [ 33 ]. Each principal component is uncorrelated [ 29 , 32 ].…”

Section: Methodsmentioning

confidence: 99%

The Predominant Sources of Heavy Metals in Different Types of Fugitive Dust Determined by Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF) Modeling in Southeast Hubei: A Typical Mining and Metallurgy Area in Central China

Chen

Wen

Wang

et al. 2022

IJERPH

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To develop accurate air pollution control policies, it is necessary to determine the sources of different types of fugitive dust in mining and metallurgy areas. A method integrating principal component analysis and a positive matrix factorization model was used to identify the potential sources of heavy metals (HMs) in five different types of fugitive dust. The results showed accumulation of Mn, Fe, and Cu can be caused by natural geological processes, which contributed 38.55% of HMs. The Ni and Co can be released from multiple transport pathways and accumulated through local deposition, which contributed 29.27%. Mining-related activities contributed 20.11% of the HMs and showed a relatively high accumulation of As, Sn, Zn, and Cr, while traffic-related emissions contributed the rest of the HMs and were responsible for the enrichment in Pb and Cd. The co-applied source-identification models improved the precision of the identification of sources, which revealed that the local geological background and mining-related activities were mainly responsible for the accumulation of HMs in the area. The findings can help the government develop targeted control strategies for HM dispersion efficiency.

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“…These winning variables embody essential features that shape specific characteristics and behaviours of data within a given area. Understanding these localized influences is vital as it unveils unique spatial patterns and anomalies that may remain concealed when considering spatial scales (Aidoo et al ., 2021).…”

Section: Navigating the Influence: Discovering Winning Variables Thro...mentioning

confidence: 99%

Hilly terrain and housing wellness: Geo‐visualizing spatial dynamics of urban household quality in the Himalayan town of Darjeeling, India

Roy,

Majumder,

Bose

et al. 2024

Singap J Trop Geogr

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Darjeeling, renowned as the ‘Queen of the Himalayas’, is one of the high‐altitude towns in India, distinguished by its exceptional topography and picturesque landscape. Given the challenges posed by limited land availability, susceptibility to natural hazards, and the need for context‐specific housing interventions in such hilly terrains, understanding housing conditions becomes paramount. Thus, this research explores the spatial patterns and heterogeneity of urban housing wellness in Darjeeling's hilly urban centre. A comprehensive assessment was conducted utilizing 15 key indicators. Three principal indices were formulated: Residence Quality, Residence Essential Services, and Residence Asset & Possession, culminating in the Urban Housing Wellness Index (UHWI). The index construction employed the Geographically Weighted Principle Component Analysis (GWPCA) technique. In further analysis, Univariate Local Indicators of Spatial Association (LISA) were used to determine clustering and spatial dependence, while Moran's I was utilized to gauge the spatial autocorrelation of housing conditions. A notable clustering pattern and spatial autocorrelation was observed in the urban housing wellness of the study area. The present study offers novel insights into the intricate dynamics of housing conditions in unique hilly terrains.

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Geographically weighted principal component analysis for characterising the spatial heterogeneity and connectivity of soil heavy metals in Kumasi, Ghana

Cited by 32 publications

References 30 publications

Geranium robertianum L. tolerates various soil types burdened with heavy metals

Geranium robertianum L. tolerates various soil types burdened with heavy metals

The Predominant Sources of Heavy Metals in Different Types of Fugitive Dust Determined by Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF) Modeling in Southeast Hubei: A Typical Mining and Metallurgy Area in Central China

Hilly terrain and housing wellness: Geo‐visualizing spatial dynamics of urban household quality in the Himalayan town of Darjeeling, India

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