A Fuzzy-Based Analysis of Air Particle Pollution Data: An Index IMC for Magnetic Biomonitoring

Chaparro, Mauro A. E.; Chaparro, Marcos A. E.; Molinari, Daniela A.

doi:10.3390/atmos15040435

Cited by 1 publication

(1 citation statement)

References 37 publications

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“…In addition to traditional analytical methods, recent advancements have introduced the use of fuzzy models in magnetic monitoring. For instance, a study by Chaparro proposed a magnetic index (IMC) that leverages fuzzy clustering, fuzzy arithmetic, and a fuzzy inference system to analyze relationships between the IMC and PLI, suggesting a promising approach for broader application in environmental matrices such as soils and sediments [45]. The application of magnetic susceptibility measurement techniques for the characterization of soils and geochemistry has consistently been a focal point of research.…”

Section: Introductionmentioning

confidence: 99%

Worldwide Examination of Magnetic Responses to Heavy Metal Pollution in Agricultural Soils

Zhao,

Zhang,

et al. 2024

Agriculture

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Over the last decade, a large number of studies have been conducted on heavy metals and magnetic susceptibility () measurement in soils. Yet, a global understanding of soil contamination and magnetic responses remains elusive due to the limited scope or sampling sites of these studies. Hence, we attempted to explore a pollution proxy on a global scale. Through a meta-analysis of data from 102 published studies, our research aimed to provide a worldwide overview of heavy metal pollution and magnetic responses in agriculture soils. We mapped the geographic distribution of nine heavy metals (Cr, Cu, Zn, Pb, Ni, As, Cd, Mn, and Fe) in agricultural soils and explored their pollution sources and contributions. Since 2011, The accumulation of heavy metals has escalated, with industrial activities (31.5%) being the largest contributor, followed by agricultural inputs (27.1%), atmospheric deposition (22.66%), and natural sources (18.74%). The study reports ranging from 6.45 × 10−8 m3/kg to 319.23 × 10−8 m3/kg and from 0.59% and 12.85%, with the majority of the samples being below 6%, indicating heavy metal influence mainly from human activities. Pearson’s correlation and redundancy analysis show significant positive correlations of Pb, Zn, and Cu with (r = 0.51–0.53) and Mn and Fe with (r = 0.50–0.53), while Pb, Zn, Cu, and As metals were shown to be key factors of variation in magnetic response. The average heavy metal pollution load index of 2.03 suggests moderate global agricultural soil pollution, with higher heavy metal contamination in areas of high . Regression analysis confirms soil is considered to be non-polluted below of and polluted above this threshold, with all contamination factors of metals showing a linear correlation with (R = 0.72), indicating that a significant relationship between and the geochemical properties of soils continues to exist on a global scale. This study provides new insights for large-scale agricultural soil quality assessment and magnetic response.

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Section: Introductionmentioning

confidence: 99%