Assessment of Soil Heavy Metal Pollution with Principal Component Analysis and Geoaccumulation Index

Wu, Zhiyuan; Wang, Dengfeng; Zhou, Haifang; Zhang, Qi

doi:10.1016/j.proenv.2011.09.305

Cited by 132 publications

(47 citation statements)

References 10 publications

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“…Bearing the above in mind, it can be declared that, based on the background values for heavy metals pollution, the mean concentrations of heavy metals obtained by this study were generally lower than mean values reported by other Iranian studies and the understudy area further showed relatively high levels of contamination. In addition, the assessment result of RI was mainly determined by the higher concentrations and the concentration of Cd in the soil samples, which may be attributed to the fact that the toxic response factor value of Cd is significantly higher than the other three elements (Zhiyuan et al 2011). However, the mean RI for all of the study soil samples suggested the presence of a significantly high risk in Aran-o-Bidgo.…”

Section: Resultsmentioning

confidence: 99%

Spatial Eco-Risk Assessment of Heavy Metals in the Surface Soils of Industrial City of Aran-o-Bidgol, Iran

Ravankhah

Mirzaei

Masoum

2016

Bull Environ Contam Toxicol

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Heavy metals usually accumulate and migrate in soil environment due to human activities and this in return poses potential ecological and health risks. A total of 135 surface soil samples were collected from Aran-o-Bidgol City, Iran and were analyzed for their Cd, Pb, Ni and Cu concentrations to determine these elements' spatial distribution and potential ecological risks. To this end, interpolation mapping was conducted using Ordinary Kriging. The mean concentrations of Cd, Pb, Ni and Cu in the samples were 0.72, 11.41, 29.87 and 14.82 mg kg(-1), respectively, which were all higher than their background values. The spatial variation in the concentration of heavy metals could be attributable to point sources and vehicle emissions. Considering the severity of the potential ecological risks of metals, the descending order of contaminants' concentrations was Cd > Cu > Ni > Pb. The Potential Ecological Risk Index (RI) suggested that approximately 40 % and 59 % of the samples posed high and significantly high potential ecological risks, respectively. Moreover, ecological risk decreased progressively going from southwest to northeast over the under-study area. Considering the background values and RI, this area presented a relatively high level of contamination.

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Section: Resultsmentioning

confidence: 99%

Spatial Eco-Risk Assessment of Heavy Metals in the Surface Soils of Industrial City of Aran-o-Bidgol, Iran

Ravankhah

Mirzaei

Masoum

2016

Bull Environ Contam Toxicol

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“…The relatively high importance factor revealed its permutations by using the RF algorithm. Since the whole process is dependent on permutation without reducing the dimension, dimensionality reduction techniques such as PCA [66] were replaced by RF to explore the distribution of soil parameters at depth. Additionally, the mobility assessment of important soil heavy metals at the individual level is necessary for further investigations at subsoil horizons.…”

Section: Assessment Of Soil Contaminant Parametersmentioning

confidence: 99%

Spatial Distribution and Mobility Assessment of Carcinogenic Heavy Metals in Soil Profiles Using Geostatistics and Random Forest, Boruta Algorithm

Shaheen

Iqbal

2018

Sustainability

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Abstract:In third world countries, industries mainly cause environmental contamination due to lack of environmental policies or oversight during their implementation. The Sheikhupura industrial zone, which includes industries such as tanneries, leather, chemical, textiles, and colour and dyes, contributes massive amounts of untreated effluents that are released directly into drains and used for the irrigation of crops and vegetables. This practice causes not only soil contamination with an excessive amount of heavy metals, but is also considered a source of toxicity in the food chain, i.e., bioaccumulation in plants and ultimately in human body organs. The objective of this research study was to assess the spatial distribution of the heavy metals chromium (Cr), cadmium (Cd), and lead (Pb), at three depths of soil using geostatistics and the selection of significant contributing variables to soil contamination using the Random Forest (RF) function of the Boruta Algorithm. A total of 60 sampling locations were selected in the study area to collect soil samples (180 samples) at three depths (0-15 cm, 15-30 cm, and 60-90 cm). The soil samples were analysed for their physico-chemical properties, i.e., soil saturation, electrical conductivity (EC), organic matter (OM), pH, phosphorus (P), potassium (K), and Cr, Cd, and Pb using standard laboratory procedures. The data were analysed with comprehensive statistics and geostatistical techniques. The correlation coefficient matrix between the heavy metals and the physico-chemical properties revealed that electrical conductivity (EC) had a significant (p ≤ 0.05) negative correlation with Cr, Cd, and Pb. The RF function of the Boruta Algorithm employed soil depth as a classifier and ranked the significant soil contamination parameters (Cr, Cd, Pb, EC, and P) in relation to depth. The mobility factor indicated the leachate percentage of heavy metals at different vertical depths of soil. The spatial distribution pattern of Cr, Cd, and Pb revealed spatial variability regarding subsoil horizons. Significant contamination was discovered near the Deg drain and the Bed Nallah irrigated area that indicated a high Cr topsoil contamination, and in a homogenous pattern in Cd and Pb (p < 0.05). Consequently, different soil management strategies can be adopted in an industrial irrigated area to reduce the contamination load of heavy metals in soil.

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“…Bellanca et al (1996) and Vingiani et al (2018) founded TOP/BOT for Terra rossa soils in Italy of 1.16 and 1.14 respectively (Table 3) Original scientific paper DOI: /10.5513/JCEA01/21.3.2588 Miloš and Bensa: Lead and zinc concentration in genetic horizons of Terra rossa soil at a... to highlight those soil properties that contribute most to their variability. This approach has been widely used (Facchinelli et al, 2001;Mico et al, 2006;Wei et al, 2011;Lovrenčić Mikelić et al, 2011;Sun et al, 2010;Kelepertzis, 2014) to identify the origin of HMs and quantify the anthropogenic impact. Facchinelli et al (2001) Table 5.…”

Section: The Top/bottom Ratiomentioning

confidence: 99%

Lead and zinc concentration in genetic horizons of Terra rossa soil at a local scale. How these concentrations differ?

Miloš¹,

Bensa²

2020

JCEA

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The goals of this study were to (i) determine the concentration of the Pb and Zn in Terra rossa soil, (ii) reveal its origin and (iii) establish how the concentration of these elements between genetic horizons differ. In a total of 128 soil samples collected from the genetic A and B horizon of 64 Terra Rossa soil profiles were analyzed for aqua regia soluble concentration of lead (Pb A and Pb B) and zinc (Zn A and Zn B) as well as soil properties: pH in KCl, CaCO 3 , soil organic carbon (SOC) and particle size distribution. The median value of Pb A , Pb B , Zn A , and Zn B were 41.6, 33.8, 106.1 and 103.8 mg/ kg, respectively. The principal component analyses have shown that the Pb and Zn contribute most to the variability of A horizon and have the same and natural origin-they are accumulated by geochemical weathering and soil-forming processes. Using non-parametric techniques (the two-sided Kolmogorov-Smirnov test (K-S) and a shift function (SF) we estimated that (i) the Pb A and Pb B distribution differ in spread and median and (ii) the Pb B distribution need to be progressively shifted, at the whole range starting from 2.8 mg/kg for the first decile; 7.2 mg/kg for the fifth decile (median) to 12.2 mg/kg for the ninth decile to match Pb A distribution. In the case of Zn, there is no evidence that the distribution of Zn A is in any way different than the distribution of Zn B. This implies that the Zn A and Zn B have a remarkably similar shape of their distribution functions and a noticeable slight shift with a value of 2.3 mg/kg at fifth decile. The analysis presented here illustrated why detailed statistical descriptions can help to better understand how and by how much lead and zinc distributions in Terra rossa genetic horizon differ.

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Assessment of Soil Heavy Metal Pollution with Principal Component Analysis and Geoaccumulation Index

Cited by 132 publications

References 10 publications

Spatial Eco-Risk Assessment of Heavy Metals in the Surface Soils of Industrial City of Aran-o-Bidgol, Iran

Spatial Eco-Risk Assessment of Heavy Metals in the Surface Soils of Industrial City of Aran-o-Bidgol, Iran

Spatial Distribution and Mobility Assessment of Carcinogenic Heavy Metals in Soil Profiles Using Geostatistics and Random Forest, Boruta Algorithm

Lead and zinc concentration in genetic horizons of Terra rossa soil at a local scale. How these concentrations differ?

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