2015
DOI: 10.1016/j.ecolind.2014.08.032
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Geochemical baseline determination and pollution assessment of heavy metals in urban soils of Karachi, Pakistan

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Cited by 159 publications
(57 citation statements)
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“…In most studies, traffic emission was recognized as the most important factor that would lead to significant quantities of metals deposited in roadside soils, for example, in China (Chen et al 2010;Wei and Yang 2010;Lu et al 2012), Turkey (Guney et al 2010;Kadıoğlu et al 2010), Greece (Massas et al 2009), Pakistan (Karim et al 2015), and Nigeria (Azeez et al 2014). Some studies have focused on the concentrations of heavy metals in roadside soils derived from vehicular emissions and demonstrated their detrimental impacts on the surrounding environment (Khan et al 2011;Yan et al 2012;Vural 2013).…”
Section: Introductionmentioning
confidence: 97%
“…In most studies, traffic emission was recognized as the most important factor that would lead to significant quantities of metals deposited in roadside soils, for example, in China (Chen et al 2010;Wei and Yang 2010;Lu et al 2012), Turkey (Guney et al 2010;Kadıoğlu et al 2010), Greece (Massas et al 2009), Pakistan (Karim et al 2015), and Nigeria (Azeez et al 2014). Some studies have focused on the concentrations of heavy metals in roadside soils derived from vehicular emissions and demonstrated their detrimental impacts on the surrounding environment (Khan et al 2011;Yan et al 2012;Vural 2013).…”
Section: Introductionmentioning
confidence: 97%
“…The calculation of coefficient of variation also confirms the variability in the dispersion of the heavy metals in the study area. Coefficient of variation value lower than 20% indicates low variability, while value between 20 and 50% implies moderate variability, value greater than 50% but less or equal to 100% shows high variability and coefficient of variation value greater than 100% is regarded as exceptionally high variability (Karim et al 2015). From the results, all the heavy metals show low to moderate variability in the rock, soil and water samples.…”
Section: Heavy Metalsmentioning
confidence: 70%
“…By making assumptions about the normal distribution of potentially toxic metals in non-contaminated sediments, the GBC can be established by removing data outliers and then applying several statistical techniques (Karim et al, 2015;Jiang et al, 2013;Wang et al, 2015). Cumulative distribution function (CDF) for potentially toxic metal concentration can be used to remove artificially-influenced potentially toxic metal concentration data points regardless of grain size, mineral influences, or potentially toxic metal sources (Karim et al, 2015;Jiang et al, 2013;Wang et al, 2015); however, this approach might delete naturally high values and cause unexpected errors.…”
Section: Determination Of Gbcfmentioning
confidence: 99%
“…The first approach relies on non-contaminated samples collected from pristine areas unaffected by human activities or by oceanic modification processes such as scavenging (Song et al, 2014;Xu et al, 2014). In the second approach, statistical methods are applied to infer GBC in surface sediments (Jiang et al, 2013;Wang et al, 2015;Karim et al, 2015). By making assumptions about the normal distribution of potentially toxic metals in non-contaminated samples, the GBC can be estimated by first removing data outliers, and then by applying several statistical techniques (Matschullat et al, 2000).…”
Section: Introductionmentioning
confidence: 99%