Multivariate and geostatistical analyses of the spatial distribution and origin of heavy metals in the agricultural soils in Shunyi, Beijing, China

Abstract

An extensive survey was conducted in this study to determine the spatial distribution and possible sources of heavy metals in the agricultural soils in Shunyi, a representative agricultural suburb in Beijing, China. A total of 412 surface soil samples were collected at a density of one sample per km 2 , and concentrations of As, Cd, Cu, Hg, Pb and Zn were analyzed. The mean values of the heavy metals were 7.85± 2.13, 0.136 ±0.061, 22.4 ±6.31, 0.073 ±0.049, 20.4 ±5.2, and 69.8 ±16.5 mg kg − 1 for As, Cd, Cu, Hg, Pb, and Zn, respectively, slightly higher than their background values of Beijing topsoil with the exception of Pb, but lower than the guideline values of Chinese Environmental Quality Standard for Soils. Multivariate and geostatistical analyses suggested that soil contamination of Cd, Cu and Zn was mainly derived from agricultural practices. Whereas, As and Pb were due mainly to soil parent materials, and Hg was caused by the atmospheric deposits from Beijing City. The identification of heavy metal sources in agricultural soils is a basis for undertaking appropriate action to protect soil quality.

Evaluation of semi-arid arable soil heavy metal pollution by magnetic susceptibility in the Linfen basin of China

“…The coefficients of variation (CV) ranged from 0.09 to 0.35 indicating minor variability. The concentrations of Zn, Ni, and Cd were all slightly higher than those that have been previously reported in the other agricultural soils excluding Linfen (Liao et al 2007;Lu et al 2012;Liu et al 2015;Cao et al 2015), while Cu was lower than in the other agricultural soils (Table 2), indicating soil contamination of heavy metal as a result of excessive fertilization and pesticide application. The ordinary kriging interpolation method for heavy metal was used for the spatial distribution maps.…”

Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data

“…For human and ecological risk assessment, the prediction of toxic metals accumulation in soils has been determined using a variety of geostatistical methods and environmental data at various scales [15][16][17][18][19][20][21][22]. Lado et al [16] used regression-kriging with a large number of auxiliary variables (1-km resolution) as predictors (e.g., land cover, geology, night-lights images) to model and map the distribution of eight toxic metals (As, Cd, Cr, Cu, Hg, Ni, Pb, Zn) for the topsoil in Europe.…”

Spatial variation of soil quality and pollution assessment of heavy metals in cultivated soils of Henan Province, China

“…That indicates that these metals had similar sources (Nagarajan et al, 2010). Mn, Zn and Cr also had a significant relationship with AP, and their contents could increase with the prolonged application of organic and mineral fertilisers (Kong et al, 2006;Fan et al, 2005;Lu et al, 2012) and pesticides-fungicides (Kelepertzis 2014). Potential problems could arise following additions of metals considered harmful for agriculture, such as soil contamination and plant toxicity (Cheng et al, 2012;Manna et al, 2007;Cai et al, 2012).…”

“…This imbalanced nutrient management causes many problems in the ecoagriculture system (Ma et al, 2010;Zhang et al, 2004). For example, excessive accumulation of metals in agriculture soils may result in elevated uptake by food crops (Lu et al, 2012;Cai et al, 2012;Cheng et al, 2012) and subsequently transport into humans through food. This is of great concern because of the potential health risks to local inhabitants (Arora et al, 2008;Cui et al, 2004;Zhuang et al, 2009).…”