Source identification of eight hazardous heavy metals in agricultural soils of Huizhou, Guangdong Province, China

Cai, Li‐Xuan; Xu, Zhencheng; Ren, Mingchun; Guo, Qi; Hu, Xibang; Hu, Guocheng; Wan, Hongfu; Peng, Ping’an

doi:10.1016/j.ecoenv.2011.07.004

Cited by 309 publications

(152 citation statements)

References 29 publications

Supporting

Mentioning

121

Contrasting

Unclassified

Order By: Relevance

“…If a variogram reaches a certain limit that results in a certain effective range, the spatial structure and conditions for inherent spatial assumption can be achieved. The Matern variogram models were calculated [25] using the given Equation (3).…”

Section: Geostatistical Analysis and Variogram Modelling For Soil Heamentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Geostatistical Analysis and Variogram Modelling For Soil Heamentioning

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

View full text Add to dashboard Cite

show abstract

“…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).…”

Section: Discussionmentioning

confidence: 99%

“…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).…”

Section: Introductionmentioning

confidence: 99%

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

Cheng

Guo

Wang

et al. 2014

Chemical Speciation & Bioavailability

View full text Add to dashboard Cite

Eight chemical and physical characteristics and seven biological properties of the soils from five cultivated areas in the Henan Province were quantified. Concentrations of Cu, Cr, Zn and Mn were determined using the flame atomic absorption spectrometry. In addition available phosphorus, chloride, soluble salt and pH were measured. The activities of enzymes were determined by a literature method. The results showed that the concentrations of Mn, Zn and Cr had close relationships with that of available phosphorus, and their concentrations increased with the prolonged application of organic and mineral fertilisers and pesticides-fungicides. The activities of cellulase have a relationship to the concentrations of Zn and Mn at 0.95 levels respectively, Zn and Mn stimulate many enzyme activities. The relationship between protease and available phosphorus was nearly distinctive at 0.95 levels, relating with the use of nitrogen fertiliser in the farmlands. Local organic manures and urea fertilisers did not change the soil characteristics to a great extent. However, the soils showed moderate and moderate-to-strong contamination by Mn and Zn in the study areas. Wastewater and groundwater irrigation, pesticidesfungicides, and fertiliser may be responsible for the elevated concentrations of Zn and Mn in the soil samples.

show abstract

“…These parameters will be helpful for comparing datasets, and subsequently summarizing the obtained results and facilitating its consequent interpretations (Burgos et al 2005;Cai et al 2012;Martínez et al 2007). Figure 3 shows the histogram of the metals Pb, Ni, Se, Mo, and Zn.…”

Section: Descriptive Statisticsmentioning

confidence: 99%

Assessment of the metals contamination and their grading by SAW method: a case study in Sarcheshmeh copper complex, Kerman, Iran

et al. 2015

View full text Add to dashboard Cite

Studying the distribution of metals in the soil of mineral regions is one of the most important environmental issues. These metals are transferred to the cycle of nature by geogenic and anthropogenic resources, causing serious short-and long-term effects, and posing serious risks for the survival of living organisms. Various mining activities, smelting and beneficiation processes are important anthropogenic factors in the presence of metals in soil, which play a far more effective role in soil pollution than natural factors. Sarcheshmeh copper mine in Kerman is one of the largest copper sulfide mines in the world, for which evaluation and grading of metals contamination has paramount importance due to the high volume of mining. In this study, in order to determine the metal content in the surface soil from the Sarcheshmeh copper complex soils area, 120 surface soil samples were taken from a depth of 30 cm, and analyzed for the metals Pb, Ni, Se, Mo and Zn using the ICP-MS method. Then, the contamination coefficient, enrichment factor and geoaccumulation index of these metals were calculated. Based on expert views on the relative importance of each of the indicators and their final weight, risk assessment and grading by the simple additive weighting (SAW) method was performed. According to the results, the highest risk of pollution was obtained for nickel and the lowest was for selenium.

show abstract

Source identification of eight hazardous heavy metals in agricultural soils of Huizhou, Guangdong Province, China

Cited by 309 publications

References 29 publications

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

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

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

Assessment of the metals contamination and their grading by SAW method: a case study in Sarcheshmeh copper complex, Kerman, Iran

Contact Info

Product

Resources

About