An integrated approach to identify quantitative sources and hazardous areas of heavy metals in soils

Lv, Jianshu; Liu, Yang

doi:10.1016/j.scitotenv.2018.07.257

Cited by 93 publications

(40 citation statements)

References 59 publications

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“…Heavy metals are of great concern because of their toxicity and persistence in the environment [1]. Heavy metals in soil, such as Hg, Pb, As, Cd, and Cr, can directly poison plants growing nearby as well as the ground and surface water systems, which may also harm human health by enriching agricultural products and water.…”

Section: Introductionmentioning

confidence: 99%

Ecological Risk and Restoration Measures Relating to Heavy Metal Pollution in Industrial and Mining Wastelands

Cheng

Huang

et al. 2019

IJERPH

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In this study, we applied an integrated approach to an ecological risk evaluation of heavy metal pollution in industrial and mining wastelands in Yangxin County, China. A total of 72 sampling sites were designated in the study area. The results show that the potential ecological risk levels of Hg and Cd are higher, and the coefficient of variation of mercury levels is large. Cr, Cu, Zn, Pb, Ni, and As are all at low potential ecological risk. The land types with relatively high ecological risks are alum and coal mines. In the soil of alum mines, the risk due to mercury is higher, while in coal mine soil, the risk due to cadmium is relatively higher.

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Section: Introductionmentioning

confidence: 99%

Ecological Risk and Restoration Measures Relating to Heavy Metal Pollution in Industrial and Mining Wastelands

Cheng

Huang

et al. 2019

IJERPH

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“…F1 had a strong spatial autocorrelation that represented a natural factor [ 37 , 43 ]. Martin et al [ 44 ] studied potentially toxic element concentrations in topsoils in the Ebro basin and suggested that the grouping of Cr and Ni with other potentially toxic elements by multivariate analysis was generally regarded as the influence of natural source factors, which is consistent with the results of Nanos and Rodríguez Martin [ 45 ] for the Duero River basin, Lv et al [ 27 ] for Ju County, Jiang et al [ 46 ] for Changshu and Lv and Liu [ 6 ] for Boshan.…”

Section: Discussionmentioning

confidence: 61%

“…The concentration of soil potentially toxic elements is subjected to natural background levels and human inputs [ 5 ]. The former arises from the weathering of geological parent rocks [ 1 , 6 ]. Human input pathways include mining, waste disposal, sewage irrigation, vehicle exhaust emissions, atmospheric deposition, fertilizer and pesticide application, among other human activities [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Source apportionment of potentially toxic elements in soils using APCS/MLR, PMF and geostatistics in a typical industrial and mining city in Eastern China

Cao

Zhang³

et al. 2020

PLoS ONE

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Source apportionment of potentially toxic elements in soils is a critical step for devising soil sustainable management strategies. However, misjudgment or imprecision can occur when traditional statistical methods are applied to identify and apportion the sources. The main objective of the study was to develop a robust approach composed of the absolute principal component score/multiple linear regression (APCS/MLR) receptor model, positive matrix factorization (PMF) receptor model and geostatistics to identify and apportion sources of soil potentially toxic elements in typical industrial and mining city, eastern China. APCS/ MLR and PMF were applied to provide robust factors with contribution rates. The geostatistics coupled with the variography and kriging methods was used to present factors derived from these two receptor models. The results indicated that mean concentrations of As, Cd, Cr, Cu, Hg, Ni, Pb and Zn exceeded the local background levels. Based on multivariate receptor models and geostatistics, we determined four sources of eight potentially toxic elements including natural source (parent material), agricultural practices, pollutant emissions (industrial, mining and traffic) and the atmospheric deposition of coal combustion, which accounted for 68%, 12%, 12% and 9% of the observed potentially toxic element concentrations, respectively. This study provides a reliable and robust approach for potentially toxic elements source apportionment in this particular industrial and mining city with a clear potential for future application in other regions.

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“…Zhao et al (2010) reported that fertilizer applied to vegetable fields influenced the excessive content of Cd. Lv and Liu (2019) concluded that Cd was a major mark for the use of chemical fertilizer.…”

Section: Geostatistical Independent Simulation Of Hms In Soilsmentioning

confidence: 99%

“…The kriging interpolation and stochastic simulation are the main tools for spatial analysis in geostatistics, and were used to provide spatial structures of contaminants (Y. M. Wang et al, 2020; Zhao et al, 2010). Kriging interpolation with fast operation speed could show rough spatial distribution (Lv & Liu, 2019). The results of the stochastic simulation are more realistic than kriging interpolation.…”

Section: Introductionmentioning

confidence: 99%

Combining finite mixture distribution, receptor model, and geostatistical simulation to evaluate heavy metals pollution in soils: Source and spatial pattern

Zhu

Zhang²,

Wang³

et al. 2021

Land Degrad Dev

Self Cite

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Soil heavy metals (HMs) pollution has caused significant land degradation by affecting soil properties and functions. Clarifying sources and spatial distributions of soil HMs is necessary for the investigation of land degradation, but lacks accurate and efficient methods. This study proposes a combined method for improving source apportionment and spatial prediction of soil HMs. Finite mixture distribution modelling (FMDM) was used to explore the backgrounds and contamination thresholds, and can verify the source contribution calculated by positive matrix factorization (PMF). To improve the efficiency of traditional multivariate geostatistical simulation, independent component analysis and sequential Gaussian simulation were integrated into geostatistical independent simulation for revealing the spatial patterns and the polluted areas. The combined method was applied to an HMs dataset of Longkou City, eastern China as a case-study. Both PMF and FMDM produced three sources of HMs. Cr and Ni were dominated by natural sources. Agricultural inputs mainly affected Cd, Cu, and Zn concentrations. Hg, 66.8%, and Pb, 8.1%, originated from industrial, mining, and traffic emissions. Hazardous areas exceeding moderate pollution cutoffs comprised 203.88 km 2 , accounting for 23.2% of the study area. Less than 0.1% of the areas for all HMs were identified as hazardous zones exceeding the high pollution thresholds. The reasonable management of industrial and agricultural activities, traffic emissions, and mining could have an important influence on the protection of land resources. Combining FMDM, PMF, and geostatistical independent simulation proved valid for investigating the land degradation due to HMs pollution.

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An integrated approach to identify quantitative sources and hazardous areas of heavy metals in soils

Cited by 93 publications

References 59 publications

Ecological Risk and Restoration Measures Relating to Heavy Metal Pollution in Industrial and Mining Wastelands

Ecological Risk and Restoration Measures Relating to Heavy Metal Pollution in Industrial and Mining Wastelands

Source apportionment of potentially toxic elements in soils using APCS/MLR, PMF and geostatistics in a typical industrial and mining city in Eastern China

Combining finite mixture distribution, receptor model, and geostatistical simulation to evaluate heavy metals pollution in soils: Source and spatial pattern

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