Fuzzy synthetic evaluation and health risk assessment quantification of heavy metals in Zhangye agricultural soil from the perspective of sources

Zhao, Rui; Guan, Qingyu; Luo, Haiping; Lin, Junqi; Yang, Libin; Wang, Feifei; Pan, Ninghui; Yang, Yanyan

doi:10.1016/j.scitotenv.2019.134126

Cited by 79 publications

(43 citation statements)

References 83 publications

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“…Cd, as the most prominently contaminated metal in riverine sediments, was determined with an r 2 coefficient of 0.999, which was followed by Cu (0.961), Zn (0.895), Pb (0.883), Ni (0.563), and Cr (0.521). These r 2 coefficients are similar to the results from previous studies using PMF model for metal concentration prediction (Yang et al, 2019;Kolakkandi et al, 2020). Combined with the pollution levels and risk assessment results, metals identified with high pollution levels and risks had a stronger predictability in the study region.…”

Section: Source Apportionment For Metal Concentrations and Ecological Risk By Pmf Modelsupporting

confidence: 87%

“…Metal contamination mainly originates from anthropogenic activities and comprises various sources such as industry, mining/smelting, agriculture, coal burning and traffic (Men, et al, 2020). Multiple methods are applied to qualitatively investigate metal source apportionment, which include correlation analysis, principal component analysis and regression (Yang et al, 2019;Zhao, et al, 2019). For example, Fei et al (2019) employed a synthesis model using Bayesian Maximum Entropy theory and Geographically Weighted Regression to determine that Cd contamination in Shanghai soils was mainly derived from agricultural activities while Cr originated from natural sources; Principal component analysis-multiple linear regression extracted 4 factors for soil heavy metals in a electroplate factory area (Duan et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Positive matrix factorization (PMF), a typical quantitative receptor model, has distinct advantage of non-negative constraint in identifying source categories and apportioning corresponding contributions for priority contaminants and remediation strategies (Yang et al, 2019), which has been widely applied for source apportionment of metal contamination in the atmosphere, sediment and soil (Jorquera & Barraza, 2013;Wang et al, 2020;Lv, 2019). contamination prevention For example, a modified PMF approach was used for source apportionment of metals in agricultural soil in Tianjin (China) where it identified irrigation and atmospheric deposition as the two main pollution sources having contributions of 26.6% and 19.6%, respectively (Wu et al, 2020); Duan et al (2020) showed better prediction of soil heavy metals by PMF model than that of principal component analysis-multiple linear regression (PCA-MLR) due to mathematical constraints in PCA-MLR method.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Integrated source-risk and uncertainty assessment for metals contamination in sediments of an urban river system in eastern China

Xia

Niu²,

et al. 2021

CATENA

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Integrated source apportionment and risk assessment of metals is of great importance for contamination source control and remediation at the regional watershed scale. To identify metal sources and source-specific ecological risks, sediments were collected in the Wen-Rui Tang urban watershed for metal analysis. Risk assessment showed considerable and extremely high risk for Cu and Cd with large spatial variation. Positive matrix factorization model extracted three main sources with reasonable prediction efficacy for metal concentrations. Due to different toxicity coefficients for various metals, the low concentration contribution of source factor 2 (27.2%) contributed 83.7% of total risk, while a high concentration loading for factor 3 (40.8%) only contributed 4.6% to total risk. Predicted source-specific risk was similar to determined risk level for Cr, Ni and Pb; however, Cu and Cd were predicted with decreased risk, while Zn had increased risk. Triangular fuzzy number (TFN) coupled with stochastic simulation showed elevated trend in risk simulation for Cd and Cu when compared with determined risk. The uncertainties for risk evaluation appear to result from spatial variations in metal concentrations. Source apportionment and specific-risk assessment results suggest that different strategies may be required to address mitigation of elevated metal concentrations versus ecological risk.

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Section: Source Apportionment For Metal Concentrations and Ecological Risk By Pmf Modelsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Integrated source-risk and uncertainty assessment for metals contamination in sediments of an urban river system in eastern China

Xia

Niu²,

et al. 2021

CATENA

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show abstract

“…As shown in Table 8, among the existing methods based on the FS and IFS by Hernandez and Uddameri, 11 Wang et al, 12 Zhao et al, 13 Santos et al, 18 and Pilevar et al, 20 the characterization of index information is limited to the fact that the sum of the MD and NMD is less than 1. When the evaluator makes an independent evaluation in a more complex information environment, the sum of the MD and NMD is greater than 1; then, the IFS and FS cannot process this evaluation information 24 .…”

Section: Discussionmentioning

confidence: 99%

Assessment and selection of smart agriculture solutions using an information error‐based Pythagorean fuzzy cloud algorithm

Yang

Lin

et al. 2021

Int J of Intelligent Sys

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Smart agriculture can enhance agricultural production efficiency, improve the ecological environment, and realize the sustainable development of agriculture. Many countries and companies are working hard to develop or introduce smart agricultural solutions. Because of the shackles of traditional agricultural management methods and fierce competition with a variety of different solutions, it is a difficult task for enterprises to select and implement smart agricultural solutions smoothly. Hence, enterprises must assess alternative solutions and select a feasible solution in advance. This study drew a novel assessment and selection for smart agriculture solutions using an information error‐based Pythagorean fuzzy cloud algorithm. First, an evaluation index system built on smart agriculture solutions was constructed from four aspects. Then, a new concept of Pythagorean fuzzy clouds was defined to express the evaluation information for each indicator. Simultaneously, the Pythagorean fuzzy cloud weighted Bonferroni mean (PFCWBM) operator was developed to aggregate the assessment information of multiple indicators. Next, an assessment and selection decision framework for smart agriculture solutions based on the PFCWBM operator was presented. In addition, an example was given to illustrate the effectiveness of the proposed algorithm. Finally, a discussion was conducted to verify the superiority of our approach. The results showed that our algorithm can characterize and evaluate complex information and has high sensitivity and environmental adaptability.

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“…Human activities as anthropogenic sources cover a wide range and can be divided into urban industrial and mining activities, sewage irrigation of farmland, unreasonable use of chemical fertilizers in agricultural activities, and atmospheric deposition (Luo et al, 2009;Shi et al, 2018). Recently, owing to excellent performances in determining the potential sources and apportioning sources contribution, the positive matrix factorization (PMF) model has been extensively applied to the source apportionment of soil heavy metals (Chen & Lu, 2018;Lv & Wang, 2019;Wang et al, 2020;Zhao et al, 2019). Risk assessment is a key step for further analysis of soil pollutant hazards, which helps to formulate appropriate control measures under the premise of understanding different risk levels of pollutants.…”

Section: Introductionmentioning

confidence: 99%

A joint method to quantify source contributions of heavy metals to ecological and human health risks in oasis farmland soil

Tian

Guan

Shao

et al. 2021

Soil Science Soc of Amer J

Self Cite

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Source identification and risk assessment of heavy metals are ongoing hot topics in current research, but few studies have been performed on the linkage mechanism between them. In the past, the amount of heavy metals discharged was the identification criterion for high-risk source but failed to consider different toxicity of heavy metals in the risk level of each pollution source. Therefore, it is impossible to accurately determine high-risk pollution source. For this situation, this study introduced a risk assessment model based on the source apportionment model, which can quantitatively analyze the source-based risk. Meanwhile, pollution assessment indexes and a risk assessment model were applied to evaluate the levels of pollution and risk of heavy metals, showing that lead (Pb) caused relatively serious pollution and arsenic (As) generated the highest ecological risk and noncarcinogenic risk. Positive matrix factorization (PMF) model identified and quantified the sources of heavy metals (coal-related activities source, mixed source of mining and traffic emissions, industrial activity source, agricultural source related to the application of agrochemicals) with the corresponding contributions of 42, 30, 26, and 2%, respectively. Then PMF was combined with potential ecological risk index and human health risk assessment model to quantify the risk from pollution sources, indicating that the coal-related activities source was the largest pollution source (31-36%) that caused human health risks, while the mixed source of mining and transportation emissions posed the greatest threat (29%) to the ecosystem health. Therefore, both sources should be identified as the priority pollution sources.

show abstract

Fuzzy synthetic evaluation and health risk assessment quantification of heavy metals in Zhangye agricultural soil from the perspective of sources

Cited by 79 publications

References 83 publications

Integrated source-risk and uncertainty assessment for metals contamination in sediments of an urban river system in eastern China

Integrated source-risk and uncertainty assessment for metals contamination in sediments of an urban river system in eastern China

Assessment and selection of smart agriculture solutions using an information error‐based Pythagorean fuzzy cloud algorithm

A joint method to quantify source contributions of heavy metals to ecological and human health risks in oasis farmland soil

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