Assessment of heavy metal accumulation and potential risks in surface sediment of estuary area: A case study of Dagu river

Han, Xiran; Wu, Hao; Li, Qingyu; Cai, Wenrui; Hu, Shuya

doi:10.1016/j.marenvres.2024.106416

Cited by 35 publications

(3 citation statements)

References 19 publications

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“… 5 , 6 The HMs (Pb, Cd, As, Cu, and Hg) become a potential environmental hazard once their concentrations reach a toxic level. 7 , 8 These HMs have a major role in altering the pH of the soil, making it unsuitable for the growth of the plant. 9 , 10 These toxic HMs could originate from either natural sources, for instance, volcanic eruptions, or anthropogenic activities like smelting.…”

Section: Introductionmentioning

confidence: 99%

Lead toxicity in plants: mechanistic insights into toxicity, physiological responses of plants and mitigation strategies

Gupta,

Dwivedi,

Kumar

et al. 2024

Plant Signaling & Behavior

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Section: Introductionmentioning

confidence: 99%

Lead toxicity in plants: mechanistic insights into toxicity, physiological responses of plants and mitigation strategies

Gupta,

Dwivedi,

Kumar

et al. 2024

Plant Signaling & Behavior

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“…Excessive levels of soil heavy metals not only impede soil productivity but also pose risks to human and animal health through their inhalation and ingestion pathways [10,11]. Thus, understanding the spatial distribution of soil heavy metal contents and evaluating the status of soil heavy metal pollution are crucial for safeguarding agricultural production and protecting human health [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Spatial Distribution Prediction of Soil Heavy Metals Based on Random Forest Model

Nie,

Chen,

Sun

et al. 2024

Sustainability

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Mastering the spatial distribution of soil heavy metal content and evaluating the pollution status of soil heavy metals is of great significance for ensuring agricultural production and protecting human health. This study used a machine learning model to study the spatial distribution of soil heavy metal content in a coastal city in eastern China. Having obtained six soil heavy metal contents, including Cr, Cd, Pb, As, Hg, and Ni, environmental variables such as precipitation, soil moisture, and population density were selected. Random forest (RF) was used to model the spatial distribution of soil heavy metal content. The research findings indicate that the RF model demonstrates a robust predictive capability in discerning the spatial distribution of soil heavy metals, and environmental factor variables can explain 60%, 52.3%, 53.5%, 63.1%, 61.2%, and 51.2% of the heavy metal content of Cr, Cd, Pb, As, Hg, and Ni in soil, respectively. Among the chosen environmental variables, precipitation and population density exert notable influences on the predictive outcomes of the model. Specifically, precipitation exhibits the most substantial impact on Cr and Ni, whereas population density emerges as the primary determinant for Cd, Pb, As, and Hg. The RF prediction results show that Cr and Ni in the study area are less affected by human activities, while Cd, Pb, As, and Hg are more affected by human industrial and agricultural production. Research has shown that using RF models for predicting soil heavy metal distributions has certain significance.

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“…Therefore, in order to address the inherently multiscale and highly dimensional control problems at the level of environmental systems, an innovative strategy is required. Utilizing the inherent capabilities of biological agents, bioremediation emerges as an especially advantageous strategy for mitigating the adverse effects of environmental contaminants 56 , 57 . When the potential of bioremediation is disregarded, achieving optimum efficacy remains a formidable undertaking.…”

Section: Introductionmentioning

confidence: 99%

Optimizing bioremediation techniques for soil decontamination in a linguistic intuitionistic fuzzy framework

Alolaiyan,

Hayat,

Shuaib

et al. 2024

Sci Rep

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Bioremediation techniques, which harness the metabolic activities of microorganisms, offer sustainable and environmentally friendly approaches to contaminated soil remediation. These methods involve the introduction of specialized microbial consortiums to facilitate the degradation of pollutants, contribute to soil restoration, and mitigate environmental hazards. When selecting the most effective bioremediation technique for soil decontamination, precise and dependable decision-making methods are critical. This research endeavors to tackle the aforementioned concern by utilizing the tool of aggregation operators in the framework of the Linguistic Intuitionistic Fuzzy (LIF) environment. Linguistic Intuitionistic Fuzzy Sets (LIFSs) provide a robust framework for representing and managing uncertainties associated with linguistic expressions and intuitionistic assessments. Aggregation operators enrich the decision-making process by efficiently handling the intrinsic uncertainties, preferences, and priorities of MADM problems; as a consequence, the decisions produced are more reliable and precise. In this research, we utilize this concept to devise innovative aggregation operators, namely the linguistic intuitionistic fuzzy Dombi weighted averaging operator (LIFDWA) and the linguistic intuitionistic fuzzy Dombi weighted geometric operator (LIFDWG). We also demonstrate the critical structural properties of these operators. Additionally, we formulate novel score and accuracy functions for multiple attribute decision-making (MADM) problems within LIF knowledge. Furthermore, we develop an algorithm to confront the complexities associated with ambiguous data in solving decision-making problems in the LIF Dombi aggregation environment. To underscore the efficacy and superiority of our proposed methodologies, we adeptly apply these techniques to address the MADM problem concerning the optimal selection of a bioremediation technique for soil decontamination. Moreover, we present a comparative evaluation to delineate the authenticity and practical applicability of the recently introduced approaches relative to previously formulated techniques.

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Assessment of heavy metal accumulation and potential risks in surface sediment of estuary area: A case study of Dagu river

Cited by 35 publications

References 19 publications

Lead toxicity in plants: mechanistic insights into toxicity, physiological responses of plants and mitigation strategies

Lead toxicity in plants: mechanistic insights into toxicity, physiological responses of plants and mitigation strategies

Spatial Distribution Prediction of Soil Heavy Metals Based on Random Forest Model

Optimizing bioremediation techniques for soil decontamination in a linguistic intuitionistic fuzzy framework

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