Potential bioavailability assessment, source apportionment and ecological risk of heavy metals in the sediment of Brisbane River estuary, Australia

Duodu, Godfred Odame; Goonetilleke, Ashantha

doi:10.1016/j.marpolbul.2017.02.017

Cited by 133 publications

(30 citation statements)

References 42 publications

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“…Multiple methods, including principle component analysis (PCA), correlation analysis, geographical detector method and geographically weighted regression have been applied to qualitatively identify various sources of metals in sediments (Duodu et al, 2017;Xia et al, 2018;Luo et al, 2019). These methods identify common characteristics among metals and capture the features of potential sources, for example, Giri and Singh used PCA method to recognize and extract two potential common sources of innate and anthropogenic activities for metals in surface water of the Subarnarekha River (Giri and Singh, 2014).…”

Section: Introductionmentioning

confidence: 99%

A comprehensive analysis and source apportionment of metals in riverine sediments of a rural-urban watershed

Xia

Zhang

et al. 2020

Journal of Hazardous Materials

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Quantitative assessment of metal sources in sediments is essential for implementation of source control and remediation strategies. This study investigated metal contamination in sediments to assess potential ecological risks and quantify pollutant sources of metals (Cu, Zn, Pb, Cd, Cr, Co and Ni) in the Wen-Rui Tang River watershed. Total and fraction analysis indicated high pollution levels of metals. Zinc and Cd posed high ecological risk based on the risk assessment code, with the highest ecological risk found in the southwestern of the watershed. The positive matrix factorization (PMF) model was highly effective in predicting total metal concentrations and identified three contributing metal sources. An agricultural source (factor 1) contributed highly to Cu (74.1%) and Zn (42.5%), and was most prominent in the west and south-central portions of the watershed. Cd (93.5%) showed a high weighting with industrial sources (factor 2) with a hot spot in the southwest. Factor 3 was identified as a mixed natural and vehicle traffic source that showed large contribution to Cr (65.2%), Ni (63.9%) and Pb (50.7%). Spatial analysis indicated a consistent pattern between PMF-identified factors and suspected metal sources at the watershed scale demonstrating the efficacy of the PMF modeling approach for watershed analysis.

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Section: Introductionmentioning

confidence: 99%

A comprehensive analysis and source apportionment of metals in riverine sediments of a rural-urban watershed

Xia

Zhang

et al. 2020

Journal of Hazardous Materials

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“…Site S5 is downstream where the other stretch of the river ends up, receiving some of its contents apart from the effluents received from the nearby industrial plants. Downstream has been reported in several studies to have more pollutants affecting the natural wellbeing of that particular ecosystem setting [44][45][46].…”

Section: Discussionmentioning

confidence: 99%

“…However, CAT activities ranged from 6.67-72. 46 Ug −1 protein and SOD 2.70-12.49 Ug −1 protein. The sequence for activities of CAT and SOD across the sites; S5 > S3 > S4 > S1 > S2 corresponded with that of heavy metals concentrations, Igeo and ErF.…”

Section: Antioxidant Enzyme Activitiesmentioning

confidence: 99%

Metallic Pollution and the Use of Antioxidant Enzymes as Biomarkers in Bellamya unicolor (Olivier, 1804) (Gastropoda: Bellamyinae)

Aljahdali

Alhassan

2020

Water

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Industrial and domestic discharges of effluent is one of the major causes of heavy metal pollution in aquatic ecosystems. Samples of benthic sediment and freshwater mollusc Bellamya unicolor were collected from 5 sites in the River Kaduna to determine heavy metal concentration, their ecological risk, and antioxidant enzymes activities in Bellamya unicolor. The results revealed the level of pollution based on heavy metal concentrations across the sites in the order S5 > S3 > S4 > S1 > S2. The ecological risk factor (ErF) revealed that Cd made the highest contribution to pollution, recording the highest ErF (2206.41). Moreover, the results of correlation base multivariate analysis showed that urban and industrial waste were the sources of Cu and Pb in the River Kaduna. The significant positive correlation between metal concentration and antioxidants catalase (CAT) and superoxide dismutase (SOD) was established, with maximum activities of antioxidants at site S5. Results from this study have revealed potential ecological risk as a result of heavy metals pollution in the River Kaduna. Hence the need for approaches and policies be put in place to prevent the discharge of untreated industrial and domestic waste into this aquatic ecosystem.

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“…Logam berat yang terdapat di perairan berasal dari sumber alami berupa pelapukan bebatuan dan aktivitas vulkanik (Peng et al, 2018). Selain itu, logam berat masuk ke perairan melalui sumber antropogenik yang terkait dengan aktivitas manusia seperti pelayaran, pertambangan, pertanian, industrialisasi, urbanisasi, limpasan air hujan, dan limbah perkotaan (Duodu et al, 2017;Ismail et al, 2016;Ke et al, 2017;Vu et al, 2017). Sumber antropogenik yang masuk ke lingkungan akuatik terakumulasi di sedimen melalui proses adsorpsi, presipitasi, copresipitasi, dan efek biologi (Peng et al, 2018) sehingga konsentrasi logam berat di sedimen jauh lebih besar dibandingkan di badan air.…”

Section: Pendahuluanunclassified

Distribusi Logam Berat Dalam Air Laut Dan Sedimen Di Perairan Cimanuk, Jawa Barat, Indonesia

Harmesa¹,

Lestari²,

Budiyanto³

2020

OLDI

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Distribution of Heavy Metals in Seawater and Sediments in Cimanuk Estuary, West Java, Indonesia. Increasing economic activities in the north coastal of Java have the potential to contribute to anthropogenic contaminants that threaten the water quality of Cimanuk coastal and estuary in Indramayu. Heavy metal which is one of the wastes from these activities has not been studied in detail. The aim of this study was to determine the distribution of Cu, Pb, Cd, Zn, and Ni in seawaters and sediments of the Cimanuk Estuary. Seawater and sediment samples were collected at 18 stations in May 2017. Seawater samples were extracted using the back extraction method while sediment samples were extracted using acids according to USEPA 3050B. Measurement of heavy metals from seawater extracts or sediments was carried out using Flame Absorption Spectrophotometry according to the USEPA 3050B method. The spatial distribution of heavy metals in seawater and sediments is modeled using ArcGIS® version 10.6.1. The results showed that ranges of metals in seawater : 0,0004 – 0,0038 mg/L (Cu), <0,0001 – 0,0044 mg/L (Pb), 0,0002 – 0,0003 mg/L (Cd), 0,0005 – 0,0119 mg/L (Zn), and 0,0020 – 0,0052 mg/L (Ni). While the metal content in sediments are 12,36 – 54,08 mg/kg (Cu), 6,43 – 15,72 mg/kg (Pb), 0,07 – 0,37 mg/kg (Cd), 64,53 – 85,16 mg/kg (Zn), and 19,66 – 62,85 mg/kg (Ni). Spatial distribution models show that heavy metals in seawater and sediments show identical patterns. High level of metals are generally detected at the stations located closed to the mainland, indicating that heavy metals are enrichment from terrestrial anthropogenic activities.

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Potential bioavailability assessment, source apportionment and ecological risk of heavy metals in the sediment of Brisbane River estuary, Australia

Cited by 133 publications

References 42 publications

A comprehensive analysis and source apportionment of metals in riverine sediments of a rural-urban watershed

A comprehensive analysis and source apportionment of metals in riverine sediments of a rural-urban watershed

Metallic Pollution and the Use of Antioxidant Enzymes as Biomarkers in Bellamya unicolor (Olivier, 1804) (Gastropoda: Bellamyinae)

Distribusi Logam Berat Dalam Air Laut Dan Sedimen Di Perairan Cimanuk, Jawa Barat, Indonesia

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