The Characteristics and Source Analysis of Heavy
Metals in the Sediment of Water Area of Urban
Scenic: A Case Study of the Delta Park in Suzhou
City, Anhui Province, China

Jiang, Ying; Gui, Herong; Chen, Chen; Wang, Chunlei; Zhang, Yaru; Huang, Yong-Zhen; Yu, Hao; Wang, Meichen; Fang, Hongxia; Qiu, Huili

doi:10.15244/pjoes/127279

Cited by 10 publications

(7 citation statements)

References 15 publications

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“…The present results confirm the results from previous studies, showing that HM concentrations are mainly connected with point sources of pollution [ 6 , 23 , 41 ]. An analysis of 47 rivers in Poland shows that there are two main sources of point pollution, industrial and urban.…”

Section: Discussionsupporting

confidence: 92%

“…Studies indicate that the natural biogeochemical cycle of HMs has been perturbed by anthropogenic activities [ 1 , 2 , 3 , 4 , 5 ]. Nowadays, the presence of HMs in the aquatic environment is mainly associated with anthropogenic sources [ 6 ]. In general, there is always a considerably greater pool of HMs in sediments that have a distinct anthropogenic origin [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…There are also other statistical methods used to identify sources of pollution such as conditional inference tree (CIT) [ 85 ]. Moreover, artificial neural network methods, including Kohonen’s self-organizing map, are also used to reduce and organize the structure of large datasets [ 6 ]. On the other hand, a completely different issue is the spatial presentation of environmental pollution using geostatistical methods [ 60 ].…”

Section: Introductionmentioning

confidence: 99%

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Heavy Metals in River Sediments: Contamination, Toxicity, and Source Identification—A Case Study from Poland

Sojka

Jaskuła

2022

IJERPH

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This study investigated the spatial distribution, contamination, potential ecological risks and quantities of pollutant sources of six heavy metals (HMs) in sediments of 47 rivers. The catchments of the investigated rivers are situated in Poland, but some of them are located in Slovakia, the Czech Republic, and Germany. Cluster analysis was applied to analyze the spatial distribution of Cd, Cr, Cu, Ni, Pb, and Zn in river sediments. Moran I and Getis-Ord Gi* statistics were calculated to reveal the distribution pattern and hotspot values. Principal component analysis (PCA) and positive matrix factorization (PMF) were used to identify pollution sources. Furthermore, geochemical indices and sediment quality guidelines allowed us to assess sediment contamination and potential toxic effects on aquatic biota. The results showed that in 1/3rd of the rivers, the HM pattern and concentrations indicate sediment contamination. The EF, PLI, and MPI indices indicate that concentrations were at a rather low level in 2/3rd of the analyzed rivers. Only in individual rivers may the HMs have toxic effects on aquatic biota. Spatial autocorrelation analysis using the Moran I statistic revealed a random and dispersed pattern of HMs in river sediments. PCA analysis identified two sources of HMs’ delivery to the aquatic environment. Cr, Cu, Ni, Pb, and Zn originate from point and non-point sources, while Cd concentrations have a dominant natural origin. The PMF identified three sources of pollution. Among them, urban pollution sources are responsible for Cu delivery, agricultural pollution for Zn, and industrial pollution for Ni and Cr. Moreover, the analysis showed no relationship between catchment land-use patterns and HM content in river sediments.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Heavy Metals in River Sediments: Contamination, Toxicity, and Source Identification—A Case Study from Poland

Sojka

Jaskuła

2022

IJERPH

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“…For a better graphical representation, the projection was constructed in two-dimensional datasets that are fundamentally the computational grid size in the selfdirecting procedure (Park et al 2014). However, the k-means process is one of the robust initialization tools for properly clustering the SOM model (Jiang et al 2021). This equation is used to construct and calculate the SOM neurons (Vesanto and Alhoniemi 2000a;Kim et al 2019).…”

Section: Environmental and Ecological Indices For Sedimentsmentioning

confidence: 99%

Distribution, sources, and pollution levels of toxic metal(loid)s in an urban river (Ichamati), Bangladesh using SOM and PMF modeling with GIS tool

Nasiruddin

Islam

Siddique

et al. 2022

Environ Sci Pollut Res

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Indexical assessment coupled with a self-organizing map (SOM) and positive matrix factorization (PMF) modeling of toxic metal(loid)s in sediment and water of the aquatic environment provides valuable information from the environmental management perspective. However, in northwest Bangladesh, indexical and modeling assessments of toxic metal(loid)s in surface water and sediment are still rare. Toxic metal(loid)s were measured in sediment and surface water from an urban polluted river (Ichamati) in northwest Bangladesh using an atomic absorption spectrophotometer to assess distribution, pollution levels, sources, and potential environmental risks to the aquatic environment. The mean concentrations (mg/kg) of metal(loid)s in water are as follows: Fe (871) > Mn (382) > Cr (72.4) > Zn (34.2) > Co (20.8) > Pb (17.6) > Ni (16.7) > Ag (14.9) > As (9.0) > Cu (5.63) > Cd (2.65), while in sediment, the concentration follows the order, Fe (18,725) > Mn (551) > Zn (213) > Cu (47.6) > Cr (30.2) > Ni (24.2) > Pb (23.8) > Co (9.61) > As (8.23) > Cd (0.80) > Ag (0.60). All metal concentrations were within standard guideline values except for Cr and Pb for water and Cd, Zn, Cu, Pb, and As for sediment. The outcomes of eco-environmental indices, including contamination and enrichment factors and geoaccumulation index, differed spatially, indicating that most of the sediment sites were moderately to highly polluted by Cd, Zn, and As. Cd and Zn content can trigger ecological risks. The positive matrix factorization (PMF) model recognized three probable sources of sediment, i.e., natural source (49.39%), industrial pollution (19.72%), and agricultural source (30.92%), and three possible sources of water, i.e., geogenic source (45.41%), industrial pollution (22.88%), and industrial point source (31.72%), respectively. SOM analysis identified four spatial patterns, e.g., Fe-Mn-Ag, Cd-Cu, Cr-Pb-As-Ni, and Zn-Co in water and three patterns, e.g., Mn-Co-Ni-Cr, Cd-Cu-Pb-Zn, and As-Fe-Ag in sediment. The spatial distribution of entropy water quality index values shows that the southwestern area possesses "poor" quality water. Overall, the levels of metal(loid) pollution in the investigated river surpassed a critical threshold, which might have serious consequences for the river's aquatic biota and human health in the long run.

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“…Rivers carry most of the eroded material that is moved from the continents to the oceans (Jiang et al, 2021). This material is transported by rivers in both suspended and bed load sediment forms or dissolved and solid forms (Jaskuła, 2021).…”

Section: Introductionmentioning

confidence: 99%

Chemical Composition of Sediment in Bernam Catchment, Selangor Malaysia

Sumayyah Aimi Mohd Najib,

Nur Aziemah Zainal,

Jumarmac Niza Jupinis

et al. 2024

jag

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The main sources of metal pollution are identified and the potential toxicity of dredged materials on agricultural land is estimated by sediment investigations. Here, we investigated chemical composition of sediment in Bernam catchment. Bernam river catchment has been engulfed by certain anthropogenic activities such as urbanization, illicit logging, farming. Serious concerns have been raised by stakeholders and local people because of the level of pollution due to the release of chemicals and sediments into water bodies by these human activities. Chemical composition elements such as Iron, Aluminium, Copper, Total Organic Carbon, Zinc, Potassium, Calcium, Magnesium, Sodium, Chromium, Cobalt, Nikel, Arsenic and Cadmium have been detected in this study. Characterising potential sediment sources by their diagnostic chemical and physical properties and contrasting them with the properties of transported fluvial material are key components of the methodology used to determine the source of the sediment. The methodology focuses on fluvial sampling techniques used in sediment tracing studies and their suitability for various hydrologic and morphologic river conditions. Based on cluster analysis, there are three cluster of group classification in this catchment. From a management standpoint, determining the sediment is essential to putting into practice the right controls to prevent sediment mobilisation and the ensuing siltation of river channels in the sensitive areas.

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The Characteristics and Source Analysis of Heavy Metals in the Sediment of Water Area of Urban Scenic: A Case Study of the Delta Park in Suzhou City, Anhui Province, China

Cited by 10 publications

References 15 publications

Heavy Metals in River Sediments: Contamination, Toxicity, and Source Identification—A Case Study from Poland

Heavy Metals in River Sediments: Contamination, Toxicity, and Source Identification—A Case Study from Poland

Distribution, sources, and pollution levels of toxic metal(loid)s in an urban river (Ichamati), Bangladesh using SOM and PMF modeling with GIS tool

Chemical Composition of Sediment in Bernam Catchment, Selangor Malaysia

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