Distribution of heavy metals and environmental assessment of surface sediment of typical estuaries in eastern China

Bi, Shipu; Yang, Yuan; Xu, Chengfen; Yong, Zhang; Zhang, Xiaobo; Zhang, Xianrong

doi:10.1016/j.marpolbul.2017.06.013

Cited by 74 publications

(10 citation statements)

References 9 publications

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“…According to the Guide for black-odor water classification standards, the characteristic indicators are defined as: The equation for the NCPI is as follows [41][42][43]:…”

Section: Characteristic Indicatormentioning

confidence: 99%

Monitoring of Urban Black-Odor Water Based on Nemerow Index and Gradient Boosting Decision Tree Regression Using UAV-Borne Hyperspectral Imagery

et al. 2019

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The formation of black-odor water in urban rivers has a long history. It not only seriously affects the image of the city, but also easily breeds germs and damages the urban habitat. The prevention and treatment of urban black-odor water have long been important topics nationwide. "Action Plan for Prevention and Control of Water Pollution" issued by the State Council shows Chinese government's high attention to this issue. However, treatment and monitoring are inextricably linked. There are few studies on the large-scale monitoring of black-odor water, especially the cases of using unmanned aerial vehicle (UAV) to efficiently and accurately monitor the spatial distribution of urban river pollution. Therefore, in order to get rid of the limitations of traditional ground sampling to evaluate the point source pollution of rivers, the UAV-borne hyperspectral imagery was applied in this paper. It is hoped to grasp the pollution status of the entire river as soon as possible from the surface. However, the retrieval of multiple water quality parameters will lead to cumulative errors, so the Nemerow comprehensive pollution index (NCPI) is introduced to characterize the pollution level of urban water. In the paper, the retrieval results of six regression models including gradient boosting decision tree regression (GBDTR) were compared, trying to find a regression model for the retrieval NCPI in the current scenario. In the first study area, the retrieval accuracy of the training dataset (adjusted_R 2 = 0.978), and test dataset (adjusted_R 2 = 0.974) was higher than that of the other regression models. Although the retrieval effect of random forest is similar to that of GBDTR in both training accuracy and image inversion, it is more computationally expensive. Finally, the spatial distribution graphs of NCPI and its technical feasibility in monitoring pollution sources were investigated, in combination with field observations.

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“…According to the Guide for black-odor water classification standards, the characteristic indicators are defined as: The equation for the NCPI is as follows [41][42][43]:…”

Section: Characteristic Indicatormentioning

confidence: 99%

Monitoring of Urban Black-Odor Water Based on Nemerow Index and Gradient Boosting Decision Tree Regression Using UAV-Borne Hyperspectral Imagery

et al. 2019

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“…With the rapid growth of population, and industrial and agricultural development, heavy metal pollution in aquatic systems has gradually become a global issue and attracted widespread attention of studies [ 1 , 2 ]. In the aquatic environment, river sediments serve as the repository of metals [ 3 ], and are considered as one of the important monitoring indicators for long-term metal deposition pollution in ecosystems [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…Sediments may directly affect overlying waters and become a potential secondary non-point source of metals pollution [ 3 , 6 ]. Due to the persistent, toxic, less degradable nature and bioaccumulation of metals in the environment, they could be released under favorable conditions such as redox potential, pH, dissolved oxygen, and temperature, and pose a great potential threat to aquatic ecosystems and the local inhabitants through the food chain [ 2 , 5 , 7 , 8 , 9 ]. Metals in river sediments originate from both natural sources and anthropogenic activities, such as chemical leaching of bedrock, water drainage, mining, the discharge of urban industrial and rural agricultural wastewaters [ 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Concentrations, Distribution, and Pollution Assessment of Metals in River Sediments in China

Lian

Lee

2021

IJERPH

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This study conducted a review on the concentrations, spatial distribution and pollution assessment of metals including As, Hg, Cd, Co, Cr, Cu, Mn, Ni, Pb and Zn in 102 river sediments in China between January 2008 and July 2020 based on the online literature. The geo-accumulation index (Igeo) and potential ecological risk index (RI) were used for the pollution assessment of the metals. The results showed that the ranges of metals were: 0.44 to 250.73 mg/kg for As, 0.02 to 8.67 mg/kg for Hg, 0.06 to 40 mg/kg for Cd, 0.81 to 251.58 mg/kg for Co, 4.69 to 460 mg/kg for Cr, 2.13 to 520.42 mg/kg for Cu, 39.76 to 1884 mg/kg for Mn, 1.91 to 203.11 mg/kg for Ni, 1.44 to 1434.25 mg/kg for Pb and 12.76 to 1737.35 mg/kg for Zn, respectively. The median values of these metals were descending in the order: Mn > Zn > Cr > Cu > Pb > Ni > Co > As > Cd > Hg. Compared with the SQGs, As and Cr manifested higher exceeding sites among the metals. Metals of river sediments manifested a significant spatial variation among different regions, which might be attributed to the natural weathering and anthropogenic activity. The mean Igeo values of the metals presented the decreasing trends in the order: Cd > Hg > Zn > Cu > As > Pb > Ni > Co > Cr > Mn. Cd and Hg manifested higher proportions of contaminated sites and contributed most to the RI, which should be listed as priority control of pollutants. Southwest River Basin, Liaohe River Basin, and Huaihe River Basin manifested higher ecological risks than other basins. The study could provide a comprehensive understanding of metals pollution in river sediments in China, and a reference of the control of pollutant discharge in the river basins for the management.

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“…Over the past 10 years, the pollution of the marine environment of China has become increasingly severe, particularly for heavy metal pollution (Wang et al, 2013;Zhang et al, 2013;Bi et al, 2017;Xiao et al, 2017;Cao et al, 2020). More than 20 large rivers and irrigation canals along the coast of mid-eastern China bring terrestrial materials, including heavy metals, into the tidal flats and seas (Cao et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Multi-Year Survey and Bayesian Modeling Approach for Characterizing Arsenic Pollution in Marine Clams Cultivated Along the Tidal Flats of Mid-Eastern China

Wang

Ying

Chen

et al. 2021

Front. Environ. Sci.

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Marine clam is one of the main sources of arsenic (As), and the tidal flats of Jiangsu Province are considered major culturing areas for clams in mid-eastern China. In view of increasingly severe pollution in this region, concerns have been raised by the consumers with the safety of clams they purchased or may purchase. To address these concerns, we conducted a multi-year survey to determine the levels of As and As species in five major clam species cultivated in eight production areas of this region. Based on the above analysis data, Bayesian statistics used a Markov Chain Monte Carlo approach was applied to predict the toxic As residue distributions in clams produced in this region and their health risks to Chinese adults. It was found that the bioaccumulation ability of total As (tAs) and inorganic As (iAs) was species-specific, while Mactra veneriformis (MV) had the strongest accumulation capacity for toxic iAs (0.22–2.85 mg/kg dw). Up to 6.7% of the tested MV samples exceeding the iAs limit of China Food and Drug Administration. The content of iAs was also found to be related significantly to the harvest seasons, with clam in the spawning period (June) having the lowest iAs concentrations. The non-carcinogenic and carcinogenic health risk from dietary exposure to iAs associated with MV consumption was rather high, which suggested that specific attention should be paid to the safety of clam consumption in this region.

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Distribution of heavy metals and environmental assessment of surface sediment of typical estuaries in eastern China

Cited by 74 publications

References 9 publications

Monitoring of Urban Black-Odor Water Based on Nemerow Index and Gradient Boosting Decision Tree Regression Using UAV-Borne Hyperspectral Imagery

Monitoring of Urban Black-Odor Water Based on Nemerow Index and Gradient Boosting Decision Tree Regression Using UAV-Borne Hyperspectral Imagery

Concentrations, Distribution, and Pollution Assessment of Metals in River Sediments in China

Multi-Year Survey and Bayesian Modeling Approach for Characterizing Arsenic Pollution in Marine Clams Cultivated Along the Tidal Flats of Mid-Eastern China

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