Spatial distribution of heavy metals in the West Dongting Lake floodplain, China

2020 ◽  
Vol 22 (5) ◽  
pp. 1256-1265 ◽  
Author(s):  
Dong Peng ◽  
Ziyu Liu ◽  
Xinyue Su ◽  
Yaqian Xiao ◽  
Yuechen Wang ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Spatial Distribution ◽  
Metal Contamination ◽  
Heavy Metal Contamination ◽  
Dongting Lake ◽  
Migration Route ◽  
The West ◽  
Rare And Endangered ◽  
And Migration

The protection of Dongting Lake is important because it is an overwintering and migration route for many rare and endangered birds of East Asia and Australasia, but an assessment of heavy metal contamination in West Dongting Lake is lacking.

Spatial distribution characteristics and source identification of heavy metals in river waters of the Huaihe River Basin, China

2018 ◽  
Vol 69 (5) ◽  
pp. 840 ◽  
Author(s):  
Jiqiang Yang ◽  
Yun Wan ◽  
Jingjing Li ◽  
Dawei Zou ◽  
Xin Leng ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Spatial Distribution ◽  
River Basin ◽  
Metal Contamination ◽  
Heavy Metal Contamination ◽  
Huaihe River Basin ◽  
Huaihe River ◽  
River Waters ◽  
The Huaihe River

Rapid rates of industrialisation and urbanisation have led to heavy metal contamination of many rivers in China. Identification of the main sources of heavy metal contamination in river waters and description of their spatial distribution are essential for the control of river water pollution. In this study, water samples were collected from 218 sampling sites on rivers of the Huaihe River Basin during summer 2014. Fourteen heavy metals were detected (As, Ba, Co, Cr, Fe, Pb, Mn, Mo, Ni, Zn, Se, Sn, Sr and V). The concentrations of these heavy metals showed significant regional variation and the areas could be divided into four groups based on pollution levels: a pollution-free group (Group C), a low pollution group (Group D), a moderate pollution group (Group A), and a high pollution group (Group B). Pearson correlation coefficients verified the common sources of some of the heavy metals. Further analysis revealed that the release of effluents associated with mining, smelting, welding, fertilisers, pesticides and the chemical and electronics industries are the principal sources of heavy metal contamination in the waters of rivers of the Huaihe River Basin.

scholarly journals DISTRIBUTION AND BIOACCUMULATION OF HEAVY METALS Pb AND Cd ON BIVALVES Anadara nodifera, Meretrix lyrata, AND Solen lamarckii IN COASTAL WATERS OF THE WEST MADURA STRAIT

2018 ◽  
Vol 9 (2) ◽  
pp. 631-643 ◽  
Author(s):  
. Ahyar ◽  
Dietriech G. Bengen ◽  
Yusli Wardiatno
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Coastal Waters ◽  
Metal Contamination ◽  
Heavy Metal Contamination ◽  
Quality Standards ◽  
High Concentration ◽  
Metal Analysis ◽  
The West ◽  
Negative Impacts

Madura strait receives waste disposal from various activites in the mainland, making it vulnerable to contamination of harmful pollutants. Some of the harmful pollutants are heavy metal Pb and Cd. The high concentration Cd and Pb give negative impacts to the biota including bivalves. This study was conducted to assess the distribution of heavy metals Pb and Cd in the waters and their accumulation in bivalves Anadara nodifera, Meretrix lyrata, and Solen lamarckii in coastal waters of the West Madura Strait. Heavy metal analysis refer to APHA 2012 using AAS. The results showed that concentration of Cd and Pb in water were below the quality standards, while in sediment were above the standard (Cd of 7.20 mg/kg and Pb of 62.06 mg/kg). The concentration of Cd in bivalve were different among the species tested A. nodifera of 6.10 mg/kg, M. lyrata of 3.65 mg/kg, and S. lamarckii of 2.74 mg/kg, as well as Pb concentration A. nodifera of 60.10 mg/kg, M. lyrata of 51.48 mg/kg, and S. lamarckii of 45.29 mg/kg). These results indicated that the three shells have exceeded the maximum limit of heavy metal contamination in food (Cd of 1.0 mg/kg and Pb of 1.5 mg/kg). Keywords: bivalve, heavy metal, Madura Strait

Feasibility of using environmental covariates and machine learning to predict the spatial variability of selected heavy metals in soils

2020 ◽  
Author(s):  
Mojtaba Zeraatpisheh ◽  
Rouhollah Mirzaei ◽  
Younes Garosi ◽  
Ming Xu ◽  
Gerard B.M. Heuvelink ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heavy Metals ◽  
Heavy Metal ◽  
Spatial Distribution ◽  
Random Forest ◽  
Metal Contamination ◽  
Heavy Metal Contamination ◽  
Prediction Performance ◽  
Anthropogenic Sources ◽  
Environmental Covariates

<p>Heavy metal contamination in soil is a major environmental issue intensified by rapid industrial and population growth. Understanding the spatial distribution of soil contamination by heavy metals in the ecosystem is a necessary precondition to monitor soil health and to assess the ecological risks. The main sources of heavy metals in soil are natural and anthropogenic sources. Natural sources are typically released of heavy metals from rock by weathering and atmospheric precipitation. Anthropogenic sources are related to industrialization, rapid urbanization, agricultural practices, and military activities. We analyzed a total of 358 topsoil samples (0–30 cm) collected in Golestan province in the northeast of Iran based on a regular square grid networks with 1,700 squares each sized 2.5 km²(random sampling within the grid). From these samples, we determined the spatial distribution of Cd, Cu, Ni, Zn, and Pb using random forest (RF). A multi-spectral image (Landsat 8), and environmental derivatives calculated from terrain attributes, climatic parameters, parent material, land use maps, distances to mine sectors, main roads, industrial sites, and rivers were used as covariates to predict the spatial distribution of concentrations of heavy metals. The multi-collinearity of the predictors was examined by the variance inflation factor (VIF), and a feature selection process (genetic algorithm) was applied to avoid noise and optimize the selected input variables for the final model. The predictive accuracy of RF model was assessed by the mean prediction error (ME), root mean squared error (RMSE), and coefficient of determination (R<sup>2</sup>) using 5-fold cross-validation technique. The results showed that the concentration levels (mg kg<sup>-1</sup>) of Cd, Cu, Pb, Ni, and Zn varied from 0.02 to 2.75, 9.70 to 93.70, 6.80 to 114.20, 9.50 to 93.20, and 25.10 to 417.4, respectively. The best prediction performance was for Ni (RMSE=9.9 mg kg<sup>-1 </sup>and R<sup>2</sup>=56.6%), and the lowest prediction performance for Cd (RMSE=0.4 mg kg<sup>-1 </sup>and R<sup>2</sup>=28.0%). Environmental covariates that control soil moisture and water flow along with climatic factors were the most important variables to define the spatial distribution of soil heavy metals. We conclude that the RF model using easily accessible environmental covariates is a promising, cost-effective and fast approach to monitor the spatial distribution of heavy metal contamination in soils.</p><p><strong>Keywords:</strong> Heavy metals; digital soil mapping; machine learning; random forest; spatial variation; soil pollution.</p>

A brief Survey of Assessment models to Predict stress Level of Heavy Metals in Soils

2020 ◽  
Vol 13 ◽  
Author(s):  
Sangeetha Annam ◽  
Anshu Singla
Keyword(s):  
Economic Growth ◽  
Heavy Metals ◽  
Heavy Metal ◽  
Ecological Risk ◽  
Stress Level ◽  
Metal Contamination ◽  
Heavy Metal Contamination ◽  
Human Life ◽  
High Stress ◽  
Heavy Metal Stress

Abstract: Soil is a major and important natural resource, which not only supports human life but also furnish commodities for ecological and economic growth. Ecological risk has posed a serious threat to the ecosystem by the degradation of soil. The high-stress level of heavy metals like chromium, copper, cadmium, etc. produce ecological risks which include: decrease in the fertility of the soil; reduction in crop yield & degradation of metabolism of living beings, and hence ecological health. The ecological risk associated, demands the assessment of heavy metal stress levels in soils. As the rate of stress level of heavy metals is exponentially increasing in recent times, it is apparent to assess or predict heavy metal contamination in soil. The assessment will help the concerned authorities to take corrective as well as preventive measures to enhance the ecological and hence economic growth. This study reviews the efficient assessment models to predict soil heavy metal contamination.

scholarly journals Correction: Spatial distribution of heavy metals in the West Dongting Lake floodplain, China

2020 ◽  
Vol 22 (5) ◽  
pp. 1306-1306
Author(s):  
Dong Peng ◽  
Ziyu Liu ◽  
Xinyue Su ◽  
Yaqian Xiao ◽  
Yuechen Wang ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Spatial Distribution ◽  
Dongting Lake ◽  
The West

Correction for ‘Spatial distribution of heavy metals in the West Dongting Lake floodplain, China’ by Dong Peng et al., Environ. Sci.: Processes Impacts, 2020, DOI: 10.1039/c9em00536f.

scholarly journals TINGKAT BIOKONSENTRASI LOGAM BERAT DAN GAMBARAN HISTOPATOLOGI IKAN MUJAIR (Oreochromis Mossambicus) YANG HIDUP DI PERAIRAN TUKAD BADUNG KOTA DENPASAR

2015 ◽  
Vol 9 (1) ◽  
pp. 25
Author(s):  
Made Rahayu Kusumadewi ◽  
I Wayan Budiarsa Suyasa ◽  
I Ketut Berata
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Hexavalent Chromium ◽  
Metal Contamination ◽  
Heavy Metal Contamination ◽  
Quality Standards ◽  
Oreochromis Mossambicus ◽  
Metal Exposure ◽  
Histopathological Changes ◽  
Food Ingredient

Tukad Badung River is one of the potential contamination of heavy metal sare very highin the city of Denpasar. Tilapia (Oreochromis mossambicus) isa commonspecies of fish found in the river and became the object of fishing by the public. The fish is usually consume das a food ingredient forever yangler. Fish can be used as bio-indicators of chemical contamination in the aquatic environment. Determination of heavy metal bioconcentration and analysis of liver histopathology gills organs and muscles is performed to determine the content of heavy metals Pb, Cd, and Cr+6, and the influence of heavy metal exposure to changes in organ histopathology Tilapia that live in Tukad Badung. In this observational study examined the levels of heavy metal contamination include Pb, Cd and Cr+6 in Tilapia meat with AAS method (Atomic Absorption Spectrofotometric), and observe the histopathological changes in organ preparations gills, liver, and muscle were stained with HE staining (hematoxylin eosin). Low Pb content of the fish that live in Tukad Badung 0.8385 mg/kg and high of 20.2600 mg/kg. The content of heavy metals Pb is above the quality standards specified in ISO 7378 : 2009 in the amount of 0.3 mg / kg. The content of Cr+6 low of 1.1402 mg / kg and the highest Cr+6 is 6.2214 mg / kg. The content of Cr+6 is above the quality standards established in the FAO Fish Circular 764 is equal to 1.0 mg / kg. In fish with Pb bioconcentration of 0.8385 mg / kg and Cr+6 of 1.1402 mg / kg was found that histopathological changes gill hyperplasia and fusion, the liver was found degeneration, necrosis, and fibrosis, and in muscle atrophy found. Histopathologicalchangessuch asedema and necrosis ofthe liveris foundin fishwith Pb bioconcentration of 4.5225mg/kg and Cr+6 amounted to2.5163mg/kg. Bio concentration of heavy metal contamination of lead (Pb) and hexavalent chromium (Cr+6) on Tilapia ( Oreochromis mossambicus ) who lives in Tukad Badung river waters exceed the applicable standard. Histopathological changes occur in organs gills, liver, and muscle as a result of exposure to heavy metals lead and hexavalent chromium. Advised the people not to eat Tilapia that live in Tukad Badung

scholarly journals Study on Heavy Metal Contamination in High Water Table Coal Mining Subsidence Ponds That Use Different Resource Reutilization Methods

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3348
Author(s):  
Min Tan ◽  
Kun Wang ◽  
Zhou Xu ◽  
Hanghe Li ◽  
Junfeng Qu
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Coal Mining ◽  
Control Region ◽  
Metal Contamination ◽  
Heavy Metal Contamination ◽  
High Water ◽  
Aquaculture Pond ◽  
Coal Mining Subsidence ◽  
Resource Reutilization

Heavy metals accumulate in high water table coal mining subsidence ponds, resulting in heavy metal enrichment and destruction of the ecological environment. In this study, subsidence ponds with different resource reutilization methods were used as study subjects, and non-remediated subsidence ponds were collectively used as the control region to analyze the heavy metal distributions in water bodies, sediment, and vegetation. The results revealed the arsenic content in the water bodies slightly exceeded Class III of China’s Environmental Quality Standards for Surface Water. The lead content in water inlet vegetation of the control region and the Anguo wetland severely exceeded limits. Pearson’s correlation, PCA, and HCA analysis results indicated that the heavy metals at the study site could be divided into two categories: Category 1 is the most prevalent in aquaculture pond B and mainly originate from aquaculture. Category 2 predominates in control region D and mainly originates from atmospheric deposition, coal mining, and leaching. In general, the degree of heavy metal contamination in the Anguo wetland, aquaculture pond, and fishery–solar hybrid project regions is lower than that in the control region. Therefore, these models should be considered during resource reutilization of subsidence ponds based on the actual conditions.

scholarly journals Heavy Metals Concentration in Contaminated Soils from Southern Part of Romania

2011 ◽  
Vol 68 (2) ◽  
Author(s):  
Diana FLORESCU ◽  
Andreea IORDACHE ◽  
Claudia SANDRU ◽  
Elena HORJ ◽  
Roxana IONETE ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Contaminated Soils ◽  
Metal Contamination ◽  
Industrial Waste ◽  
Heavy Metal Contamination ◽  
Land Application ◽  
Industrial Wastes ◽  
Treatment Processes ◽  
Contamination Of Soils

As a result of accidental spills or leaks, industrial wastes may enter in soil and in streams. Some of the contaminants may not be completely removed by treatment processes; therefore, they could become a problem for these sources. The use of synthetic products (e.g. pesticides, paints, batteries, industrial waste, and land application of industrial or domestic sludge) can result in heavy metal contamination of soils.

scholarly journals Organic Versus Conventional – a Comparative Study on Quality and Nutritive Value of Selected Vegetable Crops of Southern India

2020 ◽  
Vol 18 (1) ◽  
pp. 99-116
Author(s):  
JR Xavier ◽  
V Mythri ◽  
R Nagaraj ◽  
VCP Ramakrishna ◽  
PE Patki ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Vitamin C ◽  
Metal Contamination ◽  
Nutritive Value ◽  
Heavy Metal Contamination ◽  
Plate Count ◽  
Microbial Quality ◽  
Edible Plant ◽  
Lead And Cadmium

Vegetables are defined as edible plant parts generally consumed raw or cooked with a main dish, in a mixed dish, as an appetizer or as a salad. Food safety aspects related to microbial quality (total plate count, yeast and mold and food borne pathogens) and toxic residue (heavy metals) and mineral content were investigated for vegetables such as green leafy vegetable, salad vegetables, sprouts, brinjal, green chilies and French beans collected from organic and conventional outlets from Mysore region, Karnataka, India. Microbial analysis was carried out using standard procedures and mminerals (Ca, K, Fe, Cu, Mg, Mn and Zn) and heavy metals (Cd and Pb) were determined. Significant variations (p>0.05) were observed for microbial quality among organic and conventional vegetables. Mineral and vitamin C content were also significantly higher (p>0.01) in organic samples. Heavy metal contamination for lead and cadmium tested positive for conventional samples while organic samples tested negative. The variables that contributed most for the variability were heavy metal contamination, mineral and vitamin C content. Organically grown vegetables were free from heavy metals and safe for consumption, as well as they are rich in mineral and vitamin C content in comparison to conventional samples. SAARC J. Agri., 18(1): 99-116 (2020)

scholarly journals Origin and spatial distribution of metals in moss samples in Albania: A hotspot of heavy metal contamination in Europe

Chemosphere ◽  
2018 ◽  
Vol 190 ◽  
pp. 337-349 ◽  
Author(s):  
Pranvera Lazo ◽  
Eiliv Steinnes ◽  
Flora Qarri ◽  
Shaniko Allajbeu ◽  
Sonila Kane ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Spatial Distribution ◽  
Metal Contamination ◽  
Heavy Metal Contamination
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