Distribution and heavy metal pollution of the suspended particulate matter on the barcelona continental shelf (North-Western Mediterranean)

1994 ◽  
Vol 85 (2) ◽  
pp. 205-215 ◽  
Author(s):  
Albert Palanques
2021 ◽  
Author(s):  
Wenye Li ◽  
ZHANG Wenqiang ◽  
SHAN Baoqing ◽  
SUN Baoping ◽  
GUO Xiaoping ◽  
...  

Abstract Suspended particulate matter (SPM) is a major contamination source in urban rivers. In this work, the Beiyun River, northern China, was used as a case study to determine the characteristics of heavy metal spatial distribution in SPM, and to evaluate the potential ecological risks and identify heavy metal sources. The concentration of seven heavy metals and associated indicators (TC, TN, TP, and OM) were measured at 12 sites and analyzed by Pearson correlation (PC) and principal component analyses (PCA). The average concentrations of Cr, Ni, Cu, Zn, As, Cd, and Pb were 70.72, 27.88, 31.35, 115.70, 27.77, 0.23, and 29.62 mg/kg, respectively, and significant spatial differences occurred between some elements. Igeo values indicated the ranking of heavy metal pollution in SPM as As > Cd > Zn > Cu > Pb > Cr > Ni. The Eir analysis demonstrated that the order of potential ecological risk of the seven metals was Cd > As > Cu > Pb > Ni > Cr > Zn. RI (potential ecological risk index) results confirmed high potential ecological risk in objective area. Of the measured heavy metals, Cd represented the highest pollution risk. Significant positive correlations were found between TC, TN, TP, and Cu. Three element pairs, Zn-Cd, Cr-Cu, and Cr-Ni, had strong correlations. Zn, Cu, and Ni were mainly introduced by human activities, and Cr was mainly from natural processes. This information on the concentration, risk, and sources of SPM in Beiyun River provides an important reference for reducing heavy metal pollution in SPM of a typical river in the Haihe River Basin.


Ocean Science ◽  
2011 ◽  
Vol 7 (5) ◽  
pp. 705-732 ◽  
Author(s):  
F. Gohin

Abstract. Sea surface temperature, chlorophyll, and turbidity are three variables of the coastal environment commonly measured by monitoring networks. The observation networks are often based on coastal stations, which do not provide a sufficient coverage to validate the model outputs or to be used in assimilation over the continental shelf. Conversely, the products derived from satellite reflectance generally show a decreasing quality shoreward, and an assessment of the limitation of these data is required. The annual cycle, mean, and percentile 90 of the chlorophyll concentration derived from MERIS/ESA and MODIS/NASA data processed with a dedicated algorithm have been compared to in-situ observations at twenty-six selected stations from the Mediterranean Sea to the North Sea. Keeping in mind the validation, the forcing, or the assimilation in hydrological, sediment-transport, or ecological models, the non-algal Suspended Particulate Matter (SPM) is also a parameter which is expected from the satellite imagery. However, the monitoring networks measure essentially the turbidity and a consistency between chlorophyll, representative of the phytoplankton biomass, non-algal SPM, and turbidity is required. In this study, we derive the satellite turbidity from chlorophyll and non-algal SPM with a common formula applied to in-situ or satellite observations. The distribution of the satellite-derived turbidity exhibits the same main statistical characteristics as those measured in-situ, which satisfies the first condition to monitor the long-term changes or the large-scale spatial variation over the continental shelf and along the shore. For the first time, climatologies of turbidity, so useful for mapping the environment of the benthic habitats, are proposed from space on areas as different as the southern North Sea or the western Mediterranean Sea, with validation at coastal stations.


2011 ◽  
Vol 184 (12) ◽  
pp. 7113-7124 ◽  
Author(s):  
Ahmet Demirak ◽  
Hanife Aydın Yılmaz ◽  
Feyyaz Keskin ◽  
Yalçın Şahin ◽  
Oğuz Akpolat

2006 ◽  
Vol 178 (1-4) ◽  
pp. 373-384 ◽  
Author(s):  
Shaik Basha ◽  
Premsingh Mansingh Gaur ◽  
Ravikumar Bhagwan Thorat ◽  
Rohitkumar Harikrishna Trivedi ◽  
Sandip Kumar Mukhopadhyay ◽  
...  

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