scholarly journals Multivariate statistical evaluation of heavy metals in the surface water sources of Jia Bharali river basin, North Brahmaputra plain, India

2016 ◽  
Vol 7 (5) ◽  
pp. 2577-2586 ◽  
Author(s):  
Nayan J. Khound ◽  
Krishna G. Bhattacharyya
Author(s):  
Songtao Wang ◽  
Zongjun Gao ◽  
Yuqi Zhang ◽  
Hairui Zhang ◽  
Zhen Wu ◽  
...  

This study investigated the characteristics and sources of heavy metals in a soil–ginger system and assessed their health risks. To this end, 321 topsoil samples and eight soil samples from a soil profile, and 18 ginger samples with root–soil were collected from a ginger-planting area in the Jing River Basin. The average concentration of heavy metals in the topsoil followed the order: Cr > Zn > Pb > Ni > Cu > As > Cd > Hg. In the soil profile, at depths greater than 80 cm, the contents of Cr, Ni, and Zn tended to increase with depth, which may be related to the parent materials, whereas As and Cu contents showed little change. In contrast, Pb content decreased sharply from top to bottom, which may be attributable to external environmental and anthropogenic factors. Multivariate statistical analysis showed that Cr, Ni, Cu, Zn, and Cd contents in soil are affected by natural sources, Pb and As contents are significantly affected by human activities, and Hg content is affected by farmland irrigation. Combined results of the single pollution index (Pi), geo-accumulation index (Igeo), and potential ecological risk assessment (Ei and RI) suggest that soil in the study area is generally not polluted by heavy metals. In ginger, Zn content was the highest (2.36 mg/kg) and Hg content was the lowest (0.0015 mg/kg). Based on the bioconcentration factor, Cd and Zn have high potential for enrichment in ginger. With reference to the limit of heavy metals in tubers, Cr content in ginger exceeds the standard in the study area. Although Cr does not accumulate in ginger, Cr enrichment in soil significantly increases the risk of excessive Cr content in ginger.


2021 ◽  
Author(s):  
Tao Lin ◽  
Huiqing Yu ◽  
Qi Wang ◽  
Lin Hu ◽  
Jing Yin

Abstract The river is a vital component of the water ecosystem in both urban and rural regions. However, its rapidly increasing pollutants are posing a severe threat for water ecosystem security. Using Multivariate statistical technique and Integrated water quality index model (IWQI) to evaluate surface water quality and its spatial distribution based on Geographic information system (GIS). This combinatorial model have been proved to be a feasible tool for evaluating surface water quality at large-scale basin. This study analyzed the spatio-temporal variations of surface water quality, which were determined monthly from samples collected in the Maozhou River Basin Guangdong Province, China from 2018 to 2020. The results demonstrated that the surface water quality status of in the Maozhou River Basin has been steadily improved during the study period. The surface water quality of 82.17% of monitoring site reached the water quality target of function zones (surface water quality of the class V standards), with the IWQI values ranging from 12.118 to 3.650. By the end of 2019, black-odorous water in Maozhou River basin has disappeared from our sight. By 2020, the water quality status of the Maozhou River Basin has been steadily maintained at “Medium and good” level, and the main background pollutants for the water quality target of function zones is NH3-N. However, the some area in which the surface water quality still need to further improve is estuary and southwest tributary in the basin. This finding calls for further efforts to improve surface water quality and to properly deal with various sources of pollution in the watershed. It is concluded that this combined surface water quality evaluation model is more efficient and reasonable for surface water quality evaluation at a larger scale. It can provide scientific foundation for the water ecosystem management and planning in efficiently managing and evaluating surface water quality at river or basin scales.


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