Heavy metal pollution in a reforested mangrove ecosystem (Can Gio Biosphere Reserve, Southern Vietnam): Effects of natural and anthropogenic stressors over a thirty-year history

2020 ◽  
Vol 716 ◽  
pp. 137035 ◽  
Author(s):  
Sandra Costa-Böddeker ◽  
Lê Xuân Thuyên ◽  
Philipp Hoelzmann ◽  
Henko C. de Stigter ◽  
Piet van Gaever ◽  
...  
2021 ◽  
Author(s):  
Ruili Li ◽  
Minwei Chai ◽  
Xiaoxue Shen ◽  
Cong Shi ◽  
Guoyu Qiu ◽  
...  

Based on Chinese ecological policy, we have been studying mangrove ecosystems in southern China, especially from the perspective of pollutants deposition in mangrove wetlands, physiological ecology of mangrove species on the impact of heavy metal pollution and seeking ecosystem restoration. For these, we explored in three aspects: 1) pollutants distribution and ecological risk in main distribution of mangrove, China, 2) eco-statistics and microbial analyses of mangrove ecosystems (including shellfish) in representative locations where mangrove plants are well developed, especially in Shenzhen, a rapid developing economic city in Guangdong Province, 3) ecophysiological experiments on a representative species of mangrove for evaluating combination effects of major nutrient elements and heavy metal pollution on growth and physiological responses of the seedlings. Based on the results, we proposed how to rehabilitate mangrove ecosystem in China under rapidly changing environmental conditions, with a view to our future survival and to provide nature-based solution as well as the public with more ecosystem services.


2019 ◽  
Vol 29 (3SI) ◽  
pp. 411
Author(s):  
N. H. Quyet ◽  
Le Hong Khiem ◽  
V. D. Quan ◽  
T. T. T. My ◽  
M. V. Frontasieva ◽  
...  

The aim of this paper was the application of statistical analysis including principal component analysis to evaluate heavy metal pollution obtained by moss technique in the air of Ha Noi and its surrounding areas and to evaluate potential pollution sources. The concentrations of 33 heavy metal elements in 27 samples of Barbula Indica moss in the investigated region collected in December of 2016 in the investigated area have been examined using multivariate statistical analysis. Five factors explaining 80% of the total variance were identified and their potential sources have been discussed.


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