Assessment of river ecosystem health in Tianjin City, China: index of ecological integrity and water comprehensive pollution approach

Author(s):  
Yan Wang ◽  
Shang Zhao ◽  
Mingdong Sun ◽  
Xubo Lv ◽  
Wenqian Cai ◽  
...  
2021 ◽  
Vol 13 (11) ◽  
pp. 6287
Author(s):  
Suyeon Kim ◽  
Sang-Woo Lee ◽  
Se-Rin Park ◽  
Yeeun Shin ◽  
Kyungjin An

It is imperative to develop a methodology to identify river impairment sources, particularly the relative impact of socioeconomic sources, to enhance the efficiency of various river restoration schemes and policies and to have an internal diagnosis system in place. This study, therefore, aims to identify and analyze the relative importance of the socioeconomic factors affecting river ecosystem impairment in South Korea. To achieve this goal, we applied the Analytical Hierarchy Process (AHP) to evaluate expert judgement of the relative importance of different socioeconomic factors influencing river ecosystem impairment. Based on a list of socioeconomic factors influencing stream health, an AHP questionnaire was prepared and administered to experts in aquatic ecology. Our analysis reveals that secondary industries form the most significant source of stream ecosystem impairment. Moreover, the most critical socioeconomic factors affecting stream impairment are direct inflow pollution, policy implementation, and industrial wastewater. The results also suggest that the AHP is a rapid and robust approach to assessing the relative importance of different socioeconomic factors that affect river ecosystem health. The results can be used to assist decision makers in focusing on actions to improve river ecosystem health.


2013 ◽  
Vol 726-731 ◽  
pp. 958-962 ◽  
Author(s):  
Zhen Chun Hao ◽  
Xiao Li Liu ◽  
Qin Ju

Healthy river ecosystem has been acknowledged as the object of river management, which is crucial for the sustainable development of cities. Simple and practical evaluation methods with great precision are necessary for the evaluation of river ecosystem health. Fuzzy system has been widely used in evaluation and decision making for its simple reasoning and the adoption of experts knowledge. However, much artificial intervention decreases the precision. Neural network has a strong ability of self-leaning while it is not good at expressing rule-based knowledge. The T-S fuzzy neural network model combines the advantages of fuzzy system and neural network. In this paper, the T-S fuzzy neural network model was used to establish a river ecosystem health evaluation model. Results show that the combination of T-S fuzzy model and neural network eliminates the influences of subjective factors and improve the final precisions efficiently.


2019 ◽  
Vol 667 ◽  
pp. 500-510 ◽  
Author(s):  
C. Zhao ◽  
T. Pan ◽  
T. Dou ◽  
J. Liu ◽  
C. Liu ◽  
...  

2014 ◽  
Vol 1065-1069 ◽  
pp. 2956-2963
Author(s):  
Jun Xian Chen ◽  
Guo Hua Fang ◽  
Ren Fei Jiang ◽  
Xian Feng Huang ◽  
Yan Chen

With the theory of ecology and ecosystem health evaluation, the factors impacting river ecosystem health are analyzed and an evaluation system is built. The connotation of river ecosystem responding to the cascade development of reservoirs is studied. The response of typical plankton, benthos, fish to cascade development is identified. According the living and breeding habits of key species, typical representatives are selected to establish the grading standards of ecological health and evaluate ecosystem health as well as the effect of eco-hydrology regulation under cascade development of typical reservoirs. Results show that the annual distribution of runoff tends to be balanced, with reduced amount of suspended sediment in downstream; species fond of shallow water and rapids suffered decreased habitat space and a decline in its number, resulting in decreased diversity of fish species; and the quality of some river segments failed to meet the standard.


2020 ◽  
Vol 40 (10) ◽  
Author(s):  
孙然好,魏琳沅,张海萍,陈利顶 SUN Ranhao

2010 ◽  
Vol 55 ◽  
pp. 223-240 ◽  
Author(s):  
S. E. BUNN ◽  
E. G. ABAL ◽  
M. J. SMITH ◽  
S. C. CHOY ◽  
C. S. FELLOWS ◽  
...  

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