Watershed-Level Optimization of BMP Selection for Cost-Effective Pollutant Load Reduction in the Lower Fox River Basin and Green Bay, Wisconsin

2009 ◽  
Vol 2009 (6) ◽  
pp. 570-599
Author(s):  
Laura J. Blake ◽  
Corey Godfrey
1996 ◽  
Vol 34 (12) ◽  
pp. 33-40 ◽  
Author(s):  
Y. Hosoi ◽  
Y. Kido ◽  
H. Nagira ◽  
H. Yoshida ◽  
Y. Bouda

The inflow of pollutant load from urban areas and the stagnation of water due to sea water intrusion cause the deterioration of river water quality in tidal zone. In order to improve water quality, various measures such as the reduction of pollutant load by sewage systems, discharge control from sewage treatment plants considering river flow, nutrient removal by aquatic plants, and the dredging of bottom sediments have been examined. The choice of these measures depends on the situation of the river environment and finances. In this study, a field survey was carried out in a typical urban river basin, first. Secondly, on the basis of this survey, a mathematical model was formed to simulate flow and water quality. Several purification alternatives designed for the investigated river basin were comparatively evaluated from the viewpoint of the effect of water quality improvement and their cost. Finally, they were prioritized. Through this case study, a planning process of river water quality management was shown.


2020 ◽  
Vol 20 (2) ◽  
pp. 251-263
Author(s):  
Kyeong Hwan Kang ◽  
Junghyeon Kim ◽  
Hyeonjin Jeon ◽  
Kyoungwoo Kim ◽  
Imgyu Byun

In 2006, the Korean Ministry of Environment established <The 1st Water Environment Management Master Plan>. The plan aimed at “Clean Water, Eco River 2015” and guided water quality protection and strengthened water management. This study evaluated the achievement of the target water quality among the 33 mid-level basins in the Nakdong River basin and assessments of the causes of non-achievement of the target water quality by mid-level basins. According to the 2015 water quality data, only 16 of the 33 mid-level basins achieved the target water quality. The low achievement of the target water quality was attributed to the failure to predict the pollutant load at the time of planning, problems with the management of tributaries, implementation of the <Four major river restoration project>, and problems with the representativeness of the water quality representative points. In addition, feasibility studies on the water quality monitoring representative point used in each mid-level basin were also performed; some mid-level basins required improvement or change of the representative points. This study also suggested further research to improve water quality, such as detailed studies of the management of pollutant load, mainstream tributaries, and water quality indicators, for the revision of the current ongoing <The 2nd Water Environment Management Master Plan>.


Author(s):  
G Simm ◽  
W S Dingwall ◽  
S V Murphy ◽  
J FitzSimons ◽  
W R Brown

It is likely that returns from lamb production in future will depend, much more than at present, on producing leaner carcasses. There are several short-term changes in management which could produce leaner carcasses. However, In the longer term genetic Improvement, particularly by within-breed selection In terminal sire breeds, is likely to provide permanent, cumulative and cost-effective benefits In carcass composition. In the early 1980s a research project was started at the Edinburgh School of Agriculture, using Suffolk sheep, to examine the genetic potential for Improving carcass composition In terminal sires. The work commenced with an evaluation of techniques for in vivo measurement of carcass composition (Simm, 1987) and derivation of selection indices to incorporate In vivo measurements (Simm and Dingwall, 1989). Since 1985 In vivo measurement and Index selection have been practised In the experimental flock, which now numbers about 220 ewes. This paper reports the interim results of selection.


2020 ◽  
Vol 32 (12) ◽  
pp. 2297-2309
Author(s):  
Kai Han ◽  
Yuntian He ◽  
Keke Huang ◽  
Xiaokui Xiao ◽  
Shaojie Tang ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document