摘要
有关城市暴雨径流初期冲刷现象(FEE)的评估方法迄今为止饱受争议,针对传统初期冲刷现象评估方法存在的弊端,提出了用最优分割模式(OSM)进行初期冲刷现象识别.两场暴雨(2009-8-3和2010-7-31)径流的案例分析表明:应用最优分割模式识别初期冲刷现象,对于无坡度的混凝土道路径流,平均初期径流控制量为6mm,而传统方法按照80/30(初期30%的暴雨径流携带了80%的污染负荷)标准无法识别初期冲刷现象,按照50/25标准评估的初期径流控制量为5mm;对于平均坡度为2.3%的大尺度城市流域,最优分割模式识别的初期径流控制量为2.7mm,而传统方法无法判断实际存在的中期冲刷现象.影响因子敏感性分析表明,分段数k、控制污染物的选取均会对初期径流控制量的判断结果产生影响.最优分割模式能够克服传统方法判断标准不一致,不能直接获取需要控制的初期径流量,无法识别中后期冲刷现象的缺点,作为初期冲刷现象新识别模式的初步探索,研究结果可为初期冲刷现象的科学判别提供参考.
Assessment methods in identifying the first flush effect（FFE） in urban stormwater runoff have been disputed so far.Based on the analysis of the limitations of current methods,an optimal segmentation mode （OSM） was presented in this paper to detect the FFE.The results from two case studies（2009-8-3 and 2010-7-31） showed that through using the OSM,the initial runoff volume from concrete road with no slope can be controlled within 6mm,while FFE can＇t be recognized by traditional method based on the 83/30（80% of pollutant mass is transported in the first 30% of runoff volume） standard,but 5mm can be controlled by 50/25 standard.For a large-scale urban watershed with an average slope of 2.3%,the initial controlled volume can be ascertained as 2.7mm,while the actual metaphase flush effect can＇t be recognized by using the traditional method.The analysis of the influential factors indicated that the results could be affected by both subsection amount and the selected pollutants.Limitations existing in traditional method could be overcome by the OSM,such as the difference among estimation standards,inability to capture initial volume needing to be controlled directly,and failure in detecting the flush effect if middle or end pollutant concentrations were high,and so on.As a preliminary study,the results provided a new reference for more scientific assessment of FFE.
出处
《环境科学学报》
CAS
CSCD
北大核心
2011年第11期2432-2439,共8页
Acta Scientiae Circumstantiae
基金
国家水体污染控制与治理科技重大专项(No.2008ZX07315-001)
重庆市自然科学基金项目(No.CSTC
2010BB1351)~~
关键词
暴雨径流
初期冲刷现象
识别
模式
stormwater runoff
first flush effect
identification
mode
