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Water quality prediction of marine recreational beaches receiving watershed baseflow and stormwater runoff in southern California, USA

机译:美国南部加利福尼亚接受分水岭底流和雨水径流的海洋休闲海滩的水质预测

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Beach advisories are issued to the public in California when the concentration of fecal indicator bacteria (FIB), including total coliform, fecal coliform (or Escherichia coli), and Enterococcus, exceed their recreational water health standards, or when the amount of a rainfall event is above the pre-determined threshold. However, it is not fully understood about how and to what degree stormwater runoff or baseflow exerts impacts on beach water quality. Furthermore, current laboratory methods used to determine the FIB levels take 18-96 h, which is too slow to keep pace with changes in FIB levels in water. Thus, a beach may not be posted when it is contaminated, and may be posted under advisory when bacterial levels have already decreased to within water quality standards. The study was designed to address the above critical issues. There were large temporal and spatial variations in FIB concentrations along two popular State Beaches in San Diego, CA, USA. The rainstorm-induced runoff from the watersheds exerts significant impacts on the marine recreational water quality of the beaches adjacent to lagoons during the first 24-48 h after a rain event. The large volume of stormwater runoff discharging to beaches caused high FIB concentrations in beach water not only at the lagoon outlet channel and the mixing zone, but also at the locations 90 m away from the channel northward or southward along the shoreline. The geomorphology of beach shoreline, distance from the outlet channel, wind strength, wind direction, tide height, wave height, rainfall, time lapse after a rainstorm, or channel flow rate played a role in affecting the distribution of FIB concentrations in beach water. Despite the great temporal and spatial variability of FIB concentrations along a shoreline, the artificial neural network-based models developed in this study are capable of successfully predicting FIB concentrations at different beaches, different locations, and different times under baseflow or rainstorm conditions. The models are based on readily measurable variables including temperature, conductivity, pH, turbidity, channel water flow, rainfall, and/or time lapse after a rainstorm. The established models will help fill the current gap between beach posting and actual water quality and make more meaningful and effective decisions on beach closures and advisories.
机译:当包括总大肠菌群,粪大肠菌群(或大肠杆菌)和肠球菌在内的粪便指示菌(FIB)的浓度超过其娱乐用水卫生标准时,或当发生降雨事件时,将向加利福尼亚发布海滩咨询高于预定阈值。但是,关于雨水径流或底流如何以及在多大程度上对海滩水质产生影响尚不完全清楚。此外,当前用于确定FIB含量的实验室方法需要18-96 h,这太慢了,无法跟上水中FIB含量的变化。因此,被污染的海滩可能不会被张贴,而当细菌水平已经降低到水质标准以内时,可以在建议下被张贴。该研究旨在解决上述关键问题。在美国加利福尼亚州圣地亚哥的两个受欢迎的州立海滩上,FIB浓度存在较大的时空变化。在降雨事件发生后的头24至48小时内,暴雨引起的流域径流对邻近泻湖的海滩的海洋娱乐水质量产生了重大影响。大量的雨水径流排入海滩,不仅在泻湖出口河道和混合区,而且在沿河道向北或向南距河道90m的位置,都使海滩水中的FIB浓度很高。泳滩海岸线的地貌,距出水道的距离,风强度,风向,潮高,浪高,降雨,暴雨后的时间流逝或泳道流速对泳滩水中FIB浓度的分布有影响。尽管沿海岸线的FIB浓度存在很大的时空变化,但本研究开发的基于人工神经网络的模型仍能够成功预测基流或暴雨条件下不同海滩,不同地点和不同时间的FIB浓度。这些模型基于易于测量的变量,包括温度,电导率,pH,浊度,河道水流量,降雨量和/或暴雨后的时间流逝。既定的模型将有助于填补目前在海滩张贴和实际水质之间的差距,并对海滩关闭和咨询做出更有意义和有效的决策。

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