Bioswale column experiments and simulation of pollutant removal from urban road stormwater runoff

被引:4
作者
Li, Jiake [1 ]
Li, Ya [1 ]
Zhang, Jiayang [1 ]
Li, Yajiao [2 ]
机构
[1] Xian Univ Technol, State Key Lab Base Ecohydraul Engn Arid Area, Xian 710048, Peoples R China
[2] Xian Univ Sci & Technol, Sch Architecture & Civil Engn, Xian 710054, Peoples R China
基金
中国国家自然科学基金;
关键词
Road runoff pollution; Bioretention; Multiple linear regression; Partial least squares; Pollutant removal; FIELD PERFORMANCE; BIORETENTION; HYDROLOGY; RETENTION;
D O I
10.1080/19443994.2016.1151833
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
The concomitant flux of pollutants carried by urban storm water runoff to the surface of water bodies causes a series of environmental issues. Bioswale, which is an effective purification measure of urban surface runoff pollutants, was investigated to determine the relationship between bioswale composition and purification. Results indicate that the pollutant removal rate of bioswales decreases with an increase in influent concentration. Various selected vegetation combinations showed no significant difference in terms of pollutant removal in two months after being planted within a single rainfall. Increasing the thickness of the planting soil and artificial filler layers can improve the contaminant removal rate. Various artificial filler compositions were proven to have different effects on pollutant purification. Planting soil and a mixture of coal ash and sand were the best choice, whereas sand was the worst. The total nitrogen (TN) removal rate increased with submerged zone height, whereas the removal of chemical oxygen demand (COD) and total phosphorus (TP) showed no significant change. The addition of an external carbon source increased TN concentration removal and decreased COD concentration removal without significantly affecting TP. The curve analysis of the pollutant removal rate and various factors was established by processing the statistical results through the SPSS software. The equation of linear regression, which reveals the relationship between the control effect and its influencing factors, was obtained based on the partial least squares model.
引用
收藏
页码:24894 / 24912
页数:19
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