中文关键词城市面源不透水地表粗糙度街尘降雨径流粒径分布 英文关键词urban non-point sourceimpervious surfaceroughnessstreet dustrainfall runoffparticle size distribution

作者	单位	E-mail
单溪环	青岛大学环境科学与工程学院, 青岛 266071 中国科学院生态环境研究中心城市与区域生态国家重点实验室, 北京 100085	15689989376@163.com
谢文霞	青岛大学环境科学与工程学院, 青岛 266071	xwx080312@163.com
廖云杰	中国科学院生态环境研究中心城市与区域生态国家重点实验室, 北京 100085
房志达	中国科学院生态环境研究中心城市与区域生态国家重点实验室, 北京 100085
杨晓晶	中国科学院生态环境研究中心城市与区域生态国家重点实验室, 北京 100085 兰州交通大学环境与市政工程学院, 兰州 730070
苏静君	中国科学院生态环境研究中心城市与区域生态国家重点实验室, 北京 100085
赵洪涛	中国科学院生态环境研究中心城市与区域生态国家重点实验室, 北京 100085
李叙勇	中国科学院生态环境研究中心城市与区域生态国家重点实验室, 北京 100085

中文摘要 目前国内外关于不透水地表微观结构特征如何影响街尘对径流输出过程的研究鲜见报道.本研究基于12场降雨事件的野外观测，以粗糙度（构造深）量化不透水地表微观结构特征，分析粗糙度与晴天街尘的累积特征及雨天冲刷特征之间的相关关系.结果表明，不透水地表粗糙度显著影响街尘的晴天累积-雨天冲刷过程，晴天累积天数对粗糙度与街尘累积量的相关性（r=0.664，P<0.01）具有增强效应，降雨量对粗糙度与街尘冲刷量的相关性（r=0.527，P<0.01）具有增强效应；各粒径段街尘累积量与粗糙度的相关性（0.529≤ r<0.757）随颗粒物粒径变大而提高，各粒径段街尘冲刷量与粗糙度的相关性（0.603 > r > 0.209）随颗粒物粒径变大而降低.通过建立粗糙度和降雨量的线性回归模型可以较好地预测场降雨径流中TSS累积污染负荷.粗糙度和降雨量对<20 μm以及>250 μm粒径段的累积负荷作用效果显著.上述结果揭示了粗糙度和降雨量对街尘输出为地表径流污染物的作用，为准确模拟城市面源颗粒污染物的径流冲刷过程提供了科学依据. 英文摘要 At present, there are few reports about how impervious surface microstructure characteristics affect the runoff output process of street dust. Based on field observations of 12 rainfall events, this study quantified the microstructure characteristics of impervious surfaces by structural depth (roughness) and analyzed the correlation between roughness and accumulation characteristics of street dust on sunny days as well as scouring characteristics in rainy days. The results show that the roughness of the underlying surface notably affects dust accumulation on sunny days and scouring in rainy days. The correlation between roughness and street dust accumulation (r=0.664, P<0.01) was enhanced on sunny days, and the correlation between roughness and street dust erosion (r=0.527, P<0.01) was enhanced by rainfall. The correlation of street dust accumulation and roughness of each particle size segment increased as particle size increased (0.529 ≤ r<0.757), and the correlation between street dust scouring amount and roughness decreased as particle size increased (0.603 > R > 0.209). By establishing the linear regression model of roughness and rainfall, the cumulative pollution load of TSS in rainfall runoff can be well predicted. The effects of roughness and rainfall on the cumulative load of grain sizes<20 μm and >250 μm are significant. These results elucidate the role of roughness and rainfall analysis in predicting surface runoff pollution load characteristics, which can provide new information for predicting and evaluating urban non-point source pollution.

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