Modelling heavy metals build-up on urban road surfaces for effective stormwater reuse strategy implementation
Modelling heavy metals build-up on urban road surfaces for effective stormwater reuse strategy implementation
复制标题
对城市道路表面的重金属堆积进行建模，以有效实施雨水再利用策略
DOI：
10.1016/j.envpol.2017.08.056
复制
发表时间：
2017
影响因子：
8.9
通讯作者：
An Liu
中科院分区：
文献类型：
--
作者：
Nian Hong;Panfeng Zhu;An Liu
城市道路雨洪是世界范围内缓解水资源短缺的一种替代水源。重金属在城市道路表面的沉积（积聚）会进入道路雨水径流，破坏雨水回用的安全性。由于重金属累积负荷在空间分布方面表现出很高的变异性，并受到周围土地利用的强烈影响，因此必须制定一种方法来确定暴雨径流可能包括高重金属浓度的热点，因此如果不进行适当的处理，就不能再利用。本研究开发了一个强大的建模方法来估计城市道路上使用土地利用分数（代表给定区域内的土地利用百分比）的人工神经网络（ANN）模型技术的重金属累积负荷。根据模拟结果，生成了一系列重金属负荷空间分布图和综合生态风险图。这些地图提供了一个可视化平台，以确定雨水可以安全再利用的优先区域。此外，这些地图可以作为城市土地利用规划工具，在有效的雨水再利用战略的实施。
Urban road stormwater is an alternative water resource to mitigate water shortage issues in the worldwide. Heavy metals deposited (build-up) on urban road surface can enter road stormwater runoff, undermining stormwater reuse safety. As heavy metal build-up loads perform high variabilities in terms of spatial distribution and is strongly influenced by surrounding land uses, it is essential to develop an approach to identify hot-spots where stormwater runoff could include high heavy metal concentrations and hence cannot be reused if it is not properly treated. This study developed a robust modelling approach to estimating heavy metal build-up loads on urban roads using land use fractions (representing percentages of land uses within a given area) by an artificial neural network (ANN) model technique. Based on the modelling results, a series of heavy metal load spatial distribution maps and a comprehensive ecological risk map were generated. These maps provided a visualization platform to identify priority areas where the stormwater can be safely reused. Additionally, these maps can be utilized as an urban land use planning tool in the context of effective stormwater reuse strategy implementation.