Hong Nian, Zhu Panfeng, Liu An
College of Chemistry and Environmental Engineering, Shenzhen University, 518060 Shenzhen, China; Shenzhen Key Laboratory of Environmental Chemistry and Ecological Remediation, 518060 Shenzhen, China.
College of Chemistry and Environmental Engineering, Shenzhen University, 518060 Shenzhen, China.
Environ Pollut. 2017 Dec;231(Pt 1):821-828. doi: 10.1016/j.envpol.2017.08.056. Epub 2017 Sep 25.
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.
城市道路雨水是缓解全球水资源短缺问题的一种替代水源。沉积在城市道路表面的重金属会进入道路雨水径流，危及雨水回用安全。由于重金属累积负荷在空间分布上具有高度变异性，且受周边土地利用的强烈影响，因此开发一种方法来识别热点区域至关重要，在这些热点区域，雨水径流可能含有高浓度重金属，若未经适当处理则无法回用。本研究开发了一种稳健的建模方法，通过人工神经网络（ANN）模型技术，利用土地利用比例（代表给定区域内土地利用的百分比）来估算城市道路上的重金属累积负荷。基于建模结果，生成了一系列重金属负荷空间分布图和一张综合生态风险图。这些地图提供了一个可视化平台，以识别雨水可安全回用的优先区域。此外，在有效实施雨水回用策略的背景下，这些地图可作为城市土地利用规划工具。
