State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China; Science and Engineering Faculty, Queensland University of Technology (QUT), GPO Box 2434, Brisbane 4001, Queensland, Australia.
State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China.
Sci Total Environ. 2019 Feb 15;651(Pt 1):114-121. doi: 10.1016/j.scitotenv.2018.09.013. Epub 2018 Sep 3.
Accurate modelling of particulates build-up process is essential for designing effective stormwater management strategies. However, current modelling practice relies on the classical 'power model' which has limitations in accounting for the variability in the build-up process. This research study investigated the relationships between influential factors of the build-up process and coefficients in the power model. The outcomes showed that the coefficient, which determines the build-up rate, is predominantly influenced by land use factors (pervious area, road area, commercial area and residential area), such that land use factors exerted 23 times more influence than the site characteristics (distance to pervious area and road surface texture depth). The coefficient, which determines how quickly build-up reaches equilibrium, was found to be equally influenced by anthropogenic activities (sweeping frequency and traffic volume) and site characteristics. Further, site characteristics were found to play a major role in generating build-up process variability with three times more influence than that of anthropogenic activities. It was found that the power model satisfactorily replicates the build-up of particles <74 μm. For the build-up of particles >74 μm, a new coefficient, namely, 'coefficient of variability' was introduced in order to improve the prediction performance (up to 17% compared to original power model). The study outcomes provide a deeper understanding into particulates build-up modelling, and can contribute to the formulation of effective stormwater treatment strategies.
准确模拟颗粒物的积累过程对于设计有效的雨水管理策略至关重要。然而，当前的建模实践依赖于经典的“幂模型”，该模型在解释积累过程的可变性方面存在局限性。本研究调查了积累过程的影响因素与幂模型中的系数之间的关系。研究结果表明，决定积累速率的系数主要受土地利用因素（透水区、道路区、商业区和住宅区）的影响，土地利用因素的影响是场地特征（与透水区的距离和路面纹理深度）的 23 倍。决定颗粒物达到平衡速度的系数同样受到人为活动（清扫频率和交通量）和场地特征的影响。此外，场地特征在产生颗粒物积累过程的变异性方面起着重要作用，其影响是人为活动的三倍。研究发现，幂模型能够很好地模拟粒径<74μm 的颗粒物的积累。对于粒径>74μm 的颗粒物，引入了一个新的系数，即“变异系数”，以提高预测性能（与原始幂模型相比，提高了 17%）。研究结果深入了解了颗粒物的积累建模，并有助于制定有效的雨水处理策略。
