Abstract

Among the urban aquatic pollutants, the most common is sediment which also acts as a transport medium for many contaminants. Hence there is an increasing interest in being able to better predict the sediment wash–off from urban surfaces. The exponential wash-off model is the most widely used method to predict the sediment wash-off. Although a number of studies proposed various modifications to the original exponential wash-off equation, these studies mostly looked into one parameter in isolation thereby ignoring the interactions between the parameters corresponding to rainfall, catchment and sediment characteristics. Hence in this study we aim (a) to investigate the effect of rainfall intensity, surface slope and initial load on wash-off load in an integrated and systematic way and (b) to subsequently improve the exponential wash-off equation focusing on the effect of the aforementioned three parameters. A series of laboratory experiments were carried out in a full-scale setup, comprising of a rainfall simulator, a 1 m2 bituminous road surface, and a continuous wash-off measuring system. Five rainfall intensities ranging from 33 to 155 mm/h, four slopes ranging from 2 to 16% and three initial loads ranging from 50 to 200 g/m2 were selected based on values obtained from the literature. Fine sediment with a size range of 300–600 µm was used for all of the tests. Each test was carried out for one hour with at least 9 wash-off samples per test collected. Mass balance checks were carried out for all the tests as a quality control measure to make sure that there is no significant loss of sand during the tests. Results show that the washed off sediment load at any given time is proportional to initial load for a given combination of rainfall intensity and surface slope. This indicates the importance of dedicated modelling of build-up so as to subsequently predict wash-off load. It was also observed that the maximum fraction that is washed off from the surface increases with both rainfall intensity and the surface slope. This observation leads to the second part of the study where the existing wash-off model is modified by introducing a capacity factor which defines this maximum fraction. This capacity factor is derived as a function of wash-off coefficient, making use of the correlation between the maximum fraction and the wash-off rate. Values of the modified wash-off coefficient are presented for all combinations of rainfall intensities and surface slopes, which can be transferred to other urban catchments with similar conditions.

Highlights

  • Pollutant wash-off is the process by which non-point source pollutants including sediment, nutrients, bacteria, oil, metals and chemicals are removed from urban surfaces by the action of rainfall and runoff

  • In these cases there is an increasing pattern of values of Fw with increasing initial load. These combinations of high rainfall intensity and steep slope where the initial load has an impact on Fw are very rare in reality (MetOffice UK, 2017; Manual for Streets, 2009). It implies that the effect of initial load on Fw is negligible for most general combinations of rainfall intensity and surface slope

  • This essentially means the actual mass of sediment washed off at any given time is proportional to initial load for a given rainfall intensity and surface slope

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Summary

Introduction

Pollutant wash-off is the process by which non-point source pollutants including sediment, nutrients, bacteria, oil, metals and chemicals are removed from urban surfaces by the action of rainfall and runoff. Considering the above gaps and room for improvements in sediment wash-off modelling, we designed and carried out a series of laboratory experiments to: Study the effect of three dominant parameters corresponding to rainfall, surface and sediment characteristics in an integrated and systematic way. These parameters are, rainfall intensity (i), surface slope and initial load (wo) respectively., and Improve Eq (1) using the experimental results focusing on the effect of the above three parameters

Experimental setup
Quality control
Experimental results
Model improvement
Conclusions

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