Abstract

Green roofs are recognized as effective means of stormwater quantity control through runoff volume reduction and peak discharge attenuation. A properly designed and constructed green roof can improve stormwater quality by reducing the pollutant loads to receiving waters. In this study, with onsite data monitored at the rooftop, green roof stormwater was analyzed from the perspectives of both runoff quantity and runoff quality in an attempt to interpret the basic relationship between rainfall and runoff, and to characterize pollutant loads along with event mean concentrations (EMCs). From correlation analysis of a total of twelve major pollutants found in green roof runoff, it is seen that the correlation between the loads of two pollutants appears to be stronger than the correlation between the EMCs of the two pollutants. As one of the common pollutants is suspended solids, the correlations between suspended solids and other pollutants were evaluated for the possibility of using the loads of suspended solids as a surrogate to predict other pollutant loads. Further statistical analysis of the cumulative density functions (CDFs) of pollutant loads reveals that the log-normal distribution appears to fit the observed data reasonably well when compared with the CDFs of normal and exponential distributions. The normal distribution tends to significantly overestimate the CDFs of pollutant loads. From the extrapolation of the exceedance probability of pollutant loads based on statistics of the sample data, it is seen that the log-Pearson distribution is capable of providing estimates of pollutant loads which are close to the estimates from the log-normal distribution for a given return period. In comparison, the Pearson distribution may significantly underestimate pollutant loads with reference to the estimates of the log-normal or log Pearson distributions.

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