The application of permeable pavements has been promoted to reduce pressures on traditional stormwater management systems and enhance urban water. However, the performance of permeable pavement under cold climate context is still uncertain. This thesis focused on assessing the hydraulic and water quality performance of permeable pavements based on field and laboratory experiments and developing a modeling approach for assisting engineering design of permeable pavements.
In a series of field experiments, simulated 100-year storm events with durations of 20 minutes were applied to the pavement surfaces in order to examine and compare the hydraulic and environmental performance of the three permeable pavement types under cold climate conditions. Results demonstrated that PA, PC and PICP are all effective in mitigating storm runoff under cold climate conditions. All pavement types in general have the same level of performance in removing TSS, TP, TN, and heavy metals.
A series of laboratory experiments were designed to assess the ability of the three pavement types to remove TSS, TP and TN within their surface and sub-surface layers individually. PA, PC and PICP with sub-surface layers consisting of different gravel sizes were investigated at various thicknesses. The lab-scale pavements were also compared with the field-scale pavements in terms of pollutant removal. Superior performance in removing pollutants was found in the PC surface layer compared to surface layers of PA and PICP. A regression model based on these results was developed to provide estimates of water quality performance in the field.
A mathematical model for predicting hydraulic and water quality performance in both the short- and long-term is proposed based on field measurements for the three types of permeable pavements. The proposed model can simulate the outflow hydrographs with a coefficient of determination (R2) ranging from 0.762 to 0.907, and normalized root-mean-square deviation (NRMSD) ranging from 13.78% to 17.83%. Comparison of the time to peak flow, peak flow, runoff volume and TSS removal rates between the measured and modeled values in model validation phase had a maximum difference of 11%.