No abstract

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.

No abstract

Due to their carcinogenic effects, Polycyclic Aromatic Hydrocarbons (PAHs) deposited on urban surfaces are a major concern in the context of stormwater pollution. However, the design of effective pollution mitigation strategies is challenging due to the lack of reliability in stormwater quality modelling outcomes. Current modelling approaches do not adequately replicate the interdependencies between pollutant processes and their influential factors. Using Bayesian Network modelling, this research study characterised the influence of vehicular traffic on the build-up of the sixteen US EPA classified priority PAHs. The predictive analysis was conditional on the structure of the proposed BN, which can be further improved by including more variables. This novel modelling approach facilitated the characterisation of the influence of traffic as a source of origin and also as a key factor that influences the re-distribution of PAHs, with positive or negative relationship between traffic volume and PAH build-up. It was evident that the re-distribution of particle-bound PAHs is determined by the particle size rather than the chemical characteristics such as volatility. Moreover, compared to commercial and residential land uses, mostly industrial land use contributes to the PAHs load released to the environment. Carcinogenic PAHs in industrial areas are likely to be associated with finer particles, while PAHs, which are not classified as human carcinogens, are likely to be found in the coarser particle fraction.

The preparation of Ru-based single atom catalysts with SMSI effect.