Urban stormwater from simulated rainfall on three different landuses in Queensland State, Australia (residential, industrial, commercial) was analysed for heavy metals and physico-chemical parameters such as Dissolved Organic Carbon (DOC) and Total Suspended Solids (TSS). Rainfall events were simulated using a specially designed rainfall simulator for paved surfaces. Event mean concentration samples were separated into five different particle sizes and analysed individually for eight metal elements (Zn, Fe, Cr, Cd, Cu, Al, Mn and Pb). Multivariate data analysis was carried out for the data thus generated. It was found that DOC and TSS influence the distribution of the metals in the different particle size classes. Zn was correlated with DOC at all three sites. Similarly, Pb, Fe and Al were correlated with TSS at all sites. The distribution of Cu was found to vary between the three sites, whilst Cd concentrations were too low to assess any relationships with other parameters. No correlation between Electrical Conductivity (EC), pH and heavy metals was found at the three sites. The identification of physico-chemical parameters influencing the distribution process kinetics of heavy metals in urban stormwater will significantly enhance the efficiency of urban stormwater management systems.

Road-deposited sediments were analysed for heavy metal concentrations at three different landuses (residential, industrial, commercial) in Queensland State, Australia.The sediments were collected using a domestic vacuum cleaner which was proven to be highly efficient in collecting sub-micron particles. Five particle sizes were analysed separately for eight heavy metal elements (Zn, Fe, Pb, Cd, Cu, Cr, Al and Mn). At all sites, the maximum concentration of the heavy metals occurred in the 0.45-75 m particle size range, which conventional street cleaning services do not remove efficiently. Multicriteria decision making methods (MCDM), PROMETHEE and GAIA, were employed in the data analysis. PROMETHEE, a non-parametric ranking analysis procedure, was used to rank the metal contents of the sediments sampled at each site. The most polluted site and particle size range were the industrial site and the 0.45-75 m range respectively. Although the industrial site displayed the highest metal concentrations, the highest heavy metal loading coincided with the highest sediment load, which occurred at the commercial site. GAIA, a special form of Principal Component Analysis, was applied to determine correlations between the heavy metals and particle size ranges and also to assess possible correlation with Total Organic Carbon (TOC). The GAIA-planes revealed that irrespective of the site, most of the heavy metals are adsorbed to sediments below 150 m. A weak correlation was found between Zn, Mn and TOC at the commercial site. This could lead to higher bioavailability of these metals through complexation reactions with the organic species in the sediments.

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This paper reports the distribution of Polycyclic Aromatic Hydrocarbons (PAHs) in wash-off in urban stormwater in Gold Coast, Australia. Runoff samples collected from residential, industrial and commercial sites were separated into a dissolved fraction (<0.45 microm), and three particulate fractions (0.45-75 microm, 75-150 microm and >150 microm). Patterns in the distribution of PAHs in the fractions were investigated using Principal Component Analysis. Regardless of the land use and particle size fraction characteristics, the presence of organic carbon plays a dominant role in the distribution of PAHs. The PAHs concentrations were also found to decrease with rainfall duration. Generally, the 1- and 2-year average recurrence interval rainfall events were associated with the majority of the PAHs and the wash-off was a source limiting process. In the context of stormwater quality mitigation, targeting the initial part of the rainfall event is the most effective treatment strategy. The implications of the study results for urban stormwater quality management are also discussed.

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As the concept of sustainable communities is gaining increasing recognition around the world it is of critical importance to investigate the water quality of urban environments. The contamination of waterways in urban communities seriously affects the utility of water for different purposes and degrades the aesthetic value of natural watercourses. Research investigations in the past have generally focused on suspended solids and nutrients, which are relatively easy to monitor. Unfortunately the build-up and wash-off of micro pollutants such as polycyclic aromatic hydrocarbons (PAH) and heavy metals (HM) have received limited research interest in urban water quality research even though these can cause significant health and environmental impacts even at low concentrations. This paper describes how artificial rainfall, using a specially designed highly portable rainfall simulator was employed in order to generate water quality data from urban environments. This approach was adopted in order to investigate the wash-off of pollutants from paved surfaces and to overcome constraints due to the highly unreliable rainfall in South-East Queensland Australia. The rainfall simulator was able to demonstrate its ability to satisfactory simulate natural rainfall in the area. The results obtained confirmed that the rainfall simulator is a reliable tool for urban water quality research and can be used to simulate pollutant wash-off.