Prediction of the wash-off of traffic related semi- and non-volatile organic compounds from urban roads under climate change influenced rainfall characteristics
Highlights
► A robust tool based on orthogonal rotatable central composite design and factor analysis has been presented. ► The wash-off of traffic generated SVOCs and NVOCs from urban roads is primarily caused by the intensity and durations of low to moderate rain events. ► The model is capable to predict concentrations of seventeen SVOCs and NVOCs in particulate fractions >300–1 μm.
Introduction
Traffic related semi- and non-volatile organic compounds (SVOCs and NVOCs) are primarily associated with diesel fuels, fuel oils, heavier engine oils and lubricants [1]. Homologous series of n-alkanes from decane to tetracontane are amongst the most common constituents of these products, which are widely used in motor vehicles, and have the potential to pollute the urban water environment through deposition and wash-off from urban roads [2]. Rainfall characteristics such as, intensity, duration and frequency or average recurrence intervals (ARIs) are predicted to undergo significant changes as a result of climate change. In this context, the Commonwealth Scientific and Industrial Research Organisation (CSIRO) has forecasted longer periods of dry weather with fewer, but more intense storms in Australia due to climate change [3]. Such climate change driven changes in the rainfall characteristics will affect the wash-off processes of various stormwater pollutants including the SVOCs and NVOCs.
The detrimental effects of SVOCs and NVOCs on human health have been widely reported in research literature. Mutagenic evidence in mammalian cells caused by diesel engine exhaust particles has been cited by Bao et al. [4]. Morgan et al. [5] attributed the long term exposure to diesel engine exhaust particles to respiratory allergy, cardiopulmonary mortality and risk of lung cancer. Petroleum related activities have also been attributed to significant wetland loss in the Mississippi Delta [6]. While studies on the impacts of traffic generated volatile organic compounds such as BTEXs (benzene, toluene, ethylbenzene and xylene) in urban roads [7] and ambient atmosphere have commonly been undertaken [8], [9], such pollutants have only been characterised in terms of concentrations and modelled for the ambient atmosphere [10]. However, it is important to note that pollutants present in the urban atmosphere are not necessarily deposited on the urban roads due to various climatic factors. Therefore, compartment-based multimedia models (e.g. separate wash-off models from pervious and impervious surfaces) are particularly suitable in rationalising the differences in environmental fate and transport of pollutants in a defined environment [11]. Nevertheless, the wash-off phenomenon from urban roads becomes complex when the changed rainfall characteristics due to climate change affect the wash-off processes of such pollutants. In this context, the current state of knowledge on traffic generated semi- and non-volatile organic compounds (SVOCs and NVOCs) available on roads for wash-off is very limited.
Mahbub et al. [12] recently proposed a prediction model for the build-up of five traffic generated volatile organic compounds on urban roads. However, their model did not investigate the uncertainties involved in the wash-off of the wide range of traffic generated pollutants from roads under climate change driven changes to rainfall characteristics. Accurate estimations of the concentrations of available SVOCs and NVOCs on roads in wash-off under climate change are required in order to undertake mitigation measures for the management of such pollutants in stormwater runoff. Accordingly, this research study presents a framework for predicting the concentrations of traffic generated SVOCs and NVOCs in wash-off under climate change influenced rainfall characteristics. This approach is expected to contribute to overcome the uncertainties inherent in the wash-off estimation of traffic generated SVOCs and NVOCs by predicting these pollutants based on the significance of individual predictors and consequently, strengthening the appropriate measures for pollution mitigation.
Section snippets
Site selection
Four road sites within a 5 km radius from a meteorological gauging station were selected as the wash-off study sites. The station was located at 27.90°S and 153.31°E at an elevation of 6 m above mean sea level with daily rainfall data recorded since 1894. The selected road sites were situated in three relatively new suburbs in the Gold Coast region, Australia with the transport infrastructure developed in the last decade. The sites were in different land uses such as residential, commercial and
Exploratory principal component analysis
Wash-off data matrices for light SVOCs, heavy SVOCs and NVOCs were analysed for all five size fractions. Fig. 1 shows the PCA biplots for total particulate (<300–1 μm) and dissolved fractions (<1 μm). This study adopted the rain events classification under climate change proposed by Mahbub et al. [25]. Events with intensity <40 mm/h with relatively low ARI were classified as low events; those having intensity between 50 and 100 mm/h but with relatively higher ARIs of up to 50 years were classified
Conclusions
This research study established a prediction framework for SVOCs and NVOCs under climate change induced rainfall scenario and presented a useful tool for estimating the concentrations of these pollutants under a dynamic situation. The study found that the intensity and durations of low to moderate rain events mainly affect the wash-off of semi- and non-volatile organic compounds from urban roads. The study also proposed that the extraction of the underlying uncorrelated factors within the data
Acknowledgements
This study was undertaken as a part of an Australian Research Council funded Linkage Project (LP0882637). The first author gratefully acknowledges the postgraduate scholarship awarded by Queensland University of Technology to conduct his doctoral research. Support from the Gold Coast City Council and Queensland Department of Transport and Main Roads are also gratefully acknowledged.
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