BITS Faculty Publications
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Item Mathematical interpretation of pollutant wash-off from urban road surfaces using simulated rainfall(Elsevier, 2007-07) Goonetilleke, AshanthaIn the context of stormwater quality modelling, an in-depth understanding of underlying physical processes and the availability of reliable and accurate mathematical equations, which can replicate pollutant processes are essential. Stormwater pollutants undergo three primary processes, namely, build-up, wash-off and transport, before accumulating into receiving waters. These processes are expressed mathematically by equations in stormwater quality models. Among the three processes, wash-off is the least investigated. This paper presents the outcomes of an in-depth investigation of pollutant wash-off processes on typical urban road surfaces. The study results showed that a storm event has the capacity to wash-off only a fraction of pollutants available and this fraction varies primarily with rainfall intensity, kinetic energy of rainfall and characteristics of the pollutants. These outcomes suggest that the exponential equation commonly used for mathematically defining pollutant wash-off would need to be modified in order to incorporate the wash-off capacity of rainfall. Consequently, the introduction of an additional term referred to as the ‘capacity factor’ CF is recommended. CF primarily varies with rainfall intensity. However, for simplicity three rainfall intensity ranges were identified where the variation of CF can be defined. For rainfall intensities less than 40 mm/h, CF varies linearly from 0 to 0.5. For rainfall intensities from 40 to around 90 mm/h, CF is a constant around 0.5. Beyond 90 mm/h, CF varies between 0.5 and 1.Item Understanding road surface pollutant wash-off and underlying physical processes using simulated rainfall(IWA Publishing, 2008-04) Goonetilleke, AshanthaPollutant wash-off is one of the key pollutant processes that detailed knowledge is required in order to develop successful treatment design strategies for urban stormwater. Unfortunately, current knowledge relating to pollutant wash-off is limited. This paper presents the outcomes of a detailed investigation into pollutant wash-off on residential road surfaces. The investigations consisted of research methodologies formulated to overcome the physical constraints due to the heterogeneity of urban paved surfaces and the dependency on naturally occurring rainfall. This entailed the use of small road surface plots and artificially simulated rainfall. Road surfaces were selected due to its critical importance as an urban stormwater pollutant source. The study results showed that the influence of initially available pollutants on the wash-off process was limited. Furthermore, pollutant wash-off from road surfaces can be replicated using an exponential equation. However, the typical version of the exponential wash-off equation needs to be modified by introducing a non dimensional factor referred to as ‘capacity factor' CF. Three rainfall intensity ranges were identified where the variation of CF can be defined. Furthermore, it was found that particulate density rather than size is the critical parameter that influences the process of pollutant wash-off.Item Evaluation of pollutant build-up and wash-off from selected land uses at the Port of Brisbane, Australia(Elsevier, 2009-02) Goonetilleke, AshanthaThe quality of stormwater runoff from seaports can be an important source of pollution to the marine environment. Currently, little knowledge exists with regards to the pollutant generation capacity specific to seaports as they do not necessarily compare well with conventional urban land use. The research project focussed on the assessment of pollutant build-up and wash-off. The study was undertaken using rainfall simulation and small impervious plots for different port land uses with the results obtained compared to typical urban land uses. The study outcomes confirmed that the Port land uses exhibit comparatively lower pollutant concentrations. However, the pollutant characteristics varied across different land uses. Hence, the provision of stereotypical water quality improvement measures could be of limited value. Particle size <150 μm was predominant in suspended solids. Therefore, if suspended solids are targeted as the surrogate parameter for water quality improvement, this particle size range needs to be removed.Item Distribution of polycyclic aromatic hydrocarbons in urban stormwater in Queensland, Australia(Elsevier, 2010-09) Goonetilleke, AshanthaThis 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 μm), and three particulate fractions (0.45–75 μm, 75–150 μm and >150 μm). 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.Item Effects of climate change on the wash-off of volatile organic compounds from urban roads(Elsevier, 2011-09) Goonetilleke, AshanthaThe predicted changes in rainfall characteristics due to climate change could adversely affect stormwater quality in highly urbanised coastal areas throughout the world. This in turn will exert a significant influence on the discharge of pollutants to estuarine and marine waters. Hence, an in-depth analysis of the effects of such changes on the wash-off of volatile organic compounds (VOCs) from urban roads in the Gold Coast region in Australia was undertaken. The rainfall characteristics were simulated using a rainfall simulator. Principal Component Analysis (PCA) and Multicriteria Decision tools such as PROMETHEE and GAIA were employed to understand the VOC wash-off under climate change. It was found that low, low to moderate and high rain events due to climate change will affect the wash-off of toluene, ethylbenzene, meta-xylene, para-xylene and ortho-xylene from urban roads in Gold Coast. Total organic carbon (TOC) was identified as predominant carrier of toluene, meta-xylene and para-xylene in < 1 μm to 150 μm fractions and for ethylbenzene in 150 μm to > 300 μm fractions under such dominant rain events due to climate change. However, ortho-xylene did not show such affinity towards either TOC or TSS (total suspended solids) under the simulated climatic conditions.Item Prediction of the wash-off of traffic related semi- and non-volatile organic compounds from urban roads under climate change influenced rainfall characteristics(Elsevier, 2012-04) Goonetilleke, AshanthaTraffic generated semi- and non-volatile organic compounds (SVOCs and NVOCs) pose a serious threat to human and ecosystem health when washed off into receiving water bodies by stormwater. Climate change influenced rainfall characteristics makes the estimation of these pollutants in stormwater quite complex. The research study discussed in the paper developed a prediction framework for such pollutants under the dynamic influence of climate change on rainfall characteristics. It was established through principal component analysis (PCA) that the intensity and durations of low to moderate rain events induced by climate change mainly affect the wash-off of SVOCs and NVOCs from urban roads. The study outcomes were able to overcome the limitations of stringent laboratory preparation of calibration matrices by extracting uncorrelated underlying factors in the data matrices through systematic application of PCA and factor analysis (FA). Based on the initial findings from PCA and FA, the framework incorporated orthogonal rotatable central composite experimental design to set up calibration matrices and partial least square regression to identify significant variables in predicting the target SVOCs and NVOCs in four particulate fractions ranging from >300 to 1 μm and one dissolved fraction of <1 μm. For the particulate fractions in >300–1 μm range, similar distributions of predicted and observed concentrations of the target compounds from minimum to 75th percentile were achieved. The inter-event coefficient of variations for particulate fractions of >300–1 μm was 5–25%. The limited solubility of the target compounds in stormwater restricted the predictive capacity of the proposed method for the dissolved fraction of <1 μm.Item Time as the critical factor in the investigation of the relationship between pollutant wash-off and rainfall characteristics(Elsevier, 2014-03) Goonetilleke, AshanthaThe approach adopted for investigating the relationship between rainfall characteristics and pollutant wash-off process is commonly based on the use of parameters which represent the entire rainfall event. This does not permit the investigation of the influence of rainfall characteristics on different sectors of the wash-off process such as first flush where there is a high pollutant wash-off load at the initial stage of the runoff event. This research study analysed the influence of rainfall characteristics on the pollutant wash-off process using two sets of innovative parameters by partitioning wash-off and rainfall characteristics. It was found that the initial 10% of the wash-off process is closely linked to runoff volume related rainfall parameters including rainfall depth and rainfall duration while the remaining part of the wash-off process is primarily influenced by kinetic energy related rainfall parameters, namely, rainfall intensity. These outcomes prove that different sectors of the wash-off process are influenced by different segments of a rainfall event.Item Sectional analysis of the pollutant wash-off process based on runoff hydrograph(Elsevier, 2014-03) Goonetilleke, AshanthaThe validity of using rainfall characteristics as lumped parameters for investigating the pollutant wash-off process such as first flush occurrence is questionable. This research study introduces an innovative concept of using sector parameters to investigate the relationship between the pollutant wash-off process and different sectors of the runoff hydrograph and rainfall hyetograph. The research outcomes indicated that rainfall depth and rainfall intensity are two key rainfall characteristics which influence the wash-off process compared to the antecedent dry period. Additionally, the rainfall pattern also plays a critical role in the wash-off process and is independent of the catchment characteristics. The knowledge created through this research study provides the ability to select appropriate rainfall events for stormwater quality treatment design based on the required treatment outcomes such as the need to target different sectors of the runoff hydrograph or pollutant species. The study outcomes can also contribute to enhancing stormwater quality modelling and prediction in view of the fact that conventional approaches to stormwater quality estimation is primarily based on rainfall intensity rather than considering other rainfall parameters or solely based on stochastic approaches irrespective of the characteristics of the rainfall event.Item A Bayesian regression approach to assess uncertainty in pollutant wash-off modelling(Elsevier, 2014-05) Goonetilleke, AshanthaDue to knowledge gaps in relation to urban stormwater quality processes, an in-depth understanding of model uncertainty can enhance decision making. Uncertainty in stormwater quality models can originate from a range of sources such as the complexity of urban rainfall–runoff-stormwater pollutant processes and the paucity of observed data. Unfortunately, studies relating to epistemic uncertainty, which arises from the simplification of reality are limited and often deemed mostly unquantifiable. This paper presents a statistical modelling framework for ascertaining epistemic uncertainty associated with pollutant wash-off under a regression modelling paradigm using Ordinary Least Squares Regression (OLSR) and Weighted Least Squares Regression (WLSR) methods with a Bayesian/Gibbs sampling statistical approach. The study results confirmed that WLSR assuming probability distributed data provides more realistic uncertainty estimates of the observed and predicted wash-off values compared to OLSR modelling. It was also noted that the Bayesian/Gibbs sampling approach is superior compared to the most commonly adopted classical statistical and deterministic approaches commonly used in water quality modelling. The study outcomes confirmed that the predication error associated with wash-off replication is relatively higher due to limited data availability. The uncertainty analysis also highlighted the variability of the wash-off modelling coefficient k as a function of complex physical processes, which is primarily influenced by surface characteristics and rainfall intensity.Item Assessing uncertainty in pollutant wash-off modelling via model validation(Elsevier, 2014-11) Goonetilleke, AshanthaStormwater pollution is linked to stream ecosystem degradation. In predicting stormwater pollution, various types of modelling techniques are adopted. The accuracy of predictions provided by these models depends on the data quality, appropriate estimation of model parameters, and the validation undertaken. It is well understood that available water quality datasets in urban areas span only relatively short time scales unlike water quantity data, which limits the applicability of the developed models in engineering and ecological assessment of urban waterways. This paper presents the application of leave-one-out (LOO) and Monte Carlo cross validation (MCCV) procedures in a Monte Carlo framework for the validation and estimation of uncertainty associated with pollutant wash-off when models are developed using a limited dataset. It was found that the application of MCCV is likely to result in a more realistic measure of model coefficients than LOO. Most importantly, MCCV and LOO were found to be effective in model validation when dealing with a small sample size which hinders detailed model validation and can undermine the effectiveness of stormwater quality management strategies.