http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/1867 2026-04-01T12:12:32Z http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/20835 Title: Influence of traffic and land use on urban stormwater quality Authors: Goonetilleke, Ashantha Abstract: This book presents a detailed analysis in relation to pollutant processes and transport pathways encompassing atmospheric pollutants, atmospheric deposition and build-up on road surfaces of traffic generated key pollutants. The research study undertaken by the authors created extensive knowledge relating to the relevant processes and establishing their relationships as a chain of processes. The information presented in this book was derived based on comprehensive experimental investigations including field sampling, laboratory testing, mathematical modelling and multivariate and univariate statistical data analyses. The knowledge presented will be of particular interest to readers such as stormwater treatment design specialists, decision-makers and urban planners since these outcomes provide practical suggestions and recommendations to effective urban stormwater treatment design. 2018-01-01T00:00:00Z http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/20834 Title: Use of surrogate indicators for the evaluation of potential health risks due to poor urban water quality: A Bayesian Network approach Authors: Goonetilleke, Ashantha Abstract: Urban water pollution poses risks of waterborne infectious diseases. Therefore, in order to improve urban liveability, effective pollution mitigation strategies are required underpinned by predictions generated using water quality models. However, the lack of reliability in current modelling practices detrimentally impacts planning and management decision making. This research study adopted a novel approach in the form of Bayesian Networks to model urban water quality to better investigate the factors that influence risks to human health. The application of Bayesian Networks was found to enhance the integration of quantitative and qualitative spatially distributed data for analysing the influence of environmental and anthropogenic factors using three surrogate indicators of human health risk, namely, turbidity, total nitrogen and fats/oils. Expert knowledge was found to be of critical importance in assessing the interdependent relationships between health risk indicators and influential factors. The spatial variability maps of health risk indicators developed enabled the initial identification of high risk areas in which flooding was found to be the most significant influential factor in relation to human health risk. Surprisingly, population density was found to be less significant in influencing health risk indicators. These high risk areas in turn can be subjected to more in-depth investigations instead of the entire region, saving time and resources. It was evident that decision making in relation to the design of pollution mitigation strategies needs to account for the impact of landscape characteristics on water quality, which can be related to risk to human health. 2018-02-01T00:00:00Z http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/20833 Title: Evaluating the relationship between temporal changes in land use and resulting water quality Authors: Goonetilleke, Ashantha Abstract: Changes in land use have a direct impact on receiving water quality. Effective mitigation strategies require the accurate prediction of water quality in order to enhance community well-being and ecosystem health. The research study employed Bayesian Network modelling to investigate the validity of using cross-sectional and longitudinal data on water quality and land use for predicting water quality in a mixed use catchment and the role it plays in the generation of blue-green algae in the receiving marine environment. Bayesian Network modelling showed that cross-sectional and longitudinal data analyses generate contrasting information about the influence of different land uses on surface water pollution. The modelling outcomes highlighted the lack of reliability in cross-sectional data analysis, based on the indication of spurious relationships between water quality and land use. On the other hand, the longitudinal data analysis, which accounted for changes in water quality and land use over a ten-year period, informed how catchment water quality varies in response to temporal changes in land use. The longitudinal data analysis further revealed that the types of anthropogenic activities have a more significant influence on pollutant generation than the change in the area extent of different land uses over time. Therefore, the careful interpretation of the findings derived solely from cross-sectional data analysis is important in the design of long-term strategies for pollution mitigation. 2018-03-01T00:00:00Z http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/20832 Title: Intrinsic and extrinsic factors which influence metal adsorption to road dust Authors: Goonetilleke, Ashantha Abstract: The adsorption behaviour of metals deposited on road surfaces is a complex process and influenced by a range of factors common to the urban environment. However, all factors do not have the same level of importance. It is therefore important to identify the most crucial factors for accurate stormwater quality predictions and to implement effective stormwater pollution mitigation measures. Accordingly, this study investigated the extrinsic and intrinsic factors in terms of their degree of influence on the adsorption of individual metal cations to particulates. The variability associated with the adsorption of Zn, Cu, Pb, Cd, Cr and Ni to road dust was found to be influenced by changes to the antecedent dry days and land use characteristics. The initial dry days after a storm event exerts a significant influence on adsorption compared to the later dry days in all land uses. In terms of the intrinsic physico-chemical properties of road dust, the parameters that influence the adsorption process differ in terms of the type of metal cation and particle size fractions of solids. Based on the influential parameters identified, the bioavailability characteristics of Zn, Cd, Pb and Ni in < 150 μm size fraction of road dust and potential stormwater quality impacts can be highlighted. 2018-03-01T00:00:00Z