Kaveh Deilami

Correlation or Causality between Land Cover Patterns and the Urban Heat Island Effect? Evidence from Brisbane, Australia

Numerous studies have identified associations between the surface urban heat island (SUHI) effect (i.e., SUHI, hereinafter is referred to as UHI) and urban growth, particularly changes in land cover patterns. This research questions their causal links to answer a key policy question: If cities restrict urban expansion and encourage people to live within existing urban areas, will that help in controlling UHI? The question has been answered by estimating four models using data from Brisbane, Australia: Model 1—cross-sectional ordinary least square (OLS) regression—to examine the association between the UHI effect and land cover patterns in 2013; Model 2—cross-sectional geographically weighted regression (GWR)—to examine whether the outputs generated from Model 1 possess significant spatial variations; Model 3—longitudinal OLS—to examine whether changes in land cover patterns led to changes in UHI effects between 2004 and 2013; and Model 4—longitudinal GWR—to examine whether the outputs generated from Model 3 vary significantly over space. All estimations were controlled for potential confounding effects (e.g., population, employment and dwelling densities). Results from the cross-sectional OLS and GWR models were consistent with previous findings and showed that porosity is negatively associated with the UHI effect in 2013. In contrast, population density has a positive association. Results from the longitudinal OLS and GWR models confirm their causal linkages and showed that an increase in porosity level reduced the UHI effect, whereas an increase in population density increased the UHI effect. The findings suggest that even a containment of population growth within existing urban areas will lead to the UHI effect. However, this can be significantly minimized through proper land use planning, by creating a balance between urban and non-urban uses of existing urban areas.

Catchment scale assessment of risk posed by traffic generated heavy metals and polycyclic aromatic hydrocarbons

Heavy metals (HMs) and polycyclic aromatic hydrocarbons (PAHs) are among the most toxic chemical pollutants present in urban stormwater. Consequently, urban stormwater reuse is constrained due to the human health risk posed by these pollutants. This study developed a scientifically robust approach to assess the risk to human health posed by HMs and PAHs in urban stormwater in order to enhance its reuse. Accordingly, an innovative methodology was created consisting of four stages: quantification of traffic and land use parameters; estimation of pollutant concentrations for model development; risk assessment, and risk map presentation. This methodology will contribute to catchment scale assessment of the risk associated with urban stormwater and for risk mitigation. The risk map developed provides a simple and efficient approach to identify the critical areas within a large catchment. The study also found that heavy molecular weight PAHs (PAHs with 5-6 benzene rings) in urban stormwater pose higher risk to human health compared to light molecular PAHs (PAHs with 2-4 benzene rings). These outcomes will facilitate the development of practical approaches for applying appropriate mitigation measures for the safe management of urban stormwater pollution and for the identification of enhanced reuse opportunities.

Did Brisbane Grow Smartly? Drivers of City Growth 1991-2001 and Lessons for Current Policies

Urban areas are growing unsustainably around the world; however, the growth patterns and their associated drivers vary between contexts. As a result, research has highlighted the need to adopt case study based approaches to stimulate the development of new theoretic understandings. Using land-cover data sets derived from Landsat images (30 m × 30 m), this research identifies both patterns and drivers of urban growth in a period (1991-2001) when a number of policy acts were enacted aimed at fostering smart growth in Brisbane, Australia. A linear multiple regression model was estimated using the proportion of lands that were converted from non-built-up (1991) to built-up usage (2001) within a suburb as a dependent variable to identify significant drivers of land-cover changes. In addition, the hot spot analysis was conducted to identify spatial biases of land-cover changes, if any. Results show that the built-up areas increased by 1.34% every year. About 19.56% of the non-built-up lands in 1991 were converted into built-up lands in 2001. This conversion pattern was significantly biased in the northernmost and southernmost suburbs in the city. This is due to the fact that, as evident from the regression analysis, these suburbs experienced a higher rate of population growth, and had the availability of habitable green field sites in relatively flat lands. The above findings suggest that the policy interventions undertaken between the periods were not as effective in promoting sustainable changes in the environment as they were aimed for.

Use of surrogate indicators for the evaluation of potential health risks due to poor urban water quality: a Bayesian Network approach

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.

Ranking the factors influencing polycyclic aromatic hydrocarbons (PAHs) build-up on urban roads.

An in-depth understanding of polycyclic aromatic hydrocarbons (PAHs) build-up on urban roads is essential for effective stormwater treatment design. Past research studies have pointed out the relationship between influential factors and PAHs build-up individually. However, these studies do not provide a comprehensive analysis of the relationships and the hierarchy of factors in terms of their importance in influencing PAHs build-up. This paper presents the outcomes of an in-depth investigation into the range of influential factors, including traffic volume, land use, distance to highway and roughness of road surfaces by ranking them in terms of their influence on PAHs build-up. A number of data analysis techniques including forward stepwise linear regression (FSWLR), principal component analysis (PCA) and multiple linear regression (MLR) were employed for the analyses undertaken. The outcomes confirmed that traffic volume is ranked first while land use and roughness of road surfaces are second and the third, respectively. Distance to highway did not show a significant influence on PAHs build-up. Additionally, it was noted that a high traffic volume tended to produce high loads of PAHs with more than 4 rings and the spatial variability of PAHs build-up were relatively higher in high traffic volume areas. These outcomes contributed to the formulation of a robust stormwater treatment strategy and generation of priority area maps focusing on the removal of PAHs.

Application of landscape epidemiology to assess potential public health risk due to poor sanitation.

Clear identification of areas vulnerable to waterborne diseases is essential for protecting community health. This is particularly important in developing countries where unsafe disposal of domestic wastewater and limited potable water supply pose potential public health risks. However, data paucity can be a compounding issue. Under these circumstances, landscape epidemiology can be applied as a resource efficient approach for mapping potential disease risk areas associated with poor sanitation. However, in order to realise the full potential offered by this approach, an in-depth understanding of the impact of different classes of an explanatory variable on a target disease and the validity of hotspot analysis using limited datasets is needed. Accordingly, this research study focused on typhoid and diarrhoea incidence with respect to different classes of elevation, flood inundation, land use, soil permeability, population density and rainfall as explanatory variables. An integrated methodology consisting of hot spot analysis and Poisson regression was employed to map potential disease risk areas. The study findings confirmed the significant differences in the influence exerted by the various classes of an explanatory variable in relation to a target disease. The results also confirmed the feasibility of the hotspot analysis for identifying areas vulnerable to the target diseases using a limited dataset. The study outcomes are expected to contribute to creating an in-depth understanding of the relationship between disease prevalence and associated landscape factors for the delineation of disease risk zones in the context of data paucity.