Influence of land use class and configuration on water-sediment partitioning of heavy metals.

Influence of land use and population characteristics on solid-liquid partitioning of heavy metals in aquatic ecosystems is little understood. This study hypothesised that the partitioning of heavy metals (Cd, Cr, Cu, Ni, Pb and Zn) between water and sediments is influenced by different land use classes, their configuration patterns including patch density, Shannon's diversity index, largest patch index, and splitting index and population density. Relationships between variables were investigated from different distances to the stream network (sub-catchment and riparian scales) and considering land use patterns within individual land use classes and individual sub-catchments as a whole (class and landscape levels, respectively). The study outcomes confirmed that the influence of land use and configuration on metals partitioning is scale independent. However, population density increases metal bioavailability at the riparian scale compared to the sub-catchment scale. Agricultural lands discharge the highest fractions of dissolved metals at both spatial scales (eigenvectors = 0.409 - sub-catchment, and -0.533 - riparian, whilst metals have opposite loadings). Positive relationships between splitting index and metal partitioning confirmed that the division of anthropogenic land uses into smaller patches reduces water pollution. However, high fragmentation of forested areas increases the fraction of soluble metals. Further, high patch density and patch diversity are beneficial for controlling the solubility of some metals. Configuration metrics at the landscape level fundamentally reproduce the patterns of the largest land use type and are not effective for assessing metal partitioning. Therefore, analyses at the class level are preferred. This research investigation contributes essential knowledge to improve land use management strategies and, thereby, help safeguard urban waterways.

Implications of Nonstationary Effect on Geographically Weighted Total Least Squares Regression for PM2.5 Estimation.

Land use regression (LUR) models are used for high-resolution air pollution assessment. These models use independent parameters based on an assumption that these parameters are accurate and invariable; however, they are observational parameters derived from measurements or modeling. Therefore, the parameters are commonly inaccurate, with nonstationary effects and variable characteristics. In this study, we propose a geographically weighted total least squares regression (GWTLSR) to model air pollution under various traffic, land use, and meteorological parameters. To improve performance, the proposed model considers the dependent and independent variables as observational parameters. The GWTLSR applies weighted total least squares in order to take into account the variable characteristics and inaccuracies of observational parameters. Moreover, the proposed model considers the nonstationary effects of parameters through geographically weighted regression (GWR). We examine the proposed model's capabilities for predicting daily PM2.5 concentration in Isfahan, Iran. Isfahan is a city with severe air pollution that suffers from insufficient data for modeling air pollution with conventional LUR techniques. The advantages of the model features, including consideration of the variable characteristics and inaccuracies of predictors, are precisely evaluated by comparing the GWTLSR model with ordinary least squares (OLS) and GWR models. The R2 values estimated by the GWTLSR model during the spring and autumn are 0.84 and 0.91, respectively. The corresponding average R2 values estimated by the OLS model during the spring and autumn are 0.74 and 0.69, respectively, and the R2 values estimated by the GWR model are 0.76 and 0.70, respectively. The results demonstrate that the proposed functional model efficiently described the physical nature of the relationships among air pollutants and independent variables.

Creating a hierarchy of hazard control for urban stormwater management.

Urban stormwater reuse is becoming increasingly prevalent to overcome the serious urban water scarcity being experienced around the world. Therefore, the adoption of reliable approaches to minimise the human health risk posed by pollutants commonly present in urban stormwater such as heavy metals and polycyclic aromatic hydrocarbons (PAHs) is critical for safe stormwater reuse. This study collected a total of 40 pollutant build-up samples and analysed the concentrations of nine heavy metals and 15 PAH species. Based on pollutant build-up data, pollutant concentrations in stormwater were estimated through modelling. Risk assessment was conducted using an existing model developed by previous studies. The study outcomes confirmed that simply evaluating the individual pollutant concentrations based on guideline threshold values cannot comprehensively estimate the overall human health risk posed by these pollutants. Accordingly, it is recommended that the assessment of the overall human health risk should be based on the pollutant mix present as provided by the models discussed in this paper. The study also demonstrated the practical application of a robust risk assessment model to derive the hierarchy of hazard control to provide a reliable underpinning to urban stormwater risk management. The outcomes suggest that decentralised hazard control methods such as the provision of custom designed Water Sensitive Urban Design (WSUD) measures can be implemented in priority areas with high risk from stormwater pollution based on the risk assessment undertaken. Distributed hazard control methods can be applied to reduce the generation of primary toxic pollutants, especially chromium (Cr) and heavy PAHs, through elimination and substitution measures. The percentage reduction in traffic volume required to mitigate the human health risk can be quantified through the risk models presented. The study outcomes will contribute to the development of efficient, targeted and reliable stormwater management strategies and to identify viable opportunities for stormwater reuse.

Nutrients and metals interactions between water and sediment phases: An urban river case study.

The provision of water to meet the needs of an ever increasing urban population is a significant challenge. This is because urban receiving waters are constantly at risk from pollutant inputs via stormwater runoff and wastewater discharge. This research study employed multiple approaches including principal component analysis, Bayesian Networks (BNs) modelling and geospatial analysis to identify patterns in the distributions of nutrients and metals in water and sediments in an urban river and the interactions between the two phases. In both, water and sediments, nutrient concentrations/loads varied in the order of total carbon (TC) > total nitrogen (TN) > total phosphorus (TP). The river sediments were found to contain the highest crustal metal loads, while in water, the marine-related metals had the highest concentrations. The BNs modelling of pollutant interactions between water and sediment phases indicated that nitrogen is more likely to be transferred from water to sediment than the opposite, while anthropogenic metals are more likely to be transferred from sediments to water. Further, geospatial analysis showed that TN, crustal metals and anthropogenic metal loads in sediments increased from upstream to downstream, while having a decreasing pattern in water. However, marine-related metals in both, water and sediments had increasing concentrations/loads from upstream to downstream. These spatial patterns are attributed to the interactions between water and sediment phases, sediment transport along the river and seawater intrusion in the estuarine area. The study outcomes are expected to contribute to enhancing the knowledge required for developing mitigation strategies to improve urban receiving water quality.

Evaluating the relationship between temporal changes in land use and resulting water quality.

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.

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.

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.

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.