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The speciation of heavy metals (Ni, Cr, Cu, Zn, Pb, and Cd) was studied in surface sediments of the Cross River Estuary (CRE), Gulf of Guinea, South East Nigeria. Pb (~56 %), Cd (~71 %), Zn (~67 %), and Cr (~76 %) were mainly available in non-residual phases, suggesting potential bioavailability. High contents of Ni and Cu in residual phase indicated immobilization of these metals in aluminosilicate minerals. Cd was the most polluted heavy metal with the highest bioavailability risk. Bayesian Network model results revealed that sedimentary organic carbon (OC) from terrestrial C

Current stormwater quality modelling tools lack robust mathematical replication of nutrient entrainment in runoff. This makes it challenging to design effective stormwater treatment systems such as nature based solutions with adequate resilience to future changes in nutrient inputs in urban environments. Consequently, poorly treated stormwater can be discharged into receiving waters, leading to nutrient enrichment and in turn, environmental and human health impacts. This study integrated empirically based with statistical modelling techniques to incorporate nutrient dynamics into commonly used Intensity-Frequency-Duration (IFD) distributions of design rainfall. Field based nutrient wash-off experiments were conducted to understand nutrient behaviour during a runoff event. New mathematical formulations were derived to describe the decay (wash-off) of nutrients. Rainfall intensity, duration and initially accumulated pollutant load exert positive influence on the decay of nitrogen and phosphorous, while organic carbon has a negative impact on phosphorus decay. It was also evident that nitrogen species would decay at a similar rate, while phosphorus species may decay at different rates. Compared to nitrogen species, phosphorous species were found more likely to be washed-off during a rainfall event. Using the mathematical formulations developed, wash-off of nitrogen and phosphorous was simulated for 435 very frequent and frequent/infrequent design rainfall events leading to the creation of Intensity-Frequency-Duration-Wash-off (IFDW) curves. Analysis of uncertainty associated with IFDW indicated that total phosphorous could be completely washed-off during most of the design rainfall events, while total nitrogen would only be completely washed-off by very few events that are rarer than 10 % AEP (annual exceedance probability). IFDW can act as a tool for supporting effective stormwater treatment design in order to promote sustainable stormwater management and reuse.

The presence of microplastics (MPs) has been recognized as a significant environmental threat due to adverse effects spanning from molecular level, organism health, ecosystem services to human health and well-being. MPs are complex environmental contaminants as they bind to a wide range of other contaminants. MPs associated contaminants include toxic chemical substances that are used as additives during the plastic manufacturing process and adsorbed contaminants that co-exist with MPs in aquatic environments. With the transfer between the water column and sediments, and the migration within aquatic systems, such contaminants associated MPs potentially pose high risk to aquatic systems. However, only limited research has been undertaken currently to link the environmental risk associated with MPs occurrence and movement behaviour in aquatic systems. Given the significant environmental risk and current knowledge gaps, this review focuses on the role played by the abundance of different MP species in water and sediment compartments as well as provides the context for assessing and quantifying the multiple risks associated with the occurrence and movement behaviour of different MP types. Based on the review of past literature, it is found that the physicochemical properties of MPs influence the release/sorption of other contaminants and current MPs transport modelling studies have primarily focused on virgin plastics rather than aged plastics. Additionally, risk assessment of contaminants-associated MPs needs significantly more research. This paper consolidates the current state-of-the art knowledge on the source to sink movement behaviour of MPs and methodologies for assessing the risk of different MP species. Moreover, knowledge gaps and emerging trends in the field are also identified for future research endeavours.

Though abundant studies have targeted the sorption of Cr(VI) and As(V) anions by organic polymers or magnetic metal oxides, there is no research literature on the sorption characteristics of Cr(VI) and As(V) by thiourea-formaldehyde resin (TF) and its magnetic derivative (MTF). TF resin is a strong chelating agent, which has several practical applications. This paper reports on the removal of Cr(VI) and As(V) oxoanions by TF and MTF sorbents. The sorbents were characterized by Fourier transform infrared (FTIR), nuclear magnetic resonance (NMR) spectroscopy, elemental analysis, zetametry, Brunauer-Emmett-Teller (BET) analysis, and differential light scattering (DLS). The synergistic influence of magnetite incorporation and ultrasonic power on sorption kinetics, isotherms, and oxoanion desorption were investigated, including the analysis of the uncertainty in the study results. The relationship between kinetic and equilibrium constants of the two sorbents under normal shaking and ultrasound shaking was analysed. Ultrasound power improved the mass transfer and makes the sorption ultra-fast while magnetite enhanced the sorption capacity. The MTF particles sorbed 4.28 and 1.97 mmol g−1 of Cr(VI) and As(V), respectively, under optimum conditions. Further, desorption kinetics and efficiency of Cr(VI) and As(V) were estimated using normal shaking and ultrasonic agitation. Ultrasound power reduced the time and the concentration of NaCl required for the stabilization of desorption efficiency.

Despite well documented studies on metal pollutants in aquatic ecosystems, knowledge on the combined effects of catchment characteristics, sediment properties, and emerging pollutants, such as microplastics (MPs) on the presence of metals in urban river sediments is still limited. In this study, the synergistic influence of MPs type and hazard indices, catchment characteristics and sediment properties on the variability of metals present in sediments was investigated based on a typical urban river, Brisbane River, Australia. It was noted that the mean concentrations of metals in Brisbane River decreases in the order of Al (94,142 ± 12,194 μg/g)Fe (62,970 ± 8104 μg/g)Mn (746 ± 258 μg/g)Zn (196 ± 29 μg/g)Cu (50 ± 19 μg/g)Pb (47 ± 25 μg/g)Ni (25 ± 3 μg/g) while the variability of metals decreases in the order of PbCuMnAlNiZnFe along the river. According to enrichment factor (Ef) contamination categories, Mn, Cu and Zn exert a moderate level of contamination (Ef2), while Fe, Ni, and Zn show slight sediment pollution (1Ef2). In the case of Pb, extremely high enrichment (Ef3) was found at sampling locations having a high urbanisation level and traffic related activities. Crustal metal elements (namely, Al, Fe, Mn) were found to be statistically significantly correlated with sediment properties (P0.05). Anthropogenic source metals (namely, Cu, Ni, Pb, Zn) were observed to be highly correlated with catchment characteristics. Additionally, the presence of metals in sediments were positively correlated with MPs concentration, and negatively correlated with MPs hazard indices. The outcomes of this study provide new insights for understanding the relationships among metals and various influential factors in the context of urban river sediment pollution, which will benefit the formulation of risk assessment and regulatory measures for protecting urban waterways.

The design of an industrial water treatment system using sorption is based on laboratory column tests. To verify the applicability of a column sorption system at industrial scale, it is necessary to determine the system's breakthrough time (BT) in a laboratory setting. In a laboratory column set-up, BT is referred to as the time taken by the adsorbate to appear at column outlet for the first time. This is when the mass transfer zone (MTZ), where the equilibrium sorption occurs, reaches the end of the sorbent bed. However, such laboratory set-up requires significant resources including laboratory space, time and multiple trials, which is the opposite to the batch experimental approach that is commonly used to assess efficiency of sorbents. This study identified batch sorption parameters that can be used to determine BT for a column sorption setting for three toxic heavy metals commonly found in industrial wastewater, namely, Pb

Metal pollution of stormwater runoff can cause potential toxic impacts on the receiving water environment and human health. Effective pollution mitigation requires accurate stormwater quality modeling. Even though a significant knowledge base exists on the factors influencing metal build-up on urban roads, very limited studies have investigated how metal-particulate interaction influences metal build-up. This study quantitatively assessed the influence of particulate characteristics, together with vehicular traffic and land use, on the build-up of Zn, Cu, Pb, Cr, Ni and Cd on urban roads. The study outcomes revealed that the variability in metal build-up is highly influenced by the variability associated with metal adsorption to particulates. The percentage contribution from particulate properties influencing metal adsorption in the case of150 μm size road dust particles was found to be higher (Zn 44%, Cu 52%, Cr 16%, Ni 27% and Cd 45%) when compared to traffic and land use characteristics (Zn 21%, Cu 13%, Cr and Ni10% and Cd 34%). Similar adsorption behavior was noted for metals associated with150 μm size road dust particles. Among different particulate properties influencing metal adsorption, effective cation exchange capacity showed a strong positive relationship with the build-up of Cd compared to other metals, highlighting the potential role of Cd in stormwater quality as a readily available metal. The build-up of metals such as Cr and Ni are highly influenced by metal oxides of Al, Fe and Mn and clay forming minerals, indicating that Cr and Ni are relatively stable in nature.

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.

Accurate modelling of particulates build-up process is essential for designing effective stormwater management strategies. However, current modelling practice relies on the classical 'power model' which has limitations in accounting for the variability in the build-up process. This research study investigated the relationships between influential factors of the build-up process and coefficients in the power model. The outcomes showed that the coefficient, which determines the build-up rate, is predominantly influenced by land use factors (pervious area, road area, commercial area and residential area), such that land use factors exerted 23 times more influence than the site characteristics (distance to pervious area and road surface texture depth). The coefficient, which determines how quickly build-up reaches equilibrium, was found to be equally influenced by anthropogenic activities (sweeping frequency and traffic volume) and site characteristics. Further, site characteristics were found to play a major role in generating build-up process variability with three times more influence than that of anthropogenic activities. It was found that the power model satisfactorily replicates the build-up of particles74 μm. For the build-up of particles74 μm, a new coefficient, namely, 'coefficient of variability' was introduced in order to improve the prediction performance (up to 17% compared to original power model). The study outcomes provide a deeper understanding into particulates build-up modelling, and can contribute to the formulation of effective stormwater treatment strategies.

Water scarcity, which is exacerbated by climate change, is a major challenge to ensure human well-being. Therefore, it is equally important to protect conventional water resources from degradation and at the same time to identify cost-effective alternatives with a low carbon footprint. In this regard, stormwater plays a key role as it is a largely under-utilised resource for both, potable and non-potable use. However, stormwater carries substantial loads of pollutants to receiving waters such as rivers. Unfortunately, the lack of comprehensive stormwater quality modelling strategies, which account for the effects of climate change, constrains the formulation of effective measures to improve the quality of stormwater. Currently, there is a significant knowledge gap in the merging of stormwater quality modelling and climate modelling. This paper critically reviews current stormwater quality modelling approaches (quantity and quality) and the role of climate modelling outputs in stormwater quality modelling. This is followed by the presentation of a robust framework to integrate the impacts of climate change with stormwater quality models.

With global number of cases 100 million and death toll surpassing 2.16 million as of late-January 2021, the COVID-19 pandemic is certainly one of the major threats that humankind have faced in modern history. As the scientific community navigates through the overwhelming avalanche of information on the multiple health impacts caused by the pandemic, new reports start to emerge on significant ancillary effects associated with the treatment of the virus. Besides the evident health impacts, other emerging impacts related to the COVID-19 pandemic, such as water-related impacts, merits in-depth investigation. This includes strategies for the identification of these impacts and technologies to mitigate them, and to prevent further impacts not only in water ecosystems, but also in relation to human health. This paper has critically reviewed currently available knowledge on the most significant potential impacts of the COVID-19 pandemic on the wastewater pathway into surface water, as well as technologies that may serve to counteract the major threats posed, key perspectives and challenges. Additionally, current knowledge gaps and potential directions for further research and development are identified. While the COVID-19 pandemic is an ongoing and rapidly evolving situation, compiling current knowledge of potential links between wastewater and surface water pathways as related to environmental impacts and relevant associated technologies, as presented in this review, is a critical step to guide future research in this area., Graphical abstract Unlabelled Image

Rivers are regarded as sinks and pathways of plastic pollutants from terrestrial environments to various other aquatic systems such as lakes and oceans. Although extensive research has been conducted in recent years on microplastic pollution in river sediments, knowledge on the relationships between potential influential factors such as catchment characteristics and sediment properties, and microplastics occurrence in river sediments, is still considered an under-researched area. This study evaluated the influence of land use, population, and sediment particle size on the presence of microplastics abundance and types. Based on a Bayesian Network modelling approach to characterise the correlations between influencing factors and microplastics occurrence, it was evident that microplastics type had a positive correlation with different land use types and population. Catchment characteristics were found to play a more important role in influencing microplastics type than microplastics concentration. A statistically significant positive relationship was observed between microplastics concentration and clay particles which suggested that the occurrence of microplastics in sediments can be impacted by the presence of clay particles.

This book discusses the physicochemical changes (transformations) that metals deposited on urban road surfaces undergo during dry weather periods, in order to provide insights into their potential impacts on stormwater quality. Based on extensive field experiments, and laboratory and data analyses, it examines transformation characteristics of metals with respect to the particle size of road dust, antecedent dry days and land uses. Further, it proposes a new risk-assessment methodology, improving the original human health-risk indices based on the transformation characteristics and potential bioavailability of metals in order to evaluate the risks posed by metals in stormwater. This book is of interest to researchers and decision-makers developing appropriate pollution mitigation measures to enhance the quality of stormwater, targeting the effective reuse of stormwater in urban areas.

Reliable approaches for accurately assessing the performance of stormwater treatment systems is essential for their effective design, including filter media selection which can be a significant constituent in stormwater treatment systems. This study presents an innovative modelling approach integrating the Tank Model with the adsorption-desorption characteristics of the filter media. The resulting modelling approach was applied to simulate a field-scale bioretention basin where biochar was used as filter media with over ten years of rainfall records. The resulting outflow and overflow volumes were compared with observed data for calibration. The Stormwater Treatment Tank Model (STTM) was validated using the Leave-One-Out-Cross-Validation (LOOCV) method. The simulation outcomes include water outflow and overflow (quantity) from the bioretention basin as well as outflow water quality represented by three heavy metals (Pb, Cu, and Zn). The modelling approach developed was found to be capable of accurately simulating outflow and overflow volumes, with outlet water quantity being significantly influenced by the total rainfall depth. The modeling results also suggested that a sole treatment system would not be adequate, particularly for large rainfall events (100 mm) and a treatment train would be more effective. Simulating long-term (over ten years) pollutant removal performance in the bioretention basin indicated that heavy metals outflow event mean concentration (EMCs) values calculated using simulated results of 30% biochar application rate generated the best pollutant removal with consistent values (2.7 μg/L, 3.0 μg/L, 17.2 μg/L for Pb, Cu, and Zn, respectively). These results confirm that the modelling approach is reliable for assessing long-term treatment performance, as well as a robust tool able to contribute to more effective treatment system design, particularly filter media selection and evaluation.

Heavy metal pollution of urban stormwater poses potential risks to human and ecosystem health. The design of reliable pollution mitigation strategies requires reliable stormwater modelling approaches. Current modelling practices do not consider the influence of urbanisation characteristics on stormwater quality. This could undermine the accuracy of stormwater quality modelling results. This research study used a database consisting of over 1000 datasets to compare the characteristics of heavy metal build-up (one of the most important stormwater pollutant processes) on urban surfaces under the influence of anthropogenic and natural factors specific to different urban regions from China (Shenzhen) and Australia (Gold Coast), using Bayesian Networks. The outcomes show that the differences in heavy metals build-up loads between the two regions (mean value for Shenzhen - mean value for Gold Coast)/mean value for Shenzhen) were 0.45 (Al), 0.88 (Cr), 0.99 (Mn), 0.68 (Fe), 0.98 (Ni), 0.24 (Cu), 0.47 (Zn) and 0.13 (Pb), respectively. The research outcomes also confirmed that the influence of traffic on the build-up of different sized particles varies between Shenzhen and Gold Coast, and traffic plays distinct roles as a source and as a factor that drives heavy metal re-distribution. The road surface roughness was also found to influence build-up process differently between the two regions. More importantly, the assessment of inherent process uncertainty revealed that heavy metal build-up between different road sites in Shenzhen varies over a wider range than in Gold Coast. The study highlighted a clear distinction in the influence of sources and key anthropogenic factors on the variability of particle-bound heavy metals build-up between geographically different urban regions. The study outcomes provide new knowledge to enhance the accuracy of urban stormwater quality modelling.

The deposition and the re-suspension of particulate matter (PM) in urban areas are the key processes that contribute not only to stormwater pollution, but also to air pollution. However, investigation of the deposition and the re-suspension of PM is challenging because of the difficulties in distinguishing between the resuspended and the deposited PM. This study created two Bayesian Networks (BN) models to explore the deposition and the re-suspension of PM as well as the important influential factors. The outcomes of BN modelling revealed that deposition and re-suspension of PM10 occurred under both, high-traffic and low-traffic conditions, and the re-suspension of PM2.5 occurred under low-traffic conditions. The deposition of PM10 under low-volume traffic condition is 1.6 times higher than under high-volume traffic condition, which is attributed to the decrease in PM10 caused by relatively higher turbulence under high-volume traffic conditions. PM10 is more easily resuspended from road surfaces compared to PM2.5 as the particles which larger than the thickness of the laminar airflow over the road surface are more easily removed from road surfaces. The increase in wind speed contributes to the increase in PM build-up by transporting particulates from roadside areas to the road surfaces and the airborne PM2.5 and PM10 increases with the increase in relative humidity. The study outcomes provide a step improvement in the understanding of the transfer processes of PM2.5 and PM10 between atmosphere and urban road surfaces, which in turn will contribute to the effective design of mitigation measures for urban stormwater and air pollution.

Investigations into the transformation characteristics of metals deposited on urban roads required a scientifically robust research design. Sample collection and laboratory analyses were two important steps in the overall research design. This chapter provides a detailed discussion on the methods adopted for road dust sample collection, sample handling and preservation, and laboratory experiments for the analysis of selected metals and key physicochemical parameters of road dust.

A detailed understanding of the metal transformation processes and consequent stormwater quality impacts provides a platform for the implementation of effective stormwater pollution mitigation strategies. This chapter discusses the geochemical behaviour of common metals associated with road dust and the processes they undergo during dry weather periods, in order to understand their mobility characteristics and potential stormwater quality impacts. Among the analysed metals, Zn and Cd have the greatest affinity for the readily available exchangeable geochemical fraction, whilst Cu and Pb are likely to associate with the reducible and oxidisable geochemical fractions. The characteristic affinity of Ni and Cr for the residual geochemical fraction indicates their low mobility. With increasing residence time, weakly bound metals gradually transform into more stable chemical forms. The transformation rate of metals follow the order: Pb > Cu > Zn > Cd, which is inversely opposite to the order of their mobility. Based on the transformation rate and mobility order, metals such as Cd and Zn can adversely influence stormwater quality due to their readily available nature.

Stormwater reuse is one of the viable alternatives to overcome the growing water demand in urban areas. However, effective use of stormwater as an alternative water resource is constrained due to the presence of toxic pollutants generated by natural and anthropogenic processes. Stormwater quality is further impacted by the behavioural changes to pollutants, resulting from the transformation of their physical and chemical characteristics. The transformed pollutants can have different persistence, toxicity, mobility and bioavailability characteristics compared to their original form. Therefore, improving stormwater quality by understanding the behavioural changes of pollutants and quantification of the associated risks of transformed pollutants is essential to ensure the safe use of stormwater. This chapter discusses the potential transformation characteristics of metals associated with road dust, which is among the major toxic pollutants found in urban areas, and the factors influencing metal transformations and resulting stormwater quality impacts.

Risks posed by metals in stormwater is directly influenced by their geochemical fractionation and, in turn, the bioavailability. Therefore, accurate quantification of the risks from metals should be undertaken by considering these two key aspects. This chapter presents a novel approach to quantify the human health risks of metals, among the common stormwater pollutants found in urban areas, considering their geochemical fractionation and bioavailability characteristics. Stormwater quality and quantity modelling approaches were used to convert metal build-up loads to corresponding metal concentrations in stormwater. The results of the modified risk assessment discussed in this chapter highlights the drawbacks in using classical health risk indices and the importance of upgrading the risk assessment methods to avoid the over-estimation of risk posed by metals. Based on the modified hazard index, there is a low or no health risk from metals in stormwater runoff originating from the urban land uses investigated in this study.

Due to their carcinogenic effects, Polycyclic Aromatic Hydrocarbons (PAHs) deposited on urban surfaces are a major concern in the context of stormwater pollution. However, the design of effective pollution mitigation strategies is challenging due to the lack of reliability in stormwater quality modelling outcomes. Current modelling approaches do not adequately replicate the interdependencies between pollutant processes and their influential factors. Using Bayesian Network modelling, this research study characterised the influence of vehicular traffic on the build-up of the sixteen US EPA classified priority PAHs. The predictive analysis was conditional on the structure of the proposed BN, which can be further improved by including more variables. This novel modelling approach facilitated the characterisation of the influence of traffic as a source of origin and also as a key factor that influences the re-distribution of PAHs, with positive or negative relationship between traffic volume and PAH build-up. It was evident that the re-distribution of particle-bound PAHs is determined by the particle size rather than the chemical characteristics such as volatility. Moreover, compared to commercial and residential land uses, mostly industrial land use contributes to the PAHs load released to the environment. Carcinogenic PAHs in industrial areas are likely to be associated with finer particles, while PAHs, which are not classified as human carcinogens, are likely to be found in the coarser particle fraction.

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.

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.

Sanitary landfill is the most popular way to dispose solid wastes with one major drawback: the generation of landfill leachate resulting from percolation of rainfall through exposed landfill areas or infiltration of groundwater into the landfill. The landfill leachate impacts on the environment has forced authorities to stipulate more stringent requirements for pollution control, generating the need for innovative technologies to eliminate waste degradation by-products incorporated in the leachate. Natural attenuation has no effect while conventional treatment processes are not capable of removing some the pollutants contained in the leachate which are reported to reach the natural environment, the aquatic food web, and the anthroposphere. This review critically evaluates the state-of-the-art engineered materials and technologies for the treatment of landfill leachate with the potential for real-scale application. The study outcomes confirmed that only a limited number of studies are available for providing new information about novel materials or technologies suitable for application in the removal of pollutants from landfill leachate. This paper focuses on the type of pollutants being removed, the process conditions and the outcomes reported in the literature. The emerging trends are also highlighted as well as the identification of current knowledge gaps and future research directions along with recommendations related to the application of available technologies for landfill leachate treatment.

The adsorption-desorption behaviour of heavy metals in aquatic environments is complex and the processes are regulated by the continuous interactions between water and sediments. This study provides a quantitative understanding of the effects of nutrients and key water and sediment properties on the adsorption-desorption behaviour of heavy metals in riverine and estuarine environments. The influence levels of the environmental factors were determined as conditional regression coefficients. The research outcomes indicate that the mineralogical composition of sediments, which influence other sediment properties, such as specific surface area and cation exchange capacity, play the most important role in the adsorption and desorption of heavy metals. It was found that particulate organic matter is the most influential nutrient in heavy metals adsorption in the riverine environment, while particulate phosphorus is more important under estuarine conditions. Dissolved nutrients do not exert a significant positive effect on the release of heavy metals in the riverine area, whilst dissolved phosphorus increases the transfer of specific metals from sediments to the overlying water under estuarine conditions. Furthermore, the positive interdependencies between marine-related ions and the release of most heavy metals in the riverine and estuarine environments indicate an increase in the mobility of heavy metals as a result of cation exchange reactions.

Assessing the risk to human health posed by stormwater pollutants is critical, because stormwater is considered as an alternative water resource to mitigate water deficiencies, which is becoming increasingly common around the world. In this context, hydrocarbons with different volatility that are common in the urban environments pose significant human and ecosystems health risks resulting from wash-off into receiving waters during rainfall events. Effective mitigation strategies require reliable stormwater quality modelling approaches incorporated with accurate assessment of uncertainty in stormwater quality predictions. This study employed Bayesian Network modelling to investigate how anthropogenic and climatic factors influence the build-up of particle-bound hydrocarbons, with specific focus on volatile hydrocarbons, and thereby assessed the uncertainty inherent to their build-up process. The study found that volatile hydrocarbons build-up rapidly in the early stage of the antecedent dry period, and volatility plays a key role in the build-up of these pollutants. Significant differences in the temporal variations in build-up could be observed between different seasons. Further, season specific conditions and physico-chemical characteristics of particulates were found to influence the behaviour of volatile hydrocarbons during build-up.

Stormwater is viewed as an alternative resource to mitigate water shortages. However, stormwater reuse is constrained due to the presence of many toxic pollutants such as hydrocarbons. Effective mitigation requires robust mathematical models for stormwater quality prediction based on an understanding of pollutant processes. However, the rise in global temperatures will impose changes to pollutant processes. This study has proposed a new perspective on modelling the build-up process of hydrocarbons, with a focus on volatile organic compounds (VOCs). Among organic compounds, VOCs are the most susceptible to changes as a result of global warming due to their volatility. Seven VOCs, namely, benzene, toluene, ethylbenzene, para-xylene, meta-xylene, ortho-xylene and styrene in road dust were investigated. The outcomes are expected to lay the foundation to overcoming the limitations in current modelling approaches such as not considering the influence of temperature and volatility, on the build-up process. A new conceptualisation is proposed for the classical build-up model by mathematically defining the volatility of VOCs in terms of temperature. Uncertainty in the re-conceptualised build-up model was quantified and was used to understand the build-up patterns in the future scenarios of global warming. Results indicated that for the likely scenarios, the variability in VOCs build-up gradually increases at the beginning of the dry period and then rapidly increases after around seven days, while the build-up reaches a near-constant value in a shorter dry period, limiting the variability. These initial research outcomes need to be further investigated given the expected impacts of global warming into the future. Build-up of volatile organic components was investigated by taking temperature and volatility into account.

The high pollutant loads discharged from cities pose risks to urban waterways, and in turn the estuarine environments, making it challenging to improve urban liveability. Past studies on the behaviour of pollutants in rivers have largely investigated their transport along the waterway, primarily focusing on the movement of water and sediment. However, the current approaches in pollutant transport modelling provide limited insights into how pollutant transfer between water and sediment phases influences their transport from the upstream towards the estuarine environment. This research study firstly identified typical patterns of metal loads along an urban river in a highly populated city in China. The outcomes were then used to conceptualise metal transfer between water and sediment phases. It was noted that physico-chemical characteristics of water and sediments play a key role in metal transfer between the two phases, and the dominant transfer path (sediment to water/water to sediment) is different between different metals, independent of their origin (crustal, anthropogenic or marine-related). Several scenarios were derived from the conceptualisation of metal behaviour. These in turn were then used to develop real-world scenarios of metal transport in rivers based on the field data. The conceptualisation of metal behaviour confirmed that each metal is likely to have a dominant phase of transport (sediment/water), which is influenced by the dominant transfer path of that metal between water and sediments.

Anthropogenic activities which are common to urban areas generate a range of physical, chemical and biological pollutants which are subsequently incorporated in stormwater runoff, leading to the deterioration of receiving water environments. This poses risks to both human and ecosystem health including carcinogenic and neurological effects and the loss of aquatic biodiversity. Water environments are an essential asset for enhancing urban liveability. Significant research has been undertaken in relation to stormwater pollutant characterisation and pollutant processes, which forms the baseline knowledge for developing effective stormwater pollution mitigation strategies. The current practice of formulating strategies to improve stormwater quality relies on the fundamental understanding that pollutants accumulate on urban surfaces during dry weather periods and are subsequently washed-off during rainfall. However, there are significant gaps in the current knowledge base in relation to how pollutant load and composition could vary temporally and spatially, which is critical for understanding the dynamic nature of stormwater quality in urban catchments. This acts as a major constraint to informed decision-making in the context of designing effective stormwater pollution mitigation strategies. Moreover, climate change is a significant influential factor in relation to urban stormwater pollution. The predicted changes to dry and wet weather conditions would lead to changes to pollutant accumulation on urban surfaces, change pollutant characteristics and increase the likelihood of discharging shock loads of pollutants to receiving waters. Research is needed to understand the complex mechanisms underpinning pollutant processes and their influential factors and the role of climate change in order to enhance the well-being of urban communities.

This chapter presents the outcomes from a case study which investigated how uncertainty inherent in build-up and wash-off of metals commonly present in urban catchments, could influence stormwater quality. The investigation found consistent and significantly high concentrations of Al, Cr, Mn, Fe, Ni, Cu, Zn, Cd and Pb in the particle size fraction 150 µm. When considering consecutive events of build-up and wash-off, the temporal variations in the build-up loads of metals associated with particle size fractions 150 µm were not consistent with their wash-off loads. These inconsistencies could be potentially due to the interactions between metals and particles that are determined by the particle physico-chemical characteristics. While particle behaviour was found to drive the variability in metal build-up and wash-off, the need for characterising process variability in stormwater quality modelling was highlighted, enabling the quantitative assessment of process uncertainty associated with stormwater quality predictions.

Current practice in stormwater quality modelling constraints the generation of reliable information about catchment scale stormwater quality due to the lack of robust methods to assess the inherent uncertainty in pollutant build-up and wash-off processes. This chapter presents an approach to quantify process uncertainty as an integral part of stormwater quality predictions. The approach primarily aims to mathematically incorporate the characteristics of process variability into stormwater quality models, and thus quantifying the resulting uncertainty. The application of the new approach revealed that compared to wash-off process uncertainty, the build-up process uncertainty has a greater influence on the prediction of event mean concentrations (EMCs) of particulate solids in urban catchments. Further, it was found that process uncertainty differently influences stormwater quality predictions corresponding to storm events with different intensities, durations and resulting runoff volumes. Planning and management decision making needs to specifically address the changes in the load and composition of particulate solids and associated pollutants during dry weather periods and the storm events that can potentially influence high variations in stormwater quality.

The outcomes of a series of mathematical simulations of pollutant build-up and wash-off are presented in this chapter, strengthening the knowledge base on process variability. It was found that the build-up of particles 150 µm have different temporal patterns. These patterns could be used to differentiate between the behaviour of particles. The behaviour of particles 150 µm is significant during rainfall events with relatively shorter duration.

This chapter discusses implications of the outcomes of this research study in relation to stormwater pollution mitigation. It also presents a novel approach for implementing the research outcomes, using the concept of Logic Models. This approach is a logical sequence to a set of activities that enable the designing of effective stormwater pollution mitigation strategies utilising reliable information generated by stormwater quality models. The chapter further identifies opportunities for future research, namely, pollutant-particulate interactions, uncertainty assessment in relation to models with different complexity, and the need for integrating different modelling frameworks to improve the quantification of overall uncertainty associated with stormwater quality modelling outcomes.

Stormwater quality modelling is the common practice for generating information necessary for decision making in the design of stormwater pollution mitigation measures. However, the reliability of modelling outcomes largely depends on two types of uncertainty, namely, uncertainty inherent to stormwater pollutant processes and process modelling uncertainty. The inherent process uncertainty arises due to the intrinsic variability in stormwater pollutant processes. The modelling uncertainty arises from model structure and parameters, and input data and calibration data. The chapter establishes the context for defining and quantifying the uncertainty inherent to pollutant build-up and wash-off processes by bringing together current scientific knowledge from research literature. The temporal changes in particle size results in different particle behaviour during build-up and wash-off, leading to variations in particle-bound pollutant load and composition. Accordingly, the variation in particle size over time can be used as a basis for accounting for process variability in stormwater quality modelling, and thereby assessing process uncertainty.

Urban road build-up solids contain toxic metals posing potential risks to human health. Management of human health risks arising from these metals is critical in urban areas. This study collected solids build-up data from 16 study sites with various land use and traffic characteristics. Source quantification was conducted using PCA/APCS receptor model. It was found that soil and asphalt wear are the largest contributors (69.43%) to risk and mainly contribute Al, Cr, Mn, Fe, Ni, Zn and Pb to build-up solids. Brake wear is the second largest contributor accounting for 17.20% and contributes Cd and Cu. Tyre wear is the third major contributor (11.38%) and it primarily contributes Ni, Zn and Cr. Mathematical equations were fitted to estimate the risk against daily traffic volume and land use fractions, and the uncertainty analysis highlighted that risk assessment should account for the variability in metal concentrations rather than a point value of concentrations at a given time and space. Based on source quantification and risk assessment, an integrated risk management model was developed to manage human health risks from toxic metals in build-up solids. This risk model provides guidance for urban planning and land use development to mitigate risk arising from urban road deposited solids.