James McGree

Human health risk assessment of heavy metals in urban stormwater.

Toxic chemical pollutants such as heavy metals (HMs) are commonly present in urban stormwater. These pollutants can pose a significant risk to human health and hence a significant barrier for urban stormwater reuse. The primary aim of this study was to develop an approach for quantitatively assessing the risk to human health due to the presence of HMs in stormwater. This approach will lead to informed decision making in relation to risk management of urban stormwater reuse, enabling efficient implementation of appropriate treatment strategies. In this study, risks to human health from heavy metals were assessed as hazard index (HI) and quantified as a function of traffic and land use related parameters. Traffic and land use are the primary factors influencing heavy metal loads in the urban environment. The risks posed by heavy metals associated with total solids and fine solids (<150μm) were considered to represent the maximum and minimum risk levels, respectively. The study outcomes confirmed that Cr, Mn and Pb pose the highest risks, although these elements are generally present in low concentrations. The study also found that even though the presence of a single heavy metal does not pose a significant risk, the presence of multiple heavy metals could be detrimental to human health. These findings suggest that stormwater guidelines should consider the combined risk from multiple heavy metals rather than the threshold concentration of an individual species. Furthermore, it was found that risk to human health from heavy metals in stormwater is significantly influenced by traffic volume and the risk associated with stormwater from industrial areas is generally higher than that from commercial and residential areas.

A Sequential Monte Carlo Algorithm to Incorporate Model Uncertainty in Bayesian Sequential Design

This article presents a sequential Monte Carlo (SMC) algorithm that can be used for any one-at-a-time Bayesian sequential design problem in the presence of model uncertainty where discrete data are encountered. Our focus is on adaptive design for model discrimination but the methodology is applicable if one has a different design objective such as parameter estimation or prediction. An SMC algorithm is run in parallel for each model and the algorithm relies on a convenient estimator of the evidence of each model that is essentially a function of importance sampling weights. Methods that rely on quadrature for this task suffer from the curse of dimensionality. Approximating posterior model probabilities in this way allows us to use model discrimination utility functions derived from information theory that were previously difficult to compute except for conjugate models. A major benefit of the algorithm is that it requires very little problem-specific tuning. We demonstrate the methodology on three applications, including discriminating between models for decline in motor neuron numbers in patients suffering from motor neuron disease. Computer code to run one of the examples is provided as online supplementary materials.

Equine hyperinsulinemia: investigation of the enteroinsular axis during insulin dysregulation

Compared with some other species, insulin dysregulation in equids is poorly understood. However, hyperinsulinemia causes laminitis, a significant and often lethal disease affecting the pedal bone/hoof wall attachment site. Until recently, hyperinsulinemia has been considered a counterregulatory response to insulin resistance (IR), but there is growing evidence to support a gastrointestinal etiology. Incretin hormones released from the proximal intestine, glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic peptide, augment insulin secretion in several species but require investigation in horses. This study investigated peripheral and gut-derived factors impacting insulin secretion by comparing the response to intravenous (iv) and oral d-glucose. Oral and iv tests were performed in 22 ponies previously shown to be insulin dysregulated, of which only 15 were classified as IR (iv test). In a more detailed study, nine different ponies received four treatments: d-glucose orally, d-glucose iv, oats, and commercial grain mix. Insulin, glucose, and incretin concentrations were measured before and after each treatment. All nine ponies showed similar iv responses, but five were markedly hyperresponsive to oral d-glucose and four were not. Insulin responsiveness to oral d-glucose was strongly associated with blood glucose concentrations and oral glucose bioavailability, presumably driven by glucose absorption/distribution, as there was no difference in glucose clearance rates. Insulin was also positively associated with the active amide of GLP-1 following d-glucose and grain. This study has confirmed a functional enteroinsular axis in ponies that likely contributes to insulin dysregulation that may predispose them to laminitis. Moreover, iv tests for IR are not reliable predictors of the oral response to dietary nonstructural carbohydrate.

Sequential Monte Carlo for Bayesian sequentially designed experiments for discrete data

In this paper we present a sequential Monte Carlo algorithm for Bayesian sequential experimental design applied to generalised non-linear models for discrete data. The approach is computationally convenient in that the information of newly observed data can be incorporated through a simple re-weighting step. We also consider a flexible parametric model for the stimulus-response relationship together with a newly developed hybrid design utility that can produce more robust estimates of the target stimulus in the presence of substantial model and parameter uncertainty. The algorithm is applied to hypothetical clinical trial or bioassay scenarios. In the discussion, potential generalisations of the algorithm are suggested to possibly extend its applicability to a wide variety of scenarios.

Process variability of pollutant build-up on urban road surfaces

Knowledge of the pollutant build-up process is a key requirement for developing stormwater pollution mitigation strategies. In this context, process variability is a concept which needs to be understood in-depth. Analysis of particulate build-up on three road surfaces in an urban catchment confirmed that particles <150 μm and >150 μm have characteristically different build-up patterns, and these patterns are consistent over different field conditions. Three theoretical build-up patterns were developed based on the size-fractionated particulate build-up patterns, and these patterns explain the variability in particle behavior and the variation in particle-bound pollutant load and composition over the antecedent dry period. Behavioral variability of particles <150 μm was found to exert the most significant influence on the build-up process variability. As characterization of process variability is particularly important in stormwater quality modeling, it is recommended that the influence of behavioral variability of particles <150 μm on pollutant build-up should be specifically addressed. This would eliminate model deficiencies in the replication of the build-up process and facilitate the accounting of the inherent process uncertainty, and thereby enhance the water quality predictions.

Compound Optimal Design Criteria for Nonlinear Models

Three approaches for combining parameter estimation with opposing design criteria are proposed for nonlinear models. The first method discussed is the technique found in the literature and as such is the reference method for this paper. The compound crtierion is formed by maximizing a weighted product of efficiencies. The second criterion involves maximizing an opposing criterion while minimizing a defined loss function. The third method simultaneously maximizes both efficiencies with respect to parameter estimation and an opposing criterion with a multiple objective simulated annealing algorithm. The examples presented are based on a PK-model and a generalized linear model found in the literature.

Influence of pollutant build-up on variability in wash-off from urban road surfaces.

Variability in the pollutant wash-off process is a concept which needs to be understood in-depth in order to better assess the outcomes of stormwater quality models, and thereby strengthen stormwater pollution mitigation strategies. Current knowledge about the wash-off process does not extend to a clear understanding of the influence of the initially available pollutant build-up on the variability of the pollutant wash-off load and composition. Consequently, pollutant wash-off process variability is poorly characterised in stormwater quality models, which can result in inaccurate stormwater quality predictions. Mathematical simulation of particulate wash-off from three urban road surfaces confirmed that the wash-off load of particle size fractions < 150 μm and > 150 μm after a storm event vary with the build-up of the respective particle size fractions available at the beginning of the storm event. Furthermore, pollutant load and composition associated with the initially available build-up of < 150 μm particles predominantly influence the variability in washed-off pollutant load and composition. The influence of the build-up of pollutants associated with > 150 μm particles on wash-off process variability is significant only for relatively shorter duration storm events.

Understanding the uncertainty associated with particle-bound pollutant build-up and wash-off: A critical review.

Accurate prediction of stormwater quality is essential for developing effective pollution mitigation strategies. The use of models incorporating simplified mathematical replications of pollutant processes is the common practice for determining stormwater quality. However, an inherent process uncertainty arises due to the intrinsic variability associated with pollutant processes, which has neither been comprehensively understood, nor well accounted for in uncertainty assessment of stormwater quality modelling. This review provides the context for defining and quantifying the uncertainty associated with pollutant build-up and wash-off on urban impervious surfaces based on the hypothesis that particle size is predominant in influencing process variability. Critical analysis of published research literature brings scientific evidence together in order to establish the fact that particle size changes with time, and different sized particles exhibit distinct behaviour during build-up and wash-off, resulting in process variability. Analysis of the different adsorption behaviour of particles confirmed that the variations in pollutant load and composition are influenced by particle size. Particle behaviour and variations in pollutant load and composition are related due to the strong affinity of pollutants such as heavy metals and hydrocarbons for specific particle size ranges. As such, the temporal variation in particle size is identified as the key to establishing a basis for assessing build-up and wash-off process uncertainty. Therefore, accounting for pollutant build-up and wash-off process variability, which is influenced by particle size, would facilitate the assessment of the uncertainty associated with modelling outcomes. Furthermore, the review identified fundamental knowledge gaps where further research is needed in relation to: (1) the aggregation of particles suspended in the atmosphere during build-up; (2) particle re-suspension during wash-off; (3) pollutant re-adsorption by different particle size fractions; and (4) development of evidence-based techniques for assessing uncertainty; and (5) methods for translating the knowledge acquired from the investigation of process mechanisms at small scale into catchment scale for stormwater quality modelling.

Influence of uncertainty inherent to heavy metal build-up and wash-off on stormwater quality.

Uncertainty inherent to heavy metal build-up and wash-off stems from process variability. This results in inaccurate interpretation of stormwater quality model predictions. The research study has characterised the variability in heavy metal build-up and wash-off processes based on the temporal variations in particle-bound heavy metals commonly found on urban roads. The study outcomes found that the distribution of Al, Cr, Mn, Fe, Ni, Cu, Zn, Cd and Pb were consistent over particle size fractions <150 μm and >150 μm, with most metals concentrated in the particle size fraction <150 μm. When build-up and wash-off are considered as independent processes, the temporal variations in these processes in relation to the heavy metals load are consistent with variations in the particulate load. However, the temporal variations in the load in build-up and wash-off of heavy metals and particulates are not consistent for consecutive build-up and wash-off events that occur on a continuous timeline. These inconsistencies are attributed to interactions between heavy metals and particulates <150 μm and >150 μm, which are influenced by particle characteristics such as organic matter content. The behavioural variability of particles determines the variations in the heavy metals load entrained in stormwater runoff. Accordingly, the variability in build-up and wash-off of particle-bound pollutants needs to be characterised in the description of pollutant attachment to particulates in stormwater quality modelling. This will ensure the accounting of process uncertainty, and thereby enhancing the interpretation of the outcomes derived from modelling studies.

Predictive models for water sources with high susceptibility for bromine-containing disinfection by-product formation: implications for water treatment

This study examines a matrix of synthetic water samples designed to include conditions that favour brominated disinfection by-product (Br-DBP) formation, in order to provide predictive models suitable for high Br-DBP forming waters such as salinity-impacted waters. Br-DBPs are known to be more toxic than their chlorinated analogues, in general, and their formation may be favoured by routine water treatment practices such as coagulation/flocculation under specific conditions; therefore, circumstances surrounding their formation must be understood. The chosen factors were bromide concentration, mineral alkalinity, bromide to dissolved organic carbon (Br/DOC) ratio and Suwannee River natural organic matter concentration. The relationships between these parameters and DBP formation were evaluated by response surface modelling of data generated using a face-centred central composite experimental design. Predictive models for ten brominated and/or chlorinated DBPs are presented, as well as models for total trihalomethanes (tTHMs) and total dihaloacetonitriles (tDHANs), and bromide substitution factors for the THMs and DHANs classes. The relationships described revealed that increasing alkalinity and increasing Br/DOC ratio were associated with increasing bromination of THMs and DHANs, suggesting that DOC lowering treatment methods that do not also remove bromide such as enhanced coagulation may create optimal conditions for Br-DBP formation in waters in which bromide is present.