Spatiotemporal prediction of Escherichia coli and Enterococci for the Commonwealth Games triathlon event using Bayesian Networks.

A number of Bayesian Networks were developed in order to nowcast and forecast, up to 4 days ahead and in different locations, the likelihood of water quality within the 2018 Commonwealth Games Triathlon swim course exceeding the critical limits for Enterococci and Escherichia coli. The models are data-driven, but the identification of potential inputs and optimal model structure was performed through the parallel contribution of several stakeholders and experts, consulted through workshops. The models, whose main nodes were discretised with a customised discretisation algorithm, were validated over a test set of data and deployed in real-time during the Commonwealth Games in support to a traditional water quality monitoring program. The proposed modelling framework proved to be cost-effective and less time-consuming than process-based models while still achieving high accuracy; in addition, the added value of a continuous stakeholder engagement guarantees a shared understanding of the model outputs and its future deployment.

Leaching behavior of mineral processing waste: comparison of batch and column investigations.

In this study, a comparison of laboratory batch and column experiments on metal release profile from a mineral processing waste (MPW) is presented. Batch (equilibrium) and column (dynamic) leaching tests were conducted on ground MPW at different liquid-solid ratios (LS) to determine the mechanisms controlling metal release. Additionally, the effect of pH on metal release is also discussed. It was observed that acidic pH conditions induced dissolution of As, Zn and Cu. Negligible leaching at alkaline pH was observed. However, Se depicted amphoteric behavior with high release at low and high pH. The batch and column data showed that As and Se release increased with LS ratio, while that of Cu and Zn increased initially and tapered towards equilibrium values at high LS ratios. The results on metal release from the MPW suggested that dissolution of the metal was the controlling mechanism. Leaching profiles from the batch and column data corresponded well for most LS ratios. This is most likely due to the acidic character of the waste, minimal changes in pH during the column operation and granular structure of the waste. From a waste management perspective, low cost batch equilibrium studies in lieu of high cost column experiments can be used for decision making on its disposal only when the waste exhibits characteristics similar to the mineral processing waste.

Arsenic release from iron rich mineral processing waste: Influence of pH and redox potential.

This paper presents the effect of pH and redox potential on the potential mobility of arsenic (As) from a contaminated mineral processing waste. The selected waste contained about 0.47 g kg(-1) of As and 66.2 g kg(-1) of iron (Fe). The characteristic of the waste was identified by acid digestion, X-ray diffraction and sequential extraction procedures. Less than 2% of the total As was acid extractable with the remaining 98% associated with Fe-oxyhydroxides and oxides. Batch leaching tests at different pH conditions showed a strong pH dependence on arsenic and iron leaching. Arsenic leaching followed a "V" shaped profiles with significant leaching in the acidic and alkaline pH region. Acid extractable phases dissolved at acidic pH, while desorption of arsenic due to increase in pH resulted in high arsenic concentration at alkaline pH. Under aerobic conditions and pH 7, As solubility was low, probably due to its precipitation on Fe-oxyhydroxides. Maximum As solubilization occurred at pH 11 (3.59 mg l(-1)). Similarity in the As and Fe leaching profiles suggested that the release of As was related to the dissolution of Fe in the low pH region. In general, redox potential did not play a significant role in arsenic or iron solubilization. It was thus concluded that for this solid waste, desorption was the predominant mechanism in arsenic leaching. A simple thermodynamic model based on arsenic and iron redox reactions was developed to identify the more sensitive redox couple.