Adsorption of Geosmin and MIB on Activated Carbon Fibers–Single and Binary Solute System

The adsorption of two taste- and odor-causing compounds, namely MIB (2-methyl isoborneol—C₁₁H₂₀O) and geosmin (C₁₂H₂₂O) on activated carbon was investigated in this study. The impact of adsorbent pore size distribution on adsorption of MIB and geosmin was evaluated through single solute and multicomponent adsorption of these compounds on three types of activated carbon fibers (ACFs) and one granular activated carbon (GAC). The ACFs (ACC-15, ACC-20, and ACC-25) with different degrees of activation had narrow pore size distributions and specific critical pore diameters whereas the GAC (F-400) had a wider pore size distribution and lesser microporosity. The effect of the presence of natural organic matter (NOM) on MIB and geosmin adsorption was also studied for both the single solute and binary systems. The Myers equation was used to evaluate the single solute isotherms as it converges to Henry’s law at low coverage and also serves as an input for predicting multicomponent adsorption. The single solute adsorption isotherms fit the Myers equation well and pore size distribution significantly influenced adsorption on the ACFs and GAC. The ideal adsorbed solute theory (IAST), which is a well-established thermodynamic model for multicomponent adsorption, was used to predict the binary adsorption of MIB and geosmin. The IAST predicted well the binary adsorption on the ACFs and GAC. Binary adsorption isotherms were also conducted in the presence of oxygen (oxic) and absence of oxygen (anoxic). There were no significant differences in the binary isotherm between the oxic and anoxic conditions, indicating that adsorption was purely through physical adsorption and no oligomerization was taking place. Binary adsorptions for the four adsorbents were also conducted in the presence of humic acid to determine the effect of NOM and to compare with IAST predictions. The presence of NOM interestingly resulted in deviation from IAST behavior in case of two adsorbents, ACC-15 and F-400.     

A Bayesian network analysis of workplace accidents caused by falls from a height

This article analyses, using Bayesian networks, the circumstances surrounding workplace tasks performed using auxiliary equipment (ladders, scaffolding, etc.) that may result in falls. The information source was a survey of employees working at a height. We were able to determine the usefulness of this approach – innovative in the accident research field – in identifying the causes that have the greatest bearing on accidents involving auxiliary equipment: in these cases, the adoption of incorrect postures during work and a worker’s inadequate knowledge of safety regulations. Likewise, the duration of tasks was also associated with both these variables, and therefore, with the accident rate. Bayesian networks also enable dependency relationships to be established between the different causes of accidents. This information – which is not usually furnished by conventional statistical methods applied in the field of labour risk prevention – allow a causality model to be defined for workplace accidents in a more realistic way. With this statistic tool, the expert is also provided with useful information that can be input to a management model for labour risk prevention.