Green infrastructure drainage systems are innovative treatment units that capture and treat stormwater. Unfortunately, highly polar contaminants remain challenging to remove in conventional biofilters. To overcome treatment limitations, we assessed the transport and removal of stormwater vehicle-related organic contaminants with persistent, mobile, and toxic (in short: PMTs) properties, such as 1H-benzotriazole, NN'-diphenylguanidine, and hexamethoxymethylmelamine (PMT precursor), using batch experiments and continuous-flow sand columns amended with pyrogenic carbonaceous materials, like granulated activated carbon (GAC) or wheat-straw derived biochar. Our results indicated that all investigated contaminants were subjected to nonequilibrium interactions in sand-only and geomedia-amended columns, with kinetic effects upon transport. Experimental breakthrough curves could be well described by a one-site kinetic transport model assuming saturation of sorption sites, which we inferred could occur due to dissolved organic matter fouling. Furthermore, from both batch and column experiments, we found that GAC could remove contaminants significantly better than biochar with higher sorption capacity and faster sorption kinetics. Hexamethoxymethylmelamine, with the lowest organic carbon-water partition coefficient (K_OC) and largest molecular volume among target chemicals, exhibited the lowest affinity in both carbonaceous adsorbents based on estimated sorption parameters. Results suggest that sorption of investigated PMTs was likely driven by steric and hydrophobic effects, and coulombic and other weak intermolecular forces (e.g., London–van der Waals, H-bonding). Results from extrapolating our data to a 1-m depth geomedia-amended sand filter suggested that GAC and biochar could enhance the removal of organic contaminants in biofilters and last for more than one decade. Overall, our work is the first to study treatment alternatives for NN'-diphenylguanidine and hexamethoxymethylmelamine, and contributes to better PMT contaminant removal strategies in environmental applications.

A learning-based strategy that uses support vector machines and k nearest neighbours is proposed for locating the faulted zone in radial power systems, specifically in distribution networks. The main goal is to reduce the multiple estimation of the fault location, inherent in those methods that use single end measurements. A selection of features obtained from the fundamentals of voltages and currents, measured at the power substation, are analysed and used as inputs of the proposed zone locator. Performance of several combinations of these features considering all fault types, different short-circuit levels and variation of the fault resistance, and the system load is evaluated. An application example illustrates the high precision to locate the faulted zone, obtained with the proposed methodology. The proposal provides appropriate information for the prevention and opportune attention of faults, requires minimum investment and overcomes the multiple estimation problem of the classic impedance based methods.