The circular valorization of industrial residues as functional materials offers a promising strategy to reduce the environmental footprint of water treatment technologies. In this study, raw coal fly ash (FA), applied without pretreatment or structural modification, was evaluated as a waste-derived photocatalyst for the degradation of metamizole, a widely detected contaminant of emerging concern. A systematic assessment under five irradiation sources (visible light, UV-A, UV-B, UV-C, and V-UV) demonstrated that genuine photocatalytic activation occurred exclusively under UV-B irradiation, achieving 96.2% removal after 150 min and outperforming direct UV-B photolysis by 14.9 percentage points. In contrast, UV-C and V-UV induced near-complete photolysis independently of catalyst presence, while visible and UV-A irradiation produced negligible degradation, consistent with the measured band gap of FA (3.1 eV). Kinetic and scavenger experiments indicated a mechanism mainly governed by valence-band holes (h+) and hydroxyl radicals (circle OH), with minor contribution from superoxide species (O2 circle-). The photocatalytic performance remained stable in the presence of common inorganic salts, confirming robustness under realistic water matrix conditions. HPLC-MS/MS analysis enabled the detection of fourteen potential transformation products and the proposal of a degradation pathway distinguishing photocatalytic activity from direct photolysis. Under optimized UV-B/FA conditions, COD and TOC removals reached 67.11% and 56.73%, respectively, while metal leaching remained minimal. Overall, unmodified coal fly ash behaves as an intrinsic semiconductor photocatalyst under UV-B irradiation and represents a promising lowcost material for sustainable and circular water treatment applications.
Fisheries subsidies are often linked to overcapacity, overfishing, and environmental harm. As a result, global efforts have increasingly focused on eliminating harmful subsidies, while paying less attention to how public support can be designed to promote positive environmental outcomes. This study analyzes an alternative paradigm from Chile’s industrial shrimp (Heterocarpus reedi) and prawn (Cervimunida johni and Pleuroncodes monodon) fisheries, where a government-supported Clean Production Agreement (CPA) incentivized the adoption of cleaner production methods. Using a mixed-methods approach that combines qualitative documentary analysis with quantitative evaluation of environmental and economic indicators, we assessed the design, implementation, and outcomes of the CPA. Our findings reveal that the scheme enhanced environmental performance by reducing fuel consumption, bycatch, freshwater use, waste, and air emissions, while simultaneously delivering tangible economic benefits for participating companies. The initiative’s success was driven by four key design features: conditional funding tied to environmental goals, mandatory private co-financing, independent auditing and certification, and collaborative governance involving both public and private sectors. Importantly, subsidies did not cover fuel, fleet expansion, or other activities that could increase fishing capacity, thereby mitigating the risks of overcapacity and greenwashing. The Chilean case demonstrates that well-designed environmental subsidies can effectively support sustainability transitions in fisheries and offers valuable policy lessons for differentiating beneficial subsidies from those that threaten marine sustainability.
El desierto de Atacama, especialmente en la región de Antofagasta, enfrenta crecientes riesgos de remoción en masa agravados por el cambio climático. Este artículo propone un modelo teórico que integra estrategias de mitigación y adaptación en proyectos de arquitectura urbana a escala del barrio, enfocados en zonas desérticas en pendiente. El proceso metodológico contempla una etapa de clasificación deductiva seguida de otra de análisis cualitativo inductivo. Se identifican dentro de 13 proyectos académicos de la Escuela de Arquitectura de la Universidad Católica del Norte en la ciudad de Antofagasta dos tipologías: una orientada a la mitigación mediante piscinas aluviales y muros de contención, y otra mixta que incorpora adaptación al terreno, barreras verdes, zonas inundables, rampas y canalización subterránea. El modelo contribuye a llenar un vacío en la literatura sobre gestión de riesgos en contextos áridos, alineándose con el ODS 11 y ODS 13. Asimismo, ofrece una guía práctica para arquitectos y urbanistas, promoviendo políticas y proyectos urbanos más resilientes, sostenibles y seguros.
Heap leaching is widely used for the recovery of copper from low-grade oxide ores. Continuous monitoring of copper concentration in the pregnant leach solution (PLS) is essential for tracking leaching kinetics, determining optimal irrigation termination, and balancing metallurgical inventories at the individual-heap level; however, measurement at heap drainage channels remains operationally challenging owing to limited accessibility and reliance on manual sampling with delayed laboratory analysis. This study characterizes the photographic and modeling parameters of a laboratory prototype that estimates copper concentration in copper sulfate solutions using empirical, image-based colorimetric regression with low-cost consumer imaging hardware rather than a laboratory spectrometer, as a basis for future in-line sensing. Synthetic PLS solutions (0 to 40 g Cu/L in sulfuric acid) were imaged under controlled illumination (fixed-color, fixed-intensity LED light, diffused inside a dark chamber and not collimated) using a Nikon D3100 digital single-lens reflex (DSLR) camera across four channels (red, green, blue, and a white composite) at three intensity levels. For each channel, an absorbance-like feature was derived from the channel intensity relative to a zero-copper blank. A dataset of 432 images was subdivided into 256 spatial sub-samples per image across four channels, generating 442,368 channel-level observations, which were reduced to 15,120 physically coherent observations by sequential filtering (Kendall rank correlation, then linear and quadratic absorbance-concentration bounds). A feedforward artificial neural network (ANN) predicted copper concentration from the four color features and was benchmarked against a multivariate linear model used as a Lambert-Beer baseline. On a separate simulated, controlled dataset, the ANN achieved an RMSE of approximately 0.55 g/L (residual standard deviation of 0.40-0.41 g/L), a reduction of approximately 37 % in residual standard deviation relative to the best linear baseline (from 0.64 to 0.40 g/L) on simulated data. Experimental validation yielded a prediction error of approximately 25 %. This error is structured rather than random, arising from intensity-level displacement caused by specular reflections on the cylindrical sample cell, and is comparable to the similar to 20 % uncertainty of current manual sampling; it is therefore a hardware and dataacquisition limitation rather than a limitation of the sensing concept. Under real PLS conditions, the error would be expected to increase, so the laboratory value should be read as a best-case bound. A flat-window flow cell, collimated illumination, and inclusion of the recorded illumination intensity as a model input are identified as the priority improvements.
