Sources, characteristics, toxicity, and control of ultrafine particles: an overview

Air pollution by particulate matter (PM) is one of the main threats to human health, particularly in large cities where pollution levels are continually exceeded. According to their source of emission, geography, and local meteorology, the pollutant particles vary in size and composition. These particles are conditioned to the aerodynamic diameter and thus classified as coarse (2.5–10 μm), fine (0.1–2.5 μm), and ultrafine (<0.1 μm), where the degree of toxicity becomes greater for smaller particles. These particles can get into the lungs and translocate into vital organs due to their size, causing significant human health consequences. Besides, PM pollutants have been linked to respiratory conditions, genotoxic, mutagenic, and carcinogenic activity in human beings. This paper presents an overview of emission sources, physicochemical characteristics, collection and measurement methodologies, toxicity, and existing control mechanisms for ultrafine particles (UFPs) in the last fifteen years

Recent Developments in the Photocatalytic Treatment of Cyanide Wastewater: An Approach to Remediation and Recovery of Metals

For gold extraction, the most used extraction technique is the Merrill-Crow process, which uses lixiviants as sodium or potassium cyanide for gold leaching at alkaline conditions. The cyanide ion has an affinity not only for gold and silver, but for other metals in the ores, such as Al, Fe, Cu, Ni, Zn, and other toxic metals like Hg, As, Cr, Co, Pb, Sn, and Mn. After the extraction stage, the resulting wastewater is concentrated at alkaline conditions with concentrations up to 1000 ppm of metals. Photocatalysis is an advanced oxidation process (AOP) able to generate a photoreaction in the solid surface of a semiconductor activated by light. Although it is well known that photocatalytic processes can remove metals in solution, there are no compilations about the researches on photocatalytic removal of metals in wastewater with cyanide. Hence, this review comprises the existing applications of photocatalytic processes to remove metal and in some cases recover cyanide from recalcitrant wastewater from gold extraction. The use of this process, in general, requires the addition of several scavengers in order to force the mechanism to a pathway where the electrons can be transferred to the metal-cyanide matrices, or elsewhere the entire metallic cyanocomplex can be degraded by an oxidative pathway

Aplicación de Redes Neuronales Artificiales y Teoría de la entropía de la información para evaluar las precipitaciones Distribución de estaciones: un estudio de caso de Colombia

18 páginasAn assessment of the rainfall station distribution in the mountainous area of the Regional Autonomous Corporation of Cundinamarca (CAR, for its acronym in Spanish), Colombia, was conducted by applying concepts from information entropy and artificial neural networks (ANNs). This study was divided into two phases: first, a classification of the meteorological stations using two-dimensional self-organizing maps; second, the evaluation of the performance of the ANN by applying concepts of information entropy. Three scenarios were raised for the classification of the meteorological stations by adjusting the number of neurons in the output layer. A high number of neurons in the output layer were obtained, causing the model to over-fit while emphasizing differences amid patterns. When comparing the results of the scenarios, the permanence of certain characteristics and features was found in the system, validating the model classification. Subsequently, the results of the first scenario were used to evaluate the entropy of the historical series. Finally, the results show that the area of study presents a lack of information due to the uncertainty associated with the probabilistic arrangement, which can be corrected with the developed model. Consequently, some recommendations for the redesign of the rainfall are providedSe realizó una evaluación de la distribución de las estaciones pluviométricas en la zona montañosa de la Corporación Autónoma Regional de Cundinamarca (CAR), Colombia, aplicando conceptos de entropía de la información y redes neuronales artificiales (RNAs). Este estudio se dividió en dos fases: primero, una clasificación de las estaciones meteorológicas utilizando mapas bidimensionales autoorganizados; segundo, la evaluación del desempeño de la RNA aplicando conceptos de entropía de la información. Se plantearon tres escenarios para la clasificación de las estaciones meteorológicas ajustando el número de neuronas en la capa de salida. Se obtuvo una gran cantidad de neuronas en la capa de salida, lo que provocó que el modelo se ajustara demasiado y enfatizara las diferencias entre los patrones. Al comparar los resultados de los escenarios, se encontró la permanencia de ciertas características y rasgos en el sistema, validando la clasificación del modelo. Posteriormente, los resultados del primer escenario se utilizaron para evaluar la entropía de la serie histórica. Finalmente, los resultados muestran que el área de estudio presenta carencia de información debido a la incertidumbre asociada al arreglo probabilístico, lo cual puede ser corregido con el modelo desarrollado. En consecuencia, se brindan algunas recomendaciones para el rediseño del pluvial

Application of Artificial Neural Network and Information Entropy Theory to Assess Rainfall Station Distribution: A Case Study from Colombia

An assessment of the rainfall station distribution in the mountainous area of the Regional Autonomous Corporation of Cundinamarca (CAR, for its acronym in Spanish), Colombia, was conducted by applying concepts from information entropy and artificial neural networks (ANNs). This study was divided into two phases: first, a classification of the meteorological stations using two-dimensional self-organizing maps; second, the evaluation of the performance of the ANN by applying concepts of information entropy. Three scenarios were raised for the classification of the meteorological stations by adjusting the number of neurons in the output layer. A high number of neurons in the output layer were obtained, causing the model to over-fit while emphasizing differences amid patterns. When comparing the results of the scenarios, the permanence of certain characteristics and features was found in the system, validating the model classification. Subsequently, the results of the first scenario were used to evaluate the entropy of the historical series. Finally, the results show that the area of study presents a lack of information due to the uncertainty associated with the probabilistic arrangement, which can be corrected with the developed model. Consequently, some recommendations for the redesign of the rainfall are provided. </p