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Metallurgy and recovery of gold in electronic waste sometimes involve the reduction of tetrachloroaurate ion (AuCl4-) to elemental gold form. Currently, for the reduction of tetrachloroaurate ion, people use reducing agents such as hydroquinone and sodium borohydride. Photocatalysts of Fe3O4/SiO2/TiO2 nanoparticles were prepared and tested for the reduction of tetrachloroaurate ion under UV light illumination. The magnetite (Fe3O4) nanoparticle was first prepared by coprecipitation and sonication, followed by SiO2 and TiO2 coatings via the sol-gel process and calcination. The products were confirmed by XRD and TEM. The photocatalytic reduction of tetrachloroaurate ion was performed in a closed reactor equipped with a UV light source. The results indicated that Fe3O4/SiO2/TiO2 nanoparticles were successfully prepared, which retained good magnetic and photocatalytic properties. The photocatalytic reaction is best performed at a pH of 5 under UV irradiation for 2 h, which is capable of reducing 96% of the tetrachloroaurate present in the mixture. The co-presence of Ni2+ and Cu2+ ions in the solution leads to a decrease in yield due to competitive reduction and adsorption. The photocatalyst is recoverable by the use of a magnetic bar and may find application for gold recovery and metallurgy.

Particulate matter (PM), classified according to aerodynamic diameter, is one of the harmful pollutants causing health damaging effects. It is considered as cancerogenic by the World Health Organization (WHO) because of the substances found in the chemical composition of PM. In this study, short-term prediction of PM2.5 pollution at 1, 2 and 3 hours was modelled using deep learning methods. Three deep learning algorithms and the combination thereof were evaluated: Long-short term memory units (LSTM), recurrent neural networks (RNN) and gated recurrent unit (GRU). Air Quality Monitoring Stations of the Ministry of Environment and Urbanization of Turkey were utilized to obtain the data. Specifically, meteorological and air pollution data were obtained from a monitoring station located in Keçiören District of Ankara. Several trials were conducted using different combinations of RNN, GRU and LSTM models. Pollutant concentrations and meteorological factors were integrated into the model as input parameters to predict PM2.5 concentration for 1,2 and 3 hours. Best results with R2 of 0.83, 0.7 and 0.63 for 1-, 2-, and 3-hour predictions, respectively, were obtained by using a combination of GRU and RNN models. The results of this study are promising for explaining the effect of different deep learning models on prediction performance.

The identification of PM sources and their contribution to measured PM concentrations is crucial for the environmental policy making since the findings be able to conduce to the development of relevant legislation in order to achieve effective air quality manage. In this study, the sources of PM10 at three receptors with different characteristics, within the Western Macedonia (WM) in NW Greece, were investigated: S1 in the center of Kozani, a medium sized city located at the southern edge of the industrial axis of WM, where urban activities and traffic density occur. S2 in the city of Ptolemaida, a medium sized city located in the centre of industrial zone, and S3 in the village of Eratyra, a rural residential district outside of the industrial area. For this purpose the multivariate Positive Matrix Factorization (EPA PMF 5.0) receptor model was applied on elemental data. Specifically, PM10 samples, obtained by filtration during 1-year sampling campaign, were analyzed by ICP-MS instrument. Twenty five elements were detected at quantifiable concentrations in the investigated PM samples. For the particle samples obtained in areas within and proximal from mining and power station operations, a six-factor model gave source profiles that attributed to be vehicle exhaust, road dust, soil dust, coal combustion, oil combustion and biomass burning. Furthermore, at the background site, the major contributors were biomass burning, soil dust and oil burning while no distant transport from industrial axis was recorded.

Volatile Organic Compounds (VOCs) are toxic for the environment and human health and their tendency to readily volatilize in the atmosphere can lead to problems connected to odours annoyance. Conventional VOCs gaseous emissions treatments entail the application of chemical-physical processes, only promoting the transfer of the contaminants from gas to liquid and/or solid phases. Advanced Oxidation Process (AOPs) and biological processes, conversely, support the oxidation of the organic pollutants, promoting their conversion into harmless and odourless compounds. The integration of booth processes is suggested to increase treatability of VOC. The research presents the application of an innovative treatment system composed by an AOPs pretreatment coupled with a bio-scrubbing unit for the abatement of VOCs, with the aim to increase the removal efficiency. The evaluation of the performance of the proposed system is discussed with reference to the analysis carried out using toluene as model substance. Different operating conditions have been analyzed and investigated to optimize the removal efficiency. The results show that the ozonation applied as pretreatment to the biological process may promote an increase of the pollutant biodegradability along with synergic effects due to the absorption of the ozone derived compounds into the culture growth, resulting in a significant enhancement of removal performances respect to the conventional biotechnologies. A toluene removal efficiency up to 95% were obtained under the investigated conditions.