The drill-and-blast method is widely used for the excavation of hard rock tunnels. Toxic gases such as carbon monoxide and nitrogen oxides are released immediately after blasting by the detonation of explosives. To provide a safe working environment, the concentration of noxious gases must be reduced below the threshold limit value according to health and safety regulations. In this paper, one-dimensional mathematical models and three-dimensional CFD numerical simulations were conducted to analyze the concentration, propagation and dilution of the blasting fumes under different operating conditions. Forced, exhaust and mixed ventilation modes were compared to determine the safe re-entry times after blasting in a 200 m-long tunnel excavated using the top-heading-and-benching method. Based on the numerical simulations, carbon monoxide was the most critical gas, as it required a longer ventilation time to reduce its concentration below the threshold limit value. The safe re-entry time reached 480 s under the typical forced ventilation mode, but was reduced to 155 s when a mixed ventilation system was used after blasting, reducing the operating costs. The reduction of the re-entry time represents a significant improvement in the excavation cycle. In addition, the results obtained show that 1D models can be used to preliminary analyze the migration of toxic gases. However, to reliably determine the safe re-entry times, 3D numerical models should be developed. Finally, to verify the accuracy of the CFD results, field measurements were carried out in a railway tunnel using gas sensors. In general, good agreements were obtained between the 3D numerical simulations and the measured values.

Benzene is a pollutant which is very harmful to our health, so models are necessary to predict its concentration and relationship with other air pollutants. The data collected by eight stations in Madrid (Spain) over nine years were analyzed using the following regression-based machine learning models: multivariate linear regression (MLR), multivariate adaptive regression splines (MARS), multilayer perceptron neural network (MLP), support vector machines (SVM), autoregressive integrated moving-average (ARIMA) and vector autoregressive moving-average (VARMA) models. Benzene concentration predictions were made from the concentration of four environmental pollutants: nitrogen dioxide (NO2), nitrogen oxides (NOx), particulate matter (PM10) and toluene (C7H8), and the performance measures of the model were studied from the proposed models. In general, regression-based machine learning models are more effective at predicting than time series models.

Million cubic meters from abandoned mines worldwide could be used as subsurface reservoirs for large scale energy storage systems, such as adiabatic compressed air energy storage (A-CAES). In this paper, analytical and three-dimensional CFD numerical models have been conducted to analyze the thermodynamic performance of the A-CAES reservoirs in abandoned mines during air charging and discharging processes. Unlike other research works, in which the heat transfer coefficient is considered constant during the operation time, in the present investigation a correlation based on both unsteady Reynolds and Rayleigh numbers is employed for the heat transfer coefficient in this type of application. A tunnel with a 35 cm thick concrete lining, 200 m3 of useful volume and typical operating pressures from 5 to 8 MPa were considered. Fiber-reinforced plastic (FRP) and steel were employed as sealing layers in the simulations around the fluid. Finally, the model also considers a 2.5 m thick sandstone rock mass around the concrete lining. The results obtained show significant heat flux between the pressurized air and the sealing layer and between the sealing layer and concrete lining. However, no temperature fluctuation was observed in the rock mass. The air temperature fluctuations are reduced when steel sealing layer is employed. The thermal energy balance through the sealing layer for 30 cycles, considering air mass flow rates of 0.22 kg s−1 (charge) and −0.45 kg s−1 (discharge), reached 1056 and 907 kWh for FRP and steel, respectively. In general, good agreements between analytical and numerical simulations were obtained.

Mining is an essential activity for obtaining materials necessary for the well-being and development of society. However, this activity produces important environmental impacts that must be controlled. More specifically, there are different soils near new or abandoned mining productions that have been contaminated with potentially toxic elements, and currently represent an important environmental problem. In this research, a contaminated soil from the mining district of Linares was studied for its use as a raw material for the conforming of ceramic materials, bricks, dedicated to construction. Firstly, the contaminated soil was chemically and physically characterized in order to evaluate its suitability. Subsequently, different families of samples were conformed with different percentages of clay and contaminated soil. Finally, the conformed ceramics were physically and mechanically characterized to examine the variation produced in the ceramic material by the incorporation of the contaminated soil. In addition, in this research, leachate tests were performed according to the TCLP method determining whether encapsulation of potentially toxic elements in the soil occurs. The results showed that all families of ceramic materials have acceptable physical properties, with a soil percentage of less than 80% being acceptable to obtain adequate mechanical properties and a maximum of 70% of contaminated soil to obtain acceptable leachate according to EPA regulations. Therefore, the maximum percentage of contaminated soil that can be incorporated into the ceramic material is 70% in order to comply with all standards. Consequently, this research not only avoids the contamination that contaminated soil can produce, but also valorizes this element as a raw material for new materials, avoiding the extraction of clay and reducing the environmental impact.

The data obtained from air quality monitoring stations, which are used to carry out studies using data mining techniques, present the problem of missing values. This paper describes a research work on missing data imputation. Among the most common methods, the method that best imputes values to the available data set is analysed. It uses an algorithm that randomly replaces all known values in a dataset once with imputed values and compares them with the actual known values, forming several subsets. Data from seven stations in the Silesian region (Poland) were analyzed for hourly concentrations of four pollutants: nitrogen dioxide (NO2), nitrogen oxides (NOx), particles of 10 μm or less (PM10) and sulphur dioxide (SO2) for five years. Imputations were performed using linear imputation (LI), predictive mean matching (PMM), random forest (RF), k-nearest neighbours (k-NN) and imputation by Kalman smoothing on structural time series (Kalman) methods and performance evaluations were performed. Once the comparison method was validated, it was determine that, in general, Kalman structural smoothing and the linear imputation methods best fitted the imputed values to the data pattern. It was observed that each imputation method behaves in an analogous way for the different stations The variables with the best results are NO2 and SO2. The UMI method is the worst imputer for missing values in the data sets.