Air pollution has become a major concern in industrial or highly populated areas. Although legislation has been enacted to limit pollution levels, air quality monitoring still needs to be carried out by stations which are located at fixed points unable to provide the spatial evolution of pollutants. This research, focused on the city of Gijón (Asturias), includes a Computational Fluid Dynamics model capable of simulating the dispersion of pollutants in a large urban environment (12x18 km $$^{\textrm{2}}$$2 ). Different wind conditions were simulated with two sources of emission. The results show the influence of the terrain on the dispersion of pollutants in open spaces whilst simultaneously scrutinizing the origin of diffuse industrial pollution circulating over the city of Gijon. The simulation allows us to set limits in the areas with higher levels of contamination or to analyse the variations of particle concentration in height. Therefore, this research defines and validates a methodology to generate numerical models which grant us the opportunity to observe the spatial evolution of pollutants in large areas. This result endorses further use in other lines of research, such as the evaluation of corrective measures to improve air quality in highly polluted environments.

Air pollution, representing one of the major environmental crises affecting us all, is responsible for half a million deaths each year in Europe. This research shows a numerical model based on Computational Fluid Dynamics (CFD) and a methodology for its validation allowing to know the evolution of particles in open urban environments. In this case, the model represents an area of Gijón (Asturias), specifically chosen as it serves to highlight a location at which the daily limit values of PM₁₀ set by the regulations are most frequently exceeded. A numerical model has been developed at a scale of 1:10,000 to simulate the dispersion of pollutants, including particulate emissions. A physical model at the same scale was built using 3D printing. This model was tested in a wind tunnel and analysed in the four main wind directions. To carry out the tests, a particle generation system was designed and manufactured, and the necessary infrastructure was built to take measurements using an optical particle meter. Results show that the numerical model meets the expected objectives and is capable of predicting the behaviour of particle dispersion in the air. The numerical model produces results in the same order of magnitude as the physical model, although there is a tendency to underestimate the maximum values with respect to the measured ones.

Tides can be a vast and predictable source of renewable energy. Due to the solar and lunar influx on our planet, they move large amounts of water periodically, and this energy can be harnessed using devices designed and positioned adequately, such as current turbines. However, the relation between the energy obtained with actual devices and the economic and environmental cost of their installation limits the practical application of these solutions. In order to optimize the design of this technology and achieve its successful installation and use, a detailed knowledge about the energy potential of tides at the specific location is necessary. This calculation is not easy and requires the use of specialized software tools. Currently, there is no specific software to evaluate the tidal currents energy potential, but there are more than a few codes able to calculate the hydraulic flow in rivers, estuaries and coastal regions. These programs are usually used for the calculation of pollutant dispersion and floods, but they can be adapted with more or less success. This paper reviews the available 1D, 2D, and 3D software tools with the aim of analyzing their functionality and their validity to evaluate the energy potential of tidal currents.

Air pollution is one of the major environmental problems, especially in industrial and highly populated areas. Remote sensing image is a rich source of information with many uses. This paper is focused on estimation of air pollutants using Landsat-5 TM and Landsat-8 OLI satellite images. Particulate Matter with particle size less than 10 microns (PM10) is estimated for the study area of Principado de Asturias (Spain). When a satellite records the radiance of the surface received at sensor, does not represent the true radiance of the surface. A noise caused by Aerosol and Particulate Matters attenuate that radiance. In many applications of remote sensing, that noise called path radiance is removed during pre-processing. Instead, path radiance was used to estimate the PM10 concentration in the air. A relationship between the path radiance and PM10 measurements from ground stations has been established using Random Forest (RF) algorithm and a PM10 map was generated for the study area. The results show that PM10 estimation through satellite image is an efficient technique and it is suitable for local and regional studies.

In a context where air quality has become a global concern, modelling techniques are becoming very popular for analysing pollutant dispersion conditions. While models based on empirical formulations are most widely used for industrial scenarios, singular cases involving complex terrain or large obstacles in the vicinity of emission sources require a more robust approach to evaluate the dispersion conditions. In this research, a computational fluid dynamics (CFD) model is developed to analyse the dispersion of pollutants from an industrial facility whose location and characteristics suggest the occurrence of complex flow features that affect the dispersion patterns. In addition, the variation of the gas exit temperatures of waste heat recovery systems is proposed as a measure to mitigate the impact of the plume. The simulation results show unexpected deviations in the plume path affecting vulnerable areas in a nearby mountainside, but increasing the gas exit temperature is useful to prevent this event. Therefore, the proposed methodology can be considered a decision support tool to find a compromise between the environmental impact and the fuel consumption of the plant.