In the last years, research efforts have been addressed on the effects of single and multiple pollutants on human health, in particular in densely populated areas. Modelling tools, integrating atmospheric science with the latest evidence available from air pollution epidemiology and exposure science, represent a valuable support to health impact assessment. This article considers the latest developments of the DIATI Dispersion and Externalities Model (DIDEM). To extend DIDEM’s scope of analysis, the inte- gration with different pollutant dispersion models was recently implemented. Particularly, in this arti- cle, a comparative evaluation between CALPUFF (California Puff) Lagrangian puff model and SPRAY Lagrangian particle model is presented. To help reaching this objective, the case study of Turin’s district heating system, presented in previous publications, was re-considered and deepened. CALPUFF and SPRAY models were compared on the same emission scenario. NO_x and total PM concentrations result- ing from the simulations were of the same magnitude, with some difference in the spatial distribution. Total health damage costs differed between 8.5% and 9.7%, with lower values corresponding to SPRAY simulations. This difference mostly corresponds to the different spatial distribution of pollutant con- centrations which, in turn, correspond to different exposure levels. The possibility of selecting different modelling tools extends the usability of DIDEM to a larger set of applications, including a wider scope of application and a larger range of users. The results provide important information in the view of the characterization of the overall uncertainty of the impact pathway approach methodology.

The monitoring of air pollution, especially the detection and characterization of biological aerosols (bioaerosols) in the atmosphere continues to be a challenging task. Most biosensors rely on the presence of specific molecules, such as antigens on the surface, DNA sequences, or the common fluorescents tryptophan, flavins, or reduced form of nicotinamide adenine dinucleotide (NADH). However, the de- tection signatures from either of these technologies can change significantly when the bioaerosol is released into the atmosphere, and the observed changes are strongly dependent upon the environmental conditions. In developing bioaerosol detection and characterization methods, researchers must account for the potential changes in their physical, chemical, and biological properties caused by various atmospheric conditions. The experimental results presented here show how the fluorescence spectral profile and intensity, the viability, and the PCR signature of bioaerosols, in particular for the vegetative bacteria Escherichia coli, change with time in the presence of one, or combinations of two, three, or four of the following variables: relative humidity <30% or ~75%, ozone ~100 ppb, α-pinene ~5 ppb, toluene ~45 ppb, and simulated solar ultra-violet light illumination with the typical levels in common atmospheric constituents and meteorological conditions. Large changes have been observed, e.g. UV fluorescence intensity dropped to be less than 1/10 of its initial value and the ratio of UV/visible fluo- rescence intensity flipped from 2 to ½ within 3 h. These changes could happen on a typical day in any city or suburban area. Recording data of the ageing processes measured here should be very useful in developing biosensors and monitoring air pollution.

The World Health Organization (WHO) estimates that air pollution kills around 6.5 million people around the world every year. The European Environment Agency, in turn, points out that about 50,000 people die annually in Poland due to this. PM10 pollution arises in the form of smog (smoke and fog) and is an unnatural phenomenon created by adverse weather conditions and human activity. The aim of this article is to assess the possibilities of tasking modern neural networks to predict PM10 air pollution levels in the following hours of the subsequent day. In evaluating the prediction task, several types of error are considered, and machine learning algorithms and structures are utilized as learning models. Of note, the algorithm selected for stochastic optimization is a form of convolutional neural networking and deep learning neural networking that is used in machine learning when considering Big Data issues. The obtained results were then analysed and compared with other methods of prediction. As a result of this research, the proposed convergent neural network could be used effectively as a tool for calculating detailed air quality forecasts for the subsequent 24-h period.

Air quality improvement is a major concern in developed countries. In the past decade, especially in Eu- rope, legislative measures have been taken to reduce air pollution. The present article promotes photoca- talysis as an air quality improvement technique towards NO pollution. Indoor air depollution by painted plasterboards treated with photocatalytic coating was investigated. First, at laboratory scale, using a bed flow reactor, depollution efficiency of the photocatalytic system was evaluated. Experimental conditions were adapted as much as possible to match indoor environment. Thus, pollution levels remained at ppb scale, temperature and relative humidity (RH) were kept constant (20 °C and 50% RH) and typical indoor lighting systems (fluorescent tubes, Light-Emitting Diode (LED) and halogen bulbs) were used for photoactivation. UV-A fluorescent tube was also used to optimise photocatalytic activity. Second, experiments were conducted at real scale, in a 10-m3 experimental chamber developed at our laboratory. Interior walls were covered with the photocatalytic system and the chamber was used as a reactor. Employing a specific experimental procedure, aiming at keeping pollution level constant in the chamber, photocatalytic depollution was evaluated. The same lighting systems were used for photoactivation. NO₂ abatement efficiency was evaluated through the photocatalytic oxidation potential and rate. Results show that NO₂ can be significantly removed by this technique. However, the light used for photoactivation is at utmost importance. Furthermore, the results show that at laboratory scale, photocatalytic depollution efficiency of NO₂ could be underestimated.

One of the strategies for sustainable development is to promote a quality health care system, available to all without discrimination, and improving protection against health threats. In this context, arsenic removal from groundwater for drinking purposes presents challenges at national and global levels. Thus, the present article focuses on removing arsenic from groundwaters by using a new class of materials based on cellulose modified with crown ether (dibenzo-18-crown-6) doped with iron ions. Using such extractants involves only a small amount of crown ether, indicating higher efficiency of produced material, and in order to improve the adsorbent properties and selectivity for arsenic removal, the modified cellulose was functionalized with iron ions. The new adsorbent material was characterized by using energy-dispersive X-ray analysis and Fourier-transform infrared spectroscopy. To investigate its adsorption properties for arsenic removal, equilibrium, kinetic and thermodynamic studies were performed. Arsenic adsorption from water onto new class of adsorbent material was studied under different experimental conditions such as reac- tion time, initial arsenic concentration and temperature. Kinetic of adsorption process was better de- scribed by pseudo-second-order model. The equilibrium adsorption data were well described by the Sips adsorption isotherm. The values of thermodynamic parameters (ΔGº, ΔHº, ΔSº) showed that the adsorption process was endothermic and spontaneous. The possibility of reuse of the adsorbent material through adsorption and desorption cycles was also studied, and it was found that the material can be used in three adsorption–desorption cycles.

A new method that evaluates dominant local dynamics by skeletonization, mathematical term decom- position and the re-combination of a reduced number of dominant terms around the skeleton points is proposed to clarify the dynamics of hairpin vortices generated during the boundary-layer transition under free-stream turbulence (FST). The development of the method is based on the results of direct numerical simulations conducted for the laminar-turbulent transition on a flat plate with FST intensities of 0–6% and a free-stream Mach number of 0.5. Regarding the skeletonization, a new algorithm for extracting the interior points of vortex structures represented by enclosed iso-surfaces is developed. To identify the dominant terms, governing equations are decomposed into non-further-decomposable (NFD) terms. The proposed method is also extended to time series flow field data to reveal the variation of the combination set of dominant NFD terms during the evolution of vortex structures. The present method enables the automatic finding and categorization of the variations of the sets of dominant terms that govern local dynamics during the evolution of hairpin vortices.

In spite of their positive role in the framework of circular economy, waste-to-energy processes are responsible for the emissions of a large number of air pollutants. Although this sector has made significant improvements in the air pollution control of primary emissions, the role of other sources (i.e. secondary emissions) has been often neglected. This paper aims at investigating the contributions of primary and secondary emissions expected from a waste gasification plant that is planned for the construction in an Alpine valley. The results from this analysis show that secondary emissions would play a significant role in the overall emissive footprint of the plant, contributing to 29% and 10%, respectively, of the overall emissions of dusts and total organic carbon. In the light of such results, secondary emissions would require an appropriate monitoring approach, which should complement the existing monitoring protocols for primary emissions.