Air, Volume 3, Issue 2 (June 2025) – 3 articles

A project to assess air pollution at the National Archeological Museum in Naples was carried out. The main goal of the project was to develop and test a reliable yet simple monitoring system to be adopted at the same time in several exposition rooms. Nitrogen dioxide, hydrogen chloride, nitrous acid, and sulphur dioxide were the chemical species addressed by the technique. Monitoring was simultaneously performed in five rooms, and pollutant concentrations were determined using two passive samplers. The sampling time was approximately one month per period. In addition to passive samplers, environmental data loggers were used to obtain temperature and relative humidity data. Results show high concentrations of nitrogen dioxide inside rooms, which were consistent with those found in outdoor environments and are close to the values calculated considering the air exchange rates, estimated through time gradients of ambient temperature. The minimum values were recorded in a basement room that had a low ventilation rate. The conversion of nitrogen dioxide to real surfaces produces nitric acid and nitrous acid. Large amounts of nitrous acid, up to 15 µg/m³, were found in exposition rooms, with maximum values in the basement room, where the air exchange rate is limited, and the surface-to-volume ratio is the highest among the monitored rooms. Data analysis demonstrated that the system could discriminate between nitrous acid and nitrogen dioxide. The results show that, for the first time, passive samplers can overcome the problem of mutual interference between nitrogen-containing species. Nitrates and nitrites found in the alkaline passive sampler were generally found not to be interfered by nitrogen dioxide. Nitric acid was also found in the gas phase, likely generated by dissociation of ammonium nitrate in particulate matter. Hydrogen chloride and sulphur dioxide were present at few µg/m³. Nitrous acid is the most relevant acidic species found indoors. The presence of pollutants was discussed in terms of the reliability of the analytical procedure and its significance for indoor air pollution. Full article

The air we breathe contains contaminants such as particulate matter (PM), carbon dioxide (${\mathrm{CO}}_2$), nitrogen dioxide (${\mathrm{NO}}_2$), and nitric oxide (NO), which, when inhaled, bring about several changes in the autonomous responses of our body. Our previous work showed that we can use the human body as a sensor by making use of autonomous responses (or biometrics), such as changes in electrical activity in the brain, measured via electroencephalogram (EEG) and physiological changes, including skin temperature, galvanic skin response (GSR), and blood oxygen saturation (${\mathrm{SpO}}_2$). These biometrics can be used to estimate pollutants, in particularly ${\mathrm{PM}}_1$ and ${\mathrm{CO}}_2$, with high degree of accuracy using machine learning. Our previous work made use of the Welch method (WM) to obtain a power spectral density (PSD) from the time series of EEG data. In this study, we introduce a novel approach for obtaining a PSD from the EEG time series, developed by Astrapi, called the Astrapi Spectrum Analyzer (ASA). The physiological responses of a participant cycling outdoors were measured using a biometric suite, and ambient ${\mathrm{CO}}_2$, ${\mathrm{NO}}_2$, and NO were measured simultaneously. We combined physiological responses with the PSD from the EEG time series using both the WM and the ASA to estimate the inhaled concentrations of ${\mathrm{CO}}_2$, ${\mathrm{NO}}_2$, and NO. This work shows that the PSD obtained from the ASA, when combined with other physiological responses, provides much better results (RMSE = 9.28 ppm in an independent test set) in estimating inhaled ${\mathrm{CO}}_2$ compared to making use of the same physiological responses and the PSD obtained by the WM (RMSE = 17.55 ppm in an independent test set). Small improvements were also seen in the estimation of ${\mathrm{NO}}_2$ and NO when using physiological responses and the PSD from the ASA, which can be further confirmed with a large number of dataset. Full article

The transportation sector has a significant impact on climate change, as it is responsible for 20% of the global greenhouse gas (GHG) emissions. This paper evaluates the role of electric vehicles (EVs) in achieving Europe’s ambitious target of carbon neutrality by 2050. The limitations of internal combustion engines (ICEs) along with the recent advancements, such as Euro 6 standards, are examined with a pseudo–lifecycle analysis (pseudo-LCA). While ICEs remain cost-effective initially, their higher long-term cost and environmental impact make them unsustainable. The benefits of EVs, including high energy efficiency, minimal maintenance, and reduced GHG emissions, are stated. However, challenges such as range limitations, charging infrastructure, and the environmental cost of battery production persist. Hybrid electric vehicles (HEVs) are highlighted as transitional technologies, offering improved thermal efficiency and reduced emissions, enhancing air quality in both urban and rural areas. The analysis extends to the use of alternative fuels, such as bioethanol, biodiesel, and hydrogen. These provide interim solutions but face scalability and sustainability issues. Policy interventions, including subsidies, tax incentives, and investments in renewable energy, are crucial factors for EV adoption. As EVs are pivotal to decarbonization, integrating renewable energy and addressing systemic challenges are essential for a sustainable transition. Full article