Search Results (56)

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

The goal of this study is to describe a design architecture for a self-powered IoT (Internet of Things) sensor network that is currently being deployed at various locations throughout the Dallas-Fort Worth metroplex to measure and report on Particulate Matter (PM) concentrations. This system leverages diverse low-cost PM sensors, enhanced by machine learning for sensor calibration, with LoRaWAN connectivity for long-range data transmission. Sensors are GPS-enabled, allowing precise geospatial mapping of collected data, which can be integrated with urban air quality forecasting models and operational forecasting systems. To achieve energy self-sufficiency, the system uses a small-scale solar-powered solution, allowing it to operate independently from the grid, making it both cost-effective and suitable for remote locations. This novel approach leverages multiple operational modes based on power availability to optimize energy efficiency and prevent downtime. By dynamically adjusting system behavior according to power conditions, it ensures continuous operation while conserving energy during periods of reduced supply. This innovative strategy significantly enhances performance and resource management, improving system reliability and sustainability. This IoT network provides localized real-time air quality data, which has significant public health benefits, especially for vulnerable populations in densely populated urban environments. The project demonstrates the synergy between IoT sensor data, machine learning-enhanced calibration, and forecasting methods, contributing to scientific understanding of microenvironments, human exposure, and public health impacts of urban air quality. In addition, this study emphasizes open source design principles, promoting transparency, data quality, and reproducibility by exploring cost-effective sensor calibration techniques and adhering to open data standards. The next iteration of the sensors will include edge processing for short-term air quality forecasts. This work underscores the transformative role of low-cost sensor networks in urban air quality monitoring, advancing equitable policy development and empowering communities to address pollution challenges. Full article

Tobacco smoking and electronic cigarette (EC) use are associated with elevated levels of particulate matter (PM) and nicotine in indoor environments. This study assessed filtration and nicotine capture efficiency of untreated and citric acid-treated high efficiency particulate air (HEPA) filters from two manufacturers, “on-brand” (original) and “off-brand” (replacement). When challenged with salt aerosol, the filtration efficiency (FE) (Mean ± RSD) of original HEPA filters (99.9% ± 0.1) was significantly higher than replacements (94.4% ± 1.7), but both were significantly below the HEPA designation of 99.97%. No significant differences in FE were observed between treated and untreated HEPA filters. All filters had lower FE for EC aerosol compared to salt aerosol, especially among replacement filters. Nicotine capture efficiency was significantly higher in citric acid-treated HEPA filters for originals (99.4% ± 0.22) and replacements (99.0% ± 1.07) compared to untreated originals (57.4% ± 2.33) and replacements (42.0% ± 14.20). This study demonstrated that our citric acid treatment of HEPA filters was effective and efficient at capturing airborne nicotine and did not affect the FE for PM. Use of citric acid-treated HEPA filters would be an effective exposure reduction strategy for both nicotine and PM in indoor settings. Full article

This study aims to determine the optimal frequency for monitoring airborne pollutants in densely populated urban areas to effectively capture their temporal variations. While environmental organizations worldwide typically update air quality data hourly, there is no global consensus on the ideal monitoring frequency to adequately resolve pollutant (particulate matter) time series. By applying temporal variogram analysis to particulate matter (PM) data over time, we identified specific measurement intervals that accurately reflect fluctuations in pollution levels. Using January 2023 air quality data from the Joppa neighborhood of Dallas, Texas, USA, temporal variogram analysis was conducted on three distinct days with varying ${\mathrm{PM}}_{2.5}$ (particulate matter of size ≤ $2.5 \mathsf{\mu }\mathrm{m}$ in diameter) pollution levels. For the most polluted day, the optimal sampling interval for ${\mathrm{PM}}_{2.5}$ was determined to be 12.25 s. This analysis shows that highly polluted days are associated with shorter sampling intervals, highlighting the need for highly granular observations to accurately capture variations in PM levels. Using the variogram analysis results from the most polluted day, we trained machine learning models that can predict the sampling time using meteorological parameters. Feature importance analysis revealed that humidity, temperature, and wind speed could significantly impact the measurement time for ${\mathrm{PM}}_{2.5}$. The study also extends to the other size fractions measured by the air quality monitor. Our findings highlight how local conditions influence the frequency required to reliably track changes in air quality. Full article

Indoor air pollution constitutes a public health problem due to the long time that individuals spend in enclosed spaces every day. The present study aims to investigate the level of volatile organic compounds (VOCs) in indoor air in households in Senegal, and to assess health risks related to residents’ exposure. Of the 17 VOCs identified, 16 were detected in Medina accommodations versus 14 in Darou Khoudoss. Toluene levels reached 70.9 μg/m³ in Medina and 18.5 μg/m³ in Darou Khoudoss, which were the highest compared to other compounds. The sum of Benzene, Toluene, Ethylbenzene, o-Xylene, and 1,2,4-trimethylbenzene concentrations were two times higher in Medina (79.57 µg/m³ versus 37.1 µg/m³). Furthermore, VOCs were found at higher levels in living rooms compared to other living spaces. The highest benzene and acetone concentrations were estimated at 13.6 µg/m³ and 8.4 µg/m³, respectively, in households where incense was burnt daily, while the highest formaldehyde levels were observed in households using incense seasonally (6.8 µg/m³). As regards the health risks associated with exposure of residents, the lifetime cancer risks were all above the WHO tolerable limit (10⁻⁵–10⁻⁶). Exposure to benzene (8.5 µg/m³) associated with a lifetime risk of leukemia (51.3 per million people exposed) was higher in Darou Khoudoss, while the risk of nasopharyngeal cancer (600 per million people exposed) associated with exposure to formaldehyde (4.23 µg/m³) was higher in Medina. Full article

Museums play a vital role in preserving cultural heritage and for this reason, they require strict indoor environmental controls. Balancing indoor environmental quality with reduced energy consumption poses significant challenges. Over the course of a year (2023), indoor microclimate conditions, atmospheric pollutant concentrations (O₃, TVOC, CO, CO₂, particulate matter), and energy use were monitored at the Archaeological Museum of Kavala. Maximum daily fluctuations in relative humidity were 15% in summertime, while air temperature variations reached 2.0 °C, highlighting unstable microclimatic conditions. Particulate matter was the primary threat to the preservation of artworks, followed by indoor O₃ and NO₂, whose concentrations exceeded recommended limits for cultural conservation. In 2023, the Energy Use Intensity (EUI) was 86.1 kWh m⁻², a value that is significantly correlated with the number of visitors and the outdoor air temperature. Every person visiting the museum was assigned an average of 7.7 kWh of energy. During the hottest days and when the museum was crowded, the maximum amount of energy was consumed. Over the past decade (2013–2023), the lowest EUI was recorded during the COVID-19 pandemic at 53 kWh m⁻². Energy consumption is linked to indoor environmental quality; thus, both must be continuously monitored. Full article

Particulate matter (PM) is a critical component of urban air pollution, with severe implications for human health and environmental ecosystems. This study investigates the capacity of green roofs at the Warsaw University Library to mitigate air pollution by analyzing the retention of PM and associated trace elements (TEs) across eight perennial plant species during spring, summer, and autumn. The results highlight significant interspecies variability and seasonal trends in PM retention, with peak levels observed in summer due to increased foliage density and ambient pollution. Sedum spectabile and Spiraea japonica emerged as the most effective species for PM capture, owing to their wax-rich surfaces and dense foliage, while Betula pendula demonstrated a high retention of TEs like manganese and zinc. Seasonal shifts from surface-bound PM (_SPM) to wax-bound PM (_WPM) in autumn underline the importance of adaptive plant traits for sustained pollutant capture. These findings underscore the critical role of green roofs in urban air quality management, emphasizing the need for species-specific strategies to maximize year-round phytoremediation efficacy. Expanding the implementation of diverse vegetation on green roofs can significantly enhance their environmental and public health benefits. Full article

Indoor air quality (IAQ) significantly impacts human health, particularly in enclosed spaces where people spend most of their time. This study evaluates the performance of low-cost IAQ sensors, focusing on their ability to measure carbon dioxide (CO₂) and particulate matter (PM) under real-world conditions. Measurements provided by these sensors were verified against calibrated reference equipment. The study utilized two commercial devices from inBiot and Kaiterra, comparing their outputs to a reference sensor across a range of CO₂ concentrations (500–1200 ppm) and environmental conditions (21–25 °C, 27–92% RH). Data were analyzed for relative error, temporal stability, and reproducibility. Results indicate strong correlation between low-cost sensors (LCSs) and the reference sensor at lower CO₂ concentrations, with minor deviations at higher levels. Environmental conditions had minimal impact on sensor performance, highlighting robustness to temperature and humidity within the tested ranges. For PM measurements, low-cost sensors effectively tracked trends, but inaccuracies increased with particle concentration. Overall, these findings support the feasibility of using low-cost sensors for non-critical IAQ monitoring, offering an affordable alternative for tracking CO₂ and PM trends. Additionally, LCSs can assess long-term exposure to contaminants, providing insights into potential health risks and useful information for non-expert users. Full article

It is well known that carbonyl compounds play an important role in air pollution and the formation of secondary pollutants, such as peroxyacetyl nitrates (PAN). Additionally, airborne carbonyls have been described as cytotoxic, mutagenic and carcinogenic. In this research, several carbonyl compounds, including aldehydes and ketones, as well as ozone, were monitored during a campaign conducted in July and September-October 2023 at Golf Ciudad Real, a golf course located in a non-industrial area of a south-central province in Spain. Extraction and analysis were carried out following procedures outlined by Radiello^®. Analyses were performed using HPLC-DAD and UV-Visible spectrophotometry. Ozone shows seasonal variation (temperature-dependent) concentrations displaying lower values in September/October. Among all the identified carbonyls, butanal was the most abundant, accounting for 40% of the total concentration. The C₁/C₂ and C₂/C₃ ratios were also calculated to provide information about the main emissions sources of the analyzed carbonyl compounds, indicating that mainly anthropogenic sources contribute to air quality in the area. The data were further supported by Quantitative Structure-Activity Relationship (QSAR) models using the ProtoPRED online server, which employs in silico methods based on European Chemicals Agency (ECHA) regulations to assess the (eco)toxicity of the measured carbonyl compounds. Full article

Because PM_2.5 concentrations are not regularly monitored in Mbarara, Uganda, this study was implemented to test whether correlations exist between weather parameters and PM_2.5 concentration, which could then be used to estimate PM_2.5 concentrations. PM_2.5 was monitored for 24 h periods once every week for eight months, while weather parameters were monitored every day. The mean dry and wet season PM_2.5 concentrations were 70.1 and 39.4 µg/m³, respectively. Diurnal trends for PM_2.5 levels show bimodal peaks in the morning and evening. The univariate regression analysis between PM_2.5 and meteorological factors for the 24 h averages yields a significant correlation with air pressure when all data are considered, and when the data are separated by season, there is a significant correlation between PM_2.5 concentration and wind speed in the dry season. A strong correlation is seen between diurnal variations in PM_2.5 concentration and most weather parameters, but our analysis suggests that in modeling PM_2.5 concentrations, the importance of these meteorological factors is mainly due to their correlation with underlying causes including diurnal changes in the atmospheric boundary layer height and changes in sources both hourly and seasonally. While additional measurements are needed to confirm the results, this study contributes to the knowledge of short-term and seasonal variation in PM_2.5 concentration in Mbarara and forms a basis for modeling short-term variation in PM_2.5 concentration and determining the effect of seasonal and diurnal sources on PM_2.5 concentration. Full article

Mineral dust from desert areas accounts for a large portion of aerosols globally, estimated at 3–4 billion tons per year. Aerosols emitted from arid and semi-arid areas, e.g., from parched lakes or rivers, are transported over long distances and have effects on a global scale, affecting the planet’s radiative balance, atmospheric chemistry, cloud formation and precipitation, marine biological processes, air quality, and human health. Desert dust transport takes place in the atmosphere as the result of a dynamical sequence beginning with dust uplift from desert areas, then followed by the long-range transport and terminating with the surface deposition of mineral dust in areas even very far from dust sources. The Mediterranean basin is characterized by frequent dust intrusion events, particularly affecting Spain, France, Italy, and Greece. Such events contribute to the increase in PM₁₀ and PM_2.5 concentration values, causing legal threshold values to be exceeded. In recent years, these events have shown a non-negligible increase in frequency and intensity. The present work reports the results of an analysis of the dust events that in recent years (2018–2023) affected the Mediterranean area and in particular central Italy, focusing on the more recurrent meteorological configurations leading to long-range transport and on the consequent increase in aerosol concentration values. A method for desert intrusion episodes identification has been developed using both numerical forecast model data and PM₁₀ observed data. A multi-year dataset has been analyzed by applying such an identification method and the resulting set of dust events episodes, affecting central Italy, has been studied in order to highlight their frequency on a seasonal basis and their interannual variability. In addition, a first attempt at a meteorological classification of desert intrusions has been carried out to identify the most recurrent circulation patterns related to dust intrusions. Understanding their annual and seasonal variations in frequency and intensity is a key topic, whose relevance is steeply growing in the context of ongoing climate change. Full article

Research into the adverse health effects of air pollution exposure has repeatedly considered smaller particles, to the point where particle number concentration might be a more relevant metric than mass concentration. Here, we highlight some historical research which developed metrics for air pollution severity based on particle number concentration. Because this work was published in a national journal and prior to the internet and open access, this historical research is not easy to find, and it was more through the history of the aerosol research community in Ireland that this work is now being presented. Multiple online searches for published research papers on “particle number concentrations” and “air pollution severity” were undertaken. Even when specific searches were undertaken using the author names and publication year, these featured papers were not found on any internet search. O’Dea and O’Connor proposed that air pollution severity could be classified based on particle number concentration of condensation nuclei, with ‘little’ air pollution <50 × 10³ particles per cm³, ‘mean’ 50–70 × 10³ particles per cm³, ‘strong’ 70–100 × 10³ particles per cm³, and ‘very strong’ >100 × 10³ particles per cm³. Applying their assumptions on density and mean particle size, equated to mass concentrations for a mean of 6 µgm⁻³, strong at 8.5 µgm⁻³, and very strong >10 µgm⁻³. These are consistent with the current WHO guideline values for PM_2.5. Additionally, we highlight the 1955 work by Burke and Nolan on the retention of inhaled particles, where ~40% of the inhaled number concentration is retained in the respiratory system. This is also consistent with the more recently published work on particle retention. In summary, the proposed categories of pollution severity, based on number concentrations, could form a basis for the development of future guidelines. This paper highlights that sometimes research has already been published, but it is difficult to find. We challenge researchers to find publications from their own countries which pre-date the WWW to inform current and future research. Additionally, there is scope for a repository for such information on historical publications. We have presented historical research on aerosol number concentrations, classifications of air pollution severity, and particle retention, which present lessons for current researchers. Full article

This study assesses the performance of the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis dataset in estimating ground-level concentrations (GLCs) of aerosols and criteria pollutants across the Himawari-8 observational area, covering China, Indonesia, and Australia, from 2016 to 2023. Ground-based monitoring networks in these regions are limited in scope, making it necessary to rely on satellite-derived aerosol optical depth (AOD) as a proxy for GLCs. While AOD offers broad coverage, it presents challenges, particularly in capturing surface-level pollution accurately during episodic events. CAMS, which integrates satellite data with atmospheric models, is evaluated here to determine its effectiveness in addressing these issues. The study employs square root transformation to normalize pollutant concentration data and calculates monthly–hourly long-term averages to isolate pollution anomalies. Geographically weighted regression (GWR) and Jacobian matrix (dY/dX) methods are applied to assess the spatial variability of pollutant concentrations and their relationship with meteorological factors. Results show that while CAMS captures large-scale pollution episodes, such as the 2019/2020 Australian wildfires, discrepancies in representing GLCs are apparent, especially when vertical aerosol stratification occurs during short-term pollution events. The study emphasizes the need for integrating CAMS data with higher-resolution satellite observations, like Himawari-8, to improve the accuracy of real-time air quality monitoring. The findings highlight important implications for public health interventions and environmental policy-making, particularly in regions with insufficient ground-based data. Full article

Overtraffic is one of the main keys to air pollution in urban areas. The aim of the present work is to review the approaches and explore the potentiality of AI in reducing traffic pollution in urban areas, ranging over three main areas: the optimization of traffic lights timing to reduce delays, the use of AI-powered drones to monitor pollution levels in real-time, and the use of fixed AI-based sensors to detect the levels of pollutants in the air with the use of AI models to identify patterns in the collected data and predict air quality in near-real time. Some attention was also dedicated to possible problems arising from privacy protection and data security, and the case study of the Piemonte area and of the city of Turin in the north–west of Italy is presented: the current situation is depicted, and possible local future applications of AI are explored. The use of AI has proven to be very promising in all three areas, particularly in the field of optimization of traffic lights’ timing and coordination in increasingly larger traffic networks. Full article