Air

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

This paper presents a novel machine learning methodology able to extend the results of detailed local emission inventories to larger domains where emission estimates are not available. The first part of this work consists in the development of an emission inventory of elemental carbon (EC), black carbon (BC), organic carbon (OC), and levoglucosan (LG) obtained from the detailed emission estimates available from the Project LIFE PREPAIR for the Po Basin in north Italy. The emissions of these chemical species in combination with particulate primary emissions and gaseous precursors are very important information in source apportionment and in the impact assessment of the different emission sources in air quality. To gain a better understanding of the origins of atmospheric pollution, it is possible to combine measurements with emission estimates for the particulate matter fractions known as EC, BC, OC, and LG. To identify the sources of emissions, it is usual practice to use the ratio of the measured EC, OC, TC (Total Carbon), and LG. The PREPAIR emission estimates and these new calculations are then used to train the Random Forest (RF) algorithm, considering a large array of local variables, such as taxes, the characteristics of urbanization and dwellings, the number of employees detailed for economic activities, occupation levels and land cover. The outcome of the comparison of the predictions of the machine learning implemented model (ML) with the estimates obtained for the same areas by two independent methods, local disaggregation of the national emission inventory and Copernicus Air Modelling Service (CAMS) emissions estimates, is extremely encouraging and confirms it also as a promising approach in terms of effort saving. The implemented modelling approach identifies the most important variables affecting the spatialization of different pollutants in agreement with the main emission source characteristics and is suitable for harmonization of the results of different local emission inventories with national emission reporting. Full article

Harsh winters, aging infrastructure, and the demand for modern amenities are major factors contributing to the deteriorating air quality in Bishkek. The city meets its winter heating energy needs through coal combustion at the central heating plant, heat-only boilers, and in situ heating equipment, while diesel and petrol fuel its transportation. Additional pollution sources include 30 km² of industrial area, 16 large open combustion brick kilns, a vehicle fleet with an average age of more than 10 years, 7.5 km² of quarries, and a landfill. The annual PM_2.5 emission load for the airshed is approximately 5500 tons, resulting in an annual average concentration of 48 μg/m³. Wintertime daily averages range from 200 to 300 μg/m³. The meteorological and pollution modeling was conducted using a WRF–CAMx system to evaluate PM_2.5 source contributions and to support scenario analysis. Proposed emissions management policies include shifting to clean fuels like gas and electricity for heating, restricting secondhand vehicle imports while promoting newer standard vehicles, enhancing public transport with newer buses, doubling waste collection efficiency, improving landfill management, encouraging greening, and maintaining road infrastructure to control dust emissions. Implementing these measures is expected to reduce PM_2.5 levels by 50–70% in the mid- to long-term. A comprehensive plan for Bishkek should expand the ambient monitoring network with reference-grade and low-cost sensors to track air quality management progress and enhance public awareness. Full article

High concentrations of ozone in the Uinta Basin, Utah, can occur after sufficient snowfall and a strong atmospheric anticyclone creates a persistent cold pool that traps emissions from oil and gas operations, where sustained photolysis of the precursors builds ozone to unhealthy concentrations. The basin’s winter-ozone system is well understood by domain experts and supported by archives of atmospheric observations. Rules of the system can be formulated in natural language (“sufficient snowfall and high pressure leads to high ozone”), lending itself to analysis with a fuzzy-logic inference system. This method encodes human expertise as machine intelligence in a more prescribed manner than more complex, black-box inference methods such as neural networks, increasing user trustworthiness of our model prototype before further optimization. Herein, we develop an ozone forecasting system, Clyfar, informed by an archive of meteorological and air-chemistry measurements. This prototype successfully demonstrates proof-of-concept despite rudimentary tuning. We describe our framework for predicting future ozone concentrations if input values are drawn from numerical weather prediction forecasts rather than observations as Clyfar initial conditions. We evaluate inferred values for one winter, finding our prototype demonstrates mixed performance but promise after optimization to deliver useful forecast guidance for decision-makers when forecast data are used as input. This early version model is the basis of ongoing optimization through machine learning. Full article

There are meteorological situations when huge amounts of Saharan dust are transported from Africa to Europe. These natural dust events may have a significant impact on particulate matter concentrations at monitoring sites. This phenomenon affects mainly the countries in Southern Europe; however, some strong advections can bring Saharan dust to higher latitudes too. The number of Saharan dust events in the Carpathian Basin is believed to increase due to the changing patterns in the atmospheric circulation over the Northern Hemisphere’s mid-latitudes. The jet stream becomes more meandering if the temperature difference between the Arctic areas and the lower latitudes decreases. This favours the northward transport of the North African dust. The European regulation makes it possible to subtract the concentration of Saharan-originated aerosol from the measured PM₁₀ concentration. This manuscript describes the methodology used by the HungaroMet to calculate the amount of natural dust contributing to measured PM₁₀ concentrations. Full article

This study compares the PM10 (particulate matter of diameter smaller than 10 μm) organic aerosol composition between urban and suburban stations in Heraklion, Crete, during winter 2024 in order to highlight the impact of local anthropogenic activities on urban atmospheric particulate matter pollution. Using an HPLC-ESI-MS Orbitrap analyzer (High Performance Liquid Chromatography-Electrospray Ionization-Mass Spectrometry) in full MS scan mode at a resolution of 140,000, 48 daily aerosol filter extracts were analyzed in both positive and negative modes, resulting in the detection of 2809 and 3823 features, respectively. Features with at least five times higher intensity in the urban environment compared to the suburban, and p < 0.05, were deemed significant. A correlation with black carbon (r > 0.6) was observed for 71% of significant urban features in positive mode. These features showed a predominance of low O:C ratios (<0.2) and the majority were classified as intermediate volatility organic compounds (IVOCs), indicating fresh primary emissions. A clear urban–suburban distinction was shown by PCA of positive mode features, unlike the negative mode features. Regarding the total intensity of the features, urban samples were on average 55% higher than suburban samples in positive mode and 39% higher in negative mode. This study reveals the molecular profile of locally emitted combustion related organics observed in positive mode in an urban environment. Full article