Authors: Samyak Shrestha David J. Lary Shisir Ruwali Faiz Ahmad
Life expectancy in the United States varies significantly by region, a gap often explained by socioeconomic factors like income and education. However, the relative contribution of atmospheric exposures is less understood. We identify formaldehyde exposure and wet-bulb temperature as leading predictors of county-level life expectancy. Our analysis of 22,540 county-year observations (2012–2019) shows that formaldehyde ranked as the second-strongest predictor, surpassed only by educational attainment. Wet-bulb temperature, a physiological measure of heat stress, ranked sixth and was the leading meteorological predictor. We identified these patterns using XGBoost with SHAP analysis, integrating atmospheric exposures, livestock density, socioeconomic conditions, and smoking prevalence within an external exposome framework. These results suggest that air pollutants and heat stress provide predictive information beyond traditional socioeconomic indicators.
]]>Authors: El Jirie Baticados Sergio Capareda
Ammonia (NH3) emissions from concentrated animal feeding operations (CAFOs) are recognized contributors to secondary fine particulate matter (PM2.5) formation, yet empirically derived secondary PM2.5 emission factors applicable to livestock operations remain limited. This study investigated NH3-derived secondary PM2.5 formation under controlled laboratory conditions using a PTFE flow reactor in which NH3 was reacted with sulfur dioxide (SO2) across ammonia-rich NH3:SO2 ratios, with and without zero air. The resulting aerosols were characterized using gravimetric analysis, elemental analysis, Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM/EDS), and particle size distribution (PSD) measurements. The recovered particles were dominated by inorganic ammonium–sulfur species, with FTIR and elemental trends indicating sulfite-related intermediates under no-zero-air conditions and more oxidized ammonium–sulfur products under oxygenated conditions. Accounting for both filter-collected and wall-deposited particles, unit particulate emission factors normalized to ammonia input were derived. Size-based apportionment using PSD data indicated that approximately 76.6% of the recovered particulate mass was within the PM2.5 size range. Scaling the experimentally derived unit emission factors using literature-based ammonia emission rates yielded an estimated secondary PM2.5 emission factor of 0.351 ± 0.084 g PM2.5 per animal head per day for cattle feedlots, corresponding to approximately 3–4% of reported total PM2.5 emissions. Because the experimental system isolates NH3–SO2 interactions under idealized conditions and does not represent full atmospheric chemistry, the derived values should be interpreted as screening-level estimates of NH3-derived secondary PM2.5 formation potential intended to support comparative air quality assessments of CAFOs rather than direct predictions of ambient PM2.5 concentrations.
]]>Authors: Chikumbusko Chiziwa Kaonga Fabiano Gibson Daud Thulu Gunseyo Dickson Dzinjalamala Upile Chitete-Mawenda Gladys Chimwemwe Banda Darlington Chimutu Stella James Kingsley Kabango Petra Chiipa Estiner Walusungu Katengeza Tawina Mlowa Harold Wilson Tumwitike Mapoma Ishmael Bobby Mphangwe Kosamu
This study presents an assessment of ambient air quality in Chichiri and Malawi University of Business and Applied Sciences (MUBAS) locations, Blantyre City, Southern Malawi. The study aimed at assessing temporal trends, identifying exceedance of thresholds, investigating relationships between pollutants and meteorological factors, and exploring the predictability of air quality index (AQI). Five pollutants: PM2.5, PM10, NOx, CO2 and TVOC were assessed over a two-month period using fixed low-cost sensors. Daily and hourly temporal analysis showed that pollutants peak during morning and evening hours. A significant number of exceedances for PM2.5 and PM10 were observed when compared to indicative thresholds. Chichiri exhibited more frequent AQI classifications in the “unhealthy” range. A strong positive relationship between PM2.5 and PM10 (r = 0.84) and positive correlations between NOx and CO2 were observed. A multiple linear regression model achieved a high coefficient of determination (R2 = 0.938), identifying PM10 and NOx as dominant predictors of AQI variability. Temperature and humidity showed modest inverse relationship with AQI, suggesting dispersion effects. A comparison with African cities showed that the study areas’ pollution levels were within regional norms, but that there is a need for targeted mitigation. These findings underscore the importance of continuous monitoring, data-driven policy making and regional collaboration to address urban air quality challenges.
]]>Authors: Christos Mountzouris Grigorios Protopsaltis John Gialelis
Exposure to poor indoor air quality (IAQ) conditions represents a major public health concern, with adverse effects on human health and well-being. The adoption of innovative technological solutions can support timely risk awareness, enable informed decision-making, and ultimately mitigate this health burden. In this context, Large Language Models (LLMs) emerge as a promising technological avenue through the Retrieval-Augmented Generation (RAG) paradigm, which extends their inherent natural language understanding capabilities with explicit access to external knowledge bases, enabling evidence-grounded reasoning and informed recommendations. The present work introduces an integrated GraphRAG-based Question Answering (QA) system that couples a domain-specific knowledge graph encoding fundamental IAQ concepts and relationships with a RAG-based natural language interface, thereby enabling explainable, context-aware, and advanced analytical reasoning over IAQ data. The evaluation results demonstrate the effectiveness of the proposed QA system across both retrieval and generation stages. The retrieval mechanism achieved a context recall of 0.914 and a precision of 0.838, while the generation mechanism attained a faithfulness score of 0.906 and an answer relevancy score of 0.891.
]]>Authors: Ibironke T. Enitan Stuart J. Piketh Joshua N. Edokpayi
Vehicular emissions are a significant anthropogenic source of air pollutants in South Africa, driven by urbanisation and industrialisation. Thulamela Municipality in Limpopo Province faces increasing air quality challenges associated with rising vehicle kilometres travelled (VKT) and population growth. A reliable baseline emission inventory is therefore required to inform effective air quality management. This study quantified emissions and developed a vehicular emission inventory (VEI) for Thulamela Municipality using a bottom-up approach for the period 2012–2021. VKT was estimated using odometer readings obtained through a questionnaire-based seven-day vehicle survey, together with registered vehicle population data from the National Traffic Information System (NaTIS). Results indicate that VKT increased over the study period, with light-duty vehicles (LDVs) contributing the most, followed by passenger cars (PCs), heavy-duty vehicles (HDVs), and heavy-passenger vehicles (HPVs). Cumulative emissions of CO, NOx, PM10, PM2.5, and SO2 over the 10 years were 32,781.1, 22,326.0, 1367.8, 1291.7, and 547.2 tons, respectively, with growth rates ranging from 39% to 41%. In 2021, total vehicular emissions reached 6647.6 tons, dominated by CO (56%) and NOx (38%), with PM10 (3%), PM2.5 (2%), and SO2 (1%). LDVs contributed 82% of total emissions, followed by PCs (9%), HDVs (6%), and HPVs (3%). A positive correlation between vehicle numbers and Gross Domestic Product (GDP) further suggests that economic growth is associated with higher emissions. These findings show that vehicular emissions are a key contributor to air pollution in the area and highlight the need for targeted mitigation strategies to improve air quality and protect public health.
]]>Authors: Omosehin D. Moyebi Azhar Siddique Mirza M. Hussain David O. Carpenter Haider A. Khwaja
Ambient air pollution, particularly fine particulate matter (PM2.5), poses significant health risks, especially concerning cardiovascular diseases (CVDs). This study assesses the association between PM2.5 exposure and CVD hospital admissions (HAs) and emergency room (ER) visits in Karachi, Pakistan. Daily PM2.5 samples were collected from four Karachi sites (Makro, Karachi University, Keamari, and Malir) between October 2009 and June 2011. CVD morbidity data, including HAs and ER visits, were gathered from major hospitals. A single-pollutant model was employed to evaluate associations between PM2.5 levels and CVD outcomes, adjusting for meteorological variables and other potential confounders. PM2.5 concentrations and CVD morbidity were significantly associated across all sites Stratification by age and gender revealed stronger associations among males and individuals aged 40 and above. Exposure to elevated levels of PM2.5 in Karachi was significantly associated with increased CVD HAs and ER visits, with the highest association found between PM2.5 exposure and arrhythmias. The study underscores the need for effective air quality management policies and interventions to reduce PM2.5 levels. Karachi’s high PM2.5 levels demand urgent attention from regulatory agencies and public health professionals to implement interventions that mitigate air pollution and protect vulnerable populations.
]]>Authors: Brian Naess Vlad Isakov Mathilde Teyssier
The massive influx and subsequent anaerobic decomposition of pelagic Sargassum on Caribbean coasts release toxic gases, including hydrogen sulfide (H2S), and pose a real public health hazard, as evidenced by thousands of reported acute exposure cases in Martinique in 2018. To effectively characterize exposure and identify at-risk areas, we utilized the interactive web-based dispersion modeling system C-PORT, representing Sargassum accumulation zones as area sources derived from recent aerial and in situ monitoring data. Inverse modeling, comparing C-PORT output against Madininair observation data from 2024 to 2025, established emission flux rates ranging from 0.45 to 3.58 mg/m2 per second for H2S, depending on Sargassum density. The resulting modeled concentrations exhibit a low average fractional bias (approx. 0.04) when compared to observations. This study demonstrates that C-PORT can be used to estimate spatially resolved concentrations for H2S, generate health-risk maps for H2S, evaluate options to mitigate exposure from varying Sargassum intensity levels, and serve as a crucial tool for public health agencies across vulnerable coastal regions.
]]>Authors: Ivan Notardonato Mario Lovrić Pasquale Avino
Limonene is one of the most abundant, natural, bio-based monoterpenes. In recent years, it has attracted growing attention in both industrial and scientific communities due to its versatile physicochemical properties and wide spectrum of biological activities, including antimicrobial, antioxidant, and anti-inflammatory effects. Its renewable origin and biodegradability make limonene an ideal candidate for sustainable development and as a key building block in green chemistry. The industrial relevance of limonene spans multiple sectors, ranging from its use as a solvent and flavoring agent to its application in pharmaceuticals, cosmetics, polymers, and renewable fuels. Nevertheless, despite its numerous advantages, certain limitations and safety concerns have emerged. Prolonged or high-level exposure may result in sensitization, irritant reactions, or secondary oxidation products that pose potential health risks. Moreover, its oxidative instability can lead to the formation of reactive compounds under specific environmental conditions that influence indoor air quality and may contribute to secondary organic aerosol formation. Current research focuses on several key challenges: improving extraction and purification yields through biotechnological and enzymatic pathways; enhancing oxidative stability via encapsulation or chemical modification; and standardizing toxicological assessment protocols for both occupational and clinical settings. In this review, we analyze and discuss studies published predominantly in the last five years that explore the dual nature of limonene, its valuable industrial applications and its potential environmental and health-related challenges.
]]>Authors: Vongani Chabalala Craig Rudolph Karabo Mosala Edward Khomotso Nkadimeng Chuene Mosomane Thuso Mathaha Pallab Basu Muhammad Ahsan Mahboob Jude Kong Nicola Bragazzi Iqra Atif Mukesh Kumar Bruce Mellado
Air pollution, particularly fine particulate matter (PM2.5), poses significant public health and environmental risks. This study explores the effectiveness of spatiotemporal graph neural networks (ST-GNNs) in forecasting PM2.5 concentrations by integrating remote-sensing hyperspectral indices with traditional meteorological and pollutant data. The model was evaluated using data from Switzerland and the Gauteng province in South Africa, with datasets spanning from January 2016 to December 2021. Key performance metrics, including root mean squared error (RMSE), mean absolute error (MAE), probability of detection (POD), critical success index (CSI), and false alarm rate (FAR), were employed to assess model accuracy. For Switzerland, the integration of spectral indices improved RMSE from 1.4660 to 1.4591, MAE from 1.1147 to 1.1053, CSI from 0.8345 to 0.8387, POD from 0.8961 to 0.8972, and reduced FAR from 0.0760 to 0.0719. In Gauteng, RMSE decreased from 6.3486 to 6.2319, MAE from 4.4891 to 4.4066, CSI from 0.9555 to 0.9560, and POD from 0.9699 to 0.9732, while FAR slightly increased from 0.0154 to 0.0181. Error analysis revealed that while the initial one-day ahead forecast without spectral indices had a marginally lower error, the dataset with spectral indices outperformed from the two-day ahead mark onwards. The error for Swiss monitoring stations stabilized over longer prediction lengths, indicating the robustness of the spectral indices for extended forecasts. The study faced limitations, including the exclusion of the Planetary Boundary Layer (PBL) height and K-index, lack of terrain data for South Africa, and significant missing data in remote sensing indices. Despite these challenges, the results demonstrate that ST-GNNs, enhanced with hyperspectral data, provide a more accurate and reliable tool for PM2.5 forecasting. Future work will focus on expanding the dataset to include additional regions and further refining the model by incorporating additional environmental variables. This approach holds promise for improving air quality management and mitigating health risks associated with air pollution.
]]>Authors: Ahmed Benabed Adrian Arfire Hanaa ER-Rbib Safwen Ncibi Elizabeth Fu Pierre Pousset
Road dust resuspension is widely recognized as a major contributor to traffic-related particulate matter (PM) in urban environments. Nevertheless, reported emission factors exhibit substantial variability. These discrepancies stem not only from the intrinsic complexity of the resuspension process but also from limitations in measurement techniques, which often fail to adequately control or characterize the influencing parameters. As a result, the contribution of each parameter remains difficult to isolate, leading to inconsistencies across studies. This study presents an experimental protocol developed to quantify PM10 and PM2.5 emission factors associated with vehicle-induced road dust resuspension. Experiments were conducted on a dedicated test track seeded with alumina particles of controlled mass and size distribution to simulate road dust. A network of microsensors was strategically deployed at multiple upwind and downwind locations to continuously monitor particle concentration variations during vehicle passages. Emission factors were derived through time integration of the mass flow rate of resuspended dust measured by the sensor network. The estimated PM10 emission factor showed excellent agreement, within 2.5%, with predictions from a literature-based formulation, thereby validating the accuracy and external relevance of the proposed protocol. In contrast, comparisons with U.S. EPA formulas and other empirical equations revealed substantially larger discrepancies, particularly for PM2.5, highlighting the persistent limitations of current modeling approaches.
]]>Authors: Daniel P. Johnson Gabriel Filippelli Asrah Heintzelman
Fine particulate matter (PM2.5) poses substantial urban health risks that vary across space, time, and population vulnerability. We integrate a spatio-temporal INLA–SPDE PM2.5 field with an agent-based model (ABM) of 10,000 daily home–work commuters in Indianapolis’s Pleasant Run airshed (50 weeks; 250 m grid). The PM2.5 surface fuses 23 corrected PurpleAir PA-II-SD sensors with meteorology, land use, road proximity, and MODIS AOD. Validation indicated strong agreement (leave-one-out R2 = 0.79, RMSE = 3.5 μg/m3; EPA monitor comparison R2 = 0.81, RMSE = 3.1 μg/m3). We model a spatial-equity counterfactual by assigning susceptibility independently of residence and workplace, isolating vulnerability from residential segregation. Under this design, annual PM2.5 exposure was statistically indistinguishable across groups (16.22–16.29 μg/m3; max difference 0.07 μg/m3, <0.5%), yet VWDI differed by ~10× (High vs. Very Low). Route-level maps reveal recurrent micro-corridors (>20 μg/m3) near industrial zones and arterials that increase within-group variability without creating between-group exposure gaps. These findings quantify a policy-relevant “floor effect” in environmental justice: even with perfect spatial equity, substantial health disparities remain driven by susceptibility. Effective mitigation, therefore, requires dual strategies—place-based emissions and mobility interventions to reduce exposure for all, paired with vulnerability-targeted health supports (screening, access to care, indoor air quality) to address irreducible risk. The data and code framework provides a reproducible baseline against which real-world segregation and mobility constraints can be assessed in future, stratified scenarios.
]]>Authors: Angelo Robotto Cristina Bargero Enrico Racca Enrico Brizio
When it rains or snows over a city, water droplets capture airborne pollutants and transport them to the ground. Prolonged precipitation over the same area can remove a larger amount of pollution; however, rainfall systems vary in duration and tend to move rapidly across regions. Wet deposition sprinklers replicate this natural scavenging process. They can operate for extended periods as needed and can be installed at specific locations where pollution mitigation is most necessary. Despite encouraging experimental results and the widespread use of similar technologies in industrial sectors—such as mining, the construction industry, and waste management—very limited scientific research has focused on their application in urban environments. In particular, their use as an emergency measure during severe pollution episodes as a protective intervention for sensitive subjects, while awaiting the effects of long-term structural solutions, remain largely unexplored. In the present work, we systematically discuss the key elements required to design and implement a network of anti-smog cannons (ASC) to protect sensitive receptors from severe air pollution events in large cities. Based on this analysis, we established a generalized framework that can be applied to any urban context worldwide. We also examine the potential application of the proposed method to the city of Turin (≈850,000 inhabitants, north-western Italy), which is considered a representative case study for other cities in Western Europe. Our findings indicate that such a network is both technically feasible and economically sustainable for local government authorities.
]]>Authors: Carlo Volf Kristoffer Negendahl
A large proportion of the existing building stock in northern Europe is facing energy renovation in the coming years. In this process, existing architecture in cold and temperate climates, originally designed for natural ventilation, is renovated, implementing mechanical ventilation with heat recovery, in the belief that mechanical ventilation performs better than natural ventilation. Yet, can natural ventilation outperform mechanical ventilation when comparing life cycle carbon emissions, cost, and indoor environmental parameters? This study compares two different ventilation strategies in a full-scale renovation of two identical Danish residential buildings: (1) natural ventilation with passive controlled NOTECH ventilation and two-layered high-transmittance windows vs. (2) mechanical ventilation with heat recovery and three-layered low energy windows. The study compares energy performance, life cycle carbon footprint, capital cost investments, payback period, and indoor environmental quality (IEQ). Under the observed conditions, the results show that natural ventilation outperforms mechanical ventilation when it comes to energy consumption for heating (MWh), global warming potential (t. CO2-equivalent), and total costs, while mechanical ventilation has a slightly higher indoor environmental quality. The study shows that two-layered windows and natural ventilation, based on passive solar heating, can reduce the global warming potential and act as a viable alternative to three-layered windows and mechanical ventilation when renovating existing building stock.
]]>Authors: Mohammad Saleh Nikoopayan Tak Ehsan Mousavi
The COVID-19 pandemic has reshaped the global understanding of airborne disease transmission, particularly in healthcare environments. This literature review examines how building ventilation and indoor air quality strategies have evolved in response to SARS-CoV-2, with a specific focus on healthcare settings. A systematic review of 163 post-pandemic studies, alongside a selective review of pre-COVID-19 literature, was conducted to assess how scientific knowledge, practical recommendations, and HVAC-related interventions have changed. The review categorizes studies across detection methods, simulation models, observational analyses, and policy recommendations, drawing attention to novel findings and evidence-supported practices. While the body of research reaffirms the critical role of ventilation, many recommendations remain unevaluated through empirical methods. This study identifies the gaps in evidence and highlights the most impactful advances that can inform future design, maintenance, and operational protocols in healthcare facilities to mitigate airborne infection risks.
]]>Authors: Christos Mountzouris Grigorios Protopsaltis John Gialelis
Exposure to degraded indoor air quality (IAQ) conditions represents a major concern for health and well-being. PM2.5 is among the most prevalent indoor air pollutants and constitutes a key indicator in IAQ assessment. Conventional IAQ frameworks often neglect personalization, which in turn compromises the reliability of exposure estimation and the interpretation of associated health implications. In response to this limitation, the present study introduces a human-centric framework that couples wearable sensing with deep learning, employing a low-cost wearable device to capture PM2.5 concentrations in the immediate human vicinity and an attention-based Long-Short Term Memory (LSTM) to deliver 5-min-ahead exposure predictions. During evaluation, the proposed framework demonstrated strong and consistent performance across both stable conditions and transient spikes in PM2.5, yielding a Mean Absolute Error (MAE) of 0.181 µg/m3. These findings highlighted the synergistic potential between wearable sensing and data-driven modeling in advancing personalized IAQ forecasting, informing proactive IAQ management strategies, and ultimately promoting healthier built environments.
]]>Authors: Federica Paladini Fabiana D’Urso Francesco Broccolo Mauro Pollini
The COVID-19 pandemic catalyzed unprecedented innovation in indoor disinfection technologies, fundamentally transforming the patent landscape and commercial development in this sector. This comprehensive analysis examined patent filings from global databases and commercial market data spanning January 2020 to December 2025. Patent data were collected up to September 2022, while market data include both historical figures (2020–2023) and future projections (2024–2025) derived from industry research reports. A systematic review identified significant technological developments across five major categories: ultraviolet-C (UV-C) systems, ozone generators, photocatalytic oxidation systems, plasma disinfection technologies, and electromagnetic field applications. The analysis revealed that while patent activity surged dramatically during the pandemic period, commercial success rates varied significantly across technology categories. UV-C systems demonstrated the highest market penetration with established commercial viability, while emerging technologies such as electromagnetic disinfection faced substantial barriers to commercialization. Geographic analysis showed concentrated innovation in developed economies, with China leading in patent volume and South Korea achieving notable commercial success despite smaller patent portfolios. The study provides critical insights into the relationship between patent activity and commercial viability in emergency-driven innovation contexts.
]]>Authors: David Moretón Pavón Sandra Rodríguez-Sufuentes Alicia Aguado Rubèn González-Colom Alba Gómez-López Alexandra Kristian Artur Badyda Piotr Kepa Leticia Pérez Jose Fermoso
Identifying tobacco smoke exposure in indoor environments is critical for public health, especially in vulnerable populations. In this study, we developed and validated a machine learning model to detect smoking households based on indoor air quality (IAQ) data collected using low-cost sensors. A dataset of 129 homes in Spain and Austria was analyzed, with variables including PM2.5, PM1, CO2, temperature, humidity, and total VOCs. The final model, based on the XGBoost algorithm, achieved near-perfect household-level classification (100% accuracy in the test set and AUC = 0.96 in external validation). Analysis of PM2.5 temporal profiles in representative households helped interpret model performance and highlighted cases where model predictions revealed inconsistencies in self-reported smoking status. These findings support the use of sensor-based approaches for behavioral inference and exposure assessment in residential settings. The proposed method could be extended to other indoor pollution sources and may contribute to risk communication, health-oriented interventions, and policy development, provided that ethical principles such as transparency and informed consent are upheld.
]]>Authors: Kristina Leontjevaite Aoife Donnelly Tadhg Eoghan MacIntyre
In the original publication [...]
]]>Authors: David Veloz Ricardo Cisneros Paul Brown Sulin Gonzalez Hamed Gharibi Rudiel Fabian Gilda Zarate-Gonzalez
This study examines the perceived air quality and contributors to air pollution among residents of Merced and Stanislaus Counties in California’s San Joaquin Valley (SJV), one of the most polluted regions in the United States. A survey was conducted during the summer of 2017, gathering responses from 176 participants to assess their perceptions of air quality, sources of pollution, and behaviors related to air pollution awareness. Findings indicate that only 3.5% of participants perceived the air quality in their city as good, while 57.9% categorized it as unhealthy or unhealthy for sensitive groups. Participants identified cars and trucks as the primary sources of air pollution, followed by forest fires and factories. Seasonal differences in perception were also observed, with summer months being viewed as the most polluted. Additionally, participants living near major roadways reported higher concerns regarding air pollution’s impact on health. Multivariate regression analysis revealed that education was significantly associated with perceived air quality, while proximity to highways influenced perceptions of health risks. This study underscores the need for targeted interventions to raise awareness and promote self-protective behaviors, especially for vulnerable populations living near highways. These findings highlight the importance of localized public health strategies to address air quality concerns in SJV communities.
]]>Authors: Bonface O. Manono Fatihu Kabir Sadiq Abdulsalam Adeiza Sadiq Tiroyaone Albertinah Matsika Fatima Tanko
Air pollution is an escalating global challenge with profound implications for agricultural production and food security. This review explores the impacts of deteriorating air quality on global crop yields and food security, emphasizing both direct physiological effects on plants and broader environmental interactions. Key pollutants such as ground-level ozone (O3), fine particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), volatile organic compounds (VOCs), and polycyclic aromatic hydrocarbons (PAHs) reduce crop yield and quality. They have been shown to inhibit plant growth, potentially by affecting germination, morphology, photosynthesis, and enzyme activity. PAH contamination, for example, can negatively affect soil microbial communities essential for soil health, nutrient cycling and organic matter decomposition. They persist and accumulate in food products through the food chain, raising concerns about food safety. The review synthesizes evidence demonstrating how air pollution undermines the four pillars of food security: availability, access, utilization, and stability by reducing crop yields, elevating food prices, and compromising nutritional quality. The consequences are disproportionately severe in low- and middle-income countries, where regulatory and infrastructural limitations exacerbate vulnerability. This study examines mitigation strategies, including emission control technologies, green infrastructure, and precision agriculture, while stressing the importance of community-level interventions and real-time air quality monitoring through IoT and satellite systems. Integrated policy responses are urgently needed to bridge the gap between environmental regulation and agricultural sustainability. Notably, international cooperation and targeted investments in multidisciplinary research are essential to develop pollution-resilient crop systems and inform adaptive policy frameworks. This review identifies critical knowledge gaps regarding pollutant interactions under field conditions and calls for long-term, region-specific studies to assess cumulative impacts. Ultimately, addressing air pollution is not only vital for ecosystem health, but also for achieving global food security and sustainable development in a rapidly changing environment.
]]>Authors: Gopika Indu Shiva Nagendra Saragur Madanayak Richard J. Ball
In developing countries, indoor air pollution in rural areas is often attributed to the use of solid biomass fuels for cooking. Such fuels generate particulate matter (PM), carbon monoxide (CO), carbon dioxide (CO2), polyaromatic hydrocarbons (PAHs), and volatile organic compounds (VOCs). PM created from biomass combustion is a pollutant particularly damaging to health. This rigorous study employed a personal sampling device and multi-stage cascade impactor to collect airborne PM (including PM2.5) and deposited ash from 20 real-world kitchen microenvironments. A robust analysis of the PM was undertaken using a range of morphological, physical, and chemical techniques, the results of which were then compared to a controlled burn experiment. Results revealed that airborne PM was predominantly carbon (~85%), with the OC/EC ratio varying between 1.17 and 11.5. Particles were primarily spherical nanoparticles (50–100 nm) capable of deep penetration into the human respiratory tract (HRT). This is the first systematic characterisation of biomass cooking emissions in authentic rural kitchen settings, linking particle morphology, chemistry and toxicology at health-relevant scales. Toxic heavy metals like Cr, Pb, Cd, Zn, and Hg were detected in PM, while ash was dominated by crustal elements such as Ca, Mg and P. VOCs comprised benzene derivatives, esters, ethers, ketones, tetramethysilanes (TMS), and nitrogen-, phosphorus- and sulphur-containing compounds. This research showcases a unique collection technique that gathered particles indicative of their potential for penetration and deposition in the HRT. Impact stems from the close link between the physico-chemical properties of particle emissions and their environmental and epidemiological effects. By providing a critical evidence base for exposure modelling, risk assessment and clean cooking interventions, this study delivers internationally significant insights. Our methodological innovation, capturing respirable nanoparticles under real-world conditions, offers a transferable framework for indoor air quality research across low- and middle-income countries. The findings therefore advance both fundamental understanding of combustion-derived nanoparticle behaviour and practical knowledge to inform public health, environmental policy, and the UN Sustainable Development Goals.
]]>Authors: Michael J. Davies John R. Lawson Trevor O’Neil Seth N. Lyman KarLee Zager Tristan D. Coxson
After heavy snowfall in the Uinta Basin, Utah, elevated surface ozone occurs if a cold-air pool persists and traps emissions from oil and gas industry operations. Sunlight and actinic flux from a high-albedo snowpack drive ozone buildup via photolysis. Snow coverage is paramount in initiating the cold pool and driving ozone generation. Its depth is critical for predicting ozone concentrations. The Basin’s location leeward of the Wasatch Mountains provides conditions for a precipitation shadow, where sinking air suppresses snowfall. We analyzed multiple years of ground-based snow depth measurements, surface ozone data, and meteorological observations; we found that ozone levels track with snow coverage, but diagnosing a shadow effect (and any impact on ozone levels) was difficult due to sparse, noisy data. The uncertainty in linking snowfall variation to ozone levels hinders forecast quality in, e.g., machine-learning training. We highlight the importance of a better understanding of regional variation when issuing outlooks to protect the local economy and health. A wider sampling of snow depth across the Basin would benefit operational forecasters and, likely, predictive skill.
]]>Authors: Karoline K. Barkjohn Catherine Seppanen Saravanan Arunachalam Stephen Krabbe Andrea L. Clements
Data is needed to understand local air quality, reduce exposure, and mitigate the negative impacts on human health. Measuring local air quality often requires a hybrid monitoring approach consisting of the national air monitoring network and one or more networks of air sensors. However, it can be challenging to combine this data to produce a consistent picture of air quality, largely because sensor data is produced in a variety of formats. Users may have difficulty reformatting, performing basic quality control steps, and using the data for their intended purpose. We developed an R-Shiny application that allows users to import text-based air sensor data, describe the format, perform basic quality control, and export the data to standard formats through a user-friendly interface. Format information can be saved to speed up the processing of additional sensors of the same type. This tool can be used by air quality professionals (e.g., state, local, Tribal air agency staff, consultants, researchers) to more efficiently work with data and perform further analysis in the Air Sensor Network Analysis Tool (ASNAT), Google Earth or Geographic Information System (GIS) programs, the Real Time Geospatial Data Viewer (RETIGO), or other applications they already use for air quality analysis and management.
]]>Authors: Nicholas C. Newman Deborah Conradi Alexander C. Mayer Cole Simons Ravi Newman Erin N. Haynes
Regulatory air pollution monitoring is performed using a sparse monitoring network designed to provide background concentrations of pollutants but may miss small area variations due to local emission sources. Low-cost air pollution sensors operated by trained citizen scientists provide an opportunity to fill this gap. We describe the development and implementation of an air pollution monitoring and community engagement plan in response to resident concerns regarding excessive smoke production from a neighborhood restaurant. Particulate matter (PM2.5) was measured using a low-cost, portable sensor. When cooking was taking place, the highest PM2.5 readings were within 50 m of the source (mean PM2.5 36.9 µg/m3) versus greater than 50 m away (mean PM2.5 13.0 µg/m3). Sharing results with local government officials did not result in any action to address the source of the smoke emissions, due to lack of jurisdiction. A review of air pollution regulations across the United States indicated that only seven states regulate food cookers and six states specifically exempted cookers from air pollution regulations. Concerns about the smoke were communicated with the restaurant owner who eventually changed the cooking fuel. Following this change, less smoke was observed from the restaurant and PM2.5 measurements were reduced to background levels. Although current environmental health regulations may not protect residents living near sources of food cooker-based sources of PM2.5, community engagement shows promise in addressing these emissions.
]]>Authors: Anita Tóth Zita Ferenczi
Air quality forecasts play a crucial role in informing the public about atmospheric pollutant levels that pose risks to human health and the environment. The accuracy of these forecasts strongly depends on the quality and resolution of the input data used in the modelling process. At HungaroMet, the Hungarian Meteorological Service, the CHIMERE chemical transport model is used to provide two-day air quality forecasts for the territory of Hungary. This study compares two configurations of the CHIMERE model: the current operational setup, which uses climatological averages from the LMDz-INCA database for boundary conditions, and a test configuration that incorporates real-time boundary conditions from the CAMS global forecast. The primary objective of this work was to assess how the use of real-time versus climatological boundary conditions affects modelled concentrations of key pollutants, including NO2, O3, PM10, and PM2.5. The model results were evaluated against observational data from the Hungarian Air Quality Monitoring Network using a range of statistical metrics. The results indicate that the use of real-time boundary conditions, particularly for aerosol-type pollutants, improves the accuracy of PM10 forecasts. This improvement is most significant under meteorological conditions that favour the long-range transport of particulate matter, such as during Saharan dust or wildfire episodes. These findings highlight the importance of incorporating dynamic, up-to-date boundary data, especially for particulate matter forecasting—given the increasing frequency of transboundary dust events.
]]>Authors: Mayra Chavez Leonardo Vazquez-Raygoza Evan Williams Wen-Whai Li
This project assesses the feasibility of employing mobile air pollutant concentration monitoring along fixed routes within an urban community to evaluate near-road exposure. Continuous mobile air monitoring measurements of four pollutants (PM2.5, PM10, NO2, and O3) were collected using high-quality air monitors paired with a GPS device to track coordinates and vehicle speed. Simultaneous near-road measurements of the same pollutants were taken at two stationary sites to establish correlations with the mobile air monitoring data. The results indicate that pollutant concentrations recorded by mobile air monitors align closely with those from near-road stationary sites. This study demonstrated strong concordance between mobile and stationary monitoring for particulate matter concentrations, with PM2.5 and PM10 showing high correlation coefficients (R2 = 0.74 and 0.75, respectively). Ozone (O3) exhibited particularly consistent spatial distributions across all measurement platforms—mobile, near-road, and community stationary sites—as reflected in even stronger correlations (R2 = 0.93 and 0.89 for the two near-road sites). These robust associations suggest that mobile monitoring could serve as a viable alternative to stationary approaches for O3 assessment. In contrast, nitrogen dioxide (NO₂) measurements displayed greater variability, with mobile concentrations consistently exceeding near-road stationary values and demonstrating weaker correlation (R2 = 0.19), indicating potential limitations in mobile NO₂ monitoring reliability. This study highlights that mobile air pollutant monitoring in less congested communities can effectively capture exposure concentrations representative of both the community and near-road receptors represented by stationary air monitoring sites. Future research should explore how mobile air monitoring data can be utilized in exposure and health assessments, as well as how this technique can be applied in areas where stationary monitoring is impractical or prohibited due to cost or access limitations.
]]>Authors: Behrang Chenari Shiva Saadatian Manuel Gameiro da Silva
This study investigates the performance of different demand-controlled ventilation strategies for improving indoor air quality while optimizing energy efficiency. The experimental research was conducted at the Indoor Live Lab at the University of Coimbra using a smart window equipped with mechanical ventilation boxes, occupancy sensors, and a real-time CO2 monitoring system. Several occupancy-based and CO2-based ventilation control strategies were implemented and tested to dynamically adjust ventilation rates according to real-time indoor conditions, including (1) occupancy period-based control, (2) occupancy level-based control, (3) ON-OFF CO₂-based control, (4) multi-level CO₂-based control, and (5) modulating CO₂-based control. The results indicate that intelligent control strategies can significantly reduce energy consumption while maintaining indoor air quality within acceptable limits. Among the CO₂-based controls, strategy 5 achieved optimal performance, reducing energy consumption by 60% compared to the simple ON-OFF strategy, while maintaining satisfactory indoor air quality. Regarding occupancy-based strategies, strategy 2 showed 58% energy savings compared to the simple occupancy period-based control, but with greater CO₂ concentration fluctuation. The results demonstrate that intelligent DCV systems can simultaneously reduce ventilation energy use by 60% and maintain compliant indoor air quality levels, with modulating CO₂-based control proving most effective. The findings highlight the potential of integrating sensor-based ventilation controls in office spaces to achieve energy savings, enhance occupant comfort, and contribute to the development of smarter, more sustainable buildings. Future research should explore the integration of predictive analytics and multi-pollutant sensing to further optimize demand-controlled ventilation performance.
]]>Authors: Ian M. Kinsella Anna N. Petrbokova Rongjie Yang Zheng Liu Gokul Nathan Nicklaus Thompson Alexander V. Mamishev Sep Makhsous
Soldering is a common engineering practice that releases airborne particulate matter (PM), contributing to significant long-term respiratory risk. The health impact of this exposure is significant, with up to 22% of soldering workers worldwide being diagnosed with conditions such as occupational asthma, restrictive lung disease, and bronchial obstruction. Studies have reported that soldering can produce PM2.5 concentrations up to 10 times higher than the U.S. Environmental Protection Agency’s (EPA) 24 h exposure limit of 35.0 μg/m3—posing significant respiratory and cognitive health risks under chronic exposure. These hazards remain underappreciated by novice engineers in academic and entry-level industrial environments, where safety practices are often informal or inconsistently applied. Air purification systems offer a mitigation approach; however, performance varies significantly with model and placement, and independent validation is limited. This study uses an indoor air quality monitoring system consisting of six AeroSpec sensors to measure PM2.5–10 concentrations during soldering sessions conducted with and without commercial air purifiers. Tests were conducted with and without a selection of commercial air purifiers, and measurements were recorded under consistent spatial and temporal conditions. Datasets were analyzed to evaluate purifier effectiveness and the influence of placement on pollutant distribution. The findings provide independent validation of air purifier capabilities and offer evidence-based suggestions for minimizing particulate exposure and improving safety in laboratory soldering environments.
]]>Authors: Minyi Wang Takayuki Kameda
Benzo[a]pyrene is an important indicator of polycyclic aromatic hydrocarbons pollution that exhibits complex atmospheric dynamics influenced by meteorological factors and suspended particulate matter (SPM). Herein, the factors influencing B(a)P concentration were elucidated by analyzing the monthly environmental data for Kyoto, Japan, from 2001 to 2021 using an improved association rule algorithm. Results revealed that B(a)P concentrations were 1.3–3 times higher in cold seasons than in warm seasons and SPM concentrations were lower in cold seasons. The clustering performance was enhanced by optimizing the K-means method using the sum of squared error. The efficiency and reliability of the traditional Apriori algorithm were enhanced by restructuring its candidate itemset generation process, specifically by (1) generating C2 exclusively from frequent itemset L₁ to avoid redundant database scans and (2) implementing the iterative pruning of nonfrequent subsets during Lk → Ck+1 transitions, adding the lift parameter, and eliminating invalid rules. Strong association rules revealed that B(a)P concentrations ≤ 0.185 ng/m3 were associated with specific meteorological conditions, including humidity ≤ 58%, wind speed ≥ 2 m/s, temperature ≥ 12.3 °C, and pressure ≤ 1009.2 hPa. Among these, changes in pressure had the most substantial impact on the confidence of the association rules, followed by humidity, wind speed, and temperature. Under the influence of high SPM concentrations, favorable meteorological conditions further accelerated pollutant dispersion. B(a)P concentration increased with increasing pressure, decreasing temperature, and decreasing wind speed. Principal component analysis confirmed the robustness and accuracy of our optimized association rule approach in quantifying complex, nonlinear relationships, while providing granular, interpretable insights beyond the traditional methods.
]]>Authors: Maria-Aliki Chasapi Konstantinos Moustris Kyriaki-Maria Fameli Georgios Spyropoulos
This research focuses on monitoring and analyzing air pollutant emissions, mainly from passenger vehicles, at a busy urban intersection with 19 traffic lanes at the junction of Thivon Avenue and Iera Odos, located in the Egaleo municipality, an urban region of Athens, Greece. To collect data, a monitoring study was conducted specifically on the four central traffic streams of this specific intersection. On each segment of the road, a specific length was assigned through which vehicles pass at an average speed in order for their emissions to be estimated. For each vehicle, the engine type (gas or diesel) and engine displacement were taken into account to calculate the predicted mass of vehicle emissions. These measurements were conducted separately for each segment and recorded during three signal phases (from green to red) for two weekdays and one non-working day. This approach allows pollutant levels to be monitored at various hours and under various traffic conditions. The analysis revealed not only the overall quantity of emissions from vehicles but also their fluctuations throughout the day and traffic conditions, comparing them with the regulatory limits set by the EU. Significant findings regarding the impact of traffic on air quality are highlighted.
]]>Authors: Anita Lee Mitchell Kyle A. Chapman
It is crucial to assess health impacts of PM2.5, especially from wildfire smoke, to ensure proper planning for healthcare services. Studies often focus on respiratory conditions; fewer estimate the additional burden of cardiovascular complications. This study aims to extend previous work on the impacts of wildfire smoke and associated PM2.5 on health in Southern Oregon by expanding study areas and timeframes, including cardiovascular conditions, and applying improved and novel air quality measurement data. This study adopts a case-crossover approach using conditional Poisson regression to analyze time stratified patient counts while controlling for mean temperature. Every 10 μg/m3 increase in PM2.5 is associated with a 1.6% increase in same-day hospital or emergency room admission rates for respiratory conditions (OR = 1.0157; 95% CI: 1.0024–1.0287) and no significant increase in admission rates for cardiovascular conditions. A 10 μg/m3 increase in PM2.5 lasting fifteen days is associated with a 6.5% increase in hospital or emergency room admission rates for respiratory conditions (OR = 1.0645; 95% CI: 1.0400–1.0894) and a 4.9% increase in hospital or emergency room admission rates for cardiovascular conditions (OR = 1.0493; 95% CI: 1.0265–1.0723). As the duration of poor air quality increases, the risk of negative respiratory and cardiovascular health outcomes increases.
]]>Authors: Federica Valentini Ivo Allegrini Irene Colasanti Camilla Zaratti Andrea Macchia Cristiana Barandoni Anna Neri
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.
]]>Authors: Shisir Ruwali Jerrold Prothero Tanay Bhatt Shawhin Talebi Ashen Fernando Lakitha Wijeratne John Waczak Prabuddha M. H. Dewage Tatiana Lary Matthew Lary Adam Aker David Lary
The air we breathe contains contaminants such as particulate matter (PM), carbon dioxide (CO2), nitrogen dioxide (NO2), 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 (SpO2). These biometrics can be used to estimate pollutants, in particularly PM1 and CO2, 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 CO2, NO2, 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 CO2, NO2, 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 CO2 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 NO2 and NO when using physiological responses and the PSD from the ASA, which can be further confirmed with a large number of dataset.
]]>Authors: Dimitrios Rimpas Dimitrios E. Barkas Vasilios A. Orfanos Ioannis Christakis
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.
]]>Authors: Lakitha O. H. Wijeratne Daniel Kiv John Waczak Prabuddha Dewage Gokul Balagopal Mazhar Iqbal Adam Aker Bharana Fernando Matthew Lary Vinu Sooriyaarachchi Rittik Patra Nora Desmond Hannah Zabiepour Darren Xi Vardhan Agnihotri Seth Lee Chris Simmons David J. Lary
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.
]]>Authors: Toluwanimi M. Oni Changjie Cai Evan L. Floyd
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.
]]>Authors: Gokul Balagopal Lakitha Wijeratne John Waczak Prabuddha Hathurusinghe Mazhar Iqbal Rittik Patra Adam Aker Seth Lee Vardhan Agnihotri Christopher Simmons David J. Lary
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 PM2.5 (particulate matter of size ≤ 2.5 μm in diameter) pollution levels. For the most polluted day, the optimal sampling interval for PM2.5 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 PM2.5. 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.
]]>Authors: Salimata Thiam Mouhamadou Lamine Daffe Fabrice Cazier Awa Ndong Ba Anthony Verdin Paul Genevray Dorothée Dewaele Dominique Courcot Mamadou Fall
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.
]]>Authors: Glykeria Loupa Georgios Dabanlis Evangelia Kostenidou Spyridon Rapsomanikis
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.
]]>Authors: Katarzyna Gładysz Mariola Wrochna Robert Popek
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.
]]>Authors: Alicia Aguado Sandra Rodríguez-Sufuentes Francisco Verdugo Alberto Rodríguez-López María Figols Johannes Dalheimer Alba Gómez-López Rubèn González-Colom Artur Badyda Jose Fermoso
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.
]]>Authors: Alberto Moreno Yoana Rabanal-Ruiz Andrés Moreno-Cabañas Carlos Sánchez Jiménez Beatriz Cabañas
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.
]]>Authors: Shilindion Basemera Silver Onyango Jonan Tumwesigyire Martin Mukama Data Santorino Crystal M. North Beth Parks
Because PM2.5 concentrations are not regularly monitored in Mbarara, Uganda, this study was implemented to test whether correlations exist between weather parameters and PM2.5 concentration, which could then be used to estimate PM2.5 concentrations. PM2.5 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 PM2.5 concentrations were 70.1 and 39.4 µg/m3, respectively. Diurnal trends for PM2.5 levels show bimodal peaks in the morning and evening. The univariate regression analysis between PM2.5 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 PM2.5 concentration and wind speed in the dry season. A strong correlation is seen between diurnal variations in PM2.5 concentration and most weather parameters, but our analysis suggests that in modeling PM2.5 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 PM2.5 concentration in Mbarara and forms a basis for modeling short-term variation in PM2.5 concentration and determining the effect of seasonal and diurnal sources on PM2.5 concentration.
]]>Authors: Francesca Calastrini Gianni Messeri Andrea Orlandi
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 PM2.5 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.
]]>Authors: Patrick Goodman Eoin J. McGillicuddy R. Giles Harrison David Q. Rich John A. Scott
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 PM2.5. 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.
]]>Authors: Miles Sowden
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.
]]>Authors: Angelo Robotto Cristina Bargero Luca Marchesi Enrico Racca Enrico Brizio
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.
]]>Authors: Alessandro Marongiu Gabriele Giuseppe Distefano Marco Moretti Federico Petrosino Giuseppe Fossati Anna Gilia Collalto Elisabetta Angelino
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.
]]>Authors: Sarath K. Guttikunda Vasil B. Zlatev Sai Krishna Dammalapati Kirtan C. Sahoo
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 PM2.5 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 PM2.5 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 PM2.5 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.
]]>Authors: John R. Lawson Seth N. Lyman
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.
]]>Authors: Anita Tóth Zita Ferenczi
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.
]]>Authors: Evangelos Stergiou Anastasia Chrysovalantou Chatziioannou Spiros A. Pergantis Maria Kanakidou
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.
]]>Authors: Tristan Soubrié Julien Néchab Romain Viala Milena Creton Michael Jousserand
This paper examines aerosol propagation in wind instruments through numerical analysis, focusing on particle trajectories within five types of wind instruments: saxophone, clarinet, flute, oboe, and trumpet. Using a computational fluid dynamics approach, it is found that larger particles are deposited within the instruments, while smaller micron-sized particles predominantly exit through the bell. The impact of the instrument’s geometry on aerosol dynamics is quantified; cylindrical instruments (clarinet, flute) show an increased rate of small droplet deposition or escape through tone holes compared to conical instruments (saxophone, oboe). Instruments with steep turnings, such as the trumpet, exhibited significant particle deposition. The study suggests that deposited particles are likely to move towards re-emission points, driven by gravity and airflow, especially in straight-shaped instruments. Integrating computational fluid dynamics (CFD) as a complementary approach to traditional experimental methods provides insights into aerosol transmission mechanisms in musical settings. This methodology not only aids in understanding aerosol behavior but also supports the development of safer musical and educational environments, contributing to the field.
]]>Authors: Kristina Leontjevaite Aoife Donnelly Tadhg Eoghan MacIntyre
Air pollution’s effects on physical health, especially cardiovascular and respiratory, are well known. Exposure to air pollution may damage every organ and cell in the human body. New evidence is emerging showing that air pollution adversely affects human mental health. Current research suggests that high air pollution levels have long-term mental health effects, such as reduced mental capacity and increased cognitive decline, leading to increased stress, anxiety, and depression. Objectives: This scoping review aims to provide a comprehensive overview of the methods used in epidemiological literature to ascertain the existence of links between outdoor particulate matter (PM) and multiple adverse mental health (MH) effects (depression, anxiety, and/or stress). A better understanding of the practical research methodologies could lead to improved air quality (AQ) management and enhanced well-being strategies. Methods: This paper undertakes a scoping review. PubMed and EMBASE databases from 2010 to 2024 were searched for English-language human cohort observational studies stating methodologies used in analyzing the link between outdoor particulate matter (ultrafine (UFT) (<0.1 μm), fine (<2.5 μm), and course (<10 μm)) and mental health outcomes (depression, anxiety, and stress) in adults (>18 years), excluding vulnerable populations (i.e., elderly, children, and pregnant women). The study focuses on urban, suburban areas, and rural areas. Results: From an initial search of 3889 records, 29 studies met the inclusion criteria and were included in the review. These studies spanned various countries and employed robust quantitative methodologies to assess AQ and MH. All included studies investigated the impact of PM on mental health, with some (n = 19/65.52%) also examining nitrogen oxides (NOx), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and carbon monoxide (CO). Depression was the most frequently studied outcome (n = 10/34.48%), followed by anxiety and depression (n = 6/20.69%), and anxiety, stress, and depression, and stress (n = 4/13.79%, each). Depression, anxiety, and stress together were examined in a single study (n = 1/3.45%). Standardized questionnaires involving psychological scales such as Patient Health Questionnaire (PHQ) (n = 7/24.14%) and The Center for Epidemiological Studies-Depression (CES-D) (n = 3/10.34%) for depression and Generalized Anxiety Disorder Questionnaire (GAD) (n = 2/6.90%) for anxiety were commonly used MH tools. 27 out of 29 studies found a significant negative impact of air pollution on mental health, demonstrating a solid consensus in the literature. Two studies did not find a significant correlation. The results consistently indicated that higher levels of air pollution were associated with increased symptoms of depression, anxiety, and stress. Conclusion: Of the 3889 identified studies, 29 were suitable for inclusion in the scoping review per inclusion criteria. The results show the most preferred methods in assessing air quality and mental health in relevant studies, providing a detailed account of each method’s strengths and limitations used in studies. This scoping review was conducted to assist future research and relieve the decision-making process for researchers aiming to find a correlation between air quality and mental health. While the inclusion criteria were strict and thus resulted in few studies, the review found a gap in the literature concerning the general adult population, as most studies focused on vulnerable populations. Further exploration of the methodologies used to find the relationship between air quality and mental health is needed, as reporting on these outcomes was limited.
]]>Authors: Sarath K. Guttikunda
Air pollution knows no boundaries, which means for a city or a region to attain clean air standards, we must not only look at the emission sources within its own administrative boundary but also at sources in the immediate vicinity and those originating from long-range transport. And there is a limit to how much area can be explored to evaluate, govern, and manage designated airsheds for cities and larger regions. This paper discusses the need for an official airshed framework for India’s air quality management and urban airsheds designated for India’s 131 non-attainment cities under the national clean air program, and proposes climatically and geographically appropriate regional airsheds to support long-term planning. Between 28 states, eight union territories, 36 meteorological sub-regional divisions, and six regional meteorological departments, establishing the proposed 15 regional airsheds for integrated and collaborative air quality management across India is a unique opportunity.
]]>Authors: Dani Khoury Maurice Millet Yasmine Jabali Thomas Weissenberger Olivier Delhomme
For the current article, forty-two fogwater samples are collected at four sites in Alsace (Strasbourg, Geispolsheim, Erstein, and Cronenbourg) between 2015 and 2021, except 2019 and 2020. Spatio-temporal evolution is studied for their inorganic fraction (ions and heavy metals), and physico-chemical properties (pH, conductivity (K), liquid water content (LWC), and dissolved organic carbon (DOC)). The analyses show a remarkable shifting in pH from acidic to basic mainly due to the significant decrease in sulfate and nitrate levels. The calculated median LWC is somehow low (37.8–69.5 g m3) in fog samples, preventing the collection of large fog volumes. The median DOC varies between 14.3 and 24.4 ppm, whereas the median conductivity varies from 97.8 to 169.8 µS cm−1. Total ionic concentration (TIC) varies from 1338.3 to 1952.4 µEq L−1, whereas the total concentration of metals varies in the range of 1547.2 and 2860.3 µg L−1. The marine contribution is found to be negligible at all sites for the investigated elements. NH4+, in most samples, is capable alone to neutralize the acidity. On one hand, NH4+, Ca2+, NO3−, and SO42− are the dominant ions found in all samples, accounting for more than 80% of the TIC. On the other hand, Zn and Ni are the dominant metals accounting for more than 78% of the total elemental concentration. Heavy metals are found to primarily originate from crust as well as human-made activities. The median concentrations of individual elements either decrease or increase over the sampling period due to the wet deposition phenomenon or weather conditions. A Pearson analysis proves some of the suggested pollutant sources due to the presence of strong and significant correlations between elements.
]]>Authors: Mieczysław Szyszkowicz
In this study, we examine and assess the potential impact of diesel engine age on the levels of fine particulate matter (PM2.5) in school buses. The concentration of air pollutants is influenced by several factors, including the technical characteristics of the bus and its engine, the type of fuel used, the length of the commute, the weather conditions, and the ambient air pollution. The behavior of the bus on the road, during the commute to and from school, is also important. This includes its position in traffic, the number of bus stops, boarding procedures, as well as the opening of doors and windows. Data were collected by accompanying a student during their commute to and from school, with bus commutes serving as the sampling unit. A semi-parametric regression was applied to assess the link between the PM2.5 concentration and the bus engine age. It was demonstrated that the bus engine age has a statistically significant positive correlation with the PM2.5 concentration inside the bus. The fine particulate matter concentrations during boarding at the school also depend on the engine age, indicating that bus idling affects the PM2.5 concentration. In the first two minutes before boarding in front of the school and the first two minutes inside the bus, the PM2.5 concentrations were 26.3 and 40.3 μg/m3, respectively. The findings of this study highlight the impact of bus engine age on the PM2.5 concentration, showing that the PM2.5 concentration increases with the engine age. However, the effect becomes less visible as the duration of the bus ride increases.
]]>Authors: Ivo Allegrini Cinzia Perrino Elena Rantica Federica Valentini
Passive samplers have long been used to measure atmospheric pollutants in both indoor and outdoor environments. They are simple to operate, and can now monitor several chemical species. However, their use is limited because they usually require a long exposition time and provide a mean value that cannot control or evidence expected or non-expected events of environmental significance. A new apparatus specifically developed for exposing Analyst© passive samplers has been used to monitor ozone and nitrogen dioxide by automatically selecting a sampling duration of 8 h, as most legislation requires. The instrument was designed to accumulate ozone or NO2 in one passive sampler for 8 h over each day, and in another passive sampler for the remaining hours. This allows for a long-time accumulation of the 8 h ozone or nitrogen dioxide in a dedicated sampler. Measurements were carried out NE of Rome at a rural site. A description of the experiments is given, with special emphasis on the quality controls. Very low uncertainties and good comparability of the data with the reference methods were obtained for both pollutants.
]]>Authors: Hao He Zhanqing Li Russell R. Dickerson
To study air pollution in the North China Plain (NCP), the Air Chemistry Research in Asia (ARIAs) campaign conducted airborne measurements of air pollutants in spring 2016. High pollutant concentrations, with O3 > 100 ppbv, CO > 500 ppbv, and NO2 > 10 ppbv, were observed. CMAQ simulations with the 2010 EDGAR emissions capture the spatial and temporal variations in ozone and its major precursors such as NO2 and VOCs, with significant underestimation. Differences between CMAQ simulations and satellite observations reflect changes in anthropogenic emissions, decreased NOx emissions in megacities such as Beijing, but slight increases in other cities and rural areas. CMAQ also underestimates HCHO and CO, suggesting adjustments of the 2010 EDGAR emissions are necessary. HCHO/NO2 column ratios derived from OMI measurements and CMAQ simulations show that VOC-sensitive chemistry dominates the ozone photochemical production in eastern China, suggesting the importance of tightening regulations on anthropogenic VOC emissions. After adjusting emissions based on satellite observations, better model performance was achieved. Because of the VOC-sensitive environment in ozone chemistry over the NCP, the underestimation of anthropogenic emissions could be important for CMAQ simulations, while future study and regulations should focus on VOC emissions with continuous controls on NOx emissions in China.
]]>Authors: Syed A. Q. Rizvi Suzanne Kearns Shi Cao
As the global aviation sector expands to accommodate increasing air travel demand, the subsequent rise in flights exacerbates carbon dioxide (CO2) emissions, challenging the sector’s environmental sustainability. Targeting net-zero emissions by 2050, international aviation agencies are stressing the imperative of reducing emissions directly at their source. While the literature provides abundant estimates of aviation emissions from airline flights, there has been a lack of work aimed at quantifying CO2 emissions specific to the general aviation sector. This study investigates CO2 emissions attributed to the pilot training sub-sector within Canada’s general aviation sector. It specifically examines the initial phase of pilot training, known as ab initio training, extending through to the attainment of a commercial pilot license. Utilizing a mathematical framework alongside assumptions, combined with data on license issuances over a 23-year period, it estimated that each hour of flight training emits about 70.4 kg of CO2, varying between 44.9 kg and 94.9 kg per hour. Annual CO2 emissions from Canada’s ab initio pilot training are estimated at approximately 30,000 tons, with a possible range of 19,000 to 40,000 tons. The study also explores mitigation opportunities, such as flight simulation training devices and electric aircraft. Though focusing on Canada’s ab initio pilot training, the findings have international relevance.
]]>Authors: Aspen Morgan Jeremy Crowley Raja M. Nagisetty
Wildfires pose a direct threat to the property, life, and well-being of the population of Montana, USA, and indirectly to their health through hazardous smoke and gases emitted into the atmosphere. Studies have shown that elevated levels of particulate matter cause impacts to human health ranging from early death, to neurological and immune diseases, to cancer. Although there is currently a network of ground-based air quality sensors (n = 20) in Montana, the geographically sparse network has large gaps and lacks the ability to make accurate predictions for air quality in many areas of the state. Using the random forest method, a predictive model was developed in the Google Earth Engine (GEE) environment to estimate PM2.5 concentrations using satellite-based aerosol optical depth (AOD), dewpoint temperature (DPT), relative humidity (RH), wind speed (WIND), wind direction (WDIR), pressure (PRES), and planetary-boundary-layer height (PBLH). The validity of the prediction model was evaluated using 10-fold cross validation with a R2 value of 0.572 and RMSE of 9.98 µg/m3. The corresponding R2 and RMSE values for ‘held-out data’ were 0.487 and 10.53 µg/m3. Using the validated prediction model, daily PM2.5 concentration maps (1 km-resolution) were estimated from 2012 to 2023 for the state of Montana. These concentration maps are accessible via an application developed using GEE. The product provides valuable insights into spatiotemporal trends of PM2.5 concentrations, which will be useful for communities to take appropriate mitigation strategies and minimize hazardous PM2.5 exposure.
]]>Authors: Constance Utsale Chikumbusko Kaonga Fabiano Thulu Ishmael Kosamu Fred Thomson Upile Chitete-Mawenda Hiroshi Sakugawa
The increase in industrial activities has raised concerns regarding air quality in urban areas within Malawi. To assess the source apportionment of air quality parameters (AQPs) and noise levels, concentrations of AQPs (CO, TSP, PM 2.5, PM10) and noise levels were monitored at 15 sites in Makata, Limbe, Maselema, Chirimba, and Maone during dry and wet seasons, respectively. Active mobile multi-gas monitors and a Dylos DC1100 PRO Laser Particle Counter (2018 model) were used to monitor AQPs, while Integrated Sound Level Meters were used to measure noise levels. Monitoring and analysis were guided by the World Health Organization (WHO) and Malawi Standards (MS). A Positive Matrix Factorization (PMF) model was used to determine the source apportionment of AQPs, and matrix trajectories analysed air mass movement. In the wet season, the average concentration values of CO, TSP, PM10, and PM2.5 were 0.49 ± 0.65 mg/m3, 85.03 ± 62.18 µg/m3, 14.65 ± 8.13 µg/m3, and 11.52 ± 7.19 µg/m3, respectively. Dry season average concentration values increased to 1.31 ± 0.81 mg/m3, 99.86± 30.06 µg/m3, 24.35 ± 9.53 µg/m3, and 18.28 ± 7.14 µg/m3. Noise levels remained below public MS and WHO standards (85 dB). Positive correlations between AQPs and noise levels were observed, strengthening from weak in the dry season to moderately strong in the wet season. PMF analysis identified key factors influencing AQP accumulation, emphasizing the need for periodic sampling to monitor seasonal pollution trends, considering potential impacts on public health and environmental sustainability. Further studies should look at factors affecting the dynamics of PMF in Blantyre City.
]]>Authors: Hélène Niculita-Hirzel Maria Serena Merli Kyle Baikie
Sidewalk cleaning operations are essential to maintaining a clean and safe urban environment. Despite their vital role, these activities, particularly the blowing of road dust, can lead to the resuspension of road dust and associated pollutants, which poses risks to human health and the environment. While the role of blowers on particulate matter resuspension has been investigated, there is limited information on emitted bioaerosols. This study aimed to compare the occupational exposure of operators and passersby during sidewalk cleaning using two manual methods—blowing and jet washing—in two distinct urban environments. The study focused on metal road traffic tracers (copper (Cu), zinc (Zn), manganese (Mn), cadmium (Cd), and lead (Pb)) and cultivable/non-cultivable microorganisms. We showed that blowing resuspends inhalable particles containing metals (Cu, Zn, and Mn, but not Cd or Pb) and bioaerosols (fungi and Gram-negative bacteria) throughout the year. This represents an important source of exposure for the blower operators and poses a potential long-term respiratory health risk for them. Operators working in cabs are shielded from such exposure, but passersby, especially vulnerable populations, may be at risk. While jet washing reduces operator exposure to Gram-negative bacteria in comparison to blowing, it does not mitigate fungal exposure, particularly in vegetated sites. These findings underscore the necessity for the implementation of effective protective measures and the development of alternative cleaning methods to mitigate exposure risks.
]]>Authors: Roberto De Lotto Riccardo Bellati Marilisa Moretti
The authors present an analysis of the correlation between demographic and territorial indicators and greenhouse gas (GHG) emissions, emphasizing the spatial aspect using statistical methods. Particular attention is given to the application of correlation techniques, considering the spatial correlation between the involved variables, such as demographic, territorial, and environmental indicators. The demographic data include factors such as population, demographic distribution, and population density; territorial indicators include land use, particularly settlements, and road soil occupancy. The aims of this study are as follows: (1) to identify the direct relationships between these variables and emissions; (2) to evaluate the spatial dependence between geographical entities; and (3) to contribute to generating a deeper understanding of the phenomena under examination. Using spatial autocorrelation analysis, our study aims to provide a comprehensive framework of the territorial dynamics that influence the quantity of emissions. This approach can contribute to formulating more targeted environmental policies, considering the spatial nuances that characterize the relationships between demographics, territory, and GHGs. The outcome of this research is the identification of a direct formula to obtain greenhouse gas emissions from data about land use starting from the case study of Pavia Province in Italy. In the paper, the authors highlight different methodologies to compare land use and GHG emissions to select the most feasible correlation formula. The proposed procedure has been tested and can be used to promote awareness of the spatial dimension in the analysis of complex interactions between anthropogenic factors and environmental impacts.
]]>Authors: Sébastien Artous Eric Zimmermann Cécile Philippot Sébastien Jacquinot Dominique Locatelli Adeline Tarantini Carey Suehs Léa Touri Simon Clavaguera
Airports are significant contributors of atmospheric pollutant aerosols, namely ultrafine particles (UFPs). This study characterizes the particle number concentration (PNC), the median particle size (dmn50), and the metallic composition of medium-haul area and engine aerosols at two French airports (Paris-CDG and Marseille). This study followed the standard operating procedures for characterizing aerosol emissions from 5 nm to 8 μm (OECD, 2015; EN 17058:2018). It allows determining which are the specific parameters directly related to the emission sources and their contribution to the overall aerosols measured at workplace in airports. The particulate emissions observed during aircraft engine start-up were ~19× higher than the average airborne concentration. The particle size distributions remained mostly <250 nm with dmn50 < 100 nm (showing a specificity for the medium-haul area with an average dmn50 of ~12 nm). The dmn50 can be used to distinguish emission peaks due to aircrafts (dmn50~15 nm) from those due to apron vehicle activities (dmn50 > 20 nm). Chemical elements (titanium and zinc) were identified as potential tracers of aircraft emissions and occurred mainly at the micrometric scale. For aircraft engine emissions, UFPs are mainly due to fuel combustion with the presence of carbon/oxygen. The study concludes with suggestions for future research to extend on the findings presented.
]]>Authors: Satoshi Irei Seiichiro Yonemura Satoshi Kameyama Asahi Sakuma Hiroto Shimazaki
Biomass burning is one of the largest sources of particulate matter emissions globally. However, the emission of particulate inorganic species from prescribed grassland burning in Japan has not yet been characterized. In this study, we collected total suspended particulate matter from prescribed grassland burning in Hirado, Akiyoshidai, and Aso, Japan. The collected filter samples were brought to the laboratory, and water-soluble inorganic components were analyzed via ion chromatography. The measurement results showed high excess concentrations of potassium, calcium, and magnesium, and these substances were highly correlated, which agreed with previously reported findings. In contrast, the concentrations of sodium, chloride, nitrate, and sulfate were insignificant, even though their high concentrations were reported in other biomass burning studies. Among these low concentration substances, a high correlation was still observed between sulfate and nitrate. It is possible that the low concentrations of those species could have been biased in the measurements, particularly as a result of subtracting blank and background values from the observed concentrations. Building up more data in this area may allow us to characterize the significance of domestic biomass burning’s contribution to inorganic particulate components in Japanese air, which may consequently contributes to better understanding of adverse health effect of airborne particulate matter.
]]>Authors: Alessandro Marongiu Anna Gilia Collalto Gabriele Giuseppe Distefano Elisabetta Angelino
This paper describes an innovative method that recursively applies the machine learning Random Forest to an assumed homogeneous aerographic domain around measurement sites to predict concentrations and emissions of ammonia, an atmospheric pollutant that causes acidification and eutrophication of soil and water and contributes to secondary PM2.5. The methodology was implemented to understand the effects of weather and emission changes on atmospheric ammonia concentrations. The model was trained and tested by hourly measurements of ammonia concentrations and atmospheric turbulence parameters, starting from a constant emission scenario. The initial values of emissions were calculated based on a bottom-up emission inventory detailed at the municipal level and considering a circular area of about 4 km radius centered on measurement sites. By comparing predicted and measured concentrations for each iteration, the emissions were modified, the model’s training and testing were repeated, and the model converged to a very high performance in predicting ammonia concentrations and establishing hourly time-varying emission profiles. The ammonia concentration predictions were extremely accurate and reliable compared to the measured values. The relationship between NH3 concentrations and the calculated emissions rates is compatible with physical atmospheric turbulence parameters. The site-specific emissions profiles, estimated by the proposed methodology, clearly show a nonlinear relation with measured concentrations and allow the identification of the effect of atmospheric turbulence on pollutant accumulation. The proposed methodology is suitable for validating and confirming emission time series and defining highly accurate emission profiles for the improvement of the performances of chemical and transport models (CTMs) in combination with in situ measurements and/or optical depth from satellite observation.
]]>Authors: Nina E. Ahlers Jue Lin Sandra J. Weiss
Background: Growing evidence suggests that air pollution may influence fetal development, with potential consequences for later health. Alteration of telomere length (TL) is one possible mediating mechanism for the link between fetal exposure to air pollution and the development of disease. However, the few studies exploring associations between prenatal pollution and infant TL have assessed varied trimesters of pregnancy and shown mixed results. The aim of this study was to examine the differential relationships between prenatal exposure to air pollutant PM2.5 during the first, second, and third trimesters of pregnancy with infant TL at one month of age. Methods: Women (n = 74) were recruited in obstetric clinics during their third trimester. Data on PM2.5 exposure for each woman’s residential area during each trimester was acquired from the regional Air Quality Management District. At one month postnatal, a salivary sample was collected from the infant, which provided DNA for the telomere assay. Women completed questionnaires about stressors in their lives, perceived stress, depression, and sociodemographics for inclusion as covariates. Multiple linear regression was used to analyze the results. Results: PM2.5 exposure during the second (β = 0.31, p = 0.003) and third (β = 0.24, p = 0.02) trimesters was associated with longer infant TL. Exposure in the first trimester was not related to TL. Covariates of maternal depression and age and infant female sex were also associated with longer TL. Variables in the model contributed to 34% of the variance in TL (F = 10.58, p = 0.000). Discussion: Fetal programming of longer telomeres in response to pollution may have adaptive value in preparing the neonate for a postnatal environment that is less than optimal in terms of air quality. Alternatively, longer telomeres may forecast later health risks, considering established links between longer TL and diseases such as cancer. Future research needs to address how prenatal pollution interacts with TL to influence health over time.
]]>Authors: Sayanti Kar Santanu Chowdhury Tanya Gupta Dipsita Hati Arindam De Ziniya Ghatak Tahsin Tinab Iffa Tasnim Rahman Shreyashi Chatterjee Abhishek RoyChowdhury
The global concern of escalating ambient air pollution and its profound impact on human health is paramount. While traffic police personnel are critical for maintaining the road safety and transportation system of any city in India, they are susceptible to occupational health risks due to ambient air pollution. This study investigated health challenges faced by traffic police personnel due to prolonged exposure to air pollutants prevalent in traffic-congested areas, including particulate matter (PM2.5 and PM10), nitrogen dioxide, and sulfur dioxide. The first phase of this study collected and analyzed secondary air quality data over five years (2019–2023) across six locations in Kolkata, India. The second phase employed a questionnaire-based survey to assess the health implications of air pollution exposure. The survey questionnaire captured information on physical health symptoms, stress-related indicators, lifestyle habits, and work hours of around 100 police personnel from Kolkata with indoor (control group) and outdoor (exposed group) work responsibilities. The results of this study established a strong positive correlation between air pollution and a range of health issues experienced by the exposed group. The outcome of this study is significant for urban planning, policy formulation, and public health interventions geared toward minimizing the adverse impacts of air pollution on traffic police personnel.
]]>Authors: Andrew Shapero Stella Keck Adam H. Love
Source apportionment of observed PM2.5 concentrations is of growing interest as communities seek ways to improve their air quality. We evaluated publicly available PM2.5 data from the USEPA in the Dallas–Fort Worth metropolitan area to determine the contributions from various PM2.5 sources to the total PM2.5 observed. The approach combines interpolation and fixed effect regression models to disentangle background from local PM2.5 contributions. These models found that January had the lowest total PM2.5 mean concentrations, ranging from 5.0 µg/m3 to 6.4 µg/m3, depending on monitoring location. July had the highest total PM2.5 mean concentrations, ranging from 8.7 µg/m3 to 11.1 µg/m3, depending on the location. January also had the lowest mean local PM2.5 concentrations, ranging from 2.6 µg/m3 to 3.6 µg/m3, depending on the location. Despite having the lowest local PM2.5 concentrations, January had the highest local attributions [51–57%]. July had the highest mean local PM2.5 concentrations, ranging from 2.9 µg/m3 to 4.1 µg/m3, depending on the location. Despite having the highest local PM2.5 concentrations, July had the lowest local attributions [33–37%]. These results suggest that local contributions have a limited effect on total PM2.5 concentrations and that the observed seasonal changes are likely the result of background influence, as opposed to modest changes in local contributions. Overall, the results demonstrate that in the Dallas–Fort Worth metropolitan area, approximately half of the observed total PM2.5 is from background PM2.5 sources and half is from local PM2.5 sources. Among the local PM2.5 source contributions in the Dallas–Fort Worth metropolitan area, our analysis shows that the vast majority is from non-point sources, such as from the transportation sector. While local point sources may have some incremental site-specific local contribution, such contributions are not clearly distinguishable in the data evaluated. We present this approach as a roadmap for disentangling PM2.5 concentrations at different spatial levels (i.e., the local, regional, or state level) and from various sectors (i.e., residential, industrial, transport, etc.). This roadmap can help decision-makers to optimize mitigatory, regulatory, and/or community efforts towards reducing total community PM2.5 exposure.
]]>Authors: Abdullah Umair Bajwa Hassan Aftab Sheikh
The urban areas of Pakistan exhibit some of the world’s highest levels of air pollution, primarily due to sub-2.5 μm particulate emissions. This issue significantly impairs both the country’s economy and the quality of life of its residents. Road transport is a significant contributor to anthropogenic air pollution but there are discrepancies about the extent of its share. Source apportionment and sectoral inventory studies attribute anywhere between 5 and >80% of the total air pollution to vehicular sources. This uncertainty propagates into the transport policy interventions that are informed by such studies and can thus hinder the achievement of desired pollution mitigation targets. In an effort to reconcile such discrepancies and guide future studies and policy-making efforts, this paper critically reviews source apportionment studies conducted in the urban centres of Pakistan over the past two decades. The strengths and weaknesses of different approaches are compared, and results from the studies are discussed based on the emissions profile of Pakistan’s automotive fleet that emerges. Inconsistencies in the reporting of pollutant concentrations and interpreting their impacts without accounting for the relative disease burden of different pollutant species are found to be the major reasons for the large variations in the reported sectoral shares. At the end, a framework for regular air pollution monitoring and source tracking is proposed in which high-fidelity receptor-based studies inform lower-fidelity but economical sectoral inventory assessments.
]]>Authors: Austin C. Flueckiger Christopher N. Snyder Giuseppe A. Petrucci
The impact of relative humidity (RH) on organic new particle formation (NPF) from the ozonolysis of biogenic volatile organic compounds (BVOCs) remains an area of active debate. Previous reports provide contradictory results, indicating both the depression and enhancement of NPF under conditions of high RH. Herein, we report on the impact of RH on NPF from the dark ozonolysis of cis-3-hexenyl acetate (CHA), a green-leaf volatile (GLV) emitted by vegetation. We show that RH inhibits NPF by this BVOC, essentially shutting it down at RH levels > 1%. While the mechanism for the inhibition of NPF remains unclear, we demonstrate that it is likely not due to increased losses of CHA to the humid chamber walls. New oxidation products dominant under humid conditions are proposed that, based on estimated vapor pressures (VPs), should enhance NPF; however, it is possible that the vapor phase concentration of these low-volatility products is not sufficient to initiate NPF. Furthermore, the reaction of C3-excited state Criegee intermediates (CIs) with water may lead to the formation of small carboxylic acids that do not contribute to NPF. This hypothesis is supported by experiments with quaternary O3 + CHA + α-pinene + RH systems, which showed decreases in total α-pinene-derived NPF at ~0% RH and subsequent recovery at elevated RH.
]]>Authors: Vivien Pohl Alan Gilmer Vivienne Byers John Cassidy Aoife Donnelly Stig Hellebust Eoin J. McGillicuddy Eugene McGovern David J. O’Connor
Air quality monitoring in Ireland is under the jurisdiction of the Environmental Protection Agency in compliance with the Gothenburg Protocol, EU/national legislation, and the National Clean Air Strategy. Secondary inorganic aerosols (SIAS) have been acknowledged as a key atmospheric pollutant, with serious public health impacts and no safe exposure threshold in place to date. Ammonia (NH3) emissions are linked to the secondary production of aerosols through atmospheric reactions occurring with acidic atmospheric components such as sulfuric, nitric, and hydrochloric acid. These reactions result in the formation of ammonium sulfate, ammonium nitrate and ammonium chloride, among others. Approximately 98% of NH3 emissions occurring in Ireland arise from agriculture, with minor contributions from transport and natural sources. A better understanding of NH3 emissions and SIA formation can be achieved through monitoring emissions at the source level. Additionally, mitigation strategies with a more thorough understanding of NH3 dynamics at the source level and consequential SIA formation allow for more efficient action. This project monitored ambient NH3 and SIA on two selected arable agricultural sites and a control site in a rural site close to Dublin on the east coast of Ireland to establish emission levels. Meteorological factors affecting emissions and SIA formation were also measured and cross-correlated to determine micro-meteorological effects. Monitoring at the agricultural sites observed ambient NH3 concentrations ranging from 0.52 µg m−3 to 1.70 µg m−3, with an average of 1.45 µg m−3. At the control site, ambient NH3 measured concentrations ranged from 0.05 µg m−3 to 1.76 µg m−3 with an average of 0.516 µg m−3. Aerosol NH4+ ranged from 0.03 µg m−3 to 1.05 µg m−3 with an average concentration of 0.27 µg m−3 at the agricultural site. The potential effects of meteorological conditions and the implications for the effects of these emissions are discussed, with recommendations to aid compliance with the National Emissions Ceiling and the National Clean Air Strategy (Directive 2001/81/EC).
]]>Authors: Justin Holder Jamelia Jordan Kera Johnson Ayodele Akinremi Dawn Roberts-Semple
Air pollution is a leading cause of death in the United States and is associated with adverse health outcomes, including increased vulnerability to coronavirus disease 2019 (COVID-19). The AirBeam2 was used to measure particulate matter with a diameter of 2.5 μm or smaller (PM2.5) to investigate differences between indoor and ambient levels at seven private homes in New York during and after the COVID-19 lockdown. Measurements taken in 2020 fall, 2021 winter, and 2022 fall showed that at 90% of the sites, indoor PM2.5 levels exceeded outdoor levels both during and after the COVID-19 lockdown, p = 0.03, possibly exceeding safety levels. Higher indoor PM2.5 levels attributed to little or no ventilation in the basement and kitchens from cooking and smoke were greater in fall than in winter. Higher ambient PM2.5 levels were attributed to vehicular traffic at a street-facing sampling site. PM2.5 sources identified in this study may help in devising control strategies to improve indoor air quality (IAQ) and consequently alleviate respiratory health effects. These findings may be used as a basis for in-house modifications, including natural ventilation and the use of air purifiers to reduce exposures, mitigate future risks, and prevent potential harm to vulnerable residents.
]]>Authors: Nadir Hafs Mokhtar Djeddou Ahmed Benabed Georges Fokoua Amine Mehel
The vehicle in-cabin is subject to several types of pollutants infiltrating from the outdoors or emitted directly inside it, such as Volatile Organic Compounds (VOCs). The concentration of TVOC (total volatile organic compounds) is the result of the emission from different equipment surfaces that compose the car cabin. In the present study, the experimental characterization of TVOC emission from the interior surfaces of a car cabin is discussed by considering the influence of two parameters: the temperature and ventilation modes. A measurement location grid was used to measure TVOC’s emissions from 267 points on all surfaces of the car’s interior equipment. Three different temperatures and two ventilation modes (recirculation and outdoor air) were investigated. The results indicate that the concentration of TVOC increases with the temperature inside the cabin with a contribution that varies with the type of cabin equipment including the dashboard, center console, seats, and carpets. On the other hand, the concentration distributions of TVOC showed relative differences of 10–13% and 2–5% for surface and volumetric measurements, respectively. This implies no preferential positioning of the in-cabin probe for TVOC volumetric concentration measurements. In addition, the recirculation ventilation mode results in a higher accumulation of TVOC; therefore, higher concentrations are measured.
]]>Authors: Skyler Spooner Rod Handy Nancy Daher Rachel Edie Trent Henry Darrah Sleeth
Ethylene oxide (EtO) is a colorless, flammable gas at room temperature produced by the catalytic oxidation of ethylene. EtO is widely used by medical sterilization facilities to clean medical supplies and equipment. Recent epidemiological studies showed that EtO is a more potent carcinogen than previously documented, leading the Environmental Protection Agency (EPA) to update, in December 2016, the inhalation unit risk estimate for EtO. This resulted in the identification of EtO as a potential health concern in several areas across the US, including the state of Utah. The geography surrounding Salt Lake Valley creates a bowl, which is ideal for collecting air pollution emissions. The region often experiences inversion episodes which inhibit vertical mixing and cause an accumulation of air pollutants, leading to unhealthy pollution levels. Using the EPA’s dispersion modeling software, AERMOD, this study estimated EtO concentrations through facility stack and fugitive emissions modeling results. These values were compared with those of canister-based concentrations from ambient air samples taken near a medical device sterilization facility in Salt Lake Valley. Stainless steel whole-air passivated canisters were used to collect 24 h ambient concentration samples of EtO. Eight locations surrounding a Salt Lake Valley medical device sterilization facility and four background sites were chosen to measure the ambient concentrations. Accounting for potential atmospheric impacts on EtO, measurements were sampled in winter 2022 (January–March) and summer 2022 (July–September). The modeled EtO concentrations were adjusted to account for background values associated with the winter or summer data. Then, the two methodologies were compared using a Wilcoxon signed-ranked paired test. The statistical analysis resulted in six of the eight sample locations surrounding the sterilization facility being significantly different when comparing the canister-based measurements of ambient EtO to modeled estimates. Canister-based measurements taken at sites one, three, and four were statistically greater than the modeled estimates, while sites two, five, and seven were statistically less than the modeled estimates. Also, the summer background value calculated was almost 2.5 times greater than the winter one. The results do not suggest whether one method is more or less conservative than the other. In conclusion, the five of the closest sites and site seven were statistically different when comparing measured and modeled ambient concentrations of EtO. The comparison results do not clearly indicate if a correction factor could be derived for future human exposure to cancer risk assessment modeling. However, it is reasonable that the closer to the sterilization facility, the more total EtO exposure will be realized.
]]>Authors: Amanda Pontes Maia Pires Alcantara Mona Lisa Moura de Oliveira Jesuína Cássia Santiago de Araújo Rinaldo dos Santos Araújo Rita Karolinny Chaves de Lima André Valente Bueno Maria Eugênia Vieira da Silva Paulo Alexandre Costa Rocha Enrique Rodríguez-Castellón
In this work, the catalytic performance of clinoptilolite (CLIN) and SBA-15 catalysts, doped with Fe and Cu, was evaluated in the selective catalytic reduction of NO using NH3 as a reducing agent (SCR-NH3). Both Cu-CLIN and Fe-CLIN were obtained by ion-exchange using natural clinoptilolite zeolite originating from the Hrabovec deposit (northeast Slovakia region). Cu-SBA-15 and Fe-SBA-15 were prepared by impregnation into SBA-15 mesoporous synthesized silica. Standard catalytic activity tests were carried out on a bench-scale laboratory apparatus using a reaction mixture of a standard test. GHSV of 48,000 h−1 was adopted based on the space velocity of a real NH3-SCR catalyst for diesel vehicles (100–550 °C). All Cu-doped samples showed better NO conversion values than Fe-doped samples. Clinoptilolite catalysts were more active than those based on SBA-15. Maximum NO conversions of about 96% were observed for Cu-CLIN and Fe-CLIN at 350–400 °C, respectively. Moreover, Fe-CLIN also showed higher stability in the presence of SO2 and water steam at 350 °C. These results demonstrate the potential of metal-doped natural clinoptilolite to be used as cost-effective catalysts applied to the abatement of NOx emissions generated in automotive combustion processes.
]]>Authors: Ioannis Logothetis Christina Antonopoulou Georgios Zisopoulos Adamantios Mitsotakis Panagiotis Grammelis
One of people’s greatest concerns about air quality degradation is its impact on human health. This work is a case study that aims to investigate the air quality and the related impact on people’s health in a coastal city over the eastern Mediterranean. The analysis proceeded during a low-tourist density period, covering the days from 17 to 27 November 2022. Hourly PM2.5, NO2 and O3 concentration records from three, mobile, Air Quality Monitoring Systems (AQMS), established in an urban location, port and central area of Rhodes city, are analyzed. To investigate the impact of pollution levels on human health, the Air Quality Health Index (AQHI) is calculated. The daily and diurnal variation of pollutants’ concentration and AQHI among the different areas, as well as the relation among the ambient air pollutants and AQHI, are studied. Additionally, to investigate the impact of wind regime on the variation of pollution and AQHI levels, the hourly zonal and meridional wind-speed components, as well as the temperature at 2 m, the dew point temperature at 2 m, and the height of the boundary layer from ERA5 reanalysis, are retrieved for the region of the southeastern Mediterranean. Results show that the highest pollution level occurs in the city center of Rhodes, compared to the rest of the studied locations. In general, the findings do not show exceedances of the pollutants’ concentration according to the European Directive 2008/50/EC. Moreover, findings show that in some cases, the health risk is classified from Low to Moderate in terms of AQHI. The analysis indicates that the climate conditions affect the pollutants’ concentration due to dispersion, and likely, the atmospheric transport of pollutants. Finally, this work aims to improve the knowledge regarding the air quality of southeastern Greece, promoting the framework for the green and sustainable development of the South Aegean Sea.
]]>Authors: Jayanarayanan Kuttippurath Vikas Kumar Patel Gopalakrishna Pillai Gopikrishnan Hamza Varikoden
The increasing population and its associated amenities demand innovative devices, infrastructure, methods, plans and policies. Regional climate has a great role in deciding the air quality and energy demand, and therefore, weather and climate have an indisputable role in its consumption and storage. Here, we present the changes in trace gases and associated regional weather in India during lockdown and unlock periods of COVID-19. We observe a reduction of about 30% in sulphur dioxide (SO2) and 10–20% in aerosols in the Indo-Gangetic Plain (IGP), large cities, industrial sites, mining areas and thermal power plants during lockdown as compared to the same period in the previous year and with respect to its climatology. However, a considerable increase in aerosols is found, particularly over IGP during Unlock 1.0 (1–30 June 2020), because of the relaxation of lockdown restrictions. The analyses also show a decrease in temperature by 1–3 °C during lockdown compared to its climatology for the same period, mainly in IGP and Central India, possibly due to the significant reduction in absorbing aerosols such as black carbon and decrease in humidity during the period. The west coast, northwest and central India show reduced wind speed when compared to its previous year and climatological values, suggesting that there was a change in regional weather due to the lockdown. Energy demand in India decreased by about 25–30% during the first phase of lockdown and about 20% during the complete lockdown period. This study thus suggests that the reduction of pollution could also modify local weather, and these results would be useful for drafting policy decisions on air pollution reduction, urban development, the energy sector, agriculture and water resources.
]]>Authors: Bogusław Andres Izabela Betlej Wojciech Bagiński
This article is a case study of museum premises at the Museum of King John III’s Palace at Wilanow (Warsaw, Poland), wetted as a result of a failure of the water supply system to the air conditioning unit located in the attic of the building. As a result of flooding, discoloration and cracks appeared on the plaster and stucco decoration of the ceiling, located mainly in the central part of the ceiling of the King’s Library. The paintings (plafonds) mounted on the ceiling of this room also became damp. The article analyzes the microbiological contamination of air and damp paintings in the context of promptly proceeding with the drying of damp building partitions. The obtained results of microbiological air pollution in the flooded rooms were significantly lower than the permissible values recommended by Interdepartmental Commission for Maximum Admissible Concentrations and Intensities for Agents Harmful to Health in the Working Environment. In the King’s Library, i.e., the room with the dampest plaster and stucco as a result of the accident, the concentration of mold spores in the air was only 15 cfu/m3. This means that the immediate commencement of intensive drying of the building partitions (walls, ceilings with wooden floors) brought very good results. The rapid reduction in the moisture of the building partitions contributed to the worsening conditions for the development of microorganisms, which can have an adverse effect on wooden building partitions, plaster, stucco, etc.
]]>Authors: Sai Deepak Pinakana Esmeralda Mendez Ismaila Ibrahim Md. Salahuddin Majumder Amit U. Raysoni
Air pollution is a major public health concern. The region of South Texas in the United States has experienced high levels of air pollution in recent years due to an increase in population, cross-border trade between the U.S.A. and Mexico, and high vehicular activity. This review assesses the relationships between human health and air pollution in South Texas. A thorough scientific search was performed using PubMed, Science Direct, and ProQuest, with most of the literature focusing on the source apportionment of particulate matter that is 2.5 microns or less in width (PM2.5), Carbon Dioxide (CO2), carbon monoxide (CO), Black Carbon (BC), and associated health risks for children and pregnant women. Findings from the source apportionment studies suggest the role of industries, automobiles emissions, agricultural burning, construction work, and unpaved roads in the overall deterioration of air quality and deleterious health effects, such as respiratory and cardiovascular diseases. This review demonstrates the pressing need for more air pollution and health effects studies in this region, especially the Brownsville–Harlingen–McAllen metropolitan area.
]]>Authors: Ling Tim Wong
Air (ISSN 2813-4168) is a new peer-reviewed, international, open access online academic journal for scientists in different disciplines related to air’s composition and impacts [...]
]]>Authors: Steven A. Kolmes
The greatest challenge of the coming century will be the consequences of an imbalanced atmosphere. Currently, projections of global heating due to an increasingly imbalanced atmosphere are dire, but they underestimate the near-term heating impacts of the growing concentrations of methane. Industrially mediated carbon capture and storage sometimes gets raised as a promising solution on the CO2 front, but it is presently commercially inviable. Despite these facts, we nonetheless need to act globally to reduce the atmospheric concentrations of greenhouse gases, although our increasingly separate information ecosystems make finding a way to express the reality of the atmospheric imbalance crisis to a wide audience daunting. One approach to presenting the atmospheric imbalances leading to global heating is to strip the discussion down initially to its bare bones with a sharp focus on the variables of the logistic growth equation. Although virtually anything can be politicized, the logistic growth equation’s variables are at least apolitical in their origin. After examining those variables, we can proceed to focus on density-dependent mortality factors (DDMFs) and their relationship to visible climatic changes driven by atmospheric imbalances. Both the Global North and the Global South need to do all that we do to reduce atmospheric greenhouse gas accumulation, reducing DDMFs, while paying careful attention to Indigenous rights and to the need for global gender equity, so that our efforts to control DDMFs do not produce a new expression of colonialism.
]]>Authors: Gleidson Junior Silva Risely Ferraz-Almeida
Fluorine (F) produced from the fertilizer factory occurs in the process of phosphate fertilizer production, using sulfur and phosphate rocks as raw materials. Technologies to control atmospheric pollution with F should be adopted to reduce the impact on agricultural production. This study has the hypothesis that the emission of F, derived from the chimneys of fertilizer factories, is influencing the quality of corn (Zea mays L.) and increasing the F levels in the soil and plants. The objective of the study was to monitor the contents of F in corn leaves and soil in properties located close to the fertilizer production industry (between 1.5 and 2.0 km) before and after the installation of scrubber filters in the chimneys of the factory. A field study was carried out during the 2020/2021 harvest to evaluate the contents of F in corn plants and soil. Results showed that the scrubber filter installation represented a F reduction average of 92% in leaves comparing the average before the scrubber filter installation. Corn showed symptoms of F toxicity, such as leaf chlorosis, caused by the disintegration of chloroplasts, inhibition of photosynthesis, and others. In addition, there was a reduction of 40% (from the first to the second collecting) and 75% (from the first to the third collecting) in the levels of F in the soil after the scrubber filter installation. Based on the results, we conclude that the implementation of a scrubber filter is an optimal alternative to reduce F levels in corn leaves and the soil in properties located close to a fertilizer factory.
]]>Authors: Rab Nawaz Muhammad Aslam Iqra Nasim Muhammad Atif Irshad Sajjad Ahmad Maria Latif Fida Hussain
Urban air and soil quality has been deteriorating during the past few years due to urbanization, industrialization and increased number of vehicles. The goal of the current study was to assess the Air Pollution Tolerance Index (APTI) and heavy metal absorption (Pb, Cd, Zn, and Ni) potential by ten selected trees planted along the roadside in the metropolitan city of Lahore, Pakistan. APTI was estimated on the basis of biochemical parameters (chlorophyll content, ascorbic acid, pH and relative water contents) of plant extract, while heavy metals (HMs) accumulation potential was measured by a digestion method. The highest APTI was estimated in P. longifolia (78.9), followed by A. scholarils (75.9) and M. indica (71.9). Overall, these three species have significant closeness among the higher pollution-tolerance results. The poor APTI result was determined in F. religiosa (19.5) and E. citriodora (14.9). The highest Pb contents were observed in P. longifolia and M. indica i.e., 135 and 132 mg/kg, respectively. Similarly, the highest Zn contents were found in P. longifolia and S. cumini with 130 and 132 mg/kg, respectively. The Ni concentration was observed highest in P. longifolia (34 mg/kg), but in the remaining species, it is almost the same trend of Ni accumulation. Combining these trees can be useful for fostering green-belt growth along roadsides to reduce air and soil pollution and achieve environmental sustainability. But unfortunately, these species are not planted well across the roadside as they have very little biodiversity index, as compared to other species. These species should be planted in urban areas to enhance biodiversity in the urban ecosystem and make them sustainable cities and communities.
]]>Authors: Vivien Pohl Alan Gilmer Stig Hellebust Eugene McGovern John Cassidy Vivienne Byers Eoin J. McGillicuddy Finnian Neeson David J. O’Connor
Ammonia (NH3) is a naturally occurring, highly reactive and soluble alkaline trace gas, originating from both natural and anthropogenic sources. It is present throughout the biosphere, yet plays a complicated role in atmospheric acid–base reactions resulting in the formation of inorganic secondary inorganic aerosols (SIAs). While the general mechanisms are recognised, factors controlling the reactions leading to SIA formation are less explored. This review summarises the current knowledge of NH3 sources, emission and deposition processes and atmospheric reactions leading to the formation of SIA. Brief summaries of NH3 and SIA long-range transport and trans-boundary pollution, a discussion of precursor species to SIAs (other than NH3), abiotic and biotic controls and state-of-the-art methods of measurement and modelling of pollutants are also included. In Ireland, NH3 concentrations remained below National and European Union limits, until 2016 when a rise in emissions was seen due to agricultural expansion. However, due to a lack of continuous monitoring, source and receptor relationships are difficult to establish, including the appointment of precursor gases and aerosols to source regions and industries. Additionally, the lack of continuous monitoring leads to over- and underestimations of precursor gases present, resulting in inaccuracies of the estimated importance of NH3 as a precursor gas for SIA. These gaps in data can hinder the accuracy and precision of forecasting models. Deposition measurements and the modelling of NH3 present another challenge. Direct source measurements are required for the parameterization of bi-directional fluxes; however, high-quality data inputs can be limited by local micrometeorological conditions, or the types of instrumentation used. Long-term measurements remain challenging for both aerosols and precursor gases over larger areas or arduous terrains.
]]>Authors: Rita Hagan Emma Markey Jerry Clancy Mark Keating Aoife Donnelly David J. O’Connor Liam Morrison Eoin J. McGillicuddy
Non-Road Mobile Machinery (NRMM) incorporate a wide range of machinery, with or without bodywork and wheels, and are installed with a combustion engine and not intended for carrying passengers or goods on the road. These are used in many different sectors including construction, agriculture, forestry, mining, local authorities, airport and port ground operations, railways, inland waterways and within the household and gardening sector. This article presents a review of the state of knowledge with regard to non-road mobile machinery, particularly focusing on their regulation and the atmospheric emissions associated with them. This was undertaken as there is currently a lack of this information available in the literature, which is an oversight due to the potential for Non-Road Mobile Machinery to form a greater part of atmospheric emissions in the future, as other areas of emissions are tackled by regulations, as is outlined in the article. Emissions such as particulate matter (PM), carbon oxide (CO), carbon dioxide (CO2), hydrocarbons (HC), nitrogen oxides (NOx) and sulphur oxides (SOx) from NRMM contribute considerably to total emissions released into the air. NRMM are diverse in application, engine type and fuel use, and are therefore difficult to categorise. This leads to numerous issues when it comes to the control and regulation of their emissions. The most recent European and international regulations are outlined in this article. Due to the divergent nature of NRMM, their emissions profiles are highly varied, and in-use emissions monitoring is challenging. This has led to a lack of data and inaccuracies in the estimation of total emissions and emission inventories. It was assumed in the past that emissions from non-road sources did not contribute as significantly to total emissions as those from on-road sources. This assumption was partly due to the difficulty in gathering relevant data, and it was disproven in the 1990s by studies in The Netherlands, Finland and Sweden. It is now understood that NRMM will eventually surpass on-road vehicles as the leading source of mobile pollution due to the continuing efforts to reduce emissions from other sources. Many states worldwide gather emissions data from NRMM, and EU member states are required to report their emissions. As of January 2017, a new European regulation establishing limits for gaseous and particulate pollutants from NRMM applies, and this regulation also defines administrative and technical requirements for EU approval. The exact number of NRMM and the total amount of fuel they use is currently not known. In Ireland, for example, their fuel use has been reported under stationary boilers and engines. However, this results in the underestimation of emissions of some pollutants (NOx in particular) because emissions of air pollutants tend to be higher in mobile than in stationary machinery.
]]>Authors: Victoria A. Lang Jonathan D. W. Kahl
Fine particulate matter (PM2.5) from fireworks displays have been linked to serious health concerns, particularly in infants and children. Outdoor displays in large, recurring festivals such as state fairs thus may threaten local air quality, particularly given the proximity of fairgrounds to substantial, nearby residential populations. Here, we identify state fairs with known firework displays and assess their impact on air quality in nearby communities. We assessed the impact of three large, recurring festivals on PM2.5 levels in nearby communities. Overall, our multi-year analysis failed to identify measurable increases in PM2.5 concentrations during festival days at air quality monitoring sites within 4–10 km of the fairgrounds, even when data were filtered by wind direction. Results suggest that firework displays from such festivals are unlikely to violate PM2.5 air quality standards in communities near the fairgrounds. The results suggest that identifying a potential air pollution signal associated with fireworks is challenging, particularly in urban fairgrounds where air quality is impacted by multiple local and distant pollution sources. Local impacts may yet be identified in future studies if air quality is monitored closer to the fairgrounds and if the fireworks pyrotechnic content is known.
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