Air

Ammonia (NH₃) emissions from concentrated animal feeding operations (CAFOs) are recognized contributors to secondary fine particulate matter (PM_2.5) formation, yet empirically derived secondary PM_2.5 emission factors applicable to livestock operations remain limited. This study investigated NH₃-derived secondary PM_2.5 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 PM_2.5 size range. Scaling the experimentally derived unit emission factors using literature-based ammonia emission rates yielded an estimated secondary PM_2.5 emission factor of 0.351 ± 0.084 g PM_2.5 per animal head per day for cattle feedlots, corresponding to approximately 3–4% of reported total PM_2.5 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 PM_2.5 formation potential intended to support comparative air quality assessments of CAFOs rather than direct predictions of ambient PM_2.5 concentrations. Full article

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: ${\mathrm{P}\mathrm{M}}_{2.5}$, ${\mathrm{P}\mathrm{M}}_{10}$, ${\mathrm{N}\mathrm{O}}_{\mathrm{x}}$, ${\mathrm{C}\mathrm{O}}_2$ 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 ${\mathrm{P}\mathrm{M}}_{2.5}$ and ${\mathrm{P}\mathrm{M}}_{10}$ were observed when compared to indicative thresholds. Chichiri exhibited more frequent AQI classifications in the “unhealthy” range. A strong positive relationship between ${\mathrm{P}\mathrm{M}}_{2.5}$ and ${\mathrm{P}\mathrm{M}}_{10}$ (r $=$ 0.84) and positive correlations between ${\mathrm{N}\mathrm{O}}_{\mathrm{x}}$ and ${\mathrm{C}\mathrm{O}}_2$ were observed. A multiple linear regression model achieved a high coefficient of determination (${\mathrm{R}}^2$ $=$ 0.938), identifying ${\mathrm{P}\mathrm{M}}_{10}$ and ${\mathrm{N}\mathrm{O}}_{\mathrm{x}}$ 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. Full article

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, NO_x, PM₁₀, PM_2.5, 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 NO_x (38%), with PM₁₀ (3%), PM_2.5 (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. Full article

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. Full article

Ambient air pollution, particularly fine particulate matter (PM_2.5), poses significant health risks, especially concerning cardiovascular diseases (CVDs). This study assesses the association between PM_2.5 exposure and CVD hospital admissions (HAs) and emergency room (ER) visits in Karachi, Pakistan. Daily PM_2.5 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 PM_2.5 levels and CVD outcomes, adjusting for meteorological variables and other potential confounders. PM_2.5 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 PM_2.5 in Karachi was significantly associated with increased CVD HAs and ER visits, with the highest association found between PM_2.5 exposure and arrhythmias. The study underscores the need for effective air quality management policies and interventions to reduce PM_2.5 levels. Karachi’s high PM_2.5 levels demand urgent attention from regulatory agencies and public health professionals to implement interventions that mitigate air pollution and protect vulnerable populations. Full article

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. Full article

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. Full article

Air pollution, particularly fine particulate matter (PM_2.5), poses significant public health and environmental risks. This study explores the effectiveness of spatiotemporal graph neural networks (ST-GNNs) in forecasting PM_2.5 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 PM_2.5 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. Full article

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. Full article

Fine particulate matter (PM_2.5) poses substantial urban health risks that vary across space, time, and population vulnerability. We integrate a spatio-temporal INLA–SPDE PM_2.5 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 PM_2.5 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 PM_2.5 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. Full article

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. Full article

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. Full article

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. Full article

Exposure to degraded indoor air quality (IAQ) conditions represents a major concern for health and well-being. PM_2.5 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 PM_2.5 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 PM_2.5, 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. Full article

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. Full article

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 PM_2.5, 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 PM_2.5 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. Full article

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. Full article

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 (PM_2.5 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. Full article