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.
This manuscript examines the total toxic releases from electric utilities and mining facilities in the United States in 2020, focusing on their relationships with human health outcomes. The research highlights the adverse effects of air and water pollution, linking exposure to toxic emissions to several health issues, such as low birth weight, respiratory and cardiovascular diseases, and cancers. It underscores the disproportionate impact of these pollutants on low-income and minority populations. This research project utilizes two sets of data: (1) environmental data, the EPA’s Toxic Release Inventory (TRI) data that records total emissions of all-electric and mining facilities, and (2) health data, the PLACES health data. The results of this study show that census tracts exposed to higher toxic releases are expected to have worse health outcomes. The coefficients for total toxic release indicate that higher toxic release corresponds to a higher rate of cancer, chronic obstructive pulmonary disease (COPD), diabetes, kidney diseases, arthritis, cardiac heart disorders (CHD), and stroke. Except for diabetes and kidney diseases, the associations are statistically significant. The analysis indicates the need for comprehensive public health strategies to mitigate the risks posed by toxic releases, particularly for vulnerable communities.
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.
Forest fires cause societal damage, including injuries, burns, and the development and exacerbation of cardiorespiratory diseases. One of the damaging factors of forest fires is carbonaceous particles heated up to high temperatures. These particles are carried from the forest fire front and can interact with human tissue. Three scenarios for the interaction of a heated carbonaceous particle with human tissue are considered. The first scenario involves particle impact on the skin. The second scenario involves particle impact on the nasopharyngeal mucosa. The third scenario involves the impact on the tissues of the upper airways. A two-dimensional mathematical statement is considered in the “carbonaceous particle–human tissue” system. Mathematically, the heat transfer process is described by non-stationary parabolic partial differential equations with corresponding initial and boundary conditions. The problem is solved using locally one-dimensional and finite-difference methods. Difference analogs of the differential equations are solved using the marching method. Temperature distributions for particles of varying sizes and initial heat contents were obtained. The software realization was implemented using the high-level Object Pascal programming language in the RAD Studio environment. Conclusions were drawn regarding the potential practical applications of the developed software in healthcare and environmental protection.
Monitoring ambient air quality is essential for assessing environmental conditions and examining relationships among pollution indicators. This study presents a cross-sectional comparative analysis of key air quality indicators (PM2.5, O3, NO2, SO2, CO, and volatile organic compounds), together with a contextual variable related to pollution exposure (household solid fuels), across countries in Latin America and the Caribbean for the year 2024. The objective is to characterize air quality profiles by analyzing the interrelationships among indicators and the relative positioning of countries, integrating both elements within a multivariate framework. Multivariate statistical techniques, including HJ-Biplot and cluster analysis, were applied to provide an integrated representation of the data. The results indicate differences in the configuration of air quality indicators across countries, with some Caribbean countries associated with lower levels of pollution indicators, while several South and Central American countries are associated with higher levels. These results also suggest associations between air quality indicators and factors such as industrial activity proxies, population density, and the use of household solid fuels. Given the cross-sectional nature of the data, these findings should be interpreted as associations rather than causal relationships.
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 over 24,000 county-year observations (2012–2019) reveals that formaldehyde consistently ranks as the second-strongest predictor of life expectancy, surpassed only by educational attainment. Wet-bulb temperature, a physiological measure of heat stress, emerges as a top-five predictor. We identified these patterns using XGBoost with SHAP analysis, integrating atmospheric, livestock, and socioeconomic data within an external exposome framework. These results suggest that air pollutants and heat stress capture health-relevant information beyond traditional socioeconomic indicators.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
