Search Results (98)

Unmanned aerial vehicles (UAVs) equipped with lightweight gas sensors offer a promising approach for greenhouse-gas emission monitoring. However, airflow generated by multirotor propellers can disturb the atmosphere and influence measured gas concentrations. This study investigates rotor-induced downwash effects on vehicle exhaust plume measurements using experimental and numerical approaches. Experiments were conducted with a stationary diesel vehicle at idle, while a propeller system reproduced UAV downwash at rotor-sensor separation distances of 0.5–5.5 m above a fixed CO₂ sensor. A low-cost Feather-based sensing platform was evaluated against a commercial IoTSens monitoring station. CFD simulations were performed in OpenFOAM^® using a compressible multi-species solver, Large Eddy Simulation (LES), and a Multiple Reference Frame (MRF) approach. Experiments showed CO₂ reductions of up to 52.7%, while CFD predicted reductions of 50.4–88.5%. Both approaches showed decreasing rotor-wake influence with increasing separation distance, with strongest effects below approximately 2–3 m. Experimentally, downwash effects became weak between 3.5 and 5.5 m, consistent with reduced plume–wake interaction predicted by CFD. These findings highlight the importance of accounting for rotor-induced downwash when designing UAV-based gas monitoring missions. Full article

We present FDS and AERMOD simulations of the Alaska Clean Seas (ACS) oil burn experiments to improve dispersion modeling of large, open-air fires relevant to industrial settings. We propose a method in which FDS smoke fields with available ground measurements are used to empirically calibrate AERMOD configured using volume sources to represent the fire source. FDS is first run for the three ACS experiments at high resolutions (~10 m) and verified against ground monitoring to provide detailed three-dimensional smoke fields. The fractional allocation of total fire emissions (weights, w_i) is then empirically specified for each volume source i so AERMOD smoke predictions fit both the ground level measurements and FDS simulations to acceptable accuracy. Runs for volumes at the surface (i = 1), 100 m AGL (i = 2) and 300 AGL (i = 3) and w_i = [0.01, 0.09, 0.9]–[0.04, 0.36, 0.6] accurately represent these data, suggesting this range as suitable for fire heat fluxes (~800–3000 kW/m²), wind speeds (5–10 m/s) and PBL depths (300–500 m with and without capping temperature inversions) of the three ACS experimental burns. Further work exploring the applicability of this AERMOD setup to a broader range of conditions is ongoing. Full article

Air pollution in urban and coastal regions is increasingly shaped by interactions among port operations, freight logistics, urban mobility systems, spatial development patterns, and governance arrangements. Although emission reduction technologies and regulatory measures have advanced significantly, their implementation frequently remains distributed across sector-specific institutional and operational domains. This paper introduces the concept of Air-Aware Port–City–Logistics Systems, proposing an integrated governance framework that treats air quality as a system-level outcome of linked maritime, logistics, urban transport, spatial, technological, and adaptive processes. Drawing on a structured interdisciplinary synthesis of literature in transport planning, maritime economics, logistics, environmental governance, air-quality management, and resilience, the study identifies key gaps in cross-sectoral coordination and in the governance of interdependencies influencing air-quality outcomes. The framework maps relationships among emission sources, transport and logistics flows, spatial configurations, population exposure, institutional coordination, technological and data systems, and adaptive capacity, thereby identifying potential intervention points across the port–city–logistics system. Its distinctive contribution lies not merely in combining these dimensions, but in organising them through a governance-oriented architecture in which monitoring, operational decisions, institutional responses, and adaptive learning are treated as interconnected processes. The framework also incorporates resilience and risk perspectives, emphasising adaptive governance approaches capable of responding to disruptions, changing demand patterns, climatic pressures, and other external environmental influences. By advancing a systems-based perspective, the paper contributes to air quality management and policy development through the integration of port–city interfaces, freight distribution networks, mobility planning, exposure considerations, and institutional decision-making within a unified analytical framework. As a conceptual framework, AAPCLS provides a transferable basis for future empirical application, contextual adaptation, and policy-oriented analysis rather than a validated operational model. Full article

India has one of the highest public health burdens attributable to air pollution globally. Despite national air quality regulations and control policies, improvements in air quality have been limited, restricting opportunities to evaluate the health benefits of cleaner air under real-world conditions. COVID-19 lockdowns substantially reduced air pollution in India’s megacities, creating a natural experiment for assessing pollution-attributable health benefits. Using ambient PM_2.5 concentration data from United States Embassy and Consulate monitors in Chennai, Hyderabad, Kolkata, Mumbai, and New Delhi during the 2020 and 2021 lockdowns, and World Health Organization AirQ+ software, we estimated the short- and long-term health benefits of maintaining lockdown-related improvements in air quality. PM_2.5 reductions ranging from 28–77% during the first lockdown, if sustained long term, would lead to about 42,778 fewer premature deaths in the short term and 132,000 fewer annual avoidable deaths from all causes under the modeled scenario across the five cities. We also estimated annual reductions of 11,100 deaths from chronic obstructive pulmonary disease, 3900 from lung cancer, 5500 from ischaemic heart disease, 3900 from stroke, and 1240 from acute lower respiratory infection. These findings support stronger air quality standards, emission controls, and policy enforcement to reduce air pollution-related mortality in Indian megacities. Full article

Public fireworks shows are widely used for national holiday celebrations, religious ceremonies, and sports events. However, fireworks produce short-lived atmospheric particulate matter (PM) peaks, posing a risk to spectators’ health. Every year, public fireworks shows expose over 140 million Americans to episodic transient PM2.5 peaks that exceed air quality standards by up to 10 times in the United States alone. Yet the magnitude and timing of this representativeness gap have not been measured within an event. This paper investigates the Independence Day fireworks display over Lake Union, Seattle, WA, USA, using a low-cost PM network deployed up to 2 km from the launch site. We evaluated the network observations against routine monitoring data from regulatory stations within 10 km of the launch site and generated a backward–forward Lagrangian stochastic dispersion model, calibrated with the network. Spectator zone PM2.5 concentrations varied by over threefold across sensors within 1.5 km. Duration above the World Health Organization (WHO) 24 h PM2.5 guideline concentration level at network sensors varied from 1 min to nearly 30 min, and regulatory hourly averaging retained 57% of the near-field peak signal on average and only 22% in the worst case. Peak detection at some regulatory stations was delayed by one to two hours compared with the network. The dispersion model, using only network data and public regional wind data, captured the launch site location to within ~100 m and provided minute-scale estimates of PM2.5 and PM1 concentrations across the spectator zone. The findings demonstrate and quantify, for a single event, the representativeness gap expected when transient near-field fireworks plumes are evaluated using spatially sparse and hourly averaged regulatory observations. Full article

Chronic obstructive pulmonary disease (COPD) remains a major cause of respiratory morbidity and healthcare utilization, creating challenges for healthcare planning, resource allocation, and early intervention. This study aimed to develop a forecasting framework for quarterly age- adjusted COPD outpatient visit rates across 762 regions in Pennsylvania from 2019 to 2023, using multi-source data including PHC4 outpatient records, satellite-derived environmental pollutant variables, and meteorological variables. To compare models that capture nonlinear relationships with those that explicitly model temporal dependencies, several machine learning models and a deep learning long short-term memory (LSTM) model, designed to learn sequential patterns and lagged temporal effects, were evaluated. The seasonal naïve baseline achieved $R^2=0.570$, classical machine learning models achieved $R^2=0.58$–$0.62$, and the LSTM model achieved $R^2=0.705$ with lower prediction error. Lagged COPD activity was the strongest predictor, while environmental, meteorological, and geographic variables provided additional predictive information. These findings highlight the value of integrating multi-source environmental data with sequence-based modeling for forecasting regional COPD activity and suggest that environmental and meteorological variables can provide additional predictive information beyond historical COPD activity alone. Full article

Advanced biofuels represent a key option for transport decarbonization, particularly in sectors where electrification is constrained by technical and economic barriers. Their compatibility with existing vehicle fleets and fuel distribution infrastructure enables rapid deployment without the need for major capital investments. In local public transport, biodiesel (FAME), hydrotreated vegetable oil (HVO), and biomethane are mature solutions capable of delivering greenhouse gas emission reductions of 60–90% compared with fossil fuels. Among these, HVO is particularly promising, as an extensive body of literature has consistently shown its potential to significantly reduce engine-out emissions, especially particulate matter (PM) and nitrogen oxides (NO_x). This study reports the results of an experimental campaign carried out on a diesel-powered local public transport bus equipped with a Euro III engine and lacking particulate matter and NO_x after-treatment systems. Emissions were measured using a portable emissions measurement system (PEMS) under real driving conditions, operating the vehicle with neat diesel, a 15% HVO blend, and a 70% HVO blend. Tests were conducted over urban and extra-urban routes. The results show that NO_x emissions decrease proportionally with increasing HVO content, with high-blend ratios (HVO70) yielding estimated reductions of approximately 13–18%, and up to 23% under carefully controlled and comparable urban driving conditions. Based on these findings and the existing literature, HVO proves to be a useful instrument to meet 2025–2030 climate and air quality targets (particularly NO_x and PM emission reductions), alongside electrification and modal shift measures, if used in public transport fleets. Full article

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. Full article

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 (PM_2.5, 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. Full article

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. Full article

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. Full article

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

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

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