Air, Volume 4, Issue 3 (September 2026) – 6 articles

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