Air, Volume 4, Issue 1 (March 2026) – 5 articles

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