Abstract Airborne microplastics from a variety of sources, including textiles and environmental degradation, pose a challenging problem due to their ability to infiltrate respiratory tissues and trigger inflammatory response. Microplastics may contribute to respiratory impairment and other respiratory-related problems. Airborne microplastics influence the lung metabolism and toxic mechanism reactions that are interpreted using human responsive biomarkers for airway inflammation. This study aims to evaluate the airborne microplastic exposure influences on human responsive biomarkers. This study employs systematic scoping review using PRISMA to explore human biomarkers response to airborne microplastic exposure. The reviewed publications are from databases like ScienceDirect, SpringerLink, PubMed, and Google Scholar. Papers were selected using four keywords: “airborne microplastic”, “human biomarker”, “biological response”, and “health effects” with associated synonyms. A total of 14 publications met the inclusion criteria for this scoping review, comprising 7 from ScienceDirect, 4 from PubMed, 2 from Google Scholar, and 1 from SpringerLink. Among the 14 studies reviewed, polystyrene (PS) was reported in 46% of the studies, polypropylene (PP) and polyethylene terephthalate (PET) were each reported in 15%, polyvinyl chloride (PVC) in 8%, and other microplastic types were reported in 15% of the studies, noting that some studies examined more than one polymer type. Previous research showed a significant relationship between asthma symptoms and biomarkers such as TNF-α, IL-6, IL-8, IL-17 and IL-1β in human lung cells and other studies related to human and animal respiratory systems. 6 out of 14 reviewed articles have investigated biological responses and disease mechanisms using mice, a reliable mammalian model for comparison with human systems. Standardizing human-responsive biomarkers, such as IL-6 and IL-8 which are commonly used for airborne microplastic exposure, will improve early detection of airway inflammation and strengthen environmental health risk assessments, thus leading to policy development. The consistent biomarker thresholds and measurement protocols facilitate the comparison of exposure levels, identification of at-risk populations, and development of evidence-based guidelines to mitigate microplastic-related health impacts. Graphical Abstract

Abstract In late December 2023, the Ministry of Ecology and Environment of China implemented a joint reginal air pollution prevention and control initiative in key areas encompassing Beijing, Tianjin, Hebei, Shandong, and Henan to mitigate severe pollution episodes. Taking Henan Province as a case study, this research employs the WRF-SMOKE-CAMx modeling system to assess the impacts of emergency emission reductions during a heavy pollution period spanning late December 2023 to early January 2024 (Y2023). Despite unfavorable meteorological conditions, observed PM 2.5 concentrations in Henan during Y2023 declined by 20.9% compared to a comparable heavy pollution episode during the 2022–2023 year-end transition (Y2022), attributable to stringent control measures. Sulfate exhibited the most pronounced response to emission controls, showing a 95.4% reduction in relative concentration change after meteorological normalization, followed by ammonium at 50.3%. These results demonstrate the effectiveness of the Y2023 intervention in curtailing sulfate and ammonium formation. Moreover, earlier activation of orange alert measures in northern Henan cities yielded significantly greater emission reduction benefits compared to southern cities. The findings underscore that in industrialized, energy-intensive regions such as Henan, controlling sulfate precursors remains a critical strategy for achieving rapid PM 2.5 reductions. Graphical Abstract

Abstract The European standard EN 149:2001+A1:2009 specifies tests for filtering respiratory protective equipment (RPE). One of them is the total inward leakage (TIL) test designed to assess the fit of RPE and its performance in providing protection against harmful airborne particles and dust. The test is typically conducted using a sodium chloride aerosol with a mass median aerodynamic diameter of 0.6 µm. The objective of this study was to conduct TIL tests for the evaluation of the performance of high-quality filtering RPE against an aerosol containing reduced graphene oxide particles, considering the nanometric scale and varied shapes of these particles, and with particular attention to adequate fit. TIL was measured for four types of FFP2 respirators as well as four types of FFP3 respirators of various designs using a head-and-torso dummy that simulated a sequence of human movements consisting of head rotation (right-to-left), head tilt (up-and-down), and speech, followed by a rest period. TIL measurements followed a fit test performed using a PortaCount system (TSI, Inc., St. Paul, MN) and lasted 10 min in accordance with the exercise protocol specified in the standard EN 149. Analysis involved only particles no larger than 100 nm. The results showed that high-quality FFP3 respirators provide the highest level of protection, with an average TIL value of 0.75%. The study offers new insights into the appropriate fit and design of filtering facepiece respirators and contributes to minimizing the risk of leakage. It also demonstrates that the fit factor and filtering effectiveness influence measurements and TIL results for respirators challenged with a reduced graphene oxide aerosol. Graphical abstract

Abstract Air quality in South Korea is influenced by long-range transported air pollutants from China and domestically emitted pollutants. Among the chemical components of PM 2.5 , nitrate (NO 3 − ) is generally more dominant than sulfate (SO 4 2− ), which makes it necessary to investigate nitrate formation mechanisms in the domestic atmosphere. Although nitric acid (HNO 3 ) and ammonia (NH 3 ) are key precursors of particulate nitrate, their concentrations are not currently provided by the National Institute of Environmental Research (NIER) of Korea. HNO 3 data are extremely limited due to the lack of real-time measurement technology. Field measurements were conducted in Gwangju across all four seasons (spring, summer, fall, and winter) in 2021; in Seoul during summer, fall, and winter from 2021 to 2022; and at Ansan and Baengnyeong Island, observations were conducted in different seasons during the period 2022–2025. HNO 3 was measured using a semi-real time monitoring system developed by the Hankuk University of Foreign Studies (HUFS), while NH 3 and major PM 2.5 components were analyzed simultaneously. Results revealed that ammonium-rich conditions prevailed at all sites. A strong correlation (R 2 > 0.9) between NO 3 ⁻ and excess ammonium confirmed that NH 4 NO 3 formation is primarily governed by HNO 3 and NH 3 . Seasonal characteristics of formation and dissociation were assessed by comparing the theoretical equilibrium constant (K p ) with the observed reaction constant (K m ) derived from measured HNO 3 and NH 3 concentrations. NH 4 NO 3 showed distinct temperature-dependent behavior: dissociation in summer and formation in winter, with both regimes observed during spring and fall. Additional analysis of the molar ratio (R) and SNA (sulfate-nitrate-ammonium) composition provided insight into precursor limitations. In Seoul, Ansan, and Gwangju, HNO 3 limitation was dominant regardless of season, reflecting the influence of anthropogenic emissions and pollutant transport. In contrast, Baengnyeong Island, a background site, generally exhibited HNO 3 limitation but showed NH 3 limitation in winter, highlighting the enhanced role of NH 3 under low-NO x emission conditions. Graphical Abstract

Abstract Introduction Low-cost particulate matter sensors (LCPMS) require calibration for reliable PM 2.5 measurements. We propose a novel data-analytic framework to evaluate machine learning (ML) calibration approaches for fine particulate matter (PM 2.5 ) across the complete environmental and temporal scales relevant to the geographic location via a long-term co-location study in Vishakhapatnam, India. Methods We compared Random Forest (RF) and eXtreme Gradient Boosting (XGB) models while systematically incorporating temperature (T), relative humidity (RH), hour of day (HD), and month of year (MY) variables against baseline linear regression (LR) models. Additionally, a hybrid ensemble LR model combining the best-performing ML models was also explored. Overall model performance was assessed against the United States Environmental Protection Agency (USEPA) recommended performance metrics for low-cost PM₂.₅ sensors. In addition, the developed models were evaluated across environmental (T and RH) and temporal (TD and MY) scales by binning the respective variables. Categorical accuracies across air quality index (AQI) categories are also explored. Results and Discussion The best ML model reduced RMSE by 58% compared to the baseline LR model; the hybrid model performed better with a 63% reduction in RMSE compared to the baseline. The hybrid model exhibited the least errors across most environmental and temporal conditions, while satisfying the USEPA performance criteria. Conclusions The hybrid ensemble approach mitigates environmental and temporal variability effects on measurement accuracy, improving PM 2.5 quantification in diverse field conditions. Our framework provides a robust, computationally efficient approach that is sensor-model agnostic and adaptable to various target pollutants and calibration methodologies. Graphical abstract

Abstract The OP of the gas phase is a significant yet often overlooked aspect of environmental toxicity. While long-term exposure to reactive oxygen species (ROS) associated with particulate matter (PM) is known to have adverse health effects, the potential for gas phase compounds to cause similar effects has yet to be fully understood. The sources and atmospheric chemistry of ambient air in the gas phase remain poorly understood, possibly due to limitations in current measurement techniques. This study utilized the Dithiothreitol (DTT) assay, a widely used method for quantifying OP in epidemiological research because it is less time and resource-intensive compared to other techniques. We measured the volumetric OP values (OPDTTv) in the gas phase and ambient air (PM + gas) during sampling in spring, summer, and autumn. The results indicate that the OP in the ambient air PM + gas is nearly twice that of the gas phase. Specifically, the value of OP in gas phase during autumn (6.98 ± 1.8) was higher than in spring (4.6 ± 1.6) and summer (4.4 ± 2.2), with minimal difference observed between spring and summer. Furthermore, the results revealed an indirect correlation between ozone (O 3 ) and OP in the gas phase (DTTv.gas) as well as between O 3 and ambient air (DTTv.PM + gas). However, other variable correlations were found to be positive and direct. Graphical abstract

Abstract Air pollution in urban areas remains a significant public health concern, particularly affecting schoolchildren commuting during rush hours. This study examined the association between school travel patterns during peak traffic periods and respiratory disorders among urban primary school students. This cross-sectional study conducted structured interviews with 557 parents from schools in Khon Kaen Municipality, Thailand, based on calculated sample size. Multistage sampling was employed and multiple logistic regression was utilized. The analysis revealed that 87.6% (95% CI: 84.61–90.11) of the students revealed symptoms of respiratory disorders. Statistically significant factors associated with respiratory abnormalities included the mode of travel to and from school, with students commuting by motorcycle/bicycle (AOR:4.06, 95% CI: 1.92–8.59) showing the highest odds, followed by those using private cars (AOR:3.80, 95% CI: 1.90–7.63) compared to school bus/van users ( p -value < 0.001). Crossing traffic lanes before reaching the school gate was also associated with increased odds of experiencing respiratory symptoms (AOR:3.10, 95% CI: 1.56–6.13, p -value < 0.001), as was spending more than 10 min in the pick-up area in the afternoon (AOR:2.91, 95% CI: 1.35–6.26, p -value = 0.006). In addition, age was another significant factor, with younger children showing higher likelihood of experiencing respiratory symptoms. Additionally, students with a history of exposure to secondhand smoke environments were also more likely to report respiratory disorders. These findings highlighted the urgent need for air pollution control measures around urban schools and the development of traffic and environmental policies to protect students’ health from daily exposure to air pollution. Graphical Abstract

Abstract Few studies have empirically examined the hypothesis that age, sex, and season would be acting as effect modifiers in the association between exposure factor and health outcome when the population are simultaneously exposed to multiple air pollutants and meteorological conditions. We couple air pollution and meteorological data with reported mental disorders (MDs) drawn from approximately 1.8 million hospital outpatient visits in Nanjing, China between 2015 and 2019. Subsequent to predicting the illness risks of MDs using deep learning model, SHAP approach quantifies excess risks attributed to the feature being explained. We generally find the effect modifications in the association between air pollutant or meteorological factor and MD risk by comparison of feature-importance estimates for each stratified group. For example, PM 10 increases the risk of depression disorder of females by 1.94 (P 2.5 -P 97.5 : 1.54, 2.34) visit counts, which is significantly higher than that of males [0.25 (− 0.01, 0.50)]; the female-male difference in the risk of anxiety disorder posed by precipitation associates with 0.50 (0.31, 0.69) visit counts. The findings reasoned that efforts should address the effect of multiple risk factors that simultaneously interact with each other on MDs, as surrounding air pollution and climate change proceed. Graphical Abstract

Abstract Open biomass burning (OBB) emits large amounts of air pollutants, significantly impacting air quality and climate change. Herein, chemical (carbonaceous content, metals, NR-PM 1 : organics, SO 4 2− , NO 3 − , NH 4 + , Cl − ) and physical (number size distribution, absorption and scattering) properties of OBB-derived aerosols, and their vertical distribution, and night-time evolution were investigated during the country-wide burning of the witches (BoW) in the Czech Republic and neighboring states. The mass concentrations of most of the aerosol components sharply increased during the BoW, and a fast change in their chemical composition, size distribution, and optical properties was observed. The BoW led to significantly higher concentrations of carbonaceous aerosols, with a concentration enhancement ratio ranging from 4.5 to 11.4. The vertical distribution of equivalent black carbon (eBC) was also altered during the BoW due to OBB emissions (eBC BB ) contributing up to > 90% and 67% of eBC at 4 m and 230 m a.g.l., respectively. At 4 m, eBC BB was affected by local OBB plumes trapped within the mixing layer, whereas eBC BB at 230 m was most probably affected by longer distance transported plumes. Cl − and K, Zn, Pb and Cu were also significantly enhanced (2.6–10.8) due to OBB and coexisting sources (combustion of old tires and trash and fireworks activities). The freshest OBB emissions, made of 73% organics, with mobility diameter of 20 nm and 80 nm, rapidly grew during the first hours of the event with a single peak of ~ 100 nm. The higher concentrations of brown carbon led to an elevated absorption Ångström exponent (2.13 ± 0.26) and may also explain the enhanced scattering coefficients observed during this event. The BoW may significantly affect air quality and represents an excellent context to provide valuable background information on the physicochemical properties and night-time aging of non-heating OBB-derived aerosols in temperate climates. Graphical Abstract

Abstract Air pollution is a critical global concern, significantly affecting human health and the environment. Urbanization, industrialization, and population growth have intensified the release of hazardous particles and gases, particularly in developing countries like India, where many cities rank among the most polluted. Filtration is a widely adopted particulate matter (PM) control technology for air purification across various settings. However, studies on ultra-fine particle (UFP) removal efficiency at different face velocities (FVs) remain limited. To address this pressing issue, a systematic investigation is conducted to evaluate the removal efficiency of filtration technology. In this study, eight distinct fibrous media based on their minimum efficiency reporting value (MERV) were employed to assess their effectiveness using a custom-built air filter holder in capturing ultra-fine PM. The primary objective was to assess their performance in trapping sub-micron particles, known for their potential health impacts. The study analyzed filtration efficiency using key metrics such as particle removal efficiency, pressure drop, and quality factor. To evaluate filter performance, UFP penetration was tested across all media at FVs of 0.2, 0.5, and 0.8 m/s. The quality factor was also examined to understand the impact of effective fiber diameter, thickness, and solidity on filtration. A single-fiber model for a mechanical filter was applied for theoretical calculations, with model predictions aligning well with experimental results across all tested media. These findings offer valuable insights into selecting suitable fibrous media for air cleaner applications, particularly in environments where controlling UPFs is crucial for air quality and public health. Graphical Abstract