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Abstract Positive Matrix Factorization (PMF) is a commonly used receptor model for source apportionment of PM2.5. However, PMF results often retrieve an individual factor mainly composed of secondary aerosols, making it difficult to link with primary emission sources and formulate effective air pollution control strategies. To overcome this limitation, we employed a two-stage PMF modeling approach with adjustments of the species weighting, which was fused with a robust regression model to better characterize the sources of PM2.5 secondary aerosols. Additionally, organic molecular tracers were incorporated into PMF for source identification. A field campaign was conducted between May and December 2021 in Taichung, Taiwan. An improved PMF model was utilized to resolve the multiple time resolution data of 3-h online and 24-h offline measurements of PM2.5 compositions. Retrieved factors from PMF were averaged over 24-h intervals and then applied in robust regression analysis to re-apportion the contributions. Comparing with conventional PMF, downweighting the secondary aerosol-related species in the model was more effective in linking them to primary emission sources. The results from the fusion model showed that the majority of secondary aerosols (sum of secondary aerosol-related species = 2.67 µg m−3) within three hours were mainly contributed by oil combustion, while the largest contributor of secondary aerosols (1.65 µg m−3) over 24 hours was industry, highlighting the need for regulation of these two sources based on various temporal scales. The developed fusion strategy of two-stage PMF and robust regression provided refined results and can aid in the management of PM2.5.

During the unprecedented COVID-19 city lockdown, a unique opportunity arose to dissect the intricate dynamics of urban air quality, focusing on ultrafine particles (UFPs) and volatile organic compounds (VOCs). This study delves into the nuanced interplay between traffic patterns and UFP emissions in a subtropical urban setting during the spring-summer transition of 2021. Leveraging meticulous roadside measurements near a traffic nexus, our investigation unravels the intricate relationship between particle number size distribution (PNSD), VOCs mixing ratios, and detailed vehicle activity metrics. The soft lockdown era, marked by a 20–27% dip in overall traffic yet a surprising surge in early morning motorcycle activity, presented a natural experiment. We observed a consequential shift in the urban aerosol regime: the decrease in primary emissions from traffic substantially amplified the role of aged particles and secondary aerosols. This shift was particularly pronounced under stagnant atmospheric conditions, where reduced dilution exacerbated the influence of alternative emission sources, notably solvent evaporation, and was further accentuated with the resumption of normal traffic flows. A distinct seasonal trend emerged as warmer months approached, with aromatic VOCs such as toluene, ethylbenzene, and xylene not only increasing but also significantly contributing to more frequent particle growth events. These findings spotlight the criticality of targeted strategies at traffic hotspots, especially during periods susceptible to weak atmospheric dilution, to curb UFP and precursor emissions effectively. As we stand at the cusp of widespread vehicle electrification, this study underscores the imperative of a holistic approach to urban air quality management, embracing the complexities of primary emission reductions and the resultant shifts in atmospheric chemistry.

Abstract Our study covers the chemical characteristics and health risks of PM2.5 from 2019–2020 at National Taiwan University and City Tunnel in Taipei, Taiwan. Positive Matrix Factorization (PMF) was used to quantify the potential sources of polycyclic aromatic hydrocarbon (PAH). The influences of local pollution (LP), long range transport (LRT) and Power Plant are shown by using bivariate polar plot (BPP), Potential source contribution function (PSCF) and Enrichment Factor (EF) with Positive Matrix Factorization (PMF). Besides, chemical analysis included PAHs, carbonaceous species (OC/EC), water-soluble ions, and trace metals, respectively. Our results indicated that vehicle emissions (gasoline engines: 20.8%, diesel engines: 23.3%) and coal combustion (55.9%) were the major sources of PAHs in the urban site, with higher levels in LP than in LRT. The mean PM2.5 concentrations during the daytime was 13.0 ± 5.64 µg m−3, higher than 10.4 ± 5.16 µg m−3 at nighttime, reflecting high traffic emissions and anthropogenic aerosols during the day. Similar phenomenon was observed for PAHs in the daytime of 3.26 ± 2.36 ng m−3 and 1.91 ± 1.14 ng m−3 at nighttime. Also, due to higher NO3− and K levels, the ions and metals had higher values in the daytime and weekdays than at nighttime and weekends. In contrast, in the City Tunnel, the PM2.5 concentrations difference at the tunnel inlet and tunnel outlet during the daytime was significantly higher 36.4 µg m−3 compared to 27.4 µg m−3 at nighttime due to the heavy traffic during the day. As well, the OC/EC mass fractions at the tunnel inlet and tunnel outlet were 4.00 and 2.73. PSCF revealed a greater value (> 0.6) in the coastal areas of Inner Mongolia and Mainland China, due to long-range transport and industrial emissions. The incremental lifetime cancer risk (ILCR) of Cr6+ was higher in metal (2.60 × 10‒5) but did not exceed its tolerable limits.

AbstractThis review article delves into the multifaceted relationship between climate change, air quality, and respiratory health, placing a special focus on the process of particle deposition in the lungs. We discuss the capability of climate change to intensify air pollution and alter particulate matter physicochemical properties such as size, dispersion, and chemical composition. These alterations play a significant role in influencing the deposition of particles in the lungs, leading to consequential respiratory health effects. The review paper provides a broad exploration of climate change’s direct and indirect role in modifying particulate air pollution features and its interaction with other air pollutants, which may change the ability of particle deposition in the lungs. In conclusion, climate change may play an important role in regulating particle deposition in the lungs by changing physicochemistry of particulate air pollution, therefore, increasing the risk of respiratory disease development.

In the quest to reconcile public perception of air pollution with scientific measurements, our study introduced a pioneering method involving a gradient boost-regression tree model integrating PM2.5 concentration, visibility, and image-based data. Traditional stationary monitoring often falls short of accurately capturing public air quality perceptions, prompting the need for alternative strategies. Leveraging an extensive dataset of over 20,000 public visibility perception evaluations and over 8,000 stationary images, our models effectively quantify diverse air quality perceptions. The predictive prowess of our models was validated by strong performance metrics for perceived visibility (R = 0.98, RMSE = 0.19), all-day PM2.5 concentrations (R: 0.77–0.78, RMSE: 8.31–9.40), and Central Weather Bureau visibility records (R = 0.82, RMSE = 9.00). Interestingly, image contrast and light intensity hold greater importance than scenery clarity in the visibility perception model. However, clarity is prioritized in PM2.5 and Central Weather Bureau models. Our research also unveiled spatial limitations in stationary monitoring and outlined the variations in predictive image features between near and far stations. Crucially, all models benefit from the characterization of atmospheric light sources through defogging techniques. The image-based insights highlight the disparity between public perception of air pollution and current policy implementation. In other words, policymakers should shift from solely emphasizing the reduction of PM2.5 levels to also incorporating the public's perception of visibility into their strategies. Our findings have broad implications for air quality evaluation, image mining in specific areas, and formulating air quality management strategies that account for public perception.

Electronic cigarettes (ECs), considered a healthier alternative to traditional cigarettes (TCs), vaporize e-liquid, which may produce harmful by-products due to thermal decomposition and metal transfer. These by-products' deposition in the respiratory tract is largely determined by particle size distribution (PSD). We employ the Multiple-Path Particle Dosimetry (MPPD) model to assess particle deposition within the human and mouse respiratory tracts. Leveraging the known connection between TC smoke inhalation and atherosclerosis, we used human aortic endothelial cells (HAECs) and ApoE-/- mice to explore the potential effects of EC aerosol inhalation on atherosclerosis. Our findings reveal that TCs exhibit a highly variable PSD, with mean diameters of approximately 300 nm for mainstream (MS) smoke and 120 nm for side stream (SS) smoke. Conversely, ECs demonstrate a more stable PSD. Combined with MPPD, the deposition fraction in the human respiratory system and mice is mainly deposited in the pulmonary region and head airway. For the Apoe-/- mice exposure experiment, preliminary findings suggest a potential impact on atherosclerosis, although not statistically significant, likely due to the limited sample size and exposure duration. This study highlights the importance of considering PSD, exposure dosage, and species-specific differences in risk assessments of EC aerosols.

Abstract Rapid economic development and urbanization have significantly increased PM2.5-induced and hyperglycemia-induced toxicological effects. Tempeh is a traditional Indonesian food and may be beneficial for patients with type II diabetes. However, the toxicological effects of co-exposure to traffic-related-air-pollutant (TRAP) PM2.5 with high glucose and the potential therapeutic effect of tempeh remain unclear. Using Caenorhabditis elegans (C. elegans) as an in vivo animal model, we found that exposure to 12.74 mg L−1 TRAP PM2.5 and 80 mM D-glucose could induce toxicity in nematodes that affects growth, reproduction, locomotion behavior, and lifespan. Moreover, TRAP PM2.5 and high glucose diet co-treatment reinforced these adverse effects on C. elegans. However, pretreatment with 200 µg of tempeh extract had the greatest improvement in the adverse effects of treatment with or without 12.74 mg L−1 TRAP PM2.5 and with or without 80 mM D-glucose on C. elegans. In addition, tempeh treatment also ameliorated the altered mRNA expression of the antioxidant gene in C. elegans treated with or without TRAP PM2.5 and with or without high-dose glucose diets. These findings reveal that co-exposure to TRAP PM2.5 and high-dose glucose causes more serious health effects, while tempeh could modulate oxidative stress which ameliorated TRAP PM2.5-induced and hyperglycemia-induced toxicological effects.

Fine particulate matter (PM2.5) is thought to exacerbate Parkinson's disease (PD) in the elderly, and early detection of PD progression may prevent further irreversible damage. Therefore, we used diffusion tensor imaging (DTI) for probing microstructural changes after late-life chronic traffic-related PM2.5 exposure. Herein, 1.5-year-old Fischer 344 rats were exposed to clean air (control), high-efficiency particulate air (HEPA)-filtered ambient air (HEPA group), and ambient traffic-related PM2.5 (PM2.5 group, 9.933 ± 1.021 µg/m3) for 3 months. Rotarod test, DTI tractographic analysis, and immunohistochemistry were performed in the end of study period. Aged rats exposed to PM2.5 exhibited motor impairment with decreased fractional anisotropy and tyrosine hydroxylase expression in olfactory and nigrostriatal circuits, indicating disrupted white matter integrity and dopaminergic (DA) neuronal loss. Additionally, increased radial diffusivity and lower expression of myelin basic protein in PM2.5 group suggested ageing progression of demyelination exacerbated by PM2.5 exposure. Significant production of tumor necrosis factor-α was also observed after PM2.5 exposure, revealing potential inflammation of injury to multiple fiber tracts of DA pathways. Microstructural changes demonstrated potential links between PM2.5-induced inflammatory white matter demyelination and behavioral performance, with indication of pre-manifestation of DTI-based biomarkers for early detection of PD progression in the elderly.

Modeling is a cost-effective measure to estimate ultrafine particle (UFP) levels. Previous UFP estimates generally relied on land-use regression with insufficient temporal resolution. We carried out in-situ measurements for UFP in central Taiwan and developed a model incorporating satellite-based measurements, meteorological variables, and land-use data to estimate daily UFP levels at a 1-km resolution. Two sampling campaigns were conducted for measuring hourly UFP concentrations at six sites between 2008–2010 and 2017–2021, respectively, using scanning mobility particle sizers. Three machine learning algorithms, namely random forest, eXtreme gradient boosting (XGBoost), and deep neural network, were used to develop UFP estimation models. The performances were evaluated with a 10-fold cross-validation, temporal, and spatial validation. A total of 1,022 effective sampling days were conducted. The XGBoost model had the best performance with a training coefficient of determination (R2) of 0.99 [normalized root mean square error (nRMSE): 6.52%] and a cross-validation R2 of 0.78 (nRMSE: 31.0%). The ten most important variables were surface pressure, distance to the nearest road, temperature, calendar year, day of the year, NO2, meridional wind, the total length of roads, PM2.5, and zonal wind. The UFP levels were elevated along the main roads across different seasons, suggesting that traffic emission is an important contributor to UFP. This hybrid model outperformed prior land use regression models and thus can provide more accurate estimates of UFP for epidemiological studies.

Air pollution has been linked to respiratory diseases, and urban air pollution can be attributed to a number of emission sources. The emitted particles and gases are the primary components of air pollution that enter the lungs during respiration. Particulate matter with an aerodynamic diameter of ≤ 2.5 µm (PM2.5) can deposit deep into the respiratory tract via inhalation and has been proposed as a causative agent for adverse respiratory health. In addition, the lung contains a diverse microbial community (microbiome) that maintains normal homeostasis and is significantly altered in a variety of pulmonary disorders. Air pollution, specifically PM2.5, has previously been shown to significantly alter the composition of the lower airway microbiome, which has been linked to decreased lung function in chronic obstructive pulmonary disease (COPD) patients. Surprisingly, the intestinal microbiome has also been implicated in the modulation of pulmonary inflammatory diseases. Therefore, dysbiosis of the lung and intestinal microbiomes pose significant negative effects on human health.This dataset describes the microbial community profiles of the lungs and intestines of ageing rats exposed to ambient unconcentrated traffic-related air pollution for three months. The whole-body exposure system was equipped with and without high efficiency particulate air (HEPA) filtration (gaseous vs. PM2.5 pollution). The data can provide valuable information on lung and intestinal microbiome changes, including that which was only found after traffic-related air pollution exposure.

We investigated the effects of antibiotics, drugs, and metals on lung and intestinal microbiomes after sub-chronic exposure of low-level air pollution in ageing rats. Male 1.5-year-old Fischer 344 ageing rats were exposed to low-level traffic-related air pollution via whole-body exposure system for 3 months with/without high-efficiency particulate air (HEPA) filtration (gaseous vs. particulate matter with aerodynamic diameter of ≤2.5 µm (PM2.5) pollution). Lung functions, antibiotics, drugs, and metals in lungs were examined and linked to lung and fecal microbiome analyses by high-throughput sequencing analysis of 16 s ribosomal (r)DNA. Rats were exposed to 8.7 μg/m3 PM2.5, 10.1 ppb NO2, 1.6 ppb SO2, and 23.9 ppb O3 in average during the study period. Air pollution exposure decreased forced vital capacity (FVC), peak expiratory flow (PEF), forced expiratory volume in 20 ms (FEV20), and FEF at 25∼75% of FVC (FEF25–75). Air pollution exposure increased antibiotics and drugs (benzotriazole, methamphetamine, methyl-1 H-benzotriazole, ketamine, ampicillin, ciprofloxacin, pentoxifylline, erythromycin, clarithromycin, ceftriaxone, penicillin G, and penicillin V) and altered metals (V, Cr, Cu, Zn, and Ba) levels in lungs. Fusobacteria and Verrucomicrobia at phylum level were increased in lung microbiome by air pollution, whereas increased alpha diversity, Bacteroidetes and Proteobacteria and decreased Firmicutes at phylum level were occurred in intestinal microbiome. Lung function decline was correlated with increasing antibiotics, drugs, and metals in lungs as well as lung and intestinal microbiome dysbiosis. The antibiotics, drugs, and Cr, Co, Ca, and Cu levels in lung were correlated with lung and intestinal microbiome dysbiosis. The lung microbiome was correlated with intestinal microbiome at several phylum and family levels after air pollution exposure. Our results revealed that antibiotics, drugs, and metals in the lung caused lung and intestinal microbiome dysbiosis in ageing rats exposed to air pollution, which may lead to lung function decline.

Abstract Epidemiological and toxicological evidence indicates that fine particulate matter (PM2.5, particulate matter < 2.5 µm in aerodynamic diameter) causes cardiopulmonary toxicity; however, its neurotoxic effects remain unclear. The objective of this study was to investigate the role of the neurofilament light (NEFL) serum polypeptide in neurotoxicity. 6-month-old male Sprague Dawley (SD) rats were exposed to traffic-related PM1 (< 1 µm in aerodynamic diameter; 16.3 µg m −3) and gaseous pollutants (via high-efficiency particulate air, HEPA) for 3 and 6 months through a whole-body exposure system. According to our observations, the levels of interleukin (IL)-4, IL-10, and tumor necrosis factor (TNF)-α in the serum of the rats significantly increased (p < 0.05) after 3 months of exposure to PM1, whereas that of NEFL polypeptide significantly increased (p < 0.05) after 3 and 6 months of exposure. Additionally, increases in the IL-2, IL-6, IL-10, IL-17a, TNF-α, and interferon (IFN)-γ levels after 3 and/or 6 months of exposure to this pollutant (p < 0.05) were observed in the cerebrospinal fluid (CSF). In terms of their respective levels, the IL-6 correlated well with the CSF IL-2 and IL-10; the TNF-α correlated well with the CSF IL-6, IL-17a, TNF-α, and IFN-γ; and the NEFL polypeptide correlated well with the CSF IL-2, IL-4, IL-6, IL-10, IL-17a, TNF-α, and IFN-γ. In summary, systemic neuroinflammatory and immune responses in rats occurred after chronic exposure to PM1. Hence, NEFL polypeptide in serum may be a suitable biomarker for neurotoxicity caused by chronic exposure to this pollutant.

Human umbilical cord-derived mesenchymal stem cells (hUC-MSCs) were shown to have potential for immunoregulation and tissue repair. The objective of this study was to investigate the effects of hUC-MSCs on emphysema in chronic obstructive pulmonary disease (COPD). The C57BL/6JNarl mice were exposed to cigarette smoke (CS) for 4 months followed by administration of hUC-MSCs at 3 × 106 (low dose), 1 × 107 (medium dose), and 3 × 107 cells/kg body weight (high dose). The hUC-MSCs caused significant decreases in emphysema severity by measuring the mean linear intercept (MLI) and destructive index (DI). A decrease in neutrophils (%) and an increase in lymphocytes (%) in bronchoalveolar lavage fluid (BALF) were observed in emphysematous mice after hUC-MSC treatment. Lung levels of interleukin (IL)-1β, C-X-C motif chemokine ligand 1 (CXCL1)/keratinocyte chemoattractant (KC), and matrix metalloproteinase (MMP)-12 significantly decreased after hUC-MSC administration. Significant reductions in tumor necrosis factor (TNF)-α, IL-1β, and IL-17A in serum occurred after hUC-MSC administration. Notably, the cell viability of lung fibroblasts improved with hUC-MSCs after being treated with CS extract (CSE). Furthermore, the hUC-MSCs-conditioned medium (hUC-MSCs-CM) restored the contractile force, and increased messenger RNA expressions of elastin and fibronectin by lung fibroblasts. In conclusion, hUC-MSCs reduced inflammatory responses and emphysema severity in CS-induced emphysematous mice.

Introduction Angiotensin-converting enzyme 2 (ACE2) provides an adhesion site for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Patients with COPD could have severe outcomes after SARS-CoV-2 infection. The objective of this study was to investigate ACE2 regulation by air pollution during the development of COPD. Methods Sprague Dawley rats were exposed to unconcentrated traffic-related air pollution for 3 and 6 months. We examined lung injury markers, oxidative stress, inflammation, emphysema, ACE2 and angiotensin II receptor type 1 (AT1) and 2 (AT2) in the lungs after exposure. Results Lung injury occurred due to an increase in permeability and lactate dehydrogenase cytotoxicity was observed after 6 months of exposure to fine particulate matter of

Cigarette smoke (CS) has been reported to induce oxidative stress and inflammatory process in the lungs. However, the role of human umbilical cord-derived mesenchymal stem cells (hUC-MSCs) in the regulation of pulmonary inflammation remains unclear. The objective of this study is to investigate the effects of hUC-MSCs on lung inflammation in the acute CS-induced pulmonary inflammation animal model. Eight-week-old male C57BL/6 mice were intravenously administered 3 × 106, 1 × 107, and 3 × 107 cells/kg of hUC-MSCs as well as normal saline alone (control) after 3 days of CS exposure. Mice exposed to high-efficiency particulate air (HEPA)-filtered room air served as the CS control group. High-dose (3 × 107 cells/kg) hUC-MSC administration significantly decreased tumor necrosis factor (TNF)-α in the bronchoalveolar lavage fluid (BALF) of CS-exposed mice (p < 0.05). The chemokine (CXC motif) ligand 1/keratinocyte chemoattractant (CXCL1/KC) in BALF were significantly reduced by low-dose (3 × 106 cells/kg) and high-dose (3 × 107 cells/kg) hUC-MSC (p < 0.05). Medium-dose hUC-MSC administration decreased interleukin (IL)-1β in lung of mice, and TNF-α and caspase-3 were decreased in the lung of CS-exposed mice by medium- and high-dose MSC (p < 0.05). Low-dose hUC-MSCs significantly elevated serum CXCL1/KC and IL-1β in CS-exposed mice (p < 0.05). Our results suggest that high-dose hUC-MSCs reduced pulmonary inflammation and had antiapoptotic effects in acute pulmonary inflammation.

Abstract Background Effects of air pollution on neurotoxicity and behavioral alterations have been reported. The objective of this study was to investigate the pathophysiology caused by particulate matter (PM) in the brain. We examined the effects of traffic-related particulate matter with an aerodynamic diameter of

The vertical distribution of aerosols is important for accurate surface PM2.5 retrieval and initial modeling forecasts of air pollution, but the observation of aerosol profiles on the regional scale is usually limited. Therefore, in this study, an approach to aerosol extinction profile fitting is proposed to improve surface PM2.5 retrieval from satellite observations. Owing to the high similarity of the single-peak extinction profile in the distribution pattern, the log-normal distribution is explored for the fitting model based on a decadal dataset (3248 in total) from Micro Pulse LiDAR (MPL) measurements. The logarithmic mean, standard deviation, and the height of peak extinction near-surface (Mode) are manually derived as the references for model construction. Considering the seasonal impacts on the planetary boundary layer height (PBLH), Mode, and the height of the surface layer, the extinction profile is then constructed in terms of the planetary boundary layer height (PBLH) and the total column aerosol optical depth (AOD). A comparison between fitted profiles and in situ measurements showed a high level of consistency in terms of the correlation coefficient (0.8973) and root-mean-square error (0.0415). The satellite AOD is subsequently applied for three-dimensional aerosol extinction, and the good agreement of the extinction coefficient with the PM2.5 within the surface layer indicates the good performance of the proposed fitting approach and the potential of satellite observations for providing accurate PM2.5 data on a regional scale.