Brachyury positively regulates extracellular matrix synthesis via directly promoting aggrecan transcription in nucleus pulposus.

Nucleus pulposus (NP) degeneration is characterized by the decreased cellularity of nucleus pulposus cells (NPCs) and diminished content of hydrophilic extracellular matrix (ECM). Overexpression of brachyury has been reported to reverse the degenerated NPCs into healthy phenotypes. However, the direct correlation between brachyury and ECM has not been fully elucidated. This study revealed that brachyury expression decreased in human degenerated NP tissues and Lipopolysaccharide (LPS)-induced degenerated rat NPCs model. In vitro and in vivo experiments further showed that brachyury deficiency suppressed the synthesis of aggrecan and collagen II in NP. Mechanistically, ChIP-qPCR assays demonstrated that brachyury bound to the promoter region of aggrecan in NPCs. Furthermore, luciferase reporter assays revealed that brachyury transcriptionally activated aggrecan expression through binding with a novel specific motif. In rat in vivo model, brachyury overexpression partially reversed the degenerative phenotype. In conclusion, brachyury positively regulated ECM synthesis via directly promoting aggrecan transcription in NPCs. Accordingly, it may be helpful to be developed into a promising therapeutic target for NP degeneration.

A facile one-pot strategy for the preparation of porous polymeric microspheres via UV irradiation-induced polymerization in emulsions.

In this study, a facile one-pot strategy was developed to prepare porous polymeric microspheres via photopolymerization, where organic solvents functioned as porogens. In this strategy, an oil phase containing organic solvents and photopolymerizable materials was stabilized in water to form a stable oil-in-water emulsion. Upon UV irradiation, the photopolymerizable materials (photosensitive monomers/photosensitive prepolymers) underwent polymerization to form microspheres and the subsequent removal of organic solvents left pores in microspheres, leading to the generation of porous polymeric microspheres with high yielding. The effects of organic solvents and the chemical structure and concentration of photopolymerizable materials on the microsphere structure were systematically explored. It was found that the polarity of the organic solvents played a decisive role in the preparation of porous microspheres. In addition, the increases in the solvent content and functionalities of photopolymerizable materials were more favorable for the generation of porous microspheres. This strategy could be applicable for a wide selection of photopolymerizable materials, which endowed this strategy with good applicability. The preparation of porous microspheres by this method was facile and easy to handle, enabling the scalable preparation of porous microspheres. In addition, the whole process can be completed within a few minutes at ambient temperature, which was time-saving and energy-saving.

Characteristics and Admission Preferences of Pediatric Emergency Patients and Their Waiting Time Prediction Using Electronic Medical Record Data: Retrospective Comparative Analysis.

BACKGROUND: The growing number of patients visiting pediatric emergency departments could have a detrimental impact on the care provided to children who are triaged as needing urgent attention. Therefore, it has become essential to continuously monitor and analyze the admissions and waiting times of pediatric emergency patients. Despite the significant challenge posed by the shortage of pediatric medical resources in China's health care system, there have been few large-scale studies conducted to analyze visits to the pediatric emergency room. OBJECTIVE: This study seeks to examine the characteristics and admission patterns of patients in the pediatric emergency department using electronic medical record (EMR) data. Additionally, it aims to develop and assess machine learning models for predicting waiting times for pediatric emergency department visits. METHODS: This retrospective analysis involved patients who were admitted to the emergency department of Children's Hospital Capital Institute of Pediatrics from January 1, 2021, to December 31, 2021. Clinical data from these admissions were extracted from the electronic medical records, encompassing various variables of interest such as patient demographics, clinical diagnoses, and time stamps of clinical visits. These indicators were collected and compared. Furthermore, we developed and evaluated several computational models for predicting waiting times. RESULTS: In total, 183,024 eligible admissions from 127,368 pediatric patients were included. During the 12-month study period, pediatric emergency department visits were most frequent among children aged less than 5 years, accounting for 71.26% (130,423/183,024) of the total visits. Additionally, there was a higher proportion of male patients (104,147/183,024, 56.90%) compared with female patients (78,877/183,024, 43.10%). Fever (50,715/183,024, 27.71%), respiratory infection (43,269/183,024, 23.64%), celialgia (9560/183,024, 5.22%), and emesis (6898/183,024, 3.77%) were the leading causes of pediatric emergency room visits. The average daily number of admissions was 501.44, and 18.76% (34,339/183,204) of pediatric emergency department visits resulted in discharge without a prescription or further tests. The median waiting time from registration to seeing a doctor was 27.53 minutes. Prolonged waiting times were observed from April to July, coinciding with an increased number of arrivals, primarily for respiratory diseases. In terms of waiting time prediction, machine learning models, specifically random forest, LightGBM, and XGBoost, outperformed regression methods. On average, these models reduced the root-mean-square error by approximately 17.73% (8.951/50.481) and increased the R2 by approximately 29.33% (0.154/0.525). The SHAP method analysis highlighted that the features "wait.green" and "department" had the most significant influence on waiting times. CONCLUSIONS: This study offers a contemporary exploration of pediatric emergency room visits, revealing significant variations in admission rates across different periods and uncovering certain admission patterns. The machine learning models, particularly ensemble methods, delivered more dependable waiting time predictions. Patient volume awaiting consultation or treatment and the triage status emerged as crucial factors contributing to prolonged waiting times. Therefore, strategies such as patient diversion to alleviate congestion in emergency departments and optimizing triage systems to reduce average waiting times remain effective approaches to enhance the quality of pediatric health care services in China.

pH-Responsive Liquid Marbles Based on Dihydroxystearic Acid.

Herein, we report pH-responsive liquid marbles stabilized by 9,10-dihydroxystearic acid (DHSA). The particle morphology and the pH-responsive behavior of the liquid marbles were investigated. The rolling time during the preparation of liquid marbles has a great influence on the thickness of powder adsorption and the stability of the marbles. Compared with the liquid marbles stabilized by other fatty acids (e.g., stearic acid and docosoic acid), the liquid marbles prepared by DHSA have a much higher mechanical robustness. The increase in the number of hydroxyl groups on the carbon chain of fatty acids improves the mechanical robustness of the liquid marbles. Such liquid marbles immediately disintegrated on the surface of an alkaline solution or after exposure to NH3 gas, which extends their applications in the NH3 sensor and chemical reactions.

Water-in-Oil Emulsion Gels Stabilized by a Low-Molecular Weight Organogelator Derived from Dehydroabietic Acid.

High concentrations of surfactants or gelators are usually necessary to prepare emulsions gels with unusual physicochemical properties. This situation may be improved by innovating the aggregate morphology in systems. Herein, a rosin-based molecule is designed and synthesized using dehydroabietic acid as the starting material (denoted as R-Lys-R). The molecule acts as an effective organogelator and can gelate several hydrocarbon compounds with a minimum gelation concentration of 0.2% (w/v). Analysis using atomic force microscopy (AFM) and circular dichroism (CD) reveals that in n-decane, R-Lys-R forms left-handed helical fibers with a cross-sectional diameter of approximately 15 nm. The directional hydrogen bonding of the amide group is helpful to the formation of aggregates. At concentrations of R-Lys-R above 2%, water-in-oil emulsions are transformed into emulsion gels owing to the aptitude of R-Lys-R in gelating the oil phase. The concentrations of the emulsifier can be adjusted to obtain emulsion gels with different formulations. This work reveals the potential of rosin derivatives for the formation of small molecular weight organogels and provides a novel method for the utilization of natural resources in soft materials and home care products.

Comparison of Mortality Predictive Models of Sepsis Patients Based on Machine Learning.

Objective To compare the performance of five machine learning models and SAPS II score in predicting the 30-day mortality amongst patients with sepsis. Methods The sepsis patient-related data were extracted from the MIMIC-IV database. Clinical features were generated and selected by mutual information and grid search. Logistic regression, Random forest, LightGBM, XGBoost, and other machine learning models were constructed to predict the mortality probability. Five measurements including accuracy, precision, recall, F1 score, and area under curve (AUC) were acquired for model evaluation. An external validation was implemented to avoid conclusion bias. Results LightGBM outperformed other methods, achieving the highest AUC (0.900), accuracy (0.808), and precision (0.559). All machine learning models performed better than SAPS II score (AUC=0.748). LightGBM achieved 0.883 in AUC in the external data validation. Conclusions The machine learning models are more effective in predicting the 30-day mortality of patients with sepsis than the traditional SAPS II score.

Associations between TNFSF4 gene polymorphisms (rs2205960 G > A, rs704840 T > G and rs844648 G > A) and susceptibility to autoimmune diseases in Asians: a meta-analysis.

BACKGROUND: Tumor necrosis factor superfamily member 4 (TNFSF4) has significant role in modulating autoimmune diseases (ADs) and single nucleotide polymorphism (SNP) is also related with the susceptibility to some diseases. So a meta-analysis aimed at systematically assessing the associations between TNFSF4 polymorphisms (rs2205960 G > A, rs704840 T > G and rs844648 G > A) and ADs risk was performed in Asians. METHODS: Total 14 eligible articles published before March 2019 involving 35 studies, of which 21 studies (16,109 cases and 26,378 controls) for rs2205960 G > A, 8 studies (2,424 cases and 3,692 controls) for rs704840 T > G, and 6 studies (3,839 cases and 5,867 controls) for rs844648 G > A were included. Effects of the three respective polymorphisms on the susceptibility to ADs were estimated by pooling the odds ratios (ORs) with their corresponding 95% confidence interval (95% CI) in allelic, dominant, recessive, heterozygous and homozygous models. RESULTS: The overall analysis revealed that all the rs2205960 G > A, rs704840 T > G and rs844648 G > A polymorphisms could increase the risk of ADs in allelic, dominant, recessive, heterozygous and homozygous models. Furthermore, subgroup analysis showed that both rs2205960 G > A and rs704840 T > G were significantly associated with the susceptibility to systemic lupus erythematosus (SLE). What's more, statistically significant association between rs2205960 G > A polymorphism and primary Sjögren's syndrome (pSS) susceptibility was also observed in allelic, dominant and heterozygous models. CONCLUSIONS: This current meta-analysis suggested that all of the three TNFSF4 polymorphisms may be associated with ADs susceptibility in Asians.

Synthesis and identification of quinoline derivatives as topoisomerase I inhibitors with potent antipsoriasis activity in an animal model.

Psoriasis is a chronic inflammatory and immune-mediated skin disease. Although certain agents have shown clinical success in treating psoriasis, development of safe and effective strategies for the treatment of this condition remains important. Research suggests that DNA topoisomerase I (Topo I) inhibitors may have potent psoriasis-ameliorating effects. Here, 25 quinoline derivatives were synthesized and identified as Topo I inhibitors. These compounds inhibited the 12-O-tetradecanoylphorbol-13-acetate-induced mouse ear inflammation. The most potent analogs, 5i and 5l, suppressed the expression of inflammatory cytokines in lipopolysaccharide-stimulated HaCaT cells. Additionally, the lead compounds significantly improved imiquimod-induced psoriasis-like inflammation in mice. Moreover, the expression levels of cytokines and inflammatory mediators, such as interleukin (IL)-17A, IL-22, IL-23, nuclear factor-κB subunit p65, tumor necrosis factor-α, and interferon-γ, were dramatically inhibited in the dorsal skin of 5i- and 5l-treated mice. These findings indicate that the inhibition of Topo I activity may potentially be an effective strategy for psoriasis treatment.

In situ surface formation of TiO2/Ti(NO2) hybrid nanocomposites with N2 APPJ treatment for efficient C2H4 photodegradation.

TiO2/Ti(NO2) hybrid films were prepared using N2 atmospheric pressure plasma jet treatment on TiO2 coating. The film structure and morphology have been investigated using optical emission spectra, Fourier-transform infrared spectroscopy, X-ray diffraction, and X-ray photoelectron spectroscopy. The formed TiO2/Ti(NO2) photocatalystic thin films were applied for C2H4 photodegradation under UV irradiation. The results showed that the composite films exhibited superior photocatalytic activity over the untreated TiO2 film. The C2H4 concentration after 120 min varied from 12 to 6.2 mg/L, 6.7 mg/L, 7 mg/L for TiO2 with 1 min, 2 min and 3 min plasma treatment, respectively. In the banana storage experiment, the concentration of C2H4 was reduced from 15 to 9 ppm after 36 h with TiO2/Ti(NO2) nanocomposite film illuminated by UV light. The photocatalytic mechanism has been discussed. The composite film is able to more effectively separate the photo-excited electrons and holes, thus leading to the much high activity in C2H4 degradation. The current work has paved a way towards postharvest fruit preservation.

Inhibition effect of graphene oxide on the catalytic activity of acetylcholinesterase enzyme.

Variations in the enzyme activity of acetylcholinesterase (AChE) in the presence of the nano-material, graphene oxide (GO), were investigated with the use of molecular spectroscopy UV-visible and fluorescence methods. From these studies, important kinetic parameters of the enzyme were extracted; these were the maximum reaction rate, Vm , and the Michaelis constant, Km . A comparison of these parameters indicated that GO inhibited the catalytic activity of the AChE because of the presence of the AChE-GO complex. The formation of this complex was confirmed with the use of fluorescence data, which was resolved with the use of the MCR-ALS chemometrics method. Furthermore, it was found that the resonance light-scattering (RLS) intensity of AChE changed in the presence of GO. On this basis, it was demonstrated that the relationship between AChE and GO was linear and such models were used for quantitative analyses of GO.

Preparation of Graphene Oxide/Polymer Hybrid Microcapsules via Photopolymerization for Double Self-Healing Anticorrosion Coatings.

In this work, graphene oxide (GO)/polymer hybrid microcapsule-loaded self-healing agents were prepared via the combination of the emulsion template method and photopolymerization technology. The incorporation of GO in the microcapsule shell not only improved the impermeability, mechanical property, and solvent resistance property of the microcapsules significantly but also endowed the microcapsules with photothermal conversion property. By incorporating GO/polymer hybrid microcapsules in water-borne epoxy resin, a novel kind of anticorrosion coating with a double self-healing property was successfully fabricated. When the coating was scratched, the linseed oil (LO) encapsulated in the microcapsules could fill the crack, and the photothermal conversion property of GO could promote the molecular chain movement of the damaged area under near-infrared (NIR) irradiation to realize the close of the crack. Based on the filling of LO and photothermal conversion-induced scratch narrowing, the "filling" and "close" double self-healing effect can be realized under temporal NIR irradiation, which could lead to the complete recovery of the scratched coating. The |Z|f=0.1Hz value of the damaged coating with GO/polymer microcapsules after double healing was comparable to that of the intact coating, which was about 4 orders of magnitude higher than that of the scratched blank coating and single self-healing coating. As to the neutral salt spray test, the scratched blank coating failed in protection after 100 h, while the healed composite coating did not show any corrosion after 300 h.

Polyaniline Microspheres with Corrosion Inhibition, Corrosion Sensing, and Photothermal Self-Healing Properties toward Intelligent Coating.

A smart coating integrating functions of corrosion inhibition, self-healing, and corrosion-sensing was developed based on a polyaniline (PANI) microsphere-loading corrosion sensing probe (8-hydroxyquinone, 8-HQ). The PANI microsphere was prepared in a facile one-pot process via the combination of photopolymerization and an emulsion template. The 8-HQ-loaded PANI microsphere achieved three synergetic effects simultaneously: corrosion inhibition, corrosion sensing, and photothermal self-healing abilities. Benefiting from the corrosion inhibition effect of PANI, the coating with the PANI microsphere exhibited significantly enhanced anticorrosion behavior. After soaking in NaCl solution for 35 days, its impedance was maintained at 1.26 × 109 Ω·cm2, nearly 3 orders of magnitude higher than that of pure resin coating. Meanwhile, the encapsulated 8-HQ exhibited pH-responsive release behavior thanks to the pH-responsive characteristics of PANI, which could chelate with Al3+ ions to form 8-HQ-Al3+ coordinates with a conspicuous fluorescence, achieving a real-time corrosion diagnosing function. Moreover, benefiting from the photothermal property of PANI, the coating with the PANI microsphere displayed rapid crack closure behavior under NIR light irradiation, and the healing efficiency could reach 83.56% under near-infrared irradiation. This work presents an innovative strategy for fabricating an intelligent self-healing, self-reporting, and anticorrosion coating, which provides a new vision to prolong the lifetime of metals.

Predicting Secondary Vertebral Compression Fracture After Vertebral Augmentation via CT-Based Machine Learning Radiomics-Clinical Model.

RATIONALE AND OBJECTIVES: Secondary vertebral compression fractures (SVCF) are very common in patients after vertebral augmentation (VA). The aim of this study was to establish a radiomic-based model to predict SVCF and specify appropriate treatment strategies. MATERIALS AND METHODS: Patients diagnosed with osteoporotic vertebral compression fracture (OVCF) and undergoing VA surgery at our center between 2017 and 2021 were subject to a retrospective analysis. Radiological features of the T6-L5 vertebrae were derived from CT images. Clustering analysis, t-test, and LASSO (least absolute shrinkage and selection operator) regression were used to identify the optimization characteristics. A radiological signature model was constructed through the best combination of 13 machine learning algorithms. Radiomics signature was integrated with clinical characteristics into a nomogram for clinical applications. The model reliability was assessed by receiver operating characteristic (ROC) curve, calibration curve, clinical decision analysis (DCA), log-rank test, and confusion matrix. RESULTS: A total of 470 eligible patients (81 with SVCF and 389 without) were identified in the clinical cohort. Eight radiomics features were identified and incorporated into machine learning, and "XGBoost" model showed the best performance. Final logistic nomogram included radiomics signature (P < 0.001), bone cement volume (P = 0.034), and T-scores of L1-L4 (P = 0.001), and showed satisfactory prediction capability in training set (0.986, 95%CI 0.969-1.000) and verification set (0.884, 95%CI 0.823-0.946). CONCLUSION: Our radiomics-clinical model based on machine learning showed potential to prospectively predict SVCF after VA and provide precise treatment strategies.

Application of Short T1 Inversion Recovery Sequence in Increased Signal Intensity Following Cervical Spondylotic Myelopathy.

OBJECTIVES: To compare magnetic resonance (MR) short T1 inversion recovery (STIR) sequence with MR T2-weighted (T2W) sequence for detecting increased signal intensity (ISI) and assessing outcomes of ISI in cervical spondylotic myelopathy (CSM). METHODS: Data of patients with CSM who showed ISI on MR imaging and had undergone cervical spine surgery were retrospectively reviewed. STIR and T2W images were examined to assess signal intensity ratio (SIR), length and grading of the ISI, maximal spinal cord compression, canal narrowing ratio, and ligamentum flavum hypertrophy. The patients were divided into good and poor groups based on their outcomes. χ2 tests and variance analysis were used to assess intergroup differences. Univariate and multivariate logistic regression analyses were performed to identify risk factors for poor outcomes, and receiver operating characteristic curves were plotted to detect prognostic effects. RESULTS: SIR and ISI lengths were significantly different between the STIR and T2 images. In the univariate logistic regression analysis, age, diabetes, SIRT2, SIRSTIR, and ISISTIR grading were significant factors. Accordingly, in the multivariate logistic regression analysis, age, diabetes, SIRT2, and SIRSTIR were included in the model. Among patients with diabetes, we observed a significant difference between SIRT2 and SIRSTIR. CONCLUSIONS: The STIR sequence demonstrated superior capability to the T2W sequence in detecting ISI; however, there was no obvious difference in predicted outcomes. STIR sequence has a better prognostic value than T2W sequence in patients with diabetes who have CSM. ISI grading based on the STIR sequence may be a clinically valuable indicator.

Effects of dapagliflozin on body weight in patients with type 2 diabetes mellitus: Evidence­based practice.

The dose-dependent pharmacological response to dapagliflozin in patients with type 2 diabetes mellitus (T2DM) with regard to weight loss remain unknown. The aim of the present study was to investigate the effects of dapagliflozin on weight loss in patients with T2DM. A total of 8,545 patients with T2DM from 24 randomized controlled trials reported in the literature were selected for inclusion in the study. Data from these trials were analyzed using maximal effect (Emax) models with nonlinear mixed effects modeling; the evaluation index was the body weight change rate from baseline values. Patients treated with 2.5 mg/day dapagliflozin exhibited an Emax of -3.04%, and the time taken for therapy to reach half of the Emax (ET50) was estimated to be 30.8 weeks for patients treated with this dose. Patients treated with 5, 10 and 20 mg/day dapagliflozin exhibited Emax values of -6.57, -4.12 and -3.23%, respectively, and their ET50 values were estimated to be 27.3, 20.4 and 4.23 weeks, respectively. The data indicated ideal linear relationships between individual predictions and observations, suggesting the optimal fitting of the final models. The present study is the first systematic analysis of the effect of dapagliflozin on weight loss in patients with T2DM. The application of dapagliflozin at 5 mg/day exhibited a greater weight loss effect compared with the other doses used, and the weight loss onset time shortened as the dose of dapagliflozin increased.

Antitumor effect of the antimicrobial peptide GLI13-8 derived from domain of the avian ß-defensin-4.

We previously reported that GLI13-8, one of cationic antimicrobial peptides from linear avian ß-defensin-4 (RL38) analogs, exhibited high antimicrobial activities against both Gram-negative and Gram-positive bacteria. In the present study, we reported the in vitro cytotoxicity of GLI13-8 using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. Results showed that the cytotoxicity of GLI13-8 in three human carcinoma cells (HepG2, SGC7901, and A375) was in a dose-dependent manner. When the concentration of GLI13-8 is <128 µM, it had no toxicity towards the normal human fibroblasts (MRC-5). The Annexin-V-FITC/PI staining assay, the Hoechst 33258/PI staining assay, the permeability of fluorescein macromolecules and scanning electron microscope assays, mitochondrial membrane potential assay, caspases-3 and poly ADP-ribose polymerase (PARP) assays have been carried out. Results indicated that apoptosis was induced by GLI13-8 in HepG2 cells, and demonstrated that GLI13-8 induced loss of mitochondrial membrane potential, disruption of HepG2 cell membranes, and activation of caspase-3 and PARP. These findings suggested that GLI13-8 may be an effective agent for HepG2 cells.

CO2-switchable emulsions with controllable size and viscosity.

CO2-responsive emulsions have attracted considerable attention in recent years because of their biocompatibility and easy removal of CO2. However, most CO2-responsive emulsions are only used in stabilization and demulsification processes. In this paper, we report CO2-switchable oil-in-dispersion (OID) emulsions co-stabilized with silica nanoparticles and anionic NCOONa, in which the required concentrations of NCOONa and silica particles were as low as 0.01 mM and 0.0001 wt%, respectively. Besides reversible emulsification/demulsification, the aqueous phase containing the emulsifiers was recycled and reused with the CO2/N2 trigger. More importantly, the properties of the emulsions, such as droplet sizes (40-1020 µm) and viscosities (6-2190 Pa s), were intelligently controlled by the CO2/N2 trigger, and meanwhile reversible conversion between OID emulsions and Pickering emulsions was achieved. The present method offers a green and sustainable way to regulate the emulsion states, which enables smart control of emulsions and widens their potential applications.

Deep-learning-based denoising of X-ray differential phase and dark-field images.

PURPOSE: Statistical photon noise has always been a common problem in X-ray multi-contrast imaging and significantly influenced the quality of retrieved differential phase and dark-field images. We intend to develop a deep learning-based denoising algorithm to reduce the noise of retrieved X-ray differential phase and dark-field images. METHODS: A novel deep learning based image noise suppression algorithm (named DnCNN-P) is presented. We proposed two different denoising modes: Retrieval-Denoising mode (R-D mode) and Denoising-Retrieval mode (D-R mode). While the R-D mode denoises the retrieved images, the D-R mode denoises the raw phase stepping data. The two denoising modes are evaluated under different photon counts and visibilities. RESULTS: Experimental results show that with the algorithm DnCNN-P used, the D-R mode always exhibits a better noise reduction under diverse experimental conditions, even in the case of a low photon count and/or a low visibility. With a detected photon count of 1800 and a visibility of 0.3, compared to the differential phase images without denoising, the standard deviation is reduced by 89.1% and 16.4% in the D-R and R-D modes. Compared to the dark-field images without denoising, the standard deviation is reduced by 83.7% and 12.6% in the D-R and R-D modes, respectively. CONCLUSIONS: The novel supervised DnCNN-P algorithm can significantly reduce the noise in retrieved X-ray differential phase and dark-field images. We believe this novel algorithm can be a promising approach to improve the quality of X-ray differential phase and dark-field images, and therefore dose efficiency in future biomedical applications.

[Chemical Characteristics and Source Apportionment for VOCs During the Ozone Pollution Episodes and Non-ozone Pollution Periods in Qingdao].

The ambient concentration of ozone is high in Qingdao, and ozone pollution episodes occur frequently in summer. The refined source apportionment of ambient volatile organic compounds (VOCs) and their ozone formation potential (OFP) during ozone pollution episodes and non-ozone pollution periods can play an important role in effectively reducing air ozone pollution in coastal cities and continuously improving ambient air quality. Therefore, this study applied the online VOCs monitoring data with hourly resolution in summer (from June to August) in 2020 in Qingdao to analyze the chemical characteristics of ambient VOCs during the ozone pollution episodes and non-ozone pollution periods and conducted the refined source apportionment of ambient VOCs and their OFP using a positive matrix factorization (PMF) model. The results showed that the average mass concentration of ambient VOCs in Qingdao in summer was 93.8 µg·m-3, and compared with that during the non-ozone pollution period, the mass concentration of ambient VOCs during the ozone pollution episodes increased by 49.3%, and the mass concentration of aromatic hydrocarbons increased by 59.7%. The total OFP of ambient VOCs in summer was 246.3 µg·m-3. Compared with that in the non-ozone pollution period, the total OFP of ambient VOCs in the ozone pollution episodes increased by 43.1%; that of alkanes increased the most, reaching 58.8%. M-ethyltoluene and 2,3-dimethylpentane were the species with the largest increase in OFP and its proportion during the ozone pollution episodes. The main contributors of ambient VOCs in Qingdao in summer were diesel vehicles (11.2%), solvent use (4.7%), liquefied petroleum gas and natural gas (LPG/NG) (27.5%), gasoline vehicles (8.9%), gasoline volatilization (26.6%), emissions of combustion- and petrochemical-related enterprises (16.4%), and plant emissions (4.8%). Compared with that in the non-ozone pollution period, the contribution concentration of LPG/NG in the ozone pollution episodes increased by 16.4 µg·m-3, which was the source category with the largest increase. The contribution concentration of plant emissions increased by 88.6% in the ozone pollution episodes, which was the source category with the highest increase rate. In addition, emissions from combustion- and petrochemical-related enterprises were the largest contributor to the OFP of ambient VOCs in summer in Qingdao, with its OFP and contribution proportion being 38.0 µg·m-3and 24.5%, respectively, followed by that of LPG/NG and gasoline volatilization. Compared with the non-ozone pollution period, the total contributions of LPG/NG, gasoline volatilization, and solvent use to the increase in OFP for ambient VOCs in the ozone pollution episodes were 74.1%, which were the main contribution source categories.

Source apportionment of consumed volatile organic compounds in the atmosphere.

Conventional source apportionments of ambient volatile organic compounds (VOCs) have been based on observed and initial concentrations after photochemical correction. However, these results have not been related to ozone (O3) and secondary organic aerosol (SOA) formation. Thus, the apportioned contributions could not effectively support secondary pollution control development. Source apportionment of the VOCs consumed in forming O3 and SOA is needed. A consumed VOC source apportionment approach was developed and applied to hourly speciated VOCs data from June to August 2022 measured in Laoshan, Qingdao. Biogenic emissions (56.3%), vehicle emissions (17.2%), and gasoline evaporation (9.37%) were the main sources of consumed VOCs. High consumed VOCs from biogenic emissions mainly occurred during transport from parks to the southwest and northwest of study site. During the O3 pollution period, biogenic emissions (46.3%), vehicle emissions (24.2%), and gasoline evaporation (14.3%) provided the largest contributions to the consumed VOCs. However, biogenic emissions contribution increased to 57.1% during the non-O3 pollution period, and vehicle emissions and gasoline evaporation decreased to 16.5% and 9.01%, respectively. Biogenic emissions and the mixed source of combustion sources and solvent use contributed the most to O3 and SOA formation potentials during the O3 pollution period, respectively.