[Determination of 13 chemical components of Epimedii Folium in pharmacopoeia by UPLC method combined with quantitative analysis of multicomponents by single-marker].

The quantitative analysis of multicomponents by single-marker(QAMS) was established for 13 chemical components of Epimedii Folium, including neoglycolic acid, chlorogenic acid, cryo-chlorogenic acid, magnolidine, hypericin, epimedin A, epimedin B, epimedin C, icariin, baohuoside Ⅱ, sagittatoside A, icariin subside Ⅰ, and baohuoside Ⅰ, so as to investigate the feasibility and accuracy of this method in evaluating the quality of Epimedii Folium materials from different origins and different varieties. Through the scientific and accurate investigation of the experimental method, the external standard method was used to determine the content of 13 chemical components in epimedium brevieornu. At the same time, icariin was used as the internal standard, and the relative correction factors of icariin with neoglycolic acid, chlorogenic acid, cryo-chlorogenic acid, magnolidine, hypericin, epimedin A, epimedin B, epimedin C, icariin, baohuoside Ⅱ, sagittatoside A, icariin subside Ⅰ, and baohuoside Ⅰ were established, respectively. The contens of neoglycolic acid, chlorogenic acid, cryo-chlorogenic acid, magnolidine, hypericin, epimedin A, epimedin B, epimedin C, icariin, baohuoside Ⅱ, sagittatoside A, icariin subside Ⅰ, and baohuosideⅠ in Epimedii Folium were calculated by QAMS. Finally, the difference between the measured value and the calculated value was compared to verify the accuracy and scientific nature of QAMS in the determination. The relative correction factor of each component had better repeatability, and there was no significant difference between the results of the external standard method and those of QAMS. With icariin as the internal standard, QAMS simultaneously determining neoglycolic acid, chlorogenic acid, cryo-chlorogenic acid, magnolidine, hypericin, epimedin A, epimedin B, epimedin C, icariin, baohuoside Ⅱ, sagittatoside A, icariin subside Ⅰ, and baohuoside Ⅰ can be used for quantitative analysis of Epimedii Folium.

Quantitative measurement of acute myocardial infarction cardiac biomarkers by "All-in-One" immune microfluidic chip for early diagnosis of myocardial infarction.

Acute myocardial infarction (AMI) is a life-threatening condition with a narrow treatment window, necessitating rapid and accurate diagnostic methods. We present an "all-in-one" convenient and rapid immunoassay system that combines microfluidic technology with a colloidal gold immunoassay. A degassing-driven chip replaces a bulky external pump, resulting in a user-friendly and easy-to-operate immunoassay system. The chip comprises four units: an inlet reservoir, an immunoreaction channel, a waste pool, and an immunocomplex collection chamber, allowing single-channel flow for rapid and accurate AMI biomarker detection. In this study, we focused on cardiac troponin I (cTnI). With a minimal sample of just 4 µL and a total detection time of under 3 min, the chip enabled a quantitative visual analysis of cTnI concentration within a range of 0.5 âˆ¼ 60.0 ng mL-1. This all-in-one integrated microfluidic chip with colloidal gold immunoassay offers a promising solution for rapid AMI diagnosis. The system's portability, small sample requirement, and quantitative visual detection capabilities make it a valuable tool for AMI diagnostics.

A deep learning-based approach for fully automated segmentation and quantitative analysis of muscle fibers in pig skeletal muscle.

Muscle fiber properties exert a significant influence on pork quality, with cross-sectional area (CSA) being a crucial parameter closely associated with various meat quality indicators, such as shear force. Effectively identifying and segmenting muscle fibers in a robust manner constitutes a vital initial step in determining CSA. This step is highly intricate and time-consuming, necessitating an accurate and automated analytical approach. One limitation of existing methods is their tendency to perform well on high signal-to-noise ratio images of intact, healthy muscle fibers but their lack of validation on more complex image datasets featuring significant morphological changes, such as the presence of ice crystals. In this study, we undertake the fully automatic segmentation of muscle fiber microscopic images stained with myosin adenosine triphosphate (mATPase) activity using a deep learning architecture known as SOLOv2. Our objective is to efficiently derive accurate measurements of muscle fiber size and distribution. Tests conducted on actual images demonstrate that our method adeptly handles the intricate task of muscle fiber segmentation, yielding quantitative results amenable to statistical analysis and displaying reliability comparable to manual analysis.

Quantitative analysis of lesion images to evaluate the efficiency of vascular-targeted photodynamic therapy for port-wine stains: Four case reports.

SIGNIFICANCE: Vascular-targeted photodynamic therapy (V-PDT) is a clinically approved therapeutic approach for treating vascular-related diseases, such as port-wine stains (PWS). For accurate treatment, varying light irradiance is required for different lesions due to the irregularity of vascular size, shape and degree of disease, which commonly alters during different stages of V-PDT. This makes quantitative analysis of the treatment efficiency urgently needed. APPROACH: Lesion images pre- and post- V-PDT treatment of patients with PWS were used to construct a quantitative method to evaluate the differences among lesions. Image analysis techniques were applied to evaluate the V-PDT efficiency for PWS by determining the Euclidean distances and two-dimensional correlation coefficients. RESULTS: According to the image analysis, V-PDT with good treatment efficiency resulted in a larger Euclidean distance and a smaller correlation coefficient compared with the case having lower V-PDT efficiency. CONCLUSIONS: A new method to quantify the Euclidean distances and correlation coefficients has been proposed, which is promising for the quantitative analysis of V-PDT efficiency for PWS.

Phantom and clinical evaluation of the Bayesian penalised likelihood reconstruction algorithm Q.Clear without PSF correction in amyloid PET images.

PURPOSE: Bayesian penalised likelihood (BPL) reconstruction, which incorporates point-spread-function (PSF) correction, provides higher signal-to-noise ratios and more accurate quantitation than conventional ordered subset expectation maximization (OSEM) reconstruction. However, applying PSF correction to brain PET imaging is controversial due to Gibbs artefacts that manifest as unpredicted cortical uptake enhancement. The present study aimed to validate whether BPL without PSF would be useful for amyloid PET imaging. METHODS: Images were acquired from Hoffman 3D brain and cylindrical phantoms for phantom study and 71 patients administered with [18F]flutemetamol in clinical study using a Discovery MI. All images were reconstructed using OSEM, BPL with PSF correction, and BPL without PSF correction. Count profile, %contrast, recovery coefficients (RCs), and image noise were calculated from the images acquired from the phantoms. Amyloid ß deposition in patients was visually assessed by two physicians and quantified based on the standardised uptake value ratio (SUVR). RESULTS: The overestimated radioactivity in profile curves was eliminated using BPL without PSF correction. The %contrast and image noise decreased with increasing ß values in phantom images. Image quality and RCs were better using BPL with, than without PSF correction or OSEM. An optimal ß value of 600 was determined for BPL without PSF correction. Visual evaluation almost agreed perfectly (κ = 0.91-0.97), without depending on reconstruction methods. Composite SUVRs did not significantly differ between reconstruction methods. CONCLUSION: Gibbs artefacts disappeared from phantom images using the BPL without PSF correction. Visual and quantitative evaluation of [18F]flutemetamol imaging was independent of the reconstruction method. The BPL without PSF correction could be the standard reconstruction method for amyloid PET imaging, despite being qualitatively inferior to BPL with PSF correction for [18F]flutemetamol amyloid PET imaging.

Contribution of Phenolamides to the Quality Evaluation in Lycium spp.

ETHNOPHARMACOLOGICAL RELEVANCE: Goji berry is a general term for various plant species in the genus Lycium. Goji has long been historically used in traditional Chinese medicines. Goji is a representative tonic medicine that has the effects of nourishing the liver and kidney and benefiting the essence and eyesight. It has been widely used in the treatment of various diseases, including tinnitus, impotence, spermatorrhea and blood deficiency, since ancient times. AIM OF THE REVIEW: This study aims to comprehensively summarize the quality evaluation methods of the main compounds in goji, as well as the current research status of the phenolamides in goji and their pharmacological effects, to explore the feasibility of using phenolamides as quality control markers and thus improve the quality and efficacy in goji. MATERIALS AND METHODS: Relevant literature from PubMed, Web of Science, Science Direct, CNKI and other databases was comprehensively collected, screened and summarized. RESULTS: According to the collected literature, the quality evaluation markers of goji in the Pharmacopoeia of the People's Republic of China are Lycium barbarum polysaccharide (LBP) and betaine. As a result of its structure complexity, only the total level of LBP can be determined, while betaine is not prominent in the pharmacological action of goji and lacks species distinctiveness. Neither of them can well explain the quality of goji. KuA and KuB are commonly used as quality evaluation markers of the Lycii cortex because of their high levels and suitable pharmacological activity. Goji is rich in polyphenols, carotenoids and alkaloids. Many studies have used the above compounds to establish quality evaluation methods but the results have not been satisfactory. Phenolamides have often been neglected in previous studies because of their low single compound levels and high separation difficulty. However, in recent years, the favourable pharmacological activities of phenolamides have been gradually recognized, and studies on goji phenolamides are greatly increasing. In addition, phenolamides have higher species distinctiveness than other compounds and can be combined with other compounds to better evaluate the quality of goji to improve its average quality. CONCLUSIONS: The phenolamides in the goji are rich and play a key role in antioxidation, anti-inflammation, neuroprotection and immunomodulation. As a result of their characteristics, it is suitable to evaluate the quality by quantitative analysis of multi-components by single-marker and fingerprint. This method can be combined with other techniques to improve the quality evaluation system of goji, which lays a foundation for their effectiveness and provides a reference for new quality evaluation methods of similar herbal medicines.

Analysis of unsaturated alginate oligosaccharides using high-performance anion exchange chromatography coupled with mass spectrometry.

Alginate is a commercially important polysaccharide composed of mannuronic acid and its C5 differential isomer guluronic acid. Comprehensive research on alginate and alginate lyases requires efficient and precise analytical methods for alginate oligosaccharides. In this research, high-performance anion exchange chromatography (HPAEC) in parallel with pulsed amperometric detection (PAD) and mass spectrometry (MS) was applied to the analysis of oligosaccharides obtained by alginate lyase. By optimizing the chromatographic conditions including mobile phase concentration, flow rate, and elution gradient, the analysis of a single sample could be completed in 30 min. Seven unsaturated alginate oligosaccharides were separated and identified through their analysis time observed with PAD, including all structurally different unsaturated disaccharides and trisaccharides. The quantitative analysis of seven oligosaccharides was performed based on the quantitative capability of PAD. The method exhibited adequate linearity and precision parameters. All the calibration curves showed good linearity at least in the concentration range of 0.002 to 0.1 mg/mL. The HPAEC-PAD/MS method provides a general and efficient online method to analyze alginate oligosaccharides.

Using Risk Ratios to Quantify Potential Behavior-Environment Relations.

Behavior-environment functional relations are the units of explanation in applied behavior analysis (ABA). Whether hypothesized experimentally or descriptively, quantification of putative functional relations improves our ability to predict and influence behavior. Risk ratios are an accessible, straightforward quantitative analysis that can serve this purpose. They have been employed to great effect in other fields (e.g., medicine, public health), but are rarely used within ABA. In this tutorial, we describe risk ratios and how they are calculated, discuss why risk ratios are well suited for quantifying behavior-environment relations, and illustrate their utility and applicability across five demonstrations from real clinical cases. Recommendations for the use of risk ratios in research and practice are discussed.

Improved Correlation of 18F-Flortaucipir PET SUVRs and Clinical Stages in the Alzheimer Disease Continuum with the MUBADA/PERSI-Based Analysis.

The Alzheimer disease (AD) continuum is a neurodegenerative disorder with cognitive decline and pathologic changes. Tau PET imaging can detect tau pathology, and 18F-flortaucipir PET imaging is expected to visualize progression through the stages of AD, for which quantitative assessment is essential. Two measurement methods, statistically defined multiblock barycentric discriminant analysis (MUBADA)/parametric estimation of reference signal intensity (PERSI) and anatomically defined tau meta-volume of interest (VOI)/cerebellar gray matter (CGM) for SUV ratio (SUVR), were compared in this study to assess their relationship to AD clinical stage using 2 open multicenter PET databases. Methods: Data were selected for 106 cases from 2 databases, AMED Preclinical AD study (AMED-PRE) (n = 15) and Alzheimer Disease Neuroimaging Initiative 3 (n = 91). The data of the participants were categorized into 4 groups based on the clinical criteria. Tau PET imaging was conducted using 18F-flortaucipir, and the 2 SUVR measurement methods, MUBADA/PERSI and tau meta-VOI/CGM, were compared among different clinical categories: amyloid-negative cognitively normal, preclinical AD, amyloid-negative mild cognitive impairment (MCI), and amyloid-positive MCI. Results: Significant differences were found between cognitively normal and preclinical AD, as well as between cognitively normal and amyloid-positive MCI and between amyloid-negative MCI and -positive MCI in SUVR derived by MUBADA/PERSI, whereas SUVR by tau meta-VOI/CGM did not provide significant differences between any pair. The tau meta-VOI/CGM method consistently provided higher SUVRs and larger individual variations than MUBADA/PERSI, with a mean SUVR difference of 0.136 for the studied databases. Conclusion: MUBADA/PERSI provided the SUVR of 18F-flortaucipir uptake with better association with the clinical severity of the AD continuum and with smaller variability. The results support the usefulness of MUBADA/PERSI as a quantitative measure of 18F-flortaucipir uptake in multicenter studies using different PET systems and scanning methods. However, limitations of the study include the small sample size and the unbalanced distribution among clinical categories in the AMED Preclinical AD study database.

Quantitative measurement of internal quality of carrots using hyperspectral imaging and multivariate analysis.

The study aimed to measure the carotenoid (Car) and pH contents of carrots using hyperspectral imaging. A total of 300 images were collected using a hyperspectral imaging system, covering 472 wavebands from 400 to 1000 nm. Regions of interest (ROIs) were defined to extract average spectra from the hyperspectral images (HIS). We developed two models: least squares support vector machine (LS-SVM) and partial least squares regression (PLSR) to establish a quantitative analysis between the pigment amounts and spectra. The spectra and pigment contents were predicted and correlated using these models. The selection of EWs for modeling was done using the Successive Projections Algorithm (SPA), regression coefficients (RC) from PLSR models, and LS-SVM. The results demonstrated that hyperspectral imaging could effectively evaluate the internal attributes of carrot cortex and xylem. Moreover, these models accurately predicted the Car and pH contents of the carrot parts. This study provides a valuable approach for variable selection and modeling in hyperspectral imaging studies of carrots.

Viable Escherichia coli enumeration on a polydimethylsiloxane (PDMS) chip with vertical channel-well configuration.

The culture-based methods for viable Escherichia coli (E. coli) detection suffer from long detection time and laborious procedures, whereas the molecule tests and immune recognition technologies lack live/dead E. coli differentiation. Rapid, easy-to-use, and accessible viable E. coli detection is of benefit to bacterial infection diagnosis and risk warning of E. coli contamination of water and food, safeguarding human health. Herein, we propose a microwell chip-based solution to realize simple and rapid determination of viable E. coli. The vertical channel-well configuration is applied to develop the microwell array chip for increasing the microwell density (6200 wells/cm2), yielding a broad dynamic range from 103 to 107 CFU/mL. We incorporate an inducible enzyme assay with the developed chip and achieve the differentiation of live/dead E. coli within 4 h, significantly shortening the detection time from over 24 h in the standard method. By encapsulating single E. coli into microwells, the concentration of viable cells can be determined simultaneously through counting positive microwells. In addition, the air soluble PDMS that can store negative pressure for independent sample digitalization endows the developed chip with simple operation and less reliance on external equipment. With further developments for increasing the number of microwell and integrating more sample panels, the developed chip can become a useful tool for rapid viable E. coli enumeration with user-friendly operation, simple procedures, and accessibility in decentralized settings, thereby deploying this device for water and food safety monitoring, as well as clinical bacterial infection diagnosis.

Developmental patterns of extracellular matrix molecules in the embryonic and postnatal mouse hindbrain.

Normal brain development requires continuous communication between developing neurons and their environment filled by a complex network referred to as extracellular matrix (ECM). The ECM is divided into distinct families of molecules including hyaluronic acid, proteoglycans, glycoproteins such as tenascins, and link proteins. In this study, we characterize the temporal and spatial distribution of the extracellular matrix molecules in the embryonic and postnatal mouse hindbrain by using antibodies and lectin histochemistry. In the embryo, hyaluronan and neurocan were found in high amounts until the time of birth whereas versican and tenascin-R were detected in lower intensities during the whole embryonic period. After birth, both hyaluronic acid and neurocan still produced intense staining in almost all areas of the hindbrain, while tenascin-R labeling showed a continuous increase during postnatal development. The reaction with WFA and aggrecan was revealed first 4th postnatal day (P4) with low staining intensities, while HAPLN was detected two weeks after birth (P14). The perineuronal net appeared first around the facial and vestibular neurons at P4 with hyaluronic acid cytochemistry. One week after birth aggrecan, neurocan, tenascin-R, and WFA were also accumulated around the neurons located in several hindbrain nuclei, but HAPLN1 was detected on the second postnatal week. Our results provide further evidence that many extracellular macromolecules that will be incorporated into the perineuronal net are already expressed at embryonic and early postnatal stages of development to control differentiation, migration, and synaptogenesis of neurons. In late postnatal period, the experience-driven neuronal activity induces formation of perineuronal net to stabilize synaptic connections.

Integrating a Multiple Isotopologue Reaction-Monitoring Technique and LC-MS/MS for Quantitation of Small Molecules: Ten Mycotoxins in Cereals as an Example.

Accurate quantification of mycotoxin in cereals is crucial for ensuring food safety and human health. However, the preparation of traditional multisample external calibration curves (MSCCs) is labor-intensive and error-prone. Here, a multiple isotopologue reaction-monitoring (MIRM)-LC-MS/MS method for accurate quantitation of ten major mycotoxins in cereals was successfully developed and validated, where a novel one-sample multipoint calibration curve (OSCC) strategy is used instead of MSCCs. The OSCC can be established by examining the correlation between the calculated theoretical isotopic abundances and the measured abundance across various MIRM channels. In comparison to the MSCC, the OSCC strategy exhibits outstanding performance including superior selectivity, accuracy (78.4-108.6%), and precision (<12.5%). Furthermore, the proposed OSCC-MIRM-LC-MS/MS method was successfully applied to investigate mycotoxin contamination in cereal samples in China. Considering the advantages of simplified workflows and improved throughput, the OSCC-MIRM-LC-MS/MS methodology holds great promise for accurately quantifying chemical contaminants in foods.

Advancing High-Throughput MS-Based Protein Quantification: A Case Study on Quantifying 10 Major Food Allergens by LC-MS/MS Using a One-Sample Multipoint External Calibration Curve.

The LC-MS-based method has emerged as the preferred approach for quantifying food allergens. However, the preparation of a traditional calibration curve (MSCC) is labor-intensive and error-prone. Here, a sensitive and robust LC-MS/MS method for quantifying 10 major food allergens was developed and validated, where the one-sample multipoint external calibration curve (OSCC) was employed instead of MSCC. By employing the multiple isotopologue reaction monitoring (MIRM) technique with only one spiked level in the blank, OSCC can be effectively established. Results demonstrate that the proposed method exhibits excellent performance in selectivity, sensitivity, accuracy, and precision, comparable to that of the traditional MSCC. Additionally, this strategy allows for isotope sample dilution by monitoring the less abundant MIRM channel. Moreover, the developed method was successfully applied to investigate the contamination of 10 food allergens in commercial food products. With its high throughput and robustness, the MIRM-OSCC-LC-MS/MS methodology has many potential applications, especially in the MS-based protein quantification analysis.

Analysis of mousy off-flavor compound 2-Acetyl-tetrahydropyridine using Liquid Chromatography Mass Spectrometry with Electrospray Ionization in sour beer.

Mousy off-flavor describes N-heterocycles compounds related to spoilage in the brewing industry. It has also been identified in sour beers through sensory analysis. Therefore, preventing spoilage N-heterocycles development is essential to preserve end-products and obviate economic losses. To this day, no methods or protocols have been reported to identifying mousy off-flavor compounds in a beer matrix. The main objective of this work was to develop a standardized quantification method for 2-acetyl-3,4,5,6-tetrahydropyridine (ATHP) in beer matrix, by Liquid Chromatography Mass Spectrometry with Electrospray Ionization (LC-MS-ESI). Extraction of ATHP in the samples was performed using QuEChERS (quick, easy, cheap, effective, rugged, and safe) technique. Over a dozen different potentially mousy cask-aged sour beers including other spontaneously fermented beverages were provided, based on sensory analysis, to determine the variation in ATHP levels. Results indicated ATHP was found in all the samples, ranging from 1.64 ± 0.06 to 57.96 ± 2.15 µg L-1. Herein, we described our detection method of mousy-off flavor compounds which enables future research to mitigate the occurrence of such defects in fermented beverages matrix.•ATHP content in samples varied from 1.64 ± 0.06 to 57.96 ± 2.15 µg L-1.•The recovery range of ATHP using LC-MS-ESI varied from 71% to 97%.•Basified QuEChERS salting-out procedure is applicable for ATHP extraction from beer and other fermented beverages matrices.

Quantitative tyramine analysis method for Apis mellifera larvae infected with Melissococcus plutonius, the causative agent of European foulbrood.

Tyramine, a trace monoamine produced from tyrosine by decarboxylation and found naturally in foods, plants, and animals, is a suspected virulence factor of Melissococcus plutonius that causes European foulbrood in honey bee brood. In the present study, we developed a method for quantitative analysis of tyramine in culture medium and honey bee larvae with a limit of quantitation of 3 ng/mL and a recovery rate of >97% using Liquid Chromatography-Mass Spectrometry/Mass Spectrometry and deuterium-labeled tyramine, demonstrating for the first time that a highly virulent M. plutonius strain actually produces tyramine in infected larvae. This method will be an indispensable tool to elucidate the role of tyramine in European foulbrood pathogenesis in combination with exposure bioassays using artificially reared bee larvae.

Advances in microscopy characterization techniques for lipid nanocarriers in drug delivery: a comprehensive review.

This review paper provides an in-depth analysis of the significance of lipid nanocarriers in drug delivery and the crucial role of characterization techniques. It explores various types of lipid nanocarriers and their applications, emphasizing the importance of microscopy-based characterization methods such as light microscopy, confocal microscopy, transmission electron microscopy (TEM), scanning electron microscopy (SEM), and atomic force microscopy (AFM). The paper also delves into sample preparation, quantitative analysis, challenges, and future directions in the field. The review concludes by underlining the pivotal role of microscopy-based characterization in advancing lipid nanocarrier research and drug delivery technologies.

Quantitative Analysis of Trace Analytes with Highly Sensitive SERS Tags on Hydrophobic Interface.

Surface-enhanced Raman scattering (SERS) presents a promising avenue for trace matter detection by using plasmonic nanostructures. To tackle the challenges of quantitatively analyzing trace substances in SERS, such as poor enrichment efficiency and signal reproducibility, this study proposes a novel approach using Au@internal standard@Au nanospheres (Au@IS@Au NSs) for realizing the high sensitivity and stability in SERS substrates. To verify the feasibility and stability of the SERS performances, the SERS substrates have exhibited exceptional sensitivity for detecting methyl blue molecules in aqueous solutions within the concentration range from 10-4 M to 10-13 M. Additionally, this strategy also provides a feasible way of quantitative detection of antibiotic in the range of 10-4 M to 10-10 M. Trace antibiotic residue on the surface of shrimp in aquaculture waters was successfully conducted, achieving a remarkably low detection limit of 10-9 M. The innovative approach has great potential for the rapid and quantitative detection of trace substances, which marks a noteworthy step forward in environmental detection and analytical methods by SERS.

Quantitative analysis of microplastics in water environments based on Raman spectroscopy and convolutional neural network.

With the increasing interest in microplastics (MPs) pollutants, quantitative analysis of MPs in water environment is an important issue. Vibrational spectroscopy, represented by Raman spectroscopy, is widely used in MP detection because they can provide unique fingerprint characteristics of chemical components of MPs, but it is difficult to provide quantitative information. In this paper, an ingenious method for quantitative analysis of MPs in water environment by combining Raman spectroscopy and convolutional neural network (CNN) is proposed. It is innovatively proposed to collect the average mapping spectra (AMS) of the samples to improve the uniformity of Raman spectroscopy detection, and to increase the effective detection range of concentration by filtering different volumes of the same MP solutions. In order to verify the universality and effectiveness of the proposed method, 6 different sizes of Polyethylene (PE) MPs were used as detection objects and mixed into 5 different actual water environments. The R2 and RMSE of CNN for identifying the concentration of PE solutions could reach 0.9972 and 0.033, respectively. Meanwhile, by comparing machine learning models such as Random Forest (RF) and Support Vector Machine (SVM) were compared, and CNN combined with Raman spectroscopy has significant advantages in identifying the concentration of MPs.

Quantifying the contributions of road and air traffic to ambient ultrafine particles in two urban communities.

Traffic-related activities are widely acknowledged as a primary source of urban ambient ultrafine particles (UFPs). However, a notable gap exists in quantifying the contributions of road and air traffic to size-resolved and total UFPs in urban areas. This study aims to delineate and quantify the traffic's contributions to size-resolved and total UFPs in two urban communities. To achieve this, stationary sampling was conducted at near-road and near-airport communities in Seattle, Washington State, to monitor UFP number concentrations during 2018-2020. Comprehensive correlation analyses among all variables were performed. Furthermore, a fully adjusted generalized additive model, incorporating meteorological factors, was developed to quantify the contributions of road and air traffic to size-resolved and total UFPs. The study found that vehicle emissions accounted for 29% of total UFPs at the near-road site and 13% at the near-airport site. Aircraft emissions contributed 14% of total UFPs at the near-airport site. Notably, aircraft predominantly emitted UFP sizes below 20 nm, while vehicles mainly emitted UFP sizes below 50 nm. These findings reveal the variability in road and air traffic contributions to UFPs in distinct areas. Our study emphasizes the pivotal role of traffic layout in shaping urban UFP exposure.