Analysis Of Volatile Organic Compounds Research Articles

Study protocol: the ‘Endoscope CRC’ cohort, a prospective biobank study on the development and evaluation of diagnostic and prognostic biomarker profiles for colorectal cancer and premalignant lesions

IntroductionEarly detection of colorectal cancer (CRC) and clinically relevant (advanced) adenomas leads to a significant reduction of CRC-related mortality and morbidity. However, the faecal immunochemical test (FIT) suffers from a...

KI-gestützte Atemgasanalyse (E-Nose) zur Früherkennung von Lungenkarzinomen

Volatile organic compounds (VOCs) are an increasingly meaningful method for the early detection of various types of cancers, including lung cancer, through non-invasive methods. Traditional cancer detection techniques such as biopsies, imaging, and blood tests, though effective, often involve invasive procedures or are costly, time consuming, and painful. Recent advancements in technology have led to the exploration of VOC detection as a promising non-invasive and comfortable alternative. VOCs are organic chemicals that have a high vapor pressure at room temperature, making them readily detectable in breath, urine, and skin. The present study leverages artificial intelligence (AI) and machine learning algorithms to enhance classification accuracy and efficiency in detecting lung cancer through VOC analysis collected from exhaled breath air. Unlike other studies that primarily focus on identifying specific compounds, this study takes an agnostic approach, maximizing detection efficiency over the identification of specific compounds focusing on the overall compositional profiles and their differences across groups of patients. The results reported hereby uphold the potential of AI-driven techniques in revolutionizing early cancer detection methodologies towards their implementation in a clinical setting.

Rapid bacterial identification through volatile organic compound analysis and deep learning

BackgroundThe increasing antimicrobial resistance caused by the improper use of antibiotics poses a significant challenge to humanity. Rapid and accurate identification of microbial species in clinical settings is crucial for precise medication and reducing the development of antimicrobial resistance. This study aimed to explore a method for automatic identification of bacteria using Volatile Organic Compounds (VOCs) analysis and deep learning algorithms.ResultsAlexNet, where augmentation is applied, produces the best results. The average accuracy rate for single bacterial culture classification reached 99.24% using cross-validation, and the accuracy rates for identifying the three bacteria in randomly mixed cultures were SA:98.6%, EC:98.58% and PA:98.99%, respectively.ConclusionThis work provides a new approach to quickly identify bacterial microorganisms. Using this method can automatically identify bacteria in GC-IMS detection results, helping clinical doctors quickly detect bacterial species, accurately prescribe medication, thereby controlling epidemics, and minimizing the negative impact of bacterial resistance on society.

Usability of volatile organic compounds from exhaled breath compared to those from ruminal fluid, serum, urine, and milk to identify diet-specific metabolite profiles in lactating dairy cows

To investigate dietary influences on the volatilome, the volatile subcategory of the metabolome, we performed a comparative untargeted volatilome analysis of exhaled breath, ruminal fluid, serum, urine, and milk from lactating Holstein cows fed different diets. Thirty-two cows (79.4 ± 31.3 d in milk [DIM], 30.6 ± 4.83 kg milk/d) were assigned to 4 diets. The experiment lasted 16 weeks. Throughout the experiment, half of the animals were fed a hay-based diet (HAY; n = 16), and the other half were fed a silage-based diet (SIL; n = 16). In experimental wk 5 to 12, half of the animals in each group received the control concentrate (CON), and the other half was fed with the CON supplemented with a blend of essential oils (EXP). We hypothesized that the basal diet and the essential oils influence the VOC profiles of the cows through potential changes in ruminal fermentation, digestion, and metabolism (hypothesis 1). Furthermore, we hypothesized that the potential effects of essential oils would have a delayed onset and a carryover effect (hypothesis 2). Every 4 experimental weeks, i.e., in W4, W8, W12 and W16, samples of exhaled breath, ruminal fluid, serum, urine, milk, and feed were collected for dynamic headspace extraction and gas chromatographic analysis of volatile organic compounds (VOC) in their gaseous phase. Milk yield, milk composition, body weight, and feed intake were recorded regularly. Linear mixed models and multivariate and univariate data analyses were performed. The total DMI and basal diet intake was similar between HAY and SIL cows. However, SIL cows consumed less of the concentrate, neutral detergent fiber, and water-soluble carbohydrates (WSC) and more starch than HAY cows. The SIL cows had a higher milk production than the HAY cows. No effect was found regarding the concentrate type on feed intake or milk production. Irrespective of diet, 2,957 VOC were detected in the gaseous phase of serum; 2,771 in exhaled breath; 1,016 in urine; 1,001 in milk; and 921 in ruminal fluid. Across the experimental wk 4, 8, 12 and 16, the basal diet altered the VOC profiles of ruminal fluid, urine, and exhaled breath but not those of serum and milk. The concentrate type affected only the VOC profiles of the exhaled breath. Most diet-influenced VOC in the affected biological matrices were identified as dietary components. The experimental week influenced the VOC profiles of all matrices, especially those of exhaled breath. The VOC profile of exhaled breath strongly correlated with that of urine, followed by that of ruminal fluid, milk, and serum. This study provides the first description of diet- and time-specific VOC profiles from the biological matrices of dairy cows. The identified discriminatory VOC seem suitable as markers to discriminate between HAY and SIL cows. Exhaled breath may be a promising, sensitive, and less invasive tool to follow diet- and time-related metabolic changes.

Application of 1-octanol in the extraction and GC-FID analysis of volatile organic compounds produced in biogas and biohydrogen processes

Application of 1-octanol in the extraction and GC-FID analysis of volatile organic compounds produced in biogas and biohydrogen processes

An optimization protocol of the volatile organic compounds analysis in earwax samples for untargeted volatilomics

An optimization protocol of the volatile organic compounds analysis in earwax samples for untargeted volatilomics

Identifying viral infections through analysis of head space volatile organic compounds

Volatile organic compounds (VOCs) produced by human respiratory cells reflect metabolic and pathophysiological processes which can be detected with the use of modern technology. Analysis of exhaled breath or indoor air may potentially play an important role in screening of upper respiratory tract infections such as COVID-19 or influenza in the future. In this experimental study, air samples were collected and analyzed from the headspace of anin vitrocell culture infected by selected pathogens (influenza A H1N1 and seasonal coronaviruses OC43 and NL63). VOCs were measured with a real-time proton-transfer-reaction time-of-flight mass spectrometer and a differential mobility spectrometer. Measurements were performed every 12 h for 7 d. Non-infected cells and cell culture media served as references. In H1N1 and OC43 we observed four different VOCs which peaked during the infection. Different, individual VOCs were also observed in both infections. Activity began to clearly increase after 2 d in all analyses. We did not see increased VOC production in cells infected with NL63. VOC analysis seems to be suitable to differentiate the infected cells from those which are not infected as well as different viruses, from another. In the future, this could have practical value in both individual diagnostics and indoor environment screening.

Exploring nonanol from Bacillus velezensis (YEBBR6) for root-knot nematode management in bananas: Integrating experimental and computational approaches

Nematode infestations pose a severe threat to global banana production, leading to substantial yield losses and economic damage. Among these, the root-knot nematode Meloidogyne incognita is particularly detrimental, causing root galls that severely impair water and nutrient uptake, leading to stunted growth, reduced fruit quality, and significant economic losses for farmers. In the face of increasing agricultural challenges, bacterial endophytes have emerged as essential tools for sustainable agriculture, offering ecofriendly solutions to enhance plant health and manage pests. The present study aims to identify and characterise biomolecules from bacterial endophyte Bacillus velezensis (YEBBR6) with potential nematicidal properties against root-knot nematode Meloidogyne incognita infecting banana. The analysis of volatile and non-volatile organic compounds (VOCs/NVOCs) from the inhibition zone during the ditrophic interaction between B. velezensis (YEBBR6) and Fusarium oxysporum f.sp. cubense (Foc) revealed the presence of several distinctive biomolecules with antifungal and antimicrobial properties. These compounds included linoelaedic acid, nonanol, acetylvaleryl, 5–hydroxyl methyl furfural, clindamycin, allobarbital, 3-thiazolidine carboxamide, azulene, aminomorpholine, procyclidine, campholic acid, 3 amino-4 hydroxy phenyl sulfone, 3–deoxy mannoic lactone, hexadecanoic acid, oleic acid, and dihydroacridine. The in-silico analysis revealed that nonanol, exhibited strong binding efficacy against M. incognita protein targets, including cytochrome C oxidase subunit 1, calreticulin, neuropeptide G-protein coupled receptor, chorismate mutase 1, venom allergen-like proteins, and β-1,4-endoglucanase. Notably, nonanol proved more effective than the commercially used nematicide carbofuran 3G. Molecular dynamics studies further supported the potential of nonanol, and its nematicidal properties were validated through in vitro and pot culture studies. Moreover, nonanol was found to induce the expression of defense-related genes, such as MAPK10, WRKY22, ascorbate peroxidase, catalase, and NADPH oxidase, in banana seedlings, thus enhancing the plant's immune response. These findings highlight the potential of nonanol, derived from B. velezensis (YEBBR6), as a potent and environmentally friendly alternative for controlling nematode infestations in banana cultivation.

Volatile Organic Compounds as Early Detection Indicators of Wheat Infected by Sitophilus oryzae.

The rice weevil, Sitophilus oryzae (L.), is a major pest that poses a considerable threat to grain safety storage. Early detection is of great significance in reducing grain losses. Studies have demonstrated that pest infestation causes alterations in grain volatiles, potentially indicating the presence of pests. In this study, we detected volatile organic compounds (VOCs) in non-infected and pest-infected wheat on the 3rd, 9th, 17th, 22nd, and 40th days, corresponding to the developmental stages of the rice weevil at the egg, young larval, old larval, pupal, and adult stages, respectively. A total of 126 VOCs were identified, including 96 hydrocarbons, 7 alcohols, 5 aldehydes, 9 ketones, 9 esters, and 18 other compounds, 62 of which are newly produced compared to non-infected wheat. Six characteristic volatiles, namely dodecane, pentadecane, hexadecane, heptadecane, 2, 6, 10-trimethylpentadecane, and squalene, may be related to the degradation of lipids and the expression of wheat stress tolerance and underwent significant changes as infestation progressed, according to the VIP value. This study assists in interpreting the effects of rice weevil infestation on wheat at the metabolic level and establishes a foundation for storage inspection based on VOC analysis.

Identification of Volatile Organic Compounds (Distinct Biomarkers) Emitted by Cancer Cells Using Gas Chromatography-mass Spectrometry – A Review

The utilization of Gas Chromatography-Mass Spectrometry (GC-MS) for the detection of Volatile Organic Compounds (VOCs) as potential biomarkers in cancer diagnostics is an evolving and promising field. This review outlines current exploratory and pilot studies that lay a fundamental groundwork for future research. Due to the complexity of the fecal and respiratory volatilome, influenced by a myriad of biological and environmental factors, it is crucial to expand research demographics to enhance data robustness and ensure the applicability of findings across diverse populations. We have also, herein, highlighted the dual capability of VOC analysis to detect cancer and differentiate between its stages, which is vital for customizing patient treatment plans and monitoring therapeutic efficacy. Furthermore, establishing quantifiable thresholds for VOC concentrations is essential for their clinical adoption as reliable indicators of cancer. The integration of GC-MS with machine learning and Artificial Intelligence (AI) could be useful for comprehensive metabolic profiles and facilitating the development of non-invasive and real-time diagnostic tools. The adoption of multi-omics approach promises a deeper understanding of cancer biology, potentially leading to personalized medicine and strategies finely tuned to individual molecular profiles. Hence, the research on VOCs as cancer biomarkers is set to offer transformative advancements in diagnostic technologies, ultimately improving patient outcomes through tailored therapeutic interventions.

Analysis of Volatile Organic Compound Emissions in 3D Printing: Implications for Indoor Air Quality

This study provides a comprehensive analysis of volatile organic compound (VOC) emissions in the context of 3D printing, a rapidly advancing technology that is transforming manufacturing processes. As the adoption of 3D printing grows, concerns regarding its potential impact on indoor air quality have emerged. This research addresses these concerns by investigating the risks associated with VOC emissions and proposing effective mitigation strategies. Using a robust methodology, filament and resin-based 3D printers were employed alongside VOC sampling equipment (Tenax tubes and personal pumps) to assess emissions. A detailed analysis of 49 VOCs revealed variable concentrations across different printing materials, with ethyl acetate being the dominant compound in resin printing and decanal in filament printing. While individual VOC levels were below 1% of occupational exposure limits, total VOC concentrations frequently exceeded the recommended indoor threshold of 200 µg/m3, particularly in resin-based processes. This raises concerns about the combined effects of multiple VOCs, some of which are known carcinogens. These findings underscore the need for further investigation into the cumulative health impacts of prolonged exposure to multiple VOCs. The study also emphasises the importance of accounting for both facility-specific conditions and material emissions to fully understand the environmental and health consequences of 3D printing. Preventative measures, such as enclosing 3D printers and equipping them with extraction systems, are recommended to safeguard user health.

Advancing Breathomics through Accurate Discrimination of Endogenous from Exogenous Volatiles in Breath.

Breathomics, a growing field in exposure monitoring and clinical diagnostics, has faced accuracy challenges due to unclear contributing factors. This study aims to enhance the potential of breathomics in various frontiers by categorizing exhaled volatile organic compounds (VOCs) as endogenous or exogenous. Analyzing ambient air and breath samples from 271 volunteers via TD-GC × GC-TOF MS/FID, we identify and quantify 50 common VOCs in exhaled breath. Advanced quantitative structure-property relationships and compartment models are employed to obtain VOCs kinetic parameters. This in-depth approach allows us to accurately determine the alveolar concentration of VOCs and further discern their origins, facilitating personalized application of breathomics in exposure assessment and disease diagnosis. Our findings demonstrate that prolonged external exposure turns humans into secondary pollutant sources. Analysis of endogenous VOCs reveals that internal exposure poses more significant health risks than external. Moreover, by correcting environmental backgrounds, we improve the accuracy of gastrointestinal disease diagnostic models by 15-25%. This advancement in identifying VOC origins via compartmental models promises to elevate the clinical relevance of breathomics, marking a leap forward in exposure assessment and precision medicine.

Accelerated oxygenation for the production of fortified (mystelle-type) sweet wines: effects on the chemical and flavor profile.

Fortified wine is an important category in the wine world with very famous wines such as Porto or Jerez-wine type. The quality of fortified wines increased significantly with barrel aging not only because of a long oxidation process, but also because, in Porto wines such as Ruby or Vintage styles, the long period in bottle permits their fining. Reducing the time of oxidation can favor the development of this technique even for less known sweet wines, making them good quality and less expensive. In the present study, we have used Gamay red variety subjected to postharvest controlled dehydration at 20-22 °C and 70-75% relative humidity with an airflow of 1 m s-1. Then the grapes were pressed, and alcohol was added to the must up to an alcohol content of 15.85% (mystelle-type wine). The mass was split into six glass jars, three were oxygenated (OX) and three not (Control), and the oxygenation lasted 62 days. Wine that was oxygenated had a slightly higher volatile acidity, lower alcohol content (13.00%), and lower anthocyanins and polyphenols content. In term of volatile organic compounds (VOCs), the Control wine had a higher content of alcohols, whereas the OX sample had a higher content of lactones, furans and esters. Sensory evaluation confirmed the VOCs analysis; the two wines had a statistically different profile depending on the oxidation treatment. In general, OX wine was more appreciated in terms of visual attractiveness, taste and olfactory pleasantness. In conclusion, the technique described in the present study could be a valid alternative to traditional aging of fortified sweet wines, reducing time and costs. © 2024 Society of Chemical Industry.

Influence of on-site sampling conditions on the representativeness of the sample for the analysis of trace VOCs in raw renewable gases.

In the context of the energy transition and in order to fully manage the integration of renewable and/or low-carbon gases into the gas mix, these new renewable gases need to be characterized, including volatile organic compounds (VOCs) at trace levels that may have an impact on different stages of the gas chain event at low concentrations. This study focuses on sampling because it is the first step in any method for analyzing trace VOCs and its careful execution is essential to ensure reliable results even if the on-site conditions can be variable, such as the external temperature. The stabilization time, the effect of external temperature, and the impact of using an intermediate sample cylinder prior to transfer to tubes were hence studied in the laboratory using a standard gas mixture representative of renewable gases. The latter was also studied using a real sample. To perform this study, VOCs were sampled in Tenax® TA tubes and then analyzed by thermal desorption hyphenated to comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry. The results showed that 45min were required to stabilize the sampling system and that external temperature had little influence, limited to the heaviest compounds. Finally, the use of a cylinder to temporarily collect gas prior to transfer to tubes led to a loss of compounds, particularly when the cylinder was stored before the transfer.

Detection Methods for Pine Wilt Disease: A Comprehensive Review.

Pine wilt disease (PWD), caused by the nematode Bursaphelenchus xylophilus, is a highly destructive forest disease that necessitates rapid and precise identification for effective management and control. This study evaluates various detection methods for PWD, including morphological diagnosis, molecular techniques, and remote sensing. While traditional methods are economical, they are limited by their inability to detect subtle or early changes and require considerable time and expertise. To overcome these challenges, this study emphasizes advanced molecular approaches such as real-time polymerase chain reaction (RT-PCR), droplet digital PCR (ddPCR), and loop-mediated isothermal amplification (LAMP) coupled with CRISPR/Cas12a, which offer fast and accurate pathogen detection. Additionally, DNA barcoding and microarrays facilitate species identification, and proteomics can provide insights into infection-specific protein signatures. The study also highlights remote sensing technologies, including satellite imagery and unmanned aerial vehicle (UAV)-based hyperspectral analysis, for their capability to monitor PWD by detecting asymptomatic diseases through changes in the spectral signatures of trees. Future research should focus on combining traditional and innovative techniques, refining visual inspection processes, developing rapid and portable diagnostic tools for field application, and exploring the potential of volatile organic compound analysis and machine learning algorithms for early disease detection. Integrating diverse methods and adopting innovative technologies are crucial to effectively control this lethal forest disease.

Detection and Validation of Organic Metabolites in Urine for Clear Cell Renal Cell Carcinoma Diagnosis.

Clear cell renal cell carcinoma (ccRCC) comprises the majority, approximately 70-80%, of renal cancer cases and often remains asymptomatic until incidentally detected during unrelated abdominal imaging or at advanced stages. Currently, standardized screening tests for renal cancer are lacking, which presents challenges in disease management and improving patient outcomes. This study aimed to identify ccRCC-specific volatile organic compounds (VOCs) in the urine of ccRCC-positive patients and develop a urinary VOC-based diagnostic model. This study involved 233 pretreatment ccRCC patients and 43 healthy individuals. VOC analysis utilized stir-bar sorptive extraction coupled with thermal desorption gas chromatography/mass spectrometry (SBSE-TD-GC/MS). A ccRCC diagnostic model was established via logistic regression, trained on 163 ccRCC cases versus 31 controls, and validated with 70 ccRCC cases versus 12 controls, resulting in a ccRCC diagnostic model involving 24 VOC markers. The findings demonstrated promising diagnostic efficacy, with an Area Under the Curve (AUC) of 0.94, 86% sensitivity, and 92% specificity. This study highlights the feasibility of using urine as a reliable biospecimen for identifying VOC biomarkers in ccRCC. While further validation in larger cohorts is necessary, this study's capability to differentiate between ccRCC and control groups, despite sample size limitations, holds significant promise.

In-tube extraction dynamic headspace coupled to gas chromatography-mass spectrometry for the sensitive analysis of volatile compounds in aqueous samples

Aim: Volatile organic compounds (VOCs) are often human-made contaminants used and generated in the manufacturing of numerous products, presenting notable environmental and health hazards. Therefore, the development of sensitive and reliable analytical methods is crucial for their detection with accuracy, timeliness, and automation capabilities. The objective of this study is to demonstrate the suitability of the in-tube extraction dynamic headspace (ITEX-DHS) sampling method for the gas chromatographic/mass spectrometric (GC/MS) analysis of BTEX (benzene, toluene, ethylbenzene, and xylenes) compounds in aqueous matrices. It emphasizes the method’s metrological reliability and innovative approach to precisely determining VOCs in aqueous environments providing a tool to prevent contamination of the agrifood sector. Methods: Following the optimization of various experimental parameters, including salt incorporation and adjustments of both dry purging and desorption conditions. The method’s performance was evaluated for repeatability, reproducibility, and robustness. Results: Limit of detection (LOD) and limit of quantification (LOQ) were for all substances determined lower than 50 and 100 ng/L, respectively. Average relative standard deviations below 5% were achieved for all analytes, with recovery rates ranging between 93% and 101%. Subsequently, the method was applied for the determination of BTEX in one hundred groundwater samples. The findings revealed that the BTEX levels were below the LOD in 84.2% of samples. However, in the remaining samples, more than one compound was detected at concentrations higher than the LOQ. Conclusions: The ITEX method emerges as a highly favorable alternative to both solid phase microextraction (SPME) and purge and trap (P & T) methods for determining BTEX in aqueous samples, providing significant advantages. Its strengths lie in its increased robustness, extended trap lifespan, and enhanced sensitivity, underscoring its superior performance in VOC analysis. The total analytical method allows the sensitive and robust determination of VOC.

Classification of Sesame Oil Based on Processing-Originated Differences in the Volatile Organic Compound Profile by a Colorimetric Sensor.

Fragrant edible sesame oil is popular for its unique aroma. The aroma of sesame oil is determined by its volatile organic compound (VOC) profile. Sesame oils produced by different techniques could have different VOC profiles. In addition, blending fragrant sesame oil with refined oil could also alter the VOC profile of the final product. Current practices in aroma analysis, such as sensory evaluation and gas chromatography (GC), still face many restraints. Hence, there is a need for alternatives. We present a novel 14-unit multiplexed paper-based colorimetric sensor for fragrant sesame oil VOC analysis. The sensor was designed to visualize the VOC profile as a color "fingerprint". The sensor was validated with 55 branded sesame oil samples produced by two different techniques, i.e., hot pressing and small milling; the experimental results suggested a processing dependency in color VOC fingerprints. The sensor also demonstrated the potential to detect the change in sesame oil VOC profile due to blending with refined oil, with an estimated limit of detection down to 20% v/v of the refined oil. The colorimetric sensor might be used as a simple, rapid, and cost-effective analytical tool in the production and quality control of fragrant sesame oil.

Quantitative structure-property relationship for gas-chromatographic retention indices of volatile organic compounds in colored-quinoa seeds

This study outlines the development of a predictive in silico model for the retention index (RI) of 61 volatile organic compounds (VOCs) present in diverse colored-quinoa seeds. The analysis was performed using gas chromatography-ion mobility spectrometry (GC-IMS) with the FS-SE-54-CB-1 capillary column. The chemical information in the database was carefully curated, and the molecular geometries of the VOCs were properly optimized to calculate diverse molecular descriptors. The database was then divided into a training set (48 compounds) and a test set (13 molecules). For model development, unsupervised and supervised chemometric approaches were integrated to obtain a four-descriptor quantitative structure-property relationship (QSPR). The negligible differences in the coefficient of determination and residual standard deviation for the training set (R2 = 0.957, s = 34.16) and test set (R2 = 0.954, s = 35.46) indicate a stable and predictive model. The QSPR model was also subjected to cross-validation using diverse strategies, along with an applicability domain assessment. A thorough explanation of the mechanistic effects of these descriptors in predicting the RI is provided, ensuring the model accomplished all the criteria defined by the Organization for Economic Co-operation and Development. This model is simple and can assist chemists working on the analysis of VOCs in quinoa seeds using GC-IMS and to gain a deeper understanding of the molecular mechanisms involved in the retention index phenomenon. This QSPR model was also applied as a tool to predict the retention index of an external set of 89 new VOCs identified in quinoa seeds through GC-IMS analysis.

Assessment of VOCs emission inventory in Seoul through spatiotemporal observations using passive and online PAMS measurements

We have conducted a quantitative analysis of volatile organic compounds (VOCs) in Seoul from September 2022 to June 2023, employing passive air samplers (PAS) at 25 sites and continuous monitoring at five photochemical assessment monitoring stations (PAMS). Comparing VOCs concentrations and compositions from PAS and PAMS during the same time periods, we found that most VOCs showed no significant difference (p-value >0.05) between the methods, except for olefins, which were overestimated by 24 % in passive sampling, confirming PAS as an effective tool for assessing the spatial distribution of VOCs species over large areas. Throughout Seoul, median values of total volatile organic compounds (TVOCs) concentrations collected with passive samplers remained stable with a standard deviation of 1.22 ppbv, typically ranging from 11 to 13 ppbv, except for an increase during winter in the southwestern regions of the city, where intense industrial and vehicular emissions are reported in the current Clean Air Policy Support System (CAPSS) emissions inventory. Positive matrix factorization (PMF) analysis using the passive data revealed general consistency in the spatial distribution of area and road emissions when compared with CAPSS emission inventory, although localized discrepancies were observed. In quantitative assessments of TVOCs comparing PAS observations with emission-based models, modeled values were within 1.5 times the interquartile range of observed PAS concentrations over Seoul. However, the lack of detection of emission hotspots in southeastern Seoul in the PAS data, coupled with the omission of photochemical loss in our dispersion-only models, indicates that the current CAPSS emission inventory may significantly underestimate actual ambient VOCs levels, especially in summer.