Quantification Of Volatile Organic Compounds Research Articles

A longitudinal study of volatile organic compounds from cooking under ventilation and purification intervention: Health risk assessment and odor nuisance control

Cooking oil fumes (COFs) expose occupants to hazardous air pollutants and nuisance odors, hence the real controlling effects of different intervention methods deserve to be explored. This longitudinal study conducted year-round field measurements in 10 apartments to explore the synergistic effects of dual ventilation and purification interventions on the exposure level, odor intensity, and health risk control of gaseous organic compounds during cooking. Gas chromatography-mass spectrometry (GC‒MS) and high performance liquid chromatography (HPLC) were used for quantification of volatile organic compounds (VOCs) and carbonyls. A total of 59 VOCs were detected during cooking, and the average ΣVOC concentrations in summer, the transition seasons, and winter were 483.16 μg/m3, 233.97 μg/m3, and 282.82 μg/m3, respectively, and the proportion of aldehydes was approximately 50 %. The small temperature differences between the indoors and outdoors led to a 24 % reduction in the effective air changes per hour (ACH) in the summer compared with that in other seasons. The average estimated carcinogenic risks of formaldehyde, acetaldehyde and benzene were 9.20 × 10−5, 1.11 × 10−5, and 6.05 × 10−6, respectively, for daily cooking conditions. It is difficult to guarantee a healthy cooking environment by mechanical ventilation alone. The inhalation carcinogenic risk of these 3 hazardous VOCs was reduced by approximately 130.9 % under air purification intervention but was still greater than 1E-6. The noncarcinogenic risk of acrolein under the different intervention modes ranged from 69.8 to 84.9. Moreover, acetaldehyde and hexanal were found to be the key odorants in kitchen. When the effective exhaust air rate was greater than 12 m3/min with air purifier in operation, the average inhalation health risk was 1.05–1.78 times that before cooking, and more than half of the users would not smell obvious odors during the cooking process. This study quantifies the effectiveness of the synergetic control strategy of independent purification modules combine with kitchen mechanical ventilation, with a view to implementing a healthy & comfortable living environment.

Matrix-matched quantification of volatile organic compounds (VOCs) in gluten free flours and bakery products

The aim of this study deals with characterize the volatile profiles of gluten free flours and bakery products. An appropriate HS-SPME/GC–MS methods for the quantification analyses was performed and corn starch solid as standards was used. 34 different samples were analysed, and 127 compounds distributed in 4 classes (alcohols, aldehydes and ketones, heterocyclic compounds, and terpenes), that make up the aroma of these gluten free, were identified. The developed method is characterized by detection limits of 0.0004 and 0.0047 mg/kg for camphor and pyrazine, respectively, and linearity of quantification standards were between 0.990 and 0.998 for a range of 3–50 mg/kg.

Effects of psychological stress on the emission of volatile organic compounds from the skin

Thirty-five women were included in a clinical study to characterize the volatile organic compounds (VOCs) emitted by the skin during exposure to psychological stress. An original silicon-based polymeric phase was used for VOC sampling on the forehead before and after stress induction. Cognitive stress was induced using specialized software that included a chronometer for semantic and arithmetic tasks. Assessment of stress was monitored using a State-trait anxiety inventory questionnaire, analysis of participants’ verbal expressions and clinical measurements. Identification and relative quantification of VOCs were performed by gas chromatography-mass spectrometry. Stress induction was validated by a significant increase in state-anxiety as indicated by the questionnaire, modifications in electrodermal activity measurements and the expression of stress verbatims. In parallel, a sebum production increase and a skin pH decrease were observed. A total of 198 VOCs with different potential sources were identified. They were categorized in 5 groups: probable cosmetic composition, VOCs produced by the body or its microbiota, environmental origin, and dietary intake. In our qualitative statistical approach, three VOCs were found to be correlated with stress induction and 14 compounds showed significance in the paired Wilcoxon test. Fatty-acyls derived from lipids were predominantly identified as well as ethylbenzenes.

Exhaled breath and urinary volatile organic compounds (VOCs) for cancer diagnoses, and microbial-related VOC metabolic pathway analysis: a systematic review and meta-analysis.

The gradual evolution of the detection and quantification of volatile organic compounds (VOCs) has been instrumental in cancer diagnosis. The primary objective of this study was to assess the diagnostic potential of exhaled breath and urinary VOCs in cancer detection. As VOCs are indicative of tumor and human metabolism, our work also sought to investigate the metabolic pathways linked to the development of cancerous tumors. An electronic search was performed in the PubMed database. Original studies on VOCs within exhaled breath and urine for cancer detection with a control group were included. A meta-analysis was conducted using a bivariate model to assess the sensitivity and specificity of the VOCs for cancer detection. Fagan's nomogram was designed to leverage the findings from our diagnostic analysis for the purpose of estimating the likelihood of cancer in patients. Ultimately, MetOrigin was employed to conduct an analysis of the metabolic pathways associated with VOCs in relation to both human and/or microbiota. The pooled sensitivity, specificity and the area under the curve for cancer screening utilizing exhaled breath and urinary VOCs were determined to be 0.89, 0.88, and 0.95, respectively. A pretest probability of 51% can be considered as the threshold for diagnosing cancers with VOCs. As the estimated pretest probability of cancer exceeds 51%, it becomes more appropriate to emphasize the 'ruling in' approach. Conversely, when the estimated pretest probability of cancer falls below 51%, it is more suitable to emphasize the 'ruling out' approach. A total of 14, 14, 6, and 7 microbiota-related VOCs were identified in relation to lung, colorectal, breast, and liver cancers, respectively. The enrichment analysis of volatile metabolites revealed a significant enrichment of butanoate metabolism in the aforementioned tumor types. The analysis of exhaled breath and urinary VOCs showed promise for cancer screening. In addition, the enrichment analysis of volatile metabolites revealed a significant enrichment of butanoate metabolism in four tumor types, namely lung, colorectum, breast and liver. These findings hold significant implications for the prospective clinical application of multiomics correlation in disease management and the exploration of potential therapeutic targets.

Utilizing Machine Learning for Rapid Discrimination and Quantification of Volatile Organic Compounds in an Electronic Nose Sensor Array

Volatile organic compounds (VOCs) are ubiquitous in the surroundings, originating from both industrial and natural sources. VOCs directly impact the quality of both indoor and outdoor air and play a significant role in processes such as fruit ripening and the body’s metabolism. VOC monitoring has seen significant growth recently, with an emphasis on developing low-cost, portable sensors capable of both vapor discrimination and concentration measurements. VOC sensing remains challenging, mainly because these compounds are nonreactive, appear in low concentrations and share similar chemical structures that results in poor sensor selectivity. Therefore, individual gas sensors struggle to selectively detect target VOCs in the presence of interferences. Electronic noses overcome these limitations by employing machine learning for pattern recognition from arrays of gas sensors. Here, an electronic nose fabricated with four types of functionalized gold nanoparticles demonstrates rapid detection and quantification of eight types of VOCs at four concentration levels. A robust two-step machine learning pipeline is implemented for classification followed by regression analysis for concentration prediction. Random Forest and support vector machine classifiers show excellent results of 100% accuracy for VOC discrimination, independent of measured concentration levels. Each Random Forest regression analysis exhibits high R2 and low RMSE with an average of 0.999 and 0.002, respectively. These results demonstrate the ability of gold nanoparticle gas sensor arrays for rapid detection and quantification.

Application of colorimetric sensor array coupled with machine-learning approaches for the discrimination of grains based on freshness.

Volatile organic compounds (VOCs) in grain fluctuate depending on the degree of grain freshness. A new colorimetric sensor array (CSA) was developed as capture probes for the quantification of VOCs in grains in this work, and it was designed to monitor the variation of grain VOCs. CSA spectral data acquisition using visible-near-infrared spectroscopy and image processing of CSA's image imformation by computer were used comparatively. Then, machine-learning-based models - for example, synergistic interval partial least squares, genetic algorithm, competitive adaptive reweighted sampling (CARS) algorithm, and ant colony optimization (ACO) algorithm - were introduced to optimize variables. Moreover, principal component analysis, and linear discriminant analysis (LDA), and K-nearest neighbors (KNN) were used for the classification. Ultimately, quantitative models for detecting grain freshness are developed using various variable selection strategies. Compared with the pattern recognition results of image processing, visible-near-infrared spectroscopy could better separate the grains with different freshness from principal component analysis, and the prediction set of LDA models could correctly identify 100% of rice, 96.88% of paddy, and 97.9% of soybeans. In addition, compared with CARS and ACO, the LDA model and KNN model based on genetic algorithms show the best prediction performance. The prediction set could correctly identify 100% of rice and paddy samples and 95.83% of soybean samples. The method developed could be used for non-destructive detection of grain freshness. © 2023 Society of Chemical Industry.

A DFT evaluation of molecular reactivity of volatile organic compounds in support of chemical ionization mass spectrometry

Gas-phase molecular properties of volatile organic compounds (VOCs) play an important role in the selection of gas-phase reagent ions for chemical ionization mass spectrometry (CI-MS). Chemical ionization-based mass spectrometry techniques such as proton transfer reaction mass spectrometry (PTR-MS) and selected ion flow-tube mass spectrometry (SIFT-MS) provide real-time, rapid, and online detection and quantification of VOCs using thermodynamics and kinetics of ion-molecule gas-phase reactions. We apply hybrid density functional theory (DFT) to compute proton affinity (PA), ionization energy (IE), and global reactivity parameters for VOCs, which are widely regarded as the primary sources of taints and off-flavors in wine. Atomic polar tensor (APT) charges and total energies at the stationary point for neutral and protonated molecules are also computed. PA and IE values determine the CI-MS mode of reactions, either proton transfer or electron transfer from the reagent gas ions to VOCs. Global reactivity parameters, such as chemical potential (μ), chemical hardness (η), softness (σ), and electrophilic nature (ω) as obtained from frontier molecular orbitals, are considered useful in rationalizing the chemical reactivity patterns of the molecules. A benchmark calculation of indole molecule with MP2, B3LYP, and M06-2X DFT methods at thermodynamically and kinetically stable protonation sites further supports the applied DFT method. Since limited data are available on computed parameters, the reported values would support CI-MS quantification of trace-level VOCs not only in wine but also in various food products.

Graphene Oxide Thin Films for Detection and Quantification of Industrially Relevant Alcohols and Acetic Acid

Industrial environments are frequently composed of potentially toxic and hazardous compounds. Volatile organic compounds (VOCs) are one of the most concerning categories of analytes commonly existent in the indoor air of factories' facilities. The sources of VOCs in the industrial context are abundant and a vast range of human health conditions and pathologies are known to be caused by both short- and long-term exposures. Hence, accurate and rapid detection, identification, and quantification of VOCs in industrial environments are mandatory issues. This work demonstrates that graphene oxide (GO) thin films can be used to distinguish acetic acid, ethanol, isopropanol, and methanol, major analytes for the field of industrial air quality, using the electronic nose concept based on impedance spectra measurements. The data were treated by principal component analysis. The sensor consists of polyethyleneimine (PEI) and GO layer-by-layer films deposited on ceramic supports coated with gold interdigitated electrodes. The electrical characterization of this sensor in the presence of the VOCs allows the identification of acetic acid in the concentration range from 24 to 120 ppm, and of ethanol, isopropanol, and methanol in a concentration range from 18 to 90 ppm, respectively. Moreover, the results allows the quantification of acetic acid, ethanol, and isopropanol concentrations with sensitivity values of (3.03±0.12)∗104, (-1.15±0.19)∗104, and (-1.1±0.50)∗104 mL-1, respectively. The resolution of this sensor to detect the different analytes is lower than 0.04 ppm, which means it is an interesting sensor for use as an electronic nose for the detection of VOCs.

A Virtual Electronic Nose for the Efficient Classification and Quantification of Volatile Organic Compounds.

Although many chemical gas sensors report high sensitivity towards volatile organic compounds (VOCs), finding selective gas sensing technologies that can classify different VOCs is an ongoing and highly important challenge. By exploiting the synergy between virtual electronic noses and machine learning techniques, we demonstrate the possibility of efficiently discriminating, classifying, and quantifying short-chain oxygenated VOCs in the parts-per-billion concentration range. Several experimental results show a reproducible correlation between the predicted and measured values. A 10-fold cross-validated quadratic support vector machine classifier reports a validation accuracy of 91% for the different gases and concentrations studied. Additionally, a 10-fold cross-validated partial least square regression quantifier can predict their concentrations with coefficients of determination, R2, up to 0.99. Our methodology and analysis provide an alternative approach to overcoming the issue of gas sensors’ selectivity, and have the potential to be applied across various areas of science and engineering where it is important to measure gases with high accuracy.

Modeling headspace solid-phase microextraction of volatile organic compounds from water samples with porous coatings using finite element analysis

• Model was developed for simulation of HSSPME of VOCs from water by Car/PDMS at 25 °C. • Diffusivity in fluids was estimated by Fuller, and Wilke and Chang methods. • Fiber-air distribution constants were estimated using LSER model. • Southworth approach was accurate to model mass transport from water to headspace. • The model was applied for optimizing extraction parameters and preincubation time. This research was aimed at the development of the COMSOL Multiphysics® (CMP) model for simulating the headspace (HS) solid-phase microextraction (SPME) of volatile organic compounds (VOCs) from water using Carboxen/polydimethylsiloxane coating at 25 °C. The developed model is mainly based on existing theory and previous research on the numerical modeling of SPME. Fuller method was used to estimate diffusion coefficients in air as well as pores and voids of the coating. Coating-headspace distribution constants were estimated using linear solvation energy relationship (LSER) model and multiple regression obtained by Prikryl and Sevcik. Headspace-water constants were estimated using vapor pressures and activity coefficients were determined using UNIFAC model. Wilke and Chang method was chosen for estimating diffusion coefficients in water without stirring. Liss and Slater, and Southworth approaches were tested for estimating mass transfer coefficients at the headspace-water boundary under stirring. Southworth approach allowed obtaining benzene, toluene, ethylbenzene and o -xylene (BTEX) extraction profiles from water, which were closest to experimental profiles compared to other approaches. When using Southworth approach, root mean square deviation (RMSD) between experimental and simulated values for BTEX were 8.7–10% indicating the high accuracy of the model. The developed model was successfully applied for computational optimization of extraction parameters (stirring speed, fiber insertion depth, pressure, sample volume and the concentration of added salt). After minor modification, the model was also applied for optimization of preincubation time. It can be recommended for optimization of HSSPME-based analytical methods for VOCs quantification in water.

Membrane inlet mass spectrometry method for food intake impact assessment on specific volatile organic compounds in exhaled breath

This research work describes the development of a novel bioanalytical method for the assessment of food impact on selected exhaled breath volatile organic compounds (VOCs) using a fast and portable screening VOC prototype sensor based on membrane inlet mass spectrometry (MIMS). Method and sensor prototype functionality was verified by obtaining good response times, linearity in the examined concentration ranges, and sensitivity and repeatability for several breath VOCs—acetone, ethanol, n-pentane, and isoprene. A new VOC sensor prototype was also proven to be sensitive enough for selected breath VOC quantification with limits of detection at low part per billion (ppb) levels—5 ppb for n-pentane, 10 ppb for acetone and ethanol, and 25 ppb for isoprene. Food impact assessment was accomplished by tracking the levels of acetone, ethanol, n-pentane, and isoprene in exhaled breath samples collected from 50 healthy participants before the meal and 60 min and 120 min after the meal. For acetone, isoprene, and n-pentane, a larger impact was noticed 120 min after the meal, while for ethanol, it was after 60 min. Obtained VOC levels were in the expected concentration ranges. Mean values at all time points were ~ 500–900 ppb for acetone and ~ 400–600 ppb for ethanol. Most of the results for n-pentane were below 5 ppb, but the mean value for those which were detected was ~ 30 ppb. Along with samples, data about participants’ lifestyle were collected via a short questionnaire, which were compared against obtained VOC levels in order to reveal some significant correlations between habits of participants and their breath VOC levels.Graphical abstractPortable MS: monitoring of food impact on the levels of selected VOCs from exhaled breath

A simple sampling method for quantification of hazardous volatile organic compounds in mainstream cigarette smoke: Method development and prestudy validation

Cigarette smoke can be fatal to human health, as it contains a plethora of hazardous compounds such as volatile organic compounds (VOCs). To assess the toxic effects of cigarette smoke, both qualitative and quantitative information about the VOCs in cigarette smoke are essential. However, sampling and analysis of cigarette smoke are not simple tasks and require complicated validation procedures. Here, we develop and verify a simple sampling method for the quantification of VOCs in mainstream cigarette smoke using three steps: direct collection of the mainstream cigarette smoke sample by gas-tight syringe, injection of the sample into a bag sampler for dilution, and extraction of VOCs into an adsorption-tube sampler for quantitation by thermal desorption-gas chromatography-mass spectrometry. This method has been employed successfully to quantify seven target VOCs (isoprene, methyl ethyl ketone, benzene, acrylonitrile, toluene, m-xylene, and styrene) in cigarette smoke from 3R4F reference cigarettes. The quantities of these major five VOCs (isoprene, methyl ethyl ketone, benzene, acrylonitrile, and toluene: average of 459 ± 646 ng mL−1), when measured from 3R4F samples based on our method, were comparable to or moderately higher than those measured based on the established standard methods (e.g., Health Canada Intense (462 ± 557 ng mL−1) and International Standardization Organization (356 ± 438 ng mL−1)). As such, we succeeded in reliably collecting and quantifying VOCs in cigarette smoke using a simple sampling method proposed in this research. The proposed method is thus recommended as an alternative to complicated and costly representative methods based on international guidelines.

Chemical identification and quantification of volatile organic compounds emitted by sewage sludge

The recycling of organic waste products (e.g. sewage sludge, SS) is currently being promoted as a substitute for mineral fertilizers for agricultural lands. The spreading of SS allows the recycling of the nutrients and organic matter it contains. SS contains various pollutants such as volatile organic compounds (VOCs) that adversely affect the ecosystem and human health through ozone production and serve as critical precursors of atmospheric secondary organic aerosols. There are very few studies quantifying the gaseous compounds emitted from SS, and those studies primarily address their odorant properties for identifying suitable odour abatement techniques. There is an urgent need for more comprehensive quantitative information on VOCs emitted from SS as aerosol precursors. In this context, an experimental study was performed on SS samples taken from a wastewater treatment plant located in France. Undigested SS (UDSS), digested SS (DSS) and SS with 30% and 60% dryness were collected from different stages of treatment sequence and analyzed using atmospheric simulation chambers coupled to proton-transfer-reaction quadrupole ion-guide time-of-flight mass spectrometer. Our study revealed that SS samples emitted a large spectrum of VOCs. 380 compounds were detected, quantified and classified into different chemical groups. The VOC emissions increased with the increase in the dryness of the sample; the highest being in SS 60%, followed by SS 30%, UDSS and DSS. OVOCs were dominant in SS 60%. The statistical analysis showed that the anaerobic digestion and the dewatering to 60% of dryness decreased the emissions of sulphuric compounds. Aromatic compounds and indoles (e.g. skatole) were emitted significantly from the UDSS. Some of these VOCs can serve as precursor gases for atmospheric aerosol formation. The experimental dataset obtained in this study provides an accurate inventory reference for the VOC emissions from SS samples and shows the impacts of the treatment on emission characteristics of VOCs.

Classification and Quantification of Volatile Organic Compounds

Classification and Quantification of Volatile Organic Compounds

Quantification of volatile organic compounds by secondary electrospray ionization-high resolution mass spectrometry

Secondary electrospray ionization coupled with high resolution mass spectrometry (SESI-HRMS) is a direct mass spectrometry technique, which can identify trace volatile organic compounds (VOCs) in real time without sample pretreatment and chromatographic separation. SESI-HRMS has been successfully applied in multiple applications, including breath analysis, animals and plants VOCs emissions, analysis of headspace of cell cultures and indoor and outdoor air. The range of areas where the technique can potentially have a substantial impact is very broad. However, one critical aspect that requires further development to consolidate the technique is absolute quantification. Therefore, in this study we aim to develop a quantitative method for eight representative VOCs, including ketones (acetone, 2-butanone and 2-pentanone), alkenes (isoprene and α-terpinene) and aromatics (toluene, styrene and mesitylene). The mass spectrometric platform includes a commercial SESI source hyphenated with a Q-Exactive hybrid quadrupole Orbitrap high resolution mass spectrometer. Within the concentration range of 0–100 ppbv studied, the optimal coefficient of determination for linear regression (R2 = 0.993–0.999) between signal intensity and concentration is obtained in the range of 0–10 ppbv for all eight VOCs. The detection limits range between 3 (2-Pentanone) and 15 (Acetone) pptv. The intra-day (n = 10) and inter-day (n = 30) coefficients of variation (CV) are ≤ 6% and ≤10%, respectively. Finally the method is applied for the fast evaluation (<5 min) of different materials widely used for the collection, storage or pretreatment of gas sample. Better recovery of trace levels of eight VOCs is observed for PTFE gas sampling bag as compared to Nalophan and Tedlar bags; when Nafion tube is used to pretreat the gas sample, recovery of ≤50% are obtained for 2-pentanone, α-terpinene and all three aromatics.

Coating-Based Quartz Crystal Microbalance Detection Methods of Environmentally Relevant Volatile Organic Compounds

Volatile organic compounds (VOCs) that evaporate under standard atmospheric conditions are of growing concern. This is because it is well established that VOCs represent major contamination risks since release of these compounds into the atmosphere can contribute to global warming, and thus, can also be detrimental to the overall health of worldwide populations including plants, animals, and humans. Consequently, the detection, discrimination, and quantification of VOCs have become highly relevant areas of research over the past few decades. One method that has been and continues to be creatively developed for analyses of VOCs is the Quartz Crystal Microbalance (QCM). In this review, we summarize and analyze applications of QCM devices for the development of sensor arrays aimed at the detection of environmentally relevant VOCs. Herein, we also summarize applications of a variety of coatings, e.g., polymers, macrocycles, and ionic liquids that have been used and reported in the literature for surface modification in order to enhance sensing and selective detection of VOCs using quartz crystal resonators (QCRs) and thus QCM. In this review, we also summarize novel electronic systems that have been developed for improved QCM measurements.

Multivariate optimization of dual-sorbent dynamic headspace extraction of volatiles in wine analysis

The main critical point of newly developed miniaturized sample preparation techniques is a limited extraction capacity. Dynamic headspace extraction offers increased volume of sorbent which is commonly used in environmental analysis. Application of two sorbents (Carbopack B/Carbopack X and Tenax® TA) at different extraction temperatures allows enhancing a range of volatile organic compounds available for analysis. Such approach was applied in our research for quantification of volatile organic compounds in botrytized wines with gas chromatography. The central composite design was included to analysis simultaneous effects of incubation time, incubation temperature, purge volume and purge flow. In attempt to properly assess results, the data evaluation involved Pareto charts, surface response methodology and principal component analysis. Multivariate experimental design revealed statistical significance of purge volume and quadratic terms of incubation time and temperature, for response of volatiles. The quantification method with 0.2–2.0 µg/L LOD and 0.5–5.0 µg/L LOQ values, was developed under simultaneously optimized experimental conditions such as a 54 °C incubation temperature, a 20.18 min incubation time, a 344.3 mL purge volume and a 16.0 mL/min purge flow. The increased levels of linalool oxide, ethyl phenylacetate, γ-hexalactone and α-terpineol were observed in the samples, that correlated with botrytized wine technology.

Profiling Volatile Compounds in Blackcurrant Fruit using Headspace Solid-Phase Microextraction Coupled to Gas Chromatography-Mass Spectrometry.

There is an increasing interest in measuring volatile organic compounds (VOCs) emitted by ripe fruits for the purpose of breeding varieties or cultivars with enhanced organoleptic characteristics and thus, to increase consumer acceptance. High-throughput metabolomic platforms have been recently developed to quantify a wide range of metabolites in different plant tissues, including key compounds responsible for fruit taste and aroma quality (volatilomics). A method using headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS) is described here for the identification and quantification of VOCs emitted by ripe blackcurrant fruits, a berry highly appreciated for its flavor and health benefits. Ripe fruits of blackcurrant plants (Ribes nigrum) were harvested and directly frozen in liquid nitrogen. After tissue homogenization to produce a fine powder, samples were thawed and immediately mixed with sodium chloride solution. Following centrifugation, the supernatant was transferred into a headspace glass vial containing sodium chloride. VOCs were then extracted using a solid-phase microextraction (SPME) fiber and a gas chromatograph coupled to an ion trap mass spectrometer. Volatile quantification was performed on the resulting ion chromatograms by integrating peak area, using a specific m/z ion for each VOC. Correct VOC annotation was confirmed by comparing retention times and mass spectra of pure commercial standards run under the same conditions as the samples. More than 60 VOCs were identified in ripe blackcurrant fruits grown in contrasting European locations. Among the identified VOCs, key aroma compounds, such as terpenoids and C6 volatiles, can be used as biomarkers for blackcurrant fruit quality. In addition, advantages and disadvantages of the method are discussed, including prospective improvements. Furthermore, the use of controls for batch correction and minimization of drift intensity have been emphasized.

Field Tests of Smart Mos Gas Sensor Systems for Selective Quantification of VOCs

Field Tests of Smart Mos Gas Sensor Systems for Selective Quantification of VOCs

Lights and Shadows of the Voc Emission Quantification

Nowadays, Volatile Organic Compounds (VOC) are a class of air pollutants increasingly studied in the scientific literature, due to the well-known impact on human health and the environment. They can be emitted from different sources, including various types of industrial plants, such as oil refinery and petrochemical plants. For these reasons, the attention of control agencies on VOC emissions has increased and with it the request to quantify the total amount of VOC emitted by a plant, to monitor and to reduce this emission. However, several critical aspects still exist related to the classification of VOCs and their quantification. Indeed, different definitions of VOC are reported in scientific and technical literature. These diversities result in multiple strategies for quantifying the VOCs emitted by an industrial plant. Therefore, this paper reviews the available regulation about the quantification of VOC emitted from industrial plants, focusing on refineries and petrochemical plants, to underline some formal inconsistencies in VOC estimation procedures. From this comparison, it appears clear that VOC quantification can be addressed in different manners, obtaining a substantial non-uniformity of the VOC emission data. The data difference can conduct to a misleading estimation of emission rate.