Volatile Organic Compounds Data Research Articles

Floral structure, histochemistry, and volatile compounds in Bulbophyllum species of the ‘Cirrhopetalum alliance’ (Orchidaceae)

Abstract Bulbophyllum is the most species-rich orchid genus. Recent molecular data groups certain of its species into a monophyletic ‘Cirrhopetalum alliance’. Within this alliance are a monophyletic section Cirrhopetaloides and a non-monophyletic section Cirrhopetalum. Floral morphology and scent are partially conserved phylogenetically compared to the molecular data. Here, floral morphology, histochemistry, anatomy, and floral volatile organic compounds (VOCs) of selected Bulbophyllum species are analysed to assess whether there are structural or chemical differences that support separation into the two sections, to update available VOC data from this genus, and to consider evolutionary adaptations in the ‘Cirrhopetalum alliance’. We found that ‘Cirrhopetalum alliance’ flowers differ from some sections of Bulbophyllum in the structure of cells lining the longitudinal groove on the labellum or lip and report, for the first time in Bulbophyllum, a type of atypical oleiferous trichome probably involved in VOC production. We show a clear difference in floral VOC profiles between B. bicolor (sect. Cirrhopetaloides) and four other Bulbophyllum species, three previously assigned to sect. Cirrhopetalum, but VOC profiles do not support separation into the two sections. The lack of congruence between already established molecular phylogeny, floral anatomy, and VOC data suggests that VOC differences are due to recently and independently evolved pollinator specificity. We therefore show that there is no clear separation of these sections based on floral anatomy, histochemistry, or VOC profiles.

Application of an electronic nose for the diagnosis of ketosis in dairy cows

Ketosis in dairy cows is a nutritional and metabolic disease that severely affects the milk industry and dairy cow health. An electronic nose can identify volatile organic compounds (VOCs). This study applied the electronic nose to detect VOCs in dairy cow milk, feces, and blood. Blood ketone bodies (BHBA) were measured, and 10 ketosis cows, 71 subclinical ketosis cows, and 75 healthy cows were considered the research subjects. Cow milk, feces, and blood samples were collected for electronic nose testing. The measured VOC data were processed through multivariate analysis and machine learning algorithms. The electronic nose analysis results were validated through the GC-MS analysis of the milk samples. The results unveiled spatial differences in the electronic nose VOC values of milk, feces, and blood, and the cumulative contribution rates of both principal component variables (PC) 1 and PC2 reached more than 95%. The machine learning algorithm results revealed that the accuracy of the milk probabilistic neural network (PNN) was 87.5%, the stool support vector machine was 100%, and the blood PNN was 66.7%. This result revealed that electronic nose detection can effectively differentiate healthy, subclinical ketosis, and clinical ketosis cows. According to the milk GC-MS results, significant differences were noted in the VOC content of milk samples between the different groups.The aforementioned studies revealed that electronic nose can aid in the effective prediction and diagnosis of ketosis in dairy cows. This lays the foundation for establishing a new method for diagnosing ketosis in dairy cows.

Changes in factor profiles deriving from photochemical losses of volatile organic compounds: Insight from daytime and nighttime positive matrix factorization analyses

Substantial effects of photochemical reaction losses of volatile organic compounds (VOCs) on factor profiles can be investigated by comparing the differences between daytime and nighttime dispersion-normalized VOC data resolved profiles. Hourly speciated VOC data measured in Shijiazhuang, China from May to September 2021 were used to conduct study. The mean VOC concentration in the daytime and at nighttime were 32.8 and 36.0 ppbv, respectively. Alkanes and aromatics concentrations in the daytime (12.9 and 3.08 ppbv) were lower than nighttime (15.5 and 3.63 ppbv), whereas that of alkenes showed the opposite tendency. The concentration differences between daytime and nighttime for alkynes and halogenated hydrocarbons were uniformly small. The reactivities of the dominant species in factor profiles for gasoline emissions, natural gas and diesel vehicles, and liquefied petroleum gas were relatively low and their profiles were less affected by photochemical losses. Photochemical losses produced a substantial impact on the profiles of solvent use, petrochemical industry emissions, combustion sources, and biogenic emissions where the dominant species in these factor profiles had high reactivities. Although the profile of biogenic emissions was substantially affected by photochemical loss of isoprene, the low emissions at nighttime also had an important impact on its profile. Chemical losses of highly active VOC species substantially reduced their concentrations in apportioned factor profiles. This study results were consistent with the analytical results obtained through initial concentration estimation, suggesting that the initial concentration estimation could be the most effective currently available method for the source analyses of active VOCs although with uncertainty.

Characteristic, source apportionment and effect of photochemical loss of ambient VOCs in an emerging megacity of Central China

During the transportation from emission sources to sampling site, volatile organic compounds (VOCs) undergo photochemical losses which have significant effects for tropospheric ozone (O3) formation and VOCs source apportionment. In this study, based on hourly speciated VOC data from May 23, to July 8, 2019 with high O3 concentrations, the concentration level, chemical composition, and diurnal variation of observed VOCs were studied, and the initial concentration of VOCs was obtained by combining the local emission inventory in Zhengzhou, Central China. The impact of photochemical loss on source apportionment was evaluated using the Positive Matrix Factorization/Multilinear Engine 2-Species Ratio (PMF/ME2-SR) model based on observed and initial VOCs concentration (OC-PMF and IC-PMF). Results suggest that during the observed period, the average concentration of total VOCs (TVOCs) was 23.74 ± 9.20 ppbv, and alkanes (3.92 ± 2.40 ppbv) were the predominant component. The photochemical losses of TVOCs were approximately 10.15 ± 7.13 ppbv, with an average loss rate of 29.9%. The ozone formation potential (OFP) based on the initial VOCs concentration was 2.2 times higher than that observed VOCs concentration. Alkenes contributed the most to OFP and were also the largest contributors to photochemical losses. TVOC concentrations increased by 5.0% during O3 pollution compared to non-O3 pollution. Seven sources of VOCs were determined, including biomass burning, liquefied petroleum gas (LPG), vehicle emission, industrial emission, solvent usage, gasoline evaporation, and biogenic emission. Compared to the results of IC-PMF, the contributions of these sources in OC-PMF were underestimated by 15.4%, 14.4%, 13.0%, 11.7%, 31.5%, 7.6%, and 4.7%, respectively. This study still exhibits certain limitations in calculation method, and further exploration can be conducted in the future.

Characteristics of Volatile Organic Compounds Distribution in Downtown Ansan Near Industrial Complexes

There are four industrial complexes located around Ansan City, which is one of the areas with the highest VOCs emissions in Gyeonggi region based on the emissions provided by the clean policy support system (CAPSS), Korea. In this study, proton transfer reaction time-of-flight mass spectrometer (PTR-TOF-MS) was used to determine the distribution of volatile organic compounds (VOCs) in urban areas near industrial complexes in Ansan City in 2023 (May to June). The PTR-TOF-MS was operated over the mass range of 10-400amu at a drift tube pressure of 2.3mbar and temperature 80<sup>o</sup>C (E/N ~ 130Td) that enabled the collection of VOC data at 0.1Hz resolution. The mixing ratio calculations for methanol, acetaldehyde, acetonitrile, acetone, acetic acid, dimethyl sulfide, isoprene, methyl vinyl ketone (MVK), methyl ethyl ketone (MEK), benzene, toluene, styrene, xylene, trimethylbenzene and pinene reported in this study were done using the sensitivity factors obtained from the PTR-TOF-MS calibrations. As a result of the intensive measurement in springtime, among the substances constituting VOCs, methanol was the most abundant with 16.27±7.13 ppb, followed by acetone, acetaldehyde, acetic acid, methyl ethyl ketone (MEK), toluene, and xylene. VOCs compositions were dominated by oxidized VOCs, with CxHyO1 at 74%, CxHyO2 at 11%, aromatic CxHy at 10.5%, and CxHyN at 3.1%, respectively. As a result of the diurnal variations and conditional probability function (CPF) analysis, it was found that toluene and MEK were locally greatly influenced by industrial complexes distributed around Ansan City. Related with the ozone creating contribution, the ratio of VOCs to nitrogen oxide (NOx) was 6.5 (1.4 to 18.2), which appeared in the NOx-limited (4:1) and the VOCs-limited (15:1). Therefore it is suggested that the management of emission source NOx and VOCs are very important for the reduction of ozone in Ansan.

Comment on “Transport of substantial stratospheric ozone to the surface by a dying typhoon and shallow convection” by Chen et al. (2022)

Abstract. Chen et al. (2022) analyzed the event of rapid nocturnal O3 enhancement (NOE) observed on 31 July 2021 at the surface level in the North China Plain and proposed transport of substantial stratosphere ozone to the surface by Typhoon In-fa followed by downdraft of shallow convection as the mechanism of the NOE event. The analysis seems to be valid from the viewpoint of atmospheric physics. This comment revisits the NOE phenomenon on the basis of the China National Environmental Monitoring Center (CNEMC) network data used in Chen et al. (2022), together with the CNEMC data from Zibo (ZB) and O3, NOx, PAN (peroxyacetic nitric anhydride), and VOC (volatile organic compound) data from the Zibo supersite operated by the China Research Academy of Environmental Sciences (CRAES). We found (a) Ox (O3 + NO2) levels during the NOE period approaching those of O3 during 14:00–17:00 LT, (b) levels of PAN and the relationship between O3 and PAN consistent with dominance of chemical and physical processes within the boundary layer, and (c) estimated photochemical ages of air mass shorter than 1 d and showing no drastic increases during the NOE. We argue that the NOE was not caused by typhoon-induced stratospheric intrusion but originated from fresh photochemical production in the lower troposphere. Our argument is well supported by the analysis of atmospheric transport as well as ground-based remote sensing data.

Impact of Hurricanes, Tropical Storms, and Coastal Extratropical Storms on Indoor Air VOC Concentrations

AbstractUnderstanding vapor intrusion (VI) temporal variability is key for the design of sampling strategies intended to assess reasonable maximum exposure of indoor air concentrations of volatile organic compounds (VOCs) as well as risk evaluation and mitigation planning. VI temporal variability has previously been shown to be dependent on the complex interactions of multiple independent variables—meteorological, hydrogeological, and human behavioral. Several meteorological variables, including barometric pressure, wind speed, and rainfall, are linked during tropical and extratropical storm events. High‐frequency meteorological and indoor VOC data from a series of seven tropical storms and four extratropical storms were collected at a single industrial building with multiple heating, ventilation, and air conditioning (HVAC) zones. The storms and sampling zones showed a variety of effects on trichloroethylene (TCE) concentrations in indoor air. In one zone (supply room), increases in TCE concentrations often, but not always, closely coincided with decreasing barometric pressure, sustained wind speeds over 32 km/h (20 mph), and differential pressures indicating subslab to indoor flow. A second zone, in a restroom, did not show a consistent pattern of temporal correlation between meteorological factors and indoor air concentrations. While peak indoor air concentrations may be associated with the passage of cyclonic storms at some sampling locations, this does not appear to be generalizable to all sampling locations. The observed increase in indoor air concentration potentially attributable to these storms is typically less than an order of magnitude and the duration ranges from a day to a week.

Using wavelet transform and hybrid CNN – LSTM models on VOC & ultrasound IoT sensor data for non-visual maize disease detection

Early detection of plant diseases is crucial for safeguarding crop yield, especially in regions vulnerable to food insecurity, such as Sub-Saharan Africa. One of the significant contributors to maize crop yield loss is the Northern Leaf Blight (NLB), which traditionally takes 14–21 days to visually manifest on maize. This study introduces a novel approach for detecting NLB as early as 4–5 days using Internet of Things (IoT) sensors, which can identify the disease before any visual symptoms appear. Utilizing Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) models, nonvisual measurements of Total Volatile Organic Compounds (VOCs) and ultrasound emissions from maize plants were captured and analyzed. A controlled experiment was conducted on four maize varieties, and the data obtained were used to develop and validate a hybrid CNN-LSTM model for VOC classification and an LSTM model for ultrasound anomaly detection. The hybrid CNN-LSTM model, enhanced with wavelet data preprocessing, achieved an F1 score of 0.96 and an Area under the ROC Curve (AUC) of 1.00. In contrast, the LSTM model exhibited an impressive 99.98% accuracy in identifying anomalies in ultrasound emissions. Our findings underscore the potential of IoT sensors in early disease detection, paving the way for innovative disease prevention strategies in agriculture. Future work will focus on optimizing the models for IoT device deployment, incorporating chatbot technology, and more sensor data will be incorporated for improved accuracy and evaluation of the models in a field environment.

Characterization and Source Analysis of Pollution Caused by Atmospheric Volatile Organic Compounds in the Spring, Kunming, China

The surface atmospheric O3 concentration in Kunming shows a significant upward trend, with high values mainly occurring in March–May. Volatile organic compounds (VOCs) are one of the most important precursors of O3. However, the sources of VOCs are complex and difficult to identify. In order to understand the pollution levels, the spatial distribution characteristics, and possible sources of VOCs, we conducted simultaneous offline sampling at representative sites in six different functional areas of Kunming using SUMMA canisters for one week. The VOC samples were analyzed via GC/MS. The VOC data were analyzed (using the feature ratio method, ozone formation potential (OFP), and Positive Matrix Factorization (PMF) model). Some important conclusions were drawn. Firstly, VOCs during the spring in Kunming were mainly derived from oxygenated VOCs, aromatic hydrocarbons, and halogenated hydrocarbons, with significant spatial differences. Secondly, we found that the potential for atmospheric ozone formation is higher in Kunming for aromatic hydrocarbons and oxygenated VOCs. Finally, the results of the Positive Matrix Factorization model (PMF) showed that Kunming’s ambient atmospheric VOCs mainly originate from anthropogenic source emissions. These conclusions can provide useful reference information for O3 pollution control in Kunming.

Characterization of Italian honeys: integrating volatile and physico-chemical data

This article focuses on the comprehensive characterization of Italian honeys using various physico-chemical analyses and their volatile organic compounds (VOCs) fingerprint obtained through the PTR-ToF-MS technology. Honey characteristics, including pH, electrical conductivity, moisture content, hydroxymethylfurfural (HMF), and sugar content, were analyzed to assess their quality and origin. Honey samples from different flowers, including acacia, chestnut, citrus, linden, and multifloral, were collected and investigated. Furthermore, a few aged honeys were collected and analyzed and compared with the fresh ones. Physico-chemical analysis revealed that chestnut honey is characterized by high pH and EC values. Acacia honey has a higher fructose content, while aging appears to influence HMF levels, a vital indicator of honey quality, with aged samples exhibiting significant increases in HMF content. The VOC profiles have been found to vary among different honey types, suggesting that VOCs could be used as indicators of honey origin. Multivariate statistical analyses, such as partial least squares discriminant analysis (PLS-DA), have been applied to the VOCs data to differentiate honey types based on their volatile profiles. Acacia honey exhibited different physicochemical parameters but on the contrary, in the VOCs analysis, it displayed similarities with the linden honey due to their shared low emissions of volatile compounds. Citrus honey had similar chemical parameters to linden and multifloral honeys, but its distinctive VOCs emission allowed for a more accurate identification. In conclusion, the analysis performed with the PTR-ToF-MS was successful in obtaining specific volatile fingerprints of those samples and was effective for improving the characterization of honeys.

Origin and transformation of volatile organic compounds at a regional background site in Hong Kong: Varied photochemical processes from different source regions

Volatile organic compounds (VOCs) are important gaseous constituents in the troposphere, impacting local and regional air quality, human health, and climate. Oxidation of VOCs, with the participation of nitrogen oxides (NOx), leads to the formation of tropospheric ozone (O3). Accurately apportioning the emission sources and transformation processes of ambient VOCs, and effectively estimation of OH reactivity and ozone formation potential (OFP) will play an important role in reducing O3 pollution in the atmosphere and improving public health. In this study, field measurements were conducted at a regional background site (Hok Tsui; HT) in Hong Kong from October to November 2020 with proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS). VOC data coupled with air mass back trajectory cluster analysis and receptor modelling were applied to reveal the pollution pattern, emission sources and transformation of ambient VOCs at HT in autumn 2020. Seven sources were identified by positive matrix factorization (PMF) analysis, namely vehicular + industrial, solvent usage, primary oxygenated VOCs (OVOCs), secondary OVOCs 1, secondary OVOCs 2 (aged), biogenic emissions, and background + biomass burning. Secondary formation and vehicular + industrial emissions are the vital sources of ambient VOCs at HT supersite, contributing to 20.8 % and 46.7 % of total VOC mixing ratios, respectively. Integrated with backward trajectory analysis and correlations of VOCs with their oxidation products, short-range transport of air masses from inland regions of southeast China brought high levels of total VOCs but longer-range transport of air masses brought more secondary OVOCs in aged air masses. Photolysis of OVOCs was the most important contributor to OH reactivity and OFP, among which aldehyde was the dominant contributor. The results of this study highlight the photochemical processing of VOCs from different source regions which should be considered in strategy making for pollution reduction.

Volatile organic compounds during routine production and planned shutdown of a petrochemical refinery: Emission characteristics, source and risk assessment

During the routine production and planned shutdown of a petrochemical refinery in China, ambient volatile organic compound (VOC) data were continuously determined by an online monitoring system, and VOC characteristics, source apportionment, and health risks were calculated based on those data. The data showed that the total VOC concentration emitted during the planned shutdown period was significantly higher than that under daily production conditions, especially the alkane concentration, which increased from 137.4 μg m−3 to 481.8 μg m−3. A source apportionment study was performed using a positive matrix factorization model based on VOC data collected during different sampling times. The contributions of petroleum refinery emissions increased from 63.8% during routine production to 79.4% during the shutdown period. In addition, carcinogenic and noncarcinogenic risk assessment revealed that workers could be under higher acute and chronic health risks during the shutdown and maintenance period than during the routine production of the refinery. The results also indicated that VOCs emitted during maintenance might have more influence on public health. Therefore, more attention should be paid to the emission characteristics and source apportionment of VOCs during planned shutdowns. This work provides valuable data that can help regulatory agencies formulate VOC control strategies during routine production and shutdown maintenance in petrochemical refineries.

Variation and trend of nitrate radical reactivity towards volatile organic compounds in Beijing, China

Abstract. Nitrate radical (NO3) is an important nocturnal atmospheric oxidant in the troposphere that significantly affects the lifetime of pollutants emitted by anthropogenic and biogenic activities, especially volatile organic compounds (VOCs). Here, we used 1 year of VOC observation data obtained in urban Beijing in 2019 to look into the level, composition, and seasonal variation in NO3 reactivity (kNO3). We show that hourly kNO3 towards measured VOC varied widely from < 10−4 to 0.083 s−1 with a campaign-average value (± standard deviation) of 0.0032 ± 0.0042 s−1. There was large seasonal difference in NO3 reactivity towards VOC with averaged values (± standard deviation) of 0.0024±0.0026 s−1 (spring), 0.0067±0.0066 s−1 (summer), 0.0042±0.0037 s−1 (autumn), and 0.0027±0.0028 s−1 (winter). Alkenes such as isoprene and styrene accounted for the majority. Isoprene was the dominant species in spring, summer, and autumn, accounting for 40.0 %, 77.2 %, and 43.2 %, respectively. Styrene only played a leading role in winter, with a percentage of 39.8 %. A sensitivity study shows monoterpenes, the species we did not measure, may account for a large fraction of kNO3. Based on the correlation between the calculated kNO3 and VOC concentrations in 2019, we established localized parameterization schemes for predicting the reactivity by only using a part of VOC species. The historically published VOC data were collected using the parameterization method to reconstruct the long-term kNO3 in Beijing. The lower kNO3 during 2014–2021 compared with that during 2005–2013 may be attributed to anthropogenic VOC emission reduction. Finally, we revealed that NO3 dominated the nocturnal VOC oxidation, with 83 % of the annual average in Beijing in 2019, which varied seasonally and was strongly regulated by the level of kNO3, nitrogen oxide, and ozone. Our results improve the understanding of nocturnal atmospheric oxidation in urban regions and contribute to our knowledge of nocturnal VOC oxidation and secondary organic pollution.

Source apportionment of consumed volatile organic compounds in the atmosphere

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

Non-destructive method to classify walnut kernel freshness from volatile organic compound (VOC) emissions using gas chromatography-differential mobility spectrometry (GC-DMS) and machine learning analysis

Analysis of volatile organic compounds (VOCs) can be an effective strategy to inspect the quality of horticultural commodities and following their degradation. In this work, we report that VOCs emitted by walnuts can be studied using gas chromatography-differential mobility spectrometry (GC-DMS), and those GC-DMS data can be analyzed to predict the rancidity of walnuts, i.e., classify walnuts into grades of freshness. Walnut kernels were assigned a class n depending on their level of freshness as determined by a peroxide assay. VOC samples were analyzed using GC-DMS. From these VOC data, a partial least square regression (PLSR) model provided a freshness prediction value m, which corresponded to the rancid class n when m=n±0.5. The PLSR model had an accuracy of 80% to predict walnut grade and demonstrated a minimal root mean squared error of 0.42 for the m response variables (representative of walnut grade) with the GC-DMS data. We also conducted gas chromatography-mass spectrometry (GC–MS) experiments to identify volatiles that emerged or were enhanced with more rancid walnuts. The findings of the GC–MS study of walnut VOCs align excellently with the GC-DMS study. Based on our results, we conclude that a GC-DMS device deployed with a pre-trained machine learning model can be a very effective device for classifying walnut grades in the industry.

Changes in source apportioned VOCs during high O3 periods using initial VOC-concentration-dispersion normalized PMF

Ambient volatile organic compounds (VOCs) concentrations are affected by emissions, dispersion, and chemistry. This work developed an initial concentration-dispersion normalized PMF (ICDN-PMF) to reflect the changes in source emissions. The effects of photochemical losses for VOC species were corrected by estimating the initial data, and then applying dispersion normalization to reduce the impacts of atmospheric dispersion. Hourly speciated VOC data measured in Qingdao from March to May 2020 were utilized to test the method and had assessed its effectiveness. Underestimated solvent use and biogenic emissions contributions due to photochemical losses during the O3 pollution (OP) period reached 4.4 and 3.8 times the non-O3 pollution (NOP) period values, respectively. Increased solvent use contribution due to air dispersion during the OP period was 4.6 times the change in the NOP period. The influence of chemical conversion and air dispersion on the gasoline and diesel vehicle emissions was not apparent during either period. The ICDN-PMF results suggested that biogenic emissions (23.1 %), solvent use (23.0 %), motor-vehicle emissions (17.1 %), and natural gas and diesel evaporation (15.8 %) contributed most to ambient VOCs during the OP period. Biogenic emissions and solvent use contributions during the OP period increased by 187 % and 135 % compared with the NOP period, respectively, whereas that of liquefied petroleum gas substantially decreased during the OP period. Controlling solvent use and motor-vehicles could be effective in controlling VOCs in the OP period.

Imaging VOC distribution and tracing emission sources in surface water by a mobile shipborne spray inlet proton transfer reaction mass spectrometry

Volatile organic compound (VOC) pollution in surface water can affect the growth of microorganisms and even cause damage to human health. Quickly obtaining VOC distribution and tracing emission sources are of great significance to ensure the security of water quality. In this study, we developed a mobile shipborne spray inlet proton transfer reaction mass spectrometry (S-SI-PTR-MS) instrument for rapid and on-line detection of volatile organic compounds (VOCs) in surface water. It mainly consists of a shipborne platform, a spray inlet system, a proton transfer reaction mass spectrometer, and geographic information software (GIS). During the navigation, VOCs in water can be quickly extracted to gaseous state by the SI system and transferred into the proton transfer reaction mass spectrometry (PTR-MS) for detection. The GIS can receive VOC and longitude-latitude data at the same time. And then the identification and concentration information can be shown on an electronic map in real time. The limit of detection and response time were 1.32 μg/L∼9.43 μg/L and 56 s∼114 s, respectively. The spatial resolution is 0.14 m in a common navigation experiment (5 km/h, dwell time=0.1 s), allowing for high-resolution monitoring of VOCs in water. Finally, field experiments were performed on the lower Nanfeihe River and some surfaces of Chaohu Lake in China. During tracing pollution in Nanfeihe River, two VOC emission sources in the downstream were located, nearing a sewage outlet and a wharf, respectively. During imaging VOC distribution in the Chaohu Lake, we found the areas with high-concentration VOCs were mainly along the ship channel. The newly developed S-SI-PTR-MS can monitor water quality in surface water in real time and has the potential to assist environmental protection enforcement.

Spotting of Volatile Signatures through GC-MS Analysis of Bacterial and Fungal Infections in Stored Potatoes (Solanum tuberosum L.).

Potatoes inoculated with Pectobacterium carotovorum spp., Aspergillus flavus and Aspergillus niger, along with healthy (control) samples, were stored at different storage temperatures (4 ± 1 °C, 8 ± 1 °C, 25 ± 1 °C) for three weeks. Volatile organic compounds (VOCs) were mapped using the headspace gas analysis through solid phase micro extraction-gas chromatography-mass spectroscopy every week. The VOC data were arranged into different groups and classified using principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) models. Based on a variable importance in projection (VIP) score > 2 and the heat map, prominent VOCs were identified as 1-butanol and 1-hexanol, which can act as biomarkers for Pectobacter related bacterial spoilage during storage of potatoes in different conditions. Meanwhile, hexadecanoic acid and acetic acid were signature VOCs for A. flavus, and hexadecane, undecane, tetracosane, octadecanoic acid, tridecene and undecene were associated with A. niger. The PLS-DA model performed better at classifying the VOCs of the three different species of infection and the control sample compared to PCA, with high values of R2 (96-99%) and Q2 (0.18-0.65). The model was also found to be reliable for predictability during random permutation test-based validation. This approach can be adopted for fast and accurate diagnosis of pathogenic invasion of potatoes during storage.

Seasonal Variation in Raw Milk VOC Profile within Intensive Feeding Systems.

The study aimed to assess the seasonal variation in raw milk volatile organic compounds (VOCs) from three indoor feeding systems based on maize silage (n = 31), silages/hay (n = 19) or hay (n = 16). After headspace solid-phase microextraction (HS-SPME), VOC profiles were determined by gas chromatography (GC). Chemical and VOC (log10 transformations of the peak areas) data were submitted to a two-way ANOVA to assess the feeding system (FS) and season (S) effects; an interactive principal component analysis (iPCA) was also performed. The interaction FS × S was never significant. The FS showed the highest (p < 0.05) protein and casein content for hay-milk samples, while it did not affect any VOCs. Winter milk had higher (p < 0.05) proportions of protein, casein, fat and some carboxylic acids, while summer milk was higher (p < 0.05) in urea and 2-pentanol and methyl aldehydes. The iPCA confirmed a seasonal spatial separation. Carboxylic acids might generate from incomplete esterification in the mammary gland and/or milk lipolytic activity, while aldehydes seemed to be correlated with endogenous lipid or amino acid oxidation and/or feed transfer. The outcomes suggested that VOCs could be an operative support to trace raw milk for further mild processing.

Influence of ozone and humidity on PTR-MS and GC-MS VOC measurements with and without a Na2S2O3 ozone scrubber

Abstract. The measurement of volatile organic compounds (VOCs) can be influenced by ozone (O3), resulting in sampling artefacts that corrupt the data obtained. Published literature reports both positive (false enhancements of signal) and negative (loss of signal) interference in VOC data due to ozonolysis occurring in the sample gas. To assure good data quality it is essential to be aware of such interfering processes, to characterize them and to try to minimize the impact with a suitable sampling setup. Here we present results from experiments with a sodium thiosulfate ozone scrubber (Na2S2O3), which is a cost-effective and easily applied option for O3 scavenging during gas-phase sampling. Simultaneous measurement of selected organic trace gases using gas chromatography–mass spectrometry and proton transfer reaction–mass spectrometry was performed at different ozone levels (0–1 ppm) and different relative humidities (0 %–80 %). In this way both tropospheric and stratospheric conditions were examined. The measured data show that several carbonyl compounds including acetaldehyde, acetone and propanal show artificial signal enhancement when ozone is present at higher concentrations (&gt; 150 ppb) in dry air, while analytes with double bonds like isoprene (measured with GC-MS) and terpenes show lower signals due to reaction with ozone. Both effects can be eliminated or in the case of sesquiterpenes substantially reduced by using Na2S2O3 impregnated quartz filters in the inlet line. With the chosen scrubbing material, relative humidity (RH) substantially improves the scrubbing efficiency. Under surface conditions between 50 %–80 % RH, the filter allows for accurate measurement of all species examined.