Multiple Volatile Organic Compounds Research Articles

Fluorination modification enhanced the water resistance of Universitetet i Oslo-67 for multiple volatile organic compounds adsorption under high humidity conditions: Mechanism study

The construction of metal–organic frameworks (MOFs) with highly efficient capture for volatile organic compounds (VOCs) adsorption under humid conditions is a significant yet formidable task. Herein, series of fluorinated UiO-67 modified with trifluoroacetic acid (TFA) and 4-fluorobenzoic acid were successfully synthesized for VOCs adsorption under high humidity conditions. Experiments results showed that UiO-67 modified with 4-fluorobenzoic acid (67-F) presented excellent adsorption capacity of 345 mg/g for toluene adsorption and exhibited great water resistance (10.0 vol% H2O, 374 mg/g toluene adsorption capacity). Characterization results indicated that the introduction of 4-fluorobenzoic acid induced the competitive coordination between 4-fluorobenzoic acid and 4,4-biphenyl dicarboxylic acid (BPDC) with Zr4+, causing the formation of abundant defects to provide extra adsorption sites. Meanwhile, the benzene ring in 4-fluorobenzoic acid enhanced the π-π conjugation, causing the further promotion of VOCs adsorption capacity. More importantly, the water resistance mechanism was investigated and elucidated that the introduction of F decreased the surface energy of 67-F and its affinity with water. Meanwhile, the metal complex induced by the fluorinated modification produced an electron-dense pore environment, which greatly improved its chemical and water stability. This work provided a strategy for preparing an adsorbent with high water resistance for real-world VOCs adsorption at high humidity conditions.

Single and mixed effects of multiple volatile organic compounds exposure on hematological parameters in the U.S. adult population

BackgroundMost research exploring the correlation between volatile organic compounds (VOCs) and hematological parameters have focused on single VOCs. Our study aimed to explore the single and combined effects of VOCs on hematological parameters through three statistical models. MethodsData from 4 cycles of the National Health and Nutrition Examination Survey (NHANES) were used in this study. The correlations between single exposure to 16 VOCs and hematological parameters in the general population were assessed by weighted multiple linear regression. Weighted quantile sum (WQS) and Bayesian kernel machine regression (BKMR) models were used to explore the relationship between the combined important VOCs selected by the least absolute shrinkage and selection operator (LASSO) and hematological parameters, as well as the effects of smoking status on them. ResultsA total of 4089 adults were included in the study. We found that a variety of VOCs were significantly associated with hematological parameters. Among them, N-acetyl-S-(benzyl)-l-cysteine (BMA) was significantly negatively correlated with white blood cell (WBC), red blood cell (RBC), lymphocyte, and neutrophil counts. N-acetyl-S-(3-hydroxypropyl-1-methyl)-l-cysteine (HPMMA) was significantly positively correlated with WBC, monocyte, lymphocyte, and neutrophil counts. In the WQS analysis, the WQS index of the VOCs mixtures was positively correlated with WBC (β: 0.031; P < 0.001), monocyte (0.023; P = 0.021), and neutrophil (0.040; P = 0.001) counts, while negatively associated with RBC (−0.013; P < 0.001) counts. The BKMR model revealed that combined exposure to VOCs levels ≥70th percentile was significantly associated with lower RBC counts, and BMA was identified as the dominant contributor. Smoking significantly influenced the relationship between VOCs and hematological parameters. ConclusionsOur study indicated the effects of single and overall VOCs exposure on hematological parameters and suggested the hematotoxicity as well as pro-inflammatory effects of VOCs, which had strong public health implications for reducing the potential health hazards of VOCs exposure to the hematologic system.

Breath analysis system with convolutional neural network (CNN) for early detection of lung cancer

Early diagnosis of lung cancer, the leading cause of cancer-related death worldwide, is critical for reducing mortality rate. However, current diagnostic methods are invasive, time-consuming, costly, and may not always provide accurate diagnoses. For early diagnosis, recent research has focused on noninvasive approaches, including the detection of volatile organic compounds (VOCs) in human exhaled breath. Changes in the composition and concentration of VOCs in exhaled breath may indicate lung cancer, and this approach offers several advantages over traditional diagnostic methods. Moreover, the combination of a breath gas sensing system and machine learning algorithms provides a more accurate diagnosis. In this study, for the early diagnosis of lung cancer, a breath analysis system was developed using a gas sensor array and deep learning algorithm. The breath analysis system was designed to detect multiple VOCs in exhaled breath using ten semiconductor metal oxide (SMO), one photoionization detector (PID), nine electrochemical (EC) gas sensors. In total, 181 clinical breath samples (from 74 healthy controls and 107 lung cancer patients) were collected and analyzed using a 1D convolutional neural network (CNN) algorithm. The results showed an overall accuracy of 97.8% in classifying healthy controls and lung cancer patients using a complete clinical dataset. Through a comparison of the single-sensor type data and multimodal sensor data and performance analysis of three different deep learning models (multilayer perceptron, recurrent neural network, and CNN), we validated the potential of the breath analyzer with a multimodal sensor system and a 1D CNN as a lung cancer diagnostic device.

Hybrid plasmonic aerogel with tunable hierarchical pores for size-selective multiplexed detection of VOCs with ultrahigh sensitivity

Sensitive and rapid identification of volatile organic compounds (VOCs) at ppm level with complex composition is vital in various fields ranging from respiratory diagnosis to environmental safety. Herein, we demonstrate a SERS gas sensor with size-selective and multiplexed identification capabilities for VOCs by executing the pre-enrichment strategy. In particular, the macro-mesoporous structure of graphene aerogel and micropores of metal-organic frameworks (MOFs) significantly improved the enrichment capacity (1.68 mmol/g for toluene) of various VOCs near the plasmonic hotspots. On the other hand, molecular MOFs-based filters with different pore sizes could be realized by adjusting the ligands to exclude undesired interfering molecules in various detection environments. Combining these merits, graphene/AuNPs@ZIF-8 aerogel gas sensor exhibited outstanding label-free sensitivity (up to 0.1 ppm toluene) and high stability (RSD=14.8%, after 45 days storage at room temperature for 10 cycles) and allowed simultaneous identification of multiple VOCs in a single SERS measurement with high accuracy (error < 7.2%). We visualize that this work will tackle the dilemma between sensitivity and detection efficiency of gas sensors and will inspire the design of next-generation SERS technology for selective and multiplexed detection of VOCs.

Treatment of VOCs with a wet scrubbing – catalytic ozonation process: Efficiency, mechanism and pilot-scale application

A wet scrubbing – catalytic ozonation (WCO) process with activated carbon (AC) loaded MnOx (MnOx/AC) as the catalyst was developed, based on volatile organic compounds (VOCs) can be efficiently removed. The mineralization efficiency, degradation pathway and mechanism, and effects of the process operating parameters were investigated in detail using toluene as the target pollutant. The results show that the average removal efficiency of toluene reached 71.82 % within 90 min under optimal conditions (CToluene = 100 mg/m3; CO3 = 1 mg/L; catalyst loading = 5 g/L; pH = 7; gas flow = 500 mL/min), which outperformed that of the gaseous system. Quenching experiments and electron spin resonance spectroscopy indicate that OH and O2− are the two main reactive oxygen species (ROSs) in the system, which facilitate the opening of the benzene ring and significantly reduce by-products. The intermediates of toluene degradation were identified by proton-transfer reaction time-of-flight mass spectroscopy (PTR-TOF-MS). The results indicate that most of the intermediates generated in the system are captured in water rather than being emitted into the atmosphere. In the pilot-scale experiments, the WCO process exhibited a stable VOC removal efficiency (≥61.05 %) for 10 d in the treatment of actual low-concentration VOCs. Therefore, in this study, an effective process for the removal of low-concentration VOCs is proposed, which can be used for multiple VOC treatments.

Micro-heater embedded Ni-SnO2 ordered nanoporous films: On-chip fabrication for fast, sensitive, and selective gas sensing towards multiple VOCs for air quality monitoring

The ability to simultaneously detect multiple volatile organic compounds (VOCs) is crucial for assessing indoor air quality and addressing significant health hazards. In this study, we present a sacrificial template approach for on-chip fabrication of two-dimensional (2D) Ni-doped SnO2 nanoporous sensing films on micro-heater substrates. The technical analysis shows that the resulting honeycomb-like ultrathin Ni-SnO2 sensing layer on a suspended substrate maintains perfect structural integrity and consistency. The Ni-SnO2 sensors exhibit exceptional characteristics, including a ppb-level detection limit, high sensitivity, fast response/recovery time, and signal repeatability for typical VOCs, while operating at a low power consumption of 25 mW. Additionally, the Ni-doping enhances the sensors' cross-sensitivity to various forms of VOCs, while causing a discernible response inhibition towards flammable or exhaust gases. Furthermore, X-ray photoelectron spectroscopy (XPS) and in-situ Raman spectra reveal a Ni2+-substitution induced surface adsorbed oxygen species enrichment mechanism for sensing enhancement. Thus, the developed Ni-SnO2 sensors hold promise as future candidates for reliable air quality assessment by real-time monitoring of overall concentrations of multiple VOCs.

Feature extractions from transfer characteristics of hybrid GO FETs for selective detection of volatile organic compounds

The current work concerns a new strategy for selective classifications of multiple volatile organic compounds (VOCs) by using an array of field effect transistor (FET) type sensors and extraction of device-specific features from transfer characteristics of the FET-sensors to use in classification algorithm(s). A few layered graphene oxide (GO) was used as the base channel materials which was then functionalized very precisely with different metal oxides (WO3, TiO2) and metals (Au, Pd) nanoforms to construct an array of five FET structure sensors on SiO2/Si platform. The deviation in Id-Vgs characteristics in different VOCs were accounted with a distinctive features. The features were organized based on their importance score and used to construct different feature matrices. A minimum four sensors and six features were required to successful classification of seven VOCs by linear discriminant analysis (LDA). The classification accuracy was tested with supportive vector machine (SVM) classifier and it was 92.86%.

Comprehensive analysis between volatile organic compound (VOC) exposure and female sex hormones: a cross-sectional study from NHANES 2013-2016.

There is growing evidence suggesting that exposure to volatile organic compounds (VOCs) can pose significant health risks, including interference with the function of the reproductive system. However, there has been a lack of research focused on the impact of common environmental VOCs on the levels of sex hormones in the general female population. In this study, we conducted a cross-sectional analysis utilizing the database of the National Health and Nutrition Examination Survey (NHANES, 2013-2016). A total of 2633 participants were included in this study. The Pearson correlation model revealed the potential of co-exposure or co-toxicity between benzene and 2,5-dimethylfuran. According to GLM models, we discovered a significant positive association between blood levels of 2,5-dimethylfuran and benzene with testosterone levels in women. Subgroup analysis further identified that the women with underweight and healthy weight might be the high-risk subgroup. Bayesian kernel machine regression (BKMR) was applied to further assess the univariate and bivariate exposure-response relationships between multiple VOCs. Our research systemically formulated the possible relationship between exposure to VOCs and female sex hormones, indicating the role of VOCs as a risk factor for endocrine disruption, especially benzene and 2,5-dimethylfuran. These findings have important implications for public health and call for further investigation.

Single-chemical and mixture effects of multiple volatile organic compounds exposure on liver injury and risk of non-alcoholic fatty liver disease in a representative general adult population

Evidence on liver injury and non-alcoholic fatty liver disease (NAFLD) from volatile organic compounds (VOCs) exposure is insufficient. A cross-sectional study including 3011 US adults from the National Health and Nutrition Examination Survey was conducted to explore the associations of urinary exposure biomarkers (EBs) for 13 VOCs (toluene, xylene, ethylbenzene, styrene, acrylamide, N,N-dimethylformamide, acrolein, crotonaldehyde, 1,3-butadiene, acrylonitrile, cyanide, propylene oxide, and 1-bromopropane) with liver injury biomarkers and the risk of NAFLD by performing single-chemical (survey weight regression) and mixture (Bayesian kernel machine regression [BKMR] and weighted quantile sum [WQS]) analyses. We found significant positive associations of EBs for toluene and 1-bromopropane with alanine aminotransferase (ALT), EBs for toluene, crotonaldehyde, and 1,3-butadiene with asparate aminotransferase (AST), EBs for 1,3-butadiene and cyanide with alkaline phosphatase (ALP), EBs for xylene and cyanide with hepamet fibrosis score (HFS), EBs for the total 13 VOCs (except propylene oxide) with United States fatty liver index (USFLI), and EBs for xylene, N,N-dimethylformamide, acrolein, crotonaldehyde, and acrylonitrile with NALFD; and significant inverse associations of EBs for ethylbenzene, styrene, acrylamide, acrolein, crotonaldehyde, 1,3-butadiene, acrylonitrile, cyanide, and propylene oxide with total bilirubin, EBs for ethylbenzene, styrene, acrylamide, acrolein, 1,3-butadiene, acrylonitrile, and cyanide with albumin (ALB), EBs for ethylbenzene, styrene, acrylamide, N,N-dimethylformamide, acrolein, crotonaldehyde, 1,3-butadiene, acrylonitrile, cyanide, and propylene oxide with total protein (TP), and EB for 1-bromopropane with AST/ALT (all P-FDR<0.05). In BKMR and WQS, the mixture of VOC-EBs was significantly positively associated with ALT, AST, ALP, HFS, USFLI, and the risk of NAFLD, while significantly inversely associated with TBIL, ALB, TP, and AST/ALT. VOCs exposure was associated with liver injury and increased risk of NAFLD in US adults. These findings highlight that great attention should be paid to the potential risk of liver health damage from VOCs exposure.

Nanostructural evolution of hydrothermally grown SrTiO3 perovskite and its implementation in gaseous phase detection of ethanol

A group of SrTiO3 nanostructures with unique nano-architecture have been synthesized in the current study. Sol–gel derived TiO2 nanoparticles along with Sr(OH)2 solution was processed with facial hydrothermal reaction at 180 °C and highly stable and distinct morphologies of SrTiO3 were developed after different reaction time. Nanobush, nanograss, nanorod and nanosphere morphologies were created after 10, 14, 18 and 24 h of hydrothermal reaction. SrTiO3 nanosphere was transformed into nano-hollow sphere morphology after thermal annealing at 600 °C. Detailed morphological, structural and chemical characterizations were carried out for all the distinct nanoforms of SrTiO3 where they exhibited high crystallinity, and chemical stability along with excellent surface properties like high porosity, roughness, and large effective surface area. Due to having rich surface properties, all the SrTiO3 morphologies were then implemented for gaseous phase detection of multiple volatile organic compounds (VOCs). However, all the SrTiO3 nanoforms showed ethanol selective behavior among all the VOCs. Nanograss and nano-hollow spheres exhibited excellent ethanol sensing with 69 and 78 response values (R v/R a) in 50 ppm ethanol at 150 °C with appreciably fast response/recovery times of 36 s/34 s and 150 s/ 58 s, respectively. Additionally, all the SrTiO3 nanostructures exhibited anti-humidity characteristics and potential sensing in humid ambient (up to 80% RH). Later, the ethanol selective behavior of SrTiO3 was established by density functional theory simulations which envisaged the highest negative adsorption energy and smallest distance (r) for ethanol molecule, implying stable adsorption with SrTiO3 (110) system.

Potential of a Miniature Spectral Analyzer for District-Scale Monitoring of Multiple Gaseous Air Pollutants.

Gas sensors that can measure multiple pollutants simultaneously are highly desirable for on-site air pollution monitoring at various scales, both indoor and outdoor. Herein, we introduce a low-cost multi-parameter gas analyzer capable of monitoring multiple gaseous pollutants simultaneously, thus allowing for true analytical measurement. It is a spectral sensor consisting of a Fourier-transform infrared (FTIR) gas analyzer based on a mid-infrared (MIR) spectrometer. The sensor is as small as 7 × 5 × 2.5 cm3. It was deployed in an open-path configuration within a district-scale climatic chamber (Sense City, Marne-la-Vallée, France) with a volume of 20 × 20 × 8 m3. The setup included a transmitter and a receiver separated by 38 m to enable representative measurements of the entire district domain. We used a car inside the climatic chamber, turning the engine on and off to create time sequences of a pollution source. The results showed that carbon dioxide (CO2) and water vapor (H2O) were accurately monitored using the spectral sensor, with agreement with the reference analyzers used to record the pollution levels near the car exhaust. Furthermore, the lower detection limits of CO, NO2 and NO were assessed, demonstrating the capability of the sensor to detect these pollutants. Additionally, a preliminary evaluation of the potential of the spectral sensor to screen multiple volatile organic compounds (VOCs) was conducted at the laboratory scale. Overall, the results demonstrated the potential of the proposed multi-parameter spectral gas sensor in on-site gaseous pollution monitoring.

Swellable Array Strategy Based on Designed Flexible Double Hypercross-linked Polymers for Synergistic Adsorption of Toluene and Formaldehyde.

High-capacity adsorption and removal of complex volatile organic compounds (VOCs) from real-world environments is a tough challenge for researchers. Herein, a swellable array adsorption strategy was proposed to realize the synergistic adsorption of toluene and formaldehyde on the flexible double hypercross-linked polymers (FD-HCPs). FD-HCPs exhibited multiple adsorption sites awarded by a hydrophobic benzene ring/pyrrole ring and a hydrophilic hydroxyl structural unit. The array benzene ring, hydroxyl, and pyrrole N sites in FD-HCPs effectively captured toluene and formaldehyde molecules through π-π conjugation and electrostatic interaction and weakened their mutual competitive adsorption. Interestingly, the strong binding force of toluene molecules to the skeleton deformed the pore structure of FD-HCPs and generated new adsorption microenvironments for the other adsorbate. This behavior significantly improved the adsorption capacity of FD-HCPs for toluene and formaldehyde by 20% under multiple VOCs. Moreover, the pyrrole group in FD-HCPs greatly hindered H2O molecule diffusion in the pore, thus efficiently weakening the competitive adsorption of H2O toward VOCs. These fascinating properties enabled FD-HCPs to achieve synergistic adsorption for multicomponent VOC vapor under a highly humid environment and overcame single-species VOC adsorption properties on state-of-the-art porous adsorbents. This work provides the practical feasibility of synergistic adsorption to remove complex VOCs in real-world environments.

Smart Miniature Mass Spectrometer Enabled by Machine Learning.

Similar to smartphones, smart or automatic level is also a critical feature for a miniature mass spectrometer. Compared to large-scale instruments, miniature mass spectrometers often have a lower mass resolution and larger mass drift, making it challenging to identify molecules with close mass-charge ratios. In this work, a miniature mass spectrometer (the Brick-V model) was combined with intelligent algorithms to realize rapid and accurate identification. This Brick-V mass spectrometer developed in our lab was equipped with a vacuum ultraviolet photoionization (VUV-PI) source, which ionizes volatile organic compounds (VOCs) with minor fragments. Machine learning would be especially helpful when analyzing samples with multiple characteristic peaks. Four machine learning algorithms were tested and compared in terms of precision, recall, balanced F score (F1 score), and accuracy. After optimization, the multilayer perceptron (MLP) method was selected and first applied for the automatic identification and differentiation of ten different fruits. By recognizing the pattern of multiple VOCs diffused from fruits, an average accuracy of 97% was achieved. This system was further applied to determine the freshness of strawberries, and strawberry picking at different times (especially during the first 24 h at room temperature of winter) could be well discriminated. After building a database of 63 VOCs, a rapid method to identify compounds in the database was established. In this method, molecular ions, fragment ions, and dimer ions in the full mass spectrum were all utilized in the machine learning program. A satisfactory prediction accuracy for the 63 VOCs could be achieved (>99%).

CuO–ZnO p-n junctions for accurate prediction of multiple volatile organic compounds aided by machine learning algorithms

Detection and quantification of multiple volatile organic compounds (VOCs) are emerging as critical requirements for several niche applications including healthcare. It is desirable to get multiple gases identified rapidly and using minimum number of sensors. Heterojunctions of metal oxides are still among the top-picks for efficient VOC sensing because they unfold exciting sensing characteristics in addition to enhanced response. This work reports the synthesis of nanostructures of CuO, ZnO, and three CuO–ZnO p-n junctions having different weight percentages (1–0.5, 1-1, and 0.5–1) of CuO and ZnO, using a facile one-pot hydrothermal method. The nanomaterials were characterized using X-ray diffraction, field emission scanning electron microscopy, and UV–Visible spectroscopy. Resistive sensors were fabricated of all five nanomaterials and were tested for 25–200 ppm of four VOCs – isopropanol, methanol, acetonitrile, and toluene. The CuO and CuO–ZnO (1–0.5) sensors showed the highest response for isopropanol (7.5–65.3% and 19–122%, respectively) at 250 °C, CuO–ZnO (1-1) and CuO–ZnO (0.5–1) exhibited the highest responses for methanol (9–60%) and isopropanol (15–120%), respectively at 350 °C, and the intrinsic ZnO showed maximum response to toluene (29–76%) at 400 °C. All the sensing layers were observed to exhibit finite responses to the other three VOCs so, an attempt to classify and quantify the four VOCs accurately was made using support vector machine (SVM) and multiple linear regression (MLR) algorithms. The response and response times of two sensors were observed to be sufficient as inputs to the machine learning algorithms for classifying and quantifying all the four VOCs. The combinations of (CuO–ZnO (1–0.5) & (1-1) and CuO–ZnO (1-1) & (0.5–1) demonstrated the highest classification accuracy of 98.13% with SVM. The combination of CuO–ZnO (1–0.5) & (1-1) demonstrated the best quantification of the four VOCs using MLR.

Urinary metabolites of multiple volatile organic compounds, oxidative stress biomarkers, and gestational diabetes mellitus: Association analyses

Volatile organic compounds are ubiquitous in the environment, which may cause various adverse health effects. The objectives of this study were to investigate associations of single and mixture of urinary metabolites of volatile organic compounds (mVOCs) with gestational diabetes mellitus (GDM) risk, and examine the possible role of oxidative stress in the associations. This nested case-control study included 454 GDM cases and 454 healthy controls matched by maternal age and infant sex. Urinary concentrations of twenty-one mVOCs and three oxidative stress biomarkers (including 8-OHdG, 8-OHG, and HNEMA), in early pregnancy were measured. Analyses using logistic regression model showed that an interquartile range increase in urinary concentrations of six individual mVOCs (ATCA, BPMA, CEMA, 3HPMA, MU, and TGA) were significantly associated with increased odds of GDM by 19–27%. Weighted quantile sum regression analyses showed that in each quartile increment of the mixture of mVOCs, the odds of GDM increased by 39% (95% CI: 16%, 67%), with 2-aminothiazoline-4-carboxylic acid weighted the most in the associations (weight: 25%). Furthermore, significant associations of the oxidative stress biomarkers with both GDM and certain mVOCs were observed. These results suggested that certain urinary mVOCs (correspondingly, the parent VOCs such as 1-bromopropane, cyanide, and benzene should be concerned as priority ones for regulation and policy making) in early pregnancy were significantly associated with elevated GDM incidence, and the associations were potentially related with oxidative stress biomarkers.

Facile Synthesis of ZnO/WO3 Nanocomposite Porous Films for High-Performance Gas Sensing of Multiple VOCs.

Volatile organic compounds (VOCs) in indoor environments have typical features of multiple components, high concentration, and long duration. The development of gas sensors with high sensitivity to multiple VOCs is of great significance to protect human health. Herein, we proposed a sensitive ZnO/WO3 composite chemi-resistive sensor facilely fabricated via a sacrificial template approach. Based on the transferable properties of self-assembled monolayer colloidal crystal (MCC) templates, two-dimensional honeycomb-like ordered porous ZnO/WO3 sensing matrixes were constructed in situ on commercial ceramic tube substrates with curved and rough surfaces. The nanocomposite thin films are about 250 nm in thickness with large-scale structural consistency and integrity, which facilitates characteristic responses with highly sensitivity and reliability. Furthermore, the nanocomposite sensor shows simultaneous responses to multiple VOCs that commonly exist in daily life with an obvious suppression sensing for traditional flammable gases. Particularly, a detection limit of 0.1 ppm with a second-level response/recovery time can be achieved, which is beneficial for real-time air quality assessments. We proposed a heterojunction-induced sensing enhancement mechanism for the ZnO/WO3 nanocomposite film in which the formation of abundant heterojunctions between ZnO and WO3 NPs significantly increases the thickness of the electron depletion layer in the bulk film and improves the formation of active oxygen species on the surface, which is conducive to enhanced responses for reducing VOC gases. This work not only provides a simple approach for the fabrication of high-performance gas sensors but also opens an achievable avenue for air quality assessment based on VOC concentration detection.

Exposure to Volatile Organic Compounds Is Associated with Hypertension in Black Adults: The Jackson Heart Study

BackgroundThe prevalence of hypertension is higher among Black adults than among White and Hispanic adults. Nevertheless, reasons underlying the higher rates of hypertension in the Black population remain unclear but may relate to exposure to environmental chemicals such as volatile organic compounds (VOCs). MethodsWe evaluated the associations of blood pressure (BP) and hypertension with VOC exposure in non-smokers and smokers in a subgroup of the Jackson Heart Study (JHS), consisting of 778 never smokers and 416 age- and sex-matched current smokers. We measured urinary metabolites of 17 VOCs by mass spectrometry. ResultsAfter adjusting for covariates, we found that amoong non-smokers, metabolites of acrolein and crotonaldehyde were associated with a 1.6 mm Hg (95%CI: 0.4, 2.7; p = 0.007) and a 0.8 mm Hg (95%CI: 0.01, 1.6; p = 0.049) higher systolic BP, and the styrene metabolite was associated with a 0.4 mm Hg (95%CI: 0.09, 0.8, p = 0.02) higher diastolic BP. Current smokers had 2.8 mm Hg (95% CI 0.5, 5.1) higher systolic BP. They were at higher risk of hypertension (relative risk = 1.2; 95% CI, 1.1, 1.4), and had higher urinary levels of several VOC metabolites. Individuals who smoke had higher levels of the urinary metabolites of acrolein, 1,3-butadiene, and crotonaldehyde and were associated with higher systolic BP. The associations were stronger among participants who were <60 years of age and male. Using Bayesian kernel machine regression to assess the effects of multiple VOC exposures, we found that the relationship between VOCs and hypertension among non-smokers was driven primarily by acrolein and styrene in non-smokers, and crotonaldehyde in smokers. ConclusionsHypertension in Black individuals may be attributed, in part, to VOC exposure from the environment or tobacco smoke.

Urinary metabolites of multiple volatile organic compounds among pregnant women across pregnancy: Variability, exposure characteristics, and associations with selected oxidative stress biomarkers

Volatile organic compounds (VOCs) are a group of pollutants pervasive in daily life with identified adverse health effects. However, no study has investigated the variability in VOC metabolites during pregnancy and their relationships with oxidative stress biomarkers in pregnant women. In the present study, the variability of 21 selected VOC metabolites was examined and their relationships with three selected oxidative stress biomarkers measured in spot urine samples at three trimesters of 1094 pregnant women were analyzed. Nineteen VOC metabolites were ubiquitous in the urine samples with detection rates ranging from 75.9% to 100%. Monohydroxybutenyl mercapturic acid (MHBMA) and s-phenyl mercapturic acid (PMA) had detection rates lower than 1.00%. Intraclass correlation coefficients (ICCs) of the detected analytes at three trimesters ranged 0.07–0.24, and the concentrations were highest in the first trimester. Higher concentrations of some VOC metabolites were related with participant characteristics including higher pre-pregnancy body mass index (BMI), lower education level, unemployment during pregnancy, multiparity, and sampling season of summer or winter. In repeated cross-sectional analyses, interquartile range (IQR) increases in the 19 detected VOC metabolites were positively related with 8-hydroxy-2′-deoxyguanosine (8-OHdG), 8-hydroxyguanosine (8-OHG), and 4-hydroxy nonenal mercapturic acid (HNEMA) with the estimates ranging from 9.00% to 204%. The mixture effect of the VOC metabolites on the oxidative stress biomarkers was further assessed using weighted quantile sum regression (WQS) models and the results showed that the WQS index of VOC metabolite mixture was significantly associated with 8-OHdG (β: 0.37, 0,32, and 0.39 at the 1st, 2nd, and 3rd trimester, respectively), 8-OHG (0.38, 0.32, and 0.39) and HNEMA (1.21, 1.08, and 1.10). Glycidamide mercapturic acid (GAMA), and trans,trans-muconic acid (MU) were the strongest contributors of the mixture effect on 8-OHdG, 8-OHG, and HNEMA, respectively. Overall, urinary concentrations of the VOC metabolites during pregnancy were strongly associated with the oxidative stress biomarkers.

Ultra-trace detection and efficient adsorption removal of multiple water-soluble volatile organic compounds by fluorescent sensor array.

Due to the extremely low concentration, complex composition and easy to be converted into each other in water and air of water-soluble volatile organic compounds (VOCs), it is a great challenge to the traditional detection technology, pollution control and traceability, etc. Therefore, developing a convenient, swift and on-site detection method for simultaneous quantification of multiple VOCs is highly anticipated. In this paper, a multifunctional sensor array with adsorption and sensing of VOCs has been constructed by four fluorescence channels of small-sized Eu@Uio-66 and Tb@Uio-66. Due to the obvious cross-reactive characteristics between 4 fluorescence channels and VOCs, the sensor array could detect 8 VOCs simultaneously with all detection limits as low as ppb level. In addition, the detection results of sensor array for actual water samples coexisting with multiple VOCs confirmed that it has strong anti-interference performance and could be used for simultaneous detection of multiple VOCs in real water. The construction of sensor array with VOC adsorption function not only helps to reduce the detection limit of VOCs benefiting from the pre-concentration of materials, but also has significant value to reduce the harmfulness of pollutants. Predictably, this work is of great significance for VOC traceability, analysis of ecotoxicological effects and monitoring of pollution distribution characteristics.

Enrichment cultivation of VOC-degrading bacteria using diffusion bioreactor and development of bacterial-immobilized biochar for VOC bioremediation

Volatile organic compounds (VOCs) have been globally reported at various sites. Currently, limited literature is available on VOC bioremediation using bacterial-immobilized biochar (BC–B). In this study, multiple VOC-degrading bacteria were enriched and isolated using a newly designed diffusion bioreactor. The most effective VOC-degrading bacteria were then immobilized on rice husk-derived pristine biochar (BC) to develop BC-B. Finally, the performances of BC and BC-B for VOCs (benzene, toluene, xylene, and trichloroethane) bioremediation were evaluated by establishing batch microcosm experiments (Control, C; bioconsortium, BS; pristine biochar, BC; and bacterial-immobilized biochar, BC-B). The results revealed that the newly designed diffusion bioreactor effectively simulated native VOC-contaminated conditions, easing the isolation of 38 diverse ranges of VOC-degrading bacterial strains. Members of the genus Pseudomonas were isolated in the highest (26.33%). The most effective bacterial strain was Pseudomonas sp. DKR-23, followed by Rhodococcus sp. Korf-18, which degraded multiple VOCs in the range of 52–75%. The batch microcosm experiment data showed that BC-B remediated the highest >90% of various VOCs, which was comparatively higher than that of BC, BS, and C. In addition, compared with C, the BS, BC, and BC-B microcosms abundantly reduced the half-life of various VOCs, implying a beneficial impact on the degradation behavior of VOCs. Altogether, this study suggests that a diffusion bioreactor system can be used as a cultivation device for the isolation of a wide range of VOC-degrading bacterial strains, and a compatible combination of biochar and bacteria may be an attractive and promising approach for the sustainable bioremediation of multiple VOCs.