Human respiratory system as sink for volatile organic compounds: Evidence from field measurements

Exposure to volatile organic compounds and mortality in US adults: A population-based prospective cohort study

Volatile organic compounds (VOCs) are ubiquitous in both indoor and outdoor environments. Evidence on the associations of individual and joint VOC exposure with all-cause and cause-specific mortality is limited. Measurements of 15 urinary VOC metabolites were available to estimate exposure to 12 VOCs in the National Health and Nutritional Examination Survey (NHANES) 2005-2006 and 2011-2018. The environment risk score (ERS) was calculated using LASSO regression to reflect joint exposure to VOCs. Follow-up data on death were obtained from the NHANES Public-Use Linked Mortality File through December 31, 2019. Cox proportional hazard models and restricted cubic spline models were applied to evaluate the associations of individual and joint VOC exposures with all-cause and cause-specific mortality. Population attributable fractions were calculated to assess the death burden attributable to VOC exposure. During a median follow-up of 6.17 years, 734 (8.34 %) deaths occurred among 8799 adults. Urinary metabolites of acrolein, acrylonitrile, 1,3-butadiene, and ethylbenzene/styrene were significantly associated with all-cause, cardiovascular disease (CVD), respiratory disease (RD), and cancer mortality in a linear dose-response manner. Linear and robust dose-response relationships were also observed between ERS and all-cause and cause-specific mortality. Each 1-unit increase in ERS was associated with a 33.6 %, 39.1 %, 109.8 %, and 67.8 % increase for all-cause, CVD, RD, and cancer mortality risk, respectively. Moreover, joint exposure to VOCs contributed to 17.95 % of all-cause deaths, 13.49 % of CVD deaths, 35.65 % of RD deaths, and 33.85 % of cancer deaths. Individual and joint exposure to VOCs may enhance the risk of all-cause and cause-specific mortality. Reducing exposure to VOCs may alleviate the all-cause and cause-specific death burden.

Noninvasive Diabetes Detection through Human Breath Using TinyML-Powered E-Nose

Volatile organic compounds (VOCs) in exhaled human breath serve as pivotal biomarkers for disease identification and medical diagnostics. In the context of diabetes mellitus, the noninvasive detection of acetone, a primary biomarker using electronic noses (e-noses), has gained significant attention. However, employing e-noses requires pre-trained algorithms for precise diabetes detection, often requiring a computer with a programming environment to classify newly acquired data. This study focuses on the development of an embedded system integrating Tiny Machine Learning (TinyML) and an e-nose equipped with Metal Oxide Semiconductor (MOS) sensors for real-time diabetes detection. The study encompassed 44 individuals, comprising 22 healthy individuals and 22 diagnosed with various types of diabetes mellitus. Test results highlight the XGBoost Machine Learning algorithm's achievement of 95% detection accuracy. Additionally, the integration of deep learning algorithms, particularly deep neural networks (DNNs) and one-dimensional convolutional neural network (1D-CNN), yielded a detection efficacy of 94.44%. These outcomes underscore the potency of combining e-noses with TinyML in embedded systems, offering a noninvasive approach for diabetes mellitus detection.

The exposure to volatile organic chemicals associates positively with rheumatoid arthritis: a cross-sectional study from the NHANES program

Introduction

Rheumatoid arthritis (RA) is an autoimmune disease and closely associated with both genetic and environmental factors. Volatile organic chemicals (VOC), a common environment pollutant, was associated with some autoimmune diseases, while whether VOC exposure or which VOC leads to RA is yet clarified.

Methods

A cross-sectional study using data from the 6 survey cycles (2005-2006, 2011-2012, 2013-2014, 2015-2016, 2017-2018, 2017-2020) of NHANES program was performed. The RA or non-arthritis status of participant was identified through a questionnaire survey. The quantile logistic regression method was used for correlation analysis between VOC metabolites (VOCs) in urine and RA. The covariates included age, gender, race, educational level, marital status, total energy intake, physical activity, smoking, hypertension, diabetes, urine creatinine, albumin and marihuana use.

Results

A total of 9536 participants (aged 20 to 85) with 15 VOCs, comprising 618 RA and 8918 non-arthritis participants, was finally included for analysis. Participants in the RA group showed higher VOCs in urine than that in the non-arthritis group. A positive association between 2 VOCs (AMCC: Q4: OR=2.173, 95%CI: 1.021, 4.627. 3HPMA: Q2: OR=2.286, 95%CI: 1.207 - 4.330; Q4: OR=2.663, 95%CI: 1.288 -5.508.) and RA was detected in the model 3, which was independent of all the covariates. The relative parent compounds of the two VOCs included N,N-Dimethylformamide and acrolein.

Discussion

These findings suggested that the VOC exposure significantly associated with RA, providing newly epidemiological evidence for the establishment that environmental pollutants associated with RA. And also, more prospective studies and related experimental studies are needed to further validate the conclusions of this study.

A multistage fractal-like tree network model to predict VOC diffusion characteristic of indoor fabrics

Understanding the coupling mechanism between multi-material pollution sources and sinks is key to predicting the pollution load. Indoor fabric materials strongly adsorb volatile organic compounds (VOCs) owing to their high loading rates and large specific surface areas. The secondary source effects generated by their desorption easily aggravates indoor air pollution and prolongs the pollution period. The existing research conclusions on the VOC mass-transfer properties of building materials are difficult to apply directly to fabrics due to their multilayered anisotropic fiber-interlaced structure. In this study, the triple porous structure of the fabrics was characterized, and the mass-transfer network were analyzed. Moreover, a multistage fractal-like tree network model was proposed to characterize the fabric's pore structure and establish a theoretical prediction model of the VOC diffusion coefficient. Subsequently, the mass-transfer characteristic parameters of the fabrics were measured at different ambient temperatures through loading and emission experiments of formaldehyde, benzene, toluene, ethylbenzene, and xylene (BTEX) on typical indoor fabrics. A comparison of the experimentally determined and theoretically predicted values revealed that the proposed model could accurately predict the diffusion coefficient of fabrics. This study can help understand the dynamic source and sink characteristics of fabrics in an indoor environment.

Emission Rates of Volatile Organic Compounds from Humans

Human-emitted volatile organic compounds (VOCs) are mainly from breath and the skin. In this study, we continuously measured VOCs in a stainless-steel environmentally controlled climate chamber (22.5 m³, air change rate at 3.2 h^-1) occupied by four seated human volunteers using proton transfer reaction time-of-flight mass spectrometry and gas chromatography mass spectrometry. Experiments with human whole body, breath-only, and dermal-only emissions were performed under ozone-free and ozone-present conditions. In addition, the effect of temperature, relative humidity, clothing type, and age was investigated for whole-body emissions. Without ozone, the whole-body total emission rate (ER) was 2180 ± 620 μg h^-1 per person (p^-1), dominated by exhaled chemicals. The ERs of oxygenated VOCs were positively correlated with the enthalpy of the air. Under ozone-present conditions (∼37 ppb), the whole-body total ER doubled, with the increase mainly driven by VOCs resulting from skin surface lipids/ozone reactions, which increased with relative humidity. Long clothing (more covered skin) was found to reduce the total ERs but enhanced certain chemicals related to the clothing. The ERs of VOCs derived from this study provide a valuable data set of human emissions under various conditions and can be used in models to better predict indoor air quality, especially for highly occupied environments.

How Is Indoor Air Quality during Sleep? A Review of Field Studies

This review aimed to provide an overview of the characterisation of indoor air quality (IAQ) during the sleeping period, based only on real life conditions’ studies where, at least, one air pollutant was considered. Despite the consensual complexity of indoor air, when focusing on sleeping environments, the available scientific literature is still scarce and falls to provide a multipollutants’ characterisation of the air breathed during sleep. This review, following PRISMA’s approach, identified a total of 22 studies that provided insights of how IAQ is during the sleeping period in real life conditions. Most of studies focused on carbon dioxide (77%), followed by particles (PM2.5, PM10 and ultrafines) and only 18% of the studies focused on pollutants such as carbon monoxide, volatile organic compounds and formaldehyde. Despite the high heterogeneity between studies (regarding the geographical area, type of surrounding environments, season of the year, type of dwelling, bedrooms’ ventilation, number of occupants), several air pollutants showed exceedances of the limit values established by guidelines or legislation, indicating that an effort should be made in order to minimise human exposure to air pollutants. For instance, when considering the air quality guideline of World Health Organisation of 10 µg·m−3 for PM2.5, 86% of studies that focused this pollutant registered levels above this threshold. Considering that people spend one third of their day sleeping, exposure during this period may have a significant impact on the daily integrated human exposure, due to the higher amount of exposure time, even if this environment is characterised by lower pollutants’ levels. Improving the current knowledge of air pollutants levels during sleep in different settings, as well as in different countries, will allow to improve the accuracy of exposure assessments and will also allow to understand their main drivers and how to tackle them.

Target Analysis of Volatile Organic Compounds in Exhaled Breath for Lung Cancer Discrimination from Other Pulmonary Diseases and Healthy Persons

The aim of the present study was to investigate the ability of breath analysis to distinguish lung cancer (LC) patients from patients with other respiratory diseases and healthy people. The population sample consisted of 51 patients with confirmed LC, 38 patients with pathological computed tomography (CT) findings not diagnosed with LC, and 53 healthy controls. The concentrations of 19 volatile organic compounds (VOCs) were quantified in the exhaled breath of study participants by solid phase microextraction (SPME) of the VOCs and subsequent gas chromatography-mass spectrometry (GC-MS) analysis. Kruskal-Wallis and Mann-Whitney tests were used to identify significant differences between subgroups. Machine learning methods were used to determine the discriminant power of the method. Several compounds were found to differ significantly between LC patients and healthy controls. Strong associations were identified for 2-propanol, 1-propanol, toluene, ethylbenzene, and styrene (p-values < 0.001-0.006). These associations remained significant when ambient air concentrations were subtracted from breath concentrations. VOC levels were found to be affected by ambient air concentrations and a few by smoking status. The random forest machine learning algorithm achieved a correct classification of patients of 88.5% (area under the curve-AUC 0.94). However, none of the methods used achieved adequate discrimination between LC patients and patients with abnormal computed tomography (CT) findings. Biomarker sets, consisting mainly of the exogenous monoaromatic compounds and 1- and 2- propanol, adequately discriminated LC patients from healthy controls. The breath concentrations of these compounds may reflect the alterations in patient's physiological and biochemical status and perhaps can be used as probes for the investigation of these statuses or normalization of patient-related factors in breath analysis.

The exposure risk of typical VOCs to the human beings via inhalation based on the respiratory deposition rates by proton transfer reaction-time of flight-mass spectrometer

The respiratory deposition rates are the important analytical parameters for human health risk assessment related to the environmental volatile organic compounds (VOCs). In present study, the deposition rates from the linear regressions of CH₂O, CH₅N, C₂H₆O, C₂H₄O₂, C₃H₈O, C₆H₆, C₇H₈, C₈H₈, and C₈H₁₀ of 120 healthy volunteers were obtained with significantly different from the respective calculated deposition rates. The CH₂O (formaldehyde) has the highest deposition rate, indicating the highest associated exposure risk of CH₂O if the persons are exposed to the same concentrations of these VOCs through inhalation. In order to explore the effects of the breathing models and sampling time on the deposition rates of VOCs, volunteers were first asked to breathe successively with nasal-in-nasal-out, oral-in-nasal-out, and oral-in-oral-out breathing models before and after three meals for three days. Sampling time variation has no effect on the deposition rates of selected VOCs, while the deposition rates of C₂H₄O₂, C₃H₈O, C₆H₆, C₇H₈ and C₈H₁₀ by nasal-in-nasal-out were significantly different from oral-in-oral-out and nasal-in-oral-out models. Among all the breathing models, nasal-in-oral-out comprises the entire respiratory system. In order to further validate the results, the deposition rates of the selected VOCs were calculated in 120 healthy volunteers using nasal-in-oral-out breathing model for unlimited time after the conventional lung function examination. Difference in gender and body mass index had no effect on the deposition rates of VOCs, while the age affects the deposition rates of CH₂O, CH₅N and C₂H₄O₂. Positive correlation analysis between lung function factors and deposition rates revealed that the individuals with larger lung function factors are more susceptible to deposit the VOCs. Overall, the main conclusion can be drawn that the respiratory deposition rates were influenced by the physiological factors. Therefore, the major objective for future research is to accurately calculate the deposition rates of environmental VOCs for health-risk assessment.