Diffusive Samplers Research Articles

Generative modeling and augmentation of EEG signals using improved diffusion probabilistic models.

The development of deep learning models for electroencephalography (EEG) signal processing is often constrained by the limited availability of high-quality data. Data augmentation techniques are among the solutions to overcome these challenges, and deep neural generative models, with their data synthesis capabilities, are potential candidates. The current work investigates enhanced diffusion probabilistic models (DPM) and sampling methods for brain signal generation and data augmentation. We used implicit sampling and progressive distillation to shorten the inference and subsequently analysed the effects of these methods on the generated data. To assess the feasibility of our method, four classification models were trained and evaluated in an inter-subject setting on datasets augmented with synthetic signals. Our analysis of generative metrics and statistical evaluations, including subject- and group-level tests, showed that our DPMs could generate visual evoked potentials and motor imagery signals. Distilled, single-step DPMs were trained on two publicly available datasets and were used to synthesize relatively high-quality EEG samples. The performance of the classifiers was improved by the application of the synthesized signals. The present work demonstrates that DPMs are capable of augmenting data with high fidelity and improving the diversity of EEG signals. Although samples can be generated in a single step, there is a significant trade-off between the data quality and sampling steps. The findings and results of this study demonstrate the promising capability of diffusion models for EEG synthesis, which marks progress toward an efficient and generalizable augmentation method for various EEG decoding tasks.

Full-Aperture Reflective Remote Fourier Ptychography with Sample Matching

Fourier ptychography (FP) can break through the limitations of existing optical systems with a single aperture and realize large field-of-view (FOV) and high-resolution (HR) imaging simultaneously by aperture synthesis in the frequency domain. The method has potential applications for remote sensing and space-based imaging. However, the aperture stop of the imaging system was generally set to be much smaller than the system with an adjustable diaphragm, so it failed to make full use of the imaging capability of the system. In this paper, a reflective remote FP with full aperture is proposed, and the optical aperture of the camera is set to be the maximum according to the sample-matching condition, which can further improve the imaging resolution by exploring the whole capability of the system. Firstly, the physical model of the remote FP is established using oblique illumination of a convergent spherical wave. Then, the sampling characteristics of the low-resolution (LR) intensity image are analyzed. Assuming diffraction-limited imaging, the size of the aperture of the optical system needs to match the sampling of the detector. An experimental setup with an imaging distance of 2.4 m is built, and a series of LR images is collected by moving the camera for the diffused samples, including the USAF resolution test target and the banknote, where the diameter of the single aperture is set to the maximum to match the size of the CCD pixel under the practical minimum F# of the camera of 2.8. The high-resolution image is reconstructed by applying the iterative phase retrieval algorithm. The experimental results show that the reconstructed resolution is improved to 2.5×. This verifies that remote FP with full aperture can effectively improve the imaging resolution using only the present single-aperture optical system.

Accurate low-dose exposure assessment of benzene and monoaromatic compounds by diffusive sampling: Sampling and analytical method validation according to ISO 23320 for radiello® samplers packed with graphitised charcoal and suitable for thermal desorption

This research was aimed at providing an accurate low-dose benzene exposure assessment method, prospectively suitable to exposure limit values in the low ppb range, such as in the present-day chemical, petrochemical, foundry and pharmaceutical industry. The project addresses the need for a robust and fully validated method of personal exposure measurements considering that the Occupational Exposure Limit Value for benzene will be significantly lowered in the next few years. Diffusive sampling offers a reliable alternative to pumped sampling methods, intrinsic safety in potentially explosive atmospheres, lightness and ease of use. The tested diffusive sampler is radiello® with the RAD145 adsorption substrate. This configuration is packed with graphitised charcoal suitable for thermal desorption and analysis by HRGC-FID or HRGC-MS.The experiments have been conducted following the ISO 23320 standard in the range from 0.005 to 0.1 ppm (16 to 320 µg/m3), yielding a full validation of the sampling and analytical method. The sampler performances have fulfilled all requisites of the ISO 23320 standard, in particular: bias due to the selection of a non-ideal sorbent is lower than 10 % (no significant back diffusion of benzene due to concentration change in the atmosphere); bias due to storage of samples for up to 2 months is lower than 10 %; nominal uptake rate for benzene on RAD145 is 32.3 mL/min; expanded uncertainty of the sampling and analytical method is 22.1 % in the concentration range from 80 to 320 µg/m³. The sampling and analytical method is therefore fit-for-purpose for the personal exposure measurements aimed at testing compliance with lowered occupational exposure limit values for benzene. The method is also fit for short duration exposure monitoring related to specific tasks, and other volatile organic compounds, usually found in the same workplaces, such as aliphatic and aromatic hydrocarbons and some oxygenated compounds, have been also studied. In particular, n-hexane and isopropyl benzene (also known as cumene), whose classification is currently under revision, can be efficiently monitored by this technique.

Dual-Domain Collaborative Diffusion Sampling for Multi-Source Stationary Computed Tomography Reconstruction.

The multi-source stationary CT, where both the detector and X-ray source are fixed, represents a novel imaging system with high temporal resolution that has garnered significant interest. Limited space within the system restricts the number of X-ray sources, leading to sparse-view CT imaging challenges. Recent diffusion models for reconstructing sparse-view CT have generally focused separately on sinogram or image domains. Sinogram-centric models effectively estimate missing projections but may introduce artifacts, lacking mechanisms to ensure image correctness. Conversely, image-domain models, while capturing detailed image features, often struggle with complex data distribution, leading to inaccuracies in projections. Addressing these issues, the Dual-domain Collaborative Diffusion Sampling (DCDS) model integrates sinogram and image domain diffusion processes for enhanced sparse-view reconstruction. This model combines the strengths of both domains in an optimized mathematical framework. A collaborative diffusion mechanism underpins this model, improving sinogram recovery and image generative capabilities. This mechanism facilitates feedback-driven image generation from the sinogram domain and uses image domain results to complete missing projections. Optimization of the DCDS model is further achieved through the alternative direction iteration method, focusing on data consistency updates. Extensive testing, including numerical simulations, real phantoms, and clinical cardiac datasets, demonstrates the DCDS model's effectiveness. It consistently outperforms various state-of-the-art benchmarks, delivering exceptional reconstruction quality and precise sinogram.

Regularization by denoising diffusion process meets deep relaxation in phase

Fourier phase retrieval is one of the representative inverse problems where a signal needs to be recovered using only the measured magnitude of its Fourier transform. Deep learning-based algorithms for solving Fourier phase retrieval have been widely studied. These methods provide better reconstruction than the conventional algorithms, such as alternating projection approaches and convex relaxation methods. However, it is difficult to recover the phase information of 256 × 256 images accurately, and they often cannot provide fine details and textures. Recently, diffusion models have been used to solve Fourier phase retrieval problems. They offer realistic reconstruction results, but due to the nature of generative models, they often create non-existent features in the actual images. To address these issues, we introduced a novel algorithm inspired by regularization by denoising diffusion, a variational diffusion sampling for reconstructing the images from the measurements. In particular, the optimization problem in the convex relaxation approach for phase retrieval is interpreted as an additional constraint during the variational sampling process to estimate the phase from the given Fourier magnitude measurement. The proposed method stands out by leveraging not only pre-trained diffusion models as image priors but also the classical optimization approach as the regularization. This novel combination ensures not just accurate phase reconstruction, but also performance guarantees. Our experiments demonstrate that the proposed algorithm consistently provides state-of-the-art performance across various datasets of 256 × 256 images. We further showed the effectiveness of the new regularization for the performance gain in the phase estimation.

A New Look at Diffusion in Vapor Intrusion Assessments; Passive Adsorptive Diffusion Samplers

AbstractVapor intrusion of toxic volatile organic compounds (VOCs) from subsurface soil vapor, through a building slab/floor, and into the indoor air is an important environmental contaminant transport mechanism. It is widely believed that advective flow, driven by the pressure differential between the subslab and indoor air, is the primary mechanism of subslab soil vapor entry into buildings. This paper explores the hypothesis that molecular diffusion through the slab may potentially play a larger role in vapor intrusion than previously believed and may even be the predominant vapor intrusion mechanism when the subslab vapor source strength is sufficiently high or the pressure differential is relatively low. A novel sampling device, referred to as a Passive Adsorptive Diffusion Sampler (PADS), is presented for the purpose of directly measuring the diffusion of VOCs through a building slab. A vacant warehouse was identified as a case study site where historical sampling had determined that vapor intrusion of trichloroethene (TCE) was adversely impacting the indoor air. Calculations using Fick's First Law of Diffusion are presented which demonstrate that diffusion alone can theoretically account for all the TCE observed in the indoor air at this building based on an effective diffusion coefficient for concrete that was calculated from the Johnson and Ettinger Model. Two groups of nine replicate PADS were deployed at two areas on the slab and used to measure the flux and effective diffusion coefficient at each of the 18 total points, which showed an order of magnitude variability within each area and over two orders of magnitude variability overall. These results indicate that diffusion through concrete is inherently variable when measured at a sub‐meter scale. However, when combined over both areas, the overall average approached that calculated from the Johnson and Ettinger Model. An additional 12 PADS were deployed across the building slab (for a total of 30) to quantify the overall building‐wide diffusive flux. This area‐weighted average diffusive flux was consistent with the predicted diffusive flux as calculated from Fick's First Law and the vapor intrusion mass input required to achieve the observed indoor air TCE concentration. The results of this study show that PADS provides a simple way to measure diffusive flux directly without having to drill through the slab. However, significant variability in the measured flux should be expected and will need to be accounted for by the inclusion of a relatively large number of samples including replicates. When using PADs at a new site, the collection of traditional subslab vapors at a select number of locations is recommended for the verification of a building‐specific effective diffusion coefficient, which may not necessarily be the same as for this building.

Human Urinary Biomonitoring of Occupational Exposure to Dichloromethane

Background. In Indonesia, dichloromethane is primarily used as solvent in paint remover, chemical manufacturing, cellulose fibre production, film photography, textile making, as food flavourings extraction and coffee decaffeination, and as a reagent for analysis in laboratories. Exposure DCM in long term can increase risk for several specific cancers, including brain cancer, liver and biliary tract cancer, non-Hodgkin lymphoma, and multiple myeloma. This study aimed to determine human urinary biomonitoring to assess dichloromethane exposure in the worker populations. Methods. Literature databases (PubMed, Scopus and Embase) were screened using the keywords ‘dichloromethane’, ‘methylene chloride’, ‘biological monitoring’, and ‘occupational exposure’ Seven studies met inclusion criteria. Following exclusion criteria, three studies were eligible and applicable. Results. Biological monitoring of occupational DCM exposure is possible by use of analysis for un-metabolized DCM in end-of-shift urine. Time-weighted average DCM-A was measured by diffusive sampling followed by gas-chromatography (GC), and DCM in end-of-shift urine samples was by head-space GC. The detection limit of urinary DCM was found to be 0.01 mg/l. Urine sample collect in end shift work; the samples were kept in refrigerator until analyse use HC-GC-MS. Conclusion. From the studies, it’s important for worker who exposure DCM to use personal protective equipment, example hand gloves and respiratory mask. There’s no recommendation when the best time to do human urinary biomonitoring of DCM. But, if there are signs of intoxication DCM, the author suggests do human urinary biomonitoring of DCM.

Volatile gas scavenging in the paediatric intensive care unit: Occupational health and safety assessment.

The use of volatile anesthetic agents in the paediatric intensive care unit (PICU) is experiencing increased interest since the availability of the miniature vapourizing device. However, the effectiveness of scavenging systems in the presence of humidifiers in the ventilator circuit is unknown. We performed a bench study to evaluate the effectiveness of the Deltasorb® scavenging system in the presence of isoflurane and active humidity by simulating both infant and child ventilator test settings. A total of four ventilators were set to ventilate test lungs, all with active humidity and a Deltasorb scavenging canister collecting exhaled ventilation gas. Two ventilators also had isoflurane delivered using the Anesthesia Conserving Device- small (ACD®-S) on the inspiratory limb (also called alternative ventilator configuration). We performed instantaneous measurements of isoflurane and continuous sampling with passive badges to measure average environmental exposure over a test period of 6.5 hours. Scavenging canisters were returned to the company, where desorption analysis showed the volume of water and isoflurane captured in each canister. Both instantaneous point sampling and diffusive sampling results were below the occupational exposure limit confirming safety. The canisters collected both isoflurane and a portion of the water vapour delivered; the percentage of captured water and isoflurane collected in infants was higher than the child ventilator test settings. The tested scavenging configuration was effective in maintaining a safe working environment with active humidity and inspiratory limb (alternative) ventilator configuration of the the miniature vapourizing device.

32‐1: Improving QD Backplane Defect Image Generation Using Automatic Masking in Diffusion Models

The modern QD backplane production line deploys deep learning‐based defect classifier, which uses backplane images, to the auto repair system. However, one key challenge in this application is the lack of defect images available for training when a new product is first introduced. To address this issue, generative models like GAN have been used to generate defect images for new products but the image quality and diversity are not optimal due to unstable training and mode collapse. In this paper, the newest diffusion model is applied for defect image generation. It is trained on both normal (defect‐free) and defect images from new Product A and existing Product B. This allows the model to learn defects from Product B and generate synthetic defect images of Product A from normal images. Using an innovative method to automatically apply masks in diffusion sampling, the generated synthetic images are demonstrated to achieve high image quality and good defect diversity while preserving image fidelity outside the defective region when compared to the input normal image. Experiments are also conducted to train classifiers with generated defect images from different sampling methods. The proposed automatic masking method outperforms other sampling methods in the experiments.

Influence of the Mn/Fe ratio on structural, optical, electrical and magnetic characteristics of MnFeMgNiO spinel ferrites

MnxFe2-xMg0.5Ni0.5O4spinel ferrite nanoparticles (0.0 ≤ x ≤ 1) were prepared utilizing a flash auto-combustion technique. The as-prepared ferrite nanoparticles have been investigated and characterized using X-ray diffraction (XRD), high resolution transmission electron microscopy (HRTEM), and Fourier transform infrared analysis (FTIR). The dielectric, optical and magnetic characteristics of the synthesized spinel ferrite samples were investigated using broadband dielectric spectroscopy (BDS), diffuse reflectance spectroscopy (DRS) and vibration sample magnetometers (VSM). The cubic phase spinel ferrite with space group Fd-3m was identified through XRD. X-ray diffraction indicates that the lattice constant increases and the crystallite size declines with increasing Mn concentration. The positions of the cations and their vibrational modes in the crystal structure with oxygen ions are demonstrated through the FTIR spectra. The morphology was examined using HRTEM showed polycrystalline cubic spinel structure with faceted planes. The optical band gap Eg was determined using Kubelka–Munk model where, it decreased from 1.79 eV to1.24 eV as Mn content increases. The replacement of manganese significantly affects the dielectric characteristics of the synthesized spinel ferrites. The real (ε′), imaginary (ε″) parts of dielectric constant and ac conductivity exhibit an increase in their values as manganese substitution (x) increases from 0 to 0.25 mol %, and thereafter decreases. The presence of a relaxation phenomenon in all prepared samples except the undoped sample (Ni0.5Mg0.5Fe2O4) is shown by the imaginary modulus’s (M′′) dependence on frequency. The magnetic properties exhibited soft ferrite nature with small hysteresis curve for all spinel ferrites samples. The magnetic properties of MnxFe2-xMg0.5Ni0.5O4 nanoparticles were measured and analysed, including saturation magnetization (Ms), remanence (Mr), coercivity (Hc), and squareness ratio. (Ms), and (Mr), exhibit an increase in their values as manganese substitution (x) increases from 0 to 0.25 mol %, and thereafter decreases while squareness ratio decreased. The magnetic parameters greatly affected by Mn substitution ratio.

Smartphone-Based Color Evaluation of Passive Samplers for Gases: A Review

The application of smartphone-based color evaluation of passive sampling devices for gases has only been sparsely reported. The present review aims to compile available publications with respect to the configuration of the passive samplers, conditions of smartphone photographing, analytical procedures for color detection and quantification (including calibration processes), and their application to different target gases. The performance of the methods—whenever available—is presented regarding the analytical specifications selectivity, sensitivity, and limit of detection in comparison with other color evaluation methods of passive samplers. Practical aspects like requirements of instrumentation and ease of use will be outlined in view of the potential employment in education and citizen science projects. In one section of the review, the inconsistent terminology of passive and diffusive sampling is discussed in order to clarify the distinction of information obtained from the uptake of the passive samplers between gas-phase concentration and the accumulated deposition flux of gaseous analytes. Colorimetric gas sensors are included in the review when applied in passive sampling configurations and evaluation is performed with smartphone-based color evaluation. Differences in the analytical procedures employed after the passive sampling step and prior to the detection of the colored compounds are also presented.

Geometric deep learning for diffusion MRI signal reconstruction with continuous samplings (DISCUS).

Diffusion-weighted magnetic resonance imaging (dMRI) permits a detailed in-vivo analysis of neuroanatomical microstructure, invaluable for clinical and population studies. However, many measurements with different diffusion-encoding directions and possibly b-values are necessary to infer the underlying tissue microstructure within different imaging voxels accurately. Two challenges particularly limit the utility of dMRI: long acquisition times limit feasible scans to only a few directional measurements, and the heterogeneity of acquisition schemes across studies makes it difficult to combine datasets. Left unaddressed by previous learning-based methods that only accept dMRI data adhering to the specific acquisition scheme used for training, there is a need for methods that accept and predict signals for arbitrary diffusion encodings. Addressing these challenges, we describe the first geometric deep learning method for continuous dMRI signal reconstruction for arbitrary diffusion sampling schemes for both the input and output. Our method combines the reconstruction accuracy and robustness of previous learning-based methods with the flexibility of model-based methods, for example, spherical harmonics or SHORE. We demonstrate that our method outperforms model-based methods and performs on par with discrete learning-based methods on single-, multi-shell, and grid-based diffusion MRI datasets. Relevant for dMRI-derived analyses, we show that our reconstruction translates to higher-quality estimates of frequently used microstructure models compared to other reconstruction methods, enabling high-quality analyses even from very short dMRI acquisitions.

Rethinking Peculiar Images by Diffusion Models: Revealing Local Minima’s Role

Recent significant advancements in diffusion models have revolutionized image generation, enabling the synthesis of highly realistic images with text-based guidance. These breakthroughs have paved the way for constructing datasets via generative artificial intelligence (AI), offering immense potential for various applications. However, two critical challenges hinder the widespread adoption of synthesized data: computational cost and the generation of peculiar images. While computational costs have improved through various approaches, the issue of peculiar image generation remains relatively unexplored. Existing solutions rely on heuristics, extra training, or AI-based post-processing to mitigate this problem. In this paper, we present a novel approach to address both issues simultaneously. We establish that both gradient descent and diffusion sampling are specific cases of the generalized expectation maximization algorithm. We hypothesize and empirically demonstrate that peculiar image generation is akin to the local minima problem in optimization. Inspired by optimization techniques, we apply naive momentum and positive-negative momentum to diffusion sampling. Last, we propose new metrics to evaluate the peculiarity. Experimental results show momentum effectively prevents peculiar image generation without extra computation.

One at a Time: Progressive Multi-Step Volumetric Probability Learning for Reliable 3D Scene Perception

Numerous studies have investigated the pivotal role of reliable 3D volume representation in scene perception tasks, such as multi-view stereo (MVS) and semantic scene completion (SSC). They typically construct 3D probability volumes directly with geometric correspondence, attempting to fully address the scene perception tasks in a single forward pass. However, such a single-step solution makes it hard to learn accurate and convincing volumetric probability, especially in challenging regions like unexpected occlusions and complicated light reflections. Therefore, this paper proposes to decompose the complicated 3D volume representation learning into a sequence of generative steps to facilitate fine and reliable scene perception. Considering the recent advances achieved by strong generative diffusion models, we introduce a multi-step learning framework, dubbed as VPD, dedicated to progressively refining the Volumetric Probability in a Diffusion process. Specifically, we first build a coarse probability volume from input images with the off-the-shelf scene perception baselines, which is then conditioned as the basic geometry prior before being fed into a 3D diffusion UNet, to progressively achieve accurate probability distribution modeling. To handle the corner cases in challenging areas, a Confidence-Aware Contextual Collaboration (CACC) module is developed to correct the uncertain regions for reliable volumetric learning based on multi-scale contextual contents. Moreover, an Online Filtering (OF) strategy is designed to maintain representation consistency for stable diffusion sampling. Extensive experiments are conducted on scene perception tasks including multi-view stereo (MVS) and semantic scene completion (SSC), to validate the efficacy of our method in learning reliable volumetric representations. Notably, for the SSC task, our work stands out as the first to surpass LiDAR-based methods on the SemanticKITTI dataset.

Improving Diffusion-Based Image Restoration with Error Contraction and Error Correction

Generative diffusion prior captured from the off-the-shelf denoising diffusion generative model has recently attained significant interest. However, several attempts have been made to adopt diffusion models to noisy inverse problems either fail to achieve satisfactory results or require a few thousand iterations to achieve high-quality reconstructions. In this work, we propose a diffusion-based image restoration with error contraction and error correction (DiffECC) method. Two strategies are introduced to contract the restoration error in the posterior sampling process. First, we combine existing CNN-based approaches with diffusion models to ensure data consistency from the beginning. Second, to amplify the error contraction effects of the noise, a restart sampling algorithm is designed. In the error correction strategy, the estimation-correction idea is proposed on both the data term and the prior term. Solving them iteratively within the diffusion sampling framework leads to superior image generation results. Experimental results for image restoration tasks such as super-resolution (SR), Gaussian deblurring, and motion deblurring demonstrate that our approach can reconstruct high-quality images compared with state-of-the-art sampling-based diffusion models.

NOP14 as a Potential Predictor of Adult-Type Diffuse Glioma Prognosis and Immunotherapy, is Related to Cell Migration, Proliferation, and CD8+T Cell Infiltration.

World Health Organization (WHO) grade 4 adult-type diffuse glioma is the most malignant primary tumor of the brain. Nucleolar protein 14 (NOP14) is recognized to contribute significantly to the assembly of small ribosomal subunits. However, the specific involvement of NOP14 in diverse cancers remains poorly understood, particularly its role in adult-type diffuse glioma, which has yet to be elucidated. A total of 20 adult-type diffuse glioma samples with varying WHO stages were collected. The protein level of NOP14 was detected using immunohistochemistry. Additionally, NOP14 expression in LN229 and U251 cell lines and collected clinical tissue samples was quantified using the Western blot technique. Furthermore, the correlation between NOP14 and clinicopathological features, survival rates, matrix and immune scores, and immune components was investigated using data from the Cancer Gene Atlas database. NOP14 exhibited high expression in adult-type diffuse glioma patients, with the highest expression observed in the LN229 cell line. Moreover, elevated NOP14 expression was significantly correlated with poorer overall survival and demonstrated an association with unfavorable pathological features in a cohort of 703 glioblastoma (GBM) patients. Evidence of a connection between NOP14 and the tumor microenvironment was presented. Elevated NOP14 was linked to the infiltration of CD8+T cell and factors related to epithelial-mesenchymal transition. In in vitro assay, NOP14 was capable of suppressing adult-type diffuse glioma cell invasion and metastasis. NOP14 holds great promise as a candidate biomarker for detecting prognostic, molecular, and immune signatures of adult-type diffuse glioma.

VOC concentrations in houses in Japan: correlations with housing characteristics and types of ventilation

ABSTRACT Volatile organic compounds (VOCs) can cause health problems for residents and are one of the causes of sick building syndrome. Therefore, this study measured VOCs in 116 houses in the winter and 66 in the summer in Japan, including detached and apartment houses. The diffusion sampler method was used for field measurements. Living rooms and bedrooms were sampled to confirm the concentration characteristics and differences between the two locations. In addition, a questionnaire survey was conducted to identify the type of house, residence period, and type of ventilation system used. Most VOC concentrations did not exceed the Japanese guidelines during the winter and summer. However, in the winter, the acetaldehyde concentrations exceeded the Ministry of Health, Labour, and Welfare of Japan (MHLW) guidelines, reaching 14.7% in the living room and 12.9% in the bedroom. The VOC concentration was higher in apartments than in detached houses. Even among detached houses, built-for-sale houses had slightly higher VOC concentrations than custom-made houses. The concentration of VOCs according to characteristics, such as the residence period, did not show significant differences except for acetaldehyde. In houses with unbalanced ventilation, formaldehyde and other VOC concentrations were higher in the living room and bedroom than in houses with balanced ventilation.

Calibration of a passive sampling device for the determination of nitrogen dioxide in ambient air

Nitrogen dioxide (NO2), a common air pollutant, has been widely admitted to be harmful to both the environment and human health, demanding its well-control procedure and corresponding quantification. In this study, NO2 in ambient air was collected by a passive sampling method using the Willems badge diffusive sampler, followed by a derivatization step with the Griess-Saltzman solution, and analyzed by ultraviolet-visible (UV–vis) spectroscopy at 543 nm. The device can be utilized for 168 h of continuous field sampling. The experimental sampling rate (Ke) of (4.02 ± 0.29) × 10−3 m3h−1 with a relative standard deviation (% RSD) of 9.6 % was determined by conducting parallel experiments between an active sampling method (ISO 6768:1998) and the Willems samplers. After exposure time, samplers could be stored for two weeks in a refrigerator at 4 °C before analyzing. The studied passive diffusive sampler was simple, low-cost, easy to reuse; permitted determining the average concentration of NO2 in ambient air. The average NO2 concentrations for 2-hour to 4-hour sampling periods at different studied sampling sites in Ho Chi Minh city (Vietnam) were ranged from 11.5 to 189 μg m−3.

Evaluation of Passive Vapor Diffusion Samplers to Quantify Acetylene, Ethene, and Ethane in Groundwater

AbstractAcetylene, ethene, and ethane are products from the degradation of trichloroethene (TCE) in contaminated aquifers. However, the volatility of these gases makes it challenging to avoid excessive losses during sampling. The objective of this study was to compare the quantification of acetylene, ethene, and ethane using passive vapor diffusion (PVD) samplers vs. conventional low‐flow groundwater collection. Samples were obtained from 8 to 13 monitoring wells at three sites that show potential for biotic and abiotic degradation of TCE in fractured rock aquifers. Method reporting limits (MRLs) for the PVD samplers were 0.25 μg/L for acetylene (0.0094 μM) and 0.28 μg/L for ethene and ethane (0.0099 and 0.0092 μM, respectively); the MRLs for conventional low‐flow groundwater samples were ~40% higher. For two of the sites, the maximum concentrations of acetylene, ethene, and ethane obtained with the PVD samplers were comparable to the conventional low‐flow samples. The frequency of detection for these gases with the PVD samplers was also comparable to conventional low‐flow groundwater sampling. For one of the sites with higher levels of acetylene (maximum of 13 μg/L), the concentrations from the PVD samplers were approximately twofold higher than those with conventional low‐flow groundwater sampling. Based on robust detection of acetylene, ethene, and/or ethane, it appears likely that degradation of TCE is occurring at the three sites that were sampled. The use of PVD samplers can reduce the possibility of false negative results to provide another line of evidence in support of natural attenuation.

Outdoor trends and indoor investigations of volatile organic compounds in two high schools of southern Italy

Volatile organic compounds (VOCs) are a class of ubiquitous substances that are present in outdoor and indoor air. They are emitted by a wide range of sources and can penetrate and accumulate specifically in indoor environments. Concern is growing among the scientific community regarding the potential health impacts of exposure to a high concentration of VOCs in indoor spaces. Due to their still-developing respiratory and immune systems, children may be the most fragile subjects in this regard, and therefore, the study of indoor air quality in schools is of the utmost importance. In this work, the concentrations of total volatile organic compounds (TVOCs) and of 20 specific compounds belonging to this class were determined in a school in Squinzano, a town in the province of Lecce (Apulia region, southern Italy). Sampling was carried out in indoor (classrooms and bathrooms) and outdoor (terrace) areas using passive diffusive samplers for VOCs and photoionization detectors for TVOCs. Average concentrations of both TVOC (303 ± 47 µg m−3) and individual VOCs (< 0.5 µg m−3) were below levels of concern; however, specific indoor sources such as cleaning activities and student occupancy were responsible for peaks in TVOC concentrations above harmless levels for children and school staff. The data were then compared to the ones obtained in a similar study conducted in a school in Galatina, another town of the Apulia region, highlighting the impact of the surrounding outdoor environment on the indoor profile of VOCs.