Photoionization Detector Research Articles

Data Mining Virtual Contaminated Sites to Develop an Educational Learning Tool supported by a k-NN Machine Learning Predictive Algorithm for use in Environmental Engineering Education

Recent work by Mumford et al. used high-resolution numerical simulations of contaminated sites to evaluate the state of practice for contaminated site investigation1. These simulations were originally developed in collaboration with academic and industry partners, including the U.S. Department of Defense's Environmental Security Technology Certification Program (ESTCP), as a training tool for environmental monitoring and performance optimization. This project focuses on the development of an algorithm to determine available borehole information in those simulations based on user-input, to help leverage this work to create an educational tool. The algorithm begins by validating user-specified coordinates to ensure they fall within an acceptable range. Leveraging principles from linear algebra and a grid-based mapping system, it identifies the nearest existing borehole on a predefined coordinate grid. To optimize efficiency, the algorithm utilizes a 2-D array to store and retrieve coordinate data. It calculates distances from the input coordinates to all available borehole locations on the grid, to identify the shortest distance and select the closest borehole, enabling students to map contamination within virtual sites. In addition to boreholes, the application incorporates data for Membrane Interface Probes (MIPs), monitoring wells, groundwater samples, and soil samples that are used to investigate contaminated sites, providing a comprehensive tool for training environmental professionals. The k-nearest neighbors (k-NN) algorithm predicts Membrane Interface Probe (MIP) Photoionization Detector (ECD) readings at future locations, which indicate chlorinated contaminant levels. Two models were evaluated: one using spatial data and ECD values, and another incorporating electrical conductivity (EC) and hydraulic conductivity (K), which are also measured by MIP. By making use of additional parameters, variations in soil properties were better represented, leading to more accurate predictions. Through iterative testing and refinement, the tool’s accuracy and user-interface have been enhanced, ensuring robust reliability for classroom applications. These tools are slated for integration into 4th-year subsurface contamination courses at Queen's University, Carleton, and the University of Iowa. By automating borehole selection, instructors can interact more with students and dedicate less time to data processing tasks. The k-NN application will enable the use of machine learning for site assessment to optimize MIP instrument placement to reduce bias. Assistance from Cole Van De Ven (Carleton University) and Jessica Meyer (University of Iowa) in collecting virtual contaminated site data is gratefully acknowledged.

Breathomics: may it become an affordable, new tool for early diagnosis of non-small-cell lung cancer? An exploratory study on a cohort of 60 patients.

Analysis of breath, specifically the patterns of volatile organic compounds (VOCs), has shown the potential to distinguish between patients with lung cancer (LC) and healthy individuals (HC). However, the current technology relies on complex, expensive and low throughput analytical platforms, which provide an offline response, making it unsuitable for mass screening. A new portable device has been developed to enable fast and on-site LC diagnosis, and its reliability is being tested. Breath samples were collected from patients with histologically proven non-small-cell lung cancer (NSCLC) and healthy controls using Tedlar bags and a Nafion filter attached to a one-way mouthpiece. These samples were then analysed using an automated micro portable gas chromatography device that was developed in-house. The device consisted of a thermal desorption tube, thermal injector, separation column, photoionization detector, as well as other accessories such as pumps, valves and a helium cartridge. The resulting chromatograms were analysed using both chemometrics and machine learning techniques. Thirty NSCLC patients and 30 HC entered the study. After a training set (20 NSCLC and 20 HC) and a testing set (10 NSCLC and 10 HC), an overall specificity of 83.3%, a sensitivity of 86.7% and an accuracy of 85.0% to identify NSCLC patients were found based on 3 VOCs. These results are a significant step towards creating a low-cost, user-friendly and accessible tool for rapid on-site LC screening. ClinicalTrials.gov Identifier: NCT06034730.

A large scale study of portable sweat test sensor for accurate, non-invasive and rapid COVID-19 screening based on volatile compound marker detection

This study established a novel infield sensing approach based on detection of the volatile compound markers in skin secretions. This was based on analysis of volatile compounds in axillary sweat samples collected from RT-PCR-proven Coronavirus disease 2019 (COVID-19) positive and negative populations using gas chromatography-mass spectrometry (GC–MS). The analysis proposed the possible markers of the monoaromatic compounds and ethyl hexyl acrylate. A portable photo ionization detector (PID) incorporated with the selective material towards the marker compounds was then developed with the pressurized injection approach. This provided the accuracy of 100% in the research phase (n = 125). The developed approach was then applied for screening of 2207 COVID-19 related cases covering the periods of the Alpha, Beta, Delta and Omicron variants of SARS-CoV-2 infection in Bangkok, Thailand. This offered the sensitivity, specificity and accuracy ranges of 92–99, 93–98 and 95–97%, respectively.

Short-term temporal variability of volatile contaminant concentrations in soil gas related to soil-atmosphere interface dynamics: Two case studies in the Veneto region (Italy).

The study of the variability of soil gas concentrations is crucial for defining effective monitoring and remediation strategies and for the risk assessment related to the emission of vapors from the subsurface. The traditional soil gas monitoring strategy consists of seasonal surveys based on short-time-averaged sampling. Soil gas monitoring results are often used to assess the risk associated with the emission of volatile contaminants from the subsurface, using models mainly based on molecular diffusion and therefore assuming continuous emission from the soil. At two contaminated sites located in the Veneto region (Italy), continuous monitoring using a photoionization detector, pressure gauges, and an ultrasonic anemometer was used to relate soil gas variability to surface and subsurface physical parameters. At both sites a cyclic diurnal variation of volatile organic compounds concentration in soil gas was observed, correlated with the variation of several meteorological parameters and in particular with the variation of the differential pressure between soil and atmosphere and the buoyancy vertical flux. These findings question the reliability of the conventional methodology employed in the collection and assessment of soil gas data. Integr Environ Assess Manag 2024;20:2023-2032. © 2024 SETAC.

Environmental Impact Assessment of Volatile Organic Compound Emissions during Trenchless Cured-in-Place Pipe Installation

Cured-in-place pipe (CIPP) lining is a widely adopted method for pipeline renewal, offering advantages such as rapid installation and cost-effectiveness. However, concerns regarding the environmental impacts of volatile organic compound (VOC) emissions during the installation process have raised issues regarding the CIPP method. The literature review conducted in this paper indicated the need for stringent monitoring and management practices to mitigate adverse effects, emphasizing the importance of safe installation protocols. This paper presents the initial results of a case study focusing on VOC emissions, specifically investigating non-styrene vinyl ester resins and water curing. To capture emissions from CIPP activities, the methodology involved air sampling using various equipment, including photoionization detectors (PID), summa canisters, passive worker sampler sorbent tubes, and method 13 cartridges. The preliminary findings indicate that non-styrene vinyl ester resins resulted in VOC emissions well below the exposure limits set by OSHA and USEPA, with the highest measured concentration being 2.54 ppm. This demonstrates that non-styrene resins can significantly reduce environmental and health risks. The future phases of this project will explore different resins and curing methods to further validate these findings and develop comprehensive guidelines for safe CIPP installations.

Monitoring residual solvents in pharmaceutical products using a portable pre-concentration GC-PID

Pharmaceutical manufacturing utilizes solvents at different stages of production. Some of the harmful solvent residuals may be retained in the final product; therefore, they need to be monitored for quality control and to meet the regulation requirement. Here, a novel method capable of rapidly analyzing residual solvents in pharmaceutical products was developed using a compact-portable gas chromatography with a photoionization detector (GC-PID). The method consists of modified Tedlar® bag sampling, online pre-concentration, separation of volatiles by miniaturized GC, and micro-PID detection. The method detection limits of selected residual solvents were in the range of 26.00 – 52.03 pg/mL which is much lower than the pharmaceutical compliance concentration limits. Limits of detection > 520 pg of analyte per grams of sample was also determined for the over-the-counter drugs. The method performance showed rapid speed (5 min), linear calibration (r2 < 0.99), and repeatable retention time (RSD < 0.4 %). Direct analysis of residual solvents in solid samples was conducted without the need for complex sample preparation. The method validation using over-the-counter pharmaceutical products yielded excellent accuracy (recovery > 91.2 %) and precision (RSD < 6.5 %) for the selected residual solvents, including 1,4-dioxane, benzene, chlorobenzene, cyclohexane, xylenes, and toluene. This portable and rapid method could be deployed during the pharmaceutical drug manufacturing processes for quality control.

A compact breath breathalyzer for identifying the non-alcoholic fatty liver disease biomarker

Non-alcoholic fatty liver disease (NAFLD) is a prevalent chronic liver disease worldwide. Currently, its diagnosis relies primarily on imaging and histological examinations, which are invasive and prone to misdiagnosis in the early stage. To address these limitations, detection and analysis of volatile organic compounds (VOCs) in human breath can be a rapid and non-invasive screening method for NAFLD. In this study, a compact breath breathalyzer was developed, utilizing a miniaturized gas chromatography chip with the STM32 microcontroller as the main control chip to manage airflow, temperature, and receive terminal signals from the photoionization detector. In the experiment, a gas mixture comprising five VOCs (pentane, acetone, toluene, octane, and decane) was selected as the simulated typical disease biomarkers in human breath to investigate the breathalyzer's performance and optimize testing conditions for multi-polar and wide-boiling-range breath samples. Results show that the breathalyzer can detect low-boiling components (< 100°C) such as the isoprene and acetone, with a detection limit less than 50 ppb which are two commonly biomarkers of NAFLD. Furthermore, breath samples were collected from 35 non-diseased individuals, and NAFLD early-stage patient samples were simulated by increasing the isoprene concentration by 10 ppb. Convolutional neural network (CNN) were used to identify the VOC signatures in gas chromatograms with predictive accuracy of 85% for the classification model. Therefore, the compact breath breathalyzer has potential application in the rapid and early screening of NAFLD.

A High-Precision Monitoring Method Based on SVM Regression for Multivariate Quantitative Analysis of PID Response to VOC Signals

In the moist environment of soil-water-air, there is a problem of low accuracy in monitoring volatile organic compounds (VOCs) using a photoionization detector (PID). This study is based on the PID water-soil-gas VOC online monitor developed by this group, online monitoring of the concentration of different constituents of VOCs in different production enterprises of the petroleum and chemical industries in Shandong Province, with the concentration of the laboratory test, to build a relevant model. The correlation coefficient about the PID test concentration and the actual concentration correlation coefficient was obtained through the collection of a large number of data trainings. Based on the application of PID in VOC monitoring, the establishment of a PID high-precision calibration model is important for the precise monitoring of VOCs. In this paper, multiple quantitative analyses were conducted, based on SVM regression of PID response to VOC signals, to study the high-precision VOC monitoring method. To select the response signals of PID under different concentrations of environmental VOCs measured by the research group, first, the PID response to VOC signals was modeled using the support vector machine principle to verify the effect of traditional SVM regression. For the problem of raw data redundancy, calculate the time-domain and frequency-domain characteristics of the PID signal, and conduct the principal component analysis of the time-domain of the PID signal. In order to make the SVM regression more generalized and robust, the selection of kernel function parameters and penalty factor of SVM is optimized by genetic algorithm. By comparing the accuracy of PID calibration models such as PID signal feature extraction, SVM regression, and principal component analysis SVM regression, the superiority of photoionization detector using the signal feature extraction PCA-GA-SVM method to monitor VOCs is verified.

Simple and accurate on-site quantitation of BTEX in atmospheric air using solid-phase microextraction and gas chromatography with photoionization detection

This paper represents an improved method for on-site quantitation of benzene, toluene, ethylbenzene, and xylenes (BTEX) in atmospheric air using solid-phase microextraction (SPME) and portable gas chromatography with a split/splitless inlet and photoionization detector (GC-PID). The developed method includes air pumping to four replicate 250 mL gas sampling bulbs, three of which are extracted by 85 µm Carboxen/polydimethylsiloxane (Car/PDMS) coating at 40°C, while the last one is spiked with BTEX standard in nitrogen and extracted under the same conditions. Compared to other similar SPME-based methods, the developed method does not require a standard gas generator and traceable certified permeation tubes for instrument calibration.The method was applied for on-site BTEX quantitation in air of Talgar, Almaty oblast, Kazakhstan in February and March 2024, at two different extraction times, among which 15 min was chosen as optimal because it provided sufficient detection limits of ethylbenzene, and xylenes. The on-site standard addition calibration allowed accurate BTEX quantitation. In February, only benzene (18.3 and 6.8 µg/m3) and toluene (15.7 and 8.5 µg/m3) were detected. In March samples, concentrations of benzene were 12.3 and 7.4 µg/m3, toluene - 23 and 5.4 µg/m3, ethylbenzene – 5.5 µg/m3 and not detected, m-/p-xylene - 3.6 and 2.1 µg/m3, o-xylene - 6.8 and 10.6 µg/m3. The LODs for benzene, toluene, ethylbenzene, m-/p-xylene, and o-xylene were 0.07, 0.3, 2, 0.8 and 2 µg/m3, respectively. The developed method can be recommended for quick and low-cost on-site quantification of BTEX in atmospheric air, particularly at remote locations.

BTEX Assessment among Informal Charcoal-Burning Food Traders for Cleaner and Sustainable Environment

This study assessed the cleaner and sustainable environment by measuring emission levels of benzene, toluene, ethylbenzene, and xylene (BTEX) from informal food traders using charcoal as the primary source of energy at a flea market in Fordsburg, Johannesburg. Volatile organic compounds (VOCs) were measured using a real-time monitor (MiniRae 3000 photoionization detector); an indoor air quality (IAQ) monitor was used to monitor environmental parameters and passive samplers in the form of Radiello badges, which were used to determine BTEX emissions from charcoal used during food preparation. Measurements were taken at 1.5 m above ground assuming the receptor’s breathing circumference using PID and Radiello. PID data were downloaded and analyzed using Microsoft Excel(Version 2019). Radiellos were sent to the laboratory to determine the BTEX levels from the total VOCs. The total volatile organic compound (TVOC) concentration over the combustion cycle was 306.7 ± 62.8 ppm. The flaming phase had the highest VOC concentration (547 ± 110.46 ppm), followed by the ignition phase (339.44 ± 40.6 ppm) and coking with the lowest concentration (24.64 ± 14.3). The average BTEX concentration was 15.7 ± 5.9 µg/m3 corresponding to the entire combustion cycle. BTEX concentrations were highest at the flaming phase (23.6 µg/m3) followed by the ignition (13.4 µg/m3) and coking phase (9.45 µg/m3). Ignition phase versus the flaming phase, there was a significant difference at 95% at a p-value of 0.09; ignition phase versus the coking phase, there was a significant difference at 95% at a p-value of 0.039; and coking phase versus the flaming phase, there was a significant difference at 95% at a p-value of 0.025. When compared to the occupational exposure limits (OELs), none of the exposure concentrations (BTEX) were above the 8 h exposure limit. The findings of this study suggest that charcoal, as a source of energy, can still be a useful and sustainable fuel for informal food traders. Shortening the ignition and flaming phase duration by using a fan to supply sufficient air can further reduce exposure to VOCs.

Managing photon flux in a miniaturized photoionization detector

Miniaturized photoionization detectors (PIDs) are used in conjunction with gas chromatography systems to detect volatile compounds in gases by collecting the current from the photoionized gas analytes. PIDs should be inexpensive and compatible with a wide range of analyte species. One such PID is based on the formation of a He plasma in a dielectric barrier discharge (DBD), which generates vacuum UV (VUV) photons from excited states of He to photoionize gas analytes. There are several design parameters that can be leveraged to increase the ionizing photon flux to gas analytes to increase the sensitivity of the PID. To that end, the methods to maximize the photon flux from a pulsed He plasma in a DBD-PID were investigated using a two-dimensional plasma hydrodynamics model. The ionizing photon flux originated from the resonance states of helium, He(3P) and He(21P), and from the dimer excimer He2*. While the photon flux from the resonant states was modulated over the voltage pulse, the photon flux from He2* persisted long after the voltage pulse passed. Several geometrical optimizations were investigated, such as using an array of pointed electrodes. However, increasing the capacitance of the dielectric enclosing the plasma chamber had the largest effect on increasing the VUV photon fluence to gas analytes.

Appropriate control measure design by rapidly identifying risk areas of volatile organic compounds during the remediation excavation at an organic contaminated site.

Many organic contaminated sites require on-site remediation; excavation remediation processes can release many volatile organic compounds (VOCs) which are key atmospheric pollutants. It is therefore important to rapidly identify VOCs during excavation and map their risk areas for human health protection. In this study, we developed a rapid analysis and assessment method, aiming to and reveal the real-time distribution of VOCs, evaluate their human health risks by quantitative models, and design appropriate control measures. Through on-site diagonal distribution sampling and analysis, VOCs concentration showed a decreasing trend within 5 m from the excavation point and then increased after 5 m with the increase in distance from the excavation point (p < 0.05). The concentrations of VOCs near the dominant wind direction were higher than the concentrations of surrounding pollutants. In contrast with conventional solid-phase adsorption (SPA) and thermal desorption gas chromatography-mass spectrometry (TD-GC/MS) methods for determining the composition and concentration of VOCs, the rapid measurement of VOCs by photo-ionization detector (PID) fitted well with the chemical analysis and modeling assessment of cancer/non-cancer risk. The targeting area was assessed as mild-risk (PID < 10ppm), moderate-risk (PID from 10 to 40ppm), and heavy-risk (PID > 40ppm) areas. Similarly, the human health risks also decreased gradually with the distance from the excavation point, with the main risk area located in the dominant wind direction. The results of rapid PID assessment were comparable to conventional risk evaluation, demonstrating its feasibility in rapidly identifying VOCs releases and assessing the human health risks. This study also suggested appropriate control measures that are important guidance for personal protection during the remediation excavation process.

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.

Colorimetric Readout Based Photoionization Detector for Gas Chromatographs

This paper presents a new scheme for readout of a photoionization detector (PID) that utilizes the ionization luminescence of target gases in helium plasma to generate an output signal. Our signal readout method is based on processing the recorded video and correlating the light peak intensity with the sample injection time and concentration. We successfully demonstrated the feasibility of a colorimetric readout‐based photoionization detector coupled with a gas chromatography column by detecting a mixture of non‐polar compounds (alkanes) and two polar compounds (acetone and ethanol). The detection limit for our first‐generation device was calculated to be ~118 ng for acetone and around 26 ng for ethanol. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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.

Control Software Design for a Multisensing Multicellular Microscale Gas Chromatography System.

Microscale gas chromatography (μGC) systems are miniaturized instruments that typically incorporate one or several microfabricated fluidic elements; such systems are generally well suited for the automated sampling and analysis of gas-phase chemicals. Advanced μGC systems may incorporate more than 15 elements and operate these elements in different coordinated sequences to execute complex operations. In particular, the control software must manage the sampling and analysis operations of the μGC system in a time-sensitive manner; while operating multiple control loops, it must also manage error conditions, data acquisition, and user interactions when necessary. To address these challenges, this work describes the investigation of multithreaded control software and its evaluation with a representative μGC system. The μGC system is based on a progressive cellular architecture that uses multiple μGC cells to efficiently broaden the range of chemical analytes, with each cell incorporating multiple detectors. Implemented in Python language version 3.7.3 and executed by an embedded single-board computer, the control software enables the concurrent control of heaters, pumps, and valves while also gathering data from thermistors, pressure sensors, capacitive detectors, and photoionization detectors. A graphical user interface (UI) that operates on a laptop provides visualization of control parameters in real time. In experimental evaluations, the control software provided successful operation and readout for all the components, including eight sets of thermistors and heaters that form temperature feedback loops, two sets of pressure sensors and tunable gas pumps that form pressure head feedback loops, six capacitive detectors, three photoionization detectors, six valves, and an additional fixed-flow gas pump. A typical run analyzing 18 chemicals is presented. Although the operating system does not guarantee real-time operation, the relative standard deviations of the control loop timings were <0.5%. The control software successfully supported >1000 μGC runs that analyzed various chemical mixtures.

Roaming in the Unimolecular Decay of syn-Methyl-Substituted Criegee Intermediates.

Alkene ozonolysis generates transient carbonyl oxide species, known as Criegee intermediates, which are a significant nonphotolytic source of OH radicals in the troposphere. This study demonstrates that unimolecular decay of syn-methyl-substituted Criegee intermediates proceeds via 1,4 H atom transfer to vinyl hydroperoxides, resulting in OH fission to O-O products or, alternatively, OH roaming to hydroxycarbonyl products. Newly generated Criegee intermediates are shown to yield hydroxycarbonyls with sufficient internal excitation to dissociate via C-C fission to acyl and hydroxymethyl (CH2OH) radicals. The stabilized Criegee intermediates and unimolecular products are rapidly cooled in a pulsed supersonic expansion for photoionization detection with time-of-flight mass spectrometry. CH2OH products are identified by 2 + 1 resonance-enhanced multiphoton ionization via the 3pz Rydberg state upon unimolecular decay of CH3CHOO, (CH3)2COO, (CH3)(CH3CH2)COO, and (CH3)(CH2═CH)COO (methyl vinyl ketone oxide). The stabilized Criegee intermediates are separately detected using 10.5 eV photoionization. This study provides the first experimental evidence of roaming in the unimolecular decay of isoprene-derived methyl vinyl ketone oxide and extends earlier studies that reported stabilized hydroxycarbonyl products.

Air quality impacts from the development of unconventional oil and gas well pads: Air toxics and other volatile organic compounds

Unconventional oil and natural gas development (UOGD) has expanded rapidly across the United States in recent decades and raised concerns about associated air quality impacts. While significant effort has been made to quantify methane emissions, relatively few observations have been made of Volatile Organic Compounds (VOCs), especially during drilling and completion of new wells. Extensive air monitoring during development of several large, multi-well pads in Broomfield, Colorado, in the Denver-Julesburg Basin, provides a novel opportunity to examine changes in local air toxics and other VOC concentrations during well drilling and completions and production. These operations offer an especially useful case to study as several management practices were implemented to reduce emissions (e.g., electrified, grid-powered drill rigs and closed loop fluid handling systems to reduce truck traffic and limit fluid handling on the pad). With simultaneous measurements of methane and 50 VOCs from October 2018 to December 2022 at as many as 19 sites near well pads, in adjacent neighborhoods, and at a more distant reference location, we identify impacts from each phase of well development and production. Use of weekly, time-integrated canisters, a Proton Transfer Reaction Mass Spectrometer (PTR-MS), continuous photoionization detectors (PID) to trigger canister collection upon detection of VOC-rich plumes, and an instrumented vehicle, provided a powerful suite of measurements to characterize both transient plumes and longer-term changes in air quality. Prior to the start of well development, VOC gradients were small across Broomfield. Once drilling commenced, concentrations of oil and gas (O&G) related VOCs, including alkanes and aromatics, increased around active well pads. Concentration increases were clearly apparent during certain operations, including drilling, coil tubing/millout operations, and production tubing installation. Emissions of C8–C10 n-alkanes during drilling operations highlighted the importance of VOC emissions from synthetic drilling mud chosen to reduce odor impacts. More than 90 samples were collected of transient plumes. Using composition measurements, meteorological data, and information about well pad activities, these plumes were connected with specific UOGD operations including drilling, flowback, and production equipment maintenance. The chemical signatures of these plumes differed by operation type (e.g., C8–C10 n-alkanes constituted a larger fraction of measured VOCs in drilling-related plumes). Concentrations of individual, oil and gas-related VOCs in these plumes were often several orders of magnitude higher than in background air, with maximum ethane and benzene concentrations of 79,600 and 819 ppbv, respectively. Because these plumes typically impact a monitoring site for just several minutes, they are easily missed by slower-responding instruments. Study measurements highlight future emission mitigation opportunities during UOGD operations, including better control of emissions from shakers that separate drill cuttings from drilling mud, production separator maintenance operations, and periodic emptying of sand cans during flowback operations.

A Rule-Based Automated Chemical Recognition Algorithm for a Multi-Cell Multi-Detector Micro Gas Chromatograph

A chemical recognition algorithm is an integral part of any autonomous microscale gas chromatography (µGC) system for automated chemical analysis. For a multi-detector µGC system, the chemical analysis must account for the retention time of each chemical analyte as well as the relative response of each detector to each analyte, i.e., the detector response pattern (DRP). In contrast to the common approaches of heuristically using principal component analysis and machine learning, this paper reports a rule-based automated chemical recognition algorithm for a multi-cell, multi-detector µGC system, in which the DRP is related to theoretical principles; consequently, this algorithm only requires a small amount of calibration data but not extensive training data. For processing both the retention time and the raw DRP, the algorithm applies rules based on expert knowledge to compare the detected peaks; these rules are located in a customized software library. Additionally, the algorithm provides special handling for chromatogram peaks with a small signal-to-noise ratio. It also provides separate special handling for asymmetrical peaks that may result from surface adsorptive analytes. This work also describes an experimental evaluation in which the algorithm used the relative response of two complementary types of capacitive detectors as well as a photoionization detector that were incorporated into the µGC system of interest. In these tests, which were performed on chromatograms with 21–31 peaks for each detector, the true positive rate was 96.3%, the true negative rate was 94.1%, the false positive rate was 5.9%, and the false negative rate was 3.7%. The results demonstrated that the algorithm can support µGC systems for automated chemical screening and early warning applications.

Thermal desorption from zeolite layer for VOC detection

Volatile organic compounds (VOCs) are being considered for a wide range of applications, since they provide information on specific processes (e.g., metabolic processes in humans and plants). To date, environmental VOCs detection can be accomplished with low selectivity using both portable photoionization techniques or using gold standard laboratory techniques (e.g., gas and liquid chromatography, mass spectrometry), getting higher performances and limitations in terms of portability, availability of equipment and of consumption of time. Hereafter, a modified photoionization technique based on a thin zeolite layer for the selective adsorption of VOCs was investigated, exploiting the processes of thermal adsorption and desorption. Zeolite through the inherent nanoporous structure possesses specific electrical characteristics, strongly influenced by the adsorption of the molecules within the framework. Experimental analysis of its electrical and adsorption/desorption properties of propionic and succinic acids was performed. Results demonstrated how the use of a thin nanoporous layer allows to increase the selectivity of commercial photoionization detector during VOC detection, evidencing the potentiality of this technique for the development of low-cost and high-performance sensors in various applications.