Development of the GC-MS organic aerosol monitor (GC-MS OAM) for in-field detection of particulate organic compounds

Cropper, Paul M.; Overson, Devon K.; Cary, Robert A.; Eatough, Delbert J.; Chow, Judith C.; Hansen, Jaron C.

doi:10.1016/j.atmosenv.2017.09.019

Cited by 25 publications

(9 citation statements)

References 27 publications

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“…It uses a chemically deactivated quartz filter for collection followed by thermal desorption and GC-MS analysis. Laboratory tests show that detection limits range from 0.2 to 3 ng for many atmospherically relevant compounds (Cropper et al, 2017). The organic marker compounds identified and used in the PMF analysis reported here included levoglucosan, dehydroabietic acid, stearic acid, and the polycyclic aromatic hydrocarbons pyrene (a four-ring compound) and anthracene (a three-ring compound).…”

Section: Organic Marker Compound Datamentioning

confidence: 97%

“…The GC-MS organic aerosol monitor (GC-MS OAM) used in this study has been described (Cropper et al, 2017). The instrument combines fully automated filter collection of fine particles with thermal desorption, gas chromatography, and mass spectrometry to quantitatively measure the carbonaceous components of PM on an hourly averaged basis.…”

Section: Organic Marker Compound Datamentioning

confidence: 99%

“…A new instrument capable of monitoring the organic components of PM in the field on an hourly averaged basis has been built, automated, and successfully deployed by Brigham Young University (BYU). Emphasis has focused on the measurement of nonvolatile compounds, which have been used as markers of organic sources for source apportionment evaluations and which can be volatilized at about 200°C (Cropper et al, 2017;Lin et al, 2010). This instrument uses a filter collection/thermal desorption system integrated with a miniaturized gas chromatography-mass spectrometery (GC-MS) to measure hourly averaged concentrations of organic compounds in PM.…”

Section: Introductionmentioning

confidence: 99%

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Use of a gas chromatography–mass spectrometry organic aerosol monitor for in-field detection of fine particulate organic compounds in source apportionment

Cropper

Eatough

Overson

et al. 2018

Journal of the Air & Waste Management Association

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The identification of sources and secondary formation pathways leading to observed levels of PM (particulate matter with an aerodynmaic diameter <2.5 μm) is important in making regulatory decisions on pollution control. The use of organic marker compounds in this assessment has proven useful; however, data obtained on a daily, or longer, sampling schedule limit the value of the information because diurnal changes associated with emissions and secondary aerosol formation cannot be identified. A new instrument, the gas chromtography-mass spectrometry (GC-MS) organic aerosol monitor, allows for the determination on these compounds on an hourly averaged basis. The demonstrated potential value of hourly averaged data in a source apportionment analysis indicates that significant improvement in the data used for making regulatory decisions is possible.

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Section: Organic Marker Compound Datamentioning

confidence: 97%

Section: Organic Marker Compound Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Use of a gas chromatography–mass spectrometry organic aerosol monitor for in-field detection of fine particulate organic compounds in source apportionment

Cropper

Eatough

Overson

et al. 2018

Journal of the Air & Waste Management Association

Self Cite

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“…The non-real time concentrations of PAHs in PM were all lower than the results from real-time PAHs sampling, possibly because PAH derivatives and heterocyclic species were also detected as PAHs when using the real-time method, and the PAHs concentration in the atmosphere is overestimated [ 67 ]. On the other hand, the techniques with high temporal resolution and high specificity for the chemicals in PM are very expensive for routine air quality monitoring [ 68 ]. Therefore, the non-real-time determination method is still a well-recognized method to determine the PAHs, including sampling, experimental treatment, and instrumental analysis.…”

Section: Introductionmentioning

confidence: 99%

Exposure to Atmospheric Particulate Matter-Bound Polycyclic Aromatic Hydrocarbons and Their Health Effects: A Review

Yang

Zhang

et al. 2021

IJERPH

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Particulate matter (PM) is a major factor contributing to air quality deterioration that enters the atmosphere as a consequence of various natural and anthropogenic activities. In PM, polycyclic aromatic hydrocarbons (PAHs) represent a class of organic chemicals with at least two aromatic rings that are mainly directly emitted via the incomplete combustion of various organic materials. Numerous toxicological and epidemiological studies have proven adverse links between exposure to particulate matter-bound (PM-bound) PAHs and human health due to their carcinogenicity and mutagenicity. Among human exposure routes, inhalation is the main pathway regarding PM-bound PAHs in the atmosphere. Moreover, the concentrations of PM-bound PAHs differ among people, microenvironments and areas. Hence, understanding the behaviour of PM-bound PAHs in the atmosphere is crucial. However, because current techniques hardly monitor PAHs in real-time, timely feedback on PAHs including the characteristics of their concentration and composition, is not obtained via real-time analysis methods. Therefore, in this review, we summarize personal exposure, and indoor and outdoor PM-bound PAH concentrations for different participants, spaces, and cities worldwide in recent years. The main aims are to clarify the characteristics of PM-bound PAHs under different exposure conditions, in addition to the health effects and assessment methods of PAHs.

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“…While these softwares offer convenient automated analysis, they are prone to misinterpret data due to retention time shifts. Additional injection and hardware approaches have been explored to achieve better extraction of pertinent information [ 10 ], [ 11 ]. While the developed ideas have proved reliable, they require additional materials that may not be available.…”

Section: Introductionmentioning

confidence: 99%

Employing and Interpreting a Machine Learning Target-Cognizant Technique for Analysis of Unknown Signals in Multiple Reaction Monitoring

McCarthy

Gupta

2021

IEEE Access

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The aim of this interdisciplinary work is a robust signal processing and autonomous machine learning framework to associate well-known (target) as well as any potentially unknown (non-target) peaks present within gas chromatography-mass spectrometry (GC/MS/MS) raw instrument signal. Particularly, this work evaluates three machine learning algorithms abilities to autonomously associate raw signal peaks based on accuracy in training and testing. A target is a known congener that is expected to be present within the raw instrument signal and a non-target is an unknown or unexpected compound. Autonomously identifying target peaks within the GC/MS/MS and associating them with non-target peaks can help improve the analysis of collected samples. Association of peaks refers to classifying peaks as known congeners regardless if the peak is a target or non-target. Uncertainty of peaks fitted and discovered through raw instrument signals from GC/MS/MS data is assessed to create topographical illustrations of target annotated peaks among sample raw instrument signals collected across diverse locations in the Chicago area. The term ''annotated peak'' is used to assign peaks found at specific retention times as a known congener. Adaptive signal processing techniques are utilized to smooth data and correct baseline drifts as well as detect and separate coeluted (overlapped) peaks in the raw instrument signal to provide key feature extraction. 150 air samples are analyzed for individual polychlorinated biphenyls (PCB) with GC/MS/MS across Chicago, IL. 80% of the data is used for training classification of target PCBs and 20% of the data is evaluated to identify and associate consistently occurring non-target peaks with target PCBs. A random forest classifier is used to associate identified peaks to target PCB peaks. Geographical topographical representations of target PCBs in the raw instrument signal demonstrates how PCBs accumulate and degrade in certain locations.

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Development of the GC-MS organic aerosol monitor (GC-MS OAM) for in-field detection of particulate organic compounds

Cited by 25 publications

References 27 publications

Use of a gas chromatography–mass spectrometry organic aerosol monitor for in-field detection of fine particulate organic compounds in source apportionment

Use of a gas chromatography–mass spectrometry organic aerosol monitor for in-field detection of fine particulate organic compounds in source apportionment

Exposure to Atmospheric Particulate Matter-Bound Polycyclic Aromatic Hydrocarbons and Their Health Effects: A Review

Employing and Interpreting a Machine Learning Target-Cognizant Technique for Analysis of Unknown Signals in Multiple Reaction Monitoring

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