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Song K, Yang X, Wang Y, Wan Z, Wang J, Wen Y, Jiang H, Li A, Zhang J, Lu S, Fan B, Guo S, Ding Y. Addressing new chemicals of emerging concern (CECs) in an indoor office. ENVIRONMENT INTERNATIONAL 2023; 181:108259. [PMID: 37839268 DOI: 10.1016/j.envint.2023.108259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/27/2023] [Accepted: 10/09/2023] [Indexed: 10/17/2023]
Abstract
Indoor pollutants change over time and place. Exposure to hazardous organics is associated with adverse health effects. This work sampled gaseous organics by Tenax TA tubes in two indoor rooms, i.e., an office set as samples, and the room of chassis dynamometer (RCD) set as backgrounds. Compounds are analyzed by a thermal desorption comprehensive two-dimensional gas chromatography-quadrupole mass spectrometer (TD-GC × GC-qMS). Four new chemicals of emerging concern (CECs) are screened in 469 organics quantified. We proposed a three-step pipeline for CECs screening utilizing GC × GC including 1) non-target scanning of organics with convincing molecular structures and quantification results, 2) statistical analysis between samples and backgrounds to extract useful information, and 3) pixel-based property estimation to evaluate the contamination potential of addressed chemicals. New CECs spotted in this work are all intermediate volatility organic compounds (IVOCs), containing mintketone, isolongifolene, β-funebrene, and (5α)-androstane. Mintketone and sesquiterpenes may be derived from the use of volatile chemical products (VCPs), while (5α)-androstane is probably human-emitted. The occurrence and contamination potential of the addressed new CECs are reported for the first time. Non-target scanning and the measurement of IVOCs are of vital importance to get a full glimpse of indoor organics.
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Affiliation(s)
- Kai Song
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education (IJRC), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Xinping Yang
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yunjing Wang
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
| | - Zichao Wan
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education (IJRC), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Junfang Wang
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yi Wen
- China Automotive Technology and Research Center (CATARC), Beijing 100176, China
| | - Han Jiang
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Ang Li
- China Automotive Technology and Research Center (CATARC), Beijing 100176, China
| | | | - Sihua Lu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education (IJRC), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Baoming Fan
- TECHSHIP (Beijing) Technology Co., LTD, Beijing 100039, China
| | - Song Guo
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education (IJRC), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China.
| | - Yan Ding
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
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Cain CN, Ochoa GS, Synovec RE. Enhancing partial least squares modeling of comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry data by tile-based variance ranking. J Chromatogr A 2023; 1694:463920. [PMID: 36933463 DOI: 10.1016/j.chroma.2023.463920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/02/2023] [Accepted: 03/07/2023] [Indexed: 03/12/2023]
Abstract
Chemometric methods like partial least squares (PLS) regression are valuable for correlating sample-based differences hidden in comprehensive two-dimensional gas chromatography (GC × GC) data to independently measured physicochemical properties. Herein, this work establishes the first implementation of tile-based variance ranking as a selective data reduction methodology to improve PLS modeling performance of 58 diverse aerospace fuels. Tile-based variance ranking discovered a total of 521 analytes with a square of the relative standard deviation (RSD2) in signal between 0.07 to 22.84. The goodness-of-fit for the models were determined by their normalized root-mean-square error of cross-validation (NRMSECV) and normalized root-mean-square error of prediction (NRMSEP). PLS models developed for viscosity, hydrogen content, and heat of combustion using all 521 features discovered by tile-based variance ranking had a respective NRMSECV (NRMSEP) equal to 10.5 % (10.2 %), 8.3 % (7.6 %), and 13.1 % (13.5 %). In contrast, use of a single-grid binning scheme, a common data reduction strategy for PLS analysis, resulted in less accurate models for viscosity (NRMSECV = 14.2 %; NRMSEP = 14.3 %), hydrogen content (NRMSECV = 12.1 %; NRMSEP = 11.0 %), and heat of combustion (NRMSECV = 14.4 %; NRMSEP = 13.6 %). Further, the features discovered by tile-based variance ranking can be optimized for each PLS model with RReliefF analysis, a machine learning algorithm. RReliefF feature optimization selected 48, 125, and 172 analytes out of the original 521 discovered by tile-based variance ranking to model viscosity, hydrogen content, and heat of combustion, respectively. The RReliefF optimized features developed highly accurate property-composition models for viscosity (NRMSECV = 7.9 %; NRMSEP = 5.8 %), hydrogen content (NRMSECV = 7.0 %; NRMSEP = 4.9 %), heat of combustion (NRMSECV = 7.9 %; NRMSEP = 8.4 %). This work also demonstrates that processing the chromatograms with a tile-based approach allows the analyst to directly identify the analytes of importance in a PLS model. Coupling tile-based feature selection with PLS analysis allows for deeper understanding in any property-composition study.
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Affiliation(s)
- Caitlin N Cain
- Department of Chemistry, University of Washington, Box 351700, Seattle, WA, 98195, USA
| | - Grant S Ochoa
- Department of Chemistry, University of Washington, Box 351700, Seattle, WA, 98195, USA
| | - Robert E Synovec
- Department of Chemistry, University of Washington, Box 351700, Seattle, WA, 98195, USA.
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Trinklein TJ, Cain CN, Ochoa GS, Schöneich S, Mikaliunaite L, Synovec RE. Recent Advances in GC×GC and Chemometrics to Address Emerging Challenges in Nontargeted Analysis. Anal Chem 2023; 95:264-286. [PMID: 36625122 DOI: 10.1021/acs.analchem.2c04235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Timothy J Trinklein
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195-1700, United States
| | - Caitlin N Cain
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195-1700, United States
| | - Grant S Ochoa
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195-1700, United States
| | - Sonia Schöneich
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195-1700, United States
| | - Lina Mikaliunaite
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195-1700, United States
| | - Robert E Synovec
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195-1700, United States
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Geerts M, Ristic N, Djokic M, Ukkandath Aravindakshan S, Marin GB, Van Geem KM. Crude to Olefins: Effect of Feedstock Composition on Coke Formation in a Bench-Scale Steam Cracking Furnace. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.9b06702] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Moreno Geerts
- Laboratory for Chemical Technology, Ghent University, Technologiepark 125, B-9052 Gent, Belgium
| | - Nenad Ristic
- Laboratory for Chemical Technology, Ghent University, Technologiepark 125, B-9052 Gent, Belgium
| | - Marko Djokic
- Laboratory for Chemical Technology, Ghent University, Technologiepark 125, B-9052 Gent, Belgium
| | | | - Guy B. Marin
- Laboratory for Chemical Technology, Ghent University, Technologiepark 125, B-9052 Gent, Belgium
| | - Kevin M. Van Geem
- Laboratory for Chemical Technology, Ghent University, Technologiepark 125, B-9052 Gent, Belgium
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Bahaghighat HD, Freye CE, Gough DV, Sudol PE, Synovec RE. Ultrafast separations via pulse flow valve modulation to enable high peak capacity multidimensional gas chromatography. J Chromatogr A 2018; 1573:115-124. [DOI: 10.1016/j.chroma.2018.08.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 07/26/2018] [Accepted: 08/01/2018] [Indexed: 01/10/2023]
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Prebihalo SE, Berrier KL, Freye CE, Bahaghighat HD, Moore NR, Pinkerton DK, Synovec RE. Multidimensional Gas Chromatography: Advances in Instrumentation, Chemometrics, and Applications. Anal Chem 2017; 90:505-532. [DOI: 10.1021/acs.analchem.7b04226] [Citation(s) in RCA: 115] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Sarah E. Prebihalo
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195, United States
| | - Kelsey L. Berrier
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195, United States
| | - Chris E. Freye
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195, United States
| | - H. Daniel Bahaghighat
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195, United States
- Department of Chemistry and Life Science, United States Military Academy, West Point, New York 10996, United States
| | - Nicholas R. Moore
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195, United States
| | - David K. Pinkerton
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195, United States
| | - Robert E. Synovec
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195, United States
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