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Environ Int
Title: | "Multiply improved positive matrix factorization for source apportionment of volatile organic compounds during the COVID-19 shutdown in Tianjin, China" |
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Author(s): | Gu Y; Liu B; Dai Q; Zhang Y; Zhou M; Feng Y; Hopke PK; |
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Address: | "State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China. State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China. Electronic address: lbsnankai@foxmail.com. State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China. Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, NY 14642, USA; Institute for a Sustainable Environment, Clarkson University, Potsdam, NY 13699, USA" |
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Journal Title: | Environ Int |
Year: | 2022 |
Volume: | 20211116 |
Issue: | |
Page Number: | 106979 - |
DOI: | 10.1016/j.envint.2021.106979 |
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ISSN/ISBN: | 1873-6750 (Electronic) 0160-4120 (Linking) |
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Abstract: | "Ambient concentrations of volatile organic compounds (VOCs) vary with emission rates, meteorology, and chemistry. Conventional positive matrix factorization (PMF) loses information because of dilution variations and chemical losses. Multiply improved PMF incorporates the ventilation coefficient, and total solar radiation or oxidants to reduce the effects of dispersion and chemical loss. These methods were applied to hourly speciated VOC data from November 2019 to March 2020 including during the COVID-19 shutdown. Various comparisons were made to assess the influences of these fluctuation drivers by time of day. Dispersion normalized PMF (DN-PMF) reduced the dispersion variations. Dispersion-radiation normalized PMF (DRN-PMF) reduced the impact of chemical loss, especially at night, which was better than Dispersion-O(x) normalized PMF (DON-PMF). The conditional bivariate probability function (CBPF) plots of DRN-PMF results were consist with actual source locations. The DN-PMF, DRN-PMF, and DON-PMF results were consistent between 10:00 and 15:00, suggesting dispersion was significantly more influential than photochemical reactions during these times. The DRN-PMF results indicated that the highest VOC contributors during the COVID-19 shutdown were liquefied petroleum gas (LPG) (28.8%), natural gas (25.2%), and pulverized coal boilers emissions (19.6%). Except for petrochemical-related enterprises and LPG, the contribution concentrations of all other sources decreased substantially during the COVID-19 shutdown, by 94.7%, 90.6%, and 86.8% for vehicle emissions, gasoline evaporation, and the mixed source of diesel evaporation and solvent use, respectively. Controlling the use of motor vehicles and related volatilization of diesel fuel and gasoline can be effective in controlling VOCs in the future" |
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Keywords: | *Air Pollutants/analysis *covid-19 China Environmental Monitoring Humans SARS-CoV-2 Vehicle Emissions/analysis *Volatile Organic Compounds/analysis Covid-19 Improved PMF Source apportionment Volatile organic compounds; |
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Notes: | "MedlineGu, Yao Liu, Baoshuang Dai, Qili Zhang, Yufen Zhou, Ming Feng, Yinchang Hopke, Philip K eng Research Support, Non-U.S. Gov't Netherlands 2022/01/08 Environ Int. 2022 Jan; 158:106979. doi: 10.1016/j.envint.2021.106979. Epub 2021 Nov 16" |
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Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 16-11-2024
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