1
|
Li X, Wang W, Yang S, Cheng Y, Zeng L, Yu X, Lu S, Liu Y, Hu M, Xie S, Huang X, Zhou J, Shi L, Xu H, Lin S, Liu H, Feng M, Song D, Tan Q, Zhang Y. Ozone sensitivity regimes vary at different heights in the planetary boundary layer. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 944:173712. [PMID: 38830412 DOI: 10.1016/j.scitotenv.2024.173712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 05/29/2024] [Accepted: 05/31/2024] [Indexed: 06/05/2024]
Abstract
The sensitivity of tropospheric ozone (O3) to its precursors volatile organic compounds (VOCs) and nitrogen oxides (NOX) determines the emission reduction strategy for O3 mitigation. Due to the lack of comprehensive vertical measurements of VOCs, the vertical distribution of O3 sensitivity regimes has not been well understood. O3 precursor sensitivity determined by ground-level measurements has been generally used to guide O3 control strategy. Here, to precisely diagnose O3 sensitivity regimes at different heights in the planetary boundary layer (PBL), we developed a vertical measurement system based on an unmanned aerial vehicle platform to conduct comprehensive vertical measurements of VOCs, NOX and other relevant parameters. Our results suggest that the O3 precursor sensitivity shifts from a VOC-limited regime at the ground to a NOX-limited regime at upper layers, indicating that the ground-level O3 sensitivity cannot represent the situation of the whole PBL. We also found that the state-of-the-art photochemical model tends to underestimate oxygenated VOCs at upper layers, resulting in overestimation of the degree of VOCs-limited regime. Therefore, thorough vertical measurements of VOCs to accurately diagnose O3 precursor sensitivity is in urgent need for the development of effective O3 control strategies.
Collapse
Affiliation(s)
- Xin Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing, 100816, China; Collaborative Innovation Centre of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Wenjie Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; Minerva Research Group, Max Planck Institute for Chemistry, Mainz 55128, Germany.
| | - Suding Yang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing, 100816, China
| | - Yafang Cheng
- Minerva Research Group, Max Planck Institute for Chemistry, Mainz 55128, Germany
| | - Limin Zeng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing, 100816, China; Collaborative Innovation Centre of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Xuena Yu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Sihua Lu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing, 100816, China; Collaborative Innovation Centre of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Ying Liu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing, 100816, China
| | - Min Hu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing, 100816, China; Collaborative Innovation Centre of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Shaodong Xie
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing, 100816, China
| | - Xiaofeng Huang
- Environmental Laboratory, PKU-HKUST Shenzhen-Hong Kong Institution, Shenzhen 518057, China; Laboratory of Atmospheric Observation Supersite, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Jun Zhou
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China
| | - Lei Shi
- Quadrant Space (Tianjin) Technology Co., Ltd, Tianjin 301700, China
| | - Haibin Xu
- Quadrant Space (Tianjin) Technology Co., Ltd, Tianjin 301700, China
| | - Shuchen Lin
- Quadrant Space (Tianjin) Technology Co., Ltd, Tianjin 301700, China
| | - Hefan Liu
- Chengdu Academy of Environmental Sciences, Chengdu 610072, China
| | - Miao Feng
- Chengdu Academy of Environmental Sciences, Chengdu 610072, China
| | - Danlin Song
- Chengdu Academy of Environmental Sciences, Chengdu 610072, China
| | - Qinwen Tan
- Chengdu Academy of Environmental Sciences, Chengdu 610072, China
| | - Yuanhang Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing, 100816, China; Collaborative Innovation Centre of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
| |
Collapse
|
2
|
Cui Y, Liu B, Yang Y, Kang S, Wang F, Xu M, Wang W, Feng Y, Hopke PK. Primary and oxidative source analyses of consumed VOCs in the atmosphere. JOURNAL OF HAZARDOUS MATERIALS 2024; 476:134894. [PMID: 38909463 DOI: 10.1016/j.jhazmat.2024.134894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 05/27/2024] [Accepted: 06/11/2024] [Indexed: 06/25/2024]
Abstract
Consumed VOCs are the compounds that have reacted to form ozone and secondary organic aerosol (SOA) in the atmosphere. An approach that can apportion the contributions of primary sources and reactions to the consumed VOCs was developed in this study and applied to hourly VOCs data from June to August 2022 measured in Shijiazhuang, China. The results showed that petrochemical industries (36.9 % and 51.7 %) and oxidation formation (20.6 % and 35.6 %) provided the largest contributions to consumed VOCs and OVOCs during the study period, whereas natural gas (5.0 % and 7.6 %) and the mixed source of liquefied petroleum gas and solvent use (3.1 % and 4.2 %) had the relatively low contributions. Compared to the non-O3 pollution (NOP) period, the contributions of oxidation formation, petrochemical industries, and the mixed source of gas evaporation and vehicle emissions to the consumed VOCs during the O3 pollution (OP) period increased by 2.8, 3.8, and 9.3 times, respectively. The differences in contributions of liquified petroleum gas and solvent use, natural gas, and combustion sources to consumed VOCs between OP and NOP periods were relatively small. Transport of petrochemical industries emissions from the southeast to the study site was the primary consumed pathway for VOCs emitted from petrochemical industries.
Collapse
Affiliation(s)
- Yaqi Cui
- 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
| | - Baoshuang Liu
- 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.
| | - Yufeng Yang
- 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
| | - Sicong Kang
- Beijing Make Environment Science & Technology Co., Ltd., Beijing 100083, China
| | - Fuquan Wang
- Beijing Make Environment Science & Technology Co., Ltd., Beijing 100083, China
| | - Man Xu
- Shijiazhuang Environmental Prediction Center, Shijiazhuang 050022, China
| | - Wei Wang
- Shijiazhuang Environmental Prediction Center, Shijiazhuang 050022, China
| | - Yinchang Feng
- 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
| | - Philip K Hopke
- 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
| |
Collapse
|
3
|
Lamy E, Roquencourt C, Zhou B, Salvator H, Moine P, Annane D, Devillier P, Bardin E, Grassin-Delyle S. Combination of real-time and hyphenated mass spectrometry for improved characterisation of exhaled breath biomarkers in clinical research. Anal Bioanal Chem 2024; 416:4929-4939. [PMID: 38980330 DOI: 10.1007/s00216-024-05421-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Revised: 06/21/2024] [Accepted: 06/25/2024] [Indexed: 07/10/2024]
Abstract
Exhaled breath volatilomics is a powerful non-invasive tool for biomarker discovery in medical applications, but compound annotation is essential for pathophysiological insights and technology transfer. This study was aimed at investigating the interest of a hybrid approach combining real-time proton transfer reaction-time-of-flight mass spectrometry (PTR-TOF-MS) with comprehensive thermal desorption-two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (TD-GCxGC-TOF-MS) to enhance the analysis and characterization of VOCs in clinical research, using COVID-19 as a use case. VOC biomarker candidates were selected from clinical research using PTR-TOF-MS fingerprinting in patients with COVID-19 and matched to the Human Breathomic Database. Corresponding analytical standards were analysed using both a liquid calibration unit coupled to PTR-TOF-MS and TD-GCxGC-TOF-MS, together with confirmation on new clinical samples with TD-GCxGC-TOF-MS. From 26 potential VOC biomarkers, 23 were successfully detected with PTR-TOF-MS. All VOCs were successfully detected using TD-GCxGC-TOF-MS, providing effective separation of highly chemically related compounds, including isomers, and enabling high-confidence annotation based on two-dimensional chromatographic separation and mass spectra. Four VOCs were identified with a level 1 annotation in the clinical samples. For future applications, the combination of real-time PTR-TOF-MS and comprehensive TD-GCxGC-TOF-MS, at least on a subset of samples from a whole study, would enhance the performance of VOC annotation, offering potential advancements in biomarker discovery for clinical research.
Collapse
Affiliation(s)
- Elodie Lamy
- Département de Biotechnologie de la Santé UFR Simone Veil - Santé, Université Paris-Saclay, UVSQ, INSERM, Infection et Inflammation (2I), U1173, 2 avenue de la source de la Bièvre, 78180, Montigny le Bretonneux, France
- FHU SEPSIS (Saclay and Paris Seine Nord Endeavour to PerSonalize Interventions for Sepsis) and IHU PROMETHEUS, Garches, France
| | | | - Bingqing Zhou
- Département de Biotechnologie de la Santé UFR Simone Veil - Santé, Université Paris-Saclay, UVSQ, INSERM, Infection et Inflammation (2I), U1173, 2 avenue de la source de la Bièvre, 78180, Montigny le Bretonneux, France
- FHU SEPSIS (Saclay and Paris Seine Nord Endeavour to PerSonalize Interventions for Sepsis) and IHU PROMETHEUS, Garches, France
| | - Hélène Salvator
- Exhalomics®, Hôpital Foch, Suresnes, France
- Pneumologie, Hôpital Foch, Suresnes, France
- Laboratoire de recherche en Pharmacologie Respiratoire - VIM Suresnes, UMR 0892, Université Paris-Saclay, UVSQ, Suresnes, France
| | - Pierre Moine
- Département de Biotechnologie de la Santé UFR Simone Veil - Santé, Université Paris-Saclay, UVSQ, INSERM, Infection et Inflammation (2I), U1173, 2 avenue de la source de la Bièvre, 78180, Montigny le Bretonneux, France
- FHU SEPSIS (Saclay and Paris Seine Nord Endeavour to PerSonalize Interventions for Sepsis) and IHU PROMETHEUS, Garches, France
- Réanimation médicale, Hôpital Raymond Poincaré, Assistance Publique-Hôpitaux de Paris, Garches, France
| | - Djillali Annane
- Département de Biotechnologie de la Santé UFR Simone Veil - Santé, Université Paris-Saclay, UVSQ, INSERM, Infection et Inflammation (2I), U1173, 2 avenue de la source de la Bièvre, 78180, Montigny le Bretonneux, France
- FHU SEPSIS (Saclay and Paris Seine Nord Endeavour to PerSonalize Interventions for Sepsis) and IHU PROMETHEUS, Garches, France
- Réanimation médicale, Hôpital Raymond Poincaré, Assistance Publique-Hôpitaux de Paris, Garches, France
| | - Philippe Devillier
- FHU SEPSIS (Saclay and Paris Seine Nord Endeavour to PerSonalize Interventions for Sepsis) and IHU PROMETHEUS, Garches, France
- Exhalomics®, Hôpital Foch, Suresnes, France
- Laboratoire de recherche en Pharmacologie Respiratoire - VIM Suresnes, UMR 0892, Université Paris-Saclay, UVSQ, Suresnes, France
| | - Emmanuelle Bardin
- Département de Biotechnologie de la Santé UFR Simone Veil - Santé, Université Paris-Saclay, UVSQ, INSERM, Infection et Inflammation (2I), U1173, 2 avenue de la source de la Bièvre, 78180, Montigny le Bretonneux, France
- FHU SEPSIS (Saclay and Paris Seine Nord Endeavour to PerSonalize Interventions for Sepsis) and IHU PROMETHEUS, Garches, France
- Institut Necker-Enfants Malades, Paris, France
| | - Stanislas Grassin-Delyle
- Département de Biotechnologie de la Santé UFR Simone Veil - Santé, Université Paris-Saclay, UVSQ, INSERM, Infection et Inflammation (2I), U1173, 2 avenue de la source de la Bièvre, 78180, Montigny le Bretonneux, France.
- FHU SEPSIS (Saclay and Paris Seine Nord Endeavour to PerSonalize Interventions for Sepsis) and IHU PROMETHEUS, Garches, France.
- Exhalomics®, Hôpital Foch, Suresnes, France.
| |
Collapse
|
4
|
Zhang H, Wang X, Lv L, Li G, Liu X, Li X, Yao Z. Insights into quantitative evaluation technology of PM 2.5 transport at multi-perspective and multi-spatial and temporal scales in the north China plain. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 338:122693. [PMID: 37802287 DOI: 10.1016/j.envpol.2023.122693] [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: 08/08/2023] [Revised: 09/14/2023] [Accepted: 10/03/2023] [Indexed: 10/08/2023]
Abstract
Cross-border transport is a crucial factor affecting air quality, while how to quantify the transport contribution through different technologies at multi-perspective and multi-scale have not been fully understood. This study established three quantification techniques, and conducted a systematic assessment of PM2.5 transport over the North China Plain (NCP) based on numerical simulations and vertical observations. Results suggested that the annual local emissions, inter-urban and outer-regional transport contributed 44.5%-64.6%, 15.2%-27.9% and 18.0%-28.2% of total surface PM2.5 concentrations, respectively, with transport intensity stronger in July and April, yet weaker in January and October. The southwest-northeast, northeast-southwest, and southeast-northwest were three prevailing transport directions near the surface. By comparison, the annual PM2.5 transport contribution below the atmospheric boundary layer height increased by 16.8%-24.5% in Beijing, Tianjin and Shijiazhuang, with inter-urban and outer-regional contribution of 29.8%-32.1% and 18.5%-23.1%. Furthermore, observed fluxes from fixed-point and vehicle-based mobile lidar were in good agreement with the simulated flux. PM2.5 net flux intensity varied with height, with generally larger at the middle- and high-altitude layer than that of low-altitude layer. In the early, during and late period of haze peak formation (Stage Ⅰ, Ⅱ, Ⅲ, respectively), the largest absolute flux intensity on average was Stage Ⅱ (566.7 t/d), followed by Stage Ⅲ (307.0 t/d) and Ⅰ (191.4 t/d). Besides, external transport may dominate the second concentration peak, while local emissions may play a more vital role in the first and third peaks. It has been noted that joint prevention and control measures should be proposed 1-2 days before reaching PM2.5 extremes. These findings could improve our understanding of transport influence mechanism of PM2.5 and propose effective emission reduction measures in the NCP region.
Collapse
Affiliation(s)
- Hanyu Zhang
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China; State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing, 100048, China
| | - Xuejun Wang
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China; State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing, 100048, China
| | - Longyue Lv
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China; State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing, 100048, China
| | - Guohao Li
- Beijing Municipal Research Institute of Environmental Protection, Beijing Key Laboratory of Urban Atmospheric Volatile Organic Compounds Pollution Control and Application, National Urban Environmental Pollution Control Engineering Research Center, Beijing, 100037, China
| | - Xiaoyu Liu
- Beijing Municipal Research Institute of Environmental Protection, Beijing Key Laboratory of Urban Atmospheric Volatile Organic Compounds Pollution Control and Application, National Urban Environmental Pollution Control Engineering Research Center, Beijing, 100037, China
| | - Xin Li
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China; State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing, 100048, China
| | - Zhiliang Yao
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China; State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing, 100048, China.
| |
Collapse
|
5
|
Wang Z, Zhang P, Pan L, Qian Y, Li Z, Li X, Guo C, Zhu X, Xie Y, Wei Y. Ambient Volatile Organic Compound Characterization, Source Apportionment, and Risk Assessment in Three Megacities of China in 2019. TOXICS 2023; 11:651. [PMID: 37624157 PMCID: PMC10458435 DOI: 10.3390/toxics11080651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/24/2023] [Accepted: 07/25/2023] [Indexed: 08/26/2023]
Abstract
In order to illustrate pollution characterization, source apportionment, and risk assessment of VOCs in Beijing, Baoding, and Shanghai, field observations of CO, NO, NO2, O3, and volatile organic compounds (VOCs) were conducted in 2019. Concentrations of VOCs were the highest in Beijing (105.4 ± 52.1 ppb), followed by Baoding (97.1 ± 47.5 ppb) and Shanghai (91.1 ± 41.3 ppb). Concentrations of VOCs were the highest in winter (120.3 ± 61.5 ppb) among the three seasons tested, followed by summer (98.1 + 50.8 ppb) and autumn (75.5 + 33.4 ppb). Alkenes were the most reactive VOC species in all cities, accounting for 56.0%, 53.7%, and 39.4% of ozone formation potential in Beijing, Baoding, and Shanghai, respectively. Alkenes and aromatics were the reactive species, particularly ethene, propene, 1,3,5-trimethylbenzene, and m/p-xylene. Vehicular exhaust was the principal source in all three cities, accounting for 27.0%, 30.4%, and 23.3% of VOCs in Beijing, Baoding, and Shanghai, respectively. Industrial manufacturing was the second largest source in Baoding (23.6%) and Shanghai (21.3%), and solvent utilization was the second largest source in Beijing (25.1%). The empirical kinetic modeling approach showed that O3 formation was limited by both VOCs and nitric oxides at Fangshan (the suburban site) and by VOCs at Xuhui (the urban site). Acrolein was the only substance with an average hazard quotient greater than 1, indicating significant non-carcinogenic risk. In Beijing, 1,2-dibromoethane had an R-value of 1.1 × 10-4 and posed a definite carcinogenic risk.
Collapse
Affiliation(s)
- Zhanshan Wang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; (Z.W.)
| | - Puzhen Zhang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; (Z.W.)
| | - Libo Pan
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; (Z.W.)
| | - Yan Qian
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; (Z.W.)
| | - Zhigang Li
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; (Z.W.)
| | - Xiaoqian Li
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; (Z.W.)
| | - Chen Guo
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; (Z.W.)
| | - Xiaojing Zhu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; (Z.W.)
| | - Yuanyuan Xie
- Foreign Environmental Cooperation Centre, Ministry of Ecology and Environment, Beijing 100035, China
| | - Yongjie Wei
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; (Z.W.)
| |
Collapse
|
6
|
Liang S, Gao S, Wang S, Chai W, Chen W, Tang G. Characteristics, sources of volatile organic compounds, and their contributions to secondary air pollution during different periods in Beijing, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 858:159831. [PMID: 36336049 DOI: 10.1016/j.scitotenv.2022.159831] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 10/26/2022] [Indexed: 06/16/2023]
Abstract
Continuous measurements of volatile organic compounds (VOCs), ozone (O3), fine particulate matter (PM2.5), and related parameters were conducted between April 2020 and March 2021 in Beijing, China, to characterize potential sources of VOCs and their impacts on secondary organic aerosols (SOAs) and O3 levels. The annual average mixing ratio of VOCs was 17.4 ± 10.1 ppbv, with monthly averages ranging from 11.6 to 25.2 ppbv. According to the empirical kinetic modeling approach (EKMA), O3 formation during O3 season was "VOCs-limited", while it was in a "transition" regime during O3 pollution episodes. In the O3 season, higher ozone formation potential (OFP) of m/p-xylene, o-xylene, toluene, isopentane, and n-butane were evident during O3 pollution episodes, in line with the increasing contributions of solvent usage and coating, as well as gasoline evaporation to OFP obtained through a matrix factorization model (PMF). Aromatics contributed the most to the secondary organic aerosol formation potential (SOAFP). In the non-O3 season, the contribution of vehicle exhaust to SOAFP elevated on hazy days, thereby revealing the importance of traffic-derived VOCs for PM2.5 pollution. Our results indicate that the prior control of different VOC sources should vary by season, thereby facilitating the synergistic control of O3 and PM2.5 in Beijing.
Collapse
Affiliation(s)
- Siyuan Liang
- China National Environmental Monitoring Centre, Beijing 100012, China.
| | - Song Gao
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
| | - Shuai Wang
- China National Environmental Monitoring Centre, Beijing 100012, China
| | - Wenxuan Chai
- China National Environmental Monitoring Centre, Beijing 100012, China
| | - Wentai Chen
- Nanjing Intelligent Environmental Science and Technology Co., Ltd., Nanjing 211800, China
| | - Guigang Tang
- China National Environmental Monitoring Centre, Beijing 100012, China
| |
Collapse
|
7
|
Liu Y, Qiu P, Xu K, Li C, Yin S, Zhang Y, Ding Y, Zhang C, Wang Z, Zhai R, Deng Y, Yan F, Zhang W, Xue Z, Sun Y, Ji D, Li J, Chen J, Tian H, Liu X, Zhang Y. Analysis of VOC emissions and O 3 control strategies in the Fenhe Plain cities, China. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 325:116534. [PMID: 36419282 DOI: 10.1016/j.jenvman.2022.116534] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 09/23/2022] [Accepted: 10/12/2022] [Indexed: 06/16/2023]
Abstract
Long-term continuous hourly measurements of ambient volatile organic compounds (VOCs) are scarce at the regional scale. In this study, a one-year hourly measurement campaign of VOCs was performed in Lvliang, Linfen, and Yuncheng in the heavily polluted Fenhe Plain region in China. The VOC average (±standard deviation, std) concentrations in Lvliang, Linfen, and Yuncheng were 44.4 ± 24.9, 45.7 ± 24.9, and 37.5 ± 25.0 ppbv, respectively. Compared to published data from the past two decades in China, the observed VOCs were at high concentration levels. VOCs in the Fenhe Plain cities were significantly impacted by industrial sources according to calculated emission ratios but were less affected by liquefied petroleum gas and natural gas (LPG/NG) and traffic emissions than those in megacities abroad. The emission inventories and observation data were combined for verification and identification of the key VOC species and sources controlling ozone (O3). Industrial emissions were the largest source of VOCs, accounting for 65%-79% of the total VOC emissions, while the coking industry accounted for 45.2%-66.0%. The emission inventories significantly underestimated oxygenated VOC (OVOC) emissions through the verification of VOC emission ratios. O3 control scenarios were analyzed by changing VOC/NOX reduction ratios through a photochemical box model. O3 control strategies were formulated considering local pollution control plans, emission inventories, and O3 formation regimes. The O3 reduction of reactivity-control measures was comparable with emission-control measures, ranging from 16% to 41%, which was contrary to the general perception that ozone formation potential (OFP)-based measures were more efficient for O3 reduction. Sources with high VOC emissions are accompanied by high OFP on the Fenhe Plain, indicating that the control of high-emission sources can effectively mitigate O3 pollution on this region.
Collapse
Affiliation(s)
- Yafei Liu
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, 100875, China
| | - Peipei Qiu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
| | - Kai Xu
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, 100875, China
| | - Chenlu Li
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, 100875, China
| | - Shijie Yin
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, 100875, China
| | - Yunjun Zhang
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, 100875, China
| | - Yu Ding
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, 100875, China
| | - Chen Zhang
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, 100875, China
| | - Zheng Wang
- Yuncheng Municipal Ecological Environment Bureau, Yuncheng, 044000, China
| | - Ruixiao Zhai
- Yuncheng Municipal Ecological Environment Bureau, Yuncheng, 044000, China
| | - Yijun Deng
- Yuncheng Municipal Ecological Environment Bureau, Yuncheng, 044000, China
| | - Fengyu Yan
- Yuncheng Municipal Ecological Environment Bureau, Yuncheng, 044000, China
| | - Wenjie Zhang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Zhigang Xue
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Yele Sun
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Dongsheng Ji
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Jie Li
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Jing Chen
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, 100875, China
| | - Hezhong Tian
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, 100875, China
| | - Xingang Liu
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, 100875, China.
| | - Yuanhang Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China.
| |
Collapse
|
8
|
Li X, Li B, Guo L, Feng R, Fang X. Research progresses on VOCs emission investigations via surface and satellite observations in China. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2022; 24:1968-1981. [PMID: 36000414 DOI: 10.1039/d2em00175f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Volatile organic compounds (VOCs) are important precursors of severe pollution of ozone (O3) and secondary organic aerosols in China. Fully understanding the VOCs emission is crucial for making regulations to improve air quality. This study reviews the published studies on atmospheric VOCs concentration observations in China and observation-based estimation of China's VOCs emission strengths and emission source structures. The results reveal that direct sampling and stainless-steel-tank sampling are the most commonly used methods for online and offline observations in China, respectively. The GC-MS/FID is the most commonly used VOCs measuring instrument in China (in 60.8% of the studies we summarized). Numerous studies conducted observation campaigns in urban areas (76.2%) than in suburban (17.1%), rural (18.1%), and background areas (14.3%) in China. Moreover, observation sites are largely set in eastern China (83.8%). Though there are published studies reporting observation-based China's VOCs emission investigation, these kinds of studies are still limited, and gaps are found between the results of top-down investigation and bottom-up inventories of VOCs emissions in China. In order to enhance the observation-based VOCs emission investigations in China, this study suggests future improvements including: (1) development of VOCs detection techniques, (2) strengthening of atmospheric VOCs observations, (3) improvement of the accuracy of observation-based VOCs emission estimations, and (4) facilitation of better VOCs emission inventories in China.
Collapse
Affiliation(s)
- Xinhe Li
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, P. R. China.
| | - Bowei Li
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, P. R. China.
| | - Liya Guo
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, P. R. China.
| | - Rui Feng
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, P. R. China.
| | - Xuekun Fang
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, P. R. China.
- State Key Joint Laboratory for Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
- Center for Global Change Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| |
Collapse
|
9
|
Shi Y, Liu C, Zhang B, Simayi M, Xi Z, Ren J, Xie S. Accurate identification of key VOCs sources contributing to O 3 formation along the Liaodong Bay based on emission inventories and ambient observations. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 844:156998. [PMID: 35787908 DOI: 10.1016/j.scitotenv.2022.156998] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 06/20/2022] [Accepted: 06/23/2022] [Indexed: 06/15/2023]
Abstract
In order to achieve the precise control of the volatile organic compounds (VOCs) species with high ozone (O3) formation contribution from key sources in Panjin and Yingkou, two coastal industrial cities with severe O3 pollution along the Liaodong Bay, northeast China, the ambient concentrations of 99 VOCs species were measured online at urban-petrochemical (XLT), suburban-industrial (PP), and rural (XRD) sites in July 2019, contemporary monthly anthropogenic VOCs emission inventories were developed. The source contribution of ambient VOCs resolved by positive matrix factorization (PMF) model was comparable with emission inventories, and the location of VOCs sources were speculated by potential source contribution function (PSCF). 17.5 Gg anthropogenic VOCs was emitted in Panjin and Yingkou in July 2019 with potential to form 54.7 Gg-O3 estimated by emission inventories. The average VOC mixing ratios of 47.1, 26.7, and 16.5 ppbv was observed at XLT, PP, and XRD sites, respectively. Petroleum industry (22 %), organic chemical industry (21 %), and mobile vehicle emission (19 %) were identified to be the main sources contributing to O3 formation at XLT site by PMF, while it is organic chemical industry (33 %) and solvent utilization (28 %) contributed the most at PP site. Taking the subdivided source contributions of emission inventories and source locations speculated by PSCF into full consideration, organic raw chemicals manufacturing, structural steel coating, petroleum refining process, petroleum products storage and transport, off-shore vessels, and passenger cars were identified as the key anthropogenic sources. High O3-formation contribution sources, organic chemical industry and solvent utilization were located in the industrial parks at the junction of the two cities and the southeast of Panjin, and petroleum industry distributed in the whole Panjin and offshore areas. These results identify the key VOCs species and sources and speculate the potential geographical location of sources for precisely controlling ground-level O3 along the Liaodong Bay.
Collapse
Affiliation(s)
- Yuqi Shi
- College of Environmental Sciences and Engineering, State Key Joint Laboratory of Environmental Simulation and Pollution Control, Peking University, Beijing 100871, PR China
| | - Chang Liu
- Liaoning Ecological and Environmental Service Center, Shenyang, Liaoning 110161, PR China
| | - Baosheng Zhang
- Department of Ecology and Environment of Liaoning Province, Shenyang, Liaoning 110161, PR China
| | - Maimaiti Simayi
- College of Resources and Environments, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, PR China
| | - Ziyan Xi
- College of Environmental Sciences and Engineering, State Key Joint Laboratory of Environmental Simulation and Pollution Control, Peking University, Beijing 100871, PR China
| | - Jie Ren
- College of Environmental Sciences and Engineering, State Key Joint Laboratory of Environmental Simulation and Pollution Control, Peking University, Beijing 100871, PR China
| | - Shaodong Xie
- College of Environmental Sciences and Engineering, State Key Joint Laboratory of Environmental Simulation and Pollution Control, Peking University, Beijing 100871, PR China.
| |
Collapse
|
10
|
Niu Y, Yan Y, Chai J, Zhang X, Xu Y, Duan X, Wu J, Peng L. Effects of regional transport from different potential pollution areas on volatile organic compounds (VOCs) in Northern Beijing during non-heating and heating periods. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 836:155465. [PMID: 35500706 DOI: 10.1016/j.scitotenv.2022.155465] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 04/11/2022] [Accepted: 04/19/2022] [Indexed: 06/14/2023]
Abstract
Despite the adoption of air quality control measures, the influence of regional transport on volatile organic compounds (VOCs) pollution has gradually increased in Beijing. In this study, the whole observation period (September 24 to December 12, 2020) was divided into heating period and non-heating period to explore the impact of changing VOCs sources in different observation periods, and also classified into "Type-N" and "Type-S" periods both in non-heating period and heating period to explore the impact of regional transport from the northern and southern regions of sampling site, respectively. The average VOCs concentrations in northern Beijing during observation period were 22.6 ± 12.6 ppbv, which showed a decrease trend in recent years compared with other studies. And higher VOCs concentrations were observed in Type-S than in Type-N period. The positive matrix factorization results showed that vehicular exhaust dominated VOCs (26.1%-33.7%), but coal combustion could not be ignored in heating period, when it was twice that in non-heating period. In particular, coal combustion dominated VOCs in southern trajectories (30.9%) in heating period. The analysis of concentration weighted trajectory showed that coal combustion was affected by regional transport from the southeast regions of Beijing, while vehicular exhaust was affected by urban and the southeast regions of Beijing. Regarding human health risks, the carcinogenic risks of benzene and ethylbenzene exceeded the acceptable cancer risk value (1 × 10-6), and were higher in Type-S than in Type-N period. The results indicated that regional transport from urban areas and the areas south of Beijing had a significant impact on VOCs in northern Beijing. Thus, targeted control measures for different potential pollution regions are important for controlling VOCs pollution in Beijing.
Collapse
Affiliation(s)
- Yueyuan Niu
- MOE Key Laboratory of Resources and Environmental System Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
| | - Yulong Yan
- Institute of Transport Energy and Environment, Beijing Jiaotong University, Beijing 100044, China; School of Environment, Beijing Jiaotong University, Beijing 100044, China.
| | - Jianwei Chai
- MOE Key Laboratory of Resources and Environmental System Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
| | - Xiangyu Zhang
- MOE Key Laboratory of Resources and Environmental System Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
| | - Yang Xu
- MOE Key Laboratory of Resources and Environmental System Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
| | - Xiaolin Duan
- MOE Key Laboratory of Resources and Environmental System Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
| | - Jing Wu
- Institute of Transport Energy and Environment, Beijing Jiaotong University, Beijing 100044, China; School of Environment, Beijing Jiaotong University, Beijing 100044, China
| | - Lin Peng
- Institute of Transport Energy and Environment, Beijing Jiaotong University, Beijing 100044, China; School of Environment, Beijing Jiaotong University, Beijing 100044, China
| |
Collapse
|
11
|
Ding S, Chen Y, Devineni SR, Pavuluri CM, Li XD. Distribution characteristics of organosulfates (OSs) in PM 2.5 in Tianjin, Northern China: Quantitative analysis of total and three OS species. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 834:155314. [PMID: 35447194 DOI: 10.1016/j.scitotenv.2022.155314] [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: 03/03/2022] [Revised: 04/09/2022] [Accepted: 04/12/2022] [Indexed: 06/14/2023]
Abstract
Organosulfates (OSs) are important secondary organic aerosol (SOA) species in atmospheric fine particles (PM2.5) and can be considered as molecular indicators of SOA. To understand their seasonal and diurnal distribution characteristics and formation mechanism in northern China, PM2.5 samples collected in daytime and nighttime in winter and summer 2019 in Tianjin, China were studied for total OSs and three OS species (methyl sulfate (MS), glycolic acid sulfate (GAS), benzyl sulfate (BS)). The S contents of total OSs (SOSs) in winter and summer were 0.6 ± 1 μg m-3 and 0.4 ± 0.3 μg m-3, respectively, in PM2.5. BS found to be less abundant among the measured species, and accounted for only 0.8%-4.8% of methyl sulfate (MS), and 0.01%-0.3% of glycolic acid sulfate (GAS). Average content of GAS was higher in summer than in winter, while that of MS and BS were opposite. The fractions of MS, GAS, and BS in SOSs were higher in daytime than that in night during winter, despite their concentrations were higher in nighttime, indicating that the concentrations of unidentified OS species were much higher in nighttime than in daytime. Such diurnal variations implied that relative humidity (RH) played a major role in the formation processes of OSs, especially biogenic OSs and the acid catalyzed reaction of SO42- might be a main pathway of OSs formation during winter. High T, RH and O3 determined biological GAS in summer, while NO2 and SO2 determined anthropogenic OSs in winter. We also found that the fractions of SOSs in S contents of organic sulfur (SOS) and the S contents of MS + GAS+BS (SMS+GAS+BS) in SOSs were accounted for only less than 10% and 5%, respectively. Therefore, this study suggests the components of OS and OSs in PM2.5 have not been discovered fully yet and needs further research.
Collapse
Affiliation(s)
- Shiyuan Ding
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China
| | - Yingying Chen
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China
| | - Subba Rao Devineni
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China
| | - Chandra Mouli Pavuluri
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China
| | - Xiao-Dong Li
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China.
| |
Collapse
|
12
|
Li J, Deng S, Tohti A, Li G, Yi X, Lu Z, Liu J, Zhang S. Spatial characteristics of VOCs and their ozone and secondary organic aerosol formation potentials in autumn and winter in the Guanzhong Plain, China. ENVIRONMENTAL RESEARCH 2022; 211:113036. [PMID: 35283079 DOI: 10.1016/j.envres.2022.113036] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 01/20/2022] [Accepted: 02/24/2022] [Indexed: 06/14/2023]
Abstract
As critical precursors of tropospheric ozone (O3) and secondary organic aerosol (SOA), volatile organic compounds (VOCs) largely influence air quality in urban environments. In this study, measurements of 102 VOCs at all five major cities in the Guanzhong Plain (GZP) were conducted during Sep.09-Oct. 13, 2017 (autumn) and Nov. 14, 2017-Jan. 19, 2018 (winter) to investigate the characteristics of VOCs and their roles in O3 and SOA formation. The average concentrations of total VOCs (TVOCs) at Xi'an (XA), Weinan (WN), Xianyang (XY), Tongchuan (TC), and Baoji (BJ) sites were in the range of 55.2-110.2 ppbv in autumn and 42.4-74.3 ppbv in winter. TVOCs concentrations were reduced by 22.4%-43.5% from autumn to winter at XA, WN and BJ. Comparatively low concentrations of TVOCs were observed in XY and TC, ranging from 53.5 to 62.7 ppbv across the sampling period. Alkanes were the major components at all sites, accounting for 26.4%-48.9% of the TVOCs during the sampling campaign, followed by aromatics (4.2%-26.4%). The average concentration of acetylene increased by a factor of up to 4.8 from autumn to winter, indicating the fuel combustion in winter heating period significantly impacted on VOCs composition in the GZP. The OH radical loss rate and maximum incremental reactivity method were employed to determine photochemical reactivities and ozone formation potentials (OFPs) of VOCs, respectively. The VOCs in XA and WN exhibited the highest reactivities in O3 formation, with the OFP of 168-273 ppbv and the OH loss rates of 19.3-40.8 s-1. Alkenes and aromatics primarily related to on-road and industrial emissions contributed 57.8%-76.3% to the total OFP. The contribution of aromatics to the SOA formation at all sites reached 94.1%-98.6%. Considering the potential source-area of VOCs, regional transport of VOCs occurred within the GZP cities.
Collapse
Affiliation(s)
- Jianghao Li
- School of Water and Environment, Chang'an University, Xi'an, 710064, China; Key Laboratory of Subsurface Hydrology and Ecological Effect in Arid Region of Ministry of Education, Chang'an University, Xi'an, 710064, China
| | - Shunxi Deng
- School of Water and Environment, Chang'an University, Xi'an, 710064, China; Key Laboratory of Subsurface Hydrology and Ecological Effect in Arid Region of Ministry of Education, Chang'an University, Xi'an, 710064, China.
| | - Abla Tohti
- School of Water and Environment, Chang'an University, Xi'an, 710064, China
| | - Guanghua Li
- School of Water and Environment, Chang'an University, Xi'an, 710064, China
| | - Xiaoxiao Yi
- School of Water and Environment, Chang'an University, Xi'an, 710064, China
| | - Zhenzhen Lu
- School of Water and Environment, Chang'an University, Xi'an, 710064, China
| | - Jiayao Liu
- School of Water and Environment, Chang'an University, Xi'an, 710064, China
| | - Shuai Zhang
- School of Water and Environment, Chang'an University, Xi'an, 710064, China
| |
Collapse
|
13
|
Yang Y, Liu B, Hua J, Yang T, Dai Q, Wu J, Feng Y, Hopke PK. Global review of source apportionment of volatile organic compounds based on highly time-resolved data from 2015 to 2021. ENVIRONMENT INTERNATIONAL 2022; 165:107330. [PMID: 35671590 DOI: 10.1016/j.envint.2022.107330] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 05/27/2022] [Accepted: 05/28/2022] [Indexed: 06/15/2023]
Abstract
Highly time-resolved data for volatile organic compounds (VOCs) can now be monitored. Source analyses of such high time-resolved concentrations provides key information for controlling VOC emissions. This work reviewed the literature on VOCs source analyses published from 2015 to 2021, and assesses the state-of-the-art and the existing issues with these studies. Gas chromatography system and direct-inlet mass spectrometry are the main monitoring tools. Quality control (QC) of the monitoring process is critical prior to analysis. QC includes inspection and replacement of instrument consumables, calibration curve corrections, and reviewing the data. Approximately 54% published papers lacked details on the quantitative evaluation of the effectiveness of QC measures. Among the reviewed works, the number of monitored species varied from 5 to 119, and fraction of papers with more than 90 monitored species increased yearly. US EPA PMF v5.0 was the most commonly used (∼86%) for VOC source analyses. However, conventional source apportionment directly uses the measured VOCs and may be problematic given the impacts of dispersion and photochemical losses, uncertainty setting of VOCs data, factor resolution, and factor identification. Excluding species with high-reactivity or estimation of corrected concentrations were often applied to reduce the influence of photochemical reactions on the results. However, most reports did not specify the selection criteria or the specific error fraction values in the uncertainty estimation. Model diagnostic indexes were used in 99% of the reports for PMF analysis to determine the factor resolution. Due to lack of known local source profiles, factor identification was mainly achieved using marker species and characteristic species ratios. However, multiple sources had high-collinearity and the same species were often used to identify different sources. Vehicle emissions and fuel evaporation were the primary contributors to VOCs around the world. Contribution of coal combustion in China was substantially higher than in other countries.
Collapse
Affiliation(s)
- Yang Yang
- 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
| | - Baoshuang Liu
- 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.
| | - Jing Hua
- Tianjin Ecology and Environment Bureau, Tianjin 300191, China
| | - Tao Yang
- 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
| | - Qili Dai
- 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
| | - Jianhui Wu
- 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
| | - Yinchang Feng
- 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
| | - Philip K Hopke
- 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
| |
Collapse
|
14
|
Yang T, Liu B, Yang Y, Dai Q, Zhang Y, Feng Y, Hopke PK. Improved positive matrix factorization for source apportionment of volatile organic compounds in vehicular emissions during the Spring Festival in Tianjin, China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 303:119122. [PMID: 35276248 DOI: 10.1016/j.envpol.2022.119122] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 02/26/2022] [Accepted: 03/07/2022] [Indexed: 06/14/2023]
Abstract
Photochemical losses of volatile organic compounds (VOCs) and uncertainties in calculated factor profiles can reduce the accuracy of source apportionment by positive matrix factorization (PMF). We developed an improved PMF method (termed ICLP-PMF) that estimated the reaction-corrected ("initial") concentrations of VOCs. Source profiles from literature provided constraints. ICLP-PMF evaluated the vehicular emission contributions to hourly speciated VOC data from December 2020 to March 2021 and estimated gasoline and diesel vehicles contributions to Tianjin's VOC concentrations around the Chinese Spring Festival (SF). The average observed and initial total VOCs (TVOCs) concentrations were 24.2 and 42.9 ppbv, respectively. Alkanes were the highest concentration VOCs while aromatics showed the largest photochemical losses during the study period. Literature gasoline and diesel profiles from representative Chinese cities were constructed and provided constraints. Source apportionment was performed using ICLP-PMF method and three other PMF approaches. Photochemical losses of alkenes and aromatic hydrocarbons induced differences between calculated factor profiles and literature profiles. Using observed concentrations and unconstrained profiles produced underestimated SF contributions (∼121% and 72% for gasoline and diesel vehicles, respectively). According to the ICLP-PMF results, the contributions of gasoline and diesel vehicles during the SF were 25.6% and 23.2%, respectively, lower than those before and after the SF. No diel diesel vehicle contribution variations were found during the SF likely due to the decline in truck activity north of the study site during the holiday period.
Collapse
Affiliation(s)
- Tao Yang
- 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
| | - Baoshuang Liu
- 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.
| | - Yang Yang
- 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
| | - Qili Dai
- 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
| | - Yufen Zhang
- 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
| | - Yinchang Feng
- 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
| | - Philip K Hopke
- 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
| |
Collapse
|
15
|
Zhou X, Li Z, Zhang T, Wang F, Tao Y, Zhang X. Multisize particulate matter and volatile organic compounds in arid and semiarid areas of Northwest China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 300:118875. [PMID: 35074457 DOI: 10.1016/j.envpol.2022.118875] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 01/16/2022] [Accepted: 01/18/2022] [Indexed: 06/14/2023]
Abstract
To investigate the chemical components, sources, and interactions of particulate matter (PM) and volatile organic compounds (VOCs), a field campaign was implemented during the spring of 2018 in nine cities in northwestern (NW) China. PM was mainly contributed by organic matter and water-soluble inorganic ions (41% for PM10 and approximately 60% for PM2.5 and PM1). Two typical haze patterns were observed: anthropogenic pollution type (AP-type), wherein contributions of sulfate, nitrate, and ammonium (SNA) increased, and dust pollution type (DP-type), wherein contributions of Ca2+ increased and SNA decreased. Source appointment suggested that regional sources contributed close to half to PM2.5 pollution (40% for AP-type and 50% for DP-type). Thus, sources from regional transport are also important for haze and dust pollution. The ranking of VOC concentrations was methanol > acetaldehyde > formic acid + ethanol > acetone. Compared with other cities, there are higher oxygenated VOCs (OVOCs) and lower aromatics in NW China. The relationships between VOCs and PM were discussed. The dominating secondary organic aerosols (SOA) formation potential precursors were C10-aromatics, xylene, and styrene under low-nitrogen oxide (NOx) conditions, and benzene, C10-aromatics, and toluene dominated under high-NOx conditions. The quadratic polynomial was the most suitable fitting model for their correlation, and the results suggested that VOC oxidations explained 6.1-10.8% and 9.9-20.7% of SOA formation under high-NOx and low-NOx conditions, respectively.
Collapse
Affiliation(s)
- Xi Zhou
- Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China; State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources; Tianshan Glaciological Station, Chinese Academy of Sciences, Lanzhou, 730000, China
| | - Zhongqin Li
- State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources; Tianshan Glaciological Station, Chinese Academy of Sciences, Lanzhou, 730000, China; College of Sciences, Shihezi University, Xinjiang, 832000, China; College of Geography and Environmental Science, Northwest Normal University, Lanzhou, 730000, China.
| | - Tingjun Zhang
- Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Feiteng Wang
- State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources; Tianshan Glaciological Station, Chinese Academy of Sciences, Lanzhou, 730000, China
| | - Yan Tao
- Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Xin Zhang
- State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources; Tianshan Glaciological Station, Chinese Academy of Sciences, Lanzhou, 730000, China
| |
Collapse
|
16
|
Abstract
In order to investigate the seasonal variation in chemical characteristics of VOCs in the urban and suburban areas of southwest China, we used SUMMA canister sampling in Jinghong city from October 2016 to June 2017. Forty-eight VOC species concentrations were analyzed using atmospheric preconcentration gas chromatography–mass spectrometry (GC–MS), Then, regional VOC pollution characteristics, ozone formation potentials (OFP), source identity, and health risk assessments were studied. The results showed that the average concentration of total mass was 144.34 μg·m−3 in the urban area and 47.81 μg·m−3 in the suburban area. Alkanes accounted for the highest proportion of VOC groups at 38.11%, followed by olefins (36.60%) and aromatic hydrocarbons (25.28%). Propane and isoprene were the species with the highest mass concentrations in urban and suburban sampling sites. The calculation of OFP showed that the contributions of olefins and aromatic hydrocarbons were higher than those of alkanes. Through the ratio of specific species, the VOCs were mainly affected by motor vehicle exhaust emissions, fuel volatilization, vegetation emissions, and biomass combustion. Combined with the analysis of the backward trajectory model, biomass burning activities in Myanmar influenced the concentration of VOCs in Jinghong. Health risk assessments have shown that the noncarcinogenic risk and hazard index of atmospheric VOCs in Jinghong were low (less than 1). However, the value of the benzene cancer risk to the human body was higher than the safety threshold of 1 × 10−6, showing that benzene has carcinogenic risk. This study provides effective support for local governments formulating air pollution control policies.
Collapse
|