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Li Z, Zhao B, Yin D, Wang S, Qiao X, Jiang J, Li Y, Shen J, He Y, Chang X, Li X, Liu Y, Li Y, Liu C, Qi X, Chen L, Chi X, Jiang Y, Li Y, Wu J, Nie W, Ding A. Modeling the Formation of Organic Compounds across Full Volatility Ranges and Their Contribution to Nanoparticle Growth in a Polluted Atmosphere. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:1223-1235. [PMID: 38117938 DOI: 10.1021/acs.est.3c06708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2023]
Abstract
Nanoparticle growth influences atmospheric particles' climatic effects, and it is largely driven by low-volatility organic vapors. However, the magnitude and mechanism of organics' contribution to nanoparticle growth in polluted environments remain unclear because current observations and models cannot capture organics across full volatility ranges or track their formation chemistry. Here, we develop a mechanistic model that characterizes the full volatility spectrum of organic vapors and their contributions to nanoparticle growth by coupling advanced organic oxidation modeling and kinetic gas-particle partitioning. The model is applied to Nanjing, a typical polluted city, and it effectively captures the volatility distribution of low-volatility organics (with saturation vapor concentrations <0.3 μg/m3), thus accurately reproducing growth rates (GRs), with a 4.91% normalized mean bias. Simulations indicate that as particles grow from 4 to 40 nm, the relative fractions of GRs attributable to organics increase from 59 to 86%, with the remaining contribution from H2SO4 and its clusters. Aromatics contribute much to condensable organic vapors (∼37%), especially low-volatility vapors (∼61%), thus contributing the most to GRs (32-46%) as 4-40 nm particles grow. Alkanes also contribute 19-35% of GRs, while biogenic volatile organic compounds contribute minimally (<13%). Our model helps assess the climatic impacts of particles and predict future changes.
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Affiliation(s)
- Zeqi Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Bin Zhao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Dejia Yin
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Xiaohui Qiao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Jingkun Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Yiran Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Jiewen Shen
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yicong He
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Xing Chang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
- Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, China
| | - Xiaoxiao Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Yuliang Liu
- Joint International Research Laboratory of Atmospheric and Earth System Research, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
- National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, Nanjing 210023, Jiangsu Province, China
- Jiangsu Provincial Collaborative Innovation Center for Climate Change, Nanjing University, Nanjing 210093, China
| | - Yuanyuan Li
- Joint International Research Laboratory of Atmospheric and Earth System Research, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
- National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, Nanjing 210023, Jiangsu Province, China
| | - Chong Liu
- Joint International Research Laboratory of Atmospheric and Earth System Research, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
- National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, Nanjing 210023, Jiangsu Province, China
| | - Ximeng Qi
- Joint International Research Laboratory of Atmospheric and Earth System Research, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
- National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, Nanjing 210023, Jiangsu Province, China
- Jiangsu Provincial Collaborative Innovation Center for Climate Change, Nanjing University, Nanjing 210093, China
| | - Liangduo Chen
- Joint International Research Laboratory of Atmospheric and Earth System Research, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
- Jiangsu Provincial Collaborative Innovation Center for Climate Change, Nanjing University, Nanjing 210093, China
| | - Xuguang Chi
- Joint International Research Laboratory of Atmospheric and Earth System Research, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
- National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, Nanjing 210023, Jiangsu Province, China
- Jiangsu Provincial Collaborative Innovation Center for Climate Change, Nanjing University, Nanjing 210093, China
| | - Yueqi Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yuyang Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Jin Wu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Wei Nie
- Joint International Research Laboratory of Atmospheric and Earth System Research, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
- National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, Nanjing 210023, Jiangsu Province, China
- Jiangsu Provincial Collaborative Innovation Center for Climate Change, Nanjing University, Nanjing 210093, China
| | - Aijun Ding
- Joint International Research Laboratory of Atmospheric and Earth System Research, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
- National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, Nanjing 210023, Jiangsu Province, China
- Jiangsu Provincial Collaborative Innovation Center for Climate Change, Nanjing University, Nanjing 210093, China
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Tang R, Guo S, Song K, Yu Y, Tan R, Wang H, Liu K, Shen R, Chen S, Zeng L, Zhang Z, Zhang W, Shuai S, Hu M. Emission characteristics of intermediate volatility organic compounds from a Chinese gasoline engine under varied operating conditions: Influence of fuel, velocity, torque, rotational speed, and after-treatment device. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 906:167761. [PMID: 37832675 DOI: 10.1016/j.scitotenv.2023.167761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 09/14/2023] [Accepted: 10/10/2023] [Indexed: 10/15/2023]
Abstract
Improved measurement of new pollutants, particularly intermediate volatility organic compounds (IVOCs), is urgently needed due to the lack of emission data under various operating conditions and potential fuel switching for gasoline engines. This study focused on examining the emission characteristics of IVOCs and the formation of secondary organic aerosols (SOA) in a commercial gasoline direct injection (GDI) engine, considering different fuels and operating conditions. The key findings are as follows: (1) The emission factor (EF) of IVOCs ranged from 2.0 to 357.8 mg kg-fuel-1, with a median value of 87.9 mg kg-fuel-1. (2) IVOCs emission characteristics were influenced by the fuel type and engine operating conditions. The addition of ethanol resulted in a significant decrease in IVOCs emissions, while lower velocities and torques led to higher IVOCs emissions. (3) Ethanol-blended fuel scenarios (E10, E25) and CGPF (Pd/Rh catalytically coated gasoline particle filter)-equipped scenarios exhibited high proportions of oxygen-containing compounds like aliphatic alcohols, ethers, and carboxylic acids. (4) IVOCs exhibited a high potential for the formation of SOA, underscoring the importance of controlling IVOCs in future strategies to mitigate particulate matter pollution in China. These findings highlight the significance of smooth traffic flow and advancements in fuel types, engine technologies, and after-treatment designs to effectively control IVOC emissions and contribute to the realization of a carbon-neutral society.
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Affiliation(s)
- Rongzhi Tang
- 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, PR China; Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, PR China; School of Energy and Environment, City University of Hong Kong, Kowloon 999077, Hong Kong, 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, PR China; Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, PR China.
| | - Kai Song
- 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, PR China
| | - Ying Yu
- 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, PR China
| | - Rui Tan
- 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, PR China
| | - Hui Wang
- 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, PR China
| | - Kefan Liu
- 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, PR China
| | - Ruizhe Shen
- 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, PR China
| | - Shiyi Chen
- 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, PR China
| | - Limin Zeng
- 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, PR China
| | - Zhou Zhang
- State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, PR China
| | - Wenbin Zhang
- State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, PR China
| | - Shijin Shuai
- State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, PR China
| | - Min Hu
- 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, PR China
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Feng YL, Yang C, Cao XL. Intermediate volatile organic compounds in Canadian residential air in winter: Implication to indoor air quality. CHEMOSPHERE 2023; 328:138567. [PMID: 37023898 DOI: 10.1016/j.chemosphere.2023.138567] [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: 11/29/2022] [Revised: 03/30/2023] [Accepted: 03/31/2023] [Indexed: 06/19/2023]
Abstract
Intermediate volatile organic compounds (IVOCs) have recently been characterized for their contributions to the formation of secondary organic aerosol in atmospheric air. However, IVOCs in air in various indoor environments have not been characterized yet. In this study, we characterized and measured IVOCs, volatile organic compounds (VOCs) and semi-volatile organic compounds (SVOCs), in residential indoor air in Ottawa, Canada. IVOCs, including n-alkanes, branched-chain alkanes (b-alkanes), unspecified complex mixtures (UCM) IVOCs, and oxygenated IVOCs (such as fatty acids), were found to have a large impact on indoor air quality. The results indicate that the indoor IVOCs behave differently from those in the outdoor environment. IVOCs in the studied residential air ranged from 14.4 to 69.0 μg/m3, with a geometric mean of 31.3 μg/m3, accounting for approximately 20% of the total organic compounds (IVOCs, VOCs and SVOCs) in indoor air. The total b-alkanes and UCM-IVOCs were found to have statistically significant positive correlations with indoor temperature but have no correlations with airborne particulate matter less than 2.5 μm (PM2.5) as well as ozone (O3) concentration. However, indoor oxygenated IVOCs behaved differently from b-alkanes and UCM-IVOCs, with a statistically significant positive correlation with indoor relative humidity but no correlation with other indoor environmental conditions.
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Affiliation(s)
- Yong-Lai Feng
- Exposure and Biomonitoring Division, Environmental Health Science and Research Bureau, Health Canada, 251 Sir Frederick Banting Driveway, Ottawa, Ontario, K1A 0K9, Canada.
| | - Chun Yang
- Emergencies Science and Technology Section, Science and Technology Branch, Environment and Climate Change Canada, Ottawa, Ontario, Canada.
| | - Xu-Liang Cao
- Food Research Division, Bureau of Chemical Safety, Food Directorate, Health Canada, 251 Frederick Banting Driveway, AL: 2203D, Ottawa, Ontario, K1A 0K9, Canada
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Barhoumi B, Guigue C, Touil S, Johnson-Restrepo B, Driss MR, Tedetti M. Hydrocarbons in the atmospheric gas phase of a coastal city in Tunisia: Levels, gas-particle partitioning, and health risk assessment. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 879:162986. [PMID: 36958548 DOI: 10.1016/j.scitotenv.2023.162986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 03/17/2023] [Accepted: 03/17/2023] [Indexed: 05/17/2023]
Abstract
Many studies have focused on aliphatic hydrocarbons and polycyclic aromatic hydrocarbons (AHs and PAHs) in different environmental compartments, especially atmospheric particles (aerosols), due to their adverse effects on the environment and human health. However, much less information is currently available on the content of AHs and PAHs in the atmospheric gas phase, which is a major reservoir of volatile and photoreactive compounds. Here, for the first time, we assessed the levels, gas-particle partitioning, human health risks and seasonal variations of AHs and PAHs in the atmospheric gas-phase of Bizerte city (Tunisia, North Africa) over a one-year period (March 2015-January 2016). Σ34PAH concentration in the gas phase over the period ranged from 6.7 to 90.6 ng m-3 and on average was 2.5 times higher in the cold season than in the warm season. Σ28AH concentration in the gas phase over the period ranged from 14.0 to 35.9 ng m-3, with no clear seasonal variations. In the gas phase, hydrocarbons were dominated by low-molecular-weight (LMW) compounds, i.e. 3- and 4-ring for PAHs and < n-C24 for AHs. Gas-phase concentrations of PAHs and AHs accounted for up to 80 % of the total (gas + particle phases) atmospheric concentrations of PAHs and AHs. Further analysis of gas-particle partitioning showed that LMW hydrocarbons preferential accumulated in the gas phase, and that gas-particle partitioning was not in equilibrium but dominated by absorption processes into the aerosol organic matter. Benzo[a]pyrene toxic equivalency quotient (BaP-TEQ) in the gas phase represented on average 37 % of the total atmospheric BaP-TEQ concentration, which was always higher in the cold season. Atmospheric gas is a significant factor in the risks of cancer associated with inhalation of ambient air. The Monte Carlo simulation-based exposure assessment model predicted that outdoor air exposure to PAHs does not pose a cancer risk to infants, but the children, adolescent, and adult populations may face a lower cancer risk during the warm season and a higher risk in the cold season.
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Affiliation(s)
- Badreddine Barhoumi
- Laboratory of Hetero-Organic Compounds and Nanostructured Materials (LR18ES11), Department of Chemistry, Faculty of Sciences of Bizerte, University of Carthage, 7021 Zarzouna, Tunisia; Aix Marseille Univ, Université de Toulon, CNRS, IRD, MIO UM 110, 13288 Marseille, France.
| | - Catherine Guigue
- Aix Marseille Univ, Université de Toulon, CNRS, IRD, MIO UM 110, 13288 Marseille, France
| | - Soufiane Touil
- Laboratory of Hetero-Organic Compounds and Nanostructured Materials (LR18ES11), Department of Chemistry, Faculty of Sciences of Bizerte, University of Carthage, 7021 Zarzouna, Tunisia
| | - Boris Johnson-Restrepo
- Environmental Chemistry Research Group, School of Exact and Natural Sciences, University Campus of San Pablo, University of Cartagena, Zaragocilla, Carrera 50 No. 24-99, Cartagena, 130015, Colombia
| | - Mohamed Ridha Driss
- Laboratory of Hetero-Organic Compounds and Nanostructured Materials (LR18ES11), Department of Chemistry, Faculty of Sciences of Bizerte, University of Carthage, 7021 Zarzouna, Tunisia
| | - Marc Tedetti
- Aix Marseille Univ, Université de Toulon, CNRS, IRD, MIO UM 110, 13288 Marseille, France
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Zou Y, Liu X, Wu K, Zhai Y, Li Y. Role of sulphur and chlorine in condensable particulate matter formation during coal combustion. JOURNAL OF HAZARDOUS MATERIALS 2023; 443:130317. [PMID: 36356518 DOI: 10.1016/j.jhazmat.2022.130317] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 10/01/2022] [Accepted: 11/01/2022] [Indexed: 06/16/2023]
Abstract
Condensable particulate matter (CPM) is a major component of primary particulate matter emitted into the atmosphere from stationary sources. However, the factors affecting CPM generation remain unclear. In this study, we systematically investigated the role of sulphur and chlorine in CPM formation during coal combustion. To explore the influence of S, various concentrations of SO2 (0-2000 ppm) were added to the combustion process of high-S coal. The role of Cl in the generation of CPM was revealed by burning coal with a significant difference in the Cl content (0.51-9.70 mg/g). The results show that addition of SO2, especially in SO42-, to the combustion process increases the CPM inorganic fraction content from 5.83 to 48.3 mg/m3. In addition, we speculated that the presence of SO2 may have led post-break oxidation of long-chain alkanes to form esters, especially phthalates. At the same time, in experiments concerning Cl, the opposite trend was observed between S and Cl in the CPM inorganic fraction. As the Cl content in the fuel increased, the S content in the inorganic fraction of CPM gradually decreased. This is because Cl inhibits the conversion of SO2 to SO3, therefore, less S forms CPM as SO3 or as sulphides.
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Affiliation(s)
- Yue Zou
- School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China; State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, Wuhan 430074, PR China
| | - Xiaowei Liu
- School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China; State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, Wuhan 430074, PR China.
| | - Kui Wu
- School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China; State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, Wuhan 430074, PR China
| | - Yunfei Zhai
- School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China; State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, Wuhan 430074, PR China
| | - Yuyang Li
- School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China; State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, Wuhan 430074, PR China
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Han J, Kong T, Jiang J, Zhao X, Zhao X, Li P, Gu Q. Characteristic flavor metabolic network of fish sauce microbiota with different fermentation processes based on metagenomics. Front Nutr 2023; 10:1121310. [PMID: 36950329 PMCID: PMC10025566 DOI: 10.3389/fnut.2023.1121310] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 02/08/2023] [Indexed: 03/08/2023] Open
Abstract
This article purposed to discuss the connection between microbiota and characteristic flavor of different fish sauces (Natural fermentation (WQ), koji outdoor fermentation (YQ), heat preservation with enzyme (BWE), and heat preservation with koji (BWQ)) at the early (3 months) and late stage (7 months). A total of 117 flavor compounds were determined according to SPME-GC-MS analysis. O2PLS-DA and VIP values were used to reveal 15 and 28 flavor markers of different fish sauces at 3 and 7 M of fermentation. Further, the possible flavor formation pathways were analyzed using metagenomic sequencing, and the key microbes associated with flavor formation were identified at the genetic level. The top 10 genera related to flavor generation, such as Lactobacillus, Staphylococcus, Enterobacter, etc., appeared to play a prominent part in the flavor formation of fish sauce. The difference was that only BWQ and BWE groups could produce ethyl-alcohol through amino acid metabolism, while YQ, BWE and BWQ groups could generate phenylacetaldehyde through the transformation of Phe by α-ketoacid decarboxylase and aromatic amino acid transferase. Our research contributes to clarifying the various metabolic roles of microorganisms in the flavor generation of fish sauce.
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Tropical Air Chemistry in Lagos, Nigeria. ATMOSPHERE 2022. [DOI: 10.3390/atmos13071059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The Nigerian city of Lagos experiences severe air pollution as a result of emissions and subsequent atmospheric photochemistry and aerosol chemistry. A year-long study, between August 2020 and July 2021, included measurements of gas-phase and aerosol processes, with surface meteorology at six urban sites. The sites were selected to represent near seacoast conditions, urban sites, and inland locations near agricultural and grassland ecosystems. The observations included continuous concentrations for CO, SO2, NOx, O3, PM2.5, and PM10. Samples were collected and analyzed for speciated volatile organic compounds (VOCs) and particulate chemical composition including inorganic and organic chemical species. The average diel variations in concentrations indicated well-known local photochemistry resulting from the presence of combustion sources, including motor vehicles, petroleum production and use, and open burning. The annual diel characteristics were emission-dependent and were modulated by meteorological variability, including the sea breeze and the seasonal changes associated with monsoons and Harmattan winds. Gases and particulate matter varied daily, consistent with the onset of source activities during the day. Fine particles less than 2.5 μm in diameter (PM2.5) included both primary particles from emission sources and secondary particles produced in the atmosphere by photochemical reactions. Importantly, particle sources included a large component of dust and carbonaceous material. For the latter, there was evidence that particle concentrations were dominated by primary sources, with little secondary material formed in the atmosphere. From complementary measurements, there were occasions when regional chemical processes affected the local conditions, including transportation, industry, commercial activity, and open waste burning.
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