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Yin D, Zhao B, Wang S, Donahue NM, Feng B, Chang X, Chen Q, Cheng X, Liu T, Chan CK, Schervish M, Li Z, He Y, Hao J. Fostering a Holistic Understanding of the Full Volatility Spectrum of Organic Compounds from Benzene Series Precursors through Mechanistic Modeling. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:8380-8392. [PMID: 38691504 DOI: 10.1021/acs.est.3c07128] [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: 05/03/2024]
Abstract
A comprehensive understanding of the full volatility spectrum of organic oxidation products from the benzene series precursors is important to quantify the air quality and climate effects of secondary organic aerosol (SOA) and new particle formation (NPF). However, current models fail to capture the full volatility spectrum due to the absence of important reaction pathways. Here, we develop a novel unified model framework, the integrated two-dimensional volatility basis set (I2D-VBS), to simulate the full volatility spectrum of products from benzene series precursors by simultaneously representing first-generational oxidation, multigenerational aging, autoxidation, dimerization, nitrate formation, etc. The model successfully reproduces the volatility and O/C distributions of oxygenated organic molecules (OOMs) as well as the concentrations and the O/C of SOA over wide-ranging experimental conditions. In typical urban environments, autoxidation and multigenerational oxidation are the two main pathways for the formation of OOMs and SOA with similar contributions, but autoxidation contributes more to low-volatility products. NOx can reduce about two-thirds of OOMs and SOA, and most of the extremely low-volatility products compared to clean conditions, by suppressing dimerization and autoxidation. The I2D-VBS facilitates a holistic understanding of full volatility product formation, which helps fill the large gap in the predictions of organic NPF, particle growth, and SOA formation.
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Affiliation(s)
- 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
| | - 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
| | - 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
| | - Neil M Donahue
- Center for Atmospheric Particle Studies, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, United States
| | - Boyang Feng
- 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
- Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, China
| | - Qi Chen
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, BIC-ESAT and IJRC, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Xi Cheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, BIC-ESAT and IJRC, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Tengyu Liu
- Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
| | - Chak K Chan
- Division of Physical Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
| | - Meredith Schervish
- Center for Atmospheric Particle Studies, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, United States
- Department of Chemistry, University of California, Irvine, California 92697, United States
| | - 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
| | - 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
| | - Jiming Hao
- 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
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Qu Q, Wang S, Zhao B, Hu R, Liang C, Zhang H, Li S, Feng B, Hou X, Yin D, Du J, Chu Y, Zhang Y, Wu Q, Wen Y, Wu X, Hu J, Zhang S, Hao J. Response of organic aerosol in Beijing to emission reductions during the XXIV Olympic Winter Games. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 914:170033. [PMID: 38220000 DOI: 10.1016/j.scitotenv.2024.170033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/05/2024] [Accepted: 01/07/2024] [Indexed: 01/16/2024]
Abstract
Organic aerosol (OA) serves as a crucial component of fine particulate matter. However, the response of OA to changes in anthropogenic emissions remains unclear due to its complexity. The XXIV Olympic Winter Games (OWG) provided real atmospheric experimental conditions on studying the response of OA to substantial emission reductions in winter. Here, we explored the sources and variations of OA based on the observation of aerosol mass spectrometer (AMS) combined with positive matrix factorization (PMF) analysis in urban Beijing during the 2022 Olympic Winter Games. The influences of meteorological conditions on OA concentrations were corrected by CO and verified by deweathered model. The CO-normalized primary OA (POA) concentrations from traffic, cooking, coal and biomass burning during the OWG decreased by 39.8 %, 23.2 % and 65.0 %, respectively. Measures controlling coal and biomass burning were most effective in reducing POA during the OWG. For the CO-normalized concentration of secondary OA (SOA), aqueous-phase related oxygenated OA decreased by 51.8 % due to the lower relative humidity and emission reduction in precursors, while the less oxidized‑oxygenated OA even slightly increased as the enhanced atmospheric oxidation processes may partially offset the efficacy of emission control. Therefore, more targeted reduction of organic precursors shall be enhanced to lower atmospheric oxidation capacity and mitigate SOA pollution.
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Affiliation(s)
- Qipeng Qu
- 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.
| | - 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
| | - Ruolan Hu
- 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
| | - Chengrui Liang
- 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
| | - Haowen Zhang
- 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
| | - Shengyue 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
| | - Boyang Feng
- 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
| | - Xuan Hou
- 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
| | - Jinhong Du
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yangxi Chu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yanning Zhang
- 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
| | - Qingru Wu
- 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
| | - Yifan Wen
- 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
| | - Xiaomeng Wu
- 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
| | - Jingnan Hu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Shaojun Zhang
- 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
| | - Jiming Hao
- 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
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Huang J, Cai A, Wang W, He K, Zou S, Ma Q. The Variation in Chemical Composition and Source Apportionment of PM 2.5 before, during, and after COVID-19 Restrictions in Zhengzhou, China. TOXICS 2024; 12:81. [PMID: 38251036 PMCID: PMC10819188 DOI: 10.3390/toxics12010081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 01/13/2024] [Accepted: 01/15/2024] [Indexed: 01/23/2024]
Abstract
Despite significant improvements in air quality during and after COVID-19 restrictions, haze continued to occur in Zhengzhou afterwards. This paper compares ionic compositions and sources of PM2.5 before (2019), during (2020), and after (2021) the restrictions to explore the reasons for the haze. The average concentration of PM2.5 decreased by 28.5% in 2020 and 27.9% in 2021, respectively, from 102.49 μg m-3 in 2019. The concentration of secondary inorganic aerosols (SIAs) was 51.87 μg m-3 in 2019, which decreased by 3.1% in 2020 and 12.8% in 2021. In contrast, the contributions of SIAs to PM2.5 increased from 50.61% (2019) to 68.6% (2020) and 61.2% (2021). SIAs contributed significantly to PM2.5 levels in 2020-2021. Despite a 22~62% decline in NOx levels in 2020-2021, the increased O3 caused a similar NO3- concentration (20.69~23.00 μg m-3) in 2020-2021 to that (22.93 μg m-3) in 2019, hindering PM2.5 reduction in Zhengzhou. Six PM2.5 sources, including secondary inorganic aerosols, industrial emissions, coal combustion, biomass burning, soil dust, and traffic emissions, were identified by the positive matrix factorization model in 2019-2021. Compared to 2019, the reduction in PM2.5 from the secondary aerosol source in 2020 and 2021 was small, and the contribution of secondary aerosol to PM2.5 increased by 13.32% in 2020 and 12.94% in 2021. In comparison, the primary emissions, including biomass burning, traffic, and dust, were reduced by 29.71% in 2020 and 27.7% in 2021. The results indicated that the secondary production did not significantly contribute to the PM2.5 decrease during and after the COVID-19 restrictions. Therefore, it is essential to understand the formation of secondary aerosols under high O3 and low precursor gases to mitigate air pollution in the future.
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Affiliation(s)
- Jinting Huang
- College of Surveying and Mapping Engineering, Yellow River Conservancy Technical Institute, Kaifeng 475004, China;
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, College of Geography and Environmental Science, Henan University, Kaifeng 475004, China
| | - Aomeng Cai
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, College of Geography and Environmental Science, Henan University, Kaifeng 475004, China
- Henan Key Laboratory of Integrated Air Pollution Control and Ecological Security, Kaifeng 475004, China
| | - Weisi Wang
- Henan Ecological and Environmental Monitoring Center, Zhengzhou 450007, China
| | - Kuan He
- College of Surveying and Mapping Engineering, Yellow River Conservancy Technical Institute, Kaifeng 475004, China;
| | - Shuangshuang Zou
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, College of Geography and Environmental Science, Henan University, Kaifeng 475004, China
| | - Qingxia Ma
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, College of Geography and Environmental Science, Henan University, Kaifeng 475004, China
- Henan Key Laboratory of Integrated Air Pollution Control and Ecological Security, Kaifeng 475004, China
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