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Chen X, Ma W, Zheng F, Wang Z, Hua C, Li Y, Wu J, Li B, Jiang J, Yan C, Petäjä T, Bianchi F, Kerminen VM, Worsnop DR, Liu Y, Xia M, Kulmala M. Identifying Driving Factors of Atmospheric N 2O 5 with Machine Learning. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:11568-11577. [PMID: 38889013 DOI: 10.1021/acs.est.4c00651] [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: 06/20/2024]
Abstract
Dinitrogen pentoxide (N2O5) plays an essential role in tropospheric chemistry, serving as a nocturnal reservoir of reactive nitrogen and significantly promoting nitrate formations. However, identifying key environmental drivers of N2O5 formation remains challenging using traditional statistical methods, impeding effective emission control measures to mitigate NOx-induced air pollution. Here, we adopted machine learning assisted by steady-state analysis to elucidate the driving factors of N2O5 before and during the 2022 Winter Olympics (WO) in Beijing. Higher N2O5 concentrations were observed during the WO period compared to the Pre-Winter-Olympics (Pre-WO) period. The machine learning model accurately reproduced ambient N2O5 concentrations and showed that ozone (O3), nitrogen dioxide (NO2), and relative humidity (RH) were the most important driving factors of N2O5. Compared to the Pre-WO period, the variation in trace gases (i.e., NO2 and O3) along with the reduced N2O5 uptake coefficient was the main reason for higher N2O5 levels during the WO period. By predicting N2O5 under various control scenarios of NOx and calculating the nitrate formation potential from N2O5 uptake, we found that the progressive reduction of nitrogen oxides initially increases the nitrate formation potential before further decreasing it. The threshold of NOx was approximately 13 ppbv, below which NOx reduction effectively reduced the level of night-time nitrate formations. These results demonstrate the capacity of machine learning to provide insights into understanding atmospheric nitrogen chemistry and highlight the necessity of more stringent emission control of NOx to mitigate haze pollution.
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Affiliation(s)
- Xin Chen
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Wei Ma
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Feixue Zheng
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Zongcheng Wang
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Chenjie Hua
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Yiran Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Jin Wu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Boda Li
- Meta Platforms, Inc., Menlo Park, California 94025, United States
| | - Jingkun Jiang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Chao Yan
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki 00014, Finland
- Joint International Research Laboratory of Atmospheric and Earth System Research, School of Atmospheric Sciences, Nanjing University, Nanjing 210008, China
| | - Tuukka Petäjä
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki 00014, Finland
| | - Federico Bianchi
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki 00014, Finland
| | - Veli-Matti Kerminen
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki 00014, Finland
| | - Douglas R Worsnop
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki 00014, Finland
| | - Yongchun Liu
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Men Xia
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki 00014, Finland
| | - Markku Kulmala
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki 00014, Finland
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Wang Y, Chen Y, Chi S, Wang J, Zhang C, Lin W, Zhao W, Ye C. Optimizing a twin-chamber system for direct ozone production rate measurement. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 348:123837. [PMID: 38537793 DOI: 10.1016/j.envpol.2024.123837] [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: 01/30/2024] [Revised: 03/10/2024] [Accepted: 03/20/2024] [Indexed: 04/04/2024]
Abstract
High Ozone Production Rate (OPR) leads to O3 pollution episodes and adverse human health outcomes. OPR observation (Obs-OPR) and OPR modelling (Mod-OPR) have been obtained from observed and modelled peroxy radicals and nitrogen oxides. However, discrepancies between them remind of an imperfect understanding of O3 photochemistry. Direct measurement of OPR (Mea-OPR) by a twin-chamber system emerges. Herein, we optimized Mea-OPR design, i.e., minimizing the chamber surface area to volume ratio (S/V) to 9.8 m-1 from 18 m-1 and the dark uptake coefficient of O3 to 9.9 × 10-9 from 7.1 × 10-8 in the literature. In addition, control experiments further revealed and quantified a photo-enhanced O3 uptake, and therefore recommended an essential correction of Mea-OPR. We finally characterized a measurement uncertainty of ±38% and a detection limit of 3.2 ppbv h-1 (3SD), which suggested that Mea-OPR would be sensitive enough to measure OPR in urban or suburban environments. Further application of this system in urban Beijing during the Beijing 2022 Olympic Winter Games recorded a noontime OPR of 7.3 (±3.3, 1SD) ppbv h-1. These observational results added up to our confidence in future field application of Mea-OPR, to facilitate pollution control policy evaluation and to shed light on O3 photochemistry puzzle.
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Affiliation(s)
- Yaru Wang
- State Key Joint Laboratory for Environmental Simulation and Pollution Control, Center for Environment and Health, and College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
| | - Yi Chen
- Key Laboratory of Ecology and Environment in Minority Areas (Minzu University of China), National Ethnic Affairs Commission, Beijing, 100081, China
| | - Suzhen Chi
- State Key Joint Laboratory for Environmental Simulation and Pollution Control, Center for Environment and Health, and College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
| | - Jianshu Wang
- State Key Joint Laboratory for Environmental Simulation and Pollution Control, Center for Environment and Health, and College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
| | - Chong Zhang
- State Key Joint Laboratory for Environmental Simulation and Pollution Control, Center for Environment and Health, and College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
| | - Weili Lin
- Key Laboratory of Ecology and Environment in Minority Areas (Minzu University of China), National Ethnic Affairs Commission, Beijing, 100081, China
| | - Weixiong Zhao
- Laboratory of Atmospheric Physico-Chemistry, Chinese Academy of Sciences Hefei Institutes of Physical Science Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei, Anhui, 230031, China
| | - Chunxiang Ye
- State Key Joint Laboratory for Environmental Simulation and Pollution Control, Center for Environment and Health, and College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, 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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Li W, Li J, Ma T, Chang Z, Casuccio GS, Gao J, Li H. Physiochemistry and sources of individual particles in response to intensified controls during the 2022 Winter Olympics in Beijing. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 352:119946. [PMID: 38237337 DOI: 10.1016/j.jenvman.2023.119946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 11/30/2023] [Accepted: 12/23/2023] [Indexed: 02/04/2024]
Abstract
To investigate the particle sources before, during, and after the 2022 Beijing Winter Olympic and Paralympic (WOP) in Beijing, ambient particles were passively collected from January to March 2022. The physicochemical properties including morphology, size, shape parameters, and elemental compositions were analyzed by the IntelliSEM EPAS (an advanced computer-controlled scanning electron microscopy [CCSEM] system). Using the user-defined classification rules, 37,174 individual particles were automatically classified into 27 major groups and further attributed to seven major sources based on the source-associated characteristics, including mineral dust, secondary aerosol, combustion/industry, carbonaceous particles, salt-related particles, biological particles, and fiber particles. Our results showed that mineral dust (66.5%), combustion/industry (12.6%), and secondary aerosol (6.3%) were the three major sources in a wide size range of 0.2-42.8 μm. During the Winter Olympic Games period, low emission of anthropogenic particles and favorable meteorological conditions contributed to significantly improved air quality. During the Winter Paralympic Games period, more particles sourced from the dust storm, secondary formed particles, and the adverse meteorological conditions resulted in relatively worse air quality. The secondary aerosol all decreased during the competition period, while increased during the non-competition period. Sulfate-related particles had explosive growth and further aggravate the pollution degree during the non-competition period, especially under adverse meteorological conditions. These results provide microscopic evidence revealing variations of physicochemical properties and sources in response to the control measures and meteorological conditions.
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Affiliation(s)
- Wenjun Li
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; Joint Laboratory for Electron Microscopy Analysis of Atmospheric Particles, Beijing, 100012, China.
| | - Jinying Li
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; Joint Laboratory for Electron Microscopy Analysis of Atmospheric Particles, Beijing, 100012, China
| | - Tong Ma
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Zhe Chang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; Joint Laboratory for Electron Microscopy Analysis of Atmospheric Particles, Beijing, 100012, China
| | | | - Jian Gao
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; Joint Laboratory for Electron Microscopy Analysis of Atmospheric Particles, Beijing, 100012, China.
| | - Haisheng Li
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
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