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Li Y, Wang T, Wang Q, Li M, Qu Y, Wu H, Fan J, Shao M, Xie M. Deciphering the seasonal dynamics of multifaceted aerosol-ozone interplay: Implications for air quality management in Eastern China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 946:174327. [PMID: 38955271 DOI: 10.1016/j.scitotenv.2024.174327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 06/14/2024] [Accepted: 06/25/2024] [Indexed: 07/04/2024]
Abstract
We employed an enhanced WRF-Chem to investigate the discrete mechanisms of aerosol-radiation-feedback (ARF), extinction-photochemistry (AEP), and heterogeneous-reactions (AHR) across different seasons in eastern China, aiming to assess the synergistic effects arising from the simultaneous operation of multiple processes on O3 and PM2.5. Our findings demonstrated that ARF fostered the accumulation of pollutants and moisture, initiating two distinct feedback mechanisms concerning O3. The elevation in the NO/NO2 ratio amplified O3 consumption. Increased near-surface moisture diminished upper-level cloud formation, thereby enhancing photolysis rates and O3 photochemical production. The pronounced impact of heightened NO/NO2 on O3 led to a decrease of 0.1-2.7 ppb. When decoupled from ARF, AEP led to a more significant reduction in photolysis rates, resulting in declines in both O3 and PM2.5, except for an anomalous increase observed in summer, with O3 increasing by 1.6 ppb and PM2.5 by 2.5 μg m-3. The heterogeneous absorption of hydroxides in spring, autumn, and winter predominantly governed the AHR-induced variation of O3, leading to a decrease in O3 by 0.7-1 ppb. Conversely, O3 variations in summer were primarily dictated by O3-sensitive chemistry, with heterogeneous absorption of NOy catalyzing a decrease of 2.4 ppb in O3. Furthermore, AHR accentuated PM2.5 by facilitating the formation of fine sulfates and ammonium while impeding nitrate formation. In summer, the collective impact of ARF, AEP, and AHR (ALL) led to a substantial reduction of 6.2 ppb in O3, alleviating the secondary oxidation of PM2.5 and leading to a decrease of 0.3 μg m-3 in PM2.5. Conversely, albeit aerosol substantially depleted O3 by 0.4-4 ppb through their interactions except for summer, aerosol feedback on PM2.5 was more pronounced, resulting in a significant increase of 1.7-6.1 μg m-3 in PM2.5. Our study underscored the seasonal disparities in the ramifications of multifaceted aerosol-ozone interplay on air quality.
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Affiliation(s)
- Yasong Li
- School of the Environment, Nanjing University, Nanjing 210023, China
| | - Tijian Wang
- School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China.
| | - Qin'geng Wang
- School of the Environment, Nanjing University, Nanjing 210023, China
| | - Mengmeng Li
- School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
| | - Yawei Qu
- College of Intelligent Science and Control Engineering, Jinling Institute of Technology, Nanjing 211112, China
| | - Hao Wu
- Key Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing, China
| | - Jiachen Fan
- School of the Environment, Nanjing University, Nanjing 210023, China
| | - Min Shao
- School of Environment, Nanjing Normal University, Nanjing 210046, China
| | - Min Xie
- School of Environment, Nanjing Normal University, Nanjing 210046, China
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Wang F, Zhang C, Ge Y, Zhang Z, Shi G, Feng Y. Multi-scale analysis of the chemical and physical pollution evolution process from pre-co-pollution day to PM 2.5 and O 3 co-pollution day. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 945:173729. [PMID: 38839009 DOI: 10.1016/j.scitotenv.2024.173729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 05/10/2024] [Accepted: 06/01/2024] [Indexed: 06/07/2024]
Abstract
PM2.5 and O3 are two of the main air pollutants that have adverse impacts on climate and human health. The evolution process of PM2.5 and O3 co-pollution are of concern because of the increased frequency of PM2.5 and O3 co-pollution days. Here, we examined the chemical coupling and revealed the driving factors of the PM2.5 and O3 co-pollution evolution process from cleaning day, PM2.5 pollution day, or O3 pollution day, applied by theoretical analysis and model calculation methods. The results demonstrate that PM2.5 and O3 co-pollution day frequently occurred with high concentrations of gaseous precursors and higher sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR), which we attribute to the enhancement of atmospheric oxidation capacity (AOC). The AOC is positively correlated with O3 and weakly correlated with PM2.5. In addition, we found that the correlation coefficients of PM2.5-NO2 (0.62) were higher than that of PM2.5-SO2 (0.32), highlighting the priority of NOx controlling to mitigate PM2.5 pollution. Overall, our discovery can provide scientific evidence to design feasible solutions for the controlling PM2.5 and O3 co-pollution process.
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Affiliation(s)
- Feng Wang
- School of Environmental Science and Safety Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Chun Zhang
- Shaanxi Province Environmental Monitoring Center, Xi'an 710054, China
| | - Yi Ge
- Shaanxi Province Environmental Monitoring Center, Xi'an 710054, China
| | - Zhang 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; China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Guoliang Shi
- 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; China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research, College of Environmental Science and Engineering, Nankai University, 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; China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
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Zhou M, Liu Y, Lu K, Yu D, Li C, Zhai T, Yang S, Tan Z, Ma X, Li X, Dong H, Zeng L, Chen S, Chen J, Tan Q, Song D, Zhang X, Zhang Y. Ozone production sensitivity analysis for the Chengdu Plain Urban Agglomeration based on a multi-site and two-episode observation. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 950:175068. [PMID: 39094651 DOI: 10.1016/j.scitotenv.2024.175068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 07/07/2024] [Accepted: 07/24/2024] [Indexed: 08/04/2024]
Abstract
Chengdu Plain Urban Agglomeration (CPUA) is one of the most serious areas suffering from ozone pollution in China. A comprehensive field observation focused on the ozone production rate and its sensitivity was conducted at CPUA in the summer of 2019. Six sampling sites were set and two ozone pollution episodes were recognized. The daily maximum 8-h average (MDA8) O3 concentration reached 137.9 ppbv in the urban sites during the ozone pollution episode. Peak concentration of O3 was closely related to intense solar radiation, high temperatures, and precursor emissions. The OH-HO2-RO2 radical chemistry and ozone production rate (P(O3)) were calculated using an observation-based model (OBM). The daily peak OH concentration varied in the range of 3-13 × 106 molecules cm-3, and peak HO2 and RO2 were in the range of 2-14 × 108 molecules cm-3 during ozone pollution episodes. During the ozone pollution episode, the average maximum of P(O3) in suburban sites (about 30 ppbv h-1.) was compared with urban sites, and the maximum of P(O3) was 18 ppbv h-1 in rural sites. The relative incremental reactivity (RIR) results demonstrate that it was a VOCs-limited regime in the central urban area of Chengdu, with NOx suppression effect in some regions. In the southern neighboring suburb of Chengdu, it was VOCs-limited as well. However, the northern suburban area was a transition region. In the remote rural areas of the southern CPUA, it was highly NOx-limited. Local ozone production driven by the photochemical process is crucial to the ozone pollution formation in CPUA. The geographically differentiated recognition of the ozone regime found by this study can help to tailor control strategies for local conditions and avoid the negative effects of a one-size-fits-all approach.
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Affiliation(s)
- Ming Zhou
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Yuhan Liu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China; China Institute of Atomic Energy, Beijing, China
| | - Keding Lu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China.
| | - Dan Yu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Chunmeng Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China; Center for Environmental Metrology, National Institute of Metrology, Beijing, China
| | - Tianyu Zhai
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China; State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing, China
| | - Suding Yang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Zhaofeng Tan
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Xuefei Ma
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Xin Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Huabin Dong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Limin Zeng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Shiyi Chen
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Junhui Chen
- Sichuan Academy of Environmental Sciences, Chengdu, China
| | - Qinwen Tan
- Chengdu Academy of Environmental Sciences, Chengdu, China
| | - Danlin Song
- Chengdu Academy of Environmental Sciences, Chengdu, China
| | - Xiaoling Zhang
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu, China
| | - Yuanhang Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
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Chen Y, Ling Y, Liu F, Tong L, Yang M, Shi Y, Xue Y, Ye H, Xu Y, Huang C, Xiao H. Characteristics and Source Apportionment of Volatile Organic Compounds in a Coastal Industrial Area: A Case Study in the Yangtze River Delta of China. BULLETIN OF ENVIRONMENTAL CONTAMINATION AND TOXICOLOGY 2024; 113:16. [PMID: 39068285 DOI: 10.1007/s00128-024-03926-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 07/02/2024] [Indexed: 07/30/2024]
Abstract
In recent years, the coastal area in East China has experienced elevated volatile organic compounds (VOCs) levels during specific periods. VOCs have become one of the major atmospheric pollutants in these areas. In this study, 64 compounds including alkanes, alkenes, halohydrocarbons, aromatics, and oxygenated VOCs (OVOCs) were obtained by the TO-15 method through a 12-month campaign in industrial, urban and suburban areas in the Yangtze River Delta of China. The overall trends of total VOC (TVOC) concentrations at eight sampling sites were as follows: winter > autumn > spring > summer. The proportion of VOC categories was various at industrial sites, while OVOCs and halohydrocarbons had high proportions at urban sites and suburban sites, respectively. Coating, vehicle emission, petrochemical source, industrial source, and gasoline volatilization were identified as the major VOC emission sources by the positive matrix factorization model. Petrochemical and coating sources were the prime VOC sources at industrial sites. Aromatics contributed the most ozone formation potential at industrial sites, while OVOCs provided the main contributions at both urban and suburban sites during four seasons. According to the health risk assessment, a high probability of non-carcinogenic risk existed at three industrial sites. Special attention should be given to certain VOCs, such as acrolein and 1,2-dibromoethane in industrial areas.
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Affiliation(s)
- Yunbo Chen
- Air pollution prevention and control research center, Kunming Research Academy of Eco-environmental Sciences, Kunming, 650032, China
| | - Yufei Ling
- College of Biological and Environmental Sciences, Zhejiang Wanli University, Ningbo, 315100, China
| | - Fengwei Liu
- College of Biological and Environmental Sciences, Zhejiang Wanli University, Ningbo, 315100, China
| | - Lei Tong
- Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China.
- Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo, 315800, China.
| | - Mengrong Yang
- Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China
- Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo, 315800, China
| | - Yasheng Shi
- Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China
- Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo, 315800, China
| | - Yuan Xue
- College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - Haiyun Ye
- Air pollution prevention and control research center, Kunming Research Academy of Eco-environmental Sciences, Kunming, 650032, China
| | - Yilei Xu
- Air pollution prevention and control research center, Kunming Research Academy of Eco-environmental Sciences, Kunming, 650032, China
| | - Cenyan Huang
- College of Biological and Environmental Sciences, Zhejiang Wanli University, Ningbo, 315100, China.
| | - Hang Xiao
- Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China
- Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo, 315800, China
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Li Y, Ye C, Ma X, Tan Z, Yang X, Zhai T, Liu Y, Lu K, Zhang Y. Radical chemistry and VOCs-NO x-O 3-nitrate sensitivity in the polluted atmosphere of a suburban site in the North China Plain. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 947:174405. [PMID: 38960186 DOI: 10.1016/j.scitotenv.2024.174405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Revised: 06/29/2024] [Accepted: 06/29/2024] [Indexed: 07/05/2024]
Abstract
In this study, the chemical mechanisms of O3 and nitrate formation as well as the control strategy were investigated based on extensive observations in Tai'an city in the NCP and an observation-constrained box model. The results showed that O3 pollution was severe with the maximum hourly O3 concentration reaching 150 ppb. Higher O3 concentration was typically accompanied by higher PM2.5 concentrations, which could be ascribed to the common precursors of VOCs and NOx. The modeled averaged peak concentrations of OH, HO2, and RO2 were relatively higher compared to previous observations, indicating strong atmospheric oxidation capacity in the study area. The ROx production rate increased from 2.8 ppb h-1 to 5 ppb h-1 from the clean case to the heavily polluted case and was dominated by HONO photolysis, followed by HCHO photolysis. The contribution of radical-self combination to radical termination gradually exceeded NO2 + OH from clean to polluted cases, indicating that O3 formation shifted to a more NOx-limited regime. The O3 production rate increased from 14 ppb h-1 to 22 ppb h-1 from clean to heavily polluted cases. The relative incremental reactivity (RIR) results showed that VOCs and NOx had comparable RIR values during most days, which suggested that decreasing VOCs or NOx was both effective in alleviating O3 pollution. In addition, HCHO, with the largest RIR value, made important contribution to O3 production. The Empirical Kinetic Modeling Approach (EKMA) revealed that synergistic control of O3 and nitrate can be achieved by decreasing both NOx and VOCs emissions (e.g., alkenes) with the ratio of 3:1. This study emphasized the importance of NOx abatement for the synergistic control of O3 and nitrate pollution in the Tai'an area as the sustained emissions control has shifted the O3 and nitrate formation to a more NOx-limited regime.
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Affiliation(s)
- Yang Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Can Ye
- School of Environmental Science and Engineering, Tiangong University, Tianjin 300387, China.
| | - Xuefei Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Zhaofeng Tan
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Xinping Yang
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Tianyu Zhai
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yuhan Liu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Keding Lu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China.
| | - Yuanhang Zhang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China.
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Li J, Yuan B, Yang S, Peng Y, Chen W, Xie Q, Wu Y, Huang Z, Zheng J, Wang X, Shao M. Quantifying the contributions of meteorology, emissions, and transport to ground-level ozone in the Pearl River Delta, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 932:173011. [PMID: 38719052 DOI: 10.1016/j.scitotenv.2024.173011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 04/29/2024] [Accepted: 05/01/2024] [Indexed: 05/14/2024]
Abstract
Ozone pollution presents a growing air quality threat in urban agglomerations in China. It remains challenge to distinguish the roles of emissions of precursors, chemical production and transportations in shaping the ground-level ozone trends, largely due to complicated interactions among these 3 major processes. This study elucidates the formation factors of ozone pollution and categorizes them into local emissions (anthropogenic and biogenic emissions), transport (precursor transport and direct transport from various regions), and meteorology. Particularly, we attribute meteorology, which affects biogenic emissions and chemical formation as well as transportation, to a perturbation term with fluctuating ranges. The Community Multiscale Air Quality (CMAQ) model was utilized to implement this framework, using the Pearl River Delta region as a case study, to simulate a severe ozone pollution episode in autumn 2019 that affected the entire country. Our findings demonstrate that the average impact of meteorological conditions changed consistently with the variation of ozone pollution levels, indicating that meteorological conditions can exert significant control over the degree of ozone pollution. As the maximum daily 8-hour average (MDA8) ozone concentrations increased from 20 % below to 30 % above the National Ambient Air Quality Standard II, contributions from emissions and precursor transport were enhanced. Concurrently, direct transport within Guangdong province rose from 13.8 % to 22.7 %, underscoring the importance of regional joint prevention and control measures under adverse weather conditions. Regarding biogenic emissions and precursor transport that cannot be directly controlled, we found that their contributions were generally greater in urban areas with high nitrogen oxides (NOx) levels, primarily due to the stronger atmospheric oxidation capacity facilitating ozone formation. Our results indicate that not only local anthropogenic emissions can be controlled in urban areas, but also the impacts of local biogenic emissions and precursor transport can be potentially regulated through reducing atmospheric oxidation capacity.
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Affiliation(s)
- Jin Li
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Bin Yuan
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China.
| | - Suxia Yang
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Yuwen Peng
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Weihua Chen
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Qianqian Xie
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Yongkang Wu
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Zhijiong Huang
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Junyu Zheng
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Xuemei Wang
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Min Shao
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
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Tang Y, Wang Y, Chen X, Liang J, Li S, Chen G, Chen Z, Tang B, Zhu J, Li X. Diurnal emission variation of ozone precursors: Impacts on ozone formation during Sep. 2019. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 929:172591. [PMID: 38663597 DOI: 10.1016/j.scitotenv.2024.172591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 03/08/2024] [Accepted: 04/17/2024] [Indexed: 04/30/2024]
Abstract
With the issue of ozone (O3) pollution having increasingly gained visibility and prominence in China, the Chinese government explored various policies to mitigate O3 pollution. In some provinces and cities, diurnal regulations of O3 precursor were implemented, such as shifting O3 precursor emission processes to nighttime and offering preferential refueling at night. However, the effectiveness of these policies remains unverified, and their impact on the O3 generation process requires further elucidation. In this study, we utilized a regional climate and air quality model (WRF-Chem, v4.5) to test three scenarios aimed at exploring the impact of diurnal industry emission variation of O3 precursors on O3 formation. Significant O3 variations were observed mainly in urban areas. Shifting volatile organic compounds (VOCs) to nighttime have slight decreased daytime O3 levels while moving nitrogen oxides (NOx) to nighttime elevates O3 levels. Simultaneously moving both to nighttime showed combined effects. Process analysis indicates that the diurnal variation in O3 was mainly attributed to chemical process and vertical mixing in urban areas, while advection becomes more important in non-urban areas, contributing to the changes in O3 and O3 precursors levels through regional transportation. Further photochemical analysis reveals that the O3 photochemical production in urban areas was affected by reduced daytime O3 precursors emissions. Specifically, decreasing VOCs lowered the daytime O3 production by reducing the ROx radicals (ROx = HO + HO˙2 + RO˙2), whereas decreasing NOx promoted the daytime O3 production by weakening ROx radical loss. Our results demonstrate that diurnal regulation of O3 precursors will disrupt the ROx radical and O3 formation in local areas, resulting in a change in O3 concentration and atmospheric oxidation capacity, which should be considered in formulating new relevant policies.
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Affiliation(s)
- Yifan Tang
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China
| | - Yuchen Wang
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China; State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, PR China
| | - Xuwu Chen
- School of Advanced Interdisciplinary Studies, Hunan University of Technology and Business, Changsha 410205, PR China
| | - Jie Liang
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China
| | - Shuai Li
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China
| | - Gaojie Chen
- College of Mathematics and Econometrics, Hunan University, Changsha 410082, PR China
| | - Zuo Chen
- College of Information Science and Technology, Hunan University, Changsha 410082, PR China
| | - Binxu Tang
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China
| | - Jiesong Zhu
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China
| | - Xiaodong Li
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China.
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Dong Z, Jiang Y, Wang S, Xing J, Ding D, Zheng H, Wang H, Huang C, Yin D, Song Q, Zhao B, Hao J. Spatially and Temporally Differentiated NO x and VOCs Emission Abatement Could Effectively Gain O 3-Related Health Benefits. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:9570-9581. [PMID: 38781138 DOI: 10.1021/acs.est.4c01345] [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: 05/25/2024]
Abstract
The increasing level of O3 pollution in China significantly exacerbates the long-term O3 health damage, and an optimized health-oriented strategy for NOx and VOCs emission abatement is needed. Here, we developed an integrated evaluation and optimization system for the O3 control strategy by merging a response surface model for the O3-related mortality and an optimization module. Applying this system to the Yangtze River Delta (YRD), we evaluated driving factors for mortality changes from 2013 to 2017, quantified spatial and temporal O3-related mortality responses to precursor emission abatement, and optimized a health-oriented control strategy. Results indicate that insufficient NOx emission abatement combined with deficient VOCs control from 2013 to 2017 aggravated O3-related mortality, particularly during spring and autumn. Northern YRD should promote VOCs control due to higher VOC-limited characteristics, whereas fastening NOx emission abatement is more favorable in southern YRD. Moreover, promotion of NOx mitigation in late spring and summer and facilitating VOCs control in spring and autumn could further reduce O3-related mortality by nearly 10% compared to the control strategy without seasonal differences. These findings highlight that a spatially and temporally differentiated NOx and VOCs emission control strategy could gain more O3-related health benefits, offering valuable insights to regions with severe ozone pollution all over the world.
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Affiliation(s)
- Zhaoxin Dong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Formation and Prevention of the Urban Air Pollution Complex, Shanghai Academy of Environment Sciences, Shanghai 200233, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, 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
| | - 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
| | - Jia Xing
- 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
| | - Dian Ding
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
| | - Haotian Zheng
- 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
| | - Hongli Wang
- State Environmental Protection Key Laboratory of Formation and Prevention of the Urban Air Pollution Complex, Shanghai Academy of Environment Sciences, Shanghai 200233, China
| | - Cheng Huang
- State Environmental Protection Key Laboratory of Formation and Prevention of the Urban Air Pollution Complex, Shanghai Academy of Environment Sciences, Shanghai 200233, 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
| | - Qian Song
- 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
| | - 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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9
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Zhu H, Liu Z, Wilson J, Liu T, Negesa D, Li Y. Spatiotemporal evolution and key driver analysis of ozone pollution from the perspectives of spatial spillover and path-dependence effects in China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 356:124318. [PMID: 38844038 DOI: 10.1016/j.envpol.2024.124318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 05/19/2024] [Accepted: 06/03/2024] [Indexed: 06/10/2024]
Abstract
In recent years, ozone (O3) has emerged as the primary air pollutant in China, superseding PM2.5. Previous studies have concentrated on the spatiotemporal variation of ozone pollution, the analysis of its sources and drivers, as well as its environmental impacts and health benefits. Since ozone pollution can be dynamically transferred through industrial activities and meteorological factors, it is crucial to scientifically identify the spatial spillover and path-dependent effects of ozone pollution in China. However, existing studies have not yet addressed this issue. Therefore, we investigate the spatiotemporal evolution and the spatial spillover of ozone pollution by means of the exploratory spatial data analysis (ESDA) using panel data of 30 Chinese provinces from 2013 to 2020 in this study. The dynamic spatial Durbin model (SDM) was employed to reveal the key drivers of ozone pollution from the perspectives of spatial spillover and path-dependence effects. The direct and spillover effects of each driver on ozone pollution are systematically analyzed. The results show that from 2013 to 2020 ozone concentrations followed a fluctuating upward trend at national and provincial scales. Ozone pollution presented significant spatial spillover and path dependence effects. The direct effects indicated that economic growth, technological level, industrial structure, energy structure, ventilation coefficient, relative humidity and precipitation were the key drivers of local ozone pollution. The spillover effects indicated that population density, technology level, industrial structure, environmental regulations, ventilation coefficient, sunshine hours and relative humidity had significant spatial spillover effects on ozone pollution of surrounding regions. These findings will contribute to the understanding of the spatial spillover and path-dependent effects of O3 pollution, and provide scientific guidance for regional synergy and long-term ozone control policies in China.
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Affiliation(s)
- Houle Zhu
- Research Center for Resource and Environmental Management, Institute for Population and Development Studies, School of Public Policy and Administration, Xi'an Jiaotong University, Shaanxi Province, China
| | - Zhe Liu
- Research Center for Resource and Environmental Management, Institute for Population and Development Studies, School of Public Policy and Administration, Xi'an Jiaotong University, Shaanxi Province, China.
| | - Jeffrey Wilson
- School of Environment, Enterprise and Development, University of Waterloo, Waterloo, Canada
| | - Tongtong Liu
- Research Center for Resource and Environmental Management, Institute for Population and Development Studies, School of Public Policy and Administration, Xi'an Jiaotong University, Shaanxi Province, China
| | - Doryn Negesa
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China
| | - Yongzhi Li
- Economic and Technological Development Zone Branch Bureau of Shenyang Environmental Protection Bureau, Shenyang 110141, China
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10
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Chen Q, Miao R, Geng G, Shrivastava M, Dao X, Xu B, Sun J, Zhang X, Liu M, Tang G, Tang Q, Hu H, Huang RJ, Wang H, Zheng Y, Qin Y, Guo S, Hu M, Zhu T. Widespread 2013-2020 decreases and reduction challenges of organic aerosol in China. Nat Commun 2024; 15:4465. [PMID: 38796477 PMCID: PMC11127919 DOI: 10.1038/s41467-024-48902-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 05/17/2024] [Indexed: 05/28/2024] Open
Abstract
High concentrations of organic aerosol (OA) occur in Asian countries, leading to great health burdens. Clean air actions have resulted in significant emission reductions of air pollutants in China. However, long-term nation-wide trends in OA and their causes remain unknown. Here, we present both observational and model evidence demonstrating widespread decreases with a greater reduction in primary OA than in secondary OA (SOA) in China during the period of 2013 to 2020. Most of the decline is attributed to reduced residential fuel burning while the interannual variability in SOA may have been driven by meteorological variations. We find contrasting effects of reducing NOx and SO2 on SOA production which may have led to slight overall increases in SOA. Our findings highlight the importance of clean energy replacements in multiple sectors on achieving air-quality targets because of high OA precursor emissions and fluctuating chemical and meteorological conditions.
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Affiliation(s)
- Qi Chen
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Innovation Center for Engineering Science and Advanced Technology, International Joint Laboratory for Regional Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China.
- Institute of Carbon Neutrality, Peking University, Beijing, China.
| | - Ruqian Miao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Innovation Center for Engineering Science and Advanced Technology, International Joint Laboratory for Regional Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Guannan Geng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China
| | | | - Xu Dao
- China National Environmental Monitoring Centre, Beijing, China
| | - Bingye Xu
- Zhejiang Key Laboratory of Ecological and Environmental Monitoring, Forewarning and Quality Control, Zhejiang Province Environment Monitoring Centre, Hangzhou, China
| | - Jiaqi Sun
- China National Environmental Monitoring Centre, Beijing, China
| | - Xian Zhang
- China National Environmental Monitoring Centre, Beijing, China
| | - Mingyuan Liu
- China National Environmental Monitoring Centre, Beijing, China
| | - Guigang Tang
- China National Environmental Monitoring Centre, Beijing, China
| | - Qian Tang
- Zhejiang Key Laboratory of Ecological and Environmental Monitoring, Forewarning and Quality Control, Zhejiang Province Environment Monitoring Centre, Hangzhou, China
| | - Hanwen Hu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China
| | - Ru-Jin Huang
- State Key Laboratory of Loess and Quaternary Geology, Center for Excellence in Quaternary Science and Global Change, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, China
| | - Hao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Innovation Center for Engineering Science and Advanced Technology, International Joint Laboratory for Regional Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Yan Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Innovation Center for Engineering Science and Advanced Technology, International Joint Laboratory for Regional Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Yue Qin
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Innovation Center for Engineering Science and Advanced Technology, International Joint Laboratory for Regional Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
- Institute of Carbon Neutrality, Peking University, Beijing, China
| | - Song Guo
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Innovation Center for Engineering Science and Advanced Technology, International Joint Laboratory for Regional Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
- Institute of Carbon Neutrality, Peking University, Beijing, China
| | - Min Hu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Innovation Center for Engineering Science and Advanced Technology, International Joint Laboratory for Regional Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
- Institute of Carbon Neutrality, Peking University, Beijing, China
| | - Tong Zhu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Innovation Center for Engineering Science and Advanced Technology, International Joint Laboratory for Regional Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
- Institute of Carbon Neutrality, Peking University, Beijing, China
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11
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Fang H, Wang W, Wang R, Xu H, Zhang Y, Wu T, Zhou R, Zhang J, Ruan Z, Li F, Wang X. Ozone and its precursors at an urban site in the Yangtze River Delta since clean air action plan phase II in China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 347:123769. [PMID: 38499173 DOI: 10.1016/j.envpol.2024.123769] [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: 11/01/2023] [Revised: 02/05/2024] [Accepted: 03/09/2024] [Indexed: 03/20/2024]
Abstract
In response to regional ozone (O3) pollution, Chinese government has implemented air pollution control measures in recent years. Here, a case study was performed at an O3-polluted city, Wuhu, in Yangtze River Delta region of China to investigate O3 variation trend and the relationship to its precursors after implementation of Clean Air Action Plan Phase II, which aims to reduce O3 pollution. The results showed that peak O3 concentration was effectively reduced since Clean Air Action Plan Phase II. Due to significant NOx reduction, O3 formation tended to shift from volatile organic compound (VOC)-limited regimes to NOx-limited regimes during 2018-2022. VOC/NOx ratios measured in 2022 revealed that peak O3 concentration tended to respond positively to NOx. Apart from high-O3 period, Wuhu was still in a VOC-limited regime. The relationship of maximum daily 8-h ozone average and NO2 followed a lognormal distribution with an inflection point at 20 μg m-3 of NO2, suggesting that Wuhu should conduct joint control of VOC and NOx with a focus on VOC reduction before the inflection point. Alkenes and aromatics were suggested to be preferentially controlled due to their higher ozone formation potentials. Using random forest meteorological normalization method, meteorology had a positive effect on O3 concentration in 2018, 2019 and 2022, but a negative effect in 2020 and 2021. The meteorology could explain 44.0 ± 19.1% of the O3 variation during 2018-2022. High temperature favors O3 production and O3 pollution occurred more easily when temperature was over 25 °C, while high relative humidity inhibits O3 generation and no O3 pollution was found at relative humidity above 70%. This study unveils some new insights into the trend of urban O3 pollution in Yangtze River Delta region since Clean Air Action Plan Phase II and the findings provide important references for formulating control strategies against O3 pollution.
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Affiliation(s)
- Hua Fang
- School of Ecology and Environment, Anhui Normal University, Wuhu, 241000, China; Center of Cooperative Innovation for Recovery and Reconstruction of Degraded Ecosystem in Wanjiang City Belt, Wuhu, 241000, China.
| | - Wenjing Wang
- School of Ecology and Environment, Anhui Normal University, Wuhu, 241000, China
| | - Ran Wang
- Wuhu Institute of Ecological Environmental Sciences, Wuhu, 241000, China
| | - Hongling Xu
- School of Ecology and Environment, Anhui Normal University, Wuhu, 241000, China
| | - Ying Zhang
- Wuhu Ecological and Environmental Monitoring Center of Anhui Province, Wuhu, 241005, China
| | - Ting Wu
- School of Ecology and Environment, Anhui Normal University, Wuhu, 241000, China; Center of Cooperative Innovation for Recovery and Reconstruction of Degraded Ecosystem in Wanjiang City Belt, Wuhu, 241000, China.
| | - Ruicheng Zhou
- School of Ecology and Environment, Anhui Normal University, Wuhu, 241000, China
| | - Jianxi Zhang
- School of Ecology and Environment, Anhui Normal University, Wuhu, 241000, China
| | - Zhirong Ruan
- School of Ecology and Environment, Anhui Normal University, Wuhu, 241000, China
| | - Feng Li
- School of Ecology and Environment, Anhui Normal University, Wuhu, 241000, China
| | - Xinming Wang
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China; CAS Center for Excellence in Deep Earth Science, Guangzhou, 510640, China
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12
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Li R, Zhao J, Feng K, Tian Y. Development and application of a multi-task oriented deep learning model for quantifying drivers of air pollutant variations: A case study in Taiyuan, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 920:170777. [PMID: 38331278 DOI: 10.1016/j.scitotenv.2024.170777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 02/04/2024] [Accepted: 02/05/2024] [Indexed: 02/10/2024]
Abstract
Quantitative assessment of the drivers behind the variation of six criteria pollutants, namely fine particulate matter (PM2.5), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), particulate matter (PM10), and carbon monoxide (CO), in the warming climate will be critical for subsequent decision-making. Here, a novel hybrid model of multi-task oriented CNN-BiLSTM-Attention was proposed and performed in Taiyuan during 2015-2020 to synchronously and quickly quantify the impact of anthropogenic and meteorological factors on the six criteria pollutants variations. Empirical results revealed the residential and transportation sectors distinctly decreased SO2 by 25 % and 22 % and CO by 12 % and 10 %. Gradual downward trends for PM2.5, PM10, and NO2 were mainly ascribed to the stringent measures implemented in transportation and power sectors as part of the Blue Sky Defense War, which were further reinforced by the COVID-19 pandemic. Nevertheless, temperature-dependent adverse meteorological effects (27 %) and anthropogenic intervention (12 %) jointly increased O3 by 39 %. The O3-driven pollution events may be inevitable or even become more prominent under climate warming. The industrial (5 %) and transportation sectors (6 %) were mainly responsible for the anthropogenic-driven increase of O3 and precursor NO2, respectively. Synergistic reduction of precursors (VOCs and NOx) from industrial and transportation sectors requires coordination with climate actions to mitigate the temperature-dependent O3-driven pollution, thereby improving regional air quality. Meanwhile, the proposed model is expected to be applied flexibly in various regions to quantify the drivers of the pollutant variations in a warming climate, with the potential to offer valuable insights for improving regional air quality in near future.
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Affiliation(s)
- Rumei Li
- Extended Energy Big Data and Strategy Research Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China; Shandong Energy Institute, Qingdao 266101, China; Qingdao New Energy Shandong Laboratory, Qingdao 266101, China
| | - Jinghao Zhao
- Extended Energy Big Data and Strategy Research Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China; Shandong Energy Institute, Qingdao 266101, China; Qingdao New Energy Shandong Laboratory, Qingdao 266101, China
| | - Kun Feng
- Shanxi Low-carbon Environmental Protection Industry Group Co., Ltd., Taiyuan 030012, China; Shanxi Ecological Environment Monitoring Center, Taiyuan 030027, China
| | - Yajun Tian
- Extended Energy Big Data and Strategy Research Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China; Shandong Energy Institute, Qingdao 266101, China; Qingdao New Energy Shandong Laboratory, Qingdao 266101, China.
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13
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Ye C, Wang H, Li X, Lu K, Zhang Y. Atmospheric Reactive Nitrogen Species Weaken the Air Quality Response to Emission Reductions in China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:6066-6070. [PMID: 38556988 DOI: 10.1021/acs.est.3c10927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Affiliation(s)
- Can Ye
- School of Environmental Science and Engineering, Tiangong University, Tianjin 300387, China
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Haichao Wang
- School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Xuan Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Keding Lu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Yuanhang Zhang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
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14
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Ye X, Zhang L, Wang X, Lu X, Jiang Z, Lu N, Li D, Xu J. Spatial and temporal variations of surface background ozone in China analyzed with the grid-stretching capability of GEOS-Chem High Performance. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 914:169909. [PMID: 38185162 DOI: 10.1016/j.scitotenv.2024.169909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/11/2023] [Accepted: 01/02/2024] [Indexed: 01/09/2024]
Abstract
Surface background ozone, defined as the ozone in the absence of domestic anthropogenic emissions, is important for developing emission reduction strategies. Here we apply the recently developed GEOS-Chem High Performance (GCHP) global atmospheric chemistry model with ∼0.5° stretched resolution over China to understand the sources of Chinese background ozone (CNB) in the metric of daily maximum 8 h average (MDA8) and to identify the drivers of its interannual variability (IAV) from 2015 to 2019. The GCHP ozone simulations over China are evaluated with an ensemble of surface and aircraft measurements. The five-year national-mean CNB ozone is estimated as 37.9 ppbv, with a spatially west-to-southeast downward gradient (55 to 25 ppbv) and a summer peak (42.5 ppbv). High background levels in western China are due to abundant transport from the free troposphere and adjacent foreign regions, while in eastern China, domestic formation from surface natural precursors is also important. We find greater importance of soil nitric oxides (NOx) than biogenic volatile organic compound emissions to CNB ozone in summer (6.4 vs. 3.9 ppbv), as ozone formation becomes increasingly NOx-sensitive when suppressing anthropogenic emissions. The percentage of daily CNB ozone to total surface ozone generally decreases with increasing daily total ozone, indicating an increased contribution of domestic anthropogenic emissions on polluted days. CNB ozone shows the largest IAV in summer, with standard deviations (seasonal means) of ∼5 ppbv over Qinghai-Tibet Plateau (QTP) and >3.5 ppbv in eastern China. CNB values in QTP are strongly correlated with horizontal circulation anomalies in the middle troposphere, while soil NOx emissions largely drive the IAV in the east. El Nino can inhibit CNB ozone formation in Southeast China by increased precipitation and lower temperature locally in spring, but enhance CNB in Southwest China through increased biomass burning emissions in Southeast Asia.
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Affiliation(s)
- Xingpei Ye
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Lin Zhang
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China.
| | - Xiaolin Wang
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Xiao Lu
- School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Zhongjing Jiang
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY 11973-5000, United States of America
| | - Ni Lu
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Danyang Li
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Jiayu Xu
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
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15
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Yan D, Jin Z, Zhou Y, Li M, Zhang Z, Wang T, Zhuang B, Li S, Xie M. Anthropogenically and meteorologically modulated summertime ozone trends and their health implications since China's clean air actions. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 343:123234. [PMID: 38154777 DOI: 10.1016/j.envpol.2023.123234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 12/21/2023] [Accepted: 12/24/2023] [Indexed: 12/30/2023]
Abstract
Elevated ozone (O3) has emerged as the major air quality concern since China's clean air actions, offsetting the health benefits gained from improved air quality. Given the shifted ozone chemical regimes and recently boosted extreme weather in China, it's essential to rethink the O3 trends since 2013 for evaluations of air pollution mitigation policy. Here, we examine the anthropogenically and meteorologically modulated summertime O3 trends across China at different stages of the clean air actions using multi-source observations combined with multi-model calculations. Ozone increases steadily in China between 2013-2022, with a fast increase rate of 4.4 μg m-3 yr-1 in Phase I and a much smaller 0.6 μg m-3 yr-1 in Phase II of Action Plan. Results highlight that the deteriorative O3 pollution in Phase I and early Phase II is dominated by the nonlinear O3-emission response. Persistent decline in O3 precursors has shifted its chemical regime in urban areas and began to show a positive influence on ozone mitigation in recent years. Meteorological influence on O3 variations is minor until 2019 (∼10%), but it greatly accelerates or relieves the O3 pollution after then, showing comparable contribution to emissions. Epidemiological model predicts totally 0.8-3.0 thousand yr-1 more deaths across China with altered anthropogenic emissions since clean air actions, and additional health burdens by -1.5-0.3 thousand yr-1 from perturbated meteorology. This study calls for stringent emission control and climate adaptation strategies to attain the ozone pollution mitigation in China.
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Affiliation(s)
- Dan Yan
- School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
| | - Zhipeng Jin
- School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
| | - Yiting Zhou
- School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
| | - Mengmeng Li
- School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China; Frontiers Science Center for Critical Earth Material Cycling, Nanjing, 210023, China.
| | - Zihan Zhang
- School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
| | - Tijian Wang
- School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
| | - Bingliang Zhuang
- School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
| | - Shu Li
- School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
| | - Min Xie
- School of Environment, Nanjing Normal University, Nanjing, 210023, China
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16
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Xue T, Wang R, Wang M, Wang Y, Tong D, Meng X, Huang C, Ai S, Li F, Cao J, Tong M, Ni X, Liu H, Deng J, Lu H, Wan W, Gong J, Zhang S, Zhu T. Health benefits from the rapid reduction in ambient exposure to air pollutants after China's clean air actions: progress in efficacy and geographic equality. Natl Sci Rev 2024; 11:nwad263. [PMID: 38213522 PMCID: PMC10776362 DOI: 10.1093/nsr/nwad263] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 09/13/2023] [Accepted: 10/08/2023] [Indexed: 01/13/2024] Open
Abstract
Clean air actions (CAAs) in China have been linked to considerable benefits in public health. However, whether the beneficial effects of CAAs are equally distributed geographically is unknown. Using high-resolution maps of the distributions of major air pollutants (fine particulate matter [PM2.5] and ozone [O3]) and population, we aimed to track spatiotemporal changes in health impacts from, and geographic inequality embedded in, the reduced exposures to PM2.5 and O3 from 2013 to 2020. We used a method established by the Global Burden of Diseases Study. By analyzing the changes in loss of life expectancy (LLE) attributable to PM2.5 and O3, we calculated the gain of life expectancy (GLE) to quantify the health benefits of the air-quality improvement. Finally, we assessed the geographic inequality embedded in the GLE using the Gini index (GI). Based on risk assessments of PM2.5 and O3, during the first stage of CAAs (2013 to 2017), the mean GLE was 1.87 months. Half of the sum of the GLE was disproportionally distributed in about one quarter of the population exposed (GI 0.44). During the second stage of CAAs (2017 to 2020), the mean GLE increased to 3.94 months and geographic inequality decreased (GI 0.18). According to our assessments, CAAs were enhanced, from the first to second stages, in terms of not only preventing premature mortality but also ameliorating health inequalities. The enhancements were related to increased sensitivity to the health effects of air pollution and synergic control of PM2.5 and O3 levels. Our findings will contribute to optimizing future CAAs.
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Affiliation(s)
- Tao Xue
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
- Advanced Institute of Information Technology, Peking University, Hangzhou311215, China
| | - Ruohan Wang
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
| | - Meng Wang
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY14214, USA
| | - Yanying Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
| | - Dan Tong
- Department of Earth System Science, Tsinghua University, Beijing100084, China
| | - Xia Meng
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education, and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai200433, China
| | - Conghong Huang
- College of Land Management, Nanjing Agricultural University, Nanjing 210095, China
- National & Local Joint Engineering, Research Center for Rural Land Resources Use and Consolidation, Nanjing 210095, China
| | - Siqi Ai
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
| | - Fangzhou Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
| | - Jingyuan Cao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
| | - Mingkun Tong
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
| | - Xueqiu Ni
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
| | - Hengyi Liu
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
| | - Jianyu Deng
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
| | - Hong Lu
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
| | - Wei Wan
- Clean Air Asia, Beijing100600, China
| | - Jicheng Gong
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
| | - Shiqiu Zhang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
| | - Tong Zhu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
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17
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Wu H, Guo B, Guo T, Pei L, Jing P, Wang Y, Ma X, Bai H, Wang Z, Xie T, Chen M. A study on identifying synergistic prevention and control regions for PM 2.5 and O 3 and exploring their spatiotemporal dynamic in China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 341:122880. [PMID: 37944886 DOI: 10.1016/j.envpol.2023.122880] [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: 08/30/2023] [Revised: 10/18/2023] [Accepted: 11/04/2023] [Indexed: 11/12/2023]
Abstract
Air pollutants, notably ozone (O3) and fine particulate matter (PM2.5) give rise to evident adverse impacts on public health and the ecotope, prompting extensive global apprehension. Though PM2.5 has been effectively mitigated in China, O3 has been emerging as a primary pollutant, especially in summer. Currently, alleviating PM2.5 and O3 synergistically faces huge challenges. The synergistic prevention and control (SPC) regions of PM2.5 and O3 and their spatiotemporal patterns were still unclear. To address the above issues, this study utilized ground monitoring station data, meteorological data, and auxiliary data to predict the China High-Resolution O3 Dataset (CHROD) via a two-stage model. Furthermore, SPC regions were identified based on a spatial overlay analysis using a Geographic Information System (GIS). The standard deviation ellipse was employed to investigate the spatiotemporal dynamic characteristics of SPC regions. Some outcomes were obtained. The two-stage model significantly improved the accuracy of O3 concentration prediction with acceptable R2 (0.86), and our CHROD presented higher spatiotemporal resolution compared with existing products. SPC regions exhibited significant spatiotemporal variations during the Blue Sky Protection Campaign (BSPC) in China. SPC regions were dominant in spring and autumn, and O3-controlled and PM2.5-dominated zones were detected in summer and winter, respectively. SPC regions were primarily located in the northwest, north, east, and central regions of China, specifically in the Beijing-Tianjin-Hebei urban agglomeration (BTH), Shanxi, Shaanxi, Shandong, Henan, Jiangsu, Xinjiang, and Anhui provinces. The gravity center of SPC regions was distributed in the BTH in winter, and in Xinjiang during spring, summer, and autumn. This study can supply scientific references for the collaborative management of PM2.5 and O3.
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Affiliation(s)
- Haojie Wu
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi, 710054, China; Shaanxi Key Laboratory of Environmental Monitoring and Forewarning of Trace Pollutants, Xi'an, Shaanxi, 710043, China
| | - Bin Guo
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi, 710054, China.
| | - Tengyue Guo
- Department of Geological Engineering, Qinghai University, Xining, Qinghai, 810016, China
| | - Lin Pei
- School of Exercise and Health Sciences, Xi'an Physical Education University, Xi'an, Shaanxi, 710068, China
| | - Peiqing Jing
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, 430072, China
| | - Yan Wang
- School of Geography and Tourism, Shaanxi Normal University, Xi'an, Shaanxi, 710119, China
| | - Xuying Ma
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi, 710054, China
| | - Haorui Bai
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi, 710054, China
| | - Zheng Wang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi, 710054, China
| | - Tingting Xie
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi, 710054, China
| | - Miaoyi Chen
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi, 710054, China
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18
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Zheng H, Kong S, Seo J, Yan Y, Cheng Y, Yao L, Wang Y, Zhao T, Harrison RM. Achievements and challenges in improving air quality in China: Analysis of the long-term trends from 2014 to 2022. ENVIRONMENT INTERNATIONAL 2024; 183:108361. [PMID: 38091821 DOI: 10.1016/j.envint.2023.108361] [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: 11/02/2023] [Accepted: 11/29/2023] [Indexed: 01/25/2024]
Abstract
Due to the implementation of air pollution control measures in China, air quality has significantly improved, although there are still additional issues to be addressed. This study used the long-term trends of air pollutants to discuss the achievements and challenges in further improving air quality in China. The Kolmogorov-Zurbenko (KZ) filter and multiple-linear regression (MLR) were used to quantify the meteorology-related and emission-related trends of air pollutants from 2014 to 2022 in China. The KZ filter analysis showed that PM2.5 decreased by 7.36 ± 2.92% yr-1, while daily maximum 8-h ozone (MDA8 O3) showed an increasing trend with 3.71 ± 2.89% yr-1 in China. The decrease in PM2.5 and increase in MDA8 O3 were primarily attributed to changes in emission, with the relative contribution of 85.8% and 86.0%, respectively. Meteorology variations, including increased ambient temperature, boundary layer height, and reduced relative humidity, also contributed to the reduction of PM2.5 and the enhancement of MDA8 O3. The emission-related trends of PM2.5 and MDA8 O3 exhibited continuous decrease and increase, respectively, from 2014 to 2022, while the variation rates slowed during 2018-2020 compared to that during 2014-2017, highlighting the challenges in further improving air quality, particularly in simultaneously reducing PM2.5 and O3. This study recommends reducing NH3 emissions from the agriculture sector in rural areas and transport emissions in urban areas to further decrease PM2.5 levels. Addressing O3 pollution requires the reduction of O3 precursor gases based on site-specific atmospheric chemistry considerations.
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Affiliation(s)
- Huang Zheng
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China; Research Centre for Complex Air Pollution of Hubei Province, Wuhan 430078, China
| | - Shaofei Kong
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China; Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of the China Meteorological Administration, PREMIC, Nanjing University of Information Science &Technology, Nanjing, China; Research Centre for Complex Air Pollution of Hubei Province, Wuhan 430078, China.
| | - Jihoon Seo
- Climate and Environmental Research Institute, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
| | - Yingying Yan
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China; Research Centre for Complex Air Pollution of Hubei Province, Wuhan 430078, China
| | - Yi Cheng
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China
| | - Liquan Yao
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China
| | - Yanxin Wang
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China; Research Centre for Complex Air Pollution of Hubei Province, Wuhan 430078, China
| | - Tianliang Zhao
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of the China Meteorological Administration, PREMIC, Nanjing University of Information Science &Technology, Nanjing, China
| | - Roy M Harrison
- School of Geography, Earth and Environment Sciences, University of Birmingham, Birmingham B15 2TT, UK; Department of Environmental Sciences, Faculty of Meteorology, Environment and Arid Land Agriculture, King Abdulaziz University, PO Box 80203, Jeddah, Saudi Arabia.
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19
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Zhao B, Su F, Wang K, Ying Q, Zhang R, Xu Q. Age evolution of secondary organic aerosol: Impacts of regional transport and aerosol volatility. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 904:166748. [PMID: 37659525 DOI: 10.1016/j.scitotenv.2023.166748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 08/30/2023] [Accepted: 08/30/2023] [Indexed: 09/04/2023]
Abstract
In this study, a revised CMAQ model incorporating source and temporal apportioning functions has been used to analyze the aging characteristics of SOA in East Asia. The results show that in the aerosol phase, the fraction of the non-volatile components typically fluctuates around 75 %-95 %, and aromatic hydrocarbon precursors contribute significantly to SOA, accounting for 45.6 %-72.7 % in winter and 29.1 %-52.7 % in summer. Transport due to meteorological conditions does not affect the SOA volatility profile in the cities, while regional source composition has been found to be important for the characterization of the properties of SOA in cities. When the SOA regional composition type is a multi-region-imported-dominated type (MRT), its age composition type tends to be an old-age-SOA-dominated type (OAT) (>48 h). Additionally, transport also causes fluctuations in the range of hourly SOA with atmospheric age of 96 h or higher. The SOAs normally transport through seasonal monsoon and could migrate longer in winter (700-1500 km in January) than in other seasons (250-900 km in April; 500-1200 km in July; 300-1000 km in October). Additionally, in winter, non-volatile SOA generally has a longer transport distance (700-1600 km) than semi- and low-volatile SOA (300-1300 km and 600-1500 km). Furthermore, during the transport process, geographical barriers have negligible impact on SOA in the 48+ hour age group.
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Affiliation(s)
- Bingnan Zhao
- School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China
| | - Fangcheng Su
- Institute of Environmental Sciences, Zhengzhou University, Zhengzhou 450001, China; College of Chemistry, Zhengzhou University, Zhengzhou 450001, China
| | - Ke Wang
- School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China; Institute of Environmental Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Qi Ying
- Texas A&M Univ, Zachry Dept Civil Engn, College Stn, TX 77843, USA
| | - Ruiqin Zhang
- School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China; Institute of Environmental Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Qixiang Xu
- School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China; Institute of Environmental Sciences, Zhengzhou University, Zhengzhou 450001, China.
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