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Liu N, He G, Wang H, He C, Wang H, Liu C, Wang Y, Wang H, Li L, Lu X, Fan S. Rising frequency of ozone-favorable synoptic weather patterns contributes to 2015-2022 ozone increase in Guangzhou. J Environ Sci (China) 2025; 148:502-514. [PMID: 39095184 DOI: 10.1016/j.jes.2023.09.024] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 09/20/2023] [Accepted: 09/20/2023] [Indexed: 08/04/2024]
Abstract
Objective weather classification methods have been extensively applied to identify dominant ozone-favorable synoptic weather patterns (SWPs), however, the consistency of different classification methods is rarely examined. In this study, we apply two widely-used objective methods, the self-organizing map (SOM) and K-means clustering analysis, to derive ozone-favorable SWPs at four Chinese megacities in 2015-2022. We find that the two algorithms are largely consistent in recognizing dominant ozone-favorable SWPs for four Chinese megacities. In the case of classifying six SWPs, the derived circulation fields are highly similar with a spatial correlation of 0.99 between the two methods, and the difference in the mean frequency of each SWP is less than 7%. The six dominant ozone-favorable SWPs in Guangzhou are all characterized by anomaly higher radiation and temperature, lower cloud cover, relative humidity, and wind speed, and stronger subsidence compared to climatology mean. We find that during 2015-2022, the occurrence of ozone-favorable SWPs days increases significantly at a rate of 3.2 day/year, faster than the increases in the ozone exceedance days (3.0 day/year). The interannual variability between the occurrence of ozone-favorable SWPs and ozone exceedance days are generally consistent with a temporal correlation coefficient of 0.6. In particular, the significant increase in ozone-favorable SWPs in 2022, especially the Subtropical High type which typically occurs in September, is consistent with a long-lasting ozone pollution episode in Guangzhou during September 2022. Our results thus reveal that enhanced frequency of ozone-favorable SWPs plays an important role in the observed 2015-2022 ozone increase in Guangzhou.
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Affiliation(s)
- Nanxi Liu
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China; Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Key Laboratory of Tropical Atmosphere-Ocean System (Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China
| | - Guowen He
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China; Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Key Laboratory of Tropical Atmosphere-Ocean System (Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China
| | - Haolin Wang
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China; Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Key Laboratory of Tropical Atmosphere-Ocean System (Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China
| | - Cheng He
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China; Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Key Laboratory of Tropical Atmosphere-Ocean System (Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China
| | - Haofan Wang
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China; Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Key Laboratory of Tropical Atmosphere-Ocean System (Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China
| | - Chenxi Liu
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China; Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Key Laboratory of Tropical Atmosphere-Ocean System (Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China
| | - Yiming Wang
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China; Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Key Laboratory of Tropical Atmosphere-Ocean System (Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China
| | - Haichao Wang
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China; Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Key Laboratory of Tropical Atmosphere-Ocean System (Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China
| | - Lei Li
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China; Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Key Laboratory of Tropical Atmosphere-Ocean System (Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China
| | - Xiao Lu
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China; Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Key Laboratory of Tropical Atmosphere-Ocean System (Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China.
| | - Shaojia Fan
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China; Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Key Laboratory of Tropical Atmosphere-Ocean System (Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China.
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2
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Qu K, Yan Y, Wang X, Jin X, Vrekoussis M, Kanakidou M, Brasseur GP, Lin T, Xiao T, Cai X, Zeng L, Zhang Y. The effect of cross-regional transport on ozone and particulate matter pollution in China: A review of methodology and current knowledge. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 947:174196. [PMID: 38942314 DOI: 10.1016/j.scitotenv.2024.174196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 05/29/2024] [Accepted: 06/20/2024] [Indexed: 06/30/2024]
Abstract
China is currently one of the countries impacted by severe atmospheric ozone (O3) and particulate matter (PM) pollution. Due to their moderately long lifetimes, O3 and PM can be transported over long distances, cross the boundaries of source regions and contribute to air pollution in other regions. The reported contributions of cross-regional transport (CRT) to O3 and fine PM (PM2.5) concentrations often exceed those of local emissions in the major regions of China, highlighting the important role of CRT in regional air pollution. Therefore, further improvement of air quality in China requires more joint efforts among regions to ensure a proper reduction in emissions while accounting for the influence of CRT. This review summarizes the methodologies employed to assess the influence of CRT on O3 and PM pollution as well as current knowledge of CRT influence in China. Quantifying CRT contributions in proportion to O3 and PM levels and studying detailed CRT processes of O3, PM and precursors can be both based on targeted observations and/or model simulations. Reported publications indicate that CRT contributes by 40-80 % to O3 and by 10-70 % to PM2.5 in various regions of China. These contributions exhibit notable spatiotemporal variations, with differences in meteorological conditions and/or emissions often serving as main drivers of such variations. Based on trajectory-based methods, transport pathways contributing to O3 and PM pollution in major regions of China have been revealed. Recent studies also highlighted the important role of horizontal transport in the middle/high atmospheric boundary layer or low free troposphere, of vertical exchange and mixing as well as of interactions between CRT, local meteorology and chemistry in the detailed CRT processes. Drawing on the current knowledge on the influence of CRT, this paper provides recommendations for future studies that aim at supporting ongoing air pollution mitigation strategies in China.
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Affiliation(s)
- Kun Qu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China; Laboratory for Modeling and Observation of the Earth System (LAMOS), Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany
| | - Yu Yan
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China; Sichuan Academy of Environmental Policy and Planning, Chengdu 610041, China
| | - Xuesong Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China.
| | - Xipeng Jin
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China; Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Mihalis Vrekoussis
- Laboratory for Modeling and Observation of the Earth System (LAMOS), Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany; Center of Marine Environmental Sciences (MARUM), University of Bremen, Bremen, Germany; Climate and Atmosphere Research Center (CARE-C), The Cyprus Institute, Nicosia, Cyprus
| | - Maria Kanakidou
- Laboratory for Modeling and Observation of the Earth System (LAMOS), Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany; Environmental Chemical Processes Laboratory, Department of Chemistry, University of Crete, Heraklion, Greece; Center of Studies of Air quality and Climate Change, Institute for Chemical Engineering Sciences, Foundation for Research and Technology Hellas, Patras, Greece
| | - Guy P Brasseur
- Max Planck Institute for Meteorology, Hamburg, Germany; National Center for Atmospheric Research, Boulder, CO, USA
| | - Tingkun Lin
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China
| | - Teng Xiao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China
| | - Xuhui Cai
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China
| | - Limin Zeng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China
| | - Yuanhang Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China; Beijing Innovation Center for Engineering Science and Advanced Technology, Peking University, Beijing 100871, China; CAS Center for Excellence in Regional Atmospheric Environment, Chinese Academy of Sciences, Xiamen 361021, China.
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3
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Sun J, Yu X, Ling Z, Fang G, Ming L, Zhao J, Zou S, Guan H, Wang H, Wang X, Wang Z, Gao Y, Tham YJ, Guo H, Zhang Y. Roles of photochemical consumption of VOCs on regional background O 3 concentration and atmospheric reactivity over the pearl river estuary, Southern China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 928:172321. [PMID: 38604373 DOI: 10.1016/j.scitotenv.2024.172321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 04/02/2024] [Accepted: 04/06/2024] [Indexed: 04/13/2024]
Abstract
Understanding of the photochemical ozone (O3) pollution over the Pearl River Estuary (PRE) of southern China remains limited. We performed an in-depth analysis of volatile organic compounds (VOCs) data collected on an island (i.e., the Da Wan Shan Island, DWS) located at the downwind of Pearl River Delta (PRD) from 26 November to 15 December 2021. Abundances of O3 and its precursors were measured when the air masses originated from the inland PRD. We observed that the VOCs levels at the DWS site were lower, while the mixing ratio of O3 was higher, compared to those reported at inland PRD, indicating the occurrence of photochemical consumption of VOCs during the air masses transport, which was further confirmed by the composition and diurnal variations of VOCs, as well as ratios of specific VOCs. The simulation results from a photochemical box model showed that the O3 level in the outflow air masses of inland PRD (O3(out-flow)) was the dominant factor leading to the intensification of O3 pollution and the enhancement of atmospheric radical concentrations (ARC) over PRE, which was mainly contributed by the O3 production via photochemical consumption of VOCs during air masses transport. Overall, our findings provided direct quantitative evidence for the roles of outflow O3 and its precursors from inland PRD on O3 abundance and ARC over the PRE area, highlighting that alleviation of O3 pollution over PRE should focus on the impact of photochemical loss of VOCs in the outflow air masses from inland PRD.
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Affiliation(s)
- Jiayin Sun
- School of Atmospheric Sciences, Sun Yat-sen University, Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
| | - Xiaoyu Yu
- School of Atmospheric Sciences, Sun Yat-sen University, Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
| | - Zhenhao Ling
- School of Atmospheric Sciences, Sun Yat-sen University, Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China.
| | - Guizhen Fang
- School of Marine Sciences, Sun Yat-sen University, and Pearl River Estuary Marine Ecosystem Research Station, Ministry of Education, Zhuhai, China
| | - Lili Ming
- Technical Center of Gongbei Customs District of China, Zhuhai, China
| | - Jun Zhao
- School of Atmospheric Sciences, Sun Yat-sen University, Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
| | - Shichun Zou
- School of Marine Sciences, Sun Yat-sen University, and Pearl River Estuary Marine Ecosystem Research Station, Ministry of Education, Zhuhai, China
| | - Huatian Guan
- School of Atmospheric Sciences, Sun Yat-sen University, Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
| | - Haichao Wang
- School of Atmospheric Sciences, Sun Yat-sen University, Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
| | - Xuemei Wang
- College of Environment and Climate, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou, China
| | - Zhe Wang
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Yuan Gao
- Instrumentation and Service Center for Science and Technology, Beijing Normal University, Zhuhai, China
| | - Yee Jun Tham
- School of Marine Sciences, Sun Yat-sen University, and Pearl River Estuary Marine Ecosystem Research Station, Ministry of Education, Zhuhai, China.
| | - Hai Guo
- Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China
| | - Yanli Zhang
- Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, China
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4
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Liu S, Yang S, Chen D, Mao L, Cheng X, Zhou Z, Pei C, Li M, Cheng C. Influence of ozone pollution on the mixing state and formation of oxygenated organics containing single particles. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 926:171880. [PMID: 38531461 DOI: 10.1016/j.scitotenv.2024.171880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 03/17/2024] [Accepted: 03/20/2024] [Indexed: 03/28/2024]
Abstract
The formation and aging processes of oxygenated organic molecules (OOMs) are important for understanding the formation mechanisms of secondary organic aerosols (SOAs) in the field. In this study, we investigated the mixing states of OOM particles by identifying several oxygenated species along with the distributions of secondary organic carbon (SOC) during both clean and ozone (O3)-polluted periods in July and September of 2022 in Guangzhou, China. OOM-containing particles accounted for 57 % and 49 % of the total detected single particles in July and September, respectively. Most of the OOM particles were internally mixed with sulfate and nitrate, while elemental carbon and hydrocarbon species were absent. Despite the higher SOC/OC ratio in September (81 %) than it in July (72 %), comparative investigations of the mass spectra, diurnal patterns, and distributions of OOM particles revealed the same composition and aging states of OOMs in two O3 pollution periods. As the O3 concentration increased from the clean to the polluted periods, the ratio of SOC to OC increased along with the relative abundance of secondary OOM particles among total OOM particles. In contrast, the relative abundance of OC-type OOM particles gradually decreased, indicating the conversion of hydrocarbon species into OOMs as the SOC/OC ratio increased. Both the bulk analysis of SOC from filter measurement and the mixing states of OOM particles suggested that OOM production and degree of oxidation were higher in the O3-polluted periods than in the clean periods. These results elucidate the effects of O3 pollution on the OOM formation process and offer new perspectives for the joint investigation of SOA production based on filter sampling and single-particle measurements.
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Affiliation(s)
- Sulin Liu
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for on-line source apportionment system of air pollution, Jinan University, Guangzhou 510632, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China
| | - Suxia Yang
- Guangzhou Research Institute of Environment Protection Co., Ltd, Guangzhou 510620, China
| | - Duanying Chen
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for on-line source apportionment system of air pollution, Jinan University, Guangzhou 510632, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China
| | - Liyuan Mao
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for on-line source apportionment system of air pollution, Jinan University, Guangzhou 510632, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China
| | - Xiaoya Cheng
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for on-line source apportionment system of air pollution, Jinan University, Guangzhou 510632, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China
| | - Zhen Zhou
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for on-line source apportionment system of air pollution, Jinan University, Guangzhou 510632, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China
| | - Chenglei Pei
- Guangzhou Ecological and Environmental Monitoring Center of Guangdong Province, Guangzhou 510030, China
| | - Mei Li
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for on-line source apportionment system of air pollution, Jinan University, Guangzhou 510632, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China.
| | - Chunlei Cheng
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for on-line source apportionment system of air pollution, Jinan University, Guangzhou 510632, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China; State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, China; State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy Science, Xi'an 710061, China.
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Zheng Y, Jiang F, Feng S, Shen Y, Liu H, Guo H, Lyu X, Jia M, Lou C. Large-scale land-sea interactions extend ozone pollution duration in coastal cities along northern China. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2024; 18:100322. [PMID: 37860828 PMCID: PMC10582397 DOI: 10.1016/j.ese.2023.100322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 09/21/2023] [Accepted: 09/23/2023] [Indexed: 10/21/2023]
Abstract
Land-sea atmosphere interaction (LSAI) is one of the important processes affecting ozone (O3) pollution in coastal areas. The effects of small-scale LSAIs like sea-land breezes have been widely studied. However, it is not fully clear how and to what extent the large-scale LSAIs affect O3 pollution. Here we explored an O3 episode to illuminate the role of large-scale LSAIs in O3 pollution over the Bohai-Yellow Seas and adjacent areas through observations and model simulations. The results show that the northern Bohai Sea's coastal region, influenced by the Mongolian High, initially experienced a typical unimodal diurnal O3 variation for three days, when O3 precursors from Beijing-Tianjin-Hebei, Shandong, and Northeast China were transported to the Bohai-Yellow Seas. Photochemical reactions generated O3 within marine air masses, causing higher O3 levels over the seas than coastal regions. As the Mongolian High shifted eastward and expanded, southerly winds on its western edge transported O3-rich marine air masses toward the coast, prolonging pollution for an additional three days and weakening diurnal variations. Subsequently, emissions from the Korean Peninsula and marine shipping significantly affected O3 levels in the northern Bohai Sea (10.7% and 13.7%, respectively). Notably, Shandong's emissions played a substantial role in both phases (27.5% and 26.1%, respectively). These findings underscore the substantial impact of large-scale LSAIs driven by the Mongolian High on O3 formation and pollution duration in coastal cities. This insight helps understand and manage O3 pollution in northern Bohai Sea cities and broadly applies to temperate coastal cities worldwide.
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Affiliation(s)
- Yanhua Zheng
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing, 210023, China
| | - Fei Jiang
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing, 210023, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China
- Frontiers Science Center for Critical Earth Material Cycling, Nanjing University, Nanjing, 210023, China
| | - Shuzhuang Feng
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing, 210023, China
| | - Yang Shen
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing, 210023, China
| | - Huan Liu
- School of Environment, Tsinghua University, Beijing, 100084, China
| | - Hai Guo
- Air Quality Studies, Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China
| | - Xiaopu Lyu
- Air Quality Studies, Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China
| | - Mengwei Jia
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing, 210023, China
| | - Chenxi Lou
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing, 210023, China
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6
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Chen Z, Liu R, Wu S, Xu J, Wu Y, Qi S. Diurnal variation characteristics and meteorological causes of autumn ozone in the Pearl River Delta, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 908:168469. [PMID: 37967638 DOI: 10.1016/j.scitotenv.2023.168469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/22/2023] [Accepted: 11/08/2023] [Indexed: 11/17/2023]
Abstract
This study investigates the diurnal variation of ozone (O3) in the Pearl River Delta (PRD) during autumn from 2016 to 2021, focusing on the main O3 modes and their relationship with meteorological conditions. Utilizing K-means clustering, four patterns of O3 variation were identified: Cluster 1 (extremely low O3), Cluster 2 (close to autumn average), Cluster 3 (abnormally high O3 at night), and Cluster 4 (extremely high O3). In Cluster 1, the PRD was situated on the northwest side of the western Pacific subtropical high (WPSH), resulting in increased cloud cover, weakened radiation, and the lowest O3 growth rate during the day, with weak nighttime changes. Cluster 2 presents O3 changes under normal autumn conditions, closely resembling the autumn average. In Cluster 3, the PRD was located between continental high pressure and the low-pressure system over the South China Sea. The enhanced horizontal pressure gradient led to an increase in the horizontal wind speed, promoting the formation of a low-level jet (LLJ). The LLJ caused decoupling between the residual layer and stable boundary layer at night, leading to increased surface O3 concentration and a higher background O3 concentration before sunrise the next day. In Cluster 4, favorable meteorological conditions for O3 generation and accumulation were created by the influence of the WPSH and peripheral tropical cyclones. O3 rapidly increased during the day, reaching extremely high values in the afternoon, with an exceedance rate of 80 %. Comparing the four diurnal patterns and their meteorological conditions highlights the significance of meteorological processes in O3 variations.
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Affiliation(s)
- Zichao Chen
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China
| | - Run Liu
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China; Guangdong-Hong Kong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 511443, China.
| | - Shuangshuang Wu
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China
| | - Jianmin Xu
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China
| | - Yanxing Wu
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China
| | - Shumin Qi
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China
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Huang T, Li Y, Lolli S, Cheng JCH, Wang J, Lam DHY, Leung WH, Lee HF, Yim SHL. Influences of approaching tropical cyclones on water vapor and aerosols in the atmospheric boundary layer of Guangdong-Hong Kong-Macau Greater Bay Area of China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 880:163188. [PMID: 37001679 DOI: 10.1016/j.scitotenv.2023.163188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 03/09/2023] [Accepted: 03/27/2023] [Indexed: 05/27/2023]
Abstract
The outer circulation of tropical cyclones (TCs) on the western North Pacific has been reported to substantially influence the atmospheric environment over the Guangdong-Hong Kong-Macau Greater Bay Area (GBA) of China, whereas dynamic evolution and redistribution of water vapor and aerosol in the atmospheric boundary layer (ABL) responding to moving TCs have yet to be understood. This study aims to answer three key research questions related to the influences of the approaching TCs: (1) how do water vapor and aerosol particles over the GBA change during the TC approaching stage? (2) how does the ABL in terms of vertical wind structure respond to the approaching TCs? and (3) how does turbulence influence the vertical profile of aerosol during the approaching stage? Based on an intensive analysis of three-year reanalysis and Doppler LiDAR data, this study identified a dry-polluted time over the GBA when a TC was located at ~1000 km away on South China Sea. Before that, horizontal wind has consistently come from the northeast, creating a favorable condition for weak transboundary air pollution to the GBA. During the dry-polluted time, the highest surface PM2.5 concentration was resulted from the enhanced downdraft and early-stage wind shear, i.e., stronger wind started occurring at upper-level ABL, while the further turbulent mixing induced by wind shear enhancement and updrafts recovery pumped surface pollution upward to the upper level when TCs became closer. Our findings are expected to improve both weather and PM2.5 forecasts under the impacts of approaching TCs.
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Affiliation(s)
- T Huang
- Department of Geography and Resource Management, The Chinese University of Hong Kong, 999077, Hong Kong
| | - Y Li
- Department of Geography and Resource Management, The Chinese University of Hong Kong, 999077, Hong Kong
| | - S Lolli
- CNR-IMAA, Istituto di Metodologie Ambientali, Tito, PZ, Italy; Department of Physics, Kent State University, Florence Campus, 44240 Kent, OH, USA
| | - J C H Cheng
- Department of Geography and Resource Management, The Chinese University of Hong Kong, 999077, Hong Kong
| | - J Wang
- National Institute of Education, Nanyang Technological University, Singapore
| | - D H Y Lam
- Hong Kong Observatory, 999077, Hong Kong
| | - W H Leung
- Hong Kong Observatory, 999077, Hong Kong
| | - H F Lee
- Department of Geography and Resource Management, The Chinese University of Hong Kong, 999077, Hong Kong
| | - S H L Yim
- Asian school of the Environment, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Earth Observatory of Singapore, Nanyang Technological University, Singapore.
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8
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Chen Z, Xie Y, Liu J, Shen L, Cheng X, Han H, Yang M, Shen Y, Zhao T, Hu J. Distinct seasonality in vertical variations of tropospheric ozone over coastal regions of southern China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 874:162423. [PMID: 36858237 DOI: 10.1016/j.scitotenv.2023.162423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 02/18/2023] [Accepted: 02/19/2023] [Indexed: 06/18/2023]
Abstract
The surface ozone pollution is strongly coupled with ozone variations above the ground. Using sufficient airborne ozone profiles during 2012-2018, this study reveals the tropospheric ozone distributions over four cities located in coastal regions of southern China. The 7-year mean tropospheric ozone profiles in the four cities consistently show a double-maxima profile, with a local maximum at 1 km altitude and the other in the middle-to-upper troposphere. Seasonally, springtime ozone is larger than the annual mean throughout the troposphere, while ozone in summer is high in the middle-to-upper troposphere, leading to largest vertical variations among seasons. Ozone in the middle-to-upper troposphere is lower in autumn than in spring and summer. The winter ozone is characterized with a minimum in the lower troposphere, and low values in the middle-to-upper troposphere, leading to least vertical variations among seasons. We untangle the causes for these complicated vertical ozone variations using the GEOS-Chem model. The tropospheric ozone over southern China is partitioned into locally produced ozone, regionally transported native ozone, imported ozone from outside of China (foreign ozone) and natural stratospheric ozone. The results suggest that the springtime ozone abundance is due to the enhanced import of foreign and stratospheric ozone and the intensified regional transport processes of native ozone. In summer, local ozone production is enhanced and regional transport of ozone in the middle-to-upper troposphere is strengthened due to upward air motions, while such transport becomes weaker in autumn leaving low ozone in the middle-to-upper troposphere. In winter, the intensive westerly jets promote foreign and stratospheric ozone again in the middle-to-upper troposphere, but the local ozone production and regional transport are sharply reduced, resulting in low ozone near the surface. This study provides new insights into regional ozone profiles and reveals the significance of vertical ozone variations on surface ozone prevention strategy.
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Affiliation(s)
- Zhixiong Chen
- Key Laboratory for Humid Subtropical Eco-Geographical Processes of the Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, China
| | - Yangcheng Xie
- Key Laboratory for Humid Subtropical Eco-Geographical Processes of the Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, China
| | - Jane Liu
- Key Laboratory for Humid Subtropical Eco-Geographical Processes of the Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, China; Department of Geography and Planning, University of Toronto, Toronto, Ontario, Canada.
| | - Lijuan Shen
- Key Laboratory for Aerosol-Cloud-Precipitation of the China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, China
| | - Xugeng Cheng
- Key Laboratory for Humid Subtropical Eco-Geographical Processes of the Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, China
| | - Han Han
- Key Laboratory for Humid Subtropical Eco-Geographical Processes of the Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, China
| | - Mengmiao Yang
- Key Laboratory for Humid Subtropical Eco-Geographical Processes of the Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, China
| | - Yukun Shen
- Key Laboratory for Humid Subtropical Eco-Geographical Processes of the Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, China
| | - Tianliang Zhao
- Key Laboratory for Aerosol-Cloud-Precipitation of the China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, China
| | - Jun Hu
- Fujian Provincial Key Laboratory of Environmental Engineering, Fujian Academy of Environmental Sciences, Fuzhou, China
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9
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Chen A, Yang J, He Y, Yuan Q, Li Z, Zhu L. High spatiotemporal resolution estimation of AOD from Himawari-8 using an ensemble machine learning gap-filling method. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 857:159673. [PMID: 36288751 DOI: 10.1016/j.scitotenv.2022.159673] [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: 08/02/2022] [Revised: 10/08/2022] [Accepted: 10/19/2022] [Indexed: 06/16/2023]
Abstract
The data incompleteness of aerosol optical depth (AOD) products and their lack of availability in highly urbanized areas limit their great potential of application in air quality research. In this study, we developed an ensemble machine-learning approach that integrated random forest-based Space Interpolation Model (SIM) and deep neural network-based Time Interpolation Model (TIM) to achieve high spatiotemporal resolution dataset of AOD. The spatial interpolation model first filled the spatial gaps in the Level-2 Himawari-8 hourly AOD product in 0.05° (∼5 km) spatial resolution, while the time interpolation model further improved the temporal resolution to 10 min on its basis. A full-coverage AOD dataset of Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD) in 2020 was obtained as a practical implementation. The validation against in-situ AOD observations from AERONET and SONET indicated that this new dataset was satisfactory (R = 0.80), and especially in spring and summer. Overall, our ensemble machine-learning model provided an effective scheme for reconstruction of AOD with high spatiotemporal resolution of 0.05° and 10 min, which may further advance the near-real-time monitoring of air-quality in urban areas.
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Affiliation(s)
- Aoxuan Chen
- School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China
| | - Jin Yang
- School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China
| | - Yan He
- School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China
| | - Qiangqiang Yuan
- School of Geodesy and Geomatics, Wuhan University, Wuhan, Hubei 430079, China; The Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University, Wuhan, Hubei 430079, China
| | - Zhengqiang Li
- State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Liye Zhu
- School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China; Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai 519082, China; Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519082, China.
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10
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Chung CY, Yang J, Yang X, He J. Mathematical modeling in the health risk assessment of air pollution-related disease burden in China: A review. Front Public Health 2022; 10:1060153. [PMID: 36504933 PMCID: PMC9727382 DOI: 10.3389/fpubh.2022.1060153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 11/08/2022] [Indexed: 11/24/2022] Open
Abstract
This review paper covers an overview of air pollution-related disease burden in China and a literature review on the previous studies which have recently adopted a mathematical modeling approach to demonstrate the relative risk (RR) of air pollution-related disease burden. The associations between air pollution and disease burden have been explored in the previous studies. Therefore, it is necessary to quantify the impact of long-term exposure to ambient air pollution by using a suitable mathematical model. The most common way of estimating the health risk attributable to air pollution exposure in a population is by employing a concentration-response function, which is often based on the estimation of a RR model. As most of the regions in China are experiencing rapid urbanization and industrialization, the resulting high ambient air pollution is influencing more residents, which also increases the disease burden in the population. The existing RR models, including the integrated exposure-response (IER) model and the global exposure mortality model (GEMM), are critically reviewed to provide an understanding of the current status of mathematical modeling in the air pollution-related health risk assessment. The performances of different RR models in the mortality estimation of disease are also studied and compared in this paper. Furthermore, the limitations of the existing RR models are pointed out and discussed. Consequently, there is a need to develop a more suitable RR model to accurately estimate the disease burden attributable to air pollution in China, which contributes to one of the key steps in the health risk assessment. By using an updated RR model in the health risk assessment, the estimated mortality risk due to the impacts of environment such as air pollution and seasonal temperature variation could provide a more realistic and reliable information regarding the mortality data of the region, which would help the regional and national policymakers for intensifying their efforts on the improvement of air quality and the management of air pollution-related disease burden.
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Affiliation(s)
- Chee Yap Chung
- Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo, Zhejiang Province, China,*Correspondence: Chee Yap Chung
| | - Jie Yang
- Department of Mathematics, University of Hull, Hull, United Kingdom
| | - Xiaogang Yang
- Department of Mechanical, Materials and Manufacturing Engineering, University of Nottingham Ningbo China, Ningbo, Zhejiang Province, China,Xiaogang Yang
| | - Jun He
- Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo, Zhejiang Province, China
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11
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Zhan C, Xie M. Exploring the link between ozone pollution and stratospheric intrusion under the influence of tropical cyclone Ampil. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 828:154261. [PMID: 35248632 DOI: 10.1016/j.scitotenv.2022.154261] [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: 12/21/2021] [Revised: 02/20/2022] [Accepted: 02/27/2022] [Indexed: 06/14/2023]
Abstract
Tropical cyclones (TCs) are synoptic-scale systems with a diameter of up to 2000 km, and may persist for several days to weeks. They can significantly affect the atmospheric conditions and ozone (O3) concentrations in coastal areas. They also induce stratospheric intrusion (SI, the injection of stratospheric O3 into the troposphere) by changing the height of the tropopause. Focusing on the Yangtze River Delta (YRD) region, a highly urbanized coastal area with severe O3 pollution, we systematically analyze the characteristics of O3 pollution and SIs as well as their connections under the influence of tropical cyclone Ampil. Based on surface observations, 120-h backward trajectories and ERA5 reanalysis meteorological data, the urban O3 pollution affected by tropical cyclone Ampil mainly resulted from active photochemical reactions inside the boundary layer and poor diffusion conditions characterized by weak winds. SIs induced by tropical cyclone Ampil were important for the upper and middle troposphere, where O3 concentration could increase up to 180 ppbv. But they hardly reached the ground over the YRD. Therefore, there was no direct connection between O3 pollution and SIs. However, the location of SIs can predict urban O3 pollution. SIs moved with tropical cyclone Ampil but appeared on the northwest side of Ampil, usually 500-1000 km away from tropical cyclone Ampil center. At 200 hPa, they corresponded to a high-PV (>2 PVU) air mass rich in O3 (>200 ppbv). Below this stratospheric PV signature, urban O3 concentration was usually high.
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Affiliation(s)
- Chenchao Zhan
- School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
| | - Min Xie
- School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China.
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12
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Niu Y, Huang X, Wang H, Wang S, Lin X, Chen Y, Zhu B, Zhu Q, He L. Effects of nighttime heterogeneous reactions on the formation of secondary aerosols and ozone in the Pearl River Delta. CHINESE SCIENCE BULLETIN-CHINESE 2022. [DOI: 10.1360/tb-2021-0638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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13
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Mao J, Yan F, Zheng L, You Y, Wang W, Jia S, Liao W, Wang X, Chen W. Ozone control strategies for local formation- and regional transport-dominant scenarios in a manufacturing city in southern China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 813:151883. [PMID: 34826481 DOI: 10.1016/j.scitotenv.2021.151883] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 10/23/2021] [Accepted: 11/18/2021] [Indexed: 06/13/2023]
Abstract
Given the leveling off of fine particulate matter (PM2.5), ground-level ozone (O3) pollution has become one of the most significant atmospheric pollution issues in the Pearl River Delta (PRD) region in China, especially in the manufacturing city of Dongguan, which faces more severe O3 pollution. The development of strategies to control O3 precursor emissions, including volatile organic compounds (VOCs) and nitrogen oxide (NOx), depends to a large extent on the source region of the O3 pollution. In this study, by combining the Weather Research and Forecasting model coupled with chemistry (WRF-Chem), the Empirical Kinetic Modeling Approach (EKMA), and the Flexible Particle model (FLEXPART), more effective strategies of controlling O3 precursor emissions were identified under two typical types of O3 pollution episodes: local formation (LF)-dominant (8-12 September 2019) and regional transport (RT)-dominant (23-27 October 2017) episodes, distinguished by the WRF-FLEXPART model. During the LF-dominant episode, the EKMA revealed that the O3 formation in Dongguan was in a transitional regime, and the abatement of solvent use-VOCs emissions in the key area of Dongguan was more effective in reducing O3 levels, with an emission reduction benefit 1.7 times that of total VOCs emission sources throughout Dongguan. With respect to the RT-dominant episode, the reduction in VOCs emissions in the local region did not effectively curb O3 pollution, although the photochemical regime of the O3 formation in Dongguan was VOCs-limited. A 50% reduction in NOx emissions in the upwind regions (parts of Guangzhou and Huizhou) effectively decreased the O3 concentration in Dongguan by 17%. The results of this study emphasize the importance of the source region of O3 pollution in the implementation of effective O3 control strategies and provide valuable insights for region-specific precursor emission policy formulation, not only in Dongguan, but also in other regions facing severe O3 pollution.
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Affiliation(s)
- Jingying Mao
- Guangdong-Hong Kong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou 510632, PR China
| | - Fenghua Yan
- Guangdong-Hong Kong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou 510632, PR China
| | - Lianming Zheng
- Guangdong-Hong Kong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou 510632, PR China
| | - Yingchang You
- Guangdong-Hong Kong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou 510632, PR China
| | - Weiwen Wang
- Guangdong-Hong Kong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou 510632, PR China
| | - Shiguo Jia
- School of Atmospheric Sciences, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University, Guangzhou 510275, China
| | - Wenhui Liao
- Guangdong University of Finance, Guangzhou 510521, PR China
| | - Xuemei Wang
- Guangdong-Hong Kong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou 510632, PR China
| | - Weihua Chen
- Guangdong-Hong Kong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou 510632, PR China.
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14
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He G, Deng T, Wu D, Wu C, Huang X, Li Z, Yin C, Zou Y, Song L, Ouyang S, Tao L, Zhang X. Characteristics of boundary layer ozone and its effect on surface ozone concentration in Shenzhen, China: A case study. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 791:148044. [PMID: 34118664 DOI: 10.1016/j.scitotenv.2021.148044] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 05/09/2021] [Accepted: 05/21/2021] [Indexed: 06/12/2023]
Abstract
In late September 2019, the longest and most extensive ozone (O3) pollution process occurred at Pearl River Delta. Base on the observational data, surface-level O3, vertical distribution characteristics boundary layer O3 as well as its effect on surface-level O3 are thoroughly analyzed. The O3 lidar results showed similar vertical O3 profiles both in pollution episodes and clean periods, from which a high O3 concentration layer between 300 and 500 m and a sub-high O3 concentration layer between 1300 and 1700 m (near the top of the mixing layer) can be found. Besides, the downward O3 transport paths from the high/sub-high O3 concentration layers could be observed along with the boundary layer evolution: At nighttime, large amounts of O3 were effectively stored into the residual layer (RL). Due to the upward development of Mixing layer (ML) in early morning, atmospheric vertical mixing carried the O3 inside the RL down to the surface, which led to a rapid increase in the surface-level O3. The sub-high O3 layer began the downward mixing at noon, and became well-mixed after the boundary layer was fully developed in the afternoon, by which the near surface O3 pollution deteriorated again. Further analysis of the heavy O3 pollution episodes show that, the high O3 concentration inside the RL contributed 54% ± 6% of the surface-level O3 at 9:00 LT and the average contribution of O3 in the sub-high concentration layer to the surface-level O3 at 14:00 LT was 26% ± 9%. Based on the quantitative analysis of the observational data, this paper focus to reveal the importance of the contribution of O3 inside the RL and near the top of the ML to the surface O3.
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Affiliation(s)
- Guowen He
- Guangdong Engineering Research Center for Online Atmospheric Pollution Source Appointment Mass Spectrometry System, Institute of Mass Spectrometer and Atmospheric Environment, Jinan University, Guangzhou 510632, China; Institute of Tropical and Marine Meteorology/Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction, China Meteorological Administration, Guangzhou 510640, China
| | - Tao Deng
- Institute of Tropical and Marine Meteorology/Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction, China Meteorological Administration, Guangzhou 510640, China.
| | - Dui Wu
- Guangdong Engineering Research Center for Online Atmospheric Pollution Source Appointment Mass Spectrometry System, Institute of Mass Spectrometer and Atmospheric Environment, Jinan University, Guangzhou 510632, China; Institute of Tropical and Marine Meteorology/Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction, China Meteorological Administration, Guangzhou 510640, China.
| | - Cheng Wu
- Guangdong Engineering Research Center for Online Atmospheric Pollution Source Appointment Mass Spectrometry System, Institute of Mass Spectrometer and Atmospheric Environment, Jinan University, Guangzhou 510632, China
| | - Xiaofeng Huang
- Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Zhenning Li
- Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong 999077, China
| | - Changqin Yin
- Institute of Tropical and Marine Meteorology/Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction, China Meteorological Administration, Guangzhou 510640, China
| | - Yu Zou
- Institute of Tropical and Marine Meteorology/Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction, China Meteorological Administration, Guangzhou 510640, China
| | - Lang Song
- Guangdong Provincial Academy of Environmental Science, Guangzhou 510045, China
| | - Shanshan Ouyang
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China
| | - Liping Tao
- Guangdong Engineering Research Center for Online Atmospheric Pollution Source Appointment Mass Spectrometry System, Institute of Mass Spectrometer and Atmospheric Environment, Jinan University, Guangzhou 510632, China
| | - Xue Zhang
- Guangdong Engineering Research Center for Online Atmospheric Pollution Source Appointment Mass Spectrometry System, Institute of Mass Spectrometer and Atmospheric Environment, Jinan University, Guangzhou 510632, China
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15
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Huang T, Yang Y, O'Connor EJ, Lolli S, Haywood J, Osborne M, Cheng JCH, Guo J, Yim SHL. Influence of a weak typhoon on the vertical distribution of air pollution in Hong Kong: A perspective from a Doppler LiDAR network. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 276:116534. [PMID: 33611194 DOI: 10.1016/j.envpol.2021.116534] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 01/14/2021] [Accepted: 01/15/2021] [Indexed: 06/12/2023]
Abstract
High particulate matter (PM) and ozone (O3) concentration in Hong Kong are frequently observed during the summertime typhoon season. Despite the critical effect of a typhoon on air pollution, contributions of vertical wind profile and cloud movement during transboundary air pollution (TAP) on surface PM and O3 concentration have yet to be fully understood. This work is the first study to apply a network of Doppler light detection and ranging (LiDAR) as well as back trajectory analysis to comprehensively analyze the effect of a weak Typhoon (Danas) occurring during 16-19 July 2019 on different variations in PM and O3 concentration. During the typhoon Danas, three types of surface air pollution with five episodes were identified: (1) low PM and high O3 concentration; (2) co-occurring high PM and O3 concentration and (3) high PM and low O3 concentration. Employing our 3D Real-Time Atmospheric Monitoring System (3DREAMs) along with surface observations, we found the important role of TAP in the increases in surface PM and O3 concentration with significant vertical wind shear that transported air pollutants at upper levels, and strong vertical mixing that brought air pollutants to the ground level. Cloud movement related to typhoon periphery, as well as high solar radiation due to sinking motion and remote transport by continental wind, have an impact on local O3 concentration. For the substantial difference in O3 concentration between two air quality measurement sites, the similar vertical aerosol distributions and wind profiles suggest the comparable TAP contributions at the two sites and thus infer the critical role of local O3 photochemical process in the O3 difference. This work comprehensively reveals the influences of a weak typhoon on variations in PM and O3 during the five episodes, providing important references for air quality monitoring and forecast in regions under the influence of typhoon.
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Affiliation(s)
- Tao Huang
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China
| | - Yuanjian Yang
- School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China; Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong, China
| | - Ewan James O'Connor
- Finnish Meteorological Institute, Helsinki, Finland; Department of Meteorology, University of Reading, Reading, UK
| | - Simone Lolli
- CNR-IMAA, Istituto di Metodologie Ambientali, Tito (PZ), Italy; Department of Physics, Kent State University (Florence Campus), 44240, Kent, OH, USA
| | - Jim Haywood
- Department of Mathematics, College of Engineering, Mathematics and Physical Science, University of Exeter, Exeter, UK; Met Office, Exeter, UK
| | - Martin Osborne
- Department of Mathematics, College of Engineering, Mathematics and Physical Science, University of Exeter, Exeter, UK; Met Office, Exeter, UK
| | - Jack Chin-Ho Cheng
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China
| | - Jianping Guo
- State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
| | - Steve Hung-Lam Yim
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China; Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong, China; Stanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong, Hong Kong, China.
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16
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Yan X, Zang Z, Zhao C, Husi L. Understanding global changes in fine-mode aerosols during 2008-2017 using statistical methods and deep learning approach. ENVIRONMENT INTERNATIONAL 2021; 149:106392. [PMID: 33516989 DOI: 10.1016/j.envint.2021.106392] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 12/26/2020] [Accepted: 01/09/2021] [Indexed: 06/12/2023]
Abstract
Despite their extremely small size, fine-mode aerosols have significant impacts on the environment, climate, and human health. However, current understandings of global changes in fine-mode aerosols are limited. In this study, we employed newly developed satellite retrieval data and an attentive interpretable deep learning model to explore the status, changes, and association factors of the global fine-mode aerosol optical depth (fAOD) and aerosol fine-mode fraction (FMF) from 2008 to 2017. At the global scale, the results show a significant increasing trend in land FMF (2.34 × 10-3/year); however, the FMF over the ocean and the fAOD over land and ocean did not reveal significant trends. Between 2008 and 2017, high levels of both fAOD (>0.30) and FMF (>0.75) were identified over China, southeastern Asia, India, and Africa. Seasonally, global land FMF showed high values in summer (>0.70) and low values in spring (<0.65), while land fAOD was high in summer (>0.15) but low in winter (<0.13). Importantly, Australia and Mexico experienced significant increasing trends in FMF during all four seasons. At the regional scale, a significant decline in fAOD was identified in China, which indicates that government emission controls and reductions have been effective in recent decades. The deep learning model was used to interpret the result and showed that O3 was significantly associated with changes in both the FMF and fAOD. This finding suggests the importance of synergizing the regulations for both O3 and fine particles. Our work comprehensively examined global spatial and seasonal fAOD and FMF changes and provides a holistic understanding of global anthropogenic impacts.
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Affiliation(s)
- Xing Yan
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
| | - Zhou Zang
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
| | - Chuanfeng Zhao
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China.
| | - Letu Husi
- Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences (CAS), DaTun Road No. 20 (North), Beijing 100101, China
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