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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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Wang S, Zhu Y, Jang JC, Jiang M, Yue D, Zhong L, Yuan Y, Zhang M, You Z. Modeling assessment of air pollution control measures and COVID-19 pandemic on air quality improvements over Greater Bay Area of China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 926:171951. [PMID: 38537836 DOI: 10.1016/j.scitotenv.2024.171951] [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: 12/19/2023] [Revised: 03/04/2024] [Accepted: 03/23/2024] [Indexed: 04/17/2024]
Abstract
A remarkable progress has been made toward the air quality improvements over the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China from 2017 to 2020. In this study, for the first time, the emission reductions of regional control measures together with the COVID-19 pandemic were considered simultaneously into the development of the GBA's emission inventories for the years of 2017 and 2020. Based on these collective emission inventories, the impacts of control measures, meteorological variations together with temporary COVID-19 lockdowns on the five major air quality index pollutants (SO2, NO2, PM2.5, PM10, and O3, excluding CO) were evaluated using the WRF-CMAQ and SMAT-CE model attainment assessment tool over the GBA region. Our results revealed that control measures in the Pearl River Delta (PRD) region affected significantly the GBA, resulting in pollutant reductions ranging from 48 % to 64 %. In contrast, control measures in Hong Kong and Macao contributed to pollutant reductions up to 10 %. In PRD emission sectors, stationary combustion, on-road, industrial processes and dust sectors stand out as the primary contributors to overall air quality improvements. Moreover, the COVID-19 pandemic during period I (Jan 23-Feb 23) led to a reduction of NO2 concentration by 7.4 %, resulting in a negative contribution (disbenefit) for O3 with an increase by 2.4 %. Our findings highlight the significance of PRD control measures for the air quality improvements over the GBA, emphasizing the necessity of implementing more refined and feasible manageable joint prevention and control policies.
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Affiliation(s)
- Shaoyi Wang
- Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Yun Zhu
- Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China.
| | - Ji-Cheng Jang
- Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Ming Jiang
- Guangdong Ecological and Environmental Monitoring Center, Guangzhou 510308, China
| | - Dingli Yue
- Guangdong Ecological and Environmental Monitoring Center, Guangzhou 510308, China
| | - Liuju Zhong
- Guangdong Polytechnic of Environmental Protection Engineering, Foshan 528216, China
| | - Yingzhi Yuan
- Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Mengmeng Zhang
- Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Zhiqiang You
- Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
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Zhang X, Sun J, Lin W, Xu W, Zhang G, Wu Y, Dai X, Zhao J, Yu D, Xu X. Long-term variations in surface ozone at the Longfengshan Regional Atmosphere Background Station in Northeast China and related influencing factors. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 348:123748. [PMID: 38460592 DOI: 10.1016/j.envpol.2024.123748] [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: 12/13/2023] [Revised: 02/25/2024] [Accepted: 03/06/2024] [Indexed: 03/11/2024]
Abstract
Surface ozone (O3) is a crucial air pollutant that affects air quality, human health, agricultural production, and climate change. Studies on long-term O3 variations and their influencing factors are essential for understanding O3 pollution and its impact. Here, we conducted an analysis of long-term variations in O3 during 2006-2022 at the Longfengshan Regional Atmosphere Background Station (LFS; 44.44°N, 127.36°E, 330.5 m a.s.l.) situated on the northeastern edge of the Northeast China Plains. The maximum daily 8-h average (MDA8) O3 fluctuated substantially, with the annual MDA8 decreasing significantly during 2006-2015 (-0.62 ppb yr-1, p < 0.05), jumping during 2015-2016 and increasing clearly during 2020-2022. Step multiple linear regression models for MDA8 were obtained using meteorological variables, to decompose anthropogenic and meteorological contributions to O3 variations. Anthropogenic activities acted as the primary drivers of the long-term trends of MDA8 O3, contributing 73% of annual MDA8 O3 variability, whereas meteorology played less important roles (27%). Elevated O3 at LFS were primarily associated with airflows originating from the North China Plain, Northeast China Plain, and coastal areas of North China, primarily occurring during the warm months (May-October). Based on satellite products of NO2 and HCHO columns, the O3 photochemical regimes over LFS revealed NOx-limited throughout the period. NO2 increased first, reaching peak in 2011, followed by substantial decrease; while HCHO exhibited significant increase, contributing to decreasing trend in MDA8 O3 during 2006-2015. The plateauing NO2 and decreasing HCHO may contribute to the increase in MDA8 O3 in 2016. Subsequently, both NO2 and HCHO exhibited notable fluctuations, leading to significant changes in O3. The study results fill the gap in the understanding of long-term O3 trends in high-latitude areas in the Northeast China Plain and offer valuable insights for assessing the impact of O3 on crop yields, forest productivity, and climate change.
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Affiliation(s)
- Xiaoyi Zhang
- State Key Laboratory of Severe Weather, Key Laboratory for Atmospheric Chemistry, Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, Beijing, 100081, China; Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai, 200438, China
| | - Jingmin Sun
- Longfengshan Regional Atmosphere Background Station, China Meteorological Administration (CMA), Heilongjiang, 150200, China
| | - Weili Lin
- Key Laboratory of Ecology and Environment in Minority Areas (Minzu University of China), National Ethnic Affairs Commission, Beijing, 100081, China
| | - Wanyun Xu
- State Key Laboratory of Severe Weather, Key Laboratory for Atmospheric Chemistry, Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
| | - Gen Zhang
- State Key Laboratory of Severe Weather, Key Laboratory for Atmospheric Chemistry, Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
| | - Yanling Wu
- Longfengshan Regional Atmosphere Background Station, China Meteorological Administration (CMA), Heilongjiang, 150200, China
| | - Xin Dai
- Longfengshan Regional Atmosphere Background Station, China Meteorological Administration (CMA), Heilongjiang, 150200, China
| | - Jinrong Zhao
- Longfengshan Regional Atmosphere Background Station, China Meteorological Administration (CMA), Heilongjiang, 150200, China
| | - Dajiang Yu
- Longfengshan Regional Atmosphere Background Station, China Meteorological Administration (CMA), Heilongjiang, 150200, China.
| | - Xiaobin Xu
- State Key Laboratory of Severe Weather, Key Laboratory for Atmospheric Chemistry, Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
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Guo R, Shi G, Zhang D, Chen Y, Peng C, Zhai C, Yang F. An observed nocturnal ozone transport event in the Sichuan Basin, Southwestern China. J Environ Sci (China) 2024; 138:10-18. [PMID: 38135378 DOI: 10.1016/j.jes.2023.02.054] [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: 07/01/2022] [Revised: 02/26/2023] [Accepted: 02/27/2023] [Indexed: 12/24/2023]
Abstract
The ozone (O3) pollution in China drew lots of attention in recent years, and the Sichuan Basin (SCB) was one of the regions confronting worsening O3 pollution problem. Many previous studies have shown that regional transport is an important contributor to O3 pollution. However, very few features of the O3 profile during transport have been reported, especially in the border regions between different administrative divisions. In this study, we conducted tethered balloon soundings in SCB during the summer of 2020 and captured a nocturnal O3 transport event during the campaign. Vertically, the O3 transport occurred in the bottom of the residual layer, between 200 and 500 m above ground level. Horizontally, the transport pathway was directed from southeast to northwest based on the analysis of the wind field and air mass trajectories. The effect of transport in the residual layer on the surface O3 concentration was related to the spatial distribution of O3. For cities with high O3 concentrations in the upwind region, the transport process would bring clean air masses and abate pollution. For downwind lightly polluted cities, the transport process would slow down the decreasing or even increase the surface O3 concentration during the night. We provided observational facts on the profile features of a transboundary O3 transport event between two provincial administrative divisions, which implicated the importance of joint prevention and control measures. However, the sounding parameters were limited and the quantitative analysis was preliminary, more integrated, and thorough studies of this topic were called for in the future.
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Affiliation(s)
- Ruyue Guo
- Department of Environmental Science and Engineering, Sichuan University, Chengdu 610065, China; College of Carbon Neutrality Future Technology, Sichuan University, Chengdu 610065, China
| | - Guangming Shi
- College of Carbon Neutrality Future Technology, Sichuan University, Chengdu 610065, China; National Engineering Research Center on Flue Gas Desulfurization, Chengdu 610065, China.
| | - Dan Zhang
- Chongqing Academy of Eco-Environmental Science, Chongqing 401147, China
| | - Yang Chen
- Research Center for Atmospheric Environment, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
| | - Chao Peng
- Chongqing Academy of Eco-Environmental Science, Chongqing 401147, China; Key Laboratory for Urban Atmospheric Environment Integrated Observation & Pollution Prevention and Control of Chongqing, Chongqing 401147, China
| | - Chongzhi Zhai
- Chongqing Academy of Eco-Environmental Science, Chongqing 401147, China; Key Laboratory for Urban Atmospheric Environment Integrated Observation & Pollution Prevention and Control of Chongqing, Chongqing 401147, China
| | - Fumo Yang
- College of Carbon Neutrality Future Technology, Sichuan University, Chengdu 610065, China; National Engineering Research Center on Flue Gas Desulfurization, Chengdu 610065, China
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Ji X, Chen G, Chen J, Xu L, Lin Z, Zhang K, Fan X, Li M, Zhang F, Wang H, Huang Z, Hong Y. Meteorological impacts on the unexpected ozone pollution in coastal cities of China during the unprecedented hot summer of 2022. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 914:170035. [PMID: 38218482 DOI: 10.1016/j.scitotenv.2024.170035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 01/04/2024] [Accepted: 01/07/2024] [Indexed: 01/15/2024]
Abstract
Surface ozone pollution under climate warming has become a serious environmental issue. In the summer of 2022, abnormal warming spread over most of the Northern Hemisphere and resulted in the abnormal increase in O3 concentrations. In this study, we focused on the coastal cities in China and investigated the O3 trends in July during 2015 to 2022. Four regions with different locations and emission levels were selected for comparison. A significant increase of O3 concentration in July 2022 were observed in the southern coastal cities (16.7-22.8 μg m-3) while the opposite characteristics were found in the northern coastal cities (decrease of 0.26-2.18 μg m-3). The results indicated various distribution patterns of the O3 concentrations responded to heat wave across China. The weakening of East Asian summer monsoon, extension of the western Pacific subtropical high, significant warming, stronger solar radiation, lower relative humidity, less rainfall and sinking motion of atmosphere in 2022 were beneficial for O3 generation and accumulation in the southern coastal areas. Meteorological changes in July 2022 could lead to an increase of 15.6 % in O3 concentrations in southern coastal cities compared to that in 2015-2021, based on the analysis of machine learning. Air temperature was the main contributor to high O3 concentrations in the coast of Fujian province, while other coastal cities depended on relative humidity. This study indicated the challenge of O3 pollution control in coastal areas under global warming, especially in extreme heat wave events.
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Affiliation(s)
- Xiaoting Ji
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Fujian Key Laboratory of Atmospheric Ozone Pollution Prevention, Chinese Academy of Sciences, Xiamen 361021, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Gaojie Chen
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Fujian Key Laboratory of Atmospheric Ozone Pollution Prevention, Chinese Academy of Sciences, Xiamen 361021, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jinsheng Chen
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Fujian Key Laboratory of Atmospheric Ozone Pollution Prevention, Chinese Academy of Sciences, Xiamen 361021, China.
| | - Lingling Xu
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Fujian Key Laboratory of Atmospheric Ozone Pollution Prevention, Chinese Academy of Sciences, Xiamen 361021, China
| | - Ziyi Lin
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Fujian Key Laboratory of Atmospheric Ozone Pollution Prevention, Chinese Academy of Sciences, Xiamen 361021, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Keran Zhang
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Fujian Key Laboratory of Atmospheric Ozone Pollution Prevention, Chinese Academy of Sciences, Xiamen 361021, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaolong Fan
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Fujian Key Laboratory of Atmospheric Ozone Pollution Prevention, Chinese Academy of Sciences, Xiamen 361021, China
| | - Mengren Li
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Fujian Key Laboratory of Atmospheric Ozone Pollution Prevention, Chinese Academy of Sciences, Xiamen 361021, China
| | - Fuwang Zhang
- Environmental Monitoring Center of Fujian, Fuzhou 350003, China
| | - Hong Wang
- Fujian Key Laboratory of Severe Weather, Key Laboratory of Straits Severe Weather China Meteorological Administration, Fuzhou 350007, China
| | - Zhi Huang
- Xiamen Institute of Environmental Science, Xiamen, China
| | - Youwei Hong
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Fujian Key Laboratory of Atmospheric Ozone Pollution Prevention, Chinese Academy of Sciences, Xiamen 361021, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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Cheng X, Yu J, Chen L, Sun Y, Zhang H, Gao S, Kong S, Zheng H, Wang H. Influence of pollution control measures on the reduction of black carbon in an urban site of megacity, Tianjin, China based on ground-monitored and MERRA-2 reanalysis data. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169466. [PMID: 38145677 DOI: 10.1016/j.scitotenv.2023.169466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/10/2023] [Accepted: 12/16/2023] [Indexed: 12/27/2023]
Abstract
The concentration of particulate matter (PM) has been reduced significantly with the implementation of air pollution control plans in Tianjin. However, as an important component of PM that can lead to global warming and adverse health effects, the influence of pollution control measures (PCM) on black carbon (BC) has been less studied. In this study, ten years of BC concentration satellite-based reanalysis data were collected from MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, Version 2), and their reliability was verified using ground-monitored BC data. Using the proposed Kolmogorov-Zurbenko and artificial neural network (KZ-ANN) model, the influences of meteorology and emission measures were separated. The results indicated that the overall meteorological conditions were not conducive to BC diffusion, especially in autumn and winter with low temperature, surface solar radiation, boundary layer height, and high atmospheric pressure, all of which increased the BC concentration. This study also found that although a significant reduction in BC emissions was observed in Tianjin (the total emissions of BC in 2020 dropped by 52 % compared with the level in 2013), the change in emission-influenced BC was relatively low (the concentration of emission-influenced BC in 2022 dropped by only 2.39 % compared to that in 2013). The reduction of emission-influenced BC concentration during the air pollution prevention control and action plan (APPC) was higher than the level during of the three-year action plan for winning the blue sky defense war (abbreviated as the Blue Sky Defense War). In addition, the lockdown measures during the Corona Virus Disease 2019 (COVID-19) did not have beneficial effect on the reduction of emission-influenced BC concentration. This phenomenon can be explained by the long-range transport of BC from surrounding areas, which was also proven by the results of the backward trajectory analysis. Therefore, efforts on emissions reduction in Tianjin were diminished. It is necessary to cooperate with the governments in surrounding areas to implement joint BC control measures, especially in autumn and winter.
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Affiliation(s)
- Xin Cheng
- School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin 300387, China
| | - Jie Yu
- School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin 300387, China
| | - Li Chen
- School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin 300387, China
| | - Yanling Sun
- School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin 300387, China
| | - Hui Zhang
- School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin 300387, China
| | - Shuang Gao
- School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin 300387, China.
| | - Shaofei Kong
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430074, China.
| | - Huang Zheng
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430074, China; Research Centre for Complex Air Pollution of Hubei Province, Wuhan 430078, China
| | - Hui Wang
- Tianjin Changhai Environmental Monitoring Service Corporation, Tianjin, China
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Li Y, Wu Z, Ji Y, Chen T, Li H, Gao R, Xue L, Wang Y, Zhao Y, Yang X. Comparison of the ozone formation mechanisms and VOCs apportionment in different ozone pollution episodes in urban Beijing in 2019 and 2020: Insights for ozone pollution control strategies. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 908:168332. [PMID: 37949143 DOI: 10.1016/j.scitotenv.2023.168332] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 11/02/2023] [Accepted: 11/02/2023] [Indexed: 11/12/2023]
Abstract
Ground-level ozone (O3) pollution has been a tough issue in urban areas of China in the past decade. Clarifying the formation mechanisms of O3 and the sources of its precursors is necessary for the effective prevention of O3 pollution. In this study, a comparative analysis of O3 formation mechanisms and VOCs apportionment for five O3 pollution episodes was carried out at two urban sites (CRAES and CGZ) in Beijing in 2019 and 2020 by applying an observation-based modeling approach in order to obtain insights into O3 pollution control strategies. Results indicated that O3 pollution levels were generally more severe in 2019 than in 2020 during the observation periods. O3 formation at the two sites was both VOCs-limited on O3 polluted days and non-O3 polluted days. Stronger atmospheric oxidation capacity and ROx radicals cycling processes were found on O3 polluted days which could accelerate the local production of O3, and local photochemical production dominated the observed O3 concentrations at the two sites even on non-O3 polluted days. Emission reduction of VOCs should be a priority for mitigating O3 pollution, and alkenes and biogenic VOCs was the priority species at the CRAES and CGZ sites, respectively. Additionally, the reduction of oxygenated VOCs should also be important for the ozone control. Gasoline exhaust at the CRAES site, and solvent utilization and fuel evaporation at the CGZ site were main anthropogenic sources of VOCs. Therefore, local control measures should be further strengthened and differentiated control strategies of VOCs in the aspects of area, time, sources and species should be adopted in urban Beijing in the future. Overall, the findings of this study could provide a scientific understanding of the causes of O3 pollution and significant guidelines for formulating O3 control strategies from the perspective of different ozone pollution episodes in urban Beijing.
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Affiliation(s)
- Yunfeng Li
- School of Mechanical Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Zhenhai Wu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yuanyuan Ji
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Tianshu Chen
- Environment Research Institute, Shandong University, Qingdao 266237, China
| | - Hong Li
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
| | - Rui Gao
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
| | - Likun Xue
- Environment Research Institute, Shandong University, Qingdao 266237, China
| | - Yafei Wang
- School of Mechanical Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Yuxi Zhao
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Xin Yang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
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8
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Zou Y, Yan XL, Flores RM, Zhang LY, Yang SP, Fan LY, Deng T, Deng XJ, Ye DQ. Source apportionment and ozone formation mechanism of VOCs considering photochemical loss in Guangzhou, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 903:166191. [PMID: 37567293 DOI: 10.1016/j.scitotenv.2023.166191] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 08/07/2023] [Accepted: 08/08/2023] [Indexed: 08/13/2023]
Abstract
Understanding the sources and impact of volatile organic compounds (VOCs) on ozone formation is challenging when the traditional method does not account for their photochemical loss. In this study, online monitoring of 56 VOCs was carried out in summer and autumn during high ozone pollution episodes. The photochemical age method was used to evaluate the atmospheric chemical loss of VOCs and to analyze the effects on characteristics, sources, and ozone formation of VOC components. The initial concentrations during daytime were 5.12 ppbv and 4.49 ppbv higher than the observed concentrations in the summer and autumn, respectively. The positive matrix factorization (PMF) model identified 5 major emission sources. However, the omission of the chemical loss of VOCs led to underestimating the contributions of sources associated with highly reactive VOC components, such as those produced by biogenic emissions and solvent usage. Conversely it resulted in overestimating the contributions from VOC components with lower chemical activity such as liquefied petroleum gas (LPG) usage, vehicle emissions, and gasoline evaporation. Furthermore, the estimation of ozone formation may be underestimated when the atmospheric photochemical loss is not taken into account. The ozone formation potential (OFP) method and propylene-equivalent concentration method both underestimated ozone formation by 53.24 ppbv and 47.25 ppbc, respectively, in the summer, and by 40.34 ppbv and 26.37 ppbc, respectively, in the autumn. The determination of the ozone formation regime based on VOC chemical loss was more acceptable. In the summer, the ozone formation regime changed from the VOC-limited regime to the VOC-NOx transition regime, while in the autumn, the ozone formation regime changed from the strong VOC-limited regime to the weak VOC-limited regime. To obtain more thorough and precise conclusions, further monitoring and analysis studies will be conducted in the near future on a wider variety of VOC species such as oxygenated VOCs (OVOCs).
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Affiliation(s)
- Y Zou
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, China; Institute of Tropical and Marine Meteorology, China Meteorological Administration (CMA), Guangzhou 510640, China
| | - X L Yan
- State Key Laboratory of Severe Weather & Institute of Tibetan Plateau Meteorology, Chinese Academy of Meteorological Sciences, Beijing, China
| | - R M Flores
- Marmara University, Department of Environmental Engineering, Istanbul, Turkey
| | - L Y Zhang
- Institute of Tropical and Marine Meteorology, China Meteorological Administration (CMA), Guangzhou 510640, China
| | - S P Yang
- Institute of Tropical and Marine Meteorology, China Meteorological Administration (CMA), Guangzhou 510640, China
| | - L Y Fan
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, China
| | - T Deng
- Institute of Tropical and Marine Meteorology, China Meteorological Administration (CMA), Guangzhou 510640, China
| | - X J Deng
- Institute of Tropical and Marine Meteorology, China Meteorological Administration (CMA), Guangzhou 510640, China
| | - D Q Ye
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, China.
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9
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Liu X, Gao H, Zhang X, Zhang Y, Yan J, Niu J, Chen F. Driving Forces of Meteorology and Emission Changes on Surface Ozone in the Huaihe River Basin, China. WATER, AIR, AND SOIL POLLUTION 2023; 234:355. [PMID: 37275321 PMCID: PMC10219803 DOI: 10.1007/s11270-023-06345-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 05/04/2023] [Indexed: 06/07/2023]
Abstract
Surface ozone (O3) pollution in China has become a serious environmental problem in recent years. In the present study, we targeted the HRB, a large region located in China's north-south border zone, to assess the driving forces of meteorology and emission changes on surface ozone. A Kolmogorov-Zurbenko (KZ) filter method was performed on the maximum daily average 8-h (MDA8) concentrations of ozone in the HRB during 2015-2020 to decompose the original time series. The findings demonstrated that the short-term (O3ST), seasonal (O3SN), and long-term components (O3LT) of MDA8 O3 variations accounted for 34.2%, 56.1%, and 2.9% of the total variance, respectively. O3SN has the greatest influence on the daily variation in MDA8 O3, followed by O3ST. In coastal cities, the influence of O3ST was enhanced. The influence of O3SN was stronger in the northwestern HRB. Air temperature is the prevailing variable that influences the photochemical formation of ozone. A clear phase lag (7-34 days) of the baseline component between MDA8 O3 and the atmospheric temperature was found in the HRB. Using multiple linear regression, the effect of temperature on ozone was removed. We estimated that the increase in ozone concentration in the HRB was mainly caused by the emission changes (79.4%), and the meteorological conditions made a small contribution (20.6%). This study suggests that reductions in volatile organic compounds (VOCs) will play an important role in further ozone pollution reduction in the HRB. Supplementary Information The online version contains supplementary material available at 10.1007/s11270-023-06345-1.
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Affiliation(s)
- Xiaoyong Liu
- School of Geographic Sciences, Xinyang Normal University, Xinyang, 464000 China
- Henan Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution, Xinyang Normal University, Xinyang, 464000 China
| | - Hui Gao
- School of Geographic Sciences, Xinyang Normal University, Xinyang, 464000 China
- Henan Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution, Xinyang Normal University, Xinyang, 464000 China
| | - Xiangmin Zhang
- School of Geographic Sciences, Xinyang Normal University, Xinyang, 464000 China
- Henan Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution, Xinyang Normal University, Xinyang, 464000 China
| | - Yidan Zhang
- School of Geographic Sciences, Xinyang Normal University, Xinyang, 464000 China
| | - Junhui Yan
- School of Geographic Sciences, Xinyang Normal University, Xinyang, 464000 China
- Henan Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution, Xinyang Normal University, Xinyang, 464000 China
| | - Jiqiang Niu
- School of Geographic Sciences, Xinyang Normal University, Xinyang, 464000 China
- Henan Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution, Xinyang Normal University, Xinyang, 464000 China
| | - Feiyan Chen
- School of Geographic Sciences, Xinyang Normal University, Xinyang, 464000 China
- Henan Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution, Xinyang Normal University, Xinyang, 464000 China
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10
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Ding J, Dai Q, Fan W, Lu M, Zhang Y, Han S, Feng Y. Impacts of meteorology and precursor emission change on O 3 variation in Tianjin, China from 2015 to 2021. J Environ Sci (China) 2023; 126:506-516. [PMID: 36503777 DOI: 10.1016/j.jes.2022.03.010] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 02/05/2022] [Accepted: 03/03/2022] [Indexed: 06/17/2023]
Abstract
Deterioration of surface ozone (O3) pollution in Northern China over the past few years received much attention. For many cities, it is still under debate whether the trend of surface O3 variation is driven by meteorology or the change in precursors emissions. In this work, a time series decomposition method (Seasonal-Trend decomposition procedure based on Loess (STL)) and random forest (RF) algorithm were utilized to quantify the meteorological impacts on the recorded O3 trend and identify the key meteorological factors affecting O3 pollution in Tianjin, the biggest coastal port city in Northern China. After "removing" the meteorological fluctuations from the observed O3 time series, we found that variation of O3 in Tianjin was largely driven by the changes in precursors emissions. The meteorology was unfavorable for O3 pollution in period of 2015-2016, and turned out to be favorable during 2017-2021. Specifically, meteorology contributed 9.3 µg/m3 O3 (13%) in 2019, together with the increase in precursors emissions, making 2019 to be the worst year of O3 pollution since 2015. Since then, the favorable effects of meteorology on O3 pollution tended to be weaker. Temperature was the most important factor affecting O3 level, followed by air humidity in O3 pollution season. In the midday of summer days, O3 pollution frequently exceeded the standard level (>160 µg/m3) at a combined condition with relative humidity in 40%-50% and temperature > 31°C. Both the temperature and the dryness of the atmosphere need to be subtly considered for summer O3 forecasting.
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Affiliation(s)
- Jing Ding
- Tianjin Environmental Meteorological Center, Qi xiangtai road, Tianjin 300074, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300074, China
| | - Qili Dai
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300074, China.
| | - Wenyan Fan
- Tianjin Environmental Meteorological Center, Qi xiangtai road, Tianjin 300074, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300074, China
| | - Miaomiao Lu
- Tianjin Environmental Meteorological Center, Qi xiangtai road, Tianjin 300074, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300074, China
| | - Yufen Zhang
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300074, China
| | - Suqin Han
- Tianjin Environmental Meteorological Center, Qi xiangtai road, Tianjin 300074, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300074, China.
| | - Yinchang Feng
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300074, China
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11
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Yang L, Hong S, Mu H, Zhou J, He C, Wu Q, Gong X. Ozone exposure and health risks of different age structures in major urban agglomerations in People's Republic of China from 2013 to 2018. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:42152-42164. [PMID: 36645592 DOI: 10.1007/s11356-022-24809-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 12/13/2022] [Indexed: 06/17/2023]
Abstract
High concentration of surface ozone (O3) will cause health risks to people. In order to analyze the spatiotemporal characteristics of O3 and assess O3 exposure and health risks for different age groups in China, we applied multiple methods including standard deviation ellipse, spatial autocorrelation, and exposure-response functions. Results show that O3 concentrations increased in 64.5% of areas in China from 2013 to 2018. The central plain urban agglomeration (CPU), Beijing-Tianjin-Hebei (BTH), and Yangtze River Delta (YRD) witnessed the greatest incremental rates of O3 by 16.7%, 14.3%, and 13.1%. Spatially, the trend of O3 shows a significant positive autocorrelation, and high trend values primarily in central and east China. The proportion of the total population exposed to high O3 (above 160 μg/m3) increased annually. Compared to 2013, the proportion of the young, adult, and old populations exposed to high O3 increased to different extents in 2018 by 26.8%, 29.6%, and 27.2%, respectively. The extent of population exposure risk areas in China expanded in size, particularly in north and east China. The total premature respiratory mortalities attributable to long-term O3 exposure in six urban agglomerations were about 177,000 in 2018 which has increased by 16.4% compared to that in 2013. Among different age groups, old people are more vulnerable to O3 pollution, so we need to strengthen their relevant health protection of them.
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Affiliation(s)
- Lu Yang
- School of Resource and Environmental Science, Wuhan University, Wuhan, 430079, Hubei, China
| | - Song Hong
- School of Resource and Environmental Science, Wuhan University, Wuhan, 430079, Hubei, China.
| | - Hang Mu
- School of Resource and Environmental Science, Wuhan University, Wuhan, 430079, Hubei, China
| | - Jingwei Zhou
- Wageningen Institute for Environment and Climate Research, Wageningen University & Research, 6700 HB, Wageningen, Gelderland, Netherlands
| | - Chao He
- College of Resources and Environment, Yangtze University, Wuhan, 430100, China
| | - Qian Wu
- School of Resource and Environmental Science, Wuhan University, Wuhan, 430079, Hubei, China
| | - Xi Gong
- School of Low Carbon Economics, Hubei University of Economics, Wuhan, 430205, China
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12
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Zhang X, Xu W, Zhang G, Lin W, Zhao H, Ren S, Zhou G, Chen J, Xu X. First long-term surface ozone variations at an agricultural site in the North China Plain: Evolution under changing meteorology and emissions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 860:160520. [PMID: 36442628 DOI: 10.1016/j.scitotenv.2022.160520] [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: 10/01/2022] [Revised: 11/10/2022] [Accepted: 11/22/2022] [Indexed: 06/16/2023]
Abstract
Significant upward trends in surface ozone (O3) have been widely reported in China during recent years, especially during warm seasons in the North China Plain (NCP), exerting adverse environmental effects on human health and agriculture. Quantifying long-term O3 variations and their attributions helps to understand the causes of regional O3 pollution and to formulate according control strategy. In this study, we present long-term trends of O3 in the warm seasons (April-September) during 2006-2019 at an agricultural site in the NCP and investigate the relative contributions of meteorological and anthropogenic factors. Overall, the maximum daily 8-h average (MDA8) O3 exhibited a weak decreasing trend with large interannual variability. < 6 % of the observed trend could be explained by changes in meteorological conditions, while the remaining 94 % was attributed to anthropogenic impacts. However, the interannual variability of warm season MDA8 O3 was driven by both meteorology (36 ± 28 %) and anthropogenic factors (64 ± 27 %). Daily maximum temperature was the most essential factor affecting O3 variations, followed by ultraviolet radiation b (UVB) and boundary layer height (BLH), with rising temperature trends inducing O3 inclines throughout April to August, while UVB mainly influenced O3 during summer months. Under changes in emissions and air quality, warm season O3 production regime gradually shifted from dominantly VOCs-limited during 2006-2015 to NOx-limited afterwards. Relatively steady HCHO and remarkably rising NOx levels resulted in the fast decreasing MDA8 O3 (-2.87 ppb yr-1) during 2006-2012. Rapidly decreasing NOx, flat or slightly increasing HCHO promoted O3 increases during 2012-2015 (9.76 ppb yr-1). While afterwards, slow increases in HCHO and downwards fluctuating NOx led to decreases in MDA8 O3 (-4.97 ppb yr-1). Additionally, continuous warming trends might promote natural emissions of O3 precursors and magnify their impacts on agricultural O3 by inducing high variability, which would require even more anthropogenic reduction to compensate for.
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Affiliation(s)
- Xiaoyi Zhang
- Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai 200433, China; State Key Laboratory of Severe Weather, Key Laboratory for Atmospheric Chemistry, Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Wanyun Xu
- State Key Laboratory of Severe Weather, Key Laboratory for Atmospheric Chemistry, Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, Beijing 100081, China.
| | - Gen Zhang
- State Key Laboratory of Severe Weather, Key Laboratory for Atmospheric Chemistry, Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Weili Lin
- College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
| | - Huarong Zhao
- State Key Laboratory of Severe Weather, Institute of Agricultural Meteorology, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Sanxue Ren
- State Key Laboratory of Severe Weather, Institute of Agricultural Meteorology, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Guangsheng Zhou
- State Key Laboratory of Severe Weather, Institute of Agricultural Meteorology, Chinese Academy of Meteorological Sciences, Beijing 100081, China; Hebei Gucheng Agricultural Meteorology National Observation and Research Station, Baoding 072656, China
| | - Jianmin Chen
- Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai 200433, China
| | - Xiaobin Xu
- State Key Laboratory of Severe Weather, Key Laboratory for Atmospheric Chemistry, Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, Beijing 100081, China.
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13
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Pan W, Gong S, Lu K, Zhang L, Xie S, Liu Y, Ke H, Zhang X, Zhang Y. Multi-scale analysis of the impacts of meteorology and emissions on PM 2.5 and O 3 trends at various regions in China from 2013 to 2020 3. Mechanism assessment of O 3 trends by a model. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 857:159592. [PMID: 36272478 DOI: 10.1016/j.scitotenv.2022.159592] [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: 08/23/2022] [Revised: 10/14/2022] [Accepted: 10/16/2022] [Indexed: 06/16/2023]
Abstract
A multiscale analysis of meteorological trends was carried out to investigate the impacts of the large-scale circulation types as well as the local-scale key weather elements on the complex air pollutants, i.e., PM2.5 and O3 in China. Following accompanying papers on synoptic circulation impact and key weather elements and emission contributions (Gong et al., 2022a; Gong et al., 2022b), an emission-driven Observation-based Box Model (e-OBM) was developed to study the impact mechanisms on O3 trend and quantitatively assess the effects of variation in the emissions control over 2013-2020 for Beijing, Chengdu, Guangzhou and Shanghai. Compared with the original OBM, the e-OBM not only improves the performance to simulate the hourly O3 peak concentration in daytime, but also reasonably reproduces the maximum daily 8-hour average (MDA8) O3 concentrations in the four cities. Based upon the sensitivity experiments, it is found that the meteorology is the dominant driver for the MDA8 O3 trend, contributing from about 32 % to 139 % to the variations. From the mechanistic point of view, the variations of meteorology lead to the enhancement of atmospheric oxidation capacity and the acceleration of O3 production. Further evaluation to the emission changes in four cities shows that the O3-precursors relationships of the four cities have been changed from the VOC-limited regime in 2013 to the transition regime or near-transition regime in 2020. Though the NOx/VOCs ratios have been obviously decreased, the emission reductions up to 2020 were still not enough to mitigate O3 pollution in these cities. It is emphasized in this study that the strengthened control measures with maintaining a certain ratio of NOx and VOCs should be implemented to further curb the increasing trend of O3 in urban areas.
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Affiliation(s)
- Weijun Pan
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Sunling Gong
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China; National Observation and Research Station of Coastal Ecological Environments in Macao, Macao Environmental Research Institute, Macau University of Science and Technology, 999078, Macao.
| | - Keding Lu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China.
| | - Lei Zhang
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Shaodong Xie
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Yuhan Liu
- Department of Nuclear Safety, China Institute of Atomic Energy, Beijing 102413, China
| | - Huabing Ke
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Xiaoling Zhang
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China
| | - Yuanhang Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
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14
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Tong L, Liu Y, Meng Y, Dai X, Huang L, Luo W, Yang M, Pan Y, Zheng J, Xiao H. Surface ozone changes during the COVID-19 outbreak in China: An insight into the pollution characteristics and formation regimes of ozone in the cold season. JOURNAL OF ATMOSPHERIC CHEMISTRY 2022; 80:103-120. [PMID: 36248311 PMCID: PMC9540070 DOI: 10.1007/s10874-022-09443-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 09/20/2022] [Indexed: 06/16/2023]
Abstract
The countrywide lockdown in China during the COVID-19 pandemic provided a natural experiment to study the characteristics of surface ozone (O3). Based on statistical analysis of air quality across China before and during the lockdown, the tempo-spatial variations and site-specific formation regimes of wintertime O3 were analyzed. The results showed that the O3 pollution with concentrations higher than air quality standards could occur widely in winter, which had been aggravated by the emission reduction during the lockdown. On the national scale of China, with the significant decrease (54.03%) in NO2 level from pre-lockdown to COVID-19 lockdown, the maximum daily 8-h average concentration of O3 (MDA8h O3) increased by 39.43% from 49.05 to 64.22 μg/m3. This increase was comprehensively contributed by attenuated NOx suppression and favorable meteorological changes on O3 formation during the lockdown. As to the pollution states of different monitoring stations, surface O3 responded oppositely to the consistent decreased NO2 across China. The O3 levels were found to increase in the northern and central regions, but decrease in the southern region, where the changes in both meteorology (e.g. temperature drops) and precursors (reduced emissions) during the lockdown had diminished local O3 production. The spatial differences in NOx levels generally dictate the site-specific O3 formation regimes in winter, with NOx-titration/VOCs-sensitive regimes being dominant in northern and central China, while VOCs-sensitive/transition regimes being dominant in southern China. These findings highlight the influence of NOx saturation levels on winter O3 formation and the necessity of VOCs emission reductions on O3 pollution controls.
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Affiliation(s)
- Lei Tong
- Center for Excellence in Regional Atmospheric Environment & Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021 China
- Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, Ningbo (Beilun) Zhongke Haixi Industrial Technology Innovation Center, Ningbo, 315800 China
| | - Yu Liu
- Center for Excellence in Regional Atmospheric Environment & Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021 China
- Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, Ningbo (Beilun) Zhongke Haixi Industrial Technology Innovation Center, Ningbo, 315800 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Yang Meng
- Center for Excellence in Regional Atmospheric Environment & Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021 China
- Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, Ningbo (Beilun) Zhongke Haixi Industrial Technology Innovation Center, Ningbo, 315800 China
| | - Xiaorong Dai
- Center for Excellence in Regional Atmospheric Environment & Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021 China
- College of Biological and Environmental Sciences, Zhejiang Wanli University, Ningbo, 315100 China
| | - Leijun Huang
- College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou, 311300 China
| | - Wenxian Luo
- College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou, 311300 China
| | - Mengrong Yang
- Center for Excellence in Regional Atmospheric Environment & Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021 China
- Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, Ningbo (Beilun) Zhongke Haixi Industrial Technology Innovation Center, Ningbo, 315800 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Yong Pan
- Center for Excellence in Regional Atmospheric Environment & Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021 China
- Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, Ningbo (Beilun) Zhongke Haixi Industrial Technology Innovation Center, Ningbo, 315800 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Jie Zheng
- Center for Excellence in Regional Atmospheric Environment & Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021 China
- Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, Ningbo (Beilun) Zhongke Haixi Industrial Technology Innovation Center, Ningbo, 315800 China
| | - Hang Xiao
- Center for Excellence in Regional Atmospheric Environment & Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021 China
- Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, Ningbo (Beilun) Zhongke Haixi Industrial Technology Innovation Center, Ningbo, 315800 China
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15
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Sadeghi B, Ghahremanloo M, Mousavinezhad S, Lops Y, Pouyaei A, Choi Y. Contributions of meteorology to ozone variations: Application of deep learning and the Kolmogorov-Zurbenko filter. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 310:119863. [PMID: 35963387 DOI: 10.1016/j.envpol.2022.119863] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 07/07/2022] [Accepted: 07/23/2022] [Indexed: 06/15/2023]
Abstract
From hourly ozone observations obtained from three regions⸻Houston, Dallas, and West Texas⸻we investigated the contributions of meteorology to changes in surface daily maximum 8-h average (MDA8) ozone from 2000 to 2019. We applied a deep convolutional neural network and Shapely additive explanation (SHAP) to examine the complex underlying nonlinearity between variations of surface ozone and meteorological factors. Results of the models showed that between 2000 and 2019, specific humidity (38% and 27%) and temperature (28% and 37%) contributed the most to ozone formation over the Houston and Dallas metropolitan areas, respectively. On the other hand, the results show that solar radiation (50%) strongly impacted ozone variation over West Texas during this time. Using a combination of the Kolmogorov-Zurbenko (KZ) filter and multiple linear regression, we also evaluated the influence of meteorology on ozone and quantified the contributions of meteorological parameters to trends in surface ozone formation. Our findings showed that in Houston and Dallas, meteorology influenced ozone variations to a large extent. The impacts of meteorology on West Texas, however, showed meteorological factors had fewer influences on ozone variabilities from 2000 to 2019. This study showed that SHAP analysis and the KZ approach can investigate the contributions of the meteorological factors on ozone concentrations and help policymakers enact effective ozone mitigation policies.
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Affiliation(s)
- Bavand Sadeghi
- Department of Earth and Atmospheric Science, University of Houston, Texas, USA
| | - Masoud Ghahremanloo
- Department of Earth and Atmospheric Science, University of Houston, Texas, USA
| | | | - Yannic Lops
- Department of Earth and Atmospheric Science, University of Houston, Texas, USA
| | - Arman Pouyaei
- Department of Earth and Atmospheric Science, University of Houston, Texas, USA
| | - Yunsoo Choi
- Department of Earth and Atmospheric Science, University of Houston, Texas, USA.
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16
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Bi Z, Ye Z, He C, Li Y. Analysis of the meteorological factors affecting the short-term increase in O 3 concentrations in nine global cities during COVID-19. ATMOSPHERIC POLLUTION RESEARCH 2022; 13:101523. [PMID: 35996529 PMCID: PMC9385202 DOI: 10.1016/j.apr.2022.101523] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 07/25/2022] [Accepted: 08/05/2022] [Indexed: 05/15/2023]
Abstract
Surface ozone (O3) is a major air pollutant around the world. This study investigated O3 concentrations in nine cities during the Coronavirus disease 2019 (COVID-19) lockdown phases. A statistical model, named Generalized Additive Model (GAM), was also developed to assess different meteorological factors, estimate daily O3 release during COVID-19 lockdown and determine the relationship between the two. We found that: (1) Daily O3 significantly increased in all selected cities during the COVID-19 lockdown, presenting relative increases from -5.7% (in São Paulo) to 58.9% (in Guangzhou), with respect to the average value for the same period in the previous five years. (2) In the GAM model, the adjusted coefficient of determination (R2) ranged from 0.48 (Sao Paulo) to 0.84 (Rome), and it captured 51-85% of daily O3 variations. (3) Analyzing the expected O3 concentrations during the lockdown, using GAM fed by meteorological data, showed that O3 anomalies were dominantly controlled by meteorology. (4) The relevance of different meteorological variables depended on the cities. The positive O3 anomalies in Beijing, Wuhan, Guangzhou, and Delhi were mostly associated with low relative humidity and elevated maximum temperature. Low wind speed, elevated maximum temperature, and low relative humidity were the leading meteorological factors for O3 anomalies in London, Paris, and Rome. The two other cities had different leading factor combinations.
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Affiliation(s)
- Zhongsong Bi
- College of Architecture and Environment, Sichuan University, Chengdu, 610065, China
- School of Architecture and Civil Engineering, Huangshan University, Huangshan, 245041, China
| | - Zhixiang Ye
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, China
| | - Chao He
- College of Resources and Environment, Yangtze University, Wuhan, 430100, China
| | - Yunzhang Li
- College of Architecture and Environment, Sichuan University, Chengdu, 610065, China
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17
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Min R, Wang F, Wang Y, Song G, Zheng H, Zhang H, Ru X, Song H. Contribution of local and surrounding area anthropogenic emissions to a high ozone episode in Zhengzhou, China. ENVIRONMENTAL RESEARCH 2022; 212:113440. [PMID: 35526583 DOI: 10.1016/j.envres.2022.113440] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 05/03/2022] [Accepted: 05/04/2022] [Indexed: 06/14/2023]
Abstract
This study analyzed an ozone pollution episode that occurred in the summer of 2020 in Zhengzhou, the provincial capital of Henan, China, and quantified the contribution of local and surrounding area anthropogenic emissions to this episode based on the Weather Research and Forecasting with Chemistry (WRF/Chem) model. Simulation results showed that the WRF/Chem model is well suited to simulate the ozone concentrations in this area. In addition, four simulation scenarios (removing the emissions from the northern Zhengzhou, southwestern Zhengzhou, Zhengzhou local and southeastern Zhengzhou) were conducted to explore the specific contributions of local emissions and emissions from surrounding areas within Henan to this ozone pollution episode. We found that contributions from the northern, local, southwestern, and southeastern regions were 6.1%, 5.9%, 1.7%, and 1.5%, respectively. The northern and local emissions of Zhengzhou (only emissions from Zhengzhou) were prominent contributors within the simulation areas. In other words, during this episode, most of the ozone pollution in Zhengzhou appeared to be transported in from regions outside Henan Province.
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Affiliation(s)
- Ruiqi Min
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng, Henan, 475004, China; Institute of Urban Big Data, College of Geography and Environmental Science, Henan University, Kaifeng, Henan, 475004, China
| | - Feng Wang
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng, Henan, 475004, China; Institute of Urban Big Data, College of Geography and Environmental Science, Henan University, Kaifeng, Henan, 475004, China
| | - Yaobin Wang
- Henan Key Laboratory of Integrated Air Pollution Control and Ecological Security, Henan University, Kaifeng, Henan, 475004, China; Institute of Urban Big Data, College of Geography and Environmental Science, Henan University, Kaifeng, Henan, 475004, China
| | - Genxin Song
- Institute of Urban Big Data, College of Geography and Environmental Science, Henan University, Kaifeng, Henan, 475004, China.
| | - Hui Zheng
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng, Henan, 475004, China; Henan Key Laboratory of Integrated Air Pollution Control and Ecological Security, Henan University, Kaifeng, Henan, 475004, China
| | - Haopeng Zhang
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng, Henan, 475004, China; Institute of Urban Big Data, College of Geography and Environmental Science, Henan University, Kaifeng, Henan, 475004, China
| | - Xutong Ru
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng, Henan, 475004, China; Institute of Urban Big Data, College of Geography and Environmental Science, Henan University, Kaifeng, Henan, 475004, China
| | - Hongquan Song
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng, Henan, 475004, China; Henan Key Laboratory of Integrated Air Pollution Control and Ecological Security, Henan University, Kaifeng, Henan, 475004, China.
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18
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Ye F, Rupakheti D, Huang L, T N, Kumar Mk S, Li L, Kt V, Hu J. Integrated process analysis retrieval of changes in ground-level ozone and fine particulate matter during the COVID-19 outbreak in the coastal city of Kannur, India. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 307:119468. [PMID: 35588959 PMCID: PMC9109815 DOI: 10.1016/j.envpol.2022.119468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 04/25/2022] [Accepted: 05/09/2022] [Indexed: 06/15/2023]
Abstract
The Community Multi-Scale Air Quality (CMAQ) model was applied to evaluate the air quality in the coastal city of Kannur, India, during the 2020 COVID-19 lockdown. From the Pre1 (March 1-24, 2020) period to the Lock (March 25-April 19, 2020) and Tri (April 20-May 9, 2020) periods, the Kerala state government gradually imposed a strict lockdown policy. Both the simulations and observations showed a decline in the PM2.5 concentrations and an enhancement in the O3 concentrations during the Lock and Tri periods compared with that in the Pre1 period. Integrated process rate (IPR) analysis was employed to isolate the contributions of the individual atmospheric processes. The results revealed that the vertical transport from the upper layers dominated the surface O3 formation, comprising 89.4%, 83.1%, and 88.9% of the O3 sources during the Pre1, Lock, and Tri periods, respectively. Photochemistry contributed negatively to the O3 concentrations at the surface layer. Compared with the Pre1 period, the O3 enhancement during the Lock period was primarily attributable to the lower negative contribution of photochemistry and the lower O3 removal rate by horizontal transport. During the Tri period, a slower consumption of O3 by gas-phase chemistry and a stronger vertical import from the upper layers to the surface accounted for the increase in O3. Emission and aerosol processes constituted the major positive contributions to the net surface PM2.5, accounting for a total of 48.7%, 38.4%, and 42.5% of PM2.5 sources during the Pre1, Lock, and Tri periods, respectively. The decreases in the PM2.5 concentrations during the Lock and Tri periods were primarily explained by the weaker PM2.5 production from emission and aerosol processes. The increased vertical transport rate of PM2.5 from the surface layer to the upper layers was also a reason for the decrease in the PM2.5 during the Lock periods.
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Affiliation(s)
- Fei Ye
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Dipesh Rupakheti
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Lin Huang
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Nishanth T
- Department of Physics, Sree Krishna College Guruvayur, Kerala, 680102, India
| | - Satheesh Kumar Mk
- Department of Atomic and Molecular Physics, Manipal Academy of Higher Education, Karnataka, 576104, India
| | - Lin Li
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Valsaraj Kt
- Cain Department of Chemical Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Jianlin Hu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
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19
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Li XB, Fan G. Interannual variations, sources, and health impacts of the springtime ozone in Shanghai. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 306:119458. [PMID: 35561793 DOI: 10.1016/j.envpol.2022.119458] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 04/08/2022] [Accepted: 05/08/2022] [Indexed: 05/22/2023]
Abstract
In spring, ozone (O3) pollution frequently occurrs in eastern China, but key drivers remain uncertain. In this study, interannual variations in springtime ozone in Shanghai, China, from 2013 to 2021, were investigated to assess the health impacts and the effectiveness of recent air pollution control measures. A combination of ground-level measurements of regulated air pollutants, lidar observations, and backward trajectories of air masses was used to identify the key drivers for enhancing springtime O3. The results show that external imports of O3 driven by atmospheric circulation are notable sources of springtime surface O3. For example, the downward transport from the free troposphere could contribute to over 50% of surface O3 in the morning. The surface O3 mixing ratios in spring exhibited an upward trend of 0.93 ppb yr-1 (p < 0.05) from 2013 to 2021. The change in meteorological variables, particularly the increase in air temperature, could explain nearly 87% of the springtime O3 upward trend. The change in anthropogenic emissions of precursors only contributed to a small fraction (<13%) of the increase in springtime O3. The cumulative exposure of urban residents to O3 in spring also exhibited a significant upward trend (111 ppb yr-1, p < 0.05). With the rapid increase in surface O3, premature respiratory mortality attributable to O3 exposure has fluctuated at approximately 2933 deaths per year since 2016, even though the total deaths from respiratory diseases have significantly declined. Long-term exposure to high O3 concentrations is a significant contributor to premature respiratory mortality.
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Affiliation(s)
- Xiao-Bing Li
- Institute for Environmental and Climate Research, Jinan University, Guangzhou, 510632, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou, 511443, China.
| | - Guangqiang Fan
- Key Lab of Environmental Optics & Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei, 230031, China
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20
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Gong S, Zhang L, Liu C, Lu S, Pan W, Zhang Y. Multi-scale analysis of the impacts of meteorology and emissions on PM 2.5 and O 3 trends at various regions in China from 2013 to 2020 2. Key weather elements and emissions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 824:153847. [PMID: 35189213 DOI: 10.1016/j.scitotenv.2022.153847] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 01/26/2022] [Accepted: 02/09/2022] [Indexed: 06/14/2023]
Abstract
A multiscale analysis of meteorological trends was carried out to investigate the impacts of the large-scale circulation types as well as the local-scale key weather elements on the complex air pollutants, i.e., PM2.5 and O3 in China. Following an accompanying paper on synoptic circulation impact (Gong et al., 2022), using a multi-linear regression model, the trends of key meteorological elements at local scale, i.e., temperature, relative humidity, solar radiation, PBL height, precipitation and wind speed, are analyzed and correlated with the trends of PM2.5 and O3 levels to identify significantly influencing factors in seven Chinese cities. Furthermore, with additional emission surrogates introduced in the regression model, the impacts on the trends by meteorology and emission were separated and quantified. Results show that the increasing trends of O3 at most Chinese cities were largely attributed to the trends of meteorological elements of temperature and solar radiation, while the trends of PM2.5 are mostly contributed by the emission reduction measures of PM2.5 and its precursors. The meteorology alone can explain approximately 57-80% of the O3 variations and only 20-33% of the PM2.5 variations. With the addition of emission surrogates, this explanation percentage is increased to about 57-82% for O3 but significantly enhanced to 71-83% for PM2.5.
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Affiliation(s)
- Sunling Gong
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China.
| | - Lei Zhang
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China.
| | - Cheng Liu
- University of Science and Technology of China, Hefei, China
| | - Shuhua Lu
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Weijun Pan
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Yuanhang Zhang
- College of Environmental Sciences and Engineering, Peking University, China
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21
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Wen L, Yang C, Liao X, Zhang Y, Chai X, Gao W, Guo S, Bi Y, Tsang SY, Chen ZF, Qi Z, Cai Z. Investigation of PM 2.5 pollution during COVID-19 pandemic in Guangzhou, China. J Environ Sci (China) 2022; 115:443-452. [PMID: 34969472 PMCID: PMC8279957 DOI: 10.1016/j.jes.2021.07.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 06/29/2021] [Accepted: 07/07/2021] [Indexed: 05/27/2023]
Abstract
The COVID-19 pandemic has raised awareness about various environmental issues, including PM2.5 pollution. Here, PM2.5 pollution during the COVID-19 lockdown was traced and analyzed to clarify the sources and factors influencing PM2.5 in Guangzhou, with an emphasis on heavy pollution. The lockdown led to large reductions in industrial and traffic emissions, which significantly reduced PM2.5 concentrations in Guangzhou. Interestingly, the trend of PM2.5 concentrations was not consistent with traffic and industrial emissions, as minimum concentrations were observed in the fourth period (3/01-3/31, 22.45 μg/m3) of the lockdown. However, the concentrations of other gaseous pollutants, e.g., SO2, NO2 and CO, were correlated with industrial and traffic emissions, and the lowest values were noticed in the second period (1/24-2/03) of the lockdown. Meteorological correlation analysis revealed that the decreased PM2.5 concentrations during COVID-19 can be mainly attributed to decreased industrial and traffic emissions rather than meteorological conditions. When meteorological factors were included in the PM2.5 composition and backward trajectory analyses, we found that long-distance transportation and secondary pollution offset the reduction of primary emissions in the second and third stages of the pandemic. Notably, industrial PM2.5 emissions from western, southern and southeastern Guangzhou play an important role in the formation of heavy pollution events. Our results not only verify the importance of controlling traffic and industrial emissions, but also provide targets for further improvements in PM2.5 pollution.
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Affiliation(s)
- Luyao Wen
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Rm 510, Engineering Facility Building No.3, Guangzhou 510006, China
| | - Chun Yang
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Rm 510, Engineering Facility Building No.3, Guangzhou 510006, China
| | - Xiaoliang Liao
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Rm 510, Engineering Facility Building No.3, Guangzhou 510006, China
| | - Yanhao Zhang
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, China
| | - Xuyang Chai
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Rm 510, Engineering Facility Building No.3, Guangzhou 510006, China
| | - Wenjun Gao
- Guangzhou Meteorological Public Service Center, Guangzhou Meteorological Service, Guangzhou 510006, China
| | - Shulin Guo
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Rm 510, Engineering Facility Building No.3, Guangzhou 510006, China
| | - Yinglei Bi
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Rm 510, Engineering Facility Building No.3, Guangzhou 510006, China
| | - Suk-Ying Tsang
- School of Life Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Zhi-Feng Chen
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Rm 510, Engineering Facility Building No.3, Guangzhou 510006, China
| | - Zenghua Qi
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Rm 510, Engineering Facility Building No.3, Guangzhou 510006, China.
| | - Zongwei Cai
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Rm 510, Engineering Facility Building No.3, Guangzhou 510006, China; State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, China.
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22
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Wang X, Yin S, Zhang R, Yuan M, Ying Q. Assessment of summertime O 3 formation and the O 3-NO X-VOC sensitivity in Zhengzhou, China using an observation-based model. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 813:152449. [PMID: 34942256 DOI: 10.1016/j.scitotenv.2021.152449] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 12/07/2021] [Accepted: 12/12/2021] [Indexed: 06/14/2023]
Abstract
Zhengzhou, the provincial capital of Henan province in Central China and a major hub of the country's transportation network, has been suffering from severe summertime ozone (O3) pollution. Simultaneous field measurements of O3 and its precursors, including NOx, CO, HONO, and 106 volatile organic compounds (VOCs), were conducted at an urban site (the municipal environmental monitoring station, MEM) in Zhengzhou in July 2019. The Community Multiscale Air Quality (CMAQ) model, which incorporates the Master Chemical Mechanism (MCMv3.3.1), was modified to work as a 0-D observation-based photochemical box model to assess the sources and sinks of HOx radicals and O3, and the OH reactivity (KOH) and ozone formation potential (OFP) of major VOC groups. In addition, the O3-NOx-VOC sensitivity was evaluated using the relative incremental reactivity (RIR) and O3 formation isopleth techniques. The OH radicals were mainly generated from the propagation reaction of HO2 + NO (91-95%). The daily average mixing ratios of OH and HO2 radicals were significantly higher during high O3 days, reaching as high as 4.8 × 106 and 7.7 × 108 molecules cm-3, respectively. Photochemical O3 formation was mostly due to the conversion of NO to NO2 by HO2 radicals (52-54%), while the NO2 + OH reaction was the main contributor to O3 destruction (70- 76%). Alkenes and aromatics were the main anthropogenic VOC contributors to KOH and OFP. Contributions of biogenic VOCs became much more important on high O3 days, correlating with the increase in temperature and solar radiation. RIR analysis showed that the O3 formation was under the VOC-limited on low O3 days but was in the transition regime during the O3 pollution buildup and persisting days. These results are generally consistent with those based on the O3 formation isopleth. This paper provides important corroborative scientific evidence urgently needed by local governments to formulate O3 pollution control strategies.
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Affiliation(s)
- Xudong Wang
- Research Institute of Environmental Science, College of Chemistry, Zhengzhou University, Zhengzhou 450001, China
| | - Shasha Yin
- Research Institute of Environmental Science, School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China.
| | - Ruiqin Zhang
- Research Institute of Environmental Science, School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China
| | - Minghao Yuan
- Environmental Protection Monitoring Center Station of Zhengzhou, Zhengzhou 450007, China
| | - Qi Ying
- Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77843-3136, USA.
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23
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Wu C, Liu B, Wu D, Yang H, Mao X, Tan J, Liang Y, Sun JY, Xia R, Sun J, He G, Li M, Deng T, Zhou Z, Li YJ. Vertical profiling of black carbon and ozone using a multicopter unmanned aerial vehicle (UAV) in urban Shenzhen of South China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 801:149689. [PMID: 34425446 DOI: 10.1016/j.scitotenv.2021.149689] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 08/11/2021] [Accepted: 08/11/2021] [Indexed: 06/13/2023]
Abstract
Existing studies on vertical profiling of black carbon (BC) and ozone (O3) were mainly conducted in the rural areas, leading to limited knowledge of their vertical distributions in the urban area. To fill this knowledge gap, vertical profiling (0-500 m and 0-900 m, AGL) of BC and O3 was conducted in a highly urbanized area of Shenzhen in subtropical South China using a multicopter unmanned aerial vehicle (UAV) platform. In total 32 flights were conducted from the 10th to 15th, December 2017 (winter campaign) and 42 flights from the 19th to 28th, August 2018 (summer campaign) with 4 time slots per day, including morning, afternoon, evening, and midnight. In general, equivalent BC (eBC) concentration decreased as the height increased with an overall slope of -0.13 μg m-3 per 100 m in the winter campaign and -0.08 μg m-3 per 100 m in the summer campaign. On the contrary, an increase of O3 level with altitude was observed (7.8 ppb per 100 m). Absorption Ångström exponent (AAE) exhibits a slightly increasing trend with height. Seasonality of eBC vertical profiles was observed in morning, afternoon and midnight flights, but not for evening flights. The analysis showed the shape of vertical profiles of eBC and O3 can be affected by planetary boundary layer height (PBLH) and air mass origin. Calculated heating rates due to BC show distinct seasonal variability for morning but not for afternoon, because of the counteracting effects by solar irradiance in the subtropical afternoon and eBC concentration in urban South China influenced by the monsoon climate.
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Affiliation(s)
- Cheng Wu
- 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.
| | - Ben Liu
- Department of Civil and Environmental Engineering and Centre for Regional Oceans, Faculty of Science and Technology, University of Macau, Taipa, Macau
| | - Dui Wu
- 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; Institute of Tropical and Marine Meteorology, CMA, Guangzhou 510080, China
| | - Honglong Yang
- Shenzhen Meteorological Bureau, CMA, Shenzhen 518040, China
| | - Xia Mao
- Shenzhen Meteorological Bureau, CMA, Shenzhen 518040, China
| | - Jian Tan
- 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
| | - Yue Liang
- 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
| | - Jia Yin Sun
- 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
| | - Rui Xia
- 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
| | - Jiaren Sun
- Key Laboratory of urban ecological Environmental Simulation and protection, South China Institute of Environmental Sciences, the Ministry of Ecology and Environment of PRC, Guangzhou 510530, China
| | - Guowen He
- 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
| | - 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
| | - Tao Deng
- Institute of Tropical and Marine Meteorology, CMA, Guangzhou 510080, 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
| | - Yong Jie Li
- Department of Civil and Environmental Engineering and Centre for Regional Oceans, Faculty of Science and Technology, University of Macau, Taipa, Macau.
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24
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Lin C, Lau AKH, Fung JCH, Song Y, Li Y, Tao M, Lu X, Ma J, Lao XQ. Removing the effects of meteorological factors on changes in nitrogen dioxide and ozone concentrations in China from 2013 to 2020. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 793:148575. [PMID: 34175602 DOI: 10.1016/j.scitotenv.2021.148575] [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: 04/20/2021] [Revised: 05/27/2021] [Accepted: 06/16/2021] [Indexed: 06/13/2023]
Abstract
Previous studies on long-term ozone (O3) variations in China have reported inconsistent conclusions on the role of meteorological factors in controlling said variations. In this study, we used an observation-based decomposition model to conduct an up-to-date investigation of the effects of meteorological factors on the variations in nitrogen dioxide (NO2) and O3 concentrations in China in the summer from 2013 to 2020. The variations in NO2 and O3 concentrations after removing the major meteorological effects were then analyzed to improve our understanding of O3 formation regimes. Ground measurements show that both NO2 and O3 concentrations decreased in eastern, central, and southeastern China (e.g., NO2 and O3 concentrations in Wuhan reduced by 4.3 and 6.2 ppb, respectively), which was not anticipated. Analyses of meteorological effects showed that reduced wind strength, decreased temperature, and increased relative humidity significantly reduced O3 concentrations in eastern and central China (e.g., by 10.5 ppb in Wuhan). After removing the major meteorological effects, the O3 trends were reversed in eastern and central China (e.g., increased by 4.9 ppb in Wuhan). The contrasting trends in NO2 and O3 concentrations suggest that their O3 formations were sensitive to volatile organic compounds (VOC-limited regime). In southeastern China, both NO2 and O3 concentrations decreased, implying that the O3 formation regimes changed to mixed sensitive or nitrogen oxide-limited (NOx-limited) regimes. The meteorological effects varied by region and may play a dominant role in controlling the long-term O3 variation. Our results indicate that the attribution of O3 variation to emission control without accounting for meteorological effects can be misleading.
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Affiliation(s)
- Changqing Lin
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Alexis K H Lau
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China; Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, The Hong Kong University of Science and Technology, Hong Kong, China.
| | - Jimmy C H Fung
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, The Hong Kong University of Science and Technology, Hong Kong, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Yushan Song
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China; Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Ying Li
- Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Minghui Tao
- Hubei Key Laboratory of Critical Zone Evolution, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
| | - Xingcheng Lu
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Jun Ma
- Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China
| | - Xiang Qian Lao
- Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
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25
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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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