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Mao YH, Shang Y, Liao H, Cao H, Qu Z, Henze DK. Sensitivities of ozone to its precursors during heavy ozone pollution events in the Yangtze River Delta using the adjoint method. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 925:171585. [PMID: 38462008 DOI: 10.1016/j.scitotenv.2024.171585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 03/06/2024] [Accepted: 03/06/2024] [Indexed: 03/12/2024]
Abstract
Although the concentrations of five basic ambient air pollutants in the Yangtze River Delta (YRD) have been reduced since the implementation of the "Air Pollution Prevention and Control Action Plan" in 2013, the ozone concentrations still increase. In order to explore the causes of ozone pollution in YRD, we use the GEOS-Chem and its adjoint model to study the sensitivities of ozone to its precursor emissions from different source regions and emission sectors during heavy ozone pollution events under typical circulation patterns. The Multi-resolution Emission Inventory for China (MEIC) of Tsinghua University and 0.25° × 0.3125° nested grids are adopted in the model. By using the T-mode principal component analysis (T-PCA), the circulation patterns of heavy ozone pollution days (observed MDA8 O3 concentrations ≥160 μg m-3) in Nanjing located in the center area of YRD from 2013 to 2019 are divided into four types, with the main features of Siberian Low, Lake Balkhash High, Northeast China Low, Yellow Sea High, and southeast wind at the surface. The adjoint results show that the contributions of emissions emitted from Jiangsu and Zhejiang are the largest to heavy ozone pollution in Nanjing. The 10 % reduction of anthropogenic NOx and NMVOCs emissions in Jiangsu, Zhejiang and Shanghai could reduce the ozone concentrations in Nanjing by up to 3.40 μg m-3 and 0.96 μg m-3, respectively. However, the reduction of local NMVOCs emissions has little effect on ozone concentrations in Nanjing, and the reduction of local NOx emissions would even increase ozone pollution. For different emissions sectors, industry emissions account for 31 %-74 % of ozone pollution in Nanjing, followed by transportation emissions (18 %-49 %). This study could provide the scientific basis for forecasting ozone pollution events and formulating accurate strategies of emission reduction.
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Affiliation(s)
- Yu-Hao Mao
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control/Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China; Key Laboratory of Meteorological Disaster, Ministry of Education(KLME)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/International Joint Research Laboratory on Climate and Environment Change (ILCEC), NUIST, Nanjing 210044, China.
| | - Yongjie Shang
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control/Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
| | - Hong Liao
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control/Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China; Key Laboratory of Meteorological Disaster, Ministry of Education(KLME)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/International Joint Research Laboratory on Climate and Environment Change (ILCEC), NUIST, Nanjing 210044, China
| | - Hansen Cao
- Department of Mechanical Engineering, University of Colorado, Boulder, CO 80309, USA
| | - Zhen Qu
- Department of Mechanical Engineering, University of Colorado, Boulder, CO 80309, USA
| | - Daven K Henze
- Department of Mechanical Engineering, University of Colorado, Boulder, CO 80309, USA
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Zhang Z, Wang X, Cheng S, Tang G, Fu Y. Insights into multidimensional transport flux from vertical observation and numerical simulation in two cities in North China. J Environ Sci (China) 2023; 125:831-842. [PMID: 36375965 DOI: 10.1016/j.jes.2021.11.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 11/10/2021] [Accepted: 11/12/2021] [Indexed: 06/16/2023]
Abstract
This study represents the first quantitative evaluation of pollution transport budget within the boundary layer of typical cities in the Beijing-Tianjin-Hebei (BTH) region from the perspective of horizontal and vertical exchanges and further discusses the impact of the atmospheric boundary layer (ABL)-free troposphere (FT) exchange on concentration of fine particulate matter (PM2.5) within the ABL during heavy pollution. From the perspective of the transport flux balance relationship, differences in pollution transport characteristics between the two cities is mainly reflected in the ABL-FT exchange effect. The FT mainly flowed into the ABL in BJ, while in SJZ, the outflow from the ABL to the FT was more intense. Combined with an analysis of vertical wind profile distribution, BJ was found to be more susceptible to the influence of northwest cold high prevailing in winter, while sinking of strong cold air allowed the FT flowing into the ABL influence the vertical exchange over BJ. In addition, we selected a typical pollution event for targeted analysis to understand mechanistic details of the influence of ABL-FT exchange on the pollution event. These results showed that ABL-FT interaction played an important role in PM2.5 concentration within the ABL during heavy pollution. Especially in the early stage of heavy pollution, FT transport contributed as much as 82.74% of PM2.5 within the ABL. These findings are significant for improving our understanding of pollution transport characteristics within the boundary layer and the effect of ABL-FT exchange on air quality.
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Affiliation(s)
- Zhida Zhang
- Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing 100124, China
| | - Xiaoqi Wang
- Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing 100124, China.
| | - Shuiyuan Cheng
- Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing 100124, China
| | - Guiqian Tang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Yibin Fu
- Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
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Hu J, Zhao T, Liu J, Cao L, Wang C, Li Y, Shi C, Tan C, Sun X, Shu Z, Li J. Exploring the ozone pollution over the western Sichuan Basin, Southwest China: The impact of diurnal change in mountain-plains solenoid. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 839:156264. [PMID: 35644388 DOI: 10.1016/j.scitotenv.2022.156264] [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: 04/12/2022] [Revised: 05/22/2022] [Accepted: 05/23/2022] [Indexed: 06/15/2023]
Abstract
The Sichuan Basin (SCB), to the east of the Tibetan Plateau (TP), experiences severe ozone (O3) pollution. Unfavorable atmospheric diffusion conditions are considered the main causes of heavy air pollution over the basin. However, the meteorological impact of thermally driven mountain-plains solenoid (MPS) between the TP and SCB on O3 pollution has not been reported. Here we show the MPS driving the diurnal O3 changes in the atmospheric boundary layer over the SCB based on surface and high-resolution vertical observations, ERA5 reanalysis data, and the WRF-Chem model. The MPS shifts between upslope and easterly flows along the eastern slope of the TP and SCB during the day and downslope westerly flows to the western SCB at night. The daytime MPS flows drive the westward transport of O3-rich air mass in the atmospheric boundary layer from the polluted SCB and accumulate high O3 levels from the western edge of the SCB to the eastern slope of TP, subsequently aggravating O3 pollution in this region. After sunset, the MPS drainage flows carry air containing elevated O3 eastward downslope along the eastern slope of the TP into the nocturnal residual layer, enhancing the O3 concentrations aloft over the western SCB. The high-level O3 in the residual layer is transported downstream by nocturnal prevailing winds and contributes significantly to the next-day surface O3 buildup in the downwind region through daytime vertical mixing (~30 μg m-3 h-1). The present study reveals a transport mechanism driven by the MPS with coupling diurnal changes in the atmospheric boundary layer, which redistributes O3 over the basin and exacerbates O3 pollution along the western edge of the basin. This study has important implications for understanding meteorological drivers on atmospheric environment underlying the complex terrain.
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Affiliation(s)
- Jun Hu
- Fujian Provincial Key Laboratory of Environmental Engineering, Fujian Academy of Environmental Sciences, Fuzhou 350011, China; Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Tianliang Zhao
- Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China.
| | - Jane Liu
- Key Laboratory of Humid Subtropical Eco-Geographical Process, Ministry of Education, College of Geographic Sciences, Fujian Normal University, Fuzhou 350007, China; Department of Geography and Planning, University of Toronto, Toronto, Ontario M5S3G3, Canada
| | - Le Cao
- Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Chenggang Wang
- Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Yueqing Li
- Institute of Plateau Meteorology, China Meteorological Administration, Chengdu 610072, China
| | - Chengchun Shi
- Fujian Provincial Key Laboratory of Environmental Engineering, Fujian Academy of Environmental Sciences, Fuzhou 350011, China
| | - Chenghao Tan
- State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaoyun Sun
- Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Zhuozhi Shu
- Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Juan Li
- State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing 100081, China
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Luo Y, Zhao T, Yang Y, Zong L, Kumar KR, Wang H, Meng K, Zhang L, Lu S, Xin Y. Seasonal changes in the recent decline of combined high PM 2.5 and O 3 pollution and associated chemical and meteorological drivers in the Beijing-Tianjin-Hebei region, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 838:156312. [PMID: 35636546 DOI: 10.1016/j.scitotenv.2022.156312] [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: 02/27/2022] [Revised: 05/25/2022] [Accepted: 05/25/2022] [Indexed: 06/15/2023]
Abstract
China suffers from combined air pollution (CAP) comprising dual high O3 and PM2.5, particularly in the Beijing-Tianjin-Hebei (BTH) region, which is an urban agglomeration in the North China Plain. To characterize the seasonal changes in regional CAP, 82 CAP days were identified during the study period from 2015 to 2019 with the co-occurring pollution of O3 and PM2.5 in the BTH. It is found that CAP seasonality has undergone distinct changes with a declining trend in the interannual variations in CAP over recent years. It is also revealed that the monthly CAP peaks have recently shifted from summer to early spring (March and April), indicating seasonal changes in CAP in the BTH. Furthermore, the of chemical and meteorological roles in CAP changes was investigated using environmental and meteorological observation data. The recent reduction in PM2.5 and O3 concentrations had enhanced O3 production and atmospheric oxidizability, thereby causing increments in secondary PM2.5 proportion. The interaction between O3 and PM2.5 was responsible for changing the CAP of dual high O3 and PM2.5 to the transition/spring season in the context of mitigation of air pollutant emissions. Furthermore, principal component analysis in the T-mode (T-PCA) was applied to identify four synoptic circulation patterns that regulate CAP occurrence. The results show that the CAP occurrence was regulated by the dominant patterns of synoptic circulation in the BTH. Warm temperature and strong downward ultraviolet radiation anomalies were observed in the BTH, indicating the importance of meteorological drivers in O3 photochemical production on the CAP. The frequency of key synoptic circulation patterns during the spring season increased annually, thereby inducing seasonal changes in the atmospheric environment with CAP in the BTH in recent years.
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Affiliation(s)
- Yuehan Luo
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory of Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, China
| | - Tianliang Zhao
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory of Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, China.
| | - Yuanjian Yang
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory of Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, China
| | - Lian Zong
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory of Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, China
| | - Kanike Raghavendra Kumar
- Department of Engineering Physics, College of Engineering, Koneru Lakshmaiah Education Foundation (KLEF), Vaddeswaram 522302, Guntur, Andhra Pradesh, India
| | - Hong Wang
- State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Kai Meng
- Key Laboratory of Meteorology and Ecological Environment of Hebei Province, Hebei Provincial Institute of Meteorological Sciences, Shijiazhuang 050021, China
| | - Lei Zhang
- State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Shuo Lu
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory of Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, China
| | - Yushan Xin
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory of Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, China
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Relationships between Springtime PM2.5, PM10, and O3 Pollution and the Boundary Layer Structure in Beijing, China. SUSTAINABILITY 2022. [DOI: 10.3390/su14159041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Complex pollution with high aerosol and ozone concentrations has recently been occurring in several densely populated cities in China, raising concerns about the influence of meteorological factors, including synoptic circulation and local conditions. In this study, comprehensive analyses on the associations between PM2.5, PM10, and O3 and meteorological conditions were conducted based on observations from radar wind profiler, microwave radiometer, automatic weather station, and air quality monitoring sites in Beijing during the spring of 2019. The results showed that the boundary layer height and temperature inversion were negatively (positively) correlated with PM (O3) concentrations, modulating the degree of air pollution. Five identified synoptic patterns were derived using geopotential height data of the ERA5 reanalysis, among which Type 1, characterised by south-westerly prevailing winds with high pressure to the south, was considered to be associated with severe PM and O3 contamination. This indicates that air pollutants originating from southern regions exert a major influence on Beijing through the transportation effect. In addition, high temperature, relative humidity, and low wind velocity exacerbate pollution. Overall, this study provides significant information for understanding the vital roles played by meteorological elements at both the regional and local scales in regulating air contamination during spring in Beijing.
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Characteristics of PM2.5 and PM10 Spatio-Temporal Distribution and Influencing Meteorological Conditions in Beijing. ATMOSPHERE 2022. [DOI: 10.3390/atmos13071120] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
PM2.5 and PM10 in the atmosphere seriously affect human health and air quality, a situation which has aroused widespread concern. In this paper, we analyze the temporal and spatial distribution of PM2.5 and PM10 concentrations from 2016 to 2021 based on real-time monitoring data. In addition, we also explore the influence of meteorological conditions on pollutants. The results show that PM2.5 and PM10 concentrations are similarly distribution in temporal and spatial from 2016 to 2021, and the average concentrations of both show a decreasing trend. The ratio of PM2.5 to PM10 is decreasing, indicating that the proportion of fine particles is declining. PM2.5 and PM10 concentrations are higher in spring and winter, but lower in summer. Spatially, it shows a gradual shift from the characteristic of “high in the south and low in the north” to a uniform homogenization across districts. The spatial distribution of PM2.5 and PM10 mass concentrations is synchronous by applying empirical orthogonal functions (EOF). The first EOF pattern exhibits a consistent characteristic of high in the southeast and low in the northwest. The second pattern EOF reflects the effect of impairing PM2.5 concentrations in the southeast during the winter of 2016–2018. The PM2.5 and PM10 concentrations are significantly negatively correlated with wind speed and precipitation in both spring and winter. On the other hand, from the perspective of the circulation situation, the southeasterly and weak westerly wind in spring produce convergence resulting in higher particulate matter concentrations in the south than in the north in Beijing. The westerly wind is flatter at 700 hPa geopotential height, which is conducive to the formation of stationary weather. The vertical direction of airflow in spring and winter is dominated by convergence and sinking, indicating the weak dispersion ability of the atmosphere. The reason for the accumulation of particulate matter at the surface is investigated, which is beneficial to provide the theoretical basis for air quality management and pollution control in Beijing.
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Ojha N, Soni M, Kumar M, Gunthe SS, Chen Y, Ansari TU. Mechanisms and Pathways for Coordinated Control of Fine Particulate Matter and Ozone. CURRENT POLLUTION REPORTS 2022; 8:594-604. [PMID: 35991936 PMCID: PMC9376561 DOI: 10.1007/s40726-022-00229-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/25/2022] [Indexed: 05/11/2023]
Abstract
PURPOSE OF REVIEW Fine particulate matter (PM2.5) and ground-level ozone (O3) pose a significant risk to human health. The World Health Organization (WHO) has recently revised healthy thresholds for both pollutants. The formation and evolution of PM2.5 and O3 are however governed by complex physical and multiphase chemical processes, and therefore, it is extremely challenging to mitigate both pollutants simultaneously. Here, we review mechanisms and discuss the science-informed pathways for effective and simultaneous mitigation of PM2.5 and O3. RECENT FINDINGS Global warming has led to a general increase in biogenic emissions, which can enhance the formation of O3 and secondary organic aerosols. Reductions in anthropogenic emissions during the COVID-19 lockdown reduced PM2.5; however, O3 was enhanced in several polluted regions. This was attributed to more intense sunlight due to low aerosol loading and non-linear response of O3 to NO x . Such contrasting physical and chemical interactions hinder the formulation of a clear roadmap for clean air over such regions. SUMMARY Atmospheric chemistry including the role of biogenic emissions, aerosol-radiation interactions, boundary layer, and regional-scale transport are the key aspects that need to be carefully considered in the formulation of mitigation pathways. Therefore, a thorough understanding of the chemical effects of the emission reductions, changes in photolytic rates and boundary layer due to perturbation of solar radiation, and the effect of meteorological/seasonal changes are needed on a regional basis. Statistical emulators and machine learning approaches can aid the cumbersome process of multi-sector multi-species source attribution.
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Affiliation(s)
| | - Meghna Soni
- Physical Research Laboratory, Ahmedabad, India
- Indian Institute of Technology, Gandhinagar, Gujarat, India
| | - Manish Kumar
- Department of Environmental Science, Stockholm University, Stockholm, Sweden
| | - Sachin S. Gunthe
- EWRE Division, Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, India
- Laboratory for Atmospheric and Climate Sciences, Indian Institute of Technology Madras, Chennai, India
| | - Ying Chen
- Laboratory of Atmospheric Chemistry, Paul Scherrer Institut (PSI), Villigen, Switzerland
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Jia W, Zhang X, Wang Y. Assessing the pollutant evolution mechanisms of heavy pollution episodes in the Yangtze-Huaihe valley: A multiscale perspective. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2021; 244:117986. [PMID: 33052190 PMCID: PMC7543740 DOI: 10.1016/j.atmosenv.2020.117986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 09/29/2020] [Accepted: 10/05/2020] [Indexed: 06/11/2023]
Abstract
The Yangtze-Huaihe (YH) region experiences heavy aerosol pollution, characterized by high PM2.5 concentration. To unravel the pollutant evolution mechanism during the heavy pollution episodes (HPEs), this study combined observational data analysis and three-dimensional WRF-Chem simulations. From December 2, 2016 to January 15, 2017, YH region experienced 4 HPEs under the control by synoptic system, normally associated with a transport stage (TS) and a cumulative stage (CS). During the TS, pollutants are transported to the north of YH region through the near-surface, and then transported to the "mountain corridor" through the residual layer (RL) under the influence of prevailing wind. For the RL transport mechanism, the change of pollutant concentration cannot only consider the net flux in the horizontal direction, but also the role of the vertical movement is extremely important and cannot be ignored. By analyzing the mass conservation equation of pollutant, the results show that the advection transport and turbulent diffusion have a synergistic effect on the change of pollutant in the CS of three HPEs. The change of turbulence characteristics also affected by topography. For the "mountain corridors", which is accompanied by variable wind direction and turbulence diffusion is easily affected by wind shear. In addition, the turbulence characteristics are different during the TS and CS, especially the strong stable conditions in the CS at nighttime. The turbulence is intermittent, and the model has insufficient performance for turbulence, which will lead to differences for the simulation of pollutant concentration. In short, as the PM2.5 concentration linearly increases, the friction velocity (turbulent diffusion coefficient) decreases 63% (80%), 61% (78%) and 45% (68%), respectively. Therefore, the change of pollutants is less sensitive to the change of turbulence during the HPEs. The contribution of regional transport (local emissions) reaches 43% (47%), thus we need pay attention to the contribution of each part during the HPEs, which will help us to build a certain foundation for the emission reduction work in the future.
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Affiliation(s)
- Wenxing Jia
- Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
- Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Xiaoye Zhang
- Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
- Center for Excellence in Regional Atmospheric Environment, IUE, Chinese Academy of Sciences, Xiamen, 361021, China
| | - Yaqiang Wang
- Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
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Gao Y, Shan H, Zhang S, Sheng L, Li J, Zhang J, Ma M, Meng H, Luo K, Gao H, Yao X. Characteristics and sources of PM 2.5 with focus on two severe pollution events in a coastal city of Qingdao, China. CHEMOSPHERE 2020; 247:125861. [PMID: 31931317 DOI: 10.1016/j.chemosphere.2020.125861] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 12/31/2019] [Accepted: 01/06/2020] [Indexed: 06/10/2023]
Abstract
In this study, the seasonal mean PM2.5 concentration in Qingdao, a coastal city, during 2014-2018 was first analyzed and the winter, in particular of 2015, showed the highest concentration. To elucidate the sources and control factors of PM2.5, three dimensional model Weather Research and Forecasting (WRF), Community Multiscale Air Quality model (CMAQ), as well as Flexible Particle model (FLEXPART), were used. During December 2015 and January 2016, modeling results showed that the mean contribution to PM2.5 mass concentrations from local emissions in Qingdao was 25%, and the transport from north and west accounted for almost half. Over the two episodically polluted periods (29-31 December 2015; 15-17 January 2016), the local emissions in Qingdao surprisingly contributed to only 18% and 24% to PM2.5 mass concentrations, respectively, indicating the dominant contributions from other regions, such as areas outside Qingdao in Shandong and Beijing-Tianjin-Hebei (BTH). The results show the sources region and contribution may vary remarkably along with the change in the pathways of the air parcel, inferred by the FLEXPART, while the near-surface PM2.5 enhancement is largely caused by downward vertical advection and enhanced aerosol chemistry reactions, accompanied by simultaneous drop in the boundary layer height. This study also reveals that the transport contribution is sensitive to the air parcel trajectories. We, therefore, recommend the efficient emission control based on transport trajectories in short-term air quality improvement in Qingdao.
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Affiliation(s)
- Yang Gao
- Key Laboratory of Marine Environment and Ecology, Ministry of Education/Institute for Advanced Ocean Study, Ocean University of China, Qingdao, 266100, China; Laboratory for Marine Ecology and Environmental Science, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266237, China.
| | - Huayao Shan
- Key Laboratory of Marine Environment and Ecology, Ministry of Education/Institute for Advanced Ocean Study, Ocean University of China, Qingdao, 266100, China
| | - Shaoqing Zhang
- Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266237, China; Key Laboratory of Physical Oceanography, Ministry of Education/Institute for Advanced Ocean Study/Frontiers Science Center for Deep Ocean Multispheres and Earth System (DOMES), Ocean University of China, Qingdao, 266100, China; International Laboratory for High-Resolution Earth System Prediction (iHESP), Qingdao, 266237, China; College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, 266100, China
| | - Lifang Sheng
- College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, 266100, China; Ocean-Atmosphere Interaction and Climate Laboratory, Key Laboratory of Physical Oceanography, Ocean University of China, Qingdao, 266100, China
| | - Jianping Li
- Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266237, China; Key Laboratory of Physical Oceanography, Ministry of Education/Institute for Advanced Ocean Study/Frontiers Science Center for Deep Ocean Multispheres and Earth System (DOMES), Ocean University of China, Qingdao, 266100, China
| | - Junxi Zhang
- State Key Laboratory of Clean Energy, Department of Energy Engineering, Zhejiang University, Hangzhou, Zhejiang, 310027, China
| | - Mingchen Ma
- Key Laboratory of Marine Environment and Ecology, Ministry of Education/Institute for Advanced Ocean Study, Ocean University of China, Qingdao, 266100, China
| | - He Meng
- Qingdao Environmental Monitoring Station, Qingdao, 266003, China
| | - Kun Luo
- State Key Laboratory of Clean Energy, Department of Energy Engineering, Zhejiang University, Hangzhou, Zhejiang, 310027, China
| | - Huiwang Gao
- Key Laboratory of Marine Environment and Ecology, Ministry of Education/Institute for Advanced Ocean Study, Ocean University of China, Qingdao, 266100, China; Laboratory for Marine Ecology and Environmental Science, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266237, China
| | - Xiaohong Yao
- Key Laboratory of Marine Environment and Ecology, Ministry of Education/Institute for Advanced Ocean Study, Ocean University of China, Qingdao, 266100, China; Laboratory for Marine Ecology and Environmental Science, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266237, China.
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