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Li F, Shi X, Wang S, Wang Z, de Leeuw G, Li Z, Li L, Wang W, Zhang Y, Zhang L. An improved meteorological variables-based aerosol optical depth estimation method by combining a physical mechanism model with a two-stage model. CHEMOSPHERE 2024; 363:142820. [PMID: 38986777 DOI: 10.1016/j.chemosphere.2024.142820] [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: 04/16/2024] [Revised: 07/01/2024] [Accepted: 07/08/2024] [Indexed: 07/12/2024]
Abstract
A two-stage model integrating a spatiotemporal linear mixed effect (STLME) and a geographic weight regression (GWR) model is proposed to improve the meteorological variables-based aerosol optical depth (AOD) retrieval method (Elterman retrieval model-ERM). The proposed model is referred to as the STG-ERM model. The STG-ERM model is applied over the Beijing-Tianjin-Hebei (BTH) region in China for the years 2019 and 2020. The results show that data coverage increased by 39.0% in 2019 and 40.5% in 2020. Cross-validation of the retrieval results versus multi-angle implementation of atmospheric correction (MAIAC) AOD shows the substantial improvement of the STG-ERM model over earlier meteorological models for AOD estimation, with a determination coefficient (R2) of daily AOD of 0.86, root mean squared prediction error (RMSE) and the relative prediction error (RPE) of 0.10 and 36.14% in 2019 and R2 of 0.86, RMSE of 0.12 and RPE of 37.86% in 2020. The fused annual mean AOD indicates strong spatial variation with high value in south plain and low value in northwestern mountainous areas of the BTH region. The overall spatial seasonal mean AOD ranges from 0.441 to 0.586, demonstrating strongly seasonal variation. The coverage of STG-ERM retrieved AOD, as determined in this exercise by leaving out part of the meteorological data, affects the accuracy of fused AOD. The coverage of the meteorological data has smaller impact on the fused AOD in the districts with low annual mean AOD of less than 0.35 than that in the districts with high annual mean AOD of greater than 0.6. If available, continuous daily meteorological data with high spatiotemporal resolution can improve the model performance and the accuracy of fused AOD. The STG-ERM model may serve as a valuable approach to provide data to fill gaps in satellite-retrieved AOD products.
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Affiliation(s)
- Fuxing Li
- State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China; School of Geographical Sciences, Hebei Normal University, Hebei Key Laboratory of Environmental Change and Ecological Construction, Hebei Technology Innovation Center for Remote Sensing Identification of Environmental Change, Shijiazhuang, 050024, China.
| | - Xiaoli Shi
- School of Geographical Sciences, Hebei Normal University, Hebei Key Laboratory of Environmental Change and Ecological Construction, Hebei Technology Innovation Center for Remote Sensing Identification of Environmental Change, Shijiazhuang, 050024, China.
| | - Shiyao Wang
- School of Geographical Sciences, Hebei Normal University, Hebei Key Laboratory of Environmental Change and Ecological Construction, Hebei Technology Innovation Center for Remote Sensing Identification of Environmental Change, Shijiazhuang, 050024, China.
| | - Zhen Wang
- School of Geographical Sciences, Hebei Normal University, Hebei Key Laboratory of Environmental Change and Ecological Construction, Hebei Technology Innovation Center for Remote Sensing Identification of Environmental Change, Shijiazhuang, 050024, China.
| | - Gerrit de Leeuw
- State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China; Royal Netherlands Meteorological Institute (KNMI), R&D Satellite Observations, 3730AE De Bilt, Netherlands.
| | - Zhengqiang Li
- State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China.
| | - Li Li
- State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China.
| | - Wei Wang
- School of Geographical Sciences, Hebei Normal University, Hebei Key Laboratory of Environmental Change and Ecological Construction, Hebei Technology Innovation Center for Remote Sensing Identification of Environmental Change, Shijiazhuang, 050024, China.
| | - Ying Zhang
- State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China.
| | - Luo Zhang
- State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China.
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Tariq S, Nisa A, Ul-Haq Z, Mariam A, Murshed M, Sulaymon ID, Salam MA, Mehmood U. Classification of aerosols using particle linear depolarization ratio (PLDR) over seven urban locations of Asia. CHEMOSPHERE 2024; 350:141119. [PMID: 38195014 DOI: 10.1016/j.chemosphere.2024.141119] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 12/27/2023] [Accepted: 01/03/2024] [Indexed: 01/11/2024]
Abstract
Active lidar remote sensing has been used to obtain detailed and quantitative information about the properties of aerosols. We have analyzed the spatio-temporal classification of aerosols using the parameters of particle linear depolarization ratio and single scattering albedo from Aerosol Robotic Network (AERONET) over seven megacities of Asia namely; Lahore, Karachi, Kanpur, Pune, Beijing, Osaka, and Bandung. We find that pollution aerosols dominate during the winter season in all the megacities. The concentrations, however, vary concerning the locations, i.e., 70-80% pollution aerosols are present over Lahore, 40-50% over Karachi, 90-95% over Kanpur and Pune, 60-70% and over Beijing and Osaka. Pure Dust (PD), Pollution Dominated Mixture (PDM), and Dust Dominated Mixture (DDM) are found to be dominant during spring and summer seasons.This proposes that dust over Asia normally exists as a mixture with pollution aerosols instead of pure form. We also find that black carbon (BC) dominated pollution aerosols.
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Affiliation(s)
- Salman Tariq
- Department of Space Science, University of the Punjab, Lahore, Pakistan; Remote Sensing, GIS and Climatic Research Lab (National Center of GIS and Space Applications), Centre for Remote Sensing, University of the Punjab, Lahore, Pakistan.
| | - Aiman Nisa
- Department of Space Science, University of the Punjab, Lahore, Pakistan; Remote Sensing, GIS and Climatic Research Lab (National Center of GIS and Space Applications), Centre for Remote Sensing, University of the Punjab, Lahore, Pakistan
| | - Zia Ul-Haq
- Department of Space Science, University of the Punjab, Lahore, Pakistan; Remote Sensing, GIS and Climatic Research Lab (National Center of GIS and Space Applications), Centre for Remote Sensing, University of the Punjab, Lahore, Pakistan
| | - Ayesha Mariam
- Remote Sensing, GIS and Climatic Research Lab (National Center of GIS and Space Applications), Centre for Remote Sensing, University of the Punjab, Lahore, Pakistan
| | - Muntasir Murshed
- Department of Economics, School of Business and Economics, North South University, Dhaka, 1229, Bangladesh; Department of Journalism, Media and Communications, Daffodil International University, Dhaka, Bangladesh.
| | - Ishaq Dimeji Sulaymon
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Mohammed Abdus Salam
- Department of Environmental Science and Disaster Management, Noakhali Science and Technology University, Noakhali, 3814, Bangladesh
| | - Usman Mehmood
- Remote Sensing, GIS and Climatic Research Lab (National Center of GIS and Space Applications), Centre for Remote Sensing, University of the Punjab, Lahore, Pakistan; Department of Business Administration, Bahçeşehir Cyprus University, Nicosia, Northern Cyprus, Turkey
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Liang Y, Gui K, Che H, Li L, Zheng Y, Zhang X, Zhang X, Zhang P, Zhang X. Changes in aerosol loading before, during and after the COVID-19 pandemic outbreak in China: Effects of anthropogenic and natural aerosol. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 857:159435. [PMID: 36244490 PMCID: PMC9558773 DOI: 10.1016/j.scitotenv.2022.159435] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 09/22/2022] [Accepted: 10/10/2022] [Indexed: 06/03/2023]
Abstract
Anthropogenic emissions reduced sharply in the short-term during the coronavirus disease pandemic (COVID-19). As COVID-19 is still ongoing, changes in atmospheric aerosol loading over China and the factors of their variations remain unclear. In this study, we used multi-source satellite observations and reanalysis datasets to synergistically analyze the spring (February-May) evolution of aerosol optical depth (AOD) for multiple aerosol types over Eastern China (EC) before, during and after the COVID-19 lockdown period. Regional meteorological effects and the radiative response were also quantitatively assessed. Compared to the same period before COVID-19 (i.e., in 2019), a total decrease of -14.6 % in tropospheric TROPOMI nitrogen dioxide (NO2) and a decrease of -6.8 % in MODIS AOD were observed over EC during the lockdown period (i.e., in 2020). After the lockdown period (i.e., in 2021), anthropogenic emissions returned to previous levels and there was a slight increase (+2.3 %) in AOD over EC. Moreover, changes in aerosol loading have spatial differences. AOD decreased significantly in the North China Plain (-14.0 %, NCP) and Yangtze River Delta (-9.4 %) regions, where anthropogenic aerosol dominated the aerosol loading. Impacted by strong wildfires in Southeast Asia during the lockdown period, carbonaceous AOD increased by +9.1 % in South China, which partially offset the emission reductions. Extreme dust storms swept through the northern region in the period after COVID-19, with an increase of +23.5 % in NCP and + 42.9 % in Northeast China (NEC) for dust AOD. However, unfavorable meteorological conditions overwhelmed the benefits of emission reductions, resulting in a +20.1 % increase in AOD in NEC during the lockdown period. Furthermore, the downward shortwave radiative flux showed a positive anomaly due to the reduced aerosol loading in the atmosphere during the lockdown period. This study highlights that we can benefit from short-term controls for the improvement of air pollution, but we also need to seriously considered the cross-regional transport of natural aerosol and meteorological drivers.
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Affiliation(s)
- Yuanxin Liang
- State Key Laboratory of Severe Weather, Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China; Department of Atmospheric and Oceanic Sciences, Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China
| | - Ke Gui
- State Key Laboratory of Severe Weather, Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Huizheng Che
- State Key Laboratory of Severe Weather, Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China.
| | - Lei Li
- State Key Laboratory of Severe Weather, Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Yu Zheng
- State Key Laboratory of Severe Weather, Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Xutao Zhang
- State Key Laboratory of Severe Weather, Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Xindan Zhang
- State Key Laboratory of Severe Weather, Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China; Department of Atmospheric and Oceanic Sciences, Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China
| | - Peng Zhang
- Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites (LRCVES), FengYun Meteorological Satellite Innovation Center (FY-MSIC), National Satellite Meteorological Center, Beijing 100081, China
| | - Xiaoye Zhang
- State Key Laboratory of Severe Weather, Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
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Zhao H, Gui K, Ma Y, Wang Y, Wang Y, Wang H, Zheng Y, Li L, Zhang L, Che H, Zhang X. Climatology and trends of aerosol optical depth with different particle size and shape in northeast China from 2001 to 2018. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 763:142979. [PMID: 33498120 DOI: 10.1016/j.scitotenv.2020.142979] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 09/17/2020] [Accepted: 10/05/2020] [Indexed: 06/12/2023]
Abstract
Aerosol generated from the economic development and extensive urbanization in northeast China (NEC) could influence aerosol optical properties and affect the regional air quality. The level 3 aerosol optical depth (AOD) of different particle size and shape (spherical or nonspherical) obtained by Multiangle Imaging Spectroradiometer (MISR) version 23 were used to estimate their seasonal, annual, and decadal distribution and contribution in NEC from 2001 to 2018. The highest AOD of approximately 0.3 was found in the central Liaoning urban agglomeration, and the lowest AOD occurred in the mountainous area of NEC; the proportion of spherical AOD in NEC region was more than 90%. The contribution of large AOD was higher in spring, ranging from 28.8% to 29.8%. In spring and summer, small and medium AODs were concentrated in central Liaoning (approximately 0.2-0.3 and 0.06-0.08, respectively). The annual variation in the AOD of different particle size was significantly higher in Liaoning than in Jilin and Heilongjiang. The annual proportions of small and spherical AODs were approximately 60% and 90%, respectively. The annual occurrence of clean conditions with AOD < 0.05 was most common in northern Heilongjiang (approximately 20%). In NEC, the annual occurrence frequencies of 0.05 < AOD < 0.15 and AOD > 0.6 were the highest (approximately 50%) and the lowest (less than 1%), respectively. Interdecadal AOD revealed a positive trend from 2001 to 2008 and a negative trend from 2009 to 2018. The frequency of occurrence trend at different AOD levels also changed from positive to negative between these two periods. The findings in this study are based on the first aerosol retrieval of the newly released MISR in NEC. The results provide a comprehensive understanding of the regional and climatological aerosol extinction with different AOD of size and shape as well as various level bins in NEC.
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Affiliation(s)
- Hujia Zhao
- Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110166, China.
| | - Ke Gui
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory for Atmospheric Chemistry (LAC), Institute of Atmospheric Composition and Environmental Meteorology, Chinese Academy of Meteorological Sciences (CAMS), CMA, Beijing 100081, China
| | - Yanjun Ma
- Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110166, China
| | - Yangfeng Wang
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory for Atmospheric Chemistry (LAC), Institute of Atmospheric Composition and Environmental Meteorology, Chinese Academy of Meteorological Sciences (CAMS), CMA, Beijing 100081, China
| | - Yaqiang Wang
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory for Atmospheric Chemistry (LAC), Institute of Atmospheric Composition and Environmental Meteorology, Chinese Academy of Meteorological Sciences (CAMS), CMA, Beijing 100081, China
| | - Hong Wang
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory for Atmospheric Chemistry (LAC), Institute of Atmospheric Composition and Environmental Meteorology, Chinese Academy of Meteorological Sciences (CAMS), CMA, Beijing 100081, China
| | - Yu Zheng
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory for Atmospheric Chemistry (LAC), Institute of Atmospheric Composition and Environmental Meteorology, Chinese Academy of Meteorological Sciences (CAMS), CMA, Beijing 100081, China
| | - Lei Li
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory for Atmospheric Chemistry (LAC), Institute of Atmospheric Composition and Environmental Meteorology, Chinese Academy of Meteorological Sciences (CAMS), CMA, Beijing 100081, China
| | - Lei Zhang
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory for Atmospheric Chemistry (LAC), Institute of Atmospheric Composition and Environmental Meteorology, Chinese Academy of Meteorological Sciences (CAMS), CMA, Beijing 100081, China
| | - Huizheng Che
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory for Atmospheric Chemistry (LAC), Institute of Atmospheric Composition and Environmental Meteorology, Chinese Academy of Meteorological Sciences (CAMS), CMA, Beijing 100081, China
| | - Xiaoye Zhang
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory for Atmospheric Chemistry (LAC), Institute of Atmospheric Composition and Environmental Meteorology, Chinese Academy of Meteorological Sciences (CAMS), CMA, Beijing 100081, China
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Xia X, Che H, Shi H, Chen H, Zhang X, Wang P, Goloub P, Holben B. Advances in sunphotometer-measured aerosol optical properties and related topics in China: Impetus and perspectives. ATMOSPHERIC RESEARCH 2021; 249:105286. [PMID: 33012934 PMCID: PMC7518977 DOI: 10.1016/j.atmosres.2020.105286] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Revised: 09/21/2020] [Accepted: 09/21/2020] [Indexed: 06/02/2023]
Abstract
Aerosol is a critical trace component of the atmosphere. Many processes in the Earth's climate system are intimately related to aerosols via their direct and indirect radiative effects. Aerosol effects are not limited to these climatic aspects, however. They are also closely related to human health, photosynthesis, new energy, etc., which makes aerosol a central focus in many research fields. A fundamental requirement for improving our understanding of the diverse aerosol effects is to accumulate high-quality aerosol data by various measurement techniques. Sunphotometer remote sensing is one of the techniques that has been playing an increasingly important role in characterizing aerosols across the world. Much progress has been made on this aspect in China during the past decade, which is the work reviewed in this paper. Three sunphotometer networks have been established to provide high-quality observations of long-term aerosol optical properties across the country. Using this valuable dataset, our understanding of spatiotemporal variability and long-term trends of aerosol optical properties has been much improved. The radiative effects of aerosols both at the bottom and at the top of the atmosphere are comprehensively assessed. Substantial warming of the atmosphere by aerosol absorption is revealed. The long-range transport of dust from the Taklimakan Desert in Northwest China and anthropogenic aerosols from South Asia to the Tibetan Plateau is characterized based on ground-based and satellite remote sensing as well as model simulations. Effective methods to estimate chemical compositions from sunphotometer aerosol products are developed. Dozens of satellite and model aerosol products are validated, shedding new light on how to improve these products. These advances improve our understanding of the critical role played by aerosols in both the climate and environment. Finally, a perspective on future research is presented.
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Affiliation(s)
- Xiangao Xia
- LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Huizheng Che
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China
| | - Hongrong Shi
- LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Hongbin Chen
- LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaoye Zhang
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China
| | - Pucai Wang
- LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Phillipe Goloub
- Univ. Lille, CNRS, UMR 8518 - LOA - Laboratoire d'Optique Atmosphérique, F-59000 Lille, France
| | - Brent Holben
- Biospheric Sciences Branch, Code 923, NASA/Goddard Space Flight Center, Greenbelt, MD, USA
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Zhao H, Che H, Zhang L, Gui K, Ma Y, Wang Y, Wang H, Zheng Y, Zhang X. How aerosol transport from the North China plain contributes to air quality in northeast China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 738:139555. [PMID: 32534280 DOI: 10.1016/j.scitotenv.2020.139555] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 05/17/2020] [Accepted: 05/17/2020] [Indexed: 06/11/2023]
Abstract
Northeast China (NEC) has unique climate characteristics and emission sources; continued urbanisation has aggravated regional pollution. The in situ observation data concerning PM2.5, visibility, surface meteorological elements and synchronous aerosol vertical extinction profiles obtained from ground-based Lidar were investigated to better understand local and regional particulate pollution in NEC. The WRF (3.7.1)/CAMx (6.40) model was employed to quantitative investigate the contribution of regional transport to PM2.5 in Shenyang. The results suggested that PM2.5 increased significantly from 9 to 14 January over NEC and the Northern China (NC), with monthly PM2.5 highest in Shijiazhuang and Baoding of NC about 145.2 ± 88.9 and 136.8 ± 83.1 μg m-3, respectively. The distribution of SO2 and NO2 for PM2.5 implied SO2 was more influence on PM2.5 in NEC, while NO2 has larger impact on PM2.5 in NC. The significant increasing of relative humidity (RH) and temperatures exhibited in the pollution indicate water vapor and warm air flow during the transport. The development of the southwest airflow was conducive to pollutant transport across the Beijing-Tianjin-Hebei (or Jing-Jin-Ji) megalopolis to NEC, and together with the local emissions in NEC to affect air quality. The modelling results pointed out that contribution of regional transport to PM2.5 in Shenyang was about 80.12% at 00:00 LT in 10 January, of which the contribution of BTH was about 61.52%; the total regional contribution to PM2.5 in Shenyang reaching 60.70% at 02:00 LT on 13 January including 34.56% contributed by BTH region. Aerosol vertical extinction indicated the particle layer appeared in the near-surface and in the upper atmospheric layer from 0.5 to 1.0 km following the development of transport event. The findings of this study can facilitate a comprehensive understanding of the local and regional air pollution in NEC and helpful for national environment pollution controls and improvement.
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Affiliation(s)
- Hujia Zhao
- Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110016, China; State Key Laboratory of Severe Weather (LASW) and Key Laboratory for Atmospheric Chemistry, Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China
| | - Huizheng Che
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory for Atmospheric Chemistry, Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China.
| | - Lei Zhang
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory for Atmospheric Chemistry, Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China
| | - Ke Gui
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory for Atmospheric Chemistry, Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China
| | - Yanjun Ma
- Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110016, China
| | - Yaqiang Wang
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory for Atmospheric Chemistry, Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China
| | - Hong Wang
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory for Atmospheric Chemistry, Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China
| | - Yu Zheng
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory for Atmospheric Chemistry, Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China
| | - Xiaoye Zhang
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory for Atmospheric Chemistry, Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China
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Zheng Y, Che H, Xia X, Wang Y, Yang L, Chen J, Wang H, Zhao H, Li L, Zhang L, Gui K, Yang X, Liang Y, Zhang X. Aerosol optical properties and its type classification based on multiyear joint observation campaign in north China plain megalopolis. CHEMOSPHERE 2020; 273:128560. [PMID: 34756345 DOI: 10.1016/j.chemosphere.2020.128560] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 09/22/2020] [Accepted: 10/04/2020] [Indexed: 06/13/2023]
Abstract
Since haze and other air pollution are frequently seen in the North China Plain (NCP), detail information on aerosol optical and radiative properties and its type classification is demanded for the study of regional environmental pollution. Here, a multiyear ground-based synchronous sun photometer observation at seven sites on North China Plain megalopolis from 2013 to 2018 was conducted. First, the annual and seasonal variation of these characteristics as well as the intercomparsion were analyzed. Then the potential relationships between these properties with meteorological factors and the aerosol type classification were discussed. The results show: Particle volume exhibited a decreasing trend from the urban downtown to suburban and the rural region. The annual average aerosol optical depth at 440 nm (AOD440) varied from ∼0.43 to 0.86 over the NCP. Annual average single-scattering albedo at 440 nm (SSA440) varied from ∼0.89 to 0.93, indicating a moderate to slight absorption capacity. Average absorption aerosol optical depth at 440 nm (AAOD440) varied from ∼0.07 to 0.10. The absorption Ångström exponent (AAE) (∼0.89-1.40) indicated the multi-types of absorptive matters originated form nature and anthropogenic emission. The discussion of aerosol composition showed a smaller particle size of aerosol from biomass burning and/or fossil foil consumption with enhanced aerosol scattering and enlarged light extinction. Aerosol classification indicated a large percentage of mixed absorbing aerosol (∼20%-49%), which showed increasing trend between relative humidity (RH) with aerosol scattering and dust was an important environmental pollutant compared to southern China.
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Affiliation(s)
- Yu Zheng
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing, 100081, China
| | - Huizheng Che
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing, 100081, China.
| | - Xiangao Xia
- Laboratory for Middle Atmosphere and Global Environment Observation (LAGEO), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China; School of Geoscience, University of Chinese Academy of Science, Beijing, 100049, China
| | - Yaqiang Wang
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing, 100081, China
| | - Leiku Yang
- School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, 454000, China
| | - Jing Chen
- Shijiazhuang Meteorological Bureau, Shijiazhuang, 050081, China
| | - Hong Wang
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing, 100081, China
| | - Hujia Zhao
- Institute of Atmospheric Environment, China Meteorological Administration, Shenyang, 110016, China
| | - Lei Li
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing, 100081, China
| | - Lei Zhang
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing, 100081, China
| | - Ke Gui
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing, 100081, China
| | - Xianyi Yang
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing, 100081, China
| | - Yuanxin Liang
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing, 100081, China
| | - Xiaoye Zhang
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing, 100081, China
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Xiao Z, Xie X, Lin X, Xie J, Chen J, Shi Y, Chen Y. The spatio-temporal characteristics of aerosol optical thickness as well as the relationship with PM 2.5 in Xiamen city, China. ENVIRONMENTAL MONITORING AND ASSESSMENT 2020; 192:676. [PMID: 33025262 DOI: 10.1007/s10661-020-08622-6] [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: 05/15/2020] [Accepted: 09/17/2020] [Indexed: 06/11/2023]
Abstract
Long-term aerosol optical thickness (AOT) composited data (2002-2017) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft was used to evaluate the temporal and spatial variability of aerosol in Xiamen city by using wavelet analysis, and the relationship between the surface mass concentrations of particulate matter with aerodynamic diameters less than 2.5 μm (PM2.5) and the AOT was analyzed by using linear regression. The results showed that AOT increased gradually from 2002 to 2011, and then decreased. AOT displayed a significant 9-month periodicity in AOT was inferred wavelet analysis. AOT also showed significant annual variability in response to changes in weather and aerosol pollution. We observed highest AOT values in April, with a monthly mean of 1.00 ± 0.18. Lowest values were observed in December, with a mean AOT of 0.52 ± 0.11. Multi-year monthly AOT fluctuations were lowest in January with a low variation coefficient (0.14), and the largest fluctuations appeared in July with a high variation coefficient (0.29). Higher AOT values (~ 1.1) were predominantly located in the southern urban areas of Xiamen and lower AOT values (~ 0.3) were mainly located in northern rural regions. The aerosol pollution was serious in April with the smallest spatial variation coefficient of 0.25, and the highest spatial variation coefficient appeared in July. Highest intraannual variability predominantly occurred in the high-value areas in the center of Xiamen. AOT values remained high in Xiamen Island throughout the year with a multi-year mean of 0.87. There was a moderate correlation between ground-based PM2.5 and MODIS AOT. Therefore, we confirm the suitability of MODIS AOT to accurately estimate PM2.5 concentration and evaluate the temporal and spatial characteristics of air quality in Xiamen.
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Affiliation(s)
- Zhongyong Xiao
- College of Harbour and Environmental Engineering, Jimei University, Xiamen, 361021, China
| | - Xianquan Xie
- College of Harbour and Environmental Engineering, Jimei University, Xiamen, 361021, China
| | - Xiaofeng Lin
- College of Harbour and Environmental Engineering, Jimei University, Xiamen, 361021, China
| | - Jinghan Xie
- College of Harbour and Environmental Engineering, Jimei University, Xiamen, 361021, China
| | - Jiongfeng Chen
- College of Harbour and Environmental Engineering, Jimei University, Xiamen, 361021, China
| | - Yiqiang Shi
- College of Harbour and Environmental Engineering, Jimei University, Xiamen, 361021, China
| | - Yingfeng Chen
- College of Harbour and Environmental Engineering, Jimei University, Xiamen, 361021, China.
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