本文建立了气溶胶5成分模型(黑碳BC、吸收性有机碳BrC、硫酸铵AS、粉尘DU和水分AW),利用AERONET数据对喜马拉雅山及周边地区的气溶胶组分进行了反演计算.结果显示:多年平均来看,各个站点成分柱质量浓度占比相似,其中AS、DU、AW、BrC和AW依次占比53.49%、29.33%、10.63%、5.27%、1.28%.同时,喜马拉雅山南坡地区的气溶胶柱质量浓度(年平均390mg/m2)显著高于北坡(年平均49mg/m2),主要原因是高海拔山脉大量阻挡了南亚污染物气溶胶跨越喜马拉雅的传输(仅通过约13%).总体上,喜马拉雅地区春季(多年平均)气溶胶柱质量浓度最高,而夏,秋,冬季表现出大幅降低,这主要是由于春季季风前大气污染物排放的不断累积效应,以及南亚沙漠释放的粉尘气溶胶大量传输造成的,而在夏天季风期随着大量降水的淋洗而大量降低.年际变化来看,2015~2024期间的气溶胶平均柱质量浓度(南坡平均370.83mg/m2)略低于较早的2006~2015年期间(南坡平均453.26mg/m2),这反映出南亚地区近期污染状况有所改善,然而总体大气污染状况仍然十分严重.由于黑碳和棕碳等吸收性气溶胶可以显著影响冰川积雪的加速消融,南亚地区持续的高污染气溶胶排放,预估未来会继续影响喜马拉雅山冰川变化.
Abstract
This study established a five-component aerosol model (including Black Carbon (BC), Brown Carbon (BrC), Ammonium Sulfate (AS), Dust (DU), and Water (AW)) utilizing AERONET data, to perform inversion calculations on the aerosol components variability in the Himalayas and surrounding regions. The results indicated that, on a multi-year average, the proportion of the mass concentration of each component at various monitoring sites was similar, with AS, DU, AW, BrC, and AW accounting for 53.49%, 29.33%, 10.63%, 5.27%, and 1.28%, respectively. The aerosol mass concentration in the southern slope of the Himalayas (annual average of 390mg/m2) was significantly higher than that in the northern slope (annual average of 49mg/m2), primarily due to the substantial obstruction of South Asian pollutant aerosols by the high-altitude mountains during transboundary transport crossed the Himalayas (with only about 13% transported over the Himalayas). Generally, the aerosol mass concentration in the Himalayan region was highest in spring (multi-year average), while it saw significant reductions in summer, autumn, and winter. This variation was mainly attributed to the cumulative effect of atmospheric pollutant emissions prior to the South Asian Monsoon (SAM) and the substantial transport of dust aerosols from South Asian deserts, which were greatly diminished during the summer monsoon period due to heavy precipitation. Interannually, the average aerosol mass concentration from 2015 to 2024 (with southern slope average of 370.83mg/m2) was slightly lower than that during the earlier period from 2006 to 2015 (with southern slope average of 453.26mg/m2), reflecting an improvement in recent pollution levels in South Asia. However, the overall atmospheric pollution remained extremely serious. Given that the absorbing aerosols like black carbon and brown carbon can significantly accelerate the melting of glacial snow, the persistent high emissions of polluted aerosols in the South Asian region may continue affecting the accelerated glacial changes in the Himalayas in the future.
关键词
AERONET /
气溶胶组分 /
喜马拉雅山 /
污染排放 /
时空变化
Key words
AERONET /
aerosol component /
Himalayas /
pollutants emission /
spatiotemporal variability
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] Dong Z, Kang S, Qin X, et al. New insights into trace elements deposition in the snow packs at remote alpine glaciers in the northern Tibetan Plateau, China [J]. Science of the Total Environment, 2015, 529:101-113.
[2] Zhu X, Wang Z, Chen H. Advances in isotope geochronology and isotope geochemistry: a preface [J]. Journal of Earth Science, 2022, 33(1):1-4.
[3] Wang S, Nie X, Ran F, et al. Human activities control the source of eroded organic carbon in lake sediments over the last 100years: Evidence from stable isotope fingerprinting [J]. Fundamental Research, 2025,5(3):1097-1106.
[4] Dong Z, Jiang H, Baccolo G, et al. Biological and pollution aerosols on snow and ice—interplay between the atmosphere and the cryosphere [J]. Journal of Earth Science, 2023,34(6):1951-1956.
[5] Jiao X, Dong Z, Brahney J, et al. Uranium isotopes of aeolian dust deposited in northern Tibetan Plateau glaciers: Implications for tracing aeolian dust provenance [J]. Fundamental Research, 2022,2(5):716- 726.
[6] Wang L, Li Z, Tian Q, et al. Estimate of aerosol absorbing components of black carbon, brown carbon, and dust from ground-based remote sensing data of sun-sky radiometers [J]. Journal of Geophysical Research: Atmospheres, 2013,118(12):6534-6543.
[7] Zhao C, Hu Z, Qian Y, et al. Simulating black carbon and dust and their radiative forcing in seasonal snow: a case study over North China with field campaign measurements [J]. Atmospheric Chemistry and Physics, 2014,14(20):11475-11491.
[8] Li S, Zhang H, Wang Z, et al. Advances in the research on brown carbon aerosols: its concentrations, radiative forcing, and effects on climate [J]. Aerosol and Air Quality Research, 2023,23(8):220336.
[9] Qiu J. China: The third pole [J]. Nature, 2008,454:393-396.
[10] Xu B, Cao J, Hansen J, et al. Black soot and the survival of Tibetan glaciers [J]. Proceedings of the National Academy of Sciences, 2009,106(52):22114-22118.
[11] Immerzeel W W, Van Beek L P H, Bierkens M F P. Climate change will affect the Asian water towers [J]. Science, 2010,328(5984):1382- 1385.
[12] Bond T C, Charlson R J, Heintzenberg J. Quantifying the emission of light-absorbing particles: Measurements tailored to climate studies [J]. Geophysical Research Letters, 1998,25(3):337-340.
[13] Schuster G L, Dubovik O, Holben B N, et al. Inferring black carbon content and specific absorption from Aerosol Robotic Network (AERONET) aerosol retrievals [J]. Journal of Geophysical Research: Atmospheres, 2005,110(D10S17).
[14] Dey S, Tripathi S N, Singh R P, et al. Retrieval of black carbon and specific absorption over Kanpur city, northern India during 2001-2003 using AERONET data [J]. Atmospheric Environment, 2006,40(3): 445-456.
[15] Wang L, Li Z, Tian Q, et al. Estimate of aerosol absorbing components of black carbon, brown carbon, and dust from ground-based remote sensing data of sun-sky radiometers [J]. Journal of Geophysical Research: Atmospheres, 2013,118(12):6534-6543.
[16] Schuster G L, Dubovik O, Arola A. Remote sensing of soot carbon- Part 1: Distinguishing different absorbing aerosol species [J]. Atmospheric Chemistry and Physics, 2016,16(3):1565-1585.
[17] Xie Y S, Li Z Q, Zhang Y X, et al. Estimation of atmospheric aerosol composition from ground‐based remote sensing measurements of Sun-sky radiometer [J]. Journal of Geophysical Research: Atmospheres, 2017,122(1):498-518.
[18] Zhang Y, Yan C Y, Li Z, et al. Development of a multiple solution mixing mechanism based aerosol component retrieval method for polarimetric satellite measurements [J]. Atmospheric Environment, 2025,349:121120.
[19] Haywood J M, Shine K P. The effect of anthropogenic sulfate and soot aerosol on the clear sky planetary radiation budget [J]. Geophysical Research Letters, 1995,22(5):603-606.
[20] Sokolik I N, Toon O B. Incorporation of mineralogical composition into models of the radiative properties of mineral aerosol from UV to IR wavelengths [J]. Journal of Geophysical Research: Atmospheres, 1999,104(D8):9423-9444.
[21] Andreae M O, Gelencsér A. Black carbon or brown carbon? The nature of light-absorbing carbonaceous aerosols [J]. Atmospheric Chemistry and Physics, 2006,6(10):3131-3148.
[22] 程丁,吴晟,吴兑,等.深圳市城区和郊区黑碳气溶胶对比研究[J].中国环境科学, 2018,38(5):1653-1662. Cheng D, Wu S, Wu D, et al. Comparative study of black carbon aerosols in urban and suburban areas of Shenzhen [J]. China Environmental Science, 2018,38(5):1653-1662.
[23] Cai J, Zhi G R, Chen Y J, et al. A Preliminary Study on Brown Carbon Emissions from Open Agricultural Biomass Burning and Residential Coal Combustion in China [J]. Research of Environmental Sciences, 2014,27(5):455-461.
[24] Sun J, Zhi G, Hitzenberger R, et al. Emission factors and light absorption properties of brown carbon from household coal combustion in China [J]. Atmospheric Chemistry and Physics Discussions, 2017,2017:1-25.
[25] Xie M, Hays M D, Holder A L. Light-absorbing organic carbon from prescribed and laboratory biomass burning and gasoline vehicle emissions [J]. Scientific Reports, 2017,7(1):7318.
[26] Russell P B, Bergstrom R W, Shinozuka Y, et al. Absorption Angstrom Exponent in AERONET and related data as an indicator of aerosol composition [J]. Atmospheric Chemistry and Physics, 2010,10(3): 1155-1169.
[27] 程宁熹,刘湾湾,刘琼,等.北京地区对流层低层臭氧及硫酸盐气溶胶的时空分布[J].中国环境科学, 2020,40(11):4669-4678. Cheng N X, Liu W W, Liu Q, et al. Spatiotemporal distributions of tropospheric ozone and sulfate aerosols in the lower atmosphere over Beijing [J]. China Environmental Science, 2020,40(11):4669-4678.
[28] Cao J J, Shen Z X, Chow J C, et al. Winter and summer PM2.5 chemical compositions in fourteen Chinese cities [J]. Journal of the Air & Waste Management Association, 2012,62(10):1214-1226.
[29] Holben B N, Eck T F, Slutsker I, et al. AERONET—A federated instrument network and data archive for aerosol characterization [J]. Remote Sensing of Environment, 1998,66(1):1-16.
[30] Dubovik O, King M D. A flexible inversion algorithm for retrieval of aerosol optical properties from Sun and sky radiance measurements [J]. Journal of Geophysical Research: Atmospheres, 2000,105(D16): 20673-20696.
[31] Dehkhoda N, Shin J, Joo S, et al. An AERONET-based methodology to retrieve black carbon light absorption and comparison with MERRA-2data [J]. Atmospheric Pollution Research, 2024,15(2): 101994.
[32] Li Z, Li L, Zhang F, et al. Comparison of aerosol properties over Beijing and Kanpur: Optical, physical properties and aerosol component composition retrieved from 12years ground-based Sun-sky radiometer remote sensing data [J]. Journal of Geophysical Research: Atmospheres, 2015,120(4):1520-1535.
[33] Heller W. Remarks on refractive index mixture rules [J]. The Journal of Physical Chemistry, 1965,69(4):1123-1129.
[34] 王玲.大气气溶胶化学成分地基遥感反演研究[D].南京:南京大学, 2013. Wang L. Ground-based remote sensing of atmospheric aerosol chemical composition [D]. Nanjing: Nanjing University, 2013.
[35] Qiu M, Zigler C, Selin N E. Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions [J]. Atmospheric Chemistry and Physics, 2022,22(16): 10551-10566.
[36] Bey I, Jacob D J, Yantosca R M, et al. Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation [J]. Journal of Geophysical Research: Atmospheres, 2001,106(D19):23073-23095.
[37] Zhao T, Mao J, Ayazpour Z, et al. Interannual variability of summertime formaldehyde (HCHO) vertical column density and its main drivers at northern high latitudes [J]. Atmospheric Chemistry and Physics, 2024,24(10):6105-6121.
[38] Christian K E, Brune W H, Mao J. Global sensitivity analysis of the GEOS-Chem chemical transport model: ozone and hydrogen oxides during ARCTAS (2008) [J]. Atmospheric Chemistry and Physics, 2017, 17(5):3769-3784.
[39] Qiu M, Zigler C, Selin N E. Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions [J]. Atmospheric Chemistry and Physics, 2022,22(16): 10551-10566.
[40] Jiang J, Zhou T, Qian Y, et al. Precipitation regime changes in High Mountain Asia driven by cleaner air [J]. Nature, 2023,623(7987): 544-549.
[41] Sigdel S R, Zheng X, Babst F, et al. Accelerated succession in Himalayan alpine treelines under climatic warming [J]. Nature Plants, 2024,10:1909-1918.
[42] Minkina T M, Mandzhieva S S, Chaplygin V A, et al. Content and distribution of heavy metals in herbaceous plants under the effect of industrial aerosol emissions [J]. Journal of Geochemical Exploration, 2017,174:113-120.
[43] Wang L, Li Z, Tian Q, et al. Estimate of aerosol absorbing components of black carbon, brown carbon, and dust from ground-based remote sensing data of sun-sky radiometers [J]. Journal of Geophysical Research: Atmospheres, 2013,118(12):6534-6543.
[44] Xu J Z, Zhang Q, Wang Z B, et al. Chemical composition and size distribution of summertime PM2.5 at a high altitude remote location in the northeast of the Qinghai-Xizang (Tibet) Plateau: insights into aerosol sources and processing in free troposphere [J]. Atmospheric Chemistry and Physics, 2015,15(9):5069-5081.
[45] Xu J, Zhang X, Zhao W, et al. High-resolution physicochemical dataset of atmospheric aerosols over the Tibetan Plateau and its surroundings [J]. Earth System Science Data Discussions, 2024,16: 1875-1900.
[46] Wang K, Kang S C, Lin M, et al. Himalayas as a global hot spot of springtime stratospheric intrusions: Insight from isotopic signatures in sulfate aerosols [J]. Research in Cold and Arid Regions, 2024,16(1):5-13.
[47] Li J, Hendricks J, Righi M, et al. An aerosol classification scheme for global simulations using the K-means machine learning method [J]. Geoscientific Model Development Discussions, 2022,15:509-533.
[48] Dong Z, Brahney J, Kang S, et al. Aeolian dust transport, cycle and influences in high-elevation cryosphere of the Tibetan Plateau region: New evidences from alpine snow and ice [J]. Earth-Science Reviews, 2020,211:103408.
[49] Li C, Yan F, Kang S, et al. Re-evaluating black carbon in the Himalayas and the Tibetan Plateau: concentrations and deposition [J]. Atmospheric Chemistry and Physics, 2017,17(19):11899-11912.
[50] Zhang M, Zhao C, Cong Z, et al. Impact of topography on black carbon transport to the southern Tibetan Plateau during the pre- monsoon season and its climatic implication [J]. Atmospheric Chemistry and Physics, 2020,20(10):5923-5943.
[51] Chen H, Wang H. Haze days in North China and the associated atmospheric circulations based on daily visibility data from 1960 to 2012[J]. Journal of Geophysical Research: Atmospheres, 2015,120(12):5895-5909.
[52] Zhang Y, Ding A, Mao H, et al. Impact of synoptic weather patterns and inter-decadal climate variability on air quality in the North China Plain during 1980~2013[J]. Atmospheric Environment, 2016,124: 119-128.
基金
国家自然科学基金(42371139);中国地质大学(武汉)(CUG240629);甘肃省自然科学基金重点项目(23JRRA858)