سابِق|تال

Temporal and spatial relationship between fine particle aerosols and short-lived trace gas in Chinese ocean

  • role： First author第一作者
  • Affiliation：

    Beijing PieSat Information Technology Co., Ltd.,Beijing 100195, China

  • Email：xubr@radi.ac.cn
  • Introduction：徐彬仁，1990年生，男，硕士研究生，研究方向为大气遥感。E-mail: xubr@radi.ac.cn
XU Binren1,  
  • Affiliation：

    Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing 100101, China

    University of Chinese Academy of Sciences,Beijing 100049, China

WEI Yuanyuan23,  
  • Affiliation：

    Beijing PieSat Information Technology Co., Ltd.,Beijing 100195, China

ZHANG Han1,  
  • Affiliation：

    Beijing PieSat Information Technology Co., Ltd.,Beijing 100195, China

SONG Yi1,  
  • Affiliation：

    Beijing PieSat Information Technology Co., Ltd.,Beijing 100195, China

YANG Zhizhong1

ملخص

Aerosols have important effects on global energy balance, cloud properties, rainfall frequency, and atmospheric circulation. To understand the impact of aerosols on climate change, this paper explores the quantitative relationship between fine particle aerosols and gases.Aerosol Optical Depth (AOD), Fine Mode Fraction (FMF) from MODIS, and trace gas (SO2, NO2, and HCHO) from OMI were used to analyze the quantitative relationship between the fine aerosols and trace gases over the Yellow Sea, East China Sea, and South China Sea between 2006 and 2015.First, the mean values ​​of the aerosols and trace gases were analyzed. The mean values ​​of AODfine, SO2, NO2, and HCHO decreased orderly in the Yellow Sea, South China Sea, and East China Sea all decreased. Meanwhile, the sensitivity analysis of the relationship between aerosols and trace gases revealed that: AODfine is most sensitive to SO2 with a sensitivity of 0.424, which may be ascribed to the anthropogenic emissions from the coastal cities of Eastern China. Meanwhile, East China Sea and South China Sea demonstrate high sensitivity to HCHO (0.664 and 0.545, respectively), which can be ascribed to the biomass combustion in Southeast Asia and Southern China. The seasonal correlation analysis of aerosols and trace gases in these three regions reveal that the AODfine in the Yellow Sea has a strong correlation with SO2 during summer and autumn (R>0.5) mainly due to the high temperature and humidity. A significant correlation is observed between HCHO and AODfine was significant in the East China Sea (R=0.57), and a relatively good correlation is observed between HCHO and AODfine in the South China Sea was relatively good (R=0.57) due to regional and seasonal changes.In sum, fine particle modal aerosol has a significant correlation with trace gases, and such relationship provides a scientific basis for understanding the aerosol processes, especially those of artificial aerosols dominated by fine particles.

مفهوم

remote sensing;anthropogenic emissions;AODfine;aerosols;trace gases;China’s neighboring sea;spatiotemporal change

References

  1. 1.
    Baek K H, Kim J H, Park R J, Chance K and Kurosu T P. 2014. Validation of OMI HCHO data and its analysis over Asia. Science of the Total Environment, 490: 93-105
  2. 2.
    Chen D D, Guo J P, Wang H Q, Li J, Min M, Zhao W H and Yao D. 2018. The cloud top distribution and diurnal variation of clouds over East Asia: preliminary results from Advanced Himawari Imager. Journal of Geophysical Research: Atmospheres, 123(7): 3724-3739
  3. 3.
    De Smedt I, Müller J F, Stavrakou T, van der A R, Eskes H and Van Roozendael M. 2008. Twelve years of global observations of formaldehyde in the troposphere using GOME and SCIAMACHY sensors. Atmospheric Chemistry and Physics, 8(16): 4947-4963
  4. 4.
    Dickerson R R, Li C, Li Z, Marufu L T, Stehr J W, McClure B, Krotkov N, Chen H, Wang P, Xia X, Ban X, Gong F, Yuan J and Yang J. 2007. Aircraft observations of dust and pollutants over northeast China: insight into the meteorological mechanisms of transport. Journal of Geophysical Research: Atmospheres, 112(D24): D24S90
  5. 5.
    Gao Y B, Mei S Y, Ma J J, Wu S C, Zhang H H and Xu D D. 2016. Spatiotemporal variations of SO2 over China by OMI observations during 2005～2012. Journal of Atmospheric and Environmental Optics, 11(4): 299-312
  6. 6.
    Guo J P, Miao Y C, Zhang Y, Liu H, Li Z Q, Zhang W C, He J, Lou M Y, Yan Y, Bian L G and Zhai P M. 2016. The climatology of planetary boundary layer height in China derived from radiosonde and reanalysis data. Atmospheric Chemistry and Physics, 16(20): 13309-13319
  7. 7.
    Heath D F, Krueger A J, Roeder H A and Henderson B D. 1975. The solar backscatter ultraviolet and total ozone mapping spectrometer (SBUV/TOMS) for Nimbus G. Optical Engineering, 14(4): 144323
  8. 8.
    Itahashi S, Uno I, Yumimoto K, Irie H, Osada K, Ogata K, Fukushima H, Wang Z and Ohara T. 2012. Interannual variation in the fine-mode MODIS aerosol optical depth and its relationship to the changes in sulfur dioxide emissions in China between 2000 and 2010. Atmospheric Chemistry and Physics, 12(5): 2631-2640
  9. 9.
    Jones T A and Christopher S A. 2011. A reanalysis of MODIS fine mode fraction over ocean using OMI and daily GOCART simulations. Atmospheric Chemistry and Physics, 11(12): 5805-5817
  10. 10.
    Kang N, Kumar K R, Yin Y, Diao Y W and Yu X N. 2015. Correlation analysis between AOD and cloud parameters to study their relationship over China using MODIS data (2003-2013): impact on cloud formation and climate change. Aerosol and Air Quality Research, 15(3): 958-973
  11. 11.
    King M D, Menzel W P, Kaufman Y J, Tanre D, Platnick S, Ackerman S A, Remer L A, Pincus R and Hubanks P A. 2003. Cloud and aerosol properties, precipitable water, and profiles of temperature and water vapor from MODIS. IEEE Transactions on Geoscience and Remote Sensing, 41(2): 442-458
  12. 12.
    Levy R C, Remer L A, Mattoo S, Vermote E F and Kaufman Y J. 2007. Second-generation operational algorithm: retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance. Journal of Geophysical Research: Atmospheres, 112(D13): D13211
  13. 13.
    Levy R C, Remer L A, Kleidman R G, Mattoo S, Ichoku C, Kahn R and Eck T F. 2010. Global evaluation of the Collection 5 MODIS dark-target aerosol products over land. Atmospheric Chemistry and Physics, 10(21): 10399-10420
  14. 14.
    Li Z Q, Niu F, Lee K H, Xin J Y, Hao W M, Nordgren B, Wang Y S and Wang P C. 2007. Validation and understanding of Moderate Resolution Imaging Spectroradiometer aerosol products (C5) using ground-based measurements from the handheld Sun photometer network in China. Journal of Geophysical Research: Atmospheres, 112(D22): D22S07
  15. 15.
    Lohmann U and Feichter J. 2005. Global indirect aerosol effects: a review. Atmospheric Chemistry and Physics, 5(3): 715-737
  16. 16.
    Mi W, Li Z Q, Xia X G, Holben B, Levy R, Zhao F S, Chen H B and Cribb M. 2007. Evaluation of the Moderate Resolution Imaging Spectroradiometer aerosol products at two aerosol robotic network stations in China. Journal of Geophysical Research: Atmospheres, 112(D22): D22S08
  17. 17.
    Niu F and Li Z Q. 2012. Systematic variations of cloud top temperature and precipitation rate with aerosols over the global tropics. Atmospheric Chemistry and Physics, 12(18): 8491-8498
  18. 18.
    Ohara T, Akimoto H, Kurokawa J, Horii N, Yamaji K, Yan X and Hayasaka T. 2007. An Asian emission inventory of anthropogenic emission sources for the period 1980-2020. Atmospheric Chemistry and Physics, 7(16): 4419-4444
  19. 19.
    Peng J, Li Z Q, Zhang H, Liu J J and Gribb M. 2016. Systematic changes in cloud radiative forcing with aerosol loading for deep clouds in the tropics. Journal of the Atmospheric Sciences, 73(1): 231-249
  20. 20.
    Penning de Vries M J M, Beirle S, Hörmann C, Kaiser J W, Stammes P, Tilstra L G, Tuinder O N E and Wagner T. 2015. A global aerosol classification algorithm incorporating multiple satellite data sets of aerosol and trace gas abundances. Atmospheric Chemistry and Physics, 15(18): 10597-10618
  21. 21.
    Quaas J, Boucher O, Bellouin N and Kinne S. 2008. Satellite-based estimate of the direct and indirect aerosol climate forcing. Journal of Geophysical Research: Atmospheres, 113(D5): D05204
  22. 22.
    Remer L A, Kaufman Y J, Tanré D, Mattoo S, Chu D A, Martins J V, Li R R, Ichoku C, Levy R C, Kleidman R G, Eck T F, Vermote E and Holben B N. 2005. The MODIS aerosol algorithm, products, and validation. Journal of the Atmospheric Sciences, 62(4): 947-973
  23. 23.
    Remer L A, Kleidman R G, Levy R C, Kaufman Y J, Tanré D, Mattoo S, Martins J V, Ichoku C, Koren I, Yu H B and Holben B N. 2008. Global aerosol climatology from the MODIS satellite sensors. Journal of Geophysical Research: Atmospheres, 113(D14): D14S07
  24. 24.
    Shang X J. 2013. Studies on Reactions of Nitric Oxides with Volatile Organic Compounds in Atmosphere. Lanzhou: Lanzhou University
  25. 25.
    Sreekanth V. 2014. On the classification and sub-classification of aerosol key types over south central peninsular India: MODIS–OMI algorithm. Science of the Total Environment, 468-469: 1086-1092
  26. 26.
    Streets D G, Wu Y and Chin M. 2006. Two-decadal aerosol trends as a likely explanation of the global dimming/brightening transition. Geophysical Research Letters, 33(15): L15806
  27. 27.
    Streets D G, Yan F, Chin M, Diehl T, Mahowald N, Schultz M, Wild M, Wu Y and Yu C. 2009. Anthropogenic and natural contributions to regional trends in aerosol optical depth, 1980-2006. Journal of Geophysical Research: Atmospheres, 114(D10): D00D18
  28. 28.
    Tang J P, Wang P C, Mickley L J, Xia X G, Liao H, Yue X, Sun L and Xia J R. 2014. Positive relationship between liquid cloud droplet effective radius and aerosol optical depth over Eastern China from satellite data. Atmospheric Environment, 84: 244-253
  29. 29.
    Veefkind J P, Boersma K F, Wang J, Kurosu T P, Krotkov N, Chance K and Levelt P F. 2011. Global satellite analysis of the relation between aerosols and short-lived trace gases. Atmospheric Chemistry and Physics, 11(3): 1255-1267
  30. 30.
    Xie Y S, Li Z Q, Zhang Y X, Zhang Y, Li D H, Li K T, Xu H, Zhang Y, Wang Y Q, Chen X F, Schauer J J and Bergin M. 2017. Estimation of atmospheric aerosol composition from ground-based remote sensing measurements of Sun-sky radiometer. Journal of Geophysical Research: Atmospheres, 122(1): 498-518
  31. 31.
    Yao L, Lü N and Shi H D. 2012. Study on spatial-temporal variations in total NO2 column amounts over China using SCIAMACHY data. Research of Environmental Sciences, 25(4): 419-424
  32. 32.
    Yu H B, Fu R, Dickinson R E, Zhang Y, Chen M X and Wang H. 2007. Interannual variability of smoke and warm cloud relationships in the Amazon as inferred from MODIS retrievals. Remote Sensing of Environment, 111(4): 435-449
  33. 33.
    Zhang Y and Li Z Q. 2013. Estimation of PM2.5 from fine-mode aerosol optical depth. Journal of Remote Sensing, 17(4): 929-943
  34. 34.
    Zhang Y and Li Z Q. 2015. Remote sensing of atmospheric fine particulate matter (PM2.5) mass concentration near the ground from satellite observation. Remote Sensing of Environment, 160: 252-262

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