Articles | Volume 21, issue 5
https://doi.org/10.5194/acp-21-3833-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-3833-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Statistical aerosol properties associated with fire events from 2002 to 2019 and a case analysis in 2019 over Australia
Xingchuan Yang
State Key Laboratory of Earth Surface Processes and Resource Ecology, and
College of Global Change and Earth System Science, Beijing Normal
University, Beijing, China
State Key Laboratory of Earth Surface Processes and Resource Ecology, and
College of Global Change and Earth System Science, Beijing Normal
University, Beijing, China
Yikun Yang
State Key Laboratory of Earth Surface Processes and Resource Ecology, and
College of Global Change and Earth System Science, Beijing Normal
University, Beijing, China
State Key Laboratory of Earth Surface Processes and Resource Ecology, and
College of Global Change and Earth System Science, Beijing Normal
University, Beijing, China
State Key Laboratory of Earth Surface Processes and Resource Ecology, and
College of Global Change and Earth System Science, Beijing Normal
University, Beijing, China
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Cited articles
Albergel, C., Dutra, E., Munier, S., Calvet, J.-C., Munoz-Sabater, J., de Rosnay, P., and Balsamo, G.: ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better?, Hydrol. Earth Syst. Sci., 22, 3515–3532, https://doi.org/10.5194/hess-22-3515-2018, 2018.
Boschetti, L. and Roy, D. P.: Strategies for the fusion of satellite fire
radiative power with burned area data for fire radiative energy derivation,
J. Geophys. Res.-Atmos., 114, D14S05, https://doi.org/10.1029/2008jd011645, 2009.
Bouya, Z. and Box, G. P.: Seasonal variation of aerosol size distributions in Darwin, Australia, J. Atmos. Sol.-Terr. Phy., 73, 2022–2033, https://doi.org/10.1016/j.jastp.2011.06.016, 2011.
Chen, Z., Schofield, R., Rayner, P., Zhang, T., Liu, C., Vincent, C., Fiddes, S., Ryan, R. G., Alroe, J., Ristovski, Z. D., Humphries, R. S., Keywood, M. D., Ward, J., Paton-Walsh, C., Naylor, T., and Shu, X.:
Characterization of aerosols over the Great Barrier Reef: The influence of
transported continental sources, Sci. Total Environ., 690,
426–437, https://doi.org/10.1016/j.scitotenv.2019.07.007, 2019.
Commonwealth of Australia, Bureau of Meteorology: Climate classifications (base climatological data sets), available at: http://www.bom.gov.au/jsp/ncc/climate_averages/climate-classifications (last access: 3 February 2021), 2005.
Crippa, P., Castruccio, S., Archer-Nicholls, S., Lebron, G. B., Kuwata, M., Thota, A., Sumin, S., Butt, E., Wiedinmyer, C., and Spracklen, D. V.: Population exposure to hazardous air quality due to the 2015 fires in Equatorial Asia, Sci. Rep.-UK, 6, 37074, https://doi.org/10.1038/srep37074, 2016.
Dubovik, O., Smirnov, A., Holben, B. N., King, M. D., Kaufman, Y. J., Eck, T. F., and Slutsker, I.: Accuracy assessments of aerosol optical properties retrieved from Aerosol Robotic Network (AERONET) Sun and sky radiance measurements, J. Geophys. Res.-Atmos., 105, 9791–9806,
https://doi.org/10.1029/2000jd900040, 2000.
Dubovik, O., Holben, B., Eck, T. F., Smirnov, A., Kaufman,Y. J., King, M.
D., Tanré, D., and Slutsker, I.: Variability of Absorption and Optical
Properties of Key Aerosol Types Observed in Worldwide Locations, J. Atmos.
Sci., 59, 590–608, https://doi.org/10.1175/1520-0469(2002)059<0590:voaaop>2.0.co;2, 2002.
Dubovik, O., Sinyuk, A., Lapyonok, T., Holben, B. N., Mishchenko, M., Yang, P., Eck, T. F., Volten, H., Muñoz, O., Veihelmann, B., van der Zande, W. J., Leon, J.-F., Sorokin, M., and Slutsker, I.: Application of spheroid models to account for aerosol particle nonsphericity in remote sensing of desert dust, J. Geophys. Res.-Atmos., 111, D11208,
https://doi.org/10.1029/2005JD006619, 2006.
Dutta, R., Das, A., and Aryal, J.: Big data integration shows Australian bush-fire frequency is increasing significantly, Roy. Soc. Open Sci., 3, 150241, https://doi.org/10.1098/rsos.150241, 2016.
Filkov, A. I., Ngo, T., Matthews, S., Telfer, S., and Penman, T. D.: Impact of Australia's catastrophic 2019/20 bushfire season on communities and environment. Retrospective analysis and current trends, J. Safety Sci. Resilience, 1, 44–56,
https://doi.org/10.1016/j.jnlssr.2020.06.009, 2020.
Fujii, Y., Kawamoto, H., Tohno, S., Oda, M., Iriana, W., and Lestari, P.: Characteristics of carbonaceous aerosols emitted from peatland fire in Riau, Sumatra, Indonesia (2): Identification of organic compounds, Atmos. Environ., 110, 1–7, https://doi.org/10.1016/j.atmosenv.2015.03.042, 2015.
Garrett, T. J. and Zhao, C.: Increased Arctic cloud longwave emissivity associated with pollution from mid-latitudes, Nature, 440, 787–789, https://doi.org/10.1038/nature04636, 2006.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/jcli-d-16-0758.1, 2017.
Giglio, L., Schroeder, W., and Justice, C. O.: The collection 6 MODIS active fire detection algorithm and fire products, Remote Sens. Environ., 178, 31–41, https://doi.org/10.1016/j.rse.2016.02.054, 2016.
Global Modeling and Assimilation Office (GMAO): MERRA-2 tavgM_2d_aer_Nx: 2d, Monthly mean, Time-averaged, Single-Level, Assimilation, Aerosol Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), https://doi.org/10.5067/FH9A0MLJPC7N, 2015.
Grandey, B. S., Lee, H.-H., and Wang, C.: Radiative effects of interannually varying vs. interannually invariant aerosol emissions from fires, Atmos. Chem. Phys., 16, 14495–14513, https://doi.org/10.5194/acp-16-14495-2016, 2016.
He, C., Miljevic, B., Crilley, L. R., Surawski, N. C., Bartsch, J., Salimi, F., Uhde, E., Schnelle-Kreis, J., Orasche, J., Ristovski, Z., Ayoko, G. A., Zimmermann, R., and Morawska, L.: Characterisation of the impact of open biomass burning on urban air quality in Brisbane, Australia, Environ. Int., 91, 230–242, https://doi.org/10.1016/j.envint.2016.02.030, 2016.
Hersbach, H. and Dee, D.: ERA5 reanalysis is in production, ECMWF Newsletter No. 147, 2016.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 monthly averaged data on single levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), https://doi.org/10.24381/cds.f17050d7, 2019.
Holben, B. N., Eck, T. F., Slutsker, I., Tanré, D., Buis, J. P., Setzer,
A., Vermote, E., Reagan, J. A., Kaufman, Y. J., Nakajima, T., Lavenu, F.,
Jankowiak, I., and Smirnov, A.: AERONET – A Federated Instrument Network and
Data Archive for Aerosol Characterization, Remote Sens. Environ.,
66, 1–16, https://doi.org/10.1016/S0034-4257(98)00031-5, 1998.
Ito, A. and Penner, J. E.: Global estimates of biomass burning emissions based on satellite imagery for the year 2000, J. Geophys. Res.-Atmos., 109, D14S05, https://doi.org/10.1029/2003jd004423, 2004.
Jacobson, M. Z.: Effects of biomass burning on climate, accounting for heat and moisture fluxes, black and brown carbon, and cloud absorption effects, J. Geophys. Res.-Atmos., 119, 8980–9002,
https://doi.org/10.1002/2014jd021861, 2014.
Jiang, J. H., Su, H., Huang, L., Wang, Y., Massie, S., Zhao, B., Omar, A., and Wang, Z.: Contrasting effects on deep convective clouds by different types of aerosols, Nat. Commun., 9, 3874, https://doi.org/10.1038/s41467-018-06280-4, 2018.
Levy, R., Hsu, C., and MODIS Atmosphere Science Team: Aerosol Retrieval Group/MODIS Adaptive Processing System (MODAPS), MODIS Atmosphere L2 Aerosol Product, Goddard Space Flight Center, USA, https://doi.org/10.5067/MODIS/MYD04_L2.061, 2015.
Liu, Y., Zhu, Q., Huang, J., Hua, S., and Jia, R.: Impact of dust-polluted convective clouds over the Tibetan Plateau on downstream precipitation, Atmos. Environ., 209, 67–77,
https://doi.org/10.1016/j.atmosenv.2019.04.001, 2019.
Luhar, A. K., Mitchell, R. M., Meyer, C. P., Qin, Y., Campbell, S., Gras, J. L., and Parry, D.: Biomass burning emissions over northern Australia constrained by aerosol measurements: II – Model validation, and impacts on air quality and radiative forcing, Atmos. Environ., 42, 1647–1664, https://doi.org/10.1016/j.atmosenv.2007.12.040, 2008.
Mallet, M. D., Desservettaz, M. J., Miljevic, B., Milic, A., Ristovski, Z. D., Alroe, J., Cravigan, L. T., Jayaratne, E. R., Paton-Walsh, C., Griffith, D. W. T., Wilson, S. R., Kettlewell, G., van der Schoot, M. V., Selleck, P., Reisen, F., Lawson, S. J., Ward, J., Harnwell, J., Cheng, M., Gillett, R. W., Molloy, S. B., Howard, D., Nelson, P. F., Morrison, A. L., Edwards, G. C., Williams, A. G., Chambers, S. D., Werczynski, S., Williams, L. R., Winton, V. H. L., Atkinson, B., Wang, X., and Keywood, M. D.: Biomass burning emissions in north Australia during the early dry season: an overview of the 2014 SAFIRED campaign, Atmos. Chem. Phys., 17, 13681–13697, https://doi.org/10.5194/acp-17-13681-2017, 2017.
McGowan, H. and Clark, A.: Identification of dust transport pathways from Lake Eyre, Australia using Hysplit, Atmos. Environ., 42, 6915–6925, https://doi.org/10.1016/j.atmosenv.2008.05.053, 2008.
Mehta, M., Singh, R., Singh, A., Singh, N., and Anshumali: Recent global aerosol optical depth variations and trends – A comparative study using MODIS and MISR level 3 datasets, Remote Sens. Environ., 181,
137–150, https://doi.org/10.1016/j.rse.2016.04.004, 2016.
Mehta, M., Singh, N., and Anshumali: Global trends of columnar and
vertically distributed properties of aerosols with emphasis on dust,
polluted dust and smoke – inferences from 10-year long CALIOP observations,
Remote Sens. Environ., 208, 120–132,
https://doi.org/10.1016/j.rse.2018.02.017, 2018.
Meyer, C. P., Luhar, A. K., and Mitchell, R. M.: Biomass burning emissions over northern Australia constrained by aerosol measurements: I – Modelling the distribution of hourly emissions, Atmos. Environ., 42, 1629–1646, https://doi.org/10.1016/j.atmosenv.2007.10.089, 2008.
Meyer, C. P., Cook, G. D., Reisen, F., Smith, T. E. L., Tattaris, M., Russell-Smith, J., Maier, S. W., Yates, C. P., and Wooster, M. J.: Direct measurements of the seasonality of emission factors from savanna fires in northern Australia, J. Geophys. Res.-Atmos., 117, D20305,
https://doi.org/10.1029/2012jd017671, 2012.
Mitchell, R. M., O'Brien, D. M., and Campbell, S. K.: Characteristics and
radiative impact of the aerosol generated by the Canberra firestorm of
January 2003, J. Geophys. Res.-Atmos., 111, D02204,
https://doi.org/10.1029/2005jd006304, 2006.
Mitchell, R. M., Forgan, B. W., Campbell, S. K., and Qin, Y.: The
climatology of Australian tropical aerosol: Evidence for regional
correlation, Geophys. Res. Lett., 40, 2384–2389, https://doi.org/10.1002/grl.50403,
2013.
Mitchell, R. M., Forgan, B. W., and Campbell, S. K.: The Climatology of Australian Aerosol, Atmos. Chem. Phys., 17, 5131–5154, https://doi.org/10.5194/acp-17-5131-2017, 2017.
Mukkavilli, S. K., Prasad, A. A., Taylor, R. A., Huang, J., Mitchell, R. M., Troccoli, A., and Kay, M. J.: Assessment of atmospheric aerosols from two reanalysis products over Australia, Atmos. Res., 215, 149–164, https://doi.org/10.1016/j.atmosres.2018.08.026, 2019.
Murphy, B. P., Prior, L. D., Cochrane, M. A., Williamson, G. J., and Bowman, D. M. J. S.: Biomass consumption by surface fires across Earth's most fire-prone continent, Glob. Change Biol., 25, 254–268, https://doi.org/10.1111/gcb.14460, 2018.
NASA: AEROSOL OPTICAL DEPTH (V3) – SOLAR and AEROSOL INVERSIONS (V3), Goddard Space Flight Center, USA, available at: https://aeronet.gsfc.nasa.gov/ (last access: 3 February 2021), 2016.
NASA/LARC/SD/ASDC: CALIPSO Lidar Level 3 Tropospheric Aerosol Profiles, Cloud Free Data, Standard V4-20 [Data set], NASA Langley Atmospheric Science Data Center DAAC, https://doi.org/10.5067/CALIOP/CALIPSO/CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-20, 2019.
NOAA: GDAS – Daily Tar Files (1∘ by 1∘), NCEI's NOAA National Operational Model Archive and Distribution System (NOMADS), available at: ftp://ftp.arl.noaa.gov/pub/archives/gdas1 (last access: 3 February 2021), 2016.
Ohneiser, K., Ansmann, A., Baars, H., Seifert, P., Barja, B., Jimenez, C., Radenz, M., Teisseire, A., Floutsi, A., Haarig, M., Foth, A., Chudnovsky, A., Engelmann, R., Zamorano, F., Bühl, J., and Wandinger, U.: Smoke of extreme Australian bushfires observed in the stratosphere over Punta Arenas, Chile, in January 2020: optical thickness, lidar ratios, and depolarization ratios at 355 and 532 nm, Atmos. Chem. Phys., 20, 8003–8015, https://doi.org/10.5194/acp-20-8003-2020, 2020.
Omar, A. H., Winker, D. M., Tackett, J. L., Giles, D. M., Kar, J., Liu, Z.,
Vaughan, M. A., Powell, K. A., and Trepte, C. R.: CALIOP and AERONET aerosol
optical depth comparisons: One size fits none, J. Geophys.
Res.-Atmos., 118, 4748–4766, https://doi.org/10.1002/jgrd.50330, 2013.
Qin, Y. and Mitchell, R. M.: Characterisation of episodic aerosol types over the Australian continent, Atmos. Chem. Phys., 9, 1943–1956, https://doi.org/10.5194/acp-9-1943-2009, 2009.
Radhi, M., Box, M. A., Box, G. P., and Mitchell, R. M.: Biomass burning aerosol over Northern Australia, Aust. Meteorol. Ocean., 62, 25–33, 2012.
Randles, C. A., da Silva, A. M., Buchard, V., Colarco, P. R., Darmenov, A.,
Govindaraju, R., Smirnov, A., Holben, B., Ferrare, R., Hair, J., Shinozuka,
Y., and Flynn, C. J.: The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I:
System Description and Data Assimilation Evaluation, J. Climate, 30,
6823–6850, https://doi.org/10.1175/jcli-d-16-0609.1, 2017.
Ravi, S., Baddock, M. C., Zobeck, T. M., and Hartman, J.: Field evidence for differences in post-fire aeolian transport related to vegetation type in semiarid grasslands, Aeolian Res., 7, 3–10,2012.
Reisen, F., Meyer, C. P., McCaw, L., Powell, J. C., Tolhurst, K., Keywood,
M. D., and Gras, J. L.: Impact of smoke from biomass burning on air quality
in rural communities in southern Australia, Atmos. Environ., 45,
3944–3953, https://doi.org/10.1016/j.atmosenv.2011.04.060, 2011.
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.: The MODIS Aerosol Algorithm, Products, and Validation, J. Atmos. Sci., 62, 947–973, https://doi.org/10.1175/jas3385.1, 2005.
Rooney, B., Wang, Y., Jiang, J. H., Zhao, B., Zeng, Z.-C., and Seinfeld, J. H.: Air quality impact of the Northern California Camp Fire of November 2018, Atmos. Chem. Phys., 20, 14597–14616, https://doi.org/10.5194/acp-20-14597-2020, 2020.
Sayer, A. M., Munchak, L. A., Hsu, N. C., Levy, R. C., Bettenhausen, C., and Jeong, M.-J.: MODIS Collection 6 aerosol products: Comparison between Aqua's e-Deep Blue, Dark Target, and “merged” data sets, and usage recommendations, J. Geophys. Res.-Atmos., 119,
13965–13989, https://doi.org/10.1002/2014JD022453, 2014.
Torres, O., Jethva, H., Ahn, C., Jaross, G., and Loyola, D. G.: TROPOMI aerosol products: evaluation and observations of synoptic-scale carbonaceous aerosol plumes during 2018–2020, Atmos. Meas. Tech., 13, 6789–6806, https://doi.org/10.5194/amt-13-6789-2020, 2020.
van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Kasibhatla, P. S., and Arellano Jr., A. F.: Interannual variability in global biomass burning emissions from 1997 to 2004, Atmos. Chem. Phys., 6, 3423–3441, https://doi.org/10.5194/acp-6-3423-2006, 2006.
Vermote, E., Ellicott, E., Dubovik, O., Lapyonok, T., Chin, M., Giglio, L., and Roberts, G. J.: An approach to estimate global biomass burning emissions of organic and black carbon from MODIS fire radiative power, J. Geophys. Res.-Atmos., 114, D18205, https://doi.org/10.1029/2008jd011188, 2009.
Wagner, R., Jähn, M., and Schepanski, K.: Wildfires as a source of airborne mineral dust – revisiting a conceptual model using large-eddy simulation (LES), Atmos. Chem. Phys., 18, 11863–11884, https://doi.org/10.5194/acp-18-11863-2018, 2018.
Wang, C., Graham, R. M., Wang, K., Gerland, S., and Granskog, M. A.: Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution, The Cryosphere, 13, 1661–1679, https://doi.org/10.5194/tc-13-1661-2019, 2019.
Wang, Y., Khalizov, A., and Misti Levy, R. Z.: New Directions: Light absorbing aerosols and their atmospheric impacts, Atmos. Environ.,
81, 713–715, 2013.
Wardoyo, A. Y. P., Morawska, L., Ristovski, Z. D., Jamriska, M., Carr, S., and Johnson, G.: Size distribution of particles emitted from grass fires in the Northern Territory, Australia, Atmos. Environ., 41, 8609–8619, 2007.
Winker, D., Pelon, J., and McCormick, M.: The CALIPSO mission: spaceborne lidar for observation of aerosols and clouds, Proc. SPIE Int. Soc. Opt. Eng., 4893, 1–11, https://doi.org/10.1117/12.466539, 2003.
Winton, V. H. L., Edwards, R., Bowie, A. R., Keywood, M., Williams, A. G., Chambers, S. D., Selleck, P. W., Desservettaz, M., Mallet, M. D., and Paton-Walsh, C.: Dry season aerosol iron solubility in tropical northern Australia, Atmos. Chem. Phys., 16, 12829–12848, https://doi.org/10.5194/acp-16-12829-2016, 2016.
Yan, X., Li, Z., Luo, N., Shi, W., Zhao, W., Yang, X., and Jin, J.: A
minimum albedo aerosol retrieval method for the new-generation geostationary
meteorological satellite Himawari-8, Atmos. Res., 207, 14–27,
https://doi.org/10.1016/j.atmosres.2018.02.021, 2018.
Yan, X., Li, Z., Luo, N., Shi, W., Zhao, W., Yang, X., Liang, C., Zhang, F., and Cribb, M.: An improved algorithm for retrieving the fine-mode fraction of aerosol optical thickness. Part 2: Application and validation in Asia, Remote Sens. Environ., 222, 90–103,
https://doi.org/10.1016/j.rse.2018.12.012, 2019.
Yang, X., Zhao, C., Guo, J., and Wang, Y.: Intensification of aerosol
pollution associated with its feedback with surface solar radiation and
winds in Beijing, J. Geophys. Res.-Atmos., 121, 4093–4099, https://doi.org/10.1002/2015jd024645, 2016.
Yang, X., Jiang, L., Zhao, W., Xiong, Q., Zhao, W., and Yan, X.: Comparison of Ground-Based PM2.5 and PM10 Concentrations in China, India, and the U.S, Int. J. Env. Res. Pub. He., 15, 1382, https://doi.org/10.3390/ijerph15071382, 2018.
Yang, X., Zhao, C., Luo, N., Zhao, W., Shi, W., and Yan, X.: Evaluation and
Comparison of Himawari-8 L2 V1.0, V2.1 and MODIS C6.1 aerosol products over
Asia and the oceania regions, Atmos. Environ., 220, 117068,
https://doi.org/10.1016/j.atmosenv.2019.117068, 2020a.
Yang, X., Zhao, C., and Yang, Y.: Long-term multi-source data analysis about the characteristics of aerosol optical properties and types over Australia, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2020-921, in review, 2020b.
Yang, Y., Zhao, C., Dong, X., Fan, G., Zhou, Y., Wang, Y., Zhao, L., Lv, F., and Yan, F.: Toward understanding the process-level impacts of aerosols on microphysical properties of shallow cumulus cloud using aircraft observations, Atmos. Res., 221, 27–33,
https://doi.org/10.1016/j.atmosres.2019.01.027, 2019.
Zhao, C. and Garrett, T. J.: Effects of Arctic haze on surface cloud radiative forcing, Geophys. Res. Lett., 42, 557–564,
https://doi.org/10.1002/2014gl062015, 2015.
Zhao, C., Lin, Y., Wu, F., Wang, Y., Li, Z., Rosenfeld, D., and Wang, Y.: Enlarging Rainfall Area of Tropical Cyclones by Atmospheric Aerosols, Geophys. Res. Lett., 45, 8604–8611, 10.1029/2018gl079427, 2018.
Zheng, C., Zhao, C., Zhu, Y., Wang, Y., Shi, X., Wu, X., Chen, T., Wu, F., and Qiu, Y.: Analysis of influential factors for the relationship between PM2.5 and AOD in Beijing, Atmos. Chem. Phys., 17, 13473–13489, https://doi.org/10.5194/acp-17-13473-2017, 2017.
Zheng, Y., Che, H., Xia, X., Wang, Y., Wang, H., Wu, Y., Tao, J., Zhao, H., An, L., Li, L., Gui, K., Sun, T., Li, X., Sheng, Z., Liu, C., Yang, X., Liang, Y., Zhang, L., Liu, C., Kuang, X., Luo, S., You, Y., and Zhang, X.:
Five-year observation of aerosol optical properties and its radiative
effects to planetary boundary layer during air pollution episodes in North
China: Intercomparison of a plain site and a mountainous site in Beijing,
Sci. Total Environ., 674, 140–158,
https://doi.org/10.1016/j.scitotenv.2019.03.418, 2019.
Zhu, Q., Liu, Y., Jia, R., Hua, S., Shao, T., and Wang, B.: A numerical simulation study on the impact of smoke aerosols from Russian forest fires on the air pollution over Asia, Atmos. Environ., 182, 263–274, https://doi.org/10.1016/j.atmosenv.2018.03.052, 2018.
Short summary
Using long-term multi-source data, this study shows significant impacts of fire events on aerosol properties over Australia. The contribution of carbonaceous aerosols to the total was 26 % of the annual average but larger (30–43 %) in September–December; smoke and dust are the two dominant aerosol types at different heights in southeastern Australia for the 2019 fire case. These findings are helpful for understanding aerosol climate effects and improving climate modeling in Australia in future.
Using long-term multi-source data, this study shows significant impacts of fire events on...
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