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Atmospheric aerosols in Amazonia and land use change: from natural biogenic to biomass burning conditions
Open accessAbstract
In the wet season, a large portion of the Amazon region constitutes one of the most pristine continental areas, with very low concentrations of atmospheric trace gases and aerosol particles. However, land use change modifies the biosphere-atmosphere interactions in such a way that key processes that maintain the functioning of Amazonia are substantially altered. This study presents a comparison between aerosol properties observed at a preserved forest site in Central Amazonia (TT34 North of Manaus) and at a heavily biomass burning impacted site in south-western Amazonia (PVH, close to Porto Velho). Amazonian aerosols were characterized in detail, including aerosol size distributions, aerosol light absorption and scattering, optical depth and aerosol inorganic and organic composition, among other properties. The central Amazonia site (TT34) showed low aerosol concentrations (PM2.5 of 1.3 +/- 0.7 microg m(-3) and 3.4 +/- 2.0 microg m(-3) in the wet and dry seasons, respectively), with a median particle number concentration of 220 cm(-3) in the wet season and 2200 cm(-3) in the dry season. At the impacted site (PVH), aerosol loadings were one order of magnitude higher (PM2.5 of 10.2 +/- 9.0 microg m(-3) and 33.0 +/- 36.0 microg m(-3) in the wet and dry seasons, respectively). The aerosol number concentration at the impacted site ranged from 680 cm(-3) in the wet season up to 20 000 cm(-3) in the dry season. An aerosol chemical speciation monitor (ACSM) was deployed in 2013 at both sites, and it shows that organic aerosol account to 81% to the non-refractory PM1 aerosol loading at TT34, while biomass burning aerosols at PVH shows a 93% content of organic particles. Three years of filter-based elemental composition measurements shows that sulphate at the impacted site decreases, on average, from 12% of PM2.5 mass during the wet season to 5% in the dry season. This result corroborates the ACSM finding that the biomass burning contributed overwhelmingly to the organic fine mode aerosol during the dry season in this region. Aerosol light scattering and absorption coefficients at the TT34 site were low during the wet season, increasing by a factor of 5, approximately, in the dry season due to long range transport of biomass burning aerosols reaching the forest site in the dry season. Aerosol single scattering albedo (SSA) ranged from 0.84 in the wet season up to 0.91 in the dry. At the PVH site, aerosol scattering coefficients were 3-5 times higher in comparison to the TT34 site, an indication of strong regional background pollution, even in the wet season. Aerosol absorption coefficients at PVH were about 1.4 times higher than at the forest site. Ground-based SSA at PVH was around 0.92 year round, showing the dominance of scattering aerosol particles over absorption, even for biomass burning aerosols. Remote sensing observations from six AERONET sites and from MODIS since 1999, provide a regional and temporal overview. Aerosol Optical Depth (AOD) at 550 nm of less than 0.1 is characteristic of natural conditions over Amazonia. At the perturbed PVH site, AOD550 values greater than 4 were frequently observed in the dry season. Combined analysis of MODIS and CERES showed that the mean direct radiative forcing of aerosols at the top of the atmosphere (TOA) during the biomass burning season was -5.6 +/- 1.7 W m(-2), averaged over whole Amazon Basin. For high AOD (larger than 1) the maximum daily direct aerosol radiative forcing at the TOA was as high as -20 W m(-2) locally. This change in the radiation balance caused increases in the diffuse radiation flux, with an increase of Net Ecosystem Exchange (NEE) of 18-29% for high AOD. From this analysis, it is clear that land use change in Amazonia shows alterations of many atmospheric properties, and these changes are affecting the functioning of the Amazonian ecosystem in significant ways.
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Self-citationThe Amazon Boundary Layer Experiment: Wet season 1987
Harriss, R. C., Garstang, M., Wofsy, S. C., Beck, S. M., Bendura, R. J., Coelho, J. R. B., Drewry, J. W., Hoell, J. M., Jr., Matson, P. A., McNeal, R. J., et al. (1990) · Journal of Geophysical Research Atmospheres · doi:10.1029/jd095id10p16721 · Cited by 114
Further evidence for significant smoke transport from Africa to Amazonia
Baars, H., Ansmann, A., Althausen, D., et al. (2011) · Geophysical Research Letters · doi:10.1029/2011gl049200 · Cited by 111
Kühn, U., Ganzeveld, L., Thielmann, A., Dindorf, T., Schebeske, G., Welling, M., Sciare, J., Roberts, G., Meixner, F. X., KESSELMEIER, J., et al. (2010) · Atmospheric chemistry and physics · doi:10.5194/acp-10-9251-2010 · Cited by 105
Self-citationAmazonia and the modern carbon cycle: lessons learned
Ometto, J. P., Nobre, A. D., da Rocha, H. R., Artaxo, P. E., & Martinelli, L. A. (2005) · Oecologia · doi:10.1007/s00442-005-0034-3 · Cited by 105
Self-citationde Souza, J. M. A. C., Montégut, C. D. B., Cabanes, C., & Klein, P. (2011) · Geophysical Research Letters · doi:10.1029/2011gl049359 · Cited by 103
Spectral dependence of aerosol light absorption over the Amazon Basin
Rizzo, L. V., Correia, A. L., Artaxo, P. E., Procópio, A. S., & Andreae, M. O. (2011) · Atmospheric chemistry and physics · doi:10.5194/acp-11-8899-2011 · Cited by 92
Self-citationAndreae, M. O., Artaxo, P. E., Beck, V., Bela, M. M., Freitas, S. R., Gerbig, C., Longo, K. M., Munger, J. W., Wiedemann, K. T., & Wofsy, S. C. (2012) · Atmospheric chemistry and physics · doi:10.5194/acp-12-6041-2012 · Cited by 88
Self-citationImpact of biomass burning aerosols on precipitation in the Amazon: A modeling case study
Martins, J. A., Dias, M. A. F. S., & Gonçalves, F. L. T. (2009) · Journal of Geophysical Research Atmospheres · doi:10.1029/2007jd009587 · Cited by 85
Yamasoe, M. A., von Randow, C., Manzi, A. O., Schafer, J. S., Eck, T. F., & HOLBEN, B. N. (2006) · Atmospheric chemistry and physics · doi:10.5194/acp-6-1645-2006 · Cited by 84
A possible role of ground-based microorganisms on cloud formation in the atmosphere
Ekström, S., Nozière, B., Hultberg, M., Alsberg, T. E., Magnér, J., Nilsson, E. D., & Artaxo, P. E. (2010) · Biogeosciences · doi:10.5194/bg-7-387-2010 · Cited by 81
Self-citationHoeve, J. E. T., Remer, L. A., & Jacobson, M. Z. (2011) · Atmospheric chemistry and physics · doi:10.5194/acp-11-3021-2011 · Cited by 76
Rizzo, L. V., Artaxo, P. E., Karl, T., Guenther, A., & Greenberg, J. (2009) · Atmospheric Environment · doi:10.1016/j.atmosenv.2009.11.002 · Cited by 69
Self-citationDoughty, C. E., Flanner, M. G., & Goulden, M. L. (2010) · Global Biogeochemical Cycles · doi:10.1029/2009gb003670 · Cited by 65
Jiang, J. H., Su, H., Schoeberl, M. R., Massie, S. T., Colarco, P. R., Platnick, S. E., & Livesey, N. J. (2008) · Geophysical Research Letters · doi:10.1029/2008gl034631 · Cited by 65
Small farmers and deforestation in Amazonia
Brondízio, E. S., Cak, A. D., Caldas, M. M., Mena, C. F., Bilsborrow, R., Futemma, C. T., Ludewigs, T., Moran, E. F., & Batistella, M. (2009) · Geophysical monograph · doi:10.1029/2008gm000716 · Cited by 51
Large‐scale effect of aerosols on precipitation in the West African Monsoon region
Huang, J., Zhang, C., & Prospero, J. M. (2009) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.391 · Cited by 47
Christopher, S. A., Li, X., Welch, R. M., Reid, J. S., Hobbs, P. V., Eck, T. F., & HOLBEN, B. N. (2000) · Journal of applied meteorology · doi:10.1175/1520-0450-39.10.1742 · Cited by 33
Ebben, C. J., Martinez, I. S., Shrestha, M., Buchbinder, A. M., Corrigan, A. L., Guenther, A., Karl, T., Petäjä, T., Song, W., Zorn, S. R., et al. (2011) · Atmospheric chemistry and physics · doi:10.5194/acp-11-10317-2011 · Cited by 33
Self-citationA Multisensor satellite‐based assessment of biomass burning aerosol radiative impact over Amazonia
Patadia, F., Gupta, P., Christopher, S. A., & Reid, J. S. (2008) · Journal of Geophysical Research Atmospheres · doi:10.1029/2007jd009486 · Cited by 29
Cited by
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van Donkelaar, A., Hammer, M. S., Bindle, L., Brauer, M., Brook, J. R., Garay, M. J., Hsu, N. C., Kalashnikova, O. V., Kahn, R. A., Lee, C., et al. (2021) · Environmental Science & Technology · doi:10.1021/acs.est.1c05309 · Cited by 747
Air quality and human health improvements from reductions in deforestation-related fire in Brazil
Reddington, C. L., Butt, E. W., Ridley, D. A., Artaxo, P., Morgan, W. T., Coe, H., & Spracklen, D. V. (2015) · Nature Geoscience · doi:10.1038/ngeo2535 · Cited by 244
Self-citationFires increase Amazon forest productivity through increases in diffuse radiation
Rap, A., Spracklen, D. V., Mercado, L., Reddington, C. L., Haywood, J. M., Ellis, R. J., Phillips, O. L., Artaxo, P., Bonal, D., Restrepo Coupe, N., et al. (2015) · Geophysical Research Letters · doi:10.1002/2015gl063719 · Cited by 116
Self-citation
Metadata
- Authors
- Artaxo, P., Rizzo, L. V., Brito, J. F., Barbosa, H. M. J., Arana, A., Sena, E. T., Cirino, G. G., Bastos, W., Martin, S. T., & Andreae, M. O.
- Venue
- Faraday Discussions
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2013
- Volume
- 165
- Pages
- 203
- ISSN
- 1359-6640 (ISSN-L)
- 1364-5498
- OpenAlex
- W2131283510
- Cited by
- 257
- References
- 97
- FWCI
- 9.09
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.