Large wildfires reduce vegetation cover and soil moisture, leaving the temporally degraded landscapes an emergent source of dust emission. However, the global extent of post-fire dust events and their influencing factors remain unexplored. Using satellite measurements of active fires, aerosol abundance, vegetation cover and soil moisture from 2003 to 2020, here we show that 54% of the examined ~150,000 global large wildfires are followed by enhanced dust emission, producing substantial dust loadings for days to weeks over normally dust-free regions. The occurrence and duration of post-fire dust emission is controlled primarily by the extent of precedent wildfires and resultant vegetation anomalies and modulated secondarily by pre-fire drought conditions. The intensifying wildfires and drying soils during the studying period have made post-fire dust events one day longer, especially over extratropical forests and grasslands. With the predicted intensification of regional wildfires and concurrent droughts in the upcoming decades, our results indicate a future enhancement of sequential fire and dust extremes and their societal and ecological impacts.
This is a preview of subscription content, access via your institution
Subscribe to Nature+
Get immediate online access to Nature and 55 other Nature journal
Subscribe to Journal
Get full journal access for 1 year
only $9.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
The datasets for conducting the analysis presented here are all publicly available, including the MODIS Collection 6 Active Fire Detections (MCD14ML) acquired from NASA Fire Information for Research Management System (https://earthdata.nasa.gov/firms); the MODIS Deep Blue aerosol products acquired from the Level-1 and Atmosphere Archive and Distribution System (LAADS) Distributed Active Archive Center (DAAC) (https://ladsweb.modaps.eosdis.nasa.gov/); the MISR aerosol products acquired from the NASA Langley Research Center Atmospheric Science Data Center (https://l0dup05.larc.nasa.gov/cgi-bin/MISR/main.cgi); the AERONET coarse-mode aerosol optical depth data downloaded from https://aeronet.gsfc.nasa.gov; the ESA CCI soil moisture data downloaded from https://www.esa-soilmoisture-cci.org/node/238; the ERA5 hourly climate data provided by ECMWF (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5); the MODIS MCD12Q1v006 Landcover Type 1 product (https://lpdaac.usgs.gov/products/mcd12q1v006/); and the MODIS L3 EVI (MOD13C1 and MYD13C1) from DAAC (https://lpdaac.usgs.gov/products/mod13c1v006/). We generate a list of all identified dust-emission cases following large fires available at https://doi.org/10.6084/m9.figshare.20648055 (ref. 77).
The code to carry out the current analyses is available from the corresponding author on request.
Bowman, D. M. J. S. et al. Fire in the Earth system. Science 324, 481–484 (2009).
Bowman, D. M. J. S. et al. Human exposure and sensitivity to globally extreme wildfire events. Nat. Ecol. Evol. 1, 0058 (2017).
Hamilton, D. S. et al. Earth, wind, fire, and pollution: aerosol nutrient sources and impacts on ocean biogeochemistry. Ann. Rev. Mar. Sci. 14, 303–330 (2022).
Barkley, A. E. et al. African biomass burning is a substantial source of phosphorus deposition to the Amazon, tropical Atlantic Ocean, and Southern Ocean. Proc. Natl Acad. Sci. USA 116, 16216–16221 (2019).
Schlosser, J. S. et al. Analysis of aerosol composition data for western United States wildfires between 2005 and 2015: dust emissions, chloride depletion, and most enhanced aerosol constituents. J. Geophys. Res. Atmos. 122, 8951–8966 (2017).
Wagner, R., Schepanski, K. & Klose, M. The dust emission potential of agricultural-like fires—theoretical estimates from two conceptually different dust emission parameterizations. J. Geophys. Res. Atmos. 126, e2020JD034355 (2017).
Ichoku, C. et al. Biomass burning, land-cover change, and the hydrological cycle in northern sub-Saharan Africa. Environ. Res. Lett. 11, 095005 (2016).
Bowman, D. M. J. S. et al. Vegetation fires in the Anthropocene. Nat. Rev. Earth Environ. 1, 500–515 (2020).
Duniway, M. C. et al. Wind erosion and dust from US drylands: a review of causes, consequences, and solutions in a changing world. Ecosphere 10, e02650 (2019).
Okin, G. S., Gillette, D. A. & Herrick, J. E. Multi-scale controls on and consequences of aeolian processes in landscape change in arid and semi-arid environments. J. Arid. Environ. 65, 253–275 (2006).
Raupach, M. R. Drag and drag partition on rough surfaces. Boundary Layer Meteorol. 60, 375–395 (1992).
Webb, N. P. et al. Vegetation canopy gap size and height: critical indicators for wind erosion monitoring and management. Rangel. Ecol. Manag. 76, 78–83 (2021).
Ellis, T. M., Bowman, D. M. J. S., Jain, P., Flannigan, M. D. & Williamson, G. J. Global increase in wildfire risk due to climate-driven declines in fuel moisture. Glob. Change Biol. 28, 1544–1559 (2022).
Ravi, S. et al. Aeolian processes and the biosphere. Rev. Geophys. 49, RG3001 (2011).
Wagenbrenner, N. S., Germino, M. J., Lamb, B. K., Robichaud, P. R. & Foltz, R. B. Wind erosion from a sagebrush steppe burned by wildfire: Measurements of PM10 and total horizontal sediment flux. Aeolian Res. 10, 25–36 (2013).
Wagenbrenner, N. S. A large source of dust missing in Particulate Matter emission inventories? Wind erosion of post-fire landscapes. Elementa 5, 2 (2017).
Jeanneau, A. C., Ostendorf, B. & Herrmann, T. Relative spatial differences in sediment transport in fire-affected agricultural landscapes: a field study. Aeolian Res. 39, 13–22 (2019).
Deb, P. et al. Causes of the widespread 2019–2020 Australian bushfire season. Earths Future 8, e2020EF001671 (2020).
Nogrady, B. & Nicky, B. The climate link to Australia’s fires. Nature 577, 610–612 (2020).
Yu, Y. & Ginoux, P. Assessing the contribution of the ENSO and MJO to Australian dust activity based on satellite- and ground-based observations. Atmos. Chem. Phys. 21, 8511–8530 (2021).
Ginoux, P., Prospero, J. M., Gill, T. E., Hsu, N. C. & Zhao, M. Global-scale attribution of anthropogenic and natural dust sources and their emission rates based on MODIS Deep Blue aerosol products. Rev. Geophys. 50, RG3005 (2012).
Yu, Y., Kalashnikova, O. V., Garay, M. J., Lee, H. & Notaro, M. Identification and characterization of dust source regions across North Africa and the Middle East using MISR satellite observations. Geophys. Res. Lett. 45, 6690–6701 (2018).
Brianne, P., Rebecca, H. & David, L. The fate of biological soil crusts after fire: a meta-analysis. Glob. Ecol. Conserv. 24, e01380 (2020).
Rodriguez-Caballero, E. et al. Global cycling and climate effects of aeolian dust controlled by biological soil crusts. Nat. Geosci. 15, 458–463 (2022).
Goudie, A. S. & Middleton, N. J. Desert Dust in the Global System (Springer, 2006).
Ginoux, P. Atmospheric chemistry: warming or cooling dust? Nat. Geosci. 10, 246–247 (2017).
DeMott, P. J. et al. Predicting global atmospheric ice nuclei distributions and their impacts on climate. Proc. Natl Acad. Sci. USA 107, 11217–11222 (2010).
Yu, H. et al. The fertilizing role of African dust in the Amazon rainforest: a first multiyear assessment based on data from cloud–aerosol lidar and infrared Pathfinder satellite observations. Geophys. Res. Lett. 42, 1984–1991 (2015).
Tang, W. et al. Widespread phytoplankton blooms triggered by 2019–2020 Australian wildfires. Nature 597, 370–375 (2021).
Sarangi, C. et al. Dust dominates high-altitude snow darkening and melt over high-mountain Asia. Nat. Clim. Change 10, 1045–1051 (2020).
Cook, B. I. et al. Twenty-first century drought projections in the CMIP6 forcing scenarios. Earths Future 8, e2019EF001461 (2020).
Zheng, B. et al. Increasing forest fire emissions despite the decline in global burned area. Sci. Adv. 7, eabh2646 (2021).
Abatzoglou, J. T. & Williams, A. P. Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl Acad. Sci. USA 113, 11770–11775 (2016).
Abram, N. J. et al. Connections of climate change and variability to large and extreme forest fires in southeast Australia. Commun. Earth Environ. 2, 1–17 (2021).
Yu, Y. et al. Machine learning–based observation-constrained projections reveal elevated global socioeconomic risks from wildfire. Nat. Commun. 13, 1250 (2022).
Pu, B. & Ginoux, P. How reliable are CMIP5 models in simulating dust optical depth? Atmos. Chem. Phys. 18, 12491–12510 (2018).
Pu, B. & Ginoux, P. Climatic factors contributing to long-term variations in surface fine dust concentration in the United States. Atmos. Chem. Phys. 18, 4201–4215 (2018).
Bodí, M. B. et al. Wildland fire ash: production, composition and eco-hydro-geomorphic effects. Earth Sci. Rev. 130, 103–127 (2014).
NCAR Command Language v.6.6.2 (NCAR, 2019); https://doi.org/10.5065/D6WD3XH5
Giglio, L., Schroeder, W. & Justice, C. O. The collection 6 MODIS active fire detection algorithm and fire products. Remote Sens. Environ. 178, 31–41 (2016).
Ramo, R. et al. African burned area and fire carbon emissions are strongly impacted by small fires undetected by coarse resolution satellite data. Proc. Natl Acad. Sci. USA 118, 1–7 (2021).
Diner, D. J. et al. Multi-angle imaging spectroradiometer (MISR) instrument description and experiment overview. IEEE Trans. Geosci. Remote Sens. 36, 1072–1087 (1998).
Pu, B. et al. Retrieving the global distribution of the threshold of wind erosion from satellite data and implementing it into the Geophysical Fluid Dynamics Laboratory land–atmosphere model (GFDL AM4.0/LM4.0). Atmos. Chem. Phys. 20, 55–81 (2020).
Sayer, A. M., Hsu, N. C., Bettenhausen, C. & Jeong, M. J. Validation and uncertainty estimates for MODIS collection 6 ‘Deep Blue’ aerosol data. J. Geophys. Res. Atmos. 118, 7864–7872 (2013).
Hsu, N. C. et al. Enhanced Deep Blue aerosol retrieval algorithm: the second generation. J. Geophys. Res. Atmos. 118, 9296–9315 (2013).
Ginoux, P., Garbuzov, D. & Hsu, N. C. Identification of anthropogenic and natural dust sources using moderate resolution imaging spectroradiometer (MODIS) Deep Blue level 2 data. J. Geophys. Res. 115, D05204 (2010).
Eck, T. F. et al. Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols. J. Geophys. Res. Atmos. 104, 31333–31349 (1999).
Anderson, T. L. et al. Testing the MODIS satellite retrieval of aerosol fine-mode fraction. J. Geophys. Res. 110, 1–16 (2005).
Baddock, M. C., Bullard, J. E. & Bryant, R. G. Dust source identification using MODIS: a comparison of techniques applied to the Lake Eyre Basin, Australia. Remote Sens. Environ. 113, 1511–1528 (2009).
Baddock, M. C., Ginoux, P., Bullard, J. E. & Gill, T. E. Do MODIS-defined dust sources have a geomorphological signature? Geophys. Res. Lett. 43, 2606–2613 (2016).
Pu, B. & Ginoux, P. Projection of American dustiness in the late 21st century due to climate change. Sci. Rep. 7, 5553 (2017).
Pu, B., Ginoux, P., Kapnick, S. B. & Yang, X. Seasonal prediction potential for springtime dustiness in the United States. Geophys. Res. Lett. 46, 9163–9173 (2019).
Garay, M. J. et al. Introducing the 4.4 km spatial resolution multi-angle imaging spectroradiometer (MISR) aerosol product. Atmos. Meas. Tech. 13, 593–628 (2020).
Kalashnikova, O. V., Kahn, R., Sokolik, I. N. & Li, W.-H. Ability of multiangle remote sensing observations to identify and distinguish mineral dust types: optical models and retrievals of optically thick plumes. J. Geophys. Res. 110, D18S14 (2005).
Yu, Y. et al. Assessing temporal and spatial variations in atmospheric dust over Saudi Arabia through satellite, radiometric, and station data. J. Geophys. Res. Atmos. 118, 13253–13264 (2013).
Yu, Y., Notaro, M., Kalashnikova, O. V. & Garay, M. J. Climatology of summer Shamal wind in the Middle East. J. Geophys. Res. Atmos. 121, 289–305 (2016).
Yu, Y. et al. Disproving the Bodélé depression as the primary source of dust fertilizing the Amazon rainforest. Geophys. Res. Lett. 47, e2020GL088020 (2020).
Giles, D. M. et al. Advancements in the Aerosol Robotic Network (AERONET) version 3 database—automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements. Atmos. Meas. Tech. 12, 169–209 (2019).
O’Neill, N. T., Eck, T. F., Smirnov, A., Holben, B. N. & Thulasiraman, S. Spectral discrimination of coarse and fine mode optical depth. J. Geophys. Res. Atmos. 108, 1–15 (2003).
Winker, D. M. et al. Overview of the CALIPSO mission and CALIOP data processing algorithms. J. Atmos. Ocean. Technol. 26, 2310–2323 (2009).
Esselborn, M. et al. Spatial distribution and optical properties of Saharan dust observed by airborne high spectral resolution lidar during SAMUM 2006. Tellus B 61, 131–143 (2009).
Kim, M. H. et al. The CALIPSO version 4 automated aerosol classification and lidar ratio selection algorithm. Atmos. Meas. Tech. 11, 6107–6135 (2018).
Didan, K., Munoz, A. B., Solano, R. & Huete, A. MODIS Vegetation Index User’s Guide (Collection 6) (Univ. Arizona, 2015).
Seddon, A. W. R., Macias-Fauria, M., Long, P. R., Benz, D. & Willis, K. J. Sensitivity of global terrestrial ecosystems to climate variability. Nature 531, 229–232 (2016).
Saleska, S. R. et al. Dry-season greening of Amazon forests. Nature 531, E4–E5 (2016).
Remer, L. A., Kaufman, Y. J., Holben, B. N., Thompson, A. M. & McNamara, D. Biomass burning aerosol size distribution and modeled optical properties. J. Geophys. Res. Atmos. 103, 31879–31891 (1998).
Tegen, I. & Lacis, A. A. Modeling of particle size distribution and its influence on the radiative properties of mineral dust aerosol. J. Geophys. Res. Atmos. 101, 19237–19244 (1996).
Friedl, M. A. & Sulla-Menashe, D. User Guide to Collection 6 MODIS Land Cover (MCD12Q1 and MCD12C1) Product 6 (USGS, 2018).
Sulla-Menashe, D., Gray, J. M., Abercrombie, S. P. & Friedl, M. A. Hierarchical mapping of annual global land cover 2001 to present: the MODIS collection 6 land cover product. Remote Sens. Environ. 222, 183–194 (2019).
Dorigo, W. et al. ESA CCI Soil Moisture for improved Earth system understanding: state-of-the art and future directions. Remote Sens. Environ. 203, 185–215 (2017).
Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).
Preimesberger, W., Scanlon, T., Su, C.-H., Gruber, A. & Dorigo, W. Homogenization of structural breaks in the global ESA CCI Soil Moisture multisatellite climate data record. IEEE Trans. Geosci. Remote Sens. 59, 2845–2862 (2021).
Minola, L. et al. Near-surface mean and gust wind speeds in ERA5 across Sweden: towards an improved gust parametrization. Clim. Dyn. 55, 887–907 (2020).
Molina, M. O., Gutiérrez, C. & Sánchez, E. Comparison of ERA5 surface wind speed climatologies over Europe with observations from the HadISD dataset. Int. J. Climatol. 41, 4864–4878 (2021).
Klose, M. et al. Mineral dust cycle in the Multiscale Online Nonhydrostatic Atmosphere Chemistry model (MONARCH) version 2.0. Geosci. Model Dev. 14, 6403–6444 (2021).
Mondal, A., Kundu, S. & Mukhopadhyay, A. Rainfall trend analysis by Mann–Kendall test: a case study of north-eastern part of Cuttack District, Orissa. Int. J. Geol. Earth Environ. Sci. 2, 2277–208170 (2012).
Yu, Y. & Ginoux, P. Dust emission following large wildfires. figshare. 2022. https://doi.org/10.6084/m9.figshare.20648055.v2
This research is supported by Peking University School of Physics (Y.Y.), NASA grant number NNH19ZDA001N-HMA (P.G.), and the Earth Surface Mineral Dust Source Investigation (EMIT), a NASA Earth Ventures-Instrument (EVI-4) Mission (P.G.). We thank J. Dunne for helpful comments on the early version of this paper. We thank the MODIS, MISR, AERONET and CALIOP teams for providing data. We thank the Earth Surface Mineral Dust Source Investigation (EMIT) team, J. Lin and J. Mao for useful discussions. Computation is supported by High-performance Computing Platform of Peking University.
The authors declare no competing interests.
Peer review information
Nature Geoscience thanks Junran Li and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Xujia Jiang, in collaboration with the Nature Geoscience team.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1 Dust activity following wildfires during the 2010 and 2012 burning season in the western United States, as analyzed in Wagenbrenner et al.15,16.
Time series of number of active fires (orange bars, referring to the left y-axis), enhanced vegetation index (EVI, green line, referring to rightmost y-axis), and daily maximum dust optical depth (DOD, black dots, referring to the inter right y-axis) within ±0.05° of a. 43°40’N, 112°35’W in 2010 (Wagenbrenner et al.15) and b. 42.40°N, 117.90°W in 2012 (Wagenbrenner et al.16). The light green and light grey shadings represent the long-term 10th to 90th percentiles in EVI and DOD, respectively. The large and small black dots represent time series of DOD; large dots indicate situations with relatively small amount of biomass burning aerosols, represented by below-average coincident fine-mode optical depth (FOD). The blue square or purple triangle indicate dates when soil moisture is below long-term 10th percentile or daily maximum wind speed is above long-term 90th percentile. Based on in situ measurements of dust fluxes and particulate matter concentrations (PM10), Wagenbrenner et al.15 identified several major wind erosion events, with the largest wind erosion event in early September, in several months following the July 2010 Jefferson Fire in southeastern Idaho, United States. Based on a modeling framework, Wagenbrenner et al. 16 further quantified dust emission during August 5-6, 2012 from the burnt scars left by wildfires in western United States sagebrush landscapes in July 2012.
Extended Data Fig. 2 Aerosol vertical distribution during the 2019-2020 Australian bushfire season, detected by the space-borne lidar, Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite (CALIPSO).
Four nighttime overpasses (shown in panel a with their color matching the text of date) on b-d November 5, 2019, e-g December 22, 2019, h-j January 4, 2020, and k-m January 22, 2020 are shown here, including b, e, h, k total backscatter (m − 1 sr−1), indicating the abundance of aerosols, c, f, i, l depolarization ratio, indicating the non-sphericity of aerosol particles, and d, g, j, m aerosol type determined by CALIOP operational retrieval algorithm. The typical depolarization ratio of dust particles is between 0.2 to 0.3. The red triangles in a represent locations within the dashed box where major wildfire (weekly active fire counts exceeding 20) occurred during November 2019 within the dashed box.
a. Minimum monthly average Enhanced Vegetation Index (EVI) during 2003 to 2020. b. 95th percentile of monthly mean Dust Optical Depth (DOD) during 2003 to 2020.
Extended Data Fig. 4 Temporal evolution of dust optical depth (DOD) and dust-favoring environmental conditions during and after burning.
The probability distribution of a. DOD, b. EVI, c. soil moisture, and d. maximum wind speed during the burning period and 12 weeks afterwards. The probability distribution is represented by the frequency (%) of each variable falling below the long-term 2.5th percentile, between the 2.5th–5th, 5th–10th, 10th–25th, 25th–50th, 50th–75th, 75th–90th, 90th–95th, 95th–97.5th percentiles, and above the 97.5th percentile.
Extended Data Fig. 5 Duration of post-fire dust events regulated by the severity of proceeding wildfires and soil moisture and wind conditions.
Scatterplot of a. minimum soil moisture (m3 m−3) and b. upper decile wind speed (m s−2) during the 60 days after major fires as a function of number of precedent fires. The color of each dot represents the duration (days) of the corresponding dust event. The boxes in a indicate the 5th, 25th, 50th, 75th, and 95th percentiles of duration of post-fire dust (days, referring to the right y-axis) events with precedent fires ranging between 21–30, 31–40, 41–50, 51–60, 61–70, 71–80, 81–90, 91–100, and above 100 during the burning week.
a. Month of maximum active fire counts. Only locations with more than 18 fires detected during 2003-2020 are shown in color. b. Month of highest occurrence of post-fire dust events. c–g Joint probability distribution function (PDF, %) of the peak month of fire and post-fire dust emission.
Extended Data Fig. 7 Duration of post-fire dust events regulated by the severity of proceeding wildfires and vegetation conditions for different land cover types.
Scatterplot of minimum EVI (referring to the left y-axis) during the 60 days after major fires as a function of proceeding number of fires during the burning week, for a. forest, b. shrubland, c. savannah, d. grassland, and e. cropland ecoregions. The color of each dot represents the duration (days) of the corresponding dust event. The boxes indicate the 10th, 25th, 50th, 75th, and 90th percentiles of the duration of post-fire dust events (days, referring to the right y-axis) with proceeding fires ranging between 21–30, 31–40, 41–50, 51–60, 61–70, 71–80, 81–90, 91–100, and above 100 during the burning week.
Extended Data Fig. 8 Probability distribution of dust optical depth (DOD) as a function of concurrent soil dryness, with and without fires.
The probability distribution is represented by the frequency (%) of monthly mean DOD falling below the long-term 2.5th percentile, between the 2.5th–5th, 5th–10th, 10th–25th, 25th–50th, 50th–75th, 75th–90th, 90th–95th, 95th–97.5th percentiles, and above the 97.5th percentile. Four background conditions are analyzed here, including dry soils (monthly mean soil moisture below the 25th percentile) but no fires during the month, wet soils (monthly mean soil moisture above the 75th percentile) but no fires during the month, dry soils with more than 20 fires during the month, and wet soils with more than 20 fires during the month.
Extended Data Fig. 9 Occurrence and intensity of post-fire dust events, grouped by pre-fire drought condition during 2003–2020.
a. Occurrence of post-fire dust events with pre-fire dry condition (when soil moisture during the week before the occurrence of fires is below long-term 25th percentile) indicated by the size of dots, with color representing the dominant land cover type. b. Maximum Dust Optical Depth (DOD, color of dots) and mean duration (days, size of dots) of post-fire dust events with pre-fire dry condition. c. Occurrence of post-fire dust events with pre-fire wet condition (when soil moisture during the week before the occurrence of fires is above long-term 75th percentile) indicated by the size of dots, with color representing the dominant land cover type. d. Maximum DOD (color of dots) and mean duration (days, size of dots) of post-fire dust events with pre-fire wet condition.
Extended Data Fig. 10 Extreme dust activity associated with vegetation disturbances during the 2020 western North American extreme fire season.
a. Time series of number of active fires within ±0.05° of 38.9°N, 122.3°W (location indicated in panel b) (orange bars, referring to the left y-axis), enhanced vegetation index (EVI, green line, referring to rightmost y-axis), and daily maximum dust optical depth (DOD, black dots, referring to the inter right y-axis). The light green and light grey shadings represent the long-term 10th to 90th percentiles in EVI and DOD, respectively. The large or small dot represent DOD when dust or fine-mode particles are dominant, indicated by coincident fine-mode optical depth (FOD) below or above long-term median. The blue square or purple triangle indicate dates when soil moisture is below long-term 10th percentile or daily maximum wind speed is above long-term 90th percentile. Satellite image from NASA Earth Observatory.
About this article
Cite this article
Yu, Y., Ginoux, P. Enhanced dust emission following large wildfires due to vegetation disturbance. Nat. Geosci. 15, 878–884 (2022). https://doi.org/10.1038/s41561-022-01046-6