Nitrous acid (HONO) is a precursor of the hydroxyl radical in the atmosphere, which controls the degradation of greenhouse gases, contributes to photochemical smog and ozone production, and influences air quality. Although biomass burning is known to contribute substantially to global aerosols and reactive gas emissions, pyrogenic contributions to HONO emissions are poorly constrained and often omitted in models. Here we present a global survey of TROPOMI/Sentinel-5 Precursor satellite sounder observations and show that HONO emissions are consistently enhanced in fresh wildfire plumes. Comparing major ecosystems (savanna, grassland, shrubland and tropical and extratropical forests), we found that the enhancement ratios of HONO to nitrogen dioxide varied systematically with biome type, with the lowest in savannas and grasslands and highest in extratropical evergreen forests. Supported by airborne measurements, we demonstrate that previous assessments underestimate pyrogenic HONO emissions by a factor of 2–4 across all ecosystem types. We estimate that HONO emissions are responsible for two-thirds of the hydroxyl radical production in fresh wildfire plumes worldwide and act to accelerate oxidative plume chemistry and ozone production. Our findings suggest that pyrogenic HONO emissions have a substantial impact on atmospheric composition, which enhances regional ozone levels by up to 7 ppbv.
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The global data and validation data that support the findings of this study are available in the BIRA-IASB Data Repository (http://repository.aeronomie.be) with the identifier https://doi.org/10.18758/71021058. The TROPOMI HONO dataset used in this study is also available from the corresponding author upon request. The BB-FLUX dataset is also available on request68. http://flights.uwyo.edu/projects/bbflux18/Source data are provided with this paper.
The DOAS code used to generate the satellite and aircraft data can be accessed at http://uv-vis.aeronomie.be/software/QDOAS/index.php. The chemical mechanism of the MAGRITTE model used in this study can be obtained at https://tropo.aeronomie.be/index.php/models/magritte.
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This work was performed in the frame of the TROPOMI and the BB-FLUX projects. We acknowledge financial support from ESA S5P MPC (4000117151/16/I-LG) and Belgium Prodex TRACE-S5P (PEA 4000105598) projects. The BB-FLUX project is supported by US National Science Foundation award AGS-1754019 (principal investigator, R.V.). C.F.L. received summer support from the Department of Chemistry, CU Boulder. H.F. is recipient of a NASA graduate fellowship. L.C. is a research associate supported by the Belgian FRS-FNRS. This paper contains modified Copernicus data (2018/2019) processed by BIRA-IASB. We thank T. Stavrakou for her advices on the model simulations. We acknowledge the open access policy of the GFED4 database. We thank the entire BB-FLUX science team, the pilots and the UW Flight Center staff. The plume age estimates contain modified Copernicus Atmosphere Monitoring Service Information 2019. Neither the European Commission nor ECMWF is responsible for any use that may be made of the information it contains. We acknowledge the use of imagery from the NASA Worldview application (https://worldview.earthdata.nasa.gov), part of the NASA Earth Observing System Data and Information System (EOSDIS). We acknowledge the use of data and imagery from LANCE FIRMS operated by NASA’s Earth Science Data and Information System (ESDIS) with funding provided by NASA Headquarters.
The authors declare no competing interests.
Peer review information Primary handling editor: Clare Davis.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1 Comparison between TROPOMI and aircraft (CU DOAS) measurements of the Rabbit Foot (Idaho) and Watson Creek (Oregon) fires on August 15 and 19, 2018.
a, b, HONO slant columns from TROPOMI (rectangles) and nearly synchronized (± 15 minutes) aircraft CU-DOAS (dots), for Rabbit Foot fire (a) and Watson Creek fire (b). The fires source locations are indicated by the red points (source: https://firms.modaps.eosdis.nasa.gov/). c, d, Comparison between TROPOMI and aircraft RHN as function of plume age (Methods), respectively for (a, b). TROPOMI pixels delineated by grey lines in (a, b) are used for the comparison, and correspond to unambiguous detections of both HONO and NO2 with SCDs>2xretrieval uncertainty (other pixels are considered not suitable for comparison). The two aircraft traverses of the plumes are plotted separately. Aircraft measurements more than 5 km away from the satellite pixels are not considered. Error bars correspond to systematic uncertainties on RHNs.
Extended Data Fig. 2 RHN and normalized slant columns dependence with distance from HONO maximum.
a, Averages of RHN for extra-tropical forest and savanna ecosystems, as a function of the distance from the measured HONO maxima for the 100 largest measured HONO SCDs. Error bars are RHN standard deviations; numbers are the total pixels per distance bin. RHN peaks are found within the 5–12 km radius of temporary flight restriction (TFR) zone or affected by fire-induced turbulence. The TROPOMI results are classified using MODIS land cover type39. The inset colored bars indicate the range (mean ± standard deviation) of RHN found in the literature (Supplementary Table 2, excluding fresh plumes from large wildfires34). b, same as (a) for the HONO and NO2 slant column densities (normalized).
Extended Data Fig. 3 Modelled impact of pyrogenic HONO.
Calculated enhancement in the near-surface mixing ratios of HONO (a), OH (b) and O3 (c, d) due to the inclusion of pyrogenic HONO emissions, for the month of August 2018.
Source Data Extended Data Fig. 1
Satellite and aircraft data (HONO slant column, ratio HONO to NO2, plume age).
Source Data Extended Data Fig. 3
Calculated enhancement in the near-surface mixing ratios of HONO, OH and O3.
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Theys, N., Volkamer, R., Müller, JF. et al. Global nitrous acid emissions and levels of regional oxidants enhanced by wildfires. Nat. Geosci. 13, 681–686 (2020). https://doi.org/10.1038/s41561-020-0637-7
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