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.
Subscribe to Journal
Get full journal access for 1 year
only $8.25 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
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.
Finlayson-Pitts, B. J. & Pitts, J. N. Chemistry of the Upper and Lower Atmosphere: Theory, Experiments and Applications (Academic, 2000).
Spataro, F. & Ianniello, A. Sources of atmospheric nitrous acid: state of the science, current research needs, and future prospects. J. Air Waste Manage. Assoc. 64, 1232–1250 (2014).
Ammann, M. et al. Heterogeneous production of nitrous acid on soot in polluted air masses. Nature 395, 157–160 (1998).
Stemmler, K., Ammann, M., Donders, C., Kleffmann, J. & George, C. Photosensitized reduction of nitrogen dioxide on humic acid as a source of nitrous acid. Nature 440, 195–198 (2006).
Zhou, X. et al. Nitric acid photolysis on forest canopy surface as a source for tropospheric nitrous acid. Nat. Geosci. 4, 440–443 (2011).
Oswald, R. et al. HONO emissions from soil bacteria as a major source of atmospheric reactive nitrogen. Science 341, 1233–1235 (2013).
VandenBoer, T. C. et al. Nocturnal loss and daytime source of nitrous acid through reactive uptake and displacement. Nat. Geosci. 8, 55–60 (2015).
Andreae, M. O. & Merlet, P. Emissions of trace gases and aerosols from biomass burning. Global Biogeochem. Cycles 15, 955–966 (2001).
Akagi, S. K. et al. Emission factors for open and domestic biomass burning for use in atmospheric models. Atmos. Chem. Phys. 11, 4039–4072 (2011).
Andreae, M. O. Emissions of trace gases and aerosols from biomass burning—an updated assessment. Atmos. Chem. Phys. 19, 8523–8546 (2019).
Elshorbany, Y. F. et al. Global and regional impacts of HONO on the chemical composition of clouds and aerosols. Atmos. Chem. Phys. 14, 1167–1184 (2014).
Veres, P. et al. Measurements of gas-phase inorganic and organic acids from biomass fires by negative-ion proton-transfer chemical-ionization mass spectrometry. J. Geophys. Res. 115, D23302 (2010).
Nie, W. et al. Influence of biomass burning plumes on HONO chemistry in eastern China. Atmos. Chem. Phys. 15, 1147–1159 (2015).
Monge, M. E. et al. Light changes the atmospheric reactivity of soot. Proc. Natl Acad. Sci. USA 107, 6605–6609 (2010).
Ye, C., Zhang, N., Gao, H. & Zhou, X. Photolysis of particulate nitrate as a source of HONO and NOx. Environ. Sci. Technol. 51, 6849–6856 (2017).
Li, L. et al. Formation of HONO from the NH3-promoted hydrolysis of NO2-dimers in the atmosphere. Proc. Natl Acad. Sci. USA 115, 7236–7241 (2018).
Keene, W. C. et al. Emissions of major gaseous and particulate species during experimental burns of southern African biomass. J. Geophys. Res. 111, D04301 (2006).
Burling, I. R. et al. Laboratory measurements of trace gas emissions from biomass burning of fuel types from the southeastern and southwestern United States. Atmos. Chem. Phys. 10, 11115–11130 (2010).
Trentmann, J. et al. An analysis of the chemical processes in the smoke plume from a savanna fire. J. Geophys. Res. 110, D12301 (2005).
Yokelson, R. J. et al. The tropical forest and fire emissions experiment: overview and airborne fire emission factor measurements. Atmos. Chem. Phys. 7, 5175–5196 (2007).
Yokelson, R. J. et al. Emissions from biomass burning in the Yucatan. Atmos. Chem. Phys. 9, 5785–5812 (2009).
Akagi, S. K. et al. Measurements of reactive trace gases and variable O3 formation rates in some South Carolina biomass burning plumes. Atmos. Chem. Phys. 13, 1141–1165 (2013).
Müller, M. et al. In situ measurements and modeling of reactive trace gases in a small biomass burning plume. Atmos. Chem. Phys. 16, 3813–3824 (2016).
Neuman, J. A. et al. HONO emission and production determined from airborne measurements over the Southeast U.S. J. Geophys. Res. Atmos. 121, 9237–9250 (2016).
Beirle, S., Boersma, K. F., Platt, U., Lawrence, M. G. & Wagner, T. Megacity emissions and lifetime of nitrogen oxides probed from space. Science 333, 1737–1739 (2011).
McLinden, C. A. et al. Space-based detection of missing sulfur dioxide sources of global air pollution. Nat. Geosci. 9, 496–500 (2016).
Van Damme, M. et al. Industrial and agricultural ammonia point sources exposed. Nature 564, 99–103 (2018).
Clarisse, L., R’Honi, Y., Coheur, P.-F., Hurtmans, D. & Clerbaux, C. Thermal infrared nadir observations of 24 atmospheric gases. Geophys. Res. Lett. 38, L10802 (2011).
Platt, U., Perner, D., Harris, G. W., Winer, A. M. & Pitts, J. N. Observations of nitrous acid in an urban atmosphere by differential optical absorption. Nature 285, 312–314 (1980).
Platt, U. & Stutz, J. Differential Optical Absorption Spectroscopy (DOAS), Principle and Applications (Springer, 2008).
Hendrick, F. et al. Four years of ground-based MAX-DOAS observations of HONO and NO2 in the Beijing area. Atmos. Chem. Phys. 14, 765–781 (2014).
van der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 9, 697–720 (2017).
Kleffmann, J. Daytime sources of nitrous acid (HONO) in the atmospheric boundary layer. Chem. Phys. Chem. 8, 1137–1144 (2007).
Peng, Q. et al. HONO emissions from western U.S. wildfires provide dominant radical source in fresh wildfire smoke. Environ. Sci. Technol. 54, 5954–5963 (2020).
Liu, X. et al. Airborne measurements of western U.S. wildfire emissions: comparison with prescribed burning and air quality implications. J. Geophys. Res. Atmos. 112, 6108–6129 (2017).
Alicke, B., Platt, U. & Stutz, J. Impact of nitrous acid photolysis on the total hydroxyl radical budget during the Limitation of Oxidant Production/Pianura Padana Produzione di Ozono study in Milan. J. Geophys. Res. Atmos. 107, 8196 (2002).
Volkamer, R., Sheehy, P., Molina, L. T. & Molina, M. J. Oxidative capacity of the Mexico City atmosphere—Part 1: a radical source perspective. Atmos. Chem. Phys. 10, 6969–2991 (2010).
Kleffmann, J. et al. Daytime formation of nitrous acid: a major source of OH radicals in a forest. Geophys. Res. Lett. 32, L05818 (2005).
Friedl, M. A. et al. MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182 (2010).
Veefkind, J. P. et al. TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications. Remote Sens. Environ. 120, 70–83 (2012).
Levelt, P. F. et al. The ozone monitoring instrument. IEEE Trans. Geosci. Remote Sens. 44, 1093–1101 (2006).
Bovensmann, H. et al. SCIAMACHY: mission objectives and measurement modes. J. Atmos. Sci. 56, 127–150 (1999).
De Smedt, I. et al. Algorithm theoretical baseline for formaldehyde retrievals from S5P TROPOMI and from the QA4ECV project. Atmos. Meas. Tech. 11, 2395–2426 (2018).
Wang, Y. et al. MAX-DOAS measurements of HONO slant column densities during the MAD-CAT campaign: inter-comparison, sensitivity studies on spectral analysis settings, and error budget. Atmos. Meas. Tech. 10, 3719–3742 (2017).
Behrens, L. K. et al. GOME-2A retrievals of tropospheric NO2 in different spectral ranges—influence of penetration depth. Atmos. Meas. Tech. 11, 2769–2795 (2018).
Richter, A. & Burrows, J. P. Retrieval of tropospheric NO2 from GOME measurements. Adv. Space Res. 29, 1673–1683 (2002).
Palmer, P. I. et al. Air mass factor formulation for spectroscopic measurements from satellites: application to formaldehyde retrievals from the Global Ozone Monitoring Experiment. J. Geophys. Res. 106, 14539–14550 (2001).
Spurr, R. J. VLIDORT: a linearized pseudo-spherical vector discrete ordinate radiative transfer code for forward model and retrieval studies in multilayer multiple scattering media. J. Quant. Spectrosc. Rad. 102, 316–342 (2006).
Wang, P., Tuinder, O. N. E., Tilstra, L. G., de Graaf, M. & Stammes, P. Interpretation of FRESCO cloud retrievals in case of absorbing aerosol events. Atmos. Chem. Phys. 12, 9057–9077 (2012).
Leitão, J. et al. On the improvement of NO2 satellite retrievals—aerosol impact on the airmass factors. Atmos. Meas. Tech. 3, 475–493 (2010).
Brioude, J. et al. The Lagrangian particle dispersion model FLEXPART-WRF version 3.1. Geosci. Model Dev. 6, 1889–1904 (2013).
Kaiser, J. W. et al. Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power. Biogeosciences 9, 527–554 (2012).
Lerot, C., Stavrakou, T., De Smedt, I., Müller, J.-F. & Van Roozendael, M. Glyoxal vertical columns from GOME-2 backscattered light measurements and comparisons with a global model. Atmos. Chem. Phys. 10, 12059–12072 (2010).
Stavrakou, T. et al. Impact of short-term climate variability on volatile organic compounds emissions assessed using OMI satellite formaldehyde observations. Geophys. Res. Lett. 45, 1621–1629 (2018).
Müller, J.-F., Stavrakou, T. & Peeters, J. Chemistry and deposition in the Model of Atmospheric composition at Global and Regional scales using Inversion Techniques for Trace gas Emissions (MAGRITTEv1.0)—Part 1: chemical mechanism. Geosci. Model Dev. 12, 2307–2356 (2019).
Madronich, S. in Environmental Effects of Ultraviolet Radiation (ed. Tevini, M.) 17–69 (Lewis, 1993).
Dee, D. P. et al. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597 (2011).
Trentmann, J., Andreae, M. O. & Graf, H.-F. Chemical processes in a young biomass-burning plume. J. Geophys. Res. 108, 4705 (2003).
Jacob, D. J. et al. The Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) mission: design, execution, and first results. Atmos. Chem. Phys. 10, 5191–5212 (2010).
Simpson, I. J. et al. Boreal forest fire emissions in fresh Canadian smoke plumes: C1–C10 volatile organic compounds (VOCs), CO2, CO, NO2, NO, HCN and CH3CN. Atmos. Chem. Phys. 11, 6445–6463 (2011).
Atkinson, R. et al. Evaluated kinetic and photochemical data for atmospheric chemistry: volume II—gas phase reactions of organic species. Atmos. Chem. Phys. 6, 3625–4055 (2006).
Saunders, S. M., Jenkin, M. E., Derwent, R. G. & Pilling, M. J. Protocol for the development of the Master Chemical Mechanism, MCM v3 (Part A): tropospheric degradation of non-aromatic volatile organic compounds. Atmos. Chem. Phys. 3, 161–180 (2003).
Jenkin, M. E., Young, J. C. & Rickard, A. R. The MCMv3.3. 1 degradation scheme for isoprene. Atmos. Chem. Phys. 15, 11433–11459 (2015).
Colmenar, I. et al. UV absorption cross-sections between 290 and 280 of a series of furanaldehydes: estimation of their photolysis lifetimes. Atmos. Environ. 103, 1–6 (2015).
Gandini, A., Parsons, J. M. & Back, R. A. The photochemistry of 2-furaldehyde vapour. II. Photodecomposition: direct photolysis at 253.7 and 313 nm and Hg(3P1)-sensitized decomposition. Can. J. Chem. 54, 3095–3101 (1976).
Burkholder, J. B. et al. Chemical Kinetics and Photochemical Data for Use in Atmospheric Studies (Jet Propulsion Laboratory, 2015); http://jpldataeval.jpl.nasa.gov
Stavrakou, T. et al. Evaluating the performance of pyrogenic and biogenic emission inventories against one decade of space-based formaldehyde columns. Atmos. Chem. Phys. 9, 1037–1060 (2009).
Volkamer, R. et al. BB-FLUX: Biomass Burning Fluxes of Trace Gases and Aerosols (University of Wyoming—Research Flight Center, 2019); http://flights.uwyo.edu/projects/bbflux18/
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.
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).
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.
About this article
Cite this article
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