Isoprene is the dominant non-methane organic compound emitted to the atmosphere1,2,3. It drives ozone and aerosol production, modulates atmospheric oxidation and interacts with the global nitrogen cycle4,5,6,7,8. Isoprene emissions are highly uncertain1,9, as is the nonlinear chemistry coupling isoprene and the hydroxyl radical, OH—its primary sink10,11,12,13. Here we present global isoprene measurements taken from space using the Cross-track Infrared Sounder. Together with observations of formaldehyde, an isoprene oxidation product, these measurements provide constraints on isoprene emissions and atmospheric oxidation. We find that the isoprene–formaldehyde relationships measured from space are broadly consistent with the current understanding of isoprene–OH chemistry, with no indication of missing OH recycling at low nitrogen oxide concentrations. We analyse these datasets over four global isoprene hotspots in relation to model predictions, and present a quantification of isoprene emissions based directly on satellite measurements of isoprene itself. A major discrepancy emerges over Amazonia, where current underestimates of natural nitrogen oxide emissions bias modelled OH and hence isoprene. Over southern Africa, we find that a prominent isoprene hotspot is missing from bottom-up predictions. A multi-year analysis sheds light on interannual isoprene variability, and suggests the influence of the El Niño/Southern Oscillation.
This is a preview of subscription content
Subscription info for Chinese customers
We have a dedicated website for our Chinese customers. Please go to naturechina.com to subscribe to this journal.
Get time limited or full article access on ReadCube.
All prices are NET prices.
The CrIS Level 1B data used in this work are publicly available at https://snpp-sounder.gesdisc.eosdis.nasa.gov/data/SNPP_Sounder_Level1/SNPPCrISL1BNSR.1/. The isoprene column data employed in this work are available at https://doi.org/10.13020/v959-dr15. The airborne data are publicly available for SENEX at http://esrl.noaa.gov/csd/projects/senex/ and for SEAC4RS at http://www-air.larc.nasa.gov/missions/seac4rs/index.html. OMI QA4ECV HCHO and NO2 data are publicly available at http://www.qa4ecv.eu/ecvs.
Guenther, A. B. et al. The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions. Geosci. Model Dev. 5, 1471–1492 (2012).
Saunois, M. et al. The global methane budget 2000–2012. Earth Syst. Sci. Data 8, 697–751 (2016).
Huang, G. L. et al. Speciation of anthropogenic emissions of non-methane volatile organic compounds: a global gridded data set for 1970–2012. Atmos. Chem. Phys. 17, 7683–7701 (2017).
Trainer, M. et al. Models and observations of the impact of natural hydrocarbons on rural ozone. Nature 329, 705–707 (1987).
Hewitt, C. N. et al. Ground-level ozone influenced by circadian control of isoprene emissions. Nat. Geosci. 4, 671–674 (2011).
Mao, J. Q. et al. Ozone and organic nitrates over the eastern United States: sensitivity to isoprene chemistry. J. Geophys. Res. Atmos. 118, 11256–11268 (2013).
Lin, Y. H. et al. Epoxide as a precursor to secondary organic aerosol formation from isoprene photooxidation in the presence of nitrogen oxides. Proc. Natl Acad. Sci. USA 110, 6718–6723 (2013).
Bates, K. H. & Jacob, D. J. A new model mechanism for atmospheric oxidation of isoprene: global effects on oxidants, nitrogen oxides, organic products, and secondary organic aerosol. Atmos. Chem. Phys. 19, 9613–9640 (2019).
Arneth, A. et al. Global terrestrial isoprene emission models: sensitivity to variability in climate and vegetation. Atmos. Chem. Phys. 11, 8037–8052 (2011).
Lelieveld, J. et al. Atmospheric oxidation capacity sustained by a tropical forest. Nature 452, 737–740 (2008).
Fuchs, H. et al. Experimental evidence for efficient hydroxyl radical regeneration in isoprene oxidation. Nat. Geosci. 6, 1023–1026 (2013).
Feiner, P. A. et al. Testing atmospheric oxidation in an Alabama forest. J. Atmos. Sci. 73, 4699–4710 (2016).
Rohrer, F. et al. Maximum efficiency in the hydroxyl-radical-based self-cleansing of the troposphere. Nat. Geosci. 7, 559–563 (2014).
Bauwens, M. et al. Nine years of global hydrocarbon emissions based on source inversion of OMI formaldehyde observations. Atmos. Chem. Phys. 16, 10133–10158 (2016).
Valin, L. C., Fiore, A. M., Chance, K. & Abad, G. G. The role of OH production in interpreting the variability of CH2O columns in the southeast US. J. Geophys. Res. Atmos. 121, 478–493 (2016).
Barkley, M. P. et al. Net ecosystem fluxes of isoprene over tropical South America inferred from Global Ozone Monitoring Experiment (GOME) observations of HCHO columns. J. Geophys. Res. Atmos. 113, D20304 (2008).
Zhu, L. et al. Anthropogenic emissions of highly reactive volatile organic compounds in eastern Texas inferred from oversampling of satellite (OMI) measurements of HCHO columns. Environ. Res. Lett. 9, 114004 (2014).
Boeke, N. L. et al. Formaldehyde columns from the Ozone Monitoring Instrument: urban versus background levels and evaluation using aircraft data and a global model. J. Geophys. Res. Atmos. 116, D05303 (2011).
Fu, D. et al. Direct retrieval of isoprene from satellite-based infrared measurements. Nat. Commun. 10, 3811 (2019).
Brauer, C. S. et al. Quantitative infrared absorption cross sections of isoprene for atmospheric measurements. Atmos. Meas. Technol. 7, 3839–3847 (2014).
Razavi, A. et al. Global distributions of methanol and formic acid retrieved for the first time from the IASI/MetOp thermal infrared sounder. Atmos. Chem. Phys. 11, 857–872 (2011).
Clarisse, L., Clerbaux, C., Dentener, F., Hurtmans, D. & Coheur, P. F. Global ammonia distribution derived from infrared satellite observations. Nat. Geosci. 2, 479–483 (2009).
Franco, B. et al. A general framework for global retrievals of trace gases from IASI: application to methanol, formic acid, and PAN. J. Geophys. Res. Atmos. 123, 13963–13984 (2018).
Whitburn, S. et al. A flexible and robust neural network IASI-NH3 retrieval algorithm. J. Geophys. Res. Atmos. 121, 6581–6599 (2016).
Warneke, C. et al. Instrumentation and measurement strategy for the NOAA SENEX aircraft campaign as part of the Southeast Atmosphere Study 2013. Atmos. Meas. Technol. 9, 3063–3093 (2016).
Toon, O. B. et al. Planning, implementation, and scientific goals of the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) field mission. J. Geophys. Res. Atmos. 121, 4967–5009 (2016).
Xie, Y. et al. Understanding the impact of recent advances in isoprene photooxidation on simulations of regional air quality. Atmos. Chem. Phys. 13, 8439–8455 (2013).
Teng, A. P., Crounse, J. D. & Wennberg, P. O. Isoprene peroxy radical dynamics. J. Am. Chem. Soc. 139, 5367–5377 (2017).
Kim, S.-W., Barth, M. C. & Trainer, M. Impact of turbulent mixing on isoprene chemistry. Geophys. Res. Lett. 43, 7701–7708 (2016).
De Smedt, I. et al. Algorithm theoretical baseline for formaldehyde retrievals from S5P TROPOMI and from the QA4ECV project. Atmos. Meas. Technol. 11, 2395–2426 (2018).
Boersma, K. F. et al. Improving algorithms and uncertainty estimates for satellite NO2 retrievals: results from the quality assurance for the essential climate variables (QA4ECV) project. Atmos. Meas. Tech. 11, 6651–6678 (2018).
de Gouw, J. A. et al. Hydrocarbon removal in power plant plumes shows nitrogen oxide dependence of hydroxyl radicals. Geophys. Res. Lett. 46, 7752–7760 (2019).
Wei, D. D. et al. Environmental and biological controls on seasonal patterns of isoprene above a rain forest in central Amazonia. Agric. For. Meteorol. 256–257, 391–406 (2018).
Barkley, M. P. et al. Regulated large-scale annual shutdown of Amazonian isoprene emissions? Geophys. Res. Lett. 36, L04803 (2009).
Alves, E. G. et al. Leaf phenology as one important driver of seasonal changes in isoprene emissions in central Amazonia. Biogeosciences 15, 4019–4032 (2018).
Silvern, R. F. et al. Using satellite observations of tropospheric NO2 columns to infer long-term trends in US NOx emissions: the importance of accounting for the free tropospheric NO2 background. Atmos. Chem. Phys. 19, 8863–8878 (2019).
Belmonte Rivas, M. et al. OMI tropospheric NO2 profiles from cloud-slicing: constraints on surface emissions, convective transport and lightning NOx. Atmos. Chem. Phys. 15, 13519–13553 (2015).
Martin, S. T. et al. Introduction: observations and modeling of the Green Ocean Amazon (GoAmazon2014/5). Atmos. Chem. Phys. 16, 4785–4797 (2016).
Liu, Y. et al. Isoprene photochemistry over the Amazon rainforest. Proc. Natl Acad. Sci. USA 113, 6125–6130 (2016).
Guenther, A. et al. Isoprene emission estimates and uncertainties for the Central African EXPRESSO study domain. J. Geophys. Res. Atmos. 104, 30625–30639 (1999).
Marais, E. A. et al. Isoprene emissions in Africa inferred from OMI observations of formaldehyde columns. Atmos. Chem. Phys. 12, 6219–6235 (2012).
Otter, L. B., Guenther, A. & Greenberg, J. Seasonal and spatial variations in biogenic hydrocarbon emissions from southern African savannas and woodlands. Atmos. Environ. 36, 4265–4275 (2002).
Otter, L. et al. Spatial and temporal variations in biogenic volatile organic compound emissions for Africa south of the equator. J. Geophys. Res. Atmos. 108, 8505 (2003).
Stavrakou, T. et al. Global emissions of non-methane hydrocarbons deduced from SCIAMACHY formaldehyde columns through 2003–2006. Atmos. Chem. Phys. 9, 3663–3679 (2009).
Marais, E. A. et al. Improved model of isoprene emissions in Africa using Ozone Monitoring Instrument (OMI) satellite observations of formaldehyde: implications for oxidants and particulate matter. Atmos. Chem. Phys. 14, 7693–7703 (2014).
Wiedinmyer, C. et al. Ozarks Isoprene Experiment (OZIE): measurements and modeling of the “isoprene volcano”. J. Geophys. Res. Atmos. 110, D18307 (2005).
Kaiser, J. et al. High-resolution inversion of OMI formaldehyde columns to quantify isoprene emission on ecosystem-relevant scales: application to the southeast US. Atmos. Chem. Phys. 18, 5483–5497 (2018).
Hansen, D. A. et al. The southeastern aerosol research and characterization study: Part 1-overview. J. Air Waste Manag. Assoc. 53, 1460–1471 (2003).
Emmerson, K. M. et al. Current estimates of biogenic emissions from eucalypts uncertain for southeast Australia. Atmos. Chem. Phys. 16, 6997–7011 (2016).
Guenther, A. B. & Hills, A. J. Eddy covariance measurement of isoprene fluxes. J. Geophys. Res. Atmos. 103, 13145–13152 (1998).
Han, Y. et al. Suomi NPP CrIS measuremets, sensor data record algorithm, calibration and validation activities, and record data quality. J. Geophys. Res. Atmos. 118, 12734–12748 (2013).
Zavyalov, V. et al. Noise performance of the CrIS instrument. J. Geophys. Res. Atmos. 118, 13108–13120 (2013).
Millet, D. B. et al. A large and ubiquitous source of atmospheric formic acid. Atmos. Chem. Phys. 15, 6283–6304 (2015).
Fisher, J. A. et al. Organic nitrate chemistry and its implications for nitrogen budgets in an isoprene- and monoterpene-rich atmosphere: constraints from aircraft (SEAC4RS) and ground-based (SOAS) observations in the Southeast US. Atmos. Chem. Phys. 16, 5969–5991 (2016).
Marais, E. A. et al. Aqueous-phase mechanism for secondary organic aerosol formation from isoprene: application to the southeast United States and co-benefit of SO2 emission controls. Atmos. Chem. Phys. 16, 1603–1618 (2016).
Travis, K. R. et al. Why do models overestimate surface ozone in the Southeast United States? Atmos. Chem. Phys. 16, 13561–13577 (2016).
Liu, Y. J., Herdlinger-Blatt, I., McKinney, K. A. & Martin, S. T. Production of methyl vinyl ketone and methacrolein via the hydroperoxyl pathway of isoprene oxidation. Atmos. Chem. Phys. 13, 5715–5730 (2013).
Bates, K. H. et al. Gas phase production and loss of isoprene epoxydiols. J. Phys. Chem. A 118, 1237–1246 (2014).
Jacobs, M. I., Burke, W. J. & Elrod, M. J. Kinetics of the reactions of isoprene-derived hydroxynitrates: gas phase epoxide formation and solution phase hydrolysis. Atmos. Chem. Phys. 14, 8933–8946 (2014).
Crounse, J. D., Paulot, F., Kjaergaard, H. G. & Wennberg, P. O. Peroxy radical isomerization in the oxidation of isoprene. Phys. Chem. Chem. Phys. 13, 13607–13613 (2011).
Peeters, J., Nguyen, T. L. & Vereecken, L. HOx radical regeneration in the oxidation of isoprene. Phys. Chem. Chem. Phys. 11, 5935–5939 (2009).
Wolfe, G. M. et al. Photolysis, OH reactivity and ozone reactivity of a proxy for isoprene-derived hydroperoxyenals (HPALDs). Phys. Chem. Chem. Phys. 14, 7276–7286 (2012).
Peeters, J. & Muller, J. F. HOx radical regeneration in isoprene oxidation via peroxy radical isomerisations. II: experimental evidence and global impact. Phys. Chem. Chem. Phys. 12, 14227–14235 (2010).
Stavrakou, T., Peeters, J. & Muller, J. F. Improved global modelling of HOx recycling in isoprene oxidation: evaluation against the GABRIEL and INTEX-A aircraft campaign measurements. Atmos. Chem. Phys. 10, 9863–9878 (2010).
Squire, O. J. et al. Influence of isoprene chemical mechanism on modelled changes in tropospheric ozone due to climate and land use over the 21st century. Atmos. Chem. Phys. 15, 5123–5143 (2015).
Wennberg, P. O. et al. Gas-phase oxidation of isoprene and its major oxidation products. Chem. Rev. 118, 3337–3390 (2018).
Peeters, J. et al. Hydroxyl radical recycling in isoprene oxidation driven by hydrogen bonding and hydrogen tunneling: the upgraded LIM1 mechanism. J. Phys. Chem. A 118, 8625–8643 (2014).
Jørgensen, S. et al. Rapid hydrogen shift scrambling in hydroperoxy-substituted organic peroxy radicals. J. Phys. Chem. A 120, 266–275 (2016).
Møller, K. H. et al. The importance of peroxy radical hydrogen-shift reactions in atmospheric isoprene oxidation. J. Phys. Chem. A 123, 920–932 (2019).
Hu, L. et al. Isoprene emissions and impacts over an ecological transition region in the US Upper Midwest inferred from tall tower measurements. J. Geophys. Res. Atmos. 120, 3553–3571 (2015).
Emission Database for Global Atmospheric Research (EDGAR), Release Version 4.2 (European Commission (EC) Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency (PBL), 2011); http://edgar.jrc.ec.europa.eu.
2011 National Emissions Inventory (NEI) Data (EPA, 2015); http://www.epa.gov/air-emissions-inventories/2011-national-emissions-inventory-nei-data.
Kuhns, H., Green, M. & Etyemezian, V. Big Bend Regional Aerosol and Visibility Observational (BRAVO) Study Emissions Inventory (DRI, 2003).
Auvray, M. & Bey, I. Long-range transport to Europe: seasonal variations and implications for the European ozone budget. J. Geophys. Res. Atmos. 110, D11303 (2005).
Li, M. et al. MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP. Atmos. Chem. Phys. 17, 935–963 (2017).
van der Werf, G. R. et al. Global fire emissions estimates during 1997-2016. Earth Syst. Sci. Data 9, 697–720 (2017).
Murray, L. T. et al. Optimized regional and interannual variability of lightning in a global chemical transport model constrained by LIS/OTD satellite data. J. Geophys. Res. Atmos. 117, D20307 (2012).
Hudman, R. C. et al. Steps towards a mechanistic model of global nitric oxide emissions: implementation and space-based constraints. Atmos. Chem. Phys. 12, 7779–7795 (2012).
NASA U.S. Standard Atmosphere, 1976 Report No. NASA-TM-X-74335 (NASA, 1976).
Clough, S. A. et al. Atmospheric radiative transfer modeling: a summary of the AER codes. J. Quant. Spectrosc. Radiat. Transfer 91, 233–244 (2005).
Alvarado, M. J. et al. Performance of the Line-By-Line Radiative Transfer Model (LBLRTM) for temperature, water vapor, and trace gas retrievals: recent updates evaluated with IASI case studies. Atmos. Chem. Phys. 13, 6687–6711 (2013).
Gelaro, R. et al. The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). J. Clim. 30, 5419–5454 (2017).
Blum, E. K. & Li, L. K. Approximation-theory and feedforward networks. Neural Netw. 4, 511–515 (1991).
Hagan, M. T. & Menhaj, M. B. Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Netw. 5, 989–993 (1994).
Clough, S. A. et al. Forward model and Jacobians for tropospheric emission spectrometer retrievals. IEEE Trans. Geosci. Remote Sens. 44, 1308–1323 (2006).
Emmons, L. K. et al. Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4). Geosci. Model Dev. 3, 43–67 (2010).
Beer, R. TES on the Aura mission: scientific objectives, measurements, and analysis overview. IEEE Trans. Geosci. Remote Sens. 44, 1102–1105 (2006).
Smith, N. & Barnet, C. D. Uncertainty characterization and propagation in the Community Long-Term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS). Remote Sens. 11, 1227 (2019).
Wells, K. C. et al. Tropospheric methanol observations from space: retrieval evaluation and constraints on the seasonality of biogenic emissions. Atmos. Chem. Phys. 12, 5897–5912 (2012).
Chaliyakunnel, S., Millet, D. B., Wells, K. C., Cady-Pereira, K. E. & Shephard, M. W. A large underestimate of formic acid from tropical fires: constraints from space-borne measurements. Environ. Sci. Technol. 50, 5631–5640 (2016).
De Smedt, I. et al. QA4ECV HCHO Tropospheric Column Data from OMI (Version 1.1) (KNMI, 2017); https://doi.org/10.18758/71021031.
Zara, M. et al. Improved slant column density retrieval of nitrogen dioxide and formaldehyde for OMI and GOME-2A from QA4ECV: intercomparison, uncertainty characterisation, and trends. Atmos. Meas. Technol. 11, 4033–4058 (2018).
Zhu, L. et al. Observing atmospheric formaldehyde (HCHO) from space: validation and intercomparison of six retrievals from four satellites (OMI, GOME2A, GOME2B, OMPS) with SEAC4RS aircraft observations over the southeast US. Atmos. Chem. Phys. 16, 13477–13490 (2016).
Shen, L. et al. The 2005-2016 trends of formaldehyde columns over China observed by satellites: increasing anthropogenic emissions of volatile organic compounds and decreasing agricultural fire emissions. Geophys. Res. Lett. 46, 4468–4475 (2019).
Boersma, K. F. et al. QA4ECV NO 2 Tropospheric and Stratospheric Vertical Column Data from GOME-2A (Version 1.1) (KNMI, 2017); https://doi.org/10.21944/qa4ecv-no2-gome2a-v1.1.
This work was supported by the NASA Atmospheric Composition Modeling and Analysis Program (Grant Number NNX17AF61G) and by the Minnesota Supercomputing Institute. We thank D. Fu for providing optimal estimation isoprene retrievals over Amazonia and input on this manuscript; C. Barnet, E. Manning and R. Monarrez for providing CLIMCAPS HNO3 retrievals; M. Alvarado, K. Cady-Pereira, D. Gombos, J. Hegarty and I. Strickland for generating and testing isoprene absorption look-up tables employed here; and E. Edgerton for providing isoprene data from the SouthEastern Aerosol Research and CHaracterization (SEARCH) network. The SEARCH network was sponsored by the Southern Company and the Electric Power Research Institute. Isoprene measurements aboard the NASA DC-8 during SEAC4RS were supported by the Austrian Federal Ministry for Transport, Innovation and Technology (bmvit) through the Austrian Space Applications Programme (ASAP) of the Austrian Research Promotion Agency (FFG). T. Mikoviny is acknowledged for his support during SEAC4RS. We thank S. Springston for GoAmazon T3 data, which were supported by the ARM Climate Research Facility, the Central Office of the Large-Scale Biosphere Atmosphere Experiment in Amazonia (LBA), the Instituto Nacional de Pesquisas da Amazonia (INPA) and the Universidade do Estado do Amazonia (UEA). Part of this work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract to NASA.
The authors declare no competing interests.
Peer review information Nature thanks Klaas Boersma, Jean-Francois Müller and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
a, Brightness temperature (Tb) difference for simulated spectra with and without isoprene (black), nitric acid (red), ammonia (violet) and CFC-12 (yellow) and a 10% perturbation in water vapour (cyan). Red and blue arrows indicate the ν28 on-peak and off-peak spectral points used to calculate ΔTb. Simulations were performed with LBLRTM80,81 for an isoprene profile with 5 ppb in the boundary layer (P > 800 hPa) that decays exponentially aloft, and US standard atmosphere profiles of temperature, water vapour and nitric acid79. b, Relationship between ΔTb and isoprene column density, shaded by thermal contrast, for the full synthetic dataset used in this work. c, Global distribution of surface-atmosphere thermal contrast at the time of the CrIS overpass. Maps are derived from time-interpolated GMAO temperatures for January, April, July and October.
Extended Data Fig. 2 Global distribution of isoprene columns, emissions and lifetime as predicted by GEOS-Chem.
Predicted columns (left), emissions (middle) and lifetime (z < 500 m; right) are shown at 13:30 lt for January, April, July and October 2013 (top to bottom).
Extended Data Fig. 3 Statistical uncertainty in the global distribution of monthly mean isoprene:HCHO ratios as a function of isoprene and NOx regime.
a, Relative 95% confidence interval in the mean ratio for each isoprene and tropospheric NO2 bin. b, Number of observations in each bin.
Plotted are the mean (left) and relative standard deviation (right) across the 10 ANNs for January, April, July and October 2013 (top to bottom).
Extended Data Fig. 5 Boundaries of the four regions examined in the seasonal bar plots shown in Figs. 5, 6.
Plotted are the HCHO columns measured by OMI (left) and simulated by GEOS-Chem (right) at ~13:30 lt for January, April, July and October 2013 (top to bottom).
a, Function used for cloud screening CrIS L1B data before ΔTb calculation. The black line shows the modelled clear-sky difference between the 900 cm−1 brightness temperature and surface skin temperature, as a function of water vapour column density (calculated using LBLRTM80,81). The solid red line is the linear approximation used here, and the dashed red line represents a less stringent threshold used to test the sensitivity of the results to our cloud screening approach. b, c, Sensitivity of the CrIS brightness temperature differences (b) and isoprene columns (c) to cloud screening. Data shown represent the median relative differences between the base-case results (derived using the solid red line in a) and those derived using the less stringent cloud screening threshold (dashed red line in a). d, e, Scatterplots of the predicted versus true isoprene columns for the six-predictor ANN (d) and an ANN in which ΔTb is withheld as a predictor variable (e). Red dots show the mean of the ten ANN predictions, and blue error bars show the standard deviation across the predictions. f, The relative uncertainty (based on the difference between the mean ANN predicted value and the true value) for the six-predictor ANN, binned as a function of thermal contrast and isoprene column density.
The maps were derived using ANN- (left) and optimal estimation- (right) based approaches. Data are shown for September 2014 and displayed as absolute columns.
About this article
Cite this article
Wells, K.C., Millet, D.B., Payne, V.H. et al. Satellite isoprene retrievals constrain emissions and atmospheric oxidation. Nature 585, 225–233 (2020). https://doi.org/10.1038/s41586-020-2664-3