Pepijn Veefkind

Wildfire smoke influences air quality and climate through the scattering and absorption of solar radiation by aerosol particles, yet the factors controlling aerosol optical properties and their spatial variability within smoke plumes remain uncertain. Here, we use satellite observations of aerosol optical properties and trace gases from the TROPOspheric Monitoring Instrument (TROPOMI) to investigate the influence of combustion conditions on aerosol optical properties and their spatial variability within wildfire smoke plumes during the Australian 2018–2019 and 2019–2020 bushfire seasons. Substantial differences in aerosol optical depth (AOD) and single-scattering albedo (SSA) are observed among major land cover types. Wildfires characterized by stronger flaming combustion, inferred from nitrogen dioxide (NO₂) and carbon monoxide (CO) column density ratios (ΔNO₂/ΔCO), are associated with more absorbing plume-averaged aerosol populations, whereas smoldering-dominated fires are associated with more scattering smoke and larger AOD. These relationships indicate that combustion conditions exert a first-order control on aerosol absorptivity, while aerosol loading is more strongly influenced by total emissions. Within individual plumes, aerosol loading and trace gas abundance remain concentrated in NO₂-defined plume cores, whereas SSA exhibits much weaker spatial gradients. Independent box-size analyses further show a systematic tendency toward higher SSA away from plume cores and source regions while ΔNO₂/ΔCO decreases, indicating that these spatial changes occur concurrently with atmospheric processing. Although chemical processing, dilution, and mixing cannot be separated, these observations provide constraints for evaluating model representations of wildfire emissions, plume evolution, and smoke radiative effects.

Wildfire smoke influences air quality and climate through the scattering and absorption of solar radiation by aerosol particles, yet the factors controlling aerosol optical properties and their spatial variability within smoke plumes remain uncertain. Here, we use satellite observations of aerosol optical properties and trace gases from the TROPOspheric Monitoring Instrument (TROPOMI) to investigate the influence of combustion conditions on aerosol optical properties and their spatial variability within wildfire smoke plumes during the Australian 2018–2019 and 2019–2020 bushfire seasons. Substantial differences in aerosol optical depth (AOD) and single-scattering albedo (SSA) are observed among major land cover types. Wildfires characterized by stronger flaming combustion, inferred from nitrogen dioxide (NO₂) and carbon monoxide (CO) column density ratios (ΔNO₂/ΔCO), are associated with more absorbing plume-averaged aerosol populations, whereas smoldering-dominated fires are associated with more scattering smoke and larger AOD. These relationships indicate that combustion conditions exert a first-order control on aerosol absorptivity, while aerosol loading is more strongly influenced by total emissions. Within individual plumes, aerosol loading and trace gas abundance remain concentrated in NO₂-defined plume cores, whereas SSA exhibits much weaker spatial gradients. Independent box-size analyses further show a systematic tendency toward higher SSA away from plume cores and source regions while ΔNO₂/ΔCO decreases, indicating that these spatial changes occur concurrently with atmospheric processing. Although chemical processing, dilution, and mixing cannot be separated, these observations provide constraints for evaluating model representations of wildfire emissions, plume evolution, and smoke radiative effects.

Wildfire smoke influences air quality and climate through the scattering and absorption of solar radiation by aerosol particles, yet the factors controlling aerosol optical properties and their spatial variability within smoke plumes remain uncertain. Here, we use satellite observations of aerosol optical properties and trace gases from the TROPOspheric Monitoring Instrument (TROPOMI) to investigate the influence of combustion conditions on aerosol optical properties and their spatial variability within wildfire smoke plumes during the Australian 2018–2019 and 2019–2020 bushfire seasons. Substantial differences in aerosol optical depth (AOD) and single-scattering albedo (SSA) are observed among major land cover types. Wildfires characterized by stronger flaming combustion, inferred from nitrogen dioxide (NO2) and carbon monoxide (CO) column density ratios (ΔNO2/ΔCO), are associated with more absorbing plume-averaged aerosol populations, whereas smoldering-dominated fires are associated with more scattering smoke and larger AOD. These relationships indicate that combustion conditions exert a first-order control on aerosol absorptivity, while aerosol loading is more strongly influenced by total emissions. Within individual plumes, aerosol loading and trace gas abundance remain concentrated in NO2-defined plume cores, whereas SSA exhibits much weaker spatial gradients. Independent box-size analyses further show a systematic tendency toward higher SSA away from plume cores and source regions while ΔNO2/ΔCO decreases, indicating that these spatial changes occur concurrently with atmospheric processing. Although chemical processing, dilution, and mixing cannot be separated, these observations provide constraints for evaluating model representations of wildfire emissions, plume evolution, and smoke radiative effects.

The Twin Anthropogenic Greenhouse Gas Observers (TANGO) mission, scheduled for launch in 2028, will observe carbon dioxide (CO ₂ ), methane (CH ₄ ), and nitrogen dioxide (NO ₂ ) emission plumes from more than 10 000 industrial facilities per year using two formation-flying CubeSats. In general, NO ₂ plume structures exhibit substantially lower random noise than the corresponding CO ₂ features, motivating a synergistic exploitation of both species for improved emission quantification and for enhanced characterization of atmospheric chemistry within plumes. Using large-eddy simulations in combination with the integrated mass enhancement (IME) method, we assess NO ₂ -based masking of CO ₂ plumes for emission rates in the range 2.0–12.5 Mt yr ⁻¹ . This yields CO ₂ emission estimates with precisions between 18.5 % and 3.4 %, depending on the emission strength, and corresponding absolute biases that decrease from 15.3 % to 2.4 %. As an alternative approach, we analyze the observed CO ₂ / NO ₂ ratio. By fitting an empirical model to measurement simulations of this ratio and subsequently reconstructing the CO ₂ plume from NO ₂ observations, we obtain a substantial reduction in the apparent noise of the reconstructed CO ₂ plume. For the inferred emission rates, however, the precision remains largely unchanged, with the masking approach consistently showing lower absolute biases than the reconstruction approach across all emission strengths. Consequently, despite reduced errors in individual pixel-level observations, plume reconstruction does not enhance the precision of CO ₂ emission estimates, because it converts originally uncorrelated pixel noise into spatially correlated errors. Neglecting these spatial error correlations leads to a severe underestimation of the retrieval uncertainty. A key advantage of the empirical CO ₂ / NO ₂ ratio model is its ability to characterize plume chemistry. Here CO ₂ serves as non-decaying reference tracer. We demonstrate that an effective timescale for the nitric oxide (NO) to NO ₂ conversion in emission plumes can be inferred for sources with CO ₂ emissions >5.0 Mt yr ⁻¹ . Application of the method to Environmental Mapping and Analysis Program (EnMAP) observations demonstrates its practical utility, confirming its applicability to real satellite data.

The Fraunhofer absorption feature at 430 nm influences the retrieval of nitrogen dioxide (NO₂) from measurements by satellite-based instruments such as the Tropospheric Monitoring Instrument (TROPOMI) and Ozone Monitoring Instrument (OMI). The width and depth of the feature in the measured spectrum are affected by rotational Raman scattering (RRS) throughout the atmosphere and by vibrational Raman scattering (VRS) in open water bodies. RRS, or the Ring-effect, is accounted for in the Differential Optical Absorption Spectroscopy (DOAS) retrieval of the NO₂ slant column density (SCD) by means of a scalable reference spectrum, which will not fully pick up the variation of the depth of the 430 nm feature with the solar activity cycle. It is not possible to account for VRS with a scalable reference spectrum, since VRS characteristics depend on several aspects, including the viewing geometry and the material dissolved in the water, such as chlorophyll. From detailed inspection of DOAS fit residuals, the difference between the measured and modelled spectra, it is clear that the 430 nm feature disturbs the NO₂ SCD retrieval. In this paper we investigate the benefits of removing the wavelength range 428–433 nm from the DOAS retrieval. This “NO₂-gap approach” reduces the SCD error and the RMS error of the fit over water bodies by 10 %–20 % and the fit residual for the remaining parts of the window improves. Over some land scenes, where the residual outside the 428–433 nm range looks very good, the SCD error and RMS error are reduced by 5 %–10 %. For other areas the fit residual does not deteriorate by the NO₂-gap approach. Over ocean waters the SCD is seen to decrease by a few percent, which leads to a decrease of the stratospheric NO₂ column of on average up to −2 µmol m⁻² in the tropics. Over land the change in SCD may be positive or negative by a few percent, which in combination with the change in the stratospheric column leads to changes in the tropospheric NO₂ column of on average ±2 µmol m⁻². These changes are too small to alter the general conclusions of the routine validation of TROPOMI data. Because of the improvement of the SCD error and systematic improvements over open water it has been decided to implement the NO₂-gap approach in the new processor versions of TROPOMI (since 22 November 2025) and OMI (since April 2026, with full mission reprocessing).

The Twin Anthropogenic Greenhouse Gas Observers (TANGO) mission, scheduled for launch in 2028, will observe CO₂, CH₄, and NO₂ emission plumes from more than 10,000 industrial facilities per year using two formation-flying CubeSats. Here, NO₂ plume structures exhibit substantially lower random noise than the corresponding CO₂ features, motivating a synergistic exploitation of both species for improved emission quantification and for enhanced characterization of atmospheric chemistry within plumes. Using large-eddy simulations in combination with the Integrated Mass Enhancement (IME) method, we assess NO₂-based masking of CO₂ plumes for emission rates in the range 2.0–12.5 Mt yr⁻¹. This yields CO₂ emission estimates with precisions between 18.5 % and 3.4 %, depending on the emission strength, and corresponding absolute biases that decrease from 15.3 % to 2.4 %. As an alternative approach, we analyze the observed CO₂/NO₂ ratio. By fitting an empirical model to measurement simulations of this ratio and subsequently reconstructing the CO₂ plume from NO₂ observations, we obtain a substantial reduction in the apparent noise of the reconstructed CO₂ plume. For the inferred emission rates, however, the precision remains largely unchanged. Consequently, despite reduced errors in individual pixel-level observations, plume reconstruction does not enhance the precision of CO₂ emission estimates, because it converts originally uncorrelated pixel noise into spatially correlated errors. Neglecting these spatial error correlations leads to a severe underestimation of the retrieval uncertainty. A key advantage of the empirical CO₂/NO₂ ratio model is its ability to characterize plume chemistry. Here CO₂ serves as non-decaying reference tracer. We demonstrate that an effective timescale for the NO → NO₂ conversion in emission plumes can be inferred for sources with CO₂ emissions > 5.0 Mt yr⁻¹. Application of the method to Environmental Mapping and Analysis Program (EnMAP) observations demonstrates its practical utility, confirming its applicability to real satellite data.

The Twin Anthropogenic Greenhouse Gas Observers (TANGO) mission, scheduled for launch in 2028, will observe CO₂, CH₄, and NO₂ emission plumes from more than 10,000 industrial facilities per year using two formation-flying CubeSats. Here, NO₂ plume structures exhibit substantially lower random noise than the corresponding CO₂ features, motivating a synergistic exploitation of both species for improved emission quantification and for enhanced characterization of atmospheric chemistry within plumes. Using large-eddy simulations in combination with the Integrated Mass Enhancement (IME) method, we assess NO₂-based masking of CO₂ plumes for emission rates in the range 2.0–12.5 Mt yr⁻¹. This yields CO₂ emission estimates with precisions between 18.5 % and 3.4 %, depending on the emission strength, and corresponding absolute biases that decrease from 15.3 % to 2.4 %. As an alternative approach, we analyze the observed CO₂/NO₂ ratio. By fitting an empirical model to measurement simulations of this ratio and subsequently reconstructing the CO₂ plume from NO₂ observations, we obtain a substantial reduction in the apparent noise of the reconstructed CO₂ plume. For the inferred emission rates, however, the precision remains largely unchanged. Consequently, despite reduced errors in individual pixel-level observations, plume reconstruction does not enhance the precision of CO₂ emission estimates, because it converts originally uncorrelated pixel noise into spatially correlated errors. Neglecting these spatial error correlations leads to a severe underestimation of the retrieval uncertainty. A key advantage of the empirical CO₂/NO₂ ratio model is its ability to characterize plume chemistry. Here CO₂ serves as non-decaying reference tracer. We demonstrate that an effective timescale for the NO → NO₂ conversion in emission plumes can be inferred for sources with CO₂ emissions > 5.0 Mt yr⁻¹. Application of the method to Environmental Mapping and Analysis Program (EnMAP) observations demonstrates its practical utility, confirming its applicability to real satellite data.

The Twin Anthropogenic Greenhouse Gas Observers (TANGO) mission, scheduled for launch in 2028, will observe CO₂, CH₄, and NO₂ emission plumes from more than 10,000 industrial facilities per year using two formation-flying CubeSats. Here, NO₂ plume structures exhibit substantially lower random noise than the corresponding CO₂ features, motivating a synergistic exploitation of both species for improved emission quantification and for enhanced characterization of atmospheric chemistry within plumes. Using large-eddy simulations in combination with the Integrated Mass Enhancement (IME) method, we assess NO₂-based masking of CO₂ plumes for emission rates in the range 2.0–12.5 Mt yr⁻¹. This yields CO₂ emission estimates with precisions between 18.5 % and 3.4 %, depending on the emission strength, and corresponding absolute biases that decrease from 15.3 % to 2.4 %. As an alternative approach, we analyze the observed CO₂/NO₂ ratio. By fitting an empirical model to measurement simulations of this ratio and subsequently reconstructing the CO₂ plume from NO₂ observations, we obtain a substantial reduction in the apparent noise of the reconstructed CO₂ plume. For the inferred emission rates, however, the precision remains largely unchanged. Consequently, despite reduced errors in individual pixel-level observations, plume reconstruction does not enhance the precision of CO₂ emission estimates, because it converts originally uncorrelated pixel noise into spatially correlated errors. Neglecting these spatial error correlations leads to a severe underestimation of the retrieval uncertainty. A key advantage of the empirical CO₂/NO₂ ratio model is its ability to characterize plume chemistry. Here CO₂ serves as non-decaying reference tracer. We demonstrate that an effective timescale for the NO → NO₂ conversion in emission plumes can be inferred for sources with CO₂ emissions > 5.0 Mt yr⁻¹. Application of the method to Environmental Mapping and Analysis Program (EnMAP) observations demonstrates its practical utility, confirming its applicability to real satellite data.

The Twin Anthropogenic Greenhouse Gas Observers (TANGO) mission, scheduled for launch in 2028, will observe CO₂, CH₄, and NO₂ emission plumes from more than 10,000 industrial facilities per year using two formation-flying CubeSats. Here, NO₂ plume structures exhibit substantially lower random noise than the corresponding CO₂ features, motivating a synergistic exploitation of both species for improved emission quantification and for enhanced characterization of atmospheric chemistry within plumes. Using large-eddy simulations in combination with the Integrated Mass Enhancement (IME) method, we assess NO₂-based masking of CO₂ plumes for emission rates in the range 2.0–12.5 Mt yr⁻¹. This yields CO₂ emission estimates with precisions between 18.5 % and 3.4 %, depending on the emission strength, and corresponding absolute biases that decrease from 15.3 % to 2.4 %. As an alternative approach, we analyze the observed CO₂/NO₂ ratio. By fitting an empirical model to measurement simulations of this ratio and subsequently reconstructing the CO₂ plume from NO₂ observations, we obtain a substantial reduction in the apparent noise of the reconstructed CO₂ plume. For the inferred emission rates, however, the precision remains largely unchanged. Consequently, despite reduced errors in individual pixel-level observations, plume reconstruction does not enhance the precision of CO₂ emission estimates, because it converts originally uncorrelated pixel noise into spatially correlated errors. Neglecting these spatial error correlations leads to a severe underestimation of the retrieval uncertainty. A key advantage of the empirical CO₂/NO₂ ratio model is its ability to characterize plume chemistry. Here CO₂ serves as non-decaying reference tracer. We demonstrate that an effective timescale for the NO → NO₂ conversion in emission plumes can be inferred for sources with CO₂ emissions > 5.0 Mt yr⁻¹. Application of the method to Environmental Mapping and Analysis Program (EnMAP) observations demonstrates its practical utility, confirming its applicability to real satellite data.