Joost A. de Gouw

Climate change has led to an increase in the number and size of wildfires in western North America, and their emissions of particulate matter and reactive trace gases threaten to offset otherwise improving air quality. Most air quality models do not dynamically update the chemical composition of biomass burning emissions with each model time step. However, laboratory, field, and satellite remote sensing observations indicate that the chemical composition of wildfire emissions changes with evolving combustion conditions (i.e., flaming vs. smoldering). We have previously shown that the Tropospheric Monitoring Instrument (TROPOMI) can be used to observe day‐to‐day changes in the chemical composition of wildfire emissions, during the transition from flaming to smoldering combustion. Here, we present the use of the Hourly Wildfire Potential index (HWP) to predict the impact of meteorological changes on wildfire activity, combined with TROPOMI observations of enhanced nitrogen dioxide emissions compared with that of carbon monoxide (/CO), to parameterize how biomass burning nitrogen oxide () emissions change with evolving combustion conditions. We implemented this parameterization in the High‐Resolution Rapid Refresh model coupled with Chemistry (HRRR‐Chem), developed by National Oceanic and Atmospheric Administration's Global Systems Laboratory. HRRR‐Chem is an experimental, high‐resolution chemical transport model with 3 × 3 horizontal grid spacing over the contiguous US, based on the Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem). We compared modeled smoke and trace gases with airborne measurements to evaluate our model. Overall, we found that modifying biomass burning emissions based on HWP significantly improved predicted ozone concentrations in smoke plumes.

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.

Urban secondary organic aerosol (SOA) contributes to degraded air quality, which can affect human health. Improvements in Los Angeles (LA), CA, air quality have mainly plateaued since 2010. In summer 2022, measurements were made to quantify the SOA formation potential (SOA-FP) in ambient LA air. Two oxidation flow reactors (OFRs) ingested ambient air: one was equipped with an electrically conductive polymer inlet that denuded lower-volatility species, and the other was run without an inlet. This allowed the separate quantification of SOA-FP from higher- vs. lower-volatility precursors. To our knowledge these are the first direct measurements of these fractions. Measured ambient SOA was similar, and total SOA-FP was lower in 2022 vs. 2010, consistent with higher ambient OH causing greater consumptions of SOA precursors in 2022. The dual-OFR measurements suggest ∼ 31 % of the total SOA-FP is due to compounds with volatilities in the semi-volatile organic compound (SVOC) and lower intermediate-volatility organic compound (IVOC) ranges. Evidence suggests these lower-volatility precursors are mainly from direct emissions, rather than oxidation products from upwind aging. Results are compared to two box models: one based on CRACMM and the other adapted from a recent Caltech publication. CRACMM predicted ambient OA well but underpredicted SOA-FP by about a factor of 2, while the Caltech model underestimated OA and overpredicted SOA-FP by a factor of 2.5. Our study finds terpenoids contribute to, but do not dominate, SOA-FP.

Ground-level ozone remains a persistent challenge in East Asian urban centers, where concentrations continue rising despite significant reductions in precursor emissions. Designing effective mitigation is complicated by the non-linear relationship between precursor abundance and reactivity. Standard data-driven approaches often suffer from "survivor bias," systematically undervaluing highly reactive precursors that are rapidly depleted. We introduce a process-informed machine learning framework that uses net chemical consumption (ΔVOC) rather than ambient concentrations to resolve this attribution failure. Applied to measurements from a NOₓ-saturated roadside site in Seoul, South Korea, the approach reveals a robust quantitative relationship between intrinsic hydroxyl radical reactivity (kOH) and ozone formation sensitivity. Across a comprehensive suite of precursors—including oxygenated VOCs (e.g., formaldehyde, acetone) and aromatics—the framework identifies reactive aromatics (trimethylbenzenes, xylenes) and biogenics (isoprene, monoterpenes) as the dominant kinetic drivers. Whereas static metrics such as OFP rank precursors by stoichiometric capacity under idealized accumulated conditions, the process-informed framework shows that realized ozone production in fresh urban plumes is governed by kinetic turnover rather than abundance, with this kinetic selectivity further amplified during high-ozone episodes. These results indicate that mass-based VOC inventories and OFP-style rankings, when applied without kinetic context, can systematically misallocate control priorities in NOₓ-saturated urban regimes. Because the framework requires only sub-hourly co-located VOC and ozone observations and no prescribed mechanism, it offers a complementary empirical pathway in settings where explicit mechanism-based modeling is constrained by incomplete VOC speciation or unmeasured radical precursors.

Ground-level ozone remains a persistent challenge in East Asian urban centers, where concentrations continue rising despite significant reductions in precursor emissions. Designing effective mitigation is complicated by the non-linear relationship between precursor abundance and reactivity. Standard data-driven approaches often suffer from "survivor bias," systematically undervaluing highly reactive precursors that are rapidly depleted. We introduce a process-informed machine learning framework that uses net chemical consumption (ΔVOC) rather than ambient concentrations to resolve this attribution failure. Applied to measurements from a NOₓ-saturated roadside site in Seoul, South Korea, the approach reveals a robust quantitative relationship between intrinsic hydroxyl radical reactivity (kOH) and ozone formation sensitivity. Across a comprehensive suite of precursors—including oxygenated VOCs (e.g., formaldehyde, acetone) and aromatics—the framework identifies reactive aromatics (trimethylbenzenes, xylenes) and biogenics (isoprene, monoterpenes) as the dominant kinetic drivers. Whereas static metrics such as OFP rank precursors by stoichiometric capacity under idealized accumulated conditions, the process-informed framework shows that realized ozone production in fresh urban plumes is governed by kinetic turnover rather than abundance, with this kinetic selectivity further amplified during high-ozone episodes. These results indicate that mass-based VOC inventories and OFP-style rankings, when applied without kinetic context, can systematically misallocate control priorities in NOₓ-saturated urban regimes. Because the framework requires only sub-hourly co-located VOC and ozone observations and no prescribed mechanism, it offers a complementary empirical pathway in settings where explicit mechanism-based modeling is constrained by incomplete VOC speciation or unmeasured radical precursors.

Ground-level ozone remains a persistent challenge in East Asian urban centers, where concentrations continue rising despite significant reductions in precursor emissions. Designing effective mitigation is complicated by the non-linear relationship between precursor abundance and reactivity. Standard data-driven approaches often suffer from "survivor bias," systematically undervaluing highly reactive precursors that are rapidly depleted. We introduce a process-informed machine learning framework that uses net chemical consumption (ΔVOC) rather than ambient concentrations to resolve this attribution failure. Applied to measurements from a NOₓ-saturated roadside site in Seoul, South Korea, the approach reveals a robust quantitative relationship between intrinsic hydroxyl radical reactivity (kOH) and ozone formation sensitivity. Across a comprehensive suite of precursors—including oxygenated VOCs (e.g., formaldehyde, acetone) and aromatics—the framework identifies reactive aromatics (trimethylbenzenes, xylenes) and biogenics (isoprene, monoterpenes) as the dominant kinetic drivers. Whereas static metrics such as OFP rank precursors by stoichiometric capacity under idealized accumulated conditions, the process-informed framework shows that realized ozone production in fresh urban plumes is governed by kinetic turnover rather than abundance, with this kinetic selectivity further amplified during high-ozone episodes. These results indicate that mass-based VOC inventories and OFP-style rankings, when applied without kinetic context, can systematically misallocate control priorities in NOₓ-saturated urban regimes. Because the framework requires only sub-hourly co-located VOC and ozone observations and no prescribed mechanism, it offers a complementary empirical pathway in settings where explicit mechanism-based modeling is constrained by incomplete VOC speciation or unmeasured radical precursors.

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.

Ground-level ozone remains a persistent challenge in East Asian urban centers, where concentrations continue rising despite significant reductions in precursor emissions. Designing effective mitigation is complicated by the non-linear relationship between precursor abundance and reactivity. Standard data-driven approaches often suffer from "survivor bias," systematically undervaluing highly reactive precursors that are rapidly depleted. We introduce a process-informed machine learning framework that uses net chemical consumption (ΔVOC) rather than ambient concentrations to resolve this attribution failure. Applied to measurements from a NOₓ-saturated roadside site in Seoul, South Korea, the approach reveals a robust quantitative relationship between intrinsic hydroxyl radical reactivity (kOH) and ozone formation sensitivity. Across a comprehensive suite of precursors—including oxygenated VOCs (e.g., formaldehyde, acetone) and aromatics—the framework identifies reactive aromatics (trimethylbenzenes, xylenes) and biogenics (isoprene, monoterpenes) as the dominant kinetic drivers. Whereas static metrics such as OFP rank precursors by stoichiometric capacity under idealized accumulated conditions, the process-informed framework shows that realized ozone production in fresh urban plumes is governed by kinetic turnover rather than abundance, with this kinetic selectivity further amplified during high-ozone episodes. These results indicate that mass-based VOC inventories and OFP-style rankings, when applied without kinetic context, can systematically misallocate control priorities in NOₓ-saturated urban regimes. Because the framework requires only sub-hourly co-located VOC and ozone observations and no prescribed mechanism, it offers a complementary empirical pathway in settings where explicit mechanism-based modeling is constrained by incomplete VOC speciation or unmeasured radical precursors.