Heterogeneous chemical cycles of pyrogenic nitrogen and halides influence tropospheric ozone and affect the stratosphere during extreme pyrocumulonimbus (PyroCB) events. We report field-derived N2O5 uptake coefficients, γ(N2O5), and ClNO2 yields, φ(ClNO2), from two aircraft campaigns observing fresh smoke in the lower and mid troposphere and processed/aged smoke in the upper troposphere and lower stratosphere (UTLS). Derived φ(ClNO2) varied across the full 0–1 range but was typically < 0.5 and smallest in a PyroCB (< 0.05). Derived γ(N2O5) was low in agricultural smoke (0.2–3.6 ×10-3), extremely low in mid-tropospheric wildfire smoke (0.1 × 10-3), but larger in PyroCB processed smoke (0.7–5.0 × 10–3). Aged BB aerosol in the UTLS had a higher median γ(N2O5) of 17 × 10–3 that increased with sulfate and liquid water, but that was nevertheless 1–2 orders of magnitude lower than values for aqueous sulfuric aerosol used in stratospheric models.

Isoprene is the dominant non-methane organic compound emitted to the atmosphere, where it drives ozone and aerosol production, modulates atmospheric oxidation, and interacts with the global nitrogen cycle. Isoprene emissions are highly variable and uncertain, as is the non-linear chemistry coupling isoprene and its primary sink, the hydroxyl radical (OH). Space-based isoprene measurements can help close the gap on these uncertainties, and when combined with concurrent formaldehyde data provide a new constraint on atmospheric oxidation regimes. Here we present a next-generation machine-learning isoprene retrieval for the Cross-track Infrared Sounder (CrIS) that provides improved sensitivity, lower noise, and thus higher space-time resolution than earlier approaches. The Retrieval of Organics with CrIS Radiances (ROCR) isoprene measurements compare well with previous space-based retrievals as well as with the first-ever ground-based isoprene column measurements, with 20-50% discrepancies that reflect differing sources of systematic uncertainty. An ensemble of sensitivity tests points to the spectral background and isoprene profile specification as the most relevant uncertainty sources in the ROCR framework. We apply the ROCR isoprene algorithm to the full CrIS record from 2012-2020, showing that it can resolve fine-scale spatial gradients at daily resolution over the world’s isoprene hotspots. Results over North America and Amazonia highlight emergent connections between isoprene abundance and daily-to-interannual variations in temperature, nitrogen oxides, and drought stress.

Accurate fire emissions inventories are crucial to predict the impacts of wildland fires on air quality and atmospheric composition. Two traditional approaches are widely used to calculate fire emissions: a satellite-based top-down approach and a fuels-based bottom-up approach. However, these methods often considerably disagree on the amount of particulate mass emitted from fires. Previously available observational datasets tended to be sparse, and lacked the statistics needed to resolve these methodological discrepancies. Here, we leverage the extensive and comprehensive airborne in situ and remote sensing measurements of smoke plumes from the recent Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign to statistically assess the skill of the two traditional approaches. We use detailed campaign observations to calculate and compare emission rates at an exceptionally high resolution using three separate approaches: top-down, bottom-up, and a novel approach based entirely on integrated airborne in situ measurements. We then compute the daily average of these high-resolution estimates and compare with estimates from lower resolution, global top-down and bottom-up inventories. We uncover strong, linear relationships between all of the high-resolution emission rate estimates in aggregate, however no single approach is capable of capturing the emission characteristics of every fire. Global inventory emission rate estimates exhibited weaker correlations with the high-resolution approaches and displayed evidence of systematic bias. The disparity between the low resolution global inventories and the high resolution approaches is likely caused by high levels of uncertainty in essential variables used in bottom-up inventories and imperfect assumptions in top-down inventories.

Wildland fires involve complicated processes that are challenging to represent in chemical transport models. Recent airborne measurements reveal remarkable chemical tomography in fresh wildland fire plumes, which remain yet to be fully explored using models. Here we present a high-resolution large eddy simulation (LES) model coupled to chemistry to study the chemical evolution in fresh wildland fire plume. The model is configured for a large fire heavily sampled during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign, and a variety of airborne measurements are used to evaluate the chemical heterogeneity revealed by the model. We show that the model captures the observed cross-transect variations of a number of compounds quite well, including ozone (O3), nitrous acid (HONO), and peroxyacetyl nitrate (PAN), etc. The combined observational and modeling results suggest that the top and edges of fresh plume drive the photochemistry, while dark chemistry is also present but in the lower part of the plume. The model spatial resolution is shown to be very important as it may shift the chemical regime, leading to biases in O3 and NOx chemistry. Based on findings in this work, we speculate that the impact of small fires on air quality may be largely underestimated in models with coarse spatial resolutions.