Observationally constrained representation of brown carbon emissions from wildfires in a chemical transport model

The month of August 2015 featured extensive wildfires in the Northwestern U.S. and no significant fires in Alaska and Canada. With the majority of carbonaceous aerosols (CA), including black carbon...

Local-time Dependence of Chemical Species in the Venusian Mesosphere

Observed chemical species in the Venusian mesosphere show local-time variabilities. SO2 at the cloud top exhibits two local maxima over local time, H2O at the cloud top is uniformly distributed, and CO in the upper atmosphere shows a statistical difference between the two terminators. In this study, we investigated these local-time variabilities using a three-dimensional (3D) general circulation model (GCM) in combination with a two-dimensional (2D) chemical transport model (CTM). Our simulation results agree with the observed local-time patterns of SO2, H2O, and CO. The two-maximum pattern of SO2 at the cloud top is caused by the superposition of the semidiurnal thermal tide and the retrograde superrotating zonal (RSZ) flow. SO2 above 85 km shows a large day–night difference resulting from both photochemistry and the subsolar-to-antisolar (SS-AS) circulation. The transition from the RSZ flows to SS-AS circulation can explain the CO difference between two terminators and the displacement of the CO local-time maximum with respect to the antisolar point. H2O is long-lived and exhibits very uniform distribution over space. We also present the local-time variations of HCl, ClO, OCS, and SO simulated by our model and compare to the sparse observations of these species. This study highlights the importance of multidimensional CTMs for understanding the interaction between chemistry and dynamics in the Venusian mesosphere.

City Scale Modeling of Ultrafine Particles in Urban Areas with Special Focus on Passenger Ferryboat Emission Impact

Air pollution by aerosol particles is mainly monitored as mass concentrations of particulate matter, such as PM10 and PM2.5. However, mass-based measurements are hardly representative for ultrafine particles (UFP), which can only be monitored adequately by particle number (PN) concentrations and are considered particularly harmful to human health. This study examines the dispersion of UFP in Hamburg city center and, in particular, the impact of passenger ferryboats by modeling PN concentrations and compares concentrations to measured values. To this end, emissions inventories and emission size spectra for different emission sectors influencing concentrations in the city center were created, explicitly considering passenger ferryboat traffic as an additional emission source. The city-scale chemical transport model EPISODE-CityChem is applied for the first time to simulate PN concentrations and additionally, observations of total particle number counts are taken at four different sampling sites in the city. Modeled UFP concentrations are in the range of 1.5–3 × 104 cm−3 at ferryboat piers and at the road traffic locations with particle sizes predominantly below 50 nm. Urban background concentrations are at 0.4–1.2 × 104 cm−3 with a predominant particle size in the range 50100 nm. Ferryboat traffic is a significant source of emissions near the shore along the regular ferry routes. Modeled concentrations show slight differences to measured data, but the model is capable of reproducing the observed spatial variation of UFP concentrations. UFP show strong variations in both space and time, with day-to-day variations mainly controlled by differences in air temperature, wind speed and wind direction. Further model simulations should focus on longer periods of time to better understand the influence of meteorological conditions on UFP dynamics.

Development and evaluation of a new compact mechanism for aromatic oxidation in atmospheric models

Aromatic hydrocarbons, including benzene, toluene, and xylenes, play an important role in atmospheric chemistry, but the associated chemical mechanisms are complex and uncertain. Sparing representation of this chemistry in models is needed for computational tractability. Here, we develop a new compact mechanism for aromatic chemistry (GC13) that captures current knowledge from laboratory and computational studies with only 17 unique species and 44 reactions. We compare GC13 to six other currently used mechanisms of varying complexity in box model simulations of environmental chamber data and diurnal boundary layer chemistry, and show that GC13 provides results consistent with or better than more complex mechanisms for oxygenated products (alcohols, carbonyls, dicarbonyls), ozone, and hydrogen oxide (HOx≡OH+HO2) radicals. Specifically, GC13 features increased radical recycling and increased ozone destruction from phenoxy–phenylperoxy radical cycling relative to other mechanisms. We implement GC13 into the GEOS-Chem global chemical transport model and find higher glyoxal yields and net ozone loss from aromatic chemistry compared with other mechanisms. Aromatic oxidation in the model contributes 23 %, 5 %, and 8 % of global glyoxal, methylglyoxal, and formic acid production, respectively, and has mixed effects on formaldehyde. It drives small decreases in global tropospheric OH (−2.2 %), NOx (≡NO+NO2; −3.7 %), and ozone (−0.8 %), but a large increase in NO3 (+22 %) from phenoxy–phenylperoxy radical cycling. Regional effects in polluted environments can be substantially larger, especially from the photolysis of carbonyls produced by aromatic oxidation, which drives large wintertime increases in OH and ozone concentrations.

Unraveling pathways of elevated ozone induced by the 2020 lockdown in Europe by an observationally constrained regional model using TROPOMI

Questions about how emissions are changing during the COVID-19 lockdown periods cannot be answered by observations of atmospheric trace gas concentrations alone, in part due to simultaneous changes in atmospheric transport, emissions, dynamics, photochemistry, and chemical feedback. A chemical transport model simulation benefiting from a multi-species inversion framework using well-characterized observations should differentiate those influences enabling to closely examine changes in emissions. Accordingly, we jointly constrain NOx and VOC emissions using well-characterized TROPOspheric Monitoring Instrument (TROPOMI) HCHO and NO2 columns during the months of March, April, and May 2020 (lockdown) and 2019 (baseline). We observe a noticeable decline in the magnitude of NOx emissions in March 2020 (14 %–31 %) in several major cities including Paris, London, Madrid, and Milan, expanding further to Rome, Brussels, Frankfurt, Warsaw, Belgrade, Kyiv, and Moscow (34 %–51 %) in April. However, NOx emissions remain at somewhat similar values or even higher in some portions of the UK, Poland, and Moscow in March 2020 compared to the baseline, possibly due to the timeline of restrictions. Comparisons against surface monitoring stations indicate that the constrained model underrepresents the reduction in surface NO2. This underrepresentation correlates with the TROPOMI frequency impacted by cloudiness. During the month of April, when ample TROPOMI samples are present, the surface NO2 reductions occurring in polluted areas are described fairly well by the model (model: −21 ± 17 %, observation: −29 ± 21 %). The observational constraint on VOC emissions is found to be generally weak except for lower latitudes. Results support an increase in surface ozone during the lockdown. In April, the constrained model features a reasonable agreement with maximum daily 8 h average (MDA8) ozone changes observed at the surface (r=0.43), specifically over central Europe where ozone enhancements prevail (model: +3.73 ± 3.94 %, +1.79 ppbv, observation: +7.35 ± 11.27 %, +3.76 ppbv). The model suggests that physical processes (dry deposition, advection, and diffusion) decrease MDA8 surface ozone in the same month on average by −4.83 ppbv, while ozone production rates dampened by largely negative JNO2[NO2]-kNO+O3[NO][O3] become less negative, leading ozone to increase by +5.89 ppbv. Experiments involving fixed anthropogenic emissions suggest that meteorology contributes to 42 % enhancement in MDA8 surface ozone over the same region with the remaining part (58 %) coming from changes in anthropogenic emissions. Results illustrate the capability of satellite data of major ozone precursors to help atmospheric models capture ozone changes induced by abrupt emission anomalies.

ML-TOMCAT: machine-learning-based satellite-corrected global stratospheric ozone profile data set from a chemical transport model

High-quality stratospheric ozone profile data sets are a key requirement for accurate quantification and attribution of long-term ozone changes. Satellite instruments provide stratospheric ozone profile measurements over typical mission durations of 5–15 years. Various methodologies have then been applied to merge and homogenise the different satellite data in order to create long-term observation-based ozone profile data sets with minimal data gaps. However, individual satellite instruments use different measurement methods, sampling patterns and retrieval algorithms which complicate the merging of these different data sets. In contrast, atmospheric chemical models can produce chemically consistent long-term ozone simulations based on specified changes in external forcings, but they are subject to the deficiencies associated with incomplete understanding of complex atmospheric processes and uncertain photochemical parameters. Here, we use chemically self-consistent output from the TOMCAT 3-D chemical transport model (CTM) and a random-forest (RF) ensemble learning method to create a merged 42-year (1979–2020) stratospheric ozone profile data set (ML-TOMCAT V1.0). The underlying CTM simulation was forced by meteorological reanalyses, specified trends in long-lived source gases, solar flux and aerosol variations. The RF is trained using the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) data set over the time periods of the Microwave Limb Sounder (MLS) from the Upper Atmosphere Research Satellite (UARS) (1991–1998) and Aura (2005–2016) missions. We find that ML-TOMCAT shows excellent agreement with available independent satellite-based data sets which use pressure as a vertical coordinate (e.g. GOZCARDS, SWOOSH for non-MLS periods) but weaker agreement with the data sets which are altitude-based (e.g. SAGE-CCI-OMPS, SCIAMACHY-OMPS). We find that at almost all stratospheric levels ML-TOMCAT ozone concentrations are well within uncertainties of the observational data sets. The ML-TOMCAT (V1.0) data set is ideally suited for the evaluation of chemical model ozone profiles from the tropopause to 0.1 hPa and is freely available via https://doi.org/10.5281/zenodo.5651194 (Dhomse et al., 2021).

Modeling Biomass Burning Organic Aerosol Atmospheric Evolution and Chemical Aging

The changes in the concentration and composition of biomass-burning organic aerosol (OA) downwind of a major wildfire are simulated using the one-dimensional Lagrangian chemical transport model PMCAMx-Trj. A base case scenario is developed based on realistic fire-plume conditions and a series of sensitivity tests are performed to quantify the effects of different conditions and processes. Temperature, oxidant concentration and dilution rate all affect the evolution of biomass burning OA after its emission. The most important process though is the multi-stage oxidation of both the originally emitted organic vapors (volatile and intermediate volatility organic compounds) and those resulting from the evaporation of the OA as it is getting diluted. The emission rates of the intermediate volatility organic compounds (IVOCs) and their chemical fate have a large impact on the formed secondary OA within the plume. The assumption that these IVOCs undergo only functionalization leads to an overestimation of the produced SOA suggesting that fragmentation is also occurring. Assuming a fragmentation probability of 0.2 resulted in predictions that are more consistent with available observations. Dilution leads to OA evaporation and therefore reduction of the OA levels downwind of the fire. However, the evaporated material can return to the particulate phase later on after it gets oxidized and recondenses. The sensitivity of the OA levels and total mass balance on the dilution rate depends on the modeling assumptions. The high variability of OA mass enhancement observed in past field studies downwind of fires may be partially due to the variability of the dilution rates of the plumes.