This work aims at quantifying the relative contribution of secondary organic aerosol (SOA) precursors emitted by wildfires to organic aerosol (OA) formation during summer of 2007 over the Euro-Mediterranean region, where intense wildfires occurred. A new SOA formation mechanism, H²O_aro, including recently identified aromatic volatile organic compounds (VOCs) emitted from wildfires, is developed based on smog chamber experiment measurements under low- and high-NO_x regimes. The aromatic VOCs included in the mechanism are toluene, xylene, benzene, phenol, cresol, catechol, furan, naphthalene, methylnaphthalene, syringol, guaiacol, and structurally assigned and unassigned compounds with at least six carbon atoms per molecule (USC>6). This mechanism H²O_aro is an extension of the H²O (hydrophilic–hydrophobic organic) aerosol mechanism: the oxidation of the precursor forms surrogate species with specific thermodynamic properties (volatility, oxidation degree and affinity to water). The SOA concentrations over the Euro-Mediterranean region in summer of 2007 are simulated using the chemistry transport model (CTM) Polair3D of the air-quality platform Polyphemus, where the mechanism H²O_aro was implemented. To estimate the relative contribution of the aromatic VOCs, intermediate volatility, semi-volatile and low-volatility organic compounds (I/S/L-VOCs), to wildfires OA concentrations, different estimations of the gaseous I/S/L-VOC emissions (from primary organic aerosol – POA – using a factor of 1.5 or from non-methanic organic gas – NMOG – using a factor of 0.36) and their ageing (one-step oxidation vs. multi-generational oxidation) are also tested in the CTM. Most of the particle OA concentrations are formed from I/S/L-VOCs. On average during the summer of 2007 and over the Euro-Mediterranean domain, they are about 10 times higher than the OA concentrations formed from VOCs. However, locally, the OA concentrations formed from VOCs can represent up to 30 % of the OA concentrations from biomass burning. Amongst the VOCs, the main contributors to SOA formation are phenol, benzene and catechol (CAT; 47 %); USC>6 compounds (23 %); and toluene and xylene (12 %). Sensitivity studies of the influence of the VOCs and the I/S/L-VOC emissions and chemical ageing mechanisms on PM_2.5 concentrations show that surface PM_2.5 concentrations are more sensitive to the parameterization used for gaseous I/S/L-VOC emissions than for ageing. Estimating the gaseous I/S/L-VOC emissions from POA or from NMOG has a high impact on local surface PM_2.5 concentrations (reaching −30 % in the Balkans, −8 % to −16 % in the fire plume and +8 % to +16 % in Greece). Considering the VOC as SOA precursors results in a moderate increase in PM_2.5 concentrations mainly in the Balkans (up to 24 %) and in the fire plume (+10 %).

This study examines the uncertainties on air quality modeling associated with the integration of wildfire emissions in chemistry-transport models (CTMs). To do so, aerosol concentrations during the summer of 2007, which was marked by severe fire episodes in the Euro-Mediterranean region especially in the Balkans (20–31 July, 24–30 August 2007) and Greece (24–30 August 2007), are analyzed. Through comparisons to observations from surface networks and satellite remote sensing, we evaluate the abilities of two CTMs, Polyphemus/Polair3D and CHIMERE, to simulate the impact of fires on the regional particulate matter (PM) concentrations and optical properties. During the two main fire events, fire emissions may contribute up to 90 % of surface PM_2.5 concentrations in the fire regions (Balkans and Greece), with a significant regional impact associated with long-range transport. Good general performances of the models and a clear improvement of PM_2.5 and aerosol optical depth (AOD) are shown when fires are taken into account in the models with high correlation coefficients. Two sources of uncertainties are specifically analyzed in terms of surface PM_2.5 concentrations and AOD using sensitivity simulations: secondary organic aerosol (SOA) formation from intermediate and semi-volatile organic compounds (I/S-VOCs) and emissions' injection heights. The analysis highlights that surface PM_2.5 concentrations are highly sensitive to injection heights (with a sensitivity that can be as high as 50 % compared to the sensitivity to I/S-VOC emissions which is lower than 30 %). However, AOD which is vertically integrated is less sensitive to the injection heights (mostly below 20 %) but highly sensitive to I/S-VOC emissions (with sensitivity that can be as high as 40 %). The maximum statistical dispersion, which quantifies uncertainties related to fire emission modeling, is up to 75 % for PM_2.5 in the Balkans and Greece, and varies between 36 % and 45 % for AOD above fire regions. The simulated number of daily exceedance of World Health Organization (WHO) recommendations for PM_2.5 over the considered region reaches 30 days in regions affected by fires and ∼10 days in fire plumes, which is slightly underestimated compared to available observations. The maximum statistical dispersion (σ) on this indicator is also large (with σ reaching 15 days), showing the need for better understanding of the transport and evolution of fire plumes in addition to fire emissions.

Organic aerosols are measured at a remote site (Ersa) on the cape of Corsica in the northwestern Mediterranean basin during the winter campaign of 2014 of the CHemistry and AeRosols Mediterranean EXperiment (CharMEx), when high organic concentrations from anthropogenic origins are observed. This work aims to represent the observed organic aerosol concentrations and properties (oxidation state) using the air-quality model Polyphemus with a surrogate approach for secondary organic aerosol (SOA) formation. Because intermediate and semi-volatile organic compounds (I/S-VOCs) are the main precursors of SOAs at Ersa during winter 2014, different parameterizations to represent the emission and aging of I/S-VOCs were implemented in the chemistry-transport model of Polyphemus (different volatility distribution emissions and single-step oxidation vs multi-step oxidation within a volatility basis set – VBS – framework, inclusion of non-traditional volatile organic compounds – NTVOCs). Simulations using the different parameterizations are compared to each other and to the measurements (concentration and oxidation state). The highly observed organic concentrations are well reproduced in all the parameterizations. They are slightly underestimated in most parameterizations. The volatility distribution at emissions influences the concentrations more strongly than the choice of the parameterization that may be used for aging (single-step oxidation vs multi-step oxidation), stressing the importance of an accurate characterization of emissions. Assuming the volatility distribution of sectors other than residential heating to be the same as residential heating may lead to a strong underestimation of organic concentrations. The observed organic oxidation and oxygenation states are strongly underestimated in all simulations, even when multigenerational aging of I/S-VOCs from all sectors is modeled. This suggests that uncertainties in the emissions and aging of I/S-VOC emissions remain to be elucidated, with a potential role of formation of organic nitrate and low-volatility highly oxygenated organic molecules.

Hazardous weather, turbulence, wind, and thermals pose a ubiquitous challenge to Unmanned Aircraft Systems (UAS) and electric-Vertical Take-Off and Landing (e-VTOL) aircrafts, and the safe integration of UAS into urban area requires accurate high-granularity wind data especially during landing and takeoff phases. Two models, namely, Open-Source Field Operation and Manipulation (OpenFOAM) software package and Weather Research and Forecasting (WRF) model, are used in the present study to simulate airflow over Downtown Oklahoma City, Oklahoma, United States. Results show that computational fluid dynamics wind simulation driven by the atmospheric simulation significantly improves the simulated wind speed because the accurate modeling of the buildings affects wind patterns. The evaluation of different simulations against six Micronet stations shows that WRF-CFD numerical evaluation is a reliable method to understand the complicated wind flow within built-up areas. The comparison of wind distributions of simulations at different resolutions shows better description of wind variability and gusts generated by the urban flows. Simulations assuming anisotropy and isotropy of turbulence show small differences in the predicted wind speeds over Downtown Oklahoma City given the stable atmospheric stratification showing that turbulent eddy scales at the evaluation locations are within the inertial subrange and confirming that turbulence is locally isotropic.

The present study introduces “WindAware”, a wind and turbulence prediction system that provides nowcasts of wind and turbulence parameters every 5 min up to 6 h over a predetermined airway over Chicago, Illinois, USA, based on 100 m high-resolution simulations (HRSs). This system is a long short-term memory-based recurrent neural network (LSTM-RNN) that uses existing ground-based wind data to provide nowcasts (forecasts up to 6 h every 5 min) of wind speed, wind direction, wind gust, and eddy dissipation rate to support the Uncrewed Aircraft Systems (UASs) safe integration into the National Airspace System (NAS). These HRSs are validated using both ground-based measurements over airports and upper-air radiosonde observations and their skill is illustrated during lake-breeze events. A reasonable agreement is found between measured and simulated winds especially when the boundary layer is convective, but the timing and inland penetration of lake-breeze events are overall slightly misrepresented. The WindAware model is compared with the classic multilayer perceptron (MLP) and the eXtreme Gradient Boosting (XGBoost) models. It is demonstrated by comparison to high-resolution simulations that WindAware provides more accurate predictions than the MLP over the 6 h lead times and has almost similar performance as the XGBoost model although the XGBoost’s training is the fastest using its parallelized implementation. WindAware also has higher prediction errors when validated against lake-breeze events data due to their under-representation in the training dataset.

We used the objective analysis method in conjunction with the successive correction method to assimilate MODerate resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) data into the Chimère model in order to improve the modeling of fine particulate matter (PM${}_{2.5}$) concentrations and AOD field over Europe. A data assimilation module was developed to adjust the daily initial total column aerosol concentrations based on a forecast-analysis cycling scheme. The model is then evaluated during one-month winter period to examine how such a data assimilation technique pushes the model results closer to surface observations. This comparison showed that the mean biases of both surface PM${}_{2.5}$ concentrations and the AOD field could be reduced from $-34$ to −15% and from −45 to −27%. The assimilation, however, leads to false alarms because of the difficulty in distributing AOD${}_{550}$ over different particle sizes. The impact of the influence radius is found to be small and depends on the density of satellite data. This work, although preliminary, is important in terms of near-real time air quality forecasting using the Chimère model and can be further developed to improve modeled PM${}_{2.5}$ and ozone concentrations.