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Wildfire impacts on air quality and climate are expected to be exacerbated by climate change with the most pronounced impacts in the boreal biome. Despite the large geographic coverage, there is limited information on boreal forest wildfire emissions, particularly for organic compounds, which are critical inputs for air quality model predictions of downwind impacts. In this study, airborne measurements of 193 compounds from 15 instruments, including 173 non-methane organics compounds (NMOG), were used to provide the most detailed characterization, to date, of boreal forest wildfire emissions. Highly speciated measurements showed a large diversity of chemical classes highlighting the complexity of emissions. Using measurements of the total NMOG carbon (NMOG T), the Σ NMOG was found to be 50 % ± 3 % to 53 % ± 3 % of NMOG T , of which, the intermediate- and semi-volatile organic compounds (I/SVOCs) were estimated to account for 7 % to 10 %. These estimates of I/SVOC emission factors expand the volatility range of NMOG typically reported. Despite extensive speciation, a substantial portion of NMOG T remained unidentified (47 % ± 15 % to 50 % ± 15 %), with expected contributions from more highly-functionalized VOCs and I/SVOCs. The emission factors derived in this study improve wildfire chemical speciation profiles and are especially relevant for air quality modelling of boreal forest wildfires. These aircraft-derived emission estimates were further linked with those derived from satellite observations demonstrating their combined value in assessing variability in modelled emissions. These results contribute to the verification and improvement of models that are essential for reliable predictions of near-source and downwind pollution resulting from boreal forest wildfires. [ABSTRACT FROM AUTHOR]

We investigate the potential for aircraft-based top-down emission rate retrieval over- and under-estimation using a regional chemical transport model, the Global Environmental Multiscale-Modeling Air-Quality and CHemistry (GEM-MACH). In our investigations we consider the application of the mass-balance approach in the Top-down Emission Rate Retrieval Algorithm (TERRA). Aircraft-based mass-balance retrieval methodologies such as TERRA require relatively constant meteorological conditions and source emission rates to reliably estimate emission rates from aircraft observations. Avoiding cases where meteorology and emission rates change significantly is one means of reducing emissions retrieval uncertainty, and quantitative metrics that may be used for retrieval accuracy estimation are therefore desirable. Using these metrics has the potential to greatly improve emission rate retrieval accuracy. Here, we investigate the impact of meteorological variability on mass-balance emission rate retrieval accuracy by using model-simulated fields as a proxy for real-world chemical and meteorological fields, in which virtual aircraft sampling of the GEM-MACH output was used for top-down mass balance estimates. We also explore the impact of upwind emissions from nearby sources on the accuracy of the retrieved emission rates. This approach allows the state of the atmosphere used for top-down estimates to be characterized in time and 3D space; the input meteorology and emissions are "known", and thus potential means for improving emission rate retrievals and determining the factors affecting retrieval accuracy may be investigated. We found that emissions retrieval accuracy is correlated with three key quantitative criteria, evaluated a priori from forecasts and/or from observations during the sampling period: (1) changes to the atmospheric stability (described as the change in gradient Richardson number), (2) variations in the direction of transport, as a result of plume vertical motion and in the presence of vertical wind shear, and (3) the combined effect of the upwind-to-downwind concentration ratio and the upwind-to-downwind concentration standard deviations. We show here that cases where these criteria indicate high temporal variability and/or high upwind emissions can result in "storage-and-release" events within the sampled region (control volume), which decrease emission rate retrieval accuracy. Storage-and-release events may contribute the bulk of mass-balance emission rate retrieval under- and over-estimates, ranging in the tests carried out here from -25 % to 24 % of the known (input) emissions, with a median of -2 %. Our analysis also includes two cases with unsuitable meteorological conditions and/or significant upwind emissions to demonstrate conditions which may result in severe storage, which in turn cause emission rate under-estimates by the mass-balance approach. We also introduce a sampling strategy whereby the emission rate retrieval under- and over-estimates associated with storage-and-release are greatly reduced (to -14 % to +5 %, respectively, relative to the magnitude of the known emissions). We recommend repeat flights over a given facility and/or time-consecutive upwind and downwind (remote) vertical profiling of relevant fields (e.g., tracer concentrations) in order to measure and account for the factors associated with storage-and-release events, estimate the temporal trends in the evolution of the system during the flight/sampling time, and partially correct for the effects of meteorological variability and upwind emissions. [ABSTRACT FROM AUTHOR]

We evaluate four high-resolution model simulations of pollutant emissions, chemical transformation, and downwind transport for the Athabasca oil sands using the Global Environmental Multiscale -- Modelling Air-quality and Chemistry (GEM-MACH) model, and compare model results with surface monitoring network and aircraft observations of multiple pollutants, for simulations spanning a time period corresponding to an aircraft measurement campaign in the summer of 2013. We have focussed here on the impact of different representations of the model's aerosol size distribution and plume-rise parameterization on model results. The use of a more finely resolved representation of the aerosol size distribution was found to have a significant impact on model performance, reducing the magnitude of the original surface PM2.5 negative biases 32%, from 2.62 to - 1.72 µg m-3. We compared model predictions of SO2, NO2, and speciated particulate matter concentrations from simulations employing the commonly used Briggs (1984) plume-rise algorithms to redistribute emissions from large stacks, with stack plume observations. As in our companion paper (Gordon et al., 2017), we found that Briggs algorithms based on estimates of atmospheric stability at the stack height resulted in under-predictions of plume rise, with 116 out of 176 test cases falling below the model : observation 1 : 2 line, 59 cases falling within a factor of 2 of the observed plume heights, and an average model plume height of 289m compared to an average observed plume height of 822 m. We used a high-resolution meteorological model to confirm the presence of significant horizontal heterogeneity in the local meteorological conditions driving plume rise. Using these simulated meteorological conditions at the stack locations, we found that a layered buoyancy approach for estimating plume rise in stable to neutral atmospheres, coupled with the assumption of free rise in convectively unstable atmospheres, resulted in much better model performance relative to observations (124 out of 176 cases falling within a factor of 2 of the observed plume height, with 69 of these cases above and 55 of these cases below the 1 : 1 line and within a factor of 2 of observed values). This is in contrast to our companion paper, wherein this layered approach (driven by meteorological observations not co-located with the stacks) showed a relatively modest impact on predicted plume heights. Persistent issues with over-fumigation of plumes in the model were linked to a more rapid decrease in simulated temperature with increasing height than was observed. This in turn may have led to overestimates of near-surface diffusivity, resulting in excessive fumigation. [ABSTRACT FROM AUTHOR]

Dry deposition is an important yet poorly constrained process that removes reactive organic carbon from the atmosphere, making it unavailable for airborne chemical reactions and transferring it to other environmental systems. Using an aircraft-based measurement method, we provide large-scale estimates of total gas-phase organic carbon deposition rates and fluxes. Observed deposition rates downwind of large-scale unconventional oil operations reached up to 100 tC hour-1, with fluxes exceeding 0.1 gC m-2 hour-1. The observed deposition lifetimes (tdep) were short enough (i.e., 4 ± 2 hours) to compete with chemical oxidation processes and affect the fate of atmospheric reactive carbon. Yet, much of this deposited organic carbon cannot be accounted for using traditional gas-phase deposition algorithms used in regional air quality models, signifying underrepresented, but influential, chemical-physical surface properties and processes. Furthermore, these fluxes represent a major unaccounted contribution of reactive carbon to downwind freshwater ecosystems that outweigh terrestrial sources, necessitating the inclusion of dry deposition in aquatic carbon balances and models. [ABSTRACT FROM AUTHOR]