Airborne Emission Rate Measurements Validate Remote Sensing Observations and Emission Inventories of Western U.S. Wildfires.

Carbonaceous emissions from wildfires are a dynamic mixture of gases and particles that have important impacts on air quality and climate. Emissions that feed atmospheric models are estimated using burned area and fire radiative power (FRP) methods that rely on satellite products. These approaches show wide variability and have large uncertainties, and their accuracy is challenging to evaluate due to limited aircraft and ground measurements. Here, we present a novel method to estimate fire plume-integrated total carbon and speciated emission rates using a unique combination of lidar remote sensing aerosol extinction profiles and in situ measured carbon constituents. We show strong agreement between these aircraft-derived emission rates of total carbon and a detailed burned area-based inventory that distributes carbon emissions in time using Geostationary Operational Environmental Satellite FRP observations (Fuel2Fire inventory, slope = 1.33 ± 0.04, r2 = 0.93, and RMSE = 0.27). Other more commonly used inventories strongly correlate with aircraft-derived emissions but have wide-ranging over- and under-predictions. A strong correlation is found between carbon monoxide emissions estimated in situ with those derived from the TROPOspheric Monitoring Instrument (TROPOMI) for five wildfires with coincident sampling windows (slope = 0.99 ± 0.18; bias = 28.5%). Smoke emission coefficients (g MJ-1) enable direct estimations of primary gas and aerosol emissions from satellite FRP observations, and we derive these values for many compounds emitted by temperate forest fuels, including several previously unreported species.

Understanding the Evolution of Smoke Mass Extinction Efficiency Using Field Campaign Measurements.

Aerosol mass extinction efficiency (MEE) is a key aerosol property used to connect aerosol optical properties with aerosol mass concentrations. Using measurements of smoke obtained during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign we find that mid-visible smoke MEE can change by a factor of 2-3 between fresh smoke (<2 hr old) and one-day-old smoke. While increases in aerosol size partially explain this trend, changes in the real part of the aerosol refractive index (real(n)) are necessary to provide closure assuming Mie theory. Real(n) estimates derived from multiple days of FIREX-AQ measurements increase with age (from 1.40 - 1.45 to 1.5-1.54 from fresh to one-day-old) and are found to be positively correlated with organic aerosol oxidation state and aerosol size, and negatively correlated with smoke volatility. Future laboratory, field, and modeling studies should focus on better understanding and parameterizing these relationships to fully represent smoke aging.

Evaluating Methods To Estimate Methane Emissions from Oil and Gas Production Facilities Using LES Simulations.

Large-eddy simulations (LES) coupled to a model that simulates methane emissions from oil and gas production facilities are used to generate realistic distributions of meteorological variables and methane concentrations. These are sampled to obtain simulated observations used to develop and evaluate source term estimation (STE) methods. A widely used EPA STE method (OTM33A) is found to provide emission estimates with little bias when averaged over six time periods and seven well pads. Sixty-four percent of the emissions estimated with OTM33A are within ±30% of the simulated emissions, showing a slightly larger spread than the 72% found previously using controlled release experiments. A newly developed method adopts the OTM33A sampling strategy and uses a variational or a stochastic STE approach coupled to an LES to obtain a better fit to the sampled meteorological conditions and to account for multiple sources within the well pad. This method can considerably reduce the spread of the emissions estimates compared to OTM33A (92-95% within ±30% percent error), but it is associated with a substantial increase in computational cost due to the LES. It thus provides an alternative when the additional costs can be afforded to obtain more precise emission estimates.

Estimates of Health Impacts and Radiative Forcing in Winter Haze in Eastern China through Constraints of Surface PM2.5 Predictions.

The Gridpoint Statistical Interpolation (GSI) Three-Dimensional Variational (3DVAR) data assimilation system is extended to treat the MOSAIC aerosol model in WRF-Chem, and to be capable of assimilating surface PM2.5 concentrations. The coupled GSI-WRF-Chem system is applied to reproduce aerosol levels over China during an extremely polluted winter month, January 2013. After assimilating surface PM2.5 concentrations, the correlation coefficients between observations and model results averaged over the assimilated sites are improved from 0.67 to 0.94. At nonassimilated sites, improvements (higher correlation coefficients and lower mean bias errors (MBE) and root-mean-square errors (RMSE)) are also found in PM2.5, PM10, and AOD predictions. Using the constrained aerosol fields, we estimate that the PM2.5 concentrations in January 2013 might have caused 7550 premature deaths in Jing-Jin-Ji areas, which are 2% higher than the estimates using unconstrained aerosol fields. We also estimate that the daytime monthly mean anthropogenic aerosol radiative forcing (ARF) to be -29.9W/m2 at the surface, 27.0W/m2 inside the atmosphere, and -2.9W/m2 at the top of the atmosphere. Our estimates update the previously reported overestimations along Yangtze River region and underestimations in North China. This GSI-WRF-Chem system would also be potentially useful for air quality forecasting in China.

Improving aerosol distributions below clouds by assimilating satellite-retrieved cloud droplet number.

Limitations in current capabilities to constrain aerosols adversely impact atmospheric simulations. Typically, aerosol burdens within models are constrained employing satellite aerosol optical properties, which are not available under cloudy conditions. Here we set the first steps to overcome the long-standing limitation that aerosols cannot be constrained using satellite remote sensing under cloudy conditions. We introduce a unique data assimilation method that uses cloud droplet number (N(d)) retrievals to improve predicted below-cloud aerosol mass and number concentrations. The assimilation, which uses an adjoint aerosol activation parameterization, improves agreement with independent N(d) observations and with in situ aerosol measurements below shallow cumulus clouds. The impacts of a single assimilation on aerosol and cloud forecasts extend beyond 24 h. Unlike previous methods, this technique can directly improve predictions of near-surface fine mode aerosols responsible for human health impacts and low-cloud radiative forcing. Better constrained aerosol distributions will help improve health effects studies, atmospheric emissions estimates, and air-quality, weather, and climate predictions.

Assessing Vertical Allocation of Wildfire Smoke Emissions Using Observational Constraints From Airborne Lidar in the Western U.S.

Wildfire emissions are a key contributor of carbonaceous aerosols and trace gases to the atmosphere. Induced by buoyant lifting, smoke plumes can be injected into the free troposphere and lower stratosphere, which by consequence significantly affects the magnitude and distance of their influences on air quality and radiation budget. However, the vertical allocation of emissions when smoke escapes the planetary boundary layer (PBL) and the mechanism modulating it remain unclear. We present an inverse modeling framework to estimate the wildfire emissions, with their temporal and vertical evolution being constrained by assimilating aerosol extinction profiles observed from the airborne Differential Absorption Lidar-High Spectral Resolution Lidar during the Fire Influence on Regional to Global Environments and Air Quality field campaign. Three fire events in the western U.S., which exhibit free-tropospheric injections are examined. The constrained smoke emissions indicate considerably larger fractions of smoke injected above the PBL (f >PBL, 80%-94%) versus the column total, compared to those estimated by the WRF-Chem model using the default plume rise option (12%-52%). The updated emission profiles yield improvements for the simulated vertical structures of the downwind transported smoke, but limited refinement of regional smoke aerosol optical depth distributions due to the spatiotemporal coverage of flight observations. These results highlight the significance of improving vertical allocation of fire emissions on advancing the modeling and forecasting of the environmental impacts of smoke.

Assessment of NO2 observations during DISCOVER-AQ and KORUS-AQ field campaigns.

NASA's Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ, conducted in 2011-2014) campaign in the United States and the joint NASA and National Institute of Environmental Research (NIER) Korea-United States Air Quality Study (KORUS-AQ, conducted in 2016) in South Korea were two field study programs that provided comprehensive, integrated datasets of airborne and surface observations of atmospheric constituents, including nitrogen dioxide (NO2), with the goal of improving the interpretation of spaceborne remote sensing data. Various types of NO2 measurements were made, including in situ concentrations and column amounts of NO2 using ground- and aircraft-based instruments, while NO2 column amounts were being derived from the Ozone Monitoring Instrument (OMI) on the Aura satellite. This study takes advantage of these unique datasets by first evaluating in situ data taken from two different instruments on the same aircraft platform, comparing coincidently sampled profile-integrated columns from aircraft spirals with remotely sensed column observations from ground-based Pandora spectrometers, intercomparing column observations from the ground (Pandora), aircraft (in situ vertical spirals), and space (OMI), and evaluating NO2 simulations from coarse Global Modeling Initiative (GMI) and high-resolution regional models. We then use these data to interpret observed discrepancies due to differences in sampling and deficiencies in the data reduction process. Finally, we assess satellite retrieval sensitivity to observed and modeled a priori NO2 profiles. Contemporaneous measurements from two aircraft instruments that likely sample similar air masses generally agree very well but are also found to differ in integrated columns by up to 31.9 %. These show even larger differences with Pandora, reaching up to 53.9 %, potentially due to a combination of strong gradients in NO2 fields that could be missed by aircraft spirals and errors in the Pandora retrievals. OMI NO2 values are about a factor of 2 lower in these highly polluted environments due in part to inaccurate retrieval assumptions (e.g., a priori profiles) but mostly to OMI's large footprint (> 312 km2).

Assessment of biomass burning smoke influence on environmental conditions for multi-year tornado outbreaks by combining aerosol-aware microphysics and fire emission constraints.

We use the WRF system to study the impacts of biomass burning smoke from Central America on several tornado outbreaks occurring in the US during spring. The model is configured with an aerosol-aware microphysics parameterization capable of resolving aerosol-cloud-radiation interactions in a cost-efficient way for numerical weather prediction (NWP) applications. Primary aerosol emissions are included and smoke emissions are constrained using an inverse modeling technique and satellite-based AOD observations. Simulations turning on and off fire emissions reveal smoke presence in all tornado outbreaks being studied and show an increase in aerosol number concentrations due to smoke. However, the likelihood of occurrence and intensification of tornadoes is higher due to smoke only in cases where cloud droplet number concentration in low level clouds increases considerably in a way that modifies the environmental conditions where the tornadoes are formed (shallower cloud bases and higher low-level wind shear). Smoke absorption and vertical extent also play a role, with smoke absorption at cloud-level tending to burn-off clouds and smoke absorption above clouds resulting in an increased capping inversion. Comparing these and WRF-Chem simulations configured with a more complex representation of aerosol size and composition and different optical properties, microphysics and activation schemes, we find similarities in terms of the simulated aerosol optical depths and aerosol impacts on near-storm environments. This provides reliability on the aerosol-aware microphysics scheme as a less computationally expensive alternative to WRF-Chem for its use in applications such as NWP and cloud-resolving simulations.

Surface dimming by the 2013 Rim Fire simulated by a sectional aerosol model.

The Rim Fire of 2013, the third largest area burned by fire recorded in California history, is simulated by a climate model coupled with a size-resolved aerosol model. Modeled aerosol mass, number, and particle size distribution are within variability of data obtained from multiple-airborne in situ measurements. Simulations suggest that Rim Fire smoke may block 4-6% of sunlight energy reaching the surface, with a dimming efficiency around 120-150 W m-2 per unit aerosol optical depth in the midvisible at 13:00-15:00 local time. Underestimation of simulated smoke single scattering albedo at midvisible by 0.04 suggests that the model overestimates either the particle size or the absorption due to black carbon. This study shows that exceptional events like the 2013 Rim Fire can be simulated by a climate model with 1° resolution with overall good skill, although that resolution is still not sufficient to resolve the smoke peak near the source region.

Health impacts and economic losses assessment of the 2013 severe haze event in Beijing area.

Haze is a serious air pollution problem in China, especially in Beijing and surrounding areas, affecting visibility, public health and regional climate. In this study, the Weather Research and Forecasting-Chemistry (WRF-Chem) model was used to simulate PM2.5 (particulate matters with aerodynamic diameter≤2.5 µm) concentrations during the 2013 severe haze event in Beijing, and health impacts and health-related economic losses were calculated based on model results. Compared with surface monitoring data, the model results reflected pollution concentrations accurately (correlation coefficients between simulated and measured PM2.5 were 0.7, 0.4, 0.5 and 0.6 in Beijing, Tianjin, Xianghe and Xinglong stations, respectively). Health impacts assessments show that the PM2.5 concentrations in January might cause 690 (95% confidence interval (CI): (490, 890)) premature deaths, 45,350 (95% CI: (21,640, 57,860)) acute bronchitis and 23,720 (95% CI: (17,090, 29,710)) asthma cases in Beijing area. Results of the economic losses assessments suggest that the haze in January 2013 might lead to 253.8 (95% CI: (170.2, 331.2)) million US$ losses, accounting for 0.08% (95% CI: (0.05%, 0.1%)) of the total 2013 annual gross domestic product (GDP) of Beijing.