Wildfire Smoke Plumes Research Articles

Wildfire Smoke Contributions to Polycyclic Aromatic Hydrocarbon Loadings in Western Canadian Urban Surface Grime.

Wildfires emit large amounts of polycyclic aromatic hydrocarbons (PAHs) into the atmosphere. As PAHs emitted from anthropogenic sources are known to accumulate in urban surface grime present on building exteriors and windows, we hypothesized that PAH-containing wildfire smoke plumes could similarly increase PAH grime loadings. To explore this hypothesis, we coupled analysis of PAHs in grime samples collected from August to November 2021 in two historically smoke-affected Canadian cities, Calgary and Kamloops, with contemporaneous field- and model-based indicators of wildfire influence. In Calgary, a single wildfire smoke day contributed over 20% of total grime PAH loadings during this study's 3-month sampling period, which implies that wildfire inputs have the potential to dominate the grime composition during a typical wildfire season. In Kamloops, although the PAH congener profile displayed a sustained background wildfire influence, total PAH loadings were dominated by a hyper-local combustion event, which highlights that even small-scale urban combustion activities have the potential to control pollutant loadings on nearby surfaces. In both locations, temporal PAH congener profiles showed no evidence of reactive loss, implying that biomass burning contributes to the presence of a persistent PAH reservoir available for direct exposure or runoff-mediated contamination of downstream environmental compartments.

The impact of volcanic eruptions, pyrocumulonimbus plumes, and the Arctic polar vortex intrusions on aerosol loading over Tomsk (Western Siberia, Russia) as observed by lidar from 2018 to 2022

ABSTRACT Long-term ground-based lidar observations are important in estimating the contribution of different sources (mostly volcanic eruptions and wildfires) to aerosol loading of the atmosphere and in developing and improving various climate models. In this paper, we carefully analyse aerosol scattering ratio profiles and aerosol layers, observed with 532-nm lidar measurements at the Siberian Lidar Station in Tomsk (56.48°N, 85.05°E) over the period 2018–2022, and identify the layers’ potential sources. To compare aerosol loading over Tomsk for the last five years (2018–2022) with that for previous years, we also present and discuss time series of integrated aerosol backscatter coefficient (IABC) for three altitude ranges. The first range (15–30 km) reveals the pure stratospheric aerosol loading provided by volcanic eruptions, the second range (11–15 km) is responsible mainly for aftereffects of wildfire smoke plumes, and the third one (11–30 km) demonstrates aerosol contribution from both sources. In particular, we found for the period 2001–2022 that the annual average IABC reached its maximal values of 2.91 × 10−4 sr−1 (15–30 km) and 8.21 × 10−4 sr−1 (11–30 km) in 2022 due to the Hunga Tonga-Hunga Ha’apai volcanic eruption. The period 2020–2022 exhibited a significant increase in the relative aerosol content at altitudes of 11–15 km in the total aerosol loading at 11–30 km over Tomsk from 54.4% in 2020 to 64.8% in 2022, which indicates a shift of the main part of aerosol loading to altitudes of 11–15 km due to the increased wildfire activity in North America and north-eastern Asia. The Arctic polar vortex is also revealed to distort the real aerosol vertical distribution over Tomsk by replacing it with the aerosol distribution inside the vortex on measurement days when the vortex is over the lidar site.

Daily Fine Resolution Estimates of the Influence of Wildfires on Fine Particulate Matter in California, 2011–2020

Worsening wildfire seasons in recent years are reversing decadal progress on the reduction of harmful air pollutants in the US, particularly in Western states. Measurements of the contributions of wildfire smoke to ambient air pollutants, such as fine particulate matter (PM2.5), at fine resolution scales would be valuable to public health research on climate vulnerable populations and compound climate risks. We estimate the influence of wildfire smoke emissions on daily PM2.5 at fine-resolution, 3 km, for California 2011–2020, using a geostatistical modeled ambient PM2.5 estimate and wildfire smoke plume data from NOAA Hazard Mapping System. Additionally, we compare this product with the US Environmental Protection Agency (EPA) daily and annual standards for PM2.5 exposure. Our results show wildfires significantly influence PM2.5 in California and nearly all exceedances of the daily US EPA PM2.5 standard were influenced by wildfire smoke, while annual exceedances were increasingly attributed to wildfire smoke influence in recent years. This wildfire-influenced PM2.5 product can be applied to public health research to better understand source-specific air pollution impacts and assess the combination of multiple climate hazard risks.

A Control-Theoretic Spatio-Temporal Model for Wildfire Smoke Propagation Using UAV-Based Air Pollutant Measurements

Wildfires have the potential to cause severe damage to vegetation, property and most importantly, human life. In order to minimize these negative impacts, it is crucial that wildfires are detected at the earliest possible stages. A potential solution for early wildfire detection is to utilize unmanned aerial vehicles (UAVs) that are capable of tracking the chemical concentration gradient of smoke emitted by wildfires. A spatiotemporal model of wildfire smoke plume dynamics can allow for efficient tracking of the chemicals by utilizing both real-time information from sensors as well as future information from the model predictions. This study investigates a spatiotemporal modeling approach based on subspace identification (SID) to develop a data-driven smoke plume dynamics model for the purposes of early wildfire detection. The model was learned using CO2 concentration data which were collected using an air quality sensor package onboard a UAV during two prescribed burn experiments. Our model was evaluated by comparing the predicted values to the measured values at random locations and showed mean errors of 6.782 ppm and 30.01 ppm from the two experiments. Additionally, our model was shown to outperform the commonly used Gaussian puff model (GPM) which showed mean errors of 25.799 ppm and 104.492 ppm, respectively.

Assessment of smoke plume height products derived from multisource satellite observations using lidar-derived height metrics for wildfires in the western US

Abstract. As wildfires intensify and fire seasons lengthen across the western US, the development of models that can predict smoke plume concentrations and track wildfire-induced air pollution exposures has become critical. Wildfire smoke plume height is a key indicator of the vertical placement of plume mass emitted from wildfire-related aerosol sources in climate and air quality models. With advancements in Earth observation (EO) satellites, spaceborne products for aerosol layer height or plume injection height have recently emerged with increased global-scale spatiotemporal resolution. However, to evaluate column radiative effects and refine satellite algorithms, vertical profiles of regionally representative aerosol properties from wildfires need to be measured directly. In this study, we conducted the first comprehensive evaluation of four passive satellite remote-sensing techniques specifically designed for retrieving plume height. We compared these satellite products with the airborne Wyoming Cloud Lidar (WCL) measurements during the 2018 Biomass Burning Flux Measurements of Trace Gases and Aerosols (BB-FLUX) field campaign in the western US. Two definitions, namely, “plume top” and “extinction-weighted mean plume height”, were used to derive the representative heights of wildfire smoke plumes, based on the WCL-derived vertical aerosol extinction coefficient profiles. Using these two definitions, we performed a comparative analysis of multisource satellite-derived plume height products for wildfire smoke. We provide a discussion related to which satellite product is most appropriate for determining plume height characteristics near a fire event or estimating downwind plume rise equivalent height, under multiple aerosol loadings. Our findings highlight the importance of understanding the sensitivity of different passive remote-sensing techniques on space-based wildfire smoke plume height observations, in order to resolve ambiguity surrounding the concept of “effective smoke plume height”. As additional aerosol-observing satellites are planned in the coming years, our results will inform future remote-sensing missions and EO satellite algorithm development. This bridges the gap between satellite observations and plume rise modeling to further investigate the vertical distribution of wildfire smoke aerosols.

SNN-PDE: Learning Dynamic PDEs from Data with Simplicial Neural Networks

Dynamics of many complex systems, from weather and climate to spread of infectious diseases, can be described by partial differential equations (PDEs). Such PDEs involve unknown function(s), partial derivatives, and typically multiple independent variables. The traditional numerical methods for solving PDEs assume that the data are observed on a regular grid. However, in many applications, for example, weather and air pollution monitoring delivered by the arbitrary located weather stations of the National Weather Services, data records are irregularly spaced. Furthermore, in problems involving prediction analytics such as forecasting wildfire smoke plumes, the primary focus may be on a set of irregular locations associated with urban development. In recent years, deep learning (DL) methods and, in particular, graph neural networks (GNNs) have emerged as a new promising tool that can complement traditional PDE solvers in scenarios of the irregular spaced data, contributing to the newest research trend of physics informed machine learning (PIML). However, most existing PIML methods tend to be limited in their ability to describe higher dimensional structural properties exhibited by real world phenomena, especially, ones that live on manifolds. To address this fundamental challenge, we bring the elements of the Hodge theory and, in particular, simplicial convolution defined on the Hodge Laplacian to the emerging nexus of DL and PDEs. In contrast to conventional Laplacian and the associated convolution operation, the simplicial convolution allows us to rigorously describe diffusion across higher order structures and to better approximate the complex underlying topology and geometry of the data. The new approach, Simplicial Neural Networks for Partial Differential Equations (SNN PDE) offers a computationally efficient yet effective solution for time dependent PDEs. Our studies of a broad range of synthetic data and wildfire processes demonstrate that SNN PDE improves upon state of the art baselines in handling unstructured grids and irregular time intervals of complex physical systems and offers competitive forecasting capabilities for weather and air quality forecasting.

Investigation of 2021 wildfire impacts on air quality in southwestern Turkey

In the summer of 2021, unusually intense wildfires happened in southwestern Turkey, especially in Antalya and Mugla with destroying effects on forests, wildlife, and communities residing there. This study aims to understand the air quality impacts of these wildfires. A time period (June–September 2021) covering pre-, fire, and post-periods was investigated using NO2 ground-based observations and TROPOMI CO, HCHO, and NO2 satellite retrievals along with VIIRS FRP. Eight fire regions were selected for detailed analysis in Antalya (An-1-2), Mugla (Mu-1-4), and Mersin (Me-1-2). The highest fire intensity was found in An-1 and followed by Mu-1. CO also showed strongest signals over An-1 (6.73 × 1018 molecules/cm2) with highest levels (1.44 × 1019 molecules/cm2) in the downwind of the wildfires. NO2 showed fire signals in or in very close proximity to the fire regions with strongest signal and largest impact area in An-1 (>8.75 × 1016 molecules/cm2). HCHO showed a different pattern due to HCHO undergoing chemical production and loss in the wildfire plume. HCHO highest levels were not observed over the fire regions, but inside the transported plume with maximum levels for An-1 (3.60 × 1016 molecules/cm2) and for An-2 (3.23 × 1016 molecules/cm2). CO and NO2 increase continued not only in fire, but also in post-fire period, whereas HCHO levels indicated decreases in post-fire compared to pre-fire period. The increases in column concentrations in fire period ranges from 17.6 to 123.1% for CO, 40.6–116.5% for HCHO and 34.4–294.5% for NO2. Higher increases were observed over the Mediterranean Sea especially for CO. Correlations with FRP indicated highest correlations with CO and NO2 and low correlations with HCHO. This study showed that the capability of sparse, ground-level air quality monitoring stations to capture wildfire impacts are limited in the region, because the plume transport was driven by wind direction and wildfire smoke plumes are elevated due to buoyancy. Satellite retrievals are better for capturing the wildfire plume transport and estimating the overall air quality impacts of wildfires.

Aerosol Shortwave Radiative Heating and Cooling by the 2017 and 2023 Chilean Wildfire Smoke Plumes

AbstractThe aerosol shortwave, direct radiative effects of smoke plumes from Chilean wildfires in 2017 and 2023 were derived from satellite observations in both cloud‐free and cloud scenes. At the top of the atmosphere, the aerosol DRE changes sign when aerosol overly clouds or open ocean, confirmed by both measurements and a simulation study. The cloud‐free daily‐mean DRE, computed using an offline radiative transfer model (RTM), was 66 W m−2 in 2023 and 42 W m−2 in 2017, due to absorption by smoke. However, the total radiative effects were larger in 2017 due to a larger plume size compared to 2023. The method presented here provides a new conceptual model to quickly assess the radiative effects of wildfire smoke plumes using satellite measurements and pre‐computed RTM results. The presented estimates are strongly affected by the uncertainty of aerosol optical thickness retrievals from satellite, which can be large in the presence of clouds.

Potential Health Impacts from a Wildfire Smoke Plume over Region Jämtland Härjedalen, Sweden

In the summer of 2018, Sweden experienced widespread wildfires, particularly in the region of Jämtland Härjedalen during the final weeks of July. We previously conducted an epidemiological study and investigated acute respiratory health effects in eight municipalities relation to the wildfire air pollution. In this study, we aimed to estimate the potential health impacts under less favorable conditions with different locations of the major fires. Our scenarios focused on the most intense plume from the 2018 wildfire episode affecting the largest municipality, which is the region’s only city. Combining modeled PM2.5 concentrations, gridded population data, and exposure–response functions, we assessed the relative increase in acute health effects. The cumulative population-weighted 24 h PM2.5 exposure during the nine highest-level days reached 207 μg/m3 days for 63,227 inhabitants. We observed a small number of excess cases, particularly in emergency unit visits for asthma, with 13 additional cases compared to the normal 12. Overall, our scenario-based health impact assessment indicates minor effects on the studied endpoints due to factors such as the relatively small population, limited exposure period, and moderate increase in exposure compared to similar assessments. Nonetheless, considering the expected rise in fire potential due to global warming and the long-range transport of wildfire smoke, raising awareness of the potential health risks in this region is important.

Wildfire smoke exposure and early childhood respiratory health: a study of prescription claims data

Wildfire smoke is associated with short-term respiratory outcomes including asthma exacerbation in children. As investigations into developmental wildfire smoke exposure on children’s longer-term respiratory health are sparse, we investigated associations between developmental wildfire smoke exposure and first use of respiratory medications. Prescription claims from IBM MarketScan Commercial Claims and Encounters database were linked with wildfire smoke plume data from NASA satellites based on Metropolitan Statistical Area (MSA). A retrospective cohort of live infants (2010–2016) born into MSAs in six western states (U.S.A.), having prescription insurance, and whose birthdate was estimable from claims data was constructed (N = 184,703); of these, gestational age was estimated for 113,154 infants. The residential MSA, gestational age, and birthdate were used to estimate average weekly smoke exposure days (smoke-day) for each developmental period: three trimesters, and two sequential 12-week periods post-birth. Medications treating respiratory tract inflammation were classified using active ingredient and mode of administration into three categories:: 'upper respiratory', 'lower respiratory', 'systemic anti-inflammatory'. To evaluate associations between wildfire smoke exposure and medication usage, Cox models associating smoke-days with first observed prescription of each medication category were adjusted for infant sex, birth-season, and birthyear with a random intercept for MSA. Smoke exposure during postnatal periods was associated with earlier first use of upper respiratory medications (1–12 weeks: hazard ratio (HR) = 1.094 per 1-day increase in average weekly smoke-day, 95%CI: (1.005,1.191); 13–24 weeks: HR = 1.108, 95%CI: (1.016,1.209)). Protective associations were observed during gestational windows for both lower respiratory and systemic anti-inflammatory medications; it is possible that these associations may be a consequence of live-birth bias. These findings suggest wildfire smoke exposure during early postnatal developmental periods impact subsequent early life respiratory health.

Predicting wind-driven spatial deposition through simulated color images using deep autoencoders

For centuries, scientists have observed nature to understand the laws that govern the physical world. The traditional process of turning observations into physical understanding is slow. Imperfect models are constructed and tested to explain relationships in data. Powerful new algorithms can enable computers to learn physics by observing images and videos. Inspired by this idea, instead of training machine learning models using physical quantities, we used images, that is, pixel information. For this work, and as a proof of concept, the physics of interest are wind-driven spatial patterns. These phenomena include features in Aeolian dunes and volcanic ash deposition, wildfire smoke, and air pollution plumes. We use computer model simulations of spatial deposition patterns to approximate images from a hypothetical imaging device whose outputs are red, green, and blue (RGB) color images with channel values ranging from 0 to 255. In this paper, we explore deep convolutional neural network-based autoencoders to exploit relationships in wind-driven spatial patterns, which commonly occur in geosciences, and reduce their dimensionality. Reducing the data dimension size with an encoder enables training deep, fully connected neural network models linking geographic and meteorological scalar input quantities to the encoded space. Once this is achieved, full spatial patterns are reconstructed using the decoder. We demonstrate this approach on images of spatial deposition from a pollution source, where the encoder compresses the dimensionality to 0.02% of the original size, and the full predictive model performance on test data achieves a normalized root mean squared error of 8%, a figure of merit in space of 94% and a precision-recall area under the curve of 0.93.

Heat flux assumptions contribute to overestimation of wildfire smoke injection into the free troposphere

Injections of wildfire smoke plumes into the free troposphere impact air quality, yet model forecasts of injections are poor. Here, we use aircraft observations obtained during the 2019 western US wildfires (FIREX-AQ) to evaluate a commonly used smoke plume rise parameterization in two atmospheric chemistry-transport models (WRF-Chem and HRRR-Smoke). Observations show that smoke injections into the free troposphere occur in 35% of plumes, whereas the models forecast 59–95% indicating false injections in the simulations. False injections were associated with both models overestimating fire heat flux and terrain height, and with WRF-Chem underestimating planetary boundary layer height. We estimate that the radiant fraction of heat flux is 0.5 to 25 times larger in models than in observations, depending on fuel type. Model performance was substantially improved by using observed heat flux and boundary layer heights, confirming that models need accurate heat fluxes and boundary layer heights to correctly forecast plume injections.

Direct Constraints on Secondary HONO Production in Aged Wildfire Smoke From Airborne Measurements Over the Western US

AbstractNitrous acid (HONO) mixing ratios measured in aged wildfire smoke plumes were higher than expected from known homogeneous chemical reactions. In a representative smoke plume, intercepted hours to days downwind of the source, the missing HONO source was highly correlated to particulate nitrate photolysis and NO2 reactive uptake to particles. Using a multilinear regression involving these two sources, we could explain the missing HONO production in this plume (R2 = 0.77). The resulting fit parameters from this plume had good explanatory power (R2 = 0.64) for missing HONO production in other fire plumes. The mean enhancement factor for particulate nitrate photolysis relative to gas‐phase nitric acid photolysis was 63 and the mean NO2 reactive uptake coefficient to submicron aerosol surface area forming HONO was 4.9 × 10−4. Given the likelihood of other neglected secondary HONO sources, these values are upper‐limits, suggesting a need to revisit HONO formation mechanisms in aged wildfire smoke.

Emissions of organic compounds from western US wildfires and their near-fire transformations

Abstract. The size and frequency of wildfires in the western United States have been increasing, and this trend is projected to continue, with increasing adverse consequences for human health. Gas- and particle-phase organic compounds are the main components of wildfire emissions. Some of the directly emitted compounds are hazardous air pollutants, while others can react with oxidants to form secondary air pollutants such as ozone and secondary organic aerosol (SOA). Further, compounds emitted in the particle phase can volatize during smoke transport and can then serve as precursors for SOA. The extent of pollutant formation from wildfire emissions is dependent in part on the speciation of organic compounds. The most detailed speciation of organic compounds has been achieved in laboratory studies, though recent field campaigns are leading to an increase in such measurements in the field. In this study, we identified and quantified hundreds of gas- and particle-phase organic compounds emitted from conifer-dominated wildfires in the western US, using two two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC ToF-MS) instruments. Observed emission factors (EFs) and emission ratios are reported for four wildfires. As has been demonstrated previously, modified combustion efficiency (MCE) was a good predictor of particle-phase EFs (e.g., R2=0.78 and 0.84 for sugars and terpenoids, respectively), except for elemental carbon. Higher emissions of diterpenoids, resin acids, and monoterpenes were observed in the field relative to laboratory studies, likely due to distillation from unburned heated vegetation, which may be underrepresented in laboratory studies. These diterpenoids and resin acids accounted for up to 45 % of total quantified organic aerosol, higher than the contribution from sugar and sugar derivatives. The low volatility of resin acids makes them ideal markers for conifer fire smoke. The speciated measurements also show that evaporation of semi-volatile organic compounds took place in smoke plumes, which suggests that the evaporated primary organic aerosol can be a precursor of SOAs in wildfire smoke plumes.

The High-Resolution Rapid Refresh (HRRR): An Hourly Updating Convection-Allowing Forecast Model. Part I: Motivation and System Description

Abstract The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model with hourly data assimilation that covers the conterminous United States and Alaska and runs in real time at the NOAA/National Centers for Environmental Prediction (NCEP). Implemented operationally at NOAA/NCEP in 2014, the HRRR features 3-km horizontal grid spacing and frequent forecasts (hourly for CONUS and 3-hourly for Alaska). HRRR initialization is designed for optimal short-range forecast skill with a particular focus on the evolution of precipitating systems. Key components of the initialization are radar-reflectivity data assimilation, hybrid ensemble-variational assimilation of conventional weather observations, and a cloud analysis to initialize stratiform cloud layers. From this initial state, HRRR forecasts are produced out to 18 h every hour, and out to 48 h every 6 h, with boundary conditions provided by the Rapid Refresh system. Between 2014 and 2020, HRRR development was focused on reducing model bias errors and improving forecast realism and accuracy. Improved representation of the planetary boundary layer, subgrid-scale clouds, and land surface contributed extensively to overall HRRR improvements. The final version of the HRRR (HRRRv4), implemented in late 2020, also features hybrid data assimilation using flow-dependent covariances from a 3-km, 36-member ensemble (“HRRRDAS”) with explicit convective storms. HRRRv4 also includes prediction of wildfire smoke plumes. The HRRR provides a baseline capability for evaluating NOAA’s next-generation Rapid Refresh Forecast System, now under development. Significance Statement NOAA’s operational hourly updating, convection-allowing model, the High-Resolution Rapid Refresh (HRRR), is a key tool for short-range weather forecasting and situational awareness. Improvements in assimilation of weather observations, as well as in physics parameterizations, have led to improvements in simulated radar reflectivity and quantitative precipitation forecasts since the initial implementation of HRRR in September 2014. Other targeted development has focused on improved representation of the diurnal cycle of the planetary boundary layer, resulting in improved near-surface temperature and humidity forecasts. Additional physics and data assimilation changes have led to improved treatment of the development and erosion of low-level clouds, including subgrid-scale clouds. The final version of HRRR features storm-scale ensemble data assimilation and explicit prediction of wildfire smoke plumes.

The High-Resolution Rapid Refresh (HRRR): An Hourly Updating Convection-Allowing Forecast Model. Part II: Forecast Performance

Abstract The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Research version of the Weather Research and Forecast (WRF-ARW) Model that covers the conterminous United States and Alaska and runs hourly (for CONUS; every 3 h for Alaska) in real time at the National Centers for Environmental Prediction. The high-resolution forecasts support a variety of user applications including aviation, renewable energy, and prediction of many forms of severe weather. In this second of two articles, forecast performance is documented for a wide variety of forecast variables and across HRRR versions. HRRR performance varies across geographical domain, season, and time of day depending on both prevalence of particular meteorological phenomena and the availability of both conventional and nonconventional observations. Station-based verification of surface weather forecasts (2-m temperature and dewpoint temperature, 10-m winds, visibility, and cloud ceiling) highlights the ability of the HRRR to represent daily planetary boundary layer evolution and the development of convective and stratiform cloud systems, while gridded verification of simulated composite radar reflectivity and quantitative precipitation forecasts reveals HRRR predictive skill for summer and winter precipitation systems. Significant improvements in performance for specific forecast problems are documented for the upgrade versions of the HRRR (HRRRv2, v3, and v4) implemented in 2016, 2018, and 2020, respectively. Development of the HRRR model data assimilation and physics paves the way for future progress with operational convective-scale modeling. Significance Statement NOAA’s operational hourly updating convection-allowing model, the High-Resolution Rapid Refresh (HRRR), is a key tool for short-range weather forecasting and situational awareness. Improvements in assimilation of weather observations, as well as in physics parameterizations, has led to improvements in simulated radar reflectivity and quantitative precipitation forecasts since the initial implementation of HRRR in September 2014. Other targeted development has focused on improved representation of the diurnal cycle of the planetary boundary layer, resulting in improved near-surface temperature and humidity forecasts. Additional physics and data assimilation changes have led to improved treatment of the development and erosion of low-level clouds, including subgrid-scale clouds. The final version of HRRR features storm-scale ensemble data assimilation and explicit prediction of wildfire smoke plumes.

Wildfire Smoke Observations in the Western United States from the Airborne Wyoming Cloud Lidar during the BB-FLUX Project. Part I: Data Description and Methodology

Abstract During the summer of 2018, the upward-pointing Wyoming Cloud Lidar (WCL) was deployed on board the University of Wyoming King Air (UWKA) research aircraft for the Biomass Burning Flux Measurements of Trace Gases and Aerosols (BB-FLUX) field campaign. This paper describes the generation of calibrated attenuated backscatter coefficients and aerosol extinction coefficients from the WCL measurements. The retrieved aerosol extinction coefficients at the flight level strongly correlate (correlation coefficient, rr > 0.8) with in situ aerosol concentration and carbon monoxide (CO) concentration, providing a first-order estimate for converting WCL extinction coefficients into vertically resolved CO and aerosol concentration within wildfire smoke plumes. The integrated CO column concentrations from the WCL data in nonextinguished profiles also correlate (rr = 0.7) with column measurements by the University of Colorado Airborne Solar Occultation Flux instrument, indicating the validity of WCL-derived extinction coefficients. During BB-FLUX, the UWKA sampled smoke plumes from more than 20 wildfires during 35 flights over the western United States. Seventy percent of flight time was spent below 3 km above ground level (AGL) altitude, although the UWKA ascended up to 6 km AGL to sample the top of some deep smoke plumes. The upward-pointing WCL observed a nearly equal amount of thin and dense smoke below 2 km and above 5 km due to the flight purpose of targeted fresh fire smoke. Between 2 and 5 km, where most of the wildfire smoke resided, the WCL observed slightly more thin smoke than dense smoke due to smoke spreading. Extinction coefficients in dense smoke were 2–10 times stronger, and dense smoke tended to have larger depolarization ratio, associated with irregular aerosol particles.

Wildfire Smoke Observations in the Western United States from the Airborne Wyoming Cloud Lidar during the BB-FLUX Project. Part II: Vertical Structure and Plume Injection Height

Abstract The western U.S. wildfire smoke plumes observed by the upward-pointing Wyoming Cloud Lidar (WCL) during the Biomass Burning Fluxes of Trace Gases and Aerosols (BB-FLUX) project are investigated in a two-part paper. Part II here presents the reconstructed vertical structures of seven plumes from airborne WCL measurements. The vertical structures evident in the fire plume cross sections, supported by in situ measurements, showed that the fire plumes had distinct macrophysical and microphysical properties, which are closely related to the plume transport, fire emission intensity, and thermodynamic structure in the boundary layer. All plumes had an injection layer between 2.8 and 4.0 km above mean sea level, which is generally below the identified boundary layer top height. Plumes that transported upward out of the boundary layer, such as the Rabbit Foot and Pole Creek fires, formed a higher plume at around 5.5 km. The largest and highest Pole Creek fire plume was transported farthest and was sampled by University of Wyoming King Air aircraft at 170 km, or 2.3 h, downwind. It was associated with the warmest, driest, deepest boundary layer and the highest wind speed and turbulence. The Watson Creek fire plume intensified in the afternoon with stronger CO emission and larger smoke plume height than in the morning, indicating a fire diurnal cycle, but some fire plumes did not intensify in the afternoon. There were pockets of relatively large irregular aerosol particles at the tops of plumes from active fires. In less-active fire plumes, the WCL depolarization ratio and passive cavity aerosol spectrometer probe mass mean diameter maximized at a height that was low in the plume.

Does air pollution increase electric vehicle adoption? Evidence from U.S. metropolitan areas, 2011–2018

ABSTRACT We estimate a model of electric vehicle (EV) adoption in 427 of the largest metropolitan areas in the 48 contiguous U.S. states. We observe all new battery electric vehicles (BEV) and plug-in electric vehicles (PHEV) registrations by metro area over the 2011–2018 period, and we investigate whether adoption of new EVs is statistically related to multiple types of air pollution – long-term air pollution as measured by ambient PM2.5 and temporary pollution events as measured by the presence of wildfire smoke plumes in either the lower or upper atmosphere. Regression results show that both ambient PM2.5 and smoke plumes are related to BEV and PHEV adoptions by metro area.

Evolution of Acyl Peroxynitrates (PANs) in Wildfire Smoke Plumes Detected by the Cross‐Track Infrared Sounder (CrIS) Over the Western U.S. During Summer 2018

AbstractWe use observations of acyl peroxynitrates (PANs) from the Cross‐Track Infrared Sounder (CrIS) to investigate PANs over the western U.S. during the summer 2018 wildfire season. This period coincides with the Western Wildfire Experiment for Cloud Chemistry, Aerosol Absorption, and Nitrogen (WE‐CAN). When combined with favorable background conditions, the resolution and sensitivity of CrIS is sufficient to observe the production of PANs in smoke plumes. CrIS PANs normalized excess mixing ratios with respect to CO (NEMRs) in the Pole Creek Fire increase from 0.2% to 0.4% within 3–4 hr of physical aging, consistent with in situ NEMRs. CrIS is also able to detect PANs enhancements in plumes that have been transported hours to days downwind. On average for summer 2018, 19–56% of PANs in the free troposphere during the afternoon over the western U.S. can be attributed to smoke.