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The summer of 2018 saw intense smoke impacts on the eastern side of the Sierra Nevada in California, which have been anecdotally ascribed to the closest wildfire, the Lions Fire. We examined the role of the Lions Fire and four other, simultaneous large wildfires on smoke impacts across the Eastern Sierra. Our approach combined GOES-16 satellite data with fire activity, fuel loading, and fuel type, to allocate emissions diurnally per hour for each fire. To apportion smoke impacts at key monitoring sites, dispersion was modeled via the BlueSky framework, and daily averaged PM2.5 concentrations were estimated from 23 July to 29 August 2018. To estimate the relative impact of each contributing wildfire at six Eastern Sierra monitoring sites, we layered the multiple modeled impacts, calculated their proportion from each fire and at each site, and used that proportion to apportion smoke from each fire’s monitored impact. The combined smoke concentration due to multiple large, concurrent, but more distant fires was on many days substantially higher than the concentration attributable to the Lions Fire, which was much closer to the air quality monitoring sites. These daily apportionments provide an objective basis for understanding the extent to which local versus regional fire affected Eastern Sierra Nevada air quality. The results corroborate previous case studies showing that slower-growing fires, when and where managed for resource objectives, can create more transient and manageable air quality impacts relative to larger fires where such management strategies are not used or feasible.

Objectives/Goals: 1) Determine the association between wildfire smoke-driven PM2.5 and risk of persistent respiratory symptoms and repeated asthma exacerbations after the acute wildfire period among adults with asthma. 2) Examine how measures to reduce personal exposure to wildfire smoke, including avoiding outdoor activities, modify this association. Methods/Study Population: This is a retrospective study of adults with asthma in WHAT-NOW, a cohort study of people living in Northern California during the 2018 Camp Fire. Daily smoke-driven PM2.5 was estimated for each participant based on their home address or evacuation location. We examined the association between mean PM2.5 exposure and the presence of respiratory symptoms at both the time of the survey (6–16 months post-wildfire) and at least one other post-wildfire time-period, as well as whether they had a medically attended respiratory illness (saw a doctor, visited the ER, or were hospitalized for a respiratory symptom). We examined the interaction of PM2.5 with spending time outdoors during the wildfires. Poisson regression models with robust standard errors were adjusted for age, sex, race, smoking, allergies, and education. Results/Anticipated Results: Among 337 adults with asthma in the WHAT-NOW cohort, one standard deviation higher smoke-driven PM2.5 was associated with higher risk of any persistent respiratory symptom (risk ratio (RR) 1.38, 95% CI 1.07 – 1.78) and having at least one medically attended respiratory illness (RR 1.33, 95% CI 1.07 – 1.65), but not significantly associated with repeated asthma exacerbations (RR 1.30, 95% CI 0.92 – 1.81). However, there was a significant interaction between PM2.5 and outdoor activities during the wildfire on the outcomes of any persistent respiratory symptoms (p = 0.041) and repeated asthma exacerbations (p = 0.028). The association between PM2.5 and repeated asthma exacerbations was greater among people who spent time outdoors (RR 3.36, 95% CI 1.47 – 10.23) than those who did not (RR 1.00, p = 0.99). Discussion/Significance of Impact: This study provides evidence that exposure to wildfire smoke increases respiratory morbidity among adults with asthma beyond the acute wildfire period. Additionally, it suggests that avoiding outdoor activities on smoky days can significantly decrease the risk of future repeated asthma exacerbations associated with smoke exposure.

Speciated particulate matter (PM) 2.5 data collected as part of the Interagency Monitoring of Protected Visual Environments (IMPROVE) program in Phoenix, AZ, from April 2001 through October 2003 were analyzed using the multivariate receptor model, positive matrix factorization (PMF). Over 250 samples and 24 species were used, including the organic carbon and elemental carbon analytical temperature fractions from the thermal optical reflectance method. A two-step approach was used. First, the species excluding the carbon fractions were used, and initially eight factors were identified; non-soil potassium was calculated and included to better refine the burning factor. Next, the mass associated with the burning factor was removed, and the data set rerun with the carbon fractions. Results were very similar (i.e., within a few percent), but this step enabled a separation of the mobile factor into gasoline and diesel vehicle emissions. The identified factors were burning (on average 2% of the mass), secondary transport (7%), regional power generation (13%), dust (25%), nitrate (9%), industrial As/Pb/Se (2%), Cu/Ni/V (7%), diesel (9%), and general mobile (26%). The overall contribution from mobile sources also increased, as some mass (OC and nitrate) from the nitrate and regional power generation factors were apportioned with the mobile factors. This approach allowed better apportionment of carbon as well as total mass. Additionally, the use of multiple supporting analyses, including air mass trajectories, activity trends, and emission inventory information, helped increase confidence in factor identification.

Plume injection height influences plume transport characteristics, such as range and potential for dilution. We evaluated plume injection height from a predictive wildland fire smoke transport model over the contiguous United States (U.S.) from 2006 to 2008 using satellite-derived information, including plume top heights from the Multi-angle Imaging SpectroRadiometer (MISR) Plume Height Climatology Project and aerosol vertical profiles from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). While significant geographic variability was found in the comparison between modeled plumes and satellite-detected plumes, modeled plume heights were lower overall. In the eastern U.S., satellite-detected and modeled plume heights were similar (median height 671 and 660 m respectively). Both satellite-derived and modeled plume injection heights were higher in the western U.S. (2345 and 1172 m, respectively). Comparisons of modeled plume injection height to satellite-derived plume height at the fire location (R2 = 0.1) were generally worse than comparisons done downwind of the fire (R2 = 0.22). This suggests that the exact injection height is not as important as placement of the plume in the correct transport layer for transport modeling.

Urban smoke exposure events from large wildfires have become increasingly common in California and throughout the western United States. The ability to study the impacts of high smoke aerosol exposures from these events on the public is limited by the availability of high-quality, spatially resolved estimates of aerosol concentrations. Methods for assigning aerosol exposure often employ multiple data sets that are time-consuming to create and difficult to reproduce. As these events have gone from occasional to nearly annual in frequency, the need for rapid smoke exposure assessments has increased. The rapidfire (relatively accurate particulate information derived from inputs retrieved easily) R package (version 0.1.3) provides a suite of tools for developing exposure assignments using data sets that are routinely generated and publicly available within a month of the event. Specifically, rapidfire harvests official air quality monitoring, satellite observations, meteorological modeling, operational predictive smoke modeling, and low-cost sensor networks. A machine learning approach, random forest (RF) regression, is used to fuse the different data sets. Using rapidfire, we produced estimates of ground-level 24 h average particulate matter for several large wildfire smoke events in California from 2017–2021. These estimates show excellent agreement with independent measures from filter-based networks.

Land managers rely on prescribed burning and naturally ignited wildfires for ecosystem management, and must balance trade-offs of air quality, carbon storage, and ecosystem health. A current challenge for land managers when using fire for ecosystem management is managing smoke production. Smoke emissions are a potential human health hazard due to the production of fine particulate matter (PM 2.5 ), carbon monoxide (CO), and ozone (O 3 ) precursors. In addition, smoke emissions can impact transportation safety and contribute to regional haze issues. Quantifying wildland fire emissions is a critical step for evaluating the impact of smoke on human health and welfare, and is also required for air quality modeling efforts and greenhouse gas reporting. Smoke emissions modeling is a complex process that requires the combination of multiple sources of data, the application of scientific knowledge from divergent scientific disciplines, and the linking of various scientific models in a logical, progressive sequence. Typically, estimates of fire size, available fuel loading (biomass available to burn), and fuel consumption (biomass consumed) are needed to calculate the quantities of pollutants produced by a fire. Here we examine the 2006 Tripod Fire Complex as a case study for comparing alternative data sets and combinations of scientific models available for calculating fire emissions. Specifically, we use five fire size information sources, seven fuel loading maps, and two consumption models (Consume 4.0 and FOFEM 5.7) that also include sets of emissions factors. We find that the choice of fuel loading is the most critical step in the modeling pathway, with different fuel loading maps varying by 108 %, while fire size and fuel consumption show smaller variations (36 % and 23 %, respectively). Moreover, we find that modeled fuel loading maps likely underestimate the amount of fuel burned during wildfires as field assessments of total woody fuel loading were consistently higher than modeled fuel loadings in all cases. The PM 2.5 emissions estimates from Consume and FOFEM vary by 37 %. In addition, comparisons with available observational data demonstrate the value of using local data sets where possible.

Thermal–optical analysis (TOA) is a class of methods widely used for determining organic carbon (OC) and elemental carbon (EC) in atmospheric aerosols collected on filters. Results from TOA vary not only with differences in operating protocols for the analysis, but also with details of the instrumentation with which a given protocol is carried out. Three models of TOA carbon analyzers have been used for the IMPROVE_A protocol in the past decade within the Chemical Speciation Network (CSN). This study presents results from intercomparisons of these three analyzer models using two sets of CSN quartz filter samples, all analyzed using the IMPROVE_A protocol with reflectance charring correction. One comparison was between the Sunset model 5L (Sunset) analyzers and the Desert Research Institute (DRI) model 2015 (DRI-2015) analyzers using 4073 CSN samples collected in 2017. The other comparison was between the Sunset and the DRI model 2001 (DRI-2001) analyzers using 303 CSN samples collected in 2007. Both comparisons showed a high degree of inter-model consistency in total carbon (TC) and the major carbon fractions, OC and EC, with a mean bias within 5 % for TC and OC and within 12 % for EC. Relatively larger and diverse inter-model discrepancies (mean biases of 5 %–140 %) were found for thermal subfractions of OC and EC (i.e., OC1–OC4 and EC1–EC3), with better agreement observed for subfractions with higher mass loadings and smaller within-model uncertainties. Optical charring correction proved critical in bringing OC and EC measurements by different TOA analyzer models into agreement. Appreciable inter-model differences in EC between Sunset and DRI-2015 (mean bias ±SD of 21.7 %±12.2 %) remained for ∼5 % of the 2017 CSN samples; examination of these analysis thermograms revealed that the optical measurement (i.e., filter reflectance and transmittance) saturated in the presence of strong absorbing materials on the filter (e.g., EC), leaving an insufficient dynamic range for the detection of carbon pyrolysis and thus no optical charring correction. Differences in instrument parameters and configuration, possibly related to disagreement in OC and EC subfractions, are also discussed. Our results provide a basis for future studies of uncertainties associated with the TOA analyzer model transition in assessing long-term trends of CSN carbon data. Further investigations using these data are warranted, focusing on the demonstrated inter-model differences in OC and EC subfractions. The within- and inter-model uncertainties are useful for model performance evaluation.

We evaluated predictions of hourly PM2.5surface concentrations produced by the experimental BlueSky Gateway air quality modeling system during two wildfire episodes in southern California (Case 1) and northern California (Case 2). In southern California, the prediction performance was dominated by the prevailing synoptic weather patterns, which differentiated the smoke plumes into two types: narrow and highly concentrated during an offshore flow, and diluted and well‐mixed during a light onshore flow. For the northern California fires, the prediction performance was dominated by terrain and the limitations of predicting concentrations in a narrow valley, rather than by the synoptic pattern, which did not differ much throughout the wildfire episode. There was an over‐prediction bias for the maximum values during this episode. When the predicted values were compared to observed values, the best performance results were for the onshore flow during the southern California fires, indicating that the coarse grid used by BlueSky Gateway appropriately represented these well‐mixed conditions. Overall, the southern California fire predictions were biased low and the model did not reproduce the high hourly concentrations (>240μg/m3) observed by the monitors. The predicted results performed well against the observations for the northern California fires, with a large number of predicted values within acceptable range of the observed values. Key Points BlueSky Gateway predicted daily PM2.5 concentrations during two large wildfires A range of prediction performance was found depending on meteorology and terrain Modeled wildfire emissions and model grid size contributed to prediction error

We evaluated predictions of hourly PM 2.5 surface concentrations produced by the experimental BlueSky Gateway air quality modeling system during two wildfire episodes in southern California (Case 1) and northern California (Case 2). In southern California, the prediction performance was dominated by the prevailing synoptic weather patterns, which differentiated the smoke plumes into two types: narrow and highly concentrated during an offshore flow, and diluted and well‐mixed during a light onshore flow. For the northern California fires, the prediction performance was dominated by terrain and the limitations of predicting concentrations in a narrow valley, rather than by the synoptic pattern, which did not differ much throughout the wildfire episode. There was an over‐prediction bias for the maximum values during this episode. When the predicted values were compared to observed values, the best performance results were for the onshore flow during the southern California fires, indicating that the coarse grid used by BlueSky Gateway appropriately represented these well‐mixed conditions. Overall, the southern California fire predictions were biased low and the model did not reproduce the high hourly concentrations (>240 μ g/m 3 ) observed by the monitors. The predicted results performed well against the observations for the northern California fires, with a large number of predicted values within acceptable range of the observed values. BlueSky Gateway predicted daily PM2.5 concentrations during two large wildfires A range of prediction performance was found depending on meteorology and terrain Modeled wildfire emissions and model grid size contributed to prediction error