Estimating population exposure to particulate matter during wildfires can be difficult because of insufficient monitoring data to capture the spatiotemporal variability of smoke plumes. Chemical ...transport models (CTMs) and satellite retrievals provide spatiotemporal data that may be useful in predicting PM2.5 during wildfires. We estimated PM2.5 concentrations during the 2008 northern California wildfires using 10-fold cross-validation (CV) to select an optimal prediction model from a set of 11 statistical algorithms and 29 predictor variables. The variables included CTM output, three measures of satellite aerosol optical depth, distance to the nearest fires, meteorological data, and land use, traffic, spatial location, and temporal characteristics. The generalized boosting model (GBM) with 29 predictor variables had the lowest CV root mean squared error and a CV-R2 of 0.803. The most important predictor variable was the Geostationary Operational Environmental Satellite Aerosol/Smoke Product (GASP) Aerosol Optical Depth (AOD), followed by the CTM output and distance to the nearest fire cluster. Parsimonious models with various combinations of fewer variables also predicted PM2.5 well. Using machine learning algorithms to combine spatiotemporal data from satellites and CTMs can reliably predict PM2.5 concentrations during a major wildfire event.
Wildland fire emissions from both wildfires and prescribed fires represent a major component of overall U.S. emissions. Obtaining an accurate, time-resolved inventory of these emissions is important ...for many purposes, including to account for emissions of greenhouse gases and short-lived climate forcers, as well as to model air quality for health, regulatory, and planning purposes. For the U.S. Environmental Protection Agency's 2011 and 2014 National Emissions Inventories, a new methodology was developed to reconcile the wide range of available fire information sources into a single coherent inventory. The Comprehensive Fire Information Reconciled Emissions (CFIRE) inventory effort utilized satellite fire detections as well as a large number of national, state, tribal, and local databases. The methodology and results for CONUS and Alaska were documented and compared against other fire emissions databases, and the efficacy of the overall effort was evaluated. Results show the overall spatial pattern differences and relative seasonality of wildfires and prescribed fires across the country. Prescribed burn emissions occurred primarily in non-summer months were concentrated in the Southeast, Northwest, and lower Midwest, and were relatively consistent year to year. Wildfire emissions were much more variable but occurred primarily in the summer and fall. Overall, CFIRE represents a third of total emitted PM2.5 across all sources in the National Emissions Inventory, with prescribed fires accounting for nearly half of all CFIRE emissions. Compared with other wildland fire emissions inventories derived solely from satellite detections, the CFIRE inventory shows markedly increased emissions, reflecting the importance of the multiple national and regional databases included in CFIRE in capturing small fires and prescribed fires in particular.
Implications: Wildland fire emissions inventories need to incorporate multiple sources of fire information in order to better represent the full range of fire activity, including prescribed burns and smaller fires. For the 2011 and 2014 U.S. National Emissions Inventory, a methodology was developed to collect, associate, and reconcile fire information from satellite data as well as a large number of national, regional, state, local, and tribal fire information databases across the country. The resulting emissions inventory shows the importance of this type of integration and reconciliation when compared against other emissions inventories for the same period.
Forest fire smoke is a growing public health concern as more intense and frequent fires are expected under climate change. Remote sensing is a promising tool for exposure assessment, but its utility ...for health studies is limited because most products measure pollutants in the total column of the atmosphere, and not the surface concentrations most relevant to population health. Information about the vertical distribution of smoke is vital for addressing this limitation. The CALIPSO satellite can provide such information but it cannot cover all smoke events due to its narrow ground track. In this study, we developed a random forests model to predict the minimum height of the smoke layer observed by CALIPSO at high temporal and spatial resolution, using information about fire activity in the vicinity, geographic location, and meteorological conditions. These pieces of information are typically available in near-real-time, ensuring that the resulting model can be easily operationalized. A total of 15,617 CALIPSO data blocks were identified as impacted by smoke within the province of British Columbia, Canada from 2006 to 2015, and 52.1% had smoke within the boundary layer, where the population might be exposed. The final model explained 82.1% of the variance in the observations with a root mean squared error of 560m. The most important variables in the model were wind patterns, the month of smoke observation, and fire intensity within 500km. Predictions from this model can be 1) directly applied to smoke detection from the existing remote sensing products to provide another dimension of information; 2) incorporated into statistical smoke models with inputs from remote sensing products; or 3) used to inform estimates of vertical dispersion in deterministic smoke models. These potential applications are expected to improve the assessment of ground-level population exposure to forest fire smoke.
•A statistical model was developed to predict the smoke height observed by CALIPSO.•Variables of fire activity, geographical locations and meteorology were included.•The model explains 82% of the variance in the observations.•Wind patterns, month and fire intensity within 500km are the most predictive.•Model predictions can be used to improve remote sensing products and smoke models.
•We examine how emissions inventories for wildland fire are developed.•Comparisons are made between four emissions inventories for the contiguous USA.•Seasonal, interannual, and regional differences ...are examined.•The effects of modeling choices on emissions inventories are detailed.•Current knowledge gaps and potential for future improvements and discussed.
Emissions from wildland fire are both highly variable and highly uncertain over a wide range of temporal and spatial scales. Wildland fire emissions change considerably due to fluctuations from year to year with overall fire season severity, from season to season as different regions pass in and out of wildfire and prescribed fire periods, and from day to day as weather patterns affect large wildfire growth events and prescribed fire windows. Emissions from wildland fire are highly uncertain in that every component used to calculate wildland fire emissions is uncertain – including how much fire occurs and at what time during the year, assessments of available fuel stocks, consumption efficiency, and emissions factors used to calculate the final emissions. As shown here, these component uncertainties result in large-scale differences between estimation methods of wildland fire emissions including greenhouse gas totals, particulate matter totals, and other emissions. Four recent emissions inventories for the contiguous United States are compared to determine inter-inventory differences and to examine how methodological choices result in different annual totals and patterns of temporal and spatial variability. Inter-model variability is detailed for several current models, and current knowledge gaps and future directions for progressing fire emissions inventories are discussed.
As long-term speciated PM2.5 monitoring programs, the Interagency Monitoring of Protected Visual Environments (IMPROVE) and Chemical Speciation Network (CSN) were designed with different objectives ...but apply similar analytical methods to 24hr filter samples and report many of the same species. The two networks have different operating structures, sampling practices, analytical methods, analytical facilities, and data handling and validation practices, which require attention when data from the two networks are combined in an analysis. Data from collocated CSN and IMPROVE sites from January 1, 2016 through September 30, 2018 are presented to document the comparability between the networks. While species measured well above the method detection limit (MDL) generally agree well during this period, there is evidence of some inter-network bias for fine-soil-related elements at specific locations, as well as subtle biases for some well-measured species. Many species – particularly for CSN – are measured at or near the MDL and have poor inter- and intra-network collocated agreement; caution should be used when advancing findings on such measurements. However, comparison of reconstructed mass shows good inter-network agreement suggesting that the networks are effective at quantifying predominant mass species.
•Because of methodology differences, use caution when combining CSN/IMPROVE data.•Species measured well above the MDL generally have good collocated agreement.•Species measured at or near the MDL generally have poor collocated agreement.
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.
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.
Retrieval of aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) using the Collection 5 (C005) algorithm provides large‐scale (10 × 10 km) estimates that can be ...used to predict surface layer concentrations of particulate matter with aerodynamic diameter smaller than 2.5 µm (PM2.5). However, these large‐scale estimates are not suitable for identifying intraurban variability of surface PM2.5 concentrations during wildfire events when individual plumes impact populated areas. We demonstrate a method for providing high‐resolution (2.5 km) kernel‐smoothed estimates of AOD over California during the 2008 northern California fires. The method uses high‐resolution surface reflectance ratios of the 0.66 and 2.12 µm channels, a locally derived aerosol optical model characteristic of fresh wildfire plumes, and a relaxed cloud filter. Results show that the AOD derived for the 2008 northern California fires outperformed the standard product in matching observed aerosol optical thickness at three coastal Aerosol Robotic Network sites and routinely explained more than 50% of the variance in hourly surface PM2.5 concentrations observed during the wildfires.
Key Points
The 2.5 km estimates of AOD are derived for California wildfires
AOD estimates predict more than 50% of observed surface PM2.5 variance
High‐resolution surface reflectance ratios are critically important
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