The 2013 Rim Fire was the third largest wildfire in California history and burned 257 314 acres in the Sierra Nevada Mountains. We evaluated air-quality impacts of PM2.5 from smoke from the Rim Fire on receptor areas in California and Nevada. We employed two approaches to examine the air-quality impacts: (1) an evaluation of PM2.5 concentration data collected by temporary and permanent air-monitoring sites and (2) an estimation of intake fraction (iF) of PM2.5 from smoke. The Rim Fire impacted locations in the central Sierra nearest to the fire and extended to the northern Sierra Nevada Mountains of California and Nevada monitoring sites. Daily 24-h average PM2.5 concentrations measured at 22 air monitors had an average concentration of 20 μg/m3 and ranged from 0 to 450 μg/m3. The iF for PM2.5 from smoke during the active fire period was 7.4 per million, which is slightly higher than representative iF values for PM2.5 in rural areas and much lower than for urban areas. This study is a unique application of intake fraction to examine emissions-to-exposure for wildfires and emphasizes that air-quality impacts are not only localized to communities near large fires but can extend long distances and affect larger urban areas.
Fine particulate matter (PM2.5) is a well-established risk factor for public health. To support both health risk assessment and epidemiological studies, data are needed on spatial and temporal patterns of PM2.5 exposures. This review article surveys publicly available exposure datasets for surface PM2.5 mass concentrations over the contiguous U.S., summarizes their applications and limitations, and provides suggestions on future research needs. The complex landscape of satellite instruments, model capabilities, monitor networks, and data synthesis methods offers opportunities for research development, but would benefit from guidance for new users. Guidance is provided to access publicly available PM2.5 datasets, to explain and compare different approaches for dataset generation, and to identify sources of uncertainties associated with various types of datasets. Three main sources used to create PM2.5 exposure data are: ground-based measurements (especially regulatory monitoring), satellite retrievals (especially aerosol optical depth, AOD), and atmospheric chemistry models. We find inconsistencies among several publicly available PM2.5 estimates, highlighting uncertainties in the exposure datasets that are often overlooked in health effects analyses. Major differences among PM2.5 estimates emerge from the choice of data (ground-based, satellite, and/or model), the spatiotemporal resolutions, and the algorithms used to fuse data sources.
Implications: Fine particulate matter (PM2.5) has large impacts on human morbidity and mortality. Even though the methods for generating the PM2.5 exposure estimates have been significantly improved in recent years, there is a lack of review articles that document PM2.5 exposure datasets that are publicly available and easily accessible by the health and air quality communities. In this article, we discuss the main methods that generate PM2.5 data, compare several publicly available datasets, and show the applications of various data fusion approaches. Guidance to access and critique these datasets are provided for stakeholders in public health sectors.
Prescribed fires in forest ecosystems can negatively impact human health and safety by transporting smoke downwind into nearby communities. Smoke transport to communities is known to occur around Bend, Oregon, United States of America (USA), where burning at the wildland–urban interface in the Deschutes National Forest resulted in smoke intrusions into populated areas. The number of suitable days for prescribed fires is limited due to the necessity for moderate weather conditions, as well as wind directions that do not carry smoke into Bend. To better understand the conditions leading to these intrusions and to assess predictions of smoke dispersion from prescribed fires, we collected data from an array of weather and particulate monitors over the autumn of 2014 and spring of 2015 and historical weather data from nearby remote automated weather stations (RAWS). We characterized the observed winds to compare with meteorological and smoke dispersion models using the BlueSky smoke modeling framework. The results from this study indicated that 1–6 days per month in the spring and 2–4 days per month in the fall met the general meteorological prescription parameters for conducting prescribed fires in the National Forest. Of those, 13% of days in the spring and 5% of days in the fall had “ideal” wind patterns, when north winds occurred during the day and south winds did not occur at night. The analysis of smoke intrusions demonstrated that dispersion modeling can be useful for anticipating the timing and location of smoke impacts, but substantial errors in wind speed and direction of the meteorological models can lead to mischaracterizations of intrusion events. Additionally, for the intrusion event modeled using a higher-resolution 1-km meteorological and dispersion model, we found improved predictions of both the timing and location of smoke delivery to Bend compared with the 4-km meteorological model. The 1-km-resolution model prediction fell within 1 h of the observed event, although with underpredicted concentrations, and demonstrated promise for high-resolution modeling in areas of complex terrain.
There is an urgent need for next-generation smoke research and forecasting (SRF) systems to meet the challenges of the growing air quality, health and safety concerns associated with wildland fire emissions. This review paper presents simulations and experiments of hypothetical prescribed burns with a suite of selected fire behaviour and smoke models and identifies major issues for model improvement and the most critical observational needs. The results are used to understand the new and improved capability required for the next-generation SRF systems and to support the design of the Fire and Smoke Model Evaluation Experiment (FASMEE) and other field campaigns. The next-generation SRF systems should have more coupling of fire, smoke and atmospheric processes. The development of the coupling capability requires comprehensive and spatially and temporally integrated measurements across the various disciplines to characterise flame and energy structure (e.g. individual cells, vertical heat profile and the height of well-mixing flaming gases), smoke structure (vertical distributions and multiple subplumes), ambient air processes (smoke eddy, entrainment and radiative effects of smoke aerosols) and fire emissions (for different fuel types and combustion conditions from flaming to residual smouldering), as well as night-time processes (smoke drainage and super-fog formation).
The Fuel Characteristic Classification System (FCCS) was designed to store and archive wildland fuel characteristics within fuelbeds, defined as the inherent physical characteristics of fuels that contribute to fire behavior and effects. The FCCS represents fuel characteristics in six strata including canopy, shrubs, herbaceous fuels, downed wood, litter-lichen-moss, and ground fuels. Each stratum is further divided into one or more categories and subcategories to represent the complexity of wildland and managed fuels. A variety of techniques to measure and summarize fuelbed data are detailed in this guide. This guide is organized by strata and categories to facilitate data input into FCCS fuelbeds and provides field sampling forms by stratum. The first section provides an overview of how FCCS reference fuelbeds were constructed from databases, literature sources, and expert opinion. The guide next describes how regional pathway fuelbeds can provide a systematic set of management fuelbeds that track vegetation and fuel succession over time as well as management activities such as prescribed burning and mechanical thinning. The final section details common field sampling methodologies for users who wish to use field measurements to construct FCCS fuelbeds.
