Michael G. Billmire

Modeling acute respiratory illness during the 2007 San Diego wildland fires using a coupled emissions-transport system and generalized additive modeling

BackgroundA study of the impacts on respiratory health of the 2007 wildland fires in and around San Diego County, California is presented. This study helps to address the impact of fire emissions on human health by modeling the exposure potential of proximate populations to atmospheric particulate matter (PM) from vegetation fires. Currently, there is no standard methodology to model and forecast the potential respiratory health effects of PM plumes from wildland fires, and in part this is due to a lack of methodology for rigorously relating the two. The contribution in this research specifically targets that absence by modeling explicitly the emission, transmission, and distribution of PM following a wildland fire in both space and time.MethodsCoupled empirical and deterministic models describing particulate matter (PM) emissions and atmospheric dispersion were linked to spatially explicit syndromic surveillance health data records collected through the San Diego Aberration Detection and Incident Characterization (SDADIC) system using a Generalized Additive Modeling (GAM) statistical approach. Two levels of geographic aggregation were modeled, a county-wide regional level and division of the county into six sub regions. Selected health syndromes within SDADIC from 16 emergency departments within San Diego County relevant for respiratory health were identified for inclusion in the model.ResultsThe model captured the variability in emergency department visits due to several factors by including nine ancillary variables in addition to wildfire PM concentration. The model coefficients and nonlinear function plots indicate that at peak fire PM concentrations the odds of a person seeking emergency care is increased by approximately 50% compared to non-fire conditions (40% for the regional case, 70% for a geographically specific case). The sub-regional analyses show that demographic variables also influence respiratory health outcomes from smoke.ConclusionsThe model developed in this study allows a quantitative assessment and prediction of respiratory health outcomes as it relates to the location and timing of wildland fire emissions relevant for application to future wildfire scenarios. An important aspect of the resulting model is its generality thus allowing its ready use for geospatial assessments of respiratory health impacts under possible future wildfire conditions in the San Diego region. The coupled statistical and process-based modeling demonstrates an end-to-end methodology for generating reasonable estimates of wildland fire PM concentrations and health effects at resolutions compatible with syndromic surveillance data.

Modeling Regional-Scale Wildland Fire Emissions with the Wildland Fire Emissions Information System*

AbstractAs carbon modeling tools become more comprehensive, spatial data are needed to improve quantitative maps of carbon emissions from fire. The Wildland Fire Emissions Information System (WFEIS) provides mapped estimates of carbon emissions from historical forest fires in the United States through a web browser. WFEIS improves access to data and provides a consistent approach to estimating emissions at landscape, regional, and continental scales. The system taps into data and tools developed by the U.S. Forest Service to describe fuels, fuel loadings, and fuel consumption and merges information from the U.S. Geological Survey (USGS) and National Aeronautics and Space Administration on fire location and timing. Currently, WFEIS provides web access to Moderate Resolution Imaging Spectroradiometer (MODIS) burned area for North America and U.S. fire-perimeter maps from the Monitoring Trends in Burn Severity products from the USGS, overlays them on 1-km fuel maps for the United States, and calculates fuel ...

Santa Ana winds and predictors of wildfire progression in southern California

Santa Ana winds have been implicated as a major driver of large wildfires in southern California. While numerous anecdotal reports exist, there is little quantitative analysis in peer-reviewed literature on how this weather phenomenon influences fire progression rates. We analysed fire progression within 158 fire events in southern California as a function of meteorologically defined Santa Ana conditions between 2001 and 2009. Our results show quantitatively that burned area per day is 3.5–4.5 times larger on Santa Ana days than on non-Santa Ana days. Santa Ana definition parameters (relative humidity, wind speed) along with other predictor variables (air temperature, fuel temperature, 10-h fuel moisture, population density, slope, fuel loading, previous-day burn perimeter) were tested individually and in combination for correlation with subsets of daily burned area. Relative humidity had the most consistently strong correlation with burned area per day. Gust and peak wind speed had a strong positive correlation with burned area per day particularly within subsets of burned area representing only the first day of a fire, >500 ha burned areas, and on Santa Ana days. The suite of variables comprising the best-fit generalised linear model for predicting burned area (R2 = 0.41) included relative humidity, peak wind speed, previous-day burn perimeter and two binary indicators for first and last day of a fire event.

Rapid-response tools and datasets for post-fire remediation: linking remote sensing and process-based hydrological models

Post-wildfire flooding and erosion can threaten lives, property and natural resources. Increased peak flows and sediment delivery due to the loss of surface vegetation cover and fire-induced changes in soil properties are of great concern to public safety. Burn severity maps derived from remote sensing data reflect fire-induced changes in vegetative cover and soil properties. Slope, soils, land cover and climate are also important factors that require consideration. Many modelling tools and datasets have been developed to assist remediation teams, but process-based and spatially explicit models are currently underutilised compared with simpler, lumped models because they are difficult to set up and require properly formatted spatial inputs. To facilitate the use of models in conjunction with remote sensing observations, we developed an online spatial database that rapidly generates properly formatted modelling datasets modified by user-supplied soil burn severity maps. Although assembling spatial model inputs can be both challenging and time-consuming, the methods we developed to rapidly update these inputs in response to a natural disaster are both simple and repeatable. Automating the creation of model inputs facilitates the wider use of more accurate, process-based models for spatially explicit predictions of post-fire erosion and runoff.

The San Diego 2007 wildfires and Medi-Cal emergency department presentations, inpatient hospitalizations, and outpatient visits: An observational study of smoke exposure periods and a bidirectional case-crossover analysis

Background The frequency and intensity of wildfires is anticipated to increase as climate change creates longer, warmer, and drier seasons. Particulate matter (PM) from wildfire smoke has been linked to adverse respiratory and possibly cardiovascular outcomes. Children, older adults, and persons with underlying respiratory and cardiovascular conditions are thought to be particularly vulnerable. This study examines the healthcare utilization of Medi-Cal recipients during the fall 2007 San Diego wildfires, which exposed millions of persons to wildfire smoke. Methods and findings Respiratory and cardiovascular International Classification of Diseases (ICD)-9 codes were identified from Medi-Cal fee-for-service claims for emergency department presentations, inpatient hospitalizations, and outpatient visits. For a respiratory index and a cardiovascular index of key diagnoses and individual diagnoses, we calculated rate ratios (RRs) for the study population and different age groups for 3 consecutive 5-day exposure periods (P1 [October 22–26], P2 [October 27–31], and P3 [November 1–5]) versus pre-fire comparison periods matched on day of week (5-day periods starting 3, 4, 5, 6, 8, and 9 weeks before each exposed period). We used a bidirectional symmetric case-crossover design to examine emergency department presentations with any respiratory diagnosis and asthma specifically, with exposure based on modeled wildfire-derived fine inhalable particles that are 2.5 micrometers and smaller (PM2.5). We used conditional logistic regression to estimate odds ratios (ORs), adjusting for temperature and relative humidity, to assess same-day and moving averages. We also evaluated the United States Environmental Protection Agency (EPA)’s Air Quality Index (AQI) with this conditional logistic regression method. We identified 21,353 inpatient hospitalizations, 25,922 emergency department presentations, and 297,698 outpatient visits between August 16 and December 15, 2007. During P1, total emergency department presentations were no different than the reference periods (1,071 versus 1,062.2; RR 1.01; 95% confidence interval [CI] 0.95–1.08), those for respiratory diagnoses increased by 34% (288 versus 215.3; RR 1.34; 95% CI 1.18–1.52), and those for asthma increased by 112% (58 versus 27.3; RR 2.12; 95% CI 1.57–2.86). Some visit types continued to be elevated in later time frames, e.g., a 72% increase in outpatient visits for acute bronchitis in P2. Among children aged 0–4, emergency department presentations for respiratory diagnoses increased by 70% in P1, and very young children (0–1) experienced a 243% increase for asthma diagnoses. Associated with a 10 μg/m3 increase in PM2.5 (72-hour moving average), we found 1.08 (95% CI 1.04–1.13) times greater odds of an emergency department presentation for asthma. The AQI level “unhealthy for sensitive groups” was associated with significantly elevated odds of an emergency department presentation for respiratory conditions the day following exposure, compared to the AQI level “good” (OR 1.73; 95% CI 1.18–2.53). Study limitations include the use of patient home address to estimate exposures and demographic differences between Medi-Cal beneficiaries and the general population. Conclusions Respiratory diagnoses, especially asthma, were elevated during the wildfires in the vulnerable population of Medi-Cal beneficiaries. Wildfire-related healthcare utilization appeared to persist beyond the initial high-exposure period. Increased adverse health events were apparent even at mildly degraded AQI levels. Significant increases in health events, especially for respiratory conditions and among young children, are expected based on projected climate scenarios of wildfire frequency in California and globally.

Expanding Alaska–Canada Rail: Jointly Visualizing Revenue Freight, Development Cost, Mineral Commodity Value, and Impact of Carbon Dioxide

Recent changes in global markets have raised the value of mineral resources in northwestern Canada and Alaska. The development of these resources depends on the economics of rail infrastructure expansion. Transportation decision makers need revenue and cost assessments to plan investment in rail infrastructure. A tool based on a geographic information system was developed for mineral resource evaluation and visualization. The tool incorporated expert-augmented mineral resource data for more than 22,000 occurrences in the region. The tool included the proposed Alaska–Canada Rail Link, which would connect Alaska rail to the lower 48 states. Users selected locations of known mineral occurrences near actual or proposed rail routes and used statistical mineral deposit models to estimate resource sizes and extractable value over time by combining current or user-entered commodity prices with multimodal revenue freight volumes and optimally routed transportation costs. The tool translated the revenue scenario into likely carbon dioxide emissions according to the transport of mineral concentrates to regional and international destinations. Users could select and visualize multimodal transportation networks to understand and minimize mobile-source carbon emissions as part of their scenarios. Statistical estimates of mine capital expenditure and operating costs were also calculated according to type. The tool calculated the gross metal value of a mineral occurrence with statistical deposit models. This index was linked to the positive regional economic impact associated with the developed resource in terms of jobs, taxes and royalties, and resupply. This information helped decision makers close the loop on infrastructure investment assessments.