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.

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 (R 2 = 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.

Application of the wildland fire emissions inventory system to estimate fire emissions on forest lands of the United States.

BACKGROUND: Forests are significant terrestrial biomes for carbon storage, and annual carbon accumulation of forest biomass contributes offsets affecting net greenhouse gases in the atmosphere. The immediate loss of stored carbon through fire on forest lands reduces the annual offsets provided by forests. As such, the United States reporting includes annual estimates of direct fire emissions in conjunction with the overall forest stock and change estimates as a part of national greenhouse gas inventories within the United Nations Framework Convention on Climate Change. Forest fire emissions reported for the United States, such as the 129 Tg CO2 reported for 2022, are based on the Wildland Fire Emissions Inventory System (WFEIS). Current WFEIS estimates are included in the Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2022 published in 2024 by the United States Environmental Protection Agency. Here, we describe WFEIS the fire emissions inventory system we used to address current information needs, and an analysis to confirm compatibility of carbon mass between estimated forest fire emissions and carbon in forest stocks. RESULTS: The summaries of emissions from forests are consistent with previous reports that show rates and interannual variability in emissions and forest land area burned are generally greater in recent years relative to the 1990s. Both emissions and interannual variability are greater in the western United States. The years with the highest CO2 emissions from forest fires on the 48 conterminous states plus Alaska were 2004, 2005, and 2015. In some years, Alaska emissions exceed those of the 48 conterminous states, such as in 2022, for example. Comparison of forest fire emission to forest carbon stocks indicate there is unlikely any serious disconnect between inventory and fire emissions estimates. CONCLUSIONS: The WFEIS system is a user-driven approach made available via a web browser. Model results are compatible with the scope and reporting needs of the annual national greenhouse gas inventories.

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

BACKGROUND: A 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. METHODS: Coupled 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. RESULTS: The 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. CONCLUSIONS: The 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.

Simulating spatio-temporal dynamics of surface PM2.5 emitted from Alaskan wildfires.

Wildfire is a major disturbance agent in Arctic boreal and tundra ecosystems that emits large quantities of atmospheric pollutants, including PM2.5. Under the substantial Arctic warming which is two to three times of global average, wildfire regimes in the high northern latitude regions are expected to intensify. This imposes a considerable threat to the health of the people residing in the Arctic regions. Alaska, as the northernmost state of the US, has a sizable rural population whose access to healthcare is greatly limited by a lack of transportation and telecommunication infrastructure and low accessibility. Unfortunately, there are only a few air quality monitoring stations across the state of Alaska, and the air quality of most remote Alaskan communities is not being systematically monitored, which hinders our understanding of the relationship between wildfire emissions and human health within these communities. Models simulating the dispersion of pollutants emitted by wildfires can be extremely valuable for providing spatially comprehensive air quality estimates in areas such as Alaska where the monitoring station network is sparse. In this study, we established a methodological framework that is based on an integration of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, the Wildland Fire Emissions Inventory System (WFEIS), and the Arctic-Boreal Vulnerability Experiment (ABoVE) Wildfire Date of Burning (WDoB) dataset, an Arctic-oriented fire product. Through our framework, daily gridded surface-level PM2.5 concentrations for the entire state of Alaska between 2001 and 2015 for which wildfires are responsible can be estimated. This product reveals the spatio-temporal patterns of the impacts of wildfires on the regional air quality in Alaska, which, in turn, offers a direct line of evidence indicating that wildfire is the dominant driver of PM2.5 concentrations over Alaska during the fire season. Additionally, it provides critical data inputs for research on understanding how wildfires affect human health which creates the basis for the development of effective and efficient mitigation efforts.

Using wildland fire smoke modeling data in gerontological health research (California, 2007-2018).

Widespread population exposure to wildland fire smoke underscores the urgent need for new techniques to characterize fire-derived pollution for epidemiologic studies and to build climate-resilient communities especially for aging populations. Using atmospheric chemical transport modeling, we examined air quality with and without wildland fire smoke PM2.5. In 12-km gridded output, the 24-hour average concentration of all-source PM2.5 in California (2007-2018) was 5.16 µg/m3 (S.D. 4.66 µg/m3). The average concentration of fire-PM2.5 in California by year was 1.61 µg/m3 (~30% of total PM2.5). The contribution of fire-source PM2.5 ranged from 6.8% to 49%. We define a "smokewave" as two or more consecutive days with modeled levels above 35 µg/m3. Based on model-derived fire-PM2.5, 99.5% of California's population lived in a county that experienced at least one smokewave from 2007 to 2018, yet understanding of the impact of smoke on the health of aging populations is limited. Approximately 2.7 million (56%) of California residents aged 65+ years lived in counties representing the top 3 quartiles of fire-PM2.5 concentrations (2007-2018). For each year (2007-2018), grid cells containing skilled nursing facilities had significantly higher mean concentrations of all-source PM2.5 than cells without those facilities, but they also had generally lower mean concentrations of wildland fire-specific PM2.5. Compared to rural monitors in California, model predictions of wildland fire impacts on daily average PM2.5 carbon (organic and elemental) performed well most years but tended to overestimate wildland fire impacts for high-fire years. The modeling system isolated wildland fire PM2.5 from other sources at monitored and unmonitored locations, which is important for understanding exposures for aging population in health studies.

Mapping Modeled Exposure of Wildland Fire Smoke for Human Health Studies in California.

Wildland fire smoke exposure affects a broad proportion of the U.S. population and is increasing due to climate change, settlement patterns and fire seclusion. Significant public health questions surrounding its effects remain, including the impact on cardiovascular disease and maternal health. Using atmospheric chemical transport modeling, we examined general air quality with and without wildland fire smoke PM2.5. The 24-h average concentration of PM2.5 from all sources in 12-km gridded output from all sources in California (2007-2013) was 4.91 µg/m3. The average concentration of fire-PM2.5 in California by year was 1.22 µg/m3 (~25% of total PM2.5). The fire-PM2.5 daily mean was estimated at 4.40 µg/m3 in a high fire year (2008). Based on the model-derived fire-PM2.5 data, 97.4% of California's population lived in a county that experienced at least one episode of high smoke exposure ("smokewave") from 2007-2013. Photochemical model predictions of wildfire impacts on daily average PM2.5 carbon (organic and elemental) compared to rural monitors in California compared well for most years but tended to over-estimate wildfire impacts for 2008 (2.0 µg/m3 bias) and 2013 (1.6 µg/m3 bias) while underestimating for 2009 (-2.1 µg/m3 bias). The modeling system isolated wildfire and PM2.5 from other sources at monitored and unmonitored locations, which is important for understanding population exposure in health studies. Further work is needed to refine model predictions of wildland fire impacts on air quality in order to increase confidence in the model for future assessments. Atmospheric modeling can be a useful tool to assess broad geographic scale exposure for epidemiologic studies and to examine scenario-based health impacts.