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

Abstract

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.

Differential respiratory health effects from the 2008 northern California wildfires: A spatiotemporal approach

“…Our findings are in line with Johnston et al (2002) and Thelen et al (2013) in finding increasingly significant respiratory health impacts with increasing concentrations of PM during wildfire periods.…”

Sensitivity of global wildfire occurrences to various factors in the context of global change

“…For example, van der Werf et al (2010) reported that global carbon emissions from wildfires were 1.5e2.8 Pg C yr À1 during 1998e2007. As a result, global wildfires can significantly affect the ecosystems, carbon cycle, and climate as well as human health (Bowman et al, 2009;Thelen et al, 2013). On the other hand, the changing climate can significantly affect global wildfire activities by altering fire meteorology (Kloster et al, 2012;Pechony and Shindell, 2010;Marlon et al, 2009).…”

Spatiotemporal Prediction of Fine Particulate Matter During the 2008 Northern California Wildfires Using Machine Learning

“…1 39 Although numerous studies have investigated the acute health effects of exposure to urban 40 particulate matter (PM), fewer have investigated the health impacts of exposure to wildfire PM 41 on the general population. 2 Increasing evidence suggests that wildfire PM causes adverse 42 5 A few wildfire health studies have used air pollution dispersion models 10,[23][24][25] or 66 CTMs 26, 27 to estimate air pollution levels in space and time. Interestingly, both studies that used 67 CTM output combined it with satellite aerosol optical depth (AOD) data, 26,27 but neither included 68 other variables in their analyses despite evidence that meteorological parameters can help to 69 scale vertically full-column AOD measures to ground-level PM 2.5 estimates.…”