An empirical model to estimate daily forest fire smoke exposure over a large geographic area using air quality, meteorological, and remote sensing data.
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ABSTRACT:
SUBMITTER: Yao J
PROVIDER: S-EPMC3994508 | biostudies-other | 2014 May-Jun
REPOSITORIES: biostudies-other
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Project description:Exposure to forest fire smoke (FFS) is associated with multiple adverse health effects, mostly respiratory. Findings for cardiovascular effects have been inconsistent, possibly related to the limitations of conventional methods to assess FFS exposure. In previous work, we developed an empirical model to estimate smoke-related fine particulate matter (PM2.5) for all populated areas in British Columbia (BC), Canada. Here, we evaluate the utility of our model by comparing epidemiologic associations between modeled and measured PM2.5. For each local health area (LHA), we used Poisson regression to estimate the effects of PM2.5 estimates and measurements on counts of medication dispensations and outpatient physician visits. We then used meta-regression to estimate the overall effects. A 10 μg/m(3) increase in modeled PM2.5 was associated with increased sabutamol dispensations (RR=1.04, 95% CI 1.03-1.06), and physician visits for asthma (1.06, 1.04-1.08), COPD (1.02, 1.00-1.03), lower respiratory infections (1.03, 1.00-1.05), and otitis media (1.05, 1.03-1.07), all comparable to measured PM2.5. Effects on cardiovascular outcomes were only significant using model estimates in all LHAs during extreme fire days. This suggests that the exposure model is a promising tool for increasing the power of epidemiologic studies to detect the health effects of FFS via improved spatial coverage and resolution.
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Project description:BackgroundExposure to atmospheric particulate matter (PM) remains an important public health concern, although it remains difficult to quantify accurately across large geographic areas with sufficiently high spatial resolution. Recent epidemiologic analyses have demonstrated the importance of spatially- and temporally-resolved exposure estimates, which show larger PM-mediated health effects as compared to nearest monitor or county-specific ambient concentrations.MethodsWe developed generalized additive mixed models that describe regional and small-scale spatial and temporal gradients (and corresponding uncertainties) in monthly mass concentrations of fine (PM2.5), inhalable (PM10), and coarse mode particle mass (PM(2.5-10)) for the conterminous United States (U.S.). These models expand our previously developed models for the Northeastern and Midwestern U.S. by virtue of their larger spatial domain, their inclusion of an additional 5 years of PM data to develop predictions through 2007, and their use of refined geographic covariates for population density and point-source PM emissions. Covariate selection and model validation were performed using 10-fold cross-validation (CV).ResultsThe PM2.5 models had high predictive accuracy (CV R2=0.77 for both 1988-1998 and 1999-2007). While model performance remained strong, the predictive ability of models for PM10 (CV R2=0.58 for both 1988-1998 and 1999-2007) and PM(2.5-10) (CV R2=0.46 and 0.52 for 1988-1998 and 1999-2007, respectively) was somewhat lower. Regional variation was found in the effects of geographic and meteorological covariates. Models generally performed well in both urban and rural areas and across seasons, though predictive performance varied somewhat by region (CV R2=0.81, 0.81, 0.83, 0.72, 0.69, 0.50, and 0.60 for the Northeast, Midwest, Southeast, Southcentral, Southwest, Northwest, and Central Plains regions, respectively, for PM2.5 from 1999-2007).ConclusionsOur models provide estimates of monthly-average outdoor concentrations of PM2.5, PM10, and PM(2.5-10) with high spatial resolution and low bias. Thus, these models are suitable for estimating chronic exposures of populations living in the conterminous U.S. from 1988 to 2007.
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Project description:BackgroundMillions of people can potentially be exposed to smoke from forest fires, making this an important public health problem in many countries.ObjectiveIn this study we aimed to measure the association between out-of-hospital cardiac arrest (OHCA) and forest fire smoke exposures in a large city during a severe forest fire season, and estimate the number of excess OHCAs due to the fire smoke.MethodsWe investigated the association between particulate matter (PM) and other air pollutants and OHCA using a case-crossover study of adults (? 35 years of age) in Melbourne, Australia. Conditional logistic regression models were used to derive estimates of the percent change in the rate of OHCA associated with an interquartile range (IQR) increase in exposure. From July 2006 through June 2007, OHCA data were collected from the Victorian Ambulance Cardiac Arrest Registry. Hourly air pollution concentrations and meteorological data were obtained from a central monitoring site.ResultsThere were 2,046 OHCAs with presumed cardiac etiology during our study period. Among men during the fire season, greater increases in OHCA were observed with IQR increases in the 48-hr lagged PM with diameter ? 2.5 ?m (PM2.5) (8.05%; 95% CI: 2.30, 14.13%; IQR = 6.1 ?g/m(3)) or ? 10 ?m (PM10) (11.1%; 95% CI: 1.55, 21.48%; IQR = 13.7 ?g/m(3)) and carbon monoxide (35.7%; 95% CI: 8.98, 68.92%; IQR = 0.3 ppm). There was no significant association between the rate of OHCA and air pollutants among women. One hundred seventy-four "fire-hours" (i.e., hours in which Melbourne's air quality was affected by forest fire smoke) were identified during 12 days of the 2006/2007 fire season, and 23.9 (95% CI: 3.1, 40.2) excess OHCAs were estimated to occur due to elevations in PM2.5 during these fire-hours.ConclusionsThis study found an association between exposure to forest fire smoke and an increase in the rate of OHCA. These findings have implications for public health messages to raise community awareness and for planning of emergency services during forest fire seasons.
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Project description:Recent studies have interpreted patterns of remotely sensed tree cover as evidence that forest with intermediate tree cover might be unstable in the tropics, as it will tip into either a closed forest or a more open savanna state. Here we show that across all continents the frequency of wildfires rises sharply as tree cover falls below ~40%. Using a simple empirical model, we hypothesize that the steepness of this pattern causes intermediate tree cover (30?60%) to be unstable for a broad range of assumptions on tree growth and fire-driven mortality. We show that across all continents, observed frequency distributions of tropical tree cover are consistent with this hypothesis. We argue that percolation of fire through an open landscape may explain the remarkably universal rise of fire frequency around a critical tree cover, but we show that simple percolation models cannot predict the actual threshold quantitatively. The fire-driven instability of intermediate states implies that tree cover will not change smoothly with climate or other stressors and shifts between closed forest and a state of low tree cover will likely tend to be relatively sharp and difficult to reverse.
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Project description:BackgroundDuring the summer of 2003 numerous fires burned in British Columbia, Canada.ObjectivesWe examined the associations between respiratory and cardiovascular physician visits and hospital admissions, and three measures of smoke exposure over a 92-day study period (1 July to 30 September 2003).MethodsA population-based cohort of 281,711 residents was identified from administrative data. Spatially specific daily exposure estimates were assigned to each subject based on total measurements of particulate matter (PM) ? 10 ?m in aerodynamic diameter (PM10) from six regulatory tapered element oscillating microbalance (TEOM) air quality monitors, smoke-related PM10 from a CALPUFF dispersion model run for the study, and a SMOKE exposure metric for plumes visible in satellite images. Logistic regression with repeated measures was used to estimate associations with each outcome.ResultsThe mean (± SD) exposure based on TEOM-measured PM10 was 29 ± 31 ?g/m3, with an interquartile range of 14-31 ?g/m3. Correlations between the TEOM, smoke, and CALPUFF metrics were moderate (0.37-0.76). Odds ratios (ORs) for a 30-?g/m3 increase in TEOM-based PM10 were 1.05 [95% confidence interval (CI), 1.03-1.06] for all respiratory physician visits, 1.16 (95% CI, 1.09-1.23) for asthma-specific visits, and 1.15 (95% CI, 1.00-1.29) for respiratory hospital admissions. Associations with cardiovascular outcomes were largely null.ConclusionsOverall we found that increases in TEOM-measured PM10 were associated with increased odds of respiratory physician visits and hospital admissions, but not with cardiovascular health outcomes. Results indicating effects of fire smoke on respiratory outcomes are consistent with previous studies, as are the null results for cardiovascular outcomes. Some agreement between TEOM and the other metrics suggests that exposure assessment tools that are independent of air quality monitoring may be useful with further refinement.
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Project description:Background People who smoke with serious mental illness carry disproportionate costs from smoking, including poor health and premature death from tobacco-related illnesses. Hospitals in New Zealand are ostensibly smoke-free; however, some mental health wards have resisted implementing this policy. Aim This study explored smoking in acute metal health wards using data emerging from a large sociological study on modern acute psychiatric units. Methods Eighty-five in-depth, semi-structured interviews were conducted with staff and service users from four units. Data were analysed using a social constructionist problem representation approach. Results Although high-level smoke-free policies were mandatory, most participants disregarded these policies and smoking occurred in internal courtyards. Staff reasoned that acute admissions were not the time to quit smoking, citing the sceptres of distress and possibly violence; further, they found smoking challenging to combat. Inconsistent enforcement of smoke-free policies was common and problematic. Many service users also rejected smoke-free policies; they considered smoking facilitated social connections, alleviated boredom, and helped them feel calm in a distressing environment – some started or increased smoking following admission. A minority viewed smoking as a problem; a fire hazard, or pollutant. No one mentioned its health risks. Conclusion Psychiatric wards remain overlooked corners where hospital smoke-free policies are inconsistently applied or ignored. Well-meaning staff hold strong but anachronistic views about smoking. To neglect smoking cessation support for people with serious mental illness is discriminatory and perpetuates health and socioeconomic inequities. However, blanket applications of generic policy are unlikely to succeed. Solutions may include myth-busting education for service users and staff, local champions, and strong managerial support and leadership, with additional resourcing during transition phases. Smoke-free policies need consistent application with non-judgemental NRT and, potentially, other treatments. Smoking cessation would be supported by better designed facilities with more options for alleviating boredom, expressing autonomy, facilitating social connections, and reducing distress.
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