Estimating Ground-Level PM2.5 in the Eastern United States Using Satellite Remote Sensing

An empirical model based on the regression between daily PM2.5 (particles with aerodynamic diameters of less than 2.5 μm) concentrations and aerosol optical thickness (AOT) measurements from the multiangle imaging spectroradiometer (MISR) was developed and tested using data from the eastern United States during the period of 2001. Overall, the empirical model explained 48% of the variability in PM2.5 concentrations. The root-mean-square error of the model was 6.2 μg/m3 with a corresponding average PM2.5 concentration of 13.8 μg/m3. When PM2.5 concentrations greater than 40 μg/m3 were removed, model results were shown to be unbiased estimators of observations. Several factors, such as planetary boundary layer height, relative humidity, season, and other geographical attributes of monitoring sites, were found to influence the association between PM2.5 and AOT. The findings of this study illustrate the strong potential of satellite remote sensing in regional ambient air quality monitoring as an extension to ground networks. With the continual advancement of remote sensing technology and global data assimilation systems, AOT measurements derived from satellite remote sensors may provide a cost-effective approach as a supplemental source of information for determining ground-level particle concentrations.Earth and Planetary Science

Repeating cardiopulmonary health effects in rural North Carolina population during a second large peat wildfire

Background Cardiovascular health effects of fine particulate matter (PM2.5) exposure from wildfire smoke are neither definitive nor consistent with PM2.5 from other air pollution sources. Non-comparability among wildfire health studies limits research conclusions. Methods We examined cardiovascular and respiratory health outcomes related to peat wildfire smoke exposure in a population where strong associations were previously reported for the 2008 Evans Road peat wildfire. We conducted a population-based epidemiologic investigation of associations between daily county-level modeled wildfire PM2.5 and cardiopulmonary emergency department (ED) visits during the 2011 Pains Bay wildfire in eastern North Carolina. We estimated changes in the relative risk cumulative over 0–2 lagged days of wildfire PM2.5 exposure using a quasi-Poisson regression model adjusted for weather, weekends, and poverty. Results Relative risk associated with a 10 μg/m3 increase in 24-h PM2.5 was significantly elevated in adults for respiratory/other chest symptoms 1.06 (1.00–1.13), upper respiratory infections 1.13 (1.05–1.22), hypertension 1.05 (1.00–1.09) and ‘all-cause’ cardiac outcomes 1.06 (1.00–1.13) and in youth for respiratory/other chest symptoms 1.18 (1.06–1.33), upper respiratory infections 1.14 (1.04–1.24) and ‘all-cause’ respiratory conditions 1.09 (1.01–1.17). Conclusions Our results replicate evidence for increased risk of cardiovascular outcomes from wildfire PM2.5 and suggest that cardiovascular health should be considered when evaluating the public health burden of wildfire smoke

Peat Bog Wildfire Smoke Exposure in Rural North Carolina Is Associated with Cardiopulmonary Emergency Department Visits Assessed through Syndromic Surveillance

Background: In June 2008, burning peat deposits produced haze and air pollution far in excess of National Ambient Air Quality Standards, encroaching on rural communities of eastern North Carolina. Although the association of mortality and morbidity with exposure to urban air pollution is well established, the health effects associated with exposure to wildfire emissions are less well understood. Objective: We investigated the effects of exposure on cardiorespiratory outcomes in the population affected by the fire. Methods: We performed a population-based study using emergency department (ED) visits reported through the syndromic surveillance program NC DETECT (North Carolina Disease Event Tracking and Epidemiologic Collection Tool). We used aerosol optical depth measured by a satellite to determine a high-exposure window and distinguish counties most impacted by the dense smoke plume from surrounding referent counties. Poisson log-linear regression with a 5-day distributed lag was used to estimate changes in the cumulative relative risk (RR). Results: In the exposed counties, significant increases in cumulative RR for asthma [1.65 (95% confidence interval, 1.25–2.1)], chronic obstructive pulmonary disease [1.73 (1.06–2.83)], and pneumonia and acute bronchitis [1.59 (1.07–2.34)] were observed. ED visits associated with cardiopulmonary symptoms [1.23 (1.06–1.43)] and heart failure [1.37 (1.01–1.85)] were also significantly increased. Conclusions: Satellite data and syndromic surveillance were combined to assess the health impacts of wildfire smoke in rural counties with sparse air-quality monitoring. This is the first study to demonstrate both respiratory and cardiac effects after brief exposure to peat wildfire smoke

Mapping annual mean ground-level PM 2.5 concentrations using Multiangle Imaging Spectroradiometer aerosol optical thickness over the contiguous United States

We present a simple approach to estimating ground-level fine particulate matter (PM2.5, particles smaller than 2.5 μm in diameter) concentrations by applying local scaling factors from a global atmospheric chemistry model (GEOS-CHEM with GOCART dust and sea salt data) to aerosol optical thickness (AOT) retrieved by the Multiangle Imaging Spectroradiometer (MISR). The resulting MISR PM2.5 concentrations are compared with measurements from the U.S. Environmental Protection Agency's (EPA) PM2.5 compliance network for the year 2001. Regression analyses show that the annual mean MISR PM2.5 concentration is strongly correlated with EPA PM2.5 concentration (correlation coefficient r = 0.81), with an estimated slope of 1.00 and an insignificant intercept, when three potential outliers from Southern California are excluded. The MISR PM2.5 concentrations have a root mean square error (RMSE) of 2.20 μg/m3, which corresponds to a relative error (RMSE over mean EPA PM2.5 concentration) of approximately 20%. Using simulated aerosol vertical profiles generated by the global models helps to reduce the uncertainty in estimated PM2.5 concentrations due to the changing correlation between lower and upper tropospheric aerosols and therefore to improve the capability of MISR AOT in estimating surface-level PM2.5 concentrations. The estimated seasonal mean PM2.5 concentrations exhibited substantial uncertainty, particularly in the west. With improved MISR cloud screening algorithms and the dust simulation of global models, as well as a higher model spatial resolution, we expect that this approach will be able to make reliable estimation of seasonal average surface-level PM2.5 concentration at higher temporal and spatial resolution.Engineering and Applied Science

Additional file 1: of Repeating cardiopulmonary health effects in rural North Carolina population during a second large peat wildfire

Figure S1. Percent of population “in poverty” by county (US Census Bureau 2012). Figure S2. Comparison of SFS PM2.5, satellite imagery and FRM monitor PM2.5: May 11, 2011. Figure S3. Comparison of cRR (per 10 μg/m3 rise in wildfire PM2.5) between statistical model adjusted for county-level poverty (used in this study) and an un-adjusted (crude) statistical model. Figure S4. Percent change in ED visits and 95 % confidence intervals per 10 μg/m3 rise in wildfire PM2.5 for adults and stratified by gender. (DOCX 4293 kb

Multiangle Imaging Spectroradiometer aerosol optical thickness over the contiguous United States

Mapping annual mean ground-level PM 2.5 concentrations usin

Cardio-respiratory outcomes associated with exposure to wildfire smoke are modified by measures of community health

Abstract Background Characterizing factors which determine susceptibility to air pollution is an important step in understanding the distribution of risk in a population and is critical for setting appropriate policies. We evaluate general and specific measures of community health as modifiers of risk for asthma and congestive heart failure following an episode of acute exposure to wildfire smoke. Methods A population-based study of emergency department visits and daily concentrations of fine particulate matter during a wildfire in North Carolina was performed. Determinants of community health defined by County Health Rankings were evaluated as modifiers of the relative risk. A total of 40 mostly rural counties were included in the study. These rankings measure factors influencing health: health behaviors, access and quality of clinical care, social and economic factors, and physical environment, as well as, the outcomes of health: premature mortality and morbidity. Pollutant concentrations were obtained from a mathematically modeled smoke forecasting system. Estimates of relative risk for emergency department visits were based on Poisson mixed effects regression models applied to daily visit counts. Results For asthma, the strongest association was observed at lag day 0 with excess relative risk of 66%(28,117). For congestive heart failure the excess relative risk was 42%(5,93). The largest difference in risk was observed after stratifying on the basis of Socio-Economic Factors. Difference in risk between bottom and top ranked counties by Socio-Economic Factors was 85% and 124% for asthma and congestive heart failure respectively. Conclusions The results indicate that Socio-Economic Factors should be considered as modifying risk factors in air pollution studies and be evaluated in the assessment of air pollution impacts.</p