The air pollution monitoring paradigm is rapidly changing due to recent advances in (1) the development of portable, lower-cost air pollution sensors reporting data in near-real time at a high-time resolution, (2) increased computational and visualization capabilities, and (3) wireless communication/infrastructure. It is possible that these advances can support traditional air quality monitoring by supplementing ambient air monitoring and enhancing compliance monitoring. Sensors are beginning to provide individuals and communities the tools needed to understand their environmental exposures with these data individual and community-based strategies can be developed to reduce pollution exposure as well as understand linkages to health indicators. Each of these areas as well as corresponding challenges (e.g., quality of data) and potential opportunities associated with development and implementation of air pollution sensors are discussed.

An empirical model based on the regression between daily PM 2.5 (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 PM 2.5 concentrations. The root-meansquare error of the model was 6.2 µg/m 3 with a corresponding average PM 2.5 concentration of 13.8 µg/m 3 . When PM 2.5 concentrations greater than 40 µg/m 3 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 PM 2.5 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.

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.