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This paper describes the use of a continuous mobile monitor and passive samplers to estimate the spatial distribution of NO2 in an urban area for the purpose of siting a continuous monitor to measure population exposure. Monitoring sites were sites selected based on the State and Local Air Monitoring Stations (SLAMS) and National Air Monitoring Station (NAMS) siting criteria required by the U.S. Environmental Protection Agency (U.S. EPA). SLAMS monitoring objectives define scales in which the NO2 concentration and land use are homogeneous. The SLAMS scales relevant to NO2 monitoring for NAMS NO2 monitoring sites are neighborhood (0.5 to 4 km), and urban (several to 50 km). SLAMS siting objectives also define four categories of sites: highest concentration, representative concentration, impacts of major sources, and background sites. Mobile monitoring with a Scintrex LMA-3 luminal monitor was used on a neighborhood scale to measure the NO2 concentration at sites that covered a large geographical area. Passive samplers were then located at candidate mobile monitoring locations for long-term sampling which covered the neighborhood to the urban scale. These two methods complement each other by combining short-term continuous measurements and integrated long-term measurements which reflect the National Ambient Air Quality Standard for NO2 which is based on an annual average. The neighborhood site with the highest concentration was not only in the area of highest population density, but was also representative of the larger urban scale. The magnitude of this urban scale is approximately 20 km.

Asthma is the most common chronic illness of childhood and its prevalence is increasing, causing much concern for identification of risk factors such as air pollution. We previously conducted a study showing a relationship between asthma visits in all persons < 65 years of age to emergency departments (EDs) and air pollution in Seattle, Washington. In that study the most frequent zip codes of the visits were in the inner city. The Seattle-King County Department of Public Health (Seattle, WA) subsequently published a report which showed that the hospitalization rate for children in the inner city was over 600/100,000, whereas it was < 100/100,000 for children living in the suburbs. Therefore, we conducted the present study to evaluate whether asthma visits to hospital emergency departments in the inner city of Seattle were associated with outdoor air pollution levels. ED visits to six hospitals for asthma and daily air pollution data were obtained for 15 months during 1995 and 1996. The association between air pollution and childhood ED visits for asthma from the inner city area with high asthma hospitalization rates were compared with those from lower hospital utilization areas. Daily ED counts were regressed against fine particulate matter (PM), carbon monoxide (CO), sulfur dioxide, and nitrogen dioxide using a semiparametric Poisson regression model. Significant associations were found between ED visits for asthma in children and fine PM and CO. A change of 11 microg/m3 in fine PM was associated with a relative rate of 1.15 [95% confidence interval (CI), 1.08-1.23]. There was no stronger association between ED visits for asthma and air pollution in the higher hospital utilization area than in the lower utilization area. These findings were seen when estimated PM2.5 concentrations were below the newly adopted annual National Ambient Air Quality Standard of 15 microg/m3.

As part of the Clean Air Act, Congress has directed EPA to set air quality standards to protect sensitive population groups from air pollutants in the ambient environment. People with asthma represent one such group. We undertook a study of the relation between measured ambient air pollutants in Seattle and nonelderly hospital admissions with a principal diagnosis of asthma. We regressed daily hospital admissions to local hospitals for area residents from 1987 through 1994 on particulate matter less than 10 and 2.5 microm in aerodynamic diameter (PM10 and PM2.5, respectively); coarse particulate mass; sulfur dioxide (SO2); ozone (O3); and carbon monoxide (CO) in a Poisson regression model with control for time trends, seasonal variations, and temperature-related weather effects. With the exception of seasonally monitored O3, we supplemented incomplete pollutant measures in a multiple imputation model to create a complete time series of exposure measures. We found an estimated 4-5% increase in the rate of asthma hospital admissions associated with an interquartile range change in PM (19 microg/m3 PM(10),11.8 microg/m3 PM2.5, and 9.3 microg/m3 coarse particulate mass) lagged 1 day; relative rates were as follows: for PM10, 1.05 [95% confidence interval (CI) = 1.02-1.08]; for PM2.5, 1.04 (95% CI = 1.02-1.07); and for coarse particulate mass, 1.04 (95% CI = 1.01-1.07). In single-pollutant models we also found that a 6% increase in the rate of admission was associated with an interquartile range change in CO (interquartile range, 924 parts per billion; 95% CI = 1.03-1.09) at a lag of 3 days and an interquartile range change in O3 (interquartile range, 20 parts per billion; 95% CI = 1.02-1.11) at a lag of 2 days. We did not observe an association for SO2. We found PM and CO to be jointly associated with asthma admissions. We estimated the highest increase in risk in the spring and fall seasons.