This paper characterizes the relationship between occupant activities and indoor air particulate levels in a non-smoking office building. Occupant activities were recorded on video. Particulate concentrations were monitored by three optical particle counters (OPCs) in five size ranges at three heights. Particulate mass concentrations were measured gravimetrically and bioaerosol concentrations were determined by impaction methods. Occupant activities and number concentrations were determined with 1-min resolution over a 1-week period. Occupant activities such as walking past or visiting the monitoring site explained 24-55% of the variation of 1- to 25-micron diameter particle number concentrations. Statistical models associating particulate concentrations with occupant activities depended on the size fraction and included an autocorrelative term. Occupant activities are estimated to contribute up to 10 micrograms m-3 in particulate concentrations per person. Number concentrations of particles smaller than 1 micron had little correlation with indoor activities other than cigarette smoking and were highly correlated with outdoor levels. The method can be used to characterize emissions from activities if rapid measurements can be made and if activities can be coded from the video record.

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Measurements of gaseous and particulate concentrations are used to characterize the indoor environment, but such measurements may reflect temporary conditions that are not representative of longer time periods. Moreover, indoor air quality (IAQ) measurements are autocorrelated, a result of limited mixing and air exchange, cyclic emissions, HVAC operation, and other factors. This article analyzes the autocorrelation and variability of IAQ measurements using time series analysis techniques in conjunction with a simple IAQ model. Autocorrelations may be estimated using the air exchange rate (alpha) and ventilation effectiveness (epsilon) of the building or room under study, or estimated from pollutant measurements. From this, the variability, required sample size, and other sampling parameters are estimated. The method is tested in a case study in which particle number, fungi, bacteria, and carbon dioxide concentrations were continuously measured in an office building over a 1-week period. The estimated air exchange rate (1.4/hr) for area studied was predicted to yield autocorrelation coefficients of approximately 0.5 for measurements collected on 30-min intervals. Autocorrelation coefficients based on airborne measurements (lag 0.5 hr) ranged from 0.5 to 0.7 for 1-25 microm diameter particles, fungi, and CO2, but near zero for particles < or =1 microm diameter and bacteria. As expected, the variability of measurements with the lowest autocorrelation decreased the most at long sampling times. The implications for spaces with low alpha * epsilon products are that measurements may not benefit significantly from longer averaging periods, measurements on any single day may not be representative, and day-to-day variability may be significant. Steps to determine sample sizes, averaging times, and sampling strategies that can improve the representativeness of IAQ measurements are discussed.

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