Brian Rumburg

Statistical distributions of particulate matter and the error associated with sampling frequency

Abstract The distribution of particulate matter (PM) concentrations has an impact on human health effects and the setting of PM regulations. Since PM is commonly sampled on less than daily schedules, the magnitude of sampling errors needs to be determined. Daily PM data from Spokane, Washington were resampled to simulate common sampling schedules and the sampling error was computed for regulatory and distribution statistics. Probability density functions (pdfs) were fit to the annual daily data to determine the shape of the PM2.5 and PM8 concentration distributions and they were also fit to the less than daily sampling to determine if pdfs could be used to predict the daily high-concentration percentiles. There is an error when using a less than daily sampling schedule for all statistics. The error expressed as a percentage difference from the everyday sampling for the PM2.5 mean was as large as 1.7, 3.4 and 7.7% and the 98th percentile error was as great as 8.8, 18 and 67% for 1-in-2 day, 1-in-3 day and 1-in-6 day sampling, respectively. For PM8 the error in the mean was 2.5, 4.7 and 8.6% for and the error in the 99th percentile was 27, 18 and 46% for 1-in-2 day, 1-in-3 day, and 1-in-6 day sampling, respectively. The PM2.5 and PM8 concentration data were best fit by a three-parameter lognormal distribution and a generalized extreme value distribution, respectively. For PM2.5 and PM8, as the annual mean increased the mode concentration increased, but for PM8 the shape of the distribution also flattened. Predicting the daily high percentiles from pdfs that were fit to the less than daily sampled data produced mixed results. For PM8, the pdfs predicted high concentrations were closer to the daily percentiles than the actual less-than-daily sampling percentile while for PM2.5 they were not.