Design and evaluation of mobile monitoring campaigns for air pollution exposure assessment in epidemiologic cohorts

Mobile monitoring campaigns to estimate long-term air pollution levels are becoming increasingly common. Still, many campaigns have not conducted temporally-balanced sampling, and few have looked at the implications of such study designs for epidemiologic exposure assessment. We carried out a simulation study of fixed-site air quality monitors to better understand how different mobile monitoring designs involving short-term stationary measurements at fixed locations impact the resulting exposure surfaces. We used Monte Carlo resampling to simulate three archetypal monitoring designs using oxides of nitrogen (NOx) monitoring data from 69 regulatory sites in California: a year-around Balanced Design that sampled during all seasons of the year, days of the week, and all or various hours of the day; a temporally reduced Rush Hours Design; and a temporally reduced Business Hours Design. We evaluated the performance of each designs land use regression prediction model. The Balanced Design consistently yielded the most accurate annual averages; while the reduced Rush Hours and Business Hours Designs generally produced more biased results. A temporally-balanced sampling design is crucial for mobile monitoring campaigns aiming to assess accurate long-term exposure in epidemiologic cohorts. SynopsisAir pollution mobile monitoring campaigns rarely conduct temporally balanced sampling. We show that this results in biased annual average exposure estimates. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/21255641v2_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@126c8ddorg.highwire.dtl.DTLVardef@14d52e5org.highwire.dtl.DTLVardef@17d390dorg.highwire.dtl.DTLVardef@2cc3d1_HPS_FORMAT_FIGEXP M_FIG C_FIG