Sampling campaign design is a crucial aspect of air pollution exposure studies. Selection of both monitor numbers and locations is important for maximizing measured information, while minimizing bias and costs. We developed a two-stage geostatistical-based method using pilot NO 2 samples from Lanzhou, China with the goal of improving sample design decision-making, including monitor numbers and spatial pattern. In the first step, we evaluate how additional monitors change prediction precision through minimized kriging variance. This was assessed in a Monte Carlo fashion by adding up to 50 new monitors to our existing sites with assigned concentrations based on conditionally simulated NO 2 surfaces. After identifying a number of additional sample sites, a second step evaluates their potential placement using a similar Monte Carlo scheme. Evaluations are based on prediction precision and accuracy. Costs are also considered in the analysis. It was determined that adding 28-locations to the existing Lanzhou NO 2 sampling campaign captured 73.5% of the total kriged variance improvement and resulted in predictions that were on average within 10.9 μg/m 3 of measured values, while using 56% of the potential budget. Additional monitor sites improved kriging variance in a nonlinear fashion. This method development allows for informed sampling design by quantifying prediction improvement (accuracy and precision) against the costs of monitor deployment.
|Original language||English (US)|
|Number of pages||10|
|Journal||Journal of Exposure Science and Environmental Epidemiology|
|State||Published - Mar 1 2019|
- Air pollution
- Method development
- Monitor network
PubMed: MeSH publication types
- Journal Article
- Research Support, Non-U.S. Gov't
- Research Support, U.S. Gov't, Non-P.H.S.