Using Geostatistical Simulation to Inform the Quantity and Placement of New Monitors for a Follow-Up Air Sampling Campaign

摘要

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 NO2 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 NO2 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 both prediction precision and accuracy. Costs are also considered in the analysis. It was determined that adding 28-locations to the existing Lanzhou NO2 sampling campaign captured 73.5% of the total kriged variance improvement and resulted in predictions that were on average within 10.9mg/m3 of measured values, while using 56% of the potential budget. This method allows for informed sampling design by quantifying prediction improvement (accuracy and precision) against the costs of monitor deployment.