Ultrafine particle mobile monitoring study designs for epidemiology: Cost and performance comparisons.

Abstract

Background: Given the difficulty of collecting air pollution measurements for individuals, researchers use mobile monitoring to develop accurate models that predict long-term average exposure to air pollution allowing the investigation of its association with human health. While recent mobile monitoring studies focused on predictive models' abilities to select optimal designs, cost is also an important feature.

Objectives: This study aims to compare costs to predictive model performance for different mobile monitoring designs.

Methods: We used data on ultrafine particle stationary roadside mobile monitoring and associated costs collected by the Adult Changes in Thought Air Pollution (ACT-AP) study. By assuming a single-instrument, local monitoring, and constant costs of equipment and investigator oversight, we focused on the incremental cost of staff work days composed mostly of sampling drives and quality control procedures. The ACT-AP complete design included data collection from 309 sites, ~29 visits per site, during 4 seasons, every day of the week. We considered alternative designs by selecting subsets of fewer sites, visits, seasons, days of week, and hours of day. Then, we developed exposure prediction models from each alternative design and calculated cross-validation (CV) statistics using all observations from the complete design. Finally, we compared CV R²s and the numbers of staff work days from alternative designs to those from the complete design and demonstrate this exercise in a web application.

Results: For designs with less visits per site, the costs for number of work days were lower and model performance (CV R²) also worsened, but with mild decline above 12 visits per site. The costs were also less for designs with fewer sites when considering at least 100 sites, while the reduction in performance was minimal. For temporally-restricted designs that were constrained to have the same number of work days and thus the same cost, restrictions on the number of seasons, days of week, and/or hours of the day adversely impacted model performance.

Discussion: Our study provides practical guidance to future mobile monitoring campaigns that have the ultimate goal of assessing the health effect of long-term air pollution. Temporally-balanced designs with 12 visits per site are a cost-effective option that provide relatively good prediction accuracy with reduced costs. https://doi.org/10.1289/EHP15100.