Design and evaluation of short-term monitoring campaigns for long-term air pollution exposure assessment

Abstract

Background: Short-term 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.

Objective: We carried out a simulation study using fixed-site air quality monitors to better understand how different short-term monitoring designs impact the resulting exposure surfaces.

Methods: We used Monte Carlo resampling to simulate three archetypal short-term monitoring sampling 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 design's land use regression prediction model.

Results: The Balanced Design consistently yielded the most accurate annual averages; while the reduced Rush Hours and Business Hours Designs generally produced more biased results.

Significance: A temporally-balanced sampling design is crucial for short-term campaigns such as mobile monitoring aiming to assess long-term exposure in epidemiologic cohorts.

Impact statement: Short-term monitoring campaigns to assess long-term air pollution trends are increasingly common, though they rarely conduct temporally balanced sampling. We show that this approach produces biased annual average exposure estimates that can be improved by collecting temporally-balanced samples.

Keywords: Air Pollution; Environmental Monitoring; Exposure Modeling; New Approach Methodologies (NAMs).

Figure 1.

Best fit lines of cross-validated short-term predictions for 30 campaigns vs the gold standard predictions for NOx. Thin transparent lines are individual campaigns, colored by design version; thicker lines are the overall version trend.

Figure 2.

Site-specific NOx prediction errors for short-term designs (N = 30 campaigns) as compared to the gold standard predictions (long-term Balanced Design Version 1). Showing a stratified random sample of 12 sites, stratified by whether true concentrations were in the low (Conc < 0.25), middle (0.25 ≤ Conc ≤ 0.75) or high (Conc > 0.75) concentration quantile and arranged within each stratum with lower concentration sites closer to the bottom.

Figure 3.

Model performances (MSE-based R², Regression-based R², and RMSE), as determined by each campaign’s cross-validated predictions relative to: a) the true averages (long-term Balanced Version 1), and b) its respective campaign averages. Boxplots are for short-term approaches (30 campaigns), while squares are for long-term approaches (1 campaign).