Quantifying uncertainties in pollutant mapping studies using the Monte Carlo method

Abstract

Routine air monitoring provides accurate measurements of annual average concentrations of air pollutants, but the low density of monitoring sites limits its capability in capturing intra-urban variation. Pollutant mapping studies measure air pollutants at a large number of sites during short periods. However, their short duration can cause substantial uncertainty in reproducing annual mean concentrations. In order to quantify this uncertainty for existing sampling strategies and investigate methods to improve future studies, we conducted Monte Carlo experiments with nationwide monitoring data from the EPA Air Quality System. Typical fixed sampling designs have much larger uncertainties than previously assumed, and produce accurate estimates of annual average pollution concentrations approximately 80% of the time. Mobile sampling has difficulties in estimating long-term exposures for individual sites, but performs better for site groups. The accuracy and the precision of a given design decrease when data variation increases, indicating challenges in sites intermittently impact by local sources such as traffic. Correcting measurements with reference sites does not completely remove the uncertainty associated with short duration sampling. Using reference sites with the addition method can better account for temporal variations than the multiplication method. We propose feasible methods for future mapping studies to reduce uncertainties in estimating annual mean concentrations. Future fixed sampling studies should conduct two separate 1-week long sampling periods in all 4 seasons. Mobile sampling studies should estimate annual mean concentrations for exposure groups with five or more sites. Fixed and mobile sampling designs have comparable probabilities in ordering two sites, so they may have similar capabilities in predicting pollutant spatial variations. Simulated sampling designs have large uncertainties in reproducing seasonal and diurnal variations at individual sites, but are capable to predict these variations for exposure groups.