Use of Low-Cost Air Pollution Sensors in the Adult Changes in Thought Air Pollution Study

Abstract

Incorporating modern air pollution sensor technology into epidemiologic cohort studies is appealing because of its low cost, allowing much better spatial representation of pollution exposure. Exposure sampling design can also leverage emerging understanding from measurement error correction methods that the monitoring network design should be spatially compatible with the locations where the health study participants live. However, research has not yet been conducted to determine whether these low-cost sensor data are reliable, accurate, and consistent enough to use for epidemiological study exposure assessment. Our scientific goal is to estimate individual-level long-term average exposures to PM2.5 and oxides of nitrogen for inference about the effects of air pollution on brain health. To accomplish this we are developing new spatio-temporal pollution predictions based on recently collected low-cost sensor data combined with existing ambient monitoring data. I will discuss the necessary quality control criteria for using the low-cost sensor data in a spatio-temporal prediction model, including calibration of these measurements to federal reference measurements. I will present the improvement in the performance of the spatio-temporal predictions from adding the low-cost sensor data and discuss the implications of these results for improving inference from epidemiologic cohort studies such as the Adult Changes in Thought Air Pollution study.