Network of Low-cost Air Quality Sensor for Monitoring Indoor, Outdoor, and Personal PM2.5 Exposure in Seattle during the 2020 Wildfire Season

TL;DR

This study demonstrates a low-cost sensor network in Seattle during the 2020 wildfire season, revealing indoor/outdoor PM2.5 ratios, the effectiveness of air filtration, and the importance of personal exposure monitoring for health interventions.

Contribution

It introduces a low-cost sensor network for comprehensive indoor, outdoor, and personal PM2.5 monitoring during wildfires, highlighting the impact of air filtration and personal exposure variability.

Findings

High correlation between outdoor sensors and regional air quality data (R2=0.93)

Air filtration systems significantly reduce indoor PM2.5 levels

Personal monitors capture high variability in individual exposure

Abstract

The increased frequency of wildfires in the Western United States has raised public concerns. Exposure to wildfire smoke has been linked to an increased risk of cancer and cardiorespiratory morbidity. Evidence-driven interventions can alleviate the adverse health impact of wildfire smoke. Public health guidance during wildfires is based on regional air quality data with limited spatiotemporal resolution. We demonstrate the use of a network of low-cost particulate matter (PM) sensors to gather indoor, outdoor, and personal PM2.5 exposure data from seven locations in the urban Seattle area, along with a personal exposure monitor worn by a resident living in one of these locations during the 2020 Washington wildfire event. The data were used to determine PM concentration indoor/outdoor (I/O) ratios, PM reduction, and personal exposure levels. The result shows that locations equipped with high-efficiency particulate air (HEPA) filters and HVAC filtration systems had significantly lower I/O ratios (median I/O = 0.43) than those without air filtration (median I/O = 0.82). The median PM2.5 reduction for the locations with HEPA is 58 % compared to 20% for the locations without HEPA. The outdoor PM sensors showed a high correlation to the nearby regional air quality monitoring stations (R2 = 0.93). The personal monitor showed high variance in PM measurements as the user moved through different microenvironments and could not be fully characterized by the network of indoor or outdoor monitors. The findings imply evidence-based interventions can be developed for reducing pollution exposure based on the combination of indoor, outdoor sensors. Personal exposure monitoring in individuals' breathing zones provided the highest fidelity data capturing temporal spikes in PM exposure.