Technical note: Understanding the effect of COVID-19 on particle pollution using a low-cost sensor network

Area of Science:

• Environmental Science
• Public Health
• Atmospheric Chemistry

Background:

• The 2020 coronavirus pandemic prompted global quarantine measures, altering daily life and potentially impacting air quality.
• Preliminary studies suggested improved air quality in urban areas due to reduced human activity during lockdowns.
• Existing air quality monitoring stations offer sparse data, necessitating higher-resolution measurements for localized impact assessment.

Purpose of the Study:

• To quantify neighborhood-scale changes in air pollution using a high-resolution sensor network.
• To compare particulate matter (PM2.5) concentrations before and during COVID-19 quarantine periods in Salt Lake County.
• To investigate the relationship between lockdown stringency and observed air quality improvements.

Main Methods:

• Utilized data from the Air Quality & yoU (AQ&U) citizen-hosted, low-cost particulate matter sensor network.
• Compared monthly average estimated PM2.5 concentrations from 71 unique locations during Feb-May 2019 and Feb-May 2020.
• Employed a paired t-test to analyze significant differences in PM2.5 levels between the two years, correlating with quarantine periods.

Main Results:

• A significant reduction in monthly average PM2.5 concentrations was observed between 2019 and 2020, with reductions of 71.1% (March 11-April 10) and 21.3% (April 11-May 10).
• State monitoring sites also reported significant PM2.5 decreases in March 2020 compared to the 5-year average.
• Reductions were most pronounced in lower-elevation, urbanized areas, while higher-elevation residential areas showed less significant changes.

Conclusions:

• COVID-19 quarantine measures led to measurable improvements in air quality, specifically reducing PM2.5 concentrations in Salt Lake County.
• The study highlights the effectiveness of reduced traffic and pollutant emissions during lockdowns for improving urban air quality.
• Citizen science sensor networks provide valuable high-resolution data for understanding localized environmental changes.