Using Tower‐Based Observational Networks to Assess the Impact of COVID‐19 Lockdowns on Greenhouse Gas Emissions in Six North American Cities

Abstract

Abstract The onset of the COVID‐19 pandemic in North America and the lockdowns that followed in March 2020 brought forth a rapid change to societal functions disrupting many aspects of normal life, including the greenhouse gas emissions associated with them. In this work, we examine the capabilities of tower networks established in six North American cities in quantifying the change in these emissions. Influence functions, which relate tower‐based observational sites to their upwind source regions, were created for sites in Los Angeles, the D.C./Baltimore urban corridor, Indianapolis, Salt Lake City, Boston, and Toronto for the months of February–April of 2017–2020, and model CO 2 enhancements were generated by multiplying the influence functions by regional inventories. Scaling factors are assigned to the city emissions to minimize the difference between observed and modeled afternoon CO 2 enhancements in 15 days intervals. Scaling factors from the 2020 period are then compared directly to those from the 2017 to 2019 timeframe to calculate a relative change in the emissions during the COVID‐lockdown timeframe. Results across all six cities show a consistent message; by the end of March 2020, CO 2 emissions decreased by an average of 34% relative to the same 2017–2019 timeframe. This decrease matches values observed from bottom‐up inventories during the same period. A similar technique is performed for methane across four cities with more variable trends across cities. The results of this paper demonstrate the ability to utilize simple approaches to detect and quantify temporal changes in emissions using a tower network.