Global fine-scale changes in ambient NO2 during COVID-19 lockdowns

Matthew J Cooper,Randall V Martin,Pepijn Veefkind,Jeffrey R Brook,Melanie S Hammer,Chris A Mclinden,Lok N Lamsal,Nickolay A Krotkov,Pieternel F Levelt

doi:10.1038/s41586-021-04229-0

Abstract

Nitrogen dioxide (NO2) is an important contributor to air pollution and can adversely affect human health1–9. A decrease in NO2 concentrations has been reported as a result of lockdown measures to reduce the spread of COVID-1910–20. Questions remain, however, regarding the relationship of satellite-derived atmospheric column NO2 data with health-relevant ambient ground-level concentrations, and the representativeness of limited ground-based monitoring data for global assessment. Here we derive spatially resolved, global ground-level NO2 concentrations from NO2 column densities observed by the TROPOMI satellite instrument at sufficiently fine resolution (approximately one kilometre) to allow assessment of individual cities during COVID-19 lockdowns in 2020 compared to 2019. We apply these estimates to quantify NO2 changes in more than 200 cities, including 65 cities without available ground monitoring, largely in lower-income regions. Mean country-level population-weighted NO2 concentrations are 29% ± 3% lower in countries with strict lockdown conditions than in those without. Relative to long-term trends, NO2 decreases during COVID-19 lockdowns exceed recent Ozone Monitoring Instrument (OMI)-derived year-to-year decreases from emission controls, comparable to 15 ± 4 years of reductions globally. Our case studies indicate that the sensitivity of NO2 to lockdowns varies by country and emissions sector, demonstrating the critical need for spatially resolved observational information provided by these satellite-derived surface concentration estimates.

Highlights

NO2 observations indicate air quality relationships with combustion sources of pollution such as transportation[6,21]
It is important to consider the effect of meteorology on recent NO2 changes[22] and to quantify NO2 changes due to COVID-19 interventions in the context of longer-term trends[23]
Air quality monitoring sites tend to be preferentially located in higher-income regions, raising questions about how NO2 changed in lower-income regions where larger numbers of potentially susceptible people reside

Summary

Methods

We use tropospheric NO2 columns from the OMI (NASA Standard Product version 4)[51] and TROPOMI52,53 satellite instruments. We correct for sampling biases in the satellite records due to persistent cloudy periods or surface snow cover using a correction factor calculated with the GEOS-Chem chemical transport model described below by sampling the GEOS-Chem-simulated monthly or annual mean column densities to match the satellite. The updated algorithm uses the satellite-observed column densities and ground-monitor data as observational constraints on the shape of the boundary layer profile, reducing the sensitivity to model resolution and improving agreement between satellite-derived ground-level concentrations and in situ observations. For long-term trend analysis, we use more recent TROPOMI observations to provide fine-resolution spatial structure to the OMI-observed NO2 columns following the method of Geddes et al.[25]. Meteorological effects are estimated using GEOS-Chem simulations at 2° × 2.5° resolution with consistent emissions in both years, downscaled to ~1 × 1 km[2] resolution using the horizontal variability of TROPOMI-derived ground-level concentrations. Where xi is the NO2 concentration and Pi is the population within a ~1 × 1-km[2] grid box

AMF2020

Findings

Code availability