Counterfactual time series analysis of short-term change in air pollution following the COVID-19 state of emergency in the United States

Tanujit Dey,Francesca Dominici,M Benjamin Sabath,Pooja Tyagi,Leila Kamareddine,Lucas Henneman,Danielle Braun

doi:10.1038/s41598-021-02776-0

Abstract

Lockdown measures implemented in response to the COVID-19 pandemic produced sudden behavioral changes. We implement counterfactual time series analysis based on seasonal autoregressive integrated moving average models (SARIMA), to examine the extent of air pollution reduction attained following state-level emergency declarations. We also investigate whether these reductions occurred everywhere in the US, and the local factors (geography, population density, and sources of emission) that drove them. Following state-level emergency declarations, we found evidence of a statistically significant decrease in nitrogen dioxide (NO2) levels in 34 of the 36 states and in fine particulate matter (PM2.5) levels in 16 of the 48 states that were investigated. The lockdown produced a decrease of up to 3.4 µg/m3 in PM2.5 (observed in California) with range (− 2.3, 3.4) and up to 11.6 ppb in NO2 (observed in Nevada) with range (− 0.6, 11.6). The state of emergency was declared at different dates for different states, therefore the period "before" the state of emergency in our analysis ranged from 8 to 10 weeks and the corresponding "after" period ranged from 8 to 6 weeks. These changes in PM2.5 and NO2 represent a substantial fraction of the annual mean National Ambient Air Quality Standards (NAAQS) of 12 µg/m3 and 53 ppb, respectively. As expected, we also found evidence that states with a higher percentage of mobile source emissions (obtained from 2014) experienced a greater decline in NO2 levels after the lockdown. Although the socioeconomic restrictions are not sustainable, our results provide a benchmark to estimate the extent of achievable air pollution reductions. Identification of factors contributing to pollutant reduction can help guide state-level policies to sustainably reduce air pollution.

Highlights

There is consistent evidence that short- and long-term exposure to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) increases the risk of mortality, hospitalization, and other adverse health outcomes[1,2,3,4,5,6,11,12]
We examined the effect of the abrupt lockdown measures implemented in response to the COVID19 pandemic, which produced sudden and significant changes in how society functions, with decreases in road traffic, air traffic, and economic a ctivity[19]
Because of the good fit of the seasonal autoregressive integrated moving average models (SARIMA) model to the historical data (Figure S6), and because the counterfactual forecasting is agnostic to the date of the state level emergency, we argue that negative estimated values of j indicate that air pollution levels declined because of the state-level emergency

Summary

Introduction

There is consistent evidence that short- and long-term exposure to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) increases the risk of mortality, hospitalization, and other adverse health outcomes[1,2,3,4,5,6,11,12]. Several studies have provided preliminary evidence that short and long-term air pollution exposure increases the risk of hospitalization and death among individuals with COVID-194–10. We examined the effect of the abrupt lockdown measures implemented in response to the COVID19 pandemic, which produced sudden and significant changes in how society functions, with decreases in road traffic, air traffic, and economic a ctivity[19]. This provided us with an unprecedented opportunity to implement a quasi-experimental design with a well-defined control condition (no pandemic) to estimate the changes in air pollution because of the implementation of these extreme measures.

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