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

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 (NO 2 ) levels in 34 of the 36 states and in fine particulate matter (PM 2.5 ) levels in 16 of the 48 states that were investigated. The lockdown produced a decrease of up to 3.4 µg/m 3 in PM 2.5 (observed in California) with range (− 2.3, 3.4) and up to 11.6 ppb in NO 2 (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 PM 2.5 and NO 2 represent a substantial fraction of the annual mean National Ambient Air Quality Standards (NAAQS) of 12 µg/m 3 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 NO 2 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.