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

  1. Tanujit Dey
  2. Pooja Tyagi
  3. M. Benjamin Sabath
  4. Leila Kamareddine
  5. Lucas Henneman
  6. Danielle Braun
  7. Francesca Dominici

Reviewed by

Read the full article

Listed in

Log in to save this article

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 (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.

Article activity feed

  1. Version published to 10.1038/s41598-021-02776-0
  2. ScreenIT

    SciScore for 10.1101/2020.08.04.20168237: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Study Limitations. We relied on state-level concentration averages and the 2014 emissions inventory. While our study would benefit greatly from a more recent emissions inventory (or fine spatial emissions estimates during the interventions), to our knowledge, such data is not currently available publicly. Trading finer spatial resolution in the monitoring data—not averaging to the state level—may reveal important sub-state variability in lockdown impacts. Our approach also does not consider the spatial correlations between pollutant concentrations, which may help explain concentration changes in non-local pollutants such as PM2.5. Finally, data were available for 36 states for NO2 and 48 states for PM2.5, which limited the number of observations in the weighted regression model.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a protocol registration statement.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.08.04.20168237v1 on medRxiv
  4. Version published to 10.1101/2020.08.04.20168237v2 on medRxiv