Analysis of Factors Influencing Air Quality in Different Periods during COVID-19: A Case Study of Tangshan, China.

This study aimed to analyze the main factors influencing air quality in Tangshan during COVID-19, covering three different periods: the COVID-19 period, the Level I response period, and the Spring Festival period. Comparative analysis and the difference-in-differences (DID) method were used to explore differences in air quality between different stages of the epidemic and different years. During the COVID-19 period, the air quality index (AQI) and the concentrations of six conventional air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3-8h) decreased significantly compared to 2017-2019. For the Level I response period, the reduction in AQI caused by COVID-19 control measures were 29.07%, 31.43%, and 20.04% in February, March, and April of 2020, respectively. During the Spring Festival, the concentrations of the six pollutants were significantly higher than those in 2019 and 2021, which may be related to heavy pollution events caused by unfavorable meteorological conditions and regional transport. As for the further improvement in air quality, it is necessary to take strict measures to prevent and control air pollution while paying attention to meteorological factors.

Impact Evaluation of Coronavirus Disease 2019 Policy: A Guide to Common Design Issues.

Policy responses to coronavirus disease 2019 (COVID-19), particularly those related to nonpharmaceutical interventions, are unprecedented in scale and scope. However, evaluations of policy impacts require a complex combination of circumstance, study design, data, statistics, and analysis. Beyond the issues that are faced for any policy, evaluation of COVID-19 policies is complicated by additional challenges related to infectious disease dynamics and a multiplicity of interventions. The methods needed for policy-level impact evaluation are not often used or taught in epidemiology, and they differ in important ways that may not be obvious. Methodological complications of policy evaluations can make it difficult for decision-makers and researchers to synthesize and evaluate the strength of the evidence in COVID-19 health policy papers. Here we 1) introduce the basic suite of policy-impact evaluation designs for observational data, including cross-sectional analyses, pre-/post- analyses, interrupted time-series analysis, and difference-in-differences analysis; 2) demonstrate key ways in which the requirements and assumptions underlying these designs are often violated in the context of COVID-19; and 3) provide decision-makers and reviewers with a conceptual and graphical guide to identifying these key violations. Our overall goal is to help epidemiologists, policy-makers, journal editors, journalists, researchers, and other research consumers understand and weigh the strengths and limitations of evidence.

Effects of COVID-19 lockdown on global air quality and health.

The COVID-19 pandemic has put much of the world into lockdown, as one unintended upside to this response, the air quality has been widely reported to have improved worldwide. Existing studies examine the environmental effect of lockdowns at a city- or country-level, few examines it from a global perspective. Using a novel COVID-19 government response tracker dataset, combining the daily air pollution data and weather data across 597 major cities worldwide between January 1, 2020, and July 5, 2020, this study quantifies the causal impacts of 8 types of lockdown measures on changes of a range of individual pollutants based on a difference-in-differences design. The results show that the NO2 air quality index value falls more precipitously (23-37%) relative to the pre-lockdown period, followed by PM10 (14-20%), SO2 (2-20%), PM2.5 (7-16%), and CO (7-11%), but the O3 increases 10-27%. Furthermore, intra/intercity travel restrictions have a better performance in curbing air pollution. These results are robust to a set of alternative specifications, including different panel sizes, independent variables, estimation strategies. The heterogeneity analysis in terms of different types of cities shows that the lockdown effects are more remarkable in cities from lower-income, more industrialized, and populous countries. We also do a back-of-the-envelope calculation of the subsequent health benefits following such improvement, and the expected averted premature deaths due to air pollution declines are around 99,270 to 146,649 among 76 countries and regions involved in this study during the COVID-19 lockdown. These findings underscore the importance of continuous air pollution control strategies to protect human health and reduce the associated social welfare loss both during and after the COVID-19 pandemic.