Background: Mounting evidence has shown that long-term exposure to fine particulate matter [PM in aerodynamic diameter ( )] and ozone ( ) can increase mortality. However, the health effects associated with long-term exposure to nitrogen dioxide ( ) are less clear, in particular the evidence is scarce for at low levels that are below the current international guidelines. Methods: We constructed a population-based full cohort comprising all Medicare beneficiaries (aged , ) in the southeastern United States from 2000 to 2016, and we then further defined the below-guideline cohort that included only those who were always exposed to low-level , that is, with annual means below the current World Health Organization guidelines (i.e., ). We applied previously estimated spatially and temporally resolved concentrations and assigned annual means to study participants based on their ZIP code of residence. Cox proportional hazards models were used to examine the association between long-term exposure to low-level and all-cause mortality, adjusting for potential confounders. Results: About 71.1% of the Medicare beneficiaries in the southeastern United States were always exposed to low-level over the study period. We observed an association between long-term exposure to low-level and all-cause mortality, with a 1.042 (95% CI: 1.040, 1.045) in single-pollutant models and a 1.047 (95% CI: 1.045, 1.049) in multipollutant models (adjusting for and ), per increase in annual concentrations. The penalized spline indicates a linear exposure–response relationship across the entire exposure range. Medicare enrollees who were White, female, and residing in urban areas were more vulnerable to long-term exposure. Conclusion: Using a large and representative cohort, we provide epidemiological evidence that long-term exposure to , even below the national and global ambient air quality guidelines, was approximately linearly associated with a higher risk of mortality among older adults, independent of and exposure. Improving air quality by reducing emissions, therefore, may yield significant health benefits. https://doi.org/10.1289/EHP9044

China implemented an aggressive nationwide lockdown procedure immediately after the COVID-19 outbreak in January 2020. As China emerges from the impact of COVID-19 on national economic and industrial activities, it has become the site of a large-scale natural experiment to evaluate the impact of COVID-19 on regional air quality. However, ground measurements of fine particulate matters (PM2.5) concentrations do not offer comprehensive spatial coverage, especially in suburban and rural regions. In this study, we developed a machine learning method with satellite aerosol remote sensing data, meteorological fields and land use parameters as major predictor variables to estimate spatiotemporally resolved daily PM2.5 concentrations in China. Our study period consists of a reference semester (1 November 2018–30 April 2019) and a pandemic semester (1 November 2019–30 April 2020), with six modeling months in each semester. Each period was then divided into subperiod 1 (November and December), subperiod 2 (January and February) and subperiod 3 (March and April). The reference semester model obtained a 10-fold cross-validated R2 (RMSE) of 0.79 (17.55 μg/m3) and the pandemic semester model obtained a 10-fold cross-validated R2 (RMSE) of 0.83 (13.48 μg/m3) for daily PM2.5 predictions. Our prediction results showed high PM2.5 concentrations in the North China Plain, Yangtze River Delta, Sichuan Basin and Xinjiang Autonomous Region during the reference semester. PM2.5 levels were lowered by 4.8 μg/m3 during the pandemic semester compared to the reference semester and PM2.5 levels during subperiod 2 decreased most, by 18%. The southeast region was affected most by the COVID-19 outbreak with PM2.5 levels during subperiod 2 decreasing by 31%, followed by the Northern Yangtze River Delta (29%) and Pearl River Delta (24%).