Abstract
While a sharp decline in air pollution has been documented during early Covid-19 lockdown periods, the stability and homogeneity of this effect are still under debate. Building on pollution data with a very high level of resolution, this paper estimates the impact of lockdown policies on P M 2.5 exposure in France over the whole year 2020. Our analyses highlight a surprising and undocumented increase in exposure to particulate pollution during lockdown periods. This result is observed during both lockdown periods, in early spring and late fall, and is robust to several identification strategies and model specifications. Combining administrative datasets with machine learning techniques, this paper also highlights strong spatial heterogeneity in lockdown effects, especially according to long-term pollution exposure.