The COVID-19 lockdowns led to major reductions in air pollutant emissions. Here, we quantitatively evaluate changes in ambient NO₂, O₃, and PM_2.5 concentrations arising from these emission changes in 11 cities globally by applying a deweathering machine learning technique. Sudden decreases in deweathered NO₂ concentrations and increases in O₃ were observed in almost all cities. However, the decline in NO₂ concentrations attributable to the lockdowns was not as large as expected, at reductions of 10 to 50%. Accordingly, O₃ increased by 2 to 30% (except for London), the total gaseous oxidant (O_x = NO₂ + O₃) showed limited change, and PM_2.5 concentrations decreased in most cities studied but increased in London and Paris. Our results demonstrate the need for a sophisticated analysis to quantify air quality impacts of interventions and indicate that true air quality improvements were notably more limited than some earlier reports or observational data suggested.

The formation mechanism of aerosol sulfate during wintertime haze events in China is still largely unknown. As companions, SO₂ and transition metals are mainly emitted from coal combustion. Here, we argue that the transition metal-catalyzed oxidation of SO₂ on aerosol surfaces could be the dominant sulfate formation pathway and investigate this hypothesis by integrating chamber experiments, numerical simulations and in-field observations. Our analysis shows that the contribution of the manganese-catalyzed oxidation of SO₂ on aerosol surfaces is approximately one to two orders of magnitude larger than previously known routes, and contributes 69.2% ± 5.0% of the particulate sulfur production during haze events. This formation pathway could explain the missing source of sulfate and improve the understanding of atmospheric chemistry and climate change.

Acidic air pollutants dissolve iron in aerosols and fertilize the ocean.