Air Quality Impacts of Anthropogenic Activities During Pandemics

The interplay between anthropogenic activity and air quality during pandemics has been a focus since the COVID-19 crisis revealed the responsiveness of urban atmospheres to rapid emission changes. Restrictions on transport and industry delivered unprecedented reductions in primary pollutants such as nitrogen dioxide and fine particulate matter, yet these gains were uneven and sometimes offset by increases in secondary species. Meteorological conditions, chemical non-linearities and spatial disparities have all shaped local outcomes, leading to heterogeneous improvements in air quality across cities and regions. The natural experiment of lockdowns has highlighted both the potential of targeted emission controls and the limitations of single-pollutant approaches. Findings underscore the importance of coordinated strategies that address precursor interactions, incorporate meteorological normalisation and mitigate disparities in exposure. Moreover, advances in forecasting and causal inference methods have enhanced our ability to disentangle the effects of policy measures from background trends, offering new tools for designing resilient air-quality management plans. These insights carry global significance, informing sustainable recovery pathways and long-term policies aimed at reducing health risk and inequality associated with poor air quality.

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Air Quality Impacts of Anthropogenic Activities During Pandemics publication trend

The graph below shows the total number of articles in air quality impacts of anthropogenic activities during pandemics across all publications each year (not limited to Nature Index journals).

Technical terms

Nitrogen dioxide (NO₂): A combustion-derived gas that contributes to respiratory irritation and serves as a precursor to ozone formation.

Particulate matter (PM₂.₅ and PM₁₀): Suspended solid or liquid particles with aerodynamic diameters below 2.5 μm or 10 μm that affect human health and visibility.

Ozone (O₃): A secondary pollutant formed by photochemical reactions of nitrogen oxides and volatile organic compounds, linked to lung inflammation.

Secondary particulate matter: Aerosol particles generated in the atmosphere from gaseous precursors through chemical reactions rather than direct emissions.

Meteorological normalisation: A statistical technique used to adjust pollutant concentration data for weather variations to isolate emission-driven trends.

References

1. Using Explainable Machine Learning to Interpret the Effects of Policies on Air Pollution: COVID-19 Lockdown in London. Environmental Science and Technology (2023).
2. A machine learning approach to address air quality changes during the COVID-19 lockdown in Buenos Aires, Argentina. Earth System Science Data (2023).
3. Air pollution changes due to COVID-19 lockdowns and attributable mortality changes in four countries. Environment International (2024).
4. Enhanced secondary pollution offset reduction of primary emissions during COVID-19 lockdown in China. National Science Review (2020).