Spatiotemporal evolution of NO₂ diffusion in Beijing in response to COVID-19 lockdown using complex network.

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Urban Studies

Background:

• COVID-19 lockdowns reduced air pollution globally, particularly nitrogen dioxide (NO2) in megacities.
• The spatial diffusion patterns of these NO2 reductions during the pandemic remain unclear.

Purpose of the Study:

• To investigate the NO2 diffusion process in Beijing, a megacity with strict COVID-19 lockdown measures.
• To understand the spatiotemporal dynamics of air pollution changes during pandemic and recovery periods.

Main Methods:

• Utilized complex network methods to analyze NO2 diffusion patterns.
• Employed improved topological metrics to quantify diffusion performance.
• Examined evolutionary diffusion patterns from lockdown to recovery phases.

Main Results:

• COVID-19 significantly impacted spatial NO2 diffusion due to altered human activities and meteorological conditions.
• The difference between normal and pandemic NO2 diffusion initially increased post-lockdown then decreased with recovery measures.
• Source areas exhibited higher vulnerability and lower resilience compared to receptor areas.

Conclusions:

• The study reveals significant changes in NO2 diffusion patterns attributable to COVID-19 measures.
• Findings provide insights into urban air pollution dynamics, aiding high-resolution mapping and prediction.
• Understanding diffusion patterns is crucial for managing air quality during public health crises.