Quantifying COVID-19 enforced global changes in atmospheric pollutants using cloud computing based remote sensing

Area of Science:

• Environmental science
• Atmospheric chemistry
• Remote sensing

Background:

• COVID-19 pandemic lockdowns altered anthropogenic activities globally.
• Previous air quality studies were limited by station-based data, especially in developing regions.
• Satellite data offers a more comprehensive approach to analyzing global air quality changes.

Purpose of the Study:

• To analyze and quantify global air quality changes during COVID-19 lockdowns.
• To assess air pollution reduction in megacities, capitals, and high-standard-of-living cities.
• To provide insights into the spatial distribution of air pollutant changes.

Main Methods:

• Utilized high spatiotemporal resolution satellite data and merged air pollutant products.
• Employed Google Earth Engine for cloud computing-based remote sensing analysis.
• Focused quantitative assessment on megacities, capitals, and affluent urban areas.

Main Results:

• Observed statistically significant reductions in key air pollutants.
• Mean reductions of 19.74% in nitrogen dioxide (NO2), 7.38% in aerosol optical depth (AOD), and 49.9% in PM2.5 concentrations over megacities.
• Demonstrated the effectiveness of satellite remote sensing for global air quality monitoring.

Conclusions:

• COVID-19 lockdowns led to measurable global air quality improvements, particularly in urban centers.
• Satellite-based analysis provides valuable data for climate model validation and sensitivity studies.
• Developed Google Earth Engine apps enable real-time visualization of air quality changes.