NASA SEES: Air Quality Dynamics During COVID-19

Project Motivation

Lockdowns created a rare, large‑scale reduction in human activity—offering a unique experiment to observe pollution changes. This analysis aimed to understand which pollutants are most sensitive to human activity and how quickly they rebound once activity resumes.

Data & Study Design

Study Cities

• New York City (dense transport + building emissions)
• Los Angeles (vehicle emissions + photochemical smog)
• Chicago (industrial + transportation mix)

Data Source

Sentinel‑5P near‑real‑time products were used for: CO, HCHO, NO₂, O₃, and SO₂.

Analysis Window

• July 2019 → Pre‑lockdown baseline
• July 2020 → Lockdown period
• July 2023 → Post‑lockdown recovery

Methods

Difference Mapping

I created spatial difference maps for each pollutant between years to quantify how concentrations changed in each city.

Time Series Analysis

Monthly averages were extracted to assess trends across the four‑year period and capture rebound patterns.

Percent Change Calculations

For each city and pollutant, July‑to‑July percent changes were computed to standardize comparisons.

Key Results

Most pollutants rebounded by 2023, with some exceeding pre‑pandemic levels. O₃ showed minimal changes, reflecting complex secondary chemistry.

Anomalies & Interpretation

• SO₂ trends varied strongly by city, indicating local source dependence.
• HCHO in NYC decreased only slightly, suggesting residential activity offsets.
• O₃ remained stable due to nonlinear interactions between NOx and VOCs.

Limitations

• July averages miss seasonal extremes (winter inversions, wildfire smoke).
• Satellite data lacks hyperlocal resolution.
• Natural events (wildfires, biogenic emissions) confound signals.

Gallery (Additional Photos)