COVID-19 lockdown altered atmospheric pollutant dynamics across major Indian cities

This study provides a descriptive, comparative analysis of air quality changes across seven major Indian cities (Delhi, Mumbai, Chennai, Bengaluru, Indore, Kolkata, and Guwahati) during the COVID-19 lockdown, focusing strictly on atmospheric physical and chemical processes. Using Central Pollution Control Board (CPCB) data, daily mean concentrations of PM₂.₅, PM₁₀, NO₂, SO₂, CO, and O₃ were examined across three temporal phases: pre-lockdown (January–March 2020), lockdown (March–May 2020), and post-lockdown (May–June 2020). Results indicate significant declines in primary pollutants during the lockdown phase: AQI fell by 17–53% across cities, with NO₂ recording the most pronounced reductions (30–60%), followed by PM₂.₅ (14–40%), PM₁₀ (33–47%), and CO (21–60%). These improvements were primarily driven by direct reductions in primary emission sources (vehicular traffic, industrial combustion) with secondary aerosol suppression providing a complementary contribution. Post-lockdown periods exhibited variable rebounds, with six of seven cities showing sustained improvement while Chennai exhibited a notable rebound exceeding pre-lockdown levels. Secondary pollutant O₃ exhibited heterogeneous responses, including modest increases in several cities (1–17%), reflecting non-linear atmospheric photochemistry: reduced nocturnal NO titration combined with enhanced daytime HOₓ radical concentrations under the lower-NOₓ regime shifted urban photochemical conditions toward VOC-limited or transitional regimes, resulting in net O₃ accumulation despite overall emission suppression. Meteorological factors such as temperature, wind speed, and humidity partially influenced dispersion and chemical transformation. The findings establish a process-level understanding of emission–concentration relationships and pollutant–meteorology interactions during extreme emission reduction scenarios, with implications for integrated urban air quality management strategies.transformation. The findings establish a mechanistic understanding of emission–concentration relationships and pollutant–meteorology interactions during extreme emission reduction scenarios.