High concentrations of tropospheric ozone (O₃) is a serious concern in India. The generation and atmospheric dynamics of this trace gas depend on the availability of its precursors and meteorological variables. Like other parts of the world, the COVID-19 imposed lockdown and restrictions on major anthropogenic activities executed a positive impact on the ambient air quality with reduced primary pollutants/precursors load. In spite of this, several reports pointed towards a higher O₃ in major Indian cities during the lockdown. The present study designed with 30 pan-Indian mega-, class I-, and class II-cities revealed critical and contrasting aspects of the geographical location, source, precursor, and meteorological variable dependency of the spatial and temporal O₃ formation. This unexpected O₃ increase in the major cities might forecast the probable future risks for the National Air Quality policies, especially O₃ pollution management, in the Indian sub-continent. The results also pointed towards the severity of the north Indian air quality, followed by the western and eastern parts. We believe these results will definitely pave the way for researchers and policy-makers for predicting/framing regional and/or national O₃ management strategies in the future.

Machine learning (ML) has emerged as a powerful technique in the Earth system science, nevertheless, its potential to model complex atmospheric chemistry remains largely unexplored. Here, we applied ML to simulate the variability in urban ozone (O₃) over Doon valley of the Himalaya. The ML model, trained with past variations in O₃ and meteorological conditions, successfully reproduced the independent O₃ data (r² ~ 0.7). Model performance is found to be similar when the variation in major precursors (CO and NO_x) were included in the model, instead of the meteorology. Further the inclusion of both precursors and meteorology improved the performance significantly (r² = 0.86) and the model could also capture the outliers, which are crucial for air quality assessments. We suggest that in absence of high-resolution measurements, ML modeling has profound implications for unraveling the feedback between pollution and meteorology in the fragile Himalayan ecosystem.

To prevent the spread of the COVID-19 epidemic, the Chinese megacity Wuhan has taken emergent lockdown measures starting on January 23, 2020. This provided a natural experiment to investigate the response of air quality to such emission reductions. Here, we decoupled the influence of meteorological and non-meteorological factors on main air pollutants using generalized additive models (GAMs), driven by data from the China National Environmental Monitoring Center (CNEMC) network. During the lockdown period (Jan. 23 – Apr. 8, 2020), PM_2.5, PM₁₀, NO₂, SO₂, and CO concentrations decreased significantly by 45 %, 49 %, 56 %, 39 %, and 18 % compared with the corresponding period in 2015–2019, with contributions by S(meteos) of 15 %, 17 %, 13 %, 10 %, and 6 %. This indicates an emission reduction of NO_x at least 43 %. However, O₃ increased by 43 % with a contribution by S(meteos) of 6 %. In spite of the reduced volatile organic compound (VOC) emissions by 30 % during the strict lockdown period (Jan. 23 – Feb. 14, 2020), which likely reduced the production of O₃, O₃ concentrations increased due to a weakening of the titration effect of NO. Our results suggest that conventional emission reduction (NO_x reduction only) measures may not be sufficient to reduce (or even lead to an increase of) surface O₃ concentrations, even if reaching the limit, and VOC-specific measures should also be taken.