Atmospheric ozone (O₃) concentration is impacted by a number of factors, such as the amount of solar radiation, the composition of nitrogen oxides (NOx) and hydrocarbons, the transport of pollutants and the amount of particulate matter in the atmosphere. The oxidative potential of the atmosphere and the formation of secondary organic aerosols (SOAs) as a result of atmospheric oxidation are influenced by the prevalent O₃ concentration. The formation of secondary aerosols from O₃ depends on several meteorological, environmental and chemical factors. The relationship between PM_2.5 and O₃ in different urban environmental regimes of India is investigated in this study during the summer and winter seasons. A relationship between PM_2.5 and O₃ has been established for many meteorological and chemical variables, such as RH, WS, T and NOx, for the selected study locations. During the winter season, the correlation between PM_2.5 and O₃ was found to be negative for Delhi and Bengaluru, whereas it was positive in Ahmedabad. The city of Bengaluru was seen to have a positive correlation between PM_2.5 and O₃ during summer, coinciding with the transport of marine air masses with high RH and low wind speed (as evident from FLEXPART simulations), leading to the formation of SOAs. Further, O₃ concentrations are predicted using a Recurrent Neural Network (RNN) model based on the relation obtained between PM_2.5 and O₃ for the summer season using NOx, T, RH, WS and PM_2.5 as inputs.

Satellite observations of column-averaged carbon dioxide (XCO₂) and methane (XCH₄) mixing ratios provide essential data for monitoring greenhouse gas (GHG) emissions. However, the accuracy of emission estimates depends on the precision and bias of satellite retrievals, which require validation against ground-based reference measurements. This study presents a systematic validation of XCO₂ and XCH₄ data from GOSAT (Greenhouse gases Observing SATellite) and OCO-2 (Oribiting Carbon Observatory-2) satellites over South India using ground-based Fourier transform spectrometer (FTS) observations at Gadanki (13.5° N, 79.2° E) collected from October 2015 to July 2016. Satellite products from National Institute for Environmental Studies, Japan (NIES), NASA's Atmospheric CO₂ Observations from Space (ACOS) project, USA (ACOS), and the University of Leicester, UK (UoL) were evaluated using a three-step spatial-temporal pairing method. Results show that the UoL's proxy XCH₄ product meets the European Space Agency's Climate Change Initiative (ESA CCI) bias requirement (<10 ppb) across all spatial windows, while the NIES XCH₄ product meets the requirement only for intermediate spatial scales. For XCO₂, NASA ACOS and OCO-2 products meet the CCI bias requirement (<0.5 ppm), while NIES XCO₂ exceeds this threshold. All products satisfy the precision requirement (<8 ppm for XCO₂ and <34 ppb for XCH₄) with substantial margins. In addition, FLEXPART model simulations using regional emission inventories revealed that agricultural activities dominate seasonal methane enhancements, contributing approximately 55 %, followed by waste and wetland emissions. The model captured seasonal trends but underestimated the amplitude of observed variations, highlighting the influence of changing background methane levels. These findings demonstrate the suitability of recent satellite products for regional GHG monitoring and emphasise the need for expanding ground-based FTS networks across South Asia to support improved emission assessments.

The OCO-2 and GOSAT series of satellites provide near-global coverage of CO₂ and CH₄ mixing ratios. To accurately derive emission fluxes from the observed mixing ratios, it is crucial that these data meet specific precision and systematic error requirements. In this study, we report validation results for GOSAT and OCO-2 over South Asia, obtained using a portable Fourier Transform infrared Spectrometer (FTS) at a tropical rural site (Gadanki; Latitude: 13.45° N, Longitude: 79.18° E) in Southern India, from November 2015 to July 2016. Biases in CH₄ mixing ratios from GOSAT ranged from -9 to -18.5 ppb, depending on the collocation criteria, while CO₂ data from OCO-2 demonstrated better accuracy and precision, meeting the requirements of ESA’s Climate Change Initiative (CCI). Using the FLEXPART model, we also show that CH₄ emissions from regional sources accounted for only 35 % of the day-to-day observed variability. Both model-derived and observed mixing ratios exhibited the same seasonal variation, with higher values in October–November and lower values in June–July. However, the observed mixing ratios decreased by approximately 100 ppb, while the model-derived values decreased by only 20 ppb, suggesting that atmospheric chemistry and variations in background concentrations play a significant role over South India.