Abstract
The concentrations of atmospheric pollutants are a serious concern due to their adverse impacts on human health. The ventilation coefficient (VC) is an indicator that measures the dispersion capacity of air pollutants (air pollution potential) in the atmosphere, providing insights into air quality. In this study, we aim to investigate the spatio-temporal variation and trends of VC over the Indian subcontinent using India’s first high-resolution regional reanalysis (IMDAA) and global reanalysis datasets (ERA5) for the period 1980-2019. The spatial pattern of the seasonal climatological mean ERA5 and IMDAA derived VC shows a lower magnitude during winter and post-monsoon seasons, indicating poor air quality over the Indian region, especially in the northern parts of India. We noticed a gradual declination of VC during different seasons, implying increasing surface-level air pollutants and worsening air quality over India. The study further investigates the changes of VC during strong phases of El Niño and La Niña events. The results reveal that El Niño significantly impacts air quality over northern and western parts of India during pre-monsoon and monsoon seasons. At the diurnal scale, the VC exhibits the highest magnitude and variability during daytime due to increased dispersion of pollutants and higher human activities, while remaining low and stable during night due to stagnant atmospheric conditions. These essential characteristics of VC are well represented in IMDAA, albeit with some discrepancies. Furthermore, we have examined the fidelity of a machine learning model-Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM), in predicting the VC for the year 2019 over Delhi city. Various statistical metrics are computed to evaluate the performance of the CNN-LSTM model. The results confirm that the model successfully predicts the VC compared to observations from ERA5.