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<p>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), 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. We noticed a gradual decline in 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 Southern Oscillation events. The results reveal that El Niño and La Niña significantly impacts air quality over northern, central and western parts of India during pre-monsoon and monsoon seasons. These essential characteristics of VC are well represented in IMDAA, albeit with some discrepancies. Furthermore, we have examined the fidelity of a hybrid machine learning model–Convolutional Neural Network and Long Short-Term Memory, in predicting the VC for the year 2019 over Delhi city. The results confirm that the model successfully predicts the VC compared to observations from ERA5. This framework further helps in developing a city-based air pollution prediction.</p>

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Analyzing and predicting ventilation coefficient over India using long-term reanalysis datasets and hybrid machine learning approach

  • 期刊论文

  • 发表日期：

    2025年10月29日

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  • Amitabha Govande,
  • Raju Attada,
  • Krishna Kumar Shukla,
  • Soumya Muralidharan,
  • Ravi Kumar Kunchala,
  • Nagaraju Chilukoti,
  • Garima Kaushik

摘要

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), 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. We noticed a gradual decline in 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 Southern Oscillation events. The results reveal that El Niño and La Niña significantly impacts air quality over northern, central and western parts of India during pre-monsoon and monsoon seasons. These essential characteristics of VC are well represented in IMDAA, albeit with some discrepancies. Furthermore, we have examined the fidelity of a hybrid machine learning model–Convolutional Neural Network and Long Short-Term Memory, in predicting the VC for the year 2019 over Delhi city. The results confirm that the model successfully predicts the VC compared to observations from ERA5. This framework further helps in developing a city-based air pollution prediction.