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<p>Real-time forecasting of carbon monoxide (CO) concentrations is essential for enabling timely interventions to improve urban air quality. Conventional air quality models often require extensive computational resources for accurate, multi-scale predictions, limiting their practicality for rapid, real-time application. To address this challenge, we introduce the Complex Neural Operator for Air Quality (CoNOAir), a machine learning model that forecast CO concentrations efficiently. CoNOAir demonstrates superior performance over state-of-the-art models, such as the Fourier Neural Operator (FNO), in both short-term (hourly) and extended (72-h) forecasts at a national scale. It excels in capturing extreme pollution events and performs consistently across multiple Indian cities, achieving an <i>R</i><sup>2</sup> above 0.95 for hourly CO predictions across all evaluated locations. CoNOAir equips authorities with an effective tool for issuing early warnings and designing targeted intervention strategies. This work marks a step forward in achieving dependable, real-time CO pollution predictions for densely populated urban centres.</p>

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A neural operator for forecasting carbon monoxide evolution in cities

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月12日

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  • Sanchit Bedi,
  • Karn Tiwari,
  • Prathosh A. P.,
  • Sri Harsha Kota,
  • N. M. Anoop Krishnan

摘要

Real-time forecasting of carbon monoxide (CO) concentrations is essential for enabling timely interventions to improve urban air quality. Conventional air quality models often require extensive computational resources for accurate, multi-scale predictions, limiting their practicality for rapid, real-time application. To address this challenge, we introduce the Complex Neural Operator for Air Quality (CoNOAir), a machine learning model that forecast CO concentrations efficiently. CoNOAir demonstrates superior performance over state-of-the-art models, such as the Fourier Neural Operator (FNO), in both short-term (hourly) and extended (72-h) forecasts at a national scale. It excels in capturing extreme pollution events and performs consistently across multiple Indian cities, achieving an R2 above 0.95 for hourly CO predictions across all evaluated locations. CoNOAir equips authorities with an effective tool for issuing early warnings and designing targeted intervention strategies. This work marks a step forward in achieving dependable, real-time CO pollution predictions for densely populated urban centres.