A Neural Operator for Forecasting Carbon Monoxide Evolution in Cities

2025-01-10

AI Summary

To address the challenge of real-time urban carbon monoxide (CO) concentration forecasting, this paper proposes CoNOAir—a novel complex-domain neural operator. Unlike conventional real-valued models, CoNOAir integrates Fourier spectral modeling with multi-scale spatiotemporal feature learning, enabling superior detection of extreme pollution events and enhanced cross-city generalization. The architecture supports high-accuracy forecasting from hourly to 72-hour horizons. Empirical evaluation across multiple Indian cities yields R² > 0.95, substantially outperforming state-of-the-art baselines including the Fourier Neural Operator (FNO). Designed for operational deployment, CoNOAir features lightweight parameterization and strong robustness, meeting stringent national air quality early-warning system requirements for real-time responsiveness, predictive accuracy, and hardware efficiency. By bridging theoretical innovation with practical applicability, CoNOAir delivers reliable, actionable intelligence for urban air pollution mitigation and policy intervention.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Mixture of Experts (MoE)
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

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-theart models, such as the Fourier Neural Operator (FNO), in both short-term (hourly) and extended (72-hour) 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.

Problem

• Urban Air Quality
• Carbon Monoxide Concentration
• Real-time Prediction

Innovation

• CoNOAir
• CO concentration prediction
• high accuracy