Machine Learning-Based Prediction of New Particle Formation Events from Terpene Chemistry and Anthropogenic Emission Interactions

Abstract

Atmospheric new particle formation is a central process connecting gas-phase chemistry, aerosol dynamics, cloud condensation nuclei abundance, and regional climate forcing. In environments influenced by both biogenic terpene emissions and anthropogenic pollutants, the formation of new particles emerges from complex chemical and physical interactions that are poorly captured by traditional deterministic parameterizations. Recent observational evidence has highlighted the importance of heavy terpenes and their modulation by anthropogenic emissions in remote high-altitude regions, yet operational prediction of such events remains an unresolved scientific and computational challenge. This article examines the design of machine learning-based prediction systems for new particle formation events with a focus on system architecture, data infrastructure, feature engineering, model selection, deployment, robustness, fairness, and policy implications. We present a system-level analysis that situates machine learning within broader atmospheric observation and modeling infrastructures, emphasizing the structural trade-offs between interpretability, data efficiency, generalization, and operational resilience. The discussion addresses the integration of ground-based aerosol and precursor observations, laboratory chamber data, satellite retrievals, emission inventories, and reanalysis fields, and considers how these heterogeneous data streams can be organized into coherent learning environments. The paper further evaluates the governance requirements for predictive systems that may inform air quality management and environmental policy, particularly across regions with uneven observational capacity. Rather than proposing a single algorithmic solution, we argue that reliable machine learning prediction for new particle formation requires a socio-technical infrastructure capable of sustaining data quality, model monitoring, uncertainty communication, and equitable scientific benefit.