Air-sea exchange of PAHs in the Taiwan Strait: Seasonal dynamics and regulation mechanisms revealed by machine learning approach

In this study, to understand the seasonal dynamics of air-sea exchange and its regulation mechanisms, we investigated polycyclic aromatic hydrocarbons (PAHs) at the air-sea interface in the western Taiwan Strait in combination with measurements and machine learning (ML) predictions. For 3-ring PAHs and most of 4- to 6-ring, volatilization and deposition fluxes were observed, respectively. Seasonal variations in air-sea exchange flux suggest the influence of monsoon transitions. Results of interpretable ML approach (XGBoost) indicated that volatilization of 3-ring PAHs was significantly controlled by dissolved PAH concentrations (contributed 24.0%), and the gaseous deposition of 4- to 6-ring PAHs was related to more contaminated air masses originating from North China during the northeast monsoon. Henry’s law constant emerged as a secondary factor, influencing the intensity of air-sea exchange, particularly for low molecular weight PAHs. Among environmental parameters, notably high wind speed emerges as the primary factor and biological pump’s depletion of PAHs in surface seawater amplifies the gaseous deposition process. The distinct dynamics of exchanges at the air-water interface for PAHs in the western TWS can be attributed to variations in primary emission intensities, biological activity, and the inconsistent pathways of long-range atmospheric transport, particularly within the context of the monsoon transition. Environment implication Most polycyclic aromatic hydrocarbons (PAHs) are carcinogenic, teratogenic, and mutagenic. They can be transported globally via various pathways due to their semi-volatility and persistence. This exerts a long-term effect on the ecosystems and human beings.This work investigated the seasonal dynamics of air-sea exchange flux of PAHs in the Taiwan Strait and firstly employed an interpretable machine learning approach (XGBoost) to identify key regulation mechanisms and quantify their contributions to the air-sea exchange flux. This work provided a newly perspective to study the fate of aquatic pollutants by combining their field measurements and machine learning predictions.