Modeling the long-term fate of polycyclic aromatic hydrocarbons (PAHs) and public health risk in Bohai Bay Sea Area, China

Dynamic multimedia approach for source apportionment of polycyclic aromatic hydrocarbons

This study was performed to evaluate the variable indicators of polycyclic aromatic hydrocarbons (PAHs) source apportionment by using an unsteady-state multimedia model. The identical indicators have been used in different environmental bulks for more than 20 years, which resulted in huge errors in source apportionment. Generated through four emission arrays, the diagnostic ratios for indicators revealed dimensionless OR, in air/soil and seawater/sediment reached ∼3.63 and ∼0.24 for Fla/Pyr, and for Ant/Phe the ratio was ∼0.31 and ∼0.18, and coastal OR for air/seawater was higher than the offshore, suggesting both compartmental and spatial divergences. The PCA indicated similar loading distribution and primary factors, shared by emission, atmosphere, and seawater arrays, whereas the slow transport between air/water and soil/sediment, weak degradation, and original concentration level might result in factors in soil and sediment separated or merged in dynamic conditions. The physicochemical divergence of indicators could be intensified after long-term environmental transport, misleading the source apportionment. Therefore, the result elucidated the essential evaluation of additional inorganic indicators and necessary verification by simultaneous sampling measurement on vertical compartments.

Ocean Stratification Impacts on Dissolved Polycyclic Aromatic Hydrocarbons (PAHs): From Global Observation to Deep Learning

Ocean stratification plays a crucial role in many biogeochemical processes of dissolved matter, but our understanding of its impact on widespread organic pollutants, such as polycyclic aromatic hydrocarbons (PAHs), remains limited. By analyzing dissolved PAHs collected from global oceans and marginal seas, we found different patterns in vertical distributions of PAHs in relation to ocean primary productivity and stratification index. Notably, a significant positive logarithmic relationship (R2 = 0.50, p < 0.05) was observed between the stratification index and the PAH stock. To further investigate the impact of ocean stratification on PAHs, we developed a deep learning neural network model. This model incorporated input variables determining the state of the seawater or the stock of PAHs. The modeled PAH stocks displayed substantial agreement with the observed values (R2 ≥ 0.92), suggesting that intensified stratification could prompt the accumulation of PAHs in the water column. Given the amplified effect of global warming, it is imperative to give more attention to increased ocean stratification and its impact on the environmental fate of organic pollutants.