Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 18339-18349 |
| Number of pages | 11 |
| Journal | Environmental Science and Technology |
| Volume | 57 |
| Issue number | 46 |
| DOIs | |
| State | Published - 21 Nov 2023 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 14 Life Below Water
Keywords
- deep learning neural network
- global oceans
- marine fate
- ocean stratification
- polycyclic aromatic hydrocarbons (PAHs)
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