Toward better atmospheric polycyclic aromatic hydrocarbons pollution control in the Northern Hemisphere: Process analysis based on interpretable deep learning models

Abstract

The accurate prediction of atmospheric polycyclic aromatic hydrocarbons (PAHs) concentrations in the Arctic is crucial in guiding the development of pollution control measures for atmospheric PAHs. However, the complex formation process and strong time-dependence of PAHs in the Arctic increase the difficulty of prediction. Herein, deep learning (DL) models were developed to predict monthly air concentrations of 16 priority PAHs at Alert monitoring station by incorporating the inherent periodicity of PAH concentrations and ancillary data including PAH emissions, meteorological information, fire emissions, and sea ice area. The prediction performance of long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (Bi LSTM) algorithms were compared by multiple model evaluation metrics. The results showed that Bi LSTM models outperformed LSTM and GRU in predicting PAHs concentrations because it exhibited higher accuracy in terms of average R2ext (0.09) and errors (average MAPEext = 1.45). The DL interpretation based on SHAP values suggested that the main drivers of PAHs concentrations were PAHs emissions and sea ice area, with a more prominent contribution from meteorological conditions. The interpretable deep learning approach provides a potential shortcut for predicting time-delayed PAHs concentrations in the Arctic, promoting the management and control of global PAHs pollution.