## 5 Conclusions  
This study investigated the critical issue of airport pavement icing in saline environments, which poses a significant threat to aviation safety. To address this challenge, an experimental setup was developed to replicate real-world conditions, enabling a comprehensive analysis of the effects of variables such as the air temperature, wind speed, and deicing salt concentration on the icing process. Our findings reveal that lower air temperatures and higher wind speeds significantly accelerate pavement cooling. Specifically, wind speeds of 6 m/s decrease the icing time by 35.7%, while temperatures of \(-20\)\({}^{\circ}\)C can reduce it by 94.4%. Under severe conditions (\(-20\)\({}^{\circ}\)C, wind speed 6 m/s), pavements reach critical icing temperatures in just 15 min, underscoring the urgent need to manage the increased operational risks associated with rapid icing on runways during extreme weather. Conversely, high concentrations of deicing salt can delay the icing onset by up to 67 min and 33 s, representing a 6700% increase compared to conditions without salt, thereby effectively preventing icing and lowering pavement temperatures. However, the potential damage to pavements caused by deicing salt indicates that their optimal use warrants further study. A comparison between our collected experimental data and theoretical predictions reveals an initial concordance, with the early and middle stages of icing exhibiting an average error margin below 30%. However, the observed discrepancies increased over time, likely due to the simplified assumptions inherent in our model. Based on these results, a simplified prediction model was developed and validated against the empirical data. This model serves as a practical tool for forecasting icing events and tailoring deicing strategies to the prevailing environmental conditions. By incorporating such predictions into operational planning, it is possible to significantly enhance runway safety and efficiency. This study was conducted under controlled laboratory conditions, which may not fully capture the complexity of real-world environments. Future research will validate the prediction model in diverse operational scenarios and explore the effectiveness of deicing strategies under various climatic conditions. This will involve utilizing real environmental data collected via sensors on airport runways and applying artificial intelligence algorithms to refine the model's accuracy and usability. Additionally, further studies will address airport operational risks and optimize the use of deicing salts to enhance the effectiveness and sustainability of these strategies. Conceptualization, J.L. and S.L. ; methodology, J.Z. ; software, J.Z. ; validation, J.Z. and X.X. ; resources, S.L. ; data curation, J.Z. and X.X. ; writing--original draft preparation, S.L. ; writing--review and editing, S.L. and X.X. ; visualization, J.Z. and X.X. ; supervision, J.L. ; project administration, J.L. ; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript. This research was supported by the Natural Science Foundation of Shanghai under grant number 23 ZR1466300. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. Author [PERSON] was employed by the company Guangzhou Baiyun International Airport Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Multi-zone indoor temperature prediction with LSTM-based sequence to sequence model. _Energy Build._**2021**, _245_, 111053. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Highway icing time prediction with deep learning approaches based on data from road sensors. _Sci. China Technol.