REFERENCES
1. Shin, H. H.; Gogna, P.; Maquiling, A.; Parajuli, R. P.; Haque, L.; Burr, B. Comparison of hospitalization and mortality associated with short-term exposure to ambient ozone and PM2.5 in Canada. Chemosphere 2021, 265, 128683.
2. Sejnowski, T. J. ChatGPT and the future of AI: the deep language revolution. 2024. https://mitpress.mit.edu/9780262049252/chatgpt-and-the-future-of-ai/. (accessed 3 Jun 2025).
4. Jiang, K.; Xing, R.; Luo, Z.; et al. Unclean but affordable solid fuels effectively sustained household energy equity. Nat. Commun. 2024, 15, 9761.
5. Wang, H.; Li, C.; Li, Y. F.; Tsung, F. An intelligent industrial visual monitoring and maintenance framework empowered by large-scale visual and language models. IEEE. Trans. Ind. Cyber. Phys. Syst. 2024, 2, 166-75.
6. Acharya, K.; Velasquez, A.; Song, H. H. A survey on symbolic knowledge distillation of large language models. IEEE. Trans. Artif. Intell. 2024, 5, 5928-48.
7. Ferrer-Cid, P.; Barcelo-Ordinas, J. M.; Garcia-Vidal, J. Graph signal reconstruction techniques for IoT air pollution monitoring platforms. IEEE. Internet. Things. J. 2022, 9, 25350-62.
8. Ali, S.; Glass, T.; Parr, B.; Potgieter, J.; Alam, F. Low cost sensor with IoT LoRaWAN connectivity and machine learning-based calibration for air pollution monitoring. IEEE. Trans. Instrum. Meas. 2021, 70, 1-11.
9. Wang, Z.; Yang, Y.; Wu, F. A lightweight air quality monitoring method based on multiscale dilated convolutional neural network. IEEE. Trans. Ind. Inf. 2024, 20, 14184-92.
10. Shi, T.; Li, P.; Yang, W.; Qi, A.; Qiao, J. Research on air quality monitoring system based on STM32 single chip microcomputer. In 2022 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), Penang, Malaysia. Nov 22-25, 2022. IEEE; 2022. p. 1-4.
11. Che, W.; Frey, H. C.; Fung, J. C. H.; et al. PRAISE-HK: a personalized real-time air quality informatics system for citizen participation in exposure and health risk management. Sustain. Cities. Soc. 2020, 54, 101986.
12. Ali, S.; El-Sappagh, S.; Ali, F.; Imran, M.; Abuhmed, T. Multitask deep learning for cost-effective prediction of patient’s length of stay and readmission state using multimodal physical activity sensory data. IEEE. J. Biomed. Health. Inform. 2022, 26, 5793-804.
13. Raimondi, P. M.; Lo Bue, A.; Vitale, M. C. A CNN adaptive model to estimate PM10 monitoring. In 2005 IEEE Conference on Emerging Technologies and Factory Automation, Catania, Italy. Sep 19-22, 2005. IEEE; 2005. p. 6 pp. -810.
14. National Health Commission of the People’s Republic of China. https://en.nhc.gov.cn/. (accessed 3 Jun 2025).
15. Kavathekar, V.; Tripathy, A. K.; Chettri, S. K.; et al. Assessment and prediction of urban pollutants and its influence on human health using deep learning algorithm. In 2024 IEEE 9th International Conference for Convergence in Technology (I2CT), Pune, India. Apr 05-07, 2024. IEEE; 2024. p. 1-7.
16. Nguyen-Tan, T.; Le-Trung, Q. Lightweight model using graph neural networks for air quality impact assessment on human health. In 2022 RIVF International Conference on Computing and Communication Technologies (RIVF), Ho Chi Minh City, Vietnam. Dec 20-22, 2022. IEEE; 2022. p. 1-6.
17. Pandey, G.; Sharma, R.; Shikhola, T. Improving PM 2.5 prediction accuracy with a hybrid EEMD-CNN-BiLSTM approach. In 2024 11th International Conference on Advances in Computing and Communications (ICACC), Kochi, India. Nov 06-08, 2024. IEEE; 2024. p. 1-6.