[1] A H Al Hanai, D S Antkiewicz, J D C Hemming et al. Seasonal variations in the oxidative stress and inflammatory potential of PM2.5 in Tehran using an alveolar macrophage model; the role of chemical composition and sources. Environment International, 123, 417-427(2019).
[2] F Laden, J Schwartz, F E Speizer et al. Reduction in fine particulate air pollution and mortality: Extended follow-up of the Harvard six cities study. American Journal of Respiratory and Critical Care Medicine, 173, 667-672(2006).
[3] J Evans, A van Donkelaar, R V Martin et al. Estimates of global mortality attributable to particulate air pollution using satellite imagery. Environmental Research, 120, 33-42(2013).
[4] D Rojas-Rueda, A de Nazelle, O Teixidó et al. Health impact assessment of increasing public transport and cycling use in Barcelona: A morbidity and burden of disease approach. Preventive Medicine, 57, 573-579(2013).
[5] J Chai, C Peng. The causes of haze formation and its effects on human health. World Abstract of The Latest Medical Information, 17, 128-129(2017).
[6] Y Zhou, O Tan, H Zhang et al. Research progress of fine particulate matter PM2.5 in the atmospheric environment. China Resources Comprehensive Utilization, 39, 90-93(2021).
[7] C L Yang. A study on the relationship between urban economy and PM2.5. Science and Technology Wind, 133(2019).
[8] X Zhao, L L Hou, X L Wang et al. Simulation of spatial divergence of PM2.5 and PM10 concentrations in Beijing in 2019 based on LUR model. Journal of Environmental Science, 40, 4060-4069(2020).
[9] D D Wang, C Qin. Regional PM2.5 spatio-temporal regression modeling and prediction. China Environmental Monitoring, 35, 107-113(2019).
[10] Y S Shi. Application of support vector machine in PM2.5 prediction research. Cooperative Economics and Technology, 48-50(2022).
[12] S Hochreiter, J Schmidhuber. Long short-term memory. Neural Computation, 9, 1735-1780(1997).
[13] J Chung, C Gulcehre, K H Cho et al. Empirical evaluation of gated recurrent neural networks on sequence modeling. Eprint Arxiv(2014).
[14] J Huang, F Zhang, Z H Du et al. PM2.5 hourly concentration prediction based on RNN-CNN integrated deep learning model. Journal of Zhejiang University (Science Edition), 46, 370-379(2019).
[15] D G Duan, Z D Zhao, S H Liang et al. LSTM-based PM2.5 concentration prediction model. Computer Measurement and Control, 27, 215-219(2019).
[16] Z Zhang, S Q Zhang, X M Zhao et al. Temporal difference-based graph transformer networks for air quality PM2.5 prediction: A case study in China. Frontiers in Environmental Science, 10, 924986(2022).
[17] I Sutskever, O Vinyals, Q V Le. Sequence to sequence learning with neural networks. Advances In neural Information Processing Systems, 3104-3112(2014).
[18] S J Taylor, B Letham. Forecasting at scale. The American Statistician, 72, 37-45(2018).