Cite
Multi-step forecast of PM2.5 and PM10 concentrations using convolutional neural network integrated with spatial–temporal attention and residual learning.
MLA
Zhang, Kefei, et al. “Multi-Step Forecast of PM2.5 and PM10 Concentrations Using Convolutional Neural Network Integrated with Spatial–temporal Attention and Residual Learning.” Environment International, vol. 171, Jan. 2023, p. N.PAG. EBSCOhost, https://doi.org/10.1016/j.envint.2022.107691.
APA
Zhang, K., Yang, X., Cao, H., Thé, J., Tan, Z., & Yu, H. (2023). Multi-step forecast of PM2.5 and PM10 concentrations using convolutional neural network integrated with spatial–temporal attention and residual learning. Environment International, 171, N.PAG. https://doi.org/10.1016/j.envint.2022.107691
Chicago
Zhang, Kefei, Xiaolin Yang, Hua Cao, Jesse Thé, Zhongchao Tan, and Hesheng Yu. 2023. “Multi-Step Forecast of PM2.5 and PM10 Concentrations Using Convolutional Neural Network Integrated with Spatial–temporal Attention and Residual Learning.” Environment International 171 (January): N.PAG. doi:10.1016/j.envint.2022.107691.