APA-Zitierstil (7. Ausg.)

Zhang, D., Wang, Q., Song, S., Chen, S., Li, M., Shen, L., . . . Zheng, H. (2023). Machine learning approaches reveal highly heterogeneous air quality co-benefits of the energy transition. iScience, 26(9), . https://doi.org/10.1016/j.isci.2023.107652

Chicago-Zitierstil (17. Ausg.)

Zhang, Da, et al. "Machine Learning Approaches Reveal Highly Heterogeneous Air Quality Co-benefits of the Energy Transition." IScience 26, no. 9 (2023). https://doi.org/10.1016/j.isci.2023.107652.

MLA-Zitierstil (9. Ausg.)

Zhang, Da, et al. "Machine Learning Approaches Reveal Highly Heterogeneous Air Quality Co-benefits of the Energy Transition." IScience, vol. 26, no. 9, 2023, https://doi.org/10.1016/j.isci.2023.107652.