Articles | Volume 23, issue 1
https://doi.org/10.5194/acp-23-375-2023
https://doi.org/10.5194/acp-23-375-2023
Research article
 | 
10 Jan 2023
Research article |  | 10 Jan 2023

Capturing synoptic-scale variations in surface aerosol pollution using deep learning with meteorological data

Jin Feng, Yanjie Li, Yulu Qiu, and Fuxin Zhu

Related authors

Measurement report: Fast photochemical production of peroxyacetyl nitrate (PAN) over the rural North China Plain during haze events in autumn
Yulu Qiu, Zhiqiang Ma, Ke Li, Mengyu Huang, Jiujiang Sheng, Ping Tian, Jia Zhu, Weiwei Pu, Yingxiao Tang, Tingting Han, Huaigang Zhou, and Hong Liao
Atmos. Chem. Phys., 21, 17995–18010, https://doi.org/10.5194/acp-21-17995-2021,https://doi.org/10.5194/acp-21-17995-2021, 2021
Short summary
Assessing the formation and evolution mechanisms of severe haze pollution in the Beijing–Tianjin–Hebei region using process analysis
Lei Chen, Jia Zhu, Hong Liao, Yi Gao, Yulu Qiu, Meigen Zhang, Zirui Liu, Nan Li, and Yuesi Wang
Atmos. Chem. Phys., 19, 10845–10864, https://doi.org/10.5194/acp-19-10845-2019,https://doi.org/10.5194/acp-19-10845-2019, 2019
Short summary

Related subject area

Subject: Aerosols | Research Activity: Machine Learning | Altitude Range: Troposphere | Science Focus: Chemistry (chemical composition and reactions)
Reduction in vehicular emissions attributable to the Covid-19 lockdown in Shanghai: insights from 5 years of monitoring-based machine learning
Meng Wang, Yusen Duan, Zhuozhi Zhang, Qi Yuan, Xinwei Li, Shuwen Han, Juntao Huo, Jia Chen, Yanfen Lin, Qingyan Fu, Tao Wang, Junji Cao, and Shun-cheng Lee
Atmos. Chem. Phys., 23, 10313–10324, https://doi.org/10.5194/acp-23-10313-2023,https://doi.org/10.5194/acp-23-10313-2023, 2023
Short summary
Decoupling impacts of weather conditions on interannual variations in concentrations of criteria air pollutants in South China – constraining analysis uncertainties by using multiple analysis tools
Yu Lin, Leiming Zhang, Qinchu Fan, He Meng, Yang Gao, Huiwang Gao, and Xiaohong Yao
Atmos. Chem. Phys., 22, 16073–16090, https://doi.org/10.5194/acp-22-16073-2022,https://doi.org/10.5194/acp-22-16073-2022, 2022
Short summary

Cited articles

Bei, N., Li, G., Huang, R.-J., Cao, J., Meng, N., Feng, T., Liu, S., Zhang, T., Zhang, Q., and Molina, L. T.: Typical synoptic situations and their impacts on the wintertime air pollution in the Guanzhong basin, China, Atmos. Chem. Phys., 16, 7373–7387, https://doi.org/10.5194/acp-16-7373-2016, 2016. 
Chen, Z. H., Cheng, S. Y., Li, J. B., Guo, X. R., Wang, W. H., and Chen, D. S.: Relationship between atmospheric pollution processes and synoptic pressure patterns in northern China, Atmos. Environ., 42, 6078–6087, https://doi.org/10.1016/j.atmosenv.2008.03.043, 2008. 
Cho, K., van Merrienboer, B., Bahdanau, D., and Bengio, Y.: On the Properties of Neural Machine Translation: Encoder-Decoder Approaches, arXiv [preprint], arXiv:1409.1259, https://doi.org/10.48550/arXiv.1409.1259, 2014. 
Feng, J.: Data for “Capturing synoptic-scale variations in surface aerosol pollution using deep learning with meteorological data”, Zenodo [data set], https://doi.org/10.5281/zenodo.6982879, 2022a. 
Feng, J.: Animation for “Capturing synoptic-scale variations in surface aerosol pollution using deep learning with meteorological data”, Zenodo [video/audio], https://doi.org/10.5281/zenodo.6982971, 2022b. 
Download
Short summary
It is important to use weather data to estimate aerosol concentrations. Here, a weather index for aerosol concentration based on deep learning was developed, linking weather and short-term variations in aerosol concentrations over China. The index provides better performance than chemical transport model simulation and other data-based estimation approaches. It can be used as a robust tool for estimating daily variations in aerosol concentrations.
Altmetrics
Final-revised paper
Preprint