Articles | Volume 19, issue 7
https://doi.org/10.5194/amt-19-2507-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-19-2507-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Apr 2026
Research article |
| 16 Apr 2026
| 16 Apr 2026
An ensemble machine learning method to retrieve aerosol parameters from ground-based Sun-sky photometer measurements
Qiurui Li
Department of Atmospheric and Oceanic Sciences, Peking University, Beijing, 100871, China
Zhongxia Sun
Department of Atmospheric and Oceanic Sciences, Peking University, Beijing, 100871, China
Paul Scherrer Institut, Forschungsstrasse 111, 5232 Villigen PSI, Switzerland
Meijing Liu
Department of Atmospheric and Oceanic Sciences, Peking University, Beijing, 100871, China
Huizheng Che
Key Laboratory of Atmospheric Chemistry, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
Yu Zheng
Key Laboratory of Atmospheric Chemistry, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
Department of Atmospheric and Oceanic Sciences, Peking University, Beijing, 100871, China
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Ke Gui, Wenrui Yao, Huizheng Che, Linchang An, Yu Zheng, Lei Li, Hujia Zhao, Lei Zhang, Junting Zhong, Yaqiang Wang, and Xiaoye Zhang
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Zhongjing Jiang and Jing Li
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Yu Zheng, Huizheng Che, Yupeng Wang, Xiangao Xia, Xiuqing Hu, Xiaochun Zhang, Jun Zhu, Jibiao Zhu, Hujia Zhao, Lei Li, Ke Gui, and Xiaoye Zhang
Atmos. Meas. Tech., 15, 2139–2158, https://doi.org/10.5194/amt-15-2139-2022, https://doi.org/10.5194/amt-15-2139-2022, 2022
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Ke Gui, Huizheng Che, Yu Zheng, Hujia Zhao, Wenrui Yao, Lei Li, Lei Zhang, Hong Wang, Yaqiang Wang, and Xiaoye Zhang
Atmos. Chem. Phys., 21, 15309–15336, https://doi.org/10.5194/acp-21-15309-2021, https://doi.org/10.5194/acp-21-15309-2021, 2021
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Liangying Zeng, Yang Yang, Hailong Wang, Jing Wang, Jing Li, Lili Ren, Huimin Li, Yang Zhou, Pinya Wang, and Hong Liao
Atmos. Chem. Phys., 21, 10745–10761, https://doi.org/10.5194/acp-21-10745-2021, https://doi.org/10.5194/acp-21-10745-2021, 2021
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Using an aerosol–climate model, the impacts of El Niño with different durations on aerosols in China are examined. The modulation on aerosol concentrations and haze days by short-duration El Niño events is 2–3 times more than that by long-duration El Niño events in China. The frequency of short-duration El Niño has been increasing significantly in recent decades, suggesting that El Niño events have exerted increasingly intense modulation on aerosol pollution in China over the past few decades.
Qingyang Xiao, Yixuan Zheng, Guannan Geng, Cuihong Chen, Xiaomeng Huang, Huizheng Che, Xiaoye Zhang, Kebin He, and Qiang Zhang
Atmos. Chem. Phys., 21, 9475–9496, https://doi.org/10.5194/acp-21-9475-2021, https://doi.org/10.5194/acp-21-9475-2021, 2021
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We used both statistical methods and a chemical transport model to assess the contribution of meteorology and emissions to PM2.5 during 2000–2018. Both methods revealed that emissions dominated the long-term PM2.5 trend with notable meteorological effects ranged up to 37.9 % of regional annual average PM2.5. The meteorological contribution became more beneficial to PM2.5 control in southern China but more unfavorable in northern China during the studied period.
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Short summary
We present a fast, interpretable machine learning method to retrieve key aerosol parameters from ground-based Sun-sky photometer measurements. Trained on simulated data covering diverse aerosol and atmospheric conditions, ensuring robustness and physical consistency. Applied to real observations, it agrees well with AERONET products and reduces computation time by orders of magnitude, offering a practical tool for monitoring aerosols and their effects on air quality and climate.
We present a fast, interpretable machine learning method to retrieve key aerosol parameters from...
