Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 5.414 IF 5.414
  • IF 5-year value: 5.958 IF 5-year
    5.958
  • CiteScore value: 9.7 CiteScore
    9.7
  • SNIP value: 1.517 SNIP 1.517
  • IPP value: 5.61 IPP 5.61
  • SJR value: 2.601 SJR 2.601
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 191 Scimago H
    index 191
  • h5-index value: 89 h5-index 89
ACP | Articles | Volume 19, issue 20
Atmos. Chem. Phys., 19, 12935–12951, 2019
https://doi.org/10.5194/acp-19-12935-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Chem. Phys., 19, 12935–12951, 2019
https://doi.org/10.5194/acp-19-12935-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 18 Oct 2019

Research article | 18 Oct 2019

Development of a daily PM10 and PM2.5 prediction system using a deep long short-term memory neural network model

Hyun S. Kim et al.

Related authors

The impacts of uncertainties in emissions on aerosol data assimilation and short-term PM2.5 predictions in CMAQ v5.2.1 over East Asia
Sojin Lee, Chul Han Song, Kyung Man Han, Daven K. Henze, Kyunghwa Lee, Jinhyeok Yu, Jung-Hun Woo, Jia Jung, Yunsoo Choi, Pablo E. Saide, and Gregory R. Carmichael
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-116,https://doi.org/10.5194/gmd-2020-116, 2020
Preprint under review for GMD
Development of Korean Air Quality Prediction System version 1 (KAQPS v1) with focuses on practical issues
Kyunghwa Lee, Jinhyeok Yu, Sojin Lee, Mieun Park, Hun Hong, Soon Young Park, Myungje Choi, Jhoon Kim, Younha Kim, Jung-Hun Woo, Sang-Woo Kim, and Chul H. Song
Geosci. Model Dev., 13, 1055–1073, https://doi.org/10.5194/gmd-13-1055-2020,https://doi.org/10.5194/gmd-13-1055-2020, 2020
Short summary
Concentration Trajectory Route of Air pollution with an Integrated Lagrangian model (C-TRAIL model v1.0) derived from the Community Multiscale Air Quality Modeling (CMAQ model v5.2)
Arman Pouyaei, Yunsoo Choi, Jia Jung, Bavand Sadeghi, and Chul Han Song
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-366,https://doi.org/10.5194/gmd-2019-366, 2020
Revised manuscript accepted for GMD
GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia
Myungje Choi, Jhoon Kim, Jaehwa Lee, Mijin Kim, Young-Je Park, Brent Holben, Thomas F. Eck, Zhengqiang Li, and Chul H. Song
Atmos. Meas. Tech., 11, 385–408, https://doi.org/10.5194/amt-11-385-2018,https://doi.org/10.5194/amt-11-385-2018, 2018
Short summary
GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during the DRAGON-NE Asia 2012 campaign
Myungje Choi, Jhoon Kim, Jaehwa Lee, Mijin Kim, Young-Je Park, Ukkyo Jeong, Woogyung Kim, Hyunkee Hong, Brent Holben, Thomas F. Eck, Chul H. Song, Jae-Hyun Lim, and Chang-Keun Song
Atmos. Meas. Tech., 9, 1377–1398, https://doi.org/10.5194/amt-9-1377-2016,https://doi.org/10.5194/amt-9-1377-2016, 2016
Short summary

Related subject area

Subject: Aerosols | Research Activity: Atmospheric Modelling | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
Regional-scale modelling for the assessment of atmospheric particulate matter concentrations at rural background locations in Europe
Goran Gašparac, Amela Jeričević, Prashant Kumar, and Branko Grisogono
Atmos. Chem. Phys., 20, 6395–6415, https://doi.org/10.5194/acp-20-6395-2020,https://doi.org/10.5194/acp-20-6395-2020, 2020
Short summary
Understanding and improving model representation of aerosol optical properties for a Chinese haze event measured during KORUS-AQ
Pablo E. Saide, Meng Gao, Zifeng Lu, Daniel L. Goldberg, David G. Streets, Jung-Hun Woo, Andreas Beyersdorf, Chelsea A. Corr, Kenneth L. Thornhill, Bruce Anderson, Johnathan W. Hair, Amin R. Nehrir, Glenn S. Diskin, Jose L. Jimenez, Benjamin A. Nault, Pedro Campuzano-Jost, Jack Dibb, Eric Heim, Kara D. Lamb, Joshua P. Schwarz, Anne E. Perring, Jhoon Kim, Myungje Choi, Brent Holben, Gabriele Pfister, Alma Hodzic, Gregory R. Carmichael, Louisa Emmons, and James H. Crawford
Atmos. Chem. Phys., 20, 6455–6478, https://doi.org/10.5194/acp-20-6455-2020,https://doi.org/10.5194/acp-20-6455-2020, 2020
Short summary
Impact of topography on black carbon transport to the southern Tibetan Plateau during the pre-monsoon season and its climatic implication
Meixin Zhang, Chun Zhao, Zhiyuan Cong, Qiuyan Du, Mingyue Xu, Yu Chen, Ming Chen, Rui Li, Yunfei Fu, Lei Zhong, Shichang Kang, Delong Zhao, and Yan Yang
Atmos. Chem. Phys., 20, 5923–5943, https://doi.org/10.5194/acp-20-5923-2020,https://doi.org/10.5194/acp-20-5923-2020, 2020
Short summary
Integrated impacts of synoptic forcing and aerosol radiative effect on boundary layer and pollution in the Beijing–Tianjin–Hebei region, China
Yucong Miao, Huizheng Che, Xiaoye Zhang, and Shuhua Liu
Atmos. Chem. Phys., 20, 5899–5909, https://doi.org/10.5194/acp-20-5899-2020,https://doi.org/10.5194/acp-20-5899-2020, 2020
Short summary
Thermodynamic properties of isoprene- and monoterpene-derived organosulfates estimated with COSMOtherm
Noora Hyttinen, Jonas Elm, Jussi Malila, Silvia M. Calderón, and Nønne L. Prisle
Atmos. Chem. Phys., 20, 5679–5696, https://doi.org/10.5194/acp-20-5679-2020,https://doi.org/10.5194/acp-20-5679-2020, 2020
Short summary

Cited articles

Abdul-Wahab, S. A. and Al-Alawi, S. M.: Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks, Environ. Modell. Softw., 17, 219–228, 2002. 
Amarasinghe, K., Marino, D. L., and Manic, M: Deep neural networks for energy load forecasting, Proceedings of the 26th IEEE International Symposium on Industrial Electronics, 19–21 June, Scotland, UK, 1483–1488, 2017. 
Ayinde, B. O., Inanc, T., and Zurada, J. M.: On Correlation of Features Extracted by Deep Neural Networks, arXiv:1901.10900v1, 2019. 
Bengio, Y., Simard, P., and Frasconi, P.: Learning long-term dependencies with gradient descent is difficult, IEEE Trans. Neural Networ., 5, 157–166, 1994. 
Berge, E., Huang, H.-C., Chang, J., and Liu, T.-H.: A study of importance of initial conditions for photochemical oxidant modeling, J. Geophys. Res.-Atmos., 106, 1347–1363, 2001. 
Publications Copernicus
Download
Short summary
In this study, a deep recurrent neural network system based on a long short-term memory (LSTM) model was developed for daily PM10 and PM2.5 predictions in South Korea. In general, the accuracies of the LSTM-based predictions were superior to the 3-D CTM-based predictions. Based on this, we concluded that the LSTM-based system could be applied to daily operational PM forecasts in South Korea. We expect that similar AI systems can be applied to the predictions of other atmospheric pollutants.
In this study, a deep recurrent neural network system based on a long short-term memory (LSTM)...
Citation