International journal of advanced smart convergence

  • 제13권1호
  • /
  • Pages.48-56
  • /
  • 2024
  • /
  • 2288-2847(pISSN)
  • /
  • 2288-2855(eISSN)
한국인터넷방송통신학회 (The Institute of Internet, Broadcasting and Communication)
권호 표지
DOI QR코드

DOI QR Code

Artificial Intelligence and Air Pollution : A Bibliometric Analysis from 2012 to 2022

  • Yong Sauk Hau (Department of Business Administration, Yeungnam University)
  • 투고 : 2024.01.20
  • 심사 : 2024.02.02
  • 발행 : 2024.03.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

The application of artificial intelligence (AI) is becoming increasingly important to coping with air pollution. AI is effective in coping with it in various ways including air pollution forecasting, monitoring, and control, which is attracting a lot of attention. This attention has created high need for analyzing studies on AI and air pollution. To contribute for satisfying it, this study performed bibliometric analyses on the studies on AI and air pollution from 2012 to 2022 using the Web of Science database. This study analyzed them in various aspects such as the trend in the number of articles, the trend in the number of citations, the top 10 countries of origin, the top 10 research organizations, the top 10 research funding agencies, the top 10 journals, the top 10 articles in terms of total citations, and the distribution by languages. This study not only reports the bibliometric analysis results but also reveals the eight distinct features in the research steam in studies on AI and air pollution, identified from the bibliometric analysis results. They are expected to make a useful contribution for understanding the research stream in AI and air pollution.

키워드

참고문헌

  1. A. Masood and K. Ahmad, "A Review on Emerging Artificial Intelligence (AI) Techniques for Air Pollution Forecasting: Fundamentals, Application and Performance," Journal of Cleaner Production, Vol. 322, 2021. DOI: https://doi.org/10.1016/j.jclepro.2021.129072
  2. L. Bai, J. Wang, X. Ma, and H. Lu, "Air Pollution Forecasts: An Overview," International Journal of Environmental Research and Public Health, , Vol. 15, No. 4, 2018. DOI: https://doi.org/10.3390/ijerph15040780
  3. S. M .Cabaneros, J. K. Calautit, and B. R. Hughes, "A Review of Artificial Neural Network Models for Ambient Air Pollution Prediction," Environmental Modelling & Software, Vol. 119, pp. 285-304, 2019. DOI: https://doi.org/10.1016/j.envsoft.2019.06.014
  4. P. Asha, L. Natrayan, B. T. Geetha, J. R. Beulah, R. Sumathy, G. Varalakshmi, and S. Neelakandan, "IoT Enabled Environmental Toxicology for Air Pollution Monitoring using AI Techniques," Environmental Research, Vol. 205, 2022. DOI: https://doi.org/10.1016/j.envres.2021.112574
  5. A. Almalawi, F. Alsolami, A. I. Khan, A. Alkhathlan, A. Fahad, K. Irshad, S. Qaiyum, and A. S. Alfakeeh, "An IoT Based System for Magnify Air Pollution Monitoring and Prognosis using Hybrid Artificial Intelligence Technique," Environmental Research, Vol. 206, 2022. DOI: https://doi.org/10.1016/j.envres.2021.112576
  6. Z. Ye, J. Yang, N. Zhong, X. Tu, J. Jia, and J. Wang, "Tackling Environmental Challenges in Pollution Controls using Artificial Intelligence: A Review, Science of the Total Environment, Vol. 699, 2020. DOI: https://doi.org/10.1016/j.scitotenv.2019.134279
  7. J. He, S. Gong, Y. Yu, L. Yu, L. Wu, H. Mao, C. Song, S. Zhao, H. Liu, X. Li, and R. Li, "Air Pollution Characteristics and their Relation to Meteorological Conditions during 2014-2015 in Major Chinese Cities," Environmental Pollution, Vol. 223, pp. 484-496, 2017. DOI: https://doi.org/10.1016/j.envpol.2017.01.050
  8. X. Feng, Q. Li, , Y. Zhu, J. Hou, L. Jin, and J. Wang, "Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation," Atmospheric Environment, Vol. 107, pp. 118-128, 2015. DOI: https://doi.org/10.1016/j.atmosenv.2015.02.030
  9. C. Huang and P. Kuo, "A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities," Sensors, Vol. 18, No. 7, 2018. DOI: https://doi.org/10.3390/s18072220
  10. X. Li, L. Peng, X. Yao, S. Cui, Y. Hu, C. You, and T. Chi, "Long Short-Term Memory Neural Network for Air Pollutant Concentration Predictions: Method Development and Evaluation," Environmental Pollution, Vol. 231, pp. 997-1004, 2017. DOI: https://doi.org/10.1016/j.envpol.2017.08.114
  11. T. Li, H. Shen, Q. Yuan, X. Zhang, and L. Zhang, " Estimating ground-level PM2. 5 by fusing satellite and station observations: a geo-intelligent deep learning approach," Geophysical Research Letters, Vol. 44. No. 23, pp. 11-985, 2017. DOI: https://doi.org/10.1002/2017gl075710
  12. N. Zimmerman, A. A. Presto, S. P. N. Kumar, J .Gu, A. Hauryliuk, E. s. Robinson, A. L. Robinson, and R. Subramanian, "A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring." Atmospheric Measurement Techniques, Vol. 11, No. 1, pp.291-313, 2018. DOI: https://doi.org/10.5194/amt-11-291-2018
  13. Y. Qi, Q. Li, H. Karimian, D. Liu, "A Hybrid Model for Spatiotemporal Forecasting of PM2.5 Based on Graph Convolutional Neural Network and Long Short-Term Memory," Science of the Total Environment, Vol. 664, pp. 1-10, 2019. DOI: https://doi.org/10.1016/j.scitotenv.2019.01.333
  14. M. Stafoggia, T. Bellander, S. Bucci, M. Davoli, K. De Hoogh, F. De'Donato, ... and J. Schwartz, "Estimation of daily PM10 and PM2. 5 concentrations in Italy, 2013-2015, using a spatiotemporal land-use random-forest model," Environment international, Vol. 124, pp. 170-179, 2019. DOI: https://doi.org/10.1016/j.envint.2019.01.016
  15. J. Zhao, F. Deng, Y. Cai, and J. Chen, "Long Short-Term Memory - Fully Connected (LSTM-FC) Neural Network for PM2.5 Concentration Prediction," Chemosphere, Vol. 220, pp. 486-492, 2019. DOI: https://doi.org/10.1016/j.chemosphere.2018.12.128
  16. M. Z. Joharestani, , Cao, C., X. Ni, B. Bashir, and S. Talebiesfandarani, "PM2. 5 prediction based on random forest, XGBoost, and deep learning using multisource remote sensing data," Atmosphere, Vo. 10. No. 7, 2019. DOI: https://doi.org/10.3390/atmos10070373