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Bibliometric analysis of Indian research trends in air quality forecasting research using machine learning from 2007–2023 using Scopus database

Year 2024, Volume: 7 Issue: 3, 356 - 377, 30.09.2024
https://doi.org/10.35208/ert.1434390

Abstract

Machine-learning air pollution prediction studies are widespread worldwide. This study examines the use of machine learning to predict air pollution, its current state, and its expected growth in India. Scopus was used to search 326 documents by 984 academics published in 231 journals between 2007 and 2023. Biblioshiny and Vosviewer were used to discover and visualise prominent authors, journals, research papers, and trends on these issues. In 2018, interest in this topic began to grow at a rate of 32.1 percent every year. Atmospheric Environment (263 citations), Procedia Computer Science (251), Atmospheric Pollution Research (233) and Air Quality, Atmosphere, and Health (93 citations) are the top four sources, according to the Total Citation Index. These journals are among those leading studies on using machine learning to forecast air pollution. Jadavpur University (12 articles) and IIT Delhi (10 articles) are the most esteemed institutions. Singh Kp's 2013 "Atmospheric Environment" article tops the list with 134 citations. The Ministry of Electronics and Information Technology and the Department of Science and Technology are top Indian funding agency receive five units apiece, demonstrating their commitment to technology. The authors' keyword co-occurrence network mappings suggest that machine learning (127 occurrences), air pollution (78 occurrences), and air quality index (41) are the most frequent keywords. This study predicts air pollution using machine learning. These terms largely mirror our Scopus database searches for "machine learning," "air pollution," and "air quality," showing that these are among the most often discussed issues in machine learning research on air pollution prediction. This study helps academics, professionals, and global policymakers understand "air pollution prediction using machine learning" research and recommend key areas for further research.

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References

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Year 2024, Volume: 7 Issue: 3, 356 - 377, 30.09.2024
https://doi.org/10.35208/ert.1434390

Abstract

References

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  • D. Loomis, Y. Grosse, B. Lauby-Secretan, F. El Ghissassi, V. Bouvard, L. Benbrahim-Tallaa…, and K. Straif; International Agency for Research on Cancer Monograph Working Group, “The carcinogenicity of outdoor air pollution,” The Lancet Oncology, Vol. 14(13), pp. 1262-1263, 2013. [CrossRef]
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  • M. Kampa, and E. Castanas, “Human health effects of air pollution,” Environmental Pollution, Vol. 151(2), pp. 362–367, 2008. [CrossRef]
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  • M. I. Qureshi, A. M. Rasli, U. Awan, J. Ma, G. Ali, A. Alam…, and K. Zaman, “Environment and air pollution: Health services bequeath to grotesque menace,” Environmental Science and Pollution Research, Vol. 22, pp. 3467–3476, 2015. [CrossRef]
  • H. Orru, K. L. Ebi, and B. Forsberg, “The interplay of climate change and air pollution on health,” Currrent Environmental Health Reports, Vol. 4, pp. 504–513, 2017. [CrossRef]
  • H. Du, D. Liu, Z. Lu, J. Crittenden, G. Mao, S. Wang, and H. Zou, “Research development on sustainable urban infrastructure from 1991 to 2017: A bibliometric analysis to inform future innovations,” Earth’s Future, Vol. 7(7), pp. 718–733, 2019. [CrossRef]
  • D. L. Crouse, N. A. Ross, and M. S. Goldberg, “Double burden of deprivation and high concentrations of ambient air pollution at the neighbourhood scale in Montreal, Canada,” Social Science & Medicine, Vol. 69(6), pp. 971–981, 2009. [CrossRef]
  • J. Kerckhoffs, G. Hoek, L. Portengen, B. Brunekreef, and R. C. H. Vermeulen, “Performance of prediction algorithms for modeling outdoor air pollution spatial surfaces,” Environmental Science and Technology, Vol. 53(3), pp. 1413–1421, 2019. [CrossRef]
  • W. Wang, C. Men, and W. Lu, “Online prediction model based on support vector machine,” Neurocomputing, Vol. 71(4–6) pp. 550–558, 2008. [CrossRef]
  • R. S. Batth, M. Gupta, K. S. Mann, S. Verma, and A. Malhotra, “Comparative study of tdma-based mac protocols in vanet: A mirror review,” Proceedings of the 2019 International Conference on Innovative Computing and Communications (ICICC). Ostrava, Czech Republic, 2019.
  • M. Kaur, and S. Verma, “Flying ad-hoc network (FANET): Challenges and routing protocols,” Journal of Computational and Theoretical Nanoscience Vol. 17(6) pp. 2575–2581, 2020. [CrossRef]
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  • X. Tian, Y. Huang, S. Verma, M. Jin, U. Ghosh, K. M. Rabie, and D. T. Do, “Power allocation scheme for maximizing spectral efficiency and energy efficiency tradeoff for uplink NOMA systems in B5G/6G,” Physical Communication, Vol. 43, Article 101227, 2020. [CrossRef]
  • G. Ghosh, M. Sood, and S. Verma, “Internet of things based video surveillance systems for security applications,” Journal of Computational and Theoretical Nanoscience, Vol. 17(6), pp. 2582–2588, 2020[CrossRef]
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There are 88 citations in total.

Details

Primary Language English
Subjects Air Pollution Modelling and Control, Air Pollution and Gas Cleaning , Air Pollution Processes and Air Quality Measurement
Journal Section Research Articles
Authors

Asif Ansari 0000-0002-8171-7673

Abdur Rahman Quaff This is me 0000-0002-3304-0063

Publication Date September 30, 2024
Submission Date February 9, 2024
Acceptance Date April 20, 2024
Published in Issue Year 2024 Volume: 7 Issue: 3

Cite

APA Ansari, A., & Quaff, A. R. (2024). Bibliometric analysis of Indian research trends in air quality forecasting research using machine learning from 2007–2023 using Scopus database. Environmental Research and Technology, 7(3), 356-377. https://doi.org/10.35208/ert.1434390
AMA Ansari A, Quaff AR. Bibliometric analysis of Indian research trends in air quality forecasting research using machine learning from 2007–2023 using Scopus database. ERT. September 2024;7(3):356-377. doi:10.35208/ert.1434390
Chicago Ansari, Asif, and Abdur Rahman Quaff. “Bibliometric Analysis of Indian Research Trends in Air Quality Forecasting Research Using Machine Learning from 2007–2023 Using Scopus Database”. Environmental Research and Technology 7, no. 3 (September 2024): 356-77. https://doi.org/10.35208/ert.1434390.
EndNote Ansari A, Quaff AR (September 1, 2024) Bibliometric analysis of Indian research trends in air quality forecasting research using machine learning from 2007–2023 using Scopus database. Environmental Research and Technology 7 3 356–377.
IEEE A. Ansari and A. R. Quaff, “Bibliometric analysis of Indian research trends in air quality forecasting research using machine learning from 2007–2023 using Scopus database”, ERT, vol. 7, no. 3, pp. 356–377, 2024, doi: 10.35208/ert.1434390.
ISNAD Ansari, Asif - Quaff, Abdur Rahman. “Bibliometric Analysis of Indian Research Trends in Air Quality Forecasting Research Using Machine Learning from 2007–2023 Using Scopus Database”. Environmental Research and Technology 7/3 (September 2024), 356-377. https://doi.org/10.35208/ert.1434390.
JAMA Ansari A, Quaff AR. Bibliometric analysis of Indian research trends in air quality forecasting research using machine learning from 2007–2023 using Scopus database. ERT. 2024;7:356–377.
MLA Ansari, Asif and Abdur Rahman Quaff. “Bibliometric Analysis of Indian Research Trends in Air Quality Forecasting Research Using Machine Learning from 2007–2023 Using Scopus Database”. Environmental Research and Technology, vol. 7, no. 3, 2024, pp. 356-77, doi:10.35208/ert.1434390.
Vancouver Ansari A, Quaff AR. Bibliometric analysis of Indian research trends in air quality forecasting research using machine learning from 2007–2023 using Scopus database. ERT. 2024;7(3):356-77.