Journals / Doğal Afetler ve Çevre Dergisi / 2025 / Cilt: 11 Sayı: 2

Development and Future Perspectives of Air Pollution Research Using Artificial Intelligence-Based Methods: A Bibliometric Review

Yapay Zekâ Tabanlı Yöntemlerle Hava Kirliliği Araştırmalarının Gelişimi ve Gelecek Perspektifleri: Bibliyometrik Bir İnceleme

Pages
471–487
Publication Date
2025-07-27
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings indicate that after 2014, AI-based methods have led to a paradigm shift, playing a critical role in air pollution forecasting and modeling.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Urbanization, energy consumption, industrialization, and population growth, along with air pollution and the decline in air quality, pose a serious threat to public health and the environment.
📊
Key Findings & Data:This study presents a thorough review of the evolution of key themes in AI-driven air pollution research from 2004 to 2024, highlighting areas for future investigation.
💡
Conclusion & Impact:The findings indicate that after 2014, AI-based methods have led to a paradigm shift, playing a critical role in air pollution forecasting and modeling.
🔬Design:Review & Systematic Survey
📍Scope:2004 Verileri
⚙️Variables:Hava Kirliliği · Hava Kalitesi · Yapay Zekâ

Abstract

Urbanization, energy consumption, industrialization, and population growth, along with air pollution and the decline in air quality, pose a serious threat to public health and the environment. The detection and management of pollutants have become urgent global concerns, underscoring the growing significance of artificial intelligence (AI)-based methods in air pollution research. This study presents a thorough review of the evolution of key themes in AI-driven air pollution research from 2004 to 2024, highlighting areas for future investigation. Through bibliometric and citation analyses, the study systematically examines the literature, revealing an exponential growth in AI applications in air pollution research over time. The findings indicate that after 2014, AI-based methods have led to a paradigm shift, playing a critical role in air pollution forecasting and modeling. At the same time, the study reveals that interdisciplinary collaboration trends are strengthening and that AI-based approaches not only offer innovative solutions but also serve as a transformative force shaping the evolution of the literature. This analysis provides valuable insights into the current state of air pollution research and presents guidance for future directions, emphasizing the need for broader and more effective integration of AI techniques in this area.

Özet

Kentleşme, enerji tüketimi, sanayileşme ve nüfus artışıyla birlikte hava kirliliği ve hava kalitesindeki düşüş, halk sağlığı ve çevre üzerinde ciddi bir tehdit oluşturmaktadır. Kirleticilerin tespiti ve kontrolü, günümüzün öncelikli sorunlarından biri haline gelmiş ve bu bağlamda yapay zekâ tabanlı yöntemlerin hava kirliliği araştırmalarında artan önemi dikkat çekmiştir. Bu çalışma, 2004–2024 yılları arasında yapay zekâ tabanlı yöntemlerin kullanıldığı hava kirliliği araştırmalarındaki öncelikli temaların gelişimini ve gelecekteki araştırmalara yön verebilecek alanları kapsamlı bir şekilde incelemiştir. Bibliyometrik analiz ve atıf analizi yöntemleri kullanılarak yapılan bu araştırma, literatürün sistematik bir değerlendirmesini sunmuş ve hava kirliliği ile ilgili yapay zekâ uygulamalarının zamanla üstel bir artış gösterdiğini ortaya koymuştur. Araştırma sonuçları, 2014 sonrası dönemde yapay zekâ tabanlı yöntemlerin literatürde bir paradigma değişimi yaratarak hava kirliliği tahminleri ve modellemelerinde merkezi bir rol üstlendiğini göstermektedir. Aynı zamanda, disiplinler arası iş birliği eğilimlerinin güçlenmekte olduğunu ve yapay zekâ tabanlı yöntemlerin yalnızca yenilikçi bir çözüm sunmakla kalmayıp, aynı zamanda literatürdeki evrimi şekillendiren bir dönüşüm sağladığını ortaya koymaktadır. Bu analiz, alanın mevcut durumunu anlamak ve gelecekteki araştırma yönelimlerini belirlemek için değerli bir bilgi kaynağı sunmakta, yapay zekâ tabanlı yöntemlerin hava kirliliği çalışmalarında daha geniş ve etkili bir şekilde kullanılabileceğini güçlü bir şekilde desteklemektedir.

🔖

Cite This Article

Aydın, O., Kılar, H. (2025). Development and Future Perspectives of Air Pollution Research Using Artificial Intelligence-Based Methods: A Bibliometric Review. Doğal Afetler ve Çevre Dergisi, Cilt: 11 Sayı: 2, 471–487. https://doi.org/10.21324/dacd.1628030

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (52)

  1. 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
  2. Aria, M., & Cuccurullo, C. (2017). Bibliometrix: an R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
  3. Awasthi, A., Pattnayak, K. C., Tiwari, P. R., Panda, S. K., Gautam, S., Choudhury, T., & Sar, A. (2024). Air quality monitoring (AQM) and prediction. In A. Awasthi, K. C. Pattnayak, G. Dhiman, & P. R. Tiwari (Eds.), Artificial intelligence for air quality monitoring and prediction (Vol. 1, pp. 1–23). CRC Press.
  4. Birkle, C., Pendlebury, D., Schnell, J., & Adams, J. (2020). Web of science as a data source for research on scientific and scholarly activity. Quantitative Science Studies, 1, Article 3550, 1–14. https://doi.org/10.1162/qss_a_00018
  5. Chen, G., Li, S., Knibbs, L. D., Hamm, N. A. S., Cao, W., Li, T., Guo, J., Ren, H., Abramson, M. J., & Guo, Y. (2018). A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information. Science of the Total Environment, 636, 52–60. https://doi.org/10.1016/j.scitotenv.2018.04.251
  6. Chen, J., Chen, Q., Hu, L., Yang, T., Yi, C., & Zhou, Y. (2024). Unveiling trends and hotspots in air pollution control: a bibliometric analysis. Atmosphere, 15, Article 630. https://doi.org/10.3390/atmos15060630
  7. da Silva Filho, A. M., da Silva, I. J., Queila, K., Brito, D., Sobrinho, T. G., & Chaves, L. H. G. (2020). Air pollution: bibliometric analysis and space-temporal distribution of specialized scientific production. International Journal of Advanced Engineering Research and Science, 7(10), 331–341. https://dx.doi.org/10.22161/ijaers.710.38
  8. Di, Q., Amini, H., Shi, L., Kloog, I., Silvern, R., Kelly, J., Sabath, M. B., Choirat, C., Koutrakis, P., Lyapustin, A., Wang, Y., Mickley, L. J., & Schwartz, J. (2019). An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution. Environment International, 130, Article 104909. https://doi.org/10.1016/j.envint.2019.104909
  9. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: an overview and guidelines. Journal of Business Research, 133, 285296. https://doi.org/10.1016/j.jbusres.2021.04.070
  10. European Union. (2021). Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008 on ambient air quality and cleaner air for Europe. Official Journal of the European Union. https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2008:152:0001:0044:en:PDF
  11. Fan, H., Zhao, C., & Yang, Y. (2020). A comprehensive analysis of the spatio-temporal variation of urban air pollution in China during 2014–2018. Atmospheric Environment, 220, Article 117066. https://doi.org/10.1016/j.atmosenv.2019.117066
  12. Feng, T., Sun, Y., Shi, Y., Ma, J., Feng, C., & Chen, Z. (2024). Air pollution control policies and impacts: a review. Renewable and Sustainable Energy Reviews, 191, Article 114071. https://doi.org/10.1016/j.rser.2023.114071
  13. Guo, Q., Ren, M., Wu, S., Sun, Y., Wang, J., Wang, Q., Ma, Y., Song, X., & Chen, Y. (2022). Applications of artificial intelligence in the field of air pollution: a bibliometric analysis. Frontiers in Public Health, 10, Article 933665. https://doi.org/10.3389/fpubh.2022.933665
  14. Hamer, P. D., Walker, S-E., Sausa Santos, G., Vogt, M., Vo Thanh, D., Lopez Aparicio, S., Schneider, P., Ramacher, M. O. P., & Karl, M. (2020). The urban dispersion model EPISODE v10.0– Part 1: an eulerian and sub-grid-scale air quality model and its application in Nordic winter conditions. Geoscientific Model Development, 13, 4323–4353. https://doi.org/10.5194/gmd-13-4323-2020
  15. Hau, Y. S. (2024). Artificial intelligence and air pollution: a bibliometric analysis from 2012 to 2022. International Journal of Advanced Smart Convergence, 13(1), 48–56. https://dx.doi.org/10.7236/IJASC.2024.13.1.48
  16. Houdou, A., Badisy, I. E., Khomsi, K., Abdala, S. A., Abdulla, F., Najmi, H., Obtel, M., Belyamani, L., Ibrahimi, A., & Khalis, M. (2024). Interpretable machine learning approaches for forecasting and predicting air pollution: a systematic review. Aerosol and Air Quality Research, 24(1), Article 230151. https://doi.org/10.4209/aaqr.230151
  17. Huang, C., & Kuo, P. (2018). A deep CNN-LSTM model for particulate matter (PM2.5) forecasting in smart cities. Sensors, 18, Article 2220. https://doi.org/10.3390/s18072220
  18. Joharestani, M. Z., Cao, C., Ni, X., Bashir, B., & Talebiesfandarani, S. (2019). PM2.5 prediction based on random forest, XGBoost, and deep learning using multisource remote sensing data. Atmosphere, 10, Article 373. http://doi.org/10.3390/atmos10070373
  19. Koseoglu, M. A., Rahimi, R., Okumus, F., & Liu, J. (2016). Bibliometric studies in tourism. Annals of Tourism Research, 61, 180–198. https://doi.org/10.1016/j.annals.2016.10.006
  20. Li, Y., Wang, Y., Rui, X., Li, Y., Li, Y., Wang, H., Zuo, J., & Tong, Y. (2017a). Sources of atmospheric pollution: a bibliometric analysis. Scientometrics, 112, 1015–1045. https://doi.org/10.1007/s11192-017-2421-z
  21. Li, X., Peng, L., Yao, X., Cui, S., Hu, Y., You, C., & Chi, T. (2017b). Long short-term memory neural network for air pollutant concentration predictions: method development and evaluation. Environmental Pollution, 231, 997–1004. https://doi.org/10.1016/j.envpol.2017.08.114
  22. Li, T., Shen, H., Yuan, Q., Zhang, X. X., & Zhang, L. (2017c). Estimating ground-level PM2.5 by fusing satellite and station observations: a geo-intelligent deep learning approach. Geophysical Research Letters, 44(23), 985–993. https://doi.org/10.1002/2017GL075710
  23. Li, K., Rollins, J., & Yan, E. (2018). Web of science use in published research and review papers 1997–2017: a selective, dynamic, cross-domain, content-based analysis. Scientometrics, 115, 1–20. https://doi.org/10.1007/s11192-017-2622-5
  24. Li, Y., & Sun, Y. (2021). Modeling and predicting city-level CO2 emissions using open access data and machine learning. Environmental Science and Pollution Research, 28(15), 19260–19271. https://doi.org/10.1007/s11356-020-12294-7
  25. Li, Y., Sha, Z., Tang, A., Goulding, K., & Liu, X. (2023). The application of machine learning to air pollution research: a bibliometric analysis. Ecotoxicology and Environmental Safety, 257, Article 114911. https://doi.org/10.1016/j.ecoenv.2023.114911
  26. Lin, H. (2024). Bibliometric analysis of traffic-related air pollution: using citeSpace to explore the knowledge structure and trends. Environmental Research Communications, 6, Article 022002. https://doi.org/10.1088/2515-7620/ad2a92
  27. Luo, X., Yang, Q., Zheng, D., Tian, H., Chen, L., Wu, J., Ji, Z., Chen, Y., & Li, Z. (2022). A bibliometric and visualization analysis on the association between chronic exposure to fine particulate matter and cancer risk. Frontiers in Public Health, 10, Article 1039078. https://doi.org/10.3389/fpubh.2022.1039078
  28. Mandal, S., Boppani, S., Dasari, V., & Thakur, M. (2024). A bivariate simultaneous pollutant forecasting approach by unified spectro-spatial graph neural network (USSGNN) and its application in prediction of O3 and NO2 for New Delhi, India. Sustainable Cities and Society, 114, Article 105741. https://doi.org/10.1016/j.scs.2024.105741
  29. Marrakchi, N., Bergam, A., Fakhouri, H., & Khomsi, K. (2024). Air quality predicting using lstm recurrent neural network in Tangier: a comparative analysis detecting ozone concentration peaks. Palestine Journal of Mathematics, 13(1), 209–221.
  30. Marvi, R., & Foroudi, M. M. (2023). Bibliometric analysis. In P. Foroudi & C. Dennis (Eds.), Researching and analysing business: research methods in practice (Vol. 3, pp. 4354), Routledge. https://doi.org/10.4324/9781003107774-4
  31. Masood, A., & Ahmad, K. (2021). A review on emerging artificial intelligence (AI) techniques for air pollution forecasting: fundamentals, application and performance. Journal of Cleaner Production, 322, Article 129072. https://doi.org/10.1016/j.jclepro.2021.129072
  32. Mehmood, K., Bao, Y., Saifullah, C. W., Khan, M. A., Siddique, N., Abrar, M. M., Soban, A., Fahad, S., & Naidu, R. (2022). Predicting the quality of air with machine learning approaches: current research priorities and future perspectives. Journal of Cleaner Production, 379, Article 134656. https://doi.org/10.1016/j.jclepro.2022.134656
  33. Menares, C., Perez, P., Parraguez, S., & Fleming, Z. L. (2021). Forecasting PM2.5 levels in Santiago de Chile using deep learning neural networks. Urban Climate, 38, Article 100906. https://doi.org/10.1016/j.uclim.2021.100906
  34. Morawska, L., Thai, P. K., Liu, X., Asumadu-Sakyi, A., Ayoko, G., Bartonova, A., Bedini, A., Chai, F., Christensen, B., Dunbabin, M., Gao, J., Hagler, G. S. W., Jayaratne, R., Kumar, P., Lau, A. K. H., Louie, P. K. K., Mazaheri, M., Ning, Z., Motta, N., Mullins, B., Rahman, Md M., Ristovski, Z., Shafiei, M., Tjondronegoro, D., Westerdahl, D., & Williams, R. (2018). Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: how far have they gone?. Environment International, 116, 286–299. https://doi.org/10.1016/j.envint.2018.04.018
  35. Nazli, S. N., Vilcins. D., Sly, P. D., Razak, A. A., Sabri, N., & Ibrahim, T. N. B. T. (2024). Indoor air quality: bibliometric analysis of the published literature between 2018 and 2023. Environmental Quality Management, 34, Article e22297. https://doi.org/10.1002/tqem.22297
  36. Polezer, G., Tadano, Y. S., Siqueira, H. V., Godoi, A. F. L., Yamamoto, C. I., de André, P. A., Pauliquevis, T., Andrade, M. F., Oliveira, A., Saldiva, P. H. N., Taylor, P. E., & Godoi, R. H. M. (2018). Assessing the impact of PM2.5 on respiratory disease using artificial neural networks. Environmental Pollution, 235, 394–403. https://doi.org/10.1016/j.envpol.2017.12.111
  37. Qi, Y., Li, Q., Karimian, H., & Liu, D. (2019). 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, 664, 1–10. https://doi.org/10.1016/j.scitotenv.2019.01.333
  38. Rakholia, R., Le, Q., Vu, K., Ho, B. Q., & Carbajo, R. S. (2022). AI-based air quality PM2.5 forecasting models for developing countries: a case study of Ho Chi Minh City, Vietnam. Urban Climate, 46, Article 101315. https://doi.org/10.1016/j.uclim.2022.101315
  39. Ramadan, M. N. A., Ali, M. A. H., Khoo, S. Y., Alkhedher, M., & Alherbawi, M. (2024). Real-time IoT-powered AI system for monitoring and forecasting of air. pollution in industrial environment. Ecotoxicology and Environmental Safety, 283, Article 116856. https://doi.org/10.1016/j.ecoenv.2024.116856
  40. Rautela, K. S., & Goyal, M. K. (2024). Transforming air pollution management in India with AI and machine learning technologies. Scientific Report, 14, Article 20412. https://doi.org/10.1038/s41598-024-71269-7
  41. Schneider, A., Friedl, M. A., & Potere, D. (2010). Mapping global urban areas using MODIS 500-m data: new methods and datasets based on ‘urban ecoregions’. Remote Sensing of Environment, 114(8), 1733–1746. https://doi.org/10.1016/j.rse.2010.03.003
  42. Song, X. H., & Hopke, P. K. (1996). Solving the chemical mass balance problem using an artificial neural network. Environmental Science & Technology, 30, 531–535. https://doi.org/10.1021/es950281o
  43. Türk, B. (2022). Important factors affecting the quality of indoor air and a bibliometric analysis. Sakarya University Journal of Science, 26(3), 608–619. https://doi.org/10.16984/saufenbilder.996443
  44. Uluocak, I., Pinar, E., & Bilgili, M. (2024). Atmospheric NO2 concentration prediction with statistical and hybrid deep learning methods. Environmental and Ecological Statistics, 32(1), 89–118. https://doi.org/10.1007/s10651-024-00637-3
  45. Vidal, M. S., Menon, R., Yu, G. F. B., & Amosco, M. D. (2022). Environmental toxicants and preterm birth: a bibliometric analysis of research trends and output. International Journal of Environmental Research and Public Health, 19(5), Article 2493. https://doi.org/10.3390/ijerph19052493
  46. Villacura, L., Sánchez, L. F., Catalán, F., Toro A, R., & Leiva, G. M. A. (2024). An overview of air pollution research in Chile: bibliometric analysis and scoping review, challenger and future directions. Heliyon, 10(3), Article e25431. https://doi.org/10.1016/j.heliyon.2024.e25431
  47. World Health Organization. (2021a). Air pollution causes 13 deaths per minute worldwide. https://www.who.int/multi-media/details/air-pollution-climate-change
  48. World Health Organization. (2021b). COP26 special report on climate change and health. https://www.who.int/publications/i/item/ 9789240036727
  49. Yildirim, G., Rahman, A., & Singh, V. P. (2022). A bibliometric analysis of drought indices, risk, and forecast as components of drought early warning systems. Water, 14(2), Article 253. https://doi.org/10.3390/w14020253
  50. Zhang, J., Yu, Q., Zheng, F., Long, C., Lu, Z., & Duan, Z. (2015). Comparing keywords plus of WOS and author keywords: a case study of patient adherence research. Journal of the Association for Information Science and Technology. 67(4), 967972. https://doi.org/https://doi.org/10.1002/asi.23437
  51. Zimmerman, N., Presto, A. A., Kumar, S. P. N., Gu, J., Hauryliuk, A., Robinson, E. S., Robinson, A. L., & Subramanian, R. (2018). A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring. Atmospheric Measurement Techniques, 11, 291–313. https://doi.org/10.5194/amt-11-291-2018
  52. Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629