Global and Turkish Research Trends in PM₁₀ Prediction Using Machine Learning: A Bibliometric Perspective

Yıl 2025, Cilt: 10 Sayı: 6, 954 - 971, 30.11.2025

Duygu Odabaş Alver

https://doi.org/10.35229/jaes.1766267

Öz

Air pollution, particularly PM10 particulate matter, poses a major threat to public health and environmental sustainability on a global scale. This study conducts a bibliometric analysis of scientific publications from 2020 to 2025 that focus on PM10 and air pollution, with a specific emphasis on prediction approaches based on machine learning. A search conducted in the Web of Science database using the keywords “PM10” and “air pollution” in combination with “Estimation,” “Prediction,” “Forecasting,” or “Machine Learning,” and limited to the “topic” field and “article” document type, identified a total of 1,095 publications indexed in the SCI-EXPANDED and SSCI collections. Of these, only 32 were identified as studies based in Türkiye. According to the data, public health, atmospheric sciences, and environmental sciences accounted for the bulk of articles on a worldwide scale. Despite maintaining a certain level of publication volume, Türkiye was found to lag far behind in terms of citation impact and international collaboration, while China, the United States, India, and the United Kingdom stood out as leading nations in terms of both publication and citation counts. The keyword analysis reveals that concepts such as “air pollution”, “PM10”, “particulate matter”, and “machine learning” are prominently featured. In conclusion, although Türkiye's scientific output in this field has shown numerical growth, it still lags behind developed countries in terms of citation impact, global engagement, and research quality. This situation highlights the need for Türkiye to enhance international collaborations, produce high-impact publications, and focus on research that can support policy-makers.

Anahtar Kelimeler

Air pollution , Bibliometric analysis , Machine learning , PM10 , Türkiye.

Etik Beyan

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Destekleyen Kurum

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Teşekkür

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Makine Öğrenmesi ile PM₁₀ Hava Kirliliği Tahmini Üzerine Küresel ve Türkiye Odaklı Araştırma Eğilimleri: Bibliyometrik Bir İnceleme

Öz

Hava kirliliği, özellikle partikül madde PM10, dünyada halk sağlığını ve çevresel sürdürülebilirliği tehdit eden önemli bir sorundur. Bu bağlamda bu çalışmada, 2020-2025 yılları arasında “PM10” ve “air pollution” konulu yayınlar makine öğrenmesi temelli tahmin yöntemleri açısından ele alınarak bibliyometrik bir analiz gerçekleştirilmiştir. Web of Science veri tabanında “PM10” and “air pollution” and “Estimation” or “PM10” and “air pollution” and “Prediction” or “PM10” and “air pollution” and “Forecasting” or “PM10” and “air pollution” and “Machine Learning” anahtar sözcükleri kullanılarak yapılan bu taramada, yalnızca “topic” alanında, “article” olan dokümanlar, “tüm alanlar”da yapılan taramalardan elde edilen ve SCI-EXPANDED ve SSCI indekslerinde yer alan 1095 yayına ulaşılmıştır. Bunların yalnızca 32’sinin Türkiye merkezli araştırmalar olduğu tespit edilmiştir. Analiz sonuçlarında, küresel ölçekte yayınların büyük kısmının çevre bilimleri, atmosfer bilimleri ve halk sağlığı alanlarında yoğunlaştığı görülmüştür. Çin, ABD, Hindistan ve İngiltere hem yayın hem de atıf sayısında lider ülkeler olarak öne çıkarken, Türkiye’nin yayın hacmi bakımından belirli bir seviyede olsa da atıf sayısı ve uluslararası iş birlikleri açısından oldukça geride kaldığı görülmüştür. Anahtar kelime analizleri, “air pollution”, “PM10”, “particulate matter” ve “machine learning” kavramlarının ön plana çıktığını göstermektedir. Sonuç olarak, Türkiye’nin bu alandaki bilimsel üretimi sayısal olarak artış göstermiş olsa da, atıf gücü, küresel etkileşim düzeyi ve araştırma kalitesi bakımından gelişmiş ülkelerin gerisinde kaldığı görülmektedir. Bu durum, Türkiye’nin daha fazla uluslararası iş birliği geliştirmesi, yüksek etkili yayınlar üretmesi ve politika yapıcıları destekleyecek nitelikte araştırmalara odaklanması gerekliliğini ortaya koymaktadır.

Anahtar Kelimeler

Bibliyometrik analiz , Hava kirliliği , Makine öğrenmesi , PM10 , Türkiye.

Kaynakça

• Aksangür, İ., Eren, B., & Erden, C. (2022). Evaluation of data preprocessing and feature selection process for prediction of hourly PM10 concentration using long short-term memory models. Environmental Pollution, 311, 119973. DOI: 10.1016/j.envpol.2022.119973
• Aladağ, E. (2021). Forecasting of particulate matter with a hybrid ARIMA model based on wavelet transformation and seasonal adjustment. Urban Climate, 39, 100930. DOI: 10.1016/j.uclim.2021.100930
• Al-Janabi, S., Mohammad, M., & Al-Sultan, A. (2020). A new method for prediction of air pollution based on intelligent computation. Soft Computing, 24(2), 661-680. DOI: 10.1007/s00500-019- 04495-1
• Amagai, T., Fujii, Y., & Watanabe, M. (2023). Emerging interdisciplinary collaborations in air-quality research after the COVID-19 pandemic: A bibliometric analysis. Environmental Advances, 13, 100363. DOI: 10.1016/j.envadv.2023.100363
• 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. DOI: 10.35208/ert.1434390
• Aria, M., & Cuccurullo, C. (2017). Bibliometrix: an R- tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. DOI: 10.1016/j.joi.2017.08.007
• Babu, P., Verma, V., Khadanga, S.S., Yadav, S.K., Kumar, D.B., & Gupta, A. (2024). Exploring the association between air pollution and spontaneous abortion through systematic review and bibliometric analysis. Air Quality, Atmosphere & Health, 17(5), 1107-1133.
• Bozdağ, A., Dokuz, Y., & Gökçek, Ö.B. (2020). Spatial prediction of PM10 concentration using machine learning algorithms in Ankara, Turkey. Environmental Pollution, 263, 114635. DOI: 10.1016/j.envpol.2020.114635
• Cao, Y., Wu, X., Han, W., & An, J. (2024). Visual analysis of global air pollution impact research: a bibliometric review (1996-2022). Environmental Science and Pollution Research, 31(28), 40383- 40418. DOI: 10.1007/s11356-023-28468-y
• Chang, Y.-S., Chiao, H.-T., Abimannan, S., Huang, Y.- P., Tsai, Y.-T., & Lin, K.-M. (2020). An LSTM- based aggregated model for air pollution forecasting. Atmospheric Pollution Research, 11(8), 1451-1463. DOI: 10.1016/j.apr.2020.05.015
• Chen, J., Chen, Q., Hu, L., Yang, T., Yi, C., & Zhou, Y. (2024a). Unveiling trends and hotspots in air pollution control: A bibliometric analysis. Atmosphere, 15(6), 630. DOI: 10.3390/atmos15060630
• Chen, Y., Li, J., Zhao, X., & Wang, Z. (2024b). Deep learning-based hybrid models for PM₁₀ prediction and spatiotemporal air quality analysis. Atmospheric Environment, 320, 120153. DOI: 10.1016/j.atmosenv.2024.120153
• Cobo, M.J., López-Herrera, A.G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382–1402. DOI: 10.1002/asi.21525
• Çekim, H.O. (2020). Forecasting PM10 concentrations using time series models: a case of the most polluted cities in Turkey. Environmental Science and Pollution Research, 27, 25612–25624. DOI: 10.1007/s11356-020-08164-x
• Dastoorpoor, M., Idani, E., Goudarzi, G., & Khanjani, N. (2018). Acute effects of air pollution on spontaneous abortion, premature delivery, and stillbirth in Ahvaz, Iran: a time-series study. Environmental Science and Pollution Research, 25(6), 5447-5458.
• Dhital, S., & Rupakheti, D. (2019). Bibliometric analysis of global research on air pollution and human health: 1998–2017. Environmental Science and Pollution Research, 26, 13103-13114.
• 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. DOI: 10.1016/j.jbusres.2021.04.070
• Erden, C. (2023). Genetic algorithm-based hyperparameter optimization of deep learning models for PM2.5 time-series prediction. International Journal of Environmental Science and Technology, 20, 2959-2982. DOI: 10.1007/s13762-023-04763-6
• Fan, J., Wu, L., Ma, X., Zhou, H., & Zhang, F. (2020). Hybrid support vector machines with heuristic algorithms for prediction of daily diffuse solar radiation in air-polluted regions. Renewable Energy, 145, 2034-2045. DOI: 10.1016/j.renene.2019.07.104
• Fu, P., Guo, X., Cheung, F. M. H., & Yung, K. K. L. (2019). The association between PM2.5 exposure and neurological disorders: A systematic review and meta-analysis. The Science of the Total Environment, 655, 1240-1248. DOI: 10.1016/j.scitotenv.2018.11.218
• Gao, J., Zhu, S., Li, D., Jiang, H., Deng, G., Wen, Y., ..., & Cao, Y. (2023). Bibliometric analysis of climate change and water quality. Hydrobiologia, 850(16), 3441-3459.
• Geng, Z., Ma, Y., & Qi, X. (2025). Bibliometric analysis of the association between air pollution and allergic rhinitis. Global Health Action, 18, 2547434. DOI: 10.1080/16549716.2025.2547434
• Gurung, A., Son, J.Y., & Bell, M.L. (2017). Particulate matter and risk of hospital admission in the Kathmandu Valley, Nepal: a case-crossover study. American Journal of Epidemiology, 186(5), 573-580.
• Guo, Q., Ren, M., Wu, S., Sun, Y., Wang, J., Wang, Q., ..., & Chen, Y. (2022). Applications of artificial intelligence in the field of air pollution: A bibliometric analysis. Frontiers in Public Health, 10, 933665. DOI: 10.3389/fpubh.2022.933665
• Han, L., Yu, Z., & Chen, X. (2023). Advances in air pollution modeling using machine learning: From local prediction to global data integration. Environmental Modelling & Software, 165, 105694. DOI: 10.1016/j.envsoft.2023.105694
• Jain, S., Sharma, S.K., Vijayan, N., & Mandal, T.K. (2020). Seasonal characteristics of aerosols (PM2.5 and PM10) and their source apportionment using PMF: A four year study over Delhi, India. Environmental Pollution, 262, 114337. DOI: 10.1016/j.envpol.2020.114337
• Jain, S., Kaur, N., Verma, S., Kavita, Hosen, A.S.M.S., & Sehgal, S.S. (2022). Use of machine learning in air pollution research: A bibliographic perspective. Electronics, 11(21), 3621. DOI: 10.3390/electronics11213621
• Jeong, H., Lee, D., & Park, S. (2021). Linear regression modeling of winter PM₂.₅ variability in East Asia in relation to ENSO and the Siberian High. Environmental Research, 201, 111585. DOI: 10.1016/j.envres.2021.111585
• Jin, L., Godri Pollitt, K.J., Liew, Z., Rosen Vollmar, A.K., Vasiliou, V., Johnson, C.H., & Zhang, Y. (2021). Use of untargeted metabolomics to explore the air pollution-related disease continuum. Current Environmental Health Reports, 8(1), 7-22.
• Karimi, B., & Samadi, S. (2024). Long-term exposure to air pollution on cardio-respiratory, and lung cancer mortality: a systematic review and meta- analysis. Journal of Environmental Health Science and Engineering, 22(1), 75-95.
• Kavic, M.S., & Satava, R.M. (2021). Scientific literature and evaluation metrics: impact factor, usage metrics, and altmetrics. JSLS: Journal of the Society of Laparoscopic & Robotic Surgeons, 25(3), e2021-00010.
• Kaya, K., & Gündüz Öğüdücü, Ş. (2020). Deep flexible sequential (DFS) model for air pollution forecasting. Scientific Reports, 10(1), 3346. Kim, E., Park, H., Park, E.A., Hong, Y.C., Ha, M., Kim, H.C., & Ha, E.H. (2016). Particulate matter and early childhood body weight. Environment International, 94, 591–599. DOI: 10.1016/j.envint.2016.06.021
• Koçak, E. (2025). Comprehensive evaluation of machine learning models for real‑world air quality prediction and health risk assessment by AirQ+. Earth Science Informatics, 18, 447. DOI: 10.1007/s12145-025-01941-7
• Kong, L., Tang, X., Zhu, J., Wang, Z., Fu, J. S., Wang, X., Itahashi, S., Yamaji, K., Nagashima, T., Lee, H.-J., Kim, C.-H., Lin, C.-Y., Chen, L., Zhang, M., Tao, Z., Li, J., Kajino, M., Liao, H., Wang, Z., Sudo, K., Wang, Y., Pan, Y., Tang, G., Li, M., Wu, Q., Ge, B., & Carmichael, G.R. (2021). A 6-year-long high-resolution air quality reanalysis dataset in China obtained from the assimilation of surface observations using the ensemble Kalman filter and Nested Air Quality Prediction Modeling System (CAQRA). Earth System Science Data, 13(2), 529-570. DOI: 10.5194/essd-13-529-2021
• Kumar, R., & Kumar, P. (2023). Trends and developments in air-quality prediction using artificial intelligence: A scientometric review (2010-2023). Environmental Monitoring and Assessment, 195(4), 523. DOI: 10.1007/s10661- 023-11598-8
• Kurnaz, G., & Demir, A.S. (2022). Prediction of SO2 and PM10 air pollutants using a deep learning-based recurrent neural network: Case of industrial city Sakarya. Urban Climate, 41, 101051. DOI: 10.1016/j.uclim.2021.101051
• Liao, Q., Zhu, M., Wu, L., Pan, X., Tang, X., & Wang, Z. (2020). Deep learning for air quality forecasts: a review. Current Pollution Reports, 6(4), 399- 409.
• Liao, Z., Li, X., & Song, Y. (2021). Global research trends and hotspots in air pollution prediction using machine learning: A bibliometric perspective. Atmosphere, 12(6), 686. DOI: 10.3390/atmos12060686
• Li, Z., Ho, Y.S. (2008). Use of citation per publication as an indicator to evaluate contingent valuation research. Scientometrics, 75(1), 97-110.
• 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, 114911. DOI: 10.1016/j.ecoenv.2023.114911
• Liu, F., Wang, M., & Zheng, M. (2021). Quantifying the causal effects of COVID-19 lockdown on air quality globally. Science of the Total Environment, 755(Part 1), 142533. DOI: 10.1016/j.scitotenv.2020.142533
• Luo, W., Deng, Z., Zhong, S., & Deng, M. (2022). Trends, issues and future directions of urban health impact assessment research: a systematic review and bibliometric analysis. International Journal of Environmental Research and Public Health, 19(10), 5957
• Magazzino, C., Mele, M., & Schneider, N. (2020). Investigating the nexus between Particulate Matter (PM) and COVID-19: A machine learning approach. Applied Energy, 279, 115835. DOI: 10.1016/j.apenergy.2020.115835
• Mampitiya, L., Rathnayake, N., Ghosh, R., Hoshino, Y., & Rathnayake, U. (2025). AI‑Driven Forecasting of PM₁₀ Concentrations in the Republic of Ireland: Integrating Machine Learning for Urban Air Quality Prediction. Aerosol Science & Engineering, DOI: 10.1007/s41810-025-00325-0
• Mao, W., Wang, W., Jiao, L., Zhao, S., & Liu, A. (2021). Modeling air quality prediction using a deep learning approach: Method optimization and evaluation. Sustainable Cities and Society, 65, 102567. DOI: 10.1016/j.scs.2020.102567
• Maring, T., Kumar, S., Jha, A. K., Kumar, N., & Pandey, S. P. (2023). Airborne particulate matter and associated heavy metals: a review. In Macromolecular Symposia (Vol. 407, No. 1, p. 2100487).
• Orak, N. H., & Özdemir, O. (2021). The impacts of COVID‑19 lockdown on PM₁₀ and SO₂ concentrations and association with human mobility across Turkey. Environmental Research, 197, 111018. DOI: 10.1016/j.envres.2021.111018
• Orduña-Malea, E., & Costas, R. (2021). Link-based approach to study scientific software usage: The case of VOSviewer. Scientometrics, 126(9), 8153-8186. DOI: 10.1007/s11192-021-04082-y
• Öncel Çekim, H. (2020). Forecasting PM₁₀ concentrations using time series models: A case of the most polluted cities in Turkey. Environmental Science and Pollution Research, 27(25), 25612-25624. DOI: 10.1007/s11356-020-08164-x
• Özdemir, N., Orak, N. H., & Zeydan, O. (2020). Investigation of the relationship between extreme pressure values and particulate matter (PM₁₀) values for megacity İstanbul. Journal of Anatolian Environmental and Animal Sciences, 5(4), 509- 516. DOI: 10.35229/jaes.733816
• Qu, S., Li, H., Wu, J., & Zhao, B. (2025). Environmental and Health Benefits: A Bibliometric and Knowledge Mapping Analysis of Research Progress. Sustainability, 17(5), 2269.
• Rai, P.K. (2016). Impacts of particulate matter pollution on plants: Implications for environmental biomonitoring. Ecotoxicology and Environmental Safety, 129, 120-136.
• Right to Clean Air Platform. (2022). Dark Report 2022: Executive Summary. Temiz Hava Hakkı Platformu. https://temizhavahakki.org/wp- content/uploads/2023/04/Executive_Summary.pdf
• Rybarczyk, Y., & Zalakeviciute, R. (2018). Machine learning approaches for outdoor air quality modelling: A systematic review. Applied Sciences, 8(12), 2570. DOI: 10.3390/app8122570
• Salman Özdemir, B., Yontuç, Y., & Sivri, N. (2024). How did the COVID-19 pandemic cause plastic pollution within the scope of One Health? Recent research and identifying knowledge gaps. Journal of Anatolian Environmental and Animal Sciences, 9(1), 32-44. DOI: 10.35229/jaes.1404637
• Sarver, T., Al-Qaraghuli, A., & Kazmerski, L.L. (2013). A comprehensive review of the impact of dust on the use of solar energy: History, investigations, results, literature, and mitigation approaches. Renewable and Sustainable Energy Reviews, 22, 698-733.
• Seihei, N., Farhadi, M., Takdastan, A., Asban, P., Kiani, F., & Mohammadi, M.J. (2024). Short- term and long-term effects of exposure to PM₁₀. Clinical Epidemiology and Global Health, 27, DOI: 10.1016/j.cegh.2024.101611
• Shaddick, G., Thomas, M.L., Amini, H., Broday, D., Cohen, A., Frostad, J., ... & Brauer, M. (2018). Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environmental Science & Technology, 52(16), 9069-9078. DOI: 10.1021/acs.est.8b02864
• Song, R., Liu, L., Wei, N., Li, X., Liu, J., Yuan, J., ... & Su, H. (2023). Short-term exposure to air pollution is an emerging but neglected risk factor for schizophrenia: a systematic review and meta- analysis. Science of the Total Environment, 854, DOI: 10.1016/j.scitotenv.2022.158823
• Shi, T., Ma, H., Li, D., Pan, L., Wang, T., Li, R., & Ren, X. (2024). Prenatal exposure to fine particulate matter chemical constituents and the risk of stillbirth and the mediating role of pregnancy complications: A cohort study. Chemosphere, 349, DOI: 10.1016/j.chemosphere.2023.140858
• Sun, L., Liu, T., & Wu, Q. (2024). Integrating satellite observations with deep learning for air-pollution prediction: A global overview. Atmospheric Research, 298, 107161. DOI: 10.1016/j.atmosres.2024.107161
• Şahin, F., Kara, M.K., Koç, A., & Şahin, G. (2020). Multi-criteria decision-making using GIS-AHP for air pollution problem in Igdir Province/Turkey. Environmental Science and Pollution Research, 27, 36215-36230. DOI: 10.1007/s11356-020-09710-3
• Tao, Q., Liu, F., & Li, S. (2019). Air pollution prediction using a deep learning model based on 1D convolutional neural network and long short-term memory. Atmospheric Pollution Research, 10(3), 1068-1078. DOI: 10.1016/j.apr.2018.11.012
• U.S. Environmental Protection Agency. (2025). (U.S. EPA.) Particulate matter (PM) basics. https://www.epa.gov/pm-pollution/particulate- matter-pm-basics (Access date: 22.06.2025)
• Ünal, E., Özdemir, A., Khanjani, N., Dastoorpoor, M., & Özkaya, G. (2022). Air pollution and pediatric respiratory hospital admissions in Bursa, Turkey: A time series study. International Journal of Environmental Health Research, 32(12), 2767- 2780.
• Verma, S., & Gustafsson, A. (2020). Investigating the emerging COVID-19 research trends in the field of business and management: A bibliometric analysis approach. Journal of Business Research, 118, 253-261. DOI: 10.1016/j.jbusres.2020.06.057
• Villacura, L., Sanchez, L.F., Catalan, F., Toro, 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). e25431 DOI: 10.1016/j.heliyon.2024.e25431
• Wang, M., Kim, R.Y., Kohonen-Corish, M.R., Chen, H., Donovan, C., & Oliver, B.G. (2025). Particulate matter air pollution as a cause of lung cancer: epidemiological and experimental evidence. British Journal of Cancer, 132, 986- 996. DOI: 10.1038/s41416-025-02999-2
• Wen, S., Tan, Q., Baheti, R., Wan, J., Yu, S., Zhang, B., & Huang, Y. (2024). Bibliometric analysis of global research on air pollution and cardiovascular diseases: 2012-2022. Heliyon, 10(12). DOI: 10.1016/j.heliyon.2024.e32840
• Wong, P.-Y., Lee, H.-Y., Chen, Y.-C., Zeng, Y.-T., Chern, Y.-R., Chen, N.-T., Lung, S.-C. C., Su, H.-J., & Wu, C.-D. (2021). Using a land use regression model with machine learning to estimate ground level PM2.5. Environmental Pollution, 277, 116846. DOI: 10.1016/j.envpol.2021.116846
• World Health Organization (WHO) (2024). Ambient (outdoor) air quality and health. https://www.who.int/news-room/fact- sheets/detail/ambient-(outdoor)-air-quality-and- health (Access date: 22.06.2025)
• Wu, C., Wang, R., Lu, S., Tian, J., Yin, L., Wang, L., & Zheng, W. (2025). Time-series data-driven PM₂.₅ forecasting: From theoretical framework to empirical analysis. Atmosphere, 16(3), 292. DOI: 10.3390/atmos16030292
• Xayasouk, T., Lee, H., & Lee, G. (2020). Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models. Sustainability, 12(6), 2570. DOI: 10.3390/su12062570
• Yan, L., & Zhiping, W. (2023). Mapping the literature on academic publishing: A bibliometric analysis on WOS. Sage Open, 13(1), 1-16. DOI: 10.1177/21582440231158562
• Zeydan, Ö. (2021). 2019 yılında Türkiye’deki partikül madde (PM₁₀) kirliliğinin değerlendirilmesi. Journal of the Institute of Science and Technology, 11(1), 106-118. DOI: 10.21597/jist.745539
• Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429-472. DOI: 10.1177/1094428114562629