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Modeling of atmospheric particulate matters via artificial intelligence methods

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Abstract

Nowadays, pollutants continue to be released into the atmosphere in increasing amounts with each passing day. Some of them may turn into more harmful forms by accumulating in different layers of the atmosphere at different times and can be transported to other regions with atmospheric events. Particulate matter (PM) is one of the most important air pollutants in the atmosphere, and it can be released into the atmosphere by natural and anthropogenic processes or can be formed in the atmosphere as a result of chemical reactions. In this study, it was aimed to predict PM10 and PM2.5 components measured in an industrial zone selected by adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), classification and regression trees (CART), random forest (RF), k-nearest neighbor (KNN), and extreme learning machine (ELM) methods. To this end, in the first stage of the study, the dataset consisting of air pollutants and meteorological data was created, the temporal and qualitative evaluation of these data was performed, and the PM (PM10 and PM2.5) components were modeled using the “R” software environment by artificial intelligence methods. The ANFIS model was more successful in predicting the PM10 (R2 = 0.95, RMSE = 5.87, MAE = 4.75) and PM2.5 (R2 = 0.97, RMSE = 3.05, MAE = 2.18) values in comparison with other methods. As a result of the study, it was clearly observed that the ANFIS model could be used in the prediction of air pollutants.

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Data availability

Pollutant data are freely available from https://sim.csb.gov.tr/STN/STN_Report/StationDataDownloadNew

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Acknowledgements

The authors are thankful to Istanbul University-Cerrahpasa Graduate Education Institute for support.

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Authors and Affiliations

  1. Department of Computer Engineering, Corlu Engineering Faculty, Tekirdag Namık Kemal University, 59860, Çorlu, Tekirdag, Turkey

    Pınar Cihan

  2. Engineering Faculty, Department of Environmental Engineering, Istanbul University-Cerrahpasa, 34320, Avcilar, Istanbul, Turkey

    Huseyin Ozel & Huseyin Kurtulus Ozcan

Authors
  1. Pınar Cihan
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  2. Huseyin Ozel
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  3. Huseyin Kurtulus Ozcan
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Contributions

PC, HO, and HKO analyzed and processed the data. PC and HO developed the machine learning models. PC, HO, and HKO wrote the original draft preparation and review. All authors read and approved the final manuscript.

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Correspondence to Pınar Cihan.

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Cihan, P., Ozel, H. & Ozcan, H.K. Modeling of atmospheric particulate matters via artificial intelligence methods. Environ Monit Assess 193, 287 (2021). https://doi.org/10.1007/s10661-021-09091-1

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