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A wavelet-based random forest approach for indoor BTEX spatiotemporal modeling and health risk assessment

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Abstract

This study reports on BTEX concentrations in one of the largest parking garages in Iran with a peak traffic flow reaching up to ~9300 vehicles in the last few days of the Nowruz holidays. Samples were obtained on different days of the week at three main locations in the Zaer Parking Garage. A novel wavelet-based random forest model (WRF) was trained to estimate BTEX concentrations by decomposing temperature, day of the week, sampling location, and relative humidity data with a maximal overlap discrete wavelet transform (MODWT) function and subsequently inputted into the WRF model. The results suggested that the WRF model can reasonably estimate BTEX trends and variations based on high R2 values of 0.96, 0.95, and 0.98 for training, validation, and test data subsets, respectively. The carcinogenic (LTCR) and non-carcinogenic health risk (HI) assessment results indicated a definite carcinogenic risk of benzene (LTCR = 2.22 × 10−4) and high non-carcinogenic risk (HI = 4.51) of BTEX emissions. The results of this study point to the importance of BTEX accumulation in poorly ventilated areas and the utility of machine learning in forecasting air pollution in diverse airsheds such as parking garages.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgments

We would like to express our gratitude to the Zaer Parking Garage administration as well as the staff for providing support and the opportunity to carry out this experiment. Secondly, we acknowledge the Iran Meteorological Organization for sharing the required meteorological data. Our gratitude is also extended to the staff of the Qom University of Medical Sciences who participated in this study.

Funding

This project was funded by Research Centre for Environmental Pollutants, Qom University of Medical Sciences (Grant number: 96899).

Author information

Authors and Affiliations

  1. Independent researcher, Isfahan, Iran

    Mostafa Rezaali

  2. Research Centre for Environmental Pollutants, Qom University of Medical Sciences, Qom, Iran

    Reza Fouladi-Fard

  3. Department of Environmental Health Engineering, School of Health, Qom University of Medical Sciences, Qom, Iran

    Reza Fouladi-Fard

  4. Student Research Committee, Qom University of Medical Sciences, Qom, Iran

    Hassan Mojarad

  5. Department of Chemical and Environmental Engineering, University of Arizona, Tucson, AZ, USA

    Armin Sorooshian

  6. Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA

    Armin Sorooshian

  7. Department of Occupational Health, Faculty of Health, Qom University of Medical Sciences, Qom, Iran

    Mohsen Mahdinia

  8. Department of Environmental Health Engineering, Social Determinants of Health (SDH) Research Center, Kashan University of Medical Sciences, Kashan, Iran

    Nezam Mirzaei

  9. Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran

    Nezam Mirzaei

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  1. Mostafa Rezaali
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  2. Reza Fouladi-Fard
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  3. Hassan Mojarad
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  4. Armin Sorooshian
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Contributions

The authors’ contributions based on CRediT taxonomy are as follows:

Mostafa Rezaali: Writing—original draft, writing—review and editing, data curation, formal analysis, software, validation, conceptualization, visualization, and methodology.

Reza Fouladi-Fard: Conceptualization, data curation, methodology, funding acquisition, investigation, project administration, resources, supervision, validation, and writing—review and editing.

Hassan Mojarad: Data curation, investigation, and resources.

Armin Sorooshian: Validation, formal analysis, and writing—review and editing.

Mohsen Mahdinia: Formal analysis and writing—review and editing.

Nezam Mirzaei: Formal analysis and writing—review and editing.

Corresponding author

Correspondence to Reza Fouladi-Fard.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The study does not involve any research with human participants or animals performed by any of the authors and is approved by the Qom University of Medical Science, in the research ethics certificate: approval ID: IR.MUQ.REC.1397.035, approval date: May 22, 2018.

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Responsible Editor: Constantini Samara

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Rezaali, M., Fouladi-Fard, R., Mojarad, H. et al. A wavelet-based random forest approach for indoor BTEX spatiotemporal modeling and health risk assessment. Environ Sci Pollut Res 28, 22522–22535 (2021). https://doi.org/10.1007/s11356-020-12298-3

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