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

Mostafa Rezaali, Reza Fouladi-Fard, Hassan Mojarad, Armin Sorooshian, Mohsen Mahdinia, Nezam Mirzaei

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)22522-22535
Number of pages14
JournalEnvironmental Science and Pollution Research
Issue number18
StatePublished - May 2021


  • Health risk assessment
  • Indoor air pollution
  • Parking emissions
  • Wavelet-based random forest model

ASJC Scopus subject areas

  • Environmental Chemistry
  • Pollution
  • Health, Toxicology and Mutagenesis


Dive into the research topics of 'A wavelet-based random forest approach for indoor BTEX spatiotemporal modeling and health risk assessment'. Together they form a unique fingerprint.

Cite this