Investigation of temporal PCDD/F concentration levels and gas/particle partitioning employing recent models in the ambient air of a wastewater treatment plant site


Gülegen B., Noori A. A., TAŞDEMİR Y.

Atmospheric Pollution Research, vol.17, no.3, 2026 (SCI-Expanded, Scopus) identifier

  • Publication Type: Article / Article
  • Volume: 17 Issue: 3
  • Publication Date: 2026
  • Doi Number: 10.1016/j.apr.2025.102823
  • Journal Name: Atmospheric Pollution Research
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, INSPEC
  • Keywords: Dioxin, Gas-particle modeling, Persistent organic pollutant, Risk assessment, Seasonal variation, Source apportionment
  • Bursa Uludag University Affiliated: Yes

Abstract

Polychlorinated dibenzo-p-dioxins/dibenzofurans (PCDD/Fs) are persistent organic pollutants posing a serious threat to human health and the environment. The present work measured PCDD/F atmospheric concentrations in the ambient air of Bursa's largest wastewater treatment plant over four seasons. The average PCDD/F concentration was 410 ± 178 fg/m3 (30.83 ± 4.85 fg WHO TEQ/m3). This value was in agreement with many studies conducted worldwide. Ambient air PCDD/Fs were found to originate mainly from sewage sludge in the aeration basin and motor vehicle emissions. However, the risk values were less than 10−6, indicating that PCDD/F pollution in the site does not pose a health risk. The gas/particle (G/P) distribution is a significant concern from many different perspectives, including the mechanisms of atmospheric deposition, long-distance transport and health risks. Traditionally, adsorption and absorption-based models have been employed to define the PCDD/F G/P partitioning. In the present study, five different models (Li-Ma-Yang, Dachs-Eisenreich, QSPR, pp-LFER, mp-pp-LFER) were utilized along with these two models. Among these models, the Li-Ma-Yang model presented successful performance and revealed the importance of absorption and deposition mechanisms for the G/P transitions. Also, the model of pp-LFER generally performed well and showed that it could predict PCDD/F partitioning.