One-year dynamics of atmospheric organophosphate esters: Size-fractionated distribution, seasonal trends, and machine learning-based driver identification

Organophosphate esters (OPEs) have increasingly raised global concern due to their pervasive presence in airborne particles and potential risks they pose to human health. This study systematically characterized OPE concentrations in PM<sub>2.5</sub>, PM<sub>10</sub>, and TSP over a one-year period. The results revealed that OPEs preferentially partitioned to smaller particles and displayed higher total concentrations during the warm season. Among alkyl-, halogenated-, and aryl-OPEs, halogenated-OPEs exhibited the highest concentrations while aryl-OPEs ranked highest in ToxPi-based prioritization. To identify key environmental drivers, Random Forest model combined with SHAP analysis and structural equation model were employed. The RF-SHAP model demonstrated higher explanatory power for halogenated-OPEs compared to alkyl- and aryl-OPEs, with temperature, atmospheric pressure, and the interaction effect of temperature and relative humidity identified as dominant influencing factors. Furthermore, the findings suggest that OPEs may undergo atmospheric transformation under complex environmental conditions. Overall, this study provides a comprehensive understanding of the particle-size distribution, temporal dynamics, environmental determinants, and health risk profiles associated with particle-bound OPEs. These insights are invaluable for informing future regulations concerning emerging contaminants in the atmosphere.

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Publication Details

Journal
Environment International
Published
2025-09-25
DOI
https://doi.org/10.1016/j.envint.2025.109815
Citations
1
Primary Topic
Atmospheric chemistry and aerosols
Type
article
Field-Weighted Citation Impact
1.30
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article

One-year dynamics of atmospheric organophosphate esters: Size-fractionated distribution, seasonal trends, and machine learning-based driver identification

She-Jun Chen, Yang Tang, Dachi Hong, Guang‐Guo Ying et al.
1 citations
Environment International
Atmospheric chemistry and aerosols
1.30
article

One-year dynamics of atmospheric organophosphate esters: Size-fractionated distribution, seasonal trends, and machine learning-based driver identification

She-Jun Chen, Yang Tang, Dachi Hong, Guang‐Guo Ying, Li‐Xin Hu, Shuqing Qiu, Chenglei Pei
article en
1 citations

Abstract

Organophosphate esters (OPEs) have increasingly raised global concern due to their pervasive presence in airborne particles and potential risks they pose to human health. This study systematically characterized OPE concentrations in PM<sub>2.5</sub>, PM<sub>10</sub>, and TSP over a one-year period. The results revealed that OPEs preferentially partitioned to smaller particles and displayed higher total concentrations during the warm season. Among alkyl-, halogenated-, and aryl-OPEs, halogenated-OPEs exhibited the highest concentrations while aryl-OPEs ranked highest in ToxPi-based prioritization. To identify key environmental drivers, Random Forest model combined with SHAP analysis and structural equation model were employed. The RF-SHAP model demonstrated higher explanatory power for halogenated-OPEs compared to alkyl- and aryl-OPEs, with temperature, atmospheric pressure, and the interaction effect of temperature and relative humidity identified as dominant influencing factors. Furthermore, the findings suggest that OPEs may undergo atmospheric transformation under complex environmental conditions. Overall, this study provides a comprehensive understanding of the particle-size distribution, temporal dynamics, environmental determinants, and health risk profiles associated with particle-bound OPEs. These insights are invaluable for informing future regulations concerning emerging contaminants in the atmosphere.

Environment InternationalVol. 204
South China Normal University (CN), Guangdong Province Environmental Monitoring Center (CN)
Openalex Percentile: Top 18%
Atmospheric chemistry and aerosols
1.30
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One-year dynamics of atmospheric organophosphate esters: Size-fractionated distribution, seasonal trends, and machine learning-based driver identification — She-Jun Chen, Yang Tang, et al. · Environment International (2025) | TGRS Research Map | TGRS