Green sampling of gas-phase volatile organic compounds using time-weighted average solid phase microextraction and gas chromatography - mass spectrometry: A critical review

Time-weighted average solid-phase microextraction (TWA-SPME) is a solvent-free passive sampling technique increasingly applied to volatile organic compounds (VOCs) monitoring in air. This review critically examines the theoretical foundations of TWA-SPME based on Fick’s laws of diffusion, highlights optimization of SPME coating, diffusion path length, and sampling duration, and compares quantification strategies based on both theoretical modeling and empirical calibration. Applications across ambient and indoor air, vehicle exhaust, industrial emissions, and clinical environments demonstrate their versatility and sensitivity relative to conventional sorbent-based methods. A greenness evaluation using the AGREEprep tool confirmed strong TWA-SPME alignment with green sampling preparation principles, particularly through elimination of solvents, low waste, and high operator safety. Challenges such as adsorption on metallic components and coating saturation are discussed alongside mitigation strategies and device refinements. Emerging designs and simulation-based models improve performance predictability and sampling efficiency under diverse environmental conditions. Comparisons with emerging alternatives such as thin-film SPME and needle trap devices highlight the unique advantages of TWA-SPME in robustness, simplicity, and reproducibility. Overall, TWA-SPME represents an environmentally friendly, accurate, and practical approach to VOC sampling, with growing relevance in environmental monitoring and occupational exposure assessments.

Publication Details

Journal
Green Analytical Chemistry
Published
2025-10-01
DOI
https://doi.org/10.1016/j.greeac.2025.100302
Citations
1
Primary Topic
Nonlinear Differential Equations Analysis
Type
article
Field-Weighted Citation Impact
3.73
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Green sampling of gas-phase volatile organic compounds using time-weighted average solid phase microextraction and gas chromatography - mass spectrometry: A critical review

1 citations
Green Analytical Chemistry
Nonlinear Differential Equations Analysis
3.73
article

Green sampling of gas-phase volatile organic compounds using time-weighted average solid phase microextraction and gas chromatography - mass spectrometry: A critical review

article en
1 citations

Abstract

Time-weighted average solid-phase microextraction (TWA-SPME) is a solvent-free passive sampling technique increasingly applied to volatile organic compounds (VOCs) monitoring in air. This review critically examines the theoretical foundations of TWA-SPME based on Fick’s laws of diffusion, highlights optimization of SPME coating, diffusion path length, and sampling duration, and compares quantification strategies based on both theoretical modeling and empirical calibration. Applications across ambient and indoor air, vehicle exhaust, industrial emissions, and clinical environments demonstrate their versatility and sensitivity relative to conventional sorbent-based methods. A greenness evaluation using the AGREEprep tool confirmed strong TWA-SPME alignment with green sampling preparation principles, particularly through elimination of solvents, low waste, and high operator safety. Challenges such as adsorption on metallic components and coating saturation are discussed alongside mitigation strategies and device refinements. Emerging designs and simulation-based models improve performance predictability and sampling efficiency under diverse environmental conditions. Comparisons with emerging alternatives such as thin-film SPME and needle trap devices highlight the unique advantages of TWA-SPME in robustness, simplicity, and reproducibility. Overall, TWA-SPME represents an environmentally friendly, accurate, and practical approach to VOC sampling, with growing relevance in environmental monitoring and occupational exposure assessments.

Green Analytical ChemistryVol. 15
Responsible consumption and production
Openalex Percentile: Top 6%
Nonlinear Differential Equations Analysis
3.73
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