ION MOBILITY-ENABLED LC-HRMS FOR THE ANALYSIS OF POLLUTANTS IN INDOOR DUST: IDENTIFICATION AND PREDICTIVE CAPABILITIES

Significance of the Topic

Indoor dust serves as a reservoir for diverse anthropogenic pollutants. Ion mobility–enabled LC–HRMS introduces an orthogonal separation dimension by measuring gas-phase ion conformations, enhancing selectivity and identification confidence in non-targeted environmental screening.

Objectives and Study Overview

This work aimed to apply LC–IMS–QTof to characterize xenobiotics in dust collected from two e-waste processing facilities and domestic environments, employing both targeted and non-targeted workflows along with predictive modelling to improve compound identification without relying solely on authentic standards.

Methodology and Instrumentation

Sample preparation involved dichloromethane extraction of composite dust followed by reconstitution in methanol–water. Chromatography used a Waters ACQUITY I-Class system with a BEH C18 column (2.1×50 mm, 1.7 µm) at 65 °C and a gradient of ammonium acetate in water:methanol to methanol over an 8.5 min run at 0.45 mL/min. Ion mobility–MS analysis was performed on a Waters Vion IMS QTof in ESI± mode (50–1000 m/z), with nitrogen drift gas, low-energy 3 eV, high-energy ramp 20–55 eV, capturing collision cross section (CCS) values as an additional identification metric.

Main Results and Discussion

• Twenty-nine xenobiotic compounds across organophosphorus flame retardants, brominated phenols, pesticides, perfluoroalkyl substances, and pharmaceuticals were tentatively identified, with confirmation using available standards.
• CCS measurements demonstrated excellent repeatability (mean RSD ~0.24%) and agreed within 2% of standard values for confirmed analytes.
• Multi-halogenated compounds exhibited notably reduced CCS compared to non-halogenated analogs of similar mass, reflecting more compact structures and faster drift times.
• Machine learning CCS prediction, based on a robust development model (R² = 0.977), supported 68% of identifications within a 2% error threshold and 100% within 5.15% of experimental CCS, underscoring its value for non-targeted analysis.

Benefits and Practical Applications

• The added IMS dimension enhances discriminative power in complex mixtures, reducing false positives.
• Predictive CCS modelling extends identification capabilities when authentic standards are unavailable.
• High-throughput analysis with an 8.5 min LC run facilitates routine monitoring of indoor pollutants.

Future Trends and Potential Applications

• Expansion of CCS databases and predictive models for a wider range of environmental contaminants.
• Real-time IMS-enabled screening in indoor air and dust monitoring.
• Integration with advanced data mining and machine learning workflows for automated non-targeted surveillance.

Conclusion

The integration of ion mobility spectrometry with LC–HRMS and predictive CCS modelling offers a powerful, high-throughput approach for comprehensive detection and confident identification of indoor dust pollutants, paving the way for its adoption in environmental health assessments.

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