Towards the Real-Time Detection of Hazardous Dust from Narcotics and Explosives
JOHANNES PASSIG, Andreas Walte, Sven Ehlert, Guanzhong Wang, Heinrich Ruser, Ellen-Iva Rosewig, Julian Schade, Aleksandrs Kalamašhņikovs, Robert Irsig, Petra Hehet, Michael Pütz, Martin Seipenbusch, Simone Vinati, Karsten Wegner, Konrad Matena, Thorsten Streibel, Ralf Zimmermann,
Rostock University and Photonion GmbH Abstract Number: 317
Working Group: Identifying and Addressing Disparate Health and Social Impacts of Exposure to Aerosols and Other Contaminants across Continents, Communities, and Microenvironments
AbstractHazardous dusts pose substantial risks for human health and security. This applies not only to air pollution, e.g from combustion processes, but also to releases of hazardous substances after accidents or during illicit transport and handling of drugs and explosives. The current opioid crisis in the U.S. shed new light on the lack of technologies that can detect such dusts from narcotics in real time, e.g. to protect staff in parcel logistics, at airports or custom offices as well as to support criminal investigations. In the HazarDust project, we are developing technologies to close this gap, in a collaboration of SMEs, academic partners and authorities.
The core detection technology of our approach is single-particle mass spectrometry (SPMS), which is capable to chemically characterize individual particles in a complex aerosol (Pratt and Prather, 2012). After having introduced resonant ionization methods for particle-bound metals (Passig et al. 2020) and polycyclic aromatic hydrocarbons (Passig et al. 2022), we are ready to tailor the SPMS technology to dusts of various narcotics and explosives. We present the resulting single-particle mass spectral signatures of over-the-counter medicals, precursors of fentanyl and other drugs and show how to overcome interferences with other particle types in a complex environment. While SPMS are real-time instruments, the complex data evaluation is generally performed offline, after the measurement procedure is finished (Sultana et al., 2017). This disables on-line screening of hazardous particles and real-time risk assessment. To overcome this severe limitation, we are developing real-time software including data acquisition, preprocessing, screening and clustering. A strong emphasis is put on machine learning algorithms using open-source databases. We investigated supervised learning approaches for particle screening and classification and show that they can greatly outperform the traditional unsupervised algorithms like ART-2a clustering in terms of speed and accuracy of detecting a large variety of particle classes, facilitating real-time data analysis and immediate emergency response (Wang et al., 2023).
Beyond SPMS, we show first results from particle re-dispersion experiments with gas pulses and aerosol enrichment for direct sampling of dust residues from surfaces of luggage and parcels.
[1] Pratt, K.A. and Prather, K.A. (2012) Mass Spectrom. Rev. 31 , 17 48.
[2] Passig, J. et al. (2020) Atmos. Chem. Phys., 20, 7139–7152.
[3] Passig, J. et al. (2022) Atmos. Chem. Phys., 22, 1495–1514.
[4] Sultana, C. et al. (2017) Atmos. Meas. Tech., 10, 1323–1334.
[5] Wang, G. et al. (2023) submitted to Atmos. Meas. Tech.