Use this URL to cite or link to this record in EThOS:
Title: Forensic detection of explosives in the wastewater system : implications for intelligence gathering
Author: Gamble, S. C.
ISNI:       0000 0004 8498 5715
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2016
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
Forensic evidence has traditionally been used in the detection of crime. However, the potential for such evidence to be used for the prevention or disruption of crimes has not yet been fully realised. There is significant potential for measuring trace levels of explosives in the wastewater system to offer a viable form of forensic intelligence to inform on-going criminal and counter-terrorism investigations. This research addresses the need to provide an empirical evidence base for the monitoring of trace explosives, utilising the wastewater analysis approach to contribute to identifying the provenance of the illegal manufacture of homemade explosives (HMEs) for use in improvised explosive devices. Building upon the well-established approach of wastewater analysis for illicit drug consumption estimates and other important emerging pollutants in the environment, this work identifies the potential for trace explosives detection in situ in the sewerage network by identifying key field- and lab-based methods for this purpose. This research presents the development of solid phase extraction methods for the analysis of trace explosives in influent wastewater samples and the development of liquid-chromatography-mass spectrometry methods for the quantification of trace levels of hexamethylene triperoxide diamine (HMTD) and pentaerythritol tetranitrate (PETN). In addition, the use of passive sampling devices for the collection, pre-concentration and extraction of trace explosives as an alternative to the frequently used grab sampling and solid phase extraction methods is explored. The implementation of these methodologies to achieve 'forensic intelligence' for the prevention and disruption of criminal activity is also explored with examples of how this data could be mapped in future work using electronic data and predictive modelling. The implications for incorporating such findings with other forms of intelligence to determine attribution are addressed.
Supervisor: Morgan, R. ; Campos, L. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available