Environmental forensic investigations: The potential use of a novel heavy metal sensor and novel taggants

This chapter presents a novel hand-held instrument capable of real-time in situ detection and identification of heavy metals, along with the potential use of novel taggants in environmental forensic investigations. The proposed system provides the facilities found in a traditional laboratory-based instrument but in a hand held design, without the need for an associated computer. The electrochemical instrument uses anodic stripping voltammetry, which is a precise and sensitive analytical method with excellent limits of detection. The sensors comprise a small disposable plastic strip of screen-printed electrodes rather than the more common glassy carbon disc and gold electrodes. The system is designed for use by a surveyor on site, allowing them to locate hotspots, thus avoiding the expense and time delay of prior laboratory analysis. This is particularly important in environmental forensic analysis when a site may have been released back to the owner and samples could be compromised on return visits. The system can be used in a variety of situations in environmental assessments, the data acquired from which provide a metals fingerprint suitable for input to a database. The proposed novel taggant tracers, based on narrow-band atomic fluorescence, are under development for potential deployment as forensic environmental tracers. The use of discrete fluorescent species in an environmentally stable host has been investigated to replace existing toxic, broadband molecular dye tracers. The narrow band emission signals offer the potential for tracing a large number of signals in the same environment. This will give increased data accuracy and allow multiple source environmental monitoring of environmental parameters.

A multi-capability sensor for hydrocarbons, synthetic-based fluids and heavy metals:Applications for environmental monitoring during removal of drill cuttings piles

As part of any drilling cuttings pile removal process the requirement for monitoring the release of contaminants into the marine environment will be critical. Traditional methods for such monitoring involve taking samples for laboratory analysis. This process is time consuming and only provides data on spot samples taken from a limited number of locations and time frames. Such processes, therefore, offer very restricted information. The need for improved marine sensors for monitoring contaminants is established. We report here the development and application of a multi-capability optical sensor for the real-time in situ monitoring of three key marine environmental and offshore/oil parameters: hydrocarbons, synthetic-based fluids and heavy metal concentrations. The use of these sensors will be a useful tool for real-time in situ environmental monitoring during the process of decommissioning offshore structures. Multi-capability array sensors could also provide information on the dispersion of contamination from drill cuttings piles either while they are in situ or during their removal.

The application of surface second harmonic sensor to probe laser induced modification of titanium dioxide

The effects of high power pulsed laser light on a TiO2 photocatalyst have been investigated using a surface second harmonic generation (SSHG) sensor. When TiO2 is irradiated with a laser at 355mm a visible change in colour from white to dark blue crystals was observed. X-ray diffraction studies indicate that the crystal structure of the TiO2 developed a more rutile form following laser exposure.

A methodology for sensor modeling and placement optimization to support temperature monitoring

This paper presents a modeling and optimization approach for sensor placement in a building zone that supports reliable environment monitoring. © 2012 ACM.

A Soft Sensor for Viscosity Control of Polymer Extrusion

Viscosity represents a key indicator of product quality in polymer extrusion but has traditionally been difficult to measure in-process in real-time. An innovative, yet simple, solution to this problem is proposed by a Prediction-Feedback observer mechanism. A `Prediction' model based on the operating conditions generates an open-loop estimate of the melt viscosity; this estimate is used as an input to a second, `Feedback' model to predict the pressure of the system. The pressure value is compared to the actual measured melt pressure and the error used to correct the viscosity estimate. The Prediction model captures the relationship between the operating conditions and the resulting melt viscosity and as such describes the specific material behavior. The Feedback model on the other hand describes the fundamental physical relationship between viscosity and extruder pressure and is a function of the machine geometry. The resulting system yields viscosity estimates within 1% error, shows excellent disturbance rejection properties and can be directly applied to model-based control. This is of major significance to achieving higher quality and reducing waste and set-up times in the polymer extrusion industry.

A hydrogel based zinc(ii) sensor for use in fluorescent multi-well plate analysis

A polymeric hydrogel containing a photoinduced electron transfer (PET) based probe for Zn(ii) has been formulated into the wells of a 96-well plate. Upon addition of Zn(ii) ions to selected wells, the fluorescence of the gel was observed to increase in a concentration dependent manner in the 0.25-1.75 mM range. The millimolar binding constant observed for this probe is higher than that reported for other Zn(ii) probes in the literature and offers the possibility to determine the concentration of this ion in environments where the Zn(ii) concentration is high. The combination of the multi-well plate set-up with fluorescence detection offers the possibility of high-throughput screening using low sample volumes in a timely manner. To the best of our knowledge, this is the first reported example of a polymeric hydrogel sensor for zinc with capability for use in fluorescence multi-well plate assay.

Fusion of Static and Temporal Information for Threat Evaluation in Sensor Networks

In many CCTV and sensor network based intelligent surveillance systems, a number of attributes or criteria are used to individually evaluate the degree of potential threat of a suspect. The outcomes for these attributes are in general from analytical algorithms where data are often pervaded with uncertainty and incompleteness. As a result, such individual threat evaluations are often inconsistent, and individual evaluations can change as time elapses. Therefore, integrating heterogeneous threat evaluations with temporal influence to obtain a better overall evaluation is a challenging issue. So far, this issue has rarely be considered by existing event reasoning frameworks under uncertainty in sensor network based surveillance. In this paper, we first propose a weighted aggregation operator based on a set of principles that constraints the fusion of individual threat evaluations. Then, we propose a method to integrate the temporal influence on threat evaluation changes. Finally, we demonstrate the usefulness of our system with a decision support event modeling framework using an airport security surveillance scenario.

Kinetics of methylene blue (MB) photocatalyzed reduction and dark regeneration in a colorimetric oxygen sensor

Performance data for a dye based, regenerable oxygen sensor (Mills and Lawrie [1], Mills et al. [2]) are analyzed to develop useful kinetic models for sensor photoactivation (dye reduction) and dark, oxygen detection (dye oxidation). The titania loaded, thin film sensor exhibits an apparent first order photoactivation of the dye, which we demonstrate (Section 3.2 and Fig. 4) is due to a kinetic disguise of a zero order photoreaction occurring through a non-uniformly illuminated sensor film. The observed zero order, slow recovery due to dye oxidation by dioxygen (O2 detection) appears best rationalized by a model assuming a near O2-impermeable skin developing on the sensor surface as solvent is evaporatively removed following sensor film casting and curing.

Context-dependent Combination of Sensor Information in Dempster-Shafer Theory for BDI

There has been much interest in the belief–desire–intention (BDI) agent-based model for developing scalable intelligent systems, e.g. using the AgentSpeak framework. However, reasoning from sensor information in these large-scale systems remains a significant challenge. For example, agents may be faced with information from heterogeneous sources which is uncertain and incomplete, while the sources themselves may be unreliable or conflicting. In order to derive meaningful conclusions, it is important that such information be correctly modelled and combined. In this paper, we choose to model uncertain sensor information in Dempster–Shafer (DS) theory. Unfortunately, as in other uncertainty theories, simple combination strategies in DS theory are often too restrictive (losing valuable information) or too permissive (resulting in ignorance). For this reason, we investigate how a context-dependent strategy originally defined for possibility theory can be adapted to DS theory. In particular, we use the notion of largely partially maximal consistent subsets (LPMCSes) to characterise the context for when to use Dempster’s original rule of combination and for when to resort to an alternative. To guide this process, we identify existing measures of similarity and conflict for finding LPMCSes along with quality of information heuristics to ensure that LPMCSes are formed around high-quality information. We then propose an intelligent sensor model for integrating this information into the AgentSpeak framework which is responsible for applying evidence propagation to construct compatible information, for performing context-dependent combination and for deriving beliefs for revising an agent’s belief base. Finally, we present a power grid scenario inspired by a real-world case study to demonstrate our work.