abstract
- The field of environmental forensics emerged in the 1980s as a consequence of legislative frameworks enacted to enable parties, either states or individuals, to seek compensation with regard to contamination or injury due to damage to the environment. This legal environment requires stringent record keeping and defendable data therefore analysis can sometimes be confined to data to be obtained from certified laboratories using a standard accredited analytical method. Many of these methods were developed to target specific compounds for risk assessment purposes and not for environmental forensics applications such as source identification or age dating which often require larger data sets. The determination of persistent organic pollutants (POPs) for environmental forensic applications requires methods that are selective but also cover a wide range of target analytes which can be identified and quantified without bias. POPs are used in a wide variety of applications such as flame retardants, fire suppressants, heat transfer agents, surfactants and pesticides mainly because of their chemical inertness and stability. They also include compounds such as dioxins that can be unintentionally produced from industrial activities. POPs are persistent in the environment, bioaccumulative and/or toxic and therefore require analytical methods that are sensitive enough to meet the low detection limits needed for the protection of the environment and human health. A variety of techniques, procedures and instruments can be used which are well suited for different scenarios. Optimised methods are important to ensure that analytes are quantitatively extracted, matrix coextractables and interferences are removed and instruments are used most effectively and efficiently. This can require deviation from standard methods which can open the data up to further scrutiny in the courtroom. However, when argued effectively and strict QA/QC procedures are followed the development and optimization of methods based on investigation specific scenarios has the potential to generate better quality and more useful data.