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Polycyclic aromatic hydrocarbons (PAH) are known carcinogens and are abundant in the environment and foodstuffs. Currently the majority of PAH research focuses on benzo[a]pyrene (BaP), although a much greater range of PAH are known to have detrimental effects to human health. Monitoring a large number of PAH is expensive, time consuming and analytically demanding, yet there is currently no clear basis for determining which PAH should be monitored to give an indication of overall exposure. A thorough statistical examination of the relationships between different PAH in different foodstuffs has not previously been carried out. Using a test dataset of homogenised edible flesh from shellfish samples as a case study a modelling process using principal components analysis regression is proposed to determine which PAH subset (from a total of 27 monitored PAH) should be assessed as indicators for general PAH exposure. Multivariate ordination and clustering show that PAH concentrations of compounds of similar chemical structure can be highly correlated in the samples, e.g. the five ringed isomers PAHs benzo[b]fluoranthene, benzo[j]fluoranthene and benzo[k]fluoranthene. The model selection process determined which subsets of PAH can be used to predict the presence and abundance of other PAHs in shellfish samples. Models were more accurate in predicating PAHs concentrations of PAH where concentrations were measured above the limit of detection (LoD). PAH with values below the LoD were harder to predict accurately. The current analysis highlights that laboratories should focus on the following PAHs BaP, benzo[a]anthracene, benzo[g,h,i]perylene, phenanthrene, benzo[g,h,i]fluoranthene, chrysene, benzo[k]fluoranthene, benzo[b]fluoranthene and fluoranthene when analysing shellfish samples. Focussing monitoring on this group of PAH may give a better indication of overall PAH content of samples that the summed PAH indicator methods currently adopted.
Polycyclic aromatic hydrocarbons (PAH) are known carcinogens and are abundant in the environment and foodstuffs. Currently the majority of PAH research focuses on benzo[a]pyrene (BaP), although a much greater range of PAH are known to have detrimental effects to human health. Monitoring a large number of PAH is expensive, time consuming and analytically demanding, yet there is currently no clear basis for determining which PAH should be monitored to give an indication of overall exposure. A thorough statistical examination of the relationships between different PAH in different foodstuffs has not previously been carried out. Using a test dataset of homogenised edible flesh from shellfish samples as a case study a modelling process using principal components analysis regression is proposed to determine which PAH subset (from a total of 27 monitored PAH) should be assessed as indicators for general PAH exposure. Multivariate ordination and clustering show that PAH concentrations of compounds of similar chemical structure can be highly correlated in the samples, e.g. the five ringed isomers PAHs benzo[b]fluoranthene, benzo[j]fluoranthene and benzo[k]fluoranthene. The model selection process determined which subsets of PAH can be used to predict the presence and abundance of other PAHs in shellfish samples. Models were more accurate in predicating PAHs concentrations of PAH where concentrations were measured above the limit of detection (LoD). PAH with values below the LoD were harder to predict accurately. The current analysis highlights that laboratories should focus on the following PAHs BaP, benzo[a]anthracene, benzo[g,h,i]perylene, phenanthrene, benzo[g,h,i]fluoranthene, chrysene, benzo[k]fluoranthene, benzo[b]fluoranthene and fluoranthene when analysing shellfish samples. Focussing monitoring on this group of PAH may give a better indication of overall PAH content of samples that the summed PAH indicator methods currently adopted.
Understanding the fate and effects of spilled oil or chemicals in the marine environment is essential if the scientific and response communities are to develop best practices for the future. The timely and successful deployment of survey specialists, sampling strategy and scientific techniques to monitor the environmental consequences of an incident can be a complex challenge and, in the same way as for spill response and clean-up, requires effective planning and coordination. However, the delivery of the necessary guidance, skilled personnel and coordinated management are often not pre-identified, are rarely in the same organisation and the levels of preparedness to undertake complex monitoring programmes are often low. This paper identifies the 8 principles of effective post-spill monitoring programmes as; Scientific Guidance, Skills & Knowledge, Equipment, Funding, Responsibility & Management, Integration & Coordination, Support & Buy-in and Practice. Drawing on experience from the Premiam (Pollution Response in Emergencies: Marine Impact Assessment and Monitoring; www.cefas.defra.gov.uk/premiam) programme in the United Kingdom these principles are described and the approaches taken and challenges faced in the UK to improve post-spill monitoring practices are outlined. This paper goes on to describe how these principles can be used as the basis for the assessment of monitoring preparedness through the generation of a Monitoring Preparedness Assessment Score (MPAS). This assessment approach can be used by local, regional or national authorities to establish the level of environmental monitoring and impact assessment preparedness for incidents in their areas and to highlight areas for improvement. In addition it is of use to responders, policy makers, environmental scientists and planners as a tool through which to assess preparedness and capability for specific scenarios. The use of the approach is demonstrated through the assessment of previous incidents and potential future scenarios.
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