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Geospatial visualisation of food contaminant distributions: Polychlorinated naphthalenes (PCNs), potentially toxic elements (PTEs) and aflatoxins
- Zhihua, Li, Gong, Yunyun, Holmes, Mel, Pan, Xiaoxi, Xu, Yiwei, Zou, Xiaobo, Fernandes, Alwyn R.
- Chemosphere 2019 v.230 pp. 559-566
- aflatoxins, biomarkers, blood serum, children, chlorinated naphthalenes, crabs, family size, food contamination, issues and policy, lakes, mackerel, risk assessors, risk reduction, socioeconomics, stakeholders, statistics, tissues, turbot, China, Tanzania, United Kingdom
- Large volume of multidimensional data can be summarised, both in terms of tabulated statistics, and as graphic geospatial visualisations. The latter approach allows rapid interpretation and communication of complex information to stake-holders such as regulators, risk assessors and policy makers. In the main study on polychlorinated naphthalene (PCN), individual samples representing different edible fish species were analysed from around the UK. PCNs were observed in all samples with nearly all of the twelve measured congeners being detected. Summed congener concentrations ranged from 0.7 ng/kg ww (turbot) to 265 ng/kg ww (sprats). The highest contamination levels were recorded for sprats and mackerel with mean summed concentrations of 67 ng/kg ww and 68 ng/kg ww respectively. Two ancillary studies, on potentially toxic elements (PTEs) in crabs from China and aflatoxin in children's blood from Tanzania, demonstrate the wide applicability of this approach. The PTE contents in crab showed strong dependence on the tested tissues and elements, and crabs from Tai and Yangcheng Lakes showed obviously higher PTE levels than the other lakes. Geospatial distribution of the aflatoxin biomarker AF-alb in children's serum from 3 locations showed how individual anthropometric or socio-economic data reveals the relationship between family size, socio-economic score and magnitude of serum aflatoxin levels. In addition to facilitating the flow of interpreted data to stakeholders, these techniques can direct the formulation of risk mitigation activities and help with the identification of data gaps. When combined with hierarchical cluster analyses, correlations within the data can also be predicted.