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Outdoor human decomposition in Sweden: A retrospective quantitative study of forensic-taphonomic changes and postmortem interval in terrestrial and aquatic settings.

Clara AlfsdotterAnja Petaros
Published in: Journal of forensic sciences (2021)
This paper presents a quantitative retrospective study of gross human decomposition in central and southeastern Sweden. The applicability of methods developed abroad for postmortem interval (PMI) estimation from decomposition morphology and temperature are is evaluated. Ninety-four cases were analyzed (43 terrestrial and 51 aquatic) with a median PMI of 48 days. The results revealed differences in decomposition patterns between aquatic, surface, hanging, and buried remains. While partial saponification and desiccation occurred in cases of surface remains, complete skeletonization was observed in all cases with a PMI over two years. Aquatic skeletonization was slower due to extensive saponification in cases with PMI higher than one year. Formulae for assessing accumulated degree-days (ADD) from the original methods did not fit the study material. However, a regression analysis demonstrated that 80% of decomposition variance in surface remains could be explained by ADD, suggesting that a geographically adapted equation holds promise for assessing PMI. In contrast, the model fit was poor for aquatic cases (43%). While this may be explained by problems in obtaining reliant aquatic temperature data or an insufficient scoring system, aquatic decomposition may be highly dependent on factors other than ADD alone. This study evaluates the applicability of current PMI methods on an outdoor sample from a previously unpublished region, and represents the first scientific publication of human outdoor decomposition patterns in Sweden. Suggestions for future research are provided, including that scoring methods should incorporate saponification to fit forensic taphonomy in Swedish environments.
Keyphrases
  • risk assessment
  • endothelial cells
  • air pollution
  • magnetic resonance
  • computed tomography
  • big data
  • mass spectrometry
  • machine learning
  • artificial intelligence