Artículo

Nine decades of data on environmental chemical pollutant exposure in dogs: a bibliometric analysis

Resumen

In recent decades, a global concern associated with environmental chemical contamination has emerged as an important risk factor for the development of human diseases. Risk assessment methods based on animal approaches have shown to be very useful as early warning systems. However, questions, knowledge gaps, and limitations still need to be addressed in animals close to humans, such as dogs. The objective of this study was to analyze citation patterns, impact of publications, and most relevant authors, countries, institutional affiliations, and lines of research on environmental chemical contaminants and their relationship with dogs, in terms of exposure and biological effects. For this, a bibliometric analysis was carried out. Results revealed an increase in scientific production on this subject during the last 90 years in journals such as Health Physics, Science of the Total Environment, and Plos One, highlighting authors such as Muggenburg, Sonne, Boecker, and Dietz. The USA, Brazil, Germany, and the UK and universities such as California, Colorado State, and Purdue were the most relevant countries and institutional affiliations in scientific production and collaboration in relation to this topic. There is a growing interest in the development of lines of research related to heavy metals (mercury and lead mainly) and persistent organic compounds (PCBs, PBDEs, pesticides) using dogs as sentinels, as well as new sources of interest related to zoonosis and One Health. Finally, issues related to pollutants, sentinel lymph nodes, and epidemiology appear as new areas of research. These results highlight interesting current challenges and future research perspectives on dogs as sentinels for environmental chemical contamination.
Avila, Albert (57799669900); Prieto, Laura (57800772800); Luna-Acosta, Andrea (35196659100)
Nine decades of data on environmental chemical pollutant exposure in dogs: a bibliometric analysis
2023
10.1007/s11356-022-24791-y
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148438751&doi=10.1007%2fs11356-022-24791-y&partnerID=40&md5=e3cfe1419bb8bd9ff733bb623a976dd2
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