Human Biomonitoring of Selected Hazardous Compounds in Portugal: Part I-Lessons Learned on Polycyclic Aromatic Hydrocarbons, Metals, Metalloids, and Pesticides.
Angelina PenaSofia C DuarteAndré M P T PereiraLiliana J G SilvaCélia S M LaranjeiroMarta OliveiraCeleste LinoSimone MoraisPublished in: Molecules (Basel, Switzerland) (2021)
Human biomonitoring (HBM) data provide information on total exposure regardless of the route and sources of exposure. HBM studies have been applied to quantify human exposure to contaminants and environmental/occupational pollutants by determining the parent compounds, their metabolites or even their reaction products in biological matrices. HBM studies performed among the Portuguese population are disperse and limited. To overcome this knowledge gap, this review gathers, for the first time, the published Portuguese HBM information concerning polycyclic aromatic hydrocarbons (PAHs), metals, metalloids, and pesticides concentrations detected in the urine, serum, milk, hair, and nails of different groups of the Portuguese population. This integrative insight of available HBM data allows the analysis of the main determinants and patterns of exposure of the Portuguese population to these selected hazardous compounds, as well as assessment of the potential health risks. Identification of the main difficulties and challenges of HBM through analysis of the enrolled studies was also an aim. Ultimately, this study aimed to support national and European policies promoting human health and summarizes the most important outcomes and lessons learned through the HBM studies carried out in Portugal.
Keyphrases
- human health
- polycyclic aromatic hydrocarbons
- risk assessment
- endothelial cells
- climate change
- case control
- heavy metals
- induced pluripotent stem cells
- electronic health record
- drinking water
- randomized controlled trial
- adipose tissue
- big data
- health risk assessment
- metabolic syndrome
- insulin resistance
- high resolution
- skeletal muscle
- data analysis
- artificial intelligence
- life cycle
- deep learning