A framework for assessing human internal microplastic exposure through dataset alignment and quality screening.

Determining human microplastic exposure from external intake estimates combined with biodistribution modelling remains highly uncertain because errors propagate across multiple pathways and assumptions. We therefore present a framework for estimating internal human microplastic exposure directly from published measurements in organs, tissues, and bodily fluids. The framework integrates dataset selection, QA/QC screening, and an updated mathematical alignment method incorporating statistical fitting, goodness-of-fit testing, and comparison of alternative particle-size distributions to support cross-compartment comparison and whole-body inference. As a proof of principle, we applied this approach to published human microplastic particle data under two scenarios: an 'as measured' scenario and a 'biologically plausible' scenario in which particle size distributions were truncated at literature-based accessibility limits. The scenarios produced strongly divergent mean estimates: 159.4 mg (80.9-824; parametric 95 % Monte Carlo interval) under the 'as measured' scenario and 0.41 mg (0.22-1.56; parametric 95 % Monte Carlo interval) for the truncated 'biologically plausible' scenario. This 388-fold divergence shows that current quantitative inference is dominated by assumptions about the treatment of unusually large particles and data quality. The values should therefore be interpreted as scenario-bounded framework outputs conditional on the currently available, clinically biased dataset, and not as estimates representative of healthy populations or the general population. The framework also yielded apparent, size-dependent organ-to-blood ratios that may serve as provisional benchmarks or hypothesis-generating boundary conditions for future physiologically based kinetic (PBK) model development, but not yet as validated tissue-partitioning constants. Although current data quality limits regulatory-grade quantification, the framework provides a transparent basis for exposure assessment using human monitoring data, for identifying key uncertainties, and for illustrating potential future regulatory application.