Workflow-Dependent Observables in Mass Spectrometry-Based Micro- and Nanoplastic Analysis: Implications for Comparability and Harmonization.

Micro- and nanoplastic (MNP) measurements are increasingly used to assess environmental occurrence and biological exposure, yet the relationship between analytical outputs and the original physical form of polymeric material remains inconsistently defined across analytical workflows. Here, we reframe mass spectrometry-based MNP measurements as workflow-dependent observables rather than direct measures of particulate abundance. We show that analytical workflows partition heterogeneous samples into two distinct measurement domains depending on whether particulate structure is preserved prior to chemical analysis. Workflows incorporating structure-preserving operations (e.g., size or density fractionation followed by pyrolysis-GC-MS) generate particle-resolved-chemical (PRC) outputs linked to defined particle subsets, whereas structure-collapsing transformations (e.g., extraction, thermal desorption, or bulk pyrolysis) generate chemically integrated signals independent of original particle identity. These domains encode fundamentally different information and are not mutually invertible: multiple physically distinct particulate systems can produce indistinguishable chemically integrated outputs. This non-invertibility arises from workflow architecture rather than instrumental uncertainty, limiting direct comparison of polymer-derived measurements across environmental and biological matrices. To formalize this distinction, we introduce Polymer-Associated Chemical Signals (PACS) as an operational descriptor for outputs generated by chemically integrated workflows. PACS distinguishes transformation-derived chemical signals from particle-resolved measurements and defines the inferential scope of chemically integrated data without attributing particle-specific properties. Recognizing analytical outputs as workflow-dependent observables shifts harmonization from standardizing analytical performance alone to recognizing workflow-defined analytical observables, providing a conceptual foundation for workflow-aware reporting, evidence synthesis, and regulatory interpretation of mass spectrometry-based MNP measurements.