A modular computational framework for integrated microplastic data analysis and environmental monitoring

Microplastic pollution has emerged as a major environmental concern, increasing the demand for reliable computational tools capable of supporting standardized quantification and interpretation of contamination data. However, current microplastic assessment workflows are often fragmented, separating particle measurement, dimensional aggregation, and statistical analysis into independent procedures, which reduces reproducibility, transparency, and analytical integration. To address these limitations, this study presents IMPa, an open-source computational framework implemented in Python for integrated microplastic data analysis and environmental monitoring. The proposed framework combines count-based concentration (MP), cumulative length metrics (IMPa-L), and projected area metrics (IMPa-A) within a unified architecture that supports automated normalization, morphological stratification, multilevel aggregation, dimensional distribution analysis, matrix-based representation, and embedded multivariate analysis through Principal Component Analysis (PCA). The system was designed using a modular architecture that ensures traceability from particle-level measurements to aggregated environmental indicators while facilitating extensibility and reproducibility. The framework was evaluated using a demonstrative dataset obtained from a tidally influenced river and computational benchmark datasets containing up to 1,000,000 particles. Results showed that dimension-based indices captured variability not detected by count-based concentration alone, highlighting structural differences among aggregation schemes. The embedded PCA explained 91.9% of the total variance in the first two principal components, demonstrating the framework’s ability to support exploratory interpretation of multidimensional microplastic datasets. Performance benchmarking confirmed near-linear computational scalability (O(n)) in both execution time and memory consumption, with successful processing of datasets containing up to one million particle records. By integrating measurement, aggregation, visualization, and statistical exploration within a single computational environment, the proposed framework provides a reproducible and transparent solution for dimension-integrated microplastic assessment. The software is distributed under the MIT License, publicly available on GitHub, and archived with a persistent DOI (10.5281/zenodo.18386718), ensuring long-term accessibility and reproducibility.