Improving monitoring, analysis and reporting to assess plastic pollution: a matter of comparability

It has been two decades since scientists started reporting microplastic data in the marine environment. During that time, research on plastic pollution in aquatic systems has evolved rapidly and expanded from the ocean to upstream sources in the river basins. Despite the progress made in acquiring new data and knowledge, the issue of harmonizing methodologies for monitoring, analysis and reporting plastic pollution remains open, hindering data comparison. In the case of microplastic studies, intrinsic questions persist nowadays, e.g., representativeness of samples, minimum and maximum size of items, item size distributions, contamination of samples, meaningful polymer analyses, etc., although these issues were identified a decade ago [1] . In this work, we assessed current issues related to monitoring, analysis and reporting plastic pollution, based on a global literature review (ca. 600 studies) via the Riverine Litter Database (RLDB) implemented under the Horizon Europe Project INSPIRE, and propose a ‘requirement list’ on how to process field data to improve reporting for comparability of results.We identified that, during monitoring, sampling size was frequently not adapted to answer the scientific question in place, meaning the samples were too small to cover in a representative way the selected size ranges (micro-, meso-, and macroplastic), hindering assessment of both spatial and temporal variability. Analyses were often incomplete, lacking essential information such as particle size distribution and polymer identification based on statistical requirements. As a general overview, we highlight that, besides the quality of the monitoring and analysis methodologies, data reporting was missing important metadata and data in many studies. Some of that missing information would imply elementary data, like GPS location, date, sample size and number of particles identified per sample. Furthermore, a large part of our ‘requirement list’ for data reporting was mostly not accessible or had not been considered during the sample analyses, which would include reporting on particle size and mass distributions, concentrations per size bins (beyond distinguishing only among micro-, meso- and macroplastics concentrations), or making accessible raw data at particle level for microplastics or harmonised item classification for macroplastics. Such details would facilitate framing the significance of the results of each study and improve comparability. In INSPIRE, we implement a data processing framework following a common guideline with elementary and advance requirements for data harmonization to improve reporting of results for extended comparability, making existing data more accessible and reusable.