The worldwide growth in the production of plastics and lack of appropriate waste management has led to unsustainable environmental pollution of global concern. Hence, determining the key drivers characterizing the degradation of polymers in water is a rising topic. Here we propose a workflow based on advanced high throughput data analysis and processing tools aiming at the characterization of (a) the degradation products of polymers exposed to an aquatic environment, and (b) the taxonomical composition of the potential microbial consumers' consortia. To test this approach, polycaprolactonediol (PCLD) polymer probes were exposed to different wastewater environments (influent, aerobic, and anaerobic denitrifying reactors) in four different wastewater treatment plants (WWTPs). Probes were extracted and analyzed by non-target liquid chromatography-high resolution mass spectrometry (LC-HRMS) and the acquired data were processed using advanced chemometric tools (Regions of Interest-Multivariate Curve Resolution-Alternate Least Squares, ROIMCR-ALS). Up to 26 components explaining ca. 69% of the variance were resolved, and 20 of these were further identified. Degradation was dominated by two processes: (a) hydrolysis of the ester groups present in PCLD and (b) formation of cyclic polycaprolactone oligomers. These transformation pathways occurred regardless of what were the WWTPs and the treatment stages, varying only the respective concentrations of the formed by-products. The composition of the microbial communities attached to the polymer and free-living in the surrounding water was characterized by 16S rDNA amplicon sequencing analysis. The two communities showed taxonomic differences and were unique in each WWTP treatment stage. Polymer-attached communities were tentatively proposed as potential consumers. This work has been supported by the Spanish Ministry of Science and Innovation (MCIN) Project WATERPROT ID2019-105732GB funded by MCIN/ AEI /10.13039/501100011033 and by the Severo Ochoa Grant CEX2018-000794-S funded by MCIN/AEI/ 10.13039/501100011033. C. Pérez-López acknowledges the predoctoral scholarship Severo Ochoa FPI 2019-090182 included in the Grant CEX2018-000794-S funded by MCIN/AEI/ 10.13039/501100011033. Dr. Daniel Lundin is acknowledged for support in bioinformatic analyses. Peer reviewed