Trash Taxonomy Tool: harmonizing classification systems used to describe trash in environments

Abstract

Despite global efforts to monitor, mitigate against, and prevent trash (mismanaged solid waste) pollution, no harmonized trash typology system has been widely adopted worldwide. This impedes the merging of datasets and comparative analyses. We addressed this problem by 1) assessing the state of trash typology and comparability, 2) developing a standardized and harmonized framework of relational tables and tools, and 3) informing practitioners about challenges and potential solutions. We analyzed 68 trash survey lists to assess similarities and differences in classification. We created comprehensive harmonized hierarchical tables and alias tables for item and material classes. On average, the 68 survey lists had 20.8% of item classes in common and 29.9% of material classes in common. Multiple correspondence analysis showed that the 68 surveys were not significantly different regarding organization type, ecosystem focus, or substrate focus. We built the Trash Taxonomy Tool (TTT) web-based application with query features and open access at openanalysis.org/trashtaxonomy. The TTT can be applied to improve, create, and compare trash surveys, and provides practitioners with tools to integrate datasets and maximize comparability. The use of TTT will ultimately facilitate improvements in assessing trends across space and time, identifying targets for mitigation, evaluating the effectiveness of prevention measures, informing policymaking, and holding producers responsible.

Polymer Characterization of Submerged Plastic Litter from Lake Tahoe, United States

“…Litter was sorted into categories using the United Nations Environmental Protection/Intergovernmental Oceanographic Commission (UNEP/IOC) marine litter scheme for plastic. 43 Much like other litter sorting approaches (e.g., Hapich et al 7 ) litter was first sorted into a material class (e.g., metal, plastic, rubber, etc. ), and then into litter subcategories by item type (e.g., bottles < 2 L, bottle caps, etc.).…”

“…Plastic litter monitoring approaches reflect the scientific, management, and/or policy questions to be addressed. [5][6][7] Generally, plastic litter monitoring efforts collect data on litter typology (e.g., bottle, cap, bag, etc. ), count, weight, volume, and site information.…”

“…), count, weight, volume, and site information. 1,7 Methodologies for monitoring pelagic plastics typically include using a net or trawl, or visually counting floating plastic litter. 5,[8][9][10][11][12][13] Monitoring benthic (or submerged) plastics is more difficult and has been achieved through bottom trawl surveys, self-contained underwater breathing apparatus (SCUBA) diving surveys, or remotely operated vehicle (ROV) surveys, as well as other surveying approaches.…”

Plastic as a Sediment – A Universal and Objective practical solution to growing ambiguity in plastic litter classification schemes

“…Even within internally consistent studies, the findings differ depending on if the item classification is executed via item category, item material, or item function (Vriend et al, 2020). A unified approach would contribute to a universal perception of global plastic pollution and its consequences (Hartmann et al, 2019;Kooi & Koelmans, 2019;Hapich et al, 2022), with advantages on a multi-disciplinary and multi-regional scale (van Calcar & van Emmerik, 2019). These advances are critical for better predicting the environmental behavior of plastic and the global distribution of plastic litter (e.g.…”

“…plastic bottle), rather than on the basis of their geometrical shape (e.g. OSPAR Commission 2010;van Emmerick et al, 2020;Hapich et al, 2022). If plastic items cannot be identified, or if they are too small, they are typically described using a nomenclature that has been established over the past decades, consisting of fragments, granules, pellets or nurdles, spheres or spherules, beads, foams, filaments, fibres, films, and flakes (Hidalgo-Ruz et al, 2012;European Commission, 2013;Zhang et al, 2017;Chubarenko et al, 2018;Hartmann et al, 2019;Rochman et al, 2019) (Fig.…”