Microplastic pollution research has suffered from inadequate data and tools for spectral (Raman and infrared) classification. Spectral matching tools often are not accurate for microplastics identification and are cost-prohibitive. Lack of accuracy stems from the diversity of microplastic pollutants, which are not represented in spectral libraries. Here, we propose a viable software solution: Open Specy. Open Specy is on the web (www. openspecy.org) and in an R package. Open Specy is free and allows users to view, process, identify, and share their spectra to a community library. Users can upload and process their spectra using smoothing (Savitzky−Golay filter) and polynomial baseline correction techniques (IModPolyFit). The processed spectrum can be downloaded to be used in other applications or identified using an onboard reference library and correlation-based matching criteria. Open Specy's data sharing and session log features ensure reproducible results. Open Specy houses a growing library of reference spectra, which increasingly represents the diversity of microplastics as a contaminant suite. We compared the functionality and accuracy of Open Specy for microplastic identification to commonly used spectral analysis software. We found that Open Specy was the only open source software and the only software with a community library, and Open Specy had comparable accuracy to popular software (OMNIC Picta and KnowItAll). Future developments will enhance spectral identification accuracy as the reference library and functionality grows through community-contributed spectra and community-developed code. Open Specy can also be used for applications beyond microplastic analysis. Open Specy's source code is open source (CC-BY-4.0, attribution only) (https://github.com/wincowgerDEV/ OpenSpecy).

Urban areas are the primary source of human-made litter globally, and roadsides are a primary accumulation location. This study aimed to investigate how litter arrives at roadsides and determine the accumulation rate and composition of roadside litter. We monitored select roadsides in the Inland Empire, California, for litter abundance (count) and composition (material, item, and brand type). Receipt litter with sale time and location information was used to investigate whether wind, runoff, or human travel were dominant transport agents. Only 9% of the receipts could have experienced runoff, and wind direction was not correlated with receipt transport direction. However, human travel and receipt transport distances were similar in magnitude and distribution, suggesting that the displacement of litter from the place of purchase was predominantly affected by human travel. The median distance receipts traveled from the sale location to the litter observation location was 1.6 km, suggesting that most sources were nearby to where the litter was found. Litter accumulation rates were surprisingly stable (mean 40,349 (33,255-47,865) #/km/year or 1170 (917-1447) kg/km/year) despite repeated cleanups and the COVID-19 stay-at-home orders. A new approach was employed to hierarchically bootstrap litter composition proportions and estimate uncertainties. The most abundant materials were plastic and paper. Food-related items and tobacco products were the most common item types. The identified branded objects were from the primary manufacturers (Philip Morris (4, 2-7 %), Mars Incorporated (2, 1-3 %), RJ Reynolds (2, 1-3 %), and Jack in The Box (1, 1-3 %)), but unbranded objects were prevalent. Therefore, identifiable persistent labeling on all products would benefit future litter-related corporate social responsibility efforts. High-resolution monitoring on roadsides can inform urban litter prevention strategies by elucidating litter source, transport, and accumulation dynamics.

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.

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