DOI: 10.5061/dryad.8cz8w9gxb
title: Data from: A local-to-global emissions inventory of macroplastic pollution

description: Negotiations for a Global Treaty on plastic pollution will shape future
 policies on plastics production, use, and waste management. Its parties
 will benefit from a high-resolution baseline of waste flows and plastic
 emission sources to enable identification of pollution hotspots and their
 causes. Nationally aggregated waste management data can be distributed to
 smaller scale to identify generalised points of plastic accumulation and
 source phenomena. However, it is challenging to use this type of spatial
 allocation to assess the conditions under which emissions take place. To
 this, we develop a global macroplastic pollution emissions inventory by
 combining conceptual modelling of emission mechanisms with measurable
 activity data. We define emissions as materials that have moved from the
 managed or mismanaged system (controlled or contained state) to the
 unmanaged system (uncontrolled or uncontained state - the environment).
 Using machine learning and probabilistic material flow analysis we
 identify emission hotspots across 50,702 municipalities worldwide from
 five land-based plastic waste emission sources. We estimate global plastic
 waste emissions at 52.1 [48.3-56.3] million metric tonnes (Mt) per year,
 with approximately 57% wt. and 43% wt. open burned and unburned debris
 respectively. Littering is the largest emission source in the Global
 North, whereas uncollected waste is the dominant emissions source across
 the Global South. We suggest that our findings can help inform Treaty
 negotiations and develop national and sub-national waste management action
 plans and source inventories.
description: Detailed information on how this dataset was
 collected and processed is available in the Supplementary Materials,
 associated with the publication.
description: R studio and MSExcel were used for the analyses.
description: # Supplementary files for A local-to-global emissions inventory of
 macroplastic pollution **Authors:**  Joshua W. Cottom -
 [J.W.Cottom@leeds.ac.uk](mailto:J.W.Cottom@leeds.ac.uk) School of Civil
 Engineering, University of Leeds, Leeds, LS2 9JT, UK
 [https://orcid.org/0000-0002-3480-3982](https://orcid.org/0000-0002-3480-3982)  Ed Cook - [E.R.Cook@leeds.ac.uk](mailto:E.R.Cook@leeds.ac.uk)  School of Civil Engineering, University of Leeds, Leeds, LS2 9JT, UK [https://orcid.org/0000-0003-3902-7705](https://orcid.org/0000-0003-3902-7705) Costas Velis (correspondence) - [C.Velis@leeds.ac.uk](mailto:C.Velis@leeds.ac.uk) School of Civil Engineering, University of Leeds, Leeds, LS2 9JT, UK [https://orcid.org/0000-0002-1906-726X](https://orcid.org/0000-0002-1906-726X) **MODEL VERSION:** Spatio-temporal plastic pollution origins and transport model (SPOT) V1.1.0-G-1223 ### **DESCRIPTION:** Data and modelling files for SPOT V1.1.0-G-1223 as presented in the manuscript "A local-to-global emissions inventory of macroplastic pollution" published in the journal Nature. All modelling should be coordinated with the details provided in the supplementary information. #### FILE STRUCTURE & DESCRIPTION: Files are presented in two main folders: 1\. Supplementary_Data 2\. SPOT_V1.1.0-G-1223 The **Supplementary_Data** folder contains the essential supplementary data as referred to in the manuscript and supplementary information. Data sheets are in Microsoft Excel format and are formatted for accessibility. Thecolumn units and abbreviations are provided in the data sheets as additional header rows and as 'legend'sheets where applicable (SD02 - SD05). SD_01_Cottom_et_al_V1.1.0-G-1223_SPOT_Data_Cleaning.xlsx: ``` Steps undertaken to check data for errors, clean, harmonize and allocate to spatial vectors. ``` SD_02_Cottom_et_al_V1.1.0-G-1223_SPOT_System_of_Equations.xlsx: ``` System of equations for the material flow system. ``` SD_03_Cottom_et_al_V1.1.0-G-1223_SPOT_MFA_Outputs_National.xlsx: ``` Data predicted by probabilistic MFA at municipal level are aggregated to national level. Basic statistics (mean, 5th percentile, lower quartile, median, upper quartile & 95th percentile) presented. ``` SD_04_Cottom_et_al_V1.1.0-G-1223_SPOT_MFA_Outputs_Global_Regional_Income.xlsx: ``` Data predicted by probabilistic MFA at municipal level are aggregated by total (global), UN region, UN sub-region, OECD region, World Bank income category. Basic statistics (mean, 5th percentile, lower quartile, median, upper quartile & 95th percentile) presented. ``` SD_05_Cottom_et_al_V1.1.0-G-1223_SPOT_MFA_Outputs_Municipal.xlsx: ``` Data predicted by probabilistic MFA at municipal level. Basic statistics (mean, 5th percentile, lower quartile, median, upper quartile & 95th percentile) presented. ``` SD_06_Cottom_et_al_V1.1.0-G-1223_SPOT_MFA_Inputs.xls: ``` Input data for probabilistic MFA ``` The **SPOT_V1.1.0-G-1223** folder contains all necessary modelling files (data inputs, R code and outputs) for reproducibility of results. The input files are formatted to display the column units and provide description of any acronyms, whereas the output files are left unformatted to directly reproduce the format  of the outputs as produced by the scripts. It is divided into three sub-folders relating to the main modelling stages: 01_Imputation: ``` All data and code necessary for the imputation of independent variables across all municipalities ``` 02_Random_Forest: ``` All data and code necessary for quantile regression random forest for each dependent variable. ``` 03_MFA: ``` All data and code necessary for running the probabilistic material flow analysis at both municipal and aggregated levels. Each of these three folder is further subdivided in three folders of inputs, script and outputs as shown in the file tree directory below. The MFA folder outputs are the unformatted equivalents of the formatted data shown in the Supplementary data tables SD03 - SD_05. ``` The SPOT_V1.1.0-G-1223 folder contains all necessary modelling files (data inputs, R code and unformatted outputs) for reproducibility of results. It is divided into three sub-folders relating to the main modelling stages: Each of these three folder is further subdivided in three folders of inputs, script and outputs as shown in the file tree directory below. **DIRECTORY** **Cottom_et_al.zip** | +---**Supplementary_Data** \| SD_01_Cottom_et_al_V1.1.0-G-1223_SPOT_Data_Cleaning.xlsx \| SD_02_Cottom_et_al_V1.1.0-G-1223_SPOT_System_of_Equations.xlsx \| SD_03_Cottom_et_al_V1.1.0-G-1223_SPOT_MFA_Outputs_National.xlsx \| SD_04_Cottom_et_al_V1.1.0-G-1223_SPOT_MFA_Outputs_Global_Regional_Income.xlsx \| SD_05_Cottom_et_al_V1.1.0-G-1223_SPOT_MFA_Outputs_Municipal.xlsx \| SD_06_Cottom_et_al_V1.1.0-G-1223_SPOT_MFA_Inputs.xlsx | \\---**SPOT_V1.1.0-G-1223** ``` +---**01_Imputation** \| +---inputs \| | Global_Municipalities_for_imputation.xlsx \| | \| +---outputs \| | Global_Municipalities_imputed.xlsx \| | \| \\---script \| Imputation.R \| +---**02_Random_Forest** \| +---inputs \| | RF_Cleaned_Inputs.xlsx \| | \| +---outputs \| | Col_cov_CV_errors.csv \| | Col_cov_CV_predictions.csv \| | Col_cov_errors.csv \| | Col_cov_RF \| | Col_cov_testing_comparison.csv \| | Col_cov_training_comparison.csv \| | Cont_disp_CV_errors.csv \| | Cont_disp_CV_predictions.csv \| | Cont_disp_errors.csv \| | Cont_disp_RF \| | Cont_disp_testing_comparison.csv \| | Cont_disp_training_comparison.csv \| | Form_dry_recy_CV_errors.csv \| | Form_dry_recy_CV_predictions.csv \| | Form_dry_recy_errors.csv \| | Form_dry_recy_RF \| | Form_dry_recy_testing_comparison.csv \| | Form_dry_recy_training_comparison.csv \| | Incin_CV_errors.csv \| | Incin_CV_predictions.csv \| | Incin_errors.csv \| | Incin_RF \| | Incin_testing_comparison.csv \| | Incin_training_comparison.csv \| | Other_recv_CV_errors.csv \| | Other_recv_CV_predictions.csv \| | Other_recv_errors.csv \| | Other_recv_RF \| | Other_recv_testing_comparison.csv \| | Other_recv_training_comparison.csv \| | Plastic_MSW_CV_errors.csv \| | Plastic_MSW_CV_predictions.csv \| | Plastic_MSW_errors.csv \| | Plastic_MSW_RF \| | Plastic_MSW_testing_comparison.csv \| | Plastic_MSW_training_comparison.csv \| | Waste_gen_rate_CV_errors.csv \| | Waste_gen_rate_CV_predictions.csv \| | Waste_gen_rate_errors.csv \| | Waste_gen_rate_RF \| | Waste_gen_rate_testing_comparison.csv \| | Waste_gen_rate_training_comparison.csv \| | \| \\---script \| Random_Forest.R \| \\---**03_MFA** +---inputs \| Col_cov_RF \| Cont_disp_RF \| Form_dry_recy_RF \| Incin_RF \| Monte Carlo MFA Inputs.xlsx \| Other_recv_RF \| Plastic_MSW_RF \| Waste_gen_rate_RF \| +---outputs \| SPOT_MFA_Municipal_results_V1.1.0-G-1223_5000i.xlsx \| SPOT_MFA_National_results_V1.1.0-G-1223_5000i.xlsx \| SPOT_MFA_Other_aggregation_results_V1.1.0-G-1223_500i.xlsx \| SPOT_Sensitivity_analysis_results_V1.1.0-G-1223_5000i.rds \| \\---script 0-MasterScript.R 1-LoadInputs.R 2-Templates.R 3-Predictions.R 4a-DefineMunicipalMFA.R 4b-SystemofEquations.R 4c-DefineCoeffsOutputs.R 4d-DefineSummaryFunction.R 4e-DefineSensitivityAnalysis.R 5-RunMFA.R 6-AggregateMFA.R ```