Data from: A local-to-global emissions inventory of macroplastic pollution
Data files
Jul 19, 2024 version files 1.45 GB
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Cottom_et_al_Nature_Published.zip
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README.md
Abstract
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.
README: Supplementary files for A local-to-global emissions inventory of macroplastic pollution
Authors:
Joshua W. Cottom - J.W.Cottom@leeds.ac.uk
School of Civil Engineering, University of Leeds, Leeds, LS2 9JT, UK
https://orcid.org/0000-0002-3480-3982
Ed Cook - E.R.Cook@leeds.ac.uk
School of Civil Engineering, University of Leeds, Leeds, LS2 9JT, UK
https://orcid.org/0000-0003-3902-7705
Costas Velis (correspondence) - C.Velis@leeds.ac.uk
School of Civil Engineering, University of Leeds, Leeds, LS2 9JT, UK
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
Methods
Detailed information on how this dataset was collected and processed is available in the Supplementary Materials, associated with the publication.
Usage notes
R studio and MSExcel were used for the analyses.