Dynamic probabilistic material flow analysis (DPMFA) model for the Netherlands and the EU. Based on code by EMPA and data in collaboration with TNO.
Author: Institute of Public Health and the Environment (RIVM)
This model is used to calculate micro- and macroplastic emissions for certain product groups and polymers to the environment. The input needed to run the model is present in MainInputfile.xlsx. The plastic emissions to environmental sinks are calculated over time and probabilistically by using Monte Carlo simulations.
- Python (version 3.11.7)
- Numpy (version 1.26.4)
- Pandas (version 2.4.1)
- dpmfa (version 1.1)
- sqlite (version 3.41.2)
- Anaconda (needed to create and run batch files)
To run the model, please follow these steps:
-
Change the directory at the top of the main.py, write_metadata.py and CaseStudy_Runner.py scripts to the folder where the model code is located on your computer.
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In Create_batch_files.py, change the directory in line 65 to the directory where programs are installed on your computer. Usually this is :/C, so this probably does not need to be changed.
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Set the parameters to your preferences in config.py. Comment line 12 or 13 depending on your operating system. Comment line 16 or 17 depending on whether you want to run a dynamic probabilistic MFA or a probabilistic MFA. Comment line 20 or 21 depending on the region for which you want to run the model.
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Run main.py. This will create: 1) the databases needed as input for the model 2) CaseStudy_Runner files for each of the source-material combinations 3) a batch file to run all CaseStudy_Runner files at once named ‘Run_all’.
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Navigate to the Run_all.bat file in your file explorer and double click in order to run it. All output files can be found in the folder ‘output’ that has been created in the directory you provided in step 1.
Within the output folder is a folder for each category that the model
has run for. Within each of these folders, there is a folder for each
material within the category. In these material folders are the CSV
files containing the output. There are 4 types of CSVs:
-
Inflow: logged inflow into each compartment in kilotonnes
-
Outflow: logged outflow from one compartment to another in
kilotonnes -
Sink: logged inflows into the sinks (final compartments in MFA)
in kilotonnes -
Stock: logged quantities of material present in a stock
compartment.
Within the CSV files, the rows represent the number of runs (as
defined in config.py), and the columns represent the years.
Licensed under Attribution-NonCommercial-ShareAlike CC BY-NC-SA (https://creativecommons.org/licenses/#licenses)
Reason for license is this work being based on DPMFA package and other work by EMPA.