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Tool for Air Pollution Scenarios (TAPS) User Manual

Version 1.0

Contact: Will Atkinson (watkin@mit.edu)

Alternative Contacts: Noelle E. Selin (selin@mit.edu), Sebastian Eastham (seastham@mit.edu)

Welcome! This is the user manual for TAPS v1.0, as submitted to Geoscientific Model Development. The associated preprint is available at https://gmd.copernicus.org/preprints/gmd-2022-103/. You can report bugs, suggest features or view the source code on GitHub.

Table of contents generated with markdown-toc

1 Language, License, and Dependencies

TAPS v1.0 uses Python v3.8.2. The code is available under the open source MIT license.

Dependencies are listed for each of the three main scripts below. They can be installed by running TAPS_install_packages.py and following instructions in the below hyperlinks for gcgridobj and stackedBarGraph.

scale_emissions.py (Sect. 3)

  • numpy version 1.21.1
  • pandas version 1.4.2
  • xarray version 2022.3.0
  • netCDF4 version 1.5.4
  • os (via python)
  • io (via python)
  • datetime (via python)
  • time (via python)
  • gcgridobj
  • scipy.optimize version 1.5.2 (if used for emissions scenarios)

analyze_emissions.py (Sect. 4)

output_for_CTM.py (Sect. 5)

  • same as above

2 External Data Sources

The TAPS code uses several external data sources. The input_files directory includes the following .csv files:

  • Economic Prediction and Policy Analysis (EPPA) model scenario data (last accessed 7 May 2021): EPPA7_sectoral_energy_20210903.csv and EPPA7_nonsector_results.csv. Further information at https://globalchange.mit.edu/research/research-tools/human-system-model (Paltsev et al., 2021).
  • Food and Agricultural Organization (FAO) scenario data (last accessed 21 January 2022): FOFA2050RegionsData_all_total.csv. From https://www.fao.org/global-perspectives-studies/food-agriculture-projections-to-2050/en/ (2018)
  • Shared Socioeconomic Pathway (SSP) Integrated Assessment Model (IAM) scenarios for comparison in analyze_emissions.py (last accessed 30 April 2021): SSP_IAM_V2_201811.csv. From Version 2.0, DOI:110.1016/j.gloenvcha.2016.05.009 (Riahi et al., 2017) via the SSP database (https://tntcat.iiasa.ac.at/SspDb/).
  • Scenarios from the sixth Coupled Model Intercomparison Project (CMIP6) for comparison in analyze_emissions.py (last accessed 30 April 2021): SSP_CMIP6_201811_solvents.csv (having shortened the sector name of "Solvents Production and Application" to "Solvents" to facilitate processing). From Version 2.0, DOI:10.5194/gmd-12-1443-2019 (Gidden et al., 2019) via the SSP database (https://tntcat.iiasa.ac.at/SspDb/).
  • Global population distribution (last accessed 28 October 2020): gpw_v4_population_density_adjusted_rev11_2pt5_min.nc (used for EPPA region-to-grid mapping here). From Gridded Population of the World (GPW), Version 4.11, Population Count Adjusted to Match 2015 Revision of UN WPP Country Totals, https://doi.org/10.7927/H4PN93PB (CIESIN, 2018)

In addition, the emissions inventory files can be downloaded separately (using 2014 in the default code) from the following repositories:

Finally, the GAINS outputs are available on https://gains.iiasa.ac.at/models/ (IAM resolution, version 4.01) after creating a free account. The default version uses ECLIPSE v6b CLE and MFR scenarios at the EMF30 sector and region resolution (one .csv file for emissions, one for activities) for all species except ammonia (Amann et al., 2011; GAINS 4.01 release notes, 2021; Klimont et al., 2017; Smith et al., 2020). For ammonia (which was outside the scope of EMF30), we use a third .csv file at GAINS sector resolution, restricted to G20 regions due to the proprietary nature of the International Energy Agency source data.

3 Creating Emissions Trajectories

The script scale_emissions.py creates scenarios of future emissions based on the emissions inventory, activity projections, and intensity projections (see diagram and Equation 1 below). Users can create their own scenarios by tweaking projection trends in the code, editing the projection input files, or providing their own.

image

image

3.1 Emissions Inventories

The default TAPS code uses a base year of 2014 to match the EPPA7 model (Sect. 3.2). Other years (and corresponding file inputs) could be specified in the input_years and inventory code blocks. We use emissions inventories of precursor air pollutants from two sources:

  • CEDSGBD-MAPS for most anthropogenic emissions sources (McDuffie et al., 2020), and
  • GFED4.1s for agricultural waste burning (van Marle et al., 2017).

3.1.1 CEDSGBD-MAPS

CEDS includes global (0.5°×0.5°) monthly data of sulfur dioxide (SO2), carbon monoxide (CO), ammonia (NH3), black carbon (BC), organic carbon (OC), nitrogen oxides (NOx) as NO, and 23 separate non-methane volatile organic compounds (NMVOC).

We use the 11-sector, 4-fuel version in McDuffie et al. (2020) because it combines fuel-specific granularity with emissions totals that largely match the latest trends in https://github.com/JGCRI/CEDS. Further details can be found in the README file. CEDSGBD-MAPS does not include aviation or open burning emissions.

3.1.2 GFED4.1s

GFED includes global (0.25°×0.25°) monthly data of open burning by category, with conversion from dry matter to specific precursor species via the emissions factors in GFED4_Emission_Factors.txt from http://www.globalfiredata.org/ar6historic.html (based on Akagi et al., 2011). The default code scales the agricultural waste burning category, as the only open burning category that maps to EPPA's sectoral activity projections.

3.1.3 Integration with Activity Data

The inventory input section reads each data source and prepares it for regional and sectoral integration with activity data. The default code uses the following files:

  • spc_map.csv to map inventory species (with differing NMVOC speciation and naming conventions) for activity scaling (all NMVOC species are scaled by the same factor).
  • sectoral_mapping_EPPA7_inventories.csv to map inventory sectors to those in EPPA7.
  • CEDS_EPPA_masks_filled.nc and GFED_EPPA_masks_filled.nc to map inventory grids to EPPA7 regions.

These latter files were created from process_CEDS.py and process_GFED.py, using the files in the regional_mapping directory. To create a new regional mapping, users can edit those files via gen_GPW_to_EPPA_mappings.py and re-run the processing scripts.

Once these files are ready, the default code creates dictionaries that contain 2014's total emissions (in Tg yr-1) for CEDS (em_CEDS; by species, fuel, sector, and region) and GFED (em_GFED; by species, region, and category of open burning as "sector"). Exports are available for the analysis script (em_CEDS.csv and em_GFED.csv), as well as the proportion of each species/sector/region's emissions from each CEDS fuel (fuel_proportions.csv) and a global aggregation for manuscript Table A1.

3.2 Emissions Activity Projections

This version's activity projections use scenarios from the MIT Economic Projection and Policy Analysis model (EPPA). EPPA is a global multi-region multi-sector recursive–dynamic computable global equilibrium (CGE) model that has studied a variety of climate and economic policy impacts (Chen et al., 2015, 2017; Paltsev et al., 2005). More details are available at https://globalchange.mit.edu/research/research-tools/human-system-model.

3.2.1 EPPA Scenario Trends

The default inputs include three scenarios from the EPPA7 model (described in the below table). Input files include:

  • EPPA7_sectoral_energy_20210903.csv (energy trends mapped to CEDSGBD-MAPS fuels via ene_to_fuels), and
  • EPPA7_nonsector_results.csv (non-energy trends, such as population and land use)

for 14 economic sectors and 18 regions of the world. The default code uses the full range of years given (2014, 2015, 2020, 2025...2100); this range can be edited in the input_years code block.

image

The code block under "Calculate activity trends" uses these scenarios to scale emitting activities based on each sector's scaling choice in sectoral_mapping_EPPA7_inventories.csv. For agriculture, land production trends are the product of EPPA land use trends (in hectares) and FAO linearly extended production-per-area total crop trends (in tonnes per hectare) – using EPPA-to-FAO regional mappings in reg_map_EPPAFAO, scenario mappings in scen_map_EPPAFAO.csv, and yield values in FOFA2050RegionsData_all_yield.csv.

The resulting activity scaling trends (act_scaling; by scenario, sector, region, and fuel) are normalized to a base-year value of 1, and applied to each inventory species based on the proportion of its base-year emissions that came from each sector and region:

image

3.2.2 Developing Custom Scenarios

Users can create new scenarios in the following ways:

  • Tweak the code – editing any fraction of act_scaling for region-, sector-, or fuel-specific policy
  • Change the input files – editing the csv inputs to reflect different energy or non-energy trends
  • Provide new input files – based on different EPPA or EPPA-formatted scenarios

Users wishing to use non-EPPA models could readily do so after developing the corresponding mapping files.

3.3 Emissions Intensity Projections

The current version develops emissions intensity trends based on recent GAINS scenarios (GAINS 4.01 release notes, 2021; Klimont et al., 2017). GAINS has been similarly used in the SSPs (Rao et al., 2017) as well as more recent IAM scenarios (Rafaj et al., 2021). More details are available at https://gains.iiasa.ac.at/models/index.html.

3.3.1 Integration with Inventory and Activity Data Sources

The external files include data for the Current Legislation (CLE) scenario (2000, 2005, 2010, 2015, 2020, 2030, 2050) and Maximum Feasible Reduction (MFR) scenario (2030, 2050). Most pollutant species were provided in the Energy Modeling Forum (EMF) 30 study, while NH3 data were provided separately. The following files integrate this data with the inventory and activity sources:

  • GAINSEMF_sectoral_mapping.csv: mapping sector definitions from the GAINS EMF activity file to the GAINS EMF emissions file (given their slight formatting differences)
  • CEDS_GAINSEMF_sectoral_mapping.csv: mapping CEDS sectors and fuels to GAINS EMF sectors
  • CEDS_GAINSNH3_sectoral_mapping.csv: mapping CEDS sectors and fuels to GAINS NH3 sectors
  • EPPA7_GAINSEMF_regional_mapping.csv: mapping EPPA7 to GAINS EMF regions
  • EPPA7_GAINSG20_FAO_regional_mapping.csv: mapping EPPA7 to the G20 and FAO regions used for NH3 intensity trends

3.3.2 GAINS Scenario Trends

Emissions intensity trends are calculated by dividing the emissions time series by activity time series. This includes the following processing steps:

  • For occasional negative activities and emissions, we take their absolute value (assuming they were meant to be positive).
  • For missing EMF activity and NH3 values, we conduct an annual linear interpolation (and/or extension) for time series with at least two values. (For those with one or no values, emissions intensities are held constant). For trend extensions that reach zero before 2050, we assume values of zero thereafter.
  • For waste in EMF – where only emissions (not activities) were given – we assume constant emissions intensities for CLE, versus region-specific trends to zero by 2050 for MFR (based on MFR/CLE emissions ratios) in accordance with a recent GAINS paper (Gomez Sanabria et al., 2021).
  • For waste in NH3, we match NOx trends due to large data gaps.
  • For other NH3 trends outside of the GAINS G20 data given, we assume MFR trends follow their mapped G20 region (matching that area's maximum feasible decrease), but in CLE we assume constant intensities unless they increase in the mapped G20 region (i.e., no improvement from "current legislation" is assumed given the lack of data on baseline policies).
  • For agricultural sectors, we incorporate FAO trends from the corresponding scenarios of "Business as Usual" and "Toward Sustainability" (FOFA2050RegionsData_all.csv,FOFA2050RegionsData_all_total.csv). The resulting intensity trend I combines the GAINS trend (GI) with FAO’s trend for sector i relative to total production (Fr,t): image Performing this step allows for a region’s overall agricultural intensity to change based on shifts in the relative share of the emitting sectors within agriculture.
  • For all species, MFR intensities are not allowed to exceed CLE intensities (given the MFR definition as maximum feasible reduction).

We also convert emissions intensity trends from the GAINS resolution to the CEDS sector-fuel level, based on each GAINS sector's emissions contribution to the CEDS sector-fuel in the closest GAINS scenario year to our base-year. If a CEDS sector-fuel is not covered by GAINS, the sector's other fuels are proportionally upweighted to fill the gap. All trends are normalized to a base-year value of 1, interpolating linearly for 2014 (given the five-year GAINS values). We repeat the process to aggregate from GAINS to EPPA regions.

Intensity trends can be exported before and after GAINS-to-CEDS/EPPA consolidation (GAINSEMF_EFs.csv,GAINSNH3_EFs.csv,GAINS_EFs_by_EPPA_region.csv).

3.3.3 Developing Custom Scenarios

Users can directly follow the GAINS scenarios (to 2050) or develop their own. This initially implementation applies an exponential fit to all non-constant intensity trends, allowing the potential for future innovation (beyond today's MFR levels) in scenarios that match EPPA's timespan to 2100. MFR trends are formed by merging the historical values in CLE (2000-2015) with the MFR scenario points (2030, 2050). Exponentials are fitted by up to 10000 iterations of scipy.optimize.curve_fit and follow the form y = m * exp(-gt * x) + b, restricting b to positive values while leaving m and -gt unrestricted. Exponentials are designed to pass through base-year values of 1 and MFR waste values of zero for 2050 onward (using a base-year uncertainty weighting of 0.01 via the sigma parameter). Fit parameters and r-squared values are exported to GAINSEF_fits.csv for evaluation.

The following scenarios are presented from this illustrative exercise:

  • CLE Trend Continued (fitting CLE as described above)
  • MFR Trend Continued (fitting MFR as described above)
  • MFR Midcentury (fitting MFR as described above, but holding emissions intensities constant after 2050)

Users can create new scenarios in the following ways:

  • Tweak the functional form – adding code for another fitting function or time horizon
  • Tweak the code – editing any fraction of the pre-fit intensity trends for region-, sector-, or fuel-specific policy
  • Create hybrid scenarios from the CLE and MFR trends – as done in the SSPs using income-based assumptions (Rao et al., 2017)
  • Change the input files – editing the csv inputs to reflect different trends or region/sector combinations that are not currently specified
  • Provide new input files – based on different GAINS or GAINS-formatted scenarios

Users wishing to use non-GAINS intensity trends could do so after developing the corresponding scenarios and mapping files.

3.4 Exporting the Emissions Scaling

Finally, we combine the emissions inventories, activity scaling, and intensity scaling trends as in Equation 1. We produce separate emissions scaling exports for CEDS (CEDS_scaling.csv) and GFED (GFED_scaling.csv) inventories, with one trend per combination of activity scenario, intensity scenario, inventory species, EPPA region, inventory sector, and inventory fuel. We also include a summary file (Emissions_components.csv) that includes each trend's activity and intensity scaling values at 2030, 2050, and 2100 (along with the inventory value) to facilitate evaluation.

4 Analysis and Visualization

The script analyze_emissions.py reads the above scaling and base-year inventory files, calculates the scaled emissions, and plots them for analysis. We use NO2 weights for NOx to match the reported totals in McDuffie et al. (2020). The resulting values are exported in Emissions_scenarios.csv for evaluation, and plotted for the manuscript figures below.

4.1 Figure 2

Figure 2 compares our global total emissions trends with corollary SSP IAM trends. IAM (and CMIP6) corollaries are described in the table below.

image

IAM estimates are subtracted by sectors not scaled by TAPS (aviation and open burning beyond agricultural waste), based on their emissions proportion in the best-fitting CMIP6 scenario in CMIP6SSPdict (since sectoral IAM emissions are not available).

4.2 Figures 3 and 4

Figures 3 and 4 compare our 2050 and 2100 sectoral emissions with inventory values and CMIP6 corollaries. CEDS sectoral outputs are consolidated to the eight CEDS sectors in the earlier version that the SSPs employ (Hoesly et al., 2018). Each sector is appended to each bar in turn using stackedBarGraph.

4.3 Figures 5 and 6

Figures 5 and 6 compare our 2050 and 2100 regional emissions with inventory values and CMIP6 totals. Each region is appended to each bar in turn using stackedBarGraph, with a separate global treatment for CMIP6 (given regional definition discrepancies).

5 Creating Scaling Files for Chemical Transport Models

The script output_for_CTM.py inputs the above scaling files to create gridded netCDF outputs for global chemical transport models (to help assess pollutant concentrations and health impacts). Before running, users should specify an output location and ensure sufficient computational capacity, memory, and storage (10-100+ GB, depending on number of scenario-years). Creating monthly files for several decades could take hours of cluster computing time per scenario. The code produces files that match the file format and metadata of each inventory, to streamline the multiplication of those base-year inventories by the exported scaling factors. Alternative formats (or space-speed trade-offs of file creation) could be chosen depending on the model of application.

6 References

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Amann, M., Bertok, I., Borken-Kleefeld, J., Cofala, J., Heyes, C., Höglund-Isaksson, L., Klimont, Z., Nguyen, B., Posch, M., Rafaj, P., Sandler, R., Schöpp, W., Wagner, F., and Winiwarter, W.: Cost-effective control of air quality and greenhouse gases in Europe: Modeling and policy applications, Environ. Model. Softw., 26, 1489–1501, https://doi.org/10.1016/j.envsoft.2011.07.012, 2011.

Chen, Y.-H. H., Paltsev, S., Reilly, J., Morris, J., and Babiker, M. H.: The MIT EPPA6 Model: Economic Growth, Energy Use, and Food Consumption, MIT Joint Program on the Science and Policy of Global Change, 2015.

Chen, Y.-H. H., Paltsev, S., Reilly, J., Morris, J., Karplus, V. J., Gurgel, A., Winchester, N., Kishimoto, P., Blanc, E., and Babiker, M. H.: The MIT Economic Projection and Policy Analysis (EPPA) Model: Version 5, MIT Joint Program on the Science and Policy of Global Change, 2017.

CIESIN: Gridded Population of the World, Version 4 (GPWv4): Population Count Adjusted to Match 2015 Revision of UN WPP Country Totals, Revision 11, https://doi.org/10.7927/H4PN93PB, 2018.

FAO: The future of food and agriculture – Alternative pathways to 2050: https://www.fao.org/global-perspectives-studies/resources/detail/en/c/1157074/, last access: 1 January 2018.

GAINS 4.01 release notes: https://gains.iiasa.ac.at/gains/download/release_notes.pdf?version=4.01, last access: 6 December 2021.

Gidden, M. J., Riahi, K., Smith, S. J., Fujimori, S., Luderer, G., Kriegler, E., van Vuuren, D. P., van den Berg, M., Feng, L., Klein, D., Calvin, K., Doelman, J. C., Frank, S., Fricko, O., Harmsen, M., Hasegawa, T., Havlik, P., Hilaire, J., Hoesly, R., Horing, J., Popp, A., Stehfest, E., and Takahashi, K.: Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century, Geosci. Model Dev., 12, 1443–1475, https://doi.org/10.5194/gmd-12-1443-2019, 2019.

Gomez Sanabria, A., Kiesewetter, G., Klimont, Z., Schöpp, W., and Haberl, H.: Potentials for future reductions of global GHG and air pollutant emissions from circular municipal waste management systems, Nat. Portf., https://doi.org/10.21203/rs.3.rs-512870/v1, 2021.

Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T. C., Dawidowski, L., Kholod, N., Kurokawa, J., Li, M., Liu, L., Lu, Z., Moura, M. C. P., O’Rourke, P. R., and Zhang, Q.: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS), Geosci. Model Dev., 11, 369–408, https://doi.org/10.5194/gmd-11-369-2018, 2018.

Klimont, Z., Kupiainen, K., Heyes, C., Purohit, P., Cofala, J., Rafaj, P., Borken-Kleefeld, J., and Schöpp, W.: Global anthropogenic emissions of particulate matter including black carbon, Atmospheric Chem. Phys., 17, 8681–8723, https://doi.org/10.5194/acp-17-8681-2017, 2017.

van Marle, M. J. E., Kloster, S., Magi, B. I., Marlon, J. R., Daniau, A.-L., Field, R. D., Arneth, A., Forrest, M., Hantson, S., Kehrwald, N. M., Knorr, W., Lasslop, G., Li, F., Mangeon, S., Yue, C., Kaiser, J. W., and van der Werf, G. R.: Historic global biomass burning emissions for CMIP6 (BB4CMIP) based on merging satellite observations with proxies and fire models (1750–2015), Geosci. Model Dev., 10, 3329–3357, https://doi.org/10.5194/gmd-10-3329-2017, 2017.

McDuffie, E. E., Smith, S. J., O’Rourke, P., Tibrewal, K., Venkataraman, C., Marais, E. A., Zheng, B., Crippa, M., Brauer, M., and Martin, R. V.: A global anthropogenic emission inventory of atmospheric pollutants from sector- and fuel-specific sources (1970–2017): an application of the Community Emissions Data System (CEDS), Earth Syst. Sci. Data, 12, 3413–3442, https://doi.org/10.5194/essd-12-3413-2020, 2020.

Morris, J., Libardoni, A., Sokolov, A., Forest, C., Paltsev, S., Reilly, J., Schlosser, C. A., Prinn, R., and Jacoby, H.: A consistent framework for uncertainty in coupled human-Earth system models | MIT Global Change, MIT Joint Program on the Science and Policy of Global Change, 2021a.

Paltsev, S., McFarland, J., Reilly, J. M., Jacoby, H. D., Eckaus, R. S., Sarofim, M., Asadoorian, M., and Babiker, M.: The MIT Emissions Prediction and Policy Analysis (EPPA) Model: Version 4, MIT Joint Program on the Science and Policy of Global Change, 2005.

Paltsev, S., Schlosser, C. A., Chen, H., Gao, X., Gurgel, A., Jacoby, H., Morris, J., Prinn, R., Sokolov, A., and Strzepek, K.: 2021 Global Change Outlook, MIT Joint Program on the Science and Policy of Global Change, 2021.

Rafaj, P., Kiesewetter, G., Krey, V., Schoepp, W., Bertram, C., Drouet, L., Fricko, O., Fujimori, S., Harmsen, M., Hilaire, J., Huppmann, D., Klimont, Z., Kolp, P., Reis, L. A., and Vuuren, D. van: Air quality and health implications of 1.5 °C–2 °C climate pathways under considerations of ageing population: a multi-model scenario analysis, Environ. Res. Lett., 16, 045005, https://doi.org/10.1088/1748-9326/abdf0b, 2021.

Rao, S., Klimont, Z., Smith, S. J., Van Dingenen, R., Dentener, F., Bouwman, L., Riahi, K., Amann, M., Bodirsky, B. L., van Vuuren, D. P., Aleluia Reis, L., Calvin, K., Drouet, L., Fricko, O., Fujimori, S., Gernaat, D., Havlik, P., Harmsen, M., Hasegawa, T., Heyes, C., Hilaire, J., Luderer, G., Masui, T., Stehfest, E., Strefler, J., van der Sluis, S., and Tavoni, M.: Future air pollution in the Shared Socio-economic Pathways, Glob. Environ. Change, 42, 346–358, https://doi.org/10.1016/j.gloenvcha.2016.05.012, 2017.

Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp, A., Cuaresma, J. C., Kc, S., Leimbach, M., Jiang, L., Kram, T., Rao, S., Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenöder, F., Da Silva, L. A., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D., Masui, T., Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G., Harmsen, M., Takahashi, K., Baumstark, L., Doelman, J. C., Kainuma, M., Klimont, Z., Marangoni, G., Lotze-Campen, H., Obersteiner, M., Tabeau, A., and Tavoni, M.: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview, Glob. Environ. Change, 42, 153–168, https://doi.org/10.1016/j.gloenvcha.2016.05.009, 2017.

Smith, S. J., Klimont, Z., Drouet, L., Harmsen, M., Luderer, G., Riahi, K., van Vuuren, D. P., and Weyant, J. P.: The Energy Modeling Forum (EMF)-30 study on short-lived climate forcers: introduction and overview, Clim. Change, 163, 1399–1408, https://doi.org/10.1007/s10584-020-02938-5, 2020.

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