Preprints
https://doi.org/10.5194/gmd-2022-103
https://doi.org/10.5194/gmd-2022-103
Submitted as: model description paper
26 Apr 2022
Submitted as: model description paper | 26 Apr 2022
Status: this preprint is currently under review for the journal GMD.

A Tool for Air Pollution Scenarios (TAPS v1.0) to enable global, long-term, and flexible study of climate and air quality policies

William Atkinson1,2, Sebastian D. Eastham1,3, Y.-H. Henry Chen1, Jennifer Morris1, Sergey Paltsev1, C. Adam Schlosser1, and Noelle Eckley Selin2,4 William Atkinson et al.
  • 1Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, MA, 02139 USA
  • 2Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, 02139 USA
  • 3Laboratory for Aviation and the Environment, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, 02139 USA
  • 4Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139 USA

Abstract. Air pollution is a major sustainability challenge – and future anthropogenic precursor and greenhouse gas emissions will greatly affect human well-being. While mitigating climate change can reduce air pollution both directly and indirectly, distinct policy levers can affect these two interconnected sustainability issues across a wide range of scenarios. We help to assess such issues by presenting a public Tool for Air Pollution Scenarios (TAPS) that can flexibly construct and assess a variety of climate and air quality emissions pathways through its coupling with socioeconomic modeling of climate change mitigation. In this study, we develop and implement TAPS with three components: recent global and fuel-specific anthropogenic emissions inventories, scenarios of emitting activities to 2100 from the MIT Economic Projection and Policy Analysis model (EPPA), and emissions intensity trends based on recent scenario data from the Greenhouse Gas – Air Pollution Interactions and Synergies (GAINS) model. An initial application shows that in scenarios with less climate and pollution policy ambition, near-term air quality improvements from existing policies are eclipsed by long-term emissions increases – particularly from industrial processes that combine sharp production growth with less stringent pollution controls in developing regions. Additional climate actions would substantially reduce fossil fuel related air pollutant emissions (such as sulfur and nitrogen oxides), while further pollution controls would lead to larger reductions for ammonia and organic carbon. Future TAPS applications could explore diverse regional and global policies that affect these emissions, using pollutant emissions results to drive global atmospheric chemical transport models to study the scenarios’ health impacts.

William Atkinson et al.

Status: open (until 21 Jun 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

William Atkinson et al.

Data sets

Zenodo: frozen version of data and code William Atkinson, Sebastian D. Eastham, Y.-H. Henry Chen, Jennifer Morris, Sergey Paltsev, C. Adam Schlosser, Noelle E. Selin https://doi.org/10.5281/zenodo.6452104

Model code and software

Github: current version of data, code, and user manual William Atkinson, Sebastian D. Eastham, Y.-H. Henry Chen, Jennifer Morris, Sergey Paltsev, C. Adam Schlosser, Noelle E. Selin https://github.com/watkin-mit/TAPS/

William Atkinson et al.

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Short summary
Understanding policy effects on human-caused air pollutant emissions is key for assessing related health impacts. We develop a flexible scenario tool that combines updated emissions data sets, long-term economic modeling, and comprehensive technology pathways to clarify the impacts of climate and air quality policies. Results show the importance of both policy levers in the future – to prevent long-term emission increases from offsetting near-term air quality improvements from existing policies.