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Related Experiment Video

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A database for evaluating the InMAP, APEEP, and EASIUR reduced complexity air-quality modeling tools.

Kirk R Baker1, Meredith Amend2, Stefani Penn2

  • 1U.S. Environmental Protection Agency, Research Triangle Park, NC, USA.

Data in Brief
|December 25, 2019
PubMed
Summary

Researchers developed a database comparing air quality models for policy analysis. This resource aids in evaluating reduced complexity models against photochemical grid models for health impact assessments.

Keywords:
APEEPBenMAPCAMxCMAQEASIURInMAPPM2.5

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Area of Science:

  • Environmental Science
  • Atmospheric Chemistry
  • Public Health

Background:

  • Policy analysts use air quality models to estimate health impacts from emissions changes.
  • Reduced complexity models offer faster simulations than photochemical grid models but require validation.
  • Consistent input parameters are crucial for comparing different reduced complexity models.

Purpose of the Study:

  • To present a new database for evaluating reduced complexity air quality models.
  • To provide a benchmark dataset for comparing reduced complexity models with photochemical grid models.
  • To facilitate the assessment of health and economic impacts of emission control policies.

Main Methods:

  • Developed a database containing inputs and outputs for five emission control scenarios.
  • Included data for multiple reduced complexity models (APEEP, InMAP, EASIUR, EPA's benefit-per-ton) and photochemical grid models (CAMx, CMAQ).
  • Provided inputs for BenMAP-CE to calculate health benefits and monetized damages.

Main Results:

  • The database includes modeled changes in annual average PM2.5 concentrations and associated health effects.
  • Offers health impacts and monetized damages from both BenMAP-CE and specific reduced complexity models (EASIUR, APEEP).
  • The shared data facilitates systematic evaluation and intercomparison of reduced complexity models.

Conclusions:

  • The created database is a valuable resource for researchers evaluating reduced complexity models.
  • Enables easier comparison of reduced complexity model predictions against photochemical grid model benchmarks.
  • Supports more accurate and efficient air quality policy analysis and health impact assessments.