Improving chemical mechanisms for regional/global models in support of US air quality management: application to the GEOS-Chem model

EPA Grant Number: R840014
Title: Improving chemical mechanisms for regional/global models in support of US air quality management: application to the GEOS-Chem model
Investigators: Jacob, Daniel J.
Institution: Harvard University
EPA Project Officer: Chung, Serena
Project Period: August 1, 2020 through July 31, 2023
Project Amount: $785,010
RFA: Chemical Mechanisms to Address New Challenges in Air Quality Modeling (2019) RFA Text |  Recipients Lists
Research Category: Air Quality and Air Toxics , Air


This project will unify, improve, and condense the chemical mechanisms used in the GEOS-Chem global/regional model for air quality applications. Specific objectives include: (1) implementation of a common model framework for the different GEOS-Chem chemical mechanisms to facilitate their merging, subsetting, and updating; (2) improvement of current mechanisms for halogen, mercury, and isoprene secondary organic aerosol (SOA) chemistry; (3) speed-up of the chemical calculation through adaptive mechanism reduction and machine learning methods; (4) updated estimates of background influences on US air quality including for ozone, aerosol, formaldehyde, and nitrogen and mercury deposition.  


The work will involve development and applications of the GEOS-Chem model to achieve the above objectives. Mechanistic improvements will be based on state-of-science laboratory and theoretical information, and will be evaluated extensively with atmospheric observations. Work on speeding up the chemical calculation will focus on decreasing mechanistic complexity where not needed (adaptive mechanism reduction) and exploratory application of machine learning methods to mechanisms of increasing complexity.  GEOS-Chem simulations of US air quality will be conducted including these new developments and will be evaluated with observations from surface networks, field campaigns, sondes, and satellites. Results from these simulations will be used to better quantify background influences on US air quality including source attribution and uncertainty characterization.

Expected Results:

This project will siginificantly improve current capabilities to model air quality on regional/global scales, including in particular the role of background influences on US air quality. The new mechanistic and computational developments to come out of this project will enable the large community of GEOS-Chem users to better serve EPA for air quality applications, and will benefit other air quality modeling communities through code/algorithm sharing.  

Supplemental Keywords:

Air quality modeling, GEOS-Chem, halogen chemistry, mercury chemistry, isoprene chemistry, numerical methods, chemical solvers, machine learning methods, background  

Relevant Websites:

Atmospheric Chemistry Modeling Group - Harvard Exit