Grantee Research Project Results
Machine-learned atmospheric chemical mechanisms
EPA Grant Number: R840012Title: Machine-learned atmospheric chemical mechanisms
Investigators: Tessum, Christopher , Riemer, Nicole
Institution: University of Illinois Urbana-Champaign
EPA Project Officer: Chung, Serena
Project Period: August 1, 2020 through July 31, 2023 (Extended to July 31, 2024)
Project Amount: $399,469
RFA: Chemical Mechanisms to Address New Challenges in Air Quality Modeling (2019) RFA Text | Recipients Lists
Research Category: Early Career Awards , Air Quality and Air Toxics , Air
Objective:
The objective of the work proposed here is to produce a machine-learned condensed chemical mechanism that provides accurate results in air quality modeling simulations under a wide variety of atmospheric conditions. The proposed work builds on preliminary studies where we have created a neural-network-based chemical mechanism that operates with fewer chemical species and orders-of-magnitude less computational cost than the reference mechanism it was trained on, while reproducing diurnal cycles with low error in 90% of randomly initialized simulations. The proposed project will focus on reducing error in the remaining 10% of cases.
Approach:
We will 1) create a mass-conserving traditional chemical mechanism, which will allow us to enforce the neural network to conserve mass; 2) train an encoder-operator-decoder neural network to condense and emulate our reference mechanism with low error in 100% of randomly initialized simulations; and 3) implement the neural network chemical mechanism in the WRF-Chem atmospheric model and evaluate predictive performance.
Expected Results:
We expect our project to produce a class of condensed chemical mechanisms that are computationally much faster than conventional condensed mechanisms—without reduced accuracy—and can be created from explicit mechanisms in an automated and flexible manner. This would serve to reduce the computational expense of air quality modeling for regulatory impact assessment and would potentially enhance the air quality modeling capabilities of stakeholders who may not have access to the computing resources required to run current-generation models, such as state, local, and tribal agencies.
Publications and Presentations:
Publications have been submitted on this project: View all 15 publications for this projectJournal Articles:
Journal Articles have been submitted on this project: View all 2 journal articles for this projectSupplemental Keywords:
machine learning, neural network, MOSAIC, PartMC, MCM, ambient air, modelingProgress and Final Reports:
The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.