Grantee Research Project Results

2022 Progress Report: 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 (Extended to July 31, 2024)
Project Period Covered by this Report: August 1, 2021 through July 31,2022
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

Objective:

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 volatile organic compounds (VOCs) 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.

Progress Summary:

We have developed, implemented into GEOS-Chem, and published new model mechanisms for halogen chemistry [Wang et al., 2021], mercury chemistry [Shah et al., 2021], aromatic chemistry [Bates et al., 2021a], methanol chemistry [Bates et al., 2021b], cloud acidity [Shah et al., 2020], and NOx chemistry [Shah et al., 2022]. All of these mechanisms except the last have been brought into the standard version of the GEOS-Chem model. The NOx chemistry mechanism will be proposed for inclusion in the next standard version.
We have developed and published a new adaptive implementation of the Kinetic Pre-Processor (KPP) numerical solver for chemical mechanisms that allows chemical transport models such as GEOS-Chem to select an appropriately compact submechanism locally and on the fly [Shen et al., 2022; Lin et al., 2022]. This feature along with unification of gas- and aerosol chemistry as well as a number of performance improvements have been implemented into a new standard version KPP 3.0 hosted by Harvard as an independent GitHub repository and available for distribution to any model [Lin et al., 2022]
We have developed a prototype neural-network machine learning algorithm for numerical solution of chemical mechanisms [Kelp et al., 2020] and implemented it successfully in GEOS-Chem using the SuperFast chemical mechanism [Kelp et al., 2022]. Results highlighted some important limitations of machine-learning algorithms applied to atmospheric chemistry mechanisms.

Future Activities:

Implement the new NO_x chemistry mechanism into the standard GEOS-Chem model;
Apply this mechanism to better understand the sources of background NOx over the US:
Develop a new mechanism for isoprene SOA;
Develop a new chemical mechanism for volatile chemical products (VCP) and examine the implications for PAN and ozone.

References:

Bates, K.H., D.J. Jacob, K. Li, P. Ivatt, M.J. Evans, Y. Yan, and J.Lin, Development and evaluation of a new compact mechanism for aromatic oxidation in atmospheric models, Atmos. Chem. Phys., 21, 18351-18374, 2021a

Bates, K.H., Jacob, D.J., Wang, S., Hornbrook, R.S., Apel, E.C., Kim, M.J., Millet, D.B., Wells, K.C., Chen, X., Brewer, J.F., Ray, E.A., Diskin, G.S., Commane, R., Daube, B.C. and Wofsy, S.C., The global budget of atmospheric methanol: new constraints on secondary, oceanic, and terrestrial source, J. Geophys. Res., 126, e2020JD033439, 2021b.

Kelp, M.M., D.J. Jacob, H. Lin, M.P. Sulprizio, An online-learned neural network chemical solver for stable long-term global simulations of atmospheric chemistry, JAMES, 14, e2021MS002926. https://doi.org/10.1029/2021MS002926, 2022.

Kelp, M.M., D.J. Jacob, J.N. Kutz, J.D. Marshall, and C.W. Tessum, Toward stable, general machine-learned models of the atmospheric chemical system, J. Geophys. Res., 125, e2020JD032759, 2020.

Lin, H., M.S. Long, R. Sander, R.M. Yantosca, L.A. Estrada, L. Shen, and D.J. Jacob, An adaptive auto-reduction solver for speeding up integration of chemical kinetics in atmospheric chemistry models: implementation and evaluation in the Kinetic Pre-Processor (KPP) version 3.0.0, submitted to JAMES, https://doi.org/10.31223/X5505V, 2022.

Shah, V., D.J. Jacob, R. Dang, L.N. Lamsal, S.A. Strode, S.D. Steenrod, K.F. Boersma, S.D. Eastham, T.M. Fritz, C. Thompson, J. Peischl, I. Bourgeois, I.B. Pollack, B.A. Nault, R.C. Cohen, P. Campuzano-Jost, J.L. Jimenez, S.T. Andersen, L.J. Carpenter, T. Sherwen, and M.J. Evans, Nitrogen oxides in the free troposphere: Implications for tropospheric oxidants and the interpretation of satellite NO2 measurements, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-656, 2022.

Shah, V., D.J. Jacob, C.P. Thackray, X. Wang, E.M. Sunderland, T.S. Dibble, A. Saiz-Lopez, I. Cernusak, V. Kello, P.J. Castro, R. Wu, and C. Wang, Improved mechanistic model of the atmospheric redox chemistry of mercury, Environ. Sci. Technol., https://doi.org/10.1021/acs.est.1c03160, 2021.

Shah, V., D.J. Jacob, J.M. Moch, X. Wang, and S. Zhai, Global modeling of cloudwater acidity, rainwater acidity, and acid inputs to ecosystems, Atmos. Chem. Phys., 20, 12223-12245, 2020.

Shen, L., D.J. Jacob, M. Santillana, K. Bates, J. Zhuang, and W. Chen, A machine learning-guided adaptive algorithm to reduce the computational cost of integrating kinetics in global atmospheric chemistry models: application to GEOS-Chem versions 12.0.0 and 12.9.1, Geosci. Model Dev., 15, 1677– 1687, https://doi.org/10.5194/gmd-15-1677-2022 , 2022.

Wang, X., D.J. Jacob, W. Downs, S. Zhai, L. Zhu, V. Shah, C.D. Holmes, T. Sherwen, B. Alexander, M.J. Evans, S.D. Eastham, J.A. Neuman, P. Veres, T.K Koenig, R. Volkamer, L.G. Huey, T.J. Bannan, C.J. Percival, B.H. Lee, and J.A. Thornton, Global tropospheric halogen (Cl, Br, I) chemistry and its impact on oxidants, Atmos. Chem. Phys., https://doi.org/10.5194/acp-2021-441, 2021.

Journal Articles on this Report : 1 Displayed | Download in RIS Format

Publications Views
Other project views:	All 16 publications	16 publications in selected types	All 16 journal articles

Publications
Type	Citation	Project	Document Sources
Journal Article	Wang R, Jacob D, Shah V, Eastham S, Fritz T, Mickley L, Liu T, Wang Y, Wang J. Background nitrogen dioxide (NO2) over the United States and its implications for satellite observations and trends:effects of nitrate photolysis, aircraft, and open fires. Atmospheric Chemistry and Physics 2023;23(11):6271-6284.	R840014 (2022)	Full-text: Copernicus - HTML Exit

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 University Exit

Progress and Final Reports:

Original Abstract

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.