This dataset documents the simulations demonstrating the capabilities of the new DESID module, a part of CMAQ that allows for adjustment of emissions and expanded diagnostic output. There are three figures, each with four subpanels, that are provided here. They are all present in the supporting information of the manuscript. There are no figures with data in the main manuscript. This dataset is not publicly accessible because: See explanation above. It can be accessed through the following means: See explanation above. Format: This research paper is somewhat unique in that there is no data presented in the main manuscript. There are three figures presented in the supporting information which are generated from input and output data from CMAQ. The figures are for tutorial purposes only and do not directly contribute to any analysis or conclusions of any scientific hypothesis or policy recommendation. The CMAQ code, data and figure scripts used to generate the supporting information figures may be found on ASM in the folder: /asm/MOD3DEV/bmurphy/ScienceHub/DESID. This dataset is associated with the following publication: Murphy, B., C. Nolte, F. Sidi, J. Bash, K.W. Appel, C. Jang, D. Kang, J. Kelly, R. Mathur, S. Napelenok, G. Pouliot, and H. Pye. The Detailed Emissions Scaling, Isolation, and Diagnostic (DESID) module in the Community Multiscale Air Quality (CMAQ) Modeling System version 5.3.2. Geoscientific Model Development. Copernicus Publications, Katlenburg-Lindau, GERMANY, 14(6): 3407-3420, (2021).

Annual CMAQ model output for 2016 for approximately six different simulations. This dataset is not publicly accessible because: Data are too large to upload to ScienceHub. It can be accessed through the following means: These data can be requested from the author. Format: Data formats include I/O API and csv. This dataset is associated with the following publication: Appel, K.W., J. Bash, K. Fahey, K. Foley, R. Gilliam, C. Hogrefe, B. Hutzell, D. Kang, R. Mathur, B. Murphy, S. Napelenok, C. Nolte, J. Pleim, G. Pouliot, H. Pye, G. Sarwar, D. Schwede, F. Sidi, T. Spero, D. Wong, L. Ran, and S. Roselle. The Community Multiscale Air Quality (CMAQ) Model Version 5.3: System Updates and Evaluation. Geoscientific Model Development. Copernicus Publications, Katlenburg-Lindau, GERMANY, 14: 2867-2897, (2021).

Annual CMAQ model output for 2016 for approximately six different simulations. This dataset is not publicly accessible because: Data are too large to upload to ScienceHub. It can be accessed through the following means: These data can be requested from the author. Format: Data formats include I/O API and csv. This dataset is associated with the following publication: Appel, K.W., J. Bash, K. Fahey, K. Foley, R. Gilliam, C. Hogrefe, B. Hutzell, D. Kang, R. Mathur, B. Murphy, S. Napelenok, C. Nolte, J. Pleim, G. Pouliot, H. Pye, G. Sarwar, D. Schwede, F. Sidi, T. Spero, D. Wong, L. Ran, and S. Roselle. The Community Multiscale Air Quality (CMAQ) Model Version 5.3: System Updates and Evaluation. Geoscientific Model Development. Copernicus Publications, Katlenburg-Lindau, GERMANY, 14: 2867-2897, (2021).

The CMAQ Model Outputs data asset includes current and projected future levels of ambient concentrations and deposition to support regulatory impact analyses.

Hemispheric scale simulations with CMAQ and the Weather Research and Forecasting model are performed for multiple years. Model capabilities for a range of applications including episodic long-range pollutant transport, long-term trends in air pollution across the Northern Hemisphere, and air pollution-climate interactions are evaluated through detailed comparison with available surface, aloft, and remotely sensed observations. This dataset is associated with the following publication: Mathur, R., J. Xing, R. Gilliam, G. Sarwar, C. Hogrefe, J. Pleim, G. Pouliot, S. Roselle, T. Spero, D. Wong, and J. Young. Extending the Community Multiscale Air Quality (CMAQ) Modeling System to Hemispheric Scales: Overview of Process Considerations and Initial Applications. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, GERMANY, 17: 12449-12474, (2017).

Hemispheric scale simulations with CMAQ and the Weather Research and Forecasting model are performed for multiple years. Model capabilities for a range of applications including episodic long-range pollutant transport, long-term trends in air pollution across the Northern Hemisphere, and air pollution-climate interactions are evaluated through detailed comparison with available surface, aloft, and remotely sensed observations. This dataset is associated with the following publication: Mathur, R., J. Xing, R. Gilliam, G. Sarwar, C. Hogrefe, J. Pleim, G. Pouliot, S. Roselle, T. Spero, D. Wong, and J. Young. Extending the Community Multiscale Air Quality (CMAQ) Modeling System to Hemispheric Scales: Overview of Process Considerations and Initial Applications. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, GERMANY, 17: 12449-12474, (2017).

These files contain gridded annual dry deposition estimates from 2002 to 2019 from the Environmental Protection Agency’s Community Multiscale Air Quality (CMAQ) model version 5.3.2 (see https://github.com/USEPA/CMAQ/tree/5.3.2) with the revised Surface Tiled Aerosol and Gaseous Exchange (STAGE) model (Appel et al., 2021) created for the EPA’s Air QUAlity TimE Series (EQUATES, https://www.epa.gov/cmaq/equates) project. These deposition fields have been downscaled from the native 12 km resolution to 300 m by mapping STAGE land use specific deposition to Moderate Resolution Imaging Spectrometer (MODIS) 17 category International Geosphere-Biosphere Programme (IGBP) classification scheme. For details on model inputs, please refer to the EPA’s EQUATES (https://www.epa.gov/cmaq/equates) project and Benish et al., 2022.

Near Real Time CMAQ Simulations compared to AQS Observations. This dataset is associated with the following publication: Eder, B., R. Gilliam, G. Pouliot, R. Mathur, and J. Pleim. Continuous, Near Real-Time Evaluation of Air Quality Models: An Approach for the Rapid Scientific Evolution of Modeling Systems. EM Magazine. Air and Waste Management Association, Pittsburgh, PA, USA, 1-6, (2017).

We present the development of a multiphase adjoint for the Community Multiscale Air Quality (CMAQ) model, a widely used chemical transport model. The adjoint model provides location- and time-specific gradients that can be used in various applications such as backward sensitivity analysis, source attribution, optimal pollution control, data assimilation, and inverse modeling. The science processes of the CMAQ model include gas-phase chemistry, aerosol dynamics and thermodynamics, cloud chemistry and dynamics, diffusion, and advection. Discrete adjoints are implemented for all the science processes, with an additional continuous adjoint for advection. The development of discrete adjoints is assisted with algorithmic differentiation (AD) tools. Particularly, the Kinetic PreProcessor (KPP) is implemented for gas-phase and aqueous chemistry, and two different automatic differentiation tools are used for other processes such as clouds, aerosols, diffusion, and advection. The continuous adjoint of advection is developed manually. For adjoint validation, the brute-force or finite-difference method (FDM) is implemented process by process with box- or column-model simulations. Due to the inherent limitations of the FDM caused by numerical round-off errors, the complex variable method (CVM) is adopted where necessary. The adjoint model often shows better agreement with the CVM than with the FDM. The adjoints of all science processes compare favorably with the FDM and CVM. In an example application of the full multiphase adjoint model, we provide the first estimates of how emissions of particulate matter (PM2.5) affect public health across the US. This dataset is not publicly accessible because: Outside data. It can be accessed through the following means: contact : Amir Hakami . Format: Model outputs in ioapi format, and analysis results in database format. This dataset is associated with the following publication: Zhao, S., A. Hakami, S. Capps, M. Turner, D. Henze, P. Percell, J. Resler, H. Shen, A. Russell, A. Nenes, A. Pappin, S. Napelenok, J. Bash, K. Fahey, J. Baek, G. Carmichael, C. Stanier, A. Sandu, and T. Chai. A multiphase CMAQ version 5.0 adjoint. Geoscientific Model Development. Copernicus Publications, Katlenburg-Lindau, GERMANY, 13(7): 2925-2944, (2020).

We present the development of a multiphase adjoint for the Community Multiscale Air Quality (CMAQ) model, a widely used chemical transport model. The adjoint model provides location- and time-specific gradients that can be used in various applications such as backward sensitivity analysis, source attribution, optimal pollution control, data assimilation, and inverse modeling. The science processes of the CMAQ model include gas-phase chemistry, aerosol dynamics and thermodynamics, cloud chemistry and dynamics, diffusion, and advection. Discrete adjoints are implemented for all the science processes, with an additional continuous adjoint for advection. The development of discrete adjoints is assisted with algorithmic differentiation (AD) tools. Particularly, the Kinetic PreProcessor (KPP) is implemented for gas-phase and aqueous chemistry, and two different automatic differentiation tools are used for other processes such as clouds, aerosols, diffusion, and advection. The continuous adjoint of advection is developed manually. For adjoint validation, the brute-force or finite-difference method (FDM) is implemented process by process with box- or column-model simulations. Due to the inherent limitations of the FDM caused by numerical round-off errors, the complex variable method (CVM) is adopted where necessary. The adjoint model often shows better agreement with the CVM than with the FDM. The adjoints of all science processes compare favorably with the FDM and CVM. In an example application of the full multiphase adjoint model, we provide the first estimates of how emissions of particulate matter (PM2.5) affect public health across the US. This dataset is not publicly accessible because: Outside data. It can be accessed through the following means: contact : Amir Hakami . Format: Model outputs in ioapi format, and analysis results in database format. This dataset is associated with the following publication: Zhao, S., A. Hakami, S. Capps, M. Turner, D. Henze, P. Percell, J. Resler, H. Shen, A. Russell, A. Nenes, A. Pappin, S. Napelenok, J. Bash, K. Fahey, J. Baek, G. Carmichael, C. Stanier, A. Sandu, and T. Chai. A multiphase CMAQ version 5.0 adjoint. Geoscientific Model Development. Copernicus Publications, Katlenburg-Lindau, GERMANY, 13(7): 2925-2944, (2020).