The dataset represents the data depicted in the Figures and Tables of a Journal Manuscript with the following abstract: "The objective of this study is to determine the adequacy of using a relatively coarse horizontal resolution (i.e. 36 km) to simulate long-term trends of pollutant concentrations and radiation variables with the coupled WRF-CMAQ model. WRF-CMAQ simulations over the continental United State are performed over the 2001 to 2010 time period at two different horizontal resolutions of 12 and 36 km. Both simulations used the same emission inventory and model configurations. Model results are compared both in space and time to assess the potential weaknesses and strengths of using coarse resolution in long-term air quality applications. The results show that the 36 km and 12 km simulations are comparable in terms of trends analysis for both pollutant concentrations and radiation variables. The advantage of using the coarser 36 km resolution is a significant reduction of computational cost, time and storage requirement which are key considerations when performing multiple years of simulations for trend analysis. However, if such simulations are to be used for local air quality analysis, finer horizontal resolution may be beneficial since it can provide information on local gradients. In particular, divergences between the two simulations are noticeable in urban, complex terrain and coastal regions.". This dataset is associated with the following publication: Gan , M., C. Hogrefe , R. Mathur , J. Pleim , J. Xing , D. Wong , R. Gilliam , G. Pouliot , and C. Wei. Assessment of the effects of horizontal grid resolution on long-term air quality trends using coupled WRF-CMAQ simulations. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, USA, 132: 207-216, (2016).

These data sets were created using extensive model simulation results from the WRF-CMAQ model, population distributions, and through the use of an health impact assessment model - see manuscript for details. This dataset is associated with the following publication: Xing, J., J. Wang, R. Mathur , J. Pleim , S. Wang, C. Hogrefe , C. Gan, D. Wong , and J. Hao. Unexpected Benefits of Reducing Aerosol Cooling Effects. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 50(14): 7527–7534, (2016).

Files containing daily maximum 8-hr ozone mixing ratio observations and WRF/CMAQ simulations that were contributed by EPA/ORD/NERL/CED researchers to the manuscript “On the Limit to the Accuracy of Regional Air Quality Models”. This dataset is associated with the following publication: Rao, S.T., H. Luo, M. Astitha, C. Hogrefe, V. Cover, and R. Mathur. On the limit to the accuracy of regional-scale air quality models. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, GERMANY, 20(3): 1627–1639, (2020).

Files containing daily maximum 8-hr ozone mixing ratio observations and WRF/CMAQ simulations that were contributed by EPA/ORD/NERL/CED researchers to the manuscript “On the Limit to the Accuracy of Regional Air Quality Models”. This dataset is associated with the following publication: Rao, S.T., H. Luo, M. Astitha, C. Hogrefe, V. Cover, and R. Mathur. On the limit to the accuracy of regional-scale air quality models. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, GERMANY, 20(3): 1627–1639, (2020).

The data sets are outputs from the WRF-CMAQ modeling system. Typically these files contain a number of meteorological and atmospheric pollutant concentrations on a model grid which is either 2- or 3-dimensional and also in some instances vary with time. This dataset is associated with the following publication: Xing, J., R. Mathur , J. Pleim , C. Hogrefe , C. Gan, D. Wong , C. Wei, and J. Wang. Air pollution and climate response to aerosol direct radiative effects: A modeling study of decadal trends across the northern hemisphere. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(33): 12221-12236, (2015).

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).

This file describes the dataset used in the following article: Ngo, S., B.N. Murphy, C.G. Nolte, K.E. Brown (2024), Bridging existing energy and chemical transport models to enhance air quality policy assessment. Environmental Modelling and Software, in press, available at https://doi.org/10.1016/j.envsoft.2024.106218. The Community Multiscale Air Quality (CMAQ) model version 5.3.1 was used to simulate air pollutant concentrations over the continental United States using grid cells with 12km x 12km horizontal spacing, with the height of the lowest model layer around 38 m. The model is open source and freely available on GitHub at https://github.com/USEPA/CMAQ. Files included in this dataset: Outputs from The Integrated MARKAL-EFOM System (TIMES) model for each scenario. Scenario-specific Emission Control files for the Community Multiscale Air Quality (CMAQ) model, as well as the R script used to create the Emission Control files from the TIMES outputs. Annual mean concentrations of particulate matter smaller than 2.5 microns in diameter (PM2.5) and summer means of the daily 8-h average ozone level for each modeled scenario.

Software Model simulations were conducted using WRF version 3.8.1 (available at https://github.com/NCAR/WRFV3) and CMAQ version 5.2.1 (available at https://github.com/USEPA/CMAQ). The meteorological and concentration fields created using these models are too large to archive on ScienceHub, approximately 1 TB, and are archived on EPA’s high performance computing archival system (ASM) at /asm/MOD3APP/pcc/02.NOAH.v.CLM.v.PX/. Figures Figures 1 – 6 and Figure 8: Created using the NCAR Command Language (NCL) scripts (https://www.ncl.ucar.edu/get_started.shtml). NCLD code can be downloaded from the NCAR website (https://www.ncl.ucar.edu/Download/) at no cost. The data used for these figures are archived on EPA’s ASM system and are available upon request. Figures 7, 8b-c, 8e-f, 8h-i, and 9 were created using the AMET utility developed by U.S. EPA/ORD. AMET can be freely downloaded and used at https://github.com/USEPA/AMET. The modeled data paired in space and time provided in this archive can be used to recreate these figures. The data contained in the compressed zip files are organized in comma delimited files with descriptive headers or space delimited files that match tabular data in the manuscript. The data dictionary provides additional information about the files and their contents. This dataset is associated with the following publication: Campbell, P., J. Bash, and T. Spero. Updates to the Noah Land Surface Model in WRF‐CMAQ to Improve Simulated Meteorology, Air Quality, and Deposition. Journal of Advances in Modeling Earth Systems. John Wiley & Sons, Inc., Hoboken, NJ, USA, 11(1): 231-256, (2019).

Data sets include information on emissions of air pollutants, description of 3-D meteorological state of the atmosphere, and output from the CMAQ model over the northern hemisphere and contiguous U.S. This dataset is not publicly accessible because: This research was conducted a part of the primary author's Ph.D. dissertation at the University of North Carolina at Chapel Hill. All data sets were created on the UNC computers and are housed there. Since the data sets are not directly available to the EPA investigator, they are not included in ScienceHub. It can be accessed through the following means: Data sets can be accessed by contacting Dr. Sarav Arunachalam at UNC - sarav@email.unc.edu. Format: Model input (3D meteorological fields and 3D emission files) and output are in netcdf format. Observational data sets used are publicly available and typically available as ascii files. This dataset is associated with the following publication: Vennam, L., W. Vizuete, K. Talgo, M. Omary, F. Binkowski, J. Xing, R. Mathur, and S. Arunachalam. Modeled Full-Flight Aircraft Emissions Impacts on Air Quality and Their Sensitivity to Grid Resolution. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 122(24): 13,472–13,494, (2017).

Data sets include information on emissions of air pollutants, description of 3-D meteorological state of the atmosphere, and output from the CMAQ model over the northern hemisphere and contiguous U.S. This dataset is not publicly accessible because: This research was conducted a part of the primary author's Ph.D. dissertation at the University of North Carolina at Chapel Hill. All data sets were created on the UNC computers and are housed there. Since the data sets are not directly available to the EPA investigator, they are not included in ScienceHub. It can be accessed through the following means: Data sets can be accessed by contacting Dr. Sarav Arunachalam at UNC - sarav@email.unc.edu. Format: Model input (3D meteorological fields and 3D emission files) and output are in netcdf format. Observational data sets used are publicly available and typically available as ascii files. This dataset is associated with the following publication: Vennam, L., W. Vizuete, K. Talgo, M. Omary, F. Binkowski, J. Xing, R. Mathur, and S. Arunachalam. Modeled Full-Flight Aircraft Emissions Impacts on Air Quality and Their Sensitivity to Grid Resolution. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 122(24): 13,472–13,494, (2017).