NA. This dataset is not publicly accessible because: No EPA generated data was used in this work. It can be accessed through the following means: NA. Format: No EPA generated data was used in this work. This dataset is associated with the following publication: Woody, M., H. Hsi-Wu Wong, J.J. West, and S. Arunachalam. Multiscale predictions of aviation-attributable PM2.5 for U.S. airports modeled using CMAQ with plume-in-grid and an aircraft-specific 1-D emission model. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, USA, 147: 384-394, (2016).

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).

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.

The US EPA developed a set of modeled meteorology, emissions, air quality and pollutant deposition spanning the years 2002 through 2019. Modeled datasets cover the Conterminous US (CONUS) at a 12km horizontal grid spacing (12US1) and the Northern Hemisphere at a 108km (108NHEMI) using WRFv4.1.1 for meteorology and CMAQv5.3.2 for air quality modeling. New hemispheric and North American emissions inventories were developed using, to the extent possible, consistent input data and methods across all years, including emissions from mobile, fire, and oil and gas sources. Collectively these model outputs represent 100s of TB of data. We have selected a subset of the model input and output datasets that we hope will be most useful to the air quality research community. These datasets include: - Emissions inventory files for the CONUS for 2002-2019 suitable for input into the Sparse Matrix Operator Kernel Emissions (SMOKE) emission processor - Emissions trends data with annual total emissions, summed by pollutant and emissions source category - CMAQ-ready emissions, initial conditions and boundary condition input files for the 12US1 domain for 2002-2019 - CMAQ-ready meteorology files for the 12US1 domain for 2002-2019. - Matched meteorology model output with surface observations for 2002-2019 - Daily average CMAQ output for the 12US1 domain for 2002-2019 for 14 pollutants - Daily average 3D CMAQ output for 44 layers for the 108NHEMI domain for 2002–2019 - Annual total wet and dry deposition for the 12US1 domain for 2002-2019 - Hourly surface and 3D modeled meteorology, deposition and air concentrations for the 12US1 and 108NHEMI domains for 2002-2019. This dataset is associated with the following publication: Foley, K., G. Pouliot, A. Eyth, M. Aldridge, C. Allen, K. Appel, J. Bash, M. Beardsley, J. Beidler, J. Choi, C. Farkas, R. Gilliam, J. Godfrey, B. Henderson, C. Hogrefe, S. Koplitz, R. Mason, R. Mathur, C. Misenis, N. Possiel, H. Pye, L. Reynolds, M. Roark, S. Roberts, D. Schwede, K. Seltzer, D. Sonntag, K. Talgo, C. Toro, J. Vukovich, J. Xing, and E. Adams. 2002-2017 Anthropogenic Emissions Data for Air Quality Modeling over the United States. Data in Brief. Elsevier B.V., Amsterdam, NETHERLANDS, 47: N/A, (2023).

Files containing daily average total and speciated PM2.5 observations and WRF/CMAQ simulations that were contributed by EPA/ORD/CEMM/AESMD researchers to the manuscript “Evaluating Trends and Seasonality in Modeled PM2.5 Concentrations Using Empirical Mode Decomposition”. This dataset is associated with the following publication: Luo, H., M. Astitha, C. Hogrefe, R. Mathur, and S.T. Rao. Evaluating Trends and Seasonality in Modeled PM2.5 Concentrations Using Empirical Mode Decomposition. Atmospheric Pollution Research. Turkish National Committee for Air Pollution Research and Control, Izmir, TURKEY, 20(22): 13801-13815, (2020).

No dataset is directly uploaded to ScienceHub. The data are permanently available from the corresponding author. This dataset is not publicly accessible because: The data generated solely by the external academic team and were not shared in raw form with EPA. EPA reviewed the final figures, experimental methodology, model configuration and assumptions, and conclusions of the paper for accuracy, precision, and scientific relevance. It can be accessed through the following means: The data are, as indicated in the publication, available freely upon request from the corresponding author, a professor currently of the Department of Civil and Environmental Engineering at Northeastern University in Boston. According to the publication emails may be sent to: Yang Zhang (ya.zhang@northeastern.edu). Format: The data are comprised of model outputs from the weather forecasting model FV3 and the air quality model CMAQ, as well as measurement data for meteorological metrics and air pollution observations. The data are presented in a number of tables, maps, scatterplots and bar plots. This dataset is associated with the following publication: Chen, X., Y. Zhang, K. Wang, D. Tong, P. Lee, Y. Tang, J. Huang, P. Campbell, J. McQueen, H. Pye, B. Murphy, and D. Kang. Evaluation of the offline-coupled GFSv15–FV3–CMAQv5.0.2 in support of the next-generation National Air Quality Forecast Capability over the contiguous United States. Geoscientific Model Development. Copernicus Publications, Katlenburg-Lindau, GERMANY, 14: 3969–3993, (2021).

The US EPA developed a set of modeled meteorology, emissions, air quality and pollutant deposition spanning the years 2002 through 2019. Modeled datasets cover the Conterminous US (CONUS) at a 12km horizontal grid spacing (12US1) and the Northern Hemisphere at a 108km (108NHEMI) using WRFv4.1.1 for meteorology and CMAQv5.3.2 for air quality modeling. New hemispheric and North American emissions inventories were developed using, to the extent possible, consistent input data and methods across all years, including emissions from mobile, fire, and oil and gas sources. Collectively these model outputs represent 100s of TB of data. We have selected a subset of the model input and output datasets that we hope will be most useful to the air quality research community. These datasets include: - Emissions inventory files for the CONUS for 2002-2019 suitable for input into the Sparse Matrix Operator Kernel Emissions (SMOKE) emission processor - CMAQ-ready emissions, initial conditions and boundary condition input files for the 12US1 domain for 2002-2019 - CMAQ-ready meteorology files for the 12US1 domain for 2013-2019. (Fewer years of meteorology data are included due to space constraints.) - Matched meteorology model output with surface observations for 2002-2019 - Daily average CMAQ output for the 12US1 domain for 2002-2019 for 14 pollutants - Daily average 3D CMAQ output for 44 layers for the 108NHEMI domain for 2002–2019

This work is the first of a two‐part study that aims to develop a computationally efficient bias correction framework to improve surface PM2.5 forecasts in the United States. Here, an ensemble‐based Kalman filter (KF) technique is developed primarily for nonrural areas with approximately 500 surface observation sites for PM2.5 and applied to three (GEOS‐Chem, WRF‐Chem, and WRF‐CMAQ) chemical transport model (CTM) hindcast outputs for June 2012. While all CTMs underestimate daily surface PM2.5 mass concentration by 20–50%, KF correction is effective for improving each CTM forecast. Subsequently, two ensemble methods are formulated: (1) the arithmetic mean ensemble (AME) that equally weights each model and (2) the optimized ensemble (OPE) that calculates the individual model weights by minimizing the least‐square errors. While the OPE shows superior performance than the AME, the combination of either the AME or the OPE with a KF performs better than the OPE alone, indicating the effectiveness of the KF technique. Overall, the combination of a KF with the OPE shows the best results. Lastly, the Successive Correction Method (SCM) was applied to spread the bias correction from model grids with surface PM2.5 observations to the grids lacking ground observations by using a radius of influence of 125 km derived from surface observations, which further improves the forecast of surface PM2.5 at the national scale. Our findings provide the foundation for the second part of this study that uses satellite‐based aerosol optical depth (AOD) products to further improve the forecast of surface PM2.5 in rural areas by performing statistical analysis of model output. This dataset is associated with the following publication: Spero, T., B. Murphy, H. Huanxin Zhang1,2, Jun Wang1,2, Lorena Castro García1,2, Cui Ge, J. Wang, L. Castro García, C. Ge, and T. Plessel. Improving surface PM2.5 forecasts in the U.S. using an ensemble of chemical transport model outputs, part I: bias correction with surface observations in non-rural areas. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 125(14): e2019JD032293, (2020).

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).