Model output from the 2-way coupled WRF-CMAQ modeling system applied to China. This dataset is not publicly accessible because: EPA scientists helped with the model set-up and data analysis. The data was created by collaborators at Tsinghua University and is housed there. Since its not EPA generated data, the dataset is not included in ScienceHub. It can be accessed through the following means: The data can be accessed by contacting the corresponding author Prof. Shuxiao Wang (email: shxwang@tsinghua.edu.cn; phone: +86-10-62771466; fax: +86-10-62773650). Format: The data analyzed in this study were created by collaborators at Tsinghua University, China. The data can be accessed by contacting the corresponding author Prof. Shuxiao Wang (email: shxwang@tsinghua.edu.cn; phone: +86-10-62771466; fax: +86-10-62773650). This dataset is associated with the following publication: Xing, J., J. Wang, R. Mathur, S. Wang, G. Sarwar, J. Pleim, C. Hogrefe, Y. Zhang, J. Jiang, D. Wong, and J. Hao. Impacts of aerosol direct effects on tropospheric ozone through changes in atmospheric dynamics and photolysis rates. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, GERMANY, 17: 9869-9883, (2017).

Model output from the 2-way coupled WRF-CMAQ modeling system applied to China. This dataset is not publicly accessible because: EPA scientists helped with the model set-up and data analysis. The data was created by collaborators at Tsinghua University and is housed there. Since its not EPA generated data, the dataset is not included in ScienceHub. It can be accessed through the following means: The data can be accessed by contacting the corresponding author Prof. Shuxiao Wang (email: shxwang@tsinghua.edu.cn; phone: +86-10-62771466; fax: +86-10-62773650). Format: The data analyzed in this study were created by collaborators at Tsinghua University, China. The data can be accessed by contacting the corresponding author Prof. Shuxiao Wang (email: shxwang@tsinghua.edu.cn; phone: +86-10-62771466; fax: +86-10-62773650). This dataset is associated with the following publication: Xing, J., J. Wang, R. Mathur, S. Wang, G. Sarwar, J. Pleim, C. Hogrefe, Y. Zhang, J. Jiang, D. Wong, and J. Hao. Impacts of aerosol direct effects on tropospheric ozone through changes in atmospheric dynamics and photolysis rates. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, GERMANY, 17: 9869-9883, (2017).

Non-EPA data associated with Air Quality, Health, and Equity Benefits of Carbon Neutrality andClean Air Pathways in China. This dataset is not publicly accessible because: Data owned by Tsinghua University. It can be accessed through the following means: Contact Shuxiao Wang of Tsinghua University (shxwang@mail.tsinghua.edu.cn). Format: GCAM database, tables, and files associated with air quality simulations. This dataset is associated with the following publication: Sun, Y., Y. Jiang, J. Xing, Y. Ou, S. Wang, D. Loughlin, S. Yu, L. Ren, S. Li, Z. Dong, H. Zheng, B. Zhao, D. Ding, F. Zhang, H. Zhang, Q. Song, K. Liu, Z. Klimont, J. Woo, X. Lu, S. Li, and J. Hao. Air Quality, Health, and Equity Benefits of Carbon Neutrality and Clean Air Pathways in China. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 58(34): 15027-15037, (2024).

초거대 AI 기술을 적용하여 한국, 중국 일부 지역을 대상으로 대기오염 배출원(굴뚝탐지와 높이, 산업단지, 시가지)을 추정하는 데이터의 구축

simulation results. This dataset is not publicly accessible because: I don't own the data. It can be accessed through the following means: Please contact Dr. Jordan Schnell (jordan.schnell@noaa.gov) to obtain a copy of the data. Format: N/A. This dataset is associated with the following publication: Schnell, J., D. Peters, D. Wong, X. Lu, H. Gao, H. Zhang, P. Kinney, and D. Horton. Potential for Electric Vehicle Adoption to Mitigate Extreme Air Quality Events in China. Earth’s Future. John Wiley & Sons, Inc., Hoboken, NJ, USA, 9(2): e2020EF001788, (2021).

충청남도 보건환경연구원의 충청남도 내 고정식 측정망이 없는 지역이나 대기오염 민원 발생 지역을 대상으로 이동측정차량이 측정한 실시간 대기오염물질 농도 정보 데이터입니다.

The data for this project include fields that are output from the numerous CMAQ simulations used to build the RSM. This dataset is not publicly accessible because: The data is too large to be included on ScienceHub. It can be accessed through the following means: This data can be accessed by contacting the corresponding authors as will be noted by the peer-reviewed journal. Format: This data is generated by OAQPS and colleagues at Tsinghua university in China. The output data are stored and, if requested, provided in the format of NetCDF. It is stored and preserved on the Atmos high performance computing (HPC) system located at the National Computing Center (NCC). The folder location for raw model data and post-processed data is: /asm1/ROMO. The input data is located in the folder titled: /work/ROMO Data will be copied to the backup automatic storage management (ASM) system after completion of the project to make room for working space on Atmos. This backup system is also located at the National Computing Center, fully accessible form EPA computers at all times and backed up daily or more frequently. In order to facilitate transparency, reproducibility, and credibility throughout the project, the model code will be appropriately commented to indicate the algorithmic functioning in a manner suitable for the general computer programmer. CMAQ source code will be stored on Github at www.github.com/USEPA/CMAQ on the branch labeled ‘5.3.2’. All substantive evolution of the model code will be documented by comments within the code, and the date of the change will be recorded in the standard logs created by Git version control. Project documentation is available via https://www.epa.gov/scram

The data for this project include fields that are output from the numerous CMAQ simulations used to build the RSM. This dataset is not publicly accessible because: The data is too large to be included on ScienceHub. It can be accessed through the following means: This data can be accessed by contacting the corresponding authors as will be noted by the peer-reviewed journal. Format: This data is generated by OAQPS and colleagues at Tsinghua university in China. The output data are stored and, if requested, provided in the format of NetCDF. It is stored and preserved on the Atmos high performance computing (HPC) system located at the National Computing Center (NCC). The folder location for raw model data and post-processed data is: /asm1/ROMO. The input data is located in the folder titled: /work/ROMO Data will be copied to the backup automatic storage management (ASM) system after completion of the project to make room for working space on Atmos. This backup system is also located at the National Computing Center, fully accessible form EPA computers at all times and backed up daily or more frequently. In order to facilitate transparency, reproducibility, and credibility throughout the project, the model code will be appropriately commented to indicate the algorithmic functioning in a manner suitable for the general computer programmer. CMAQ source code will be stored on Github at www.github.com/USEPA/CMAQ on the branch labeled ‘5.3.2’. All substantive evolution of the model code will be documented by comments within the code, and the date of the change will be recorded in the standard logs created by Git version control. Project documentation is available via https://www.epa.gov/scram

Dataset includes CMAQ predicted results. This dataset is not publicly accessible because: Shanghai Jiao Tong University created the dataset - EPA does not have the dataset. It can be accessed through the following means: Contact - Ping Liu, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China, email: ping_liu@sjtu.edu.cn. Format: Dataset includes CMAQ output files using netcdf format. This dataset is associated with the following publication: Chen, H., P. Liu, Q. Wang, R. Huang, and G. Sarwar. Impact and pathway of halogens on atmospheric oxidants in coastal city clusters in the Yangtze River Delta region in China. Atmospheric Pollution Research. Turkish National Committee for Air Pollution Research and Control, Izmir, TURKEY, 15(2): N/A, (2024).

This dataset contains projected temperature and ozone data provided by EPA's Office of Research and Development in support of the manuscript "A Flexible Bayesian Ensemble Machine Learning Framework for Predicting Local Ozone Concentrations," by Xiang Ren, Panos Georgopoulos, et al. This dataset is associated with the following publication: Ren, X., Z. Mi, T. Cai, C. Nolte, and P. Georgopoulos. Flexible Bayesian Ensemble Machine Learning Framework for Predicting Local Ozone Concentrations. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 56(7): 3871-3883, (2022).