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Pinker", "name": "Rachel T. Pinker", "type": "personal" } }, { "affiliations": [ { "name": "University of Maryland" } ], "person_or_org": { "family_name": "Russell R. Dickerson", "name": "Russell R. Dickerson", "type": "personal" } }, { "affiliations": [ { "name": "Peking University" } ], "person_or_org": { "family_name": "Jintai Lin", "name": "Jintai Lin", "type": "personal" } }, { "affiliations": [ { "name": "Wageningen University" } ], "person_or_org": { "family_name": "K. F. Boersma", "name": "K. F. Boersma", "type": "personal" } }, { "affiliations": [ { "name": "Shandong University of Science and Technology" } ], "person_or_org": { "family_name": "Lin Sun", "name": "Lin Sun", "type": "personal" } }, { "affiliations": [ { "name": "University of California, Irvine" } ], "person_or_org": { "family_name": "Runze Li", "name": "Runze Li", "type": "personal" } }, { "affiliations": [ { "name": "Qingdao University" } ], "person_or_org": { "family_name": "Wenhao Xue", "name": "Wenhao Xue", "type": "personal" } }, { "affiliations": [ { "name": "Hohai University" } ], "person_or_org": { "family_name": "Yuanzheng Cui", "name": "Yuanzheng Cui", "type": "personal" } }, { "affiliations": [ { "name": "University of Science and Technology of China" } ], "person_or_org": { "family_name": "Chengxin Zhang", "name": "Chengxin Zhang", "type": "personal" } }, { "affiliations": [ { "name": "University of Iowa" } ], "person_or_org": { "family_name": "Jun Wang", "name": "Jun Wang", "type": "personal" } } ], "description": "

ChinaHighNO2 is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. 

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This is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level NO2 dataset in China from 2019 to 2020. This dataset yields a high quality with a cross-validation coefficient of determination (CV-R2) of 0.93, a root-mean-square error (RMSE) of 4.89 µg m-3, and a mean absolute error (MAE) of 3.48 µg m-3 on a daily basis.

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Note that the ChinaHighNO2 dataset is 1 km after 2019, but 10 km before 2019, which is available at https://doi.org/10.5281/zenodo.4641542. If you use the ChinaHighNO2 dataset for related scientific research, please cite the corresponding reference (Wei et al., ES&T, 2023; Wei et al., ACP, 2022):

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More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html

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