ChinaHighPMC 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. This is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) dataset of ground-level PM2.5 chemical composition (including SO42-, NO3-, NH4+, Cl-, BC, and OM) in China since 2000. This dataset yields a high quality with average cross-validation coefficients (CV-R2) of 0.74, 0.75, 0.71, 0.66, and 0.66, and root-mean-square errors (RMSEs) of 6.0, 6.6, 4.3, and 2.3 µg m-3 for SO42-, NO3-, NH4+, and Cl-, respectively, on a daily basis. All the data will be made public online once our paper is accepted, and if you want to use the ChinaHighPMC dataset for related scientific research, please contact us (Email: weijing_rs@163.com; weijing@umd.edu). More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html

Note that this dataset is continuously updated and if you need more data, please contact me (Email: weijing_rs@163.com; weijing@umd.edu).