GlobalHighPM₂.₅: Global Daily Seamless 1 km Ground-Level PM₂.₅ Dataset over Land (2000–Present)

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Authors: Jing Wei, Zhanqing Li, Alexei Lyapustin, Jun Wang, Оleg Dubovik, Joel David Schwartz, Lin Sun, Chi Li, Song Liu, Tong Fei Zhu
Cited by: 1

Abstract

GlobalHighPM2.5 is part of a series of long-term, seamless, global, high-resolution, and high-quality datasets of air pollutants over land (i.e., GlobalHighAirPollutants, GHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence and accounts for the spatiotemporal heterogeneity of air pollution. This dataset contains input data, analysis codes, and the generated dataset used for the following article. If you use the GlobalHighPM2.5 dataset in your scientific research, please cite the following reference (Wei et al., NC, 2023): Wei, J., Li, Z., Lyapustin, A., Wang, J., Dubovik, O., Schwartz, J., Sun, L., Li, C., Liu, S., and Zhu, T. First close insight into global daily gapless 1 km PM2.5 pollution, variability, and health impact. Nature Communications, 2023, 14, 8349. https://doi.org/10.1038/s41467-023-43862-3 Input Data Relevant raw data for each figure (compiled into a single sheet within an Excel document) in the manuscript. Code Relevant Python scripts for replicating and plotting the analysis results in the manuscript, as well as codes for converting data formats. Generated Dataset Here is the first big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) global ground-level PM2.5 dataset over land from 2000 to the present. This dataset exhibits high quality, with cross-validation coefficients of determination (CV-R2) of 0.91, 0.97, and 0.98, and root-mean-square errors (RMSEs) of 9.20, 4.15, and 2.77 µg m-3 on the daily, monthly, and annual bases, respectively. Due to data volume limitations, the dataset is released separately by year: All data (including daily products) for 2022 are available at: GlobalHighPM2.5 (2022) All data (including daily products) for 2021 are available at: GlobalHighPM2.5 (2021) All data (including daily products) for 2020 are available at: GlobalHighPM2.5 (2020) All data (including daily products) for 2019 are available at: GlobalHighPM2.5 (2019) All data (including daily products) for 2018 are available at: GlobalHighPM2.5 (2018) All data (including daily products) for 2017 are available at: GlobalHighPM2.5 (2017) All data (including daily products) for 2016 are available at: GlobalHighPM2.5 (2016) All data (including daily products) for 2015 are available at: GlobalHighPM2.5 (2015) All data (including daily products) for 2014 are available at: GlobalHighPM2.5 (2014) All data (including daily products) for 2013 are available at: GlobalHighPM2.5 (2013) All data (including daily products) for 2012 are available at: GlobalHighPM2.5 (2012) All data (including daily products) for 2011 are available at: GlobalHighPM2.5 (2011) All data (including daily products) for 2010 are available at: GlobalHighPM2.5 (2010) All data (including daily products) for 2009 are available at: GlobalHighPM2.5 (2009) All data (including daily products) for 2008 are available at: GlobalHighPM2.5 (2008) All data (including daily products) for 2007 are available at: GlobalHighPM2.5 (2007) All data (including daily products) for 2006 are available at: GlobalHighPM2.5 (2006) All data (including daily products) for 2005 are available at: GlobalHighPM2.5 (2005) All data (including daily products) for 2004 are available at: GlobalHighPM2.5 (2004) All data (including daily products) for 2003 are available at: GlobalHighPM2.5 (2003) All data (including daily products) for 2002 are available at: GlobalHighPM2.5 (2002) All data (including daily products) for 2001 are available at: GlobalHighPM2.5 (2001) All data (including daily products) for 2000 are available at: GlobalHighPM2.5 (2000) The dataset will be continuously updated... More GHAP datasets for different air pollutants are available at: https://weijing-rs.github.io/product.html

Fields of Study & Subjects

Atmospheric and Environmental Gas Dynamics Air Quality Monitoring and Forecasting Atmospheric chemistry and aerosols

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
17 August 2026
Language
EN
Document type
Dataset