Half-century PM2.5 over global land from visibility observations

Fine particulate matter (PM 2.5 ) significantly impacts climate, environment, and human health. PM 2.5 data are obtained through direct ground-based measurements, indirect estimations, and modelling reanalysis. However, historical PM 2.5 data are limited by the short duration of ground-based observations and satellite-based estimations, and the constraints of assimilation data scarcity in reanalysis, especially before 2000. Leveraging the strong correlation between PM 2.5 and visibility, and the extensive, long-term visibility observations, this study reconstructs a half-century (1973–2022) 0.25-degree monthly PM 2.5 data over global land using a machine-learning model and geographically weighted regression. The reconstruction integrates PM 2.5 observations (∼5000 sites), visibility data (∼12,000 stations), and auxiliary datasets. Machine-learning model evaluations show excellent performance and robust predictions for site-scale PM 2.5 data (R = 0.97 for cross-validation; R = 0.85 and 0.92 for historical and future scenarios). Space-time consistency examinations demonstrate strong agreement with independent observations and satellite-based datasets. The gridded PM 2.5 data by the interpolation model performs well against ground-truth data (R = 0.92), and error analysis indicates small, stable errors (1.95–2.32 μg m −3 ), although confidence is lower in unobserved regions such as deserts. During 1973 and 2022, spatial heterogeneity indicates high levels in Northern and Central Africa and Asia, moderate levels in Central and South America, Eastern Europe, Southern Africa, and Australia, and low levels in North America and Western Europe. Regional trends highlight a decline in Asia due to pollution controls after 2010, an increase in Western North America linked to anthropogenic emissions post-2015, and a sustained growth in Central America likely driven by urbanization. It fills historical gaps in PM 2.5 records and provides an essential foundation for climate, environment, and human health studies, available at https://doi.org/10.11888/Atmos.tpdc.301338 .