Global high-resolution ultrafine particle number concentrations through data fusion with machine learning

Atmospheric pollution causes millions of excess deaths annually, with particulate matter (PM) being a major concern. While research has traditionally focused on PM₁₀ and PM_2.5, ultrafine particles (UFPs, diameter < 100 nm) have emerged as a critical human health risk due to their ability to penetrate deeply into the respiratory system, transmigrate into the bloodstream and induce systemic health impacts. The total particle number concentration (PNC) serves as a proxy measure for UFP prevalence, as UFPs dominate particle number counts despite contributing minimally to total particle mass. This study presents the first global datasets of PNCs and UFPs at 1 km resolution over land by combining ground station measurements with machine learning. We developed an XGBoost model to predict annual PNC levels from 2010-2019, integrating diverse environmental and anthropogenic variables available at the global scale. Our model achieves an R² of ≥0.9 and a mean relative error of about 30% for polluted urban areas, based on comparison with test datasets, and its performance was evaluated by including spatial and temporal cross-validation schemes. We find that global annual mean PNCs near the Earth's surface vary between a few thousand per cm³ in pristine environments up to more than 40,000 per cm³ in some urban centres and that UFPs contribute about 91% to PNCs. The model incorporates a conformal prediction framework to provide reliable coverage intervals, making local-to-global PNC and UFP data available and supporting exposure assessments and health impact studies.