Air pollution is a major health risk factor worldwide. Regular short- and long-time exposures to ambient particulate matter (PM) promote various diseases and can lead to premature death. Therefore, in Germany, air quality is assessed continuously at approximately 400 measurement sites. However, knowledge about this intermediate distribution is either unknown or lacks a high spatial–temporal resolution to accurately determine exposure since commonly used chemical transport models are resource intensive. In this study, we present a method that can provide information about the ambient PM concentration for all of Germany at high spatial (100 m × 100 m) and hourly resolutions based on freely available data. To do so we adopted and optimised a method that combined land use regression modelling with a geostatistical interpolation technique using ordinary kriging. The land use regression model was set up based on CORINE (Coordination of Information on the Environment) land cover data and the Germany National Emission Inventory. To test the model’s performance under different conditions, four distinct data sets were used. (1) From a total of 8760 (365 × 24) available h, 1500 were randomly selected. From those, the hourly mean concentrations at all stations (ca. 400) were used to run the model (n = 566,326). The leave-one-out cross-validation resulted in a mean absolute error (MAE) of $7.68$$\mathsf{\mu }$$\mathrm{g}$${\mathrm{m}}^{-3}$ and a root mean square error (RMSE) of $11.20$$\mathsf{\mu }$$\mathrm{g}$${\mathrm{m}}^{-3}$. (2) For a more detailed analysis of how the model performs when an above-average number of high values are modelled, we selected all hourly means from February 2011 (n = 256,606). In February, measured concentrations were much higher than in any other month, leading to a slightly higher MAE of $9.77$$\mathsf{\mu }$$\mathrm{g}$${\mathrm{m}}^{-3}$ and RMSE of $14.36$$\mathsf{\mu }$$\mathrm{g}$${\mathrm{m}}^{-3}$, respectively. (3) To enable better comparability with other studies, the annual mean concentration (n = 413) was modelled with a MAE of $4.82$$\mathsf{\mu }$$\mathrm{g}$${\mathrm{m}}^{-3}$ and a RMSE of $6.08$$\mathsf{\mu }$$\mathrm{g}$${\mathrm{m}}^{-3}$. (4) To verify the model’s capability of predicting the exceedance of the daily mean limit value, daily means were modelled for all days in February (n = 10,845). The exceedances of the daily mean limit value of 50 $\mathsf{\mu }$$\mathrm{g}$${\mathrm{m}}^{-3}$ were predicted correctly in 88.67% of all cases. We show that modelling ambient PM concentrations can be performed at a high spatial–temporal resolution for large areas based on open data, land use regression modelling, and kriging, with overall convincing results. This approach offers new possibilities in the fields of exposure assessment, city planning, and governance since it allows more accurate views of ambient PM concentrations at the spatial–temporal resolution required for such assessments.

Air pollution remains a significant issue, particularly in urban areas. This study explored the prediction of hourly point-based PM₁₀ concentrations using the XGBoost algorithm to assimilate them into a geostatistical land use regression model for spatially and temporally high-resolution prediction maps. The model configuration and training incorporated meteorological data, station metadata, and time variables based on statistical values and expert knowledge. Hourly measurements from approximately 400 stations from 2009 to 2017 were used for training. The selected model performed with a mean absolute error (MAE) of 6.88 μg m⁻³, root mean squared error (RMSE) of 9.95 μg m⁻³, and an R² of 0.65, with variations depending on the siting type and surrounding area. The model achieved a high accuracy of 98.54% and a precision of 73.96% in predicting exceedances of the current EU-limit value for the daily mean of 50 μg m⁻³. Despite identified limitations, the model can effectively predict hourly values for assimilation into a geostatistical land use regression model.

Accurate estimation of the disease burden attributable to ambient particulate matter (PM_2.5) requires reliable exposure data with an adequate spatial resolution. While coarse-resolution models are commonly used for national assessments, the impact of spatial resolution on environmental burden of disease (EBD) estimates remains understudied. We compared two modelling approaches for PM_2.5 exposure in Germany: a chemical transport model (REM-CALGRID [RCG], 2 × 2 km² resolution) and a high-resolution hybrid geostatistical land-use regression model (PMR, 100 × 100 m² resolution). EBD estimates were calculated for five health outcomes using established concentration-response functions. Across all outcomes, the PMR model consistently produced slightly higher EBD estimates—approximately 2%–3% higher disability-adjusted life years (DALYs) than the coarser RCG model. The difference is due to improved detection of local pollution hotspots and more accurate exposure assignments in the high-resolution model. A larger share of the population was classified into higher exposure categories under the PMR model. Although the added value of high-resolution modelling is limited at national level, it offers clear benefits for small-area analyses and applications in environmental justice and health equity. Our findings support the integration of fine-scale exposure data into public health surveillance and burden of disease (BoD) assessments where feasible.