Air quality measurements usually consist of ground-based instrumentation at fixed locations. However, vertical profiles of pollutants are of interest for understanding processes, distribution, dilution and concentration. Therefore, a multicopter system has been developed to investigate the vertical distribution of the concentration of aerosol particles, black carbon, ozone, nitrogen oxides (NO_x) and carbon monoxide and the meteorological parameters of temperature and humidity. This article presents the requirements by different users, the setup of the quadrocopter system, the instrumentation and the results of first applications. The vertical distribution of particulate matter next to a highway was strongly related to atmospheric stratification, with different concentrations below and above the temperature inversion present in the morning. After the qualification phase described in this article, two identically equipped multicopters will be used upwind and downwind of line or diffuse sources such as highways or urban areas to quantify the influence of their emissions on the local air quality.

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.

The elemental carbon (EC)-tracer method was applied to PM₁₀ and PM₁ data of three sampling sites in the City of Berlin from February to October 2010. The sites were characterized by differing exposure to traffic and vegetation. The aim was to determine the secondary organic carbon (SOC) concentration and to describe the parameters influencing the application of the EC-tracer method. The evaluation was based on comparisons with results obtained from positive matrix factorization (PMF) applied to the same samples. To obtain site- and seasonal representative primary OC/EC-ratios ([OC/EC]_p), the EC-tracer method was performed separately for each station, and additionally discrete for samples with high and low contribution of biomass burning. Estimated SOC-concentrations for all stations were between 11% and 33% of total OC. SOC-concentrations obtained with PMF exceeded EC-tracer results more than 100% at the park in the period with low biomass burning emissions in PM₁₀. The deviations were besides others attributed to the high ratio of biogenic to combustion emissions and to direct exposure to vegetation. The occurrences of biomass burning emissions in contrast lead to increased SOC-concentrations compared to PMF in PM₁₀. The obtained results distinguish that the EC-tracer-method provides well comparable results with PMF if sites are strongly influenced by one characteristic primary combustion source, but was found to be adversely influenced by direct and relatively high biogenic emissions.