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This study analyzed the variability of equivalent black carbon (eBC) mass concentrations and their sources in urban Europe to provide insights into the use of eBC as an advanced air quality (AQ) parameter for AQ standards. This study compiled eBC mass concentration datasets covering the period between 2006 and 2022 from 50 measurement stations, including 23 urban background (UB), 18 traffic (TR), 7 suburban (SUB), and 2 regional background (RB) sites. The results highlighted the need for the harmonization of eBC measurements to allow for direct comparisons between eBC mass concentrations measured across urban Europe. The eBC mass concentrations exhibited a decreasing trend as follows: TR > UB > SUB > RB. Furthermore, a clear decreasing trend in eBC concentrations was observed in the UB sites moving from Southern to Northern Europe. The eBC mass concentrations exhibited significant spatiotemporal heterogeneity, including marked differences in eBC mass concentration and variable contributions of pollution sources to bulk eBC between different cities. Seasonal patterns in eBC concentrations were also evident, with higher winter concentrations observed in a large proportion of cities, especially at UB and SUB sites. The contribution of eBC from fossil fuel combustion, mostly traffic (eBCT) was higher than that of residential and commercial sources (eBCRC) in all European sites studied. Nevertheless, eBCRC still had a substantial contribution to total eBC mass concentrations at a majority of the sites. eBC trend analysis revealed decreasing trends for eBCT over the last decade, while eBCRC remained relatively constant or even increased slightly in some cities.

In this work, we study the different components of the UV Index, direct, diffuse, and global, for seven years of UV erythemal measurements in a Mediterranean locality at Burjassot AtmoSpheric Station (BASS). The measurements have been taken with three YES-UVB-1 radiometers: one provided with a shadow disk on a Sun tracker; a radiometer to measure the global horizontal radiation; and a Sun-tracking radiometer to measure the direct radiation with an especially designed radiance collimator. It is known that diffuse UV irradiance represents at least 50% of total UV irradiance on a horizontal plane, which means being able to obtain a high UV Index even under shade. Our study aims to establish the relationship between the UV Index at noon and the maximum daily UV erythemal irradiance in the mid-latitude area, as well as the potential erythemal effects of exposure to solar UV radiation on different skin phototypes (skin phototypes I–IV) for both direct and diffuse components.

We propose a methodology to derive the aerosol optical depth (AOD) and Angstrom exponent (AE) from calibrated images of an all-sky camera. It is based on a machine learning (ML) approach that establishes a relationship between AERONET measurements of AOD and AE and different signals derived from the principal plane radiance measured by an all-sky camera at three RGB channels. Gaussian process regression (GPR) has been chosen as machine learning method and applied to four models that differ in the input choice: RGB individual signals to predict spectral AOD; red signal only to predict spectral AOD and AE; blue-to-red ratio (BRR) signals to predict spectral AOD and AE; red signals to predict spectral AOD and AE at once. The novelty of our approach mostly relies on obtaining a cloud-screened and smoothed signal that enhances the aerosol features contained in the principal plane radiance and can be applied in partially cloudy conditions. In addition, a quality assurance criterion for the prediction has been also suggested, which significantly improves our results. When applied, our results are very satisfactory for all the models and almost all predictions are close to real values within ±0.02 for AOD and ±0.2 for AE, whereas the MAE is less than 0.005. They show an excellent agreement with AERONET measurements, with correlation coefficients over 0.92. Moreover, more than 87% of our predictions lie within the AERONET uncertainties (±0.01 for AOD, ±0.1 for AE) for all the output parameters of the best model. All the models offer a high degree of numerical stability with negligible sensitivities to the training data, atmospheric conditions and instrumental issues. All this supports the strength and efficiency of our models and the potential of our predictions. The optimum performance shown by our proposed methodology indicates that a well-calibrated all-sky camera can be routinely used to accurately derive aerosol properties. Together, all this makes the all-sky cameras ideal for aerosol research and this work may represent a significant contribution to the aerosol monitoring.

Four methods have been used for the estimation of the total cloud amount and cloud amount for low clouds: visual observations, the Long method applied on pyranometer measurements, the Automatic Partial Cloud Amount Detection Algorithm (APCADA) method applied on pyrgeometers measurements, and ceilometer measurements of the cloud base height. Records from meteorological observers indicate that clear days (0–1 octa) represent the most frequent cloud amount for low clouds. In contrast, the total cloud amount is more aleatory. Results obtained from the Long method show maximum frequency in the extreme cloud amount values. The APCADA method also indicates the predominance of cloudless skies. The ceilometer method shows a predominance of completely clear skies, but the completely cloudy (8 octas) is the second most frequent case. Automatic methods report more cloudless and overcast skies than the observer. Automatic methods agree with the visual method or differ in ±1 octa for 60–76% cases for low cloud amount and for 56–63% cases for total cloud amount. In general, low cloud amount agrees more with observer measurements than total cloud amount and the automatic methods underestimated total cloud amount observer values possibly due to the difficulty in monitoring high clouds.