Plant growth is directly related to levels of photosynthetic photon flux density, $Q_p$. The improvement of plant-growth models therefore requires accurate estimations of the $Q_p$ parameter that is often indirectly calculated on the basis of its relationship with solar irradiation, $R_S$, due to the scarcity of ground measurements of photosynthetic photon flux density. In this experimental campaign in Burgos, Spain, between April 2019 and January 2020, an average value of the $Q_p/R_s$ ratio is determined on the basis of measurements at ten-minute intervals. The most influential factor in the $Q_p/R_s$ ratio, over and above any daily or seasonal pattern, is the existence of overcast sky conditions. The CIE standard sky classification can be used to establish an unequivocal characterization of the cloudiness conditions of homogeneous skies. In this study, the relation between the CIE standard sky type and $Q_p/R_s$ is investigated. Its conclusions were that the $Q_p/R_s$ values, the average of which was $1.93\pm 0.15$ μmol·J⁻¹, presented statistically significant differences for each CIE standard sky type. The overcast sky types presented the highest values of the ratio, while the clear sky categories presented the lowest and most dispersed values. During the experimental campaign, only two exceptions were noted for covered and partial covered sky-type categories, respectively, sky types 5 and 9. Their values were closer to those of categories classified as clear sky according to the CIE standard. Both categories presented high uniformity in terms of illumination.

In the present study, different models constructed with meteorological variables are proposed for the determination of horizontal ultraviolet irradiance (${\mathrm{I}}_{\mathrm{UV}}$), on the basis of data collected at Burgos (Spain) during an experimental campaign between March 2020 and May 2022. The aim is to explore the effectiveness of a range of variables for modelling horizontal ultraviolet irradiance through a comparison of supervised artificial neural network (ANN) and regression model results. A preliminary feature selection process using the Pearson correlation coefficient was sufficient to determine the variables for use in the models. The following variables and their influence on horizontal ultraviolet irradiance were analyzed: horizontal global irradiance $({\mathrm{I}}_{\mathrm{GH}}$), clearness index $({\mathrm{k}}_{\mathrm{t}}$), solar altitude angle $(\mathsf{\alpha })$, horizontal beam irradiance $\left({\mathrm{I}}_{\mathrm{BH}}\right)$, diffuse fraction ($\mathrm{D}$), temperature $\left(\mathrm{T}\right)$, sky clearness $(\mathsf{\epsilon })$, cloud cover $(\mathrm{Cc}$), horizontal diffuse irradiance $\left({\mathrm{I}}_{\mathrm{DH}}\right)$, and sky brightness (Δ). The ANN models yielded results of greater accuracy than the regression models.

Four models for predicting Photosynthetically Active Radiation (PAR) were obtained through MultiLinear Regression (MLR) and an Artificial Neural Network (ANN) based on 10 meteorological indices previously selected from a feature selection algorithm. One model was developed for all sky conditions and the other three for clear, partial, and overcast skies, using a sky classification based on the clearness index (k_t). The experimental data were recorded in Burgos (Spain) at ten-minute intervals over 23 months between 2019 and 2021. Fits above 0.97 and Root Mean Square Error (RMSE) values below 7.5% were observed. The models developed for clear and overcast sky conditions yielded better results. Application of the models to the seven experimental ground stations that constitute the Surface Radiation Budget Network (SURFRAD) located in different Köppen climatic zones of the USA yielded fitted values higher than 0.98 and RMSE values less than 11% in all cases regardless of the sky type.

Solar Ultraviolet Radiation (UVR), which is identified as a major environmental health hazard, is responsible for a variety of photochemical reactions with direct effects on urban and aquatic ecosystems, human health, plant growth, and the deterioration of industrial systems. Ground measurements of total solar UVR are scarce, with low spatial and temporal coverage around the world, which is mainly due to measurement equipment maintenance costs and the complexities of equipment calibration routines; however, models designed to estimate ultraviolet rays from global radiation measurements are frequently used alternatives. In an experimental campaign in Burgos, Spain, between September 2020 and June 2022, average values of the ratio between horizontal global ultraviolet irradiance (GHUV) and global horizontal irradiance (GHI) were determined, based on measurements at ten-minute intervals. Sky cloudiness was the most influential factor in the ratio, more so than any daily, monthly, or seasonal pattern. Both the CIE standard sky classification and the clearness index were used to characterize the cloudiness conditions of homogeneous skies. Overcast sky types presented the highest values of the ratio, whereas the clear sky categories presented the lowest and most dispersed values, regardless of the criteria used for sky classification. The main conclusion, for practical purposes, was that the ratio between GHUV and GHI can be used to model GHUV.

Different strategies for modeling Global Horizontal UltraViolet Erythemal irradiance ($\mathrm{G}\mathrm{H}\mathrm{U}\mathrm{V}\mathrm{E}$) based on meteorological parameters measured in Burgos (Spain) have been developed. The experimental campaign ran from September 2020 to June 2022. The selection of relevant variables for modeling was based on Pearson’s correlation coefficient. Multilinear Regression Model $\left(\mathrm{M}\mathrm{L}\mathrm{R}\right)$ and artificial neural network $\left(\mathrm{A}\mathrm{N}\mathrm{N}\right)$ techniques were employed to model $\mathrm{G}\mathrm{H}\mathrm{U}\mathrm{V}\mathrm{E}$ under different sky conditions (all skies, overcast, intermediate, and clear skies), classified according to the $\mathrm{C}\mathrm{I}\mathrm{E}$ standard on a 10 min basis. $\mathrm{A}\mathrm{N}\mathrm{N}$ models of $\mathrm{G}\mathrm{H}\mathrm{U}\mathrm{V}\mathrm{E}$ outperform those based on MLR according to the traditional statistical indices used in this study (${\mathrm{R}}^2$, $\mathrm{M}\mathrm{B}\mathrm{E}$, and $\mathrm{n}\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}$). Moreover, the work proposes a simple all-sky $\mathrm{A}\mathrm{N}\mathrm{N}$ model of $\mathrm{G}\mathrm{H}\mathrm{U}\mathrm{V}\mathrm{E}$ based on usually recorded variables at ground meteorological stations.

Concern about the harmful effects that ultraviolet (UV) rays have on the skin of people who are routinely exposed to solar radiation has driven the industry of skin protection creams, sunglasses and clothing. Spanish Navy personnel are subject to different levels of exposure depending on their rank and function. The objective of this research is to analyze the behavioral variables associated to the effects on the skin caused by UV rays, denoted by the combined effects of perceived susceptibility and perceived severity, on their decision to purchase and wear uniforms with UV protection. A confirmatory analysis using a structural equation modeling (SEM) was performed on a sample of 100 respondents. The model results revealed a strong mediating characteristic of the intention to use, variable associated with the exogenous variables. Attitude towards the use of clothing and social influence, as well as the exogenous variable clothing action planning, on the sun protective clothing use during tactical maneuvers. These relationships were significant with p-values close to zero. However, exogenous variables related to perceived susceptibility and perceived severity in exposure to sunlight did not represent a significant influence when mediated by self-efficacy in use. The results revealed the consequence of awareness about the importance of protecting oneself and the influence that usage habits can have on the military with respect to the decision to purchase uniforms with UV protection.