Visibility is a critical factor for transportation, even if we refer to air, water, or ground transportation. The biggest trend in the automotive industry is autonomous driving, the number of autonomous vehicles will increase exponentially, prompting changes in the industry and user segment. Unfortunately, these vehicles still have some drawbacks and one, always in attention and topical, will be treated in this paper—visibility distance issue in bad weather conditions, particularly in fog. The way and the speed with which vehicles will determine objects, obstacles, pedestrians, or traffic signs, especially in bad visibility, will determine how the vehicle will behave. In this paper, a new experimental set up is featured, for analyzing the effect of the fog when the laser and LIDAR (Light Detection And Ranging) radiation are used in visibility distance estimation on public roads. While using our experimental set up, in the laboratory, the information offered by these measurement systems (laser and LIDAR) are evaluated and compared with results offered by human observers in the same fog conditions. The goal is to validate and unitarily apply the results regarding visibility distance, based on information arrives from different systems that are able to estimate this parameter (in foggy weather conditions). Finally, will be notifying the drivers in case of unexpected situations. It is a combination of stationary and of moving systems. The stationary system will be installed on highways or express roads in areas prone to fog, while the moving systems are, or can be, directly installed on the vehicles (autonomous but also non-autonomous).

This research focuses on analyzing concentrations of particulate matter (PM) next to a highly congested road section, with 39,900 as the maximum number of vehicles per 24 h, in the vicinity of Timisoara, Romania. The concentrations are measured in multiple episodes using two different measurement methods, gravimetric and dynamic light scattering, showing a dispersion range of the PM concentrations. The presence of metal particles in the samples are analyzed using an electron microscope. Additionally, the impact on human health is described by evaluating the results for inhalable-, thoracic-, and alveolar-sized particles.

The research is focusing on analyzing the concentrations of particulate matter (PM) next to a highly congested road section, with 39,900 as a maximum number of vehicles per 24 h, in the vicinity of Timisoara, Romania. Concentrations have been measured in multiple episodes using two different measurement methods, gravimetric and dynamic light scattering, showing a disperse range of PM concentrations. Presence of metal particles in the samples have been analyzed using an electron microscope. Additionally, impact on human health is described by evaluating results for inhalable, thoracic, and alveolar sized particles.

Solar energy projects require accurate forecasts for the collectable solar resources on a range of time scales. An intermediate step towards forecasting solar irradiance is the prediction of the Sun coverage by clouds, quantified here through the binary parameter sunshine number. In this paper the use of all-sky images to obtain forecasts for the sunshine number is presented. The steps of the algorithm comprise: the estimation of the average motion vector of the cloud field based on time series of sky images, advection of the cloud field over a forecast horizon, and computing the sunshine number from the predicted cloud field. A simplified version of the model is also introduced, with the cloud motion vector fixed through climatological considerations. A preliminary validation study shows that the new models bring significant improvements over persistence for both generic conditions (the simplified version) and under a highly unstable radiative regime (the full version).

Most studies have focused on extending the applicability of the Ångström–Prescott equation and improving its accuracy in estimating solar irradiation. Only a limited number of studies have addressed atmospheric climatology using the Ångström–Prescott equation. In contrast, this study reformulates the Ångström–Prescott equation to explore its potential for extracting long-term atmospheric transparency information from radiometric measurements. It introduces a new annual formulation of the Ångström–Prescott equation derived from its common monthly version. While the formal structure is preserved, the equation shifts from its usual role, as a solar irradiation estimator, to a new role, as a predictor of long-term atmospheric transparency. The revised equation naturally defines an annual effective sunshine duration, which assigns greater weight to relative sunshine during summer months than during winter months. To enable prediction, the revised Ångström–Prescott equation is combined with Gaussian process regression. The equation provides the historical annual time series, while Gaussian process regression predicts future values and quantifies their associated uncertainty. To demonstrate the predictive capability of the method, it is applied to the analysis and prediction of four annual parameters characterizing atmospheric transparency: mean clear-sky atmospheric transparency, mean cloud transmittance, mean atmospheric transparency, and effective relative sunshine duration. The analysis is conducted using radiometric data collected at 14 stations distributed across Europe. Predictions for the upcoming decade (2024–2033) indicate that, at most stations, mean atmospheric transparency is expected to remain stable or change within approximate margins of −5% to +10%.