A DNN-Based UVI Calculation Method Using Representative Color Information of Sun Object Images

As outdoor activities are necessary for maintaining our health, research interest in environmental conditions such as the weather, atmosphere, and ultraviolet (UV) radiation is increasing. In particular, UV radiation, which can benefit or harm the human body depending on the degree of exposure, is recognized as an essential environmental factor that needs to be identified. However, unlike the weather and atmospheric conditions, which can be identified to some extent by the naked eye, UV radiation corresponds to wavelength bands that humans cannot recognize; hence, the intensity of UV radiation cannot be measured. Recently, although devices and sensors that can measure UV radiation have been launched, it is very difficult for ordinary users to acquire ambient UV radiation information directly because of the cost and inconvenience caused by operating separate devices. Herein, a deep neural network (DNN)-based ultraviolet index (UVI) calculation method is proposed using representative color information of sun object images. First, Mask-region-based convolutional neural networks (R-CNN) are applied to sky images to extract sun object regions and then detect the representative color of the sun object regions. Then, a deep learning model is constructed to calculate the UVI by inputting RGB color values, which are representative colors detected later along with the altitude angle and azimuth of the sun at that time. After selecting each day of spring and autumn, the performance of the proposed method was tested, and it was confirmed that accurate UVI could be calculated within a range of mean absolute error of 0.3.

Optimization Algorithms As Training Approach With Deep Learning Methods To Develop An Ultraviolet Index Forecasting Model

The solar ultraviolet index (UVI) is a key public health indicator to mitigate the ultraviolet-exposure related diseases. However, in practice, the ultraviolet irradiance measurements are difficult and need expensive ground-based physical models and time-consuming satellite-observed data. Furthermore, accurate short-term forecasting is crucial for making effective decisions on public health owing to UVI related diseases. To this end, this study aimed to develop and compare the performances of different hybridized deep learning models for forecasting the daily UVI index. The ultraviolet irradiance-related data were collected for Perth station of Western Australia. A hybrid-deep learning framework was formulated with a convolutional neural network and long short-term memory called CLSTM. The comprehensive dataset (i.e., satellite-derived Moderate Resolution Imaging Spectroradiometer, ground-based datasets from Scientific Information for Landowners, and synoptic-scale climate indices) were fed into the proposed network and optimized by four optimization techniques. The results demonstrated the excellent forecasting capability (i.e., low error and high efficiency) of the recommended hybrid CLSTM model compared to the counterpart benchmark models. Overall, this study showed that the proposed hybrid CLSTM model successfully apprehends the complex and non-linear relationships between predictor variables and the daily UVI. A complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-CLSTM-based is appeared to be an accurate forecasting system capable of reacting quickly to measured conditions. Further, the genetic algorithm is found to be the most effective optimization technique. The study inference can considerably enhance real-time exposure advice for the public and help mitigate the potential for solar UV-exposure-related diseases such as melanoma.

The Ultraviolet Index Is Well Estimated by the Terrestrial Irradiance at 310 nm

Ultraviolet (UV) exposure significantly contributes to non-melanoma skin cancer. In the context of health, UV exposure is the product of time and the UV Index (UVI), a weighted sum of the irradiance I(λ) over all wavelengths from λ = 250 to 400 nm. In our analysis of the United States Environmental Protection Agency’s UV-Net database of over 400,000 spectral irradiance measurements taken over several years, we found that the UVI is well estimated by 77 I310. To further understand this result, we applied an optical atmospheric model to generate terrestrial irradiance spectra and found that it applies across a wide range of conditions. An accurate UVI radiometer can be built from a photodiode covered by a bandpass filter centered at 310 nm.

Partial COVID-19 lockdown effect in atmospheric pollutants and indirect impact in UV radiation in Rio Grande do Sul, Brazil

The COVID-19 pandemic introduced a significant decrease in industrial activities and other anthropic interventions on the environment, followed by a reduction of the emission of pollutant gases and aerosols. Monitoring of air quality is commonly performed through automatic stations, which can provide nearly real-time, accurate information. However, stations located in urban areas are subject to maintenance problems and extensive coverage for large areas is not feasible. As an alternative approach, data from orbital sensors can provide useful information for large areas at a low cost. Consequently, this study aimed to analyze the partial COVID-19 lockdown effect in atmospheric pollutants and indirect impact in UV radiation in Rio Grande do Sul, Brazil. Data on concentrations of nitrogen dioxide (NO2), total ozone (O3), and ultraviolet index (UVI) acquired by the OMI sensor aboard the Aura satellite were accessed for May, for the entire period 2010 to 2018, 2019, and 2020. Differences between these time series were calculated. Results showed significant reductions in nitrogen dioxide in most of the study area by as much as 33.9%, followed by increases in total ozone of up to 3.5% and the ultraviolet index by up to 4.8%. Although NO2 plays a fundamental role in stratospheric chemistry, our results suggest that its decrease in 2020 was not directly responsible for the increase in total O3; however, NO2 was partially the cause for the increase in UVI, which in turn led to the heating of the stratosphere, generating an increase in ozone.

A Radiação Ultravioleta e o Desporto

Sun exposure can be a cause of skin aggression, causing burns and skin cancer. The desired ultraviolet radiation for tanning and synthesis of vitamin D is responsible for the aggression and it can be monitored through the ultraviolet index. The literature review reveals that athletes place little value on individual protection against this radiation, with many reports of skin burns in outdoor sports practitioners. In addition to insisting on preventive measures, athletes need significantly more awareness strategies for their adoption.

The effect of sun elevation, cloudiness, and altitude on the ultraviolet index in Czechia

Ultraviolet (UV) radiation has recently become an important topic in relation to the loss of stratospheric ozone. High doses of UV radiation have a negative effect on many organisms. This paper focuses on the UV index (UVI), which expresses the risk of UV radiation on human health. The aim of the paper is to describe the definition of UVI, and its measurement, and to summarize geographical parameters and meteorological conditions affecting the values of UVI. The effect of sun elevation, cloudiness, and altitude is demonstrated using observed data from the Hradec Králové, Košetice and Labská bouda stations during the period 2011–2017. The results show a strong effect of both sun elevation and cloudiness. The highest values of UVI (up to 8) are generally observed on sunny days around midday from May to July. The reduction of the UVI caused by clouds, fog, and rain is, on average, 85% of values typical for sunny days. The effect of altitude is distinctly weaker; a rise of UVI with increasing altitude is 0.4 per 1 km for clear sky and the surface without snow cover.