Smartphone based Android app for determining UVA aerosol optical depth and direct solar irradiances

This research describes the development and evaluation of the accuracy and precision of an Android app specifically designed, written and installed on a smartphone for detecting and quantifying incident solar UVA radiation and subsequently, aerosol optical depth at 340 nm and 380 nm. Earlier studies demonstrated that a smartphone image sensor can detect UVA radiation and the responsivity can be calibrated to measured direct solar irradiance. This current research provides the data collection, calibration, processing, calculations and display all on a smartphone. A very strong coefficient of determination of 0.98 was achieved when the digital response was recalibrated and compared to the Microtops sunphotometer direct UVA irradiance observations. The mean percentage discrepancy discrepancy for derived direct solar irradiance was only 4% and 6% for observations at 380 nm and 340 nm respectively, lessening with decreasing solar zenith angle. An 8% mean percent difference discrepancy was observed when comparing aerosol optical depth, also decreasing as solar zenith angle decreases. The results indicate that a specifically designed Android app linking and using a smartphone image sensor, calendar and clock, with additional external narrow bandpass and neutral density filters can be used as a field sensor to evaluate both direct solar UVA irradiance and low aerosol optical depths for areas with low aerosol loads

Long-term UV dosimeter based on polyvinyl chloride for plant damage effective UV exposure measurements

Research on the influence of ultraviolet radiation (UV) on terrestrial plants and on its link with other influencing environmental factors requires information on UV exposures, both for a horizontal plane and specific portions of a plant, above and under the canopy. In this research, one set of UV dosimeters based on unstabilized polyvinyl chloride (PVC) were employed to measure the unweighted UVB (UVB) and the biologically effective UV radiation for plant damage (UVBEplant) incident on the leaves of a plant for a month, without having to change the dosimeters. The exposures were compared to the cumulative exposure concurrently measured with six sets of unstabilized polyphenylene oxide (PPO) dosimeters that required changing every four to six days. The difference in exposures between the two types of dosimeters was on average within 11%. The PVC dosimeter is the first reported polymer film dosimeter with a useable range of a month for measuring the plant damaging UV and the UVB exposures to specific parts of a plant. The exposure period of a month for the PVC dosimeter is an extension by a factor of four over the useable range of dosimeters previously reported in the literature for evaluation of the exposure of plants to UV radiation

Solar blue light radiation enhancement during mid to low solar elevation periods under cloud affected skies

Solar blue-violet wavelengths (380 nm - 455 nm) are at the high energy end of the visible spectrum, referred to as ‘high energy visible’ (HEV). Both chronic and acute exposure to these wavelengths have been often highlighted as causes of concern with respect to ocular health. The sun is the source of HEV which reaches the Earth’s surface either directly or after scattering by the atmosphere and clouds. This research has investigated the effect of clouds on HEV for low solar elevation (solar zenith angles between 60° and 80°), simulating time periods when potential ocular exposure in global populations are high during the early morning and late afternoon. The enhancement of ‘bluing’ of the sky due to the influence of clouds was found to increase significantly with the amount of cloud. A method is presented for calculating HEV irradiance from the more commonly measured global solar radiation (300 – 3,000 nm) for all cases when clouds do and do not obscure the sun. The method, when applied to global solar radiation data correlates well with measured HEV within the solar zenith angle range 60° and 80° (R2 = 0.94, MBE = -1.63%, MABE = 10.3% and RMSE = 14.6%). The technique can be used to develop repeatable HEV hazard evaluations for human ocular health applications

The Simulated Ocular and Whole-body Distribution of Natural Sunlight to Kiteboarders: A High Risk Case of UVR Exposure for Athletes Utilizing Water Surfaces in Sport [Not yet published 14/01/20 MC]

Kiteboarding is an aquatic sporting discipline that has not yet been considered in the literature to date in terms of solar ultraviolet radiation (UVR) measurement. Kiteboarders need to look upward and are placed obliquely relative to the horizon when towed behind an overhead kite over a reflective water surface. This research defines the typical body surface orientation of a kiteboarder in motion through video vector analysis and demonstrates the potential risk to ocular and skin surface damage through practical measurement of solar UVR using a manikin model. Video analysis of 51 kiteboarders were made to construct skeletal wireframes showing the surface orientation of the leg, thigh, spine, humerus, lower arm and head of a typical kiteboarder. Solar UVR dosimeter measurements made using a manikin model demonstrate that the vertex and anterior surfaces of the knee, lower leg, and lower humerus received 89%, 90%, 80% and 63% of the available ambient UVR respectively for a typical kiteboarder who is tilted back more than 15o from vertical while in motion. Ocular (periorbital) exposures ranged from 56 to 68% of ambient. These new findings show that the anterior skin surfaces of kiteboarders and the eye are at elevated risk of solar UVR damage

Evaluation of shade profiles while walking in urban environments: A case study from inner suburban Sydney, Australia

Precise shade distributions at the street level are an area of research of increasing importance to provide complete and high spatial and temporal resolutions of the amount and effectiveness of shade. Temporal shade distributions and profiles were evaluated for an inner Sydney tree-lined suburban street at different times of the day using an electronic sun journal (ESJ), providing detailed profiles of shade availability for various times of the day to provide very detailed street-level shade profiles and distributions that are often not included in shade audit methods and models. Further profiles were developed of streets adjoining shopfronts and public parks. Distributions of dense, light and no shade areas were calculated, revealing that tree canopy shade area during the middle of the day is considerably less effective and more prone to gaps than at other times. Distributions calculated using the ESJ were compatible with the paper-based shade auditing with less than 10% variation, whilst the ESJ has revealed a greater resolution of detail of gaps in the shade, thus records a higher amount of areas of no shade. The ESJ is a robust, low cost and portable tool that can efficiently and quickly produce shade profiles during walks in an urban environment, such as streetscapes

Glass transmitted solar irradiances on horizontal and sun-normal planes evaluated with a smartphone camera

This paper characterised and evaluated the ability of a smartphone camera to measure ultraviolet radiation (UVR) through various types and thicknesses of glass. Image sensor responses from a smartphone with UVA transmitting filters were measurably stronger in the red colour channel than the blue, with the green colour channel responding weakly. Strong correlations of up to R2= 0.96 have been determined from calibration of the red and blue channel image responses against measured UVA irradiances for data obtained from both the horizontal plane and the sun-normal plane. For the validation data of the red channel and the blue channel respectively, the mean absolute error was 13.7% and 17.4% for the horizontal plane and 3.8% to 5.6% for the sun-normal plane. This research has concluded that it is possible to determine UVA irradiances through glass, of different thicknesses, using a smartphone camera with high degree of accuracy

Electronic Sun Journal Versus Self-report Sun Diary: A Comparison of Recording Personal Sunlight Exposure Methods

This research compared personal sunlight exposure times monitored electronically within suburban Australian environments against self-report paper journals for determining the timing and total duration of individual exposure to daily solar radiation. A total of 90 Electronic Sun Journal (ESJ) daily readings and self-report timing and duration estimates of exposure for weekend and weekdays were compared. A Wilcoxon ranked sign test showed a significant difference (V = 157, p < 0.001) between the duration of exposure recorded electronically and the duration of exposure that was self-reported in a diary. There was also found to be a statistically significant difference between total exposure time measured using both methods for weekends (V = 10, p < 0.001) and weekdays (V = 87, p < 0.001). General trends in outdoor exposure timing confirmed that the most frequent daily exposures received over the weekend occurred between one and two hours earlier than the most frequent exposures received on weekdays. This preliminary research found that exposure durations as recorded by the ESJ were longer on the weekends compared to weekdays (W = 402, p < 0.001) and confirmed that the ESJ is a viable alternative to self-reporting diaries

Forecasting solar photosynthetic photon flux density under cloud cover effects: novel predictive model using convolutional neural network integrated with long short-term memory network

Forecast models of solar radiation incorporating cloud effects are useful tools to evaluate the impact of stochastic behaviour of cloud movement, real-time integration of photovoltaic energy in power grids, skin cancer and eye disease risk minimisation through solar ultraviolet (UV) index prediction and bio-photosynthetic processes through the modelling of solar photosynthetic photon flux density (PPFD). This research has developed deep learning hybrid model (i.e., CNN-LSTM) to factor in role of cloud effects integrating the merits of convolutional neural networks with long short-term memory networks to forecast near real-time (i.e., 5-min) PPFD in a sub-tropical region Queensland, Australia. The prescribed CLSTM model is trained with real-time sky images that depict stochastic cloud movements captured through a total sky imager (TSI-440) utilising advanced sky image segmentation to reveal cloud chromatic features into their statistical values, and to purposely factor in the cloud variation to optimise the CLSTM model. The model, with its competing algorithms (i.e., CNN, LSTM, deep neural network, extreme learning machine and multivariate adaptive regression spline), are trained with 17 distinct cloud cover inputs considering the chromaticity of red, blue, thin, and opaque cloud statistics, supplemented by solar zenith angle (SZA) to predict short-term PPFD. The models developed with cloud inputs yield accurate results, outperforming the SZA-based models while the best testing performance is recorded by the objective method (i.e., CLSTM) tested over a 7-day measurement period. Specifically, CLSTM yields a testing performance with correlation coefficient r = 0.92, root mean square error RMSE = 210.31 μ mol of photons m−2 s−1, mean absolute error MAE = 150.24 μ mol of photons m−2 s−1, including a relative error of RRMSE = 24.92% MAPE = 38.01%, and Nash Sutcliffe’s coefficient ENS = 0.85, and Legate and McCabe’s Index LM = 0.68 using cloud cover in addition to the SZA as an input. The study shows the importance of cloud inclusion in forecasting solar radiation and evaluating the risk with practical implications in monitoring solar energy, greenhouses and high-value agricultural operations affected by stochastic behaviour of clouds. Additional methodological refinements such as retraining the CLSTM model for hourly and seasonal time scales may aid in the promotion of agricultural crop farming and environmental risk evaluation applications such as predicting the solar UV index and direct normal solar irradiance for renewable energy monitoring systems

Evaluating UVA aerosol optical depth using a Smartphone camera

This research evaluates a smartphone complementary metal oxide semiconductor (CMOS) image sensor's ability to detect and quantify incident solar UVA radiation and subsequently, aerosol optical depth at 340 and 380 nm. Earlier studies revealed that the consumer grade CMOS sensor has inherent UVA sensitivities, despite attenuating effects of the lens. Narrow bandpass and neutral density filters were used to protect the image sensor and to not allow saturation of the solar images produced. Observations were made on clear days, free from clouds. The results of this research demonstrate that there is a definable response to changing solar irradiance and aerosol optical depth can be measured within 5% and 10% error margins at 380 and 340 nm respectively. The greater relative error occurs at lower wavelengths (340 nm) due to increased atmospheric scattering effects, particularly at higher air masses and due to lower signal to noise ratio in the image sensor. The relative error for solar irradiance was under 1% for observations made at 380 nm. The results indicate that the smartphone image sensor, with additional external narrow bandpass and neutral density filters can be used as a field sensor to evaluate solar UVA irradiance and aerosol optical depth

Cloud segmentation property extraction from total sky image repositories using Python

Acquiring the reflectance, radiance and related structural cloud properties from repositories of historical sky images can be a challenging and a computationally intensive task, especially when performed manually or by means of non-automated approaches. In this paper, a quick and efficient, self-adaptive Python tool for the acquisition and analysis of cloud segmentation properties that is applicable to images from all-sky image repositories is presented and a case study demonstrating its usage and the overall efficacy of the technique is demonstrated. The proposed Python tool aims to build a new data extraction technique and to improve the accessibility of data to future researchers, utilizing the freely available libraries in the Python programming language with the ability to be translated into other programming languages. After development and testing of the Python tool in determining cloud and whole sky segmentation properties, over 42,000 sky images were analysed in a relatively short time of just under 40 minutes, with an average execution time of about 0.06 seconds to complete each image analysis