Prediction of atmospheric environmental severity in Tibet based on polycarbonate (PC) aging behavior

Abstract

The spatiotemporal distribution of atmospheric environmental severity in Tibet was evaluated and predicted based on polycarbonate (PC) chromatic aberrations. This study collected the annual average data (April 2021 to March 2022) of eight types of environmental factors from 10 typical atmospheric sites in Tibet. The climatic characteristics and climate distribution areas were analyzed to obtain accurate input to evaluate environmental severity. Natural environmental tests were conducted at 10 sites to analyze the regulation of PC degradation. The results showed that the gloss decreased and chromatic aberration gradually increased during PC aging, and mechanical properties, such as tensile strength and tensile strain at break, decreased with fluctuations. Thus, chromatic aberration was selected as a PC aging evaluation index owing to its excellent performance. Pearson’s correlation analysis was used to determine the information redundancy hidden in various environmental factors and geographic information coordinates. The environmental parameters were further optimized, and the factors highly related to PC aging were sunshine time, altitude, average relative humidity, and precipitation time. The “environmental material” mapping model with excellent training precision and generalizability was established using the Back Propagation Artificial Neural Network. By inputting the environmental data of 28 cities in Tibet into the well-built models, the severity was predicted and visualized to form spatial distribution maps using the Griddate interpolation method. The results showed that the low-altitude areas in eastern Tibet presented low severity. By training with different learning accuracies, the results revealed that low learning precision caused insufficient training and led to low prediction accuracy, whereas high learning precision led to overfitting and a prediction of the local minimum. The meteorological data of the 28 cities in Tibet were loaded into a well-trained artificial neural network model to predict the chromatic aberration value of PC aging in 28 cities in Tibet. A spatial distribution map of severity in Tibet was obtained based on the Griddate interpolation calculation. The results indicated that severity was much higher in summer than in winter, and the severity of the northwest area was the highest even in winter. The exact quantitative evaluations of severity played a significant role in the safety service for the equipment and facilities in Tibet.

Keywords

• polycarbonate (pc)
• atmospheric environment of tibet
• artificial neural network
• severity
• prediction