Abstract

When the night is no longer lit up by stars, when the light that man once craved gradually swallows up the world, light pollution leaves the night sky without its back- ground, and life on the blue planet is immersed in the pain of light invasion. How to measure and solve light pollution has become an urgent global problem in today’s world, and we are deeply worried about it. So, we built an indicator model and developed effective methods to improve light pollution. For TASK I : In order to enhance the wide applicability of our model, we established TOPSIS Method based on AHP Model and Entropy Weight Method, which was used to analyze the indirect factors reflecting light pollution to the degree of regional pollution and their respective proportions. It is showed on the results that the regional vehicle ownership is the indirect factor reflecting the greatest degree of light pollution. After that, we deeply analyzed the more specific factors reflecting the impact of light pollution on the area by establishing a random forest model. Finally, we learned that biodiversity was the most important factor reflecting the impact of light pollution, accounting for 37% among the factors we consider. For TASK II: In order to control the influencing factors, light intensity and color temperature were divided into four levels as first-level factors, and the influence of artificial light was taken as second-level factors. Satellite images were simulated and optimal interval method and linear regression model were used to establish the contribution model of light pollution. Through this model, we proposed three intervention strategies and evaluated each of the four regions. The results showed that the scheme using lampshades worked best in suburban and urban communities, reducing light pollution risk levels by about 52 percent. For TASK III: We selected the most effective strategies for improving light pollution in Shanghai and produced a leaflet at last.

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