A light pollution risk model based on improved assessment and prediction methods

School of Jinan University, Guangzhou 519070, China.

Abstract

As light pollution becomes increasingly severe, its impact is becoming more widespread for instance affecting human health, social stability, and the ecological environment to varying degrees. In order to measure the risk level of light pollution and develop related measures, we propose the Light Pollution Risk Index (LPRI) and create various assessment, intervention and prediction models around it. It include three models: The LPRI Scoring System, The Light Pollution Risk Classification Model and the HSE Intervention Strategy & Potential Impact Prediction Model. Firstly, we use the improved EMW-AMP to determine the weights and elicicit the concepts for next models. We select 6 representative areas in different kinds of locations, combine the integrated weights with the Topsis method to score and rank. Followed by invoking K-Means cluster analysis, we reselect 108 areas and the consequence classifies the light pollution risk level into three levels: level A significant risk, indicator range from 0 to 4.23, level B average risk, indicator range from 4.23 to 7.64, and level C low risk, indicator range from 7.64 to 10. Then, followed by prediction of LPRI with a Grey linear regression combination prediction model. The predicted results can accurately and clearly reflect that the application of HEIS in Wuhan and HSIS in Los Angeles, which is the most effective. At the same time, we find that intervening in one or two of H, S, and E must have a non-positive effect on the risk indicator of the other side. Finally, we briefly discuss how the intervention strategies in Wuhan and Los Angeles affect the individual indicators and thus the level of risk.