In order to beautify the city, the overflowing decorative lights and billboards make the city’s night brighter. The excessive use of artificial lights will have adverse effects on the environment and human physical. This paper establishes the index system of quantitative light pollution level to monitor and control the light pollution. The combined weight evaluation model was established, and specific intervention strategies were proposed for the remediation of light pollution.
For problem 1: This paper first determines the important indicators of light pollution, such as magnitude brightness, luminosity and digital number, and establishes the index system to monitor the level of light pollution. The combined weights of nine indexes were obtained by AHP, Entropy Weight method and Coefficient of Variation method, and CRITIC weight is used to give the weight matrix and multiply the matrix weight and the combined weight to get the final weight. The light pollution risk assessment model is obtained.
For problem 2: First, we choose four typical cities with their characteristics. The night light data was obtained through the LJ-101\mathbf{L J} \mathbf{- 1} \mathbf{0 1} satellite, and then the data of nine indicators were obtained respectively. Finally, the normalized data are brought into the light pollution risk level model to calculate the final results of the four regions. The calculated risk level of light pollution is ranked from high to low as Shanghai (3.7582), Taizhou (0.9342), Yangshuo (0.2268) and Greater Khingan Mountains ( 0.2249 ). It can be concluded that the risk of light pollution and the degree of urbanization are positively correlated to a certain extent.
For problem 3: First, we selected four indicators through AHP method. Three of the indicators with a great impact on the results were selected, and the final weight was obtained by the Entropy Weight method: digital number (0.51), vegetation coverage (0.28), and population density ( 0.21 ), so as to build a light pollution risk level model without policy interference. Based on the new model and the literature of light pollution literature, several specific methods for light pollution: improving lighting technology in public areas; reducing population density in some areas; and increasing vegetation coverage in cities.
For the problem 4: First, analyzing the three methods found in problem three. The strategy resistance factors are applied to the light pollution risk level model without policy interference, and three light pollution risk level models with policy interference are constructed, and the risk level of light pollution in urban communities from 2016-2022 is calculated. The PSO-GM (1,1) model was constructed to predict the risk level of light pollution in the later years of urban communities, and the parameters -a < 0.3-a<0.3, which can be used for medium-and long-term prediction. By comparing the risk levels of light pollution such as four different measures, we know that policy interventions can reduce the level of light pollution risk at sites, and it can be concluded that improving vegetation coverage is the most effective intervention strategy.
Finally, we discuss the application of the model in multiple scenarios, verifying the wide applicability of the model.
I. Introduction … 3
1.1 Literature Review … 3
1.2 Our works … 4
II. The Description of the Problem … 4
2.1 Problem statement … 4
III. Basic assumption … 5
IV. Glossary & Symbols … 5
4.1 Glossary … .5
4.2 Symbols … 8
V. Light Pollution Level Indicator Evaluation System … 8
5.1 Light Pollution Combination Weighting Integrated Evaluation … 8
5.1.1 Model Preparation … 8
5.1.2 Model Establishment … 10
5.1.3 Light pollution risk level formula … 12
5.2 Application of Light Pollution Combination Weighting Integrated Evaluation Model … 13
5.2.1 Application Preparation … 13
5.2.2 Model Test Results … 14
5.3 Analysis and Implementation of Light Pollution Intervention Strategies … 15
5.3.1 Model Preparation … 15
5.3.2 Model Establishment … 15
5.3.3 Analysis of Results … 16
5.4 Effective Intervention Strategy Analysis … 17
5.4.1 Analysis of Strategy One: Reduce night light index … 18
5.4.2 Analysis of Strategy Two: Reduce the population density in some areas … 19
5.4.3 Analysis of Strategy Three: Increase vegetation coverage … 20
5.4.4 Result … 21
VI. Evaluation and Promotion of Model … 22
6.1 Strength … 22
6.2 Promotion … 22
VII. References … 25
I. Introduction
1.1 Literature Review
With the development of urbanization and social economy, artificial lighting devices are widely used in all corners of the city at night. However, too much artificial lighting is beyond the actual demand, bringing light pollution to the city’s night sky [1]. People are becoming more concerned about the safety, health, and environmental protection of the living environment. Simple “brightening” can no longer meet people’s needs. Light pollution like water pollution, and air pollution is more and more people concerned. How to develop accurate quantitative methods and evaluation standards, scientific monitoring and management of light pollution has become an urgent problem[2].
In this paper, we review and summarize the literature on light pollution monitoring and the model results of existing studies on light pollution and influencing factors, and find that the factors affecting light pollution levels are mainly related to the level of urban development (population density, urban scale, functional zoning, GDP), the natural environment, quantitative indicators such as nighttime lighting index, and non-quantitative indicators such as psychological factors[3]. Walker established a model for the relationship between urban nighttime lighting and urban scale and population distribution by studying DMSP satellite images and field observations in California, applicable to a per capita luminous flux of 500∼100ml500 \sim 100 \mathrm{ml} [4]. Robert, Garasting et al. He added climatic and geographical factors such as aerosol parameters, light scattering, and observational geographic location to refine the model of nighttime sky brightness in relation to population distribution[5][6][7][8]. Rabaza observed the local nighttime sky brightness in Spain and obtained a sky brightness model by Stellarium software, which can analyze the nighttime sky light pollution by comparing the brightness of known sky stars. It is also able to provide accurate background skylight fluxes. Light pollution levels are closely related to regional functional zoning[9]. Pun et al. used the SQM system to observe the night sky in several regions of Hong Kong and established a night sky brightness measurement model for the Hong Kong region, finding that the average night sky brightness in Hong Kong is 82 times higher than the standard night sky, with the average urban night sky brightness being 15 times higher than the rural area[10]. Kollath used SQM to measure the Zselie landscape reserve in Hungary and obtained a model of sky brightness over the area including the park and the subsidence area, and found that artificial lighting in the area had little effect on sky brightness[11]. Biggs et al. used the Perth urban business district in Australia as the grid center to radiate into the suburbs for measurement delineation. Using the SQM instrument and GPS navigation system to measure the nighttime sky brightness at this test site, it was found that the sky brightness was highest in the urban commercial, industrial, and highway areas, intermediate in the urban areas with vegetation cover, and lowest in the urban vegetated and agricultural areas[12]. In addition, GDP is an important indicator of urban development level, and can also reflect the relationship between light pollution and urban development level. Falchi et al. found that by comparing the relationship between GDP and light