Light pollution assessment and prediction method

Abstract

The invention belongs to the technical field of environmental assessment, and provides a light pollution assessment and prediction method, which comprises the following steps: firstly, acquiring light source chromaticity data of a target illumination area, obtaining an information base, then analyzing from three steps of risk identification, risk analysis and risk evaluation to obtain analysis data, and then substituting the data into an established model to obtain a light pollution prediction model. Secondly, evaluating according to the model to obtain an evaluation result, performing inspection and sensitivity analysis on the evaluation result to obtain an analysis result, and finally detecting the analysis result and selecting the most effective intervention measure; according to the method, light pollution can be accurately evaluated through the steps of data collection, data analysis, template construction, detection analysis, evaluation and prediction and the like, the light pollution level is accurately evaluated by setting a model, the evaluation result is detected, the most effective intervention measure is selected, and the accuracy of light pollution evaluation is improved. And the light pollution improvement degree is maximized under the limitation of budget, so that the most effective strategy for light pollution detection is formulated.

Description

Technical Field

[0001] This invention belongs to the field of environmental assessment technology, specifically a method for assessing and predicting light pollution. Background Technology

[0002] Light pollution is a new source of environmental pollution following pollution from exhaust gas, wastewater, solid waste, and noise. It mainly includes light pollution, artificial daylight pollution, and colored light pollution. With the rapid development of economy and technology, "light pollution," which was once ignored, has quietly entered people's lives. At the same time, people are gradually becoming aware of the harm of light pollution. It changes our perception of the night sky, affects the environment, and impacts our health and safety. Therefore, it is crucial to construct a light pollution risk assessment and intervention model.

[0003] To address the problems raised in the background art, those skilled in the art have proposed a method for light pollution assessment and prediction. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method for assessing and predicting light pollution, thereby resolving the issues present in the prior art.

[0005] A method for assessing and predicting light pollution includes the following steps:

[0006] S1. Data Collection: Acquire the chromaticity data of the light source in the target lighting area and obtain an information database;

[0007] S2. Data Analysis: Extract the data from the information database in step S1, and then analyze it from the three steps of risk identification, risk analysis, and risk assessment to obtain the analysis data.

[0008] S3. Build a template: Build an evaluation template, then perform statistical analysis on the data from step S2, then input the data into the established model, and then evaluate the model to obtain the evaluation results.

[0009] S4. Detection and Analysis: The evaluation results in step S3 are tested and sensitivity analyzed to obtain the analysis results;

[0010] S5. Evaluation and Prediction: The evaluation results in step S3 and the analysis results in step S4 are tested to select the most effective intervention measures, maximize the improvement of light pollution within budget constraints, and thus formulate the most effective strategy for light pollution detection.

[0011] Preferably, in step S1, the light source chromaticity data includes light source data such as chromaticity trajectory, chromaticity coordinates, and color temperature.

[0012] Preferably, in step S2, risk identification refers to the process of discovering, acknowledging, describing, and recording risks, and the three elements of risk generation are harm, pathway, and recipient;

[0013] The three elements categorize the hazards from the perspective of light pollution, classifying the factors that cause light pollution as follows:

[0014] 1) Human Activities Domain: Four sub-indicators were constructed in the human activities domain;

[0015] Population density: Too many businesses or too many residences concentrated in one area can lead to various forms of light pollution. The larger the population, the more artificial light human activities require. Light pollution occurs when artificial light appears at the wrong time or when the amount of light exceeds human needs.

[0016] Lighting duration: Light fixtures emit blue light, glare, flicker, and other forms of light pollution. Furthermore, the later the lights are turned off during sleep, the longer the lighting is used, and the more likely it is to cause light pollution.

[0017] Light usage rate: The amount of light pollution is directly proportional to the amount of light emitted in a region. The higher the light usage rate, the more serious the light pollution.

[0018] The prevalence of entertainment activities reliant on light: In areas with a vibrant nightlife, after nightfall, advertising lights and neon lights in shopping malls and hotels, as well as black lights, rotating lights, fluorescent lights, and flashing colored light sources installed in dance halls, all contribute to light pollution.

[0019] 2) Environmental field: Two sub-indicators were constructed in the environmental field;

[0020] • Geographical location. Cities have higher population density, resulting in more luminous and glare pollution compared to rural areas. The degree of light pollution is positively correlated with distance from the city.

[0021] • Biodiversity (vegetation cover). Vegetation has different absorption spectral characteristics in different wavelength bands. Therefore, the higher the vegetation cover, the more beneficial it is to reducing light pollution.

[0022] 3) Development level domain: Four sub-indicators were constructed in the development level domain.

[0023] Transportation: The more developed the transportation system, the more road lighting equipment there will be. Traditional streetlights use high-pressure sodium, and the yellow light emitted is easily diffused in the air. These scattered lights will eventually become light pollution.

[0024] Astronomical observation: When observation equipment exposes a light source at close range for a long time, it will directly block the light from the depths of the universe, thus causing light pollution.

[0025] Lighting equipment: We believe that the more advanced the lighting equipment, the less light pollution it produces, thus avoiding or reducing light pollution.

[0026] Light reflectivity of building materials: A portion of light pollution is caused by the glare from buildings, resulting in dizziness for pedestrians and drivers and creating a bright white pollution effect. The higher the light reflectivity of building materials, the more severe the light pollution.

[0027] Preferably, in step S2, the risk analysis refers to the process of understanding the nature of the risk and determining the risk level. The weights of each indicator are assigned by the AHP-EWM weighted average method, and the weights of the AHP-EWM weighted average method include the analytic hierarchy process and the entropy weight method.

[0028] The Analytic Hierarchy Process (AHP) can effectively solve complex problems with multiple objectives and criteria, but it is subject to the subjective influence of expert judgment; while the entropy weight method is an objective weighting method, which is usually difficult to consider the actual situation and is relatively rigid.

[0029] Preferably, in step S2, the risk assessment involves comparing the results of the risk analysis with the pre-realized risk criteria, or comparing the analysis results of various risks, to determine the importance level of the risk.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] This invention enables precise assessment of light pollution through steps such as data collection, data analysis, template construction, detection analysis, evaluation, and prediction. By setting up a model, it accurately assesses the level of light pollution, detects the assessment results, selects the most effective intervention measures, maximizes the improvement of light pollution within budget constraints, and thus formulates the most effective strategy for light pollution detection. Attached Figure Description

[0032] Figure 1 This is a flowchart of the light pollution assessment and prediction method of the present invention;

[0033] Figure 2 This is a schematic diagram illustrating the three risk elements of the present invention;

[0034] Figure 3 This is a schematic diagram illustrating the weighting percentages of the indicators in this invention.

[0035] Figure 4 This is a schematic diagram of satellite detection of urban light pollution according to the present invention;

[0036] Figure 5 This is a schematic diagram illustrating satellite detection of light pollution in suburban, rural, and nature reserve areas according to the present invention.

[0037] Figure 6This is a schematic diagram illustrating the light pollution index scores for urban and rural areas according to the present invention.

[0038] Figure 7 This is a schematic diagram illustrating the comprehensive evaluation results of urban and rural areas in this invention;

[0039] Figure 8 This is a schematic diagram showing the comparison of light pollution levels before and after urban intervention in the present invention over the next three years;

[0040] Figure 9 This is a schematic diagram showing the comparison of light pollution levels before and after rural intervention in the present invention over the next three years. Detailed Implementation

[0041] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0042] like Figures 1-9 As shown:

[0043] Example: This invention provides a method for assessing and predicting light pollution, comprising the following steps:

[0044] S1. Data Collection: Acquire the chromaticity data of the light source in the target lighting area. The chromaticity data includes light source data such as chromaticity trajectory, chromaticity coordinates and color temperature, and obtain an information database.

[0045] S2. Data Analysis: Extract the data from the information database in step S1, and then analyze it from the three steps of risk identification, risk analysis, and risk assessment to obtain the analysis data.

[0046] Risk identification refers to the process of discovering, acknowledging, describing, and recording risks, and the three elements of risk formation are harm, pathway, and recipient.

[0047] The three elements categorize the hazards from the perspective of light pollution, classifying the factors that cause light pollution as follows:

[0048] 1) Human Activities Domain: Four sub-indicators were constructed in the human activities domain;

[0049] 2) Environmental field: Two sub-indicators were constructed in the environmental field;

[0050] 3) Development level domain: Four sub-indicators were constructed in the development level domain.

[0051] The risk analysis refers to the process of understanding the nature of risk and determining the risk level. It assigns weights to each indicator by using the AHP-EWM weighted average method, and the weights of the AHP-EWM weighted average method include the analytic hierarchy process and the entropy weight method.

[0052] Weighted average determines weights

[0053] 1. Step 1. Calculate the weighted values ​​using AHP.

[0054] 1) Referring to the table below, consider the degree of light pollution as the target layer, the three secondary indicators—human activities, environment, and development level—as the criterion layer, and the tertiary indicators as the solution layer. Based on the judgment scale values ​​and expert experience, first complete the judgment matrix from the target layer to the criterion layer:

[0055] Human activities environment Development level Human activities 1 3 2 environment 1 / 3 1 1 / 2 Development level 1 / 2 2 1

[0056] Judgment Matrix

[0057] 2) Based on CR = Cl / Rl and the average random consistency index Rl in the table below, test the consistency of the judgment matrix.

[0058] n 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 RI 0 0 0.52 0.89 1.12 1.26 1.36 1.41 1.46 1.49 1.52 1.54 1.56 1.58 1.59

[0059] Consistency Index Table

[0060] Referring to the table above, CI = 0.0046 is calculated, and RI = 0.52 is obtained from the table. Therefore, CR = 0.0088 < 0.1, and the consistency of the judgment matrix is ​​considered acceptable.

[0061] 3) Next, we calculate the average of the results from the three weighting methods: arithmetic mean, geometric mean, and eigenvalue, and use this average as the final weight value to make the result more robust. The criterion layer weights are: ω0 = [0.5396, 0.1634, 0.2970]

[0062] 4) We used the same method to calculate the weights of the three criterion layers and the scheme layers, and finally calculated the weights of the ten indicators, resulting in the following table:

[0063]

[0064] AHP weights

[0065] 2. Step 2: EWM calculates the weighted values.

[0066] 1) After preprocessing the original data, we have obtained the standardized data matrix. First, we calculate the probability matrix P, and... Where Xij represents the j-th indicator of the i-th country.

[0067] 2) Calculate information entropy:

[0068] 3) Calculate the weights:

[0069] The final weights determined by the entropy weight method are shown in the table below:

[0070]

[0071] EWM weights

[0072] 3. Step 3. Calculate the final weight using a weighted average.

[0073] We want to combine AHP and EWM, so we will take the weighted average of the weights calculated by the two methods to obtain the final weights:

[0074] ω=α*ω1+(1-α)*ω2

[0075] Where ω1 represents the weight calculated by AHP, ω2 represents the weight calculated by EWM, and Q is the assigned weight percentage. Here, we let α = 0.5, and the final weight percentage of the ten indicators is as follows: Figure 3 As shown.

[0076] 4. Fuzzy comprehensive evaluation method for rating

[0077] Step 1. Determine the set of pollution factors. Measuring the degree of light pollution requires a comprehensive evaluation from multiple perspectives. Based on the light pollution index evaluation model established above, we use these indicators to constitute a factor set, denoted as:

[0078] U = {u1, u2, ..., u} n}

[0079] Step 2. Determine the risk assessment set. Since each indicator has a different evaluation value, different levels are often formed. The increasingly serious light pollution phenomenon means that sufficiently dark observation points are far from cities, making them inaccessible to many amateur astronomers. Consequently, they haven't accumulated enough experience to judge sky brightness. In light of this, the Bortle scale, based on observational experience, created the Bortle Dark Sky Classification. Building upon this, we simplify and synthesize these nine classification criteria, proposing a zoned approach to environmental brightness:

[0080] grade Environmental characteristics For example V1 Dark Area Nature reserves, national parks V2 low brightness area Urban outskirts and rural residential areas V3 Medium brightness area Suburban residential areas V4 High brightness area Urban commercial districts with a wide range of nighttime activities

[0081] Light pollution level zoning

[0082] We denote the risk assessment set as V = {v1, v2, ..., v}. m}

[0083] Step 3. Determine the weights of each factor. Generally, the roles of each factor in the comprehensive evaluation are different, and the comprehensive evaluation result is largely related to the weights of the factors. Therefore, we will use the results calculated by the AHP-EWM method above as the weights of each factor, denoted as:

[0084] ω = {ω1, ω2, ..., ωn}

[0085] Step 4. Determine the fuzzy comprehensive judgment matrix. Several experts judge and score the possible pollution levels of the evaluation indicators. The levels of each evaluation indicator are quantified using membership functions, and a fuzzy evaluation matrix is ​​established, denoted as:

[0086]

[0087] Where rnm represents the membership degree of the nth pollution factor to the mth pollution comment set.

[0088] Step 5. Comprehensive evaluation: We obtain the comprehensive evaluation result B = A * R, denoted as...

[0089] B = [b1, b2, ..., b m ]

[0090] The highest level, bi, is denoted as i, which represents the final light pollution level for that region.

[0091] The risk assessment involves comparing the results of risk analysis with pre-realized risk criteria, or comparing the results of analyses of various risks, to determine the importance level of the risk.

[0092] We will take cities as an example and conduct a detailed quantitative assessment of the risk of light pollution.

[0093] Considering the background information defined in the problem statement, we need to complete the following requirements:

[0094] 1) Develop a broad and applicable measurement standard to determine the light pollution risk level of a location, apply the above measurement indicators to four different types of locations and interpret the results.

[0095] 2) Propose three intervention strategies to address light pollution, discuss the specific implementation measures of the three strategies, and analyze the potential impact of these measures on light pollution.

[0096] 3) Select two different types of locations and use the metrics developed in this paper to determine the most effective intervention strategies for each location. Discuss how the selected intervention strategies affect the risk level of the location.

[0097] 4) For a location you have identified and its most effective intervention strategy, create a one-page flyer to promote the strategy for that location.

[0098] First, we can determine the factor set as follows:

[0099] U={U1,U2,U3},U1={PA,BT,LUR,PLA},U2={GP,BD},U3={TR,AO,LE,ORB}

[0100] Secondly, we can define the evaluation set as: V = {Dark, Low Brightness, Medium Brightness, High Brightness}

[0101] Then, we had several experts conduct single-factor risk ratings for the evaluation indicators, resulting in the expert comprehensive evaluation table below, which also includes the weight values ​​of each indicator:

[0102]

[0103]

[0104] Expert Comprehensive Evaluation Form

[0105] Based on the data values ​​provided in the aforementioned risk assessment set, we derive the membership functions. V1 represents small-scale, V2 and V3 represent intermediate-scale, and V4 represents large-scale. The membership degree of each indicator is then calculated as the evaluation matrix.

[0106]

[0107] Fuzzy function distribution table

[0108]

[0109] The above is the evaluation matrix of the secondary indicators for the city that we calculated. We then calculated the membership matrix of the secondary indicators based on Ai = ωi * Ri, which serves as the evaluation matrix for the primary indicators. Finally, we obtain the comprehensive evaluation result B:

[0110] B = [0 0 0.212416 0.787584]

[0111] The results show that the membership degree of the light pollution risk level in the selected Shanghai area is 0 for dark areas, 0 for low-brightness areas, 0.212416 for medium-brightness areas, and 0.787584 for high-brightness areas. This indicates that the light pollution level in this area belongs to the highest level, which may be related to the city's rapid development, high electricity consumption, and wide electricity usage area. Meanwhile, referring to... Figure 4 This is consistent with the nighttime light pollution map results observed by satellite in the region, indicating that our model accurately assesses the level of light pollution.

[0112] Meanwhile, we performed the same operation on the other three selected regions and calculated the comprehensive evaluation result Bi:

[0113] B1=[0.075031 0.165128 0.565336 0.025830]

[0114] B2=[0.149574 0.390623 0.289607 0]

[0115] B3 = [0.663712 0.166854 0 0]

[0116] The results show that the suburbs of Laoshan have the highest membership score in the medium brightness area (0.565336), while their membership scores in other levels are not high, indicating that the light pollution level in this area is relatively low. Sili rural area has the highest membership score in the low brightness area (0.390623), followed by the medium brightness area (0.289607), indicating that the light pollution risk in this area is relatively high, slightly exceeding the normal level. Songshan Nature Reserve has the highest membership score in the dark brightness area (0.663712), while its membership scores in other levels are very low, indicating that the light pollution level in this area is very weak. We will calculate the assessment results to obtain... Figure 5 Comparing the results with satellite maps revealed that they closely matched reality, thus demonstrating the effectiveness of the model evaluation.

[0117] S3. Build a template: Build an evaluation template, then perform statistical analysis on the data from step S2, then input the data into the established model, and then evaluate the model to obtain the evaluation results.

[0118] S4. Detection and Analysis: The evaluation results in step S3 are tested and sensitivity analyzed to obtain the analysis results;

[0119] S5. Evaluation and Prediction: The evaluation results in step S3 and the analysis results in step S4 are tested to select the most effective intervention measures, maximize the improvement of light pollution within budget constraints, and thus formulate the most effective strategy for light pollution detection.

[0120] We applied effective strategies to selected urban and rural areas. To select the most effective intervention, we established a single-objective optimization intervention model to maximize the improvement in light pollution within budget constraints. The results showed that the most effective measure in urban areas was improving the reflectivity of building facades, with an improvement rate of 1.8%. In rural areas, the most effective strategies were strengthening the management of light use and increasing public awareness of the hazards of light pollution, with an improvement rate of 0.93%. Further evaluation of future improvements using GM(1,1) revealed that the improvement rate increased year by year. Furthermore, we developed leaflets to publicize the most effective strategy in urban areas.

[0121] refer to Figure 6 To more intuitively understand the light pollution risk level, we performed the following operation on the calculated comprehensive evaluation result B:

[0122] S=Σ m α m b m = 0.8×b1 + 0.6×b2 + 0.4×b3 + 0.2×b4

[0123] That is, let the region be classified into four risk levels with a degree of b. m Weighting coefficient α corresponding to the level m By multiplying, we believe that the lower the risk level, the larger the weighting coefficient. The calculated S is the final total score for the region, and the larger S is, the lower the risk level.

[0124] We used light pollution risk score data from urban and rural Shanghai from 2017 to 2022 to predict the light pollution risk for the next three years under the condition of no intervention strategy using GM(1,1).

[0125] As can be seen from the above, without intervention, the degree of light pollution continues to worsen with social development, thus requiring intervention strategies to improve the pollution level.

[0126] To evaluate the effectiveness of our proposed intervention strategy, we established a single-objective optimization model with the objective of maximizing the light pollution risk score and the budget as the primary constraint. To more intuitively reflect the amount of investment, we used the proportion r of the region's GDP. The total new intervention budget is: F = r × GDP.

[0127] As shown above, the GDP of Shanghai's urban area and rural areas in 2022 were 4465.28 billion yuan and 80.622 billion yuan, respectively. Let r = 1%. For a continuous intervention variable, the intervention on light pollution at time T will continue to have an impact, which we represent using a leap function:

[0128]

[0129] The effect of the intervention event on the dependent variable remains essentially constant. Starting from time T, the intervention model for this effect can be written as:

[0130] When Yt is non-stationary, a stationary sequence can be obtained through differencing, i.e.

[0131] Where B is the shift operator and ω represents the intensity of the intervention effect. We express ω as the proportion of new funds r / the proportion of funds invested in this indicator without intervention. We calculate the ω value of the two regions under the influence of the three strategies.

[0132] ω Strategy 1 Strategy 2 Strategy 3 City 0.4 0.6 0.3 rural areas 0.6 0.5 0.2

[0133] ω value under the influence of strategy

[0134] Ultimately, we can obtain the optimization model: min S

[0135]

[0136] There are other constraints that link these indicators together; these constraints include equations 9-13, 15-20, and 23.

[0137] Taking 2022 data as an example, we believe that Strategy 1 mainly affected daylight hours and light usage, thus improving light pollution; Strategy 2 mainly affected building exterior reflectivity, thus improving light pollution; and Strategy 3 mainly affected biodiversity, thus improving light pollution. We input the intervention data into the IAE risk assessment model for calculation, thereby obtaining the comprehensive evaluation results of the two regions after the intervention:

[0138] S Normalblank Strategy1 Strategy2 Strategy3 City 0.24246 0.24582 0.24704 0.24396 rural areas 0.46987 0.47422 0.47298 0.47120

[0139] Light pollution index scores after intervention in urban and rural areas

[0140] From the table above and Figure 7 It can be seen that for urban areas, the most effective strategy is to improve the reflectivity of building exterior walls, which reduces the risk of light pollution by 1.8%; for rural areas, the most effective strategy is to strengthen the management of light use and increase publicity on the hazards of light pollution, which reduces the risk of light pollution by 0.93%. In order to further observe the improvement of pollution by the most effective strategy we selected, we will use the most effective indicator as the intervention strategy for the next three years and compare it with the pollution level under the condition of no intervention.

[0141] from Figure 8 and Figure 9 The analysis shows that the growth rate of light pollution improvement is increasing year by year, reaching 4.14% and 2.4% in the third year, respectively. This indicates that our policies are very effective in improving light pollution and can support the migration from the current state to the ideal state.

[0142] In summary, this invention, through steps such as data collection, data analysis, template construction, detection analysis, evaluation, and prediction, can accurately assess light pollution. By setting up a model, it can accurately assess the level of light pollution, detect the assessment results, select the most effective intervention measures, maximize the improvement of light pollution within budget constraints, and thus formulate the most effective strategy for light pollution detection.

[0143] The embodiments of the present invention are given for the purposes of illustration and description. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for assessing and predicting light pollution, characterized in that: Includes the following steps: S1. Data Collection: Acquire the chromaticity data of the light source in the target lighting area and obtain an information database; S2. Data Analysis: Extract the data from the information database in step S1, and then analyze it from the three steps of risk identification, risk analysis, and risk assessment to obtain the analysis data. S3. Build a template: Build an evaluation template, then perform statistical analysis on the data from step S2, then input the data into the established model, and then evaluate the model to obtain the evaluation results. S4. Detection and Analysis: The evaluation results in step S3 are tested and sensitivity analyzed to obtain the analysis results; S5. Evaluation and Prediction: The evaluation results in step S3 and the analysis results in step S4 are tested to select the most effective intervention measures, maximize the improvement of light pollution within budget constraints, and thus formulate the most effective strategy for light pollution detection.

2. The light pollution assessment and prediction method as described in claim 1, characterized in that: In step S1, the light source chromaticity data includes light source data such as chromaticity trajectory, chromaticity coordinates, and color temperature.

3. The light pollution assessment and prediction method as described in claim 1, characterized in that: In step S2, risk identification refers to the process of discovering, acknowledging, describing, and recording risks, and the three elements of risk generation are hazard, pathway, and recipient; The three elements categorize the hazards from the perspective of light pollution, classifying the factors that cause light pollution as follows: 1) Human Activities Domain: Four sub-indicators were constructed in the human activities domain; 2) Environmental field: Two sub-indicators were constructed in the environmental field; 3) Development level domain: Four sub-indicators were constructed in the development level domain.

4. The light pollution assessment and prediction method as described in claim 1, characterized in that: In step S2, the risk analysis refers to the process of understanding the nature of the risk and determining the risk level. The AHP-EWM weighted average method is used to assign weights to each indicator. The weights of the AHP-EWM weighted average method include the analytic hierarchy process and the entropy weight method.

5. The light pollution assessment and prediction method as described in claim 1, characterized in that: In step S2, the risk assessment involves comparing the results of the risk analysis with the pre-realized risk criteria, or comparing the analysis results of various risks, to determine the importance level of the risk.

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