Urban heat island risk evaluation method

Abstract

The invention discloses an urban heat island risk evaluation method, which is based on a WRF-Chem three-dimensional numerical model, can perform high-precision simulation on physical and chemical processes of real atmosphere in a meteorological field and a chemical field, and after the influence of fine particulate matters on the surface temperature is stripped, when the method is applied to the consistency evaluation of the intensity of different urban heat islands, the risk of the urban heat islands is evaluated. The influence of air pollution on the intensity of the urban heat island is eliminated, the real understanding of the urban heat environment is improved, the unscientific expansion of the urban underlying surface caused by the fact that a heavily polluted city mistakenly knows the intensity of the urban heat island is avoided, and the scientific and technical support in the aspects of policies and decisions is provided for improving the evaluation of the urban living comfort.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of atmospheric environmental pollutant monitoring, and particularly relates to a city heat island risk evaluation method. BACKGROUND

[0002] As the main air pollutant in recent decades, fine particulate matter can affect the ground temperature by scattering and absorbing solar radiation, and the uneven distribution of fine particulate matter in urban and rural areas will affect the intensity of urban heat island, that is, the intensity of urban heat island caused by human modification of nature will be affected by fine particulate matter. Different cities have different air pollution conditions, which will have different degrees of influence on the intensity of urban heat island. The thermal effect evaluation of different cities needs to remove the influence of air pollutants before comparison, otherwise it will cause inconsistency in the evaluation of urban heat island intensity. At present, the monitoring of urban heat island is mainly realized through meteorological station observation, but in fact, the data of urban heat island intensity cannot fully reflect the current thermal environment of the city. Due to the covering of fine particulate matter, in the case of low urban heat island intensity value, the actual thermal effect of the change of urban underlying surface and other natural environment on the city has been high. For example, the heat island intensity value of city A is not high, and the decision-making department will think that the city underlying surface can continue to expand, and various methods such as city ventilation corridor to relieve the urban thermal effect are not urgent, but in fact, the air pollution of city A is high, which weakens the urban heat island intensity and covers up the reality that the city underlying surface of city A cannot continue to expand and the urban thermal effect needs to be relieved urgently. SUMMARY

[0003] The present application aims to provide a city heat island risk evaluation method, which removes the influence of air pollution on the intensity of urban heat island, improves the true understanding of the urban thermal environment, avoids the false cognition of the urban heat island intensity of the heavy pollution city, leads to the unscientific expansion of the city underlying surface, and provides scientific and technological support for policy and decision-making in improving the evaluation of urban human settlement comfort.

[0004] Technical scheme: In order to achieve the above-mentioned application purpose, the present application adopts the following technical scheme: a city heat island risk evaluation method, comprising the following steps: S1, obtaining weather forecast mode data, terrain data, and emission inventory containing artificial sources and natural sources, and obtaining the concentration of scattering particulate matter, the concentration of absorbing particulate matter and the ground temperature in the air through WRF-Chem mode simulation; S2, obtaining the coordinates of the urban area and the suburban area of the target city, respectively extracting the scattering particulate matter concentration , the absorbing particulate matter concentration , the ground temperature of the urban area , and the scattering particulate matter concentration of the suburban area , the concentration of scattering particulate matter and the surface temperature ; S3, based on the WRF-Chem model, setting the concentration of scattering particulate matter or the concentration of absorbing particulate matter to 0 in the emission inventory, thereby obtaining the urban surface temperature without the influence of scattering particulate matter and the suburban surface temperature , and the urban surface temperature without the influence of absorbing particulate matter and the suburban surface temperature ; S4, calculating the influence rate of scattering fine particulate matter and absorbing fine particulate matter on the urban heat island intensity by the following formula and , S5, obtaining the urban heat island intensity observation value from the surface temperature observation value of the target city, and obtaining the consistency corrected urban heat island intensity based on the influence rate of scattering fine particulate matter and absorbing fine particulate matter on the urban heat island intensity obtained in step S4; S6, completing the consistency correction of the urban heat island intensity of all target cities according to steps S1-S5, obtaining the urban heat island intensity dataset, and evaluating the potential risk of heat island for different cities.

[0005] Further, the influence rate of scattering fine particulate matter and absorbing fine particulate matter on the urban heat island intensity and in step S4 is calculated by the following formula, , wherein, represents the original urban heat island intensity, represents the urban heat island intensity after removing the influence of scattering fine particulate matter, represents the urban heat island intensity after removing the influence of absorbing fine particulate matter, represents the difference in the concentration of scattering fine particulate matter between the city and the suburb, represents the difference in the concentration of absorbing fine particulate matter between the city and the suburb, represents the influence rate of scattering fine particulate matter on the urban heat island intensity, represents the influence rate of absorbing fine particulate matter on the urban heat island intensity.

[0006] Further, in step S5, the consistency correction of the urban heat island intensity includes the following steps: S5.1, obtaining the surface temperature and of the city and the suburb of the target city in the evaluation period, thereby obtaining the urban heat island intensity observation value , ; S5.2, obtaining the scattering fine particulate matter concentration observation value of the urban and suburban areas of the target city in the evaluation period and , and then obtaining the scattering fine particulate matter concentration urban-rural difference , ; S5.3, obtaining the absorbing fine particulate matter concentration observation value of the urban and suburban areas of the target city in the evaluation period and , and then obtaining the absorbing fine particulate matter concentration urban-rural difference , ; S5.4, correcting the urban heat island intensity in consistency by the following formula to obtain the corrected urban heat island intensity , ; In the formula, represents the urban heat island intensity observation value, represents the influence rate of the absorbing fine particulate matter on the urban heat island intensity, represents the scattering fine particulate matter concentration urban-rural difference, represents the influence rate of the scattering fine particulate matter on the urban heat island intensity, represents the absorbing fine particulate matter concentration urban-rural difference.

[0007] Further, in step S6, the corrected urban heat island intensity is determined as high risk; the corrected urban heat island intensity is determined as medium risk; and the corrected urban heat island intensity is determined as low risk.

[0008] Beneficial effects: Compared with the prior art, the present application realizes consistent evaluation of different urban heat island intensities based on the simulation results of the WRF-Chem model and the observation results of fine particulate matter and ground surface temperature, removes the air pollution factors affecting the urban heat island intensity, and further improves the risk judgment efficiency of the urban heat environment. BRIEF DESCRIPTION OF DRAWINGS

[0009] Figure 1 is the flowchart of the urban heat island risk evaluation method. DETAILED DESCRIPTION

[0010] The present application will be further illustrated below in combination with the drawings and specific embodiments, and it should be understood that these embodiments are only used to illustrate the present application and not used to limit the scope of the present application, and after reading the present application, various equivalent modifications of the present application by those skilled in the art all fall within the scope defined by the appended claims.

[0011] The present application is based on WRF-Chem three-dimensional numerical model, through using the same horizontal and vertical direction coordinate grid in meteorological field and chemical field, real-time mutual influence and feedback calculation of meteorological process and chemical process, high-precision simulation of real atmospheric physical and chemical process, application in consistent evaluation of different urban heat island intensity after stripping the influence of fine particulate matter on ground surface temperature, thereby improving the risk judgment efficiency of urban thermal environment.

[0012] As shown in Figure 1 , the urban heat island risk evaluation method of the present application specifically comprises the following steps: Step S1, WRF-Chem simulates fine particulate matter concentration, component and ground surface temperature, specifically: (1) determining the target city and determining the evaluation period, using NCEP global numerical weather prediction model FNL data as the meteorological driving field, setting the simulation area, obtaining the terrain data and pretreating the meteorological driving field and the terrain data; (2) setting the parameterization scheme combination, obtaining the artificial source and natural source emission inventory, and using WRF-Chem model to simulate the concentration of scattering fine particulate matter, the concentration of absorbing fine particulate matter and the ground surface temperature in the air.

[0013] Step S2, determining the urban area coordinates and the suburban area coordinates of the target city, extracting the fine particulate matter concentration and the ground surface temperature corresponding to the coordinates: the scattering fine particulate matter concentration of the urban area coordinates is , the absorbing fine particulate matter concentration is , and the ground surface temperature is ; the scattering fine particulate matter concentration of the suburban area coordinates is , the absorbing fine particulate matter concentration is , and the ground surface temperature is .

[0014] Step S3, calculating the ground surface temperature after removing fine particulate matter (1) setting the scattering fine particulate matter in the emission inventory to 0 and repeating step 1, at this time the scattering fine particulate matter has no influence on the ground surface temperature, obtaining the ground surface temperature of the urban area coordinates under the influence of no scattering fine particulate matter as ; the ground surface temperature of the suburban area coordinates under the influence of no scattering fine particulate matter is .

[0015] (2) setting the absorbing fine particulate matter in the emission inventory to 0 and repeating step 1, at this time the absorbing fine particulate matter has no influence on the ground surface temperature, obtaining the ground surface temperature of the urban area coordinates under the influence of no absorbing fine particulate matter as ; the ground surface temperature of the suburban area coordinates under the influence of no absorbing fine particulate matter is .

[0016] Step S4, calculate the impact rate of suburban fine particulate concentration difference on urban heat island intensity S4.1, original urban heat island intensity without consistency evaluation , urban heat island intensity after removing the impact of scattering fine particulate , urban heat island intensity after removing the impact of absorbing fine particulate , S4.2, suburban scattering fine particulate concentration difference , S4.3, suburban absorbing fine particulate concentration difference , S4.4, the impact rate of scattering fine particulate on urban heat island intensity is , the impact rate of absorbing fine particulate on urban heat island intensity is , Step S5, consistency correction of urban heat island intensity of target city S5.1, obtain the ground temperature observation value of target city in evaluation period, urban ground temperature is , suburban ground temperature is , urban heat island intensity observation value is , S5.2, obtain the scattering fine particulate concentration observation value of target city in evaluation period, urban is , suburban is , scattering fine particulate concentration urban-rural difference is , S5.3, obtain the absorbing fine particulate concentration observation value of target city in evaluation period, urban is , suburban is , scattering fine particulate concentration urban-rural difference is , S5.4, the consistency corrected urban heat island intensity is , Step S6, for other target cities that need to be evaluated for consistency, repeat steps 1-4 to obtain the consistency corrected urban heat island intensity dataset of city i , i=1,2,3....N. N is the total number of cities participating in the consistency evaluation of urban heat island intensity.

[0017] Step S7, for urban heat island intensity dataset , i = 1, 2, 3...N, the data are compared in size, the city with a value greater than 1.5 degrees Celsius should be identified as a potential high-risk city of urban heat island, and immediate urban underlying surface reconstruction and ventilation corridor establishment and other mitigation of urban heat effect work should be carried out; the city less than 0.5 degrees Celsius should be identified as a potential low-risk city of urban heat island, and the current urban underlying surface state can be maintained unchanged; the city less than 1.5 degrees Celsius higher than 0.5 degrees Celsius should be identified as a potential medium-risk city of urban heat island, and work such as urban green land restoration should be started to prevent further intensification of urban heat effect.

[0018] Table 1 Potential risk level of urban heat island Corrected urban heat island intensity Risk level Potential risk assessment <0.5℃ 1 Low risk 0.5℃- 1.5℃ 2 Medium risk ＞1.5℃ 3 High risk When evaluating the urban heat environment of different cities, The city with a larger value is considered to have a poorer control of the urban heat environment than the city with a smaller value, and the urban heat risk is higher. The technical content of the present application is described in detail through the following examples:

[0019] This example mainly evaluates the consistency of the urban heat island intensity of Nanjing, Shanghai, Hefei and Hangzhou in January 2017, and the specific steps are as follows: Step 1: Obtain the meteorological data driving the WRF-Chem operation within the simulation time, the NCEP global meteorological field reanalysis FNL data in January 2017, the terrain data, the MODIS land use type data and the ground elevation data.

[0020] Step 2: According to the geographical location and size of the simulation area, the simulation area is set, the meteorological data and terrain data are pretreated, the model grid is set, the four-layer nested grid is set by using the Lambert projection method, and the horizontal grid distance is 81km, 27km, 9km and 3km respectively, wherein the first layer grid range is 0~550N, 72~136 0E, including the East Asian region, the second layer grid range is 25~41 0N, 109~148 0E, the third layer grid range is 29~35 0N, 115~122 0E, including Nanjing, Shanghai, Hangzhou and Hefei in the Yangtze River Delta region.

[0021] Step 3: Different parameterization schemes which have great influence on the simulation of atmospheric pollutants are selected and combined, the Eta similarity theory scheme is adopted in the vertical direction, which is divided into 32 layers, the RRTM scheme is selected for long-wave radiation process, the Goddard scheme is selected for short-wave radiation process, the Noah land surface model is selected for land surface process, the MYJ scheme is selected for boundary layer process, the Lin scheme is selected for cloud microphysics process, the Grell-Devenyi scheme is selected for cumulus convection process, the MOSAIC sub-range scheme is selected for aerosol chemistry process, the FAST-J scheme is adopted for photolysis, and the CMBZ scheme is adopted for gas phase chemistry.

[0022] Step 4: Obtain anthropogenic emission inventory (China Multi-scale Air Quality Model MEIC) and natural source emission inventory (Natural Source Gas Particle Emission Model MEGAN). Preprocess the pollution source data using the same time, horizontal and vertical settings as the WRF-Chem simulation region to obtain the input data.

[0023] Step 5: Run WRF-Chem according to the parameterization scheme set in step 3 to obtain the surface air temperature and air pollutant concentration data in January 2017. Determine the coordinates of the urban area and the suburban area of Nanjing, and extract the fine particulate matter concentration and surface temperature corresponding to the coordinates to obtain the scattering fine particulate matter concentration, absorbing fine particulate matter concentration and surface temperature of the urban and suburban areas of the four cities.

[0024] Step 6: Subtract the suburban surface temperature from the urban surface temperature in step 5 to obtain the original urban heat island intensity of Nanjing.

[0025] Step 7: Subtract the suburban scattering fine particulate matter concentration from the urban scattering fine particulate matter concentration, and subtract the suburban absorbing fine particulate matter concentration from the urban absorbing fine particulate matter concentration in step 5 to obtain the urban-rural scattering fine particulate matter concentration difference and the urban-rural absorbing fine particulate matter concentration difference of Nanjing.

[0026] Step 8: Set the scattering fine particulate matter pollution source in the pollutant emission inventory obtained in step 4 to 0, and repeat steps 5-6 to obtain the urban heat island intensity of Nanjing without the interference of scattering fine particulate matter.

[0027] Step 9: Set the absorbing fine particulate matter pollution source in the pollutant emission inventory obtained in step 4 to 0, and repeat steps 5-6 to obtain the urban heat island intensity of Nanjing without the interference of absorbing fine particulate matter.

[0028] Step 10: Subtract the urban heat island intensity without the interference of scattering fine particulate matter obtained in step 8 from the original urban heat island intensity obtained in step 6, and divide by the urban-rural scattering fine particulate matter concentration difference in step 7 to obtain the influence rate of scattering fine particulate matter on the urban heat island intensity of Nanjing.

[0029] Step 11: Subtract the urban heat island intensity without the interference of absorbing fine particulate matter obtained in step 9 from the original urban heat island intensity obtained in step 6, and divide by the urban-rural absorbing fine particulate matter concentration difference in step 7 to obtain the influence rate of absorbing fine particulate matter on the urban heat island intensity of Nanjing.

[0030] Step 12: Obtain the observation values of the land surface temperature of the urban and suburban areas of Nanjing in January 2017, and subtract to obtain the observation value of the Nanjing urban heat island intensity. Obtain the concentration values of the scattering fine particulate matter of the urban and suburban areas of Nanjing in January 2017, and subtract to obtain the concentration difference of the scattering fine particulate matter between the urban and suburban areas of Nanjing. Obtain the concentration values of the absorbing fine particulate matter of the urban and suburban areas of Nanjing in January 2017, and subtract to obtain the concentration difference of the absorbing fine particulate matter between the urban and suburban areas of Nanjing.

[0031] Step 13: Multiply the concentration difference of the scattering fine particulate matter between the urban and suburban areas of Nanjing in step 12 by the influence rate of the scattering fine particulate matter on the Nanjing urban heat island intensity in step 10, and multiply the concentration difference of the absorbing fine particulate matter between the urban and suburban areas of Nanjing by the influence rate of the absorbing fine particulate matter on the Nanjing urban heat island intensity in step 11, and calculate the sum to obtain the corrected value of the Nanjing urban heat island intensity.

[0032] Step 14: Subtract the corrected value in step 13 from the observation value of the Nanjing urban heat island intensity obtained in step 12 to obtain the Nanjing urban heat island intensity after the consistency correction and not affected by air pollution.

[0033] Step 15: Repeat steps 5-14 for Shanghai, Hangzhou and Hefei to obtain the consistency-corrected urban heat island intensity dataset, and determine the potential risk level of the urban heat island of each city after table lookup, as shown in Table 2. Among the four cities, the city with the best control of urban heat risk is Hefei, and the city with the worst control of heat risk is Hangzhou. Hangzhou needs to develop more powerful urban heat environment mitigation policies as soon as possible.

[0034] Table 2: Consistency evaluation results of urban heat island intensity of four cities in Yangtze River Delta City name Corrected urban heat island intensity Risk level Potential risk assessment Nanjing 1 ℃ 2 Medium risk Shanghai 1.2 ℃ 2 Medium risk Hangzhou 1.5 ℃ 3 High risk Hefei 0.8 ℃ 1 Low risk The above merely describes preferred specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can make equivalent replacements or changes to the technical solutions and technical concepts of the present application within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A method for urban heat island risk assessment, characterized by The method comprises the following steps: S1, obtaining weather forecast mode data, terrain data, and emission inventory containing artificial sources and natural sources, and simulating the concentration of scattering particulate matter, the concentration of absorbing particulate matter and the ground temperature in the air by a WRF-Chem model; S2, obtaining the urban and suburban coordinates of the target city, extracting the scattering particle concentration , the absorbing particle concentration , and the surface temperature of the urban area, and the scattering particle concentration , the absorbing particle concentration , and the surface temperature of the suburban area, respectively S3, based on the WRF-Chem model, setting the concentration of scattering particulate matter or the concentration of absorbing particulate matter in the emission inventory to 0 respectively, thereby obtaining the urban surface temperature without the influence of scattering particulate matter and the suburban surface temperature , and the urban surface temperature without the influence of absorbing particulate matter and the suburban surface temperature ; S4, the influence rate of the scattering fine particulate matter and the absorbing fine particulate matter on the urban heat island intensity is calculated by the following formula and , S5, obtaining the ground temperature observation value of the target city to obtain the observed value of the urban heat island intensity, and obtaining the consistent corrected urban heat island intensity based on the influence rate of the scattering fine particulate matter and the absorbing fine particulate matter on the urban heat island intensity obtained in step S4; S6, completing the consistency correction of the urban heat island intensity of all target cities according to steps S1-S5 to obtain an urban heat island intensity data set, and evaluating the potential risk of the heat island of different cities.

2. The urban heat island risk assessment method according to claim 1, characterized in that: The influence rate of the scattering fine particulate matter and the absorbing fine particulate matter in step S4 on the intensity of the urban heat island and is calculated by the following formula, ， In the formula, represents the original urban heat island intensity, represents the urban heat island intensity after removing the influence of scattering fine particulate matters, represents the urban heat island intensity after removing the influence of absorbing fine particulate matters, represents the concentration difference of scattering fine particulate matters between the city and the suburb, represents the concentration difference of absorbing fine particulate matters between the city and the suburb, represents the influence rate of scattering fine particulate matters on the urban heat island intensity, represents the influence rate of absorbing fine particulate matters on the urban heat island intensity.

3. The urban heat island risk assessment method according to claim 1, characterized in that: In step S5, the consistency correction of the urban heat island intensity comprises the following steps: S5.1, obtaining the land surface temperature of the urban and suburban areas of the target city in the evaluation period and , and then obtaining the urban heat island intensity observation value , ; S5.2, obtaining the observation value of the scattering fine particulate matter concentration in the urban and suburban areas of the target city in the evaluation period and , and then obtaining the urban-rural difference value of the scattering fine particulate matter concentration , ; S5.3, obtaining the absorption fine particulate matter concentration observation value of the urban and suburban areas of the target city in the evaluation period and , and then obtaining the absorption fine particulate matter concentration urban-rural difference , ; S5.4, the urban heat island intensity is modified by the following formula to obtain the modified urban heat island intensity , ； In the formula, represents an observation value of the intensity of the urban heat island, represents an influence rate of the absorptive fine particulate matter on the intensity of the urban heat island, represents a difference between the concentration of the absorptive fine particulate matter in the city and that in the countryside, represents an influence rate of the scattering fine particulate matter on the intensity of the urban heat island, represents a difference between the concentration of the scattering fine particulate matter in the city and that in the countryside.

4. The urban heat island risk assessment method according to claim 1, characterized in that: In step S6, the modified urban heat island intensity is determined to be high risk; the modified urban heat island intensity is determined to be medium risk; and the modified urban heat island intensity is determined to be low risk.

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