Hongyuan Huo
email: huohongyuan@bjut.edu.cn

Xiaowei Geng
email: gengxw@emails.bjut.edu.cn

Wenzhi Zhang

Li Guo

Pei Leng
email: lengpei@caas.cn

Zhao-Liang Li

Simulation of Urban Functional Zone Air Temperature Based on Urban Weather Generator (UWG): A Case Study of Beijing, China

Keywords: Urban functional zone, urban air temperature, canopy urban heat island, UWG

Rapid urbanization has led to many urban thermal environment problems. Most studies focus on analyzing the urban thermal environment from the perspective of land use type, and often at a large scale. Thus, research of urban spatial thermal environment at a block scale with different urban functional zone (UFZ) becomes the focus of this paper. UFZ refers to urban planning units with similar socio-economic functions, which often have similar energy consumption and outdoor thermal environment. In this paper, the urban weather generator (UWG) model is used To quantitatively analyze the spatial thermal environment of Beijing. To accurately simulate the urban thermal environment, the underlying surface used in the simulation is replaced by the finely classified underlying surface of the urban functional area. In this paper, we first use POI + OSM data to divide the functional areas of Beijing. The recognition accuracy of functional area is 85.6%. then use UWG model to simulate the temperature and humidity differences in different functional areas, and finally use the corresponding urban and rural weather stations to validate its accuracy. The results show that the model calibration method can effectively improve the simulation accuracy of UWG thermal environment, the R 2 is increased by about 0.12, and the RMSE is reduced by about 0.84. The canopy temperature of different urban functional areas from high to low is: industrial area>commercial area>residential area>public area>road and traffic area>green space and square area. This model is more suitable for areas with relatively homogenous urban morphology and sparse urban vegetation. The thermal environment simulation results of the same urban functional area are basically the same, with slight differences in the temperature peaks, and the average temperature in the city center is higher than that in the suburbs.

1.Introduction

The continuous advancement of the world's urbanization process has caused many thermal environmental problems, such as the reduction of urban air quality, the increase in the demand for building heating and cooling loads, and the deterioration of the health of urban residents, which has seriously affected the urban ecological environment and sustainable development of cities. The underlying surface of the city is complex and changeable, and the urban climate is regional and local (Yao et al.2018). Therefore, to study the distribution of urban spatial wind-heat environment and its impact, it is of great significance to improve the urban ecological environment to study the wind-heat environment of urban canopy space at the block scale and to deeply analyze the interaction mechanism between different urban block plots and the wind-heat environment.

Urban thermal environment refers to the heat-related physical environment that can affect the human body's perception of cold and warm, health level, and human survival and development (Oak.1995). There are various means of research on urban space wind-heat environment, mainly including observation methods based on traditional stations, remote sensing technology and numerical simulation (Bherwani et al.2020). With the development of earth observation technology, remote sensing is mostly used in the study of surface urban thermal environment due to its unique advantages of large-scale macroscopic observation, and most urban canopy heat islands use station observation and numerical simulation methods. At present, methods based on numerical simulation are mostly focused on regional and global scales, and there are few simulations of the wind-thermal environment at the block scale (Huo and Chen.2022;Huo et al.2021).

In recent years, the research on urban space thermal environment based on UWG has attracted the attention of scholars, and many research results have been obtained.

UWG is mainly used in the fields of analysis of wind and heat environment inside the city or at the block scale (Salvati et al.2016;Street.2013), analysis of building energy consumption [START_REF] Salvati | Built form, urban climate and building energy modelling: Case-studies in Rome and Antofagasta[END_REF]Palme et al.2017), and research on human thermal comfort (Mackey et al.2017;Lemercier.2019). Based on the Town Energy Balance (TEB), UWG deduces the average thermal environment of urban canyons based on known rural meteorological data and a series of input urban parameters. UWG can be combined with remote sensing and GIS technology to provide an accurate data source for the initial condition input of the UWG model (Litardo et al.2020). At the same time, with the temperature as the optimization target, some input parameters of the UWG model are optimized and calibrated to improve the simulation accuracy (Mao and Leslie.2021;Shen and Wang.2021).

Most current studies focus on the analysis of the wind-heat environment at the macro scale. For metropolises and urban agglomerations, the types of urban underlying surfaces are complex, and local microclimates in cities are obvious. Therefore, it is necessary to carry out wind-thermal environment simulation based on UWG at a finer block scale. This can fully and more finely reflect the spatio-temporal pattern and intensity changes of urban heat islands. As the basic unit of the rapid development of modern cities, urban functional areas can help understand the complex interaction between human spatial activities and environmental changes, and have important impacts on the city's economy, society, and ecology (Yu et al.2021;Zhong et al.2015;Leichtle et al.2017). In this study, the spatial temperature field under different urban functional areas is simulated by the UWG model, which can gain an in-depth understanding of the interaction mechanism between the underlying surface and the thermal environment, improve the accuracy of the UWG simulation, and deepen the understanding of the urban thermal environment on a finer scale.

This study considers the spatio-temporal distribution of urban thermal environment under different functional areas, and mainly carries out two aspects of work: (1) Utilize POI+OSM data to divide the urban functional areas in the center of Beijing, which are mainly divided into six single functional areas and their combination of road and traffic area, industrial area, public area, residential area, green space and square area, and commercial area functional area; (2) Use the dragonfly plug-in of grasshopper modeling software to run the UWG model to simulate the thermal environment, and obtain the thermal environment simulation results under each functional area. According to the distribution pattern of functional areas in the center of Beijing, discuss the city's temperature field, living environment, heat island situation and other issues, and put forward corresponding measures and methods to improve the urban thermal environment.

2.Study area and data

Study area

Beijing is the capital of China and also the political, economic and cultural center of China. The total area of Beijing is 16,410.54 square kilometers, located at 115°25' E to 117°30' E and 39°26' N to 41°03' N, in

Data source and preprocessing

The experimental data required in this paper mainly include GF-2 remote sensing data, POI point of interest data, AOI surface of interest data, Beijing OSM road network data, Beijing weather data in 2019, and GIS data. The preprocessing process is as

3.Methodology

In this paper, the UWG is used to simulate and explore the thermal environment of urban functional areas. First of all, according to the method of POI+OSM to identify the function area, the spatial distribution of single function area and compound function area in Beijing is obtained, and the accuracy rate ACC is used to quantitatively evaluate the recognition accuracy of the function area. Then, based on the identification of functional areas, the UWG model was run for simulation, and we obtained the canopy temperature and relative humidity of each functional area. During this process, to further improve the prediction accuracy of UWG, the parameters of the model were calibrated with temperature as the optimization objective. Third, the temperature and humidity simulation results were verified using the weather station data near the study area, and the temperature and humidity differences between the same type and different types of functional areas were compared and analyzed. The accuracy of the model is evaluated using two indicators, the coefficient of determination (R 2 ) and the root mean square error (RMSE). Finally, we simulated and analyzed the summer thermal environment in Dongcheng District, and quantified the thermal comfort of the human body in the commercial area in summer with UTCI as the evaluation index.

Urban Functional Zoning Method

The following methods are adopted for the division of urban functional areas: (1) Extract the construction land according to the administrative division data of the research area; (2) Based on the Open Street Map platform, Beijing OSM data was obtained, and operations such as cutting and screening the main research road network, topological processing, establishment of buffer zones, intersection and inversion, and breaking up of the overall unit were performed in sequence to generate independent plot units; (3) Since the division of urban functional areas is closely related to construction land, the plot units divided by OSM were manually corrected to enhance the rationality of the division of research units; (4) For the acquired POI data, operations such as coordinate system conversion, data cleaning, data screening, and reclassification are performed in sequence, and the POI data is divided into the following six categories. According to the land area and public awareness (Chi.2016;[START_REF] Zhao | Extracting hierarchical landmarks from urban POI data[END_REF]Chen.2015), assign corresponding weights to various POIs, and conduct nuclear density analysis on the data of various POIs in the study area to obtain their radiation results (Wu.2021;Ding.2020); (5) Calculate the frequency density of various POIs in functional area units, and spatially connect the frequency density values with the divided research units to divide urban functional areas. The specific process is shown in Figure 3:

UWG simulation principle

The UWG model consists of four coupled modules, through which UWG processes the information from suburban sites to urban sites, so as to obtain the urban thermal environment in a specific area. The four main modules include Rural Station Model (RSM), Vertical Diffusion Model (VDM), Urban Boundary-Layer (UBL) Model, Urban Canopy and Building Energy Model (UC-BEM) (Bueno.2012;Bueno et al.2013).

Figure 4 shows the simulation process of the UWG model: First, the suburban and rural weather files stored in the *.EPW file format are used as initial input parameters (rural weather files can be obtained from meteorological station data); Secondly, the file in *.UWG format mainly stores parameters describing urban characteristics (urban geometry, structure, plot type and area, building density and height, building attributes, etc.); Third, input the parameters generated in the above two steps into the UWG simulation model only, and simulate and generate block-scale weather files of each functional area of the city stored in *.EPW format. What needs to be explained here is: *.EPW rural weather files define the meteorological factors of rural sites, including temperature, humidity, atmospheric pressure, solar radiation, wind speed, wind direction, etc., which can be obtained by integrating local meteorological data with Element software. The *.UWG urban feature file defines urban form parameters, structural parameters, building parameters, reference site parameters, simulation parameters, and can be obtained based on geographic statistics and geographic data platforms such as GEE. It can also be obtained by earth observation hands such as onsite measurement and remote sensing with related instruments. The output file *.EPW mainly contains temperature, humidity, atmospheric pressure, solar radiation, wind speed, and wind direction. The specific technical route is shown in Figure 4.

𝑑 1 (𝜌𝑐) 1 ∂𝑇 1 ∂ 𝑡 = 𝐶 1,2 (𝑇 2 -𝑇 1 ) + 𝑄 𝑠𝑢𝑟𝑓 (1)
For the first layer i:

𝑑 𝑖 (𝜌𝑐) 𝑖 𝜕𝑇 𝑖 𝜕 𝑡 = 𝐶 𝑖,𝑖+1 (𝑇 𝑖+1 -𝑇 𝑖 ) + 𝐶 𝑖,𝑖-1 (𝑇 𝑖-1 -𝑇 𝑖 ) (2)
For any intermediate layer n:

𝑑 𝑛-1 (𝜌𝑐) 𝑛-1 𝜕𝑇 𝑛-1 𝜕 𝑡 = 𝐶 𝑛-1,𝑛 (𝑇 𝑑𝑒𝑒𝑝 -𝑇 𝑛-1 ) (3)
where 𝑑 represents the depth,(𝜌𝑐) denotes the volumetric heat flux, 𝑇 𝑖 represents the average temperature of the i-th layer, 𝐶 𝑖,𝑗 represents the average thermal conductance between layer i and layer j, 𝑄 𝑠𝑢𝑟𝑓 represents the sum of surface net radiation, sensible heat flux and latent heat flux, 𝑇 𝑑𝑒𝑒𝑝 represents the average annual temperature of the location.

②VDM reads the temperature and humidity, sensible heat flux provided by RSM, The air temperature at different heights above the rural site is calculated by heat diffusion equation and provided to UBL. The heat diffusion equation is as follows:

𝜕𝜃(𝑧) 𝜕𝑡 = - 1 𝜌(𝑧) 𝜕 𝜕𝑧 (𝜌(𝑧)𝐾 𝑑 (𝑧) 𝜕𝜃(𝑧) 𝜕𝑧 ) (4)
where, 𝜃 represents the latent temperature in the air, 𝑧 is the vertical space component, 𝜌 is the air density, and 𝐾 𝑑 is the diffusion coefficient. The difficulty in implementing this diffusion equation in VDM lies in the estimation of the diffusion coefficient (𝐾 𝑑 ), mainly due to the difficulty in predicting stable boundary layers. This coefficient is currently calculated by the turbulent kinetic energy function:

𝐾 𝑑 = C k 𝐿 𝑘 𝐸 0.5 (5)
where 𝐸 represents the turbulent kinetic energy of the flow, C k is a model parameter of 0.4, and 𝐿 𝑘 represents the length.

③The UBL model is based on the temperature at different heights provided by VDM and the sensible heat flux provided by RSM, the air temperature above the urban canopy is calculated by energy balance to UC-BEM. The energy balance is expressed as:

𝑉 𝑐𝑣 𝜌𝑐 𝑣 𝑑𝜃 𝑢𝑟𝑏 𝑑𝑡 = 𝐻 𝑢𝑟𝑏 + ∫ 𝑢 𝑟𝑒𝑓 𝜌𝑐 𝑝 (𝜃 𝑟𝑒𝑓 -𝜃 𝑢𝑟𝑏 )𝑑𝐴 𝑓 (6)
where 𝑉 𝑐𝑣 represents the control volume, 𝜌 is the air density, 𝑐 𝑣 represents the specific heat ratio of air at a fixed volume, 𝑐 𝑝 represents the specific heat ratio of air at a fixed represents the convective exchange velocity between the air inside the canopy and the air above the canopy.

Results and discussion

Classification Results and Validation of UFZs

According to statistics, there are 6 types of single functional areas in the downtown Green Space-Industrial (GS&IN), Comprehensive Functional. Among them, the single functional area is dominated by road traffic land, public service land is also one of the main types of land use, and residential land occupies the least proportion. There is no obvious functional differentiation in most areas of the study area, and the phenomenon of multi-functional compounding is common, mainly distributed in the downtown area.

As a political and cultural center, Beijing has well-developed transportation, and the compounding of land for road traffic and other functions is particularly prominent. The composite functional area is dominated by public-transportation land, commercialtransportation land is also one of the main types of land use, and green space-industrial land is the least. The specific functional area division results are shown in Figure 5. To estimate the accuracy of the identification results of urban functional areas, Google Maps was manually interpreted visually and conducted field surveys to obtain the true value. The identification results of functional areas were used as predicted values, and 30 block units were randomly selected for comparative analysis. Referring to the research of Kang and Ding (Kang et al.2018;Ding et al.2020), in the scoring standard of accuracy, a very consistent score is 3 points, that is, the identification attribute of the functional area is consistent with the real attribute; 2 points for more conformity, if a single functional area is marked as a mixed functional area including the single functional area, and a mixed functional area is identified as a mixed functional area including one function of the mixed functional area; Generally, it is 1 point, if the mixed functional area is identified as a single functional area including one function of the mixed functional area; Total non-compliance is scored as 0 points. For example, a single functional area is identified as another type of single functional area, and a mixed functional area is identified as other mixed functional areas that have no connection. Among them, the calculation formula of the overall accuracy rate is:

𝐴𝐶𝐶 = ∑ 𝑥 𝑖 𝑛 𝑖=1 𝜇𝑛 (8)
where, 𝑥 𝑖 is the actual scoring score of the conformity of the i-th block, 𝜇 is the scoring score when it is very consistent (𝜇=3), n is the total number of blocks extracted (𝑛 = 30). According to the formula, the overall accuracy rate is 85.6%. It shows that this study can effectively identify single functional area, mixed functional area and comprehensive land use functional area.

Validation and analysis of UWG simulation results for different UFZs

Calibration of UWG Model Parameters

The model parameters contained in the *.UWG file, among which the boundary layer height, VDM reference height, circulation coefficient, vegetation albedo, and road albedo parameters are uncertain parameters. Simulation models play an important role in the design, analysis and optimization of built environment and energy systems.

Model calibration and uncertainty analysis are important for improving the accuracy of UWG simulation models. In this paper, for the 12 uncertain input parameters of UWG, the single-objective optimization algorithm is used to calibrate the model, and the temperature simulation result is used as the optimization goal. The calibrated UGW model can improve the accuracy of the simulation results. Among them, the calibration parameters and results are shown in Table 1, and the accuracy evaluation before and after parameter calibration is shown in Table 2. In addition, from the thermal environment simulation results, we found that the peak temperature of the urban canyon tends to follow the measured data offset by 1 h. Bande (2019) also found the same phenomenon in the simulated urban temperature field. Therefore, in this paper, the simulation results of the temperature field are uniformly shifted backward by 1h. It can be seen from Table 2 that the accuracy of the model before and after calibration is significantly improved, among which R^2 is increased by about 0.12, and RMSE is reduced by about 0.84. The calibrated model will be more accurately applied to the thermal environment simulation of urban functional areas. In addition to the above calibration parameters, the main input parameters and their values of each functional area are shown in Table 3:

Thermal environment of different functional areas in adjacent positions

Building form parameters, vegetation coverage parameters, and anthropogenic thermal parameters are different among different types of UFZs. UWG models can be used to explore the differences in thermal environments of different UFZs, which can improve the simulation accuracy of UWG models. Based on the UWG model, this paper simulates the temperature and humidity results of each UFZ, and selects July 21-July 22 in summer and January 21-January 22 in winter as typical research periods. Figure 6 shows the temperature prediction results of different UFZ in winter, and Figure 7 shows the temperature prediction results of different UFZs in summer. Table 4 quantitatively evaluates the prediction accuracy of the UWG model by R 2 and RMSE. From the above simulation results, it can be found that: (1) The overall temperature of the outer ring of Beijing is lower than that of the inner ring, and the heat island effect in the city center is serious; (2) The average daily temperature in Beijing varies around 10°C, and there is little difference in the temperature changes at the measured meteorological stations in different functional areas; (3) The simulation data has good simulation results in the trend of temperature changes, and the simulation results of individual high and low temperatures are not ideal; (4) From the perspective of land use attributes, UWG simulation shows that the temperature of functional areas from high to low is: industrial area > commercial area > residential area > public area > road and traffic area > green space and square area, and the canopy temperature of each functional area has little difference . From the temperature simulation accuracy evaluation table, it can be seen that the order of R 2 from high to low is: residential land>commercial land>green space and square>public land>roads and traffic>industrial land. It shows that the UWG model has a good fitting effect on the temperature prediction of residential land and commercial land, but the fitting accuracy of temperature prediction on industrial land is low. The order of RMSE from low to high is: commercial land> residential land> green space and square> road and traffic> public land> industrial land. It shows that the UWG model has high prediction accuracy for residential land and commercial land temperature, but low prediction accuracy for industrial land. Except for industrial land, the R 2 in summer is significantly higher than that in winter, and the RMSE in summer is significantly lower than that in winter. It shows that the temperature prediction performance of the UWG model in summer is better than that in winter, and the poor prediction effect in winter may be related to meteorological factors and heating. The reason for the poor prediction effect on industrial land may be that industrial production consumes a lot of energy and releases heat energy, while UWG considers more about the relationship between buildings and thermal environment. At the same time, the verified meteorological station has a certain distance from the industrial land, which cannot represent the thermal environment of the specific industrial park, and will also have a certain impact on the simulation results. The UWG model is more suitable for summer thermal environment simulation, and has higher prediction accuracy for residential land and commercial land attributes, but is not suitable for industrial land.

At the same time, it is reflected from the side that the UHI intensity in the UWG model is mainly determined by the geometry of the canyon, vegetation coverage and anthropogenic heat release (Salvati.2016). This model is more suitable for areas with relatively homogeneous urban morphology and sparse urban vegetation [START_REF] Bueno | The urban weather generator[END_REF]).

The road network data in the study area is complex, and there are many and finely activities in cities such as schools and hospitals. In addition to buildings, these small areas also have a certain amount of vegetation and water distribution [START_REF] Pang | Study on the landscape thermal environment of typical functional areas in Beijing[END_REF].

The canopy temperature simulation results in Dongcheng District combined with the distribution of functional areas show that the underlying surfaces of different functional areas show different canopy temperature differences, and the difference between the highest temperature and the lowest temperature can reach about 4 °C. In the central area of Dongcheng District, there are green squares and moats such as the Forbidden City and Beihai Park, which play a good role in alleviating the heat island effect, resulting in low temperatures near the plot. The mitigating effect of vegetation and water bodies on heat islands has a great correlation with the area occupied. The land plots for green squares in the remaining areas are small and sparse, resulting in less obvious heat island mitigation effects, and have not formed a large-scale "cold island effect" like the Forbidden City plots in the central area. In the northern part of Dongcheng District, there are traffic lands such as Heping West Bridge. Cement concrete asphalt pavement has poor air permeability and stores a lot of heat. Due to the convective heat transfer of the air, the hard paved roads will emit a large amount of heat at noon to form a surface heat wave. In addition, the traffic flow is heavy during commuting hours, which emits a large amount of exhaust gas, resulting in high temperatures at this time. The rest of Dongcheng District is dominated by public areas, where the building height is relatively low and the building density is relatively small.

The spatial distribution of vegetation has a relatively enhanced mitigation effect on the thermal environment, and the temperature and humidity are suitable.

Analysis of UWG simulation results for the same type of UFZ

Thermal environment of different patches in the same functional area

From the simulation results of UWG thermal environment in different types of functional areas, it can be seen that the model has the best prediction effect on residential land and commercial land. In order to explore the thermal environment differences among functional areas of the same type, this paper takes the residential area as an example to simulate and analyze the temperature and humidity of the residential single-functional area and residential multi-functional area with similar urban geometry. Figure 9 shows the summer temperature prediction results of residential single-functional zone plots and multi-functional zone plots located in different locations. Wherein, the spatial location distribution of Residential_1-Residential_3 is shown in Figure 10. For an urban area, UWG describes it through three key geometric parameters including average building height, site building density and facade-horizontal plane ratio. These parameters transform the complex, heterogeneous urban structure into a homogenous description defined by the TEB (Bueno et al.2013). From the simulation results in Figure 9, it can be seen that the thermal environment simulation results in residential areas located in different locations are basically the same, with slight differences in the high and low peaks of temperature. The temperature simulation results of the residential single-functional zone and the multi-functional zone are not much different, and the residential-traffic land is slightly higher than that of the other functional zones, because the residential-traffic land generates more anthropogenic heat during commuting time to raise the temperature. Therefore, the thermal environment simulation results obtained by the UWG model are similar for land parcels with similar urban geometry, underlying surface type, and anthropogenic heat. By simulating the average air temperature in the urban center and the urban border residential area, we found that the average air temperature in the central residential area is higher than that in the suburbs, especially at the temperature peak at two o'clock in the afternoon. The temperature and humidity simulation results of UWG are consistent with the actual temperature and humidity change trend. The air temperature curve has a single peak change, and gradually decreases after reaching the daily maximum value. The change trend of relative humidity is opposite to that of temperature, that is, the higher the temperature, the lower the humidity. As far as the temperature simulation results of UWG are concerned, the daytime simulation effect is better, and the temperature tends to be overestimated at night. The possible reason is that UWG simulates the temperature of the canopy (the air temperature at the average building height) and considers the influence of solar radiation, while the meteorological station measures the temperature inside the shutter 3m above the ground without considering the influence of solar radiation. As far as the humidity simulation results are concerned, the UWG daytime humidity simulation effect is better, and the relative humidity at night is somewhat underestimated.

Analysis of

Thermal comfort analysis based on UWG simulation

In this paper, the Universal Thermal Climate Index (UTCI) is selected as the evaluation index of thermal comfort. UTCI refers to the air temperature corresponding to the same thermophysiological response index in the mathematical model (Fiala multi-node model and adaptive clothing model) when the meteorological conditions in the actual environment cause the same. In simple terms, UTCI is the temperature at which a person produces the same physiological response as in the actual environment under a standard reference environment [START_REF] Bröde | Deriving the operational procedure for the Universal Thermal Climate Index (UTCI)[END_REF]. The standard reference environment mentioned here includes (1) Climatic conditions: the average radiation temperature is equal to the air temperature (that is, the radiation effect is not considered),

the wind speed at a height of 10 m is 0.5 m s -1 , the relative humidity is 50% (the temperature at a height of 2 m is < 29°C) or the water vapor pressure is 20 hPa (air temperature at 2 m height > 29°C); (2) Human activity level: adult male walking at 4 km h -1 (human metabolic rate 135 W m -2 ). UTCI must have a certain deviation from the actual ambient air temperature that can cause the same physiological response.

Define the deviation as 𝑂 𝑓𝑓𝑠𝑒𝑡 , 𝑂 𝑓𝑓𝑠𝑒𝑡 is determined by the actual 2m air temperature (𝑇 𝑎 ), 2m dew point temperature (𝑇 𝑑 ), 10m wind speed (𝑉 𝑎 ) and average radiation temperature (𝑇 𝑚𝑟𝑡 ).Therefore, UTCI can be expressed as: UTCI = 𝑇 𝑎 + 𝑂 𝑓𝑓𝑠𝑒𝑡 (𝑇 𝑎 , 𝑇 𝑑 , 𝑇 𝑚𝑟𝑡 , 𝑉 𝑎 ) = 𝑓(𝑇 𝑎 , 𝑇 𝑑 , 𝑇 𝑚𝑟𝑡 , 𝑉 𝑎 ) (9)

According to the physiological response of the human body to the external environment, the UTCI is graded accordingly, as shown in Table 5:

<46℃ 32℃≤UTCI<38℃ 26℃≤UTCI<32℃ 9℃≤UTCI <26℃ 0℃≤UTCI <9℃ -13℃≤UTCI<0℃ -27℃≤UTCI <-13℃ -40℃≤UTCI<-27℃ UTCI <-40℃
Taking the commercial area as an example in this study, Ladybug Tools was used as a simulation tool to analyze the thermal comfort of the human body, and the UTCI results of typical weather days in summer were calculated (Figure 13). According to the division level, it can be seen that Beijing is mostly in the range of thermal discomfort in summer, so it is particularly important to improve the urban thermal environment and improve the thermal comfort of the human body.

Conclusion

This paper innovatively replaces the natural underlying surface classification system used in UWG simulation with a fine urban functional area underlying surface classification system that can consider human socioeconomic activities. Using UWG simulation to explore the outdoor microclimate of urban functional areas within and between categories in Beijing. In the research on the identification method of functional areas, the remote sensing data and social perception data are comprehensively used, and the method of probability estimation is used to identify urban functional areas under the fusion of multivariate and multi-data, which improves the recognition accuracy of functional areas, with an accuracy of 85.6%. In the research on improving the accuracy of UWG thermal environment simulation, the temperature simulation results are used as the optimization goal, and the single-objective optimization algorithm is used to calibrate the model to improve the simulation accuracy, in which R2 is increased by about 0.12, and RMSE is reduced by about 0.84. Through research and analysis of the thermal environment differences between different types of functional areas and the same type of functional areas in Beijing. The simulation results show that: (1) The canopy temperature of different urban functional areas from high to low is: industrial area>commercial area>residential area>public area>road and traffic area>green space and square area. The temperature of high and dense building land is generally higher, the existence of the green square area will alleviate the thermal environment in this area.

(2) The UWG model is more suitable for thermal environment simulation in summer, in areas with relatively homogeneous urban morphology and sparse urban vegetation.

(3) The thermal environment simulation results of the same functional area located in different locations are basically consistent, with slight differences in the high and low peaks of temperature, the average temperature in the city center is higher than that in the suburbs. The temperature simulation results of the residential single-functional zone and the multi-functional zone are not much different, and the residential-transportation land is slightly higher than that of other functional zones. (4) The temperature and humidity simulation results of UWG are consistent with the actual temperature and humidity change trend. The air temperature curve has a single peak change, and gradually decreases after reaching the daily maximum value. The change trend of relative humidity is opposite to that of temperature, that is, the higher the temperature, the lower the humidity. The prediction effect of temperature and humidity during the day is better, and there is a tendency to overestimate the temperature and underestimate the relative humidity at night. (5) According to the UTCI thermal comfort evaluation, it can be seen that the thermal comfort of the human body in Beijing is poor in summer, and most of them are within the range of the thermal discomfort index. Therefore, it is particularly important to improve the urban thermal environment and improve the thermal comfort of the human body.

The urban functions of the downtown area of Beijing are developing toward diversification and individualization. However, with the continuous evolution of urban spatial structure, there are many potential thermal environmental problems. According to the thermal environment effect of urban land use type and structure, a reasonable urban land use structure and spatial allocation planning can be formulated to effectively alleviate the urban heat island effect. In the future, high albedo (reflectivity) roofs and pavements, porous/permeable pavements, and open grid pavements (such as concrete grid pavements) can be used to replace existing building materials. The lack of urban vegetation will further intensify the UHI effect, and parks are the largest urban green space and a major component of urban ecosystems. Increasing green area through landscape design is an effective way to improve microclimate and outdoor thermal comfort (Chen.2011;Yan.2012). Beijing has a well-developed industry and commerce, and the separation of work land and residential land creates heavy traffic pressure in the morning and evening peak hours. Anthropogenic thermal emissions from vehicles are an additional source of urban energy balance, enhancing the UHI effect. Future urban planning should rationally develop mixed-functional areas according to the needs of work and life, reduce single-use land types, and further improve land use efficiency.

The architectural geometric characteristics of the city, such as building height density, building orientation, and sky viewing angle, also play an important role in the radiation exchange and temperature within the urban canopy, and reasonable urban form design can also alleviate UHI.

The identification of urban functional areas in this paper mainly relies on POI data, but the amount of POI data in suburbs, urban unused land, farmland and other areas is small, and the semantic information contained in them is limited, resulting in poor identification results of functional areas in these areas. The next step of research should be to identify urban functional areas in combination with mobile phone signaling data, luminous lamp remote sensing data, and social media big data. UWG can measure the difference in thermal environment caused by subtle changes in small and medium scales, and can well reflect changes in the thermal environment. In order to more accurately simulate the thermal environment status of different urban functional areas, the multiobjective optimization model calibration method can be used in the future to further improve the accuracy of UWG. For example, the simulation results are calibrated with temperature and humidity as optimization objects, and a physics-based automatic calibration method or Bayesian calibration method is developed. The UWG model can predict the urban temperature well and reflect the overall trend of urban temperature change. However, the current latent heat exchange mechanism of UWG does not consider the comprehensive influence of solar radiation, air temperature and wind flow on the thermal environment, and its prediction effect on extreme weather is poor. The future research direction is to optimize the latent heat exchange mechanism between the four modules, and consider the effects of different types of underlying surfaces and wind speeds on the thermal environment in the vicinity of the simulated area.

  North China. The climate of Beijing is a typical semi-humid continental monsoon climate in the north temperate zone, with four distinct seasons, hot and rainy summer, cold and dry winter, short spring and autumn. The annual average temperature in Beijing is 19°C, and the average annual precipitation is 644mm. Beijing's terrain is high in the northwest and low in the southeast, surrounded by mountains on three sides in the west, north and northeast, and a plain that slopes gently towards the Bohai Sea in the southeast. The research area of this paper is Dongcheng District, Xicheng District, Chaoyang District and Haidian District of Beijing, with a total area of 994.34 square kilometers, as shown in Figure1.

Figure 1 .

 1 Figure 1. Research area: four major urban areas in the center of Beijing.

  follows: (1) Perform radiometric calibration, atmospheric correction, image fusion, image cropping and other operations on GF2 satellite data to obtain corrected remote sensing images of the study area, which are used to obtain UWG model parameters and urban functional area classification. (2) Carry out coordinate system conversion, screening and reclassification of the collected Beijing urban POI data and AOI area of interest data, and finally divide them into six functional areas: Public services, Commercial, Residential, Industrial, Green space, and Road traffic. (3) Download OSM data from Open Street Map (https://www.openstreetmap.org/), perform data screening, topology processing, symmetric difference operation, etc., and use it to divide the research area to obtain research unit plots. (4) Obtain the weather data of Beijing in 2019 based on the weather station, and generate the *.EPW file of UWG simulation input through the Element software. (5) The meteorological data of the urban stations in the center of Beijing can verify the temperature and humidity simulation results of UWG. The urban verification stations and their locations are shown in Figure 2.

Figure 2 .

 2 Figure 2. Location distribution map of urban verification meteorological stations

Figure 3 .

 3 Figure 3. Urban functional area identification process.

Figure 4 .

 4 Figure 4. Simulation process of the UWG model. ①RSM mainly reads the meteorological data measured hourly at the suburban sites, calculates the sensible heat flux of the suburban sites through the surface energy balance and provides them to VDM and UBL. The model represents the heat transfer process by dividing the soil into discrete layers and solving a finite difference equation (1):

  area of Beijing, namely Public Services, Green Space, Commercial, Residential, Industrial, and Road Traffic. There are a total of 15 types of mixed functional areas, including Public Services-Road Traffic (PS&RT), Green Space-Public Services (GS&PS), Industrial-Residential (IN&RT), Residential-Road Traffic (RE&PT), Commercial-Road Traffic (CO&RT), Commercial-Industrial (CO&IN), Residential-Road Traffic (RE&RT), Residential-Public Services (RE&PS), Residential-Commercial (RE&CO), Residential-Industrial (RE&IN), Residential-Green Space (RE&GS), Industrial-Public Services (IN&PS ), Green Space-Road Traffic (GS&RT),

Figure 5 .

 5 Figure 5. Recognition results of functional areas in the central urban area of Beijing.

  Figure 6. The temperature of each functional area from January 21st to January 22nd in winter. among them, the green solid line is the meteorological temperature of the Shunyi station, that is, the actual suburban temperature. the red solid line is the temperature of the weather stations located near the functional area, that is, the actual temperature of the city. The solid blue line is the temperature simulated by running the UWG model, that is, the predicted urban air temperature. (a) Public services; (b) Green space; (c) Residential;(d) Commercial; (e) Industrial; (f) Road traffic. The temperature of each functional area from July 21st to July 22nd in summer. (a) Public services; (b) Green space; (c) Residential;(d) Commercial; (e) Industrial; (f) Road traffic.

  divided functional blocks in the study area. Calculating the UWG simulation results of each patch requires a lot of computer calculations. Therefore, this study only takes Dongcheng District, Beijing as an example, calculates the temperature field of each patch simulated by UWG in Dongcheng District, Beijing, and analyzes its spatial pattern, as shown in Figure 8.

Figure 8 .

 8 Figure 8. Thermal environment simulation results of Dongcheng District (at 2:00 p.m. on July 23, 2019) The green square is dominated by natural air-permeable surfaces such as soil, vegetation, and water bodies. Vegetation can effectively block sunlight and reduce radiant temperature. At the same time, the transpiration of vegetation can increase air humidity and reduce temperature. Roads and traffic areas are mostly hard paved roads

Figure 9 .

 9 Figure 9. The temperature prediction results of single functional zone plots and composite UFZ plots in residential areas.

Figure 10 .

 10 Figure 10. The spatial distribution map of the Residential_1-Residential_3, The spatial

  Diurnal Variation of Atmospheric Temperature Field Simulated by UWG To explore the thermal environment of a specific day under the same type of functional area, we average the *.UWG file simulation parameters related to residential and commercial areas, taking July 23 in summer and January 23 in winter as the analysis period. The temperature and humidity simulation results obtained in winter and summer are shown in Fig. 11 and Fig. 12. Simulation results of temperature and humidity in winter and summer for residential land. (a) Temperature; (b) Humidity. (a) (b) Figure 12. Simulation results of temperature and humidity in winter and summer for commercial area land. (a) Temperature; (b) Humidity.

Figure 13 .

 13 Figure 13. UTCI Chart of Typical Meteorological Days in Summer in a Commercial District of Dongcheng District of Beijing.

  𝑤 (𝑇 𝑤 -𝑇 𝑢𝑟𝑏 ) + 𝐴 𝑟 ℎ 𝑟 (𝑇 𝑟 -𝑇 𝑢𝑟𝑏 ) + 𝐴 𝑟 ℎ 𝑟𝑑,𝑠𝑘𝑟 (𝑇 𝑠𝑘𝑟 -𝑇 𝑢𝑟𝑏 ) +𝐴 𝑤𝑖𝑛 𝑈 𝑤𝑖𝑛 (𝑇 𝑖𝑛 -𝑇 𝑢𝑟𝑏 ) + 𝑉 𝑖𝑛𝑓/𝑣𝑒𝑛𝑡 𝜌𝑐 𝑝 (𝑇 𝑖𝑛 -𝑇 𝑢𝑟𝑏 ) + 𝑢 𝑒𝑥 𝜌𝑐 𝑝 (𝑇 𝑢𝑏𝑙 -𝑇 𝑢𝑟𝑏 ) 𝑉 𝑐𝑎𝑛 represents the urban canyon air volume; 𝑈 𝑤𝑖𝑛 represents the U value of the window; 𝑉 𝑖𝑛𝑓/𝑣𝑒𝑛𝑡 represents the ventilation rate; 𝐻 𝑤𝑎𝑠𝑡𝑒 represents the sensible part of the waste heat flux released by the air conditioning system into the urban canyon; 𝐻 𝑡𝑟𝑎𝑓𝑓𝑖𝑐 represents the anthropogenic heat generated by traffic; 𝑢 𝑒𝑥

	𝑉 𝑐𝑎𝑛 𝜌𝑐 𝑣	𝑑𝑇 𝑢𝑟𝑏 𝑑𝑡	= 𝐴 𝑤 ℎ +𝐻 𝑤𝑎𝑠𝑡𝑒 + 𝐻 𝑡𝑟𝑎𝑓𝑓𝑖𝑐	(7)

pressure, 𝜃 𝑢𝑟𝑏 represents the average latent temperature in the control body, 𝐻 𝑢𝑟𝑏 represents the sensible heat flux on the surface of the control body, 𝜃 𝑟𝑒𝑓 represents the reference latent temperature in vitro, 𝑢 𝑟𝑒𝑓 represents the reference air velocity, 𝐴 𝑓 represents the heat exchange cross-sectional area between the control body and the surrounding environment. ④UC-BEM calculates the air temperature and humidity of the urban canyon through the town energy balance and building energy model, at the same time, it provides UBL with urban sensible heat flux and urban canyon air temperature and humidity. The urban canyon energy balance equation is as follows: where, 𝑇 𝑢𝑟𝑏 ,𝑇 𝑖𝑛 ,𝑇 𝑢𝑏𝑙 ,𝑇 𝑠𝑘𝑟 represent urban canyon air temperature, indoor air temperature, urban boundary layer air temperature above the urban canyon, and effective temperature;

Table 1

 1

			. Calibration parameters of UWG model
	NO.		Parameter		Value
	1	Daytime urban boundary layer height		580
	2	Nighttime urban boundary layer height		60
	3		Reference height of the VDM		110
	4		Circulation coefficien		0.95
	5		UCM-UBL exchange coefficient		1.21
	6		Vegetation albedo		0.25
	7		Latent fraction of grass		0.53
	8		Latent fraction of tree		0.68
	9		Rural vegetation coverage		0.07
	10		Road albedo		0.11
	11	Traffic sensible anthropogenic heat (peak)	24.7
	12		Fraction of waste heat into canyon		0.53
	Table 2. Accuracy evaluation table before and after parameter calibration
			R 2			RMSE
	UFZ categories	Before	After	Before	After
			calibration	calibration	calibration	calibration
	public land	summer winter	0.69 0.55	0.77 0.72	2.27 3.12	1.87 2.29

Table 3 .

 3 Main input parameters of each functional area

		Average	Site	Facade	Tree	Grass
	UFZ type	Building	Coverage	to Site	Coverage	Coverage
		Height	Ratio	Ratio	Ratio	Ratio
	public land	21.05	0.23	1.22	0.12	0.21
	commercial land	119.3	0.145	3.997	0.08	0.17
	residential land	43.8	0.21	1.85	0.12	0.20
	industrial land	7.59	0.18	0.225	0.08	0.17
	green space and square	6.08	0.053	0.054	0.22	0.42
	roads and traffic	10.52	0.195	0.457	0.09	0.17

Table 4 .

 4 Temperature simulation accuracy evaluation table for each functional area.

	UFZ categories		R 2	RMSE
	public land	summer winter	0.77 0.72	1.87 2.29
	commercial land	summer winter	0.88 0.70	1.44 2.35
	residential land	summer winter	0.90 0.68	1.32 2.66
	industrial land	summer winter	0.66 0.60	2.79 2.54
	green space and square	summer winter	0.78 0.72	1.87 2.07
	roads and traffic	summer winter	0.77 0.61	1.84 2.23

Table 5 .

 5 UTCI Thermal Comfort Rating Table

	Thermal Comfort Level	UTCI Value
		UTCI ≥46℃
		38℃≤UTCI