A study on the coupled effects of climate and built environment elements at urban block scale——A case study of Fengtai district in Beijing

Figures & data

Table 1. Classification statistics of urban land use in Fengtai district.

Urban land use	Code	Area/km^2	Proportion/%
Bare farmland	10	17.23	5.54
Building land	20	37.55	12.07
green land (forest land, grassland, Park)	30	60.91	19.57
Artificial surfaces (squares and roads)	40	190.48	61.22
Rivers, and lakes	60	4.99	1.60

Figure 1. Classification distribution of building height, building density, road density, and vegetation coverage.

Figure 2. Climate sensitive map at block scale, FTD.

Table 2. Calculation method of relevant factor data.

Elements	Calculation method
Temperature Wind Speed Relative Humidity AQI	Extract the average value of each element in the block unit
Surface Temperature	Extract the value of surface temperature in block units and calculate the average value
Building Density	The kernel density calculation tool of ArcGIS software calculates the building density in each block unit, mainly showing the degree of building aggregation, and the data unit is building/km^2.
Average building height	Total building height of units in each block ÷ total number of buildings in each block
Road Density	The linear density calculation tool in ArcGIS software calculates the road network density within each block unit, which mainly reflects the density of the road network, the data unit is stripe/km^2.
The proportion of building area	(The gross building area of block units ÷ block area)*100%
The proportion of green space	(The gross green space area of block units ÷ block area)*100%
The proportion of impervious road surface	Combined with the satellite map, it is shown that there is no river, forest, cropland-bareland, farmland, and other types of land in the high-sensitive unit block. Therefore, the proportion of impervious pavement area is estimated as ”1- (the proportion of building area) – (the proportion of green space)”
NDVI	Calculate NDVI value in block unit and get the mean value of vegetation cover index.
Mean vegetation coverage	Based on NDVI calculation, the vegetation coverage was obtained, and the average vegetation coverage in block units was extracted*100%.
Average population	Based on the six census data, extract the population point data in block units and calculate the average value.

Table 3. Correlation statistics between built environment elements and meteorological elements.

	Pearson	Sig	Correlation Level
(a) Surface temperature
The proportion of building area	.476**	0.0000	Medium
NDVI	−.557**	0.0000	Medium
Mean vegetation coverage	−0.545**	0.000	Medium
Average building height	−.495**	0.0000	Medium
The proportion of impervious road surface	−.416**	0.0000	Medium
Road Density	−.163**	0.0000	Extremely Weak
Building Density	−.163**	0.0000	Extremely Weak
The proportion of green space	−.132**	0.0003	Extremely Weak
(b) Wind Speed
The proportion of building area	.143**	0.0001	Extremely Weak
Average population	−.579**	0.0000	Medium
Average building height	−.171**	0.0000	Extremely Weak
The proportion of green space	−.169**	0.0000	Extremely Weak
The proportion of impervious road surface	−.076*	0.0388	Extremely Weak
Road Density	−.072*	0.0481	Extremely Weak
Building Density	−.072*	0.0481	Extremely Weak
(c) Temperature
Average population	.280**	0.0000	Weak
The proportion of green space	.153**	0.0000	Extremely Weak
Average building height	.135**	0.0002	Extremely Weak
The proportion of building area	−.078*	0.0326	Extremely Weak
(d) AQI
Average population	.330**	0.0000	Weak
The proportion of impervious road surface	.142**	0.0001	Extremely Weak
The proportion of building area	−.143**	0.0001	Extremely Weak
Road Density	.081*	0.0271	Extremely Weak
Building Density	.081*	0.0271	Extremely Weak
(e) Relative Humidity
The proportion of building area	.109**	0.0028	Extremely Weak
Road Density	−.218**	0.0000	Weak
Building Density	−.218**	0.0000	Weak
Average building height	−.112**	0.0021	Extremely Weak
The proportion of impervious road surface	−.102**	0.0054	Extremely Weak

*At the 0.05 level, the correlation was significant; **At the 0.01 level, the correlation was significant.

Figure 3. Scatter regression equation diagram of significant influencing factors. (a) relative humidity; (b) AQI; (c) temperature.

Figure 4. Scatter regression equation diagram of significant influencing factors. (d) surface temperature; (e) wind speed.

Table 4. Magnitude relationship between built environment elements and meteorological elements.

	Magnitude relationship
(a) Average building height
Surface Temperature	Increase 13.84m and decrease surface temperature by 1℃
Temperature	Increased by 37.03 m, enhance the temperature of .1 ℃
Wind Speed	Increase by 68.35m and decrease wind speed by 0.1m/s
Relative Humidity	Increased by 385.5 m, reduce the relative humidity by .5%
(b) Building Density
Surface Temperature	Reach 13.11, reduce the surface temperature 1℃
Wind Speed	Reach 5.33, reduce wind speed 0.1m/s
Relative Humidity	Reach 6.17, reduce .5% relative humidity
AQI	Reach 9.93, raising the AIR quality index by 1 point
(c) Average population
Temperature	An increase of 25,038 people, to improve the temperature of .1 ℃
Wind Speed	Increase 25,000 people and reduce wind speed by 0.1m/s
AQI	An increase of 10,989 people, to improve the air quality index 1 point
(d) The proportion of impervious road surface
Surface Temperature	Increase by 19.88%, decrease the surface temperature by 1℃
Wind Speed	Increased by 18.66% and decrease wind speed by 0.1m/s
Relative Humidity	Increased by 51.23%, reduce the relative humidity by .5%
AQI	Increased by 21.96%, enhance air quality index 1 point
(e) The proportion of building area
Surface Temperature	Increase by 16.98% and raise the surface temperature by 1℃
Temperature	Increased by 75.13%, reduce the temperature of .1℃
Wind Speed	Increased by 96.71% and improved wind speed by 0.1m/s
Relative Humidity	Increased by 46.59%, increase .5% relative humidity
AQI	Increased by 21.34%, reduced air quality index 1 point
(f) The proportion of green space
Surface Temperature	Increase by 23.66% and decrease the surface temperature by 1℃
Temperature	Increase by 14.89% and raise the temperature by .1℃
Wind Speed	Increase by 31.59% and reduce wind speed by 0.1m/s

Figure 5. Distribution of green land and rivers around high sensitive blocks.

Table 5. Change value of meteorological elements after spatial optimization of the built environment (“- ‘is decline;’+” is promotion).

Change elements		Surface Temperature	Wind Speed	AQI
The proportion of impervious road surface	−34.31%	+1.73℃	+0.0018m/s	−1.56
The proportion of green space	+34.31%	−1.45℃	+0.11 m/s	−0.17

Figure 6. Climate environment improvement model at block scale.

(Source: Drawing based on previous analysis and references (Liu, Song, and Wang 2014b; Ren 2018; Yang, Zhang, and Xiuzhang 2016; Yuan 2019))

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