Impact of Land Use/Land Cover on the Microclimate of
Elementary School Blocks: A Case Study of Kurosaki Area

Xincheng He1, Weijun Gao2,3, Rui Wang4

1 PhD Student, Faculty of Environmental Engineering, The University of Kitakyushu
2 Professor, Faculty of Environmental Engineering, The University of Kitakyushu
3 Professor, iSMART, Qingdao University of Technology
4 Post Doctor, Westlake Institute for Advanced Study, Westlake University

高齢者や児童の健康状態は、その狭い生活圏に固有な日照や気温、風の強さなど、いわゆる微気候の影響を受けやすい。そこで北九州市黒崎地区の４学区について、
建築密度、
緑地率、
道路舗装率、
天空率を調べ、比較検討した。

Abstract

As the global climate continues to warm, climate and environmental changes brought about by urbanization havebecome the focus of attention. Land use/land cover plays an increasingly important role in changes in outdoor
physical environments. In addition, changes in urban climate are more harmful to vulnerable groups, especially forsenior citizens and young children. However, few studies have focused on microclimate change around elementary
schools. This research took four elementary schools in the Kurosaki area as examples. According to the land use/land cover data, the peripheral types of schools were divided into four categories: commerce, high green areas,
residences, and public places. Through field measurement and ENVI-met simulation, this study tried to make clear
the land use/land cover impact on the microclimate around elementary schools based on the urban block as theresearch scale. The results indicated that the measured air temperature trends of schools in different categories
were significant differences. Also, the microclimate around the elementary school was closely related to building
density, green plot ratio, impervious ground surface fraction, and sky view factor. The findings of the research
provide a reference for the design of the environment around the elementary school and the overall urban
planning.
Keywords

land use/land cover; elementary school, ENVI-met, microclimate

1. Introduction

Rapid urbanization has been undergoing all over theworld since the second half of the 20th century.
According to the National Census in 2015, the total population of Japan was about 127 million and the urbanization rate of Japan was approximately 93%1). Cities havebecome the main settlements of humans. Previous
research has demonstrated the impact of urban microclimates on outdoor human comfort, the energy consumption of buildings, and the spread of air pollutants.
Air temperature (Ta) and wind speed are the two mostrepresentative microclimate parameters2). High temperatures sometimes induce serious health problems
such as heatstroke, cardiovascular diseases, and evendeath, especially for senior citizens and very young children3). Airborne transmission is an important way oftransmitting influenza viruses and respiratory syncytial
viruses4).
Land use/land cover (LU/LC) has become a criticaldriver of urban microclimate formation at the microscale.
The increase of artificial surfaces and the density andlayout of buildings have direct impacts on the microclimate. In previous studies, four commonly used parameters in urban planning and architecture of building
density (BD), green plot ratio (GnPR), impervious
ground surface fraction (PAVE), and sky view fraction(SVF) were used to characterize urban morphology5).
Reducing BD has a more significant influence on

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Xincheng He, et al.

mitigating the urban heat island effect and can reduce
the temperature rise and length of time of the heat islandeffect by approximately 30%6). Urban green infrastructure, such as parks, street trees, and green roofs, areoften suggested for urban planners to adjust the urban
microclimate7).
Despite the extensive research on microclimate inopen spaces, most research objects are main streets, residential quarters, and parks. School is the main place
where children learn. As children spend most of theirtime at school, it is crucial to provide students with ahealthy and comfortable learning environment. Physical,
visual, and auditory comfort conditions required forlearning and the energy consumption of school buildings have always been the focus of research. However,
little attention has been paid to the microclimate aroundelementary schools.
For these purposes, we used the research method thatcombines field measurement and ENVI-met software
numerical simulation. This study aimed to bridge thesegaps by assessing the microclimate performance ofurban morphology at the block scale by considering different elementary school block types. Finally, it revealed
the relative importance of LU/LC to the elementary
school blocks with an objective to formulate optimum
design strategies for improving the outdoor thermal
environment of elementary school blocks. The research
results arising from this study provide valuable insights
for informing future elementary school block-scale
designs.

2. Methodology

2.1. Study area
This study was carried out in Kurosaki (33°51’N,
130°45’E), located in Kitakyushu City, Japan. It has a
Pacific Ocean side climate. June to September were thehottest months, the average air temperature is 22.7–27.8°C. Most of the Kurosaki area is an urbanization promotion area, and the area around Hobashira Mountain is anurbanization control area. The land use on the north andsouth sides of Kurosaki also shows obvious differences,as shown in Fig. 1.
Four elementary schools (Kurosaki Chuo Elementary036

school [C1], Hikino Elementary School [R1], Kurogahata
Elementary School [G1], Hagiwara Elementary School[P1]) had been selected to conduct field measurements
24 h per day from August 1 to August 31, 2012. C1 is incommercial zone, R1 is in Category I residential zone,
and G1 and P1 are in Category I mid/high-rise oriented
residential zone. The elementary school facilities include
school buildings, gymnasiums, swimming pools, andplaygrounds.

Fig. 1. Land use zones in Kurosaki

This study took the school block as the researchscale. The 300 m scope centered on the elementary
school was used as the study area, as shown in Fig. 2.
According to the LU/LC database of Kitakyushu 2012,
the peripheral type of 4 schools were divided into thefollowing four categories: commerce, high green area,
residence, and public place. Table 1 summarizes themorphological parameters of each school block. Due tothe C1 containing large-volume or high-rise buildings,
BD and PAVE were the highest among all the study
areas, and GnPR was the lowest. The situation of G1
was the opposite of that of C1. The BD and PAVE werethe lowest, and GnPR was the highest. The vegetation
was dense, especially the tall trees. At the same time,
the tree canopy played a role in blocking the sky andreducing the SVF of the area. R1 is mainly middle- andlow-rise residences, and the PAVE value was higher. P1

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is surrounded by a middle school and parks, and the
GnPR was slightly higher than that of R1.

shown in Fig. 3. The air temperature measurements were
performed every minute. The reference thermometer
was used for calibration before measurement to avoid
measurement errors.
In this work, days with clear, calm, and sunny
weather conditions were most appropriate for microclimate analysis. Affected by humid air currents andtyphoons, there were many rainy days during the fieldmeasurement period. The sunshine time and precipitation data from the Yahata Weather Station were used tofilter out typical days. The sampling framework for filtering typical days consisted of a daily precipitation of
0.0 mm and a sunshine rate of 50% or more. Based onthese criteria, 13 typical days in August were selected.

Fig. 2. Spatial characteristics of 4 elementary school
blocks.
(Source: Google Earth)

Table 1. Urban morphology characteristics around4 schools.
Study
C1
R1
G1
Peripheral
High green
Commerce Residence
type
area

P1
Publicplace

BD

33.19%

23.60%

13.54%

20.33%

GnPR

5.55%

17.52%

47.36%

20.74%

PAVE

78.44%

67.09%

45.77%

58.46%

SVF

0.47

0.46

0.33

0.41

2.2. Field measurement
A louver box was placed at each elementary school,
the underlying surface of these observation points issoil. To monitor the air temperature at the pedestrianlevel, the temperature sensor Platinum resistance thermometer Pt100 and data recording device DATAMARK
LS-3300PtV were taken at 1.5 m above the ground, as

Fig. 3. Measurement tools.

2.3. ENVI-met simulation
In this study, numerical simulations were conductedusing ENVI-met software. The model area was 300 m ×
300 m × 90 m with 150 × 150 × 30 three-dimensional
grids, the horizontal and vertical grid resolutions were 2
m and 3 m respectively. The plant and soil database, andbuilding properties were obtained from Google Earthsatellite images (captured on May 26, 2012), LU/LC
data (2012 Kitakyushu), and field measurements at foursites. The simulated date was selected as a typicalsummer day on August 18, 2012, from 00:00 to 24:00.
To avoid the influence of the initialization (24 h), numerical simulations were conducted for August 17, 2012.
Table 2 shows the input configuration to the simulation

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Xincheng He, et al.

model. The initial air temperature in the atmospherewas 302.2 K. The initial wind speed was 1.4 m/s, andthe wind direction was 202.5°.
Table 2. Input configuration used in simulation with
ENVI-met.
Parameter
Start Date

Input value
August 17, 2012

Start Time

00:00

Total Simulation Time

48 hWind Speed in 10 m height

1.4 m/sWind Direction (0: N, 180: S)

202.5°

Initial Temperature of Atmosphere302.2 K

Relative Humidity in 2 m

78.59%

Special Humidity at model top

7.0 g/kg

This study selected the coefficient of determination(R ) and the root mean square error (RMSE) to evaluate
the validity of the ENVI-met model. The RMSE calculation is shown in Equation (1).
2(1)
where n is the number of measurements,
surement, andis the simulationis the mea-

Fig. 4. The comparison between measured and simulated Ta

3.2. Comparative analysis of measured data
The air temperature of 4 elementary schools on typical days during the observation period was compared,
as shown in Fig. 5. Ta reached its highest value atapproximately 14:00 and approached its lowest value atapproximately 06:00.

3. Results and discussion

3.1. Model validation
The ENVI-met model was validated considering thehourly measured and predicted Ta of each elementary
school. Fig. 4 shows the scatter diagram of the measured
and simulated Ta values. The R 2 value ranged from 0.93
to 0.98, and the RMSE value ranged from 0.50 °C to1.22 °C. Overall, a strong agreement was found between
the simulated and the measured Ta. And the ENVI-met
model can accurately simulate the outdoor microclimate
environment of elementary school blocks.
Fig. 5. Comparison between Ta of four schools on typical
days

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There were obvious differences in air temperature
throughout the day between different elementary
schools. C1 school is located in the core area of Kurosaki,
where the air temperature has been maintained highthroughout the day. This was because the Kurosaki station center is basically commercial land, with highbuilding density and a lot of high-rise buildings. The
surface cover is mainly asphalt and concrete, and theartificial heat removal of vehicles and the air conditioning equipment of buildings have increased significantly.
Therefore, the air temperature raised rapidly during thedaytime, and it was difficult to drop significantly atnighttime. The average air temperature was 29.7 °C,
which was the highest value among all schools. Becauseof the existence of large areas of the natural surface inthe study area, the air temperature of G1 school remained
low throughout the day, even during the hottest period
from 14:00 to 15:00. The average air temperature was
28.6 °C. Many middle and low-rise residential buildings
and the occupation of the natural surface by the artificial
surface made the air temperature of the R1 school
increase sharply after sunrise (6:00), even slightly surpassing C1 school between 10:00 and 16:00. At the sametime, residential land had low BD and less artificial heatremoval, so the heat was easily diffused at night, and the
air temperature was low. The average air temperature
was 29.2 °C. The air temperature of P1 school fluctuatedrelatively little throughout the day, with an average airtemperature of 29.3 °C.
3.3. Spatial distribution of air temperature and wind
speed
From the past study, the most uncomfortable microclimate hour is approximately 14:00 in summer. This
time is also the time when people are more active.
Based on the 24 h simulation results, the air temperature of 4 study areas at a height of 1.5 m above theground at 14:00 was generated. Fig. 6 and Fig. 7 show
the distribution of air temperature and wind speed.
The overall Ta of the C1 school was more consistentthan that of the R1 school and the P1 school, and theaverage air temperature of the C1 school was 33.3 °C.
High-intensity LU/LC existed around the C1 school. Ta

Fig. 6. Distribution of Ta at 14:00 on August 18, 2012.

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range of G1 school was not only relatively consistent but
also the lowest among all schools. The existence ofmany natural surfaces around the school provided agood thermal environment for the school and effectively
alleviated the rise of school Ta. Therefore, the overall Ta
difference of G1 school was the smallest, and the average air temperature was 31.2 °C. The Ta range of R1
school was relatively discrete, with an average air temperature of 34.0 °C, which was higher than that of C1
school. The lack of shelter on many asphalt surfaces ofthe school and the impact of the park on the south side of
the school were the main reasons for the local high Ta in
the R1 school. The Ta range of P1 school was relatively
more consistent than that of R1 school, and the average
Ta of the P1 school was 33.7 °C.
The distribution of Ta followed the wind direction,with a higher Ta on the southeast side of the study area.
The Ta in the sandy soil area on the left of the park in C1
was significantly higher than that in the lawn area on theright. The main road was also an area with higher Ta,
and the surface is asphalt. The overall average Ta of theG1 school block was the lowest among all school blocks.
Affected by the wind speed, the situation in the playground of the school belonged to the area with relatively
low Ta, which was opposite to that of other schools.
Land shape had a greater impact on Ta; that is, the lower
the height of the land, the higher the Ta. In the R1 study
area, the arterial roads were also areas with higher Ta.
Because the surface is asphalt, the ground vegetation issparsely covered. In addition, the school playground andthe park with a sandy soil surface formed a continuous
high Ta area. The P1 school playground and the park
where the surface is sandy soil on the left also formed acontinuous high Ta area. Building shadows played a role
in reducing Ta. Moreover, the height of the middle
school on the right was higher than that of the elementary school, the playground Ta dropped by approximately 2 °C.
When we compared the numerical distribution of thefour elementary schools, the value of the wind speed of
C1 school was relatively consistent. Because of the existence of many high-rise buildings around the school, thewind speed on the campus was more stable. In addition,

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Fig. 7. Distribution of wind speed at 14:00 on August 18, 2012.

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the park on the north side of the school increased part of
the wind speed of the C1 school. The average windspeed was 0.64 m/s. The overall wind speed of G1 school
was the highest among all schools, and the average windspeed was 1.28 m/s. The land height around the campus
fluctuated greatly, and less dense buildings on the surface promoted airflow. Furthermore, the obstruction of
the teaching building led to local low wind speed, resulting in the difference in the numerical value of windspeed in the school being relatively large. R1 school had
less vegetation, and the distribution of wind speed
values was scattered, with an average wind speed of
0.56 m/s. When the area around the school is public, theaverage wind speed of the P1 school was 0.60 m/s.
The channeling effect formed by the main road on
the left of the C1 school and the connection between theplayground and the park played an important role inoptimizing airflow within the school. The wind speed
was higher in the open area around the G1 school, andthere were fewer low-wind-speed areas in the study
area. The playgrounds of the G1 school belonged toareas with higher wind speeds in the study area, which
can enhance airflow within the school. The wind paths
around the R1 school improved the wind environment of
the school. Because of tall trees, parks cannot optimize
the wind speed of the campus. The wind path around theP1 school increased the wind speed in the school. In thesame environment, the middle school with a higher landheight on the right had a higher overall faster airflow
than the elementary school.
3.4. Correlation between school block microclimate
and urban morphology
This study analyzed the relationship between the
average air temperature and wind speed of the study
area and urban morphology, as shown in Fig. 8.
The BD was positively correlated with the average
air temperature of the study area; that is, an increase in
BD promoted an increase in Ta (R 2 = 0.7883). The average air temperature of the study area had a close positive
correlation with PAVE and SVF. The increase in thesetwo parameters deteriorated the Ta in the study area. On
the contrary, the average air temperature of the study

area had a significant negative correlation with GnPR
(R 2 = 0.9756), and the increase in the GnPR value effectively reduced the Ta of the study area.
The average wind speed of the study area had a close
negative correlation with PAVE and SVF. The coefficient of determination R2 was 0.8023 and 0.9638. The
BD was negatively correlated with the average windspeed; that is, it increased the value of BD to weaken the
average wind speed (R 2 = 0.6868). The effect of GnPR
on average wind speed was the opposite.

Fig. 8. Relationship between school block microclimate andurban morphology at 14:00 on August 18, 2012.

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4. Conclusion

This paper selected 4 elementary schools in Kurosaki,
Japan as the research objects. Through field measurement and numerical simulation, the current situation of
microclimate around the elementary school was analyzed from the block scale. According to the LU/LC
database, the four research areas were divided into four
categories. Afterward, validated ENVI-met numericalsimulations were carried out to evaluate the microclimate of school blocks. The major conclusions of thisstudy are as follows.
First, based on school blocks with different LU/LC,
there were significant differences in air temperature
values measured throughout the day in elementary
schools, especially between C1 school and G1 school.
The difference between the two schools of average airtemperature was 1.1 °C.
Second, although BD, GnPR, PAVE, and SVF had
different correlations with the microclimate, these foururban morphology parameters were important factors
affecting the microclimate of elementary school blocks.
Third, building shadows played an important role in
reducing the air temperature. The surface of sandy soil
and asphalt caused significant deterioration of local air
temperature. On the contrary, vegetation effectively
mitigated high air temperature. The land height of thestudy area affected air temperature.
Finally, the playground was an area with a high windspeed in the school. And the wind path around theschool enhanced airflow in schools. Dense buildings
and trees dropped down wind speeds. The land shape
had a significant impact on the airflow; that is the higher
the land height, the higher the wind speed.
Nonetheless, our findings may have some limitations. This study only involved four elementary schools
in the Kurosaki area, and the number of cases was small.
Subsequent studies should add cases to include all elementary schools in the Kurosaki area.

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