Journal of Heat Island Institute International Vol. 9-2 (2014)

Academic Article

Spatiotemporal Evaluation of Thermal Environmentin Kumamoto City Urban Districts
Takahito Ueno*1 Yusuke Shiki*2 Satoshi Doi*3 Chihiro Fujisue*4 Takahiro Tashiro*5*1 Professor, Faculty of Engineering, Sojo University*2 Kintetsu Engineering Co., Ltd.
3* Graduate School of International Environment Engineering, The University of Kitakyushu*4 Shinrin-Doboku Design Office
5* Associate Professor, Faculty of Engineering, Sojo University
Corresponding author: Takahito Ueno, ueno@eco.sojo-u.ac.jp
ABSTRACT
To investigate the thermal environment in urban districts, moving meteorological observations wereperformed in the location of Kumamoto City. The observations were carried out eight times in 2012 and 2013. In
this paper, the result of August 8 was used to evaluate the spatiotemporal characteristics of the areas. When
conducting the observations, two instruments were used and the results’ error caused by differences between
observation times and instruments were reviewed. The instruments were calibrated and observation times adjusted
to remove differences. Then, the results were able to show the thermal environment of the districts with theallowable margin of error. In addition, some unique areas were categorized into hot zones and cool zones for eachmeasurement time. In conclusion, it is suggested that this investigation method is useful to evaluate the thermal
environment of urban districts in time and space.
Key Words : Urban district, Heat environment, Moving meteorological observation, Air conditioning load

moving temperature measurement method. In addition, the1. Introduction

results of all points under observation were shown andIn urban areas of Japan, the thermal environment becomesconsidered next to the present conditions of the summer thermal

extreme particularly in summer. Therefore, negative healthenvironment of the central districts of Kumamoto City.

effects on the local residents, such as heat stroke, increase. Inaddition, the air conditioning load of buildings and energy

2. Thermal environment survey in urban districts ofconsumption increase. It is necessary to reduce energy

Kumamoto City

consumption and carbon dioxide emissions.

Previously, theauthors had studied the thermal environment of Kumamoto CityThe observations were carried out from 2012 through 2013 asurban districts, and considered its influence on local air

shown in Table 1. The observation times were 9:00, 12:00, 15:00(1). In the study, the results ofand 18:00. Fig.1 shows the observation points. Several shopping

investigations in 2011 and 2012 were compared and showed

arcades were included in these observation points. Temperature,conditioning load of buildings(2)strong similarities . The analysis, however, was limited to three

relative humidity, WBGT and globe temperature were observed

arcades in the center of Kumamoto City. In addition, there was ausing mobile measuring instrument (Kyoto electronics industry,problem of not being able to evaluate the reliability of theWBGT-113) (3). Operation of the measuring instruments was inmovement observation sufficiently. The observation method ofaccordance with the reference manual. For instance, theusing hand-held WBGT measurement devices at different points

influence of solar radiation on the globe temperature wasin the area is somewhat unreliable due to the time lag between

considered, as such the instruments were kept in the shaderecordings. In this report, the improvement of observation values

during transit. WBGT is an index to express the degree of risk ofusing two measuring instruments was performed using theheat stroke. In this report, the characteristics of the central- 73 -

districts of Kumamoto City were examined using observation
results of August 8. A meteorological measuring system wasinstalled near the point of B9 to observe the standard values;
however, the observation data were not provided because of themismanagement of the instrument. Therefore, the AutomatedMeteorological Data Acquisition System (AMeDAS) data wereused for a comparison of all points in this study. Thus, theinformation related to wind velocity and direction was not used,
and the local wind system such as land and sea breeze were notmentioned because the area was not so large and the influence of
these on air temperature distribution was considered to belimitted.
Fig.2 AMeDAS air temperature at Kumamoto meteorologicalobservatory (July 17, 19 and August 8, 2013) (4)Table 1 Observation period (2012 - 2013)
YearDateRemarks

2012

September 11

On August 31 was canceledtemperature was 27.2˚C at 5:00 and 6:00.

October 31

because of bad weather.

There were two observation courses. With both courses, the startpoint of the measurement was A1, and the end point was also A1,December 18
2013

July 17, 19

On July 26 and August 29

which was the “Bipuresu Hiroba (public square)” a central

August 8

were canceled because ofdowntown location. Course A moved sequentially from

September 13

bad weather.

observation point A1 of the east side to A14, and came back toOctober 31

A1. Course B moved from A1 to western point B1 to B14 andNovember 28

came back to A1. On the basis of the observation value at A1,
measuring errors were distributed proportionally depending on
the movement times, and the observation data were revised at
9:00, 12:00, 15:00 and 18:00. The expression to revise thetemperature were as follows: T Ti,cor.  Ti,obs.   
 ti,obs.  def .inst. .
 t datum... (1)

where Ti,cor. - corrected temperature at i point [˚C], Ti,obs. observation temperature at i point [˚C], (ΔT/Δt )datum - rate of
air temperature change at datum point at precise time [˚C/min],ti,obs. - elapsed time from precise time to i point observation[min], def.inst. - a measurement error of the instrument [˚C].
The observations of course A were done with measuringapparatuses No. 2 and No. 4. All apparatuses were corrected on
the basis of the value at 9:00 of measuring apparatuses No. 3.
Generally the measurement errors by the differences of the●: Moving observation point

apparatus were small.

■: Meteorological measurement station

At 9:00, the tendencies were different between the north side, A1-: Arcaded street

to A7, and the south side, A8 to A14, where these areas wereFig.1 Observation points in the central district of Kumamotoseparated by “Torichosuji” street. The temperature of the northCity (Map: Fukuoka Jinbun, Co. ‘Kumamoto Grand Machizu’)side area was approximately 1.0˚C higher than the south sidearea. It is hypothesized that this discrepancy was caused by theFig.2 shows the AMeDAS observation temperatures atcooling effect of a river. At 12:00, the temperatures exceeded

Kumamoto meteorological observatory on July 17, July 19 and33.0˚C and over 34.0˚C between A4 to A9. It was thought that

August 8, 2013. According to this data, it was seen that

this was caused by exhaust heat from cars. At 12:00, differences

temperature at this time exceeded 30˚C. On August 8, thein temperature were over 3.0˚C. At 15:00, there were manyhighest temperature was 33.2˚C at 12:00. The minimum

points at over 35.0˚C, and the places were not the same as 12:00.

- 74 -So, the temperature rose between A7 to A1 and B7 to B13. It

Table 2 Relation between WBGT and work environment (3)was thought that the temperatures at B7 to B13 became high

WBGT [˚C]

Permissible work level

Work strength

because the shade of the building disappeared at 15:00. In

32.5

Very light work

RMR-1

addition, it was thought that the high temperatures at A7 to A1130.5

Light work

RMR-2

were caused because they were facing the main street and that

29.0

Moderate work

RMR-3

those at A12 to A14 were caused because there was a27.5

Moderate work

RMR-4

concentration of cars in the small street. When it was 18:00, the26.5

Hard work

RMR-5

temperature difference decreased by 1.0˚C to 1.5˚C.

RMR: relative metabolic ratio (= energy spent by work / basal

The observations of course B were made with measuringmetabolism)

apparatus No. 1 and No. 3. In course B, remarkable differenceswere seen at 12:00. B4 and B5 were approximately 35.0˚C orTable 3 shows the differences between observed temperatures

more than other observation points. B14, in addition, wasand AMeDAS temperatures. Here, it should be noted that theseapproximately 34.0˚C.

AMeDAS values were not single-moment values but average

In case of WBGT, some small differences with the apparatus

values of several hours. The temperatures of the central districts

occurred. WBGT is defined and calculated as follows (3): WBGT  0.7  T  0.2  T  0.1 Tinwgd
WBGTout  0.7  Tw  0.3 Tg
Table 3 Differences between observed temperatures and.

AMeDAS temperatures [˚C] (August 8, 2013).

... (2)where, WBGTin - indoor WBGT [˚C], Tw - wet-bulb temperature[˚C], Tg – globe temperature [˚C], Td - dry-bulb temperature [˚C],
WBGTout – outdoor WBGT [˚C].
The expression of WBGTout was used in this measurement.
Referring to this expression, it was thought that unevenness ofWBGT measurement values occurred because the measurement
precision of the wet-bulb temperature and the blackballtemperature were lower than the case of the dry-bulb
temperature measurement, including the handling of theapparatus. Table 2 shows the relations between WBGT and the

9:00

12:00

15:00

18:00

29.1

33.2

33.2

31.5

A1 †

0.5

-2.5

0.0

0.7

†

0.7

-2.3

0.5

-1.3

A3 †

0.9

-2.3

0.4

0.4

A4

1.3

-0.4

1.6

2.0

A5

0.9

0.4

1.2

1.7

A6

1.0

1.1

2.3

1.9

A7

1.3

-0.4

2.4

2.1

A8

0.4

0.2

3.01.5

A9

0.1

-0.6

3.1

1.9

A10-0.5

-2.2

2.3

1.3

AMeDAS *A2Remarks

End of arcade, half-open

End of arcade, half-open

Along a national
highway

work environment. Regarding this, the summer thermal

A11-1.0

-1.6

3.4

1.9

environment at 9:00 was not as extreme because it was underA12-0.1

-2.9

3.3

0.8

28.0˚C in course A and in course B it was around 28.0˚C.

A13-0.4

-3.0

1.5

1.6

However, at 12:00 some points reached more than 30.0˚C in

A14-0.3-3.11.3

1.7

B1

†

1.8

-0.5

0.6

0.6

End of arcade, half-open

B2

†

1.2

-1.5

-1.3

-0.8

Store air conditioning

B3

†

1.6

0.7

1.1

1.3

Road crossing

B4

†

1.5

1.7

1.1

1.3

B5

†

1.3

1.8

0.9

1.0

B6

†

0.8

0.6

0.2

1.2

B7

†

0.6

-1.2

0.7

0.8

course A and B, and at 15:00, many points were over 30.0˚C. Inaddition, the points where WBGT were relatively high weredifferent between 12:00 and 15:00. At 12:00, between A10 toA14 and B7 to B11, such temperatures could not be seen, butthey rose remarkably at 15:00, and in places temperatures higher
than 32.0˚C were seen. This tendency was similar with the caseof air temperature.

B80.5

-0.9

1.3

0.5

3. Area classifications of the thermal environment in urban

B90.7

-0.8

1.2

1.1

districts

B10

0.3

-1.6

1.6

0.5

B110.5

-1.9

1.2

1.0

Fig.3 to Fig.6 show the results of air temperature observation ofB121.3

-1.0

0.3

1.2

course A and B. As mentioned above, all apparatuses were

B13

1.0

-1.1

0.5

2.0

corrected on the basis of a value at 9:00 of measuring apparatus

B140.4

0.4

1.3

0.3

No. 3. Generally the measurement errors caused by thedifferences of the apparatuses were small.

- 75 -Half-opening

End of arcade, half-open

In front of city hall

* : AMeDAS "a value every one hour ", † : Arcade.

(a) 9:00(b) 12:00(c) 15:00(d) 18:00

Fig.3 Revised air temperature (course A, apparatus No. 2 and 4)(a) 9:00(b) 12:00(c) 15:00(d) 18:00
Fig.4 Revised air temperature (course B, apparatus No. 1 and 3)- 76 -

(a) 9:00(b) 12:00(c) 15:00(d) 18:00
Fig.5 Revised WBGT (course A, apparatus No. 2 and 4)(a) 9:00(b) 12:00(c) 15:00(d) 18:00
Fig.6 Revised WBGT (course B, apparatus No. 1 and 3)- 77 -

were not higher than the ones by AMeDAS at 12:00, however,highway No. 3. The northern and western area were beyond

they were higher than AMeDAS data at the points along

33.0˚C, but were relatively low. B2 was lower, but this was

National highway, A10 to A12, and at the street with a lot of

because air conditioning leaked outside. In the area along thetraffic density.

national highway, drops of temperatures were slow and wereFig.7 shows the area classifications of the temperature

33.0˚C to 34.0˚C at 18:00. The temperatures fell relatively fastdifferences in comparison with the AMeDAS data as thein the center of the urban districts and were almost 32.0˚C. B13

standard temperature. The points exceeding 30.0˚C spreadwas exceptional, because the point was placed in a area of highthrough the northern and western area at 9:00. On the other hand,traffic density.

the low points of 28.5˚C to 29.0˚C spread through theThese features were merely the results caused by the regional

southwestern area. At 12:00, high temperature points of 33.0˚Ccharacteristics such us land use, local wind system, solar

to 34.0˚C were seen in the points along the National highway

radiation shielding, stagnation of wind, and direct artificial heat.

No. 3, the central area including points B1 and B14, and theTherefore, those mechanisms should be analysed in detail tosouth of the “Shimotori” arcade street including B3 to B6. The

elucidate the influences of these features. However those may be,

relatively low points spread through the northern and westernthese results show the features of air temperature distributions ofarea of the “Shimotori” arcade street. At 15:00, the points in thethe related districts and how varied they can be in a relatively

east area were very high and exceeded 35.0˚C. It was thought

small space and various times of the day. Especially, thethat this result was caused by there the traffic of National

influence of heavy traffic is considered conspicuous.

Relatively,

Relatively,

hot

hot

nutralnutralcool

cool

at each observation

at each observation

time.

time.

(a) 9:00(b) 12:00
Relatively,

Relatively,

hot

hot

nutralnutralcool

cool

at each observation

at each observation

time.

time.

(c) 15:00(d) 18:00

Fig.7 Area classification of air temperature and WBGT (August 8, 2013).

- 78 -4.

Experimental

application:examinationofair

5. Conclusions

conditioning energy load
To investigate urban climate by moving method is a simple
Based on these results, the air conditioning energy loads were

method and the reliability cannot be highly asserted. However,calculated. The proportions against the datum temperaturein this study the observation differences using two apparatuses(AMeDAS) were compared. The method and parameters werewere revised and the measured values were also revised by thethose of the building “Bipuresu” near A1 as in the last report (aelapsed observation time, which improved the fault. Thus, the2simple room model, floor space : 5,495m , air conditioning

characteristics of the summer thermal environments oftemperature : 25.0˚C, building structure : reinforced concrete

Kumamoto City districts were able to be evaluated temporally(1)building) . Table 4 shows the results of the calculation of airand spatially with some accuracy. It was also possible to classify

conditioning load for each measuring point. Additionally, Table

the characteristic districts in each observation time. The findings

5 shows the proportion of air conditioning load against datumof this study are briefly as follows:

values of each time by AMeDAS. Table 6 also shows the1) The southeastern district, in the morning, was relatively low

proportion of air conditioning load against datum values ofat a difference of 1.5˚C. It is hypothesized that this was due to12:00. Fig.8 shows the spatiotemporal distribution of air

the existence of a river.

conditioning load change against datum AMeDAS data at 12:00.
Table 5 Proportion of air conditioning load against each datum
2Table 4 Calculated air conditioning load [kJ/m h]
9:00
AMeDAS *49.3

value calculated using AMeDAS data [%]

12:00

15:00

18:00

98.7

98.7

78.2

AMeDAS *100

100

100100

†

9:00

12:00

15:00

18:00

†55.3

68.6

98.7

86.6

A1

112

70

100

111A2 †

57.7

71.0

104.7

62.6

A2 †117

72

106

80

†

122

72

105

106

A1
A3

†

60.2

71.0

103.5

83.0

A3

A4

65.0

93.8

117.9

102.3

A4

132

95

119

131

A5

60.2

103.5

113.1

98.7

A5

122

105

115

126A6

61.4

111.9

126.3

101.1A6125113

128

129

A7

65.0

93.8

127.5

103.5

A7132

95

129

132

A8

54.1

101.1

134.7

96.2

A8

110

102

136

123

A9

50.5

91.4

135.9

101.1

A9102

93138

129

A1043.3

72.2

126.3

93.8

A1088

73

128

120

A11

37.3

79.4

139.6

101.1

A1176

80

141

129

A1248.1

63.8

138.4

87.8

A1298

65

140

112

A1344.5

62.6

116.7

97.4

A139063

118

125

A14

45.7

61.4

114.3

98.7

A1493

62

116

126

†

†

71.0

92.6

105.9

85.4

B1

14494

107

109

B2 †

63.8

80.6

83.0

68.6

B2 †

129

82

84

8893.8B3†

139

109113

120

†

B1
B3

†

B4

†

67.4

119.1

111.9

93.8

B4

137

121

113

120

B5 †

65.0

120.3

109.5

90.2

B5 †

132

122

111

115

92.6

B6

†

120

107

102

118

†

115

85

109

112

112

89116

108

B6

†

B7

†

68.6

59.0

107.1

105.9

111.9

101.1

56.5

84.2

107.1

87.8

B7

B8

55.3

87.8

114.3

84.2

B8

B9

57.7

89.0

113.1

91.4

B9

117

90

115

117

B10

52.9

79.4

117.9

84.2

B10

107

80

119

108

B11

55.3

75.8

113.1

90.2

B11

112

77

115

115

B12

65.0

86.6

102.3

92.6

B12

132

88104118B1361.4

85.4

104.7

102.3

B1312587

106

131

B14

54.1

103.5

114.3

81.8

B14110105116105* : AMeDAS "a value every one hour ", † : Arcade.

* : AMeDAS "a value every one hour ", † : Arcade.
- 79 -Air conditioning

Air conditioning

Load change

Load changeover 20%

over 20%

nutralnutralunder -20%

under -20%

Against the datum data

Against the datum data

(AMeDAS at12:00)(AMeDAS at12:00)(a) 9:00(b) 12:00
Air conditioning

Air conditioning

Load change

Load changeover 20%

over 20%

nutralnutralunder -20%

under -20%

Against the datum data

Against the datum data

(AMeDAS at12:00)(AMeDAS at12:00)(c) 15:00(d) 18:00

Fig.8 Spatiotemporal distribution of air conditioning load: proportion against datum observation data by AMeDAS at 12:00.
2) There were differences of 3.5˚C to 4.0˚C at noon, and theperformed. Incidentally, data from another survey are currently

relative high temperature areas and the low temperature areas

under analysis, and a report of other findings will be taken up inwere mixed.

the future.

3) There were differences of around 2.0˚C to 3.0˚C, in theafternoon, and the high temperatures were observed along theAcknowledgment

national highway with much traffic density.

This work was supported by JSPS, KAKENHI 24560672 and4) There were differences of around 3.5˚C, in the evening, and it

we thank to the invaluable contributions of English lectures atwas shown that areas where traffic densities were high andthe Sojo International Learning Center (SILC), Hana Craig,

included extensive traffic jams had higher temperatures.

Gareth Humphreys, and Elton LaClare.

As above, it is thought that this technique is useful when thethermal environmental countermeasures of urban districts areReferences

examined because it provides effective information easily about(1) T. Ueno, T. Honda, H. Fukushima, D. Masaki, A case study

urban districts and enables an evaluation of the influences nof thermal environment and air conditiong energy loads inhealth and energy consumption of urban design. In addition, thisKumamoto City urban districts, Proceedings of 41th Annual

technique could be applied to evaluations of air conditioning

Meeting of Environmental Systems Research 2013, 515-521

loads and carbon dioxide increases due to the aggravation of the(2013)[in Japanese]thermal environment. Furthermore, it is thought that making(2) T. Ueno, T. Tashiro, Spatiotemporal evaluation of thermal

spatial figure every time, as in this study, is an effective

environment in urban districts, Journal of structures andprocedure and offers useful information when the thermalmaterials in civil engineering, 29, 161-166 (2013) [in Japanese]environmental planning and the design of urban districts are(3) Kyoto Electronic Manufacturing Co., Ltd., The heat disease- 80 -Table 6 Proportion of air conditioning load variations against
each datum value calculated using AMeDAS data at 12:00 [%]9:00AMeDAS

*

12:00
-15:00100

18:00--†+6-30

0+9A2 †+9-28+6-16A3 †+11-28+5+5A4+16-5+19+24A5+11+5+15+21A6+12+13+28+23A7+16-5+29+26A8+5+2+36+18A9+1-7+38+23A10-6-27+28+16A11-12-20+41+23A12-1-35+40+10A13-5-37+18+19A14-4-38+16+21A1†+22-6+7+7B2 †+15-18-16-10B3 †+209+13+16B4 †+1821+13+16B5 †+1622+11+12†+107+2+15B7 †+7-15+9+10B8+6-11+16+6B9+9-10+15+13B10+4-20+19+6B11+6-23+15+12B12+16-12+4+15B13+12-13+6+24B14+55+16+4B1B6* : AMeDAS "a value every one hour ", † : Arcade.
index measuring instrument, WBGT-113, manual [in Japanese](4) Japan Meteorological Agency: Search past meteorologicalobservation data, http://www.data.jma.go.jp/ (Accessed date:February 12, 2014) [in Japanese](Received Dec. 24, 2014, Accepted Dec. 27, 2014)- 81 -