ISSN 1880-8468
Technical Report ofInternational Development Engineering࿖㓙㐿⊒Ꮏቇႎ๔TRIDE-2012- 02

February 14, 2012

Abstracts of Master Theses
Presented in February 2012

Department of International Development Engineering,
Graduate School of Science and Engineering,Tokyo Institute of Technology
http://www.ide.titech.ac.jp/TR

Preface

Master theses of Department of International Development Engineering, Tokyo Institute ofTechnology were presented successfully on July 29, 2011 and February 14, 2012. This technicalreport consists of the abstracts of those theses.

iTechnical Report of International Development Engineering
TRIDE-2012-02
Table of Contents(Completing in September 2011)
Consumer incentives for purchasing wooden houses: focusing on potential
demand for domestic wood utilization....................................................................................................... Akihisa KURIYAMA

1Study of parking system in Ulaanbaatar city......................................................................................................... Erdenee ENKHBAT

5

Influence of the ions in pore solution and around the steel bar on corrosion ofsteel in BFS and FA mortars.......................................................................................... Anita II Alvarez ODCHIMAR

9Land readjustment and appraisal in ger area of Ulaanbaatar city.......................................................................................... Purevdorj ENKHMANDAKH

13Applicability of high resolution WRF-ARW and WEP on spatiallyheterogeneous urban areas............................................................................. Alvin Christopher Galang VARQUEZ

17Functional and mechanistic analysis of kanamycin biosynthetic enzymes................................................................................................................ Hilda SUCIPTO

21

Comparison of corrosion behavior of paint-coated steel in real structures andexperimental results in marine environment............................................................................................................ Myint Thet HTOO

25

(Completing in March 2012)
Effect of operating conditions on water quality control in intensive shrimp
mariculture ponds in developing countries by sterile seaweed................................................................................................................. Yuichiro AOKI

29

Experimental investigation on influences of steel and prestress on theeffectivity of desalination method............................................................................................................. Masato KUBOTA

33

Thermal image velocimetry (TIV) and applications..................................................................................................... Hideaki KUMEMURA

37

Quantitative analysis of swarm of mosquitoes based on in-situ observation in
relationship with atmospheric environment..................................................................................................................... Rai KONNO

ii

41

Explanation system based on wisdom of crowds on Android..................................................................................................... Norihisa TAKEMURA

45

A novel method for ammonia deodorization utilizing the anabolic activity ofheterotrophic bacteria........................................................................................................... Masashi TANAKA

49

Optimization of circuit and control parameters of a boost converter for
Energy Harvesting..........................................................................................................Masanori TANAKA

53

Separation of heavy metals from mine wastewater by adsorption using
Mongolian natural zeolites................................................................................................................. Saori TANABE

57

Study on the sulfuric acid resistance of cementitious material using OPC-γC2S-Quartz with autoclave curing...................................................................................................... Yuriko TSUBURAYA

61

Production of bioethanol from polysaccharides in the red seaweed..................................................................................................... Kanami NAKAMURA

65

Study on effects of body shape on propagation characteristics in 2.45GHz
dynamic wearable WBAN using multi-port VNA............................................................................................................... Tetsuo NUKITA

69

Separation of aromatic hydrocarbons from cracked kerosene by O/W/O
emulsion liquid membrane........................................................................................................... Masashi HARUNA

73

Study on fixing heavy metals by cementitious materials with municipal waste
combustion ash, OPC, blast furnace slag and anhydrite.................................................................................................... Takayuki FUKUNAGA

77

Urban aerodynamic parameterization for urban districts by using large-eddy
simulation with 3D building GIS...................................................................................................... Takashi MIYAMOTO

81

Modulation classification utilizing spectral correlation for emergency radios..................................................................................................................... Azril HANIZ

85

The decomposition of nitrous oxide over rutile structure titania supported
metal catalysts..................................................................................................................... Wanying HU

iii

89

The situation of university electricity conservation through a focus on
respondents’ consciousness and action in Tokyo Tech.............................................................................................................. Hongshan ZHAO

93

Optimum speed of vessels considering the reduction of CO2 emission ininternational maritime transportation.................................................................................................................. Jifang ZHANG

97

Microwave-assisted synthesis of thalifendine..................................................................................................................... Dao ZHANG

101

Study on relationship between road transport infrastructure and regional
economic development in China..................................................................................................................... Tianxing BA

105

Directional characteristics of the radio wave propagation through foliage.................................................................................................................... Le Vu HUNG

109

Improvement of Nomikawa river environment through H2S removal byoxygen release compound (ORC)............................................................................................................... Yuanzheng ZHU

113Study on effective communications in capacity assessment of technicalcooperation in environmental management sector...................................................................................................... Kazutoshi MACHIDA

iv

117

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STUDY OF PA
ARKING SYSTEM IN ULAANBAATAR CITY
C
Student Number: 09M51614

Name: Erdenee ENKHBAT

Superviisor: Shinya HANAOKA

࠙࡜ࡦࡦࡃ࡯࠻࡞Ꮢౝߩ㚢ゞ႐ࠪࠬ࠹ࡓߩᬌ⸛
ࠛ࡞࠺ࡀ࡯ࠛࡦࡈࡃ࠻

Abstract: The parking issue is the important component of Transportation Syystem. At present,
Parking is inadequate in Ulaannbaatar city in Mongolia. The Thesis evaluates parrking problems in
Ulaanbaatar and provides approoach to strengthen it. Learning about characteristic of parking systemand studying from experience, in this study, The Parking Behavior, Parking Occcupancy, Parking
Requirement, Parking Generaation Rate, Parking Demand and Parking Conceptual Design are
mentioned. The gap between paarking supply and demand in the current is estimated and then its cause
and the solution to reduce it are discussed.
street, i.e. parking areas along thhe main roads.
The Municipality of Ulaanbaattar is currently managing
about 112 parking lots (with a total area of nearly 21
hectares) only to meet more thaan 6 thousands car. Out ofthese mentioned parking areass above, the majority of
80% are on street parking areass, and remaining 20 % are
off-street parking areas. Numbber of illegal parkers has
increased in recent years.
The purpose of the Thesis willinng to take is strengthening
effectiveness of parking in Ulaaanbaatar city. Due to theabove-mentioned points, the majormobjectives of thisstudy for parking are:
1. To understand the characteeristic of Parker, questions
that are provided are Whenn, Where, Why do peoplepark?
2. To identify existing demandd for parking space of offstreet parking area
3. To set up proper parking establishment to reduceparking problems

1. Introduction
Mongolia is being in developmennt process. In recent
years, economic development quicckly brings aboutdevelopment of many other fields. The numbers of vehicle
increases while the infrastructure as rooad, parking …hasnot caught yet. The vehicles often parkk illegal on and offstreet because of insufficient parkinng. This situation
expects research about parking system tot improve it.
The Ulaanbaatar city’s main probblems and concernson the current traffic situation are increasing traffic
congestion, decreasing level of safetty and amenity ofroad transportation, low awareness of road users, lack oftraffic management capacity andd deficient roadtransportation infrastructure.
Currently, parking supply does not meet parking demand.
Road traffic has constantly increeased along withpopulation growth and increase in vehicle numbers.
During the period 2001 to 2009, trafficc volume in the city
center doubled. In 2009, number of registered vehicles ina still low, but it is
the city was only about 92700 which are
increased by 10.7% between 2001 annd 2009. According
to a recent statistic released by the Ministry of Road,
Transport, Construction and Urbban Development,
Mongolia, total on and off-street parking areas increased
to 210,800 square meter in 2008 wherreas it was 189,800
square meter in 2000.

2. Parking Behaviior Study
The interview survey and observation survey wasa Central Business District
conducted along Peace avenue atin core areas are Office buildinng parking area (OBPA),
Shopping building parking areaa(SBPA), and Office andShopping building parking areea (OSBPA) to answer
these questions.

Parking area (1000 m2)

210.8
2033.6

189.8

189.8

189.8

2000

2001

2002

194.8

196.5

2004

2005

-

203.6

-

191.7

2003

2
2006

2007

2008

Interview People hass vehicle parked aroundsurvey area by ask direcctly
Counting number off entering and existingvehicles was conduucted at parking lots.
Technique is when a vehiclev
enters or exists, aline will be marked inn form that was preparedand total will be recordded every 2 hours.

2.1 Discussion¾
Purpose of parking: Most of drivers usedparking for going to work, businness engagements and for
shopping and marketing as thetpercentage for such
purposes were 28%, 25% and 277%.

Figure-1 Statistic data of parrking area
This is a very unsatisfactory result considering the growthin population and number of vehicles in the city. On theother hand, this is a result of parkingg expansion on the5Table-1 Respondent Trip Purpose
P
OBPA
Purpose

SBPA

O&SBP
PA

No. of vehicles

Going to work
Businessengagement
Shopping
SchoolGoods delivery
Others
TOTAL



6
4

17
9

42
38

28
25

2
3
1
50

27
3
8
2
50

12
4
6
2
50

41
7
17
5
150

27
5
12
3
100

 





 

 

O&SBPASBPA

m parking space to the
Figure-2 The distance fromdestinatioon.
So, Government should considerr to the distance fromparking to their service are whenn has plan to constructpublic parking. If distance is so far,f people do not want to
use it.

2.2 Occupancy study
Occupancy is important asspect of parking because
it helps us to understand the dynamicdof how parking
demand fluctuates throughoutt the day. Motorists, ingeneral, perceive on-street annd off-street spaces withoccupancies greater than 855% to be at capacity,depending on the overall capaacity of the parking area.
Parking is perceived as beinng full when the 85%
occupancy is reached.

near mydestination

According to response, almost peopple (71%) want topark near destination.

Table-2 Parking Duration byy Purpose

3
7.9%

2
4.9%

31 min.
to 1 hr.

3
7.1%

4
10.5%

15
36.6%

1 hr. to 3hr.
3 hr. to 5hr.
5 hr. to 8
hr.
8 hr.
onward

15
35.7%
7
16.6%
14
33.3%

11
28.9%
18
47.4%
2
5.3%
-

24
58.5%
-

38
100%

Total

3
7.1%
42
100%






-

Time of day¾
Parking duration: The duuration of parking
differs by the purposes of parking of sellected areas.

Less than
30 min.

Figure-2 Priority choosing whenn park vehicles

Shop



Safe

Businessengagement


Free chargeWork

OBPA

Other












¾
Priority choosing: Almost driversdinterviewed
want to park near destination.

Durationofparking


Total
Percent
no. ofvehicles

19
25








 








 

  


 

 


 
 



Scho
ol

Goods
delivery

Other

2
28.6
%
5
71.4
%
-

4
23.5%

-

12
70.6%

3
60%

-

1
5.9%
-

2
40%
-

-

-

-

-

-

-

-

-

Figure-3 Parking Land Use
Us Type Comparison

41
100%

7
100%
%

17
100%

5
100%

Figure-4 shows graphically overall daytime (noon to
2:00pm) and nighttime (4:000p.m to 6:00p.m) peakoccupancy for selected areas.. The occupancy for theremainder of the evening wentw
down. Levels ofoccupancy are less than 91% of selected areas when see
the result, but demand was verry high in parking lots asobservation on site. It is avaailable to increase up to
100% in the case of level of serrvice for parking, parking
pavement marking and its inforrmation system. It means











 

 







8:00
am to
10:00
am

10:00
am to
12:00
pm



12:00
pm to
2:00
pm

2:00
pm to
4:00
pm




If the purposes are related to business matters,m
the durationof parking tends to be longer than in thhe case of shoppingand delivering of goods.
¾
The distance from Parkinng space to thedestination: About 80% of the drivers interviewed walk
between 100 m to 300 m to their destination. It can beconcluded that these drivers are wiilling to walk the
distance 300 m radius from the parkingg area.

6

4:00
pm to
6:00
pm

6:00
pm to
8:00
pm



8:00
pm to
10:00
pm





The national building code and its implementing rules andregulations. Presidential Decree No. 10096 (1977 revised 2004).

efficiency of parking is raised. Occupaancy is an important
point to consider regarding the parking supplysand demand

3.2 Parking demand estim
mation

3. Parking demand anaalysis

This analysis aims to demonnstrate the balance of theexisting parking demand/supply using the selected areas.
Parking demand is estimated on the basis of person-trips
or vehicle-trips; it is generally converted to a ratio ofspaces per unit of land use, so thaat the generation rate canbe converted to standards of parkking requirements.

The demand for parking is related to the type and levelof activity, the mobility of the com
mmunity, and thecultural attractiveness of the region. ParkingPdemand isdefined as the number of spaces thhat should be thatshould be provided to serve a particulaar land use.

3.1 Parking generation rate

In order to calculate the Parkinng demand, it is needed
to have the total floor area dataa. During the site study,
total gross floor area of each buuilding of selected study
areas was defined.
Then, the parking generation raate was applied to total
floor area to get parking demandd.

Parking generation rates used to calculate parking
demand for each building in the studyy area. These ratios
are assigned according to the type off use present in thebuildings. Parking generation rate waas calculated fromparking requirement and gross floorr area by land use
type. According to experience, regardd to buildings havesmall area, the best way is use public parking. In fact of
Ulaanbaatar, many building and new urban are requiredthat have to have parking but they still contravene it, do
not construct parking. So it is requiredd to apply standards
for parking requirements for existing anda new buildings.

PDid=TFAi×PDRi …………..(1)…
Where:
PDid: Parking demand by lannd use type i in zone d
TFAid: Total floor area by land use type i in zone(m2)
PDRi: Parking generating rate by land use type I(space/100m2)
After calculating, the result is ass follows:

Parking requirements standard is in placce in developed andas well as in developing countries, hoowever, complianceand implementation of these standaards differ due toeconomics, population density and othher factors that exist
in these cities. I have studied parkinng requirements ofAsian cities and several cities of USA
A and Australia, inwhich all buildings are required to providepparking inorder to obtain an appropriate parking generation ratefor Ulaanbaatar city. After made compparison of key data(urban density, number of car ownnership, GDP, andpublic transportation condition) abovve mentioned cities
especially Asian cities, then parking generationg
rate wasselected.
The parking requirements are based on grossgfloor area.

Table-4. Balance between Existing Supply and Demand
Parking
generating
rate
(100
m2GFA)

Total
floor
area
(m2)

RequuiredspaccesDem
mand

Existing
parking
supplyDifferences(Supply vs.
Demand)
Space
(%)

OBPA

1.2

12880

155

73

82

-53

SBPA

1.1

16720

184

107

77

-42

OSBP
A

1.1

17412

192

94

98

-51

Table-4 indicates that parking sppaces for the sample areas
are not sufficient, more specificcally demand level almost
doubles its supply level.
After analysis, the assumption of parking area for eachtypes of parking as follows:

Table-3 Recommended parking generationgrate
by land use/DQG8VH
/DQG8VH

Land
use type

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NLQJJHQHUDWLRQUDWH
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0HGLFDO'HQWDODQG2SWLFDO&OLQLFV
5HVWDXUDQWVFDIHVEDUVWDYHUQV
DQGQLJKWFOXEV0RWHOVKRWHOVEHGDQGEUHDNIDVW HDFKORGJLQJURRP 
HVWDEOLVKPHQWV%XVLQHVVDQG3URIHVVLRQDO2IILFHV
5HWDLOVWRUHVDQG6HUYLFHXVHV/LEUDULHVDUWJDOOHULHVDQG

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KLJKVFKRROV6FKRROV²9RFDWLRQDODQGFROOHJH
 HDFKWHQVWXGHQWV &KXUFKHV HDFKILYHVHDWV 
Sources: Viet Nam National Standards TCV
VN 4601: 1988 ‘Officebuilding standard’, TCVN 4391: 2009, Governm
ment of the Philippines.

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   Figure-5 Gap between Demand
D
and Supply

7

parking lot. It is needed to develop level of service
standards for parking facilities.

Given the occupancy study and demand calculation
results, it is clear to see that parking lots in central
Ulaanbaatar are extremely overloaded. For example,
occupancy of study areas is over 85 percent and demand
exceeds its supply at larger scale all selected parking lots.

Government should consider to the distance fromparking to their service are when has plan to constructpublic parking. If distance is so far, people do not want to
use it. The government can control the number of onroad parkers by parking fee and spaces to decrease thetraffic congestion

From two points: 1/. The gap between supply anddemand is very different in selected parking areas. 2/.
Public parking supply is being increased from 2002 but
not much (now, public parking is limited and investment
for parking projects is very deficiency compare tonumber of increased vehicle). Then, guesstimate thatshortage is from increase demand of old buildings(cause from the increasing of cars) and demand of newbuildings under the cut-off line.
So, Government needs to have preferential policy toencourage investors to increase public off-street parking
supply.
One rule was found out to reduce parking spacesthat may be useful for parking system in Ulaanbaatar.
That is shared parking. Shared parking means that parking
spaces shared by more than one user, which allows parking
facilities to be used more efficiently. From the survey, it isfound that there is different peak demand between different
land uses, so number of parking requirement can be
reduced.

The parking demand continues to increase while
supply cannot keep with demand because of limiting
land and investment fund.
To apply the parking requirement standard in
Ulaanbaatar, the parking space required for buildingsin Central Business District.
The gap cause from the public parking is not enough tocover all parking for old building and new buildingsthat under the cut-off line. So it is better to provide
more public off-street parking in Ulaanbaatar city.

6. References:
JICA team, 1979. Technical report: Urban Transport
Study in Greater Metropolitan Areas of Georgetown,
Bukit Mertajam Malaysia.

4. Conceptual Design

Wells & Associates, LLC. 2003. Parking demand
analysis for Washington Adventist Hospital

Conceptual design consists of preliminary siteplans and floor plans that identify various alternatives forthe basic components of parking structure. The goal is seek
an opportunity to widen or restructure of existing parking
lots will be discussed which allows to choose the best
outcome of all possible alternatives and to plan inaccordance with parking standards. It creates anopportunity to drivers to park conveniently. In this case,conceptual design is applied to O&SBPA, one of the threeselected areas in this study and matrix analysis was
conducted. In terms of parking lots where there is no
possibility to expand its size or where the building density
is high, it is appropriate to build parking lots nearby. In thiscase, parking behavior analysis should be considered for
determining new parking lots. Conceptual design also
covers numbers and sizes of geometric parking are directly
dependent from parking demand.

Donal Shoup, 2006. The practice of Parking
Requirements, Zoning Practice, American Planning
Association.
Michael Manville, Donal Shoup, 2005.
Parking,
People, and Cities, Journal of Urban Planning andDevelopment. pp. 233-245
So Morikawa, Kohei Asao, Takahiro Igo. Institutional
System and Current Problems of Car Parking in Tokyo,
Japan, 2009
Urban Land Institute, 2010. Dimensions of Parking 5theditionK.J Button &D.A. Hensher. Handbook of Transport
Systems and Traffic control, Volume 3. 2004
Asian Development Bank, 2010. Parking Policy inAsian Cities

5. Conclusion

Rich and Associates, Inc. 2008. Parking Study
JICA team, Ministry of Road, Transport, Construction
and Urban Development, ALMEC Corporation, Aero
Asahi Corporation 2009. Final report: The study on
Master Plan and Urban Development Program of
Ulaanbaatar city

The actual behavior of parkers to choose a parking
location is mainly influenced by trip purpose, walking
distance and availability of parking space. The main
potential factors that influence the preference of parkers aparking location choice are security, comfort ability,
availability of parking space and closeness of parking
place to their destination. Drivers are only willing to walk
not more than 300m.

Xiuyuan Zhang, Yaming Shao, 2005. Parking demand in
the High Tech Business District of Urban.
Transportation study Vol. 5 pp 891-899
Common Council, 2007. Middleton, Wisconsin, USA.
Off-street parking areas specifications and standards.

In general, driver’s opinion about parking facility in theCentral Business District of Ulaanbaatar is relatively not
so good. Example: not easy to maneuver, security is not
so reliable, and incomprehensible pavement marking of

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12

LAND READJUSTTMENT AND APPRAISAL IN GER AREA OFULAANBAATAR CITY
Student Number: 09M51637

N
Name:
Purevdorj ENKHMANDAKH

Supervisor: Shinya HANAOKA

࠙࡜ࡦࡃ࡯࠻࡞Ꮢ
Ꮢࠥ࡞࿾඙ߦ߅ߌࠆ࿯࿾඙↹ᢛℂᚻᴺߩㆡ↪ߣ⹏ଔࡊ࡟ࡉ࠼࡞ࠫ ࠛࡦࡈࡑࡦ࠳ࡈ
This study proposes a land readjustm
ment technique in to the Ger area of Ulaanbaatar cityy in which aims to convert
disorderly allocated land parcels into suuitable forms of development lots for public and privatte use accordance with city
planning requirements. Thus, land valuee assessment and replotting methodologies are examinned in the one of the urban
fringe areas by case study.

1. Introduction
The importance of introducing laand readjustment
technique in to urban land use planniing field has beenemphasized for past years in Mongolia.
Mongolia shifted to a market-orienteed economy at the
beginning of 1990s. Because of the transition from acentrally planned economy to a market-oriented one andthe private ownership of land usedd to be officiallyrecognized and also Ger area, mostly trraditional dwelling(Ger) and single storey houses surrouunded with fencesthat situated in suburbs, is dramaticallyy increased by thefree migration from rural to urban as showns
in Figure 1,
in which strongly influence to environm
mental degradationsuch as air pollution, soil erosion and innsanitary conditionin Ulaanbaatar city over the past two deecades.

Figure 2. Land readjustment concept
The second objective is to determine readjusted landarea and contribution ratio of laandowners which would be
described in replotting design.

80000
60000
40000
20000
0

Figure 3. Contribution ratio
Out-migration

2. Questionnaire survvey

In-migraation

Further questionnaire survey of the case study area isprovided accordingly. Main purpose of questionnaire
survey is focused more on to iddentify Ger area residents`
perceptions of needs for basic infrastructure andwillingness of improvements ofo the living environment.
The survey of the study area clarifies opportunities andwillingness to improve the livinng environment of the Ger
area development, and the top priorities that the Ger area
residents themselves have identtified.

Figure 1. In and out migration of Ulaanbbaatar
The significance to apply laand readjustment
methodology is to promote appropriatee land use in urban
fringe area that would be advantageouus both for support
to increase land use efficiency and tot limit disorderly
urban sprawl. There are two main objecctives of this study.
The first objective is to assess the lannd value of before
and after the planning.

39

30

27

2
32

28
17

27

23
8

Road network with pavement
Everyday shopping center
Pedestrian
Proper shape of land lot
Sewerage treatment
Elecricity

13

34
10

Water supply
Park and greenery
Public transportation (bus stop)
Street planting
Municipal heating servicebefore to after project implementation, in the other hand,measuring the rate of residential usage value increase.
Among the land appraisal methods of calculating land
valuations for the land readjustment project, the road sidevalue method is currently the most commonly used. Thismethod presumes a standard frontage along a road and ashape for the residential land, derives a value per unit landarea, and applies this to the roadway to obtain the roadside value. Based on the road side value, corrections are
made to calculate the final value, based on positioning
with respect to the road, landscape, usage and otherfactors. Further, land valuation for the land readjustment
project is not intended to be used for land transactions,but for uniformly measuring the mutual balance based onresidential land usage.
Therefore, the road side value represents the usage
value and utility of residential land, expressing this as thefundamental value set by area based on what the land in
the area is to be used for and its maturity as urban land
added to the value arising from functionality of facilities
and their utilization. In the other hand, it is the sum of theroad coefficient, the accessibility coefficient, the landcoefficient, and the utility coefficient.

Figure 4. Important issues for improvement of living
environment
Most of the respondents expressed a participation ofLand readjustment project, and desire to stay after the
implementation, if living environment improved. Among
Ger area respondents, air pollution, solid waste andinsanitary condition were the highest priority problems.
Large scale investment in infrastructure, such as paved
community road, pedestrian, park-greenery, water supply,
sewerage treatment and municipal heating service wereareas that the public reported should be improved a focusof public private partnership efforts.

3. Urban renewal design
A district plan consists of the vision statement and the
district improvement plan that includes the location ofcommunity access roads, small parks and provision of
public utilities.
The main vision of layout planning in the case study
area will be comfortable and convenient residential area
where residents will enjoy physical improvement of living
environment. To achieve this vision, the case study area isplanned as a layout planning according to Mongolian
Norms and Ordinance of Building and City Planning.

The road side value is expressed by the following
equation.

Road side value = Street coefficient + Accesscoefficient + Land coefficient + Utility coefficient= [(w-3)/w+X] + [mൈ(S-s)/(S-R)]
LUT + legal)+ (Shape ++ (Elect+ Heat+ Water+ Sew) & '

Where: F (W) = (w-3)/w+X is a street coefficient,Figure 5. Layout planning of the case study area
In the case study area, big trunk road is planned andapproved by Municipality. Hence, the case study area isdivided into two main zones that on the south side
commercial area which is along the trunk road and on thenorth side residential zone.

A = mൈ(S-s)/(S-R)

is access coefficient,

L = Shape + LUT + legal

is a land coefficient,

U = Elect+ Heat+ Water+ Sew is utility coefficient.

4.2 Street coefficient
The street coefficient expresses the usage value
originating only from the road that borders the land plot,
given by the following equation.

Access road connections would improve travel within
the community. Improved community road connectivity
would also make get to the suburbs or downtown areas,
supporting revitalization efforts. According to City
planning standard, community access roads are planned
two-way with 2 lanes that each has 3 meters wide in thecase study area. Also it has 1.5 meters wide sidewalks andstreet planting along with in these access roads.

F (W) = (w-3)/w+X&'

Where: F(W) is road coefficient, w is road width, 3 isroad shoulder and X is a road utility (pavement,pedestrian, street planting, etc.)
The F(W) equation has the following characteristics,
first of all, F(W) increases as the road width W increases,
reflecting the higher usage values of land plots along wide
roads compared with these along narrow roads.
Secondly, increasing road widths boost the usage
values of properties along narrow roads significantly, buthave a comparatively smaller effect on wide roads. In
other words, the rate of increase of F(W) gradually
declines as W increases (Figure 6).

Moreover, residential zone park is planned in the casestudy area. Residential zone park area is 3044 m2 or 4.5%of total planning area and per capita of green area is 8.5
m2.

4. The concept of land appraisal
4.1 Basic idea of land appraisal
Land appraisal in land readjustment projects lies on
measuring how residential land usage values change from
14

F(W)

the relevant facilities and the highest values of land
valuation indicators are defined as shown in Table 1.
Table 1. The magnitude of the value benefit arising fromthe relevant facilities

1
0.8
0.6
0.4
0.2
0

Indicators
0

5

10

15

20

25

30

35

40

45

Road width (m)Road network withpavement
Heating

F(W)=(W-3)/W

Bus stop

Figure 6. Street and road coefficient

Response ofquestionnaire

Share

Coefficient

39

12.4%

1.00

34

10.8%

0.87

32

10.2%

0.82

Water supply

30

9.6%

0.77

Park

28

8.9%

0.72
0.69

Shop

27

8.6%

4.3 Access coefficient

Sewage

27

8.6%

0.69

The access coefficient expresses the value of benefits
arising from relative distance of a land plot to existing
public and other facilities, as given by the following
equation.

Shape

23

7.3%

0.59

Pedestrian

17

5.4%

0.44

Electricity

10

3.2%

0.26

Street planting

8

2.5%

0.21

A = mൈ(S-s)/(S-R)(s  R)A =1(s < R)&'

In addition, the decreased value of above mentioned
indicators is calculated based on land market price surveyof GTZ in Ulaanbaatar city which is defined for eachindicator that significantly influencing land market priceand its calculation is based on comparison between withor without their services of 1 m2 average land price.

Where: A is the access coefficient, m is expressing themagnitude of the value of benefit arising from therelevant facilities, S is limit of the distance influence, R isconstant distance (limit of distance for which m does notdecrease but stays constant) and s is distance between
land parcel and relevant facilities.

Consequently, the indicators and coefficients of street,access, land, and utility services are shown in below
Table 2, which is used in land value assessment of thecase study area.

In urban areas, various city facilities are maintained tosupport land usage, and land use is predicated upon theusage of these city facilities. Therefore, the usage value ofresidential land is subject to differences based on the levelof convenience afforded by using these city facilities. The
access coefficient expresses such differences in theinfluences on usage value from various facilities.

Table 2. Indicators and coefficients of land value
assessment

4.4 Land coefficient
The land coefficient expresses the value arising from theusage status, legal rights, and physical condition of theland plot itself, given by the following equation.

L = Shape + LUT + legal&'

Where: L is a land coefficient, Shape is a land parcel`sshape (regular or irregular), LUT is a land use type(commerce, residence), and legal is a legal rights` statusof the land (ownership, possession, and usage).

No.

Indicator
Street coefficient

1

Road width2

Pavement

3

Pedestrian4

Street planting

Coefficient

20m over10-15m6-9m
Under 6m
Paved
No pavement
Available
Unavailable
Available
Unavailable

0.9-1.0
0.7-0.8
0.5-0.6
0.3-0.4
1.00
0.50
0.44
0.22
0.21
0.10

Under 50m
50-150m
150-200m
200m over
Under 100m
100-250m
250-500m
500m over
Under 100m
100-250m
250-500m
500m over
Under 100m
100-250m
250-500m
500m over

0.80
0.50
0.10
0.00
0.70
0.60
0.20
0.00
0.70
0.60
0.20
0.00
0.80
0.70
0.30
0.00

Regular
Irregular
Commerce
Residence

0.59
0.29
1.00
0.57

Access coefficient5

Kiosk (water
distributor)

6

Daily shop

7

Park and greenery8

Bus stop

4.5 Utility coefficient
The utility coefficient expresses the value arising fromprovision of public utility services such as water supply,
sewerage treatment, electricity and municipal heating
service in the project area, given by the following
equation.

U = Elect+ Heat+ Water+ Sew

Condition&'

Where: U is utility coefficient, Elect is connection for
central electricity network, Heat is provision of municipal
heating service, Water is availability of central watersupply system, and Sew is availability of seweragetreatment.
As a result of questionnaire survey of the case study
area, the magnitude of the value of benefit arising fromLand coefficient

15

9

Shape

10

Land use type

11

Legal

Ownership
Possession
Usage
No right

1.00
0.63
0.35
0.00

Available
Unavailable
Available
Unavailable
Available
Unavailable
Available
Unavailable

0.26
0.13
0.87
0.58
0.77
0.54
0.69
0.48

Utility coefficient
12

Electricity

13

Heating

14

Water supply

15

Sewage5. The procedure of land appraisal andreplotting
The basic procedure for valuation of readjusted land is todetermine the block value before and after the planning as
the fundamental value, then calculate increased value withrespect to the lot value before and after the planning anddefine the contribution ratio for the public facilities andreserved land. The principle of correspondence should beapplied to replotting design.
Accordingly, the value enhancement is calculated on
the basis of land value before and after the planning.
Essentially, the following steps are taken for land
valuation of case study area. These steps are as follows:

Figure 8. Replotting design map after the planning1) Confirm basic condition

6. Conclusion

2) Block evaluation

The land readjustment approach in Ger area might solve
problems of the shortcoming of basic infrastructure
provision by involving the separated landowners indevelopment, since they acquire the serviced urban landlots and increased value of land despite the area size
reduction. In addition, the serviced and regularly shaped
urban land lot promotes to the rational land use and its
marketability.

3) Calculate value increase4) Lot evaluation before the planning5) Lot evaluation after the planning and calculationof land area to be distributed6) Designing the replotting plan
The replotting design of the case study area is drawn
based on land valuation before and after the planning andthe readjustment maps of before and after are shown in
Figure 7 and 8.

Moreover, the local authorities might gain the profit
from which they do not need to purchase overall land lots
within the implementation zone. Besides, by through land
readjustment project, municipality could be able toimplement their responsibility of basic infrastructure
provision in principal of better urban spaces for residents.

References
[1]
[2]

[3]
[4]

[5]

[6]
[7]

Figure 7. Existing land parcels before the planning
16

Ito. S, "Outline of Land Readjustment Planning Manual in
Japan", 1988.
Deutsche Gesellschaft für Technische Zusammenarbeit(GTZ) GmbH in Land management and fiscal cadastre
project, "Report on Land Market Price Survey in Ger Area
of Ulaanbaatar City", 2009.
JICA. Ministry of Land, Infrastructure and Transport.
"Urban Land Use Planning System in Japan", 2007.
Kim Moon Kyo, Tumursukh.Sh, Enkhmandakh.P,"Housing: Problems and Prospects", Ulaanbaatar city,
2007.
JICA, Ministry of Road, Transport, Construction and
Urban Development, Ulaanbaatar City Government, "TheStudy on City Master Plan and Urban Development
Program of Ulaanbaatar city", 2009.
Solongo Algaa, "Growth of Internal and International
Migration in Mongolia", National University of Mongolia,
2007.
Statistical Yearbook of Capital City 2010.

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20

Functional and Mechanistic Analysis of Kanamycin Biosynthetic enzymes
Hilda Sucipto 09M51650
Supervisor: Prof. Sachio Hirose

Sub supervisor: Prof. Tadashi Eguchi

Kanamycin is a clinically important aminoglycoside (AG) antibiotic, which should be biosynthesized by a programmed
molecular assembly line consisting of kanamycin biosynthetic enzymes, even though the logic of the biosynthetic
machinery for kanamycin A is still in mystery. In my master course, I tackled to characterize the function of the kanamycin
biosynthetic enzymes to elucidate the pathway. Understanding biochemical properties of these enzymes was supposed toprovide important information to engineer proteins in generating structural diverse aminoglycoside antibiotics. This study
mainly focused on the late stage of kanamycin A biosynthesis, which consists of a glycosylation of a pseudodisaccharide
intermediate paromamine, two sets of amination, and a deamination. The candidate enzymes predicted by bioinformatic
analysis were expressed in Eschericia coli and the enzymatic activities for the corresponding reactions were investigated. A
glycosyltransferase KanM2 was found to catalyze the pseudotrisaccharide formation from paromamine with UDP-glucose.
In addition, the final deamination process from kanamycin B to kanamycin A was facilitated by two unique enzymes KanJ
and KanK, an α-ketoglutarate dependent dioxygenase and an NADPH dependent ketoreductase, respectively.
Consequently, except for two amination processes, all of kanamycin biosynthetic enzymes have been characterized to show
clear outline for the kanamycin biosynthetic pathway.

against kanamycin-resistant bacteria and except

1. Introductionarbekacin, are used as chemotherapeutic agents
Kanamycin

is

the

most

well

known

2-

against resistant bacteria of clinical importance.

deoxystreptamine (2DOS)-AG antibiotic because it

Bioinformatics

possesses a broad range antibacterial activity.

analysisofkanamycin

biosynthetic gene cluster with the other 2DOS

Although bacteria resistant to this antibiotic readily

containing AG biosynthetic gene clusters revealed

appear, investigations concerning bacterial resistantthat kan gene cluster indeed encodes five conserved

mechanism led to the development of semisynthetic

biosynthetic enzymes (kanC, 2-deoxy-scyllo-inosose

kanamycin derivatives such as dibekacin, amikacin

kanS1,

synthase,kanE,aminotransferase,

and arbekacin. These derivatives show good activities

Pseudodisaccharide biosynthesis (paromamine)
HO
HO

OH
O

KanC

OH OH

Glucose 6-Phosphate

KanM1

HO
HO

UDP-GlcNAc

O
HO
HO

OH

H2N
KanS1 HOHO

H2N
KanE HO
HO

OH

OH

OH

2-deoxy-scyllo-inosose

2-deoxy-scyllo-inosamine

OH
O
H2N
AcHN O
HO

KanN

HO
HO

NH2
OH
N-acetylparomamine

KanS12-deoxy-3-ketoscyllo-inosamine

2-deoxystreptamine

HO
HO

KanM2
OH

OH
O
H2N

NH2

1' H N
2
O
6
HO

NH2
O

1"

UDP-Glc

paromamine

NH2
OH

OH O

OH
O
H2N
H2N O
HO

H2N
HO
HO

HO

OH
OH

O
OH

3"-deamino-3"-hydroxykanamycin C
Pseudotrisaccharide biosynthesis and modification

KanD2
KanS2

HO
HO

OH
O
H2N

KanQ
KanB

H2N
O
HO
O
HO

HO
HO

NH2
O
H2N

NH2

H2N
O
HO

OH
OH

O

NH2
O
HO

OH

O

OH
NH2

NH2

kanamycin B

kanamycin C

KanJ
KanK

HO
HO

NH2
O
OH

H2N
O
HO

O
HO

NH2
O

OH
OH
NH2

kanamycin A

Figure 1. Kanamycin biosynthetic gene cluster identified from Streptomyces kanamyceticus and a proposed biosynthetic pathway for kanamycin
A. Box showed the characterized enzymes in this study.





21

dehydrogenase, kanM1, glycosyltransferase, and

2. Results and Discussion

kanN, deacetylase) for paromamine biosynthesis (Fig.

2.1 Functional analysis of KanM21)1). In fact, these five genes expressing Streptomyces

The cell-free extract of KanM2 expressing E. coillividans has been reported producing paromamine by

was reacted with paromamine and UDP-Glucose

a Korean research group.

(UDP-Glc) at 28°C for 18 h. The enzymatic reaction

Another deduced glycosyltransferase KanM2 wasproducts were treated with 2,4-dinitrofluorobenze tothen presumed to be responsible for attaching a sugar
at

the

C-6

of

paromaminetoconvert aminoglycosides to N-dinitrophenyl (DNP)

form

derivatives. LC-ESI-MS analysis of the sample

pseudotrisaccharide.

showed a new peak with m/z 982.4 which

A set of FAD-dependent dehydrogenase (KanQ)

corresponded to [M-H]– for the derivative ofand aminotransferase (KanB) is conserved inglucosylated paromamine. Further, a large-scaleneamine containing AG biosynthetic gene cluster.

KanM2 reaction with paromamine and UDP-Glc wasThose homolog NeoQ and NeoB derived fromcarried out to isolate the product, whose structure wasneomycin biosynthesis were reportedly to catalyze

determined to be 3”-deamino-3”-hydroxykanamycin

the oxidation of hydroxyl group at C-6 of

C by FABMS and NMR analysis.

glucosamine moieties of biosynthetic intermediates.

Subsequently, substrate specificity of KanM2 was

Therefore, KanQ and KanB seemed to be involved in

examined with paromamine and neamine as glycosyl

the similar enzymatic reaction step in kanamycin

acceptor,

biosynthesis (Fig. 1).

relatedpseudotrisaccharide

is a likely intermediate in the biosynthesis ofkanamycin. On the other hand, UDP-GlcNAc was not

AG has a characteristic amino group at C-3” of theaccepted by KanM2, indicating its strict substrate

third sugar. Thus, these seemed to be responsible forthe amino group incorporation before or after theattachment of the third sugars.
KanJ,

a

specificity.

Sincekanosamine(3’-amino-3’-deoxyglucose)

potential

putative

phytanoyl-CoA

weglycosyl

cannotdonorgroup, KanJ and KanK were predicted to beHOHO

responsible for this transformation. Based on the

HO
HO

OH
O
H2N H2NO
HOOH

UDP-Glc

complete

kanK)

OH
OH

O

2.2 Functional analysis of KanD2/KanS2 andKanQ/KanB

and

NH2
O

UDP

OH

In this study, those seven genes (kanM2, kanD2,
kanJ,

H2N
O
HO

Figure 2. Enzymatic reaction catalyzed by KanM2

kanamycin A biosynthesis
kanB,

be

HO

the oxidation at C-2’ and KanK reduces the

kanQ,

may

OH
O
H2N

KanM2
NH2

annotated function of these, KanJ seemed to catalyze

kanS2,

abiosynthetic pathway.

kanamycin B is somehow converted to an amino

toaskanosamine should be investigated to elucidate thekanamycin A biosynthesis. Since C-2’ position ofintermediate

which

UDP-

described below, substrate specificity with UDP-

oxidoreductase remained to be assigned for the

ketoneexcludebiosynthesized form UDP-Glc by KanD2 and KanS2

dioxygenase and KanK, a putative NADP-dependent

presumable

UDP-N-

paromamine to neamine, supporting that paromamineAG

antibiotic biosynthetic gene cluster. This family of

Finally,andAs a result, it was found that KanM2 prefer(KanD2) and aminotransferase (KanS2) is conserved
kanamycin

UDP-Glc

acetylglucosamine (UDP-GlcNAc) as glycosyl donor.

A set of NAD/NADP-dependent dehydrogenase
in

and

as

dehydrogenaseandaminotransferase

wereheterologously expressed in E. coli and their catalytic

To introduce 3”-amino group of kanamycin C,

activities were investigated based on the above-

two possible pathways were hypothesized; 1)

mentioned hypothesis.

conversion of UDP-Glc to UDP-kanosamine for
KanM2 reaction, 2) conversion of 3”-deamino-3”hydroxykanamycin C to kanamycin C, which were





22

presumably catalyzed by a set of KanD2 and KanS2

neomycin biosynthesis pathway was coupled with

(Fig. 3).

KanB, neamine formation was observed. This result

OH
O

HO
HO

KanD2 ?

HO

HO
HO

OH
O
H2N

1' H2N
O
HO

O
1"
HO

H2N

OUDP

H2N
NH2
O

OH
OH

oxo-paromamine and 6’-oxo-kanamycin C as its

H2N
O
HO

NH2

substrates. Substrate specificity of KanQ remained to

O

OH

O

HO

OH

OH

is maybe a dual functional aminotransferase with 6’-

OH
O

HO
HO

KanS2 ?

H2N
O
HOand catalyzes transamination to give neamine. KanB

HO

OH
O

NH2
O

O

HO
H2N

OUDP

HO
HO

KanD2 ?
6

KanS2 ?

HO

OUDP

indicated that KanB recognizes 6’-oxo-paromamine

OH

OH
O

HO
O

OH

O

HO

OH

O

be examined in order to determine the precise

NH2

biosynthetic pathway for kanamycin.

Figure 3. Proposed enzymatic reactions of KanD2/KanS2

OH
O
H2N
H2N O
HO

HO
HO

Many

expression

KanD2/KanS2

systems

includingsome

co-expression

systems

wereproteins.

However,

the

O
HO

NH2
O

HO
HO

KanB

H2N

H2N

NH2
OH

H2N
O
HO

H2N

NH2

OH

OH

Figure 4. Proposed enzymatic reactions of KanQ/KanB

constructed to improve the solubility of therecombinant

O
O

HO
HO

KanQ

expected

2.3 Functional analysis of KanJ and KanKThe recombinant KanJ and KanK proteins weretransformations have not been detected yet, probably
due to the expression problems.

reacted with kanamycin B and the DNP derivatives of

To introduce 6’-amino group of kanamycin B, two

the enzymatic reactions were analyzed by HPLC. As

possible pathways were hypothesized; 1) conversion

a result, it was found that KanJ and KanK catalyzed

of paromamine to neamine before KanM2 reaction,

the conversion of kanamycin B to kanamycin A in the2) conversion of kanamycin C to kanamycin B,

presence of α-ketoglutarate (α-KG), ferrous ionswhich were presumably catalyzed by a set of KanQ

Fe(II) and NADPH, but not with NADH. The productand KanB. Since kanamycin C was not commerciallyfrom a large scale KanJ and KanK reaction was thenavailable, paromamine was examined as a substrateconfirmed to be kanamycin A by FABMS and NMR

for the KanQ and KanB reaction (Fig. 4). As a result,analysis. Even in the absence of KanK, the substrate

the expected oxidation activity of KanQ was notkanamycin B was consumed and a new decomposed

detected, probably due to its insolubility. On the otherproduct was detected by LC-ESI-MS, indicating that

hand, when KanQ homolog NeoQ derived from

KanJ somehow catalyzes the oxidation of kanamycin

NH2
O

HO
HO

H2N

O2 ,α-KG
H2N

NH2
O

HO
HO

O

KanJ

O
HO

NH2

H2N
O
HO
O

OH
OH

O

CO2
Succinate

NH2

OH

NH2

O
HO

NH2
O

HO
HO

KanK

NH2
O

OH NAD(P)H NAD(P)+
OH

O

HO

H2N
O
HO

HO

NH2

OH
OH

O
NH2

kanamycin A

kanamycin B

Alternative Pathway 1 of KanJ reaction
OH

O

Asp
HO
HO

H2N

OH

O

FeIV
O
HO
HO

HO
HO

His

H2N
H2N

OR

OR

OH

HO
HO

O

O

His
NH2
HO

FeIV
O

Asp

FeIII
His

His
H2N

O–

HO
HO

H2N

OR

NH2
O
HO

O

OH
O
NH2

Asp
His

OH
OH

O
NH2

FeII

His
NH2
HO

O
HO

OR

O

His

OH

Asp

O

O

His

FeIII

R=

H

O

O

His

OH

O

Asp

OH

O

Alternative Pathway 2 of KanJ reaction
O

NH2
OH
O

His

OH

Asp

H2N

FeII
Asp

FeIII
NH2
O

O

O

His

O

O

His

His

NH2
HO

OH

O

O

O

His

HO
HO

O
H2N

OR

NH2
OH
O

H2O
HO
HO

H2N

OR
H

Final step for both alternative pathways
NH2
OH
O

HO
HO

H2N

OR

HO
HO

NH2
O H
O
H3N

OR

-NH3

HO
HO

NH2
O H
O

HO
HO

OR

NH2
O
O

KanK (NADPH)
H216O
HO
HO

OR

H

Figure 5. Proposed enzyme reaction for KanJ and KanK





23

NH2
O
HO OR

–H218O

HO
HO

NH2
O
O
OR

OR

NH2

B to form 2’-oxo-kanamycin, which is subsequently

amount of 18O (16.7%) was clearly incorporated into

reduced to be kanamycin A by KanK (Fig. 5).

kanamycin A, indicating that an equilibrium event

In order to clarify the ketone formation catalyzed

between ketone intermediate with water from theby KanJ, the KanJ enzymatic reaction product was

buffer occurred.

1

treated with NaBD4. H-NMR analysis revealed that

Finally, the substrate specificity of KanJ was

this coupled reaction indeed afforded kanamycin A.

examined with several AGs that are structurally

Further, the deuterium atom incorporation into C-2’

similar to kanamycin B. Among the alternative

1

2

position was clearly detected by H-NMR and H-

substrates tested, neomycin B, butirosin A, neamine

NMR. This result clearly demonstrated that the KanJ

and ribostamycin were converted to new products

generatedthendeaminated by KanJ and KanK within the conversion

reduced by NaBD4. Another product, ammonia, wasrange 37 to 97%. Paromamine was the only tested

also clearly detected by the use of coupling assay

substrate that was not accepted by KanJ. Thus, it waswith glutamate dehydrogenase. These results support

demonstrated that KanJ and KanK have relatively

that KanJ somehow synthesizes 2-oxo-kanamycin

relax substrate specificity. Amino group at C-6’

and release ammonia.

seems to be critical for KanJ reaction (Fig.6).

2’-oxo-kanamycin,

which

was

In the process of the ketone formation, two

Accepted

possible reaction mechanisms were hypothesizedHO
HO(Fig.5); (1) the formation of reactive [Fe(IV)=O]
kanamycin B homolitically to produce kanamycin B
radical at C-2’ and facilitate its own reduction to an
Fe(III)-OH

species.

Fe(III)-OH

providesissimultaneously

converted

to

OH

NH2

paromamine

H2N
H2N
O
O O

HO
NH2

NH2
OH

O
HO

O

OH
OH
NH2

O OH
NH2

O

NH2

a

HOOH
neomycin B

kanamycin Bhydroxyradical to afford a hemiaminal intermediate,
which

NH2
O

HO
HO

H2N
NH2
O
HO

H2N
O
HO

H2N

NH2

OH

NH2
O

HO
HO

OH
O

HO
HO

H2N

H2N

O
HO
neaminespecies breaks the inactivated C-2’-H bond in

Unaccepted

NH2
O

HO
HO

NH2
O
H2N
H2N O

2’-

HO

oxokanamycin, (2) the reaction was initiated by the

O

NH2

O
OH

NH2
O
H2N
H2N
O
O
O

HO
HO
HO

OH

H
N
OH

NH2
O

HO

HO OH

OH
ribostamycin

butirosin A

radical attack of ferryl oxo species to the lone pair of

Figure 6. Substrate specificity for KanJ

nitrogen atom at C-2’ to form an nitrogen cation
radical intermediate, which turns out to be an imine
intermediate that is easily hydrolyzed to be a ketone.

3. Conclusion

The presumed ketone intermediate formed by either

In this master thesis, I characterized KanM2 as UDP-

pathway then was reduced to form kanamycin A.

Glc: Paromamine glycosyltransferase, KanJ as α-KG

To distinguish these reaction mechanisms, KanJ

dependent kanamycin B deaminase and KanK asreaction coupled with KanK was conducted under
18

NADPH-dependent

O-enriched atmosphere. As a result of LC-ESI-MS

2’-oxokanamycin

reductase.

Although two amination steps are still unclear, most ofof the enzymatic reaction products, 7% of thethe kanamycin biosynthetic enzymes have now been

produced kanamycin A was found to be labeled withcharacterized to show clear outline for kanamycin A

molecular 18O. This result clearly suggested thatbiosynthesis.

KanJ catalyzes the hydroxylation to afford ahemiaminal intermediate like usual α-KG dependenthydroxylase. The small amount of
from

18

18

4. Reference

O incorporation

1.

O2 appeared to be caused by the equilibrium

exchange with

16

O from water of the ketone

intermediate. Thus, this equilibrium event was
confirmed in the presence of 50% 18O-labelled waterin the enzymatic solution. As expected, a significant





24

Kudo, F., Sucipto, H. & Eguchi, T. Enzymatic
activity of a glycosyltransferase KanM2
encoded in the kanamycin biosynthetic genecluster. J. Antibiot. 62, 707-10 (2009).

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8.00E+04

6.80E+04

non-divided

6.70E+04

dividedCoating Resistance (Ω)'LYLGHG5HJDUGLQJ )LJ  LQ VSODVK ]RQH FRDWLQJUHVLVWDQFH LQFUHDVHG LQ WKH VHFRQG PRQWK $IWHU
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6.60E+04
6.50E+04
6.40E+04( non-divided )6.00E+04( divided )5.00E+04
4.00E+04
3.00E+04
2.00E+04
1.00E+04

6.30E+04

0.00E+00

6.20E+04
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7.00E+04

2
3
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4

1

2

3

4

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9.00E+01

non-divided

divided

8.00E+01

non-divided

7.00E+01

7.00E+01
6.00E+01
5.00E+01
4.00E+01
3.00E+01

6.00E+01
5.00E+01
4.00E+01
3.00E+01

2.00E+01

2.00E+01

1.00E+01

1.00E+01

0.00E+00

0.00E+00
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Impedance ( Z ), ( Ωcm2 )6.24E-03

1.00E-02
5.00E-03
0.00E+00

1month (Submerged)

6.00E+06

4month, (Submerged)

5.00E+06

1month,(splash)

4.00E+06

4month,(splash)

3.00E+06
2.00E+06
1.00E+06
0.00E+00
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1

10
100
Frequency ( Hz )

1000

10000

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1month,(Submerged)-70

4month,(Submerged)-50

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7.00E+06

Phase Angle ( θ . Degree)

8.65E-03

1.50E-02

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2.00E-02

1month)LJ&RDWLQJ&DSDFLWDQFH

0.01

2.50E-02

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2.25E-02

splashimonth 2month 3month 4month

2month 3month 4month

Exposure period (month)

2.73E-02

submerged
3.00E-02

8.00E-06

1month

6.00E-06

2monthMicrocell Current Density( μA/cm2 )divided

8.00E+01

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3month
4.00E-06

4month

2.00E-06
0.00E+00

4month,(splash)-30

8.00E-06
1month
6.00E-06
2month
4.00E-06

3month4month

2.00E-06
0.00E+00

1

2

3

4

5

6

1

Measurement Location

-10

2

3

4

5

6

Measurement Location

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Macrocell Current Density (A/cm2)

 E 3KDVH$QJOHYV)UHTXHQF\
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8.00E-06

Macrocell Current Density ( μA/cm2 )

0.02

0.1

0.2

1

2

0.5

0.05

Frequency ( Hz )

5

10

20

50

100

200

500

1000

2000

5000

10000

10

1month

6.00E-06

2month

4.00E-06

3month4month

2.00E-06

0.00E+00
1

2

3

4

5

6

-2.00E-06

1month
6.00E-06
2month3month

4.00E-06

4month
2.00E-06
0.00E+00
1

2

3

4

5

6

-2.00E-06
Measurement Location

Measurement Location

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8.00E-06

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1.20E+09

2month

6.00E-06
5.00E-06

3month

4.00E-06

4month

3.00E-06
2.00E-06
1.00E-06
0.00E+00-1.00E-06

1

2

3

4

-2.00E-06
Measurement Location

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568.00E-06
7.00E-06
6.00E-06
5.00E-06
4.00E-06
3.00E-06
2.00E-06
1.00E-06
0.00E+00-1.00E-06-2.00E-06-3.00E-06

Reactance (Ωcm2)

1month

7.00E-06

Macrocell Current Density ( μA/cm2 )Macrocell Current Density ( A/cm2)

1.00E+09
8.00E-06

1month2month3month4month

8.00E+08
6.00E+08
Div ( submerged )4.00E+08

Non.Div ( submerged )Div ( splash )2.00E+08
1

2

3

4

Non.Div ( spalsh )50.00E+00
0

Measurement Location

1

2

3

4

5

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28

Effect of operating conditions on water quality control in intensive shrimp
mariculture ponds in developing countries by sterile seaweed
Student Number: 10M18016

Name: Yuichiro AOKISupervisor: Ryuichi EGASHIRA

୙⛱ᛶᾏ⸴࡟ࡼࡿ㛤Ⓨ㏵ୖᅜᆺ㞟⣙࢚ࣅ㣴ṪụࡢỈ㉁ไᚚ࡟ᑐࡍࡿ᧯స᮲௳ࡢᙳ㡪㟷ᮌ ᝆ୍㑻ࣔࢹࣝᾏỈ୰࡟࠾࠸࡚࢔࢜ࢧ(ᶓ὾⏘)ࡢ࢔ࣥࣔࢽ࢔❅⣲(AN)ᦤྲྀ㏿ᗘ࡞ࡽࡧ࡟ᡂ㛗㏿ᗘࢆᐇ ࡋࡓࠋ࢔࢜ࢧ
ࡢANᦤྲྀ࡞ࡽࡧ࡟ᡂ㛗ࢆ☜ㄆࡋࡓࠋANࠊⅣ⣲ࠊࣜࣥࡢ⃰ᗘࠊගྜᡂග㔞Ꮚ᮰ᐦᗘࡢቑຍࠊ ᗘࡢୖ᪼࡜࡜
ࡶ࡟ࠊᦤྲྀࠊᡂ㛗࠸ࡎࢀࡢ㏿ᗘࡶቑຍࡋࡓࠋAN⃰ᗘࡀ≉࡟㧗࠸⠊ᅖ࡟࠾࠸࡚࢔࢜ࢧࡢⓑ໬ࡀほᐹࡉࢀࡓࠋࡇ
ࢀࡽࡢᐇ㦂⤖ᯝ࡟ᇶ࡙࠸ࡓィ⟬࡟࠾࠸࡚ࠊ࢚ࣅ㣴ṪụࡢAN⃰ᗘࢆపࡃᢚ࠼ࡿࡇ࡜ࡀ࡛ࡁࡿ࡜࡜ࡶ࡟๪⏘≀࡜ࡋ࡚኱㔞ࡢ࢔࢜ࢧࢆᚓࡿࡇ࡜ࡀ࡛ࡁࡓࠋ
1 Introduction
Since the 1990s, the shrimp industry, which has givenhigh profit and foreign exchange to developing countries,
has suffered many viral disease outbreaks [1]. To avoid
entering viruses from external water, some shrimp
farmers introduced none or quite small water exchange
culture system. However, this system caused the serious
water deterioration and the water quality control became
more important. Especially, ammonia-nitrogen (AN) is
toxic to shrimp [2][3]. So, to control AN concentration isimportant for shrimp mariculture. However, because of
low shrimp value, shrimp mariculture in developing
countries can’t pay away a lot of money for water quality
control. In previous study [4], water quality control ofshrimp ponds using sterile seaweed was proposed.
However this system is affected on environmental
conditions. So to examine effect of operating conditions
is important. In this study, we examined effect ofoperating conditions on water quality control in intensive
shrimp mariculture ponds in developing countries bysterile seaweed. First, we measured AN uptake rate by
and growth rate of sterile seaweed and examined effectof operating conditions for growth rate. Second, we
calculated water quality control and growth of seaweed
in shrimp pond.

䐠
䐟

䐡

䐢

䐣
䐤

䐥

䐦

ձ Temperature indicatorղ Metal halide lampճ Sterile Ulva sp.
մ Nylon mesh
յ Plastic beakerն Magnetic stirrer
շ Jackո FanFig. 2.2-1 Experimental apparatus

2.3 Results and discussion
2.3.1 TAN uptake rate of sterile seaweed
The initial specific uptake rate of TAN by seaweed,
πTAN,0, was obtained byߨ୘୅୒ǡ଴ ൌ െ ͳ ‫ܥ‬୘୅୒ ฬ(1)ߩ୙ ‫ ݐ‬௧ୀ଴
Fig. 2.3.1-1 shows the effect of initial TAN
concentration on the initial specific uptake rate. It wasreconfirmed that sterile Ulva sp. could take in TAN. The
uptake rate increased, as TAN concentration increased.
The relation between TAN concentration and uptake rate
was fitted by Michaelis-Menten equation,ߨ୘୅୒ǡ଴ ൌ

2 AN uptake by and growth of sterile seaweed
2.1 Materials
Sterile Ulva sp. collected from Umi no koen (Marinepark) in Yokohama, Japan was used as sterile seaweed.
Commercial sea salt was used to prepare artificial
seawater (30 ‰). NH4Cl, Na3PO4∙12H2O, NaHCO3 wereused as sources of total ammonia-nitrogen (TAN),
dissolved inorganic phosphorus (DIP), and carbon (DIC)
in the seawater.
2.2 Experimental
Fig. 2.2-1 shows experimental apparatus. We measured
TAN uptake rate by and growth rate of seaweed, andexamined effect of total TAN concentration in culture
medium, photosynthetic photon flux density (PPFD), andwater temperature for growth rate. TAN concentration inliquid solution was determined by the indophenol blue
method and growth rate of seaweed was calculated frommass increment of biomass.

ߨ୫ୟ୶ ‫ܥ‬୘୅୒‫ ୑ܭ‬൅ ‫ܥ‬୘୅୒(2)

πTAN,0 103 [kg-N kgDM-1 h-1]

with the maximum specific uptake rate of TAN, πmax,
and the half saturation constant of TAN concentration for
uptake, KM, as shown by the solid line in Fig. 2.3.1-1.
The relation was well represented by this equation with
πmax = 11.6 × 10-3 kg-N kgDM-1 h-1
and
KM = 2.3 × 10-3 kg-N m-3.

15

10

5
‫ە‬Experimental result
㸫Calculated by Eq. (2)

0
0

5
10
15
CTAN 103 [kg-N m-3]

Fig. 2.3.1-1 Effect of TAN concentration
on initial specific uptake rate

29

Table 2.3.1-1 Experimental conditions
ρU,0 [kgDM m-3]0.65
T [°C]
27 ± 1-2 -1
I [μmol m s ]600
CTAN × 103-3[kg-N m ]0 - 13.1
CDIP × 104-3[kg-P m ]0
CDIC [mM]0

The effect of PPFD on specific growth rate of seaweed isgiven in Fig. 2.3.3-1. When PPFD was 0 μmol m-2 s-1,
the seaweed did not grow. The specific growth rateincreased with PPFD.

2.3.2 Effect of TAN concentration in culture medium
on specific growth rate of sterile seaweed
The specific growth rate of seaweed, μ, was defined by,
ߩ୙
ൌ ߤߩ୙
‫ݐ‬

(3)
0.03

μ [h-1]

where ρU is density of seaweed in culture medium.
Integration of this equation leads to,ߩ୙ǡ௧ൌ ߤ‫ݐ‬(4)ߩ୙ǡ଴

ln(ρU,t /ρU,0) [-]

1

0

10

20 30
t [h]

40

1000
I [μmol m-2 s-1]

2000

Fig. 2.3.3-1 Effect of PPFD on specificgrowth rate

2.3.4 Effect of water temperature of culture medium
on specific growth rate of sterile seaweed
Fig. 2.3.4-1 shows the effect of water temperature on
specific growth rate. The growth rate had the maximum
at around 30 °C over temperature. In higher temperature
range, the chlorosis of seaweed was observed, which
would result in the low growth rate. In this study, lower
than 30 °C, effect of water temperature to the specificgrowth rate was fitted Arrhenius equation.

00‫ە‬Experimental result
㸫Calculated by Eq. (5)0Table 2.3.2-1 Experimental conditions
ρU,0 [kgDM m-3]0.13
T [°C]
27 ± 1-2 -1
I [μmol m s ]600
CTAN × 103-3[kg-N m ]1.0
CDIP × 104-3[kg-P m ]3.0
CDIC [mM]
2.0

0.5

0.02

0.01

where ρU,0 and ρU,t are density of seaweed in culture
medium at initial and t. Fig. 2.3.2-1 shows time course ofnatural log of ratio of seaweed density to initial seaweed
density in culture medium. The growth of the seaweed
could be quantitatively detected in the range of thiswork. By eq. (4), the specific growth rate could beobtained from slope of Fig. 2.3.2-1.

Table 2.3.3-1 Experimental conditions
ρU,0 [kgDM m-3]
0.13 - 0.18
T [°C]
27 ± 1-2 -1
I [μmol m s ]0 - 2000
3
CTAN × 10[kg-N m-3]
4.0
CDIP × 104-3[kg-P m ]3.0
CDIC [mM]
2.0

50

0.03

Fig. 2.3.2-1 Time course of naturallog of ratio of seaweed density to initialseaweed density in culture medium

Table 2.3.4-1 Experimental conditions
ρU,0 [kgDM m-3]
0.13 - 0.18
T [°C]
14 - 40-2 -1
I [μmol m s ]600
3
CTAN × 10-3[kg-N m ]4.0
CDIP × 104-3[kg-P m ]3.0
CDIC [mM]
2.0

Arrenius

μ [h-1]

0.02

0.01
Chlorosis

The specific growth rate is plotted against TAN
concentration, CTAN, in Fig. 2.3.2-2. The growth rateincreased with increasing TAN concentration. In this

0
10

‫ܧ‬
‫ܥ‬୘୅୒
‫ܫ‬ ൤െ
൨‫ܭ‬ୗ ൅ ‫ܥ‬୘୅୒ ‫ܫ‬ୗ ൅ ‫ܫ‬ܴሺܶ ൅ ʹ͹͵ሻIn this study, the maximum specific growth rate, μmax,
was 3 × 1010 h-1. Half saturation constant of TAN
concentration for growth, KS, was 1.8 × 10-3 kg-N m-3.
Half saturate constant of PPFD for growth, IS
was 189 μmol m-2 s-1. The apparent activation energy for
growth of seaweed, E = 6.86 × 104 J mol-1.

(5)

3 Calculation of water quality control and seaweed
growth in shrimp pond
3.1 Nitrogen balance and growth of seaweed in
shrimp pond
Nitrogen as the form of the shrimp feed is supplied into
pond. Much of feed is eaten by shrimp and a portion of
feed is uneaten. Shrimp excretes AN through respirationand other nitrogen metabolite (feces, shed shell etc.). AN
also generate from sediment decomposition. On the other
hand, in this study, AN in water column was removed byseaweed uptake only. Growth of seaweed is affected by
AN concentration and environmental conditions (PPFD,
water temperature).
3.1.1 Basic equation
Material balance;
The material balance of TAN in a unit volume of shrimp
pond is given by,

μ [h-1]

0.03

0.01

Table 2.3.2-2 Experimental conditions
ρU,0 [kgDM m-3]
0.13 - 0.18
T [°C]
27 ± 1
I [μmol m-2 s-1]
600
CTAN × 103[kg-N m-3]
0 - 50.0
CDIP × 104-3[kg-P m ]3.0
CDIC [mM]2.0‫ە‬Experimental result
㸫Calculated by Eq. (5)

0
0

10

20

30

40

40

Fig.
2.3.4-1
Effectof watertemperature on specific growth rate

where μmax is the maximum specific growth rate. KS andIS are the half saturate constant of TAN concentration
and PPFD for growth. E is the apparent activation energy
for growth of seaweed, R is gas constant.

0.02

30
T [ḥ]

study, effect of TAN concentration, PPFD, I and watertemperature, T for specific growth could be describedby,ߤ ൌ ߤ୫ୟ୶

20

50

CTAN 103 [kg-N m-3]
Fig. 2.3.2-2 Effect of TAN
concentration on specific growth rate

2.3.3 Effect of PPFD on specific growth rate of sterile
seaweed30‫ ݐ‬െ ‫ݐ‬ୱߟܶ୫୧୬ െ ܶୗ ൈ  ቀെ ቁ ൅ ሺܶୗ െ ܶ୫୧୬ ሻ ൈ  ቀെ
ቁ
߬
߬
ܶൌ
ߟ
ͳ െ  ቀെ ቁ߬
(0 h ≤ t < 6 h, 18 h ≤ t < 24 h)
(15)

(6)

where rtotal,TAN is total generation rate of TAN, rtotal,remove
is total removal rate of TAN.
3.1.2 TAN removal
The removal rate of TAN in a unit volume of shrimp
pond is given by,with maximum daily water temperature, Tmax = 30 °C,
minimum daily water temperature, Tmin = 25 °C, the timeof maximum solar height (midday), LSH = 12 h, day
length, DL is 12 h, the delay in Tmax with respect to LSH,
P = 2 h, sunset temperature, TS = 28.5 °C, sunset time, tS= 18 h, time coefficient, τ = 4 h-1 and night length η = 24
h - DL. Fig. 3.1.2-1 and Fig. 3.1.2-2 show time course of
PPFD and water temperature in shrimp pond.

(7)‫୲ݎ‬୭୲ୟ୪ǡ୰ୣ୫୭୴ୣ ൌ ߩ୙ ߨ୘୅୒where ρU is density of seaweed in shrimp pond, πTAN is
the specific uptake rate of TAN by seaweed.
Growth yield of seaweed against TAN;
The specific uptake rate of TAN by seaweed, πTAN, isdescribed by,ߨ୫ୟ୶ ‫ܥ‬୘୅୒ߨ୘୅୒ ൌ
(8)‫ ୑ܭ‬൅ ‫ܥ‬୘୅୒I [μmol m-2 s-1]

2000

the maximum specific uptake rate of TAN, πmax, in eq.
(8) is described by,ߨ୫ୟ୶ ‫ߤ ן‬୫ୟ୶‫ܧ‬‫ܫ‬ ൤െ
൨ܴሺܶ ൅ ʹ͹͵ሻ‫ܫ‬ୗ ൅ ‫ܫ‬given KS = KM, πmax could be represented by,ߤ୫ୟ୶ߨ୫ୟ୶ ൌܻୋ(9)‫ܧ‬‫ܥ‬୘୅୒
‫ܫ‬ ൤െ
൨‫ܭ‬ୗ ൅ ‫ܥ‬୘୅୒ ‫ܫ‬ୗ ൅ ‫ܫ‬ܴሺܶ ൅ ʹ͹͵ሻ

1000

20
10
0

0

4

8

12 16 20 24

04Time of day [h]Fig. 3.1.2-1(10)ߨሺ‫ ݐ‬െ ͸ሻ቉ͳʹ

(6 h ≤ t < 18 h)

(12)

‫ܫ‬ൌͲ

(0 h ≤ t < 6 h, 18 h ≤ t < 24 h)(13)

Fig. 3.1.2-2 Time course watertemperatureΔ݄ ൌ ݂ୖୟ୧୬ ൅ ݂ୖ୳୬ ൅ ୍݂୬ െ ݂୉୴ୟ୮ െ ݂୓୳୲ െ ݂ୗୣୣ୮ െ ݂୓୴ୣ୰
(16)
where fRain, fRun, fIn, fEvap, fOut, fSeep, fOver are water flowrate to and from shrimp pond, each is rainfall, runoff,
inflow, evaporation, outflow, seepage, and overflow. In
this study, fRun = 1.25 × 10-5 m h-1, fEvap = 1.29 × 10-4 m
h-1, fSeep = 2.17 × 10-4 m h-1, fIn = fOut = fOver = 0 m h-1, andon rainy days, fRain = 5.20 × 10-4 m h-1 and fEvap = 0 m h-1.
3.1.3 TAN generation
Total generation rate of TAN is given by,(11)‫ ܫ‬ൌ ‫ܫ‬୫ୟ୶ ଶ  ቈTime course of PPFD

8 12 16 20 24
Time of day [h]Water balance in shrimp pond;
Water balance in shrimp pond is given by,

the value of specific growth rate is changed with TAN
concentration and environmental conditions in shrimp
pond.
Environmental conditions;Time course of PPFD in shrimp pond;
Time course of PPFD in shrimp pond is given by,‫୲ݎ‬୭୲ୟ୪ǡ୘୅୒ ൌ ‫ݎ‬ୗ୦୰ǡ୘୅୒ ൅ ‫ݎ‬ୗୣୢǡ୘୅୒

(17)
where rShr,TAN is rate of TAN excreted by shrimp
respiration, rSed,TAN is rate of TAN generated by
sediment decomposition. rShr,TAN is given by,‫ݎ‬ୗ୦୰ǡ୘୅୒ ൌ ‫ݍ‬୘୅୒ ‫ݎ‬୫୲ୠǡ୒

(18)
where qTAN is TAN fraction from shrimp respiration tototal nitrogen metabolite. rmtb,N is excretion rate ofnitrogen metabolite from shrimp per unit volume of
shrimp pond and given by,with PPFD at the time of maximum solar height(midday), Imax is 1800 μmol m-2 s-1 in fine days, is 100
μmol m-2 s-1 in cloudy or rainy days.
Time course of water temperature in shrimp pond;
Time course of water temperature in shrimp pond isgiven by,‫ܮܦ‬‫ ݐ‬െ ‫ ܪܵܮ‬൅ʹቍܶ ൌ ܶ୫୧୬ ൅ ሺܶ୫ୟ୶ െ ܶ୫୧୬ ሻ  ቌߨ‫ ܮܦ‬൅ ʹܲ
(6 h ≤ t < 18 h)300where growth yield of seaweed, YG, was 2.1 kgDM
kg-1-N.
The specific growth rate of seaweed;
The specific growth rate of seaweed is given by,ߤ ൌ ߤ୫ୟ୶

40

T [ʚ]

‫ܥ‬୘୅୒ൌ ‫୲ݎ‬୭୲ୟ୪ǡ୘୅୒ െ ‫୲ݎ‬୭୲ୟ୪ǡ୰ୣ୫୭୴ୣ‫ݐ‬ఊ‫ݎ‬୫୲ୠǡ୒ ൌ ܽ୫୲ୠǡ୒ ߩୗ ‫ܯ‬ୗ(19)
where amtb,N is nitrogen metabolite from shrimp per unit
shrimp mass. ρS is culture density of shrimp. MS isaverage shrimp mass and estimated from growth model.
γ is allometric scaling of shrimp metabolism. On theother hand, rSed,TAN is given by,(14)‫ݎ‬ୗୣୢǡ୘୅୒ ൌ ‫ݎ‬୰ ‫ܯ‬ୗୣୢǡ୒

(20)
where rr is rate constant of decomposition. MSed,N is

31

1.5
ρU [kgDM m-3]

CTAN 103 [kg-N m-3]

2

06:00 a.m.
1
12:00 p.m.

0

1

0.5

0
0

30

60

90

d [day]Fig. 3.3-1 Daily variation of TAN
concentration in shrimp pond at
06:00 a.m. and 12:00 p.m.

0

30

60

Flow rate of runoff water to pond [m h-1]
Flow rate of seepage water from pond [m h-1]Depth of shrimp pond [m]Photosynthetic photon flux density (PPFD) [μmol m-2 s-1]
PPFD at the time of maximum solar height (midday)[μmol m-2 s-1]
IS
Half saturation constant of PPFD for growth [μmol m-2 s-1]
Growth coefficient of shrimp [h-1]Kg
Existence coefficient of shrimp [h-1]
Km
KM
Half saturation constant of TAN concentration for uptake[kg-N m-3]
Half saturation constant of TAN concentration for growthKS[kg-N m-3]
LSH
Time of maximum solar height [h]Shrimp mass [kg-shrimp]
MSMS,0Shrimp mass at initial [kg-shrimp]
MS,maxShrimp mass at maximum grown [kg-shrimp]
Nitrogen in sediment per unit volume of pond water [kg-N
MSed,N
m-3]
P
Delay in Tmax with respect to LSH [h]Feed rate per shrimp mass [kg-feed kg-shrimp-1 h-1]
qF
qTAN
TAN fraction from shrimp respiration to total nitrogenmetabolite [-]R
Gas constant [J mol-1 K-1]
rF
Feed rate per unit area of shrimp pond [kg-feed h-1]
Nitrogen metabolite excretion rate by shrimp [kg-N m-3 h-1]
rmtb,N
Rate constant of sediment decomposition [h-1]rrRSFraction of shrimp assimilation in feed [-]RSedFraction of leftover in feed [-]TAN generation rate from sediment [kg-N m-3 h-1]
rSed,TAN
rShr,TAN
Excretion rate of TAN by shrimp respiration [kg-N m-3 h-1]
Total TAN removal rate [kg-N m-3 h-1]
rtotal,remove
Total TAN generation rate [kg-N m-3 h-1]
rtotal,TANtTime [h]T
Water temperature [°C]tmax
Cultivation time [h]Tmax
Maximum daily water temperature [°C]
Minimum daily water temperature [°C]
TmintS
Sunset time [h]TSSunset temperature [°C]
Growth yield of seaweed against TAN [kgDM kg -1-N]YGγAllometric scaling of metabolism [-]η
Night length (24 h - DL) [h]μSpecific growth rate of seaweed [h-1]
Maximum specific growth rate of seaweed [h-1]
μmax
πmax
Maximum specific uptake rate of TAN [kg-N kgDM-1 h-1]
Specific uptake rate of TAN [kg-N kgDM-1 h-1]
πTAN
Initial specific uptake rate of TAN [kg-N kgDM-1 h-1]
πTAN,0S
Culture density of shrimp [m-3]UDensity of seaweed in culture medium [kgDM m-3]
U,0Density of seaweed in culture medium at initial [kgDM
m-3]
U,t
Density of seaweed in culture medium at t [kgDM m-3]
τTime coefficient [h-1]
Literature Cited[1]Primavera, J.H.; “Tropical Shrimp Farming and Its Sustainability,”
Tropical Mariculture, S. S. De Silva ed., pp. 257-289, Academic
Press, San Diego, U.S.A. (1998)[2]Haywood, G.P.; “Ammonia Toxicity in Teleost Fishes: A Review,”
Can. Tech. Rep. Fish Aquat. Sci., 1177, 1-35 (1983)[3]Chen, J. C. and C. C. Tu; “Influence of Ammonia on Growth of
Penaeus monodon Fabricius Post–Larvae,” Aquaculture andFisheries Management, 22, 457-462 (1991)[4]Sato, K., Eksangri, T., Egashira, R.; “Ammonia-Nitrogen Uptake by
Seaweed for Water Quality Control in Intensive Mariculture Ponds,”
J. Chemical Engineering of Japan, 39, 247-255 (2006)[5]Burford, M. A., Lorenzen, K., “Modeling nitrogen dynamics inintensive shrimp ponds: the role of sediment remineralization,”Aquaculture, 229, 129-145(2004)
fRun
fSeep
h
I
Imax

amount of nitrogen contained in sediment per unit
volume of shrimp pond. MSed,N include the sediment
derived from uneaten feed and nitrogen metabolite fromshrimp.
3.2 Main calculation conditions
In this study, Penaeus monodon was selected as model
shrimp and cultivated 90 days. CTAN,0 and MSed,N,0 were
estimated 0 kg-N m-3. U,0 was 1.0 kgDM m-3 and culture
density of shrimp, S,0 was 40 m-3. Pond area, A anddepth, h were 4000 m2 and 1.0 m.
3.3 Results and discussion
In developing countries, recommended maximum TAN
concentration in shrimp ponds is 1.010-3 kg-N m-3. Fig.
3.3-1 shows daily variation of TAN concentration in
shrimp pond at 06:00 a.m. and 12:00 p.m. On each day,TAN concentration has large difference over the time ofday. Since seaweed could take in TAN efficiency around12:00 p.m., TAN concentration in shrimp pond was very
low. On the other hand, from night to morning, seaweed
could not take in TAN very well, so TAN concentrationin the morning is high. However, using seaweed, TAN
concentration was controlled under the recommended
maximum concentration. Fig. 3.3-2 shows time course of
seaweed density in shrimp pond. Seaweed increased
from 1.00 kgDM m-3 to 1.12 kgDM m-3 in 90 days. On
this cultivation, we obtained 480 kgDM seaweed as
by-product. However, since shrimp pond is large in area,
seaweed density in shrimp pond did not increase
excessively. So harvest of seaweed is not necessarily
often times.

90

d [day]Fig. 3.3-2 Time course of seaweed
density in shrimp pond

4 Conclusion
We could measure and quantitatively detect AN uptake
and growth of sterile seaweed and examine the effect ofoperating conditions for specific growth rate andmodeled mathematically. Next, the water quality control
method by using sterile seaweed could remove AN in
shrimp pond effectively and this method allows us toobtain a large amount of seaweed as by-product.
NomenclatureA
Shrimp pond area [m2]amtb,N
Nitrogen metabolite excretion rate [kg-N kg-shrimp-1 h-1]
CTAN
Total ammonia nitrogen (TAN) concentration [kg-N m-3]
d
Cultivation time [day]DLDay length [h]EApparent activation energy for growth of seaweed [Jmol-1]
fEvap
Flow rate of evaporation water from shrimp pond [m h-1]
Flow rate of inflow water to shrimp pond [m h-1]
fIn
Flow rate of outflow water from shrimp pond [m h-1]
fOut
fOver
Flow rate of overflow water from pond [m h-1]
Flow rate of rainfall water to shrimp pond [m h-1]
fRain

32

EXPERIMENTAL INVESTIGATION ON INFLUENCES OF STEEL AND PRESTRESS ON THE
EFFECTIVITY OF DESALINATION METHOD
Student Number: 10M18039

Name: Masato Kubota

Supervistor: Nobuaki Otsuki⣕ႮᎿᴺߩ᦭ലᕈߦ෸߷ߔ㍑᧚߅ࠃ߮ࡊ࡟ࠬ࠻࡟ࠬߩᓇ㗀ߦ㑐ߔࠆታ㛎⊛ᬌ⸛ਭ଻↰ ᣽⊓ᧄ⎇ⓥߢߪ‫ޔ‬ਣ㍑‫⇣ޔ‬ᒻ㋕╭‫ޔ‬PC ㍑᫔‫ޔ‬PC ࠃࠅ✢ࠍ↪޿ߡ‫(ޔ‬1)⣕Ⴎ೨ߩ㒐㘩ᕈ⢻ߦ෸߷ߔ㍑᧚ߩ⒳㘃෸߮ࡊ࡟ࠬ࠻࡟ࠬߩᓇ㗀‫(ޔ‬2)⣕Ⴎᓟߦ߅ߌࠆࠦࡦࠢ࡝࡯࠻ਛߩႮಽಽᏓߦ෸߷ߔ㍑᧚ߩ⒳㘃෸߮ࡊ࡟ࠬ࠻࡟ࠬ
ߩᓇ㗀‫(ޔ‬3)⣕Ⴎᓟߩ RC ෸߮ PC ⹜㛎૕ߩ㒐㘩ᕈ⢻ߦ෸߷ߔ㍑᧚ߩ⒳㘃෸߮ࡊ࡟ࠬ࠻࡟ࠬߩᓇ㗀ߦߟ޿ߡታ
㛎⊛ߦᬌ⸛ߔࠆߎߣࠍ⋡⊛ߣߒߚ‫⚿ߩߘޕ‬ᨐ‫(ޔ‬1)ࡊ࡟ࠬ࠻࡟ࠬߩᓇ㗀ߦࠃࠅ㍑᧚ߩ㒐㘩ᕈ⢻ߪૐਅߔࠆน⢻ᕈ߇␜ߐࠇߚ‫(ޔߚ߹ޕ‬2)㍑᧚ߩ⒳㘃෸߮ࡊ࡟ࠬ࠻࡟ࠬߩᓇ㗀ߦࠃࠅ⣕Ⴎᓟߦ߅ߌࠆࠦࡦࠢ࡝࡯࠻ਛߩႮಽಽ
Ꮣߦᓇ㗀ߪߥ޿ߎߣ߇⏕⹺ߐࠇߚ‫(ޕ‬3) RC ⹜㛎૕෸߮ PC ⹜㛎૕ߦ߅޿ߡ⣕Ⴎߦࠃࠆ㒐㘩ᕈ⢻ߩะ਄߇⏕⹺ߐࠇ‫ޔ‬RC ⹜㛎૕ߩᣇ߇ലᨐߪᄢ߈޿ߎߣ߇␜ߐࠇߚ‫ޕ‬

1. Introduction

2.1.1 Mortar Specimens

Desalination method is one of the electrochemicalcorrosion control methods. And it is effective fordeteriorated structures by chloride attack. Figure 1
shows principle of desalination method. Direct current
is applied between external electrode and reinforcing
bar in concrete members. And chloride ion is takenout by electrophoresis[1]. Desalination can recovercorrosion protection performance of deteriorated
members. Therefore it is applied for Reinforced
Concrete (RC) and Prestress Concrete (PC)structures[2]. However, there was no research toinvestigate the influences of types of steel andexistence of prestress on corrosion protection
performance after desalination.
This research focus on four different types of steel
such as round bar, deformed bar, PC steel rod and PC
strand. In the case of PC steel rod and PC strand, theexistence of prestress are also focused.
Objectives of this research are (1) to investigate
corrosion protection performance of each steel bar,(2)to investigate distribution of chloride ion in concrete
after desalination and (3) to investigate corrosion
protection performance of RC and PC specimen after
desalination depending on types of steel and existenceof prestress.

By using each type of steel, mortar specimens, 40
40  160mm with rectangular cross section, were
casted including 0 to 9kg/m3 chloride content withearly strength portland cement. After casting, eachspecimen was cured for 50 days in distilled water.
2.1.2 Concrete Specimens
Table 1 shows the details of specimens. RC
specimens were constructed with early strength
portland cement by using round bar and deformed bar,and PC specimens were constructed by using PC steel
rod and PC strand. Concrete specimens, 100100
500mm with rectangular cross section, were casted
with 9kg/m3 chloride content. After curing specimens,
desalination method has been carried out for 8weeks
with 1A/m2 or 3A/m2 direct current. After desalination
method, specimens have been exposed to corrosion
acceleration environment (wet and dry) for 40 weeks.
Table 1 Details of Specimen
Chloride
Current
Types of SteelContent
DensityRound Bar
9kg/m3
1A/m2
SR235 ‫͵ͳ׎‬
3A/m2
Deformed Bar
SD295A
9kg/m3
1A/m2
D13
3A/m2
PC Steel Rod
SBPR930/1180
9kg/m3
1A/m2‫͵ͳ׎‬3A/m2
PC Strand
SWPR7B
9kg/m3
1A/m2‫ʹͳ׎‬Ǥ͹
3A/m2

Fig.1 Priciple of Desalination Method

2. Experimental Procedure
2. 1 Specimen

330.1 ρ A/cm2, can be defined as value of chloride
threshold content for each types of steel.
From these results, value of chloride threshold
content for round bar was 2 to 3kg/m3, for deformed
bar was 0 to 2kg/m3 for PC strand was 0 to 1kg/m3. In
the case of PC steel rod, chloride threshold content
was not able to evaluate exactly. And PC steel rod and
PC strand have corrosion protection performance in 7
to 9kg/m3 chloride content in the case of no prestressspecimen.

2.2 Evaluation Method
In the test for chloride threshold content, corrosion
current density was calculated by polarization curve.
After calculation, existence of passive firm wasevaluated by the value refer to CEB standard[3].
Distribution of chloride ion in concrete was
evaluated based on extraction of total chloride content
following JIS A 1154.
Corrosion protection performance after desalination
was evaluated by electrochemical measurement suchas corrosion potential and polarization resistance, anddirect evaluation of internal steel bar such as corrosion
weight loss and visual observation.

3.2 Influence of Prestressing
Figure 3 shows comparison between polarization ofMortar and that of PC specimen with 9kg/m3 chloride
content in the case of PC steel rod and PC strand.
From the result, polarization resistances of Mortars
were much higher than those of PC specimens used in
PC steel rod and PC strand. Therefore it is verified
that corrosion protection performance is lower by
influence of prestress.

3. Corrosion Protection Performance of each SteelBar before Desalination
3.1 Influence of Types of Steel Bar
Figure 2 shows corrosion current density for eachtype of steel with 0 to 9kg/m3 chloride content. Basedon CEB standard, passive firm is deteriorated in thecase that value of corrosion current density is morethan 0.1ρA/cm2. Therefore, value of chloride content,
when corrosion current density is over more than

4. Distribution of Chloride Ion in concrete after
Desalination Depending on Types of Steel Bar andExistence of Prestress
Chloride content in concrete was measured for RC
specimen used in round bar and deformed bar and PC
specimen used in PC steel rod and PC strand. Figure4(a) shows distribution of chloride content in concrete
immediately after desalination with 1A/m2for eachtype of steel. And Figure 4(b) shows that after
exposure test for 40 weeks. Figure 5 shows in same(a) After Desalination(b) After 40 weeks
Fig. 4 Desalination with 1A/m2

Fig.2 Relationship between corrosion current density andchloride content for each type of steel(a) After Desalination(b) After 40 weeks
Fig. 5 Desalination with 3A/m2
Fig.3Polarization Resistance depending on existence ofprestress (Cl- 9kg/m3)

34

case of desalination with 3A/m2. From Figure 4(a) and
5(a), in the case of 1A/m2, desalination rate in all types

In the case of desalinated specimen used in PC steel
rod, polarization resistances of non prestressed
specimens were smaller than prestressed specimens.
In the case of PC strand, polarization resistances of
non prestressed specimens were also smaller thanprestressed specimens. Therefore, from the results byelectrochemical measurement, it was partly provedthat corrosion protection performance after
desalination was influenced by prestressing.

of steel was 25 to 35% near the location of steel bar andit was more than 40 to 50% in the case of 3A/m2. From
this results, it could be verified that desalination rate in
all types of steel found to be same.

As a result of Figure 4(b) and 5(b), total chloride
content near the location of steel bar remained about 5
to 6kg/m3 and 4 to 5kg /m3. Therefore, it could beverified chloride contents near the location of steel bar
after exposure test are almost same as immediately
after desalination.
From the results, it is verified that types of steel andexistence of prestress don’t influence on distributionof chloride ion in concrete.

5.2 Corrosion Protection Performance of RC andPC Specimen
5.2.1 Evaluation by Electrochemical Measurement
In corrosion monitoring, corrosion potential which
can evaluate corrosion protection performance
qualitatively was measured for 40 consecutive weeks.
Based on ASTM-C 846[4], more than 90% nocorrosion at > -326mV (vs Ag/AgCl), and more than
90% corrosion at < -479mV (vs Ag/AgCl).
Figure 7 shows corrosion potential for each type ofsteel under corrosion acceleration environment. In thecase of round bar and deformed bar, corrosion
potentials of desalinated specimen were higher thanthose of non desalinated specimens including 9kg/m3.
It was partly proved that desalinated specimens
remain corrosion protection performance. On the other
hand, in the case of PC specimens used in PC steel rod
and PC strand, corrosion potentials of desalinatedspecimens were found to be almost same tendency as
non desalinated specimens. From the results,
corrosion protection performance improved in thecase of both RC and PC specimens by desalination.
Especially, RC specimens show higher performance
than PC specimen.

5. Corrosion Protection Performance of RC andPC specimens after Desalination Depending on
Types of Steel and Existence of Prestress
5.1 Influence of Prestressing
Comparing between prestressed specimens and nonprestressed specimen, it would be verified how
corrosion protection performance was influenced byexistence of prestress. Figure 6 shows polarization
resistance depending on existence of prestress using in
PC steel rod and PC strand on 4th week under
exposure test. Based on CEB standard[3], at >130kΏȉ ଶ, passive firm around steel bar is exist.

5.2.2 Evaluation by Corrosion Weight loss andVisual Observation of Internal SteelAs direct evaluation of internal steel bar, corrosion
behavior for each specimen was evaluated by weightloss due to corrosion and visual observation.
As a result of Figure 8, for all types of steel,corrosion weight loss of desalinated specimen was
smaller than non desalinated specimen including
9kg/m3 chloride content. Therefore, desalination

Fig.6 Polarization resistance depending on existence ofprestress on 4th week under exposure test

Round Bar
Deformed Bar
PC Steel Rod
PC Strand٨ : Cl- 9kg/m3 No Desalination ً : Cl- 9kg/m3 Desalination with 1A/m2 ‫ع‬: Cl- 9kg/m3 Desalination with 3A/m2
Fig.7 Corrosion Potential for each type of steel under corrosion acceleration environment for 40 weeks

35

method had effects on improvement of corrosion
protection performance for all types of steel. Based on
CEB standard[3], at > 0.116mm/year is high corrosion
rate. Weight loss due to corrosion in the case ofdesalinated RC specimens using round bar anddeformed bar was smaller than PC specimens using
PC steel rod and PC strand. Therefore, RC specimens
had effects on desalination rather than in the case of
PC specimens. Especially, in the case of round bar,desalination had highest effect on improvement ofcorrosion protection performance after desalination in
all types of steel.
Figure 9(a)~(d) shows visual observation of internal
steel in the case of round bar, deformed bar, PC steel
rod and PC strand for non desalinated specimens anddesalinated specimens with 1A/m2 and 3A/m2. As aresult of visual observation, it was also verified thatround bar in desalinated specimen was not corroded
rather than non desalinated specimen. In the case of
PC specimen, desalinated specimen was not corroded
as much as non desalinated specimens. However there
were many pitting corrosion holes in surface of steel.
And PC strand in desalinated specimen, behavior ofmacro cell corrosion could be found.

No Desalination
Desalination with 1A/m2
Desalination with 3A/m2(d) PC Strand
Fig.9 Visual Observation of Internal Steelafter exposure test for 40 weeks

6. Conclusions
Objectives of this research are (1) to investigate
corrosion protection performance of each steel bar, (2)to investigate distribution of chloride ion in concrete
after desalination and (3) to investigate corrosion
protection performance of RC and PC specimen after
desalination depending on types of steel and existenceof prestress.
(1) By electrochemical measurement, it is partly
proved that value of chloride threshold content in thecase of round bar is 2 to 3kg/m3, in the case of
deformed bar is 0 to 2kg/m3 and in the case of PC
strand is 0 to 1kg/m3.In the case of PC steel rod,chloride threshold content was not able to evaluate
exactly.
Using in PC steel rod and PC strand, it is partly
proved that corrosion protection performance was
influenced by prestressing.
(2) From investigation of total chloride content in
concrete after desalination and exposure test,
distribution of chloride ion was not affected by typesof steel and existence of prestress.
(3) Using in PC steel rod and PC strand, it is partly
proved that corrosion protection performance after
desalination was influenced by prestressing.
By evaluation of electrochemical measurement,
weight loss due to corrosion and visual observation ofinternal steel, desalination method have effects on
improvement of corrosion protection performance inthe case of RC specimens rather than PC specimens.

Fig.8 Corrosion weight loss after exposure testfor 40 weeks
No Desalination
Desalination with 1A/m2[Reference][1] Japan Society of Civil Engineers : Electrochemical
Corrosion control method, Concrete Library107, 2001
[2] K. Ashida, et.al : Research on Desalination of Concrete byElectrochemical Method, Docter thesis in Kyoto University,
1999[3] CEB Working Party, commissions5 : Strategies for Testingand Assesment of Concrete Structures Affected byReinforcement corrosion, ISBN2-88394-040-01, 2004
[4] ASTM C876-09 Standard Test Method for Half-Cell
Potentials of Uncoated Reinforcing Steel in concrete

Desalination with 3A/m2(a) Round Bar
No Desalination
Desalination with 1A/m2
Desalination with 3A/m2(b) Deformed Bar
No Desalination
Desalination with 1A/m2
Desalination with 3A/m2(c) PC Steel Rod

36

THERMAL IMAGE VELOCIMETRY (TIV) AND APPLICATIONS
Student Number㸸10M18045

Name㸸Hideaki KUMEMURA⇕⏬ീ㢼㏿

Supervisor㸸Manabu KANDAᐃἲ㸦TIV㸧ࡢ㛤Ⓨ࡜ᛂ⏝ஂ⡿ᮧ⚽᫂ᮏㄽ࡛ࡣ㸪」㞧࡞㒔ᕷቃ⏺ᒙ࡟࠾ࡅࡿ஘ὶሙࡢᐃ㔞ⓗᢕᥱࢆ┠ⓗ࡜ࡋ࡚㸪㐃⥆ࡋࡓ⇕⏬ീࢆ⏝࠸࡚≀య⾲㠃㏆ഐࡢ஧ḟඖ㏿ᗘሙࢆ᥎ᐃࡍࡿ Thermal Image Velocimetry 㸦TIV㸧ἲࢆᥦ᱌ࡍࡿ㸬ᮏᡭἲࢆ᫬✵㛫ࢫࢣ࣮ࣝࡢ኱ࡁࡃ␗࡞ࡿ㸱ࡘࡢ㒔ᕷ኱Ẽ⌧㇟࡟㐺⏝ࡋ㸪௨ୗࡢ▱ぢࢆᚓࡓ㸬1㸬TIV ᡭἲࡣᘓ≀ࢫࢣ࣮ࣝ࠿
ࡽ኱Ẽቃ⏺ᒙࢫࢣ࣮ࣝࡢ஘ὶࡢ᫬✵㛫ᵓ㐀ࢆྲྀᚓྍ⬟࡛࠶ࡿࡇ࡜㸪2㸬TIV ㏿ᗘࡣ㉸㡢Ἴ㢼㏿ィ㢼㏿࡜Ⰻዲ࡞┦㛵ࢆ᭷ࡋ㸪 ᐃ⢭ᗘࡀ༑ศ࠶ࡿࡇ࡜㸪3㸬࢟ࣕࣜࣈ࣮ࣞࢩࣙࣥ࡟ࡼࡾ㸪㉸㡢Ἴ㢼㏿ィ㧗ᗘ࡟ᑐᛂࡍࡿ஧
ḟඖ㢼㏿ࢆ TIV ㏿ᗘ࠿ࡽྠᐃྍ⬟࡛࠶ࡿࡇ࡜㸪࡛࠶ࡿ㸬and the progressing high-frequency thermal video
imagery. A high-frequency infrared camera ishopefully used to quantify the velocity field.

1. Introduction
It is widely recognized in many big cities thaturban heat island and urban localized heavy rain has
become a serious problem. Recently, urban meteorology has made rapid progress owing to manycross-sectional researches and projects. However,the capture of complicated turbulent fields in urban
boundary layer is still a big challenge in modelizations and systematizations[1].

The objectives of this study are as follows: (1).
To develop the algorithm of thermal image velocimetry to quantify airflows and turbulences. (2). To
evaluate the accuracy of the quantifying TIV velocities in comparison with the conventional sonicanemometers. (3). To make a physical interpretationof the quantified velocities using meteorologicallyobserved data.

Numerical simulations such as Large Eddy
Simulation are one way to better understand theurban atmospheric turbulent fields. However, thevalidation data is very poor and the applicability ofnumerical methods in complicated urban boundary
layers is questionable. The lack of validation data ismainly coming from the restriction of measurements system; most of field data is on the basis ofpoint measurements using sonic anemometer.

2. Methodology
The velocity distribution near a surface is estimated based on the same algorithm of PIV but withusage of temperature images instead of tracer concentration. Then, special method is necessary todetect clear images of temperature fluctuation
purely corresponding to turbulent motions. For thispurpose, this section descries utilization of
StyrofoamTM and a preprocess using short time averaging.

One promising way to overcome the limitationis through Particle Image Velocimetry (PIV); it canobtain 2D spatial distribution of turbulences. Recently, PIV was used for a wide range of visualization of flows; flows around cars and airplanes inwind tunnels, river channel flows or coastal currents, medical tubing and apparatus of respiratorymodels. These applications of PIV are mostly forindoor model experiments. Recently, Takimoto et al.
successfully applied PIV to outdoor experiments[2].
However, a significant limitation of PIV in real
fields is that it can only be used during nighttime as
it utilizes laser lights.

2.1. Utilization of StyrofoamTM
An infrared camera takes continuous thermal
images. It is preferred that anomaly temperatures ofeach grid are bigger. Therefore, it is not simple toapply this method for building walls because concrete and other construction materials have highheat capacities and the temperature fluctuations are
small.
StyrofoamTMRB-GK-ϩ (Dow Chemical Company Co., Shinagawa, Japan), which has small heat
capacity was attached on a building wall.
StyrofoamTM surface temperatures respond quickly
to wind due to its small heat capacity and low
thermal conductivity.

In very recent years, some researchers have
tried to use thermal imaging to capture complicatedturbulent flows[3-5]. However, these are still trial
stage; just seeing the temporal and spatial images ofthermal camera and discussing qualitatively in relation to turbulent structure. The authors came up
with the idea of Thermal Image Vecocimetry (TIV)by combining with the previous PIV technology

2.2. Averaging time of anomaly tem37

peratures

3.2. Full-scale building wall

Not only wind but also surface shapes cause
heterogeneity in surface temperature distribution. It
is necessary to remove the latter’s influence in order
to identify the surface temperature fluctuations
purely attributed to turbulent motions.
This was done by subtracting a local and shorttime mean temperature from the raw temperature,
because the influence of the surface shapes always
stays in the same position as a background noise.

The TIV method was applied to a real building
wall. The target wall is Ishikawadai 3rd building inTokyo Institute of Technology (Tokyo Tech).
StyrofoamTM were painted black and attached to thevertical wall (Figure 3a). Thermal images weretaken in a section of 3 m horizontally and 11 m vertically at a frequency of 30 Hz. Figure 3b shows theexample of the temperature image taken in thebuilding wall. This experiment was conducted on
16-18 December 2010.

3. Experiment
TIV was applied to three different surfaces: (1).
in roof of a 1/5 scale model building (COSMO;
Comprehensive Outdoor Scale Model experimentfor urban climate), (2). full-scale building wall, (3).
in artificial turf. One of the motivations of the
choice of these areas is the difference of spatiotemporal scales (Table 1). The applicability of TIV
in various scales of field and phenomena should be
examined.

[Υ]Southernface

64

I3 buildingin Tokyo Tech

63

22.20 m

62

61

60

Table 1. Experimental data

59

z

Date

Image capture area

COSMO

09/13/2011

1.5 m×1.5 m

distance fromthe surface
0 mm

Building wall

12/16/201012/18/2010

3 m㽢11 m

35 mm

Field

10/12/2011
01/18/2012

60 m㽢80 m

1300 mm58x
y2.730 m57

Figure 3(a). The observation wall (Left)
Figure 3(b). A thermal image (Right)

3.3. Artificial
ground)turf(TokyoTech

TIV method was also applied to geophysicalground surface to obtain a footprint of boundary
layer-scale phenomena. This experiment was conducted in Tokyo Tech ground whose surface ismade of artificial turf. The test area was set to 60 m
80 m.
The experiment was conducted two times, 12
October 2011 and 18 January 2012. One of the experiments was done with that a ground based observation which includes the measurement using asonic anemometer installed at 130 cm from the surface (Figure 4).

3.1. Scale model building (COSMO)COSMO is made up of 512 concrete cubes,
which is 1.5 m on a side, on a flat plate extending
50 m by 100 m horizontally. The cubes are aligned
regularly. StyrofoamTM was attached on the entire
roof surface of a cube and its surface temperature
image is taken by an infrared camera (FSV-1100;
Apiste Co., Osaka, Japan) in 30 Hz. The experiment
was conducted on 13 September 2011.
Infrared Camera

Sonic anemometer

Wind
Sonic anemometer

1.5 m

StyrofoamTM

Observation
point

x

z

y

y

20 m

1.5 m

x

Figure 4. The observation setup (Tokyo Tech Field).

Figure 2. The observation setup. (COSMO)38

It is also shown that the TIV velocity is always
smaller than the wind velocity measured at higher
elevation than the surfaces.

4. Results and discussion
4.1. Accuracy

TIV velocity can be physically interpreted as
the advection velocity of coherent structure passing
over the surface. The advection velocity of thestructure is large if the size of the structure is large
since larger structure is affected by faster wind velocity at higher elevation from the surface. If theheight of the turbulent structure which is tracked by
TIV is lower than the location of the sonic anemometer, the TIV velocity becomes lower than thewind velocity measured by sonic anemometer. Thissituation possibly occurs because the large structureon the surface temperature fluctuation is effectively
high-pass filtered by subtracting short-time average,
and smaller structure is only tracked by TIV.

Figure 5 shows the TIV velocity plotted withwind velocity measured by sonic anemometerswhich are installed from the surface as explained in
the previous section. This figure demonstrates thatTIV velocity is almost proportional to the wind velocity with correlation coefficients higher than 0.7
for all cases. This implies that the wind velocity canbe estimated from the TIV velocity multiplying by aconstant coefficient.

R = 0.85

0.5

TIV [m/s]

0.3
0.1

-1.5

-0.5 -0.1 0

-1

0.5

4.2. Turbulence formation

1.51(a) COSMO-0.3

Figure 6 is the 2D distribution of velocity vectors obtained in the experiment in COSMO. In thisfigure, distinctive 2D-structures of turbulent motion
can be seen: (a) Irruptive motions from left below
edge are visible, (b) A divergence and (c) a convergence adjoiningly arises.

-0.5

Sonic anemometer [m/s]
Figure 5(a). Point diagram of TIV velocities and windvelocities (COSMO).

R = 0.86
䠄c䠅

-1.5

-1

-0.5

0

0.5

1

䠄b䠅

-0.1

1.5 m

TIV [m/s]

0.1

-0.3

Sonic anemometer [m/s]䠄a䠅

Figure 5(b). Point diagram of TIV velocities and windvelocities (Building wall).

TIV [m/s]

R = 0.75

0.2

0-3 -2.5 -2 -1.5 -1 -0.5 0

1.5 m

-0.2

Figure 6. TIV vectors(COSMO). (a). Incursion into thearea. (b). Divergence. (c). Convergence.

-0.4(b) Building wall

0.5

1

1.5

2

2.5

Anomaly temperatures and TIV vectors were
calculated (Figure 7 and 8). Figure 7 shows ananomaly temperature. An area of +1 °C deviations
move from bottom up. The same movement of
temperature +1 °C deviations was observed in TIV
vectors which lead upward flow of heat from bottom up (Figure 8).

-0.6

Sonic anemometer [m/s]
Figure 5(c). Point diagram of TIV velocities and windvelocities (Tokyo Tech Field).

39

6. Conclusion

11 m

A new technology to estimate velocity fields
very close to a ridged wall by using a sequence ofthermal images of the wall is proposed. We call thismethod Thermal Image Velocimetry (TIV). The
principle TIV is utilizing thermal images instead ofparticle images. Although TIV can potentially obtain highly fluctuated turbulent fields, the surface
temperature of the target wall should be sensitive tothe velocity fluctuation nearby. We apply TIV to amodel city, a building wall, and field, and discuss
the performance of TIV. The wind variations estimated by TIV correlate very well with those observed by a sonic anemometer.

-11Figure 7. Thermal image of anomaly temperature(Building wall).

11 m

Acknowledgments
This work is supported by the Research Program on
Climate Change Adaptation (RECCA). This research waspartially supported by the Scientific Research, Young
Scientific Research (B), 23760454, 2011. The Dow
Chemical Company provided some StyrofoamTMs.

Figure 8. TIV vectors (Building wall).

References[1]. Manabu Kanda : Progress in urban meteorology: A
Review, Journal of the Meteorological Society ofJapan, 85, 365-383, 2007.
[2]. Hiroshi Takimoto, Ayumu Sato, Janet F.Barlow,
Ryo Moriwaki, Atsushi Inagaki, Shiho Onomura,and Manabu Kanda : Particle Image Velocimetry
Measurements of Turbulent Flow Within Outdoor
and Indoor Urban Scale Models and Flushing Motions in Urban Canopy Layers, Boundary-Layer
Meteorology, 140, 295-314, 2011.
[3]. Christen, Andreas, Meier, Fred, Scherer, Dieter :High-frequency fluctuations of surface temperatures in an urban environment, Theoretical and Applied
Climatology,
OnlineFirst,
DOI
10.1007/s00704-011-0521-x, 2011.
[4]. Christen A. and Voogt, J. A.: Inferring turbulent
exchange processs in an urban street canyon fromhigh-frequency thermography, 9th Symposium on
the Urban Environment, J3A.3, 2010.
[5]. Christen A. and Voogt, J. A.: Linking atmosphericturbulence and surface temperature fluctuations in astreet canyon, The 7th International Conference on
Urban Climate, A3-6, 2009.

(c) Artificial turf
Figure 9 shows an example of TIV vectors on
the Tokyo Tech field. Convergence regions in thelower right area are clearly visible.

20 m
Figure 9. TIV vector of a field experiment (Tokyo Tech
field).

40

QUANTITATIVE ANALYSIS OF SWARM OF MOSQUITOESBASED ON IN-SITU OBSERVATION IN RELATIONSHIP WITH ATMOSPHERICENVIRONMENT
Student Number: 10M18051

ᐃ㔞ほ

Name: Rai KONNO

Superviser: Manabu KANDA

࡟ᇶ࡙࠸ࡓ㒔ᕷἙᕝ࿘㎶࡛Ⓨ⏕ࡍࡿ⺅ᰕ⃰ᗘࡢ᫬✵㛫ᣲື࡜኱Ẽ⎔ቃሙ࡜ࡢ㛵㐃ᛶࡢ᳨ウ௒㔝 㞾㒔ᕷἙᕝ࡛኱㔞Ⓨ⏕ࡍࡿࣘࢫࣜ࢝ࡢ⺅ᰕࡀ㏆㞄ఫẸࡢ⏕ά⎔ቃ࡟୙ᛌ࡞ᙳ㡪ࢆཬࡰࡋ࡚
࠸ࡿ㸬ࡇࡢၥ㢟࡟ᑐࡋᮏ◊✲࡛ࡣ㸪᪤ ࡢ⏕≀ᏛⓗほⅬ࠿ࡽ࡛ࡣ࡞ࡃẼ㇟Ꮫⓗ࣭ὶయຊᏛ
ⓗほⅬ࡟ᇶ࡙ࡃᐇ ࡟ࡼࡗ࡚㸪኱Ẽ⎔ቃሙ࡜⺅ᰕࡢ㛵ಀᛶ࡟ࡘ࠸᳨࡚ウࢆ⾜ࡗࡓ㸬ࡑࡢ㝿㸪ࣘࢫࣜ࢝ಶయᩘࢆほ ࡍࡿᡭἲࢆ᪂ࡓ࡟ᥦ᱌ࡋࡓ㸬⺅ᰕࢆᐃ㔞ほ ࡍࡿࡓࡵࡢᶵჾ࡜ࡋ࡚㸪࣮ࣞࢨ࣮ࢭࣥࢧ࣮฼⏝ࡋࡓ㠀᥋ゐᘧࡢࣘࢫࣜ࢝
ಶయᩘࡢ࢝࢘ࣥࢱ࣮ࢆ㛤Ⓨࡋࡓ㸬ࡇࢀࢆ⏝࠸㸪ᐃᛶⓗ࡞ほᐹ࣭ほ ࡟ࡼࡿ▱ぢࢆᐃ㔞ⓗ࡟ᢕᥱࡍࡿࡓࡵ࡟㸪㒔ᕷἙᕝ࡟࠾࠸࡚࢝࢘ࣥࢱ࣮࡟ࡼࡿほ ࢆ⾜ࡗࡓ㸬௨ୗࡀᚓࡽࢀࡓ▱ぢ࡛࠶ࡿ㸬 ᪥ࡢධࡾ᫬้ࡢศ๓࡟⺅ᰕⓎ⏕㔞ࡢࣆ࣮ࢡࢆ☜ㄆࡋࡓ㸬 Ἑᕝഃ㐨ࡢ୎Ꮠ㊰࡛ࡣ㢖⦾࡟㧗⃰ᗘࡢ⺅ᰕࡀほ ࡉࢀࡓ㸬 ᦤẶΥ௜㏆ࢆほ ᮇ㛫࡟࠾ࡅࡿ⺅ᰕⓎ⏕ࡢ㐺 ᖏ࡜ࡋ࡚☜ㄆࡋࡓ㸬

results..
The objective of this study is to identify the
relationship between the meteorological field andbehavior of a swarm of chironomids withaccordance to fluid mechanics. In order to achievethis, the swarm distribution must be preserved andobserved remotely. A new method of observation
and analysis was proposed without affecting thenatural flight of mosquitoes.

1. Introduction
This is an experimental study to investigate
the movement or deformation of swarms from aperspective of aerobiology. The movement of theswarm is not only affected by the active motion of
the individual insects but also the surrounding flow
field, which is especially focused on this study. The
aim of this study is to construct an experimental
basis on the prediction of their movement.
There are some experimental studies on the
movement of swarms. Mathumura and Kusunoki(1998) detected aerial planktons in non-precipitationecho using the Doppler radar at 300~500m height.
Seino (1987) verified that the planthoppers travelinglong distances from Vietnam to China and Japan are
flying in low-level jet stream pattern.
These studies are mainly focusing on the seasonalvariation of large swarms at macro-scale, which
extends more than few km horizontally. However,previous studies did not consider fluid mechanicalfeature in swarms’ behavior.
The area of study is Nomikawa river where
swarms of mosquitoes appear at a large scale
sometimes causing disturbance in the surrounding
neighborhood.
Traditionally, mosquitoes are trapped, collected,
and observed in laboratories apart from their naturalenvironment which weakens the reliability of2.

Methodology

The first step was to identify the location ofswarm. The selection criteria depended on theswarms’ presence, wind velocity fluctuation andrainfall occurrence. The area selection was based ona series of primary observations done along
Nomikawa River.
For a quantitative observation, a non-contact type
counter was developed using 10 laser sensors tomeasure number of swarms.
Ten laser sensors were laid out vertically as shownin  Fig.1. The center-to-center spacing of thesensors is 50m, and the length of the laser path is
500mm. The distances were optimized to capture
large swarms without causing noise (Fig.2). The
accuracy of the instrument was validated. The final
counter was also calibrated.
For safety purposes, the counter was attached on abicycle to maintain observation height and to41minimize the time lag of counting each swarm
around the river. The observer did not mount on thebicycle.
To measure selected meteorological parameters of
the surrounding environment, two thermocouples(φ=0.05mm) were attached on the counter. A video
camera and electric switch were also installed to log
the bicycle’s location at an instant. Rechargeable
batteries were used to power the whole set-up. Data
was logged at 50Hz(Fig.3).



3. Observation
The observation points were located above thefence surrounding Nomikawa’s upstream bounded
by Kodai bridge and Sakai bridge at Meguro ward,
Tokyo㸬Observing period was from 9/27 to 10/28 in2011 autumn.
The observation height ranges from 180-230 cm
from ground immediately above the 1.8m-highfence. The moving observation to measure theswarm of chironomids was conducted on theroad-side surrounding the upstream part ofNomi-kawa river (Fig.4).
Observations were conducted three (3) laps foreach day, initiated 30-min before sunset time (byNAOJ). Each lap has a 10-min interval. On average,
one (1) lap took about 6 minutes. The observation
conducted on September 27, 2010 was an exception
with seven (7) laps without intervals. The results arealso cited. Chironomid number of 16 sections on eachFig.2 Counter partFig.3 Overview of the equipment

side of the river were counted for every lap (refer toFig.4)

An ultrasonic anemometer (Climatec CYG-81000)
and thermo couples were installed at Tokyo Instituteof Technology ground (red mark in Fig.4) for
reference fixed-point observation.
10-min data of precipitation and water level at
kodai bridge from tokyo metropolitan government
were also obtained .

Fig.1 Layout of lasers
Fig.4 Observation area42

4. Spatiotemporal dynamics ofswarms

4.2 Spatial distribution
The area where a swarm of mosquitoes
reached highest concentration could be seen in

4.1 Time variation

East

Temp.

30

4.3 Meteorological factor
The impact of weather and microclimate on theswarm was confirmed by the relationship between
the temperature and the number of the chironomid.
A suitable temperature for swarm formation wasobserved. In Fig.9, it can be seen that 22.5-deg C
had the highest occurrence of chironomids. Thisobservation was also confirmed by the temperature
histogram shown in Figure 10. Fig.11 shows thenormalized number of chironomids by the numberof sections for each temperature level.

25
20
15
10
5
0
Date

Water Level

0
1
2
3
4
5

Precipitation

amount of chironomids䠄%)

50
40
30
20
10
0

precipitation䠄mm䠅

Level䠄cm䠅

Fig.5 monthly change of number ofchironomids, temperature

Date

Fig.6 precipitation and water at kodai bridge
Number of chironomids

1000
both

800

east
600

west

400
200
0
0:43

0:28

0:14
Time left

20
18
16
14
12
10
8
6
4
2
0

21.8
21.7
21.6
21.5
21.4
21.3
21.2

Temperature (Υ)

West

21.1
16w
14w
12w
10w
8w
6w
4w
2w
1e
3e
5e
7e
9e
11e
13e
15e

Ave.
Sum

800
700
600
500
400
300
200
100
0

Figure 8. The swarms frequently occur on 3 points
on 6 T-junctions on the roadside west of the
Nomikawa (Fig.8: 11w, 14w, and 16w). Thisphenomenon was seen through the observation
period.
The high concentration distribution spatially wasn't
always seen in fixed section. There were also points
with concentrations independent of time and space
which means other factors could be influencing theswarms’ presence.
T-junction points have common sunshine
distribution just before sunset. This is because theT-junction provides minimum shade from thesurrounding buildings. Previous studies also pointed
out that man has higher sensitivity to ultraviolet rays
than mosquitoes which is why light attracts
mosquitoes more than humans. The existence ofbuildings may pose as a future threat to themosquitoes survival.

Temp(Υ)Number of chironomids

The seasonal amount of increase and decline of themosquitoes is shown in black line in Fig.5. The
fluctuation of number of chironomids about forevery 2-days is regarded as the life cycle of thechironomids.
The number of mosquitoes on each side of the riveris also represented with red and blue lines. Constantdeviation between the west side and east side was
not observed for longer than 3 days.
Swarming of mosquitoes is restrained just after
rainfall during day time. On the other hand, theswarms were not affected by rainfall occurring thenight before the observation such as the observation
conducted during October 8.
The concentration of swarm of mosquitoes in theobservation area peaked 25-min on average before
the sunset (Fig.7).

21

Section name0:00

Fig.8 The sectionally-defined average appearance of thenumber of chironomid in observation period: e=east; w=west

Fig.7 Daily change of number of chironomids

43

number of chironomid

400
200
-90

0
Wind direction(°)

90

180

Fig.13 wind direction

300
250
200
150
100
50
0

5. Conclusion
The distribution of swarms were observed onconsecutive days to investigate their seasonal
concentration at the upstream of Nomikawa. To
accurately identify the number of swarms, a portablecounter was developed comprising of laser scanners.
The results showed the concentration of swarms
peak 25 minutes before the sunset. Furthermore, thearea where a swarm of mosquitoes reached highconcentration was identified. The high concentration
distribution was confirmed frequently in 3 points on
6 T-junctions near Sakai bridge. It was necessary toobtain different information as well as observational
data in this research to specify the reason why thedensity of the swarm of mosquitoes rises at a
T-junction. Another possible factor which may
affect the swarm is the sunshine as affected by thesurrounding buildings/land.

temperature(Υ)

number of sections

600

0

Fig.9 Number of chironomids vs. temperature
250
200
150
100
50
0
15 16 17 18 19 20 21 22 23 24 25 26 27 28
temperature(Υ)
Fig.10 Histogram of Temperature
number of sections

800

west
east

-180

15 16 17 18 19 20 21 22 23 24 25 26 27 28

Reference25
201) ᮧୖ⾜ၨ㸪஬༑ᔒ㝯ኵ㸪బ఑㝧Ꮚ㸪㊊❧㞝୍㸪ᯇ㔝ṇ▱㸪ᒸ⏣ᩄኵ㸪Ἑྜᖾ୍㑻㸪⇃㇂ᮁ㸪బࠎᏛ㸸ࣘࢫႍࣜ࢝ᜥ
࡟㛵ࡍࡿ◊✲㸬➨ II ሗ ࣘࢫࣜ࢝✀ู㛫ࡢ஺ཫᢠཎᛶ࡟
ࡘ࠸࡚㸬࢔ࣞࣝࢠ࣮㸪36, 81-85, 1987㸬
2) ᖹᯘබ⏨㸸ㄶゼ†ᆅᇦ࡟࠾ࡅࡿ͆㏞ᝨ᪻⹸͇ࣘࢫࣜ࢝ࡢ
኱Ⓨ⏕࡜ࡑࡢ㜵㝖⟇ ➨୍ሗ㸸࢔࣒࢝ࢩࣘࢫࣜ࢝
㸦Tokunagayusurika akamusi㸧ᡂ⹸ࡢ኱㔞㣕᮶㸪᪥ᮏ⾨⏕
Ꮫ㞧ㄅ㸪46 (2)㸪652 -661㸪1991㸬3) ᖹᯘබ⏨㸪୰㔛ு἞㸪Ἀ㔝እ㍤ኵ㸸ㄶゼ†࡟࠾ࡅࡿࣘࢫ
ࣜ࢝◊✲ (2)୙ᛌ᪻⹸࡜ࡋ࡚ࡢᡂ⹸࡜ࡑࡢ㜵㝖⟇࡟㛵ࡍࡿ᳨ウ㸪ᒣᆅỈ⎔ቃᩍ⫱ࢭࣥࢱ࣮◊✲ሗ࿌㸪1㸪53-62㸪
2003㸬
4) Kanda, M: Progress in urban meteorology: A Review,

15
10
5
0
15 16 17 18 19 20 21 22 23 24 25 26 27 28
temperature(Υ)
Fig.11 Average number of chironomid(every 0.25Υ)number of chironomids

1000

Number of chironomids

In relation to the wind field, very low
concentration of swarm of mosquitoes wereobserved when the wind velocity is large. Fewerthan 50 chironomids were observed by a 1
observation round when the wind velocity exceeds
1.5 m/s (Fig.12). The effect of the direction of thewind couldn’t be confirmed since southerly wind
dominated throughout the duration of theobservation (Fig. 13).

1000
800

Journal of the Meteorological Society of Japan, Vol.85,
pp.365-383, 2007.

600
400

5) ㏆⸨⦾⏕㸪ᖹᯘබ⏨㸪ᒾ⇃ᩄኵ㸪ୖ㔝㝯ᖹ㸸ࣘࢫࣜ࢝ࡢ
ୡ⏺㸪ᇵ㢼㤋㸪2001.
6) Kon, M.: Swarming and Mating of Chironomus yoshimatsui(Diptera: Chironomidae): Seasonal Change in the Timing of
Swarming and Mating, J. Ethol., Vol.2, pp.37-45, 1984.

200
0
0

0.5

1

1.5

2

wind velocity(m/s)
Fig.12 Number of chironomids v.s. wind velocity

44

EXPLANATION SYSTEM BASED ON WISDOM OF CROWDS ON ANDROID
Student Number: 10M18074

Name: Norihisa TAKEMURA

Supervisor: Yukihiko YAMASHITA

Android 端末で動作する集合知を利用した説明システム
竹村典久
近年，e-ラーニングの重要性が増しているが，教材の作成・維持管理に大きな問題を抱えている．
そのため集合知を用いてこの問題を解決した tobe システムが提案されているが，学習のためにパーソナルコンピュータを用いる必要があり，通勤・通学などの移動中に学習することは難しい．そこで最近保有率が高まってきたスマートフォンやタブレット端末に着目し，シェアが高く開発が容易である Android 端末を利用することを提案する．

1

Introduction

2. Online-training (ex: quiz)

Recently, more and more people use electronic learning (e-learning). It has been common not only for students but also for company workers. However, most of
e-learning softwares are for a personal computer (PC).
Because PC is still large and heavy, time and place for
e-learning are restricted. To solve this problem, mobile
learning (M-Learning) has been introduced. A deﬁnitionof M-learning is given as

These are usually mixed.
2.1.1

M-learning

The term M-Learning, or ”mobile learning” covers learning with portable technologies including but not limited
to handheld computers, MP3 players, notebooks andmobile phones. M-learning focuses on the mobility of
the learner, interacting with portable technologies, andlearning that reﬂects a focus on how society and its inAny sort of learning that happens when thestitutions can accommodate and support an increasingly
learner is not at a ﬁxed, predetermined lomobile population.
cation, or learning that happens when the
M-learning is convenient in that it is accessible fromlearner takes advantage of the learning opvirtually
anywhere. In addition, it is simple to utilize Mportunities oﬀered by mobile technologies [1].
learning for a more eﬀective and entertaining experience
In other words, M-learning decreases limitation of learn- [3].
ing location by the mobility of general portable devices.
Another problem of e-learning is that it takes much 2.2 Wisdom of crowds
time or needs cost to make and maintain course materials. If we want to publish an e-learning software, we have The wisdom of the crowd refers to the process of taking
to make all the course materials each time. To solve this into account the collective opinion of a group of individproblem, tobe (text oriented bi-stream explanation) sys- uals rather than a single expert to answer a question.
tem has been developed [2]. By using wisdom of crowds, This process, while not new to the information age, has
it enables to reduce eﬀort of individuals to make the been pushed into the mainstream spotlight by social inmaterials. I will apply the concept of tobe system to formation sites such as Wikipedia and Yahoo! Answers,
and other web resources that rely on opinion of crowds.
M-Learning on Android.

2

2.2.1

Backgrounds and related
studiesWiki

A wiki is a website whose users can add, modify, or delete
its content via a web browser using a simpliﬁed markup
language or a rich-text editor. Wikis are typically powI explain backgrounds and related studies needed for myered by wiki software and are often created collaboraresearch.
tively by multiple users [4]. But the markup language is
diﬀerent in each Wiki. Therefore, there is a tendency to
2.1 E-learning
standardize them.
E-learning comprises all forms of electronically supported
learning and teaching. The information and communica- 2.3 Android
tion systems, whether networked or not, serve as speciﬁcAndroid is a software stack for mobile devices that inmedia to implement the learning process.
cludes an operating system, middleware and key appliE-learning has two types of contents:
cations. Google Inc. purchased the initial developer of
1. Online-learning (ex: explaining with images and the software, Android Inc., in 2005. Android’s mobile
movies)operating system is based on the Linux kernel.

45

Table 1: Worldwide Smartphone Sales to End Users by Operating System in 3Q11 [5]
3Q11
3Q10
company
3Q11 Units Market Share(%) 3Q10 Units Market Share(%)
Android
60,490.4
52.5
20,544.0
25.3
Symbian OS
19,500.1
16.9
29,480.1
36.3
iOS17,295.3
15.0
13,484.4
16.6
RIM
12,701.1
11.0
12,508.3
15.4
Bada
2478.5
2.2
920.6
1.1
Windows mobile
1,701.9
1.5
2,203.9
2.7
Other OS
1,018.1
0.9
1,991.3
2.5
Total
115,185.4
100.0
81,132.6
100.0

Figure 1: XO-3 [6]Figure 2: Expositor [2]There are two reasons why I use Android for my research.

2.4

XML

1. Android is the most popular mobile OS in the
Extensible Markup Language (XML) is a markup lanworld (Table 1).
guage that deﬁnes a set of rules for encoding documents
in a format that is both human-readable and machine2. Android applications can be developed by Java.
readable. It is deﬁned in the XML 1.0 Speciﬁcation produced by the W3C, several other related speciﬁcations
and all gratis open standards [7].
2.3.1 OLPC
One Laptop per Child (OLPC) [6] is a project supportedby the Miami-based One Laptop per Child Association(OLPCA) and the Cambridge-based OLPC Foundation(OLPCF), two U.S. non-proﬁt organizations set up to
oversee the creation of aﬀordable educational devices foruse in the developing world.
OLPC has just announced that the organization’s
promised under $100 XO 3.0 (Fig. 1) tablet will beshown oﬀ. The XO tablet will ship with the OLPC developed Sugar operating system but will also support
Android and Linux. My proposing system may be useful for children in developing countries by using XO 3.0.
There are many statements that this project is failure. They say this project only provided personal computers for children. And they ask volunteers to build
the infrastructure and maintain the personal computers.
Some teachers cannot use the computer for the lecture,and some students use a computer only for games. I
hope that my system will change the situation.

46

2.5

Tobe system

Tobe system is an e-learning system for which anyone
can make the contents. The contents are described using tobeML (tobe markup language). The process ofcreating and studying contents is as follows.
1. Creators all over the world describe the tobeML
documents using text editor on the web.
2. The tobeML documents are stored in the server.
3. Users receive the tobeML documents from the server,
and load them into Expositer, that is a lecture style
player of tobeML documents.
The format of tobeML documents is based on XML. A
virtual teacher in Expositer (Fig. 2) can point an important area in text or ﬁgure and read the explanation.

㼜㼍㼏㼗㼍㼓㼑㻦㼠㼛㼎㼑㼙㼘㼠㼛㼎㼑㻚㼖㼍㼢㼍㻔㻭㼏㼠㼕㼢㼕㼠㼥㻕䞉㼛㼚㻯㼞㼑㼍㼠㼑㻔㻕䞉㼛㼚㻯㼘㼕㼏㼗㻔㻕䞉㼓㼑㼠㻴㼠㼠㼜㻾㼑㼝㼡㼑㼟㼠㻲㼕㼘㼑㻔㻕䞉㼓㼑㼠㼀㼠㼟㻲㼕㼘㼑㻔㻕䞉㼟㼔㼛㼣㻰㼕㼍㼘㼛㼓㻔㻕㼙㼍㼕㼚㻚㼤㼙㼘
㻸㼕㼚㼑㼍㼞㻸㼍㼥㼛㼡㼠㻔㼂㼕㼞㼠㼕㼏㼍㼘㻕䞉㼕㼙㼍㼓㼑㼍㼞㼑㼍䞉㼠㼑㼤㼠㼍㼞㼑㼍䞉㼜㼍㼓㼑㻌㼚㼡㼙㼎㼑㼞㻸㼕㼚㼑㼍㼞㻸㼍㼥㼛㼡㼠㻔㻴㼛㼞㼕㼦㼛㼚㼠㼍㼘㻕䞉㼜㼘㼍㼥㻮㼡㼠㼠㼛㼚䞉㼚㼑㼤㼠㻮㼡㼠㼠㼛㼚䞉㼎㼍㼏㼗㻮㼡㼠㼠㼛㼚㼟㼠㼞㼕㼚㼓㻚㼤㼙㼘
㼘㼑㼤㼠㻜
㼘㼑㼏㼠㻝
䞉䞉䞉
㼠㼍㼘㼗㻜
㼠㼍㼘㼗㻝
䞉䞉䞉

Figure 4: Class diagram

Android cannot set an image and a text at the sameplace. Therefore, I have to use images as back grounds.
The diagram of important classes in this player isshown in Fig. 4. I explain important methods and resources.
• onCreate(): This method is called at ﬁrst when
the application starts. In this application, the ﬁrst
resource is set and the language choice window isshown.

Figure 3: Display of the system

3

Tobe player on Android

In order to make easy to use tobe system outside, I focus
not on PC but on smartphones and tablet computers,
and propose a tobe player for M-learning. I develop aplayer on an Android device.

3.1

Text to speech

We use Text To Speech (TTS) to generate speech fromtext. The system sends text data to Google Translate [8],gets mp3 data, and plays the mp3 data on the device. To
get sound data, the system needs to connect to Internet
just only once, because mp3 data are saved in the device.
Android has an own TTS system, but its quality is
not so good and the number of languages that Android
can be applied to is less than that of Google Translate.

3.2• onClick(): This method is called when a button
clicked. In this application, when any button of
three is clicked, this method is called and “play”,“next”, or “back” is run.
• getHttpRequestFile(): This method is called when
the application sends the text to Google Translate.
Its return is a mp3 ﬁle.
• getTtsFile(): This method save a speech ﬁle to thedevice and return a mp3 ﬁle to getHttpRequestFile().
• showDialog(): This method is called when the dialog is displayed such as “This is the last page”.
• lect+number: This is resource text for display.
• talk+number: This is resource text for speech.

Structure of the player

The window of the proposing player is shown in Fig. 3.
The explanation of each part is as follows:
1. Image area: show the image for explanation

The process to use the proposing system is as follows:
1. Install the application.
2. Set the resources (texts for speaking, texts for display, images)

2. Text area: show the text for explanation

3. Start and choose language for speech explanation(Fig. 5).

3. Play button: start speech explanation
4. Back button: move to previous slide

4. Push the play button.

5. Next button: move to next slide47

5Conclusions

In this paper, we discussed about a tobe player on android that is an e-learning system using wisdom of crowds.
I explained the system can solve the problem that tobesystem needs PC.
The developed system can only do explanation. Thenan editor using Wiki is necessary . And the proposing
system can be use not only for e-learning but also for amanual. Because a manual is constructed by image andtext, it is sometimes diﬃcult to understand. By applying
the proposing player to explain with speech, users canunderstand it more easily.
However, when we make the contents for e-learning,
we have to consider educational engineering. Not everyone can make the contents for education easily. Therefore, we have to make contents that explains how to use
this system and make good materials. Such an integration property is an advantage of the proposed system.

5.1

Future work

The proposing player has not been completed. So wehave to do the following things:• To improve the function to point an important
point.
• To make contents in other languages.

Figure 5: Window for language choice• To play the contents automatically.

4

Experiment

References

We conducted an experiment to evaluate the proposing
system. Its outline is as follows:• Compare Tobe Player to printed contents.
is better to understand the content?

Which[1] C. O’Malley et al.:“Guidelines for learning/teaching/tutoring in a mobile environment”,
MOBIlearn, pp. 6 (Oct. 2003).

• The content explains some companies.

[2] S. Tobe: “Research on Explanation Markup Language based on XML and its Lecture Style Player”,
Master thesis of Tokyo Institute of Technology (Feb.
2005) (in Japanese).

• The content is written in English. It is 8 pages of
A4 document or 12 pages in player.

[3] “Mobile learning is for everyone”, http://www.mlearning.org/ (accessed 2012-01-25).

• Five subjects (2 Japanese, 1 Chinese, 1 Korean, 1Vietnamese)[4] N. Matias:“What is a Wiki?
Article”,
http://www.sitepoint.com/what-is-a-wiki/(accessed 2012-01-24).

For the result, all subjects answered tobe player was
better. Their impressions are as follows:[5] Gartner News, http://www.gartner.com/it/page.jsp?id=1848514 (accessed 2012-01-20).

• Because the system explains using image and speech, [6] One Laptop per Child, http://one.laptop.org/ (acit is easy to understand.
cessed 2012-01-27).
• I want to be said where I should look at such as
Look at the graph. .
• The amount of information at one page is restricted.
• Listening to English explanations is a little diﬃcult.

48[7] “Extensible Markup Language (XML) 1.0 (Fifth
Edition)”, http://www.w3.org/TR/REC-xml/ (accessed 2012-01-24).
[8] Google Translate, http://translate.google.co.jp/(accessed 2012-01-20).

A novel method for ammonia deodorization utilizing the anabolic activityof heterotrophic bacteria
Student No.:10M18080 Name:Masashi TANAKA Supervisor:Kiyohiko NAKASAKI Co-supervisor: Sachio HIROSEᓥዻᩕ㙃⚦⩶ߩหൻ૞↪ࠍ↪޿ߚᣂⷙߥࠕࡦࡕ࠾ࠕࠟࠬ⣕⥇ᴺ↰ਛᄢᔒ ᖡ⥇໧㗴ࠍ⸃᳿ߔࠆᣇᴺߣߒߡ‫ޔ‬ᓥ᧪߆ࠄᓸ↢‛ߦࠃࠆᖡ⥇ᚑಽߩಽ⸃޿ࠊࠁࠆ⇣ൻ૞↪ࠍ೑↪ߔࠆ
↢‛⣕⥇ᴺ߇ឭ᩺ߐࠇߡ߈ߚ‫⎇ᧄޕ‬ⓥߢߪ⥇᳇ᚑಽߣߒߡࠕࡦࡕ࠾ࠕࠟࠬࠍኻ⽎ߣߒ‫ޔ‬ᓥዻᩕ㙃⚦⩶߇὇⚛Ḯࠍᶖ⾌ߔࠆߣหᤨߦࠕࡦࡕ࠾ࠕࠍ៨ขߒߡ⩶૕วᚑߔࠆหൻ૞↪ࠍ೑↪ߒߡ⣕⥇ߔࠆᣂⷙߥࠕࡦ
ࡕ࠾ࠕࠟࠬ⣕⥇ᴺࠍ㐿⊒ߒߚ‫ࡓ࡜ࠞޕ‬ਛߦᓸ↢‛ࠍធ⒳ߒߚ᦭ᯏ‛ࠍలႯߒ‫ޔ‬1000 ppm ߩࠕࡦࡕ࠾ࠕࠟ
ࠬࠍㅢ᳇ߒߚߣߎࠈ‫ޔ‬ᓸ↢‛ߩჇᱺ(⩶૕วᚑ)ߦߣ߽ߥߞߡࠕࡦࡕ࠾ࠕ߇⣕⥇ߐࠇࠆߎߣࠍ⏕߆߼ߚ‫߹ޕ‬oߚ‫ ࠕ࠾ࡕࡦࠕޔ‬1000 ppm ࠍ 30 ߅ࠃ߮ 60 C ߩ᧦ઙߢ 5 ᣣ㑆ㅢ᳇ߒߚߣߎࠈ‫ޔ‬ᐔဋࠕࡦࡕ࠾ࠕಣℂ㊂ߪߘࠇߙࠇ 7.7210-4 g-NH3/g-ds/d‫ޔ‬7.3110-4 g-NH3/g-ds/d ߣߥߞߚ‫ޕ‬

additional wastewater treatment facility is required
1. Introduction
In Japan, the Offensive Odor Control Act was to treat the solution used for washing of the supporting
enacted to control air pollution associated with nuisance material.
This study aims to investigate ammonia
odor in1971. Thereafter, nuisance odor caused many
problems as a typical sensory environmental pollution deodorization utilizing the anabolic activity ofat livestock farm, wastewater treatment plant and heterotrophic bacteria where no byproducts that inhibit
the bacterial activity facility are produced and nolandfill.
At these point sources of the pollution, biofiltration additional facility would be required.
is commonly used as a deodorizing method due to its
low cost operation and construction. It is well-know 2. Materials & methods
that biofiltration is a technique to treat odor 2-1. Solid filler material used for deodorization
containing-air where microorgnisims in the supporting
In the present experiments, ammonia gas
material degrade odorous components contained in containing-air was introduced into a column which
the odor gas. Many reports have been focused on the contained organic materials inoculated withremoval of ammonia, hydrogen sulfide, and VOCs microorganisms. It is expected that ammonia gas would
which are the typical components of the odor gas using be removed from the air by being used as the nitrogencompost and activated sludge as the supporting source for the production of the microbial cell massmaterials of the microorganisms[1][2].
when heterotrophic microorganisms are incubating inAmong these various components, ammonia gas is the condition of less nitrogen source. It was thus
the most nuisance odor gas which has been generated in considered that the organic materials with high carbon
large quantities. So far, ammonia gas removal by the content was more advantageous and rice that was
biofiltration has been carried out using the catabolism discarded as kitchen refuse was used as a raw materialof the microorganisms during the nitrification. Two for the filler. Rice, sawdust, and seeding materials withdominant kinds of bacteria, Nitrosomanas sp. which the trade name Alles-G (Matsumoto Laboratory ofoxidizes ammonia to nitrite, and Nitrobacter sp. which Microorganisms, Matsumoto, Japan) with the mixing
oxidizes nitrite even to nitrate, proliferated with the ratio of 10:20:1 on dry-weight basis were used as the
progress of the nitrification[3]. Nitrogen oxide which is filler in the column. The initial pH of the filler was 7
generated by these nitrifying bacteria inhibits their while the moisture content was adjusted to 60% by
own growth. Therefore, in order to keep the activity of addition of distilled water. Some characteristics of thethe nitrifying bacteria, the supporting material needs seeding materials are shown in Table 1.
to be was  had to remove nitrogen oxide and49

2-3. Analysis
The exhaust gas from the column was introduced into

Table 1 Some characteritics of Alles G
charcteristic
Thermophiliclog(CFU/g-ds)
7.76 ±0.02*
Mesophiliclog(CFU/g-ds)
8.92 ±0.01*
pH
(-)
8.77
Moisture content(%)
4.92 ±0.74**95%ା㗬඙㑆㩿㫅㪔㪊㪀a 5L plastic bag(TedlarBagTM; Omi Odoair Service Co.
Ltd., Omihachiman, Japan) for 12 h, and the plastic bag
was changed twice daily at 12 h intervals. The volume

2-2. Ammonia deodorization experiment using the of exhaust gas captured in the plastic bag was measured,filler material
and the concentration of CO2 was analyzed using
Fig. 1 depicts a schematic diagram of the Kitagawa gas detector tubes (KomyoRikagaku Kogyo
experimental set-up for ammonia deodorization. Air K.K., Kanagawa, Japan). The quantity of CO2 evolved
was first passed through a flask containing ammonia during each 12 h period was determined by the CO2
solution before introducing into the column. The concentration and exhaust gas volume measured, andaeration rate was maintained at 5.5 mL/min throughout the cumulative CO2 that had evolved up to a certain
the experiment. 15 g of the filler was placed into the incubating time was calculated. A total of six reactors
column which was in-turn placed in an incubator were used for each experimental run in order to(Model IS 800, Yamato Scientific Co., Ltd, Tokyo, measure the changes in cell densities ofJapan) in order to maintain the temperature at the microorganisms and pH over time. These reactors wereconstant level. The column was composed of a initiated at the same time, and the incubating process inmini-reactor made of Pyrex glass cylinder which was subsequent reactors was interrupted at the point of 0, 1,45mm in diameter, 100mm in height, sealed with 3 d and at the end of the incubating period. The
concentration of ammonium was measured bysilicone rubber stoppers with glass pipes for aeration.
In the first investigation of the ammonia deodorization enzymatic method with F-kit NH3 (J.K.International
at 60 oC, four runs namely Run A60 (column with Inc., Tokyo, Japan) and the concentration of nitrite andfiller), B60 (column with sterilized filler), C60 (column nitrate was measured by colorimetric method with
with no filler), and D60 (column with filler) were Nitrite/Nitrate, Colorimetric Test(Roche Diagnostics K.
carried out. The NH3 gas with 1000 ppm concentration K., Tokyo, Japan). Total nitrogen in the filler was
was supplied from the bottom of the column in all runs measured by elemental analysis with organic micro
except Run D60 where air was instead supplied. Similar analysor (MICRO CORDER JM-10, J-Science CO.,runs were conducted at 30 oC namely A30, B30, C30, Kyoto, Japan)
and D30. All runs were operated for 5 days and thefiller was agitated every 24 h using sterilized spoon.
2-4. NH3 deodorization by isolated microorganism
High concentration of ammonia deodorization with
Two strains of the dominant bacteria were isolated
three kinds of experimental conditions (Runs A30+, from 3rd day samples of Run A60(C05T) andB30+ and C30+) were taken in 30͠. Experimental A30(C09T), respectively. C05T and C09T were
set-up is the same as that in Run A30, B30, C30, pre-cultured in Trypticase-soy broth at 30and 60oCfor 1
although this test is taken by ammonia concentration day. Bacteria were then centrifuged and washed three
2000ppm. These runs called Run A30+, B30+ and times with medium solution prior being used as an
inoculum.
C30+.
300mL conical flask with M9G liquid medium
including no nitrogen which was inoculated withprecultured microorganism was used as the reactor
instead of the column.
The NH3 deodorizations in the M9G liquid medium
with four runs (Runs E50, F50, E30 and F30) wereconducted.NH3 concentration of 700 ppm was suppliedin the four runs. C05T and C09T were used as
inoculum in Runs E50 and E30, respectively. Run F50
Fig.1 Schematic diagram of NH deodorization setup
350

and F30 were without the liquid medium and used forthe measurement of the NH3 concentration.

A60:filler+NH 1000ppm

D60:filler+Air

10
8
6
4
2
0

NH3 emissions between each run was observed. Lower
cumulative NH3 emission in Run B60 than that in Run
C60 was due to the NH3 adsorption to the filler. During

C60:no filler+NH31000ppm

123

3. Results & discussion
3-1. NH3deodorization with solid filler material
 The course of the cumulative NH3 emission at 60 oC
is shown in Fig.2. A clear difference of the cumulative

3

B60:filler(sterilized)+NH31000ppm

14(mol/batch)

Cumulative NH emission x104

16

0

24

48
72
Time (h)

96

120

o

Log. cell density of microorganisms(CFU/g-ds)

Fig. 2 The course of the cumulative NH3 emission at 60 C

the first 24 h, NH3 seemed to be trapped in the column
in Run A60 and B60. From 24 h, a sharp increase of
cumulative NH3 emission was observed in Run B60
while NH3 emission in Run A60 was lower level than in
RunB60 during which NH3 was consumed by the
microorganisms in Run A60.
The cumulative CO2 emission in Run A60 at 24 h was
higher than that in Run D60 in which air was instead
introduced into the column (data not shown). The
difference between A60 and D60 suggested that NH3

12
10
8
6

A60:NH 1000ppm
3

D60:NH 0ppm
3

4
2
0

24

48
72
Time (h)96120Fig. 3 The course of the log. cell density of microorganisms
NH -N
3

+

2

induced the vigorous microbial activities as it wasincorporated for the cell growth.
The courses of cell densities of microorganisms in

Amount of nitrogen x10 㩷(g/batch)

NH -N

Runs A60 and D60 are shown in Fig.3. At the end of
the incubating period, cell density of microorganisms in
Run A60 increased drastically to a value of 1.9x1010
CFU/g-ds which was over 2 orders of magnitude higher
than that in Run D60. Due to high C/N ratio of the filler,

7

4

-

NO -N
2

Org-N

-

NO -N
3

6
5
4
3
2
1
0

Input

Output & accumulation

Fig.4 The mass balance of nitrogen in Run A60

The course of the cumulative emission of ammonia
gas in the ammonia deodorization with solid filler at 30o
C is shown in Fig. 5. Since the difference of Run B30
and Run C30 become obvious at 48 h, and it wasconsidered that ammonia gas is consumed by the
microorganism in Run A30. Furthermore, NH3 gas
emissions was less, and 98.6% of the supplied ammonia
gas was removed was checked by the end of the
incubating period
Then, in Run A30 and D30, compared with ammonia
gas or air which was aerated, the carbon dioxide
evolution rate in Run A30 after 24 h was slowly
increasing and carbon dioxide maximum evolution rate
8.0x105 mol/g-ds/h in 96 h. Although the one in Run
D30 was not increasing slightly after 24 h (data notshown). Difference between A30 and D30 suggested
that NH3 induced the vigorous microbial activities as it
was incorporated for the cell growth.

it would be difficult for microorganisms in Run D60 toproliferate. However, the supply of NH3 gas to thecolumn could be an alternative source of nitrogen forthe cell growth as in Run A60.
 The mass balance of nitrogen of Run A60 is shownin Fig. 4. At the end of the incubating period, theincrease of Org-N was observed and the difference ofinitial and final Org-N would be equivalent to the cellmass. Moreover, a part of Org-N has been decomposedin an early stage; therefore, higher cell mass would be
produced than the present observation. Since thedifference between ammonia gas input and sum ofammonia gas output and inorganic nitrogen should have
converted to the microbial cell mass, at least 21.4 % of
the supplied ammonia gas was served for the microbial
growth.

51

The course of cell density of microorganisms in Runs compared with the case where ammonia gas
A30 and D30 is shown in Fig.6.Compared to having concentration 1000 ppm are aerated, Run A30, thebeen 3.6x109 CFU/g-ds of cell density in Run D30 by 3 effect of the ammonia gas removal by anabolism wasd, the microorganisms in Run A30 has increased well not able to be expected. If it is to the ammonia gas
which aerated ammonia gas, and became 8.4x1010 loading dose around 1.0x10-3 g-NH3/g-ds/d, in the case
CFU/g-ds.
of aerating ammonia gas concentration 1000 ppm, Run
The mass balance of nitrogen of Run A30 at 5 d is A30.
shown in Fig. 7.When it is calculated in similar way
with Run A60, it is thought that 75.2 % of the suppliedammonia gas serves as a cell mass at least.

Amount of nitrogen (g/batch)

3

A30:filler+NH31000ppm
B30:filler(sterilized)+NH31000ppm
C30:no filler+NH31000ppm

12(mol/batch)

Cumulative NH emission x104

16
14

D30:filler+Air

10
8
6
4

0

24

48
72
Time (h)

96

120

0.005

Cell mass-N-

NO2 -N

0.004
0.003
0.002
0.001
Input

Output &Input

Output &3-2. NH3deodorization by isolated microorganisms
In order to clarify that the supplied ammonia gas isoLog. cell density of microorganisms(CFU/g-ds)

NO3--N

NH4+-N

Fig.8 The mass balance of nitrogen at 50 oC (a), 30 oC

Fig. 5 The course of the cumulative NH3 emission at 30 C
124assimilated to the microorganisms, cultivation which
aerated ammonia gas using M9G liquid medium
adjusted to be nothing to use nitrogen except for thenitrogen from ammonia gas was performed.
 The result of having calculated the mass balance ofthe nitrogen of the culture solution cultivated to 2 d in2

50 oC, and to 1d in 30 oC was indicated with Figs. 7(a)10
8
A30:NH 1000ppm
3

6

D30:NH 0ppm
3

0

24

48
72
Time (h)96120

and 7(b) respectively. It was ascertained that the
microorganism propagated using the nitrogen suppliedby the ammonia gas aerated in the both results.

Fig. 6 The course of the log. cell density of microorganisms
NH3-N+

Amount of nitrogen x102(g/batch)

(b)
NH3-N

0.006

0

2
0

(a)

0.007

6

NH4 㪄㪥

NO2-㪄㪥

Org-N-

NO3 㪄㪥

4. Conclusions
The biofiltration using the present column packed
4with the solid filler containing the mixture of rice,3
sawdust, and seeding materials was effective in2
deodorizing ammonia gas with the loading rate of1
1.0x10-3 g-NH3/g-ds/d at the temperature of 30 oC.
0Moreover, ammonia gas deodorization by the anabolic
Input
Output & accumulation
Fig.7 The mass balance of nitrogen in Run A30
activity of heterotrophic bacteria was demonstrated
When the concentration of ammonia gas, 2000 ppm, using the liquid medium without nitrogen source except
was aerated, Run A30+ which is inoculating the the ammonia gas supplied.
microbe was changing like Run B30+, and the effect ofconsumption of the ammonia gas by a microbe was Reference
nothing. Moreover, cumulative emission of the carbon [1] H. W.Ryu, et al., J. Haz. Mate. 168(2009), 501–506
dioxide was about 0 by 5 d, and the activity of a [2] Y. Jun, et al., Biores. Tech., 100 (2009), 3869–3876
microbe was inhibited (data not shown). Since the cell [3]Sakuma T., et al., Water research, 42(2008),
density of Run A30+ increased only to 1/1000 at 3 d 4507–4513
552

Optimization of circuit and control parameters of a boost converter for Energy Harvesting
Student Number:10M18097

Name:Masanori TANAKA Supervisor:Kunio TAKAHASHI

エネルギーハーベスティング用昇圧コンバーターの回路パラメータと制御パラメータの最適化
田仲祐士

エネルギーハーベスティングの高効率化の為、昇圧コンバーターとキャパシターによるエネルギーハーベスターモデルが提案された。発電素子のポテンシャルエネルギーに対して蓄積されたエネルギーで定義されたエネルギー効率を理論的に解析する事で、回路パラメータ制御パラメータのエネルギー効率への影響が明らかにされた。最適な制御パラメータとしてデューティー比が解析的に、スイッチング周期が数値的に得られ、最適な回路パラメータが各パーツごとの様々な特性も含めて議論された。これをもとに設計された昇圧コンバーターと太陽電池を用いた実験により理論エネルギー効率の整合性が立証され、91 ％の高いエネルギー効率を達成した。

1

Introduction

2

Energy Harvesting with small solar cells or thermoelectric generators requires an eﬃcient storage
method. A DC-DC converter which operates a generating device at its Maximum Power Point (MPP)should be implemented for high eﬃciency. Designinghigh eﬃcient DC-DC converter for Energy Harvesting therefore requires considering not only transfer
eﬃciency but also Maximum Power Point Tracking(MPPT) and stored energy amount. In [1], a Solar
Harvester with a buck converter and a super capacitor is introduced and is optimized with the energy eﬃciency including the consideration. [1] achieves 85% of
the energy eﬃciency at 〜400[mW] of a PV module’s
output with an experimental setup. Unfortunately,
the energy eﬃciency in theoretical part of [1] is obtained as only numerical calculation so discussion for
maximizing the energy eﬃciency for disigning a buckconverter is poor. In [2] and [3], the energy eﬃciency
is introduced and discussed but they do not optimize
DC-DC converter to maximize the energy eﬃciency.
Thus, this study introduced an Energy Harvester witha capacitor as a storage device which has low Equivalent Series Resistance (ESR) for high eﬃciency. Additionally, a boost converter is used for MPPT because
it allows excellent transmission eﬃciency of electric
power [3]. The objective is to introduce an equivalent
Energy Harvester model, obtain the energy eﬃciency
analytically and discuss methodology for maximizing
the energy eﬃciency by focusing all parameters.

     







Analytical energy eﬃciency

In order to analyze electric behavior of an Energy Harvester in several switching times, equivalent circuit
model is introduced as Figure 2.



 
 







Fig 2: Equivalent circuit of an Energy Harvester
Power Source should be operated around its MPP,
therefore the i-v characteristics can be assumed to tangential line at the MPP. The i-v characteristics is obtained asv = 2VMP − RMP i
where VMP and IMP are power source’s output voltage and current at MPP, and RMP is deﬁned as
VMP /IMP .
In the same reason, the diode’s forward voltage vf
for the current i is assumed as
vf = VD + RD i.
Additionally, the capacitor as a storage device haslarge capacitance so that the capacitor’s voltage canbe assumed to be constant VC in one switching. Moreover, low resistant components of the boost converter
and the capacitor against RMP are required in order toimprove the energy eﬃciency. Thus, total resistance
where current pass at switched on can be assumed
to be nearly equal to total one where current pass at
switched oﬀ. This condition is expressed as





R = RMP + RL + RDS        !"    

#


$

  
 %  
 ≈ RMP + RL + RD + RC .


Kirchioﬀ’s voltage law at switch on and switch oﬀ
can be applied as        L$ dion+ Rion = 2VMP
dt

when switch is on and%  L

Fig 1: Maximum Power Point Tracking with a boost
converter53

dioﬀ+ Rioﬀ = 2VMP − VC − VDdtwhen switch is oﬀ. In order to analyze current behavior, constant switch on time Ton and switch oﬀ
time Toﬀ are considered. When the number of switching is deﬁned as n, current ﬂow at switch on ion(2n−1)
is obtained as ! 

 

2VMP − Rt
2V) e L + MP
R
R



where I2n−2 is a boundary condition at turning
switch from oﬀ to on, and switch oﬀ ioﬀ(2n) is obtained
as





I2n = I2n−1 e

−

RToff
L



I2n−2 =

I2n−1 =

MP

R
 2V

MP

−

RToff
L
RT

R(1 − e− L )




RTon

−

− RT
L

RToff
L

 







  



Voff

0

  

Ion

off
0

Egd = Ciss Vgs2
(1)
Esw =

Analyzed current behavior can be obtained as Figure 3 with experimental speciﬁcations and optimized
control parameters. Figure 3 indicates that top value
and bottom value of boundary conditions converge
from the initial condition immediately. Thus, steadystate current can be obtained using boundary conditions of equation (1) at n → ∞ as


RToff
VC,D 1 − e− L
2VRt e− L + MP and
ion = −
RTR−
R 1−e L
RTon
VC,D 1 − e− L
2V − VC,D
Rt
 e− L + MP

.
ioﬀ =
RT
R
−
R 1−e L



on
Tr

Time

off
Tf

However the FET operates as an ideal switch without RDS in the model. Gate drive loss Egd and switching loss Esw at the FET should be considered for Energy Harvesting. These energy loss are approximatedas(n−1)RT)
1−e− LR(1 − e) (n−1)RT
2VMP 
RTon+
1 − e− L e− L .
R
R



Fig 4: MOS-FET charac- Fig 5: Switching loss atteristics
current rise time and current fall time(n−1)RT)and1−e− L

VC,De− L (1−e−





RToff
2V − VC,D+ MP(1 − e− L ).
R

VC,D(1−e−





When initial condition I0 is given as 0, the recurrence equations can be analyzed as
 2V

 

Approximation of Switching loss

2VMP
RTon
(1 − e− L ) andR+  

Fig 3: Current for time using boundary conditions ofequation (1) and available speciﬁcations

where I2n−1 is a boundary condition at turning
switch from on to oﬀ and VC,D = VC + VD . Boundary
conditions of I2n−2 , I2n−1 and I2n have relationshipsof recurrence equations obtained as
RTon
L


 



2VMP − VC,D − Rt
2V − VC,D) e L + MP
R
R

I2n−1 = I2n−2 e−  

Current

ioﬀ(2n) = (I2n−1 −





Voltageion(2n−1) = (I2n−2 −andIon Voﬀ Tr,f6

(4)

where Ciss is input capacitance, Vgs is voltage ofcharging gate as shown Figure 4, Ion is current when
the switch is turning from on to oﬀ, Voﬀ is voltageof the diode and the capacitor, and Tr,f is total timeof current rise time and current fall time required forturning switch as shown Figure 5. Stored energy to
the capacitor should be considered with these energy
loss give as equation (4). On the other hand, an ideal
boost converter for Energy Harvesting must achieves
operating an power source at its MPP and storing theenergy to the capacitor without energy loss. Thus,
the energy eﬃciency of the Energy Harvester modelin one switching period T should be deﬁned as(2)Stored energy to the capacitor in one switching period can be calculated with equation (2). The energy
Eci is given as Toffioﬀ dt.
(3)
Eci = VCη=

Eci − Esw − Egd.
IMP VMP T(5)

Using equation (2) (3) (4), equation (5) can be expressed as

0

54

η=

−dT

)

Supression efficiency : ηos)(1−e
RMPVC gC,D(1−eRVC,DT(1 − e−T )−(1−d)T2
gC,D −2)− gC,D(1−d)(

Ion Voﬀ Tr,f + 6Ciss Vgs2−
6IMP VMP T(6)
V
RT
where gC,D = C,D , T =and d is duty ratio
VMP
L
Ton
expressed as. Duty ratio can be optimized anaT
lytically by diﬀerentiating equation (6) with respect
to d. Optimized duty ratio dgt is obtained as3

Voltage

0
100

101

Experimental result and Discussion

To validate theoretical energy eﬃciency and optimum
duty ratio, experimental energy eﬃciency for switching period T is obtained by measuring the capacitor’s voltage. In the ﬁrst experimental setup, constant voltage 2VMP =3.0[V] of MATSUSADA PL-362.2 and RMP =15[Ω] of the resistance are used asa power source with linear i-v characteristics. Additionally, L =470[μH] and RL =0.21[Ω] of Panasonic ELC18B471L, RDS =78[mΩ], Tr,f =7.0[ns] andCiss =480[pF] at Vgs =5.0[V] of TOSHIBA TPC6006H, VD =0.32[V] and RD =0.20[Ω] at 100[mA] of forward current ﬂow of TOSHIBA CMS16 are used forthe boost converter. Moreover, C =219[mF] andRC =6.0[mΩ] of BHC ALS3-224NP040 is used as thestorage device and charged from 7.45[V] to 7.55[V].

vps=2VMP-RMPi
vs=2VMP-Ri
MPP forthe system
VMP VMPR RMP~

gC,D= 50

with higher inductance L and lower resistance RL requires high magnetic permeable core which cause DC
superposition characteristics and iron loss with nonlinear inductance.

MPP for P.S.

0gC,D= 10

Fig 7: Relationship between T and ηos

MEHP for P.S.

VMP~

0.5 Monotonic ~decrease for T

102
~ RT
Normalized switching period : T = L

The optimum duty ratio dgt operates ﬁxing the center of the current oscillation on VMP /R. It indicatesthat the power source’s output point should be optimized by considering energy loss at the circuit resistance. This study deﬁnes the output point as Maximum Energy Harvesting Point as shown Figure 6.

R(2- RMP)VMP~

gC,D= 2

10-1




1 1
1
1 1 2 −T 2 −T−T
dgt = 1− log ( − )(1−e )+ ( − ) (1−e ) +e .
2 gC,D
2 gC,D
T

2VMP

1

2IMP

Current
Fig 6: Maximum Energy Harvesting Point (MEHP)
on i-v characteristics
Using optimum duty ratio dgt , the part of equation
6 is deﬁned asg2 (1−e−(1−dgt)T)(1−e−dgt T)
ηos = C,D
gC,D−2).
− gC,D(1−dgt )(
T(1 − e−T )Figure 7 shows that ηos is monotonic decrease forT.Circuit resistance and the FET’s energy loss should
be small. Moreover, the FET and the diode should beconsidered with reverse voltage which typically prevent forward energy transmission. So these should bechosen as low reverse voltage as possible to exceed
the capacitor’s voltage VC . On the other hand, lower
switching period T improve ηos but also increase the
FET’s energy loss. Using optimum duty ratio dgt ,relationship between switching period T and energy
eﬃciency η is plotted on Figure 9,12, 15 with solidlines. These ﬁgures indicate that optimum switching period achieving both of high ηos and low the
FET’s energy loss exist. Additionally, the inductor

Fig 8: Experimental setup
The experimental result indicates that approximation error of the FET’s switching loss occur at low
switching period area on Figure 9. On the other hand,at high switching period area, the experimental result
is nearly equal to theoretical one. In the second experiment, therefore, VMP =2.35[V] and IMP =74.6[mA] of
the solar cells as shown Figure 10 is used as a powersource with non-linear i-v characteristics as shown Figure 11.
The inductor is replaced to L =2.2[mH] andRL =0.88[Ω] of Panasonic ELC18B222L. The capacitor is charged from 9.55[V] to 10.05[V]. The experimental result indicates that approximation error of55

ELC18B471L

ELC18B102L

0.2

Eci
EMP

Current [A]0.5
Approximation errorof switching loss

470[μH]

0.1

0.1

0
0

2.2[mH]
0

1

2

0

10-6

10-5

10-4

5.0[mH]

0.1

0.025
0

Voltage [V]30

PMP=175[mW]

ever, energy eﬃciency is over 90% at a maximum andpossibility using high magnetic permeable core is validated. Maximizing energy eﬃciency requires optimization including magnetic permeability.

20

40
60
Current [mA]80

Fig 10: The solar cells and Fig 11: The solar cells’ i-v
halogen lights
characteristics

Energy efficiency : η1non-linear i-v characteristics occur at high switching
period area on Figure 12. However, in high eﬃciency
area from 3.16[μs] to 31.6[μs], both error is so small
and achieving 94% at a maximum. Theoretical energy eﬃciency are validated experimentally at higheﬃciency area.

0

Energy efficiency : η

10-7
Achieving 91% at
10[μs] and 31.6[μs]Theoretical energy efficiency
Experimental result

4

10-5

10-4

10-3

10-4

Conclusion

This study focuses on optimum control parametersof the boost converter for Energy Harvesting. Optimum control parameters are obtained as optimum
duty ratio analytically and optimum switching period
numerically. Fundamental information of designing answitching controller for Energy Harvesting is obtained.
Additionally, proposed Energy Harvesting model andtheoretical energy eﬃciency can help components selection of boost converter designing.

Theoretical energy efficiency
Experimental result

10-6

10-5

Switching period : T [s]

0

-0.5

10-6

Fig 15: Experimental result with the high magnetic
permeable core

Approximation error of
non-linear i-v characteristics

10-7

More than 6% ofefficiency reduction

0.5

-0.5

1

0.5

LDFM005502MT-V0E
1
2
Time [ms]Fig 13: Inductors for exFig 14: Frequency reperiments
sponse of RL circuit

VMP=2.35[V]IMP=74.6[mA]
02

0.2 Non-linear
inductance

10-3

Switching period : T [s]1

1

Theoretical current flow
Experimental result

Fig 9: Experimental result with linear i-v characteristics

2

0

1
2
Time [ms]Theoretical energy efficiency
Experimental result

-0.5
10-7

0.1

1.0[mH]00

Esw+Egd
EMP

ELC18B222L

0.2

0.2

Current [A]Energy efficiency : η

1

10-3

Switching period : T [s]Fig 12: Experimental result with the solar cells
In the third experiment, to discuss possibilityof an inductor with high magnetic permeable core,
L =5.0[mH] and RL =23[mΩ] of Nippon ChemiCon LDFM05502MT-V0E is used. The frequency response of ﬁrst order RL circuit as shown Figure 14
indicates that the inductor operates as high inductance under about 25[mA]. Therefore, constant voltage 2VMP =5.0[V] and RMP =100[Ω] are used as a
power source. The experimental result indicates thatabout 2% of energy eﬃciency decreasing is observed
which previous experiments cannot observed. How-

References[1] D. Dondi, et al., 2008, IEEE Trans. Ind. Electron., 55, 2759-2766.
[2] F. I. Simjee and P. H. Chou, 2008, IEEE Trans.
Power Electron., 23, 1526-1536.
[3] A. Chini and F. Soci, 2010, Electron. Lett, 46,
296-298.

56

Separation of Heavy metals from mine wastewaterby adsorption using Mongolian natural zeolites
Student Number: 10M18105

Name: Saori TANABE

Supervisor: Ryuichi EGASHIRA

ࣔࣥࢦࣝ⏘ኳ↛ࢮ࢜ࣛ࢖ࢺࢆ⏝࠸ࡓ྾╔࡟ࡼࡿ㖔ᒣᗫỈ࠿ࡽࡢ㔜㔠ᒓࡢศ㞳⏣㑓 Ἃ⧊
ࡲࡎࠊTsagaan Tsav㖔ᗋ⏘ࡢࢮ࢜ࣛ࢖ࢺヨᩱࡢ࢟ࣕࣛࢡࢱࣜࢮ࣮ࢩࣙࣥࢆ⾜࠸ࠊࡇࢀࡽࡢࢮ࢜ࣛ࢖ࢺࡀ࢝ࣝࢩ࢘
࣒ᆺࡢࢡࣜࣀࣉࢳࣟࣛ࢖ࢺࠊࣔࣝࢹࢼ࢖ࢺࠊࢩࣕࣂࢨ࢖ࢺ࡞࡝ࢆ୺ᡂศ࡜ࡍࡿࡇ࡜࡞࡝ࢆ᫂ࡽ࠿࡟ࡋࡓࠋࡘ࠸࡛
Oyu Tolgoi㖡㖔ᒣࡢᗫỈࢆ᝿ᐃࡋ࡚ࠊ㖡࡞ࡽࡧ࡟௜㝶ࡍࡿள㖄ࠊ࣐ࣥ࢞ࣥࢆྵࡴࣔࢹࣝỈ⁐ᾮࢆࠊୖグࢮ࢜ࣛ࢖ࢺヨᩱ࡟ࡼࡾฎ⌮ࡋࡓࠋ࠸ࡎࢀࡢࢮ࢜ࣛ࢖ࢺࡶࡇࢀࡽࡢ㔜㔠ᒓࢆ྾╔ࡋࠊ᭱኱࡛99 %⛬ᗘ㝖ཤ࡛ࡁࡓࠋࡲࡓࠊ௚
ࡢ㔠ᒓඹᏑ᮲௳ୗ࡛ࡶࢮ࢜ࣛ࢖ࢺ࡟ࡼࡿ྾╔ࢆ☜ㄆࡋࡓࠋavailable, among which ion exchange is considered
to be cost-effective if low-cost ion exchangers suchas zeolites are used.
Zeolites are microporous, aluminosilicate
minerals commonly used as commercial
adsorbents. The exchangeable cations in thezeolites can participate in ion-exchange processes.
The fact that zeolite exchangeable ions are
relatively innocuous and makes them particularly
suitable for removing undesirable heavy metal ionsfrom wastewater.
Mongolia is abundant in metal resources and thenatural zeolite deposits have been developed. It wasprospected that Tsagaan Tsav deposit of naturalzeolites should have a potency to produce about 4.8
million tons of zeolite minerals. However thedestination of the zeolite has not been fully
examined.
The use of natural zeolites as an adsorbent has
gained interest among many researchers. Althoughstill in developmental stage, T. Motsi, et al. studied
the actual treatment of mine wastewater from
Wheal Jane Mine by using Turkey natural zeolite.
They found that natural zeolite had the potential toeffectively remove heavy metals from relatively
dilute mine wastewater [2]. T. Bolortamir. et al.
studied wastewater treatment including chromiumby using Mongolian natural zeolites. They revealed
that Mongolian natural zeolites have potential toremove chromium (III and IV) from solutions [1].
This study aimed at the application of adsorption
using Mongolian natural zeolite to mine wastewater
treatment. As in its early stage, the characterizationof Mongolian natural zeolites and batch
equilibrium adsorption of model wastewater werecarried out.
2. Experimental
2.1 Characterization of natural zeolites
The used sample of natural zeolite was obtained
from Tsagaan Tsav deposit in Dornogovi province,
Mongolia (Table 1). The zeolites were crushed,ground and passed through 150150 μm sieves
and the moisture of the samples was controlled in
the desiccators with saturated ammonia chloride.
Base component of the natural zeolites were

1. Introduction
In Mongolia, the large deposits of gold, copper
ores, etc. have been found in 2000, discovered by
Ivanhoe Mines Ltd. of Canada. The deposits of themetals were estimated as 340 tons of gold, and 15
million tons of copper, and they may have great
influence on the commodity market in the world.
For the future, an Oyu Tolgoi copper deposit,
Tavan Tolgoi coal deposits and other ore deposit
are to be developed and the trade of these metals isplanned to drastically increase from around 2013.
However, the environmental contamination due tothe mining development has become serious.

Fig. 1 Generation of mine wastewater [5]When crushed or broken minerals in ore deposit
contact with air and water, it generate acidic water
called mine drainage. The sludge which has low
metal concentration wastes is also cause ofgeneration acidic water, infiltration water. Mine
wastewater means both mine drainage andinfiltrating water. It continues to flow out not only
during mining development but also mine closing(Fig. 1).
The neutralization method is generally used for
wastewater treatment. However this method has
been pointed out to have many problems, such as
generation of a lot of sludge, necessity of a largescale facilities, high cost requirement, etc.
The use of alternative low-cost materials as
potential sorbents for the removal of heavy metals
has been emphasized recently. Treatment processes
for metal contaminated waste streams include
chemical precipitation, ion exchange, adsorption,
ultra-filtration, etc; various treatment processes are57

Table 1 Information of Mongolian natural zeolites[1]SampleDepositSampling depth [m] Sampling date
CLP
Tsagaan Tsav60
09/2004
MOR Tsagaan Tsav60
09/2004
CHA
Tsagaan Tsav
150
12/2005
Table 2 Conditions of adsorption run
Mass of initial solution, L0 [g]Initial concentration of metals, C0 [g/m3]
Mass of zeolite, S [g]Contacting time, t [h]Initial pH of solution, pH0
Temperature, T [K]20
100 – 5000
1240
2.5 – 5300

Table 3 Principal characteristics of Mongolian natural zeolites
Surface area [m2/g]
Cation exchange capacity
㽢10-3 [mol/100g]
Si/Al molar ratio [-]

CLP
31.7

MOR
7.8

CHA
164.1

62 - 76

48 - 69

31 - 43

7.88

9.28

8.87

Table 4 The oxidized metal composition

final pH

Wt %
Al2O3
SiO2
SO3
K2O
CaO
Fe2O3
TiO2
Mn2O3

CLP
7.17
66.62
2.32
9.36
3.91
9.34
1.28
―

MOR
5.10
55.80
1.96
10.54
22.36
3.27
0.96
―

CHA
6.70
70.05
4.10
7.94
5.52
4.88
0.19
0.61

10
9
8
7
6
5
4
3
2
1
0
0

1

2

3

4

5

6

7

8

9

10

initial pH

qCu103 [mol/g-zeolite]

Fig. 2 pH change of CLP in Zn sol.
0.25
0.2
0.15
0.1
0.05
0
0

2

4

6
C [mol/m3]

8

10

12

Fig. 3 Adsorption isotherm of Cu with CLP
qCu103 [mol/g-zeolite]

carried out using X-Ray diffraction (XRD). Surface
elemental analysis was carried out with Energy
Dispersive X-ray Spectroscopy (EDX). Surface
areas were measured with nitrogen gas adsorption.
The maximum quantity of total cations, Cation
Exchange Capacity, was measured by Chapman
method (1965). The aqueous phases were analyzed
by ICP-AES (SPS 7800 Series, Seiko InstrumentsInc.). Copper (Cu), Zinc (Zn), and Manganese(Mn) were selected as a model heavy metals. Since
this study target at Oyu Tolgoi copper ore deposit,
mine wastewater will contain copper and basemetals such as Zn and Mn. Mine wastewater
generally contains sulfate ions, and the analyticalgrade reagents of CuSO4∙5H2O, ZnSO4∙7H2O andMnSO4∙5H2O (Wako Pure Chemical Industries,Ltd.) were used as the sources of the metals.
2.2 Batch adsorption studies
Batch adsorption studies were conducted by theconditions shown in Table 2. pH of the initialsolution was adjusted with sulfuric acid. The
mixture was shaken in 10010-6 m3 Erlenmeyer
flask in a shaker. The final concentration after
suction filtration and filtration with 0.45 μm
membrane filter was determined by ICP-AES. The
material balances of metals are represented by,
L0C0 = LC + Sq(1)
where L0 is the mass of initial solution, C0 is theinitial metal concentration, L is the mass ofsolution, C is the equilibrium metal concentration,
S is the mass of adsorbent, and q is the adsorbed
amount of metals per unit mass of zeolite. q wasdetermined by Eq. (1) with the assumption of L=L0.
To marshal the adsorption data, Langmuiradsorption model was used.
q q m u K u C /(1  K u C )(2)where qm is the saturated adsorbed mass and K is
the Langmuir constant. The isotherms wereexpressed by this equation.
3. Result and Discussion
3.1 Characterization of natural zeolites
From XRD measurement the frameworks of theused natural zeolites could be classified into three
species according to the major components, such as
Clinoptilolite (CLP), Mordenite (MOR), andChabazite (CHA). All zeolites have Ca typeframeworks. The Si/Al molar ratios were measured
by EDX analysis. The mass fraction of eachoxidized metal was determined with the assumptionthat all metals should exist in form of oxide metal
and is shown in Table 4. The measured cationexchange capacity and these results were shown in
Table 3. As expected from the results of EDX and
CEC, natural zeolites have impurity such as quarts,
Sanidine, Muscovite, and so on. It found that there
is general correlation between Si/Al ratio and CEC.
3.2. Batch adsorption studies

0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0

0.05

0.1

0.15

0.2

0.25

C [mol/m3]

Fig. 4 Adsorption isotherm of Cu with MOR

58

qCu103 [mol/g-zeolite]

The natural zeolite could adsorb all kinds ofmetals. The adsorption of sulfur content wasmeasured as well and the used zeolites could notadsorb it at all. To attain the adsorption
equilibrium it was found that 240 hours wereneeded. The pH was elevated from 3 to around 47 by the addition of zeolite since the zeolite
preferentially may adsorb H+ from solution [2], asshown in Fig. 2. The equilibrium pH decreasedslightly with initial concentration of metals.
Especially with MOR, the equilibrium pH tends torise up. The pH influences precipitation anddissolution of metals. The addition of the zeolites
causes the dissolved metals to precipitate, such as
Cu(OH)2, Zn(OH)2, and Mn(OH)2.
The adsorption isotherms of metals on thenatural zeolite are shown in Fig. 3 to 11. qm and K
are shown in Table 5. The results of pH0=5 andpH0=3 are almost same. The adsorption amountincrease with an increase in initial pH, pH0. The
reason for this is that competitive adsorption
between H+ ion and metal ions were occurred.
Cu In the case of adsorption with MOR,
equilibrium concentration of metal was 0. Since
the equilibrium pH tends to be higher and copperhydroxide has low solubility to water, it may
produce precipitation. Then, the most ofadsorption data on MOR were not fit to Langmuiradsorption isotherm. The adsorbed amountfollows the order: MOR, CLP, CHA. The legal
concentration of Cu2+ in effluent standard is 3
mg/L[4]. It is seen that 1 g of CLP, MOR and CHA
are able to treat 20 g of 200 mg/L, 600 mg/L and
200 mg/L solutions to below the legal
requirement, respectively.
Zn MOR may made zinc hydroxide precipitation.
The adsorbed amount follows the order: MOR,
CLP, CHA. The legal concentration of Zn2+ in
effluent standard is 2 mg/L[4]. It is seen that 1 g of
CLP, MOR and CHA are able to treat 20 g of 100
mg/L, 50 mg/L and 100 mg/L, respectively.
Mn The adsorbed amount follows the order:
MOR, CLP, CHA. The legal concentration of

0.25
0.2
0.15
0.1
0.05
0
0

Cu

Zn

Mn

zeolite
CLP
MOR
CHA
CLP
MOR
CHA
CLP
MOR
CHA

10

15

qZn103 [mol/g-zeolite]

0.2
0.15
0.1
0.05
0
0

2

4
6
C [mol/m3]

8

10Fig. 6 Adsorption isotherm of Zn with CLP
qZn×103 [mol/g-zeolite]

1
0.8
0.6
0.4
0.2
0
0

10

20

30

40

C [mol/m3]

qZn103 [mol/g-zeolite]

Fig. 7 Adsorption isotherm of Zn with MOR
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
0

5

10

15

C [mol/m3]

qMn103 [mol/g-zeolite]

Fig. 8 Adsorption isotherm of Zn with CHA
0.2
0.15
0.1
0.05
0
0

5

10

15

20

C [mol/m3]

Fig. 9 Adsorption isotherm of Mn with CLP

pH0=lowqm 㽢103
K [m3/[mol/gmol]
zeolite]
0.16
23.07
䇷
䇷
0.13
70.95
0.14
1.67
0.83
0.98
0.07
2.46
0.13
1.87
0.17
0.05
0.11
0.43

qMn103 [mol/g-zeolite]metal

C [mol/m3]

Fig. 5 Adsorption isotherm of Cu with CHA

Table 5 Principal characteristics of Mongolian naturalzeolites
pH0=highqm 㽢103
K [m3/[mol/gmol]
zeolite]
0.20
32.13
䇷
䇷
0.18
10.17
0.15
5.62
0.84
1.96
0.11
8.50
0.15
2.12
0.17
0.11
0.12
0.32

5

0.16
0.12
0.08
0.04
0
0

20

40

60

C [mol/m3]

Fig. 10 Adsorption isotherm of Mn with MOR

5980

Reference[1] Bolortamir. Ts, et al.; J. Chem. Eng. Japan, 41, 1003 (2008)[2] Motsi, T.et al.; Int. J. Miner. Process, 92, 42–48 (2009)[3] Web site ”Database of Zeolite Structures”
http://www.iza-structure.org/databases/[4] Environmental Bureau of the Tokyo Metropolitan Government
http://www.kankyo.metro.tokyo.jp/water/pollution/regulation/e
mission_standard/hazardous_materials_except.html[5] JOGMEC
http://www.jogmec.go.jp/

qMn103[mol/g-zeolite]

Mn2+ in effluent standard is 10 mg/L[4]. It is seenthat 1 g of CLP, MOR and CHA are able to treat 20
g of 200 mg/L, 50 mg/L and 50 mg/L, respectively.
Despite that the CEC follow the order: CLP,
MOR, CHA, adsorbed amount didn’t follow thesame order. It is because that there is an effect ofprecipitation of metal hydroxide.
Adsorption from binary wastewater
The adsorption of Zn and Mn in the binary
system with CLP was carried out (Fig. 12). The
gray line and dashed line shows the adsorbed
isotherms of Zn and Mn in the single system andblack key shows total adsorption amount of Zn and
Mn in binary system. The qm,Zn and qm,Mn havealmost the same adsorbed amount to black key. It isconsidered that Zn and Mn are adsorbed on sameadsorption site.
Adsorption from ternary wastewater
The results of adsorption from ternary
wastewater were shown in Fig. 13 to Fig. 15. It isclear that adsorbed amount from ternary solution
were lower than from single solution. With CLP, it
was considered that Zn and Mn were adsorbed onsame adsorption site in binary system, however, theresults in ternary system was different. Because
there may be other effects, such as competitive
adsorption between H+ and metal ion, change ofpore size, etc. With MOR, Cu may producehydroxide and the concentrations after equilibrium
were 0. The adsorption isotherm of total metal
shown as black lines were inaccurate due tohydroxide precipitation, and there is not leaving
much room for discussion.
4. Conclusion
XRD pattern showed that Mongolian naturalzeolites have Clinoptilolite, Mordenite andChabazite mainly with Ca-type. Silica/Alumina
molar ratio was 7.88, 9.28, and 8.87, and Cation
Exchange Capacity was 62-7610-3, 48-6910-3,
and 31-4310-3 mol/100g-zeolite, and surface area
were 32, 8, 164 m2/g-zeolite with CLP, MOR,
and CHA respectively. These zeolites could
remove copper, zinc and manganese. The
adsorption amount increase with an increase ininitial pH. Removal of the heavy metal ions was
not only due to ion exchange but also due toprecipitation of metal hydroxides. In binary system
of Zn and Mn, it was considered that Zn and Mn
adsorbed on same adsorption site with CLP.

0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
0

20

40
C [mol/m3]

6080

q 103 [mol/g-zeolite]

Fig. 11 Adsorption isotherm of CHA in Mn sol.
0.16

Zn

0.12

Mn

0.08
0.04
0
0

5

10

15

C [mol/m3]

Fig. 12 Adsorption isotherm of Zn and Mn

q×103 [mol/g-zeolite]

0.25

Total

Cu

0.2

Zn

0.15

Mn

0.1
0.05
0
0

10

20

30

C [mol/m3]

Adsorption isotherm with CLP from ternary system 㻌

Fig.13q103 [mol/g-zeolite]

1

Zn

0.8
0.6
0.4

Total

0.2

Mn

0
0

5

10

15

20

25

C [mol/m3]

Fig.14

Adsorption isotherm with MOR from ternary system 㻌q103 [mol/g-zeolite]

0.2

Cu

0.15

Total

Zn
0.1

Mn

0.05
0
0

5

10

15

20

25

C [mol/m3]

Fig.15

Adsorption isotherm with CHA from ternary system 㻌Nomenclature
CLP: Clinoptilolite, MOR: Mordenite, CHA: Chabazite, L0: Massof initial solution [g], C0: Initial concentration of metals [g/m3], C:
Final concentration of metals [g/m3], S: Mass of zeolite [g], t:Contacting time [h], pH0: Initial pH of solution, pH: equilibrium
pH, T: Temperature [K], q: adsorbed amount [mol/g-zeolite], qm:
maximum adsorption amount [mol/g-zeolite], K: Langmuir constant[m3/mol]

60

Study on the Sulfuric Acid Resistance of Cementitious Material
Using OPC-γ-C2S-Quartz with Autoclave Curing
Student Number: 10M18111 Name: Yuriko TSUBURAYA Supervisor: Nobuaki OTSUKI
࣮࢜ࢺࢡ࣮ࣞࣈ㣴⏕ࡋࡓ OPC-γ-C2S-ࢣ࢖▼ᚤ⢊ᮎ⣔ᮦᩱࡢ⪏◲㓟ᛶ࡟㛵ࡍࡿ◊✲
ᅭ㇂ⓒྜᏊ
ࡍ࡞ࢃࡕ㸪
OPC-J-C2S㏆ᖺ㸪J-C2S ࢆΰ࿴ᮦ࡜ࡋ࡚ࢥࣥࢡ࣮ࣜࢺᮦᩱ࡟ῧຍࡍࡿ◊✲ࡀὀ┠ࡉࢀ࡚࠸ࡿ㸬ࢣ࢖▼ᚤ⢊ᮎ⣔ࡢᮦᩱ࡟࣮࢜ࢺࢡ࣮ࣞࣈ㣴⏕ࢆ⾜࠺ࡇ࡜࡛㸪ᑍἲᏳᐃᛶཬࡧ໬ᏛⓗᏳᐃᛶࢆ᭷ࡍࡿ࡜࡜
ࡶ࡟እ㒊ຎ໬ᅉᏊ࡟ᑐࡋ࡚㧗࠸᢬ᢠᛶࢆ♧ࡍࡇ࡜ࡀᥦ᱌ࡉࢀ࡚࠸ࡿ㸬ᮏ◊✲࡛ࡣ㸪J-C2S ࢆΰ࿴ࡋ࣮࢜
ࢺࢡ࣮ࣞࣈ㣴⏕ࡋࡓࢭ࣓ࣥࢺ⣔ᮦᩱࢆ⏝࠸㸪◲㓟ຎ໬࡟ᑐࡍࡿ⪏ஂᛶࢆᐇ㝿ࡢ Ἠᆅཬࡧᐇ㦂ᐊ⎔ቃ࡟᳨࡚ウࡋࡓ㸬ࡑࡢ⤖ᯝ㸪᭱ࡶ⪏◲㓟ᛶࢆ᭷ࡍࡿ᭱㐺࡞J-C2S ࡢΰ࿴⋡ࢆỴᐃࡋ㸪OPC-J-C2S-ࢣ࢖▼ᚤ⢊ᮎ⣔ᮦᩱࡢᐇ⏝໬ࢆ♧၀ࡋࡓ㸬the severe environment, for concrete and mortar has
not been investigated yet. Therefore, this study aims
(1) to evaluate effects of the γ-C2S and Quartz
substitution ratio on the sulfuric acid resistance in
autoclave cementitious material. And, (2) to evaluate
the usefulness of a laboratory test, comparing an
actual environment testing to an accelerating test in alaboratory has been carried out. (3) To propose themixed proportions for improving the initial
compressive strength when removed from molds.

1. Introduction
Nowadays, sulfuric acid attacks are found in the hotspring or sewerage pipe1). The sulfuric acid attack on
the concrete structures are the serious problems. To
obtain the durability, concrete needs to havedimensional stability, chemical stability, and highresistance against external aggressive ions.
Furthermore, the use of concrete in engineered
barriers is being considered in the construction ofradioactive waste disposal facilities. In thesecircumstances, concrete needs the high durability. For
these purposes, it is obvious that high durability
concrete is in demand.
On the other hand, the research on the highdurability of the cement-based material using
γ-2CaO•SiO2 (hereafter”γ-C2S”) is focused. Initial
compressive strength is small mixed γ-C2S, because
γ-C2S does not react with water under normal curing.
However it reacts in high temperature and highpressure. In the cement-based material mixed with
γ-C2S, the amount of tobermorite (5CaO•6SiO2•5H2O)
which has high shrinkage resistance is increased after
conducting the autoclave curing. Saito et al.2) haveproposed the concept of material design for highdurability cement-based material in high shrinkage
resistance. Using autoclave curing, tobermorite is
generated in the hardened body and permeability
resistance to external aggressive ions such as chloride
ion and sulfate ion has been obtained and it becomeshigh shrinkage resistance in the hardened body.
However, the experiment on the durability
performance especially in the sulfuric acid which is

SiO2
OPC
γ-C2S
Quartz

21.6
35.0
93.3

2. Experimental Methodology
2.1 Materials and Mix proportions
The materials used in this study as binder are
Ordinary Portland Cement (OPC), γ-C2S and Quartz.
Table.1 shows the chemical composition of thesematerials. Table.2 shows the mixed proportions ofconcrete specimens and the symbols. The name of
cement paste and mortar specimens are also usedsame name with concrete. The mixed cement paste,mortar, cylinder concrete and concrete pile were castin a mold that volume are
1cm×1cm ×8cm,
4cm×4cm
×16cm,
ϕ10cm×20cm
andϕ20cm×30cm×thickness4cm respectively.
2.2 Curing
After casting, initial curing with 100% RH at 65ºC
is done for 4 hours. Then the molds are removed fromthe specimen and autoclave for 8 hours under 1MPa at
180ºC. In addition, non-autoclave specimen, only
OPC which is assumed generally cement-basedmaterial, was named after “Ref”.

Table.1 Chemical composition and Physical property of materials
Density
Al2O3
Fe2O3
CaO
SO3
f.CaO
(g/cm3)
5.08
2.93
64.5
1.95
0.4
3.16
1.7
0.10
61.9
3.01
1.6
1.4
2.66
61

Blaine(cm2/g)
3310
3060
3870

Name
Q:γ-C2S:OPC

W/B
(%)

Ref
50-0-50
50-20-30
50-30-20
50-40-1040-0-60
40-36-24
30-0-70
30-42-28

32.9

Table.2 Mixed Proportions and Specimens Symbols
Quantities (kg/m3)
Autoclave
Curing
W
OPC
γ-C2S
S*
G*
Quarz
MT150
148
450
0
924
907
0
0.00
×
148
225
0
906
889
225
3.37
○
148
135
90
904
887
225
2.88
○
148
90
135
903
881
225
2.68
○
148
45
180
902
886
225
2.56
○
148
270
0
910
893
180
3.37
○
148
108
162
906
889
180
2.59
○
148
315
0
913
896
135
4.27
○
148
126
189
909
892
135
3.00
○
*S : Fine aggregate, G : Coarse aggregate

2.3 Sulfuric Acid Exposure Test
After autoclave curing, the designed cement paste
and mortar specimens were exposed to the solution of
H2SO4 with pH1 at 20ºC for 38 weeks in thelaboratory. In order to maintain pH value, the solution
was exchanged every 1week. On the other hand, theconcrete piles were exposure in the hot spring at
Kusatsu in Japan with pH1.7-2.0 at 50-60ºC for 442
days (Figure.1).
2.4 Evaluation on Performance
Compressive strength test was carried out according
to JIS A 1108 using cylinder specimen(ϕ10cm×20cm) before exposure. And after 38 week
exposure, compressive strength test was also carriedout. After that, remained ratio of compressive strength
was calculated using following formula (1).

Fig.1 During exposure in hot spring

3. Result and Discussion
3.1 Compressive Strength Before exposureand After exposure
In figure.2, the left side bar shows compressive
strength of concrete cylinder before exposure test. As
can be seen, when replacement ratio of Quartz and
γ-C2S decreased, the compressive strength has
increased. Figure.2 in right side bar shows
compressive strength of concrete cylinder after
exposure test. As can be seen, when replacement ratioof Quartz decreased, the compressive strength has
increased. This result is the same tendency withcompressive Strength of concrete cylinder before
exposure. On the other hand, in case of before
exposure, compressive strength is decreased, whenpercentage of γ-C2S is increased. However in case ofafter exposure, compressive strength is increased,
when replacement ratio of γ-C2S is increased. Figure.2
in line graph shows remained ratio of compressive
strength. It should be noted that effect of Quartz tocompressive strength after exposure is small; however
effect of γ-C2S is very big and important. Remained
ratio of compressive strength is increased, whenpercentage of γ-C2S is increased. Therefore, it isconfirmed that γ-C2S is very important to increase thecompressive strength after exposure.

(After exposure / Before exposure)×100(%) (1)During submerging test in the laboratory, theweight of specimens were measured on the 1, 2, 3,
5days, 1, 2, 3, 5, 7, 10, 13, 20, 26 and 38 weeks. And
in the hot spring specimens were measured the weighton the 53㸪115㸪349 and 442 days.
The identification of amorphous material by using
Fourier Transform Infrared Reflection (FT-IR) was
conducted (frequency range 7800-350cm-1). In order
to measure amorphous material like gel on thespecimen surface, Attenuated Total Reflection (ATR)
was conducted.
The identification of reactive products by using
X-ray diffraction (XRD) was conducted. The samples
were grinded to give a sample, which were cut at
surface part (0-2mm) and core part (8-10mm) fromexposed surface. The samples were scanned between
5˚-70˚ with an X’Celerator detector.
In order to measure the initial compressive strength
when removed from molds, compressive strength test
after demolded (before autoclave curing) was carriedout according to JIS A 1136 using concrete piles(ϕ20cm×30cm×thickness4cm).

62

AfterExposure

80

80
70

60
40

60

-40
In Hot Spring

-7.5

-30

In Laboratory

-6
-4.5

-20

-3

-10

20
0

-9

Weight chabge in Laboratorey
(%)

100

90

Remained ratio

Remained ratio(%)

Compressive Strength (MPa)

BeforeExposure

-1.5

0

50

Fig.2 Compressive Strength Before
and after exposure, and Decreasing ratio

0

Fig.3 Weight Change After 38 weeks Exposure
Before exposure
After exposure-Surface
After exposure-Core

GypBeforeExposure

After
ExposureCa(cm-1)

1200 1100 1000 900

Gyp

Gyp
γ

Si-O
Ref
50-0-50
50-40-10

Weigth Change in Hot spring (%)

120γ-(SiO)n(cm-1)

28.5

1200 1100 1000 900

29

(2θ)
29.5

Ref

30

28.5

(2θ)
29 29.530

28.5

30-42-28(2θ)
29 29.530

50-40-10

Fig.5 XRD pattern (28.5-30°)

Fig.4 FT-IR spectra at Surface partAnd in figure.3, the more amount of Quartz, the lessweight change. And the more amount of γ-C2S shows
the less of weight change. This result is the sametendency with before exposure. Therefore, it isobvious that, the accelerating test in a laboratory isuseful comparing the actual environment testing.

3.2 Weight change Exposure
In figure.3, the left side shows weight change ofmortar for 38 weeks exposure in the laboratory. In
figure.3,
when Quartz percentage changed from 30% to 50%,
the weight change is less. Furthermore, in case ofsame percentage of Quartz, it could be confirmed thatthe sulfuric acid resistance has improved by increasing
the ratio of γ-C2S. It is contemplated that hydration
products which are tobermorite and other products arechanged due to replacement ratio of γ-C2S or Quartz.
It means 50-30-20 and 50-40-10 specimens have highresistance against sulfuric acid. Particularly, in
50-40-10 specimen, weight loss reduced by 84%
compared to Ref specimen.
Figure.3 in right side shows weight change ofconcrete pile for 442 days exposure in the hot spring.
By comparison, in the laboratory and in the hot spring,
the weight of specimens which exposed in laboratory
is decreased more than in hot spring due to low pH.

3.3 Reactive Products after Exposure testof cement paste
Figure.4 shows the FT-IR spectra of surface part
before and after exposure. Before exposure, Si-O
bonds which were comprised in tobermorite wereobserved except for Ref specimen. On the other hands,
after exposure, -(SiO)n- bonds which were comprised
in silica gels were observed except for Ref specimen.
And comparing 50-0-50 to 50-40-10 specimens, silica
gel peak becomes strong in 50-40-10 after exposure.
Figure.5 shows the XRD patterns ranged from
28.5-30°.
In the figure, “Gyp” corresponds togypsum which is an expansive product, “γ” is γ-C2S
and, “Ca” is calcite.

Fig.6 Concept of material deign for high resistance against sulfuric acid attack
63

Name
Q:γ-C2S:OPC
Series
W/B
SeriesHC

Table.3 New Mixed Proportions and Specimens Name
Quantities (kg/m3)
W/B
(%)
W
OPC
γ-C2S
QuartzFA
S*1

ϩ50-0-50
ϩ50-20-30

26.0

HC50-0-50
HC50-20-30

32.9

148
148
148
148

285
171
225*2
135*2

0
114
0
90

285
285
225
225

0
0
0
0

790
787
855
853

*1=S :Fine aggregate, G :Coarse aggregate

Compressive
Strength(MPa)

40
30
20

787
784
851
850

MT
150
5.69
4.55
3.37
3.37

Autoclave
Curing○○○○*2=Early-strength cement

-5

50

15MPa

10

Weight Change(%)

60

G*1

-4
-3
-2
-1
0

0

Fig.7 Compressive Strength when Remove from moldFig.8 Weight Change After 20 weeks exposure

In 50-40-10 specimen, γ-C2S was disappeared after
exposure at surface part, on the other hand, γ-C2S
existed in the core part after exposure. However, in
30-42-28 specimen, γ-C2S does not exist after
exposure in surface and core part due to deterioration
until core part. This result is attributed that dissolutionof γ-C2S and generation of silica gel like as shown inEq.(1) .
γ-2CaO•SiO2+4H++nH2O
Ѝ 2Ca2++SiO2•nH2O+2H2O (1)From these results, it could be concluded that theamount of tobermorite or residual γ-C2S after
autoclave curing change to silica gel and C-S-H gel at
the surface part. Therefore, it can be considered thatthe surface part was densified due to generation ofsilica gel and C-S-H gel in case of the high γ-C2S andquartz substitution ratio (such as 50-30-20 and
50-40-10 specimens)(Figure.6).

Furthermore, figure.8 shows weight change after 20
weeks exposure. There is nothing much difference
between original mixed proportions and new mixed
proportions. Therefore, it was confirmed that low W/B
and using early-strength cement show the good effectto improve compressive strength when remove frommolds.
4. Conclusion(1) In case of quartz substitution ratio 50% andγ-C2S substitution ratio more than 30% with
autoclave curing, the high sulfuric acid resistancehas obtained. Because the sulfate ion penetration
was small due to tobermorite or residual γ-C2S
change to silica gel and C-S-H gel at the surface
part, then the surface part was densified due togeneration of silica gel and C-S-H gel.
(2) The accelerating test in a laboratory and theactual environment testing show the sametendency.
(3) In order to improve the initial compressive
strength when removed from mold, using low
W/B with autoclave curing and early-strength
cement showed the good effect to improve theinitial compressive strength.
Therefore, in the view point of application, thecement based material using OPC-γ-C2S-Quart can beable to put in the practice.

3.4 The Improvement of initial compressive
strength when removed from molds
In the pre-casted concrete pile, the initial
compressive strength when removed from molds,
needs more than 15MPa. Therefore, to improve theinitial compressive strength when removed frommolds, new mixed proportions which were low
Water-Binder ratio (hereafter W/B) and use
early-strength cement were proposed. Table.3 shows
the mixed proportions of new concrete specimens andthe symbols.
Figure.7 shows the initial compressive strength
when removed from molds. 50-0-50 and 50-20-30
specimens are low compressive strength whenremoved from molds which are original mixed
proportions. On the other hand, ϩ50-0-50,
ϩ50-20-30, HC 50-0-50 and HC 50-20-30 are
improved compressive strength.

࠙Referenceࠚ
1) H. Matsusita et al., : Classification of Probability of
Deterioration of Concrete by Sulfate Attack Based on
Investigation Results of Sulfate Content of Ground, Doboku
Gakkai Ronbunshuu E,Vol.66, No.4, pp.507-519, 2010.12
2) T. Saito et al.,: Carbonation Reaction of Calcium Silicate
Hydrates by Hydrothermal Synthesis at 150˚C in
OPC-γ-Ca2•SiO4- α ̺ quartz System, Journal of Advanced
Concrete Technology, Vol.5, No.3, pp.333-341(2007)64

Production of bioethanol from polysaccharides in the red seaweed
Student Number: 10M18134 Name: Kanami NAKAMURA Supervisor: Kiyohiko NAKASAKI Co-supervisor: Sachio HIROSE⚃⮺ਛᄙ♧㘃߆ࠄߩࡃࠗࠝࠛ࠲ࡁ࡯࡞↢ᚑਛ᧛ ᄼ⟤ㄭᐕ‫ޔ‬⍹ᴤઍᦧΆᢱߣߒߡ㘩♳ߣ┹วߔࠆߎߣߩߥ޿ේᢱ߆ࠄߩࡃࠗࠝࠛ࠲ࡁ࡯࡞↢ᚑ߇ᦼᓙࠍ㓸߼ߡ޿ࠆ‫⎇ᧄޕ‬ⓥߢߪ‫ޔ‬㘩♳ߣ┹วߖߕ‫ࠍࡦ࠾ࠣ࡝ޔ‬฽߹ߥ޿ߚ߼ߦ㜞ᐲߩ೨ಣℂ
ࠍᔅⷐߣߒߥ޿ේᢱߣߒߡᶏ⮺ߦ⌕⋡ߒ‫࡮㉄ޔ‬㉂⚛ࠍ૶↪ߒߚ♧ൻߣ㉂Უߦࠃࠆ⊒㉂ࠍ⚵ߺวࠊߖࠆߎߣߢ⚃⮺ਛᄙ♧㘃߆ࠄ 55.0 g/L ߩ㜞Ớᐲࡃࠗࠝࠛ࠲ࡁ࡯࡞ࠍ↢ᚑߒߚ‫ޔߦࠄߐޕ‬㉄߿㉂⚛ࠍ૶↪ߖߕߦ‫⮺⚃ޔ‬ਛᄙ♧㘃ࠍ♧ൻߔࠆߎߣࠍ⋡ߑߒߡ‫ࡦ࠲ࠢ࡜ࠟޔ‬ಽ⸃㉂⚛ࠍ↢↥ߔࠆㆮવሶ⚵ߺ឵߃㉂Უࠍഃ⵾ߒߚ‫ޕ‬73.3, 227 and 13.6 units/g, respectively. It was
ascertained that Meicelase does not contain galactanhydrolyzing enzyme.
Saccharomyces cerevisiae IAM 4178 was used in
the fermentation of seaweed hydrolysate.

1 Introduction
In recent years, bioethanol has attracted attention as
an alternative to petroleum-derived fuel. The efficient
production of ethanol from inedible biomass orbiomass that people do not find palatable has long
been considered a crucial requirement for the effective
utilization of these materials. Many studies have been
performed on the production of ethanol fromlignocellulosic materials, such as wood, sugarcane
bagasse, switchgrass, rice straw, and wheat straw. The
presence of lignin in lignocellulosic materials makes
cellulose and hemicellulose resistant to hydrolysis,
and thus, various pretreatment methods to accelerate
the saccharification of polysaccharides have been
contrived. On the other hand, utilization of biomass
without lignocellulosic materials such as corn andsugarcane for the ethanol production caused
competition with the usage as food.
In this study, seaweed that is not utilized as food
was proposed as one of the most promising biomass
materials that can be easily converted to ethanol, sinceseaweeds are known to contain low concentration oflignin. It is expected that the production cost andenergy can be saved by the use of the seaweed. Agar
weed belonging to the red seaweed was used as thesubstrate and high concentration of bioethanol was
produced in the following saccharification andfermentation. Then, for the purpose of thesaccharification of the red seaweed without using acidand enzyme, recombinant yeast that produce
galactan-degrading enzyme was developed.

2.1.2 Enzymatic saccharification of red seaweed
Enzymatic hydrolysis was carried out to produce
glucose from agar weed. Agar weed in 0.1 M citricacid buffer solution was added to the filtered andsterilized enzyme solution. The hydrolysis was carriedout at 50 °C for 120 h under static conditions. Glucose
concentrations were determined by the HPLC method.
2.1.3 Combined saccharification of red seaweed
Combined saccharification, that is, the acidhydrolysis of certain polysaccharides followed by the
enzymatic hydrolysis of other polysaccharides, wasapplied to obtain high concentrations of sugars for use
in the following fermentation with yeast. For the acidsaccharification, agar weed was added to 2% sulfuric
acid, and the mixture was autoclaved at 121 °C for 30
min. After autoclaving, the pH value of the reaction
mixture was adjusted to approximately 5.5 by adding
calcium carbonate. Next, the Meicelase solution wasadded aseptically to the reaction mixture. The
enzymatic hydrolysis was carried out by incubating
the reaction mixture statically at 50 °C for 120 h. After
combined saccharification, concentration of glucose
was measured using the HPLC and concentration ofgalactose was measured using the F-kit for
Lactose/D-Galactose.

2 Materials & Methods
2.1
Productionofbioethanol
frompolysaccharides in the red seaweed
2.1.1 Red seaweed, enzyme, and yeast
Agar weed (Gelidium elegans Kuetzing) was used
as representatives of red seaweeds (Fig. 1). The dried
agar weed was washed once using distilled water inthe laboratory, and dried again at 60 °C for 2 days. The
dried agar weed was powdered using a mill to lessthan 0.5 mm in diameter before use.
Meicelase, a commercial enzyme was used forsaccharification of the glucans in the seaweeds, suchas cellulose, cellobiose and starch. The cellulase,cellobiase and amylase activities of Meicelase were

Agar weed
䋨Red seaweed䋩Fig. 165

Composition
GlucanStarchCellulose

21.8%

Galactan
AgaroseCarrageenan

26.5%

Crude fiber

12.3%

NFE

70.9%

Crude protein

11.6%

Crude fat

0.7%

Crude ash

4.6%

Composition of agar weed.

2.1.4 Fermentation of red seaweed hydrolysates
The seaweed hydrolysates obtained by the
combined saccharification was mixed with Polypepton,
yeast extract, and S. cerevisiae IAM 4178, which wasprecultured on an YM agar plate at 30 °C for 36 h andcollected using a sterilized spatula to prepare thestarting material for fermentation. The fermentation
was carried out at 30 °C for 5 days under static
conditions, and was ceased when almost all
fermentablesugarswere
consumed.
The
concentrations of glucose, ethanol, galactose andglycerol, the byproduct in the fermentation with yeast
in the substrate were measured.

2.2.2 Transformation of yeast by a plasmid
includingE-agarase gene
Genes of agaA and agaB were introduced to thepESC-HIS vector individually and pESC-HIS with
agaA or agaB was introduced to YPH499. Precultureof OA-2007 was prepared using N. B. medium by
incubating at 30 °C for 24 h and then, The
strainOA-2007 was cultivated using liquid agarose
medium at 30 °C for 48 h. Cells were harvested andgenomic DNA was extracted. Genes of agaA andagaB were amplified by PCR. For the amplification of
agaA,
forwardprimer,ACTAGTATGCAGCCAATCAGTTGG and reverse
primer, GAGCTCTTAGTGGGTGAAGTTT were used.
For the amplification of agaB, forward primer,
GGATCCATGAAAAAAATCACTTCAT and reverse
primer, GGATCCATGAAAAAAATCACTTCAT wereused. PCR amplified fragment of agarase genes andpESC-HIS were ligated using DNA ligation kit. The
yeast, YPH499 was transformed by the constructed
plasmid using FastͿ-Yeast Transformation kit.

2.2 Development of agarase producing yeast
Employment of acid and/or reagent grade of
enzyme can increase the cost for the ethanol
production. In order not to use the acid and enzyme,
production of agarose degrading enzyme from yeast
was attempted.
Structure of agarose was shown in Fig. 2. Agaroseisderivedfrom
D-galactose
and3,6-anhydro-L-galactose units, which condense
through D-1,3 and E-1,4 glycosidic bonds andhydrolyzation of agarose generates D-galactose,
3,6-anhydro-L-galactose and hetero-oligosaccharide
composed of two or more saccharides. Agarase is the
mixture of enzymes which hydrolyze D-1,3 and E-1,4
glycosidic bonds in the agarose and each of the arecalled D-agarase and E-agarase, respectivity.
Hydrolyzation of agarose by D-agarase generates
agaro-oligosaccharide of which component unit isagarobiose of non-reducing sugar and hydrolyzation of
agaroseby
E-agarase
generates
neo-agaro-oligosaccharide of which component unit isneoagarobiose of reducing sugar.
At first, a E-agarase gene derived fromagar-degrading bacteria was transformed to yeast anddegradation of agarose by E-agarase produced by therecombinant yeast was carried out.

2.2.3 Degradation of agarose by crude enzyme
from E-agarase producing yeast
Crude enzyme was prepared from E-agarase
producing yeast and degradation of agarose using thecrude enzyme was carried out. Recombinant yeast
strains were inoculated to SD(-HIS) medium andcultured statically at 30 °C for 24 h. Then, the culture
was inoculated to SG(-HIS) medium and shaken at
150 rpm at 30 °C for 48 h. After the cultivation, Cells
were harvested by centrifugation (3000×g, 5 min) andwere washed by phosphate buffered saline (pH=7.0).
After the washing, cells were suspended phosphatebuffered saline. Glass beads of 0.2 g with diameter ofD-galactose

2.2.1 Strains and vector
The E-agarase gene was derived fromagar-degrading bacteria, Cellvibrio sp. OA-2007.
OA-2007 was isolated from activated sludge sampled
in Kochi prefecture. DNA sequences of the genes
encoding two kinds of E-agarase were determined andnamed agaA and agaB, respectively. DNA sequence of
agaA doesn’t include the sequence corresponding to
the signal peptide and E-agarase produced from agaA
accumulates inside the cell. On the other hand, DNA
sequence of agaB includes the sequencecorresponding to the signal peptide and E-agarase
produced from agaB is secreted to outside of cells.
Saccharomyces cerevisiae YPH499 which is ahistidine-requiring yeast strain was used as a host. A
vector of pESC-HIS was used to introduce each of
agaA and agaB gene into the yeast (Fig. 3).

3,6-anhydroL-galactose

agarobiose
neoagarobiose

Fig. 2 Structure of the agarose molecule and two typesof product derived from digestion with agarases [1].

Fig. 366

Structure of the pESC-HIS vector [2].

produced in the fermentation using the hydrolysate
after only enzymatic saccharification and 0.03
g-glycerol/g-glucose of glycerol was produced.
Therefore, it was confirmed that not only the ethanol
but also glycerol concentration were increased by theadditional acid saccarification of the agar weed.
Higher productivity of glycerol was achieved in thefermentation with agar weed after combinedsaccharification than that after only enzymaticsaccharification. It was considered that the yeast
produced more amount of glycerol to regulate theosmotic pressure since osmotic pressure in themedium would have increased by the additional
product generated in the acid saccharification.
These results suggested that the combinedsaccharification enables the production of fermentablesugars at high concentrations by thoroughlyhydrolyzing the polysaccharides in the agar weed, thusincreasing the ethanol concentration accordingly
though productivity of glycerol as byproduct increased
simultaneously.

0.5 mm was added to 1 mL of the suspension in 1.5
mL microtube. Then, vortexing of the microtube for 1
min followed by cooling down on ice for 4 min was
repeated three times to disrupt the cells. After the
disruption of the cells, the microtube was centrifuged
and supernatant was used as crude enzyme.
Crude enzyme solution and agarose powder wereadded to phosphate buffered saline and shaken at 150
rpm at 30°C. Neo-agaro-oligosaccharide produced by
the degradation of agarose was qualitatively analyzed
by thin layer chromatography (TLC) using silica gelplate 60 [3].

3 Results & Discussion
3.1
Productionofbioethanol
frompolysaccharides in the red seaweed
3.1.1 Saccharification of agar weed
Fig. 4 represents the glucose and galactose
concentrations after the enzymatic saccharificationand combined saccharification of agar weed. The
concentration of glucose obtained after enzymaticsaccharification and combined saccharification were49.0 and 71.2 g/L, respectively, which corresponds toa conversion of 67.4 and 98.0% of the glucan, when
the conversion of glucan is defined as the ratio of theamount of glucose produced by saccharification to thepotential amount of glucose in the agar weed. Fromthese results, combined saccharification was able tohydrolyze nearly all of glucan. In addition, thegalactan that was not hydrolyzed in the enzymaticsaccharification could be hydrolyzed to galactose by
the acid saccharification.
More glucan was hydrolyzed by the combinedsaccharification than the saccharification with only
enzyme. It was considered that starch in the agar weed
was pregelatinized or hydrolyzed by the acidsaccharification.
These
resultssuggestedthat
combinedsaccharification enables the production of fermentablesugars at high concentrations by thoroughlyhydrolyzing the polysaccharides in the agar weed.

Concs. of glucose & galactose (g/L)

3.2 Development of E-agarase producing yeast
3.2.1 Introduction of E-agarase gene into yeast
Fig. 6 shows the photograph of wild type yeast hoststrain YPH499, transformants YPH499 with agaA
( Y P H 4 9 9 -a g aA ) a n d th e s t r a in w i th a g a B(YPH499-agaB) cultured on the SG(-His) agar
80
Glucose
Galactose

60

40

20

0

Enzymaticsaccharification

Combinedsaccharification

Concs. of glucose, galactose, Log. cell density(CFU/mL)ethanol & glycerol (g/L)

Fig. 4 Comparison of glucose and galactose
concentrations after the enzymatic saccharificationand combined saccharification of agar weed (n=3).

3.1.2 Fermentation of agar weed hydrolysates
The concentrations of cells, glucose, galactose andethanol during the fermentation of agar weedhydrolysate after combined saccharification are shownin Fig. 5. Galactose was only consumed after thecomplete consumption of glucose. The phenomenon
was considered to be caused because microorganisms
have the characteristic to consume more easily
available sugar (catabolite repression). The
concentration of ethanol obtained after 48 h offermentation was 55.0 g/L. The glycerol concentration
increased with the progress of fermentation, and theratio of glycerol produced to glucose and galactose
consumed reached 0.08 g-glycerol/(g-glucose +g-galactose). The cell density of yeast was maintained
at a high level, approximately 108.2 CFU/mL,
throughout the fermentation. As was not shown here
for the sake of brevity, 19.2 g/L of ethanol was

9.0
8.5
8.0
7.5
7.0
Glucose
Galactose
Ethanol
Glycerol

80
60
40
20
0

0

24

48

72

Time (h)Fig. 5The courses of cell density and theconcentrations of glucose, galactose, ethanol, andglycerol during the fermentation of agar weedhydrolysate formed by combined saccharification (n = 3).

67

medium for 5 days. The size of the colony of yeast
host strain YPH499 did not change during the glycerol
cultivation indicating that wild type YPH499 could not
grow on SG(-HIS) agar medium. On the other hand,the size of the colonies of YPH499-agaA andYPH499-agaB increased during the cultivationsuggesting the growth of these transformants andfurthermore, hollows around the colonies oftransformants were observed and it was confirmed thatagar was degraded by E-agarase derived from agaA
and agaB introduced into the yeast. The size of thehollow around the colony of YPH499-agaB was larger
than that of YPH499-agaA, which was consistent withthe fact that agaB include the sequence corresponding
to the signal peptide. Hollow around the colony of
YPH499-agaA was supposed to be produced by
E-agarase leaked from yeast cell.

Fig. 7 TLC of hydrolysis products of agaroseobtained by the crude enzyme agaA. Di-saccharide,
neoagarobiose; Tetra-saccharide, neoagarotetraose;
Hexa-saccharide, neoagarohexaose.

Fig. 6 Formation of hollow on SG(-His) by
wild type yeast (YPH499) and two transformed
yeasts (strain YPH499-agaA, YPH499-agaB).

3.2.2 Degradation of agarose by crude enzyme
from E -agarase producing yeast
The course of TLC pattern for the agarosehydrolysate obtained by applying the crude enzyme
derived from YPH499-agaA is shown in Fig. 7.
Tetrasaccharide and hexasaccharide were detected
after 6h degradation and disaccharide was also
detected after 12h degradation and it was confirmedthat the crude enzyme from YPH499-agaA candegrade agarose up to disaccharide. The course of
TLC pattern for the agarose hydrolysate by applying
the crude enzyme derived from YPH499-agaB isshown in Fig.8. Tetrasaccharide and hexasaccharide
were detected after 6h degradation. Disaccharide
could not be detected even after 48h degradation,suggesting that the crude enzyme from YPH499-agaB
could degrade agarose up to tetrasaccharide. Different
neo-agaro-oligosaccharides were produced by thedegradation of agarose by the crude enzymes from
YPH499-agaA and YPH499-agaB, therefore, it wassuggested that agaA and agaB encode agarase withdifferent agarose-degrading characteristics. As was notshown here for the sake of brevity, after cultivation of
YPH499-agaB on SG(-His) medium where agarose
powder was suspended, tetrasaccharide produced by
the degradation of agarose could be detected and it
was confirmed that agarose can be degraded by the
E-agarase secreted by YPH499-agaB.

Fig. 8 TLC of hydrolysis products of agaroseobtained by the crude enzyme agaB. Tetra-saccharide,
neoagarotetraose; Hexa-saccharide, neoagarohexaose.

4 Conclusions
 In this study, production of high concentration ofbioethanol from agar weed belonging to red seaweed
and development of transformed yeast producing
agarase that degrade agarose contained in the agar
weed were investigated. The concentration of ethanol
as high as 55 g/L could be produced from agar weedby the combined saccharification using reagent gradeacid and enzyme. Then, genes encoding two kinds of
E-agarase were introduced into a yeast strain andagarose could be degraded by E-agarase produced by
the transformed yeast strains.

References[1] Kazłowski, B., et al.: Evaluation of HPSEC-ELSD
method for precise measurement of E-agarase activity.
Biomed. Chromatogr., 25, 570-578 (2011)[2] STRATAGENE inc., pESG Yeast Epitope Tagging
Vectors INSTRUCTION MANUAL Revision A.01.
[3] Yun E. J., et al.: Production of
3,6-anhydro-l-galactose from agarose by agarolytic
enzymes of Saccharophagus degradans 2-40. Process
Biochem., 46, 88-93 (2011)

68

Study on Effects of Body Shape on Propagation
Characteristics in 2.45GHz Dynamic Wearable
WBAN using Multi-Port VNA
Student ID : 10M18140
Name : Tetsuo Nukita
Superviser : Jun-ichi Takada, Takahiro Aoyagi

マルチポート VNA を使用した 2.45GHz でのダイナミックウェアラ
ブルボディエリアネットワーク伝搬特性における体格の影響に関
する検討
貫田哲夫
本論文では、マルチポートネットワークアナライザを用いた、無線ボディエリアネットワークに関する屋内実験の結果を報告する。この実験により、人体の体格差が無線ボディエリアネットワークの伝搬特性にどの程度影響を与えるかを調べた。実験結果を分析した結果、無線ボディエリアネットワークの伝搬特性は、人体の体格差の影響を受ける事が判明した。具体的には、平均パスゲインが体格差によって部分的に説明できると結論づける事ができた。

I. I NTRODUCTION
Wireless body area network (WBAN) denotes shortrange wireless communication among electronic deviceson the surface (wearable WBAN) and/or inside (implantable WBAN) of the human body. Both wearable
WBAN and implantable WBAN are being discussed in
the IEEE 802.15.6 standardization[1]. Their applications
are highly expected especially in the medical treatment
and the health care.
In order to realize these applications, comprehensive
understanding of radiowave propagation characteristics isneeded.
This task is quite challenging because a lot of factors
have effects on the radiowave propagation in WBAN.
For example, the interaction between antennas and human
body, i.e. the changes in the antenna performance due tothe presence of the body and power losses in the tissues[2].
In addition, human movement should be taken into account. When human body is in motion, received signal
level is considerably fluctuating[3]. These factors leadto bad communication links between electronic devices.
Among those elements which influence the propagation
characteristics in WBAN, this paper focuses on the effectof body shape in wearable WBAN. There are only a few
researchers who are addressing this topic. Accroding to[4], no significant difference is found in propagation characteristics among ZigBee WBAN devices for two female
and two male subjects. However, the number of subjects
is insufficient to provide statistical confidence. On theother hand, [5] measured 16 subjects. They examined therelationship between body shape and signal propagation
using first order statistics (average and standard deviation), level crossing rate (LCR) and average fade duration(AFD). Their conclusion is that men suffer higher pathloss and more fade than women and that men’s chest size
is a parameter that affects signal propagation. In otherwords, physical characteristics are important parameters
in WBAN channel model.
Extending these researches above, this paper tried tocharacterize the relationship by numerical value using
analysis of variance (ANOVA) and regression analysis.
This study provides the relationship more clearly.
II. M EASUREMENT C ONDITIONSThis on-body channel experiment aimed to measure
the path gain between one transmitter antenna (Tx) and
four receiver antennas (Rx) (Fig.1) using multi-port vector
network analyzer. 12 males helped this experiment as a
subject. Fig.2 indicates the measurement setup. The transmitter antenna was installed onto the navel of subjects. The
four receiver antennas were installed onto the chest, arm,
wrist and thigh of subjects. They are designed to operate
at the frequency of 2.45 GHz. For these antennas to work
properly for wearable body area communications, theyshould be placed with a sufficient gap from human body.
Hence, a 15 mm gap was added between the antennas andthe body surface of subjects.
In addition, this experiment took account of the antennaorientation and the body posture. As seen in Fig.2, twokinds of orientation were defined by vector v and vector
h. At the analysis part of this paper, the relationshipbetween these parameters and the propagation characteristics will be discussed. This experiment measured thepath gain while subjects were still, walking, and sitting
down/standing up for the follwing cases.
• Tx : v, Rx : v• Tx : v, Rx : h
69

•
•

Tx : h, Rx : v
Tx : h, Rx : h

Notations Tx:v and Rx:v indicate that the the directionof the transmitter and the receiver antenna is parallel tovector v, and does not correspond to the polarization. Table
I shows the summary of the measurement conditions.

orientation of the transmitter and the receiver antennas isparallel to vector v. The distances between the transmitter
antenna and the receiver antennas are denoted below.

Tx and Rx (Chest) : 20〜27[cm]Tx and Rx (Arm) : 28〜37[cm]• Tx and Rx (Wrist) : 23〜35.5[cm]• Tx and Rx (Thigh) : 31.5〜42.5[cm]••

TABLE IIB ODY SHAPE INFORMATION AND AVERAGE PATH GAIN
Fig. 1.

Subject

Antenna used in the experiment

1
2
3
4
5
6
7
8
9
10
11
12
AverageStandard
Deviationσ

Height
[cm]
163
163
179
159
166.5
166
166
175
170.5
178
162
180
169

Weight
[kg]
60.2
54.9
62.2
60.4
53
55.7
63.3
66.8
67.1
65.8
80
71.1
62.8

Chest
Size[cm]
88
76
80.5
84
80.5
82
87
85
90
85
104
87
85.6

Waist
Size[cm]
83
71
70
80
68.5
71
74
75
75
71
951
80
76.1

Average Path
Loss[dB]
-46.2
-46.6
-43.8
-47.0
-44.1
-43.4
-40.7
-38.0
-40.3
-44.0
-47.0
-47.6
-44.0

6.99

6.03

6.63

7.16

2.95

Path Gain( No.1,Tx=v,Rx=v,still)
−10
−20

Chest
Arm
Wrist
Thigh

−30

Measurement Setup
Path Gain[dB]

Fig. 2.

TABLE IM EASUREMENT C ONDITION

Antenna
Antenna Dimension
Tranmitter Antenna
Receiver Antenna
Subject
Place
PostureZVT20 (Rohde Schwarz)
2.45
13
11.18
1801
1
1
Chip Antenna × 5(Mitsubishi Materials)
Model : AHD1403-244SK01[6]50mm × 25mm × 1mm
NavelArm, Wrist, Chest, Thigh
12 malesEmpty Room (13.6m × 15.1m × 2.7m)
Still
Walking on the spot
Sitting down/Standing up

−50
−60
−70
−80
−90
0

Fig. 3.

2

4

6Time[s]81012Temporal path gain (No.1, Tx : v, Rx : v, Still)

Path Gain( No.12,Tx=v,Rx=v,still)
−10
−20

Chest
Arm
Wrist
Thigh

−30

Path Gain[dB]

VNAFrequency[GHz]RF Power[dBm]
Sweep Time[s]Number of points
IFBW[kHz]Frequency Span[Hz]−40

III. R ESULT

−40
−50
−60
−70

Table II expresses body shape information of subjects
and the average path gain obtained in the experiment.
Other than them, fat percentage, body mass index (BMI),
width across the shoulder, length of arm and leg, andcircumference of arm, wrist and thigh were measured.
Fig.3 to Fig.8 show the temporal path gain where the

−80
−90
0

Fig. 4.

70

2

4

6
Time[s]81012Temporal path gain (No.12, Tx : v, Rx : v, Still)

Path Gain( No.12,Tx=v,Rx=v,updown)−10−20

Path Gain( No.1,Tx=v,Rx=v,walk)
−10

−30

Path Gain[dB]

−20

Chest
Arm
Wrist
Thigh

−30

Path Gain[dB]

Chest
Arm
Wrist
Thigh

−40

−40
−50
−60

−50
−70
−60
−80
−70
−90
0

−80
−90
0

Fig. 5.

2

4

6Time[s]81012Fig. 8.

Temporal path gain (No.1, Tx : v, Rx : v, Walk)

2

4

6
Time[s]81012Temporal path gain (No.12, Tx : v, Rx : v, Sit down/Stand up)

IV. A NALYSISIn order to characterize the relationship by numericalvalue, ANOVA and regression analysis were chosen as ameans of analysis.
A. ANOVA to select the appropriate parameters for modeling

Path Gain( No.12,Tx=v,Rx=v,walk)
−10
−20

Chest
Arm
Wrist
Thigh

Path Gain[dB]

−30
−40
−50
−60
−70
−80
−90
0

Fig. 6.

2

4

6Time[s]81012Temporal path gain (No.12, Tx : v, Rx : v, Walk)

Path Gain( No.1,Tx=v,Rx=v,updown)
−10
−20

Chest
Arm
Wrist
Thigh

Path Gain[dB]

−30
−40
−50
−60

B. Regression analysis to clearfy the relationship between
body shape and path gain

−70
−80
−90
0

Fig. 7.

ANOVA can be used to examine which explanatory
parameters are useful for modeling an observed data[7].
First the measurement data was divided into as manygroups as the number of measurement conditions (antennaorientation, antenna position, subject posture and so on).
Table III shows the F-value, the ratio of the variancewithin the data group and the one between the datagroup. In the table, “Ave”[dB] means the average of thepath gain measured, and “Max-Min” means the difference
between the maximum and the minimum value of the pathgain. Larger F-value means the high dependency on the
explanatory parameters. That is to say, the table prove
which parameter is more critical for WBAN propagation
channel. For example, as for the median of the pathgain, antenna position has the most influence on it. Pvalue indicates the probability that F-value is observedby accident. The lower the p-value, the more significant
the difference between the groups. The difference among
subjects has strong relationship with the average path gain
while the subject posture has little. Antenna position andorientation also have large effects on the path gain. With
respect to the ANOVA on the height (weight) and the pathgain, the data was separated into the two groups; a datagroup of subjects whose height (weight) is taller (heavier)than the median and the other data group whose height(weight) is shorter (lighter) than the median.

246Time[s]81012Temporal path gain (No.1, Tx : v, Rx : v, Sit down/Stand up)

Regression analysis is a statistical technique for modeling an observed data set by explanatory parameters. Bymeans of this technique, the path gain observed in theexperiment was modeled on height or weight. In otherwords, the path gain ypg [dB] can be expressed by heightxh [cm] or weight xw [kg] in the follwing equations.
ypg = a0 + a1 xh
71(1)TABLE III
F- VALUE (** : P < 0.01、*: P < 0.05)
Median[dB]
9.32(**)
10.49(**)
4.11(*)
0.66
0.49
7.4(**)
1.75

Ave
[dB]
10.34(**)
10.94(**)
4.58(*)
0.66
0.53
7.4(**)
1.04

Max
[dB]
13.34(**)
23.05(**)
0.52
0.39
0.44
8.09(**)
81.2(**)

Min[dB]
4.69(*)
1.86
7.02(**)
2.22
2.7
3.35(**)
92.56(**)

Max-Min[dB]
0.77
6.56(*)
3.88(*)
3.85
4.59(*)
1.2
826.84(**)

56.18(**)

57.44(**)

34.81(**)

35.69(**)

2.42(**)

25.78(**)

27.32(**)

19.41(**)

19.62(**)

19.37(**)−20
Path Gain (Measured)[dB]

HeightWeight
BMI
Waist
Chest
Subject
PostureAntenna
Position
Antenna
Orientation

−10

−30
−40
−50
−60
−70
Regression analysis
Measurement data−80155

ypg = b0 + b1 xw

Fig. 9.

Table IV is the result of regression analysis on height andpath gain. Table V is the result of regression analysis
on weight and path gain. R2 means the accuracy ofthe equations. This value should be 1 in an ideal case.
Therefore, there is little relationship between weight andpath gain. On the other hand, height accounts for 1.1
percent of the effects on the average path gain. Fig.9 andFig.10 indicate the relationship between height (weight)
and the path gain calculated by regression analysis.

a0-65.2444.613-63.932-67.588-72.202

a1
0.125
0.074
0.120
0.185
0.111

R2
0.011
0.002
0.01
0.019
0.005

P(a0 )
0
0.692
0
0
0

TABLE VR EGRESSION ANALYSIS ON WEIGHT AND PATH GAIN
ypg
Average
Max-MinMedianMax
Minb0-45.7626.137-45.11-44.823-50.96

b1
0.027
0.174
0.024
0.135
-0.04

R2
0
0.008
0
0.007
0

P(b0 )
0
0.22
0
0
0

P(b1 )
0.635
0.028
0.68
0.039
0.607

P(b0 ) = P-value for b0 、P(b1 ) = P-value for b1

V. C ONCLUSION
This work has reported that the relationship between
the body shape and the propagation channel in WBAN
can be characterize by numerical value using ANOVA
and regression analysis. Height accounts for about 1.1
percent effects on the average path gain. Therefore body
shape is not the critical parameter in WBAN propagation
channel. Result of ANOVA turns out which parameter ismore dominant to the WBAN propagation channel. As forthe average path gain, antenna position is most dominant
and antenna orientation is the second.
The final goal of this work is to reduce the effect ofbody shape, subject posture and antenna position using
antenna diversity. Generally, diversity technique is used to

175

180

Relationship between height and path gain

Path Gain (Measured) [dB]

−20
−30
−40
−50
−60
−70

P(a1 )
0.011
0.283
0.015
0.001
0.1

P(a0 ) = P-value for a0 、P(a1 ) = P-value for a1

165
170
Height [cm]−10

TABLE IV
R EGRESSION ANALYSIS ON HEIGHT AND PATH GAIN
ypg
Average
Max-MinMedian
Max
Min

160

(2)

Regression analysis
Measurement data
−8050

Fig. 10.

55

60
65
Weight [kg]7075

Relationship between weight and path gain

mitigate the effect of multipath fading or shadowing frombuildings and objects in wireless communication systems.
This technique is thought to be applicable in WBAN
system. In order to achieve the goal, further investigation
remains to be done.
R EFERENCES[1] IEEE 802.15 Task Group 6. [Online]. Available : http://ieee802.org/
15/pub/TG6.html[2] Y. Terao, M. Kim, Iswandi, J. Takada, “On antenna de-embedding in
BAN channels-Measurement of reflection coefficient and radiation
pattern of on-body antenna-,” IEICE Technical Report, Jan. 2010.
[3] T. Aoyagi, M. Kim, J. Takada, K. Hamaguchi, R. Kohno, “Numerical Simulations for Dynamic WBAN Propagation Channel during
Various Human Movements,” Proc. 5th International Symposium
on Medical Information and Communication Technology (ISMICT),
Mar.2011.
[4] C. Oliveira, L. Pedrosa, R. Rocha, “Characterizing On-Body Wireless Sensor Networks,” Proc. 2nd International Conference on New
Technologies, Mobility and Security (NTMS), Aug. 2008.
[5] F. Di Franco, C. Tachtatzis, B. Graham, M. Bykowski, D. C.
Tracey,N. F. Timmons, J. Morrison, “The effect of body shape andgender on Wireless Body Area Network on-body channels,” 1st
Middle East Conference on Antenna and Propagation (MECAP),
Oct.2010.
[6] MITSUBISHI MATERIALS. [Online]. Available : http://www.mmc.
co.jp/corporate/ja/index.html[7] Yasushi Nagata, “Nyuumon Toukei Kaisekihou,” Nikka Giren,
Tokyo, 1992.

72

Separation of aromatic hydrocarbons from cracked kerosene
by O/W/O emulsion liquid membrane
Student Number: 10M18170

Name: Masashi HARUNASupervisor: Ryuichi EGASHIRA

ศゎⅉἜ࡟ྵࡲࢀࡿⰾ㤶᪘Ⅳ໬Ỉ⣲ࡢ 2:2 ங໬ᾮ⭷ศ㞳᫓ྡ ᑗ㈨
ศゎⅉἜࢆཎᩱࠊ7ZHHQ㸦࣏ࣜ࢜࢟ࢩ࢚ࢳࣞࣥ  ࢯࣝࣅࢱࣥࣔࣀ࢚࣮࢜ࣞࢺ㸧Ỉ⁐ᾮࢆ⭷ᾮࠊཬࡧ࣊࢟
ࢧࣥࢆ⁐፹࡜ࡋ࡚ᅇศ㏱㐣ࢆ⾜ࡗࡓࠋཎᩱ୰ࡢ┤㙐㣬࿴Ⅳ໬Ỉ⣲ᡂศ࡟ᑐࡋ࡚ⰾ㤶᪘Ⅳ໬Ỉ⣲ᡂศࡀ㑅ᢥⓗ࡟⭷ᾮࢆ㏱㐣ࡋࠊࡇࢀࡽࡢᡂศ㛫ࡢศ㞳ࡀྍ⬟࡛࠶ࡗࡓࠋ᪤ ࡢᅇศᖹ⾮ᢳฟࡢሙྜ࡜ẚ㍑ࡋ࡚ࠊᮏศ㞳ἲ࡟࠾࠸࡚ࡣ኱ᖜ࡟㧗࠸཰⋡࡞ࡽࡧ࡟ྠ⛬ᗘࡢศ㞳ࡢ㑅ᢥᛶࢆᚓࡓࠋࡲࡓࠊศ㞳ᛶ⬟࡟ᑐࡍࡿ୺せ࡞᧯స᮲௳
ࡢᙳ㡪ࢆ᫂ࡽ࠿࡟ࡋࡓࠋ


feed to solvent phase and the separation takes place.
In this work, the separation of CK by the O/W/O
emulsion liquid membrane technique was conducted.
The effect on experimental condition for emulsion
liquid membrane separation was studied.

1. Introduction
Cracked kerosene (CK) is one of the by-products
from the process of cracking of heavier oil fraction to
produce cracked gasoline in petroleum refinery.
Nowadays, the supply of ordinary gasoline isdecreasing. Because of this, whereas the quantity ofcracked gasoline is decreasing, that of cracked
kerosene is increasing. There are many kinds ofchemical compounds contained in cracked kerosene,
the alkanes such as octane (C8), nonane (C9), decane(C10); the aromatic hydrocarbons such as m,p-xylene(M,PX), pseudo cumene (PC), tetraline (T), etc. Since
the content of aromatic hydrocarbons in the cracked
kerosene is so high that this fraction does not meet the
specification for the ordinary kerosene, the cracked
kerosene is used as a low-value heavy oil blendstock.
On the other hand, aromatic hydrocarbons are usefulas raw materials of chemical industry. It is, therefore,
desired to separate aromatic hydrocarbons from thecracked kerosene. The liquid-liquid equilibrium
extraction[1] has been widely used for aromatics
separation, such as, sulfolane process. The liquid
membrane permeation[2,3,4] is expected as analternative to the extraction. The liquid membrane
separation is governed by the difference in permeation
rates of the components through the membrane andthe difference is attributed mainly to the solubilities of
the components into the aqueous membrane liquid. In
general, the solubility of aromatic hydrocarbon inwater is about 200 times larger than that of alkanehydrocarbon, so that aromatic hydrocarbon permeates
through the aqueous membrane preferentially rather
than alkane hydrocarbon to be separated. In O/W/O
emulsion liquid membrane process, the feed oil phase
is emulsified in an aqueous solution of surfactant to
prepare the stable O/W emulsion. This O/W emulsion
is dispersed in a solvent oil phase to form O/W/O
dispersion system, where the internal feed andexternal solvent oil phases are divided by the aqueous
phase. This aqueous phase plays a role of the liquid
membrane, through which the permeate transfers from

(a)

(b)

High-speed
homogenizer

VesselBaffleVessel6 flat blade
turbin typeimpeller

Fig. 1 Apparatuses for batch emulsion liquid
membrane permeation (a) emulsifier with high-speed
homogenizer (b) permeator
Table 1 Experimental condition of batch permeation
Feed
Cracked kerosene
Solvent
HexaneMembraneAqueous solution of surfactant
Surfactant
Polyoxyethylene (20) sorbitan monooleate (Tween 80)
Stirring velocity in emulsification
NE [h-1]
1.1㽢106
Stirring velocity in permeation
NP [h-1] 1.8㽢104ࠥ3.6㽢104
Total volume of liquid in permeator
VT [m3]4.0㽢10-4
0.5Volume fraction of inner oil phase in O/W emulsion
IO,0[-]
Volume fraction of O/W emulsion in O/W/O emulsion IOW,0[-]0.25
Concentration of surfactant in a membrane liquid
CS [-]
0.005ࠥ0.2
Operation time
t [h]
0ࠥ0.5
Operation temperature
T [K]
293㼼5

Table 2 Components in cracked kerosene
Component
Mass fraction [-] Component Mass fraction [-]toluene0.003
heptane0.001
ethylbenxene
0.006
octane0.017
m,p-xylene
0.024
nonane0.074
o-xylene
0.016
decane0.036
propylbenzene
0.004
undecane
0.034
mesitylene
0.015
dodecane
0.033
pseudocumene
0.013
tridecane
0.037
tetraline
0.009
tetradecane
0.027
naphthalene
0.004
pentadecane
0.02
hexylbenzene
0.01
hexadecane
0.009
2-methylnaphthalene
0.008
heptadecane
0.002
Aromatics
0.111
Alkanes
0.291

73

2. Experimental

0.25
Cs

CK was used as an inner oil phase. Aqueous
membrane solution consisted of ion-exchanged waterand polyoxyethylene (20) sorbitan monooleate as anemulsifying agent. Hexane was used as externalsolvent phase. The feed CK and aqueous membrane
solution were stirred by a commercial high-speed
homogenizer to prepare the stable emulsion. Thisprepared emulsion was brought into contact with thesolvent by stirring vessel which was made of Pyrex
glass equipped with a six-flat-blade turbine typeimpeller and four baffles. Figure 1 shows appearanceof the apparatuses for batch emulsion liquid
membrane permeation (a) emulsifier with high-speed
homogenizer (b) permeator. Stirring started to begin apermeation run (t=0) and continued for a specified
operating time. The extract phases were sampled foranalysis by a gas chromatograph (GC-2010, Shimazu
Corp). This operation was repeated for the several
different times to obtain the time courses of thecompositions in the extract phase etc. Table 1 gives
the detailed experiment conditions.

Cs

Key

0.005

R1

R, E [kg]

E1

0.05

0.05

0.2

0.2

Key

0.005

0.2

0.15

0.1

0.05
0

0.1

t[h]

0.2

0.3

Fig.2 Time course of mass of raffinate and extract phase
10-3

i

Key

tolueneethylbenxene
m,p-xylene
o-xylene
propylbenzene

yi [-]

mesitylene
pseudocumene

10-4
9.1E-05

tetraline
naphthalene
hexylbenzene
2-methylnaphthalene
heptaneoctanePi a( xi  y i )VT0

0.1

Paromatics
Palkanest[h]tridecane

0.3

0.2

1010

Key

xi, yi [-]

10
0.1-1

0.01
10-2

0.001
10-3

Np=1.8㽢1040.0001
10-4
0

0.1

t[h]

0.2

0.3

i
Cs
0.005
total aromatics 0.05
0.2
0.005
total alkanes 0.05 x i
0.2
0.005
hexane
0.05
0.2
0.005
total aromatics 0.05
0.2
0.005
total alkanes 0.05 y i
0.2
0.005
0.05
hexane0.2

Fig.4 Time course of mass fraction in raffinate and extract phase
0.25

Key

(4)
0.2

Paromatics  aPalkanes  adodecaneFig.3 Time course of mass fraction in extract phase

Yi [-]In this equation, Pia expressed overall volumetricpermeation coefficient.
When overall mass transfer is governed by permeation
through the membrane, the overall permeation
coefficient is qualitatively expressed as(5)
Pi v Di  mi
Separation selectivity of total aromatics by the ratio ofoverall volumetric permeation coefficients relative tototal alkane was defined as,Edecaneundecane

7E-06

Cracked kerosene contained a number ofhydrocarbon components. Table 2 summarizes several
representative components and the respective contents
identified, determined by gas chromatography.
The mass fraction in raffinate phase, xi can becalculated by mass balance as follows:(1)
R0 xi ,0  E0 yi ,0 Rxi  Eyi
E and R were obtained from the experimental data.
Yield of component i, Yi, was defined as,
E  yi(2)
Yi
R0  xi 0
where xi0 and yi denote the mass fraction ofcomponent i in the inner oil phase at t=0 and that in
the external extract oil phase after a run, respectively.
R0 refers to the initial mass of inner oil phase and E
expresses the mass of out oil phase after the run.
Mass transfer rate of component i through ideal
emulsion liquid membrane was expressed as,
dEy i
dt

Cs=0.03
Np=1.8㽢104

10-5

3. Results and Discussion

nonanei
C s Key
i
Cs
0.01
0.01
total aromatics 0.05
total alkanes 0.05
0.2
0.2

0.15

0.1
total aromatics (extraction)
0.05

0

㻜

(6)

㻜㻚㻝

t[h]

㻜㻚㻞

Fig.5 Time course of yield

Figure 2 shows the time courses of mass of raffinate
and extract phase. The mass of raffinate phase74

total alkanes(extraction)
㻜㻚㻟increased as the mass of extract phase decreased withtime. The extract phase transferred to raffinate phase.
The variation of surfactant concentration didn’t havemuch effect on R and E.
Figure 3 shows the time courses of mass fraction in
extract phase. The mass fractions of all components
which were identified by gas chromatography are
shown on this figure. All of the mass fractions of
extract phase increased with time. From next figure,the total aromatics and total alkane are shown to
explain. Their variations are almost same as eachcompound.
Figure 4 shows the time courses of mass fraction in
raffinate and extract phase about total aromatics, total
alkane and hexane. The mass fraction of totalaromatics and total alkane in raffinate phase decreased
as those in extract phase increased with time. The
mass fraction of hexane in raffinate phase increased asthat in extract phase decreased with time. So the massfraction of raffinate and extract phase approachedeach other. The difference of mass fraction which is
driving force of permeation decreased with time.
Figure 5 shows the time courses of yields about totalaromatics and total alkane. Under all conditions, Yi ofaromatic compounds were larger than those of alkane
compounds, namely, it was confirmed that thesecompounds were separated into each other by theemulsion liquid membrane. The yield increased withtime. This figure also shows comparison between
emulsion liquid membrane and liquid-liquid extraction
on yields. The yields in the case of liquid-liquid
extraction method[1] about total aromatics and total
alkane are expressed in a straight lines. The yields inthe case of emulsion liquid membrane method werehigher than those of liquid-liquid extraction. The yieldof liquid-liquid extraction was 0.065 and that ofemulsion liquid membrane was 0.30 at maximum
about total aromatics.
Figure 6 shows the time courses of overall
volumetric permeation coefficient, Pia. In any case,Pia decreased with stirring time. Pia of the aromaticcompounds was larger than those of alkane
compounds. Aromatic compounds were selectivelytransferred through liquid membrane to extract phaseand could be separated from alkane compounds.
Figure 7 shows the time courses of separation
selectivity by the ratio of overall permeation
coefficients, β. The separation selectivity decreased
with time. This figure also shows comparison between
emulsion liquid membrane and liquid-liquid extraction
on separation selectivity. The separation selectivity inthe case of liquid-liquid extraction method[1] about
total aromatics is expressed in a straight line The
separation selectivity in the case of emulsion liquid
membrane method was higher than that ofliquid-liquid extraction. The separation selectivity ofliquid-liquid extraction was 12 and that of emulsion
liquid membrane was 17 at maximum about totalaromatics. Aromatic compounds were selectively

Pia [kgh-1m-3]

0.001
10-3

Key

i
C s Key
i
Cs
0.01
0.01
total aromatics 0.05
total alkanes 0.05
0.2
0.2

10-4
0.000110-50.00001
0

0.1

0.2

t[h]

0.3

Fig.6 Time course of overall volumetric permeation coefficient

total aroma (extraction)

β [-]

101

Cs
0.005
0.05
0.2

i

Key

total aromatics

100
0

0.1

0.2

0.3

t[h]
Fig.7 Time course of separation selectivity
0.001
10-3

Pia [kgh-1m-3]

Np=1.8㽢104

10-40.0001iKeyttotal aromatics

0.0083

total alkanes

10-50.00001
0

0.05

0.1

0.15

0.2

0.25

Cs[-]
Fig.8 Effect of surfactant concentration on overall volumetricpermeation coefficient

β [-]

100
102

Np=1.8㽢104

10
101

i

t

total aromatics

0.0083

Key

100
1
0

0.05

0.1

0.15

0.2

0.25

Cs[-]
Fig.9 Effect of surfactant concentration on separation selectivity

75

transferred through liquid membrane to extract phaseand could be separated from alkane compounds.
Figure 8 shows the effect of surfactant concentration
on overall volumetric permeation coefficient, Pia.
While Cs was lower, Pia increased with Cs. And thenwhile Cs was larger, Pia decreased with Cs. Pia had
maximum value around Cs = 0.05.
Figure 9 shows the effect of surfactant concentration
on separation selectivity by the ratio of overall
permeation
coefficients,β.
Thesurfactant
concentration didn’t have much effect on separation
selectivity.
Figure 10 shows the effect of surfactant
concentration on interfacial tension between inner oiland surfactant solution phase and the effect of
surfactant concentration on viscosity in surfactant
solution from previous work[5]. The surfactant was
Tween 20 in the previous work. The interfacial tension
decreased with surfactant concentration. The
decreasing trend became smaller from CMC (critical
micelle concentration). The viscosity increased withsurfactant concentration from CMC. While surfactant
concentration was lower, it was supposed that the
inner oil droplet decreased due to the decrease ofinterfacial tension. Therefore the Pia increased. Whilesurfactant concentration was higher, it was supposedthat diffusivity at liquid membrane decreased due toincrease of viscosity from Wilke-Chang equation.
Therefore the Pia decreased. The Wilke-Changequation is shown as below.
Wilke-Chang equation
Dsurfactant, water

2.946 u10 11 DM water.6
P waterVb0,surfactant

1

2

CMC(Tween20)

0.02

CMC(Tween80)

0.0011σ [N/m]

0.001

0.01

μ [Pa䞉s]

Surfactant: Tween20

0.0009experimental
range
0
0.01
10-2

0.0008
-1
0.1
10

1010

101
10

2
100
10

t[h]
Fig.10 Effect of surfactant concentration on interfacial tension andviscosity

Y
D
m
α
M
μ
V
σ

= yield= diffusivity= distribution coefficient= degree of association= molecular weight= viscosity= molar volume= interfacial tension[-][m2h-1][-][-][-][Pa࣭s][m3/mol][N/m]<Greek>β= separation selectivity of total aromatics byratio of overall volumetric permeation
coefficient relative to total alkane[-]
IO,0 = volume fraction of inner oil in O/W emulsion[-]
IOW,0 = volume fraction of O/W emulsion in totalliquid[-]<Subscript>
0= at initial statei= component i
CK = cracked kerosene
M,PX = m,p-xylene
PC = pseudocumeneT= tetraline
C8= octane
C9= nonane
C10 = decane

T

4. Conclusions
The aromatic hydrocarbon compounds wereseparated from cracked kerosene by the emulsion
liquid membrane. Then the highest yield andseparation selectivity were 0.3 and 17. These werehigher than those of liquid-liquid equilibrium
extraction. There was the appropriate surfactant
concentration range.

Literature Cited

Nomenclaturesa= specific interfacial area between O/Wemulsion and extract phase[m-1]
Cs= mass fraction of surfactant in membraneliquid[-]E= mass of extract phase
[kg]
[h-1]
NE = stirring velocity in emulsification
NP = stirring velocity in permeator
[h-1]
-1 -2
Pi
= overall permeation coefficient[kgh m ]R= mass of raffinate phase[kg]T= operation temperature[K]t= operation time[h]= total volume of liquid in permeation vessel [m3]VTx= mass fraction in raffinate phase[-]y= mass fraction in extract phase[-][1] H. Habaki, M. Miyazaki, E. Egashira,“Liquid-liquid Extraction of Cracked Oil”,
S.C.E.J, March 2011.
[2] N. N. Li, “Separation of Hydrocarbons by Liquid
Membrane Permeation”, U. S. Pat. 3,410,794,
November (1968).
[3] Kato, S. and J. Kawasaki; “Separation of aromatichydrocarbons contained in reformate andkerosene by liquid membrane permeation,”
Sekiyu Gakkaishi, 30, (1), 1-6(1987)[4] Kato, S. and J. Kawasaki; “Effects of lipophilic
surfactants on liquid membrane permeation ofhydrocarbons in naphtha and kerosene,” Sekiyu
Gakkaishi, 30, (4), 243-248(1987)[5] Yamamoto, T and Tanaka, M; “Effects ofsurfactant concentration on drop sizes in O/W and
W/O/W emulsions,” J. Chem. Eng. Japan, 36, (8),963-970(2003)

76

Study on fixing heavy metals by cementitious materials
with Municipal Waste Combustion Ash, OPC, Blast Furnace Slag and Anhydrite
Student Number: 10M18186

Name: Takayuki FUKUNAGASupervisor: Nobuaki Otsukiᐇ↝༷ᗫᲠ≀ࢆΰධࡋࡓ OPC㸫㧗⅔ࢫࣛࢢᚤ⢊ᮎ㸫▼⭯⣔ᮦᩱࡢ㔜㔠ᒓᅛᐃ໬࡟㛵ࡍࡿ◊✲⚟Ọ㝯அᮏ◊✲ࡣ㸪භ౯ࢡ࣒ࣟࢆྵࡴ↝༷ᗫᲠ≀ࢆΰධࡋࡓᬑ㏻࣏ࣝࢺࣛࣥࢻࢭ࣓ࣥࢺ࡟㧗⅔ࢫࣛࢢᚤ⢊ᮎ࠾ࡼࡧ↓Ỉ▼⭯ࢆࡑࢀࡒࢀΰ࿴ࡋࡓࢭ࣓ࣥࢺ⣔ᮦᩱࢆ⏝࠸࡚㸪 1) ྛᮦᩱࡀ㔜㔠ᒓᅛᐃ࡬୚࠼ࡿᙳ㡪ࢆ᫂ࡽ࠿࡟ࡋ㸪㔜㔠ᒓᅛᐃ⬟ຊࢆྥୖࡍࡿࡓࡵࡢᮦᩱタィࢆᥦ᱌ࡋ㸪2) ᐇ⏝໬࡬ࡢ᳨ウࢆ⾜࠺ࡇ࡜ࢆ┠ⓗ࡜ࡋࡓ㸬ࡑࡢ⤖ᯝ㸪1) ࡟ࡘ࠸࡚㸪㧗⅔ࢫࣛࢢᚤ⢊ᮎ࡜↓Ỉ▼⭯ࢆྠ᫬࡟ΰ࿴ࡍࡿࡇ࡜࡟ࡼࡾ㸪භ౯ࢡ࣒ࣟࡢᅛᐃ⬟ຊࡣྥୖࡋࡓ㸬ࡇࢀࡣ㸪භ౯ࢡ࣒ࣟࢆᅛᐃࡋࡓࣔࣀࢧࣝࣇ࢙࣮ࢺ࡟ຍ࠼࡚㸪භ౯ࢡ࣒ࣟࢆྵࡴ࢚ࢺࣜࣥ࢞࢖ࢺࡀ⏕ᡂࡋࡓࡇ࡜
࡟㉳ᅉࡋࡓࡶࡢ࡜⤖ㄽ௜ࡅࡓ㸬2) ࡟ࡘ࠸࡚㸪୍⯡ࡢᐇᵓ㐀≀࡟ࡣ㐺ࡉ࡞࠸ࡀࢥࣥࢡ࣮ࣜࢺ஧ḟ〇ရ࡟ࡣᐇ⏝໬ࡢྍ⬟ᛶࡀ࠶ࡿ࡜⤖ㄽ௜ࡅࡓ㸬

1. Introduction

2. Experimental procedure

Nowadays, it is difficult to get natural resources due
to environmental issues. From that point of view,
recycling Municipal Waste Combustion Ash (waste) ishighly demanded in construction field. One of theeffective methods is to use wastes as cementitious
admixture. However, wastes include hexavalentchromium (Cr(ϭ)) which is a deleterious effect ofhealth and difficult to remove it of all heavy metals.
Therefore, it is necessary to fix Cr( ϭ ) by using
cementitious admixture.
Recently, Takahashi reported 1) that Cr(ϭ) can be
fixed by aluminate hydration products such as AFm
phase , and Yoda reported2) that fixing Cr(ϭ) could be
increased by using industrial waste such as blast
furnace slag (BFS) and anhydrite (AH). However,these studies did not use existing waste.
For the above reason, this research are conducted toprevent leaching of Cr(ϭ) by using BFS and AH togenerate AFm phase in early age. The purpose of thisresearch is to prevent leaching of Cr( ϭ ) from
cementitious materials with waste-Ordinary Portland
Cement (OPC)-BFS-AH and To consider possibility
as a practical material.

2.1 Materials
The materials used in this study were waste, OPC
BFS and AH. Waste was generated from rubbish
disposal site and was used as an admixture. Table.1
shows chemical composition and physical propertiesof waste, OPC, BFS and AH. Waste has similar
chemical composition as OPC. However, waste
contains highest level of chromium ion (CrO42- ion) in
60 samples. If thiswaste can be reused, there is apossibility that other waste (lower level) can bereused.
2.2 Specimens
2.2.1 Cement paste specimen
Table.2 shows the mix proportions of cement paste
specimens. All mix proportions of cement paste
specimens contain waste 50% by mass. Remained
50% are OPC: BFS = 60:40, 45:55 and 30:70% and
AH=0, 2, 3, 5 and 7 mass%. Paste specimens wereprepared with water binder ratio of w/b=0.4, 0.5, 0.6
and 0.7. Dimension of cement paste specimens is
1×1×8cm. All specimens were cured in air for 6 and27- days, after demolding it at 24 hours.

Table.1 Chemical composition

Material

Ig.loss(mass%)
SiO2

Combustible waste
OPC
BFS
AH

10-15
1.07
0.49
0.85

CaO

MgO

SO3

Heavy
metal
(ppm)
Cr(䊥)

43.4
63.17
43.13
40.50

0.19
2.04
6.05
0.10

1.67
2.48
56.80

520
-

Chemical composition (mass %)Al2O3 Fe2O3

26.4 10.1
20.37 5.57
33.60 14.26
1.00 0.30

6.12
3.38
0.41
0.10

77

Specific surfacearea(cm2/g)
3700
3300
4550
3990

3.2 Influence of mixing AH with OPC and waste
Fig.2 shows relationship between mixing ratio of AH
and the amount of chromium leaching on 7day curing.
In the case of mixing AH, Chromium leaching isdecrease. Fig.3 shows the peak intensity ratio of
ettringite. The amount of ettringite generation has
increased by increasing the mixing ratio of AH. After
increasing aluminate hydration products, the amountof chromium leaching has decreased. However, it isdifficult to make the amount of chromium leaching
below environmental standard value by only mixing
AH.

2.2.2 Mortar specimen
All mix proportion contain waste 25, 50 % by (mass%). Remained 75, 50 % are OPC: BFS = 60:40, 45:55
and 30:70 % and AH= 5 mass%. The water binder
ratio was 0.5. Mortars with sand-binder ratio of 2.0
and dimension of 4×4×16cm were made forcompressive strength tests according to JIS R 52013).
All specimens were cured in the mold for 24 hours.
After demolded, specimens were cured in air 6 and27- days.
Table.2 mix proportions of cement paste specimens.
(mass%)
w/b
0.4
0.5
0.6
0.7

waste

OPC : BFS

AH

50

100 : 060 : 40
45 : 55
30 : 70023
5
7

2.3 Method
Fixed Cr(ϭ) was evaluated based on the amount ofleaching chromium, which measured by the JapaneseLeaching Test (JLT46).
In order to clarify influence of fixing ability of Cr(ϭ)
by OPC, BFS and AH, hydration products of thecement pastes were examined using X-ray diffraction
method (XRD). The cumulative pore volume wasmeasuredusing
mercury
porosimeter(CE
INSTRUMENTS Pascal240, pressure range: 0.1-200
MPa). Compressive strength were measured based on
JIS R 52013)

Fig.2 Relationshipreplacement ratio of AH
proportion and amount ofchromium leachingFig.3 Peak intensity ratioof Ettringite

3.3 Influence of mixing BFS with OPC and waste
Fig.4 shows relationship between mixing ratio of
BFS and the amount of chromium leaching on 7daycuring. By mixing BFS, Chromium leaching has been
decreased. Fig.5 shows XRD patterns of thespecimens. [In this paper, “C” corresponds to calcium
hydrate and “AFm (Cr)” is AFm phase including
chromium.] In case of mixing BFS, AFm (Cr) is
generated (AFm (Cr): θ=18.2°). Fig.6 shows the peakintensity ratio of AFm (Cr) and ettringite. The amountof AFm (Cr) has increased by mixing BFS, because
concentration of aluminous component had increasedin the specimen by mixing only BFS.
AFm (Cr) is produced by the reaction which occurs
by anion exchange of SO42- ion in monosulfate and
CrO42- ion as shown chemical equation (3.1).

3. Results and Discussion
3.1 Influence of mixing waste with OPC
Fig.1 shows relationship between water-binder ratio
and the amount of chromium leaching on 7 day
curing. All water binder ratios satisfy theenvironmental standard. Therefore, it is difficult to
reduce the amount of chromium leaching by only
mixing OPC.

3CaOAl2O3࣭CaSO4࣭12H2O monosulfate + CrO42→ 3CaOAl2O3࣭CaCrO4࣭12H2O AFm(Cr)+ SO42- (3.1)
Environmental
standard (0.05ppm)

The amount of AFm (Cr) has been increased bymixing BFS. After increasing AFm (Cr), the amountof chromium leaching has decreased.
Therefore, fixing ability of Cr( ϭ ) improved withmixing BFS because AFm phase which was taken Cr(ϭ) and changed into AFm (Cr), is able to increase in
the early age.

Fig. 1 Relationship between water - binder ratio andthe amount of chromium leaching78

C

AFm (Cr)
Ettringite
BFS70%
BFS55%
BFS40%
BFS 0%

Fig.4 Relationship between
replacement ratio of BFS
and amount of chromium
leaching

Fig.5 XRD pattern(2θ=17.5㹼19.5°)(c) BFS 55%(d) BFS 70%
Fig.7 Relationship between replacement ratio of AH
and the amount of chromium leachingFig.8 shows the peak intensity ratio of aluminate
hydration products. In the case of (a) AH 0% and (b)AH 2%, the amount of AFm (Cr) was small regardlessof BFS replacement ratio, and ettringite was
generated. In case of (c) AH3%, the amount of AFm(Cr) and ettringite has increased compared with AH
0%. Especially when mixing BFS is 55 and 70%, AFt(Cr) [AFt phase including hexavalent Chromium] had
generated. In (d) AH 5%, the amount of aluminate
hydration products such as AFm (Cr), AFt (Cr) and
ettringite becomes the maximum. In case of (d) AH
7%, the amount of aluminate hydration products isdecrease more than in case of mixing AH 5%. Fig.9
shows results of mercury intrusion porosimetry. In
case of mixing AH 7%, pore volume is larger thanmixing AH 5% because concentration of SO42- ion in
specimen was high and hydration product was notable to generate in early age.
Therefore, fixing ability of Cr(ϭ) was improved withincluding AH and BFS replacement ratio because
AFm (Cr) and AFt (Cr) were generated in the earlyage.

Fig.6 Peak Intensity Ratio of Ettringite
and AFm (Cr)
3.4 Influence of mixing BFS and AH with OPC andwaste
Fig.7 shows relationship between mixing ratio of AH
and the amount of chromium leaching. In the case of(a) BFS 0%, chromium leaching had been decreased
by mixing AH. In the case of (b) BFS 40%, it
accompanies increase of mix rate of AH and amountof chromium leaching decreases. When mixing rate of
AH is 5%, chromium leaching had shown the smallest
value. In case of (c) BFS 55%, it accompanies
increase of mix rate of AH and the amount ofchromium leaching decreases. After mixing 3% of AH
chromium leaching has decreased sharply. In AH5%,
chromium leaching shows the smallest value. In thecase of (d) BFS 70%, it was same trend with (c).

(a) AH 0%(a) BFS 0%(b) BFS 40%

79(b) AH 2%

(c) AH 3%(d) AH 5%(a) Waste 25%(b) Waste 50%
Fig.10 Relationship between compressive
strength and replacement BFS

5. Conclusions
The purpose of this research is to prevent leaching of
hexavalent chromium (Cr( ϭ )) by cementitious
materials with waste-OPC-BFS-AH and to consider
possibility as a practical material. The experimental
results could be concluded as follow.
1) In this research, when mixing 50% of waste, BFS
31.5%-OPC13.5%-AH5% ( BFS70% - OPC30% AH5% ) showed the highest fixing ability of Cr(ϭ).
2) In the case of mixing only AH, fixing ability ofCr( ϭ ) improved with including the AH
replacement ratio because aluminate hydrationsuch as ettringite was generated in early age.
3) In the case of mixing only BFS, fixing ability ofCr(ϭ) improved with mixing BFS because AFm
phase which was taken Cr(ϭ) and changes into
AFm (Cr), was able to increase in the early age.
4) In the case of mixing BFS and AH, fixing abilityof Cr(ϭ) improved with including AH and BFS
replacement ratio because AFm (Cr) and AFt (Cr)were generated in the early age. Therefore, it canconsider that the generation of AFm(Cr) andAFt(Cr) in the early age is important to improve
the fixing Cr(ϭ).
5) From the view of compressive strength, it is notenough to use as general structure material.
However, there is a possibility that the waste canbe used as Interlocking Block.

(e) AH 7%
Fig.8 Peak Intensity Ratio of Ettringite, AFm (Cr) andAFt (Cr)

Fig.9 Relationship between Pore volume andreplacement ratio of AH

4. Consideration to practical use
Fig.10 shows relationship between compressive
strength and replacement ratio of BFS. In the case of
mixing waste 25%, compressive strength was 30MPa
on curing 28 days. On the other hand, in the case of
mixing 50% of waste, compressive strength had
decreased to 15MPa on curing 28 days. Compressive
strength decreased by mixing waste. But, cementitious
materials with waste – OPC – BFS - AH exceeds
standard strength 15MPa4) in the precast concrete such
as Interlocking Block. There is a possibility that thewaste can be used as Interlocking Block.

࠙Referencesࠚ
[1] Shigeru Takahashi et al., Hydrated Products in
Ca3Al2O6-CaSO4࣭2H2O-CaCrO4 System, Journal of
the Society of Inorganic Materials, Japan (2005)[2] Yuya Yoda et al., Hexavalent chromium
immobilization by high blast-furnace slag, Cement
science and concrete technology (2010)[3] Japanese industrial standards, JIS R 5201 Physicaltesting methods for cement, (2011)[4] Japanese industrial standards, JIS A 5371 precast
unreinforced concrete products, (2011)80

URBAN AERODYNAMIC PARAMETERIZATION FOR URBAN DISTRICTS BY
USING LARGE-EDDY SIMULATION WITH 3D BUILDING GIS
Student number : 10M18200

Name : Takashi MIYAMOTO

Supervisor : Manabu KANDA

3 ḟඖᘓ≀ GIS ࢆ⏝࠸ࡓ LES ࡟ࡼࡿ㒔ᕷ⾤༊ࡢὶయຊᏛⓗࣃ࣓ࣛࢱࣜࢮ࣮ࢩࣙࣥᐑᮏ ᓫྐ
ᮾி㒔 23 ༊ཬࡧྡྂᒇᕷ࠿ࡽỈᖹ 1km ᅄ᪉ࡢ㡿ᇦࢆ 110 ࠿ᡤ㑅ᐃࡋ㸪GIS ࡛⾲⌧ࡉࢀࡓᐇ㒔ᕷ࡟࠾ࡅࡿᘓ≀࿘ࡾࡢὶࢀሙࡢ LES ࢆ⾜ࡗࡓ㸬ࡑࡢィ⟬⤖ᯝ࠿ࡽ࣓ࢯẼ㇟ࣔࢹࣝࡢࡓࡵࡢ㒔ᕷ࡟࠾ࡅࡿᆅ⾲㠃ࣃ࣓࣮ࣛࢱ࡛࠶ࡿࢮࣟ㠃ኚ఩ཬࡧ㐠ື㔞⢒ᗘࢆ⟬ᐃࡋࡓ㸬ࡇࢀࡽࡢ⤖ᯝࢆໟᣓࡋ㸪ࣂࣝࢡ࡞ᆅ⾲㠃ᗄఱࣃ࣓࣮ࣛࢱ࡜㛵㐃௜ࡅࡿࡇ࡜࡛㸪ࢮࣟ㠃ኚ఩ཬࡧ㐠ື㔞⢒ᗘࡢ᪂ࡓ࡞᥎ᐃᘧࢆᥦ᱌ࡍࡿ㸬conservation equation along ‫ ݔ‬direction is written as
Eq.ሺͳሻ;1. Introduction
There are many researches on weather prediction
such as the impact assessment of land surface
parameter for urban climate using mesoscale weather
prediction models. Numerical weather prediction
model simulations are useful and important ininvestigating the characteristics of urban weather andclimate. Since atmospheric phenomena over land arestrongly influenced by surface properties andprocesses, the geometric properties and physicalprocess of land surfaces should somehow be
incorporated into numerical models. Furthermore,
urban surface is a very complex type of landcharacteristic category.
However, most of them do not reflect the effect ofreal urban geometries because they assume roughgeometry data using simple building shapes at low
resolution (a few hundred meters at most).
In this study, two accurate urban aerodynamic
roughness parameters displacement height( ݀ ) androughness length(‫ݖ‬଴ ) corresponding to actual urban
geometries in Tokyo’s 23 wards and Nagoya city were
calculated from an output of the large eddy simulation(LES) in which the building canopy was explicitly
resolved with fine grid spacing (Tokyo : 2.0m, Nagoya
city : 2.5m). ݀ is the effective height of the surface.
This height is the level at which the mean drag on thesurface appears to act. ‫ݖ‬଴, a non-substantial length, isconsidered as a length scale to represent the
aerodynamic roughness of the surface.
Then, the two parameters calculated from an
output of LES were compared with the popular andcommonly utilized morphology-based model1) whichcalculates the two parameters based only on simple
urban morphological information (such as averagebuilding height).

ͳ ߲ܲ
‫ݑܦ‬
ൌെ
െ ‫ܨ‬ఛ௫ ሺͳሻߩ ߲‫ݔ‬‫ݐܦ‬
where, ‫ܨ‬ఛ௫ is the ground surface drag in ‫ ݔ‬direction.
By integrating Eq.ሺͳሻ in the entire numerical volume
and introducing volume velocityܷ and an area meansurface drag ߬଴ , ߬଴ can be expressed as following
Eq.ሺʹሻ;ͳ ܷ݀ ܸ ߲ܲ௦௧൅ ൬൰ൠሺʹሻ߬଴ ൌ െ ൜‫ݔ߲ ߩ ݐ݀ ܣ‬
in which, ܷ݀Ȁ݀‫ ݐ‬ൌ Ͳ in the case of constant volume
flow and ܲ௦௧ is static pressure. By assuming ahomogeneous static pressure gradient ( ߲ܲ௦௧ Ȁ߲‫ ݔ‬ൌܿ‫ݐݏ݊݋‬Ǥ), displacement height ݀ and roughness length‫ݖ‬଴ were calculated using the following logarithmic
law for wind velocity profile (Eq.ሺ͵ሻ).
‫ݑ‬ൌ

‫כݑ‬
‫ݖ‬െ݀
 ൬
൰ሺ͵ሻ‫ݖ‬଴ߢ

where ‫ כݑ‬can be expressed using ‫ כݑ‬ൌ ඥ߬଴ Τߩ andߢ is the von Karman constant 0.4.

2.2. Morphological model equation
Aerodynamic roughness parameters ݀ and ‫ݖ‬଴ ofthe morphological model were calculated according to
the following two Eqs. ሺͶሻ and ሺͶܾሻ empiricallyderived from wind tunnel experiments.
݀ൌ ͳ ൅ ͶǤͶ͵ିఒ೛ ൫ߣ௣ െ ͳ൯ሺͶሻ
‫ܪ‬
ି଴Ǥହ
݀
‫ܥ‬஽
݀
‫ݖ‬଴
ൌ ൬ͳ െ ൰ ݁‫ ݌ݔ‬ቈെ ൬ͲǤͷ ଶ ൬ͳ െ ൰ ߣ௙ ൰቉ሺͶሻ
‫ܪ‬
‫ܪ‬
‫ܪ‬
ߢ

2. Theory
2.1. Ground surface dragThe average value of ground surface drag ߬଴ inan entire numerical domain is calculated from themomentum conservation equation. The momentum

ߣ௣ is building coverage and ߣ௙ is the ratio of thetotal areas of building facets facing the wind direction
to the lot area. ‫ܥ‬஽ is the drag coefficient 1.2, ‫ ܪ‬is81

average building height. Ɉ is the von Karman
constant 0.4.

0.5
0.4

3. Calculation condition

0.3
λp

The calculation conditions in this work are as
follows. By using the following conditions, Eq.ሺʹሻ

0.2

can be applied to calculate the average value of

0.1

ground surface drag ߬଴ .
x

Constant volume flow(ܷ݀Ȁ݀‫ ݐ‬ൌ Ͳ)xNeutral stratificationx

No Coriolis forcex

Boundary conditions

0.0
0.0

0.1

0.2

‫[ ݁ݒܽܪ‬m]‫[ ݔܽ݉ܪ‬m]ߪ‫[ ܪ‬m]ߣ݂ߣ‫݌‬‫[ ݇ܽ݁݌̴ ݓݑܪ‬m]

Geometric data of the buildings in Tokyo’s 23
wards and Nagoya city(in 2009)
Resolution Tokyo’s 23 wards: 2.0m, Nagoya
city: 2.5m

xInitial horizontal wind velocity: 3.0m/sec

xNumerical domain (x, y, z) = (1000m, 1000m,

0.5

0.6

Table.1 Surface geometric parameters and ‫ܪ‬௨௪̴௣௘௔௞Horizontal surface: periodic boundaryx

0.4

Fig.1 Relation of ߣ௣ to ߣ௙Upper surface: free-slip boundary
x

0.3
λf

(a)

(b)

(c)

37.5
154
41.4
0.19
0.31
146

29.1
188
21.2
0.56
0.38
118

7.6
40
3.3
0.29
0.38
30

in Fig.2(b). The reason for this is that about 80% of
the area’s buildings have heights lower than 40m. Also,
there are very few buildings of relatively similarheights to the tallest building in the area. In otherwords, the momentum flux of the lower buildings andthat of the highest buildings are antagonistic to eachother. Therefore, the area didn’t have the clear valueof momentum flux. The commonality of the areas isthat the clear peak value of momentum flux is unclear
similar to the characteristic of Shibuya area.

600m)

4. Calculated areas
Fig.1 shows the relation of ߣ௣ and ߣ௙ . ߣ௣ andߣ௙ are building coverage and the ratio of the total
areas of building facets facing the wind direction to
the lot area, respectively. In Fig.1, the painted circles
and triangles shown in Fig.1 were selected areas from
Tokyo’s 23 wards and Nagoya city respectively. Using
this relationship, 100 areas in Tokyo’s 23 wards and
10 areas in Nagoya city (total 110 areas) were selected
to calculate the two aerodynamic roughness
parameters.

5.2. Displacement heightFig.3 shows ݀Τ‫ܪ‬௔௩௘ vs. ɉ௣ . According to Fig.3,
the result values of the LES showed larger than that of
the morphological model because the model was
adjusted so that ݀ would not exceed‫ܪ‬௔௩௘ . However,
if building height is uneven, ݀, defined as action
center of momentum, may exceed an ‫ܪ‬௔௩௘ . Therefore,
it can be said that it is insufficient to calculate theaerodynamic roughness parameters in the actual cityusing the morphological model.
Fig.4 shows the new parameterization about ݀. The
curves in Fig.4 are depicted the Eqs.ሺͷ̱ܽܿሻ by achange of ɉ௣ . Eq.ሺͷܿሻ is the regression expression
for staggered arrays with constant building height. By
using Eqs. ሺͷ̱ܽܿሻ , ݀ will be calculated more
precisely than Eq.ሺͶܽሻ. The difference of Eq.ሺͶܽሻand Eq.ሺͷሻ is shown in Fig.5.

5. Results
5.1. Vertical profiles of momentum flux andmain wind velocity
Fig.2 shows signature vertical profiles of windvelocity and momentum flux with each aerial
photograph. Table-1 shows the surface geometricparameters of the three areas and ‫ܪ‬௨௪̴௣௘௔௞ (Height of
momentum flux peak value). In Fig.2, solid lines,
chain lines and gray zones show each‫ܪ‬௠௔௫ (Maximum building height), ‫ܪ‬௔௩௘ (Averagebuilding height) and ‫ܪ‬௔௩௘ േ ߪு ( ߪு : Standard
deviation of building height) respectively. In Fig.2, thepeak values of momentum flux are located at muchhigher than ‫ܪ‬௔௩௘ and relatively close to‫ܪ‬௠௔௫ . Thismeans that the momentum roughness parameters
depend on ‫ܪ‬௠௔௫ even though this parameter is notconsidered in the morphological model. In additionߪு tends to obscure the peak value of momentum flux݀Τ‫ܪ‬௠௔௫ ൌ െʹߙ‫ ݔ‬ଷ ൅ ͵ߙ‫ ݔ‬ଶ ሺͷܽሻ‫ ݔ‬ൌ ሺ‫ܪ‬௔௩௘ ൅ ߪு ሻΤ‫ܪ‬௠௔௫ ሺͷܾሻߙ ൌ ͳ ൅ ͳ͹͸ͷͲ͵Ǥͳିఒ೛ ൫ߣ௣ െ ͳ൯ሺͷܿሻ

82

1km
600

(a)

(a)

(a)

(a)
500

z[ ]

1km

400
300
200
100
0
0%

0.0

u

154.0[ ]

[

]

uw [

]

20%

40%

60%

100%

80%

Cumulative rate of buildings[%]

600

(b)

(b)

(b)

(b)

500

z[ ]

400
300
200
100
0
0%

0.0

u

188.0[ ]

[

uw [

]

20%

40%

60%

80%

100%

Cumulative rate of buildings[%]

]

600

(c)

(c)

(c)

(c)

500

z[ ]

400
300
200
100
0

0.0

u

40.0[ ]

[

uw [

]

]

0%

20%

40%

60%

80%

100%

Cumulative rate of buildings[%]

Fig.2 Vertical profiles of wind velocity u and momentum flux uw, ((a): Shinagawa, (b): Shibuya and (c):Senzoku)Eq.(4a)Tokyo

0.05ӌλp㸺0.10
0.25ӌλp㸺0.30
0.40ӌλp㸺0.45
λp=0.20
λp=0.35

Nagoya3.5

0.15ӌλp㸺0.20
0.30ӌλp㸺0.35
λp=0.10λp=0.25λp=0.400.20ӌλp㸺0.25
0.35ӌλp㸺0.40
λp=0.15λp=0.30
λp=0.45

1

3
0.8

2
0.6
݀⁄‫ݔݔܽ݉ܪ‬d/Have

2.5

1.5

0.4

1
0.5

0.2

0
0.1

0.2

0.3
λp

0.4

0.5

0
0

Fig.3 Relation of ݀Τ‫ܪ‬௔௩௘ to ɉ௣ , (ᇞ: Shinagawa, □:Shibuya and ×: Senzoku)

0.2

0.4
0.6
(‫݁݁ݒܽܪ‬+ߪ‫) ܪ‬⁄‫ݔݔܽ݉ܪ‬0.8

Fig.4 Relation of ݀Τ‫ܪ‬௠௔௫ to ሺ‫ܪ‬௔௩௘ ൅ ߪு ሻΤ‫ܪ‬௠௔௫

83

1

200

150

y = 9.57x2 + 1.83x + 0.20
R² = 0.62‫ݖ‬0 (‫ܧܮ‬‫)ܵܧ‬⁄‫ݖ‬0 (Eq.(4b))݀(Eq.(5)) or ݀(Eq.(4a)) [m]4y = 0.90x
R² = 0.90

100
y = 0.26x
R² = 0.11
50

0

3

2

1

0
0

50

100

150

200

0

0.1

0.2

݀(‫[ )ܵܧܮ‬m]

Fig.5 Comparison between Eq.ሺͷሻ and Eq.ሺͶܽሻ,(○:Eq.ሺͷሻ, ᇞ:Eq.ሺͶܽሻ)
0.05- λf -0.15
0.05-λf-0.15
0.35-λf-0.45
0.35- λf -0.45

0.6

0.15- λf -0.25
0.15-λf-0.25
0.45-λf-0.55
0.45- λf -0.55

0.5

2.0

0.4

0.8

0.3

0.6
0.5
0.4

0.25- λf -0.35
0.25-λf-0.35
0.55-λf-0.65
0.55- λf -0.65

0.2

0.1

0.1

0.5

20
y = 0.88x
R² = 0.85=700.0 (Eq.(4b))

0.3

0.2

0.4

Fig.7 Relation of ‫ݖ‬଴ ሺ‫ܵܧܮ‬ሻΤ‫ݖ‬଴ ሺǤ ሺͶሻሻ toɉ௣ ൈ ߪு Τ‫ܪ‬௔௩௘‫ݖ‬0 (Eq.(6)) or ‫ݖ‬0(Eq.(4b)) [m]z0/Have30.00.3

λ‫ܪߪ݌‬⁄‫݁݁ݒܽܪ‬

15

10

5

y = 0.51x
R² = 0.49
0
0

5

10

15

20

‫ݖ‬0 (‫[)ܵܧܮ‬m]

0.0
0.1

0.2

0.3
λp

0.4

Fig.8 Comparison between Eq.ሺ͸ሻ and Eq.ሺͶሻ, (○:Eq.ሺ͸ሻ, ᇞ:Eq.ሺͶሻ)

0.5

Fig.6 Relation of ଴ Τ‫ܪ‬௔௩௘ to ɉ௣

6. Conclusions
The methods for estimating two aerodynamic
roughness parameters, displacement height androughness length were discussed. The numericalresults of vertical profiles of momentum flux andwind velocity were shown. It was found that dragcoefficients, calculated from the numericalsimulations, became significantly different from those
calculated using the widely used conventional
morphological models. On the basis of the discussion
above, a new simple aerodynamic parameterization
was proposed.

5.3. Roughness length
Fig.6 shows the relation of ଴ Τ‫ܪ‬௔௩௘ to ɉ௣ . Itshould be noted that even if the ɉ௙ of Shibuya has
about three times as large as that of Shinagawa (cf.
Table.1), the value of ‫ݖ‬଴Τ‫ܪ‬௔௩௘ is almost equal toeach other. In this regard, Xie et al. (2008)2) pointed
out that tall buildings have a significant effect on the
total drag of a building array with non-uniform
heights. It seems that areas that have a very large ߪு
increase the momentum exchange.
Fig.7 shows the newly proposed parameterizationof ଴ . The curve in Fig.7 (Eq.ሺ͸ሻ) is the regression
expression to calculate ଴ more precisely. By using
Eq.ሺ͸ሻ, ଴ will be calculated more precisely thanEq.ሺͶܾሻ. The difference of Eq.ሺ͸ሻ and Eq.ሺͶܾሻ isshown in Fig.8.

Reference
R. W. Macdonald, R. F. Griffiths, and D. J. Hall,
An improved method for estimation of surface
roughness of obstacle arrays, Atmospheric
Environment, vol. 32, pp. 1857-1864, 1998.
2) Xie Z-T, Coceal O, Castro IP(2008), Large-eddy
simulation of flows over random urban-likeobstacles, Boundary-Layer Meteorol, 129:1-231)‫ݖ‬଴ൌ ͻǤͷ͹‫ ݔ‬ଶ ൅ ͳǤͺ͵‫ ݔ‬൅ ͲǤʹͲሺ͸ሻ‫ݖ‬଴ ሺ‫ݍܧ‬Ǥ ሺͶܾሻሻ‫ ݔ‬ൌ ߣ௣ ൈ ߪு Τ‫ܪ‬௔௩௘ ሺ͸ܾሻ
84

M ODULATION C LASSIFICATION U TILIZING S PECTRAL C ORRELATION FOR
E MERGENCY R ADIOS
Student Number: 10M18223

Name: Azril HANIZSupervisor: Jun-ichi TAKADA⏵ ␖⏫ ␄ ␧৷ԟ⏓ ๆ⎅⎠ ‫ݍ‬қߗ⎯‫מ‬փ෌ভઅද⎯ബ૛൘ߧ⎯ߨധ൚⎬ ԟ⎚⏌ ٥֋⏞ ⏶␦ ␧わ ␋␇⏶੒ઘझ⎯⏉ ⎇ ⎫ ଼‫౎⎍ݍ‬१⎘ ⎠ࣟ‫܄‬る ॓ҏү܎⎌⏊ ⎯քࢊ⏽⍬␜ ⎍ಎ‫ݍ‬ઘ⎬ ‫⎒⎥ ⎒ױ‬る քූӑஓ⎬ ‫ދ‬ю⎚⏌ ⎩า২⎖ ⏍⏌ れ ஔλ࠵ఉॅ⎯‫ޡ‬ๆ⎬⏉ ⏌ Ӻࢷ෻੕⏓ ѽ‫⎰⎬⏂⎠ ⏌⎚ى‬る ಎ‫ݍ‬ઘ⎬⎋⎒ ⏌ ࠵ఉॅ๭ๆֺࣨ⎋⏉ ⎴࠵ఉॅ⏓ ‫ޡ‬ๆ⎘ ⎧ ⎅⏌ ‫מ‬փ෌ভઅද⎯ஔ଑⎍಩์⎨⎃ ⏌ れ ඕ٥֋⎨⎰る ബ૛ࣿ‫࠵⎯܃‬Ճ଑࣢ड़⎋⏉ ⎴⏵ ␖⏫ ␄ ␧৷ԟව୞⏓ ๆ⎅⎠ ബ૛൘ߧ⎯ߨധ൚⎬ શෳ⎘ ⎠ れ ஔࠡ൚⏓ ๭ๆ⎘ ⎠ Ղਣ٥֋⎰ඕ٥֋⎨২଑⎚⏌ ബ૛൘ߧ⏓ ߨധ⎚⏌ ⎠⏂⎯թ໱⎍ࡒഇ⎨⎰⎫ ⎅れ ඕ٥֋⎨⎰る ⏵ ␖⏫ ␄ ␧৷ԟ‫ޔظ‬൚⎯҉াる ⎋⏉ ⎴ई⎠ ⎫ ஦૎ળࡥ൚⏓ ஖௡⎚⏌ ⎔ ⎩⎨る ⏉ ⏋ ਪ⎐ ⎯ബ૛൘ߧ⎯ߨധ⏓ я௾⎩ ⎘ ⎠ れ ⎖ ⏊ ⎬ ‫ޔظ‬Ն⏳ ␛ ␡ ␨ ⍬⏳␣ ␯ ⎬ ⏉ ⏋ ଚΕࠡ൚⎯٠࣍⏓ ۱⎅る ࣿ‫ށ਻܃‬цಅ (SNR) ⎍ 4dB Ιࣘ⎃ ⏍⎱ 0.9 Ιࣘ⎯ҷ๻⎨ 6 ࠦຳ⎯ബ૛൘ߧ⎍ߨധя௾⎨⎃ ⏌ ⎔ ⎩ ⏓ ߟ⎘ ⎠ れ

1Introduction

this resulted in an oversimpliﬁcation of the SCD plot, leading to a loss of useful information to differentiate modulations [3].
The main objective of this study is to study the feasibility of exploiting signal cyclostationarity as a mean to classify a large number of modulation schemes commonly usedby disaster emergency radios, and to obtain the optimum parameters for the system to successfully classify the targeted
modulation schemes.

Whenever a large disaster occurs, damage to the telecommunication infrastructure can be expected, and emergency
rescue teams from all over the world would have to set
up their own mobile wireless network to communicate between their emergency personnel. As the number of rescue
teams increases, the availability of unused frequency spectrum will become a problem, and once two different teams
communicate on the same wireless channel, there is a highpossibility of interference, which will then hamper their rescue operations.
In these kinds of situations, a radio surveillance system would prove to be useful. The system’s main purpose
would be to scan the radio environment and identify any
transmitters on the scene. This identiﬁcation may be performed by extracting parameters from the received signal,
and recognizing the modulation scheme used by the transmitter. Modulation is the process of putting data such as
human voice on a high frequency carrier signal which issuitable for wireless transmission. The main focus of these
thesis will be on modulation classiﬁcation.
A survey has been done on commercial wireless radio
systems commonly used by rescue teams in the event of adisaster. In Japan, there still exists local councils which still
utilize analog modulation (AM), although the government
encouraged the use of digital radios since 2002. Elsewherearound the world, there have been a few efforts to standardize emergency radios, but unfortunately these efforts were
only limited to certain parts of the world, and not worldwide. We have found that many different commercial systems utilize various modulation schemes such as AM, π/4DQPSK, π/8-D8PSK, GMSK and 4FSK.
Automatic modulation classiﬁcation (AMC) has been along ongoing research topic, and different approaches havebeen taken to tackle this issue. [1] has conducted an extensive survey of recent AMC techniques. There exists muchliterature regarding modulation classiﬁcation based on exploitation of signal cyclostationarity [2]. This research focuses on the technique which utilizes the spectral correlation density (SCD). Existing literature used the cyclic domain proﬁle (CDP) to extract features from the SCD, but

2 Cyclostationarity
2.1 Signal Model
For this thesis, we assume the received signal can be expressed byx(t) = Re h · u(t)ej2πFc t + v(t) ,(1)where x(t) is the received real IF signal, h is time invariant
channel response and Fc is the pseudo-carrier frequency.
u(t) and v(t) denote the complex envelope of the transmitted signal s(t) and noise respectively. Noise is assumed tobe additive white Gaussian noise (AWGN) with zero meanand σv 2 variance. The signal-to-noise ratio (SNR) of thereceived signal is deﬁned as the power of the signal divided
by the in-band noise power.
2.2 Spectral Correlation Density (SCD)
Most communication signals can be modeled as cyclostationary processes that exhibit underlying periodicities intheir signal structures. These periodicities can be exploitedin order to differentiate between the transmitted signal andnoise, and also between signals. A useful way to measure
the spectral correlation of a cyclostationary signal is by using the spectral correlation density (SCD) [4]. The SCD isdeﬁned as
Sxα (f ) =85

1
lim limT →∞ Δt→∞ Δt

 Δt
2
− Δt
2

1
α
XT (t, f + )T
2α
XT∗ (t, f − )dt ,
2

(2)

1

0.75

0.75
C̃ α (Fc )C̃ α (Fc )

1

0.5
0.25
0
0

20

40

60
80
α (kHz)100(a) Rectangular window

Figure 1: SCD plot of a BPSK signal

XT (t, f ) =t− T2x(u)e−j2πf u du

(3)

1
M

(M−1)/2
v=−(M−1)/2

XT∗ (t, f −

0
0

20

40

60
80
α (kHz)100120(b) Kaiser window (β = 10)

0.3

is the Fourier transform of the input signal. Here, f is the
spectral frequency and α is the cyclic frequency. The integrand in Eq.(2) is called the cyclic periodogram. In practice,
the SCD can only be estimated using a ﬁnite set of samples.
Frequency-smoothing was performed on the SCD to reduceany random ﬂuctuations caused by this, and it can be expressed in the discrete domain by
SxαT (t, f )Δf =120

Figure 2: Effect of various window functions on the SCD along
the f = Fc axis (BPSK signal, Rs = 29kSps,
SNR= 20dB, Fsamp = 240kHz, L = 4096, M =
65)

where
 t+ T2

0.5
0.25

C̃ α (Fc )0.25

After filtering

0.15
0.1
0.05
0
8

α
1
XT (t, f + + vFs )T
2α+ vFs ) .
2

Before filtering

0.2

9

10
α (kHz)11

12

Figure 3: Effect of using emphasis ﬁlter (QPSK signal, Rs =10kSps, SNR= 20dB, Fsamp = 240kHz, L = 4096,
M = 65, K = 4, Rect. window)(4)
3.2 Dip in Noise Floor

Here, Δf = M Fs is the width of the frequencysmoothing interval, Fs = Fsamp /L is the frequency sampling increment, and L is the FFT size. Next, the spectral
coherence function was used to normalize the SCD plot,
and can be expressed by

One more issue that is faced when implementing the SCD
discretely is there is a dip in the noise ﬂoor close to the symbol rate peak. This dip is a result of the frequency smoothing across the SCD. If the symbol rate peak magnitude islowered due to this dip, it would be difﬁcult to identify it. A
αsimple ﬁlter is proposed to whiten the noise ﬂoor and em(t,
f
)
S
Δf
xT
. phasize the symbol rate peak, and we refer to this ﬁlter as
CxαT (t, f )Δf = 0
α
α
0
1/2
[SxT (t, f + 2 )Δf SxT (t, f − 2 )Δf ](5) an emphasis ﬁlter.

Finally, the SCD was also averaged over many blocks
over time to improve reliability, and this operation can be
written asĈ(α) = C̃(α) −

Kαs
Kαs
1
C̃(α −) + C̃(α +
)
2
2
2

(7)

Here, C̃(α) denotes the SCD in Eq.(6) when f = Fc , andαs denotes the cyclic frequency increment. This ﬁlter aver(6)ages the difference between the peak and the K/2-th cyclic
frequencies from both the left and right side. A comparison
where N is the number of time-averaging blocks used. As between the SCD along the f = Fc axis before and afteran example, the SCD plot of a BPSK modulated signal is ﬁltering is shown in Figure 3. Here, the magnitude of theshown in Figure 1.
SCD after ﬁltering is adjusted to match the noise ﬂoor of
3 Implementation Issues in Discrete Time the original SCD for easier comparison.
C̃xαT (f )Δf =N −1

1
C α (t + nT, f )Δf ,N n=0 xT

4 Automatic Modulation Classiﬁcation

Domain3.1 FFT Scalloping Loss

4.1 Feature Extraction

When the cyclic frequency α of the peak associated withthe symbol rate does not fall exactly in the center of the
FFT bin, scalloping loss occurs. The FFT can be thought ofas a series of ﬁlters which are centered on the FFT bins. The
ﬁlter responses will be attenuated as the frequencies moveaway from the center, and if a frequency is located exactly
in between two adjacent FFT bins, then it would experiencethe largest scalloping loss. Instead of a rectangular window,
we propose to use a Kaiser window (β = 10) to reduce
this scalloping loss, and obtain a higher peak magnitude, as
shown in Figure 2.

Different modulation schemes produce distinct SCD plots
which may be exploited for AMC, and the location of thepeaks on the f − α plane is related to the carrier frequency(Fc ) or the symbol rate (Rs ). To reduce the amount of
data, [3] took the cyclic domain proﬁle (CDP) which is
the maximum of the SCD taken over spectral frequency f .
However, some peaks may overlap, resulting in ambiguityon the actual location of the peaks. In this research, we propose to calculate only certain points on the SCD to be usedas features in order to avoid this ambiguity, and also reduce
computational load.
86

Table 1: List of simulation parameters(a) QPSKFFT Size, L
Freq. smoothing interval, M
No. of time-avg. blocks, N
Sampling freq., Fsamp
Pseudo-carrier freq., Fc
Symbol rate, Rs
SNRWindow function(b) 8PSK

Figure 4: Comparison of SCD plots between several higherorder modulation schemes (Rs = 30kSps)

4096
65 bins100
240 kHz60 kHz
30 kSymbol/s−10 ∼ 20dB
Rectangular window

F2

F3 F4 F5
00
Rs 2RsCyclic Frequency, α2Fc 2Fc−2Fc−Rs Rs/2(a) Original received signal

F6Probability of Correct
Classification

F1
Fc

Frequency, fFrequency, f

1
F7

Fc
F8

F9 F10 F11

2Fc−
2Rs

2Fc 2Fc−2Fc−Rs Rs/200
Rs 2RsCyclic Frequency, α(b) Squared received signal

AM
BPSK
QPSK
8PSK
π/2−DBPSK
π/4−DQPSK
OQPSK
MSK
GMSK
2FSK
4FSK

0.8
0.6
0.4
0.2
0

−8 −4

Figure 5: Feature locations on the f−α plane

0 4 8 12 16 20
SNR (dB)(a) Boolean data

Probability of Correct
Classification

1

Figure 6: Decision tree (Numeric data, Rs = 30kSps, SNR=
20dB)

AM
BPSK
QPSK
8PSK
π/2−DBPSK
π/4−DQPSK
OQPSK
MSK
GMSK
2FSK
4FSK

0.8
0.6
0.4
0.2
0

−8 −4

0 4 8 12 16 20
SNR (dB)(b) Numeric data

Higher-order modulation schemes such as QPSK and
8PSK do not exhibit second-order cyclostationary properties, and therefore have very similar SCD plots, and this isshown in Figure 4. Therefore, we propose to square thereceived signal before calculating the SCD, which will remove lower modulations orders and allow classiﬁcation using the SCD. The location of the peaks on the SCD which
are selected as features is shown in Figure 5.

Figure 7: Probability of correct classiﬁcation for each modulation(Rs = 30kSps)

The second type is numeric data, where the peak magnitude itself was used as training data. Comparison between
using these 2 types data will be shown in the results section.
Figure 6 shows an example of a decision tree generated by
WEKA using numeric training data.

4.2 Classiﬁcation

4.3 Simulation Results

There exists much literature which discuss the use of neuralnetworks together with spectral correlation analysis [3]. In
this research, the traditional binary decision tree was usedbecause of its simplicity in implementation. A supervised
learning algorithm is employed, where the system is trained
beforehand with supervised training data to obtain an optimal decision tree. The C4.5 algorithm, which was used tobuild the decision tree, uses information gain ratio as thecriterion in selecting the best features to be nodes in thedecision tree [5]. The java implementation of the C4.5 algorithm is called J48 and is included in the WEKA data
mining tool.
Two types of training data was used to generate decision trees. The ﬁrst type is nominal (Boolean) training data,
which consists of a yes or no, representing the presenceor absence of a peak at feature locations shown in Figure
5. To decide the presence of a peak, the peak magnitudes
were compared with a threshold, which was obtained empirically from additive white Gaussian noise data and basedon a probability of false alarm (PFA ).

4.3.1 Simulation Setup
Monte Carlo simulations were run with AM, BPSK,
π/2-DBPSK, QPSK, π/4-DQPSK, 8PSK, OQPSK, MSK,GMSK, 2CPFSK and 4CPFSK modulated signals. PSK
signals used a raised cosine ﬁlter for pulse-shaping with aroll-off factor of 0.5, and GMSK used a Gaussian ﬁlter witha BT product of 0.5.
1000 sets of training data were used to build the decision
tree. Using this decision tree, a Monte Carlo simulation of1000 trials was performed on each modulation. A very low
PFA = 0.1% was chosen because training the system using data with many false alarms will hamper the training
process.
4.3.2 Results
Figure 7 shows the probability of correct classiﬁcation of
11 modulation schemes for 2 cases, using Boolean (nominal) data and numeric data. Performance using numeric
data fared better than using Boolean data because WEKA
87

could catch smaller differences of peak magnitude when using numeric data. We can see that the system can correctlyclassify all 11 modulations with a high probability down to
SNR= 4dB. At low SNRs, the feature peaks on the SCD
plot diminish and is buried under the noise, thus reducing
the probability of correct classiﬁcation.
Figure 8 shows the results when the symbol rate is varied uniformly between Rs = 1 ∼ 30 kSps at 1 kSps steps.
It is assumed that Rs is known using a symbol rate estimator, and a decision tree was generated for each Rs . It canbe seen that the performance is degraded, especially whenutilizing Boolean training data. Scalloping loss reduces themagnitude of the peak on the SCD, and thus making it more
difﬁcult for the system to classify modulation schemes at
small SNRs.
Figure 9 shows the probability of correct classiﬁcation
which is plotted against SNR and symbol rate Rs , when aKaiser window is used. It can be seen that the performanceat low symbol rates (Rs < 5kSps) is deteriorated. This isbecause if the signal bandwidth (which is roughly equal toRs ) is smaller than M , the peak will be smoothed out. This
will result in a smaller peak magnitude, thus reducing theprobability of correct classiﬁcation.

Average Probability ofCorrect Classification

0.8
Boolean(Fixed Rs)

0.6

Boolean(Varying Rs)

0.4

Numeric(Fixed Rs)

0.2
0Numeric(Varying Rs)

−8

−4

0

4
8
SNR (dB)

12

16

20

SNR (dB)

Figure 8: Comparison between ﬁxed Rs and varying Rs
20

1

15

0.8

10

0.6

5
0.4

0

0.2

−5
−10

5

10
15
20
25
Symbol Rate, Rs (kSps)

30

0

Figure 9: Avg. prob. of correct classiﬁcation for proposed AMC technique with perfect knowledge of Rs(Kaiser window, numeric data)

SNR (dB)

5

1

Symbol Rate Estimation Utilizing SCD

20

1

15

0.8

10

0.6

5

0.4
In a ideal blind AMC system, the symbol rate Rs of the0received signal is unknown, and it is necessary to estimate
0.2−5
it beforehand. In this thesis, a technique to estimate the
−10
0
5
10
15
20
25
30
symbol rate Rs using the SCD plot is proposed. For many
Symbol Rate, Rs (kSps)
digital modulation schemes, the cyclic peak which appears
along this f = Fc axis is associated with the symbol rate Figure 10: Avg. prob. of correct classiﬁcation for proposed
AMC technique with Rs estimation (Kaiser window,
Rs . Therefore, to estimate the symbol rate, ﬁrst the SCD
numeric data)
along the f = Fc is calculated. Then the proposed emphasis ﬁlter of width Δα = Kαs is applied, where αs is 7 Conclusion
the cyclic frequency increment, and K is the emphasis ﬁlIn this thesis, the modulation scheme of received signals is
ter length. The cyclic frequency associated with the highest
used to try to identify emergency radios in a disaster scepeak equals the symbol rate Rs of the received signal.
nario. A novel AMC technique which utilizes speciﬁc fea(8) tures from the SCD plot of both the original received sigR̃s = arg max ĈxαT (Fc )ΔαΔfαnal and the squared received signal was proposed. Several
6 Whole System
issues when implementing the SCD algorithm discretely
In this section, the simulation results for the whole system is were identiﬁed, and methods of compensation was alsopresented, where the proposed symbol rate estimation tech- discussed. The performance evaluation of the whole sysnique is used together with the proposed AMC technique. tem incorporating both the symbol rate estimator and theThis brings the system one step close to blind AMC, where modulation classiﬁer revealed that the overall system could
there is no prior information about the received signal. correctly classify 6 modulation schemes down to an SNR
However, since there a several modulation schemes whose of 4 dB. However, the optimum length of the frequencysymbol rates cannot be properly estimated using the pro- smoothing interval M still remains an issue. The effect of
posed symbol rate estimation technique, this simulation was the carrier frequency offset, and also multipath channel conperformed with a smaller number of modulation schemes, ditions will have to be addressed in future work.

which are BPSK, π/2-DBPSK, QPSK, π/4-DQPSK, 8PSK References
and GMSK. However, the decision trees used here are gen[1] O. A. Dobre et al., “A survey of automatic modulation classiﬁcation
erated using all 11 modulation schemes in Section 4.3.
techniques: classical approaches and new trends,” IET Commun.,vol. 1, pp. 137-156, April 2007.
[2] B. Ramkumar, “Automatic modulation classiﬁcation for cognitive
radios using cyclic feature detection,” IEEE Circuits Syst. Mag.,
vol.2, no.2, pp. 27-45, Second Quarter 2009.
[3] E. Like et al., “Signal Classiﬁcation in Fading Channels Using
Cyclic Spectral Analysis,” EURASIP Journal on Wireless Communications and Networking, Volume 2009, 2009.
[4] W. A. Gardner, “Cyclostationarity in Communications and Signal
Processing,” IEEE Press, 1993.
[5] J.R. Quinlan, “C4.5 : Programs for Machine Learning,” Morgan
Kauffman Publishers, 1993.

6.1 Results
Figure 10 shows the results of AMC with prior symbol
rate estimation. The result presented here is the probability of correct classiﬁcation averaged over all the 6 targeted
modulation schemes. The proposed technique can correctlyclassify 6 modulation schemes with a probability of close
to 1 down to SNR= 4 dB within symbol rate range of
Rs = 5 ∼ 30 kSps.
88

THE DECOMPOSITION OF NITROUS OXIDE OVER RUTILESTRUCTURE TITANIA SUPPORTED METAL CATALYSTS
Student number: 10M18230 Name: Wanying HU Supervisor: Hirofumi HINODEࣝࢳࣝᆺࢳࢱࢽ࢔ᢸᣢ㔠ᒓゐ፹࡟ࡼࡿ୍㓟໬஧❅⣲ࡢศゎ⬌ ፣⍧ ᮏ◊✲࡛ࡣࠊᕤᴗ⏕⏘㐣⛬࠿ࡽ᤼Ẽࡉࢀࡓ N2O ࡢศゎࢆ┠ⓗ࡟ࠊ୕✀㢮ࡢࣝࢳࣝᆺࢳࢱ
ࢽ࢔ࢆᢸయ࡜ࡋ࡚ࠊࢳࢱࢽ࢔ᢸᣢ㔠ᒓ㸦㖟ࠊࢭ࣒ࣜ࢘ࠊ࣐ࣥ࢞ࣥࠊ࣐ࢢࢿࢩ࣒࢘㸧ゐ፹ࢆ
ㄪ〇ࡋࠊN2O ศゎ࡟ᑐࡍࡿゐ፹άᛶࢆホ౯ࡋࡓࠋࡲࡓࠊㄪ〇᮲௳࡛࠶ࡿ㔠ᒓᢸᣢ㔞ࠊᢸయࡢ✀㢮ཬࡧ↝ᡂ ᗘ࡟ࡼࡿゐ፹άᛶࡢኚ໬ࢆ᳨ウࡋࡓࠋㄪ〇ࡋࡓゐ፹ࡢ୰࡛ࠊ550 Υ࡛↝ᡂࡋࡓࣝࢳࣝᆺࢳࢱࢽ࢔(Toho-1)ᢸᣢ㖟(10 wt%)ゐ፹ࡣ᭱ࡶ㧗࠸ゐ፹άᛶࢆ♧ࡋࠊ㓟⣲ࡀ࡞
࠸ሙྜࠊN2O ࡣ 350 Υ࠿ࡽศゎࡋጞࡵࠊ650 Υ࡟࠾࠸࡚᏶඲࡟ศゎࡋࡓࠋ㓟⣲Ꮡᅾୗ࡛ࡣࠊ
N2O ࡢศゎ ᗘࡣ㧗 ഃ࡟ࢩࣇࢺࡋࡓࠋゐ፹ࡢ࢟ࣕࣛࢡࢱࣜࢮ࣮ࢩࣙࣥ࡟ࡼࡾࠊẚ㍑ⓗ࡟ᑠࡉ࠸ᢸᣢ㔠ᒓ⢏Ꮚᚄࢆᣢࡘゐ፹ࡣࠊࡼࡾⰋ࠸ゐ፹άᛶࢆ♧ࡋࡓࡇ࡜ࡀࢃ࠿ࡗࡓࠋ
TiO2 were shown in Table 1. The precursors of four
metals are Ag(NO3) (99.8%, Wako Co.),(98.0%,Wako
Co.),
Ce(NO3)3·6H2O(CH3COO)2Mn·4H2O (99.0%, Wako Co.) and(99.9%,Wako
Co.)(CH3COO)2Mg·4H2O
respectively.

1 Introduction
Nitrous oxide (N2O) is one of the powerfulgreenhouse gases with a global warming potential permolecule of about 300 times that of carbon dioxide(CO2) [1]. N2O is also identified as a contributor to thedestruction of ozone layer in the stratosphere. The
major stationary sources of N2O are agricultural
byproducts and biomass burning [2]. Therefore, it isimportant to study the decomposition of N2O from theenvironmental point of view.
Previous studies reported that the rutile structuretitania had a better activity for the decomposition of
N2O to N2 than the anatase structure [3].
In this research, to decompose the N2O emission
from industrial process, the decomposition of N2O
over rutile structure TiO2 supported metal (silver,
cerium, manganese and magnesium) catalysts wasinvestigated.

Table 1 Characteristics of each TiO2

TiO2 No.

Surface area

Particle size Rutile content

2(m /g)(ȣm)(%)

Toho-1

32.8

≤0.42

93.3

Toho-2

50.5

≤6.02

91.7

Toho-3

73.8

≤3.48

95

In preparation, TiO2 was impregnated in an aqueous
solution of each precursor. Then, the solution mixture
was stirred at room temperature for one day followedby drying up for 12 hours at 70 Υ. After that, thesample was calcined for 5 hours under air flow. In
order to reduce the pressure drop, the sample was
finally pelletized, crushed and sieved into between
0.71 mm to 1.00 mm.
The following nomenclatures for the catalyst
samples are used: M (x wt%)/TiO2 (y)-z Υ where M
means the loading metal, x means the loading level, ymeans the type of TiO2 and z means the calcination
temperature.

2 Experimental
2.1 Catalyst preparation
M/TiO2 (M=Ag, Ce, Mn and Mg) catalysts wereprepared by impregnation method. Three types ofTiO2 (Toho-1, Toho-2 and Toho-3, Toho Titanium Co.)
were used as supports. The characteristics of each89

concluded that N2O was completely decomposed intoN2.

2.2 Catalytic activity experiment
The catalytic reaction was carried out in a fixed-bed
flow reactor under atmospheric pressure. The reactant
gas was prepared by mixing N2O, O2 and He as abalance gas. The mixed gas of 1000 ppm N2O,
0%/10% O2 and He was fed to the catalyst at a flowrate which correspond to a space velocity of 16000
h-1.
Gas chromatography (GC323w; GL Science Co.,
with Porapak N, Porapak Q and Molecular Sieve 13X
columns) was used to analyze N2O, N2 and O2.

1  2FRQYHUVLRQWR1  



2.3 Catalyst characterization$JZW
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Characterizations of the catalysts were performedby Thermogravimetric Analysis (TG8120), X-ray
Diffraction(MultiFlex),
Nitrogen
Adsorption(Autosorb 1) and Scanning Electron Microscope(JSM-5310LV)-Electronic Data Systems (JED-2140).

Fig. 1 The effect of metal loading of Ag/TiO2 (Toho-1) 600 Υ and Ce/TiO2 (Toho-1) - 600 Υ catalysts on catalytic
activity for the decomposition of N2O to N2 without O20QZW
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3. Results and discussion
1  2FRQYHUVLRQWR1  

3.1 The effect of metal loading on catalytic
activity for N2O decomposition without
O2
Figure 1 shows the catalytic activity results of
Ag/TiO2 (Toho-1) - 600 Υ and Ce/TiO2 (Toho-1) 600 Υ with different metal loading levels for thedecomposition of N2O to N2 without O2. Compared tobare TiO2 (Toho-1) which only decomposed 48% N2O
at 700 Υ, loading of Ag and Ce on TiO2 improved
catalytic activity.
Figure 2 shows the catalytic activity results of
Mn/TiO2 (Toho-3) - 500 Υ and Mg/TiO2 (Toho-3) 400 Υ with different metal loading levels for thedecomposition of N2O to N2 without O2. Compared tobare TiO2 (Toho-3) which only decomposed 45% N2O
at 700 Υ, loading of Mn on TiO2 improved catalytic
activity. However, loading of Mg on TiO2 showed no
improvement on catalytic activity, the conversion of
N2O was even lower than bare TiO2 (Toho-3).
Variation in metal loading levels showed optimum
activity result using 10 wt% for Ag and Mn while 15
wt% for Ce. Ag (10 wt%)/ TiO2 (Toho-1) - 600 Υ
showed the best catalytic activity. Since no NO andNO2 were detected in all conditions, it could be

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Fig. 2 The effect of metal loading of Mn/TiO2 (Toho-3) 500 Υ and Mg/TiO2 (Toho-3) - 400 Υ catalysts on catalytic
activity for the decomposition of N2O to N2 without O2

3.2 The effect of support on catalytic activity
for N2O decomposition without O2
Figure 3 shows the catalytic activity results of Ag(10wt%)/TiO2-600 Υ , Ce(15 wt%)/TiO2-600 Υ andMn(10 wt%)/TiO2- 500 Υ with different support forthe decomposition of N2O to N2 without O2.
Variation in supports showed optimum activity result
using TiO2(Toho-1) for Ag and Ce, TiO2(Toho-3) forMn.

90

3.4 The influence of oxygen on catalytic
activity for N2O decomposition$J 7RKR$ J 7RKR$J 7RKR& H 7RKR&H 7RKR& H 7RKR
0Q 7RKR
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

Figure 5 shows the catalytic activity results ofAg(10 wt%) /TiO2 (Toho-1)- 600 Υ, Ce (15 wt%)/
TiO2 (Toho-1)- 600 Υ and Mn (10 wt%)/ TiO2
(Toho-3)- 500 Υfor the decomposition of N2O to N2
in the presence of O2 (10%) and without O2.

1  2FRQYHUVLRQWR1  

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


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7HPSHUDWXUH ć
Fig. 3 The effect of support of Ag (10 wt%)/ TiO2 (Toho-1),

Ce (15 wt%)/ TiO2 (Toho-1), and Mn (10 wt%)/ TiO2
(Toho-3) catalysts on catalytic activity for the decompositionof N2O to N2 without O2

3.3 The effect of calcination temperature on
catalytic activity for N2O decomposition
without O2

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Fig. 5 The influence of oxygen of Ag (10 wt%)/ TiO2
(Toho-1)- 600 Υ, Ce(15 wt%)/TiO2(Toho-1)-600 Υ, andMn (10 wt%)/ TiO2(Toho-3)-500 Υ catalysts on catalytic
activity for the decomposition of N2O to N2

For all three samples, it is shown that theconversion temperature of N2O was shifted to higher
temperature in the presence of oxygen. This result
indicated that the catalytic activity was inhibited by
the presence of oxygen. The O2 could compete with
the N2O in the process of adsorption on active site,
reducing the contact of N2O with the active site; thus
reducing the effectiveness of N2O degradation. [4].

$J
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&H
& H
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0 Q
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


Figure 4 shows the catalytic activity results ofAg(10 wt%)/TiO2(Toho-1), Ce(15 wt%)/TiO2(Toho-1)
and Mn (10 wt%)/ TiO2 (Toho-3) with different
calcination temperature for the decomposition of N2O
to N2 without O2.

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&HZLWKRXW2
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3.5 XRD results



3.5.1 XRD patterns











Figure 6 shows the results of XRD patterns of Ag(10wt%)/ TiO2 (Toho-1)- 600 Υ, Ce (15 wt%)/ TiO2
(Toho-1)-600 Υ, Mn (10 wt%)/TiO2(Toho-3)-500 Υ
and Mg (1 wt%)/ TiO2 (Toho-3)- 400 Υ.
In the cases of Ag, Ce and Mn loading, it isconfirmed that each metal was supported in the formof metallic Ag, CeO2 and Mn3O4 on TiO2, respectively.
Previous study suggested that the role of Ag, CeO2
and Mn3O4 is to provide active site for N2O



7HPSHUDWXUH ć

Fig. 4 The effect of calcination temperature of Ag (10 wt%)/
TiO2 (Toho-1), Ce (15 wt%)/ TiO2 (Toho-1), and Mn (10wt%)/ TiO2 (Toho-3) catalysts on catalytic activity for thedecomposition of N2O to N2 without O2

Variation in calcination temperatures showed
optimum activity result using 550 Υ for Ag, 600 Υ
for Ce and 500 Υ for Mn.

91decomposition [3].
However, in the case of Mg loading, only MgTiO3
peaks could be observed. Obviously Mg reacted with
TiO2 producing MgTiO3 which has low activity
towards N2O decomposition.

may increase the reactivity of the active site and also
increase the area of active site exposed to N2O. The
catalysts with larger surface area showed higher
catalytic activity to some extent. However, metal
particle size seemed to be the prevailing factor insteadof surface area on N2O decomposition.

Ag(10 wt%)/TiO2(Toho-1)-600 Υ

Table 2 Supported metal particle size andsurface area of each sample,QWHQVLW\ DXCe(15 wt%)/TiO2(Toho-1)-600 Υ

Mg( 1 wt%)/TiO2(Toho-3)-400 Υ











© e
RutileAnatase

Ag

CeO2

Mn3O4

MgTiO3

Surface

N2O

metal particle

area

conversion

size (nm)2(m /g)

at 700 ć

Ag/TiO2(Toho-1)-500 Υ

6.27

22.02

87%

Ag/TiO2(Toho-1)-550 Υ

5.61

24.46

100%

Ag/TiO2(Toho-1)-600 ć

5.79

18.34

100%

Ag/TiO2(Toho-2)-600 Υ

6.90

16.94

90%

Ag/TiO2(Toho-3)-600 Υ

5.71

17.88

94%

Ce/TiO2(Toho-3)-400 Υ

4.61

51.18

67%

Ce/TiO2(Toho-3)-500 Υ

2.49

45.83

76%

Ce/TiO2(Toho-3)-600 Υ

1.73

57.06

82%

Ce/TiO2(Toho-1)-600 ć

1.60

28.21

84%

Ce/TiO2(Toho-2)-600 Υ

3.17

26.86

72%

Mn/TiO2(Toho-3)-400 Υ

3.38

58.15

34%

Mn/TiO2(Toho-3)-500 ć

2.78

41.40

75%

Mn/TiO2(Toho-3)-600 Υ

4.76

26.00

66%

Mn/TiO2(Toho-1)-500 Υ

4.46

19.42

69%

Mn/TiO2(Toho-2)-500 Υ

4.98

30.85

71%

Catalyst

Mn(10 wt%)/TiO2(Toho-3)-500 ΥSupported

Fig. 6 XRD patterns of each sample

3.5.2 Supported metal particle size

4. Conclusions
The supported metal particle size could be
calculated from XRD patterns. The Scherrer Equation
was used in the calculation.
D=(Kλ)/(βcosθ)
D: Particle size (nm)
K: The Scherrer constant (-)λ: Wavelength (Å)β: Full width at half maximum(FWHM) (rad)θ: angle (°)

Loading of Ag, Ce and Mn onto TiO2 improved
catalytic activity compared to bare TiO2. In contrast,
loading of Mg showed no improvement. Ag(10wt%)/TiO2(Toho-1)-550 Υ showed the best catalytic
activity with the N2O decomposition beginning at
350 Υ and achieving 100% at around 650 Υ. By
introducing oxygen, the catalytic activity was
inhibited. The catalyst with smaller supported metal
particle size showed higher catalytic activity.

Table 2 shows the results of supported metal
particle size and surface area of Ag (10 wt%)/TiO2, Ce(15 wt%)/TiO2 and Mn (10 wt%)/TiO2 with different
calcination temperature or support. The samples withhigher catalytic activity of each metal were showed bydeeper color. The catalysts with smaller supported
metal particle size showed higher catalytic activity. It
is possible that smaller supported metal particle size

References[1]FAO, Livestock a major threat to environment (29Nov. 2006.)[2]IPCC AR4[3]K.Yanagida, Master thesis, Tokyo Institute ofTechnology (2006)[4] S. Suarez, M. Yates, A.L. Petre, J.A. Martin, P.
Avila and J. Blanco, “Development of a new
Rh/TiO2-sepiolite monolithic catalyst for N2O
decomposition” Appl. Catal. B64 (2006), p.302

92

The situation of university electricity conservation through a focus on respondents’ consciousness
and action in Tokyo TechStudent number: 10M18246

Name: Hongshan ZHAOSupervisor: Naoya ABEᮾிᕤᴗ኱Ꮫ࡟࠾ࡅࡿ⮬୺ⓗ⠇㟁ព㆑࣭⾜ື࡟╔┠ࡋࡓ㟁ຊᾘ㈝㔞ᢚไᑐ⟇ࡢᐇែศᯒ㉿ ⣚ጟᮏ◊✲ࡣࠊᮾ᪥ᮏ኱㟈⅏ᚋࡢᏛෆ฼⏝⪅ࡢ⠇㟁ព㆑࣭⾜ືࢆ࢔ࣥࢣ࣮ࢺㄪᰝ࡟ࡼࡾᢕᥱࡋࠊ⎔ቃ᝟ሗ฼⏝ࡢ᭷↓࡟ᙳ㡪୚࠼ࡿᅉᏊࢆศᯒࡋࡓࠋࡇࢀࡽࡢศᯒࢆ㏻ࡋ࡚ࠊࡼࡾຠᯝⓗ࡞⎔ቃ᝟ሗᥦ౪᪉ἲࢆ᳨ウࡋࡓࠋ

1 Background and Objective

unclear and it is possible that few respondents
know about the existence of the information and

Due to electricity shortages after the accident ateven some information is not significant for

the Fukushima nuclear power plant last Marchother users. Therefore there are three mainof 2011, electricity conservation measures have

objectives of this research. The first one is tobeen taken in Tokyo Tech. As an effect of suchclarify the situation of electricity consumption

measures, electricity consumption was reduced

reduction

by about 18% through Apr. to Dec. 2011

respondents’compared with the electricity consumption inmeasures. The second one is to determine how

the same period in 2010. Considering that most

respondents change their energy conservation

of

the

action at home and lab. The third one is toelectricity

investigate the factors which influence usage ofthereductionconservationwasmeasures,reduced
the

by

and

the

thinking

relationship
about

with

electricity

electricity consumption information.

consumption may probably rebound to theprevious level. Therefore it is necessary to

2 Data Collection

discuss long term electricity conservation.
The

research

about

the

relationshipbetween energy conservation behaviors at home

Based on the electricity consumption data of

and

done

Apr.~Dec. 2010 and the same data of 2011, area

(Katatani, 2004.)[1], the energy conservationof building data of 24 mainly used buildings

actions such as air conditioner temperature

which are got from Facility Department ofcontrolling, and get together in one room with

Tokyo Tech, the consumption reduction, the rateother family members are effective. But theof the reduction were calculated.

energyconsumption

has

been

situation in university is probably different,

A questionnaire survey was conducted frombecause of the little incentive to do the energy

30th Nov to 12th Dec.

conservation actions. Therefore the relationshipand message with the link of survey) were sent

between mind-settings and behavioral change

to 426 individuals in O-okayama campus with aoftotal population of about 5,500 respondents onelectricity

consumption

at

Tokyo

Tech

especially after 3.11 should be discussed.

Questionnaires (petition

the same campus.

The electricity consumption information

242 responses with 233 effective responses

was publicized in Tokyo Tech’s homepage.

were collected, including responses from 27

Providing electricity consumption information

departments within 24 main buildings in

is considered as a way to reduce electricity

O-okayama campus.

consumption by enhancing electricity users’

The contents of the questionnaire andconsciousness (Maria Gleerup, 2010) [2]. But

response attribute were shown as Table 1 andthe actual situation of the information usage is

Fig.1.

93

xij. In correspondence analysis, we consider

Table 1 Contents of the questionnaire㻽㼡㼟㼑㼠㼕㼛㼚
㻺㼡㼙㼎㼑㼞

㻽㻝㻦㻭㼠㼠㼞㼕㼎㼡㼠㼕㼛㼚㼛㼒㻌㼠㼔㼑
㼞㼑㼟㼜㼛㼚㼐㼑㼞㼟㻽㻞㻦㻌㻔㻝㻕㼀㼔㼑㻽㻞㻦㻌㻔㻞㻕㻌㼀㼔㼑㼠㼔㼕㼚㼗㼕㼚㼓㻌㼍㼎㼛㼡㼠 㼑㼚㼑㼞㼓㼥㼠㼔㼑㻌㼑㼘㼑㼏㼠㼞㼕㼏㼕㼠㼥 㼏㼛㼚㼟㼑㼞㼢㼍㼠㼕㼛㼚㼙㼑㼍㼟㼡㼞㼑㼟㻌㼠㼍㼗㼑㼚 㼍㼏㼠㼕㼛㼚㻌㼍㼠㻌㼘㼍㼎㻌㼍㼚㼐㼕㼚㻌㼠㼔㼑㻌㼘㼍㼎㼔㼛㼙㼑㻌㼎㼑㼒㼛㼞㼑㻌㼍㼚㼐㼍㼒㼠㼑㼞㻌㻟㻚㻝㻝term fij=xij/n, the probability of carrying both㻽㻟㻦㻌㼀㼔㼑㻌㼡㼟㼍㼓㼑㼍㼚㼐㻌㼠㼔㼕㼚㼗㼕㼚㼓㻌㼍㼎㼛㼡㼠㼠㼔㼑㼑㼚㼑㼞㼓㼥㻔㼑㼘㼑㼏㼠㼞㼕㼏㼕㼠㼥㻕㼕㼚㼒㼛㼞㼙㼍㼠㼕㼛㼚㻌㼣㼔㼕㼏㼔㼕㼟㻌㼜㼡㼎㼘㼕㼏㼕㼦㼑㼐㻌㼕㼚㻌㼠㼔㼑㼔㼛㼙㼑㼜㼍㼓㼑㻌㼛㼒㼀㼛㼗㼥㼛㻌㼀㼑㼏㼔the categories i(of V1) and those of j(V2). The
margins of this table, which also known as
marginal probabilities, are defined by୎୧‫ ל‬ൌ σ୧ୀଵ ୧୨ ‫ל‬୨ ൌ σ୍୧ୀଵ ୧୨ ‫ לל‬ൌ σ୧ǡ୨ ୧୨ ൌ ͳ㻯㼛㼚㼠㼞㼛㼘㼘㼕㼚㼓㻌㼠㼔㼑 㻯㼛㼚㼠㼞㼛㼘㼘㼕㼚㼓㻌㼠㼔㼑㼀㼔㼑㻌㼞㼑㼍㼟㼛㼚㻌㼛㼒㼠㼑㼙㼜㼑㼞㼍㼠㼡㼞㼑㻌㼛㼒 㼠㼑㼙㼜㼑㼞㼍㼠㼡㼞㼑㻌㼛㼒㻌㼍㼕㼞㻰㼑㼜㼍㼞㼠㼙㼑㼚㼠㼡㼟㼕㼚㼓㻌㼔㼛㼙㼑㼜㼍㼓㼑㼍㼕㼞㻌㼏㼛㼚㼐㼕㼠㼛㼚㼑㼞 㼏㼛㼚㼐㼕㼠㼛㼚㼑㼞㻮㼡㼕㼘㼐㼕㼚㼓㻌㼣㼔㼕㼏㼔㼀㼔㼑㻌㼒㼞㼑㼝㼡㼑㼚㼏㼥㻌㼛㼒㻸㼕㼓㼔㼠㻌㼑㼒㼒㼕㼏㼕㼑㼚㼏㼥 㻸㼕㼓㼔㼠㻌㼑㼒㼒㼕㼏㼕㼑㼚㼏㼥㼕㼟㻌㼟㼠㼡㼐㼥㼕㼚㼓㻌㼕㼚㼕㼚㼒㼛㼞㼙㼍㼠㼕㼛㼚㻌㼡㼟㼍㼓㼑㻽㼡㼟㼑㼠㼕㼛㼚
㻿㼍㼠㼕㼟㼒㼍㼏㼠㼕㼛㼚㻌㼣㼕㼠㼔㼁㼟㼕㼚㼓㻌㼟㼠㼍㼕㼞㼟㼁㼟㼕㼚㼓㻌㼟㼠㼍㼕㼞㼟㼑㼚㼢㼕㼞㼛㼙㼑㼚㼠㼍㼘㼓㼑㼚㼐㼑㼞
㼕㼚㼟㼠㼑㼍㼐㻌㼛㼒㼕㼚㼟㼠㼑㼍㼐㻌㼛㼒㼕㼚㼒㼛㼞㼙㼍㼠㼕㼛㼚㼑㼘㼑㼢㼍㼠㼛㼞㼟㼑㼘㼑㼢㼍㼠㼛㼞㼟㼜㼡㼎㼘㼕㼏㼕㼦㼑㼐㻌㼕㼚㼀㼛㼗㼥㼛㻌㼀㼑㼏㼔䈈䈈䈈㻺㼡㼙㼎㼑㼞
㼛㼒㻝㻤㻌㼝㼡㼑㼟㼠㼕㼛㼚㼟 㻝㻝㼝㼡㼑㼟㼠㼕㼛㼚㼟㻝㻝㼝㼡㼑㼟㼠㼕㼛㼚㼟㽢㻠㻝㻣㻌㼝㼡㼑㼟㼠㼕㼛㼚㼟㼝 㼡㼑㼟㼠㼕㼛㼚

GenderThe ɖଶ test is used to test the significance ofthe overall deviation.
For objective 2, the cross session data wasused to see respondents’s behavior change after
3.11. To investigate the factors which influence
usage of electricity consumption information(objective3), the logit model was used.
About

Nationality of Respondents

logit

model,

supposetheprobability is π . The logit function of ȧ

Foreign Student
and do notunderstandJapanese
9%

Famale22%

theleading to the model
Foreign Student
19%ሺɎሻ ൌ  ቀ
Japanese
72%

Male
78%

where

Ɏ
ቁ ൌ Ⱦ଴ ൅ Ⱦଵ ଵ ൅ ‫ ڮ‬൅ Ⱦ୯ ୯ ൅ ɂ୯ͳെɎ

ș associatedwithexplanatory

variables xj is such that exp(șj) is the odds thatthe response variable takes the value one when

Status of Respondents

Secretary2%
Instructor
3%
B3
1%

Others1%

Associate
Professor
4%

xj increases by one, conditional on the other

Professor/Assist
ant Professor
6%

explanatory variables remaining constant.
The logit of a probability is simply the log ofthe odds of the response taking the value one.

Doctor student
12%

B4
20%

To see the relationship between parameters andprobability, marginal effect is needed and can becalculated by

Master/Research Student
51%ୢ஠ሺଢ଼ୀଵሻ
ୢଡ଼ൌୢሺଵି୊ሺି஑ିஒଡ଼ሻሻ
ୢଡ଼ൌ ሺെȽ െ Ⱦሻ

where F is cumulative density function oflogistic distribution.

Fig 1 Response attribute

4 Results

3 Methodologies
Toclarifythesituationof

The results of buildings grouping is shown as
Fig.2. The variable of the horizontal axis is
for the scores of the first component from
PCA, the variable of the vertical axis is forthe scores of second component. Reductionlevel can be seen as the first component, andreduction amount can be seen as the second
component. The results of grouping are:electricity

consumption reduction and the relationshipwith respondents’ thinking about electricity
measures(objective1),

PCA(Principle

Component Analysis) and Cluster Analysis
were used to group buildings. Correspondence
analysis was also used to see the relationship.

Group1: High reduction amount, low reduction level
Group2: Both reduction amount and level are large
Group3: Middle reduction amount, high level
Group4: Both middle reduction amount and level
Group5: Low reduction amount, middle level

About correspondence analysis, generalindications for a contingency table with the twocategorical variables V1 and V2 defined for n
individuals ; individual l carries the categoriesi(for V1)and j(for V2): these are accounted for in

94

0.0

0.5

14

-1

13

0.0

3
7

-0.5

6

OthersNothing

24

-0.5

16
20
17 15
11
4

Airconditione
5
Deviceoff
GaswaterLighteffeciency
Monitoroff
3
StairsPcsettingGroup1
6

0.5

2

Lightoff

1
5

0

2

23
22

Roomtogether
418Group 5
-3

Group 3

-1.5

-1.0

10

-1.0

1

2

Group 1

-2

datapc$scores[, 2]

-0.5

8
12

Group 4-1.0

0.0Group 2
9

1

-1.5

0.5

-2.0
19

-1.0
-2.0

0.5
-2.0

-1.5

-1.0

-0.5

0.0

The relationship between electricity
reduction pattern and thinking about measurestaken in lab was analyzed by correspondence
analysis and results of group1 and group 5 wereshown as Fig.3
For measures to control air conditioner
temperature and to turn off the light whenleaving the room, all of the groups’ respondents
think it is effective and will do them voluntarily.
As for measures to work in “room together”,
responders think it is effective but will not do.
In group 5 which has low electricity reduction
amount, responders will not do them voluntarily.
In group 1,3, and 4, although they think they
are ineffective, respondents will do something
about it.
About the energy conservation behavior
change after 3.11, the percentages that
respondents do the energy conservation action
before 3.11 and after 3.11 results are shown in
fig.4.
The results show that comparing before3.11
and after, the number of respondents who began
to take the energy conservation action increased.
The number of respondents who began to takethe conservation actions at lab also increased.
The electricity reduction measures taken inTokyo Tech can be seen as an important reasonof the increased respondent’s number.
The number of respondents who began totake actions such as air conditioner
temperature control, using light efficiently
when in the room alone(such as use the desk
light), using the stairs, PC efficiency setting,increased.

-0.5

-1.5

-1.0

0.0

-0.5

0.5

Roomtogether
4Lighteffeciency
Monitoroff
Deviceoff

Group5
6 Nothing
1Others2
Pcsetting

1: The action was not betaken in the lab
2: Don't think it is effective
and will do nothing aboutthat
3: Don't think it is effective
but will do something aboutthat
4: Think it is effective but
will not do voluntarily
5: Think it is effective andwill do voluntarily
6: I don’t know
-2.0

0.5

0.0

0.5

-1.5

Fig2 Results of PCA and cluster analysis

0.0

2

-1.5

-1.0

-0.5

3
-0.5

1

StairsGaswaterAirconditioner
5

-1.0

0

Lightoff-1.5-1

datapc$scores[, 1]

-2.0

-2

-1.5

-3

-2.0

21

0.0

0.5

Fig.3 Results of correspondence analysis
Behavior change at home and lab after 3.11
AirconditonerLight off

PC efficiency

PC offDevices off

Room togtherGas and water

StairsOthersNothing

Before3.11 athome

66%

After 3.11
at home77%

Before3.11 at lab

50%

86%

33% 36%

86%

42%

34%

42%

41% 10% 53%

42%Light efficiency

4%
25% 6%

46% 18%61%2%
32% 7%

30% 35% 25% 29%9% 36% 31% 2%10%81%4%After 3.11
at lab

79%

88%

50%

52%

47%

56%

15% 46%Percentage of people acted (%)

Fig. 4 Behavior change at home and lab after 3.11

About the factors which influence usage ofelectricity consumption information, the resultsof the logit model are shown below.
The factors that influence willingness of
homepage electricity consumption information:

95

53% 4%

㻰㼑㼜㼑㼚㼐㼑㼚㼠㻌㼂㼍㼞㼕㼍㼎㼘㼑㻦㻌㼣㼛㼡㼘㼐㻌㼘㼕㼗㼑㻌㼠㼛㻌㼡㼟㼑㻌㼠㼔㼑㻌㼕㼚㼒㼛㼞㼙㼍㼠㼕㼛㼚㻌㼛㼞㻌㼚㼛㼠㻵㼚㼐㼑㼜㼑㼚㼐㼑㼚㼠㻹㼍㼞㼓㼕㼚㼍㼘
㼏㼛㼑㼒㼒㼕㼏㼕㼑㼚㼠 㻿㼠㼐㻚㻱㼞㼞㻚㻿㼠㼐㻚㻱㼞㼞㻚
㼂㼍㼞㼕㼍㼎㼘㼑㼟㼑㼒㼒㼑㼏㼠㼟
㻲㼛㼞㼑㼕㼓㼔
㼟㼠㼡㼐㼑㼚㼠㻌㼣㼔㼛㻞㻚㻜㻣㻖㻖
㻜㻚㻢㻣
㻜㻚㻠㻤
㻜㻚㻝㻟
㼏㼍㼚㼚㼛㼠㻌㼟㼜㼑㼍㼗㻶㼍㼜㼍㼚㼑㼟㼑
㻮㼑㼔㼍㼢㼕㼛㼞
㻜㻚㻠㻥
㻜㻚㻞㻝
㻜㻚㻜㻥
㼏㼔㼍㼚㼓㼑㼐㻌㼍㼒㼠㼑㼞 㻝㻚㻜㻜㻖㻖
㻟㻚㻝㻝
㼋㼏㼛㼚㼟
㻙㻝㻚㻢㻜㻖㻖
㻜㻚㻠㻟

0.0

-0.5

-1.0

0.5

1.0
1.0

1 ЋEveryday
0.4
0.5
0.2

Don’t see the detailЎ
Group55

ЎEvery week

Group10.0
2Group2**Significant at 5% level

Group40.0

4 ЋIrregularlyGroup3-0.2-0.5

The factors that influence usage
electricity
consumption
information
visualization system:

-0.4

of
of

-1.0

-0.6

Every monthЍ 3-0.6

㻰㼑㼜㼑㼚㼐㼑㼚㼠㻌㼂㼍㼞㼕㼍㼎㼘㼑㻦㻌㼡㼟㼕㼚㼓㻌㼠㼔㼑㻌㼢㼕㼟㼡㼍㼘㼕㼦㼍㼠㼕㼛㼚㻌㼟㼥㼟㼠㼑㼙㻌㼛㼞㻌㼚㼛㼠㻵㼚㼐㼑㼜㼑㼚㼐㼑㼚㼠㻹㼍㼞㼓㼕㼚㼍㼘
㼏㼛㼑㼒㼒㼕㼏㼕㼑㼚㼠 㻿㼠㼐㻚㻱㼞㼞㻚㻿㼠㼐㻚㻱㼞㼞㻚
㼂㼍㼞㼕㼍㼎㼘㼑㼟㼑㼒㼒㼑㼏㼠㼟
㼘㼕㼢㼑㻌㼍㼘㼛㼚㼑㻝㻚㻟㻞㻖㻖
㻜㻚㻡㻤
㻜㻚㻞㻥
㻜㻚㻝㻝
㼋㼏㼛㼚㼟
㻙㻜㻚㻜㻠
㻜㻚㻞㻤


-0.4

-0.2

0.0

0.2

0.4

   Fig.5 Homepage information usage
0.0

0.5

ЎMedium
Stronglywant to useЍ1 3Group5

-1.0

-0.5

0.4


0.2

There is no significant variable that
influence usage of electricity consumption
information and willingness of visualizationelectricity consumption information.
Next, to see whether there is relationshipbetween the electricity reduction pattern andinformation usage, correspondence analysis was
conducted. Similarly with PCA, both variables

0.5Strongly Ѝ 5
don’t want
to useGroup10.0Group30.0Group4

Group2
-0.2

-0.5
2

4

ЋWant to use

Don’t wantЌ
to use-0.4-1.0

of the horizontal axis and the vertical axis arefor the scores of component. In Fig.5 andFig.6, the component scores of the group andfrequency or wishes of information usage are
distributed in the same figure to see therelationship.

-0.4

-0.2

0.0

0.2

0.4


Fig.6 Wishes to use visualization information

Tech will change their perception and would
like to do the energy conservation action when
have incentive such as energy conservation
measures. Providing the energy (electricity)
information can be a way to provide thecontinuous incentive of energy reduction. As ascience and engineering university, Tokyo Techneeds to consume a certain amount of electricity
for experimental devices and various facilities
so that it is crucial to investigate more whereand for what we could conserve the electricity.
There is potential to reduce electricity by people’voluntary action, without the sacrifice of doing
research. And seeking the compatible way to
reduce the electricity consumption is necessary.

About information usage, the respondents
in those groups which have large consumption
reduction amount are always using theinformation and would like to use it.

5 Conclusions and Discussion
This study found the following points.
1:
In
smaller
consumptionbuilding,
respondents of the building also don’t prefer toperform conservation actions.
2: An increase number of respondents who
performed conservation actions were not onlyin laboratories (mandated by the university),
but also at their houses (voluntarily.)
3: Foreign respondents, the respondents who
have changed their behavior, and the
respondents in the high consumption reductiongroup have been found to express willingness touse information system.
It is considered that respondents in Tokyo

References[1] Katatani Noritaka: An Analysis of the Relationshipbetween Household Electricity Consumption and Life-style,
the Japan Institute of Energy (12), 456-457, 2003-07-30[2] Maria Gleerup: The Effect of Feedback by Text Message(SMS) and Email on Household Electricity Consumption:
Experimental Evidence, The Energy Journal, Vol.31, No3,
113-132, 2010
[3] Franois Husson: Exploratory Multivariate Analysis by
Example Using R, CRC Press

96

OPTIMUM SPEED OF VESSELS CONSIDERING THE REDUCTION OF CO2 EMISSIONIN INTERNATIONAL MARITIME TRANSPORTATION
Student Number: 10M18252

Name: Jifang ZHANG

Supervisor: Shinya HANAOKA

࿖㓙ᶏㆇߦ߅ߌࠆⅣႺࠍ⠨ᘦߒߚ⦁⥾ߩᦨㆡㅦᐲߩផ⸘ᒛ 㔿⧐ᧄ⺰ᢥߢߪ‫ࠍ⦁࠽࠹ࡦࠦޔ‬ኻ⽎ߣߒߡ‫ߩ␠⦁ޔ‬೑⋉෸߮⦁⥾߆ࠄߩ CO2 ឃ಴㊂ߩ㑐ଥࠍ⠨ᘦߒߚᦨㆡ
⥶ㅦࠍឭ᩺ផ⸘ߔࠆ‫ ߡߒߣࠖ࠺࠲ࠬࠬ࡯ࠤޕ‬3 ⥶〝ࠍኻ⽎ߦ⸘▚ߒߚ⚿ᨐ‫ޔ‬5000TEU એ਄ߩ⦁⥾ߢߪ
18.5~19.5 ࡁ࠶࠻‫ࠇߘޔ‬એਅߩ⦁⥾ߢߪ 13.2~18.3 ࡁ࠶࠻߇ᦨㆡ⥶ㅦߣ޿߁⚿ᨐࠍᓧߚ‫⁁⃻ޔ߽ࠇߕ޿ޕ‬ࠃࠅ 13%~23%ߩᷫㅦ߇ᔅⷐߦߥࠆ‫ߩ〝⥶ޔߚ߹ޕ‬㆑޿߿ࠨࠗ࠭ߩ㆑޿߇⚿ᨐߦਈ߃ࠆᓇ㗀ࠍಽᨆߒߚ‫ޕ‬1.

CO2, and the world fleet have a 7.9% reduction potential to reduce
98.7 million tones. It was also mentioned that “The MAC curve
adopts a social perspective. It doesn’t represent the expenditures theship operators would have to pay.”
Currently, some research considered the optimal speed as the
speed when profit of shipping company is maximized [3]. Some
research considered optimal speed as the minimum cost speed, whileconsidering the cost as a social cost [4].

Introduction

International maritime transportation response for 90% (ton-miles) of
the world trade, while in 2007, emitted 3.3% of global CO2
emissions. Because the growth in the world seaborne trade, it is said
that shipping’s contribution to GHG emissions could reach 18% by
2050 [1].
In 2006, International Maritime Organization (IMO) made theframe work of a package of 3 measures to reduce emissions fromshipping on global basis. Technical Measures including
superstructure optimization, waste heat recovery, to make vessel
themselves more environmental efficient. Operational Measures
including weather-routing, speed management to make the voyages
more efficient. Market based Measures including GHG Fund, EIS toencourage the GHG reduction by giving economic incentives.
IMO established a system of energy efficiency design indexing(EEDI) for new built vessels. In July 15th 2011, MEPC62 determinedthat all vessels built after January 1st 2013 need to satisfy the EEDI
Index as an obligation. For operational measures, a template for a
Ship Energy Efficiency Management Plan (SEEMP) allows
companies to improve performance with regard to factors that may
contribute to CO2 emission. But emitted CO2 during voyage would
not be checked by any 3rd organizations. There is no regulation for
the achievement of EEOI (Energy Efficiency Operational Index) at
this moment.
Speed reduction considered as a mean to reduce fuelconsumption and consequently reduce CO2 emissions. Generally, a
10% reduction in speed for all vessels would lead to a 19% reductionof emissions per ton-mile.[2] However, on the negative side speed
reduction not only means longer delivery times, but also less reliable
service schedules. During 2009, only 53% of vessels tracked on
major East-West routes arrived on time, compared with 60% in
2008[2]. For carriers, speed reduction means a reduction in thenumber of miles traveled per year. In 2010 ton-mile per dwt wasestimated to be declined 26% from 2008. [2]Shipping companies insist that they reduce speed for CO2
emission reduction, while according to UNCTAD [2] speed
reduction is only a consequence of overcapacity. From March 2007
to March 2009, around 240 new ships were being delivered,equivalent to a 10% increase in supply. While demand fromcontainerized trade decreased by a similar amount. Speed reduction
helped the carriers to reduce the need to lay off ships. When demand
increase, shipping companies are possibly to resume higher speed torestore the vessel productivity.
Current speed reduction is burdened by many problems. It isnecessary to define the optimum operational speed considering boththe profit of shipping companies and the CO2 emissions.
IMO estimated the Marginal Abatement Cost (MAC) for 10%
speed reduction of the world fleet in 2020, considered the cost as asum of purchase new vessels and additional operational cost causedby new vessels. It was found that the cost would be 80~135$/ton

2.

Objective and Case Study

The objective of this research is to estimate the most reasonable
speed for container vessels operated in specific routes, considering
the relationship of profit of shipping company and CO2 emissions.
The effect caused by practicing the proposed speed, and theinfluence of vessel sizes and routes would also be discussed.
Three transportation routes have top seaborne trade amount wereselected as case study routes. Shanghai to Los Angles represent
Asia-North America routes, Shanghai to Rotterdam represent
Asia-EU routes, and Shanghai to Singapore represent inside Asia
routes. Container ships were selected as the case vessels, for theirhighest CO2 emissions in maritime transportation sector. In 2007,
container ships transported 13% of the world freight while emitted
28.5% CO2 of the international maritime transportation sector.
3.

Model Description

The model calculated the profit of shipping company, and emissions
during voyages for individual ship classes as a function of speed. The
model consists of 2 main equations, Eq (1) describes the profit as adifference between revenue and cost, when vessel k traveled fromport i to port j. Eq (2) describes the emissions relative to fuelconsumption when vessel k traveled from i to j.

The model gave four assumptions. 1st, average freight rates remain
generally constant. 2nd, load factor for global container vessels isaround 70%. 3rd, coefficient 3.17 would be defined as fuel’s carbon
fraction 0.864 multiply a factor for converting carbon to CO2 (equal to44/12). 4th, the depreciation of vessel and export value of freight remain
constant. Parameters were described in Table 1.

97

Table1. Parameter Description
Pt: Freight Rate ($/TEU)
Pf: Fule Price ($/ton)
M: Average Transport amount TCijk:(TEU Capacity*Load Factor%) Vessel Depreciation($/day)
MFk , AFk :CM:,
ExportValue of Freight($/TEU)Engine Fuel Consumption (g/h)
v:
Operational
speed (knot)k=Engine Power (kw)
*Fuel Consumption rate (g/kwh) vkd: Design speed (knot)

4.

Profit (million $)

5
0
0

-5

30

speed (knot)

Tijkb:
Stopped time per voyage (day)Pf: Fule Price($/ton)Data for container vessels operated in three target routes were used
including vessel characteristic data and voyage data.
Table2. Data Description
Vessel Characteristic
VoyageEngine Power (kw)
Distance (mile)
Engine Fuel Consumption (g/kwh) Freight Rate ($/TEU)Engine Load Factor (%)
Export Value ($/ton)
Designed Speed (knot)
Fuel Price ($/ton)
Vessel Depreciation ($/day)
Stopped time (day)TEU Capacity(TEU)
Interest Rate (%)+8000TEU
5000~7999TEU
3000~4999TEU
2000~2999TEU
1000~1999TEU~999TEU

250
200
150
100
50
0
-50 0

10

20
speed (knot)

30

Figure2. Relationship of Speed and Annual Profit
10000+8000TEU
5000~7999TEU
3000~4999TEU
2000~2999TEU
1000~1999TEU~999TEU

Emissions (ton)

8000
6000
4000
2000

According to IMO [1], main engine fuel consumption was set to
be 175 to 195 g/kwh, and load factor was 67% or 65%. Auxiliary
engine fuel consumption was set be 220 or 230g/kwh, and loadfactor was 60%.
Main engine power, Designed speed and TEU capacity for each
vessel was bought from Lloyd's Marine Intelligence Unit, while stopped
time per voyage was calculated from vessel movement data.
According to Japan Marine Center, Freight Rate Pt was set to be
2548$/TEU in LA route, 1718$/TEU in Rotterdam route, and 782$/TEU
in Singapore route. Export value was set to be 5000$/ton, fuel price Pf
was set to be 400$/ton and Interest rate was set to be 5%, Vessel
Depreciation was set to be 10000$/day~30000$/day depending on
vessel sizeᴾ [4]. Distance was referred from Google map, where LA routewas 11632km, Rotterdam route was 21310km,ᴾ Singapore route was
4141km.

00

10 speed (knot) 20

30

Figure3. Relationship of Speed and Emissions per trip
Annual Emissions (ton)120000100000
80000
60000
40000
20000
0

㧗8000TEU
3000~4999TEU
5000~7999TEU
2000~2999TEU
1000~1999TEU~999TEU
0

5

10

15

20

25

30

speed (knot)

Figure4. Relationship of Speed and Annual Emissions
5.1.2

Result and Discussion

Speed of Profit
Minimization

Maximization

andEmission

From Figure 1 and 3, it could be found that speed of maximum profits(VPmax) and speed of minimum emissions (VEmin) are different inShanghai to Los Angeles route. Similar results were obtained also in
the other two routes. Table 3 shows the results of each route.

5.1 Hypothesis Testing
5.1.1

20

Figure1. Relationship of Speed and Profit per trip

Data Set

5.

10

-10

Annual Profit (million $)*Load factor (%)
Dij: Distance between i and j (km)CIR: Annual interest rate (%)
Pt: Freight Rate ($/TEU)+8000TEU
5000~7999TEU
3000~4999TEU
2000~2999TEU
1000~1999TEU~999TEU

15
10

Optimum Speed on Single and Annual Voyages

If set the annual transportation time Ta remain constant, thefrequency would be N=Ta/Tk, where Tk=Tijkb+D/24vk. Annual profit
and emissions could be calculated as Eq(1) and Eq(2) multiply N
respectively.
According to IMO, the vessels are divided into 6 groups by size:
Vessels larger than 8000TEU, between 5000and 7999TEU, 3000 to
4999TEU, 2000 to 2999TEU, 1000 to 1999TEU and vessels smaller
than 999TEU.
Data of 90 vessels operated in Shanghai to LA route in 2010 wasused to test this hypothesis. Results were described as follow. Figure
1 and 2 showed the relationship of speed and profit per trip,
relationship of speed and annual profit, Figure 3 and 4 showed therelationship of speed and emissions per trip, speed and annual
emissions.
Profit maximum speed and emission minimum speed weredifferent by focusing on one voyage or annual voyages. Thishypothesis only consist when the annual travel time remain constant,but in fact it is hard to predict the total travel time and this amount isalso thought to change depend on transportation demand, thus the

Table3. Max Profit Speed and Min Emission Speed(knot)
Shanghai to

TEU
+8000
5~79993~49992~29991~1999~999

Los Angeles (LA)
VPmax
VEmin
21
8
21
8
20
7
18
7
15.5
6
13.5
6

Rotterdam (R)
VPmax
VEmin
21
8
21
7
21
16.5
14.5

7
7
6

Singapore (S)
VPmax
VEmin
22
8
21
8
20
7
20
7
16.5
7
15
6

It is thought to be hard to let the shipping company practice theminimum emission speed, because of the huge difference in profit.
Take +8000TEU vessels in LA route as an example: under the speedof 21 knot, the profit was 13.3 million$, when the speed become 8knot, the profit decline to 11.8 million$, which means a 1.5 million
dollar loss in profit for shipping company.
It would be necessary to find the reasonable speed, which has lessemission and small risk for the shipping company considering therelationship of profit and emissions.

shipping companies would tend to optimize every voyage rather
than annual voyages.

98Price of one ton CO2 reduction could control the scale of thevertical axis of Figure7, so the chose point would be different bychanging CO2 price.
To choose any point x between A and B as the optimum point, it isconsidered to be necessary to satisfy the relationship in inequality (5),comparing with point A, saved cost for CO2 reduction must be larger
than lost profit.

5.2 Reasonable Speed Estimation
Objective functions to maximize profit Eq(3), and minimum
emissions Eq(4) were defined, speed vk was the variable.

(3)

PA – PX(4)≤ (EA – Ex)* EpriceX(5)

In the case of +8000TEU vessels in LA routes, the chose speed
and the CO2 price at that time was calculated as follow.
5.2.1 Smallest Profit Loss & Largest Emission Reduction

Table5. Result of +8000TEU vessels (LA route)

Speed v was the same variable for both profit and emission, therelationship of them could be found as Figure5.
The point with smallest change rate in profit and biggest change
rate in emissions (min (Δp/Δe)) would be chosen as the most
reasonable point in this part. The speed at that point would be definedas the most reasonable speed.
In the case of +8000TEU vessels in LA routes, relationship ofΔp/Δe and speed was shown in Figure 6. The result was shown in
Table 4. As Δp/Δe was biggest at Maximum Profit point, smallest at
Minimum Emission point, the speed at Maximum Profit should bechosen as the most reasonable speed in this case.

P&R
Speed(knot)
8
9
10
11
12
13
14

A: Maximum Profit

Emissions(ton)

6000
4000
2000
0

-10

-5

10

5.2.3 Results of Other Two Routes

15

Same estimation was done with all the vessels in target routes.
Because most of vessels operated under service speed and some were
under the 10% speed reduction, the comparison is shown in Table 6.

15000.0Δp/Δe10000.0

Table6. Comparison of Proposed Speed and Other Speed
TEU

5000.0
0.0
0

5

10
15
Speed (knot)

20

Table4. Result of +8000TEU vessels (LA route)䅜1. Max Profit
2.Min Emission

Speed(knot)
21
8

Profit(million $)
13.06
11.9

Emissions(ton)
4617.2
1626.3

V-10%(average)

V profit
Max(av)

VP&R

VP&R

VP&R

(LA)

(R)

(S)

24.6
24.6
23.3
20.8
17.0
14.6

22.1
22.1
21.0
18.7
15.3
13.2

21
21
20
20
16
14

18.5
18.9
17.5
16.6
14.3
13.2

19.5
19.4

19.3
19.2
18.3
17.8
14.5
14.4

Table 7 shows the average change in profit and emissions. ΔE isthe change in emissions under the proposed speed comparing withthe profit maximum speed, which could be so called as reduction
potential.
It could be found that with a small change in profit (-0.18%
to -1.45%), a big reduction of emissions could be achieved (-10to -17%). Shipping company should choose the proposed speed
rather than 10% speed reduction or service speed.

Average CO2 trade price was around 22$ in 2010, in the case of Kyoto
Credit. If monetize the emissions, by multiply the amount of emissions
with CO2 price, relationship in Figure 7 could be obtained. Most
reasonable speed was considered as some point between A and B in
Figure 5 and 7.

Table7. Average change in Profit and Emissions

A: Maximum Profit

TEU

ΔP (%)

ΔE (%)+8000
5~79993~49992~29991~1999
~999

-0.18
-0.21
-0.23
-0.45
-0.81
-1.45

-17.12-13.70-16.93-16.53-14.34-10.32

0.2
0.15
0.1
0.05
12.4

12.6

12.8

13

-22.49%-21.89%-23.06%-16.33%-13.15%-5.07%

5.3 Changes in Profit and Emissions under Proposed Speed

5.2.2 Monetize CO2 Emissions

B : Min Emission
0
11.6
11.8
12
12.2

17.8
14.9
14.1

Reduction
from
Vservice

It could be found that, necessary percentage of speed reduction
for most vessels were from 13% to 23% different by vessel sizes.
10% speed reduction was similar with profit maximum speed, which
was not reasonable for all the world fleet.

This result implies that, if there is no regulation, even shipping
companies consider the emission reduction, they will still choose theprofit maximum speed. Same estimation was done with all vessels in
the other 3 routes, similar results were obtained.

Monetized
CO2(million$)

Vservice(average)+8000
5~79993~49992~29991~1999~999

25

Figure6. Relationship of Δp/Δe and speed㩷㩷 (speed є VPmax ,VEmin)

0.3

Price of CO2($/ton)
77.2
58.1
41.9
28.0
16.1
6.6

B : Min Emission

05Profit(million $)Figure5. Relationship of Emissions and Profit

0.25

P&R
Speed(knot)
15
16
17
18
19
20

If the price of CO2 was set to be 22$, the most reasonable speed
would between 18~19knot, which was calculated to be 18.5knot.
This scenario considered the shipping companies need to pay an
additional cost for CO2 emissions. The payment of this cost could be
described as follow. At first, make the shipping companies to pay forthe planned emissions amount, and then give back the paid amount tothe companies operated under the proposed speed. Thus, it would
become an incentive to the companies to achieve the proposed speed.

10000
8000

Price
of
CO2 ($/ton)
402.6
315.3
249.9
199.3
159.1
126.5
99.7

13.2

Profit (million $)Figure7. Relationship of Monetized CO2 and Profit

99

ΔP(million$)-0.015-0.013-0.011-0.009-0.006-0.005

ΔE
(ton)
725.6
527.2
315.1
378.4
199.1
97.3

Profit Loss per TEU($/TEU)

5.4 Comparison of Three Routes
Changes in profit and emissions caused by practicing the proposed
speed in three routes are shown in Figure 8 and 9.
If focus on vessels with same size, loss of profit (ΔProfit) in
Singapore route was smallest, while in Rotterdam route was biggest.
At the same time, emission reduction potential (ΔEmission) was
smallest in Singapore route, and biggest in Rotterdam route.

0.00
-2.00

-6.00
-8.00

-10.00Shanghai to
Singapore

Reduction Potential(ton/TEU)ΔProfit(million $)+8000 5~7999 3~4999 2~2999 1~1999 ~999

-0.010Shanghai toLA-0.015

Shanghai toRotterdam-0.020-0.025vessel size (TEU)

ΔEmission (ton)vessel size(TEU)

6.

Shanghai toLA

600.000
400.000

0.000
+8000

5~7999 3~4999 2~2999 1~1999 ~999

Figure9. Emission Reduction Potential in 3 Routes
Thus, in short-term vessels operated in Singapore route should
work on speed reduction as soon as possible, while in long-termemission reduction in Rotterdam route should be addressed with.
Freight Rate, Stopped time, Distance were different by route,while distance was thought to be the factor influenced the result most.
When speed reduced, route with longer distance would have morecost increase, consequently would be more sensitive.

5.5 Comparison of Vessel Sizes
Profit & Emissions per TEU Under Proposed Speed

Figure 10 and 11 show the profit and emissions per TEU under
optimum speed. It could be found that bigger vessels are more
profitable and have less emission per TEU.
Shanghai to
LAShanghai toRotterdam
Shanghai to
Singapore

2000
1500
1000
500
0
+8000 5~7999 3~4999 2~2999 1~1999~999
Vessel size (TEU)

Figure10. Relationship of Size and Profit per TEU
Shanghai toRotterdam
Shanghai to LA

0.00
5~7999 3~4999 2~2999 1~1999~999

Shanghai to
Singapore

0.00
+8000

5~79993~49992~29991~1999Conclusion

Reference[1] International Maritime Organization (2009) “Second IMO GHG Study,
2009”[2] UNCTAD SECRETARIAT (2011) “Review of Maritime transport
2010” United Nations Conference on Trade and Development Sales No.
E.10.II.D.4[3] James J.Corbett, HaifengWang (2009), “The effectiveness and costsof speed reductions on emissions from international shipping”“Transportation Research” Part D14, P593–598[4] Haakon Lindstad (2011), “Reductions in greenhouse gas emissions and costby shipping at lower speeds”, Energy Policy 39, P3456–3464

2.00
1.00

0.10

Speed that maximize profit and speed that minimize CO2
emissions were different. The relationship of profit and emissions
could be found.
If consider the speed with smallest change rate in profit andbiggest change rate in emissions (min (Δp/Δe)) as the most
reasonable speed. Profit maximize speed would be selected.
If monetize CO2 emissions by multiplying the amount ofemissions (ton) with the price of CO2 reduction ($/ton), some speed
between max profit speed and minimum emissions speed could beselected. Comparing with profit maximum speed, saved emissions
reduction cost must be larger than loss in profit by choosing thisspeed. Thus, this speed would be defined as the most reasonable
speed in this research.
To let the shipping company operate with lower speed for less
CO2 emission, CO2 price need to be kept higher. To make theshipping company practice the proposed speed, it would benecessary to combine the proposed speed with market based measure,which could give economic incentive to the companies.
Proposed reasonable speed was different by vessel size. Vessels
larger than 5000 TEU were supposed to have optimum speed around18.5~19.5knot, 2000~4999TEU around 16.6~18.3knot, vessels
smaller than 2000TEU were 13.2~14.9knot..
Shanghai to Singapore route had least profit loss and leastemission reduction potential under proposed speed, while Rotterdam
route had both biggest. This imply that, in short-term vessels
operated in Singapore route should work on speed reduction as soon
as possible, while in long-term emission reduction in Rotterdam
route should be addressed with.
Vessels with larger capacity were estimated to be more profitable
and have less emission per TEU, while smaller vessels tend to lose
more profit, but have higher reduction potential at the same time. It
would be better to introduce new bigger vessels, while speed
reduction for the current smaller vessels need to be started.

Shanghai toRotterdam

200.000

3.00

0.20

Figure13. Emission Reduction Potential per TEU (ΔE/TEU)
Shanghai to
Singapore800.000

5.5.1

0.30

Shanghai to
LAShanghai toRotterdam
Shanghai to
Singaporevessel size (TEU)

1200.000
1000.000

vessel size (TEU)

0.40

+8000

Figure8. Profit Loss (ΔP) in each route

Profit Per TEU
under proposed
speed($)~999

Figure12. Profit Loss per TEU (ΔP/TEU)-0.005

CO2 Emission per
TEU(ton/TEU)

Shanghai to
LAShanghai toRotterdam
Shanghai to
Singapore

-4.00

0.000

~999

Vessel Size(TEU)

Figure11. Relationship of Size and Emission per TEU
5.5.2+8000 5~7999 3~4999 2~2999 1~1999

Changes in Profit and Emissions per TEU

From Figure 12 &13, it could be found that smaller sizes tend to lose
more profit, while have higher reduction potential at the same time.

100Microwave-assisted Synthesis of ThalifendineStudent Number: 10M18269

Name: Dao Zhang

Supervisor: Prof. Sachio Hirose, Prof. Kiyohiko Nakasaki࣐࢖ࢡࣟἼຍ⇕࡟ࡼࡿ࣋ࣝ࣋ࣜࣥㄏᑟయࢱࣜࣇ࢙ࣥࢹ࢕ࣥࡢ⏕ᡂᙇ 㐨ᮏ◊✲࡛ࡣࠊ࣋ࣝ࣋ࣜࣥㄏᑟయࢱࣜࣇ࢙ࣥࢹ࢕ࣥࢆ⏕ᡂࡍࡿࡓࡵࠊ࣐࢖ࢡࣟἼࢆ⏝࠸࡚࣋ࣝ࣋ࣜࣥ࠿ࡽࢱࣜࣇ࢙ࣥࢹ࢕ࣥࡢ⏕ᡂ᪉ἲࢆ᳨ウࡋࠊ཯ᛂ ᗘࠊึᮇ S+ࠊ཯ᛂ᫬㛫ࡢ཯ᛂ࡬ࡢᙳ㡪ࢆ᫂ࡽ࠿࡟ࡍࡿࠋ࣮࢜ࢺࢡ࣮ࣞࣈ࡟ࡼࡿຍ⇕᪉ἲ࡜࣐࢖ࢡࣟἼ࡟ࡼࡿຍ⇕᪉ἲ࡜ࢆẚ㍑
ࡍࡿࠋḟ࡟ࠊᵝࠎ࡞ῧຍ๣ࢆῧຍࡋࠊῧຍ๣࡟ࡼࡿ཯ᛂ࡬ࡢᙳ㡪ࢆ᫂ࡽ࠿࡟ࡋࡓࠋ&X2 ࢆῧຍࡍࡿࡇ࡜࡟ࡼࡾࠊࢱࣜࣇ࢙ࣥࢹ࢕ࣥࢆ㑅ᢥⓗ࡟⏕ᡂ࡛ࡁࡿࡇ࡜ࢆ᫂ࡽ࠿࡟ࡋࡓࠋ

atmospheric pressure [8][9] and the characteristics of
microwave-assisted synthesis are not fully understood.
In addition, no method has been reported for thesynthesis of TLF.
The purpose of this study is to synthesize TLF
by using microwave. The effect of temperature,
initial pH, radiation time, additives, andComparison
microwave
synthesis
withautoclave synthesis were investigated. In
addition, the synthesis of other protoberberines
those similar to TLF were also studied by using
microwave.

1. Introduction
Protoberberine alkaloids have various activities suchas antimicrobial [1], anti-inflammatory [2], antimalarial[3], and anti-diabetic activities [4]. Among them,berberine (BER) has a long history of medicinal use in
traditional medicine and it has been widely
investigated. However, only few researches have beenstudied the activities of other protoberberines.
Recently, it was reported that BER was metabolized to
be protoberberines such as berberrubine (BBR),
columbamine, dehydro-discretamine, demethyleneberberine (DMB), jatrorrhizine (JAT), and thalifendine(TLF), which were observed in human urine (Fig. 1)[5]. This evidence suggested that the pharmacologicaleffect of BER could be derived from its metabolites.
However, they are too complex for cost-effective
chemical synthesis and the plants contain very small
amount of protoberberines. Therefore, it is difficult to
study the pharmacological effects of protoberberines.
Microwave energy has been widely used for heating
food materials, and recently, there has been an
increasing interest in using microwave as heat source
for chemical synthesis. Microwave-assisted synthesis
has been used for the synthesis of some chemicalcompounds, for example, for deacetylation [6], and forcross-coupling reaction [7]. In many cases, shorter
reaction time and higher selectivity were observed
comparing with conventional heating method.
However, only an experiment for producing of DMB
from BER was conducted using microwave at

MeO

O
+

O

HO
+

N

HO
OH

Berberrubine
322

N

OMe

Columbamine
338

HO
+

HO

Demethyleneberberine
324

N

OMe

Dehydrodiscretamine
324
OMe

OMe
HO

+

OH

O
+

MeO
OMe

N

MeO

+

N

Jatrorrhizine
338
OMe

O
OMe
OMe

N

OMe
Thalifendine322

OH

Fig.1 Metabolites of BER

2. Materials and Methods
2.1 Materials
All chemicals were purchased from Wako Pure
Chemicals Co., Japan. Ultra pure water produced by
the Milli-Q Advantage vessel (Millipore, USA) wasused in this study.
2.2 Microwave-assisted synthesis

101

The microwave apparatus Mars-X (CEM, USA), as
shown in Fig. 2, was used for microwave-assisted
synthesis. It can be operated with an output power ofup to 1600 W (2450 MHz). The temperature wasmeasured using the fiber optic temperature probe
RTP-300 Plus, and pressure was measured using the
ESP-1500 Plus.
The microwave apparatus automatically adjusted
the radiated power when the actual temperaturereached the set temperature. The experiments were
performed in a Teflon-made Green Chem vessel witha capacity of 100 mL, which could operate under atemperature of up to 180Υ and a pressure of up to
1.38 MPa (200 PSI). The initial output power was set
at 300 W. A volume of 20 mL of BER solution (2mmol / L) was put in the vessel and radiated by
microwave. The reaction vessel was cooled down for
30 min before filtration.

by No. 40 (Whatman, UK). The filtered additive wasdesorbed in methanol (20mL) for 24 h.

Fig. 3 Autoclave

2.6 Effect of weight of Cu2O
The weight of Cu2O was adjusted to 0.1-2.0g.
All the sample was filtered through filter paper
No.40. The filtrate was diluted 5 times in ultra
pure water, and then filtered using a 0.45-ȝm
membrane filter (Toyo Roshi Kaisha, Japan) for
liquid chromatography mass spectrometry(LC/MS) analysis.
2.7 LC/MS analysis
The quantification of BBR, BER, DMB, andTLF was performed using LC/MS-2010EV
apparatus (Shimadzu, Japan). The separation was
carried out in a ZORBAX Eclipse XDB column(3.5 ȝm, 2.1 × 150mm) (Agilent Technology,
USA). The mobile phase, which consisted of a
0.1% formic acid aqueous solution (A) andmethanol (B), was delivered at a flow rate of 0.2
mL/min using the following gradient program:
25% (B) for 0–15 min, 25–30% (B) for 15–32 min,30-70% (B) for 32–47 min 70% (B) for 47–52 min,and 25% (B) for 52–60 min. The sample injection
volume was 1 µL, and the column temperature wasmaintained at 40°C. The UV spectra were obtainedby scanning the samples in the range of 200–470
nm. The amounts of BBR, BER, DMB, and TLF
were calculated from the peak area at 345 nm.

Fig. 2 Microwave apparatus

2.3 Autoclave synthesis
The autoclave used for autoclave synthesis wasshown in Fig. 3. The temperature was measured
using thermocouple and pressure was measured bypressure gauge. The autoclave automaticallyadjusted the output power when the actual
temperature reached the set temperature. The output
power was set at 100 V. A solution (20 mL) of 2
mmol/L berberine was used for the reaction. The
reaction vessel was cooled down for 1 h before
filtration.
2.4 Effect of initial pH
The initial pH of the solution was adjusted to 3-11
using sodium hydroxide and hydrochloric acid. The
pH was measured by pH meter model F-55 (Horiba,
Japan).

3. Results and discussion
3.1 LC/MS analysis
Fig. 4 shows HPLC at 345 nm of the BER
solution after radiated by microwave for 30 min.
DMB , TLF , BBR , and BER were observed at
retention time of 14.5 min, 21.4 min, 29.3 min,and 29.9 min, respectively. The compounds were
determined by its retention time, mass to charge
radio (m/z) and comparing with literature.

2.5 Effect of additives
Each additive (0.5g) was added into 20 mL of BER
solution. After the reaction, the solution was filtered

102

mAU
5



17.5




12.5

BER



10.0



7.5


8
911
10
13
1412

7

3
0.0

6

2.5

TLF
4

DMB
2



5.0
1

Intensity (mAU)

15.0

BBR

-2.5





-5.0


0

10

20

30

40

50

min

time (min)Fig. 4 HPLC at 345 nm
Fig. 5 the mechanism of temperature

3.2 Effect of reaction time
Fig.5 shows the mechanism of temperature rise formicrowave and autoclave. Microwave heated BER
solution to 180Υ in 5 min. On the other hand, it took
30 min for the experiment by autoclave. So it’s easily
to control the temperature with the method of
microwave than autoclave. Fig. 6 and Fig. 7 show theeffect of reaction time by microwave and autoclave.
The peak area of protoberberines increased with anincrease in the reaction time. Microwave-assisted
synthesis gave faster reaction than that of autoclave.
But TLF couldn’t be produded in large quantities.

Fig. 6 Effect of reaction time (MW)

3.3 Effect of temperature
Fig. 8 shows the peak area of protoberberines by
microwave-assisted synthesis. TLF was not observedat the temperature up to 140Υ. The TLF yield
increased with an increase in the temperature above
150Υ. The highest TLF yield was obtained at 180
degree centigrade.The reaction was performed at thetemperature up to 180Υ because the limit
thermal-resistance of microwave vessel is up to200 Υ.

Fig. 7 Effect of reaction time (Autoclave)

3.4 Effect of initial pH
Fig. 9 shows the effect of initial pH. A larger
amount of TLF was obtained in pH range from
6 to 8. DMB was produced when the experimentconducted in the initial pH from 3 to 8, but DMB
peak area was the highest around the initial pH 6-7.
However, DMB peak area was decreased at pH 9 andwas not observed at pH 11. On the other hand, BBR
was generated at pH 11 as a major production
compound.

Fig. 8 Effect of temperature

3.5 Effect of additives
The additive material such as cellulose and Iron
oxide could absorbed microwave and increased thesurface temperature. Thus, they can enhance theprotoberberines. Fig. 10 and Fig. 11 show the effect ofdegradation of BER and increase the amount of otheradditives by microwave and autoclave. In microwave
radiation, Cellulose gave higher yield of DMB
compared to experiment without the additive. Fe andFig. 9 Effect of initial pH103

Co3O4 produced BBR as main product. SiC, Al, andFe3O4 gave low yield of DMB. On the other hand,
DMB was not observed when using CuO, CuI, Fe3O4,
and Co. TLF was produced when using Cu2O andCoO as the additives. From this result, only some
additives increased protoberberines. The othercharacteristic of the additives rather than microwave
absorbing ability could cause the difference in theamount and molecular structure of protoberberines.
In most experiment using the additives,
microwave-assisted synthesis gave higher yields ofprotoberberines compared to those of autoclave
synthesis. However, SiC, Al, and CuI gave lower
yields of DMB.
.

Fig. 12 Comparison additives and initial pH

Fig. 13 Effect of Cu2O weight (MW)4. Conclusion
This study shows that it is possible to use
microwave as a heat source for synthesis of TLF.
The higher yield was obtained by using Cu2O
more than 0.2g at 180 Υ. Microwave-assisted
synthesis gave fast and precise reaction for
production of protoberberines than that of
autoclave. Using additive Cellulose DMB wassynthesized from BER with microwave radiation.
BBR was produced selectively when using Co3O4
as the additive.

Fig. 10 Effect of additive (MW)Reference[1] Iwasa, et al..,J. Nat. Prod., 61, 1150-1153 (1998)[2] Kuo, et al.,Cancer Lett., 203, 127-137 (2004)[3] Iwasa, et al., Eur. J. Med. Chem., 34, 1077-1083 (1999)[4] Ko, et al., Biol.Pharm. Bull., 28, 1431-1437 (2005)[5] Feng, et al.: DMD., 36, 2159-2165 (2008)[6] Varma, R. S. et al., J. Chem. Soc. Perkin. Trans. 1, 999(1993)[7]Kabalka. G. W, et al., Tetrahedron Lett., 41, 5151
(2000)[8]Das, B. et al., Synth. Commun., 32, 19, 3027-3029(2002)[9]Zhang, D. Microwave-Assisted Synthesis of
Demethyleneberberine. Bachelor Thesis (2010)

Fig. 11 Effect of additive (Autoclave)Fig.12 shows a Comparison between the result of
the effect of additives by microwave with the effect of
initial pH, It was found that the effect of additives havelarger influence than the effect of initial pH.
3.6 Effect of weight of Cu2O
TLF was produced when using Cu2O as the
additives. Fig. 13 shows the effect of amount of Cu2O on
the degradation of BER and peak area of protoberberines.
As the amount of Cu2O increased to 0.8g, peak area of TLF
was increased, while the peak area of DMB and BBR weredecreased. In addition, the peak area of TLF by using 0.9 g
Cu2O was decreased compared to 0.6g.

104

STUDY ON RELATIONSHIP BETWEEN ROAD TRANSPORT INFRASTRUCTURE
AND REGIONAL ECONOMIC DEVELOPMENT IN CHINA
Student Number: 10M18275

Supervisor: Shinya HANAOKA

Name: Tianxing BAਛ࿖ߦ߅ߌࠆ㆏〝ᢛ஻ߣ࿾ၞ⚻ᷣ⊒ዷߩ㑐ଥ              Ꮙ ᄤᤊᧄ⎇ⓥߪ‫ޔ‬ਛ࿖ߦ߅ߌࠆ㆏〝ᢛ஻ߣ࿾ၞ⚻ᷣ⊒ዷߩ㑐ଥߩಽᨆࠍ⋡⊛ߣߔࠆ‫ޕ‬1995 ᐕ߆ࠄ 2009 ᐕ߹ߢߩᦼ
㑆ࠍኻ⽎ߦ‫ޔ‬࿾ၞ㑆ߩ⋧੕㑐ଥࠍ⠨ᘦߒߚ਄ߢ‫⥄ޔ‬࿾ၞౝߩ㆏〝ᢛ஻ߣ࿾ၞ㑆㆏〝ߩᢛ஻߇࿾ၞ⚻ᷣߩᚑ㐳ߦਈ߃ࠆᓇ㗀ߦߟ޿ߡᬌ⸽ߒߚ‫⚿ߩߘޕ‬ᨐ‫ޔ‬࿾ၞ㑆㆏〝߇⚻ᷣജߦᏅ߇޽ࠆ㓞ធ࿾ၞߩ GDP ᚑ㐳ߦኻߒ‫࠻ࠬޔ‬ࡠ࡯ലᨐ޽ࠆ޿ߪಽᢔലᨐࠍ߽ߚࠄߔߎߣࠍ᣿ࠄ߆ߦߒߚ‫ޕ‬ߚˊ Introduction:
1ˊ
There is one proverb in China – ‘㽕ᛇᆠ‫ׂܜ‬䏃’ that means“if you want a country to be rich, build more roads there
first.” . The proverb gives us an idea that roads, as one of the
major transport infrastructures, are indeed vital for a nation’s
economic growth, social development and security.
Since 1978, China has increasingly become aware of thebenefits and the importance of road constructions. Its total
length of roads increased from 890,200 km in 1978 to
3,683,600 km in 2008. In 2005, China’s road investment
ranked the first in the world. Recently, as the roadinfrastructures in China is developing rapidly, it is vital toconduct studies on relationship between road infrastructureand economic development in China.
While a number of studies on the topic have been done,
studies using econometric model analysis are not enough.
Here are three unsolved questions:First, the previous studies generally focus on a certain typeof road infrastructure such as highways, national roads. Thereis no comparison on the impacts of inter-regional roads andintra-regional roads.
Second, most of the literatures analyzed the relationshipbetween road infrastructures economic development on theassumption that the regional economy is a closed. However,with the expansion of economic activities, the interdependent
relationship among regions has been strengthened. Spillover
Effect or Straw Effect of Inter-regional road infrastructureson economic growth may differ across regions.
Third, most studies have ignored that the impacts of roadinfrastructures on economic growth may differ in different
periods.
Finally, most studies used nominal GDP to analyze theimpact of road infrastructure. However, Real GDP and
Nominal GDP are very different in China.
This study attempts to fill the gaps above. And it aims toanalyze the relationship between road transportation
infrastructure and regional economic development in China
by econometric model analysis.
The specific objectives of this study:1) Analyze the difference between inter-regional roads
impacts and intra-regional roads impacts.
2) Verify whether the road infrastructures will bring spillover
impacts on real GDP growth rate to several regions to make adifference in economy.
3) Compare the influences of road infrastructure on economic
development in different periods.
The framework of this study is shown in figure 1.

Figure 1: Study Framework2ˊ Research Methodology
2.1 Selection of Study Target
This study focuses on the inter-regional roads andintra-regional roads from 1995~2009 in 30 Chinese provinces.
According to ‘the orientation of Roads in China’, (Ma, 2004),
highways in China are defined as the inter-regional roads, andgeneral national roads, provincial roads, prefectural roads andtownship roads are defined as the intra-regional roads.

Figure 2: Inter-regional roads and intra-regional roads

2.2 Selection of Methodology
2.2.1 Barro Regression (Barro, 1991)
In Barro Regression, the region’s rate of GDP per capita
growth during a certain time period is regressed on an initialcondition for GDP per capita and other potential output
growth determinants. Barro Regression can verify which
factors affect economic development.
Barro Regression also has been applied widely in analysesof convergence hypothesis. It can verify whether economic
growth tend to convergence. In this model, the effect of road105

infrastructure is reflected by GDP growth, and thequantitative distribution of roads corresponds to social andeconomic development can be calculated.

Refer to Barro(1991) and Nakasato(2001) :¾
Convergence:
It implies that poor regions (with lower GDP per capita)
tend to grow faster than rich regions.
¾
Divergence:
It implies that rich regions (with higher GDP per capita)
tend to grow faster than poor regions.
¾
Straw Effect:
As road construction is developing, more people andfactories of the slow-growing regions will move to thefast-growing regions which more logistic centers, factories
and technical economic zones have settle in.
¾
Spillover EffectAs road construction is developing, more people andfactories of fast-growing regions will move to slow-growing
regions depending on the expansion of trade area and thedispersion of economic activities.
Figure 3 shows the generalization of Straw Effect andSpillover Effect.

2.2.2 Estimating equation
In order to analyze the impacts of inter-regional roads andintra-regional roads on GDP growth rate in China, this study
refers to the literature from Nakasato (2001). The equations
are shown in Eq.(2.1).
1§ §
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T© ©

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1

it

(2.1)

it

Where, if ln(yit) > ln(yit*): D1=1; otherwise: D1=0;.if ln(yit) < ln(yit*): D2=1; otherwise: D2=0;
Table 1 has explanations for the variables in the estimating
equation.
Table 1: Explanations of variables
Dependent Variable

1§ §
¨ ln¨
T© ©

Independent Variables
Constantln(yit)DINFRAitIINFRAit (ln(yit) > ln(yit*))IINFRAit (ln(yit) < ln(yit*))y·  ln§¨¸©

i ,t  T ¹

y ·¸¹ ·¸¹
itParameters
Alpha
Beta
Gamma
Theta
Fai

Figure 3: Straw Effect and Spillover Effect
Table 2 shows the definitions of major independent
variables in Eq.(2.1).

2.4 Analysis step
Figure 4 shows the analysis steps of this study.

Table 2: Definitions of major variables
Variablesyit
DINFRAitIINFRAit
yit*

Definition
Real GDP per capita of i Province in t time point;
Increase in intra-regional road density;
Increase in inter-regional road density;
Average of real GDP per capita of the surrounding
provinces ( the provinces next to i province) in t time
point;The inter-regional roads impacts depend on the economic
gap between one region and other regions next to it with roadinfrastructure connections. In* this estimating equation, cross
* ··
·
§
§
§
¸ ‘TIINFRAit u ¨ ln§¨ y ·¸  ln¨ y ¸’¸terms are used ‘.IIINFRAit u ¨© ln§¨© yit ·¸¹  ln§¨© yit ·¸¹ ’,© it ¹ ¹¹© © it ¹
as independent variables.
In China, road infrastructure constructed depending on
traffic demand. The average traffic volume of national road is20000 vehicles / day. There is serious traffic congestion in
two-thirds cities of China with automobiles increasing. The
utilization of road is relatively high, so this study refers toNakasato (2001), using road density data as index of roadinfrastructure to analyze the impacts of road infrastructure inbasic analysis.

Figure 4: Analysis steps of this study
In this study, there are 2 estimation methods: Least Squares
Method and Instrumental Variable Method. Figure 5 shows
the image of 2 estimation method.
Least Squares Method

2.3 Interpretation of parameters’ sign

Figure 5: Images of 2 estimation method

Table 3 shows the interpretation of each parameter’s sign inEq.(2.1).

In econometric model analysis, if we only verify whether
the parameters are significant, comparing with T value, R
square is not very important.

Table 3: Interpretation of each parameter’s sign
Parameters
Beta
Gamma
Theta
Fai

sign
+
+
+
+
-

Instrumental Variable Method

Effects
Divergence;
Convergence;
Positive effects;
Negative effects;
Straw effects ;Spillover effect ;Spillover effect;
Straw effects;

3. Results and Discussion
3.1 Results
Base on the three points of ‘Possibility of Data Collection’,‘Sufficiency of Study Analysis’ and ‘Difference of
influences’, this study analyze the impacts of roadinfrastructures in China.
The table 4 is the summary of basic analysis. In basic

106analysis, this study refers to Nakasato (2001), using panel
data (T=5) as input-data and road density data as index ofroad infrastructure to analyze the impacts of roadinfrastructure all over China by Least Squares Method.

with the error terms of a regression relationship. Suchcorrelation may occur when the dependent variable causes atleast one of the covariates ("reverse" causation). Therefore, itis essential to use Instrumental Variables Method foranalyzing the impacts of road infrastructure on GDP growthrate in China. Table 7 shows the Instrumental Variables.

Table 4: Summary of basic analysis

A1

Data

Data of roadinfrastructure

Parameters

Data sections

MethodPanel data
(T=5)
30 samples

Road densityBasicequation

All
of
the
provinces(Except Hainan
province)

Least
Squares
MethodTable 7: Instrumental Variables
Instrumental Variables
Total population in initial period;
The ratio of employee population over total population in initial period;
Area ;
GDP per capita of the provinces next to the study province in initial period;
GDP per capita of study province in initial period
Road length in initial period;

The basic analysis indicates that most T-values are low.
Therefore, from Discussion 1 to Discussion 5, this studychanged the data, parameters and study sections over again to
analyze the impacts of road infrastructure on GDP growthrate by Least Square Method.
Table 5 is the summary of Discussion 1 to Discussion 5 byLeast Square Method.

Table 8 is Summary of Discussion 6 and Discussion 7 by
Instrumental Variable Method.
Table 8: Summary of Instrumental Variable Method
Data

Data of roadinfrastructure

Parameter

Data sections

MethodPanel data
(T=1)
150 samples(each period)
420 samples(all periods)

D6-1

Basic
equation

All
of
the
provinces(Except Hainan
province)

Instrumental
Variables
MethodPanel data
(T=1)
115 samples(each period)
322 samples(all periods)

D7-1Road densityBasicequation

Omitting
4
municipalitiesand 3 provinces
in west. .

Table 5: Summary of Least Square Method
Data

D1
D2

Panel data
(T=5)
30 samples

Data of roadinfrastructure

Parameter

Data sections

Road length

Basic equation

D2-1
Road densityBasicequation+ stockAll of the provinces(Except Hainan
province)Basicequation

Omitting 4
municipalities and
3 provinces in west.

D6

Method

D7

D2-2
Road length
D3

Panel data
(T=5)
23 samples

D3-1
Road density
D3-2
Road length
D3-3Road density

leastsquaresmethodD5

Basicequation+ stock

D7-2
Road length

Table 9: Results of Discussion 6-1

Panel data
(T=5)

New Barro regression refer to LIN (2010)

Panel data
(T=1)
150 samples(each period)
420 samples(all periods)

Road density

Basic equation

Barro
Regression
Alphat-valueBetat-valueGammat-valueThetat-valueFait-value
Adjusted
R square

All of the provinces(Except Hainan
province)

Discussion 1 to Discussion 5 indicate that most T-value arestill low in any applied Least Squares Methods (Table 6).
There are probably some endogeneityproblems. In general,
when estimating the impacts of road infrastructure on
economic growth by Least-Squares Method, it should be
noted that there is a potential for estimate bias. Also, there are
Endogeneityproblems caused by reverse causality.

1)
2)

Table 6: Results of Least Squares Method
Barro
Regression
Alphat-valueBetat-valueGammat-valueThetat-valueFait-value
Adjusted
R square

D6-2
Road length

Comparing the results from basic analysis to Discussion 7,
some T-value of Discussion 6-1 are relatively significant.
Table 9 shows the results of Discussion 6-1.

D3-4
Road length
D4Road density

Least Squares Method
1995-2000
2000-2005
0.015-0..039(0.358)(-0.874)
0.008
0.017***(1.537)(3.473)-0.025-0.003(-0.565)(-0.159)-0.694-1.027(-0.263)(-0.963)5.512
21.479**(1.049)(2.586)
0.026
0.086

2005-2009
0.288***(3.809)-0.018**(-2.258)
0.049(1.193)-0.066(-0.508)
0.101(0.923)0.064

Instrumental Variables Method
1995-2000-0.294(-0.621)0.046(0.787)-1.074(-0.798)-12.283(-0.282)
96.330(0.946)
0.096

2000-2005-0.739(-1.169)0.010(1.337)-0.482(-0.896)-21.097(-1.420)123.469(1.177)
0.162

2005-20090.430***(2.922)-0.034**(-2.163)
0.103*(1.701)13.745(1.368)0.029(0.002)0.070

1995-2009
0.212**(2.398)0.041)***(2.842-0.371(-1.436)-42.769**(-2.349)
14.478**(2.357)0.039*, **, *** indicate that it is statistically significant at 10%, 5%,
1% level.
The values in ( ) represent the T-values of each parameter.

3.1.1 Intra-regional Roads
In the 2005~2009 period, the parameter of intra-regionalroads is significantly positive at 10% level. In China,
2005~2009 are the years in which road infrastructure grew
rapidly. The average growth rate of intra-regional road length
at that time is 22%. There are relatively sufficientconstruction comparing with the 1995~2000 period and the
2000~2005 period, so the intra-regional road impact on GDP
growth in the 2005~2009 period is relatively obvious.
However, in other periods, the parameters of intra-regionalroads are almost insignificant. One reason is that the roadinfrastructure construction of the 1995~2000 period and the
2000~2005 period are relatively insufficient. Another reason
is that the growth rate of road length is slower than the real
GDP growth rate.
Overall, in the 1995~2009 entire period, the intra-regional

1995-2009-0.033
㧔-1.182㧕
0.015***
㧔4.868㧕
0.025
㧔1.390㧕-1.280㧔-1.156㧕
18.727***
㧔2.976㧕
0.087

Instrumental Variable Methods allow consistent estimation
when the explanatory variables (covariates) are correlated107

roads did not have significant effects on GDP growth.

to use Instrumental Variables Method for analyzing in China.

3.1.2 Inter-regional Roads

3.2.3 Reasons for the less prominent promotion ofroad constructions

Comparing the 1995~2000, 2000~2005, and 2005~2009
periods, the inter-regional roads bring significant effects on
GDP growth in the 1995~2009 period. It means the longer the
opening of the inter-regional roads, the greater the effects on
GDP growth rate in regions.
In the 1995~2009 period, the parameter of inter-regionalroads in the regions with higher GDP per capita than thesurrounding regions is significantly negative at 5% level. And
the parameter of inter-regional roads in the regions withlower GDP per capita than the surrounding regions is
significantly positive at 5% level.
Thus, based on the above results, in the 1995~2009 period,
the inter-regional roads brought Spillover Effect on regional
economic development and reduced economic disparities
between regions through spillover effects. As road
construction develops, more people and factories offast-growing regions will move to slow-growing regions
depending on the expansion of trade area and the dispersionof economic activities.

Except for the years of 2005 to 2009, the increase of road
constructions in China has been smaller than the growth ofGDP. To some degree, the road constructions did not meet theneeds of the economic development during those years.
Figure 6 shows growth rate of real GDP PC and road length.

Figure 6: Growth rate of real GDP PC and road length

3.2.4 Comparison of road construction between
China and other countries

3.1.3 GDP per capita in initial Period
In the 2005~2009 period, the parameter of real GDP per
capita in initial Period is significantly negative at 5% level, so
according to the neo-classical growth theory, thedevelopment of economic growth tends to Convergence.
However, in the 1995~2009 entire period, the parameter ofreal GDP per capita in initial Period is significantly positive
at 1% level, so according to the neo-classical growth theory,the development of economic growth tends to Divergence.

Recently, the road constructions in China have been
increasing rapidly. However, there is still a long way to go tobe sufficient infrastructure with developed countries like
Japan and UK, especially on road density. For example,
average ratio of territory unit area to highway lengthof China
in 2007 is equivalent to that of Japan in 1977 (Table 10).
Thus road constructions in China require much more
attention and promotion.

3.2 Discussion
3.2.1Effect of Road Type

Table 10: The ratio of territory unit area to highway length

The significant economical influences brought byinter-regional roads and intra-regional roads in China aredifferent in this study.
Today, with the expansion of economic activities, theinterdependent relationship among regions has beenstrengthened. Under such a situation, in the 1995~2009 entire
period, the inter-regional road impacts are greater thanintra-regional road impacts. The economic effect ofinter-regional roads in China is differed from region to region
as a result of the Spillover Effect or the dispersion of
economic activities.

(km/1000km2)

China

Japan

Average ratio of territory
unit area to highway length.

5.63(2007)

5.81(1977)

4. Conclusions
The significant economical influences brought byinter-regional roads and intra-regional roads in China areextensively discussed in this study. Only in the years of 2005
to 2009, the intra-regional roads brought significantly
positive effects to GDP growth. In 1995~2009 period, theinter-regional road brought significant effects to GDP growth.
In China, it means the longer the opening of theinter-regional roads, the greater the effects on GDP growthrate in regions.
This study also estimates the impacts of inter-regional roads
on regional economies by considering the interdependent
relationship among regions. In the 1995~2009 entire period,
the inter-regional roads brought Spillover Effect on regional
economic development and reduced economic disparities
between regions through spillover effects.
As the “five columns and seven lines” highway construction
program progresses, it is expected that those roadinfrastructure would accelerate regional economies in China.

3.2.2 Estimation Method
Referring to Barro econometric model analysis, this study
aims to analyze the impacts of road infrastructures by using
least-squares method and instrumental variables method.
Compare with the least squares method, T-value of
instrumental variables method are relatively better.
In general, when estimating the impact of road infrastructure
on economic growth by using least-squares method, there is a
probability of bias estimation. There are also someendogeneity problems that occurred due to reverse causality.
As the interdependent relationship among regions has beenstrengthened, it is very likely that the development of roadinfrastructure will develop if the regional economic grows.
1).In regions with many opportunities for economic growth,
the demand for road construction could increase to support
the expansion of companies.
2).In some richer areas, their economy develops faster.
Accordingly, the local governments have more funds for road
constructions. There are more roads being built in thoseareas.
Therefore, a country in which road construction andeconomic development have reverse-causality, it is essential

References
1.Barro (1991): Economic Growth in Cross Section of Countries,
quarterly journal of Economics 106,P407-443
2.Nakasato(2001): Transport infrastructure and economic
development, No43,P101-114
3.Abhijit Banerjee, Esther Duflo (2009):On the Road: Access toTransportation Infrastructure and Economic Growth in China
4. Jeffrey P(2010):The broad effects of transportation infrastructure:
Spatial econometrics and productivity approaches, Transportation
Research Part E 46, P317-326

108

D IRECTIONAL C HARACTERISTICS OF THE R ADIO WAVE P ROPAGATION
THROUGH F OLIAGE
Student Number: 10M18306

Name: Le Vu Hung

Supervisor: Jun-ichi TAKADA

樹木における電波伝搬の空間特性
レ

ヴ

フン

本研究は村落における移動通信サービスを主な対象とし，樹木による電磁波の散乱の角度広がりに関する実験的な検討を行う．広帯域チャネルサウンダによる信号を測定した．SAGE(Space Alternating Generalized Expectation)の超分解能到来方向推定アルゴリズムを適用した空間チャネル特性について検討を行った．結果はアジマスには
水平偏波の角度広がりが垂直偏波の角度広がりより大きいが，逆にエレベションに対しては水平偏波の角度広がりが垂直偏波の角度広がりより小さいということが分かった．平均アジマス広がりが 25◦ で平均エレベションが6◦ である．また，基地局の高さと角度広がりの関係は全く明らかにされていない．

1

Introduction

The aim of this research is to evaluate the directional
characteristics of radio wave propagation through foliage.
A wideband channel sounder was employed to perform
a directional measurement of polarimetric signal throughdense forest in Kanagawa. Discrete plane wave model isdeployed in the data modeling, where individual multipath component is represented as a discrete plane wave
with specific complex amplitude, angle of arrival and delay time. To estimate these parameters, the SAGE algorithm is applied. The selection of the SAGE algorithm
as the estimation technique is attributed to several factors:
accuracy, rapid convergence, and robustness in low SNR
scenarios. In order to understand the statistical characteristics, such as the direction spread, analysis of the multipoint
mobile measurement acquisition results was performed.

In recent years, the research of the radio propagation
mechanism in the urban environment has been studied extensively. On the other hand, only a small amount of research on environments with low population density andlarge amounts of vegetation and foliage are present. Presence of foliage in propagation path plays a significant rolein the propagation of radio waves. In many studies in
the field of radio wave propagation through foliage, there
have been a number of theoretical as well as empirical pathloss models developed in the literatures [1–3]. A theoretical description of penetration into vegetation is given by
the theory of radiative energy transfer [4]. These models
are generic and applicable to any foliage wave propagation scenario, but are more complicated than the empirical models. The empirical models are based on specificmeasured data and fail to relate the foliage path-loss tothe foliage dependent parameters such as tree species anddensity. All the models, as mentioned above, are focused
mainly on path loss characteristics. An important aspectin the understanding of radio wave propagation in rural
areas are the spatio-temporal characteristics of the channel. Of special note is the angular spread of the channel,
which is an essential parameter for diversity and MIMO.
Currently, there are few studies on the directional analysis of the dispersion effects of foliage in the radio channel. In [5], the delay characteristics and the polarimetricspatio-temporal channel characteristics have been studied
in detail, but is limited only to V polarization and withboth the base station (BS) and mobile station (MS) situated below the trees. Thus, from the viewpoint of the design of mobile communication systems, the investigation
is insufficient. Based on the above background, the spatiotemporal channel characteristics at the MS side in the foliage environment based on measurements using channelsounder has been studied [6]. The results show that somelarge spreads in azimuth and the distance of the nominal
azimuth values from zeros may be related to the dispersion
caused by the foliage. However, this result had only investigated the direct path with the location of the MS fixed.
Further investigation is essential to extract the effects ofother paths that may be present in the environment.

2 Measurement
The measurement was performed using the channelsounder at the center frequency of 2.22 GHz and an operation bandwidth of 44.8 MHz. The sounder specification and measurement set up parameters are found in Table 1. A sleeve and a slot antenna were used to send thevertically and horizontally polarized signal. The receiverantenna array was installed on the roof-top of a measurement van with the horizontal distance between the transmitter and receiver ranging from 109 to 123 m. This antenna array is a stacked polarimetric uniform circular patch
array (SPUCPA). It comprises of 4 stacked rings of 24 polarimetric patches yielding 192 ports in total. The MS
was equipped with a differential global positioning system (GPS) to accurately track its location. A measurement
campaign was carried out in a forest at Kanagawa on October 26–27, 2009 (Figure 1). The average height of the trees
is 10 m and spread out with the average density of 0.32
per square meter. The measurement area is surrounded byother forests, low rise housing and a nearby sea, the physical feature of this forest mainly consist of 90 percent is a
specie of Japanese pine tree. The BS antenna height wasvaried from 4 to 15 m by using a man lift, whereas the
MS antenna was mounted on a van with a height of 2.8 m.
The number of measurement points per each BS antennaheight was 64 with 1 m interval. As indicated to Figure 1,109 






 


Figure 2: Signal Model.




L(t) is the varying number of multipath components as the
MS moves.
√ Since αl is complex valued, it can be written as
αl = | Pl |ejϕl (t) , where we assume ϕ(t) are independentand uniformly distributed in the interval [0, 2π), and |Pl |is the received power of the path l. s(t) is the transmitted
signal and n represents the zero-mean complex Gaussian
measurement noise with variance σn2 . e(θl , φl ) is the array
response of the receiver antenna array at θl in co-elevationand φl in azimuth.












Figure 1: Measurement scenario.

Table 1: Specification of experimentCenter frequency
Bandwidth
Trans. power
Trans. signal
BS antenna
MS antenna

2.22 GHz
44.8 MHz
40 dBm
Wideband Multitone, 897 tones
sleeve, slot
SPUCPA (4 ring, 96 elements)

BS antenna height
MS antenna height
BS-MS separation
Average tree height
Number of meas. points
Number of snapshots
per measurement point
The distance between
the measurement points

4, 9, 15 m
2.8 m
109–123 m
10 m
64
20
1m

the azimuth-of-arrival (AoA) φ relative to the movement
direction of the MS was found to be 0 ∼ −180◦ for lefthand side and 0 ∼ 180◦ for right-hand side. Moreover,the co-elevation-of-arrival (EoA) θ was defined to have avalue 0◦ ∼ 180◦ , θ = 0◦ corresponds to the zenith.

3

Analysis Approach

3.1

Signal Model

Fleury [8] proposed an alternative approach to represent
the angular spread to avoid the ambiguity of angular definition due to the rotational periodicity. In this paper, 2dimensional definition of Fleury’s direction spread is introduced:
 L| exp(jφl ) − μφ |2 ·Plσφ =  l=1,(2)
L
l=1 PlwithL(t)αl e(θl , φl )s(t − τl ) + n(t),The parameters of each impinging wave are estimatedfrom measurement data using the SAGE algorithm. This
scheme is based on maximum likelihood estimation(MLE). A detailed description of the SAGE algorithm isshown in [7].
Although the SAGE algorithm is capable of estimatinga large number of propagation paths, not all of these estimated paths are meaningful. There are two reasons forthis. First, the received power of most of the paths after
extracting 80-th path was less than the actual power level
threshold of 25 dB, which had no significant contribution
to the power transmission from BS to MS. Second, the detected paths were not always reliable due to the low SNR.
In this regard, only paths falling within the 25 dB dynamic
range were used find the delay and angular spreads. The
paths that do not fall within the dynamic range were notused for further analysis.

3.3 Fleury’s Direction Spread

Figure 2 shows the signal model considering the directional information. In the data analysis, radio channel ismodeled as the sum of impinging waves. Each impinging
wave is represented as a discrete plane wave with specificcomplex amplitude, angle of arrival and delay time.
x(t) =

3.2 Estimation of Channel Parameters

μφ =

(1)

l=1

L

l=1 exp(jφl ) · Pl,L
l=1 Pl(3)

where Pl is the normalized azimuth angular power spectrum. It is noted that the direction spread in Eq. (2) is close
to the root-mean-square (RMS) azimuth spread when thewhere αl , τl denote the complex channel gain, the propagation delay of l-th multipath component, respectively.
110

1



Meas.
Gaussian Fitting
Sim. (σφ = 30◦ )Sim. (σφ = 25◦ )Sim. (σφ = 20◦ )

0.8










CDF

0.6

 











 

0.4



0.2




0◦
0

10◦
20◦
30◦
40◦
Azimuth direction spread

50◦



(a) CDF of azimuth direction spread
1








Figure 4: The obtained SAGE estimates of the mobile
measurement at the first and last point (strongest 20 paths).

0.8

CDF

0.6
0.4
Meas.
Gaussian Fitting
Sim. (σθ = 5◦ )Sim. (σθ = 4◦ )Sim. (σθ = 3◦ )

0.2
0◦
0

5◦
10◦
Co-elevation direction spread

4.2 Direction Spread
The obtained SAGE estimates of the mobile measurement
at the first and last points with a BS antenna heigh hBS of
15 m are indicated by the red dots in Figure 4. In this caseboth ports V and H polarization were used for the estimation of the mentioned parameters. It can be confirmed thatthe nominal direction of arrival calculated by Eq. (3) seenfrom MS is quite close to the actual direction.
Figures 5, 6 show the cumulative distribution function (cdf) of direction spread for the azimuth and the coelevation domains compared with Gaussian distribution
fitting. It can be confirmed that the azimuth direction
spread for H polarization is always greater than that for
V polarization and the opposite is true in the case of coelevation direction spread. It can be seen from the figuresthat changes in the BS antenna height produce no significant changes in the azimuth direction spread values for
V polarization. The relatively high values of azimuth direction spread indicate that the incoming field at the MS
is highly varying in azimuth. This is due to the dispersion caused by the absorption, scattering, and diffractionof the waves propagating through the foliage. For the EoA,
the BS direction is set to 0◦ , 3◦ and 6◦ corresponding to
the BS antenna height hBS of 4 m, 9 m, 15 m, respectively. There is no clear relation found between hBS andco-elevation direction spread. The relatively low valuesof co-elevation direction spread indicate that the incoming field at the MS is highly concentrated in co-elevation.
Also on these figures, the mean, standard deviation of thedirection spread σ̄, σsd are also shown. The mean azimuth
direction spread is about 25◦ and the mean co-elevation
direction spread is about 6◦ . This value is close to the average azimuth spread which was found to be about 25◦
in [9]. Furthermore, when the BS antenna height is 9 m,
all the values are minimum. In this case, a better understanding of the mechanism of radio wave propagation isrequired to describe this result. To confirm the goodnessof fitting between the Gaussian and experimental distributions, a Kolmogolov-Smirnov (KS) test has been applied.
A Kolmogorov-Smirnov test is used to compare the values

15◦(b) CDF of co-elevation direction spread

Figure 3: Comparison of simulated and measured CDF of
direction spread.

Table 2: Summary of direction spread statistics
Mean
Standard deviation
Direction spread σ̄sim σ̄meas σstd,sim σstd,meas
AzimuthCo-elevation

27.1◦
6.0◦

27.6◦
5.9◦

3.6◦
1.5◦

5.4◦
1.3◦

impinging waves are highly concentrated within small azimuth range [8]. By replacing φ with the co-elevation angle θ in Eq. (2) and (3), we can compute the co-elevation
direction spread σθ .

4Results

4.1

Comparison Between Measurement and
Simulation

Figure 3 shows the CDFs of the simulated and measured
results of direction spread with its Gaussian fitting. The
width of angle for simulation can be calculated. The
widths of azimuth are 42◦ , 52◦ , and 62◦ corresponding to
the azimuth spread of 20◦ , 25◦ , and 30◦ and the width ofco-elevation are 8◦ , 10◦ , and 13◦ corresponding to the coelevation spread of 3◦ , 4◦ , and 5◦ , respectively. The simulated CDF of direction spread with the azimuth spread of25◦ , the co-elevation spread of 4◦ are in agreement withthe measurements. Table 2 summaries the statistics of direction spread in case of of azimuth spread is 25◦ and coelevation spread is 4◦ . It can be confirmed that the simulations results are in agreement with the measurements.
111

1

1

V pol., σ̄ = 27.6◦ , σsd = 5.4◦
V pol. Fitting
H pol., σ̄ = 22.6◦ , σsd = 6.6◦
H pol. Fitting

0.8

0.8

0.8

CDF

0.4

0.4

0.4

0.2

0.2

0.2

0◦
0

10◦

20◦

30◦

Azimuth direction spread

0◦
0

40◦

V pol., σ̄ = 26.9◦ , σsd = 5.3◦
V pol. Fitting
H pol., σ̄ = 25.3◦ , σsd = 5.7◦
H pol. Fitting

0.6

CDF

0.6

CDF

0.6

1

V pol., σ̄ = 25.9◦ , σsd = 5.8◦
V pol. Fitting
H pol., σ̄ = 22.3◦ , σsd = 7.1◦
H pol. Fitting

10◦

20◦

30◦

Azimuth direction spread(a) hBS = 4m

0◦
0

40◦

(b) hBS = 9m

10◦

20◦

30◦

Azimuth direction spread40◦(c) hBS = 15m

Figure 5: CDF of azimuth direction spread.

0.8

0.8

0.8

0.6

0.6

0.6

0.4

CDF

1

CDF

1

CDF

1

0.4
◦

◦

V pol., σ̄ = 5.9 , σsd = 1.3
V pol. Fitting
H pol., σ̄ = 6.6◦ , σsd = 1.9◦
H pol. Fitting

0.2

0◦
0

5◦

10◦

Co-elevation direction spread

0.4◦

V pol., σ̄ = 5.2 , σsd = 1.4
V pol. Fitting
H pol., σ̄ = 5.7◦ , σsd = 1.5◦
H pol. Fitting

0.2

15◦

◦

0◦
0

5◦

10◦

Co-elevation direction spread(a) hBS = 4m

V pol., σ̄ = 7.1◦ , σsd = 2.3◦
V pol. Fitting
H pol., σ̄ = 7.6◦ , σsd = 2.7◦
H pol. Fitting

0.2

15◦

(b) hBS = 9m

0◦
0

5◦

10◦

Co-elevation direction spread15◦(c) hBS = 15m

Figure 6: CDF of co-elevation direction spread.
spread was about 6◦ .

Table 3: KS-Test results
Height of BS
Polarization
4 [m] 9 [m] 15 [m]VCo-elevation
Azimuth

A*
A

A
A

A
A

H

Co-elevation
Azimuth

A
A

A
A

A
A

*

References[1] International Telecommunication Union (ITU), “Attenuation in vegetation,” Rec. ITU-R, Geneva, Switzerland, Tech. Rep., 2007.
[2] N. C. Rogers, A. Seville, J. Richter, D. Ndzi, N. Savage, R. Caldeirinha, A. K. Shukla, M. O. Al-Nuaimi, K. Craig, E. Vilar, andOthers, “A generic model of 1–60GHz radio propagation throughvegetation-Final report,” Radio Agency, UK, 2002.

A: accepted, R: rejected (Confidence interval of 95%).

[3] G. M. Whitman, M. Y.-C. Wu, and F. K. Schwering, “Propagationand scattering of spherical wave pulses in vegetation using scalartransport theory,” IEEE Trans. Antennas Propag., vol. 58, no. 5, pp.
1662–1676, May 2010.

in the measured data to a Gaussian distribution. The null
hypothesis is that the measured data has a Gaussian distribution. The alternative hypothesis is that the measured
data does not have a Gaussian distribution. The result is A
(accepted) if the test rejects the null hypothesis at the 5%
significance level, R (rejected) otherwise. Table 3 summarizes the results of one-sample Kolmogolov-Smirnov testof Gaussian distribution for all the parameter sets. In allof the cases, empirical distribution was matched with theGaussian distribution.

5[4] A. Ishimaru, Wave propagation and scattering in random media.
Wiley-IEEE Press, 1999, vol. 12.
[5] C. Oestges, B. Villacieros, and D. Vanhoenacker-Janvier, “Radio
channel characterization for moderate antenna heights in forest areas,” IEEE Trans. Veh. Technol., vol. 58, no. 8, pp. 4031–4035, Oct.
2009.
[6] M. Ghoraishi, J. Takada, C. Phakasoum, T. Imai, and K. Kitao,“Azimuth and delay dispersion of mobile radio wave propagation
through vegetation,” in 4th European Conference on Antennas andPropagation (EuCAP 2010), Mar. 2010, pp. 1–4.
[7] B. H. Fleury, M. Tschudin, R. Heddergott, D. Dahlhaus, and K. Ingeman Pedersen, “Channel parameter estimation in mobile radio environments using the SAGE algorithm,” IEEE J. Select. Areas in Commun., vol. 17, no. 3, pp. 434–450, Mar. 1999.

Conclusion

In this research, the effect of the foliage on the radio wave
propagation has been analyzed by using a SAGE algorithm. The analysis result shows that the azimuth direction
spread for H polarization is always more than that for V
polarization and opposite is true in the case of co-elevation
direction spread. The mean azimuth direction spread wasfound to be about 25◦ and the mean co-elevation direction[8] B. Fleury, “First- and second-order characterization of direction dispersion and space selectivity in the radio channel,” IEEE Trans. Inf.
Theory, vol. 46, no. 6, pp. 2027–2044, Sep. 2000.
[9] C. Phakasoum, M. Ghoraishi, J. Takada, K. Kitao, and T. Imai, “Frequency characteristics of angular spread for radio wave propagation through foliage,” in 5th European Conference on Antennas andPropagation (EuCAP 2011), Mar. 2011, pp. 3289–3292.

112

IMPROVEMENT OF NOMIKAWA RIVER ENVIRONMENT THROUGH H2SREMOVAL BY OXYGEN RELEASE COMPOUND (ORC)
Student Number: 10M18312

Name: Yuanzheng ZHUSupervisor: Hirofumi HINODE㓟⣲౪⤥๣ࢆ⏝࠸ࡓ◲໬Ỉ⣲㝖ཤ࡟ࡼࡿ࿐ᕝỈ㉁ᨵၿ
ᮒ 㐲ᚁ
ᮾிࢆὶࢀࡿ࿐ᕝࡣᮾி‴࡟㏆࠸ୗὶ㒊ศ࡟࠾࠸࡚ῐỈᒙ࠾ࡼࡧᾏỈᒙ࠿ࡽ࡛ࡁ࡚࠸ࡿࠋ኱㞵࡞࡝ࡢ≉Ṧ࡞≧ἣ࡛ࡣᕝ࡟኱㔞ࡢ᭷ᶵ≀ࢆྵࡴୗỈࡀὶࢀ㎸ࢇ࡛ࠊᗏἾ࡞࡝࡟Ꮡᅾࡍࡿ◲㓟㑏ඖ⳦ࡢάື࡟ࡼ
ࡗ࡚◲໬Ỉ⣲ࡀከࡃ⏕ᡂࡉࢀᝏ⮯ࡢཎᅉ࡜࡞ࡗ࡚࠸ࡿࠋᮏ◊✲࡛ࡣ࿐ᕝࡢ⦪᩿㠃ࡢࢩ࣑࣮ࣗࣞࢩࣙࣥ⿦
⨨ࢆ⏝࠸࡚ࠊ◲໬≀࢖࢜ࣥ⃰ᗘࠊS+ࠊ'2 ࠾ࡼࡧ 62ࡢኚ໬ࢆほᐹࡋࡓࠋ◲໬Ỉ⣲⏕ᡂࡢᢚไࡣ㓟⣲౪⤥๣㸦25&㸧ࢆ⏝࠸࡚⾜ࡗࡓࠋ25& ࡣỈ࡜ࡢ཯ᛂ࡟ࡼࡗ࡚㓟⣲ࢆ⏕ᡂࡋࠊ◲໬≀࢖࢜ࣥࢆ◲㓟࢖࢜ࣥ࡟㓟໬ࡉࡏࡿᶵ⬟ࢆᣢࡗ࡚࠸ࡿࠋ+6㸸0J2 ࡢ཯ᛂࣔࣝẚࡣࡑࢀࡒࢀ㸯㸸㸯ࠊ㸯㸸㸰ࠊ㸯㸸㸱ࠊ㸯㸸㸲ࢆ᳨ウ
ࡋࠊ+6㸸0J2 ࡢ཯ᛂࣔࣝẚࡣ㸯㸸㸰ࡢ᫬ࠊ+6 ࡢ㝖ཤ㏿ᗘࡀ୍␒㧗࠸ࠋ+6㸸0J2 ࡢ཯ᛂࣔࣝẚࡣ㸯㸸࡟ᇶ࡙࠸࡚ࠊ25& ࡟ࡼࡿ࿐ᕝࡢ◲໬Ỉ⣲㝖ཤ࡟㛵ࡍࡿ⡆༢࡞ࢥࢫࢺࡣ  ෇ᖺ㛫ࠋ
is focused on investigating the effective and economic
method to remove H2S from Nomikawa River in orderto improve the river environment.
Eliminating the growth of SRB is a primary approach
to inhibit the generation of H2S in groundwater.
Disinfectant is generally added to water to kill SRB.
However, the H2S level recovers rapidly. The growth
pattern of SRB population is consequently changed since
the SRB in suspension have been killed and from thatpoint the immobilized SRB predominate in the water
system. Therefore, the SRB population becomes moreresistant to disinfectant [3]. In addition, many
commercial disinfectants contain heavy metals and/or are
toxic [4], which makes their use inappropriate whenapplied to an observational ground water system.
Addition of an oxidizing agent to the wastewater likehydrogen peroxide, chlorine gas, sodium/calcium
hypochlorite, potassium permanganate etc. is anotherway to remove H2S in wastewater [5][6][7]. H2S can
also be removed by precipitating with ferrous/ferricsulfate [8][9]provided the pH value is greater than 7.5.
The study deals with the evaluation of various
chemicals like hydrogen peroxide, sodium/calcium
hypochlorite, ferrous/ferric sulfate and a combination of
sodium hypochlorite and sodium hydroxide for oxidationof dissolved sulfide in wastewater on laboratory scale.
At present, aeration technology [10] on treatment ofpolluted rivers was an adopted method in the world.
Mechanized power must be needed for aeration
technology in the oxygen supply system, construction
cost and maintenance charge are also very high. So it isvery difficult to enforce the process in actual application.
An oxygen release compound (ORC) is composed
mainly of magnesium peroxide (MgO2) and this wasapplied in this study. It can be reacted with water torelease oxygen slowly to produce magnesium hydroxide(Mg(OH)2). There are two major mechanisms by which
ORC may be able todecrease H2S concentration in water.
One is the abiotic oxidation of sulfide to sulfate, and theother is biotic inhibition of SRB by reducing thegeneration of sulfide. Since ORC can release oxygen 

1 Introduction
Nomikawa River (Fig.1) flows through Setagaya ward,
Meguro ward and Ota ward in Tokyo. Since Nomikawa
River connects to Tokyo Bay, the section close to thesea has both fresh and salt water layers in the flow. At
particular conditions where abundant of organic matters
are present due to the flow of large amount of sewage
water into the river, hydrogen sulfide (H2S) is largely
generated causing bad odor.

Fig.1 Nomikawa River
Hydrogen sulfide (H2S) is one of the main states ofsulfur in the natural sulfur cycle. The dominant sourceof H2S is the reduction of sulfate, the most common
state in the natural aquatic system, by sulfate-reducing
bacteria (SRB) under anaerobic conditions [1]. H2S may
cause several problems to both sewers and the watersupply system such as odor and toxicity [2].This study

113

gradually over a long period of time, the addition of
ORC would seem a suitable method of preventing H2S
production in wastewater systems.
The purpose of this study is to evaluate thepracticability of using ORC to prevent the generation of
H2S under anaerobic conditions in the simulation set-upof a vertical section of Nomikawa River.

2 Experimental
2.1 Simulation set-up
Fig.3 Solubility equilibrium of H2S[11]2.4 Experimental principle
MgO2 can be reacted with water to release oxygen
slowly and produce magnesium hydroxide (Mg(OH)2).
Produced Mg(OH)2 can also be reacted with H2S toproduce MgSO4.
ķ2MgO2(s)+2H2Oė2Mg(OH)2(s)+O2(g)O2(g)+2H2S(g)ė2S(s)+2H2O
ĸ
MgO2(s)+ H2S(g)ėMg(OH)2(s)+S(s) ķ+ĸ=Ĺ
Mg(OH)2 + x H2S ė Mg(OH)2.(H2S)xė Mg(HS)2 + 2O2ėMgSO4
ĺ
pH value will increase as the amount of H2S decrease.
If there are enough MgO2, the generation of H2S can beinhibited.



Fig.2 Simulation set-up
The set-up was started on June 6th, 2011.
The set-up was divided into 6 parts. From part 1 toPart 3 are fluid units. And from part 4 to part 6 arepacked bed units. The sea water in the set-up simulates
the environment of Nomikawa River. And 5ml artificial
sewage is added into the set-up every week to simulate
the pollution poured into Nomikawa River.

2.5 Experimental materialGlucose(min.98.0%),KH2PO4(min.99.5%),NaHCO3(min.99.0%), from Kanto Chemical Co.,Inc.; Polypeptoneand Na2Sg5H2O(min.99.99%˅from Wake Pure Chemical
Industries,
Ltd.
ORC(Magnesium
peroxidecomplex(MgO2 24-28%)) from Sigma-Aldrich Co. Ltd.

2.2 Formation of Hydrogen Sulfide (H2S)
Hydrogen sulfide (H2S) occurs naturally through theanaerobic decay of organic matter and is recognized byits characteristic rotten egg odor.

2.6 Analysis method
H 2S(Hydrogensulfide )㸦WAK-SSRB (sulfate-reducing bacteria) can chemicallychange natural sulfates (SO42-) in water to hydrogensulfide (H2S).

pack Test㸧

2.3 Effect of pH on H2S equilibrium

㸦DPR-SO4
Pack Test㸧

(Dissolved
Oxygen )㸦AZ-DO-30
Pack Test㸧

0.05 to

10 to 100

2.0 to 11.0

Range

0.80 ppm

ppm

ppm

3 minutes

3 minutes

2 minutes

Time
Color
changeReaction
Principle

114(Sulfate)

DO

Measuring
Sampling

It is shown in Fig.3 that at a pH of 7.0, 50% of the
dissolved sulfides are present as H2S and at a pH of 6.0,
90% of the dissolved sulfides are present as H2S. Less
than 3% of the dissolved sulfides would form gaseous
H2S at a pH above 8.5. The distinction between the typesof sulfide ions is important since only H2S can escapefrom the solution and create odors and corrosion
problems.

SO42-

Colorless

Colorless

Colorlessto Whitish

Cyan

Methylene

BariumIndigoblue

chloridecarminemethodturbidimetry

method

to
Blue

to

The concentration of H2S, SO42-, and DO(Dissolved
Oxygen) can be measured by Digital Pack Test DPM-MT.
" Pack Test" is the most simplified water quality test. The
color will be changed after the reaction time.

3 Results and discussion
3.1 pH, the concentration of H2S, SO42-, DO ofeach reactor
Fig.5 H2S and SO42- concentration changed hour by hour on thecondition of different mol ratio of H2S:MgO2

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ORC was added into the set-up on the condition of the
mol ratio of H2S: MgO2 is 1:1.
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After 6 hours, the concentration of H2S in reactor 2
decreased to 0ppm.After 8 hours, the concentration of
H2S in reactor 3 decreased to 0ppm.After 48 hours, theconcentration of H2S in reactor 4 decreased to
1.16ppm.After 48 hours, the concentration of H 2S inreactor 5 decreased to 5.56ppm. After 48 hours, theconcentration of H2S in reactor 6 decreased to 15.66ppm.


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Fig.4 pH, the concentration of H2S,SO42-,DO in each reactor

The simulation set-up was started after 4 months, pH,
the concentration of H2S, SO42-, DO in each reactor werestabilized. The concentration of H2S and SO42- increased
along with the depths increased. pH and DO decreased
along with the depths increased.(Fig.4)

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3.2 The effect of different mol ratio of
H2S:MgO2

5HDFWRUThe solution taken from reactor 4 was divided into 4
portions. The same concentration of Na2S was as areference. ORC was added into each beaker on the
different mol ratio of H2S:MgO2 including 1:1,1:2,1:3,1:4
and Na2S:MgO2=1:1 as a reference. pH increased in eachbeaker. The concentration of H2S all decrease to nearly0ppm after 24 hours in each beaker(Fig.5). When the mol
ratio of H2S: MgO2 is 1:2, the removal rate is highest.
When the mol ratio of H2S:MgO2 is equal to 1:4, theamount of MgO2 is too much to prevent the rate of thereaction.

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pH of each reactor increased to around 7.75-8.3 as thetime passed.

115

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Fig.10 The vertical section of Nomikawa River
7.4×103
Volume between two bridges˄m3˅
13.4
ORC˄JPY/g˅from Sigma-Aldrich Co. Ltd
ORC needed for two days (g)1.7×105
The cost of ORC (JPY/one time)
2.2×106
The cost of ORC (JPY/one month)
3.5×107
The cost of ORC (JPY/one year)
1.4×108





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



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Fig.8 DO changed in each reactor

DO of each reactor increased at the beginning and at
the same time O2 was consumed by H2S. And then Do
decreased in reactor 4-6, because O2 release rate is lessthan consumption rate.

If the price of ORC is lower, this method can be used in
the future. ORC could be used as a common method toimprove the effect of aeration treatment in a part ofNomikawa River.

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5 Conclusions

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The simulation set-up was successfully simulating
Nomikawa River. After 4 months, the concentration of
H2S, SO42-were stabilized. The effect of H2S removal
influenced by different concentrations of ORC wasdiscussed. When the mol ratio of H2S: MgO2 is 1:2, theremoval rate is highest.
ORC could effectively remove H2S from simulation
set-up of Nomikawa River.
The cost of removing H2S from Nomikawa River was
calculated. If ORC will be added into Nomikawa Riverfor 4 times in one year, the cost of ORC is 1.4h108
JPY/year. ORC could be used as a common method toimprove the effect of aeration treatment in a part ofNomikawa River.

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2-

Fig.9 The concentration of SO4 changed in each reactor
2-

The concentration of SO4 of reactor 1 to reactor 4
increased at the beginning and then stabilized. The
concentration of SO42- of reactor 5 and reactor 6
increased as MgSO4 produced.

References[1] Grossman, E.L., 2001. Microbial sulfur cycling in terrestrial
subsurface environments. In: Fredrickson, J.K., Fletcher, M.
(Eds.),Subsurface Microbiology and Biogeochemistry. John Wiley& Sons, Inc., London, pp. 219–248.
[2] Tomar, M., Abdullah, T., 1994. Evaluation of chemicals to control
the generation of malodorous hydrogen-sulfide in waste-water.
Water Res. 28, 2545–2552.
[3] Jones, D.A., 1996. Principles and Prevention of Corrosion. Prentice
Hall, NJ, USA.
[4] Davidova, I., Hicks, M.S., Fedorak, P.M., SuXita, J.M., 2001. The
influence of nitrate on microbial process in oil industry production
waters. J. Ind. Microbiol. Biotech. 27, 80–86.
[5] Dague R. R. (1972) Fundamentals of odour control. J.Wat. Pollut.
Control Fed.44, 583-594.
[6] Fraser J. A. L. and Sims A. F. E. (1984) Hydrogen peroxide inmunicipal, landfill and industrial effluent treatment Effluent
Wat. Treat. J., pp. 184-188.
[7] Henry J. G. (1980) Odour control: an operator's guide. J. Wat.
Pollut. Control Fed. 52, 2523-2537.
[8] Waltrip G.D. and Synder E.G.(1985) Elimination of odour at six
major waste water treatment plants. J. Wat. Pollut. Control Fed.
57, 1027-1032.
[9] Metcalf and Eddy Inc. (1991) Waste Water Engineering:
Collection and Pumping of Waste water, 3rd edn. McGraw-Hill,
New York.
[10]Marc W Beutel, Alex J Horne. A review of the effects ofhypolimnetic oxygenation on lake and reservoir water quality.
Journal of Lake and Reservoir Management, 1999, 15(4):285-297.
[11]Peter ChurchillˈDavid Elmer. Hydrogen sulfide odor control in
wastewater collection systems. Newer Journal, May 1999(33)NO1,57-63.
[12] Investigation results of Nomikawa River. 2010

4 The cost of ORC for Nomikawa River
Based on the result of the previous experiment, The
cost of ORC for removing H2S from Nomikawa River
was calculated on the condition of the mol ratio of H2S:
MgO2 is 1:1.The depth and concentration of H2S undereach bridge are on the following table according to theinvestigation results of Nomikawa River[12].
According to the investigation results, theconcentration of H2S will increase after heavy rain, andthen decreased because O2 produced by photosynthesisin the river. In one year, the concentration of H2S
increased for four times, so ORC will be added into
Nomikawa River four times one year.
Table 1 The Cost of ORC for Nomikawa River
BridgeSanya-bashi Onari-bashi

Month(H2S odor detected)
Depth(m˅The average depth(m)The concentration of H2S(ppm)
The average concentration of
H2S(ppm)
The length between two bridges(m)5 8 10 1 5
10
2.1
1.48
1.79
6 6 1 2 5
2
3.67
730

116

Study on effective communications in capacity assessment oftechnical cooperation in environmental management sector
Student Number: 10M51580   Name: Kazutoshi MACHIDA   Supervisor: Naoya ABE⎔ቃ⟶⌮ศ㔝ࡢᢏ⾡༠ຊࡢ࢟ࣕࣃࢩࢸ࢕࣭࢔ࢭࢫ࣓ࣥࢺ࡟࠾ࡅࡿ
᭷ຠ࡞ࢥ࣑ࣗࢽࢣ࣮ࢩࣙࣥ࡟㛵ࡍࡿ◊✲⏫⏣ ࿴ಇ
ᮏㄽᩥ࡛ࡣࠊ㛤Ⓨ㏵ୖᅜࡢ⎔ቃ⟶⌮ศ㔝ࡢ᭷ຠ࡞⬟ຊホ౯(CA)ᡭἲࡢᥦ᱌ࢆ┠ᣦࡋࠊᢏ⾡༠ຊࡢ⌧ሙ࡟⢭㏻ࡍࡿ㛤Ⓨࢥࣥࢧࣝࢱࣥࢺ࡟ᑐࡍࡿ࢔ࣥࢣ࣮ࢺㄪᰝࢆᐇ᪋ࡋࠊᅜ㝿༠ຊᶵᵓ㸦JICA㸧࡜ࢥࣥࢧࣝࢱࣥࢺࡢ᭷ຠ࡞㐃ᦠࡸ⬟ຊホ౯ࢳ࢙ࢵࢡࣜࢫࢺࡢᨵၿࡢࡓࡵࡢศᯒࢆ⾜ࡗࡓࠋࡲࡓࠊ࣋ࢺࢼ࣒ࡢᗫ
Რ≀⟶⌮ࢆ㢟ᮦ࡜ࡋ࡚ཧຍᆺ࣮࣡ࢡࢩࣙࢵࣉࢆ㛤ദࡋࠊ⬟ຊ㛤Ⓨ࡟㛵ࢃࡿ㏵ୖᅜഃ࡜ࡢຠᯝⓗ࡞ࢥ࣑ࣗࢽࢣ࣮ࢩࣙࣥࢶ࣮ࣝ࡜ࡋ࡚㝵ᒙศᯒἲ㸦AHP㸧ࡢ᭷⏝ᛶࢆ᳨ドࡋࡓࠋ

1. Introduction
In recent years, the concept of capacity development(CD) has been paid huge attention by donor agencies
in order to ensure sustainability of the impact ofinternational development projects. In order torealize CD in practice, its assessment (capacity
assessment, CA) is crucial for donor agencies such as
the Japan International Cooperation Agency (JICA).
Definition of CA by JICA includes not only the“process of diagnosing the current state of thedeveloping countries’ capabilities for handling issues(capacity)” but also “sharing results” with concerned
parties (JICA, 2008). In this sense, CA has a role as acommunication tool between developing countries
and donors for encouraging a shared understandingof capacity for the sake of CD support.
However, the current practices of CA in realprojects and its effectiveness as a communication
tool have not been examined thoroughly. Since theTechnical Cooperation (TC) projects by JICA are
now increasingly implemented by Japanesedevelopment consultants through bidding, thecoordination of JICA staff and consultants has
become more important than ever (Fig. 1). Also, inthe view of recipient countries, Fanany et al. (2011)claims that the meaning and nature of capacity
building is not well understood locally and differs
considerably from its generally accepted sense in thewest. Thus, careful communication with counterpart(C/P) staff is an essential process for better CD
support for JICA and consultants.
    In this study, taking JICA’s TC inenvironmental management sector as an example,
effective communications in CA was studied withthe following specific research objectives. The firstJapanese
Taxpayers

Implementer(Japanese Consultants)

Bidding & Job
Assignment

Elect &
Tax
payment

Evaluation
ReportCDsupportCompletion
Report TORs

Inception
Report+ Mutual
Evaluation

Japanese
Government

Project Designer(JICA)+ Mutual

Project evaluation Beneficiary
mmaryy Table
Taab
Summary(Counterpart in
PDM

partner country)

R/D(MOFA)
Jurisdiction

Stakeholders ofTechnical Cooperation+
Mutual Evaluation

Major Actors ofTechnical Cooperation

Fig. 1. Document-based communication between major
actors and stakeholders of TC of Japanese ODA

is to confirm the past and current CA practices of
JICA. The second is to ask consultants on existing
CA checklist, which is prepared by JICA in eachsubsector and supposed to be a key tool for the better
communication on capacity, in order to improve its
practicality. The third is to seek better ways ofcommunication between C/P staff and donor staff by
verifying effectiveness of another possible tool.

2. Coordination between JICA staff
and development consultants
In order to meet the objectives regarding thecoordination between JICA staff and consultants,
questionnaire of consultants was conducted. A
sample of 40 projects was selected from past TC
projects from year 2004 to 2009 managed by the
Global Environment Department of JICA. Then thequestionnaire sheets were sent to the managers of thesample project and there were 28 responses for thequestionnaire survey to consultants (collection rate:

11780%).
Main questions of the questionnaire were themeans and contents of instruction on CA by JICA
and the recognition of existing checklist. The
checklist provided to respondents was the total of
129 CA items, which is integrated by Machida(2010) from the lists used in subsectors inenvironmental management sector. In the survey,
items that are not appropriate for CA are enumerated
with the reasons because capacity is very much
qualitative and hard to gauge the degree of suitabilityof each item as included in checklist by asking what
is appropriate.
2.1 Instruction of JICA to the consultants on CA
According to the survey, 9 respondents (32%)
answered that they did not get any explanation or
instruction about CA (Fig. 2 (a)). Furthermore, even
if there are some, the instructions were limited togeneral one: mostly only document basis and specific
CA item was rarely mentioned (Fig. 2 (b)). Yet
further, the reporting style was instructed in only two
projects (Fig. 2 (c)) although the sharing of CA
results among stakeholders is regarded as important(JICA , 2008). Thus it is safe to say that theimplementation of CA had been largely dependingon the willingness of individual consultants.

2.2 The consultants’ recognition of existing CA
checklist
Regarding the recognition of existing CA checklist,
the results of negative votes by each item are shown
first. Since respondents could vote as many items as
they like, there were great variances only by numberof items that regarded as not appropriate. As a result,more than half (55%) of the items got negative votes
at least from one respondent. Table 1 shows theitems that got 4 votes or more as inappropriate.
According to the list, those items that describe
stakeholders who are not the direct C/P tends to be
regarded as inappropriate.
The frequency of reasons of inappropriatenessas CA item is shown in Fig. 3 (a). From the result,difficult to assess turned out to be the primary causes
for being not appropriate. The follower was
irrelevant and other (e.g. the item is rather a goal
itself of CD project/it does not make sense
individually). The detailed reasons for the difficulties
of assessment are shown in Fig. 3 (b). Technicallydifficult (e.g. difficulty in definition) was the mostcommon one and time-consuming followed it. The
example of the description on other (in difficult toassess category) was political difficulty such as
inaccessibility to data).
Table 1. Ranking of negative votes (N=28)
Rank

Type

CA Item

Negative votesn(a) Whether there was any instruction on CA (N=28)
N/A, 1(5%)

Both, 2(11%)
Verbal, 3(16%)

Writen,13 (68%)(b) Means of the instruction (Multiple Answer, N=19)70%
60%

Human resource and their capabilities incivil society organization
Human resource and their capabilities inbusinesses
Environmental management capacity ofcommunities

1

Human assets

2

Human assets

3

Social organizations

3

Social aspects

Implication of religion

3

Social aspects

Customs

6

Org structure/management

6

Personnel

Aw areness of policymakers on putting
solution into practice
Performance evaluation (method,reflection to promotion)

6

Source of information Literature

6

NGO

Activities of NGO for project formulation

80

35

70

30

60

25

50

20

40

15

30

10

20

50%

40%

10

30%

0

%
7

25.0

6

21.4

5

17.9

5

17.9

5

17.9

4

14.3

4

14.3

4

14.3

4

14.3

5

0

20%
10%
0%

Methodology

Subjects ofAssessment

CA Items

Reporting Styleof the ResultsFrequency

Others(b) Detailed reasons fordifficulties of assessment
Fig. 3. Reasons of inappropriateness as CA item(a) General reasons(c) Contents of the instruction (Multiple Answer, N=19)
Fig. 2. Results of questions regarding instruction on CA(Total number of frequency by reasons for all votes, N=140,
regardless of voter, Multiple answer for each votes)

118

The data set of negative votes by consultants
was further analyzed by the Principal Component
Analysis (PCA) in order to understand the similarityof each reason. PCA is a multivariate analysis
method that can reduces the dimensionality ofnumerical multivariate data set, while retaining as
much variation as present in the data set. The
detailed calculation can be found in Jolliffe (2002),
for example.
The proportions of components and the plots ofloadings with respect to the first two components areshown in Table 2 and Fig. 4. Component 1 can be
regarded as feasibility axis (whether the item can beassessed) and Component 2 can be interpreted asmeaningfulness axis (irrelevance or uselessness). For
those items that is technically difficult (DIFF_TECH
in Fig. 4) or politically difficult (DIFF_OTHER in
Fig.4), there needs to be further two investigations
since they are not unimportant despite theirchallenges in assessment. First, their relevance tocapacity should be reconsidered as they got highscore in irrelevance direction. Second, if they are still
relevant, elaboration of gauging would be necessary
for technically difficult items. Also JICA’s initiativeon politically difficult items would be important as
consultants are not deployed to tackle the difficulty.

Table 2. Eigen value and proportion ofcomponents (by comp.4)
Component

Eigenvalue

Proportion

Cumulative

Comp1

3.04262

0.38310.3831Comp2

1.26075

0.16420.5473Comp3

1.03143

0.13410.6814Comp4.8399090.12090.8023.6

Irrelevant = “It’s not capacity”.2 (0.16).4

IRRELEVANT
DIFF_TECH
DIFF_OTHER

0

DIFF

Infeasible

Feasible-.2= “It can’t bemeasured”
DIFF_TIMEUNIMPORTANT
DIFF_COST

-.4

OTHER

-.2

Useless = “There’s no use in assessing it”
0.2
Component 1 (0.38).4

Fig. 4. The result of PCA (Component loadings).6

3. Case study on communications of
CD for solid waste management
sector in Vietnam
In a project formulation stage, JICA (2008) suggeststhat CA result should be used for helping developing
countries to draft a CD strategy with scenarios to an
aimed future vision. Nevertheless, communication on
capacity with C/P is challenging task according to
the questionnaire survey to C/P conducted during thepre-field visit.
In order to gain actual recognition about CD
and CA by C/P, a participatory workshop was
conducted in Vietnam. The workshop was held in
suburban Hanoi on 30th November and 1st Decemberand 20 participants were gathered from variousorganizations related to solid waste management(SWM) in Hanoi city. The participants were divided
into 4 groups and discussed an effective action planand strategic development of necessary capacities(NCs) for future SWM.
The structure of the workshop is as follows.
First is to confirm effectiveness of past and current
policy of SWM. Second is to conduct scenario
planning and share the preferred future vision. Third
is to identify the NCs to realize a future vision (a gap
from current status). Lastly, activities to develop
each NCs were prioritized to develop CD strategy.
The main analytical tool used in the workshop
is a decision-making support tool called Analytic
Hierarchy Process (AHP). It is a simple but powerfultool developed by Thomas Saaty in 1970’s for
multi-criteria multiple alternatives decision problems(Tone, 1986). It constructs hierarchy that consists ofgoal, criteria and alternative options first. Thencriterion is weighted by pair-wise comparison by aperson or by a group. The data is then used for the
calculation of weights for each criteria andConsistency Index (C.I.). In the participatory
workshop held in Hanoi, five criteria were set by theauthor (effectiveness, time, budget, human resources
and technology) to prioritize necessary capacities on
the basis of prior discussion with Japanese andVietnamese co-organizers. The main reason why
AHP was adopted is that the development of
hierarchy can assist the context of capacity andpromote firm and accurate discussions regarding CD.
According to the first session, 96% ofparticipants recognized the existing national strategy
for integrated SWM is very challenging or
challenging and the lack of capacity to achieve thestrategy were implied. This step could be regarded as

119

(Fully satisfied: 4 and good: 16) and AHP wereregarded as manageable (95%), the workshop could
show one case to utilize AHP as a promoting tool on
communication regarding capacity.

Participatory & Feasible
Approach

Future vision
Necessarycapacities
(NCs)

NC2
Human
Resource

NC1
Environmental
Education
•

•

•
•

Integrate ineducation system,
extra activities
Launchcommunication
campaign with
community
Provide trainings
Strength
enforcement•

Train technical& management
staff
Renew
curriculums
Provideincentives toattract talents

•
•

NC3

NC4
Technology
Selection

Involvement of
Stakeholders

•
•

•

•

Highlight roles ofcivil societies
Enhancecoordination
among
stakeholders
Establish legal
framework for
enforcement••

Organizetechnology
exhibition
workshop
Organize pilot
projects
Monitor andevaluate theprojects

NC5
Investment
Mobilization•••

4. Conclusion and further studies

Setup mechanism
for investment
from private
sectors
Organize
investment
promotion
workshops
Simplify procedureof administration

Required actions

Fig. 5. Hierarchy of AHP by a group
Table 3. Result of the pair-wise comparison of NCs
under each criterion by a group㻱㼒㼒㼑㼏㼠㼕㼢㼑㼚㼑㼟㼟㻳㼛㼢㼑㼞㼚㼙㼑㼚㼠㻌㻮㼡㼐㼓㼑㼠㻼㼡㼎㼘㼕㼏㻌㻭㼣㼍㼞㼑㼚㼑㼟㼟㻵㼚㼏㼑㼚㼠㼕㼢㼑㼟㻸㼍㼣㻌㻒㻌㼑㼚㼒㼛㼞㼏㼑㼙㼑㼚㼠㻴㼡㼙㼍㼚㻌㻾㼑㼟㼑㼛㼡㼞㼏㼑㻌㻒㻰㼍㼠㼍㼎㼍㼟㼑㻌㻿㼥㼟㼠㼑㼙
㻯㻚㻵㻚

㼀㼕㼙㼑

㻜㻚㻝㻠㻤㻠
㻜㻚㻞㻢㻞㻤
㻜㻚㻝㻝㻤㻣
㻜㻚㻞㻣㻡㻟

㻮㼡㼐㼓㼑㼠

㻜㻚㻞㻞㻜㻤
㻜㻚㻞㻠㻣㻟
㻜㻚㻜㻤㻤㻜
㻜㻚㻝㻢㻣㻢

㻜㻚㻝㻤㻟㻝
㻜㻚㻞㻞㻠㻥
㻜㻚㻜㻣㻡㻠
㻜㻚㻟㻡㻠㻢

㻴㼡㼙㼍㼚㼀㼑㼏㼔㼚㼛㼘㼛㼓㼥㻾㼑㼟㼛㼡㼞㼏㼑
㻜㻚㻟㻞㻡㻝
㻜㻚㻝㻠㻢㻢
㻜㻚㻞㻥㻤㻠
㻜㻚㻝㻤㻣㻢
㻜㻚㻝㻝㻥㻥
㻜㻚㻝㻡㻢㻝
㻜㻚㻝㻡㻜㻡
㻜㻚㻟㻞㻠㻢

㻜㻚㻝㻥㻠㻤

㻜㻚㻞㻣㻢㻠

㻜㻚㻝㻢㻝㻥

㻜㻚㻝㻜㻢㻝

㻜㻚㻝㻤㻡㻝

㻜㻚㻜㻡㻝㻞

㻜㻚㻜㻠㻞㻠

㻜㻚㻜㻞㻢㻟

㻜㻚㻜㻢㻜㻥

㻜㻚㻝㻞㻡㻡

NC1.Environemental
Education
NC2.Human Resource

Effectiveness (0.154)
Time (0.085)

NC3.Involvement of
Stakeholder

Budget (0.197)

Human Resource (0.193)

NC4.Technology Selection/Suitable technology

Technology (0.370)

NC5.Investment Mobilization
0.000

0.050

0.100

0.150

0.200

0.250

0.300

Fig. 6. Overall assessment score of five NCs by a groupself-assessment of the national capacity. On the basisof the following shared future vision in the group,
weights of the five criteria and NCs with its CD
activities were listed and prioritized. The example ofhierarchy of AHP by a group is shown in Fig. 5. The
overall assessment score by AHP and C.I. forpair-wise comparison of NCs under each criterion isshown in Table 3 and Fig. 6. The C.I. of pair-wisecomparison of NCs under criterion Technology in thegroup was 0.1255, which is a little high and there ispossibility on inconsistent answer. The possible
reasons are as follows. First, the AHP was applied
only one time and answered individually. Second,
one of five preset criteria was technology (whether
the technologies utilized in activities of the NC) andsomewhat overlapped. This could be solved byletting participants to decide their own criteria orusing advanced method of AHP, Analytic Network
Process (ANP) but neither feasible options in theworkshop due to logistical reason.
Although the workshop had some points to be
improved, the satisfaction of participants were high

The following are the conclusion of this study. First,
instructions on capacity assessment by JICA toconsultants were confirmed to have been ad-hoc andunsystematic and thus there is room for improvementin efficiency to address CA in an integrated way in
TC. Second, the criterion of whether each capacity
assessment item is in the CA checklist, operational
definition of capacity, converges to feasibility andmeaningfulness (irrelevance and uselessness). The
relatively inappropriate items and its reasons aresuggested to be shared for reference, not to say to beomitted from the checklist. Third, the result ofparticipatory workshop in Vietnam on SWM implies
that the meaning of capacity by C/P staff is subject tochange on the basis of preferred future visions orcontext. Also, it showed a case for the application of
AHP to promote structured and firm discussions andcontinuous learning regarding capacity development,
which tends to have highly sensitive meaning.
As a further study, a research on theapplicability of CA items depending on the projectstage is necessary for better coordination on CA by
JICA staff and consultants. Second, since thevisualization and dissemination of CA result iscurrently limited, model development of index of
capacity is important. Third, the meaning ofaccountability should be reexamined because theprogress of CD is becoming more and moreimmeasurable by objective manners.

References[1] JICA, 2008. Capacity Assessment Handbook, JICA:Tokyo[2] Fanany, I., Fanany, R., Kenny, S., 2011. The meaning of
capacity building in Indonesia, Community Development
Journal (2011) 46(1):89-103[4] Machida, K., 2010. Empirical Analysis on Capacity
Assessment Framework for Environmental Management
Sector in Developing Countries (in Japanese), Bachelor
Thesis, Tokyo Institute of Technology[5] Jolliffe, I. T., 2002. Principal Component Analysis, 2 nd ed.,Springer-Verlag: New York
[6] Tone, K., 1986. Gemu kan-kaku ishi-kettei hou (Game-like120

decision-making method: Introduction to AHP), JUSE Press