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Department of Civil and Environmental Engineering
Graduate School of Engineering
Tohoku University

Sponsors
http://www.g-safety.tohoku.ac.jp/
Tohoku University, Main Administration Office
Organization for Leading Graduate School Programof Tohoku University, Sendai, Miyagi 980-8578, Japan
http://www.eng.tohoku.ac.jp/
Tohoku University, Graduate School of Engineering,Sendai, Miyagi 980-8579, JapanORGANIZERSDr. So Kazama(Professor, Tohoku University)

Dr. Yu-You Li(Professor, Tohoku University)

Dr. Daisuke Komori(Associate Professor, Tohoku University)

Dr. Daisuke Sano

(Associate Professor, Tohoku University)

SECRETARIESAssistant Professor, Toshimasa Hojo
toshimasa.hojo.b5@tohoku.ac.jp
Assistant Professor, Yoshiya Touge
yoshiya.touge.a6@tohoku.ac.jp
DC Student, Jialing Ni
jialing@dc.tohoku.ac.jp
DC Student, Yemei Li
li.yemei.s5@dc.tohoku.ac.jp
DC Student, Noriko Uchidanoriko.uchida.s8@dc.tohoku.ac.jp
DC Student, Grace Puyang Emang
grace.puyang.emang.r2@dc.tohoku.ac.jp
Research Student, Ying Songsong.ying.q2@dc.tohoku.ac.jp

PARTICIPANTS
Tohoku university
Name
Yemei Li
Siti Nur Fatihah Binti Moideen
Ying Song & Weikang Qi (Tohoku Univ. & BUCT)
Yuanfan Zhang
Chenglei Xie
Yujie Chen
Chaimaa Mribet
Jialing Ni
Aijun Zhu
Hiroshi Iwano
Runda Du & Jinmei Sun(Tohoku Univ. & TJU)
Bo Feng
Ming Cong
Eli Hendrik Sanjaya(Tohoku Univ. & SUM)
Noriko Uchida
Grace Puyang Emang
Chatuphorn Somphong
Qing Chang
Mbugua Jacqueline Muthoni
Vempi Satriya Adi Hendrawan
Sopha Soliya
Akihiro Ichiba
Koji Sakamoto
Ryosuke Inomata
Keitaro Yamada
Andri Taruna Rachmadi
Sital Uprety

Fukushima University
Name
Thuy Thi Thanh LE
Chris LeongJapan
Identity
Student
Student
Student
Student
Student
Student
Student
Student
Student
Student
Student
Student
Student
Student
Student
Student
Student
Student
Student
Student
Student
Student
Student
Student
Student
Postdoctoral researcher
Student

Japan
Identity
Student
Student

Tianjin University
Name
Ping Gu
Guanghui Zhang

China
Identity
Professor
Associate Professor

Korea Institute of Civil Engineering and Building Technology
Korea
Name
Identity
Seongsim Yoon
Senior Researcher

Universiti Teknologi Malaysia
Name
Khairunnisa ABDUL HALIM

Malaysia
Identity
Visitor scholar

VenueGraduate School of Engineering, Aobayama campus, Tohoku University
Lectures and oral presentations
Room 101, Engineering Laboratory Complex building() (C10)
Posters exhibition
Room 201, Engineering Laboratory Complex building () (C10)Dinner party
Center hall Aoba restaurant ()

Engineering Laboratory Complex building()()

Access to Aobayama Campus(Subway)

Map of East-west line subway() in Sendai

Please take this subway line and get off on the stop Aobayama.

Tips
If you start from Sendai Station, please make sure you are enteringrather than South-north line

East-west line subway
subwaynor JR station.

ProgramThursday, 29 Nov 2018
Fieldwork
The$reconstruction$of$$Minami1gamo$sewage$treatment$plant2011/3/11&Tsunami&hitting&the&Sewage&Facilities

Remains&of&311:tsunami&shocking&on&the&wall&of&pump&station

P1

Plan&view&of&the&sewage&facilities

Location&of&Minami:gamo&sewage&treatment&plant

P2

Friday, 30 Nov 2018
The 6th International Symposium on Water Environment Systems
Place: Room 101, Engineering Laboratory Complex building, Tohoku University
09:00 ~ 09:10 Opening address
09:10 ~ 09:20 Memorial photo

Session I
09:20 ~ 09:50 Development of urban flood disaster prediction techniques using
weather forecasting information: a case study of Seoul, Korea
Seongsim Yoon
Korea Institute of Civil Engineering and Building Technology
09:50 ~ 10:20 Evaluation of landslide hazards in North Vietnam using TRIGRS model
under impact of climate change
Thuy Thi Thanh LE
Fukushima University
10:20 ~ 10:50 Estimating runoff in perennial and ephemeral catchments: adisaggregated flow duration curve approach
Chris Leong
Fukushima University
10:50 ~ 11:00 Coffee break

Session II
11:00 ~ 11:30 Characteristics and seasonal flactuations of invertebrate species eDNA
concentration in forest, agriculture, and urban river
Noriko Uchida
Tohoku University
11:30 ~ 12:00 Forest fire severity estimation for the 2017 Kamaishi forest fireGrace Puyang Emang
Tohoku University
12:00 ~ 13:00 Lunch break
13:00 ~ 14:00 Poster exhibition
Place: Room 201, Engineering Laboratory Complex building, Tohoku University

P3 Beach Nourishment as an Adaptation to Future Beach Loss due to Sea
Level Rise in Thailand
Chatuphorn Somphong
Tohoku University

 The impact of spatial discretization scale on urban hydrologicalmodeling performance and prediction
Qing Chang
Tohoku University

 Using MODIS to detect crop type changes in the Aral Sea Basin in the
21st Century
Mbugua Jacqueline Muthoni
Tohoku University

 Remote estimation of rice crop yield using MODIS Vegetation Indicesdata:
A case study of Solo river basin rice field, Indonesia
Vempi Satriya Adi Hendrawan
Tohoku University

 Preliminary study of grape farmers' perception to changing climate in
Takahata city, Yamagata prefecture, Japan
Sopha Soliya
Tohoku University

 Estimation of woody debris recruitment by landslides:
A Case study of Typhoon Lionrock in Iwaizumi, Japan
Akihiro Ichiba
Tohoku University

 Evaluation and sensitivity analysis for surface and bottom snowmelt
process by runoff analysis
Koji Sakamoto
Tohoku University

 Estimation of nutrient distribution in Mekong river floodplain using
satellite images
Keitaro Yamada
Tohoku University

P4

 Long-term change and spatial anomaly of precipitation in Osaka, Japan
Ryosuke Inomata
Tohoku University

 Performance of sewage treatment and bioenergy production using
duckweed
Hiroshi Iwano
Tohoku University

 Mesophilic and Thermophilic Temperature Phased Anaerobic
Digestion of Paper Waste Containing Food Waste: the Effects of
Hydraulic Retention Time
Aijun Zhu
Tohoku University

 Use of PMA-PCR for revelation of viable microbial members inanaerobic digesters
Jialing Ni
Tohoku University

 Optimization of cultivation of humic-reducing bacteria based on
ORP/pH feedback
Runda Du & Jinmei Sun
Tohoku University & Tianjin University

 The effects of pre-acidification on the granule sludge perperties in theanaerobic treatment of starch wastewater
Bo Feng
Tohoku University

 Development of urban resource recovery system by methane
fermentation and disposal waste water treatment
Ming Cong
Tohoku University Co-digestion of food waste and sewage sludge in Anaerobic
Membrane Reactor
Yemei Li
Tohoku University

P5 The potential appearance of free-chlorine resistant norovirus after
disinfection process in wastewater
Andri Taruna Rachmadi
Tohoku University

Session III
14:00 ~ 14:30 The effectRemoval of Cesium from Simulated Wastewater by aCountcurrent Two-stage Adsorption-Microfiltration Process of organic
matter on one-stage anammox process
Ping Gu & Guanghui Zhang
Tianjin University
14:30 ~ 14:50 Biohydrogen Production (BHP) and Biomethane Production (BMP)
Potential from Palm Oil Mill Effluent (POME)
Khairunnisa ABDUL HALIM
Universiti Teknologi Malaysia
14:50 ~ 15:00 Coffee break

Session IV
15:00 ~ 15:20 Methane Fermentation Treatment of Fish Processing Wastewater by
A Self-agitated Anaerobic Baffled Reactor (SA-ABR)
Eli Hendrik Sanjaya
Tohoku University & State University of Malang
15:20 ~ 15:40 The Potential Anaerobic Digestion Treatment for Palm Oil Mill Effluent(POME)
Siti Nur Fatihah Binti Moideen
Tohoku University
15:40 ~ 16:00 Assessment of slaughterhouse wastewater treatment plant using a
combination of UASB and Activated Sludge
Chaimaa Mribet
Tohoku University
16:00 ~ 16:20 Using microbial quantification for Water, Sanitation and Hygiene(WASH) intervention study in rural Nepal
Sital Uprety

P6

Tohoku University
16:20 ~ 16:35 Coffee break

Session V
16:35 ~ 16:55 Nitrogen removal performance of an anammox membrane reactor
operated at 25 with increasing sludge loading rate
Ying Song & Weikang Qi
Tohoku University &Beijing University Of Chemical Technology
16:55 ~ 17:15 Successful Operation Performance of Partial Nitritation and Anammox
Reactor Treating LowStrength
Yujie ChenAmmonium Wastewater at Room Temperature
Tohoku University
17:15 ~ 17:35 The combination application study of anaerobic fermentation andanammox technology in waste organic matter control
Chenglei Xie
Tohoku University
17:35 ~ 17:55 Simultaneous Nitrogen and Phosphorus Recovery using an anammox
Expanded Bed Reactor at Low Temperature
Yuanfan Zhang
Tohoku University
17:55 ~ 18:15 Closing speech
18:15 ~ 20:00 Dinner party

P7

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G7Safety$
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Development of urban flood disaster prediction techniques using weather forecasting
information: a case study of Seoul, Korea
Seongsim Yoon1*
1

Department of Land, Water and Environment Research, Korea Insitute of Civil Engineering and Building Technology,
Gyeonggi-do 10223, Korea*E-mail: ssyoon@kict.re.kr

Abstract
This research developed the urban flash flood forecasting techniques and real-time system in order to prevent urban flash
flood damages in Seoul and Metropolitan areas, Korea. The real-time flood forecasting system provides predicted
inundation map, river risk information, and flash flood index in mountainous area using the forecast rainfall. The forecast
rainfall is estimated from the numerical weather prediction model and radar to secure the lead time for evacuation. Thissystem can offer flood disaster information and support the decision of evacuation to reduce the damage for the citizens
and the government.

Keywords: Urban flood forecasting system, Seoul, Numerical weather prediction model

1.#Introduction#
The recent changes in climate and abnormal weather
phenomena have resulted in increased occurrences of
localized torrential rainfall. Urban areas in Korea havesuffered from localized heavy rainfall, including the notable
Seoul flood disaster in 2010 and 2011. The urbanhydrological environment has changed in relation toprecipitation, such as reduced concentration time, adecreased storage rate, and increased peak discharge. Thesechanges have altered and accelerated the severity of damage
to urban areas. In order to prevent such urban flash flood
damages, we have to secure the lead time for evacuation
through the flood forecasting system with the forecast rainfall.
For Seoul and Metropolitan area, the urban flood forecasting
system is developed using numerical weather prediction
model (NWP).

2.#Urban#flash#flood#forecasting#techniques# #In order to provide effective flood forecast information for
Seoul and the metropolitan area, this research developed
three techniques considering the type of flood, 1) urban
inundation prediction technique for roads and low-lying
ground, 2) Flash flood risk prediction technique formountainous area 3) Risk prediction technique of the river
due to rising river water level. In particular, in order tosupport the decision-making in flood prevention effectively,
we developed a real-time system and display six-houradvanced prediction information in cooperation with highresolution weather forecast information (Yoon and Lee,2016).

Figure 1. Concepts of urban flash flood information production

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and severe flood warning depends on the maximum waterlevel from all scenarios.

2.1#Seoul#Inundation#forecasting#technique#
The process of producing urban inundation forecasting
information is divided into high resolution rainfall data
production, urban runoff analysis model construction, twodimensional flood analysis model construction, and real-timeconnection.
For urban flood discharge estimation, SWMM (Storm
Water Management Model) is set up over the whole area of
Seoul, Korea. SWMM estimates the surface and pipe flow inthe sewer system of the urban area through the RUNOFF and
EXTRAN modules. For urban inundation simulation, themodel is based on the two-dimensional shallow waterequation of continuity and momentum. When surcharging
occurs at manholes, a two-dimensional diffusion model was
adopted to simulate inundation on the surface by routing
overland flow with the sources coming from surcharging
flow. This spatial resolution of inundation model is 6m. The
real-time urban flood forecasting system is consisted of thesemodels and also uses rainfall as input. Figure 2 shows theresults of inundation simulation for the 50-year probability
rainfall through Seoul inundation forecasting technique(Choi et al., 2015; Lee and Yoon, 2017).

Figure 3. Risk warning using waterlevel of Tan river

2.3#Flash#flood#forecasting#technique#in#
mountainous#area#
Flash floods can be defined as a phenomenon in which signsof flooding like rapids appear in a short time within six hours
after rainfall occurs in small mountainous catchment areas(~10 km2).
In order to predict the sudden flood in the mountainous
area, it is necessary to predict the localized heavy rain andsurface runoff analysis technique. Therefore, this study
generated the gridded flash flood index using the griddedhydrologic components of TOPLATS land surface modeland statistic flash flood index model. The study area is thenational capital region of Korea, and 38 flash flood damages
had occurred from 2009 to 2012. The spatio-temporal
resolutions of land surface model are 1 h and 1 km,
respectively. The gridded meteorological data is generatedusing the inverse distance weight method with automaticweather stations (AWSs) of Korea Meteorological
Administration (KMA). The hydrological components (e.g.,surface runoff, soil water contents, and water table depth) of
cells corresponding to the positions of 38 flood damages
reasonably respond to the cell based hourly rainfalls. Under
the total rainfall condition, the gridded flash flood index
shows 71% to 87% from 4 h to 6 h in the lead time based on
the rescue request time and 42% to 52% of accuracy at 0 h,
which means that the time period of the lead time is in alimited rescue request time. From these results, it is known
that the gridded flash flood index using the cell basedhydrological components from land surface model and thestatistic flash flood index model have a capability to predict
flash flood in the mountainous area (Lee et al., 2016).

Figure 2. Pipe network data set-up in Seoul

2.2.#Risk#prediction#technique#for#Tan#River#
There are about 40 urban rivers, with Han River as the mainstream in Seoul. These rivers are used as space for leisure andparking lots. In the recent years (2009 to 2014), the parking
places and people had experienced damages caused by floods.
A technique to predict the rise of the waterlevel was used toreduce flood damage at river in this study. Especially, inorder to secure the lead time for evacuation in the case ofheavy rainfall, it is necessary not only to utilize the highresolution rainfall prediction of KMA, but also to reduce thetime-consuming runoff estimation. This forecasting
technique is based on the determined waterlelvel scenarios.
HEC-HMS, a rainfall-runoff model, was constructed on the
Tan River watershed in order to simulate the upstream
inflows, and the tributary inflows to the main stream wererequired. A total of 143 rainfall scenarios were used as inputs
to the HEC-HMS to simulate the lateral flow of the Yangjae
Stream and the upstream flow of the Yangjae Bridge, and the
HEC-RAS model was constructed to simulate the river waterlevel. The water level information of 25 meter cross section
interval from Han river junction to Daegok bridge wasobtained and a total of 143 water level scenarios were
calculated. The flood criteria are set such as alert, warning

Figure 4. Flash flood occurrences and risk ratio2

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3.#Seoul#Inundation#Analysis#Prediction#System#
We developed a real-time Seoul inundation analysis
prediction system using each flash flood technique withobserved rainfall and forecasted rainfall of NWP model. The
system provides the predicted inundation map for the next
one to three hours, and Tan river flood risk information, suchas alert / warning / severe flooding water level that is
calculated every hour, includes cross-sectional information atfive points and rainfall information of Tan river from the last
six hours to the future six hours. Flash flood index is provided
in the metropolitan area using forecasted rainfall of NWP
through the system.

#

Figure 5. Seoul inundation analysis system

4.#Conclusions#This study developed urban flood prediction technique forSeoul and metropolitan areas. In particular, we used
forecasted rainfall from KMA to secure lead time. We
estimated high resolution inundation map, mountain flash
flood index, and river flood risk information reflecting thevarious flood occurrence mechanisms. Through this research,
the citizens and the government can easily obtain the flood
disaster information and can act on the evacuation to reducethe damage.
#
References#1)! Byongju Lee, Sumin Choi, Seongsim Yoon, Youngjean
Choi : The Evaluation of TOPLATS Land Surface
Model Application for Forecasting Flash Flood in
mountainous areas, J. Korea Water Resour. Assoc.
49(1), pp. 19-28, 2016.
2)! Byongju Lee, Seongsim Yoon : Development of Grid
based Inundation Analysis Model(GIAM), J. Korea
Water Resour. Assoc. Vol. 50, No. 3, pp. 181-190, 2017.
3)! Seongsim Yoon, Byongju Lee: Development of urban
flash flood forecasting technique, water for future, 49(9),2016.
4)! Sumin Choi, Seongsim Yoon, Byongju Lee, Youngean
Choi : Evaluation of High-Resolution QPE data for
Urban Runoff Analysis, J. Korea Water Resour. Assoc.
48( 9), pp.719-728, 2015.

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Evaluation of landslide hazards in North Vietnam using TRIGRS model under impact ofclimate changeThuy Thi Thanh LE1*, Seiki Kawagoe1
1Faculty of Symbiotic Systems Science, Fukushima University, Fukushima 960-1296, Japan*E-mail:lethanhthuy.wru@gmail.com

Abstract
This paper evaluates the probability of landslide occurrence through the regional application of a physical model TRIGRS
considering the roles of a triggering parameter (rainfall) in relation to conditioning factors under the influence of the warming
climate. The scenario-base approach is adopted basing on various options such as rainstorm events and land-cover condition.
Comparing to the decline of Fs in case of changing rain-event from actual storm in 2013 to 24-hour-PMP (126.9 mm – 332.4
mm rise in rainfall amount), the average value of factor of safety in the whole region decreased significantly when the downpour
total of 24-hour-PMP increased by 21.7 mm – 169.1 mm in additional 24 hours to reach the magnitude of 48-hour-PMP. In
addition, under the same amount of the storm-rain and storm duration, for example 24-hour-PMP, the landslide prone areas
would expanse in case of changing the storm distribution from type 1 (peak rainfall locates at the beginning of the storm) totype 3 (peak precipitation appears at the end of the storm). The slope stability also reduced when the land cover changed suchas from forest cover (Land-use 2010) to crop land and construction site (Land-use 2015). Interestingly when the whole region
was assumed to be replaced by soil (Ha02), a significant reduction of Fs was shown when compared to the option of changes inland-use database (Ha12) and changes in soil-depth option (Ha33). The findings therefore provide an important information for
local authorities in developing adequate land-management in the Cau river basin considering the relationship between landslideprone area and changes in land-cover situation.

Keywords: Landslide, TRIGRS, PMP, North Vietnam

1.#Introduction#
Landslide has been evaluating as a major hazard to thesociety because of their catastrophic consequences.
Governing by the hilly terrain in addition to higher
frequencies of extreme precipitation , rainfall-induced
landslides are considered as one of the major risks inNortheast region, in parallel with severe hazards from floods
and debris flows. In Vietnam, a developing nation, land-falls
were triggered not only by heavy intensity rainfalls, but
human activities such as slope-cut and tree-cutting as well. It
is a clear evidence that economic development has brought
about the expansion of urbanization to the whole country,
including hilly region in Northeast Vietnam; a large numberof transportation routes and other infrastructures have been
constructed and rehabilitated in mountainous area.
Vegetation cover on hill-slopes has been rapidly replaced bybared land due to exploiting activities such as slope cutting
for houses, mining and deforestation. Extreme climatic eventin a relationship with changes in land cover condition could
burden the slope stability and result in destructive impacts tothe communities’ safety. In 2012, the Vietnamese
Government deployed a national project 2012-2020 toproduce the landslide inventory maps in mountainous
provinces. However, it is quite difficult to obtain records ofslope failure in mountainous regions that locate far from
populated areas; the project therefore set up investigations
along main traffic routes to map historical landslides related
to the man-made slope failures along transportation arteries
and residential construction. In addition, recent studies
analyzed the landslide risks in North Vietnam often based on
statistical method for a regional scale; the determination

Fig. 1: a) Elevation distribution of study area; b)
Deforestation and afforestation data from 2006 to 2015
in Bac Kan; c) Deforestation and afforestation data from2006 to 2015 in Thai Nguyen
method however was mainly applied in a local scale. This
paper hence would like to evaluate the probability oflandslide occurrence through the regional application of aphysical model considering the roles of a triggeringparameter (rainfall) in relation to conditioning factors under
the influence of the warming climate.

2.#Study#area#
The study site in this paper covered the area of Cau river
basin belonging to Bac Kan and Thai Nguyen provinces. This

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and an unsaturated zone expanding to the ground surface 2) .
The dimensionless Fs at depth Z was derived from thebalance between the downslope component of thegravitational driving stress and the resisting stress for
saturated soils (Iverson 2000). By separating the time-variant
term and the steady terms, Iverson (1991) defined Fs as inequation (1):catchment is covered by a mixture of mountainous and hilly
terrain condition (Fig. 1a) with average annual precipitationof 1360 (Cho Ra) - 2572 mm (Diem Mac). The highest 24hour precipitation in each rainy month varies from 294 mm(May) to 374.9 mm (August), the largest 24-hour rainfall was
496.1 mm at Ky Phu station on 1978/10/04. According to thenational statistic data, during period 2011-2016, thepopulation in Bac Kan rose by 18.6 thousand to reach the
population of 319 thousand people in 2016 with the
population density of 65.64 people/km2. A rapid growth rateof Thai Nguyen’s population from 1139.4 thousand (2011) to1227.4 thousand people (2016) led to the higher population
density in this province (348.03 people per km2 in 2016). The
population growth required higher pressure in settlement
issues in the whole region. New residential areas in relation
to inadequate land-use management brought about asignificant reduction of forest cover in this region (Fig. 1b &1c).

!" #, % =

'() *
'() +

+

-./(1,')34 '() *(1)

35 1 "6) + -7" +Where ! is the soil friction angle (o); " is the slope angle (o);c is the cohesion (kN/m2),; #s is the unit weight of soil
(kN/m3); #w is the unit weight of water (kN/m3); Z is the depth
below the ground surface (m); t is the time (sec); $ refers to
the ground water pressure head as a function of depth Z andtime t (m);In addition to the Digital elevation model providing
information of terrain condition, information of soil andprecipitation are discretized and assigned in each cell tosimulate rainfall infiltration and vertical flow through theunsaturated zone. In this study, a scenario-based approach isadopted for landslide risk assessment under extreme weather
condition; main input layers for TRIGRS model was shownin (Fig. 2). Thickness of topsoil is often coincides with the
failure depth, and is considered as one major parameter forslope-stability evaluation. However, this information is notoften mapped, and is not feasible for field investigation in alarge scale region. It therefore its spatial distribution is often
predicted based on empirical relationship between terrain
attributes and thickness of soil. In this study, we applied themethod of Saulnier (1997) 3) to evaluate soil depth in eachpixel with the assumption of decreasing linear function
between effective soil depth and topographic slope (eq. 2):

3.#Methodology#and#database#In this paper, the landslide susceptibility is analysed based
the deterministic method using a physical model: TRIGRS(Transient Rainfall Infiltration and Grid-Based Region
Slope-Stability Analysis model) model. This FORTRAN
program utilizes analytical solutions of partially differential
equations that represent 1-D vertical subsurface flow in either
saturated or unsaturated conditions. An infinite slope stability
model is then used to assess the spatial and temporaloccurrences of landslides. The pore-water pressure and thevalue of factor of safety (Fs) are calculated on a cell-by-cellbasis and can be operated in the geographic information
system 1) . This model could be applied to analyze the impactsof rainfall flow on the stability of soil, thus is often used for
rainfall induced shallow landslides. It evaluates the changes
in the transient pore-pressure and the safety factors due torainfall infiltration. This program requires input parameters
including soil physics, hydro-geology, slope condition, as
well as rainfall intensities and durations. Regarding to thecharacteristics of the Cau River basin in Bac Kan and Thai
Nguyen provinces,
the infiltration model for initiallyunsaturated conditions of finite depth of the bedrock was
selected; the soil profile was divided into two main layers(Fig. 2) including a saturated zone beneath the water table,#6 = #8(9 1 −'()+< .'()+=<>'()+=?@ .'()+=<>

1−

1=<>
1=?@

(2)

In which Zmin and Zmax refer to minimum and maximum
values of effective soil depths; "min and "max are the minimaland maximal values of slope angle; Zi and "i represent the soil
depth and slope angle at the calculated cell i.
To evaluate the role of vegetation cover on the hilly slope, inthis paper, we considered the contribution of root systems to
the shear strength (s) by additional cohesion component (%c)
following the Mohr-Coulomb formulation 4) (eq. 3):
A = B + CD E%FGHE = B" + ∆B + CD E%FGH (3)Where the cohesion c in equation (1) in vegetation cover areais the sum of soil cohesion cs (kN/m2) and apparent cohesion
provided by roots (root cohesion) %c (kN/m2); &N refers to
the normal stress on the shear plane (kN/m2). For simplicity,in this paper the influence of tree –surcharge is ignored, andthe mechanical impact of tree cover on the shear strength ofsoil with vegetation cover is represented only by the rootcohesion (%c).

4.#Results#
In order to simulate shallow landslide under different
extreme precipitation, it is necessary to evaluate spatial andtemporal distribution of storm events. We firstly analyzed 68
heavy storms events from 1960-2016 in three provinces inNortheast (Bac Kan, Tuyen Quang, and Thai Nguyen) andfound out that typical forms of rainstorm in this region differ
not only in rain durations (from 5 hours in Tuyen Quang on

Fig. 2: a) Conceptual Diagram of TRIGRS model;b) Basic input layers for TRIGRS model.

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Table 1: Total rainfall amount regarding to historicalrainfall (H) in 2013 and 24-hour PMP, and 48-hourPMP (mm)Rainstorm
in 2013
Bac Kan
186.3
Cho Don
178.2
Phu Thong
294.0
Dinh Hoa
189.0
Dai Tu164.7
Thai Nguyen 222.2
Pho Yen200.7
Vo Nhai
250.0
Station24hr-PMP

48hr-PMP

482.1
493.8
420.9
468.9
497.1
499.0
496.2
492.4

651.3
572.8
524.7
552.9
542.9
520.7
529.3
530.7

Fig. 4: Relationship between factor of safety for option
Ha32 (Fs_Ha32) and delta Fs (%Fs) [difference between
Fs_Ha32 and factor of safety for other options ]Table 2: Calculation options for factor of safety
using TRIGRS (Fs_abcd)[Fs_abcd] a
Option Rainfall
event
H
Historical
event
a

b
RainfalldistributioncLand usedSoil depth

selected regarding to main geology regions. In addition to theworst case of bared land across the region (the cohesion c inequation (1) refers to the soil cohesion cs), the land-use dataobtaining from two main sources -USGS 2010 and JAXA
2015- was put in the model to set up scenarios (Table 2); thecohesion c in equation (1) is the sum of the soil cohesion cs
and the root cohesion %c . Applying the Saulnier method 3)(Eq. 2), information of average depth of top soil was gatheredfrom recent studies and reports, and was set up in three
options including i) case 1: upper value of soil depth (1.1 m– 4 m) based on lithology map across the whole region; ii)case 2: average value of soil depth (0.5 m – 3 m) based onlithology map and geology distribution across the whole
region; iii) case 3: average value (0.5 m – 3 m) of soil depth
based on geology distribution and rainfall regions. Since theinformation of groundwater table was unavailable, it was
assumed that the initial groundwater table was at the bottomof the weather soil layer in summer time without antecedentrain.
Based on options of rainstorm amount, rainfall distribution,
land-use condition, and soil depth assumption, we computed
the factor of safety for the whole region of Cau river basin.
In case of actual rainstorm distribution, we calculated thefactor of safety Fs regarding to two options of soil-depth(case 2 and case 3) as well as the changes in land-use option,i.e. from land use database in 2010 (USGS) (Ha12) to landuse database in 2015 (JAXA) (Ha32 & Ha33) in addition to
the worst option of land cover with bare soil (Ha02). To
estimate the changes in Fs, we consider the option Ha32 as abase case and compute the difference between Fs_Ha32 andfactor of safety for other scenarios (Fig 4). Within the rangeof below 1.5 in the value of Fs_Ha32, the option Ha12 (landcover in 2010) obtained higher threshold of %Fs; in otherwords, the slope stability reduced when the land coverchanged such as from forest cover (2010) to crop land andconstruction site (2015). Interestingly when the whole regionis assumed to be replaced by soil (Ha02), a significant
reduction of Fs was shown when compared to the option ofchanges in land-use database (Ha12) and changes in soildepth option (Ha33). When the rainstorm amount changed
from actual event (Ha32) to 24-hour-PMP (1132 and 1332)
and 48-hour-PMP (2332 and 2333), the factor of safety Fs
also declined in the whole basin. Overall the top file valuesof average %Fs in the whole basin are 0.196 (Ha32_2333),

Actualdistribution

0
1

24-hour PMP Type 1

2

48-hour PMP Type 2

JAXA
Case 2
2015- 100m
resolution

3

Type 3

JAXA
Case 32015- 50m
resolution

Bare soil
USGS 2010 Case 1

For example: Fs_Ha32 refers to the factor of safety (Fs) for an
option applying the historical rainfall event (a=H) having an
actual distribution (b=a) using JAXA land-use in 2015 with 50
m resolution (c=3), and soil depth case 2 (d=2).

1984/6/22 to 53 hours in Bac Kan on 1990/9/21) but thehourly-distributions as well. Duration of almost all heavy
storm events were often below 48 hours with three main
distribution types: i) Type 1: peak rainfall locates at the
beginning of the storm; ii) Type 2: peak rain locates at the
middle of the storm time; and iii) Type 3: peak precipitation
appears at the end of the storm (Fig. 3). In addition, within
the period 2010-2016 , the average annual rainfall in Bac Kan
and Thai Nguyen provinces varied from 1391 mm (2011) to2029 mm (2013); the highest values of the maximum-1-day
precipitation at most rain-stations occurred in the year 2013.
Heavy rainstorm events in 2013 also led to a large number oflandslides
problems across the region; for example,
according to the report of Bac Kan province, the road 258
from Bac Kan to Ba Be (40 km) were closed for 1 month andrepaired for about 3 months as the results of landslide
occurrences after the downpour in May 2013. Additionally,
under the influence of a changing climate, increases oftemperature are believed to drive higher intensity of
precipitation in many regions throughout the world. The
magnitudes of extreme rainstorm have been estimated using
the Probable maximum precipitation (PMP) 5) . The
evaluation of PMP was implemented using the moisturemaximization method. The value of a heavy rainstorm in
2013, 24-hour-PMP, and 48-hr-PMP are shown in Table 1.
Regarding to other input data in TRIGRS, we gathered
information of soil and hydro-geology from literature
reviews and field investigation; average values were then6

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0.189(Ha32_2332),
0.097(Ha32_1332),(Ha32_Ha02), and 0.040 (Ha32_1132).

0.069

5.#Conclusions#
In conclusion, under the changes in climatic condition,
extreme precipitation would result in higher probability of
unstable regions. It is noted that under the same amount of
the storm-rain and storm duration, for example 24-hour-PMP,
the landslide prone areas would expanse in case of changing
the storm distribution from type 1 (scenario 1132) to type 3(scenario 1332). Comparing the rise of %Fs in case ofchanging rain-event from actual storm in 2013 to 24-hourPMP (126.9 mm – 332.4 mm rise in rainfall amount), thefactor of safety still decreased significantly when thedownpour total of 24-hour-PMP increased by 21.7 mm –169.1 mm in additional 24 hours to reach the magnitude of
48-hour-PMP (Fig 4). This reveals the importance of threemajor components of a storm events including rain-amount,rain-duration, and rain-distribution to the stability conditionof hilly site. The findings also provide an important
information for local authorities in developing adequate landmanagement in this river basin considering the relationshipbetween landslide-prone area and changes in land-coversituation.
Acknowledgements#
This research was supported by the Vietnamese Government
scholarship (project 911); and the Social Implementation
Program on Climate Change Adaptation Technology (SICAT) of MEXT, Japan.
References#
1. Baum, R. L., Savage, W. Z., Godt, J. W.: TRIGRS —A Fortran Program for Transient Rainfall Infiltration
and Grid-Based Regional Slope-Stability Analysis,
Version 2.0. US Geol Surv Open-File Rep.
2008;(2008-1159):75. doi:Open-File Report 2008–
1159.
2. Baum, R. L., Godt, J. W., Savage, W. Z.: Erratum:
Estimating the timing and location of shallow
rainfall-induced landslides using a model for transient,
unsaturated
infiltration.
J
GeophysRes.
2010;115(F03013):1-26.
doi:doi:10.1029/2009JF001321.
3. Saulnier, G.-M., Beven, K., Obled, C.: Including
spatially variable soil depth in TOPMODEL. J
Hydrol. 1997;202:158-172.
4. Pollen-Bankhead, N., Simon, A.: Enhanced
application of root-reinforcement algorithms forbank-stability modeling. Earth Surf Process
Landforms. 2009;34:471-480. doi:10.1002/esp.1690.
5. WMO.: Manual on Estimation of Probable Maximum
Precipitation (PMP)., 2009.

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Estimating runoff in perennial and ephemeral catchments: a disaggregated flow duration
curve approach.

Chris Leong1*, Yoshiyuki Yokoo1
1

Graduate School of Symbiotic Systems Science and Technology, Fukushima University, Fukushima 960-1296, Japan*E-mail:leongchris3@gmail.com

Abstract
The flow duration curve (FDC) is a simple tool that has been used over decades in hydrologic studies. The use of simplistichydrologic models is important to further development of models relating to ungauged catchment runoff estimations.
Therefore, this study uses the FDC and disaggregates it into three parts, high, middle, low and attempts to make reasonable
runoff estimations in each part. Based on previous literature, it was determined that FDC high flow part was controlled
by precipitation, herein the Curve Number method was used for runoff estimations in the high flow parts and the middle
flow parts could use mean monthly flow (MMF). For the low flow part of the FDC, evapotranspiration was suggested be
an important controlling factor. Therefore, perennial and ephemeral catchments were used because of the different natureof the FDC shapes especially at the low flow ends. The results show reasonable runoff estimations in the perennial
catchments low flow sections and can be comparable to the MMF, therefore, using either approach is suitable. For theephemeral catchment, the runoff did not have proper estimations. Further analysis on ephemeral catchments identify
precipitation index as a suitable tool for making runoff estimations in the low flow part of the FDC.

Keywords: Flow duration curve, Disaggregation, Runoff estimation, Mean monthly flow, Precipitation andaridity index

runoff components (fast and slow runoff). Secondly, themethod by Chiba and Yokoo (2015) was used to estimate
storage. This method improves a storage estimation method
by Kobayashi and Yokoo (2013). The Kobayashi and Yokoo(2013) storage estimation method had combined thenonlinear storage estimation method by Kirchner (2009) andthe Hino and Hasebe (1984) hydrograph separation method.
Lastly a process based Tank model by Yokoo et al., (2017)
similar to the Suguwara (1995) Tank model but with few
applied differences was used to make runoff estimations.

1.#Introduction#
The flow duration curve (FDC) is a simple and easy to usehydrologic tool that has provided valuable insight intohydrological nature of catchments. This study the second partof a preceding one, aims to make potential runoff estimationsin the low flow section of the FDC. The first study by Leongand Yokoo (2017) disaggregated the FDC into three sections
namely, high, middle and low flow sections based on the
Yokoo and Sivapalan (2011) proposal. The difference
between the two studies is the latter disaggregated the FDC
into two part, high and low. A principal finding of the Yokoo
and Sivapalan (2017) was that the FDC high flow part wascontrolled by precipitation and the middle flow parts could
use mean monthly flow (MMF) for runoff estimation. The
authors suggested that for the low part of the FDC,
evapotranspiration could be an important controlling factor.
Using the information from this research, the Leong andYokoo study used a calibrated curve number method toestimate high flows and for the middle flow part the results
confirmed that the MMF can be used for runoff estimations.
This study aims to bring light to the low flow section. This
study uses perennial and ephemeral catchments because of
the different nature of the FDC shapes.

3.#Results#and#Concluding#Discussion#
By using these methods, the results show the perennial
catchment making proper runoff estimations in the low flow
sections and is comparable to the MMF, meaning that
because perennial catchments have continuous runoff either
approach is suitable. However, for the ephemeral catchment,
the runoff estimation did not have a desired outcome.
Therefore, for this study the authors isolated the ephemeral
catchments runoff estimations. Focusing on the short falls of
the Yokoo et al., (2017) Tank model, where it does notinclude evapotranspiration which can be an important factor
especially in dry ephemeral catchment regions. Two factorsthat stand out in ephemeral catchments annual data is the lackof precipitation and the high evapotranspiration. Based on
this, two indices are calculated, the aridity and precipitation
indices. By performing regression analysis of the two indicesfor each catchment at selected runoff percentiles, ageneralized regression equation was obtained for eachselected percentile. Inputting the indices into the generalized

2.#Methodology#
There are three distinctive methods used, first is thehydrograph separation, storage estimation and lastly a Tank
model. The hydrograph separation method by Hino andHasebe (1984) was first used to separate the runoff into two

8

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regression equation will give the runoff estimates based on
the aridity and precipitation. The results show the
precipitation index making sound runoff estimations morethan the aridity index, resulting in the conclusion that for thestudied ephemeral catchments usual climate is the importcontrolling factor

References#1)! Chiba takahiko, Yokoo yoshiyuki : Revision estimation
method of watershed-scale rainwater storage and its
effect, Journal of JSCE B1 (Hydraulic Engineering),
71(4), 289-294, 2015.
2)! Hino mikio, Hasebe masahiko : Identification andprediction of nonlinear hydrologic systems by the filterseparation autoregressive (AR) method: Extension tohourly hydrologic data, Journal of Hydrology, 68(1),181-210, 1984.
3)! Kirchner James : Catchments as simple dynamicalsystems: Catchment characterization, rainfall-runoff
modeling, and doing hydrology backward, Water
Resources Research, 45(2), 2009.
4)! Leong Chris, Yokoo yoshiyuki : Estimating flow
duration curve in the humid tropics: a disaggregation
approach in Hawaiian watersheds, HydrologicalResearch Letters, 11(4), 175-180, 2017.
5)! Suguwara M : Tank Model. In Computer Models of
Watershed Hydrology, Singh VJ (ed), Water Resources
Publications, pp.165-214, 1995.
6)! Syuhei kobayashi, Yokoo yoshiyuki : Estimatingwatershed-scale storage changes from hourly discharge
data in mountainous humid watersheds: toward a new
way of dominant process modeling, HydrologicalResearch Letters, 7(4), 97-103, 2013.
7)! Yokoo yoshiyuki, Chiba takahiko, Shikano yudai,
Leong Chris : Identifying dominant runoff mechanisms
and their lumped modeling: a data-based modelingapproach, Hydrological Research Letters, 11(2), 128133, 2017.
8)! Yokoo yoshiyuki, Murugesu Sivapalan : Identifying
dominant runoff mechanisms and their lumped
modeling:a data-based modeling approach, Hydrology
and Earth System Sciences, 15(9), 2805-2819, 2011.

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Characteristics and seasonal flactuations of invertebrate species eDNA concentration
in forest, agriculture, and urban river
Noriko Uchida1*, So Kazama1 , Kubota Kengo1 , Shunsuke Aita1
1

Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan*E-mail:noriko.uchida.s8@tohoku.ac.jp

Abstract
The concentration of total DNA and invertebrate species DNA were quantified from the samples that were collectedfrom rivers that had different land use property (i. e. forest, agriculture, urban). The sampling had been conducted fromMarch of 2017 to May 2018, in Natori river basin, Miyagi Prefecture, Japan. From the observation results, total DNA
concentration in forested rivers showed significantly low value comparing with agricultural and urban rivers.
Concentration of invertebrate species DNA showed maximum value on December for all rivers. That may be resulted
from decreasing of dilution effect because of small river discharge and increasing of riparian animal’s population. Specificdischarge of invertebrate species DNA of urban river was the largest in 3 land used rivers. This study clarified thatdifferent terrestrial land use influenced the amount of eDNA load to basin. Furthermore, the dynamics of eDNA
concentration were varied by season and land use. Thus, we should consider land use of inflowing river to basin andsampling season when we try to evaluate basin ecosystem by eDNA methods.

Keywords: Invertebrate species, Environmental DNA, Land use, qPCR, Watershed ecosystemhave correlation with eDNA preservation and degradation
time. Consequently, eDNA concentration should becharacterized by terrestrial land use type because water
quality indices are influenced by land uses.
In this study, we aim to find relationship between terrestrial
land use and eDNA concentration, especially in aquatic
invertebrate species. To meet this target, we focused on small
rivers with 3 different type of land use and measured total
DNA concentration and invertebrate species DNA
concentration for 1 year.

1.#Introduction#
Exploitations and climate changes have resulted in habitat
deterioration of wild animals. To conserve wild life, it isrequired to understand distribution of keystone or rare
1)
species . Additionally, in streams, understanding
assemblage component (population size or faunalcomposition) of stream invertebrate is fruitful to assess2)
biological diversity . Monitoring of aquatic wild life has
been practically carried out by visual observation, and fishingin several manners. However, in terms of monitoring in
broader scale or longer term, these conventional methods aretime- and effort- consuming, require special skills to identify
species and involve observer errors or bias.
Environmental DNA (eDNA) is remarked as to be abreakthrough method for biological monitoring. eDNA isoriginated by organism’s bodies and metabolites, and exists
3)
in environmental samples such as water, soil, and air . By
detecting species specific genome sequences in a given
environmental samples using molecular biological methods(e.g. PCR, Next Generation Sequencing) presence / absenceof a target species can be determined with high accuracy.
Remarkably, it is already verified that using eDNA method
is more efficient to know species composition or existence
4)than conventional monitoring methods by Minamoto et al.
Taking environmental sample less sampling effort and timethan animal trap or capturing or visual detection, and thisadvantage will enhance broader and longer biologicalmonitoring. In fact, some reports surveyed animal habitat
5)
distribution and found biological construction with high6)
accuracy . To make eDNA method more practically usable,
fundamental knowledge such as eDNA maintain time or
7, 8)
flow-down distancehas been accumulated. Specifically,
9)10)
water quality such as water temperature , turbidity and pH

2.#Methodologies#
We sampled eDNA at 2 forest rivers, 2 agricultural rivers,and 2 urban rivers which are included Natori River basin in
northeast Japan (Fig. 1 and Table. 1). The samplings weremonthly conducted at no-rain day, through March 2017 tothth
May 2018 except 22 July 2017 to 26 August 2017 becauseof one month-continuous rain. Invertebrate species aretargeted as eDNA detection because invertebrate species
exist even though river environment is totally different (i.e.
forest, agriculture, and urban river).
We collected 4.5L of water from surface to sterilized plastic
container by gloved hands. The sampled stream water wastransported to the laboratory and kept in the cold condition
11)
following previous reports . Subsequently, filtering process
was conducted immediately in the same day. The water
samples were then filtered by the vacuum filtration with
47mm diameter glass-fiber filters (normal pore size, 0.7µm;
Whatman) and GF/D filter (Whatman, diameter 90mm,
normal pore size 0.27µm) as a pre-filter. These samples werewrapped in commercial aluminum foil and zipped plastic bag
and stored in -20ºC until DNA extraction. DNA wasextracted from filters by combination of Phenol-ChloroformIsoamyl alcohol method and ethanol precipitation to10

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Tohoku$University!

LCO1490 (5‘-GGT CAA CAA ATC ATA AAG ATA TTG
G-3’) and HCO2198 (5’-TAA ACT TCA GGG TGA CCA
8)AAA AAT CA-3’) . We initially validated the primers on
tissue samples for a larva of the caddis fly species(Stenopsyche marmorata) which was also sampled in the
study catchment.
The qPCR was performed using SYBR®Premix Ex Taq(TaKaRa) and LightCycler®2.0 (Roche). The condition ofqPCR consists of an initial incubation at 95ºC for 20 seconds
followed by 40 cycles of 95ºC for 5 seconds, 53ºC for 30
seconds, 72ºC for 1 minute. Fragment size of amplicons wasverified with agarose gels electrophoresis and qPCR products
were purified using a MicroSpin S-400 Columns (GE Life
Science). Further, we set a template DNA which was
extracted from tissue of S. marmotara and amplified it using
the above primer pair. Then we created a dilution series from1
the PCR products of as standard DNA thorough 1.0x10 to
7
1.0x10 (copies/µl) and conducted qPCR for environmental
samples.
At the same time of water sampling, we measured average
flow velocity (AEM1-D, electromagnetic flow velocimeter)
and average water depth in one cross section of eachsampling site and calculated river discharges.

Fig. 1. Field observation points on land use map in Natori
River basin, Miyagi Prefecture. Circles represent forestsites, triangles represent agricultural sites, and squares
represent urban sites. Each sampling sites is written withland use type, display name, and river or place name. The
black rhomboid represents Ochiai river observatory andthe double circle represent Sendai meteorologicalobservatory. Land use types are classified by 15 color:
Agriculture (1~4), Forest (5, 6), Urban (7 ~10), and others.

3.#Result#3.1. Comparing eDNA concentration in each land use
Over annual monitoring results of total DNA concentration
an invertebrate species DNA concentration in forest,
agriculture, and urban rivers were showed in Fig. 2. Forest
rivers (F-1, F-2) total DNA median value were about 0.15~
0.49 times of one of agriculture river (A-1) and both urbanriver (U-1, U-2). By Mann-Whitney U test, median of total
DNA in F-1 and F-2 were significantly different from A-1,
U-1, and U-2. On the other hand, agriculture river A-2 total
DNA was higher than both of forest rivers but was not
significantly different statistically. This may be related thatland use of A-2 watershed is 40% of agriculture area and 60
of forest area besides A-1 had 80% of agriculture area. A-2
might have agriculture and forest characteristics and did notshow clear agricultural pattern. Invertebrate DNA (inv.
DNA) concentration in F-1 and U-1, F-1 and U-1 showedsignificant differences. The inv. DNA median of F-1 was
0.43 times; F-2 was 0.58 times of U-1.

enrichment DNA concentration. To remove PCR inhibitor,
DNA samples were purified using PCR Inhibitor Removal
Kit (Zymo Research). Finally, concentration of the total
extracted DNA was measured using Qubit 
R 3.0 (Thermo
Fisher Scientific Inc). In this report, we called this measured
DNA concentration as “total DNA” because it contained not
only invertebrate species but also other organisms such as
microbe vertebrate species, and algae DNA.
Mitochondrial DNA (mtDNA) was employed as the target
genetic marker because copy number of mtDNA in stream
water could be larger than that of nuclear DNA per cell sothat detection rate is expected to be higher. We used auniversal primer targeting mtDNA cytochrome c oxidasesubunit 1 (CO1), which is divergent among all animal phyla
12). We amplified the target DNA fragments of the samples
using the PCR primers designed to amplify invertebratespecific loci from the CO1 region. A 658 bp fragment from
CO1 region was amplified with the following primer pair;

Table 1. Field information

River/place

Display

Location

Elevation

Catchment

name

name

(N. LatitudeE. Longitude)(m)

Area (km2)

Aoshita R.

F-1

38.34107, 140.65442

360.0

4.813

Koyanosawa R.

F-2

38.21021, 140.54447

361.4

27.750Agriculture

Imosawa R.

A-1

38.30602, 140.76431

121.6

17.625

River

Motoisago R.

A-2

38.24180, 140.63376

209.0

12.563

KunimiU-1

38.26907, 140.84255

37.9

0.125

Zarukawa R.

U-2

38.22217, 140.84882

30.9

1.063

River type

Forest River

Urban River

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4.#Conclusion#
This study monitored that eDNA concentration of
invertebrate species in different land use types for over a
year. It is usual that survey area is located or includes
various kind of terrestrial land use types but land use impact
to eDNA detection had not been remarked. The monitoring
result in this report demonstrated that terrestrial condition
characterized eDNA concentration, especially from riverwater from urban area contained high eDNA concentration.
Secondary, seasonal fluctuations pattern of eDNA werecommon among forest, agriculture, and urban rivers. From
this result, winter season may be suitable to get high eDNA
concentration in case of invertebrate species. To clearlyinterpret biological information from eDNA survey, further
understanding of eDNA dynamics in the field should be
required.

Fig. 2. Concentration of total DNA (a) and invertebrate DNA(b) in each river. Forest rivers showed significant lower
concentration compared with other land use rivers (***:
p<0.01, **: p<0.05, *: p<0.1). Horizontal bars in box plots
represent median (50 percentile), and upper ends of boxes
represent 75 percentiles, and lower ends of boxes represent
25 percentiles. Open circles represent outlier values that are
1.5 times larger than maximum datum and 1.5 times smaller
than minimum datum.
3.2. Seasonal fluctuation of invertebrate DNA
Inv. DNA concentration in every river demonstrated
5
maximum value at December (1.0 x10 copies/L order) eventhough the land use types were different. Generally, inv.
DNA had increased through October to December, and had
gradually decreased after January to May. This fluctuation
trend was observed commonly in all survey rivers. According
to fluctuation of March to July in 2017, inv. DNA will
recover after May and decrease towards summer season.

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Acknowledgements
This research was partly supported by the Ministry of
Education, Science, Sports and Culture through a Grant-inAid for Scientific Research (grant nos. 25241024, Yasuhiro
Takemon, 26630247, Kozo Watanabe, 26820196, Kei
Nukazawa) and Japan society for the Promotion of Science(JSPS) Research Fellowship (grant no. 256493). We express
deep appreciating for all of supports.

#
References#1)! Hurlbert, A. H., Jetz W.: Species richness, hotspots,
and the scale dependence of range maps in ecology andconservation. PNAS. 104 (33): 13384-13389. 2007
2)! Heino, J.: Biodiversity of aquatic insects: spatial
gradients and environmental correlates of assemblage-

Fig. 3. Seasonal change of invertebrate species eDNA concentration (copies/L) at each sites (March 2017 to May 2018). Opencircles represent measured data (n=3), and small black rectangles represent average value of them in left axis. Gray bar
represents observed specific discharge (m/sec/km2) that discharges are divided by catchment areas in right axis.

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level measures at large scales. Freshwater Reviews. 2:
1-29, 2008
3)! Rees, H. C., Maddison, B. C., Middleditch, D. J.,
Patmore, J. R. M., Gough K. C.: The detection of
aquatic animal species using environmental DNA-areview of eDNA as a survey tool in ecology, Journal ofApplied Ecology. 51: 1450-1459, 20144)! Minamoto, T., Yamanaka, H., Takahara, T., Honjo, M.
N., Kawabata, Z.: Surveillance of fish species
composition using environmental DNA, Limnology, 13(2): 193-197. 20125)! Port J.A., O'Donnell J.L., Romero  MaracciniO. C.,
Leary P. R., Litvin S. Y., Nickols K. J., Yamahara K. M.,
Kelly R.P.: Assessing vertebrate biodiversity in a kelp
forest ecosystem using environmental DNA. Molecular
Ecology. 25 (2): 527-541. 20156)! Miya M., Sato Y., Fukunaga T., Sado T., Poulsen J. Y.,
Sato K., Minamoto T., Yamamoto S., Yamanaka H.,
Araki H., Kondoh M., Iwasaki W.: MiFish, a set ofuniversal
PCR
primersformetabarcoding
environmental DNA from fishes: detection of more than
230 subtropical marine species. Royal Society OpenScience. 2 (7): 150088. 20157)! Nukazawa K., Hamasuna Y., Suzuki Y.: Simulating theAdvection and Degradation of the Environmental DNA
of Common Carp along a River. Environmental Science& Technology. DOI: 10.1021. 20188)! Tsuji S., Uchio M., Sakurai S., Minamoto T., Yamanaka
H.: Water Temperature-Dependent Degradation ofEnvironmental DNA and Its Relation to Bacterial
Abundance. PLOS ONE. 12 (4): e0176608, 20179)! Strickler, K. M., FremierA. K., Goldberg C. S.:
Quantifying Effects of UV-B, Temperature, and PH on
EDNA Degradation in Aquatic Microcosms. BiologicalConservation. 183 : 8592, 201510)! Fukumoto S., Ushimaru A., Minamoto T.: A basinscale application of environmental DNA assessment forrare endemic species and closely related exotic species
in rivers: a case study of giant salamanders in Japan.
British Ecological Society. 52 (2): 358-365. 2015.
11)! Folmer, O., Black, M., Hoeh, W., Lutz, R., Vrijenhoek,
R.: DNA primers for amplification of mitochondrial
cytochrome c oxidase subunit 1 from diverse metazoan
invertebrates, Molecular Marine Biology andBiotechnology. 3: 294-299. 1994

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G-Safety

The 6th International Symposium on Water Environment Systems----- with Perspective of Global Safetythth(November 29 - 30 , 2018)

Tohoku University

Forest fire severity estimation for the 2017 Kamaishi forest fireGrace Puyang Emang1*, Yoshiya Touge 1, So Kazama1
1

Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan*E-mail:grace.puyang.emang.r2@dc.tohoku.ac.jp

Abstract
The 2017 Kamaishi forest fire occurred for 14 days from 8th till 22nd May 2017 and the total burnt area was 413 hawhich is greater than the total burnt area for the whole Japan in 2016. This burnt area was estimated based on burnt
and unburned area. However, in the burnt area itself, there were differences of fire severity observed. The objectiveof this research was to estimate the fire severity in this burnt area using Normalized Difference Vegetation Index(NDVI) and post fire observations of stem-bark char height, ℎ# and crown scorch height, ℎ$ . The results showsthat NDVI and ℎ$ has stronger relationship than ℎ# suggesting both are sensitive towards fire severity on leaves
while ℎ# is related to fire severity on stem-bark.
Keywords: Fire severity, NDVI, Post-fire observations, Kamaishi

1. Introduction
Forest fire occurs annually in developed and developing
countries. Between 2010 till 2014, forest and field fire arereported annually with an average of 1635 cases and an
average annual loss at approximately 576 million yen(Statistic Bureau Japan, 2018). Forest fire causes not only
economic loss but also losses of animals’ habitat and foodresources.
Ecosystem of the forest is also affected based on fireseverity as increased in sedimentation yield andconcentration was observed in burned watershed in Oldman
River Basin, Alberta (Silins et al., 2009) and in Colorado
Front Range, higher sedimentation production rate was foundin high severity forest fire than moderate and low severity
forest fire (Benavides-Solorio and MacDonald, 2005). Most
research focused on estimation of burnt area and hotspots
using remote sensing data such as NDVI (Kasischke andFrench, 1995) but in burnt area, various degree of fireseverity exist and can be seen from loss of vegetation.
Fire severity is used to measure the loss or change of theabove and below ground organic matter (Keeley, 2009) suchas char height and scorch crown height. This research will
use NDVI and char height on stem- bark, ℎ# and height ofscorch on crown, ℎ$ to estimate the fire severity in the 2017
Kamaishi Forest Fire.

Fig.1 Historical large scale forest fire occurring in Kamaishi

3. Methodology
3.1 Remote sensing data
NDVI which were cloud free from Landsat 8 were used inthis research for the period of 2017. These images were thenprocessed to create a two weeks average in an image andsubsequently, %&'()*++ were computed by subtracting thepost-fire image from the pre-fire image.

3.2 Post-fire observation: stem-bark char height,,- and scorch crown height, ,.

2. Study area

Two types of post-fire observations were made, namely
stem-back char height, ℎ# and scorch crown height, ℎ$ . For
every 30 m, a tree was observed of its fire severity bymeasuring the char height on its stem bark, ℎ# and thescorch height on its crown, ℎ$ . The observation were madein the estimated burnt area and in total 650 points of ℎ# andℎ$ respectively were collected.

In the last 42 years, Kamaishi had three major fire incidents
with burnt area of 200 ha, 392 ha and 130 ha in Kamaishi
Hamacho, Kamaishi Higashimae-cho and Kamaishi Tonicho respectively and recently, in Kamaishi Heita with anestimated burnt area of 413 ha (Touge et al., 2018), the study
area for this research as shown in Fig. 1. This indicates that
Kamaishi is prone to forest fire and burnt areas were wide.

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The 6th International Symposium on Water Environment Systems----- with Perspective of Global Safetythth(November 29 - 30 , 2018)

4. Results and discussion
To estimate the fire severity of the 2017 Kamaishi forest fire,
corresponding %&'()*++ with 650 points of ℎ# and ℎ$
were extracted and plotted in dots distribution maps as shownin Fig. 2.

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This suggests using direct value of ℎ# and ℎ$ would not bea good indicator for fire severity as it may represent only theheight of fire but by taking into account the ℎ# and ℎ$ inrelation to its total stem-bark height and crown heightrespectively, it could be a better representative of fire severity.

5. Conclusions
All three observation indicates variation of fire severity with%&'()*++ and ℎ$ exhibiting the most variations. Moreover,results indicate using rate of ℎ# and ℎ$ based on its overall
stem-bark height and crown height could offer a better
estimation of fire severity. These results open new prospects
for estimating the fire severity in the 2017 Kamaishi forest
fire more definite.

6. Acknowledgements
We wish to thank Nohmura Foundation for Membrane
Structure’s Technology for their generous financial
assistance.
References
1) Benavides-Solorio JDD, MacDonald LH: Measurement
and prediction of post-fire erosion at the hillslope scale,Colorado Front Range. International Journal of
Wildland Fire 14(4), 457–474, 2005.
2) Kasischke ES, French NHF: Locating and estimating theareal extent of wildfires in alaskan boreal forests using
multiple-season AVHRR NDVI composite data.
Remote Sensing of Environment 51(2), 263–275, 1995.
3) Keeley JE: Fire intensity, fire severity and burn severity:
A brief review and suggested usage. International
Journal of Wildland Fire 18(1), 116–126, 2009.
4) Silins U, Stone M, Emelko MB, Bladon KD: Sedimentproduction following severe wildfire and post-firesalvage logging in the Rocky Mountain headwaters of
the Oldman River Basin, Alberta. Catena 79(3), 189–
197, 2009.
5) Statistic Bureau Japan: Japan Statistical Yearbook 2018
6) Touge Y, Emang GP, Kazama S, Takahashi Y, Sasaki
K: Introduction of the Tohoku Forest Fires on May
2017; case in Kamaishi city of Iwate Prefecture andKurihara city of Miyagi Prefecture,  J.
JSNDS 36(4), 361–370, 2018

Fig. 2 Distribution of %&'()*++ , ℎ# and ℎ$
The results indicate %&'()*++ and ℎ$ can show morevariation of fire severity than ℎ# . However, %&'()*++ has
a slight stronger relationship with ℎ# (R = 0.29) than ℎ$ (R= 0.27) as shown in Fig. 3a and 3b.

0.5

NDVIdiff

Post - Pre (May 2017)
0
0

10

20

hc, cm30

R = 0.29-0.5-1Fig. 3a

NDVIdiff

0.5

Post - Pre (May 2017)

0
0

10

20

30 hs, cm40
R = 0.27-0.5-1
Fig. 3(b)Fig. 3a and 3b Correlation between %&'()*++ , ℎ# and ℎ$respectively

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The 6th International Symposium on Water Environment Systems----- with Perspective of Global Safety(Nov. 29th - Nov. 30th, 2018)

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Removal of Cesium from Simulated radioactive water by a Countcurrent Two-stage
Adsorption-Microfiltration Process○Zhang Guanghui, Xu Xiaotian, Gu Ping*, Xu Yao
Department of Environmental Engineering, School of Environmental Science and Engineering, Tianjin University,
Tianjin 300072, China.
*E-mail: guping@tju.edu.cn

Abstract
A hybrid process of adsorption, coagulation and ultrafiltration was developed to remove cesium from thesimulated radioactive water, which could effectively alleviate membrane fouling without seriously deteriorating
decontamination performance. In the meanwhile, a simple calculation method was established based on the
Freundlich adsorption isotherm, and it could accurately predict the cesium concentration in the effluent. When the
dosages of cupric ferrocyanide were 0.025 g/L and 0.04 g/L, respectively, ignoring the influence of sludge discharge,
the relative errors between the experimental and calculation values were no more than 10%. As a result, thecalculation method could be used to determine the consumption of cupric ferrocyanide at a given requirement for
the effluent.
Keywords: Copper ferrocyanide, Cesium, Carrying-adsorption, ultrafiltration, radioactive water

alleviate the fouling rates of the membrane without seriously
deteriorating decontamination performance. In the
meanwhile, a simple calculation method was establishedbased on the Freundlich adsorption isotherm, which could
accurately predict the concentrations of the cesium in theeffluent. When the dosages of CuFC were 0.025 g/L and 0.04
g/L, respectively, ignoring the influence of sludge discharge,
the relative errors between the experimental and calculation
values were no more than 10 percent. That is to say, if thecesium level in the effluent is determined, the dosage of
CuFC could be predicted very quickly. As a result, thecalculation method could be used to determine theconsumption of cupric ferrocyanide at a given level in thetreated water.
Considering the upgrade of WTP and emergency watersupply in the radioactive nuclear accidents. We tried the test
that the ultrafiltration was substituted by the sand filtration,and the hybrid process of adsorption, coagulation andfiltration was also explored for the removal of the cesium
from drinking water. The process could also highly andsafely remove the cesium ions in the simulated radioactive
water, and it could be compatible with the existing facilities
in the water treatment plants. The development of theprocesses would provide the technical supports for largescale applications to cope with the radioactive cesium
pollution caused by the nuclear leakage or accident.

1. Introduction
Fukushima nuclear leakage accident in 2011 caused theworldwide extensive research on the radioactive pollutions,among which the released nuclides including 137Cs, 134Cs,
90
Sr and so on, has drawn considerable attention due to theirlong half-life, high biological toxicity and strong mobility inthe receiving water body.
Copper ferrocyanide (CuFC) is a highly efficient andenvironmentally friendly adsorbent, and it could remove theradioactive cesium ions from water or wastewater. When theradioactive 137Cs and non-radioactive 133Cs simultaneously
existed in the water or wastewater, after adsorption and solidliquid separation, the decontamination factor for cesium
based on the radioactivity concentration was equal to thatbased on the mass concentration. This result explicitly
answered that 137Cs and 133Cs own the same decontamination
characteristics.
The effects of the initial concentration of cesium, CuFC
dosage and coexisting ions on the adsorption by CuFC wasevaluated by the means of Freundlich adsorption isotherm
and response surface methodology (RSM). A novel index
called volumetric distribution coefficient (Kvd) was
proposed to comprehensively evaluate the adsorption process.
Based on the calculation results, it could be found that there
existing an optimum initial cesium concentration, at which
the highest decontamination factor could be obtained at a
certain dosage of CuFC. In the application for theengineering, artificially dosing 133Cs to increase cesium
concentration to a suitable level could significantly improve137
Cs removal. This method was named as carryingadsorption. When the dosage of CuFC was 0.08 g/L and theinitial cesium concentration was 2500 μg/L, respectively, thedecontamination factor could achieve 1.65×105.
A hybrid process of adsorption, coagulation andultrafiltration was developed to remove the cesium ions fromthe simulated radioactive water and it could effectively

2. Acknowledgements
The authors would like to thank the Ministry of Science andTechnology, China, for the funding support under the Major
Science and Technology Program for Water Pollution
Control and Treatment (2015ZX07406006) and National
Natural Science Foundation of China for the funding support(51238006).

1
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Biohydrogen Production (BHP) and Biomethane Production (BMP) Potential from Palm Oil
Mill Effluent (POME)
Khairunnisa ABDUL HALIM1*, Muhammad Faiezul PAUZI1 & Ee Ling YONG1
1
Department of Environmental Engineering, School of Civil Engineering, Faculty of Engineering, University Teknologi
Malaysia, 81310 UTM Johor Bharu, Johor Darul Takzim, Malaysia.
*E-mail: khairunnisaabdulhalim17@gmail.com

Abstract
Biogas production from the POME treatment system has been extensively studied. High organic content in raw
POME is a good source for biogas production via anaerobic digestion. Nonetheless, 80% of the palm oil millers
still persist with ponding treatment system due to lower operational and maintenance cost. Owing to the large
treatment areas and long retention time, high-rate anaerobic digestion treatment system was developed.
However, unstable and low yield of biogas production the treatment paradigm has shifted to multi-stage anaerobic
digester. Presently, most studies focused on the biogas production performance but evaluation on the biogas
production potential under controlled temperature is still lacking. Therefore, this study performs a batch scale
thermophilic-mesophilic anaerobic digestion evaluating biohydrogen and biomethane production of POME at different
organic loading rate (7.5 – 44 g COD/L). The potentials are termed as BHP and BMP, respectively. Highest hydrogenproduction in BMP test was attained at 44 g COD/L, i.e. 112.1 mL-H2, after 5 days. Meanwhile, methane production(540.7 mL-CH4) was spotted also peaked at 44 g COD/L within 45 operational days. Both BHP and BMP revealed thatbiogas production using a multi-stage anaerobic treatment system provide a better yield.

Keywords: biohydrogen, biomethane, production potential, palm oil mill effluent.
1.! Introduction
Palm oil industry is one of the larger agricultural industries
in Malaysia where about 20 million tonne of crude palm oil(CPO) was produced in 2017. However, along theclarification and extraction of the CPO massive productionof byproduct known as palm oil mill effluent (POME) was
generated. Roughly 3.35 million tonnes of oily-thicksluggish POME was produced at every million tonne of palm
oil processed. Proper waste management needs to be
implemented to avoid the pollution of water bodies that canseverely affect the aquatic biodiversity.
POME has preference of high organic contaminants
concentrations and biodegradable, best to be treatedbiologically through anaerobic process. Conventional
ponding treatment system has been employed for decades
due to low cost and manpower needed. Although anaerobic
treatment has been implemented to treat fresh POME, thisprocess alone is unable to achieve the standard discharged
limit outlined by the Department of Environment (DOE),
Malaysia. Furthermore, the inefficient anaerobic activities
will impede the production of biogas leading to unstable yield.
Consequently, millers chose to flare the biogas (e.g:methane) to the atmosphere contributing to global warming.
Currently, methane gas generated from a single-staged
anaerobic digestion system ranged between 0.012 to 0.9 LCH4/g-COD. The unstable performance and low yield in
biogas production made the millers to persevere withponding treatment system. The limitations of the single-stage
anaerobic process altered the pattern of ideas of theresearchers by introducing principle of multi-stage anaerobic
digestion in order to enhance the biogas. Nevertheless, thestudies on the hydrogen and methane production potentialfrom POME through two-stage anaerobic digestion undercontrolled temperature are still limited.

17

Thus, this study monitors and evaluates the production ofhydrogen and methane from a batch sequence ofthermophilic-mesophilic anaerobic digestion.

2.! Materials and Methods
2.1
Seed sludge preparation
Sludge was collected from closed anaerobic digester from
Palm Oil Mill, Felda Penggeli, Wilayah Tenggara, Johor
Malaysia. The sludge has the following characteristics:
pH=7.46; total suspended solids (TSS)=59.5 g/L; and volatilesuspended solids (VSS)=11 g/L. Seed sludge was heated at
95°C for 1 hour to inhibit the methanogens and enrich thehydrogen producing bacteria, prior to the BHP test.
Meanwhile, for BMP test, the anaerobic sludge without heat
treatment was first degassed by incubating at 37°C for 5 days
before used as an inoculum to the test.
2.2
Feedstock
The fresh POME was collected from same palm oil mill,characterized and stored at 4°C prior to any test to avoid
biodegradation to the sample. The fresh POME has following
characteristics: pH=4.89; TSS=19.6-22.0 g/L; VSS=2.0-2.4
g/L; and COD=22-44 g/L.
2.3 Biohydrogen Production (BHP) and Biomethane
Production (BMP) Potential
The experiments were carried out in batch conditions, using
a 550-mL Schott bottles. The glass bottle was initially addedwith 50-mL of inoculum and 150-mL of POME, without any
nutrient supplement. These experiments were carried out atfour different concentrations of 7.5, 15 and 44 g/L. The
reaction flasks were purged with nitrogen; tighten andincubate at 55°C for 5 days HRT. The reaction flask was
manually swirled every day and maintained at the static

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condition at desired conditions. The hydrogen production
was monitored daily.
After 5 days, the bottles were opened, and another 150-mLof anaerobic sludge without heat treated were added into thereaction flasks. Reaction flasks were purged with nitrogen
again before the closed flasks were held at 37°C andincubated for 45 days, for methane evaluation. The flasks
were manually swirled every day at first 5-7 days; and 2-3
days once for the entire experimental period; and then later
maintained static at desired condition.
The hydrogen and methane gases production were evaluatedby applying wet displacement method in which 5% NaOH
displaced was considered as volume of hydrogen andmethane generated, respectively.

Tohoku$University!

Cumulative Biomethane Production (BMP) Potential
7.5!g1COD/L
15!g1COD/L
44!g1COD/L

Volume!of!Methane,!mL1CH4

600.0

540.7

500.0
400.0
300.0

212.2

200.0

185.4

100.0
0.0
5

10

15

20

25

30

Operational!Day

35

40

45

50

3.! Results and Discussion

Figure 2 Cumulative biomethane production of POME
at different loading rate in a batch experiment.

3.1 Biohydrogen Production (BHP) Potential
Figure 1 illustrates the cumulative hydrogen production fromPOME at different loading rate. Throughout theexperimental period, 112.1 mL-H 2 was obtained at 44 gCOD/L loading rate; 89.5 mL-H 2 at 15 g-COD/L and 78.9
g-COD/L at 7.5 g-COD/L.

Each concentration displays sudden increment before slowly
reach to the steady state at the end of the experimental time.
At 7.5 g-COD/L and 15 g-COD/L, maximum daily methane
production is at day 8 and 7, respectively before theproduction started to decrease while at 44 g-COD/L was atday 16. This supports the theory that substrate fermentation
to generate methane requires about 15 days.

Volume!of!Hydrogen,!mL1H2

4.! Conclusions•! Throughout the 5 days of BHP test, 112.1 mL-H 2 wasobserved at 44 g-COD/L loading rate; 89.5 mL-H 2 at
15 g-COD/L and 78.9 g-COD/L at 7.5 g-COD/L.
•! Approximately 540.7 mL-CH4 of methane gas
production was attained within 45 operational days of
BMP test at 44 g-COD/L; 212.2 mL-CH4 at 7.5 gCOD/L and 185.4 mL-CH4 at 15 g-COD/L.
•! Replication of BHP and BMP test is needed tovalidate the findings - that will be carried out in the
future work.

Cumulative Biohydrogen Production (BHP) Potential

120

112.1

100

89.5

80

78.9

60
40

7.5!g1COD/L
15!g1COD/L
44!g1COD/L

20
0
0

1

2
3
Operational!Day

4

5

Figure 1 Cumulative biohydrogen production of POME
at different loading rate in a batch experiment.

5.! Acknowledgements
The authors would like to thank Japan International
Cooperation Agency for the financial support through
AUN/SEED-Net Collaborative Research (CR) grant. The
cooperation from Palm Oil Mill, Felda Penggeli, Wilayah
Tenggara, Johor also was acknowledged.

It shows that the biogas production is increase withloading rate. However, at low loading rate as 7.5 and 15
g-COD/L stiffen increment trend was spotted from day 0
to 2 while at 44 g-COD/L this trend was seen from day 0
to 3. From this, it can be concluded that fast degradation
occurs at low loading rates.

3.2
Biomethane Production (BMP) Potential
Figure 2 demonstrates the cumulative methane production
from POME at different loading rate. Methane gas
production was cumulated about 540.7 mL-CH4 within 5 to
50 of operational days at 44 g-COD/L; 212.2 mL-CH4 at 7.5
g-COD/L and 185.4 mL-CH4 at 15 g-COD/L. These trends
were predictable and support the logic that high volume of
methane gas will be produced at high loading rate.
Nevertheless, at the end of operating period of day 46 to 50,
slight increment trend was spotted at 7.5 g-COD/L indicatesthat substrate degradation still occurs although the
production is unstable.

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Methane Fermentation Treatment ofFish Processing Wastewater by A Self-agitatedAnaerobic Baffled Reactor (SA-ABR)
Eli Hendrik SANJAYA1,2* and Yu-You LI1
1
Department of Civil and Environmental Engineering, Tohoku University, 6-6-06 Aoba, Aramaki, Aoba-ku, Sendai,
Miyagi, 980-8579, Japan.
2
Department of Chemistry, State University of Malang (Universitas Negeri Malang), Jl. Semarang No. 5, Malang,
East Java, 65145, Indonesia.
*E-mail: eli.hendrik.fmipa@um.ac.id

Abstract
New design of self-agitated anaerobic baffled reactor (SA-ABR) was developed for methane fermentation treatment offish processing wastewater (FPW). The performance, in term of gas production, organic removal, and inhibition ofammonia and salinity were investigated during 263 days by changing the hydraulic retention time (HRT) and organic
loading rate (HRT) from 100 to 10 days and from 0.461 to 4.905 g/L/day. FPW which had TCOD, proteins, carbohydrates
and lipids of 46.90, 6.83, 1,37 and 4.04 g/L, respectively, was used as substrate. Overall, the SA-ABR had very good
performance. Gas production was increased by increasing the OLR and little bit fluctuating when the OLR 2.934 and 4.905
gCOD/L/day. Methane composition and pH were quite stable about 70-80% and 7.9 to 8.1, respectively. COD, proteins, andcarbohydrates removal were stable about 82.48, 76.05, and 60.72%, respectively. However, these were gradually
decreased when the OLR was about 2.706 gCOD/L/day. This pattern was also similar to TS, VS, SS, and VSS conversion
removal. There were not significantly ammonia, VFA, and salinity inhibitions. However, it was needed advance studies
of SA-ABR for optimization with the higher OLR or shorter HRT.

Keywords: methane fermentation, self-agitated anaerobic baffled reactor (SA-ABR), fish processing wastewater
1.! Introduction
Generally, world capture fisheries and aqua culture
production always grow up as reported by FAO (2016). On
the other hand, the most utilization of fish it selves is for food.
Hence, fish processing industry will be increased along withthe human population growth.
Most of the fish processing industries are located along thecoasts, and they have historically discharged their liquid
waste directly into the sea or into the estuaries, with little orno treatment at all (Veiga et al., 1994). FPW is containing
high levels of organic substances such as fat, soluble proteins,acetic acid, and carbohydrates, phosphorus, and nitrogen(Renata et al., 2015). Therefore, it will cause eutrophication.
Moreover, the biodegradable level of organic matter isusually high along with the potential for bioenergy
production using an anaerobic process (Rollon et al., 2002).
Panpong et al. (2014) and Rollon et al. (2002) haveinvestigated the anaerobic digestion of fish processing
wastewater by using UASB, it show COD removal 92% and96.8%, respectively. The other study showed that the resultsof anaerobic digestion of fish wastewater processing of MBR
were better than those of CSTR in the removal of totalorganic carbon with efficiencies from 97.9% to 98.6%.
Nevertheless, salinity with increasing OLR aggravates
fouling in MBR while leads to deterioration of sludge settle
ability and effluent quality in CSTR (Jemli et al., 2015).
On the other hand, FPW has some limitations in anaerobic
digestion such as high salinity, protein and oil content. These
compounds can inhibit fermentation process by each own
way.
Various of anaerobic bioreactor were developed for
anaerobic digestion, such as upflow anaerobic sludge blanket(UASB), continuously stirred tank reactor (CSTR), anaerobic

19

membrane bioreactor (AnMBR) and baffled reactor. The firstanaerobic baffled reactor (ABR) was developed by
Bachmann (1983). Then, many researchers have beenworking on baffled reactor and resulting several advantages.
ABR has faster granulation process (HutnÏan, M. et al., 1999),
higher microbial diversity in every chamber, good resilienceto hydraulic and organic shock loads, prevent the sludge
wash-out (Tanikawa, D., 2017 and Motteran et al., 2013),
high resistance in environmental parameters such as pH andalkalinity (Rongrong et al., 2011; Zhong and Yang, 2012),
simple design low cost for construction and operating( Tanikawa, D., 2017; Ji et al., 2009).
Since many superiorities of the ABR, our research group
has developed new ABR called self-agitation anaerobic
baffled reactor (SA-ABR). The SA-ABR has been
investigated and the result show that SA-ABR is comparable
in digestion performance to the completely stirred tankreactor (CSTR) (Kobayashi and Li, 2011). On the other hand,
The SA-ABR also has been simulated in computational
program to investigate the fluid dynamics of the reactor. The
result show that the necessary periodical mixing is performedby the biogas generated during the process. It is applicablefor the waste substrate fermentation treatment, especially formedium and small scale reactor (Qi et al., 2013). Therefore,
the SA-ABR is compatible to be applied in fish processing
industry since majority the fish industries in the world aresmall up to medium scale (Nations, 2007).
The aim of this research are to investigate the performanceof SA-ABR in methane fermentation of FPW in term of COD
conversion; biogas production; ammonia, VFA, and salinity
inhibition.

2.! Materials and Methods
2.1! Reactor design and operation

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when the OLR 2.934 and 4.905 gCOD/L/day. Methanecomposition and pH were quite stable about 70-80% and 7.9

Fig. 1. SA-ABR Design

The reactor design is shown on Fig.1 with working volume
o10.4 L (total volume 15.4 L), temperature 35±1 C, substratetank with stirrer. The seed sludge was taken from anaerobic
wastewater treatment SEN-EN Sendai City and granularsludge from slaughter house wastewater treatment.

2.2! Analysis
Analyses of pH, alkalinity, COD, protein, carbohydrate,+
lipid, NH4 , TS and TVS were performed according to theJapan standard methods (JSWA, 1997). VFA was determinedby gas chromatography (Agilent-6890). Biogas composition
was measured by a gas chromatograph (SHIMADZU GC8A). In addition, Free ammonia (FA) was calculated
according to Hansen et al (1998) formula.
3.! Results and Discussion
3.1! Substrate profile
As substrate, fish processing wastewater had beencharacterized. pH, TAN and VFA were gradually increased
during in substrate tank with the average 7.14, 4.719 g/L and
16.886 g/L, respectively. On the other hand, TS, VS, SS, VSS,
COD, protein, carbohydrate, and lipid were vice versa. They
would be decreased during in substrate tank, the averages
were 8.21, 2.69, 1.59, 1.22, 46.90, 6.83, 1.37, and 4.04 g/L.
These phenomena because some bacteria which wereoriginally from FPW were working at substrate tank.
3.2! Performance and stability of fermentation
process
Overall, the performance of SA-ABR in treatment of fishprocessing wastewater was very good. Gas production wereincreased by increasing the OLR and little bit fluctuating

to 8.1, respectively. Based on the VFA concentration, it
was indicated that ammonia inhibition at the beginningand at the middle of OLR 1.253 gCOD/L/day, the total
VFA were more than 12.000 mg/L. However, it was very
good when the HRT 15 to 10 days, the free ammonia (FA)less than 110mg/L. The SA-ABR had very good
performance in resilience of ammonia inhibition. In theprevious studies of chicken manure fermentation by
Anaerobic Membrane Bioreactor (AMBR), thefermentation process had a good performance when TAN
concentration lower than 5000mg/L (Niu et al., 2013).
Salinity were also investigated in term of the sodium
concentration. Sodium content in substrates were notmore than 3341 mg/L. On the other hand, there were
sodium concentration accumulation in the reactor 317 to2121 mg/L. However, these were far from the salinity
inhibition since the previous research show that inhibition
would be happened when the concentration more than
4000 mg/L (Aslan and Şekerdağ, 2016; Feijoo et al., 1995;
Wang et al., 2017; Zhang et al., 2017).
In addition, the substrate conversions were evaluated inseveral form, these were COD, protein, carbohydrate, TS,
VS, SS and VSS. The COD, protein, and carbohydrate
removal were stable about 82.48, 76.05, and 60.72%,
respectively. However, these were gradually decreased
when the OLR reached 2.706 gCOD/L/day. Similar
pattern upon TS, VS, SS, and VSS removal. The
performance data can be more clearly when the OLR wereincrease as shown on Fig. 2.
4.! Conclusions
The SA-ABR has shown satisfying performance in treated
FPW. The COD conversion was 82.48% with methane
content in gas production 70-80%. There was not
significantly ammonia, VFA, and salinity inhibition.
However, it was needed advance studies of SA-ABR for
optimization with the higher OLR or shorter HRT.

Fig. 2. SA-ABR Performance in Fish Processing Wastewater Fermentation When the OLR Was Increase

20

The 6th International Symposium on Water Environment Systems----- with Perspective of Global Safety(Nov. 29th - Nov. 30th, 2018)

G-Safety
Tohoku University

The Potential Anaerobic Digestion Treatment for Palm Oil Mill Effluent (POME)○Siti Nur Fatihah MOIDEEN1* & Yu-You LI1,2
1

Graduate School of Environmental Studies, Tohoku University, 6-6-06 Aza-Aoba, Aramaki, Aoba-ku, Sendai,
Miyagi 980-8579, Japan.
2
Department of Civil and Environmental Engineering, Graduate School of Engineering, Tohoku University, 6-6-06
Aza-Aoba, Aramaki, Aoba-ku, Sendai, Miyagi 980-8579, Japan.
*E-mail: siti.nur.fatihah.binti.moideen.t3@dc.tohoku.ac.jp

Abstract
Palm oil is primarily used in the developing countries. Palm oil is coming from oil palm trees which can only growin the tropical country. Malaysia, is one of the palm oil main producer in the world after Indonesia. However, thisindustry has contributed to the massive generation of palm oil mill effluent (POME). POME having high content oforganic content and it became abundancies as the industry kept growing. Thus, a promising treatment system isrequired in order to treat POME accordingly and became harmless to the environment. Anaerobic digestion isfavorable treatment since this treatment able to degrade organic matters in POME and emit biogas as the end product.
Generally, 85% of Malaysian palm oil mill adopting conventional anaerobic pond to treat POME, hence, it isrequired to explore advance treatment method since conventional method leads to excessive emissions ofgreenhouse gaseous to the atmosphere. This paper reviews the potential treatment for POME. A part of that, theprospective treatment also reviewed.
Keywords: Palm oil mill effluent (POME), organic content, anaerobic digestion, treatment system

1. Introduction
In 2016, the production of 3.8 tons of oil per hectare has
been produced in 2016. Globally, the palm oil production
increased from 15.2 million tons in 1995 to 62.6 million tons
in 2015. Malaysia is the second largest producer of palm oil
and it is the main leading industry. The production of crude
palm oil is more than 13 million tons per year and covers
about 11% of total plantations land area.
However, the drawback from persistent demand of palm
oil is the generation of vast amount of palm oil mill effluent(POME). As stated by Vijaya et al. (2008), in every tons ofcrude palm oil (CPO), 3.05 tons of POME was generated.
Moreover, in 2015 itself, almost 60.88 million tons of POME
produced by the palm oil industry in Malaysia (Choong et al.,2018).
Basically, Malaysian palm oil industries adapting
conventional treatment plant to treat POME. The
disadvantages of this conventional method are large land area
required and excessive emission of greenhouse gaseous
emitted to the environment. As cited by Poh and Chong(2010) from Ma et al. (1993), 85% of Malaysian palm oil mill
adapting low cost ponding system to treat POME.
There are plenty of published papers discussing about theeffective POME treatment and also it potential in producing
biogas from the degradation process of organic matters in
POME.
Hence, this article is representing general review on POME
production from fresh fruit bunch (FFB), the characteristics
of POME and the potential treatment system for POME
treatment and biogas generation.

2. From FFB to POME: The Process
2.1
Oil Processing
The process of palm oil from oil palm started when fresh
fruit bunch (FFB) were brought to the mill and been sterilized.
During sterilization phase, huge amount of fresh water was
utilized. Then, sterilized fruits were stripped and the fruits
were detached from empty fruit bunch (EFB). The fruits flow
to the digester and converted into homogeneous oily mash.
In pressing phase, the press cake was separated from the
mixture oil, water, debris and other material and discarded as
dirty crude oil.
The solid press cake and palm nuts and fed into depericarper.
The crude oil was clarified and the oil was sifted based on its
density. The oil from top were skimmed off and flows to thenext step. The final CPO is then cooled and stored.
The lower layer of sludge is separated using separator andthe final liquid waste; POME flows to the cooling pond forbiogas production.
2.2
POME Characteristics
The characteristics of POME were varies depending upon
the time of sample withdrawn from the mill. Commonly,
POME having colloidal suspension of 95-96% of water, 0.6-0.7% of oil and 4-5% total solids including 2-4% of
suspended solid (Ahmad et al., 2003).
POME is rich in organic carbon and having BOD more than
20 gL-1. Moreover, POME contains nitrogen compoundaround 0.2 gL-1 and 0.5 gL-1 of total nitrogen.
Table 1: Characteristic of raw POME (source: MalaysianPalm Oil Board, MPOB, 2017)21

The 6th International Symposium on Water Environment Systems----- with Perspective of Global Safety(Nov. 29th - Nov. 30th, 2018)

Parameter
POME (average)pH
4.2
Oil and grease
4000
Biochemical oxygen
25,000demand (BOD)
Chemical oxygen demand
51,000(COD)
Total solid
40,000
Suspended solid
18,000Total volatile solid
34,000
Ammoniacal nitrogen
35(NH3-N)
Total nitrogen750
All values are in mgL-1 except pH

2.3
Anaerobic Treatment of POMEPOME is in high organic properties, thus, anaerobic process
is the most appropriate method of treating POME. Biologicaltreatment has many benefits compared to other method (i.e.
aerobic and facultative process). The advantages of
biological treatment are less energy demand, minimum
sludge accumulation and most valuable point is methane
production from efficient breakdown of organic matter byanaerobic bacteria (Rincón et al., 2006; Iskandar et al., 2018).
Even though by conventional method could generatemethane gas, the liberation of methane gas to the atmosphere
will affecting the ozone layer thus eventually causing
greenhouse effect.
Up to this date, the conventional treatment method is out ofleague and now a new method to treat POME is developed.

No. Publication

25

G-Safety
Tohoku University

Anaerobic membrane bioreactor (AnMBR) is a hybrid
process where membranes are integrated into anaerobic
processes and comes with various advantages such as
produces high quality effluent, less energy consumption withno aeration, less sludge production and bio-methane as byproduct which can be used as energy (Jeong, Hermanowicz
and Park, 2017).
Based on the previous research by Li et al. (2015),
AnMBR removed almost 100% of soluble COD (S-COD)
and about 80% of total COD. On the other hand, reactor
without membrane only 70% of total COD was removed.
Hence, in the future research AnMBR system will beapplied for the treatment of synthetic palm oil mill effluent.
The AnMBR system are as can be seen in Figure 2. The
AnMBR system will be assembled with hollow fiber
membrane. However, main problem related to membrane
system is membrane fouling but this can be encountered byinjecting gas from the continuous stirred tank to themembrane console to remove attached material on themembrane to prevent the membrane fouling.

Figure 2: Schematic diagram of AnMBR for synthetic
POME treatment

20
15
10
5
0

Malaysia

Indonesia

Thailand

Japan

China

Figure 1: Treatment system for palm oil mill effluent(POME)
Figure 1 showing hybrid system studies conducted in the
recent years. These systems were studied since it having lesser
process time and higher efficiency. Continuous stirred tankreactor (CSTR), anaerobic sequencing batch reactor(ASBR),
anaerobic baffled reactor (ABR), up flow anaerobic sludge
fixed-film (UASFF), anaerobic membrane bioreactor(AnMBR), anaerobic filtration, expanded granular sludge
blanket (EGSB) and anaerobic fluidized bed have been studied.
These methods successfully reduced the hydraulic retention
time (HRT) on a laboratory scale.

3. Prospective Treatment for POME

22

4. Conclusions
The palm oil industry has caused an undeniable source ofpollution if it is not well treated. Wise action and regulations
could counter this effect. As the demand increased, the palm
oil industries in Malaysia are expanding, hence, the waste
generation also increasing.
Despite there is anaerobic and aerobic treatment being
used to treat POME, but, it is still not able to achieve thestandard set by the authority. Furthermore, this method also
having many disadvantages such as large area requirement
and longer HRT. On the other hand, the gas releases from thistreatment has causing greenhouse effect.
Although there were many studies has been conducted totreat POME, AnMBR seems to be a promising treatment
method. AnMBR system possibly able to treat POME andresulting to low COD effluent and high biogas production

G7Safety$

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77777$with$Perspective$of$Global$Safety$ $ $ $ (Nov.$29 7$Nov.$30 ,$2018)

Tohoku$University!

Optimization of cod removal of a full scale slaughterhouse wastewater treatment plant using a
combination of uasb and activated sludge processChaimaa Mribet 1, Jiang Wu 2, Hong Chen 2,3, Bo Jiang 2, Yu-You Li1,2
1Graduate School of Environmental Studies, Tohoku University, 6-6-06 Aoba, Sendai 980-8579, Japan.
2Department of Civil and Environmental Engineering, Graduate School of Engineering, Tohoku University, 6-606 Aza-Aoba, Aramaki, Aoba-ku, Sendai, Miyagi 980-8579, Japan.
3 Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province, School of
Hydraulic Engineering, Changsha University of Science & Technology, Changsha, 410004, China.
*E-mail: eeling@utm.my

Abstract
In this case study, a wastewater treatment and biogas production plant performance was investigated. The
influent wastewater comes mainly from a meat factory (50%), poultry feathers (30%) and other sources. The
current treatment process consists of a Dissolved Air floatation unit, followed by an Upflow Anaerobic Sludge
Blanket (UASB) and an activated sludge unit. The investigation involves the analysis and interpretation of thevariations in the performance of the three main treatment units (DAF, UASB and Activated sludge), along withthe treatment efficiency of the whole process. The main objective was to optimize the performance of the plantby increasing the removal efficiency of COD, SS and Nitrogen compounds, while reducing the plant energy
costs.
The results indicate a fluctuation of the treatment efficiency in the different treatment units, mainly due toseasonal changes in temperature, as well as influent flow and composition. The Dissolved Air floatation unit has
increased its performance considerably, reaching 81% of the total COD removal efficiency, while the UASB has
proven to be best performant during summer time. The AS was considered to be the most efficient unit and has
an average removal efficiency of 80%. The maximum biogas Yield obtained was 730 Nm3/day

Keywords: Dissolved-air flotation (DAF), Full-scale slaughterhouse wastewater, Upflow anaerobic sludge
blanket (UASB) reactor, Activated Sludge, COD removal, Methane yield.
Among the different treatment systems, high rateanaerobic reactors have been proposed as good alternativeand effective system because of its numerous advantages
like low initial and operational costs, smaller spacerequirements, high organic removal efficiency and low
sludge production, combined with a net energy benefit
through the production of biogas (Banu et al., 2007; Behera
et al., 2007; Juang and Chiou, 2007; Liu et al., 2010;
Bicheldey and Latushkia, 2010; Karapidakis et al., 2010).
Our case study is a slaughterhouse wastewater treatment
plant that uses both aerobic and anaerobic processes for thetreatment. The objective is to optimize the treatment of thewastewater and reduce energy consumption, focusing on theperformance of the UASB reactor operating conditions. The
effects of the flow rate, the pH, temperature, and theirinteractions on the overall treatment efficiency as well as thebiogas yield are studied.

1.! Introduction
Meat and poultry industries use a huge amount of water.
The wastewater coming out of these factories is mainlycomposed of diluted blood, fat, suspended solids. The meat
processing industry is one of the largest consumers of total
freshwater used in the agricultural and livestock industry
worldwide. It uses 24% of the total freshwater consumed by
the food and beverage industry and up to 29% of that
consumed by the agricultural sector worldwide (Mekonnen
and Hoekstra, 2012; Gerbens-Leenes et al., 2013). And
with the population increase, it is expected that the global
consumption of meat will continue to rise in the future,
resulting in a higher amount of wastewater coming frommeat processing industries that needs to be treated before
being discharged into the environment. With the high blood,
proteins and organic matter concentrations, it ischaracterized as a high strength wastewater and is classified
as one of the most detrimental industrial wastewaters to theenvironment by the United States Environmental Protection
Agency (US EPA) and therefore needs special attention andrequires meticulous treatment.
Anaerobic Digestion is a controlled biological degradation
process, in which stabilization of organic substrates isachieved through the combined action of different microbialconsortia, in the absence of oxygen. The main products ofthis process are biogas (a mixture of CH4, CO2 and othergases in traces) and digested sludge. The former can be used
for energy applications and the latter to potentially improvesoil fertility (Singh et al., 2010).

2.! Materials and Methods
The investigation involves the analysis and interpretationof the variations in the performance of the three main
treatment units (DAF, UASB and Activated sludge), alongwith the treatment efficiency of the whole process.
Monitoring involves temperature, pH, DO, ORP, biogas
production, daily flow and pressure which are measured insitu by a plant operator. COD, SS, VSS, MLSS, MLVSS,
NH4+, NO2- and NO3- are analysed in the laboratory in aweekly basis. The data analysis from the plant is from23

G7Safety$

The$6th$International$Symposium$on$Water$Environment$Systems$th$th

77777$with$Perspective$of$Global$Safety$ $ $ $ (Nov.$29 7$Nov.$30 ,$2018)
January 2017 to December 2017, while laboratory analysis
period is from July 2017 to December 2017. CODCr
measurements were carried out according to APHA Standard
Methods, SS measurement were analysed according to 2540
D. The nitrogen compounds (NH4+-N, NO2−-N and NO3−N) were analysed by capillary electrophoresis (Agilent 7100).
Protein was analysed by Lowry method. Volatile fatty acids(VFA) were analysed by gas chromatography (Agilent 6890)
and pH was measured by an Orion 370 PerpHecT meter(Thermo Fisher Scientific, USA). The biogas composition
including CH4, CO2 and N2 was measured using a gas
chromatograph (Shimaszu GC-8A, Japan) equipped with athermal conductivity detector.

3.! Results and Discussion
An important factor influencing the efficiency of thewastewater treatment process is the intensity and variabilityof inflows and their characteristics. All the results obtained
and graphs have a linear correlation with the concentrationsof the influent wastewater.
From a whole scale level, the plant’s removal efficiency
has got an average of 96%, where, 79% of the removal
occurs in the activated sludge system that removes most of
the COD, while the UASB’s average removal rate is 52%.
The dissolved air floatation system has an average
efficiency of 40%. The latest data suggests a remarkableincrease of the DAF efficiency and a decrease performanceof the Activated Sludge unit.
The data shows a direct correlation between temperatureand the performance of the plant. The unique weather of theregion in which the plant is located contributes largely inthe performance, affecting energy consumption that tends torise during winter. the UASB is being operated undermesophilic conditions, and its treatment efficiency was at its
best during summer time. Whereas in winter time, as thetemperatures tend to decrease significantly, the TCOD
removal rate decreased and biogas production rate wasunstable. pH levels have also been affected, and dropped toless than 6.5. The period of April and May the COD appears
to be easily removed by the UASB.
The results herein show the importance of primary
treatment to decrease substantially the organic load to betreated in combined systems, and the role of dissolved air
flotation in conjunction with anaerobic and aerobic processes
for the treatment of meat industry wastewater in achieving
low discharge parameters of organic persistent pollutants.
DAF

UASB

Conclusions
The overall results suggest that due to seasonal changes,
it is best to first alternate between the UASB unit andActivated Sludge unit depending on the temperature.
During hot weather opt for an anaerobic treatment, and use
aerobic treatment during cold weather. As the latest data
indicates that more than 90% of the COD treatment occurs
in both DAF and UASB units, it could be suggested torestraint the treatment to these two units; hence lowering
energy consumption, and saving money which could be
used in recovering the biogas that is still kept unexploitedand released into the atmosphere. A further study on cost
analysis of the WWTP could estimate the profitability andfeasibility of this scenario, comparing it with the previous
one where the treatment process was using the DAF unit
and Activated Sludge unit.

4.! References
Alvarez(R,(L.(G.((2008).(Semi6continuous(co6digestion(of(solid(slaughterhouse(waste,(manure,(and(fruit(and(vegetable(waste.(Renew(Energy(33,(7266734.(
Bustillo6Lecompte,( C.( F.( (2015).( Slaughterhouse(
wastewater(

characteristics,(

treatment,(and(management( in( the( meat( processing( industry:( A(review( on( trends( and( advances.( .( Journal( of(Environmental(Management(,(161,(287–302.(
Ciro( Fernando( Bustillo6Lecompte,( M.( M.( (2017).( ,(Treatment(of(actual(slaughterhouse(wastewater(by(combined( anaerobic–aerobic( processes( for( biogas(
generation( and( removal( of( organics( and( nutrients:(
An(optimization(study(towards(a(cleaner(production(in(the(meat(processing(industry.(Journal(of(Cleaner(
Production,(Volume(141,(2786289.(
D.A.( Lovett,( S.( T.( (1986).( Dissolved( air( flotation( for(AS

abattoir(wastewater(.(Water(Research,(20(,(4216426.(

100%
TCOD removal rate %Tohoku$University!

Ihsan( Hamawand,( A.( G.( (n.d.).( A( Critical( Review( on(80%

Processes(and(Energy(Profile(of(the(Australian(Meat(

60%

Processing(Industry(.(
MIRANDA,(L.(A.,(HENRIQUES,(J.(A.,(&(MONTEGGIA,(

40%

L.( O.( (Dec.( 2005).( A( full6scale( UASB( reactor( for(20%

treatment( of( pig( and( cattle( slaughterhouse(

0%
7

9

11 1 3 5
Months (2017-2018)7

wastewater(with(a(high(oil(and(grease(content.(Braz.(

9

J.(Chem.(Eng.,(São(Paulo(,(v.(22,(n.(4,(6016610.(

Figure(1:(Total(COD(removal(efficiency(by(the(treatment(process(((24

G-Safety

The 6th International Symposium on Water Environment Systems----- with Perspective of Global Safety(Nov. 29th - Nov. 30th, 2018)

Tohoku University

Using Microbial Quantification for Water, Sanitation and Hygiene (WASH) Intervention
Study in Rural Nepal
1,3

2

3

3,4

1

Sital UPRETY *, Bipin DANGOL , Mohan AMRASIRI , Daisuke SANO Thanh NGUYEN
1Department of Civil and Environmental Engineering, University of Illinois at Urbana Champaign, Urbana, Illinois,
USA
2
Environment and Public Health Organization (ENPHO), Kathmandu, Nepal
3
Department of Frontier Science for Advanced Environment, Tohoku University, Sendai, Japan
4
Department of Civil and Environmental Engineering2, Graduate School of Engineering, Tohoku University, Sendai,Japan*E-mail: uprety2@illinois.edu

Abstract

Poor water quality and limited sanitation is responsible for approximately 3.5 billion diarrheal episodes and1.87 million diarrhea-related deaths in children under five each year, mostly occurring in the developing world.
Recent outbreaks of water-related diarrheal disease following extreme weather events such as those in Indonesia
and Haiti in 2004 and 2010, respectively, demonstrate the interdependent relationship between climate change,
water, and public health. Water, Sanitation and Hygiene (WASH) intervention in low-income countries is mostcommon way to improve the water quality and sanitation in the household. This study focused on quantifying
bacterial pathogen in water and surrounding environment. Two villages of similar climatic condition and similarsocioeconomic condition but one going through extensive WASH intervention for next two years and one without
any planned intervention, were selected for this study. We hypothesize that over the same study period, village
with intervention will have a decreases pathogen load in water and household, while pathogen load will notchange drastically in the village with no intervention. To test this hypothesis, twenty-four pathogens were selected
and the assays were designed for the targeted pathogen using microfluidic quantitative polymerase chain reaction(mfqPCR). Results indicated the deteriorated sanitation practices in addition to contaminated drinking water.

Keywords: WASH, Intervention, Pathogen, microbial quantification, Nepal
districts is by improving Water, Sanitation and Hygiene(WASH) behavior and practices in the household. This isbeing done by extensive community level WASH
intervention through capacity building, water quality
testing and monitoring and promotion and demonstrationof WASH solutions. Dhading, one of the districts in the
SUAAHARA project, was selected for the study. Tenhouseholds each from two villages were selected from
Dhading district, one going through extensive WASH
intervention (V1) and one village where no intervention
activities are planned. Drinking water (DW), cleaningwater (CW), handwash (HW) and swab samples werecollected from each household. For swab samples, toilet
handle (TH), utensil swabs(U) and water vessel (WV)swab samples were collected. Three rounds of sampling
were conducted from June-July 2017 in these two villagesof Dhading district.

1. Introduction
Waterborne diseases pose a major threat to human healthrisk all over the world causing 1.5 to 15 million deaths
every year . Like several low- and middle – income
countries, Nepal faces several problems in water,
sanitation and hygiene from cities to villages. The
objectives of this study were to identify and quantify
pathogenic genes commonly associated with humanintestinal infections in drinking water, cleaning water andenvironmental samples over three months in villages in
Nepal. Water samples were collected from drinking
water, drainage channels, and surface water in Dhading,
Nepal from May 2017 to August 2017. Target genes
commonly representative of pathogenic organisms were
quantified over time at multiple water sources. To the bestof our knowledge, this is the first study to longitudinallymeasure waterborne pathogen presence in multiple water
source types in a developing region going under water,
sanitation and hygiene intervention.

2.2
Sampling protocol
Two-liter water samples (DW, CW and HW) werecollected at each sampling site in sterile Whirl-pak£sampling bags and were processed within 24 h ofcollection. Thereafter, water samples were sequentiallyvacuum filtered through a 1.6-µm-pore glass fiber filter(Millipore, Ballerica, MA) followed by a 0.45-µm-pore
nitrocellulose filter placed in a 47-mm filtration funnel.
filtrate were sterilized prior to each sample filtrationaccording to EPA guidelines . At UIUC, filters were

2. Materials and Methods
2.1
Location
SUAAHARA is a $63 million integrated program
dedicated to improving the health and nutrition status of
women and children in 40/75 districts of Nepal. One of
the strategies to improve the nutrition in these targeted

25

The 6th International Symposium on Water Environment Systems----- with Perspective of Global Safety(Nov. 29th - Nov. 30th, 2018)

G-Safety
Tohoku University

stored at -80°C until DNA extraction. BD Liquid Eswabs(El Paso, TX) were used for sampling toilet handles,
utensils and water vessels and was stored at -20°C until
DNA extraction.
2.3
Simultaneous quantification using Biomark:24 pathogens of interest were selected for quantification
based on diseases incidences in Nepal and validity of
assays. Assay designs for all target genes were adaptedfrom previous studies to detect Enterococcus, two genesof General E. Coli, Enteropathogenic E. Coli, two genesof Shiga-toxin producing E. Coli, two genes of Shigellaspp, Shigella flexneri, two genes of Camopylobacter
jejuni, Campylobacter lari, two genes of Salmonella,
Colostridium prefringes, Listeria monocytogenes, twogenes of Vibrio cholerae, Adenovirus Type 40/4, Giardia
lambia and Cryptosporidium parvum. NH8B was used as
Internal Amplification Control (IAC) in the experiment.
Forward, reverse primers and standards for all assays wereobtained as Custom DNA Oligos (Integrated DNA
Technologies, Coralville, IA). Probes were obtained from
Universal Probe Library (UPL) (Rosche, Basel,
Switzerland). Prior to enumeration by MFQPCR(Microfluidic Quantitative Polymerase Chain Reaction),all DNA samples and standard pool dilutions underwent
standard target amplification (STA) PCR to increase
template DNA yields. mfqPCR was performed in a
Biomark HD Real-Time PCR (Fluidigm) using thefollowing thermal conditions: 70qC for 30 min, 25qC for
10 min, 95qC for 1 min, followed by 35 cycles of 96qC for
5 sec and 60qC for 20 sec.

Figure 1 Swab sample quantification in copies/swab

3. Results and Discussion
Figure 1 shows the quantification of pathogens in swab
sample in copies/swab in three sampling period andFigure 2 shows the quantification of water samples incopies/L in the same sampling period.
Overall results showed high presence of several
enteropathogenic bacteria. Presence was seen higher
in swab samples compared to water samples. Whenswab samples were investigated in detail, utensil
samples seemed to be of highest risk compared towater vessel sample and toilet handle samples
indicating the need to wash utensils in a proper way
between meals to avoid any potential enteric diseases.
In water samples, drinking water samples were thecleanest but cleaning water samples and handwashingsamples showed the potential of being of high.
Handwash samples especially had multiple pathogen
detected with higher concentrations, again indicating
deteriorated sanitation practices in household. Better
handwashing practices would be essential to avoid therisk of getting sick from diarrheal diseases.

Figure 2 Water sample quantification in copies/L

4. Conclusions(1) Several pathogenic bacteria detected with higher
concentration like Salmonella and Legionalla which canbe life threatening to individuals(2) Swab samples showed higher detection andconcentration indicating deteriorated sanitation conditionsin the households.
(3) Intervention strategies targeting behavioral change insanitation might be more impactful
5. Acknowledgments
The authors would like to thank National Science Foundation
International Research Experience for Undergraduates (NSFIRES) Grant and Japan Society for Promotion of Sciences(JSPS) Fellowship.

26

G-Safety

The 6th International Symposium on Water Environment Systems----- with Perspective of Global Safety(Nov. 29th - Nov. 30th, 2018)

Tohoku University

Nitrogen Removal Performance of an Anammox Membrane Reactor Operated at 25℃ with
Increasing Sludge Loading Rate
○Ying Song1,2, Shaohua Sun1, Wei-kang Qi1,2*, Yu-You Li2*
1

2

School of Chemical Engineering, Beijing University of Chemical Technology, Beijing, 100029, China
Department of Civil and Environmental Engineering, Graduate School of Engineering, Tohoku University,
980-8579, Sendai, Japan*E-mail: song.ying.q2@dc.tohoku.ac.jp

Abstract

An anaerobic ammonium oxidation (anammox) membrane bioreactor operated at 25 °C was used to investigate
nitrogen removal under different sludge retention time (SRT) and nitrogen loading rate (NLR). Specific anammox
activity (SAA) tests were performed to evaluate the effects of ammonium and nitrite on the anammox sludge.
Over an SRT of 500 d, the content of volatile suspended solids (VSS) gradually increased from 125 mg/L to 5590
mg/L, and the maximum nitrogen removal rate (NRR) reached 1.18 kg N/m 3/d. When the SRT was decreased to
50 d, the sludge concentration declined gradually, which necessitated an increase in the sludge loading rate (SLR),
and the maximum sludge nitrogen removal rate (SNRR) of 0.67 g total nitrogen (TN)/g VSS/d was achieved at
this time.

Keywords: Anammox, membrane bioreactor, nitrogen removal, high sludge loading rate(DO) concentration in the reactor were controlled at 7.8– 8.2
and < 0.05 mg/L, respectively. The specific anammox
activity (SAA) tests were carried out in 400 mL glass bottles
with 200 mL biomass taken from the reactor. The batch test
bottles were maintained at a temperature of 25 °C in a waterbath. The initial total nitrogen (TN) concentration was 200
mg/L with a NO2- -N to NH4 + -N ratio of 1.32.

1. Introduction
The anaerobic ammonium oxidation (anammox) process isa novel and cost-effective nitrogen removal biotechnology.
The method is suitable for the treatment of low C/N
wastewater and has low energy consumption levels, norequirements for an external carbon source, and negligible
sludge production. Use of a membrane allows for complete
separation of solids and liquids in the suspended sludge.
Therefore, compared to other reactors, MBR is more suitable
for retaining sludge and preventing sludge loss and thesuspended sludge contributes greatly to the anammox
activity. The optimum temperature for the anammox process
is about 30-40°C. However, the temperature of wastewater
is generally below 30 °C. Thusly, achieving the required
effluent quality at temperatures of 10 °C to 25 °C represents
one of the main current challenges towards realizing fullscale application of the technology.
In this study, a completely stirred anammox membranebioreactor was used to investigate nitrogen removal at 25 °C.
Low anammox sludge concentration（125mg-VSS/L）was
seeded into the reactor to investigate the long-term anammox
performance under different operation conditions. The stable
and inhibited phases during the long-term process were
investigated with increasing sludge loading rate. These
results are expected to contribute to the development ofstrategies to maintain stable operations in anammox reactors
at 25 °C.

Figure 1 Schematic diagram of anammox MBR andbatch test setup

2.2
Analyses
The concentrations NO2- -N to NH4 + -N and NO3- -N were
measured in accordance with APHA. The TN was calculated
as the sum of the three kinds of nitrogen compounds. The
measurement of suspended solid(SS) and volatile suspended
solids (VSS) was according to APHA. The sludge nitrogen
removal rate (SNRR) was calculated as an indicator of the
anammox activity during long-term operations as shown in
Eq. (1), in which NRR represents nitrogen removal rate.

2. Materials and Methods
2.1
Experimental setup and operating conditions
As shown in Fig. 1, a cylindrical anammox MBR with aworking volume of 10 L was designed. A submerged hollow
fiber membrane module with a pore size of 0.1 µm was used
to retain all the bacterial biomass in the MBR. The reactor
was operated over the course of 800 d with a hydraulicretention time (HRT) of 12 h. The pH and dissolved oxygen

SNRR = NRR/VSS

3. Results and Discussion
3.1

27

Reactor performanceEq. (1)

The 6th International Symposium on Water Environment Systems----- with Perspective of Global Safety(Nov. 29th - Nov. 30th, 2018)

G-Safety
Tohoku University

sludge activity under a high SLR. However, the reactor
performance still deteriorated with the increased SLR. Until
the NRR decreased to a minimum of 25%, the NLR was
gradually reduced to 0.22 kg N/m3/d to recover the reactor
performance. During this period, the abnormal phenomenon
mentioned above appeared again. From the 640th day to the729th day, the NLR was increased and the TN removal
efficiency was maintained at 80% with an RS and RP of 1.379
and 0.380, respectively. From the 730th day to the 800th day,the influent concentration was continuously increased while
the nitrogen removal rate remained at 80%.

3.2

Sludge characteristics

Figure 3 Long-term capacity of anammox sludge
The concentrations of VSS and SS in the anammox MBR
at different periods were monitored continuously throughout
the operating process from the 300th day onward, as shown in
Figure 1. The maximum concentration of VSS was 5590
mg/L with an SS of 19,565 mg/L at the 453rd day causing a
VSS/SS ratio of 0.28. From the 505th day onward, the SRT
was shortened from 500 days to 50 days and theconcentrations of VSS and SS decreased simultaneously and
immediately. The VSS/SS increased from 0.3 to 0.8 with theshort SRT, thus indicating that the amount of biomass in the
total sludge increased when metabolites were discharged
from the reactor. As shown in Figure 3, The SNRR was
calculated as an indicator of the anammox activity over the
long-term operation period, which was in accordance with
the nitrogen removal efficiency, VSS and NLR. The highest
SNRR of 0.67 g N/g VSS/d with an SLR of 0.8 g N/g VSS/d
was obtained at the 740th day. For in-depth study of the
anammox activity, batch tests were performed by using
sludge samples from the reactor at different stages during thelong-term process. the comparisons with SAA and SNRR
provided more information and allowed us to adjust the
influent concentration under different periods. For example,
when the SAA was higher than SNRR, the potential removal
capacity was higher than that demonstrated during the longterm operation, which could increase the NLR. When the
SAA was lower than SNRR, the anammox reactor might
have been in an over-loading state.

Figure 2 Long-term performance of the anammox MBR
From the beginning of the operations to the 160th day, theconcentration of effluent NH4 + -N and NO2- -N fluctuated. RS
and RP were highly unstable and irregular during this period.
After the 161st day, the effluent NH4 + -N and NO2- -N
concentrations decreased simultaneously, which wasconsistent with nitrate production. At the 190th day, the NH4+-N, NO2- -N, and TN removal efficiency reached 90%,
100%, and 85%, respectively, which indicated that the startup of the anammox MBR was successful. Starting on the
189th day, the influent ratio of NH4 + -N and NO2- -N was
modified to the theoretical value of 1.32. The TN removal
efficiency was over 80% and the ratio of consumptive NH4 +-N and NO2- -N was close to the theoretical value of 1.32.
However, the productive NO3- -N was less than theoreticalvalue, which may have been the result of accompanying
biological reactions like denitrification. Starting on the 278th
day, the nitrogen loading rate (NLR) was increased from 0.45
kg N/m3/d to 0.86 kg N/m3/d and correspondingly the TN
removal efficiency decreased slightly from 88.01% to77.48%. However, when the NLR reached 1.14 kg N/m3/d,
the TN removal efficiency was only 70%, which is obvious
indicative of substrate inhibition and then NLR was adjusted
to 0.59 kg N/m3/d according to capacity. From the 324th day
to the 416th day, increasing the concentration of the influent
TN from 300 mg/L to 600 mg/L led to a stepwise increase in
the NLR from 0.6 kg N/m3/d to 1.26 kg N/m3/d, whereby the
TN removal efficiency was maintained at 80%. Although thereactor experienced twice abnormal increase of the effluent
concentration of NH4 + -N and NO2- -N, which led to forceful
reduction of NLR to recover the performance. The highsubstrate concentration with low sludge concentrationnecessitated an increase in the sludge loading rate (SLR),
which led to instability in the reactor. On 535th day, NLR wasup to 0.9 kg N/m3/d, the TN removal efficiency was dropped
to 20%. The long-term inhibition without application ofadjustment strategies was then investigated to document the

4. Conclusions
In this study, an anammox MBR was operated for 800
days, and the anammox process was successfully started
up at 25℃ over a period of 180 days with a low seedsludge concentration of 125 mg VSS/L. The maximum
NLR of 1.35 kg N/m3/d corresponded to an NRR of 1.18
kg N/m3/d, and the highest SLR of 0.80 g N/g VSS/dcorresponded to an SNRR of 0.67 g N/g VSS/d. Several
sudden increases of the effluent concentration over a
period of a few days occurred during the long-term
process, and this phenomenon requires further study in the
future.

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Tohoku University

Successful Operation Performance of Partial Nitritation and Anammox Reactor Treating
Low-Strength Ammonium Wastewater at Room Temperature○Yujie Chen1, Rong Chen1,2, Yasuyuki Takemura1, Jiayuan Ji1, Yu-You Li1
1

Department of Civil and Environmental Engineering, Tohoku University, Sendai, Miyagi 980-8579, Japan
2
Xi’an University of Architecture and Technology, Xi’an, Shaanxi 710055, P.R.C.
*E-mail: gyokuyu.ri.a5@tohoku.ac.jp

Abstract

The treatment effect of low-strength ammonium wastewater at room temperature by the partial
nitritation/anammox (PN/A) process is still far from satisfactory. This study realized the stable treatment
performance of 50 mg/L ammonium wastewater with a granule-based PN/A reactor. In the long-term operation,
the nitrogen removal efficiency of 71.8±9.9% was obtained stably under a relatively short hydraulic retention
time (HRT) of 2 h. Most of the granules were in a size range of 265-536 μm according to the analysis on thephysicochemical properties of the granules. The three-layer structure of the granules was found: the majority of
the anammox bacteria agglomerated and created a granule kernel, and ammonium oxidizing bacteria (AOB)
formed an outer thick rim and a little nitrite oxidizing bacteria (NOB) co-exist with AOB in the surface layer. The
tests showed the activity of NOB was well limited through dissolved oxygen (DO) regulation.

Keywords: Anammox; AOB; partial nitritation; low-strength
unit for PN/A, while an inclined-plate zone with 0.4 L ofvolume was employed to serve as sedimentation unit where
the sediments can be returned to the reaction unit under thedriving force of air lift. The temperature in the reactor wasmaintained at 25oC with a water jacket and pH was 7.6 ± 0.3
during the entire operation.
As Figure 2 shows, the experiment was operated
continuously for 250 days, which can be divided into threestages in different conditions: the start-up period (Stage I),
the hydraulic retention time (HRT) decreasing period (Stage
II), and the stable operation period (Stage III).

1. Introduction
The nitrification-denitrification process in the conventional
activated sludge system needs much energy mainly for theaeration to oxidize the ammonium. The discovery of
anammox in 1990s makes it possible to save energy andreduce cost by autotrophic nitrogen removal. Furthermore,
the application of PN/A process can further simplify thenitrogen removal procedure through the synergy between
nitritation by AOB, oxidizing part of NH4+ to NO2-, andanaerobic ammonium oxidizing by anammox bacteria, whichconvert NH4+ and NO2- to nitrogen gas in the same reactor as
the following equation.
NH4++0.85O2→0.11NO3-+0.445N2+1.13H++1.43H2O
However, the application of mainstream PN/A remains inslow progress, due to the unstable partial nitritation andinactivation of anammox bacteria resulting from low
nitrogen concentrations, oxygen inhibition and temperatures
lower than its optimum temperature of about 35oC. In
addition, the long doubling time of bacteria makes it difficult
to offset the run off of microorganisms, which can be solved
by using the granulation of slow-growing anammox bacteria
endowing excellent biomass retention and settleability.
Additionally, studies about treating low-strength wastewater(nitrogen concentration < 100 mg/L) with the granules,
whose size are smaller than 1 mm, are still scarce.
This study focused on the efficiencies of PN/A process withthe co-immobilization of AOB-anammox bacteria fortreating the low-strength ammonium wastewater by using a
granule-based reactor at room temperature.

Figure 1 Diagram of PN/A process

3. Results and Discussion
3.1
Operation Performance
During the start-up period, the influent was 100 mg NH4+N /L at a HRT of 6 h to provide an adaptive phase for theseed sludge.
In Stage II, the NH4+-N concentration in the influent was
reduced to 50 mg/L and the HRT was shortened stepwise to
3h, 2h and 1 h, resulting the increase of NLR from 0.8 up to
2.4 g N/L/d. As Fig. 2A shows, aeration rates of 0.3 L/min at
an HRT of 3 h, and 0.35 L/min at an HRT of 2 h resulted in

2. Materials and Methods
A lab-scale integrated reactor was developed toconcurrently achieve PN/A, sedimentation and sludge
circulation. As shown in Figure 1, an air-lift completely
mixed zone with 2 L effective volume was used as reaction

29

The 6th International Symposium on Water Environment Systems----- with Perspective of Global Safety(Nov. 29th - Nov. 30th, 2018)a DO range of 0.06-0.17 mg/L and 0.06-0.21 mg/L
respectively. At the two HRTs, more NO3--N was found thanNH4+-N in the effluent with little NO2—N remaining (Fig. 2B).
As a result, the average total nitrogen (TN) concentration in
the effluent was 13.5 mg/L and average nitrogen removal
efficiency was 72.5%. The stable performance was disrupted
when the HRT was decreased to an extremely short HRT of
1 h. Firstly, a lot of NH4+ was not consumed and remained in
the effluent. Then the aeration rate was stepwise increased to
0.4 and finally to 0.6 L/min to enhance the oxidization of
NH4+, and the DO increased accordingly, gradually
approaching 0.6 mg/L. Therefore, the NH4+ was reduced
significantly in the effluent with the augment of NO3- at thesame time, which reached more than 30 mg/L in the later
period of HRT 1 h. Therefore, TN in the effluent increased toan average of 36.1 mg/L and the nitrogen removal
efficiencies were down to an almost 29.3%.
In Stage III, the HRT was returned to 2 h and the aeration
rate was lowered to 0.35 L/min, causing an average DO
concentration of 0.13 mg/L. As a consequence, the TN in theeffluent decreased to approximately 15.9 mg/L and remained
stable from the 167th day on and the nitrogen removal
efficiency of 71.8±9.9% was gotten for over 80 days. Finally,
the NH4+-N, NO2--N and NO3--N concentrations in theeffluent averaged 5.65, 0.72 and 7.61 mg/L, respectively,
which indicated a stable performance.
In conclusion, according to the performance at the HRT of
1h, the activity of anammox bacteria was deemed to be
inhibited when the DO concentration was more than 0.5
mg/L. Furthermore, the relatively high DO content could
boost NOB overgrowth which outcompeted anammox
bacteria by consuming NO2-. In addition, a large portion of
the granules was easy to be washed out from the reactor when
the HRT was extremely short, which resulted in the further
deterioration of the reactor performance.

G-Safety
Tohoku University

According the results of physicochemical properties test,
most granules are elliptical with clear outlines and bright red.
The mean size and median size of the granules wererespective to be 338.3 μm and 388 μm, which were muchsmaller than those formed in many fixation-growth granule
reactors. In addition, more than 50% of the micro granules
were in size range of 265-536 μm.

3.3
Spatial AOB-anammox association
The spatial distribution of AOB and Anammox bacteria in
the granules was visualized by Fluorescent in situ
hybridization (FISH). Four images are shown in Figure 3. In
these pictures, all of the bacteria are shown in blue, while thespecific anammox and AOB population are respectively
indicated in red and green. Fig. 3A and B show the images of
the granule clusters, and Fig.3C and D shows the images of
one granule. It can be concluded that anammox bacteria and
AOB were the two most dominant bacteria in the granules
and anammox bacteria accounted for an average 37.7% of thetotal bacteria, while AOB accounted for 34.7%. Furthermore,
the FISH test illustration unveiled the spatially syntrophic
co-immobilization of the two most important bacteria,anammox bacteria and AOB, within the granules: amajority of the anammox bacteria agglomerated andcreated a kernel of the granule with AOB and a little NOB
formed an outer thick rim, which established a three-layer
structure.

Fig. 3 FISH analysis of AOB and anammox bacteria in
granules4.
Conclusions
The results of this study were concluded as below:
1) The PN/A reactor successfully achieved the treatmentof low-strength ammonia wastewater with nitrogen
removal efficiencies of 71.8±9.9% at a short HRT of 2 h.
2) The size of the granules formed in the reactor were
measured and the mean size was 338.3 μm.
3) The special structure of AOB and anammox bacteria
could be revealed by FISH illustration.
Fig. 2 Reactor performance

3.2

Physicochemical Properties

30

The 6th International Symposium on Water Environment Systems----- with Perspective of Global Safety(Nov. 29th - Nov. 30th, 2018)

G-Safety
Tohoku University

Effective post treatment of methane fermentation effluent by using two-stages system
combined with BOD oxidation and anammox.
○Chenglei XIE1*, Yu QIN1, Yan GUO1 & Yu-You Li1
1

Department of Civil and Environmental Engineering, Graduate School of Engineering, Tohoku University, 6 6-06 Aramaki Aza Aoba, Aoba-ku, Sendai, Miyagi, 980-8579, Japan*E-mail: xie.chenglei.q6@dc.tohoku.ac.jp

Abstract
Owing to biogas plant is an effective measure to manage food waste and recover energy, its number in operation
is increasing yearly all over Japan. During the biogas fermentation process, volatile fatty acids and alcohols often
accumulate in the digestive liquid. Also the concentration of ammonia nitrogen in digestive liquid is about 100040000mg/L. The accumulation of volatile fatty acids will inhibit methane production amount. And nitrogen should
be removed from the discharged water. In order to analyze the influence of volatile fatty acids on the fermentation
process, a biogas plant located in Sendai was chosen to study. A set of pilot scale experiment device combines
BOD oxidation and anammox was installed in the downstream of the drainage port of the plant’s sewage treatment
facility. The aim of this device is to remove the organics and nitrogen from the methane fermentation effluent.
Biogas samples from each tanks and digestive liquid samples in this plant and the pilot scale experiment device
were taken regularly. Then these samples were analyzed in our laboratory. The results show both fermentation tank
No. 2 and No. 4 were acidulated. The pH average of these two tanks were 5.42 and 4.99 respectively. While pH
average of tank No. 1 and No. 3 were 7.85 and 7.66 respectively. As a result of acidification, the biogas producedby tank No. 2 and No. 4 contained approximately 8% hydrogen. The average methane content of all tanks below
50%. The average ammonia nitrogen concentration of these four tanks were 4124, 2772, 3089, and 2379 mg/L
respectively.
Keywords: Methane fermentation, sewage effluent, BOD oxidation, anammox

More and more cases combining laboratory conclusions withindustrial applications are burgeoning around the earth.
In addition to analyzing this plant operation condition, we
conduct a pilot scale experiment to remove nitrogen by using
two-stages system combined with BOD oxidation andanammox in this plant. Because the anammox bacteria arehighly sensitive to environmental conditions, we use the
BOD acidification tank of this device to remove as many
inhibitors as possible, so that anammox tank can receive astable water supply quality.
Since the pilot scale experiment is still in its initial stage,
this study mainly focuses on the operation of the methane
fermentation plant at present.

1.
Introduction
Although the amount of food waste generated in Japan rises
to approximately 20 million tons per year, the recycling rateis as low as about 27.0%. Due to measures to promote
recycling of food waste such as the Food Recycling Law and
the Feed-in tariff Law, the number of biogas plants inoperation is increasing yearly. During the decade from 2000
to 2009, more than one hundred plants had been built. As of
2015, the total number of biogas plants is about 150.
During the biogas fermentation process, volatile fatty acids
and alcohols often accumulate in the digestive liquid. The
accumulation of volatile fatty acids will inhibit methane
production amount. In order to analyze the influence ofvolatile fatty acids on the fermentation process, a biogas plant
located in Sendai was chosen to study.
The concentration of ammonia nitrogen in food waste
fermentation plant’s digestive liquid is about 100040000mg/L generally. This nitrogen should be removed fromthe discharged water. The anammox bacteria use nitrite as theelectron acceptor and ammonium as the electron donor toallow for the removal of ammonium from wastewater in theabsence of oxygen and organic matters. Since the anaerobic
and autotrophic nature of anammox bacteria permits
significant reductions in energy consumption and organiccarbon investments for biological nitrogen removal, theapplication potential of the anammox process has been
attracted more attention in recent years. By operation inlaboratory, many facts such as substrate, organic matter,
sulfide, salinity, and other nutrient salts can have negative
effects on anammox bacteria activity have been concluded.
Among the inhibitors, the substrate with too highconcentration is the most common and important factor.

2. Materials and Methods
2.1
Brief introduction of biogas plant
The target biogas plant is called J-Nex Corporation(abbreviated as JNEX), which is located in the northern partof Sendai city. It processes food waste collected as industrial
waste with processing capacity around 48 tons per day. Input
materials consist of various kinds of food wastes discharged
from restaurants, hotels, and food factories. Typically, wastes
discharged from the former include vegetables, fruit scraps
and leftovers; while the latter contains animal and vegetable
residues, waste drinks and dairy products. It needs to be
pointed out that waste sludge and grease trap sludge
discharged from food factories were treated as sludge. The
biogas produced in this plant was used to generate electricenergy, while dehydrated sludge was made into organic
fertilizer.
This plant consists of four methane fermentation tanks, adigestive liquid tank. While the pilot scale experiment device

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The 6th International Symposium on Water Environment Systems----- with Perspective of Global Safety(Nov. 29th - Nov. 30th, 2018)

Tohoku University

consists of raw water tank, BOD oxidation tank, post
treatment water tank and anammox tank. The inlet of raw
water tank is located in the downstream of the waste water
treatment facility of this plant.

2.2
Samples collection and analysis
Samples were collected in Monday, Wednesday and Friday
of per week. Among them, biogas samples were collected byusing a gas pump blowing gas into air bags from the tank No.
2, No. 3 and No. 4. Digestive liquid samples were collectedfrom the top, the middle and the bottom of each methane
fermentation tank. Since blockage accidents had happened tothe bottom sampling nozzles of tank No. 1 and No. 3,
corresponding samples were absent.
All samples were brought to our laboratory in less than 3
hours after they were collected. Biogas samples wereanalyzed by two Gas Chromatograph (GC-8A Series,
SHIMADZU, JAPAN). Digestive liquid samples wereanalyzed to obtain pH, alkalinity and concentration ofammonia nitrogen.

(b)

3. Results and Discussion
Figure 1 shows the pH trend of each fermentation tank.
From the results, we can know fermentation tank No. 2 andNo. 4 were acidulated. Among them, tank No. 2 seems to be
going to further deteriorate. Figure 2 shows the biogas
composition variation trends of each fermentation tank over
time.

(c)
Figure 2: Biogas composition of each fermentation tank
The average pH, average content of methane, hydrogen andammonia nitrogen of these four methane fermentation tanks
were as shown in Table 1.
Table 1: Average pH, content of methane, hydrogen andammonia nitrogen in each tank
Tank No.

1

2

3

4

7.85

5.42

7.66

4.99

CH4(%)

34.34

46.36

25.18

H2(%)

6.47

4.22

9.59

2772

3089

2379

Average value
pH

Figure 1: Trend of pH in each fermentation tank

NH4-N(mg/L)

4124

From table above, we can know methane content of biogas
produced in these two tanks decreased greatly compared to anormally operating tank and hydrogen content approximately
to be 8%. Since hydrogen has bad impact on the generator
boilers during its combustion process, this plant’s power
generation facility has to suspend operation. It is suggested
that alkaline substance such as caustic soda be added into
fermentation tank No. 2 and No. 4.

4. Conclusions(1) Fermentation tank No. 2 and No. 4 were acidulated
and alkaline substance should be added into them.
(2) Methane content of biogas produced in this plant’s
three tanks significantly lower than normally operating
methane fermentation tank, which will do great harm togenerator biogas combustion boiler.

(a)

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The 6th International Symposium on Water Environment Systems----- with Perspective of Global Safety(Nov. 29th - Nov. 30th, 2018)

G-Safety
Tohoku University

Simultaneous Nitrogen and Phosphorus Recovery using an Anammox Expanded Bed Reactor
at Low Temperature○Yuanfan ZHANG1*, Haiyuan MA1, Yi XUE2, Yu-You LI1

1Department of Civil and Environmental Engineering, Graduate School of Engineering, Tohoku University, Sendai, Miyagi
980-8579, Japan.
2Graduate School of Environmental Studies, Tohoku University, Sendai, Miyagi 980-8579, Japan.
*E-mail: zhang.yuanfan.s1@dc.tohoku.ac.jp

Abstract

High concentrations of nitrogen and phosphorus treatment contained in the digester liquids becomes a big issue inrecent years. In this study, the concept of crystallization reactor used for side-stream phosphorus recovery wasborrowed to be integrated with anammox reactor. This study was aimed at the performance of simultaneous nitrogen
removal and phosphorus recovery in an anammox expanded granular sludge bed at 15°C and detected the sludge
characteristics variation according to operation condition and granule structure in this new process.

Keywords: Anammox, Phosphorus recovery, HAP, Granular sludge

An AAFEB reactor with a 5 L working volume was used inthis study shown in Fig.1. The operational temperature wasrun at 35 °C at start-up period and then controlled at 15 °C.
Effluent recirculation was applied to dilute the substrateconcentration and to maintain fluidization condition in the
AAFEB reactor. Diluted sulfuric acid (2–5%) was
continuously fed into the recirculation water to adjust theoperational pH to around 8.5 for both the anammox processand HAP crystallization. The synthetic wastewater
simulating the composition of effluent from a previous partial
nitritation reactor treating anaerobic digestion effluent
consist of (NH4)2SO4 0.67 g/L, NaNO2 0.84 g/L, KH2PO4
0.057 g/L, CaCl2·2H2O 0.3 g/L, NaHCO3 0.5 g/L, KCl
0.57 g/L, MgSO4·7H2O 0.2 g/L and trace elements. The
reactor was operated for more than 250 days.

1. Introduction
The extensive use of fertilizers in modern agriculture has
increased world crop yields, making it possible for the global
population to exceed 7.4 billion. However, the nitrogen andphosphorus consumed in food typically exceed nutrient
requirements, and the remainder leaches into waterbodies,leading to eutrophication and other concomitantenvironmental problems. In addition, an estimated 40 million
tons of phosphorus rock is used for fertilizer each year forfood crops and at the present population growth rate
phosphorus reserves are estimated to be depleted by 2090.
Phosphorus recovery at WWTPs can have a significant
impact on extending the life of phosphorus reserves andfertilizer cost. An obvious way to reduce risks to water
quality and resource exhaustion is to remove phosphorus andnitrogen from wastewater and recover phosphorus in theform of valuable products.
Anammox have been made over the approximately last
30 years as an innovative and sustainable nitrogen removal
process. The Anammox process is a biological mediatedreaction in which ammonia is oxidized to nitrogen gas using
nitrite as the electron acceptor under anaerobic conditions. In
a partial nitritation-anammox process, the operation cost is
reduced due to the lower aeration energy and because noorganic carbon is required.
In this study, a simultaneous nitrogen and phosphorus
recovery process at low temperature was developed. And
discussed the effects of nitrogen load and phosphorus loadingon processing performance and sludge properties of thisprocess.

Figure 1. Reactor configuration used in this study

2. Materials and Methods
2.1
Inoculum
The reactor was initially inoculated with a mixture ofactivated sludge and anaerobic digestion sludge taken from amunicipal wastewater treatment plant in Sendai, Japan. The
initial suspended solids (SS) and volatile suspended solids(VSS) were 19.64 g/L and 11.64 g/L, respectively.
2.2
Reactor set-up

2.3
Sampling and analysis
Influent and effluent samples were collected from thereactor every two days, filtered by 0.45-μm-pore-diam
membrane filter and stored in a refrigerator at 4 °C before
analysis. Nitrogen components of NH4+-N, NO2−-N andNO3−-N were analyzed by an Agilent 7100 capillaryelectrophoresis (CE) system (Agilent Technologies,
Wilmington, USA). Total phosphorus and other inorganic

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The 6th International Symposium on Water Environment Systems(Nov. 29th - Nov. 30th, 2018)

elements were analyzed by an Agilent 720 inductively
coupled plasma optical emission spectroscopy (ICP-OES)
system (Agilent Technologies, Wilmington, USA). pH wasmonitored by a pH meter (TOA, HM-30V).

VSS/SS proportion increased with NLR/PLR but decreased
to 25% due to Ca2+ input and increase of phosphorus loadfrom Phase 6 and Phase7.
60%

Elemental content (%)

2.4
Sludge analysis
Sludge samples were taken from different heights (reactor
heights of 23 cm, 43 cm, 63 cm) of the reactor for sludge
analysis. The VSS (volatile suspended solid) and SS(suspended solid) were analyzed according to APHA
standard methods.
A fluorescence in situ hybridization (FISH) analysis was
conducted on microtome section of typical composite
granule in the reactor obtained from the end of the operation
period to reveal the microbe distribution. Fixation and FISH
with fluorescently monolabeled oligonucleotide probes was
conducted according to standard FISH protocol provided by
the SILVA database (Llobet-Brossa et al., 1998, Manz et al.,1992).

6.84±1.26
6.54±1.99
8.58±3.56
9.40±1.95
12.67±1.55
15.66±1.97
7.88±1.22
8.36±3.25

70.0±5.571.3±8.762.4±15.6
58.8±8.544.4±6.831.3±8.6
84.2±2.4
91.2±3.7

Ca

Ca/P

40%

2.0
13.6%

13.6%

13.3%

13.6%

13.4%

29.8%

30.0%

30.0%

30.5%

0

1

2

3

12.7%

12.8%

29.9%

27.9%

28.8%

4

5

6

30%

1.5
1.0

20%
10%

2.5

0.5
0.0

Phase

The results of the content of P and Ca indicated that
granule sludge in this process contains high Ca and P
concentrations. More than 40% of the sludge is composed ofCax(PO4)y. The proportion of Ca/P contained in the sludge
was within the range of 2.1- 2.3, showing a composition close
to 2.15 of HAP.
Fu ll gran u le

TN removal
NRR
Eff. TP conc. TP removal
Phaseeff. (%)(gN/L/d)(mg/L)
eff. (%)
8.48±0.41
2.17±0.04
3.07±0.12
4.46±0.10
6.67±0.18
8.83±0.18
8.66±0.28
8.45±0.49

P

Fig. 3 Elemental content of sludge

Table 1 Performance summary of this study

84.8±4.1
86.9±1.787.6±3.489.1±2.1
88.9±2.3
88.3±1.886.6±2.884.5±4.9

Others(S, Na, Fe, K, Mg)

50%

0%

3. Results and Discussion
Table 1 shows the summary of the operation performancein this expanded bed reactor reactor.

0
1
2
3
4
5
6
7

Tohoku University

Ca/P ratio----- with Perspective of Global Safety

PRR(gP/L/d)0.25±0.02
0.06±0.01
0.06±0.01
0.05±0.01
0.05±0.01
0.05±0.01
0.30±0.01
0.63±0.06

1m m

Cross section

20u mDuring the operation period, an average TN removal
efficiency of 87.4 % was obtained while the NLR range from
2.5 to 10 gN/L/d. The average TP removal efficiency was
67.6 % with an influent TP concentration of 22.8 mgP/L inperiod 1-5. However, the TP removal efficiency wasdecreased with shorter HRT. Ca2+ was added into therecirculation system in phase 6 and 7. The increasing of TP
removal efficiency indicated that Ca2+ can obviously
increase P recovery efficiency.
VSS

VSS, SS (g/L)

300

SS

35%

VSS/SS

30%

250

25%

200

20%

150
100

228.83232.74267.50226.67224.6415%

4. Conclusions(1) Simultaneous nitrogen removal and phosphorus
recovery is realized in an anammox expanded granularsludge bed reactor operated at 15 °C.
(2) Nitrogen removal efficiency of 85-89% at nitrogenloading rate of 10 gN/L/d was obtained.
(3) TP removal efficiency of 84.2% at 50 mg-TP/L and
91.2% at 100 mg-TP/L were obtained with proper Ca/P
ratio and pH control.
(4) Two-layer structure of anammox biofilm attached tocalcium phosphate inner core was demonstrated.

10%

182.5850
0270.88233.11The FISH image results indicated that anammox bacteria
mainly distributed in the outer layer of the granule andanammox bacteria predominated in the most bacteria. The
fluorescence intensity in the inner part was obviously lower
than that of the outer layer, which showed the low microbial
density in the core of composite granules. The results of the
Anammox bacteria mainly distributed in the outer side of
granules. Anammox predominated in most bacteria.

VSS/SS

350

Fig. 4 Fluorescence in situ hybridization analysis for fullgranule and cross section. (Yellow for “Ca. Kuenenia
stuttgartiensis” and “Ca. Brocadia anammoxidans”;
green for most of the bacteria.)

48.25

48.06

52.10

59.37

60.09

55.64

62.35

65.83

0

1

2

3

4

5

6

7

5%
0%

Phase

Figure 2. Sludge characteristics
The VSS concentration was gradually increased during
this process and up to 65.83g/L in phase 7. A very high SS
concentration was observed in this reactor rose from
228.83g/L in Phase 1 to 270.88 g/L in Phase 7 mainly due
to the mineral precipitation of calcium and phosphorus. The

34

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Tohoku$University!

Beach Nourishment as an Adaptation to Future Beach Lossdue to Sea Level Rise in Thailand
Chatuphorn Somphong1*, Keiko Udo2, Sompratana Ritphring3, Hiroaki Shirakawa4 and So Kazama2
1 Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan
2 Graduate School of Environmental Studies, Tohoku University, Sendai 980-8579, Japan
3

4

Faculty of Engineering, Kasetsart University, Bangkok 10900, Thailand
Graduate School of Environmental Studies, Nagoya University, Nagoya 464-8601, Japan*E-mail:

Chatuphorn.Somphong.p1@dc.tohoku.ac.jp

Abstract
A recent study suggested that in the future (2081-2100), Thailand sandy beach areas were projected to be lost
approximately 46%-72% of its current condition due to sea level rise (SLR) based on Intergovernmental Panel on Climate
Change’s (IPCC) projection of SLR. This study proposes a framework and provides preliminary results of sand volume
and costs required for beach nourishment for each coastal zone in Thailand by using Yoshida method. The results indicatethat it would require a minimum of 1,062 million USD (RCP2.6) to a maximum of 3,190 million USD (RCP8.5) to keep
all the beaches at present width. The results of this study can be used as an overview and will be useful for decision
makers to develop more feasible adaptations plan in Thailand.

Keywords: Beach nourishment, Sea level rise, Beach loss

into 64 zones based on the physical characteristics of beaches,
where 51 zones are composed of sandy beaches. Generally,
sandy beaches in Thailand has a small beach width withentirely-averaged of 34.8 m. The sand particle size isaveragely 0.3 mm and the beach slopes range between 1-14
degrees 2).

1.#Introduction#
The global sea level rise and climate change can result incoastal erosion and cause a serious problem for the1)
population at the low-laying coastal area . The IPCC 5th
assessment reports also showed that the projection ofregional sea level rise along Thailand’s coastlines ranges
2)
between about 0.39-0.61 m. Recently, Ritphring et al. hasprojected the sandy beach loss by the future sea level rise for
entire Thailand’s coastlines based on 4 Representative
Concentration Pathway (RCP) scenarios and indicated, by
2081-2100, Thailand possibly loss 46-72% of the present
beach areas.
The sea level rise may cause the physical and socioeconomic damages to the coastal regions. Since Sandy beachis one of the most important coastal resources as tourism
areas or environmental preservations function. Therefore, it
3)
is important to preserve the beach areas. Yoshida et al.
proposed a framework for beach nourishment as an
adaptation to beach erosion induced by sea level rise for theentire Japanese’s coastlines and it aims to specify venerable
beach areas and estimates the sand volume and costs tomaintain beach width in terms of different beach functions.
Since there is no adaptation plans to deal with the future sea
level rise in Thailand. This study aims to develop aframework for a proper beach nourishment and estimate thesand volume and costs by using Yoshida method.

Fig. 1 Study Area
The beach nourishment volume estimation in present study
followed Yoshida’s method 3) in which the optimal
nourishment volume was determined based on the concept of
the Bruun rule 4).
The concept of beach nourishment for keeping beach for adesign width (Y*) is shown in Fig. 2. The profile increases byadding sand with the amount of B. After the nourishment, theshoreline would retreat due to sea level rise. The nourishment
can maintain the future beach width to larger or equal to Y*.

2.#Method#Thailand’s coastlines are located nearby the South China
Sea (Fig. 1) and covers approximately 3,148 km including
2,055 km in the Gulf of Thailand (GOT) and 1,053 km in the
Andaman Sea. The Department of Coastal and Marine
Resources (DMCR) of Thailand have categorized beaches

35

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77777$with$Perspective$of$Global$Safety$ $ $ $ (November$29 $7$30 ,$2018)
The beach model was based on the assumption of theequilibrium profile concept 5). When sea level rise occurs, tomaintain the equilibrium profile, the beach profile needs tobe raised vertically to compensate the amount of sea level rise.
Based on this assumption, the sand volume can be calculated
by equation (1).
.

!" = $%& + & ∗ ()

*

++$

.

,) − & ∗ ()

*

+

,)

1∗ 234.∗%& − %∗

Tohoku$University!

minimum of 1,062 million USD to the maximum of 3,190
million USD to keep the shorelines at the present position for
all sandy beach zone as shown in Fig. 4.

(1)

where Vp is the profile change volume (m3/m), Y0 is the drybeach width, A is scaling parameter, B is the height of beachnourishment, i.e. the amount of vertical increase of
equilibrium profile, to maintain the beach width to beprotected, Y*, and W* is the cross-shore distance to closure
the depth h*. The amount of vertical increase of equilibrium
profile B can be described in equation (2) which is modified
from the Bruun equation 4).
$=0−

G7Safety$Fig. 3 Total sand volume required for beach nourishment
for 4 RCP scenarios.

(2)

where S is the sea level rise, h* is the closure depth and Bh is
the berm height. The amount of sea level rise after beachnourishment causes an allowable retreat (Y0 -Y*) after the
profile increases the amount of B. The beach data set used inthis study are from Ritphring et al.’s study including sea level
rise data, sediment size, and beach slope. It should be notedthat this study assumes that the native sediment size and thefilling sediment size are same and the design width (Y*) isthe beach width at present condition of each coastal zone.

Fig. 4 Costs of sands required for entire coastlines for 4
RCP scenarios.

Fig. 2 The concept of beach nourishment for keeping beachwidth for each option

3.#Results#
This study calculated the beach nourishment volume and thecosts for each Thailand sandy beach zone. Fig. 3 shows thesand volume required for the nourishment of each RCP
scenarios and indicated that it would need a minimum of 193
million m3 (RCP2.6) to a maximum of 303 million m3(RCP8.5) to preserve the existing shorelines. According to
the Thai government’s coastal engineering’s project reports,a cubic meter of sand costs approximately 5.5-10.5 USD.
Hence, the costs of beach nourishment could reach theFig. 5 The volume of sand for beach nourishment for eachsandy beach zone

36

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This study also shows the zonal beach nourishment volume
and its costs in Fig 5 for each beach zone. By overall, topreserve the present beach width, sandy beach zones in thelower Gulf of Thailand need larger amounts of sand than theother part. This is because of the beach width in those areas
are larger compared to the upper Gulf of Thailand and theAndaman Sea.

4.#Conclusions#This study provides preliminary results of sand volume andcosts required for beach nourishment for each coastal zone in
Thailand for keeping beach width in present condition.
However, this is only one-time nourishment (to a future
period, 2081-2100) and this study only considered beach theimpact of Sea-Level Rise and the beach replishment timeinterval should be assessed in future work. The benefit of thebeach is needed to be evaluated to determine optimum beachwidths and further cost-benefit analysis is required forrealistic nourishment in design practice.
References#1)! Roshanka Ranasinghe, David Callaghan, Marcel J. F.
Stive : Estimating coastal recession due to sea level rise:
beyond the Bruun rule. Climatic Change, 110, 561–574,
2012.
2)! Sompratana Ritphring, Chatuphorn Somphong, Keikoudo, So kazama : Projections of future beach loss due tosea level rise for sandy beaches along Thailand’s
coastlines, Proceedings from the International Coastal
Symposium (ICS) 2018 (Busan, Republic of Korea).
Journal of Coastal Research, Special Issue No. 85, 16–
20, 2018.
3)! Jun yoshida, Keiko udo, Yuriko takeda, Akira mano :Framework for proper beach nourishment as an
adaptation to beach erosion due to sea level rise. In:
Green, A.N. and Cooper, J.A.G. (eds.), Proceedings 13th
International Coastal Symposium (Durban, South
Africa), Journal of Coastal Research, Special Issue No.
70, 467-472, 2014.
4)! Per Bruun : Sea-Level rise as a cause of shore erosion,
Journal Waterways, and Habors Division. ASCE, 88,
117-132, 1962.
5)! Robert G. Dean: Equilibrium beach Profiles:
characteristics and applications. Journal of Coastal
Research, 7(1), 53-84, 1991.

37

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The$6th$International$Symposium$on$Water$Environment$Systems$thth

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The impact of spatial discretization scale on urban hydrological modeling performance andprediction
Qing Chang1*, Yoshiya Touge1, So Kazama1
1

Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan*E-mail:chang.qing.q3@dc.tohoku.ac.jp

Abstract
Model subdivision is used to capture spatial heterogeneity in input parameters and it is well-established that spatial
resolution (i.e., degree of aggregation) affects model output. However, a general consensus about the effect does not exist.
The objective of this study was to investigate the effects of spatial resolution on model predictions in an urban catchment,
and to understand the mechanism(s) responsible for the scale effect. The general approach is to develop models at various
spatial resolutions, perform simulations, and compare the predictions of total outflow volume and peak flow. Models
were developed on the basis of actual drainage networks, urban parcels and specific land use. Simulations were performedby using the EPA Storm Water Management Model (SWMM), and model output was compared for 50 storms. There wasvery little difference in the total annual outflow volumes predicted by the different resolutions. However, peak flows
showed a dual scale effect. Peak flow reduced with model aggregation, which can be explained by differences ininfiltration. The result of independent calibration, all the models showed good performances. The performance of thecalibrated models were equal to or better than the high resolution model which meant that calibration could completely
compensate models’ scale effect. The consideration of EIA (effective impervious area) as a calibration parameterincreased performance of calibration period but decreased performance in validation period which indicated theimportance of detailed EIA identification.

Keywords: Spatial resolution, Model performance, Automatic calibration, EIA (effective impervious area)

1.#Introduction#
Urbanization had significantly altered the land cover andgeomorphology characteristics of natural catchment.
Consequently the hydrological processes in urbanized area
are quite different and more complex compared with naturalstate. The large proportion of impervious area decreased
infiltration capacity and increased catchment response time,
meanwhile manmade structures like roofs, roads andchannels largely impacted overland flow and runoff routing
process. Thus it is essential to make an accurate
representation or characterization of urbanized catchment forhydrological modeling.
Modeling urban hydrological process at proper scale is nota trivial issue. However, increasing the modeling detail andreducing model uncertainty are naturally two contrary targets.
Thus, practically speaking, a compromise or tradeoff should
be find between these targets (Pertrucci, 2014). In recent 10
years, the computational capacity and availability of highresolution distributed data had increased in a large degree. As
a consequence, more and more researchers build up theirmodel in high resolutions and using detailed methods.
Schubert et al. (2012) emphasized importance of the rooftop
footprints extraction and found that the rooftop
representation in model could improve the model
performance. Chang et al. (2015) explored the interactions
between 1d sewer flow and 2d surface flow and different
rooftop drainage physics. Leandro et al. (2016) had pointed
out the importance of identify heterogeneity urban key
features (roof type and land surfaces) in successful urban

38

flood modeling. Most recent efforts in this category focusedon the impact of collecting inlets modeling in urban flood
simulation (Chang et al, 2018; Jang et al, 2018). Both studies
claimed the necessity of inclusion of inlets for accurate flood
extent and duration estimation because these inlets mean amore realistic representation of the actual drainage capacity
between surface and sewer system.
All these research modeled urban hydrological processes atvery fine scales (single rooftop or urban blocks), and thiscalled for a lot of tedious work in delineating and dataprocessing. Recent development, however, posed apossibility to overcome this problem. The public available
database like the Open Street Map, can provide information
like roof top footprint and detailed surface land use
distribution which can be directly transferred to objects in
GIS tools. On the other hand, (semi)automatic surface
delineation tools had been developed and it can largely
alleviate the model building work. Sanzana et al. (2016)
presented a semi-automatic tool Geo-PUMMA which cangenerate well-shaped vector meshes or Urban Hydrological
Elements (UHEs). In order to avoid the tedious task ofbuilding the SWMM model, Warsta et al. (2017) developed
a tool called GisToSWMM5 that can automatic generateraster based sub-catchments as well as their parameters thatcan be directly used in model. A more recent work is using
deep learning technology to identify the buildings or rooftops
footprints all over the America.
Although it is an irreversible trend to model urbanhydrology at higher resolution and in more detailed method,

The$6th$International$Symposium$on$Water$Environment$Systems$thth

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the benefit and drawbacks of doing so should be further
discussed. One of the concerns was that the high resolution
may lead to the increase of uncertainty or overparameterization (Pertrucci, 2014). The other concern is theexistence of effective parameters. These effective parameters
can representing a global hydrological behavior, and some ofthem cannot be directly measured or linked to geographicaldata. This means that some low resolution can have same
performance with more detailed model and can save moreresources. What is more, although the high resolution model
can provide simulate more detailed hydrological processes,
most of the current calibration technology are focused on
fitting the hydrograph data of one or few points. This raise anissue called equifinality which means that the different
parameter sets may produce equal model performance. This
also reduced the fidelity of the simulated results at the local
scale of a distributed model.
Therefore, the scale issue of distributed model is an
important question to be understood. It is also a naturally
concomitant problem in urban hydrology. The research ofscale or resolution issues in urban hydrology had a long
history. One of the earliest studies was conducted byZaghloul (1981), who studied the effect of surface
discretization on simulated runoff applying SWMM to one
hypothetical and four real urban catchments in the UnitedStates, Australia and Canada. Stephenson (1989) studied thedependency of model parameter values on the level of surface
discretization for a 0.74 ha residential catchment in South
Africa. Park et al. (2008) studied the effects of spatial
resolution on simulated runoff and pollutant loads using
SWMM for an urban catchment in South Korea. Elliott et al.
(2009) studied the effect of aggregation of stormwater
control devices for a small medium-density catchment in
New Zealand (0.83 km2) with primarily residential land-use
using the dynamic stormwater model MUSIC. Ghosh andHellweger (2012) evaluated the effects of spatial resolution
on simulated runoff using SWMM and both real and artificial
networks in a 3.7 km2 catchment dominated by residential
housing. While both Ghosh and Hellweger (2012) and Parket al. (2008) reported that the simulated runoff volume was
not affected by perturbations in spatial resolution,
Stephenson (1989) observed a reduction of simulated runoff
volume with increasing sub catchment aggregation. In termsof runoff peaks, simulations were clearly affected byvariations in spatial resolution in this study. Whereas
Stephenson (1989) and Elliott et al. (2009) reported similar
observations with increasing sub-catchment aggregation,
Ghosh and Hellweger (2012) and Zaghloul (1981) reported
both increase and decrease of peak flow for lower catchment
resolutions. While the variation reported in Ghosh andHellweger (2012) were found for the same catchment but
different storms, Zaghloul (1981) found these results forsame storms but different catchments. However, theseresearch are always lack of a detailed description of how theparameters values were determined during the change ofscale. One exception was G.krebs et al. (2014), who used themethod of area weight average to determine the parametersacross different resolutions. However, the value of one
important parameter EIA (effective impervious area) was not
maintained during the sub-catchment upscale process. Whatis more, most of these previous studies neglect a calibration
process. Those who conducted calibration did not or failed tocalibrate each model with different resolutions to assess how

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calibration can compensate the scale non-linearity of themodel.
The objective of the present study is to evaluate the effects
of horizontal spatial discretization on performance of urbanhydrology model. The EPA Storm Water Management
Model was used in this study because this model is one of themost popular model in urban hydrology simulation. Threequestions were attempted to be answered in this research:(1) What is the impact of spatial resolution change on model
performance?
(2) If calibration can compensate the scale effect and how thecalibrated parameters will change?
(3) If the calibrated parameters have the same performance atsmaller local scale?
The evaluation is performed using model fit and performance
criteria. The improvement of model performance obtainedwith an automatic calibration process is also investigated.
The results of this study can help modelers define thehorizontal spatial discretization for their models by better
perceiving its influence on model performance and model
prediction uncertainties.

2.#Method# #Study site description
We chose the Kunimigaoka area (KA) in Sendai City, Japanfor the case study. KA is located in the north-western part of
the uptown of Sendai City, which covers approximately 50
hector with a medium gradient slope topography. KA isfeatured with a temperate monsoon climate and the annual
average rainfall and temperature are 1254 mm and 12.4C,
respectively. KA is an old uptown where urbanization degree
is rather complete and the land use shows little change after
2000. The urban land use accounts for over 90% of the total
in this region. The storm water was firstly drained to aregulating pond and then to a downstream river. The drainage
system of KA can be divided into two parts, one being thesewer network in the Northern part of KA which accounted
for most of the drainage areas; the other is a located in thesouthern part beside the regulating pond. The wholecatchment can be divided into two independently drained subarea: a larger one in the North part accounting for around 45
hector and a small one in the South with an area of 5 hector.
Those 2 sub areas drained to the pond by circular pipes withdiameters of 2400 mm and 450 mm.
Data preparation
The DEM of KA was available in the form of a highresolution (5x5m) elevation data set, which was provided andquality controlled by the Land and Resources Department ofJapan. In order to represent the blockage effect of buildings
on surface flows, the building profiles were distinguishedusing the planar graph and Google satellite image.
The underground pipeline data were obtained from Sewer
Administration Office of Sendai City which contained
geographic and geometric information of more than 400
pipelines and manholes. Most of the pipes were circular withdiameters ranging from 0.3 to 2.4 m, while some pipes wererectangular whose widths and heights varied from 0.4 to 0.8
m. The pipe slopes showed a wide range, varying from 0.5 to
38%.
The rainfall was collected by two tipping-bucket rain
gauges within the catchment. The record resolution of the39

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rain gauge was 0.2 mm and 1minute. Since these rain gauge
had not yet collected enough data, some additional rainfall
data were got from the Japan Metrological Agency. These
rainfall data was collected by a metrological station at Sendai
city which was around 4.2 km to the study site.
At the outlet, flow rate is calculated from water levelmeasurements at 5 min intervals obtained by two fieldcameras and velocity measurements at different levels. The
water level–flow rate relationship is obtained from a previous
measurement campaign. There are some very small dry
weather flow rate, may be due to ground water exfiltration, is
subtracted from records in order to obtain the storm waterflow rate. Continuous measurements used for this study cover
the period from the 26th of February to the 29rd of June 2018.

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5)! Huber, W.C., Dickinson, R.E., 1988. Storm Water
Management Model Manual, Version 4: User’s Manual,
EPA/600/3-88/001a. US EPA, Athens, Georgia, USA.
6)! Jang, J. H., Chang, T. H., & Chen, W. B. (2018). Effectof inlet modelling on surface drainage in coupled urban
flood simulation. Journal of Hydrology, 562, 168-180.
7)! Krebs, G., Kokkonen, T., Valtanen, M., Setälä, H., &Koivusalo, H. (2014). Spatial resolution considerations
for urban hydrological modelling. Journal of hydrology,
512, 482-497.
8)! Leandro, J., Schumann, A., & Pfister, A. (2016). A step
towards considering the spatial heterogeneity of urban
key features in urban hydrology flood modelling.
Journal of Hydrology, 535, 356-365.
9)! Park, S. Y., Lee, K. W., Park, I. H., and Ha, S. R. (2008).
“Effect of the aggregation level of surface runoff fields
and sewer network for a SWMM simulation.”
Desalination, 226(1–3), 328–337.
10)! Petrucci, G., & Bonhomme, C. (2014). The dilemma of
spatial representation for urban hydrology semidistributed modelling: Trade-offs among complexity,
calibration and geographical data. Journal of Hydrology,
517, 997-1007.
11)! Rawls, W.J., Ahuja, L.R., Brakensiek, D.L.,
Shirmohammadi, A., 1992. Infiltration and soil water
movement. In: Maidment, D.R. (Ed.), Handbook ofHydrology. McGraw-Hill, New York, USA, pp. 5.1–
5.51.
12)! Rossman, L.A., 2010. Storm Water Management Model
User’s Manual Version 5.0, EPA/600/R-05/040, US
EPA National Risk Management Research Laboratory,
Cincinnati, Ohio, USA.
13)! Sanzana, P., Gironás, J., Braud, I., Branger, F.,
Rodriguez, F., Vargas, X., ... & Jankowfsky, S. (2017).
A GIS-based urban and peri-urban landscape
representation toolbox for hydrological distributedmodeling. Environmental Modelling & Software, 91,
168-185.
14)! Schubert, J. E., & Sanders, B. F. (2012). Building
treatments for urban flood inundation models andimplications for predictive skill and modeling efficiency.
Advances in Water Resources, 41, 49-64.
15)! Stephenson, D. (1989). “Selection of stormwater model
parameters.” J. Environ. Eng., 115(1), 210–220.
16)! Warsta, L., Niemi, T. J., Taka, M., Krebs, G., Haahti, K.,
Koivusalo, H., & Kokkonen, T. (2017). Development
and application of an automated subcatchment generator
for SWMM using open data. Urban water journal, 14(9),954-963.
17)! Zaghoul, N. A. (1981). “SWMM model and level of
discretization.” J. Hydraul. Div., 107(11), 1535–1545.

The Storm Water Management Model (SWMM)
The EPA Stormwater management model (SWMM) was
selected as the modelling platform for this study. SWMM
was primarily developed for urban areas and allows shortand long-term simulations for both water quantity and quality(Huber and Dickinson, 1988; Rossman, 2010). Conceptually,catchments in SWMM are treated as nonlinear reservoirs,
which receive inflows from precipitation and adjacentcatchments and generate different components of outflows
and losses including surface runoff, infiltration, andevaporation. The surface storage available through ponding,surface wetting, and interception determines the capacity ofthese reservoirs. This capacity is defined in SWMM throughthe depression storage parameter and surface runoff is only
generated when the catchment water depth exceeds thedesignated storage depth, at which the outflow is computed
using the Manning’s equation. Catchments may be
subdivided into a pervious and an impervious sub-area(defined by the imperviousness parameter) with individual
parameters (such as the depression storage or Manning’s n
for overland flow) assigned for either sub-area. Whileavailable surface water over the entire catchment is subject
to evaporation, infiltration occurs only for water on thepervious fraction of the catchment (Rossman, 2010). In thisstudy, flow routing computations used the dynamic wave
theory and infiltration from pervious areas was based on the
GREEN_AMPT method (Rawls et al., 1992).

References#1)! Chang, T. J., Wang, C. H., & Chen, A. S. (2015). A
novel approach to model dynamic flow interactions
between storm sewer system and overland surface fordifferent land covers in urban areas. Journal of
Hydrology, 524, 662-679.
2)! Chang, T. J., Wang, C. H., Chen, A. S., & Djordjević, S.
(2018). The effect of inclusion of inlets in dual drainage
modelling. Journal of Hydrology, 559, 541-555.
3)! Elliott, A. H., Trowsdale, S. A., and Wadhwa, S. (2009).
“Effect of aggregation of on-site storm-water control
devices in an urban catchment model.” J. Hydrol. Eng.,
14(9), 975–983
4)! Ghosh, I., Hellweger, F. L., and Fritch, T. G. (2006).
“Fractal generation of artificial sewer networks forhydrologic simulations.” Proc., 2006 ESRI International
User Conf., Environmental Systems Research Institute(ESRI), Redlands, CA.

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Tohoku University

Using MODIS to detect crop type changes in the Aral Sea Basin in the 21stCentury○Mbugua Jacqueline Muthoni*1, Yoshiya Touge 1, So Kazama1
1

Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan*

E-mail: mbugua.jacqueline.muthoni.s8@dc.tohoku.ac.jp

Abstract
The Aral Sea Basin in Central Asia is a region plagued by a severe water crisis. The Union of Soviet
Socialist Republics (USSR); installed a massive scale irrigation project here. This led to a rapid
expansion of irrigation in the region from the late 19th century until the collapse of the USSR in 1991.

Extensive irrigation in the Aral Sea Basin has resulted in some emerging environmental
problems. Central Asia is not only a region plagued by excessive water loss through low
irrigation efficiency but is also faced with increasing soil salinity problems. This necessitates
the cropping pattern to continuously change in order to allow crop growth. The main objective of thisresearch is to clarify the local farming situation. The study assesses the crop type focusing on wheat
and cotton which changes annually due to climate variability and as a strategy for farmers to adapt toirrigation induced problems such as increasing salinity levels. Crop type was identified through GPS
drive recorder and camera data and the NDVI based phenology was assessed and checked against
cropping calendar to identify representative phenology for crop type detection in a wider area. Somepoints located in regions that are heavily affected by salt in Uzbekistan showed great changes in thecrop type suggesting local adaptation measures undertaken by farmers in place to mitigate the salinity.

Key words: Remote sensing, Irrigated crop type identification, MODIS

1

Introduction

Cropping pattern; which refers to the proportion ofarea under different crops at a particular period oftime is decided before the start of the irrigation season.
A spatial-temporal analysis of the annual variability
in the cropping pattern will not only help us to
identify a dry year period or a drought period but also
assess farmers’ decision and judgment in theimplementation of irrigation management practices at
the farm scale.

Agriculture is the main water consuming sector inCentral Asia and is responsible for approximately 90
percent of the total consumptive water use (Hoekstra,
Chapagain, Aldaya, & Mekonnen, 2009). The main
crops under irrigation are cotton, and cereal which
includes wheat and rice, however, wheat is the mostdominant crop in the region (Frenken, 2012). Cereals
account for 49 percent of the total harvested irrigatedcrops in the Central Asia region. Of this figure, wheat
alone represents 39 percent of the total. Cotton is thesecond most harvested crop accounting for 23 percent
average of the total harvested irrigated crops in theregion from 2005 to 2011.

Some studies using NDVI in the detection of crop
type include: Yorozu et al, 2005 who uses seasonalchange of n-NDVI which is normalized NDVI toprepare global 1-degree crop type dataset. Thisdataset is however fixed for all years. Kotsuki &41
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The 6th International Symposium on Water Environment Systemsthth----- with Perspective of Global Safety (November 29 – 30 , 2018)

Tanaka, (2014) use seasonal NDVI to prepare a crop
calendar for use in a hydrological model. These twostudies use the GLCC dataset to divide irrigated andnon-irrigated areas.
2

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Information about the type of crop and the longitude
and latitude coordinates were retrieved. The total
number of crop type points retrieved from 2011 to2017 was 482. Of this, more than 90 percent was
dominated by cotton and wheat. See Figure 3.1 above.
3.2
NDVI phenology retrieval and analysis

Study area

Analysis for the identification of crop type wascarried out in Uzbekistan. One of the main challenges
in crop type identification and mapping is the lack ofquality data (Shao, Y., Lunetta, R.S., Ediriwickrema,J. and Iiames, 2010). The reason why Uzbekistan was
selected is that this region has a larger availability of
local data on irrigation and hydrology (collected fromfield based observations carried out between 2011
and 2017 excluding the year 2016) than the otherCentral Asia nations. Verification data was also
readily available from FAO’s AQUASTAT. Recently
the cropping pattern in Uzbekistan has been changing
drastically due to increasing salinity.

Longitude and latitude coordinates obtained were thenused to locate the individual points in a gridded
Sinusoidal projection for the retrieval of NDVI datafrom MODIS. To retrieve NDVI based phenology forthe different crops types identified in each year,
MOD13Q1 product was employed. In order to reducenoise in the NDVI time series data, Best Index Slope
Extraction (BISE) method was applied (Viovy, Arino, &Belward, 1992). Noise is the resultant impact to NDVI
values due to cloud contamination, atmosphericvariability and bidirectional effect.

3

Methodology

3.1

Drive recorder and camera data assessment

Quality data is a key requirement in order to produce
representative phenology for different crops. Therefore,
crop types which had insufficient representation were
eliminated from further analysis. In this case, all crop
types except cotton (61 percent), wheat (30 percent) andrice (4 percent) were removed from further analysis. The
eliminated crop types are also not dominant in theCentral Asia region. NDVI time series data was thenplotted for each year for all the remaining points.

3.3

A large amount of data in the form of pictures andvideo recordings with GPS information taken during
previous visits to the study area were available. Thisdata contained a record of various activities relating
to irrigation and hydrology in the basin. Firstly, thedrive recorder and camera data were assessed.

Selection of representative phenology for
different crop types

The resultant phenology data for each point was thenreferenced with the crop calendar information for thatcrop type. This was done in order to isolate noisy points
which did not correspond to the crop calendar
information provided. One of the major drawbacks of
MODIS data is that the sensor has a course resolution.
This may in turn cause the resultant phenology to havemixed pixels, especially when carrying out analysis inrelatively small fields.
In order to obtain representative points, homogenous
data is required. Therefore, points which exhibited
mixed pixel properties were eliminated. This was doneusing an intelligent selection approach based on local
knowledge (Zheng, Myint, Thenkabail, & Aggarwal,2015). Crop types usually have unique NDVI signals
which can be used for visual interpretation (Shao, Y.,

Figure 3.1 Distribution of total crop type data
2011-2017

This data was enlisted and used to pick out picturesand videos where the crop type could be identified.

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A

Lunetta, R.S., Ediriwickrema, J. and Iiames, 2010;
Wardlow & Egbert, 2008). Visual analysis of NDVI
signature has been utilized in a number of studies forland cover classification and crop type identification(Carrão, Gonçalves, & Caetano, 2008; Conrad, Colditz,
Dech, Klein, & Vlek, 2011). Figure 3.2 below shows
the representative phenology derived after checking
each point. After removing points with mixed pixel
effect only wheat and cotton had enough points left to
identify a trend and therefore pick representative
phenology related to the cropping calendar.

NDVI phenology 2001-2010 in a farm in
Khorezm region Uzbekistan
B

Figure 3.2 Representative phenology for wheat
and cotton

3.4

NDVI phenology from 2001-2010 in a farmin Zeravshan river basin Uzbekistan

Crop type identification in Uzbekistan region

To identify crop type, an algorithm was created basedon observation of the phenology in the representative
points. Different thresholds were set for theidentification of crop type in a wider temporal andspatial scale. GLCC dataset was then employed toisolate irrigated cropland.

Figure 3.3 NDVI phenology showingchanges in crop type4

Conclusion

The study found that majority of the points analyzed in
the basin from 2001 to 2010 showed a variability in crop
types. Some of the points are located in regions that areheavily affected by salt in Uzbekistan. Changes in thecropping pattern could suggest local adaptation
measures undertaken by farmers to mitigate the salinity
problem.

An evaluation of the crop type variability from 2001 to2017 showed a temporal change in the crop type inmajority of the farms in the study area. Due to the
problems with irrigation infrastructure, farmers try toadopt to the situation using strategies such as switching
to drought- or salt resistant crops (Bucknall et al., 2003)
Figure 3.3 below shows some points with changing crop
type. This can be seen through changes in the NDVI
based phenology evaluated from 2001 to 2010. These
two points are located in some of the most salt affected
regions in Uzbekistan; Khorezm and the Zeravshan river
basin. Changes in the cropping pattern suggest theadaptation measures that the farmers could beundertaking to mitigate the salinity effect.

5

Acknowledgement

This paper is based on achievements of thecollaborative research program (29G-11) of theDisaster Prevention Research Institute of Kyoto
University.

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6

Reference

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Tohoku University

Shao, Y., Lunetta, R.S., Ediriwickrema, J. and Iiames,
J. (2010). Mapping Cropland and Major Crop
Types across the Great Lakes Basin using
MODIS-NDVI Data. PhotogrammetricEngineering and Remote Sensing, 75(1), 73–84.
http://doi.org/10.14358/PERS.76.1.73

Bucknall, J., Klytchnikova, I., Lampietti, J., Lundell,M., Scatasta, M., & Thurman, M. (2003).
Irrigation in Central Asia. Social, economic andenvironmental considerations. Europe andCentral Asia Region, (February), 104. Retrieved
from www.worldbank.org/eca/environment

Viovy, N., Arino, O., & Belward, A. S. (1992). The
Best Index Slope Extraction ( BISE): A method
for reducing noise in NDVI time-series.
International Journal of Remote Sensing, 13(8),1585–1590.

Carrão, H., Gonçalves, P., & Caetano, M. (2008).
Contribution of multispectral and multitemporal
information from MODIS images to land coverclassification. Remote Sensing of Environment,112(3), 986–997.

Wardlow, B. D., & Egbert, S. L. (2008). Large-area
crop mapping using time-series MODIS 250 m
NDVI data: An assessment for the U.S. Central
Great Plains. Remote Sensing of Environment,112(3), 1096–1116.
http://doi.org/10.1016/j.rse.2007.07.019

Conrad, C., Colditz, R. R., Dech, S., Klein, D., & Vlek,
P. L. G. (2011). Temporal segmentation of
MODIS time series for improving cropclassification in Central Asian irrigation systems.
International Journal of Remote Sensing, 32(23),8763–8778.
http://doi.org/10.1080/01431161.2010.550647

Yorozu, K., Tanaka, K., & Ikebuchi, S. (2005).
Creating a global 1- degree dataset of crop typeand cropping calendar through timeseries analysis
of NDVI for GSWP simulation considering
irrigation effect. In 85th AMS Annual Meeting,
19th conference of Hydrology (p. Vol.6).

Frenken, K. (FAO). (2012). Irrigation in Central Asia
in figures. AQUASTAT Survey-2012. Irrigation inCentral Asia in figures. AQUASTAT Survey-2012(Vol. 39). Retrieved fromhttp://doi.wiley.com/10.1111/j.14676346.2007.00685.x

Zheng, B., Myint, S. W., Thenkabail, P. S., &Aggarwal, R. M. (2015). A support vector
machine to identify irrigated crop types using
time-series Landsat NDVI data. International
Journal of Applied Earth Observation andGeoinformation, 34(2015), 103–112.

Hoekstra, A. Y., Chapagain, A. K., Aldaya, M. M., &Mekonnen, M. M. (2009). Water Footprint
Manual State of the Art 2009. Water Footprint
Network, (november), 131. Retrieved fromhttp://waterfootprint.org/media/downloads/Water
FootprintManual2009.pdf
Kotsuki, S., & Tanaka, K. (2014). Improvement of
Global Crop Calendar Product using SatelliteSensed Vegetation Indexes. Journal of Japan
Society of Civil Engineers, Ser. B1 (HydraulicEngineering), 70.

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Remote estimation of rice crop yield using MODIS Vegetation Indices data:
A case study of Solo river basin rice field, Indonesia
Vempi Satriya Adi Hendrawan1, Daisuke Komori1
1

Graduate School of Environmental Studies, Tohoku University, Sendai 980-8579, Japan*E-mail: vempi@dc.tohoku.ac.jp

Abstract
Food security for half of the entire world’s population relies on the rice supply. As one of the largest rice producer
country in the world, Indonesia has faced a challenge to ensure the sustainability of rice production every year. Predicting
the crop yield is one of the critical issues for the country that requires technical skill utilizing various methods and datasets.
As one of the widely applied approaches, this study was done to develop a regression model for rice crop yield estimation
using remote sensing imagery data. We applied the approach for a case study in the Solo river basin as one of the largest
rice crops plain in the country. We employed Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized
Difference Vegetation Indices (NDVI) and Enhanced Vegetation Indices (EVI) as predictors and annual average yielddata on a city scale as the dependent variable. We processed it in three main steps: (1) rice crop field masking, (2)extracting raw NDVI and EVI value averaged in each city for selected years, and (3) establishing regression crop yieldmodel. We selected the year of 2007 and 2008 as samples. The results showed that the adjusted coefficient of thedetermination (R2adj) of both models are 0.215 and 0.238 respectively. From the result, it indicates a low correlation
between yield and VI for the expected ripening period (November-December) for the October-January rice crops. Somefactors were revealed presumably as a subject of uncertainty in this study. First, there was a lack of the quality of raw gridvalue due to atmospheric contamination and other optical noises. Second, there was an inconsistent use of yield data as apredictor. We used an annual average yield data on a city scale to build a model while the predictor is the vegetation
phenology information from one crop season which is October-January rice crop. Therefore, a further analysis isabsolutely needed to deal with the above issues in order to improve the model for estimating the crop yield.

Keywords: NDVI, EVI, Rice, Crop, Yield

1.#Introduction#
Food security for half of the entire world’s population relieson the rice supply (FAO, 2004a). Rice production plays animportant role in the economy of Asian countries, especially
Southeast Asian countries (Timmer, 2009; Evenson and
Rosegrant, 2003), as a region that annually produces
approximately 150 million tons of rice grain or 25% of global
rice production (Raitzer, 2009). Rice production has fallen
short of consumption since 2000, while the global rice trade
increased by more than 30% from 1995 to 2005 as theworld’s population growing at 1.1% annually (FAO, 2004b;
OECD-FAO 2006). Furthermore, the impacts of climate suchas temperature increases, rising seas, and changes in rainfall
patterns, could lead to decreased crop production. The
reduction of rice production in major rice-producing
countries may trigger increases in rice prices, consequently
causing food security issues (Son, 2013).
As one of the largest rice-producing country in the world(MoA, 2015), Indonesia has faced a challenge to ensure thesustainability of rice production every year. Predicting thecrop yield is one of the critical issues for the country that
requires technical skill utilizing various methods and datasets.
Thus, timely and accurate prediction of rice crop yield at thecountry could allow for a more precise assessment of food44
45

production and better crop management strategies to provide
solutions to food challenges.
Remote estimation of crop yield has been getting attention
by many researchers since remote sensing technology allows
in an advance way to capture any geo-parameters remotely inhigher spatio-temporal resolution. Estimating yield using
remote sensing technique is considered as an inexpensive andfast way as it is publicly available. It is also considered as apromising complement to the survey yield assessment thatcannot be neglected to do as well (Lopestri, 2015).
Estimating crop yield using remote sensing data has been
widely investigated since the early 1980s. Vegetation Indices(VI) has been recognized for its ability to monitor crops andas an estimator of crop yield by both using a single image ora time-series image (Lobell, 2013; Son, 2014; Sakamoto,2014; Johnson, 2016). Previous studies proved that yield
estimation using data from a single image at the peak of cropdevelopment showed good result. It encompassed the critical
period for grain production. Moreover, yield estimation
could be also done by extracting time-series VI value tophenology information as the estimation parameters. Bothmethods can be applied in any crop including rice in any areaof interest.
As there is no physical correlation between VI value
retrieved from optical sensor and yield in the real field, anG7Safety$

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empirical model has been proposed as a commonly usedmodel of crop yield estimation using remote sensing data.
The empirical model has been often preferred because it
requires fewer data and is simple to implement at a local orregional scale. Many researchers have coupled remotesensing data with crop models or agrometeorological models(Doraiswamy, 2004; Moriondo, 2007; Yuping, 2008). Crop
growth simulation models are probably more accurate toestimate yield, but they require numerous specific inputs thatare not always available (e.g. soil characteristics,
management practices, agrometeorological data, and cropparameters). Therefore, the empirical model using without
incorporating such kind of data seems more applicable since
the approach is applied in a developing country where suchdata are challenging to be collected.
Among all of the available remote sensing products, The
Moderate Resolution Imaging Spectroradiometer (MODIS)
was selected to be used in this study. The reason to choose
MODIS data are: (1) its fair spatial and temporal resolutionthat allows to monitor and mapping the vegetation at local orregional level, (2) having cloud-free images due to the highrevisit frequency of the sensor (per one or two days), and (3)no cost and publicly available on the website. Thus, based on
above several merits, we used MODIS as a predictor toestablish crop yield estimation model in this study.
The main objective of this research is to remotely estimate
rice crop yield using MODIS VI data for a case study in theselected major rice field in Indonesia. This paper discusses
the applicability and capability of MODIS for assessing crop
yield using simple linear regression using raw VI value. The
robustness of the established models was evaluated bycomparisons between the predicted yields and crop yieldstatistics in a city scale.

Tohoku$University!

Figure 1 Solo River Basin

2.2.$Data$
In this study, three types of data were used.

2.#Method#
2.1.$Case$study$
Solo river basin was selected as a case study to estimate rice
crop yield. Solo river basin is the largest river basin in Javaisland Indonesia with approximately 16,100 km2 of the total
area and 600 km total length of the main river. As shown in
Figure 1, the downstream of the river basin is characterized
by a plain area which is suitable for crop cultivation. There
are approximately 526,000 ha rice crop cultivated in the basin,or 26% of the total basin area. Total rice field area in thisbasin accounts 28% of the total rice field area in East Java
which has a significant amount of production in Java andeven in the national scale. There are mainly two rice-crop
pattern system in the river basin (Shresta, 2016): (1) westernpart of basin rice cultivation pattern in October-January andFebruary-June and (2) eastern part- rice cultivation pattern inDecember-April and April-June. It is varying spatially andtemporarily based on local farmer’s practice and climate
condition.
The annual average precipitation is around 1,800-2,100
mm/year whose climate is dominated by a maritime monsoon
climate (Iwami, 2017). There are two main seasons: (1) dry
season during May-October and (2) wet season during
November-March. Heavy rains usually occurred in
December-February inducing flood causing damage toproperty and destruction of crops in the basin annually.

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1.# MODIS surface reflectance data
Moderate Resolution Imaging Spectroradiometer (MODIS)
250-meter 16-day Vegetation Indices (VI) average value
provided by the Land Processes Distributed Active Archive
Centre (LP DAAC) were used to evaluate surface vegetation
color condition. The Earth Observing System (EOS) Terra
and Aqua MODIS surface reflectance MOD13Q1 and
MYD13Q1 Version 6 products provide a VI value at a per
pixel basis. There are two vegetation layers were used in thisstudy. The first is the Normalized Difference Vegetation
Index (NDVI) and the second vegetation layer is the
Enhanced Vegetation Index (EVI), which has improved
sensitivity over high biomass regions. The formulas for
obtained NDVI and EVI are as follows:

NDVI =/01 = 2.5*×

Rnir*–*Rred
Rnir*+*Rred**

Rnir*–*Rred
Rnir*+*6*×Rred*–*7.5*×*Rblue*+*1*

where RNir, RRed and RBlue refer to near-infrared, red and blue
bands of surface reflectance imagery images.
2.# Land use layer map
The national land cover/land use database was provided by
the Geospatial Information Agency (BIG). The vector layerof land use was obtained using Landsat Imagery map
classification in 2010. Although it might be changing fromyear to year, we found that there were no big changes in thecrop field area between 2007 and 2010. Therefore, we usedthat same crop field layer in this study.
3.# City-level crop statistics
Agricultural data were provided by the Central Bureau of
Statistic (BPS). BPS collect annual city level agricultural
information across East Java and Central Java Province
covering Solo river basin. Yield data (t/ha) for rice in 2007
and 2008 were obtained. Land use map was used for masking
Vegetation Indices raster map. The Vegetation Indices from

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MODIS surface reflectance data and the yield statistic werethen employed to develop a regression model.

Tohoku$University!

Table 1 Yield Regression Model

$

Year#

Regression Equation #R2adj#

2.3.$Methodology$
We employed Normalized Difference Vegetation Indices(NDVI) and Enhanced Vegetation Indices (EVI) as
predictors and annual average yield data on a city scale as thedependent variable. We processed it in three main steps: (1)image processing and data analysis, (2) rice crop fieldmasking, and (3) establishing regression crop yield model.

2007$

Y = 6.226 – 4.103*NDVInov +0.215$

2.834*EVIdec $2008$

6.19*EVInov

1.# Image Processing and Data Analysis
Two MOD13Q1 scenes (h28-v09 and h29-v09) were usedcovering the study area. Then a mosaic was done for eachdate within the month of November-December where aripening stage was taken in place for October-January crop
based on the crop calendar for the year of 2007 and 2008 as
samples. GIS software was used to extract NDVI and EVI
information of each date.
2.# Rice Crop Field Masking
Based on field layer map, NDVI and EVI mask for each city
crop field was obtained to isolate the disruption signal fromanother land use surface reflectance (Freund, 2005). The
average of NDVI and EVI values per city mask was
calculated. This was considered to represent the interceptedsignal within the city mask when the yield value is an annual
yield for each city.
3.# Establishing Regression Crop Yield Model
Vegetation indices and agricultural data statistic were used
to develop an empirical model to estimate crop yield. A
number study as reviewed by Funk (2009) documented aclose relationship between NDVI and EVI. Most of thestudies showed the capability of a linear regression equation
to model crop yield by various advance pre-processing andremote-sensing based approach. However, as a preliminary
analysis, in this study, we used the raw value of Vegetation
Index (VI) value of several dates in the ripening stage ofOctober-January crop.
The regression model derived in this study as follows:
F

0.238$+ 6.005*NDVIdec $Figure 2 Actual and modeled yield
Table 2 Actual and modeled yield
City

A

<=

Y = 6.565 – 2.789*NDVInov –=>? ×@>? + DE
>BG ?BC

where y is the crop yield, x is vegetation indices (NDVI and/
EVI), i represents the vegetation indices index, j represents
the targeted month from c ton (12 months), a is regression
slopes and b0 is the model intercept.

3.#Results#and#Discussion#
In this paper, a crop yield model for the river basin scale wasobtained based on the city-based mask of averaged NDVI and
EVI value and the annual average rice yield data on a city
scale. The yield model equation is shown in Table 1. From
the adjusted coefficient of determination (R2adj) of both
equations, it can be seen that there is no high correlation
enough between yield and NDVI and EVI in the month ofNovember and December by R2adj = 0.215 and R2adj = 0.238
for 2007 and 2008 model respectively. Figure 2 and Table 2
shows the actual yield from statistic data and the model
estimation. Table 2 shows the percent error between actualand modeled city average yield.

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2007

2008

Model

Data

Percent

Model

Data

Percent(t/ha)(t/ha)

error(t/ha)(t/ha)

error

Blora

5.27

5.10

3.35%

5.29

5.04

5.11%

Bojonegoro

5.57

5.34

4.32%

5.55

5.84

4.94%

Boyolali

5.48

5.57

1.50%

5.62

5.86

3.94%

Gresik

6.01

5.79

3.73%

5.89

6.41

8.05%

Karanganyar

5.68

5.74

1.01%

5.62

5.96

5.71%

Klaten

5.54

5.61

1.32%

5.71

5.97

4.36%

Kota Madiun

5.69

5.73

0.70%

5.63

6.33

11.10%Surakarta

5.60

5.27

6.30%

5.58

4.83

15.38%

Lamongan

5.83

5.98

2.52%

5.98

6.52

8.40%

Madiun

5.69

5.54

2.71%

5.59

6.13

8.79%

Magetan

5.76

5.95

3.16%

5.53

6.48

14.65%

Ngawi

5.66

5.49

3.13%

5.43

5.89

7.81%

Pacitan

5.60

3.91

43.21%

5.37

4.65

15.43%

Ponorogo

5.56

5.86

5.15%

5.54

6.35

12.68%

Semarang

5.26

5.22

0.94%

5.84

5.04

16.03%

Sragen

5.68

5.53

2.61%

5.50

5.85

5.94%

Sukoharjo

5.42

5.79

6.32%

5.50

6.22

11.58%

Tuban

5.69

5.63

1.09%

5.52

6.14

9.98%

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Percent
Model
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2015. Relationship between MODIS-NDVI data and(t/ha)(t/ha)
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wheat yield: A case study in Northern Buenos Aires
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10.41%
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5.56
15.85%
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Programmes.” In Crop Variety Improvement and Its
MODIS NDVI-based production anomaly estimates for
Effects on Productivity: The Impact of International
Zimbabwe. Remote Sensing of Environment, 113(1),Agricultural Research, edited by Robert E. Evenson andpp.115-125.
Douglas Gollin. Wallingford: CABI Publishing.
6)$ Raitzer, D. A., J. Roseboom, M. K. Maredia, Z. Huelgas,
and M. I. Ferino. 2009. Prioritizing the Agricultural
Research Agenda for Southeast Asia: Refocusing
Investments to Benefit the Poor. Bangkok: Asia Pacific
Association of Agricultural Research Institutions.
7)$ N. T. Son , C. F. Chen , C. R. Chen , L. Y. Chang , H. N.
Duc & L. D. Nguyen. 2013. Prediction of rice crop yieldusing MODIS EVI−LAI data in the Mekong Delta,
Vietnam. International Journal of Remote Sensing.
8)$ MoA (Ministry of Agriculture). 2015. Rencana Strategi
Kementrian Pertanian Tahun 2015-2019. Kementrian
PertanianRepublikIndonesia.
(Website:
City

2007

2008

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Preliminary study of grape farmers' perception to changing climate
in Takahata city, Yamagata prefecture, Japan
Sopha Soliya1*, Daisuke Komori 1, Shinsuke Furuno2, Eisuke Adachi2, Noriko Kaneda22

1Graduate School of Environmental Studies, Tohoku University, Sendai 980-8579, Japan
Yamagata Prefectural Government, Agriculture, Forestry and Fisheries Horticulture Promotion Division, Japan*

Email: sopha.soliya.r6@dc.tohoku.ac.jp

Abstract
This study examined the influence of climatic conditions toward grape cultivation in Takahata city, Yamagata
prefecture. To do so, the historical of grape yields and climate data have been collected during the last 38 years. Then
We have classified the yields of each year into two groups namely high and low yields years, and we have analyzed theclimate data including monthly and seasonally of rainfall and temperature by applying the Principle Component
Analysis (PCA). The results showed that in high yield years’ have found the warm temperature and less rainfall in berry
development and maturity phases. However, in low yield years’ have appeared the cold temperature and heavy rainfall
during those phases. In addition, this study investigated the grape farmers’ perception to changing climates. The
qualitative data has been conducted by using structure questionnaire forms and face to face interview by interviewing
the farmers. The results showed that farmers’ have perceived that the climatic factors including heavy rainfall and cold
climate have negatively influenced to grape cultivation.

Keywords: Changing climate, Perception, Grape farmers
1.# Introduction# #Agricultural productions have been affected by changing
climate (Adger et al.2007). In Japan, the annual precipitation andtemperature have been confirmed of changes during the last
decades (JMA., 2011). The changes are likely to affect fruit trees,
vegetables and rice cultivation (Sugiura et al. 2012). Especially,
grape berry has been impacted by temperature and sunlight on
the skin coloration in last decade (Shinomina et al., 2015;
Sugiura et al., 2017). The extremely events of changing climatesand its responses are challenges for the farmers (Adger et al.
2007; Belliveau et al.2006). And, farmers’ perception tochanging climate is not well understood (Bradshaw et al., 2004).
Most of the studies have been discussed aboutå the perception,
adaptation and influenced climate to grape cultivation separately.
Hence, by understanding the influences of climatic conditions
and farmers’ perception to the changes, it would be provided us
the preliminary fundamental data for developing adaptation
strategy and it would lead to make the effective accountabilities
and implementation of agricultural cultivation. Therefore, theobjectives of this study are to examine the climatic conditions
influencing grape cultivations and to investigate the grape
farmers’ perception to changing climate in Takahata city,which is located in Yamagata prefecture as a case study.

Figure 1: Location of study area

3.# Methodology# #1.!
Data collection: the data has been collected fromthe primary and secondary data sources, which is included
qualitative and quantitative data as following examination:
Primary data: The qualitative data has been conducted byface to face interview by interviewing the farmers who have
major income from grape cultivation. The questionnaire
included multiple choices and open-and close-ended

2.# Study#areas#
Takahata city is selected as the study area, because it isranked as the largest area of grape cultivation in the Tohoku
region (MAFF 2016), and the grape cultivation is the firstmain source of income, following by rice, soba and etc. The
total areas of grape farming are 268 hectares and the total
number of grape farmers are 461 people (Takahata Town
Hall Agriculture, 2018).

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informants namely agricultural officers and field observation.
Secondary data: the quantitative data has been collected thehistorical of grape yields and climate data during the last
38years (1977-2015). The climate data obtained including
daily, monthly and seasonally rainfall and temperature.
2.!
Data analysis
Quantitative data analysis: grape yields data has beenclassified into two groups namely high and low yields years.
The climatic observation data has been classified into 26
variables based on the grape planting seasons (A.Kobayashi
et al 1968; santos et al 2011) including total rainfall andmonthly mean temperature in April, May, June, July, August,
September and October; seasonal mean temperature, day andnight time temperature and total rainfall in shooting andflowering (April-May), berry development (June-July) andmaturity (August, September, October). Then to select thehigh correlation of dependent variables as grape yields andindependent variables as climate, we used the linear modeland Variance Inflation Factors (VIF) to remove thecollinearity (or multi-collinearity), the model has beenrounded 16 times to get the final results of VIF which is lessthan 2.5. Therefore, finally, we obtained 10 variables ofclimate parameters namely mean temperature in April, May,
June, July, August; total rainfall in April, May, July,
September and October. The set of data have been analyzed
and compared the different climate of high and low yields
years by applying the Principle Component Analysis (PCA).
Qualitative data analysis: the exploratory and descriptive
methods have been used to analyze and explain thequalitative data.

4.# Results# #Principle Components Analysis (PCA) of climatic
variables in high and low yields
The results show that the standard deviation is greater than
1 and cumulative proportion is exceeded 79%. The details ofprinciple component for high yields years shows in Table 1,
in the first principle component (PC1) has a high negative
correlation with rainfall in July and September (berry
development and maturity phases). This mean that there wasless rainfall in those months. In the PC2 has a high positive
correlation with mean temperature in May and July, and also
shows a high negative correlation with rainfall in May andJuly. Therefore, we conclude that there was warm
temperature and less rainfall in May and July. In the PC3 has
a positive correlation with mean temperature in June and highnegative correlation with mean temperature in April. This
determines about there was still cold climate in April andwarmer climate in June.
For Table 2 shows the principle component in low yields,
the first principle component (PC1) has a negative correlation
with mean temperature in April and May (shooting andflowering phases), and it has a positive correlation with
rainfall of those months. We conclude that there was highrainfall and cold temperature in both months. In the PC2 has
a positive correlation with mean temperature in August andrainfall in July and September. That mean it was warm
climate in August and heavy rainfall in July and September.
Meanwhile, in the PC3 has the highest positive correlation
with mean temperature in June, which concludes about warm
climate.

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Table 1: Principle component for high yields
climatic parameters

PC1

PC2

PC3

mean. temp. Apr

-0.41

-0.02

-0.32

mean. temp. May

-0.41

0.12

-0.2

mean. temp. June

-0.20

-0.19

0.66

mean. temp. Jul

-0.06

0.4

0.23

mean. temp. Aug-0.0070.44

0.28

rainfall. Apr

0.4

-0.17

0.26

rainfall. May

0.33

-0.21

-0.38

rainfall. Jul

-0.15

0.42

0.02

rainfall. Sep

-0.11

0.36

-0.11

rainfall. Oct

-0.30

-0.35

0.19

( factors loading)

Table 2: Principle component for low yield
climatic parameters

PC1

PC2

PC3

mean. temp. Apr

-0.24

0.04

-0.60

mean. temp. May

-0.24

0.43

-0.11

mean. temp. June

0.17

-0.01

0.64

mean. temp. Jul

0.2

0.44

0.16

mean. temp. Aug

-0.11

0.30

-0.05

rainfall. Apr

-0.34

0.14

0.2

rainfall. May

0.1

-0.47

-0.25

rainfall. Jul

-0.37

-0.36

0.11

rainfall. Sep

-0.42

-0.06

-0.19

rainfall. Oct

0.26

0.34

-0.04

( factors loading)

Farmers’ perception of climatic conditions
After the preliminary field survey, we summarized thefarmers’ perception of climatic conditions shows in the Table
3. Farmers perceived that in some years they got the low andhigh yields, which they considered that climatic factors haveinfluenced to grape yields such as heavy rainfall and snow.
For example, in low yields year as in 2018, there was heavy
snow and humidity in April. In 2008, 2012, 2013 and 2015,
farmers noted that there was heavy rainfall and they got low
yields, in those years there was high amount of rainfall in Julyand September, which was between 315 to 627mm/month(JMA, 2017). Besides, there also had low temperature in
April, which was about -2.58 to -4.17 °C (JMA, 2017). In
addition, farmers perceived that in some years namely 2007,2010, 2014, they got high yields. In those years, the amountof rainfall in July and September was between 134 to 219

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mm/month (JMA, 2017); and the temperature in April wasamong -1.82 to 2 °C (JMA, 2017). Therefore, in high yields
years, farmers mentioned that there was less rainfall insummer and warm climate in April. Thus, we conclude that
farmers’ perception and scientific observation data have been
similarly consequences. Farmers’ perceived as heavy rain
and low climate occurrence in low yield years. At those years,
the scientific observation was showed higher amount of
rainfall and lower climate than other years.
Furthermore, famers mentioned that the greater amount andlate melting of snow in winter have impacted in the first stageof growing season in April. Besides, farmers have understoodthat heavy rainfall in summer has affected the grape berry,
especially for the berry size, color and berry cracking, which
are important factors for the markets’ requirement.
Table 3: Farmers’ perception of climatic conditions

Discussion#
Descriptions

Farmer’perception
1
Year: 2018

Low yield
Heavysnow andhumidity
Year: 2014
High yields
Less
rainfallFarmer’perception
2
Years:
2008,
2013, 2015

Farmer’s
perception
3
Years:
2012

Heavy
rainfallHeavy
rainfallYears:
2007, 2010

Year: 2014

Less snowLessrainfall

The principle component analysis results demonstrate thedifferent climatic conditions between high and low yields
years. The high yields years (Table 1) is represented the lessrainfall in July and September (berry development andmaturity phases). In addition, it is appeared the warm climate
in May and July. However, in the low yields years (Table 2),
is represented the cold temperature in April and May, and it
is indicated the heavy rainfall in May, July and September.
These results are accurate with the farmers’ perception andscientific observation data of each year, which found that in
low yields years there was heavy rainfall in July and
September, and there was cold climate in April. That mean
humid conditions have the trend to slow down the berry
development and promote diseases (Morinaga et al., 2005).
Oppositely, in high yield years there was less rainfall in Julyand September and warm climate in April, which determinesthat warm temperature has made the benefits to grape yields
later than cold temperature (Santos et al., 2011).
Furthermore, another relevant factors namely occurring
diseases (Kenny et al., 1992) and decreasing in soil moisture(Malheiro et al., 2005) would lead the significant effects toyields in the future.

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and maturity phases. And the low yields years had lower
temperature and high rainfall during those phases. These
results are precise with the farmers’ perception and scientificobservation data, which found that heavy rainfall and cold
temperature in low yields years. Oppositely, there was lessrainfall and warm climate in high yield years.
This preliminary study can conclude that farmers’
perception, climatic observation data and PCA results, aresimilarly consequences. The results would be the baseline
data for further study about farmers’ adaptation, adoption of
innovation and its diffusion to the changing climate in the
future.

References# #1)! Adger WN. Agrawala S., et al., 2007. Assessment ofadaptation practices, options constraints and capacity.
Cambridge University Press, Cambridge, 717–743.
2)! Belliveau S, Smit B, Bradshaw B., 2006. Multiple
exposures and dynamic vulnerability: evidence from thegrape industry in the Okanagan Valley, Canada. Glob
Environ Change 16:364–378
3)! Bradshaw B, Dolan H, Smit B., 2004. Farm-level
adaptation to climatic variability and change: crop
diversification in the Canadian prairies. Climate Change
67:119–141.
4)! Fujisawa M, Kobayashi K., 2015. Shifting from apple topeach farming in Kazuno, northern Japan: perceptions ofand responses to climatic and non-climaticimpacts. Reg
Environ Change. 13(6):1211–1222.
5)! Japan Meteorological Agency (JMA), 2017. Statistic ofclimatic
information,
http://www.data.jma.go.jp/
gmd/risk/obsdl/#!table.
6)! Kobashi et al., 1968. Favorable day and night temperature
combination for the fruit growth of Delaware grape andSatsuma oranges. Division of Pomology, Kyoto Uni.
7)! Morinaga et al., 2005. Grape production in Japan.
Persimmon and Grape Research center, National
Institute of Fruit Tree Science, MAFF.
8)! Santos J.A., Malheiro A.C., et al., 2011. Statisticalmodelling of grapevine yield in the Port Wine region underpresent and future climate conditions. Int. J.
Biometeorol.55, 119-131.
9)! Shinomiya et al., 2015. Impact of temperature and sunlighton skin coloration of Kyoho table grape. Fukuoka
Agricultural and forest research center. Scientia
Horticulture 193: 77-83.
10)! Statistics of Agriculture, Forestry and Fisheries (2016)
Ministryof
Agriculture,
Forestry
andFisheries.http://www.maff.go.jp/j/tokei/kouhyou/sakum
otu/sakkyou_kazyu/index.html
11)! Sugiura et al., 2012. Overview of recent effects of global
warming on agriculture production in Japan. JARQ 46 (1),
7-13.
Yamada et al., 2016. Advances in table grape breeding inJapan. NARO Institute of fruit tree science. Breeding Science
66: 34-45.1

Conclusion# #The PCA results illustrated that the high yields years had
less rainfall and warmer temperature in berry development

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Estimation of woody debris recruitment by landslides:
A Case study of Typhoon Lionrock in Iwaizumi, Japan
Thapthai Chaithong1, Yuto Sukegawa2, Satoshi Anzai2, Akihiro Ichiba1*, Daisuke Komori1
1

Graduate School of Environmental Studies, Tohoku University, Sendai 980-8579, Japan
2
Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan*E-mail:akihiro.ichiba.r2@dc.tohoku.ac.jp

Abstract
A landslide can produce large amounts of debris in the form of boulders, cobbles, soil, sand and log or wood. The woody
debris produced by a landslide flows into a downstream river or village. It can form obstructions in the stream and makethe stream flow over. In this study, we aimed to develop a procedure for estimation of woody debris recruitment bylandslides. Understanding the volume of woody debris can help predict and prevent hazards from this debris. The
proposed procedure combines a shallow landslide model, tree density data, and observational data flowing landslide
occurrence. The study site is a sub-watershed of the Omoto River watershed in Iwaizumi town, Iwate Prefecture. This
town got a big damage from Typhoon No.8, Typhoon Lionrock. This typhoon delivered over 200mm rainfall per a day
and induced many landslides. Based on field surveys, we found that about 520m3 of woody debris gathered in a narrowsection under a railway bridge. Moreover, about 180m3 of woody debris compose natural woody debris dam in the stream
channel of the target watershed. Using the proposed protocol, we estimate that woody debris recruitment to the stream
was approximately 638m3.

Keywords: Woody debris, Lionrock, Landslide, Woody debris dam,

1.# Introduction#
Japan has been damaged by typhoon for a long time.
Typhoons cause not only direct damage by their strong windand heavy rain, but also indirect damage by landslides.
Landslides produce a lot of woody debris. Our target,
Typhoon No.8 in 2016 is without exception. On 30, August,
2016, Typhoon Lionrock struck the Pacific coast of northeastern Japan. The typhoon caused flash flooding, landslides,and debris flows in the north part of Japan, destroying houses
and infrastructure.
Although there are a lot of studies about how woody debris
or drift woods flow in the river, there are not many casestudies about how woody debris can be produced. However,
the estimation of the amount of woody debris is important.
Woody debris are mainly produced by landslides in themountain and river-side forests. The mechanism of landslides
of mountain area are stated as follows. Rainwater infiltrates
into a sloping surface of forest area. Therefore, thegroundwater level go up, or pore water pressure increases.
These factors reduce the effective stress of soil and the soil
can be easily sheared by its own weight1).
Although how the several standing on saturated soil trees
fall down can be explained by distinct element method2), it is
not so efficient to the estimation of estimation of woody
debris recruitment over the watershed. In this paper, we aim
to use data collected after a landslide to develop a method of
estimation of woody debris recruitment.

2.# Methods#and#targets#
2.1# Targets#
We focused our target on Omoto river water disaster caused
by Typhoon Lionrock. The total area of the study site isapproximately 0.87km2 and the total length of the stream isapproximately 3.75km.
2.2# Methods#and#dataset#
The proposed procedure can be divided into four steps.
#First# step:# hillslope# hydrology# model#

and#Infinite#stability#model#
The first step involves simulating the unstable area with ananalytical shallow land slide model that describes the sourceof woody debris recruitment to watershed. This model makes
two major assumptions: (1) the landslides characteristics are
based on an infinite slope and (2) the hydrological conditions
are based on steady state subsurface flow and infiltration. The
model is analytical and simulates the physical process of
slope destabilization caused by an increase in ground water.
The hillslope hydrology model used in this study consists of
two parts. One part simulates the saturated subsurface flow
and the other part simulates rainfall infiltration into sloping
surface. The equation of saturated surface flow is based on
Darcy’s low and the equation of rainfall infiltration into thesloping surface is based on the modified Green-Amptequation 3). From the water balance equation and Darcy’s law,
the following expression can be obtained:

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In addition, elevation data can be obtained from ASTER
Global Digital Elevation Model (GDEM) by National
Aeronautics and Space Administration (NASA) of the U.S.
and Ministry of Economy, Trade, and Industry of Japan.
From the source, we can calculate β using ArcGIS. The soil
parameters at this site are in Table 1. For the rainfall dataset,
we adopted Inverse distance weighted method (IDW) anddetermined the distribution of rainfall using the data of 5 rain
gauges in Iwaizumi town.

!" = $%&'()*+ … (1)
I: rainwater infiltration, A: catchment area, η: the porosity ofthe soil, m: steady water table height, b: width of the
topographic elements, k: saturated hydraulic conductivity, β:slope angle
Equation 1 can be translated as follows.

%=0123$()*+Parameters
Total soil cohesion (c)Total friction angle (φ)
Saturated soil unit weight (γsat)
Water unit weight (γw)
Saturated hydraulic conductivity (k)The porosity of the soil (η)
The suction at wetting front (ψ)
Volumetric water content deficit (Δθ)… (2)The Green-Ampt equation is simplified mathematicalexpression that represents the infiltration process. The rate of
infiltration for the sloping surface and the cumulative
infiltration are as follows:) = '[67(+ +! ? −Tohoku$University!

9:;] … (3)
0 <

Unit
kPa
Degree
kN/m3
kN/m3
cm/hr
cm
-

Value
10.8
29.7
16.7
9.81
2.99
0.437
6.13
0.27

#
Second#step:#Forest#analysis
By a field survey of the height, diameter of trees and tree
density and global tree density dataset, we analyzed theforest.
To determine the volume of downed woody debris in thisstudy, we used Huber’s formula (9). Then we modified
this formula as (10).

9:;
0 < ABCDE*F[G +] = 3< … (4)
ABCD
9:;

i: infiltration rate at time, I(t): cumulative infiltration at time,Δθ: volumetric water content deficit, ψ: the suction at wetting
front, t: time.
Based on field observation, a slip-type landslide occurred
and was parallel to the slope surface. Thus, the infinite slope
stability model can be used to analyze the stability of theslope based on the factor of safety (FS). The shear strengthof soil used in this study is based on the Mohr-Couloms
failure criterion and can be expressed as follows:_ = ` 1V … 9_ = b1(Wcd) … (10)

V: volume of standing trees, L: the length of a downed
woody debris piece, Am: the cross-sectional area atlongitudinal midpoint, H: height of the trees, A (DHB): thecross-sectional area at breast height.
Tree density is important, and we obtained tree density datafrom the global scale tree map by Crowther4) (2015) (Fig 1).

IJ = 6 + K?L*M … (5)τ: shear strength of soil, c: the total soil cohesion, σ: thenormal stress, φ: total friction angle.
Effective normal stress resists downslope movement andcan be expressed as follows:
K=%OCP< + Q − % O< ABCR D … (6)D: depth of soil, γsat: saturated soil unit weight, γt: thesubmerged soil unit weight.
The mobilized shear force and therefore the FS can be
expressed as follows:
TU = [ VOCP< + W − V O< CXYD + OZ VCXYD … (7)\] =A + V OCP< + W − G O< ABCR DCXYD
V OCP< OZ + W − G O< ABCDCXYD

Fig. 1: Tree Density map of the study site from the global
man of tree density… (8)Tree height data are found to be normally distributed(P=0.461) and diameter shows a right-skewed distribution(P-0.183). Mean of the height is 15.09m (Coefficient
Variation is 0.39) and that of diameter is 0.30m (CV: 0.52)

τdmobilized shear force, γw: water unit weight

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By Monte Carlo method, the peak of volume of standing is
calculated. The volume peak is 0.75m3.

#

Third# step:# Active# stream# channel#

identification
We use satellite images and aerial photography of Google
Earth and Geospatial Information Authority of Japan todetermine the active stream channel zone.
#

Final#step:#Estimation#of#woody#debris#

recruitment# From 1st step, we can gain the FS map for August 30,
Iwaizumi, 2016 (Fig. 2). I this figure red zone means unstablearea, that is, area of FS<1. Unstable areas are located on steep
hillslope (>30), which is consistent with results of fieldsurvey. The field surveys showed that slope failures occurredalong the channel banks and in high elevation areas withsteep slopes. Compared to the actual landslide scars fromaerial photograph, the GIS-Based Shallow model
overestimated the unstable areas, maybe because the spatial
variance of soil parameters was limited; in addition, access tothe study site was difficult. Moreover, determining land slide
scars from aerial photos may be mistaken because the effectof tree cover and shadow. In this study, the unstable area wasestimated as 0.42km2.
As mentioned previously, woody debris recruitment wascalculated based on the values stated above (2.2.1). In
addition, by field survey, we checked that woody debris dams
exist along the stream channel and slopes. We estimates thatthe total the total volume of woody debris dams in the site
was approximately 668.5m3 (including void, soil, sand androcks), and those in the stream was 178m3.
The volume of woody debris deposited downstream was
524m3 and that of woody debris dam in the stream channel
was 178m3 from the survey, so the total volume with thistyphoon may be 702m3 in total. However, on our analysis,
the calculated value of woody debris recruitment is 638m3,
so we are sorry to conclude that this study underestimated the
woody debris entrained downstream by 9.1%.

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3.# Conclusions#
In our study, we improved a procedure to analyze theamount of woody debris that could be transported
downstream. From the formulas and equations, we canestimate the woody debris recruitment over a watershed.
However, FS value overestimates the actual landslides.
Moreover, we found that our procedure underestimates thisamount compared to field-based measurements. However,
our results was able to demonstrate that the landslide or slope
failures are significant, procedure of woody debris in thecatchment area, which was confirmed by field surveys
following the landslides.
References#1)! Youichi Ohkura,:Fluidization mechanism of landslides
and prediction of the travel distance, Bulletin of theForestry and Forest Products Research Institute, 3(2),p109-164, 2004
2)! Hitoshi Gotoh, Eiji Harada and Tetsuo Sakai: Effect offallen trees on landslides at mountain stream, Annual
Journal of Hydraulic Engineering, 45, p721-726, 2001
3)! Chen L, Yong MH: Green-Ampt infiltration model for
sloping surfaces, Water Resources 42, p1-9,2006
4)! Crowther TW, Glick HB, Covey KR et al: Mapping tree
density at a global scale, Nature, 525, p201-205, 2015.

Fig. 2 Map of the FS, the locations of shallow landslides
and the active stream channel

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Evaluation and sensitivity analysis forsurface and bottom snowmelt process by runoff analysis
Koji Sakamoto1*, So Kazama1
1

Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan*E-mail:koji.sakamoto.r5@dc.tohoku.ac.jp

Abstract
Bottom snow melting is affected by groundwater and bottom soil type, and bottom snowmelt amount varies within theyear even at the same spot. Runoff analysis was conducted to validate the accuracy of bottom snowmelt processes
proposed by Saito et al. (2017) using laboratory snowmelt experiments and a one-dimensional advection diffusion model.
In addition, sensitivity analysis of meteorological data was conducted to identify the cause of error in the runoff analysis.
A Nash-Sutcliffe (NS) coefficient of 0.70 was obtained from the runoff analysis. Months when bottom snowmelt isdominant had little influence in lowering the NS coefficient, but months when surface snowmelt is dominant needs further
consideration. Also, lack of sufficient meteorological data of the basin can be a cause of low accuracy, and sensitivity
analysis showed that water vapor pressure has high sensitivity on the runoff analysis in winter.

Keywords: Bottom snow melting, Heat balance method, Lorentz curve, Gini coefficient, Water vapor pressure

1.#Introduction#
Japan is one of the world's heaviest snowfall countries, andmore than 50% of the area is designated as a heavy snowfall
area that accumulated snow accumulation value of 50 meters
or more in a year. For that reason, long-term and spatial graspof snow cover on basin scale is important for flood control
and water use. Therefore, the purpose of this research is to
examine the validity of calculation of bottom snowmelt
amount by runoff analysis and consider the accuracy of thewhole snowmelt model. In addition, we will clarify the causeof the error in the outflow analysis.

Noshiro(6m)
Futatsui(19m)
0

Hachimantai(578m)

2.#Study#basin#and#data#
The study basin is the Yoneshiro River Basin located in thenorthern part of Akita Prefecture (Fig-1). Yoneshiro River isa first-class river, and catchment area is 4,100 km 2 and trunkline extension is 136 km. The calculation period is from
September 1, 2014 to May 31, 2016, and the analysis period
is from September 1, 2015 to May 31, 2016. Table-1
summarizes the data required for calculation of snow
accumulation model, snowmelt model, hydrological modeland its data source.

Fig-1 Study basin
Table-1 Data details

Dataelevation
climatestreamflow

3.#Models#and#methods#
3.1#Snow#accumulation#model#
The SWE (Snow Water Equivalent) model of Kazama et al.
1)
was used to estimate the distribution of SWE in the basin.
The time change of SWE is expressed by the following
equation.
! "#$= "' − ")!%20km

SourceNational Land Numeral Information
AMeDAS
Hydrology water quality database

3.2#Snowmelt#model#
The amount of snowmelt was calculated by the sum of
surface snowmelt amount and bottom snowmelt amount, thesurface snowmelt amount was obtained by the heat balance
equation, and the bottom snowmelt amount was obtained byequation (3)2). The calculation formula of snowmelt amountis shown below.

(1)

where, "#$ is snow water equivalent (m), "' is snowfall(m/s), ") is snowmelt (m/s).

*+ = 1 − -./ " ↓ + 23 − 24 − 5 − 6$ + *7

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observation

culculation

Tohoku$University!

bottom snowmelt

1000

surface snowmelt

10

snowmelt (mm/d)

streamflow (m3/s)

800
600
400

5

200

0

0

9

10

11

12

1

2

3

4

11

5 month

12

1

2

3

4

month

Fig-3() Snowmelt in Noshiro

Fig-2 Streamflow in Futatsui

bottom snowmelt

surface snowmelt

30

*+
")8
") =
+
E
6F ×HI24(3)

25

snowmelt (mm/d)")8 = 9 − 0.378 logBC D E(4)where, *+ is the amount of heat used for surface snowmelt(W/m2), -./ is albedo, " ↓ is the shortwave radiation
amount (W/m2), 23 is the downward long-wave radiation
amount from the Earth's atmosphere (W/m2), 24 is the
upward long-wave radiation amount from the snow coversurface (W/m2), 5 is the sensible heat transport amount(W/m2), 6$ is the latent heat transport amount (W/m2), *7
is the rainfall heat amount (W/m2), 6F is the latent heat offusion (= 3.33×10L J/kg), HI is the density of water(=1.00×10M kg/m3), 9 is the parameter given for each landuse,(9=4.5767 in building land and truck transportation land,
9 =4.0767 in the field, agricultural land, and golf course,
9=3.5767 in the forest and wasteland), D is the time since
the start of snow cover (h).

1 L⁄M B Z
Uℎ EY
V

N[
= QRS − \8N%T

[ = ]\8

15
10
5
0
11

12

1

2

3

4

month

Fig-3() Snowmelt in Hachimantai

3.3#Method#of#sensitivity#analysis#
Due to the lack of observatory in the basin, atmosphericpressure data, water vapor data and total solar insolation
amount data were obtained only at one point. For this reason,
sensitivity analysis was performed on these data to assess theimpact of these data on runoff analysis. First, the maximum
observation value and the minimum observation value ofeach data were obtained. Then, the interval between theminimum value and the maximum value was equally divided
into 10, 10 types of input values were created, and the runoff
analysis was performed. The input values were named rank
1, rank 2 ... rank 10 from low value.

3.2#Runoff#model#
The amount of runoff was estimated based on thecontinuous equation and the Manning equation in the groundsurface and the riverway part, and the storage function
method in the ground. The calculation formula of snowmelt
amount is shown below.
NO N*
(5)
+
= Q + ") − QRS − $T E U
N% NP
*=

20

3.4#Evaluation#index#of#analysis#
Nash-Sutcliffe efficiency coefficient was used as anevaluation index of runoff analysis. The following formula
was used for the Nash-Sutcliffe efficiency coefficientequation.

(6)_" = 1.0 −

(7)

e
Z
RfB *`8aR − *bcdReZ
RfB *`8aR − *`8a(9)where, *`8aR is the observation river flow rate at timestep g(m3/s), *bcdR is the calculated river flow rate at timestep g(m3/s), _ is the number of hours analyzed, _" is the NashSutcliffe efficiency coefficient. Accuracy was defined asvery good at 0.75 <NS≤1.00, good at 0.65 <NS≤0.75, normal
at 0.50 <NS≤0.65 and bad at NS≤0.50.And, Gini coefficient
was used as an evaluation index of sensitivity analysis. The
calculation formula of Gini coefficient is shown below.

(8)

where, O is the flow cross-sectional area (m2), U is themesh width (m), * is the outflow flow rate (m3/s), % is thetime (s), P is the distance in the downward flow direction(m), Y is a slope gradient, QRS is the permeation amount(m/s), $T is the evapotranspiration amount (m/s), [ is thestorage height (m), \8 is the baseline flow outflow rate(m/s), and ] and ^ are the model parameter (]=120.0, ^=0.5)3).

B

hgVg = 2

P − 2 P !P

(14)

C

where, 2 P is the Lorenz curve, and P is the level of thestreamflow.

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level1level2level3level4level5level1level2level3level4level5level6level7level8level9level10level6level7level8level9level10

1000

1200

streamflow(m3/s)

1400

streamflow(m3/s)

1200

1000

800
600

400
200

800
600
400
200

0

0

11

12

1

2

3

4

5

11

month

12

Fig-5() Sensitivity of solar insolation

1

2

3

4

5

month

Fig-5() Sensitivity of water vapor

level1level2level3level4level5level6level7level8level9level100.5
0.4

Gini coefficient

1200

streamflow(m3/s)

Tohoku$University!

1000
800
600
400

total solar
insolation

0.3

water vapor

0.2
atmosphericpressure

0.1
0

200

11

0
11

12

1

2

3

4

5

month

Fig-5() Sensitivity of atmospheric pressure

12

1

2

3

4

Fig-6 Gini coefficient

5

month

coefficient is shown in Fig-5. As shown in Fig-4, large
fluctuations are observed in runoff analysis with steampressure data changed. It can be seen that the top 5 andbottom 5 rankings show different behaviors than in early
March. The top five was the maximum flow rate in early
April and the bottom 5th was the maximum flow rate in earlyMay. As for atmospheric pressure, there is little disparity inrunoff analysis. Regarding the amount of solar radiation,although there are some disparities in peak flow rate, there isno variation as much as vapor pressure. Regarding the Gini
coefficient, from Fig-5, it is early March that the Gini
coefficient fluctuates greatly, which is consistent with thetime when the surface snowmelt begins to dominate. In otherwords, the surface snowmelt amount calculation shows alarge influence on the error of the runoff analysis. In addition,
the maximum value of the Gini coefficient of the solar
radiation amount and the atmospheric pressure is both 0.10
or less, while the Gini coefficient of the vapor pressure is alarge value of 0.46. From the above, it is concluded that thecause of the error in the outflow analysis is the vapor pressure
data.

4.#Results#and#discussions#
4.1#Runoff#analysis#Fig-2 shows the calculation results of the river flow rate at
the Futatsui flow rate observation station. The NS coefficient
was 0.70, which was judged to be good as the set precision.
However, it underestimates the flow rate from the middle ofDecember to the beginning of February and from the middleof February to the end of March. In addition, the flow rate
was overestimated from the beginning of April to the snow
disappearance in early May, and although the observation
value is increasing, the calculated value is decreasing.
Although the reproducibility up to the start of snowfall was
high, errors in winter were noticeable.
Fig-3(i) ~ (ii) show the bottom snowmelt amount andsurface snowmelt calculation results at Noshiro AMeDAS
Observatory and Hachimantai AMeDAS Observatory from
November to April. On both points, it can be seen that snow
melting on the bottom is dominant from December to midFebruary. In addition, it is only a few days in late Februarythat surface snow melting is predominant at Noshiro
AMeDAS Observatory, and it can be seen that the snowmelt
was snowed almost by bottom snowmelt. However, at
Hachimantai AMeDAS Observatory, there is almost no snow
melting until the end of February compared with Noshiro
AMeDAS Observatory, and from the beginning of March
there has been a large amount of snow melting due to surface
snowmelt. That’s why it is thought that the cause of the errorof the flow rate is the underestimation of snowmelt amountfrom the end of February to the beginning of March in themountainous area and the excessive snowmelt estimation
after the beginning of April.

5.#Conclusions#1) It was suggested that the cause of the error in the runoff
analysis is the calculation of snowmelt amount.
2) It was found that the vapor pressure data greatly
influences the calculation of snowmelt amount.

!
!

References#1)! Kazama S, Izumi H, Sarukkalige R P, Nasu T,
Sawamoto M: Estimating snow distribution over a large
area and its application for water resources,
Hydrological processesVol.22pp.2315-23242008
2)! Masato Saito, So Kazama, Syunsuke Aita: Evaluation ofmutual effect of the storage of the subsurface water andsnow in basin areas, technical presentation meeting of
Tohoku branch of the Japan Society of Civil Engineers,
2017.

4.2#Sensitivity#analysis#Fig-4(i) ~ (iii) shows the outflow analysis results obtainedby changing the atmospheric pressure, vapor pressure, andsolar radiation amount data at the Futatsui flow rate
observation station, and the time series change of the Gini

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Estimation of nutrient distribution
in Mekong river floodplain using satellite images
Keitaro Yamada1, So Kazama1
1

1Graduate School of Engineering, Tohoku University*E-mail: keitaro.yamada.r4@dc.tohoku.ac.jp

Abstract
Estimation of the total phosphorus concentration distribution using satellite images on the Mekong River floodplain was
carried out. Partial least squares regression analysis was applied to satellite images of rainy season and dry season, andchlorophyll a concentration was obtained. Past studies about for relational expression between chlorophyll a and total
phosphorus were examined, and the total phosphorus concentration was also determined from the concentration ofchlorophyll a. From the regression equation in the rainy season the concentration distributions in the rainy season and the
dry season were estimated. From the regression equation in the dry season, the concentration distributions in the rainyseason and dry season were estimated. The correlation coefficient between the estimated value of the rainy season andthe observation value by the regression equation of the rainy season was 0.97, and the correlation coefficient in the dry
season was 0.070. The correlation coefficient in the dry season by the regression equation of the dry season was 0.66,
whereas the correlation coefficient in the rainy season was -0.49. It is understood that the regression equation cannot beapplied to different seasons and it was found that it is necessary to obtain a regression equation on suitable season for
more accurate estimation.

Keywords: remote sensing, Cambodia, chlorophyll, total phosphorus
1.#Introduction#
It is said that the floodplain is important for agriculture andecosystem. In the downstream of the Mekong river, thelongest river in Southeast Asia, especially Cambodia, Rice
cultivation using the floodplain is done. For Cambodia,
where the main industry is agriculture, the floodplain brings
significant benefits and it is necessary to evaluate theinfluence of this flood on agriculture recently. Hiraga
estimated nutrient distribution using nutrient transportation
1)
model . However, although this model contains observation
information by onsite observation, it is information at several
points, not in wide area. Therefore we thought it would bepossible to estimate more accurately by using satellite images.
This estimation is thought to lead to the assessment of theinfluence of flood on agriculture. Based on the above, thepurpose of this research is to estimate the nutrient distributionof flooded water using satellite images.

Fig.1 Study area

3.#Dataset#and#Method#
Since the limiting factor in this area is phosphorus, we treat
only phosphorus in this study. Moreover, it is difficult to
directly estimate phosphorus present in various forms inwater from satellite images. Therefore, according to the pastcase, the concentration of chlorophyll a which was estimatedfrom the satellite image is first obtained. By clarifying thespectral reflection characteristics of chlorophyll a which is
the observation target, its amount can be estimated from thesatellite image. We tried to estimate total phosphorus
concentration from chlorophyll a concentration.

2.#Study#Area#
The study area is a 140km×110km area (fig.1). There is
Phnom Penh in the center of this area. In this area, water
overflows from the Mekong river during the rainy season andspreads to the downstream area. In the floodplain, ricecultivation using nutrient-rich soil is done. In addition, thefloodplain is not only an agriculture land, it also becomes a
habitat for fish. The study area belongs to the tropicalmonsoon climate, the rainy season from May to October andthe dry season from November to April. The rainfall is thehighest in September, and the depth of the Mekong river isthe largest in October.

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In this study, Landsat data was used as satellite images.
Landsat is an earth observation satellite aiming at observing
the global environment by optical observation at multiple
wavelengths. It has seven bands between 33 mm and 2300
mm, and the DN value is measured from the reflectance ofthe lights of each wavelength. The data used are satellite
images of Landsat-8 / OLI in September 2017 and February
2018.

Tohoku$University!

Table.1 Result of PLS(2) Conversion to reflectanceThe conversion equation for obtaining the reflectance fromthe DN value is shown in equation (1).
1.4./0123where ρλ# reflectance of bandi(%) '()* 4 DN value of bandi%& multiplicative rescaling factor -& additive rescaling
factor 123 local sun elevation angle.
(3) Estimation of chlorophyll a concentration
PLS analysis was performed using chlorophyll aconcentrations of flood water at 14 sites in the rainy season
and 12 dry seasons obtained by site observation as objective
variables, and 7 DN values of Landsat data at the same point
as explanatory variables. The equation of estimation isequation (2)5ℎ78 = 9: + 9; ×ρλ; + 9= ×ρλ= + 9> ×ρλ>+9? ×ρλ? + 9@ ×ρλ@ + 9A ×ρλA + 9B ×ρλB

(2)

Dillon(1974)

1

Carlson(1977)

0.6
0.4

Rainy season

0.2

Dry season

0
0.02

0.04

0.06

0.08

Chl-a concentration

mg/l

0.1

4.#Result#and#Discussion#(1) Conversion to reflectance
Table 1 shows the results of PLS analysis for rainy season
and dry season, respectively. The concentration ofchlorophyll a was estimated using this equation. And we
calculate only in the water area extracted by normalized
difference water index.,(2) Estimation of nutrient concentration
As a result of calculating the square sum of the difference
between the estimated value and the observation value, theequation by Dillon was minimized. (Fig.2).

(Sakamoto, 1966)(3) Distribution of TP concentration
Chlorophyll-a concentration was estimated using a PLS
equation, and the total phosphorus concentration was
estimated using Dillon's equation. The concentration
distribution map of total phosphorus in the rainy season andthe dry season was prepared by the regression equation
obtained from the observation information of the rainyseason, and the concentration distribution map of the rainyseason and the dry season was similarly prepared by the
regression equation of the dry season (Fig. 3 ~ Fig. 6). The
correlation coefficient between the estimated total
phosphorus concentration obtained by two regression
equations and the total phosphorus concentration obtained byfield observation was obtained. The correlation coefficient
between the estimated concentration in the rainy season
estimated from the observation information of the rainyseason and the observation value is 0.97 and the correlation
coefficient between the estimated concentration in the dry(3a)(Dillon et al 1974)(3b)(Carlson, 1977)
log;: 5ℎ78 = 1.45log;: K − 1.06

0.8

Sakamoto(1966)

Fig. 2 TP and Chlorophyll concentration(4)Estimation,of,nutrient,contribution,
We examined whether the equation of the past study 2)~4) can
also be used in this area. Equation (3) shows these relational
equation.

log;: 5ℎ78 = 1.45log;: K − 1.13

1.2

0Where 5ℎ78 chlorophyll concentration (µg/l) 9: intercept
9; ~9B regression coefficient.

log;: 5ℎ78 = 1.53log;: K − 1.13

mg/l

(1)

TP concentration

ρλ# =%& '()*+ + -&(3c)

Where 5ℎ78 chlorophyll concentration (mg/l) PK total
phosphorus concentration (mg/l)	
 

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Phnom Penh
Prey Veng

Takeo

TP concentration

TP concentration(mg/l)

(mg/l)



5

5

0

0

Fig.3 TP concentration in rainy season(rainy season PLS equation)

Fig.4 TP concentration in rainy season(dry season PLS equation)

TP concentration

TP concentration(mg/l)

(mg/l)



5

5

0

0
Fig.5 TP concentration in dry season(rainy season PLS equation)

Fig.6 TP concentration in dry season(dry season PLS equation)season and the observation value by the regression equation
obtained from observation information in the dry season is0.66. While the correlation coefficient between the estimatedconcentration in the dry season and the observation by the
regression equation in the rainy season is 0.07, the correlation
coefficient between the estimated concentration in the rainyseason and the observation value by the regression equationin the dry season is as low as -0.49 Value. It was shown thatthe regression equation obtained from observation
information of rainy season and dry season cannot be used
for estimating the concentration of different seasons.

cannot be used for estimating the concentration of different
seasons. By conducting PLS analysis for each season, moreaccurate concentration estimation is possible.

References#1), - 2/*  &%'!
.( 1+ #,"0 B1$) 73  ) 4  pp283-288 20172), Sakamoto, M: Primary production by phytoplankton
community in some Japanese lakes and its dependence
on lake depth. Arch. Hydrobiol. 62: 1-28, 1966.
3), Dillon, P. J. & Rigler, F. H.: The phosphoruschlorophyll relationship in lakes. Limnol. Oceanogr. 19
p.767 773 1974.
4), Carlson, R. E.: A trophic state index for lakes. Limnol.
Oceanogr. 22: 361–369, 1977.

5.#Conclusion## In this research, the TP concentration in the rainy season
was calculated based on the observation information of therainy season and dry season. The concentration in dry season
was also estimated. It was shown that the concentration
increases in the edge of the flood plain and around the urban
area. However, the regression equation obtained fromobservation information of rainy season and dry season

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Long-term change and spatial anomaly of precipitation in Osaka, JapanRyosuke Inomata1*, Daisuke Komori2, So Kazama1
1

2

Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan
Graduate School of Environmental Studies, Tohoku University, Sendai 980-8579, Japan*E-mail:ryosuke.inomata.p4@dc.tohoku.ac.jp

Abstract!
In this study, we statistically analyzed the spatial deviation of the precipitation and rainfall frequency distribution in
the urban area of Osaka and its surrounding areas using the radar AMeDAS precipitation data for 23 years (1993 to 2015).
As a result, the following findings were obtained. (1) In western part of the urban areas, precipitation, frequency of rain,
frequency of heavy rain (precipitation (mm/hour) 	
 5mm) was greater than that in the surrounding area, especially during
the time zone during afternoon. (2) In northwestern part of the urban areas, the tendency that precipitation and frequencyof rain was greater than that of the surrounding areas has been expanded over time (3) In the southwestern part of theurban areas, the tendency of precipitation and frequency of rain was lower than that of the surrounding areas has been
expanded over time. From the above, we clarified that the impact of cities on weather is different inside the city.

Keywords: Precipitation, Spatial deviation, Interpolation, Radar AMeDAS

1.*Introduction*
As one of the influences of cities on weather, there is achange in precipitation. Many studies have pointed out thatrainfall phenomena in urban areas is different from
1)2)3)4)
5)
surrounding areas. Fujibe calculated spatial deviationof precipitation in the urban area and its surrounding areasand statistically analyzed precipitation and precipitation
frequency distribution in urban areas. However, Fujibe’s
studies have used AMeDAS data observed at fixed point,2
which was established at about 1/17 km (pieces/km ) in Japan,
so challenges remain in spatial representation. Therefore, inthis research, we will clarify the spatial deviation of the
precipitation and rainfall frequency distribution in the urban
area of Osaka and its surrounding areas, using radar
AMeDAS analytical precipitation data that synthesized
surface observation by rainfall radar and AMeDAS
stationary observation.

N

5km

Fig.1 Study area

2.*Methodology*
Fig. 1 shows the study area, the "urban area" (yellow mesh)and "surrounding area" (green mesh) in this study. In thisresearch, 4 meshes which are occupied by populated areas ofhigh - rise buildings and densely low - rise buildings wereregarded as "urban areas" from the land use map of Osaka.
The size of the mesh is 5 km square, and it matches the spatial
resolution of the radar AMeDAS analytical precipitation. The"surrounding area" was 20 mesh outside of 2 mesh from the"urban area" mesh. Note that land use in meshes in the"surrounding area" is different from "urban area", and low
rise buildings occupy a large proportion. The study period
is the period from June to August of 1993 to 2015 when radar
AMeDAS analysis precipitation data is available at thepresent time. The reason for targeting the period from June
to August is that it seems that there is a biggest difference inrainfall between urban areas and their surrounding areas.

The flow chart of the research is shown in Fig 2. For
analyzing the precipitation phenomenon in the target area,
precipitation amount (mm), rainfall amount	  1 mm hournumber as an index of rainfall frequency, rainfall amount	 5 mm hour number as index of heavy rainfall frequency wasused as an analysis index. For each mesh, we calculate theaverage value of the 30-day precipitation and the 30-day
integration of the number of precipitation amount ≥ 1 mm,
and the precipitation ≥ 5 mm hour (hereafter referred as pj (jrepresents each mesh). Interpolation value (pi) in urban area
was calculated from pj in the surrounding area by using leastsquares method defining quadratic surface based on Fujibe 1).
A quadratic surface expression defined as an interpolation
formula is optimized by the least squares condition todetermine each coefficient b1 to b6 of the quadratic surface
f(!, #) = b1! $ + b2# $ + b3!# + b4! + b5# +b6
(1)
+
$
[&
−
)
!
,
#
]
(2)
'
' '
'

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xj, yj represents the latitude and longitude of each mesh, andJ represents the total number of meshes (= 20) in thesurrounding area. The spatial deviation of rainfall and rainfall
frequency (hereinafter referred as ri) in urban areas andsurrounding areas was defined as equation (3).
/,- = 0 (3)/G8Safety$
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Spatial deviation ri



1

time

Spatial deviation ri

Fig. 3 Diurnal variation of precipitation in the northwest

Fig 2 Flowchart*
3.*Result*and*discussion*
Spatial deviation for the convenience of the paper, Fig. 3,
4 and 5 show only the diurnal change of ri for each index in
the northwestern part of the urban area where the mostprominent trend in the spatiotemporal analysis in the June toAugust period was observed. The error bars show standard
deviations. The day was classified into three time zones fromsunrise to noon (4 to 11 o'clock), noon to sunset (12 to 19
o'clock), sunset to sunrise (20 o'clock to 3 o'clock, and weanalyzed. In ri's space-time analysis, in the western part of
the urban area, ri of precipitation, rainfall frequency, heavy
rainfall frequency was large during the noon to afternoon. In
the northwestern part of the urban area which is the upwindside of the westerly wind, the trend of the ri of the
precipitation, frequency of rainfall ,frequency of heavy
rainfall to increase becomes consistent with the findings 1) 2)
5)of domestic and overseas studies. The finding that ri of
rainfall frequency also showed a trend to increase in the noon
to afternoon time is a new finding obtained by enhancing the
spatial representation of precipitation data by using the radar
AMeDAS analysis precipitation. In the analysis of aging ofri, it became clear that ri tends to increase over time in thenorthwestern part of urban area. It became clear that the timezone during which ri gets larger spreads over a wider timezone. On the other hand, in the southwestern part of the urban
area, ri of rainfall tended to decrease with age. That is, weclarified that the influence of the city on weather is different
inside the city. This is a new finding obtained in this researchby analyzing urban areas divided by 5 km resolution.

time

Fig. 4 Diurnal variation of precipitation ≥ 1 mm in northwest

Spatial deviation ri3)! In the southwestern part of the urban areas, the tendency

timeFig. 5 Diurnal variation of precipitation ≥ 5 mm in northwestof precipitation and frequency of rain was lower thanthat of the surrounding areas has been expanded overtime.

5.*Reference*1)! Huff and Vogel: Urban, Topographic and Diurnal
Effects on Rainfall in the St. Louis Region
J.Appl .Meteor,17,1141 −1152, 1978
2)! Wesccot: Summertime cloud–to-ground lightning
activity around major Midwestern urban areas
J.Appl .Meteor, 34,1633-1642, 1995
3)! Balling, R. C. Jr. and S.W. Brazel: Changes in Phoenix
Arizona summertime diurnal precipitation patterns.
Theor. Appl .Climato1,38,50-54, 198
4)! JaureguiUrban effects on convective precipitation in
Mexico CityAtmos, Environ303383-33891996

4.*Conclusions*1)! In western part of the urban areas, precipitation,
frequency of rain , frequency of heavy rain was greaterthan that in the surrounding area, especially during thetime zone during afternoon2)! In northwestern part of the urban areas, the tendency thatprecipitation and frequency of rain was greater than thatof the surrounding areas has been expanded over time

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Performance of sewage treatment and bioenergy production using duckweed○Hiroshi IWANO1*, Takashi OTANI1, Syo HATOHARA1 ,Tadashi TAGAWA2, Hideyuki TAMAKI3,
Yu-you LI1 & Kengo KUBOTA1
1

Department of Civil and Environmental Engineering, Tohoku University, 6-6-06 Aoba, Aramaki, Aoba-ku, Sendai,
Miyagi, 980-8579, Japan.
2
Department of Civil Engineering, National Institute of Technology, Kagawa College, 355 Chokushicho, Takamatsu,
Kagawa, 761-8058, Japan3
Bioproduction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST),
Central 6, Higashi 1–1–1, Tsukuba, Ibaraki 305–8566, Japan*E-mail: hiroshi.iwano.q7@dc.tohoku.ac.jp

Abstract

In this study, two species of duckweed (Lemna sp. and Spirodela sp.) were cultivated using three different
treated sewage (i.e., sewage treated by primary sedimentation, conventional activated sludge process, anddownflow hanging sponge process (DHS)) to evaluate sewage treatment efficiency, biomass production
performance and bioenergy production potential. Sewage treatment and biomass production were stable with
DHS- and primary-treated sewage, but not with conventional activated sludge process-treated sewage. Spirodela
sp. was superior to Lemna sp. in terms of nitrogen removal performance. On the other hand, biomass production rateof Lemna sp. was greater than Spirodela sp.. Methane production potential of Lemna sp. and Spirodela sp. were
320 and 304 NmL/g-VS, respectively, and eventually approximately 65% of the input COD was converted tomethane. These results indicated that duckweed is a suitable substrate for methane fermentation.

Keywords: duckweed, sewage treatment, anaerobic digestion

phosphorus (compared with primary-treated sewage) are acharacteristic of conventional activated sludge-treatedsewage. Some sewage treatment processes can achieve
nitrification, and generate nitrate. One process achieving
successful BOD removal and nitrification is down-flow
hanging sponge (DHS). DHS is an aerobic biologicaltreatment process that employs polyurethane sponges as thecarrier for retaining bacteria. The energy requirement for theoperation of DHS is very low due to no need of aeration,and the amount of excess sludge generated from DHS isextremely low. The primary-treated sewage is often
subjected to DHS, and the effluent water from DHS
contains low BOD (compatible to a conventional activatedsludge) and nitrate.
In this study, treated sewage with three different
characteristics (i.e., high ammonium-nitrogen andphosphorus
concentration
withhigherBOD[primary-treated sewage], moderate ammonium-nitrogenand low phosphorus concentration with low BOD [activatedsludge-treated sewage], and moderate nitrate-nitrogen andphosphorus concentration with low BOD [DHS-treatedsewage]) was used to evaluate biomass production fromtreated sewage using duckweed. In addition, the sewage
treatment performance in duckweed cultivation pond wasevaluated. Eventually, bioenergy potential of duckweed wasevaluated for energy production by methane fermentation.

1. Introduction
In recent years, it has attracted attention to produce
bioresources using nutrients such as nitrogen andphosphorus in sewage/wastewater, rather than removing
them using expensive treatment systems. One approach is toutilize aquatic plants, especially duckweed. Duckweed cangrow with uptaking nutrients from various wastewater suchas sewage, swine wastewater and effluent from anaerobic
digester treating sewage sludge. A growth rate of duckweed
is fast, and duckweed is easy to be harvested because they
are floating on water surface. Duckweed contains a large
amount of starch and protein and less lignin and cellulose.
Due to this characteristic, duckweed is being attractive as asource for biofuel (e.g., bioethanol and biomethane),
livestock feed, and even human food.
The potential of bioresource recovery using sewage as a
medium is huge due to its great amount. The characteristics
of sewage change upon the degree of the treatment, and it
has been developed many processes for treating sewage.
One of the most applied process is a conventional activatedsludge process. In this process, sewage is primary treated inprimary sedimentation tank, where large particles of organic
matters (i.e., suspended solids) are removed. Sewage after
primary sedimentation tank contains a certain degree oforganic matters, ammonium-nitrogen, and phosphorus. The
primary-treated sewage is subjected to a reaction tank,where microorganisms take a place for nutrient removal,
especially for BOD removal. Low BOD concentration and alittle bit lower concentrations of ammonium-nitrogen and

2.
2.1

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Materials and Methods
Duckweed pond system

The 6th International Symposium on Water Environment Systems----- with Perspective of Global Safety(Nov. 29th - Nov. 30th, 2018)

Two species of duckweed, Lemna sp. and Spirodela sp.,were used for experiments. After one week acclimation toeach treated sewage, approximately 30 g (wet-weight) of
duckweed was subjected to 7.4 L of a duckweed pond witha raceway flow system (DP) and sewage treated by different
processes (i.e., a primary sedimentation process (PS), a
conventional activated sludge process (AS), and a DHS
process) was supplied to DP with HRT of 1 day. The
experiment was conducted at 25˚C under about 8,000 lx (16h/day irradiation). Water quality of influent and effluent
water was analyzed to evaluate sewage treatment efficiency
by duckweed, and grown duckweed was harvested every
week to evaluate biomass production performance.

Tohoku University

experiments using PS- or DHS-treated sewage. The TIN
concentration and the PO43--P concentration decreased from36.4 mg-N/L to 27.3 mg-N/L in average (0.50 g-N/m2/day)and from 3.7 mg-P/L to 1.7 mg-P/L in average (0.11
g-P/m2/day), respectively, with Lemna sp.. The
concentrations of TIN and PO43--P decreased from 36.4
mg-N/L to 22.2 mg-N/L in average (0.79 g-N/m2/day) andfrom 3.7 mg-P/L to 1.6 mg-P/L in average (0.12g-P/m2/day), respectively, with Spirodela sp.. The average
TIN removal rate were 25% for Lemna sp. and 39% forSpirodela sp.. The PO43--P removal rates were similar forboth species. The maximum biomass production rate was
19.7 ton-dry/ha/year (2nd week) for Lemna sp., and 14.6
ton-dry/ha/year (1st week) for Spirodela sp. when
PS-treated sewage was subjected.
In the experiment using DHS-treated sewage, the TIN and
PO43--P concentrations decreased from 19.3 mg-N/L to 12.2
mg-N/L in average (0.40 g-N/m2/day) and from 1.6 mg-P/L
to 0.7 mg-P/L in average (0.05 g-P/m2/day), respectively,
with Spirodela sp.. The average TIN removal rates were25% and 37% for Lemna sp. and Spirodela sp., respectively.
The average removal rates of PO43--P were 57% for both
species. Likewise, the maximum biomass production rate
was 11.7 ton-dry-weight/ha/year for Lemna sp. (2nd week),
and 10.6 ton-dry-weight/ha/year for Spirodela sp. (1stweek). Note that remarkable algae growth was observed
when the initial supply of duckweed was reduced to 15 gfrom 30 g, indicating that duckweed should be supplied tocover the surface area in order to carry out stable sewage
treatment and biomass production.

2.2
Biodegradability test of duckweed
The biodegradability test of Lemna sp. and Spirodela sp.
was carried out for 30 days at 35˚C using a 124 ml vial
bottle. The composition of generated biogas was analyzed
by gas chromatography and biodegradability of duckweed
was calculated as methane gas production potential per VS.
3.

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Results and Discussion

3.1
Sewage treatment and biomass production
Monitoring results of NH4+-N, NO2--N, NO3--N, andPO43--P concentrations in DP-treated water (with Lemna sp.)and wet-weight change of Lemna sp. in DP during thetreatment are shown in Figure 1.
When AS-treated sewage was supplied, sewage treatment
efficiency and biomass production performance were
unstable. The total ionic nitrogen (TIN = NH4+-N + NO2--N+ NO3--N) removal rate of Lemna sp. decreased from 0.54
N-g/m2/day (1st week) to 0.24 N-g/m2/day (4th week). As
with the biomass production rate, the biomass production
rate decreased from 7.3 ton-dry-weight/ha/year (1st week)
to 0.2 ton-dry/ha/year (3rd week), and eventually biomass
production rate became negative in the 4th week. Since thePO43--P concentration in AS-treated sewage was very low
compared with PS- and DHS-treated sewage, it wassuggested that phosphorus may be a limiting factor for thegrowth of duckweed.
On the other hands, sewage treatment efficiency andbiomass production performance were stable in the

3.2
Biodegradability of duckweed
Methane gas production was immediately observed after
the start of the experiment. Methane gas production rates ofboth duckweeds were high and approximately 50% of an
input COD was converted to methane in the first 4 days.
Then, sudden decrease in gas production rate was observed
and eventually about 65% of the input COD was converted
to methane. Methane gas production potentials of Lemna sp.
and Spirodela sp. were 320 and 304 NmL/g-VS,
respectively. The results demonstrate that both duckweeds
are a suitable substrate for methane fermentation.

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G-Safety
Tohoku University

Mesophilic and Thermophilic Temperature Phased Anaerobic Digestion of Paper Waste Containing
Food waste: the Effects of Hydraulic Retention Time
○Aijun ZHU1*, Jing WU1, Yu Qin2 & Yu-You Li,1,2
1Department of Frontier Science for Advanced Environment, Graduate School of Environmental Sciences,
Tohoku University, 6-6-20 Aoba, Aramaki-Aza, Aoba-Ku, Sendai, Miyagi 980-8579,
2
Department of Civil and Environmental Engineering, Graduate School of Engineering, Tohoku University,
6-6-06 Aoba, Aramaki-Aza, Aoba-Ku, Sendai, Miyagi 980-8579, Japan;*E-mail: zhu.aijun.t4@dc.tohoku.ac.jp

Abstract

The aim of this study was to investigate the effect of hydraulic retention time (HRT) on methane production
using a single-stage anaerobic process in different temperature phase . Two continuously stirred tank reactors(CSTRs) were used under thermophilic (55±1o C) and mesophilic(35±1o C) conditions in order to enhancemethanogenesis. A mixture of paper waste and food waste (1:1, TS/TS) was utilized. The reactor was operated
at different HRTs to optimize the methane production and organic removal efficiency. Comparison of the t hemesophilic and thermophilic anaerobic digestion of paper waster containing food waste.
The maximum CH4 production rate was achieved at HRT of 7.5 d in thermophilic condition. The TS, VS,carbohydrate and COD removal efficiency was almost same at different HRT and protein showed negative
removal efficiency.

Keywords: temperature phased; anaerobic digestion; paper waste; food waste; Hydraulic retention timeretention time and temperature have been reported as one of
the most important parameters significantly affecting
microbial ecology in CSTR digesters and must be thus
optimized for the particular feedstock fermented in thedigester. Organic loading rate and hydraulic retention time
have been investigated in the literature due to their effect on
biogas production. More specifically, our aim was to study
the effect of HRT and different temperature phase, as thecritical operating parameters on methane production. The
purpose of this work was to assess the possibility ofco-digestion of food waste and high carbon content of waste
paper at different HRT.

1. Introduction
Renewable energy sources have received great interest
from the international community during the last decades.
Organic fraction of municipal solid waste (food waste) is
one of the most promising energy sources. However, despite
its high carbohydrate content, the anaerobic treatment offood waste is quite problematic due to its low bicarbonate
alkalinity high COD concentration and tendency to rapid
acidification. Except for the resistant to biodegradation, thelow C/N ratio of food wasteis also a serious problem to theanaerobic digestion. Although, an optimum C/N range infeedstock for the anaerobic digestion is still debatable in theliterature, 20/1–30/1 is a most acceptable range.
One method to avoid excessive ammonia accumulation isto adjust low feedstock C/N ratios by adding high carbon
content materials, thereby improving the digestion
performance. Most food waste consists of paper material(including office paper and newspaper), which has a C/Nratio ranging from 173/1 to greater than 1000/1. Bothwastes are produced in large quantities and in many places,
and much research has focused on this particular issue.
Co-digestion using paper waste containing food waste as
substrate may enhance the performance of the anaerobic
digestion (AD) due to better carbon and nutrients balance.
That is, the use of a co-substrate that in most cases improves
the biogas yields due to positive synergisms established in
the digestion medium and the supply of missing nutrients by
the co-digestion.
A series of operational parameters including pH,
temperature, reactor configuration, Among them, hydraulic

2. Materials and Methods
2.1 Materials
The compositions of food waste used as substrate in thetest based on previous study which consisted of 30% fruits,
36% vegetables, animal 14% and staple food 20%.. The
collected food waste were ground and homogenized to lessthan 5 mm with tap water in a blender and were stored at
4°C before feeding. FW was supplemented with the traceelements, Fe, Co and Ni: 100 mg/L of Fe (FeCl2∙4H2O), and10 mg/L of both Co (CoCl2∙6H2O) and Ni (NiCl2∙6H2O) .
The paper waste was consisted of office paper toilet paperand newspaper which are the most large amount of recycle
paper in Japan. The ratio of three paper waste was 1：1：1,
cut by shredder before mixing with food waste for feeding.
Seed sludge was from a mesophilic sewage sludge digesterat the Sendai municipal sewage treatment plant locatedalong Sendai, Japan.

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The 6th International Symposium on Water Environment Systems(Nov. 29th - Dec. 30st, 2018)----- with Perspective of Global Safety

Tohoku University

Table 1 The operating conditions and removal rate in digester
mesophilic
Run 1

Run 2

thermophilic
Run 3

Run 1

Run 2

Run 3

35℃

temperature

Run 4

Run 5

55℃

substrate

50%PW

40%PW

40%PW

50%PW

50%PW

50%PW

50%PW

50%PW

HRT(d)

30

20

10

30

20

10

7.5

5

Operation time

1-89

90-142

143-170

1-47

48-119120-142

143-171

172-192

OLR
ｇCOD/L/d

4.3

6.53

13.06

4.3

6.45

12.89

17.1

25.7

TS

65.6±571.3±2.1

39.3±478.1±1.1

73.6±4.1

69.6±0.2

69.2±0.1

54.77±3.1

CODCr

67.22

74.33

30.27

78.94

74.76

72.75

71.17

59.67

(CH2O)

78.03

82.53

75.88

91.53

89.06

87.48

86.34

83.99

protein

16.55

10.97

6.68

29.46

13.05

8.29

9.89

2.12

removalrate %

2.2 Analysis methods
Daily biogas production is measured by wet gas meter, thecomposition of biogas (CH4, CO2 and N2) were measured bya ShimadzuGC-8A gas chromatograph. and pH, COD, TS,ammonium nitrogen, alkalinity, VS and VSS were measured
according to Standard Methods. Sludge samples were
sampled twice a week from the digesters and the substratetank to determine the total and soluble parameters. Samples
for the analysis of soluble items, such as soluble COD(SCOD), total ammonia nitrogen (TAN), VFAs andalkalinity, were centrifuged at 8,000 rpm for 15 min andthen filtered with 0.45 μm filters before they were analyzed.
A GC, equipped with a flame ionization detector ((GC-FID,
Shimadzu GC-14B) and a DB-WAXetr column, wasutilized to detect VFAs and ethanol. A 0.5 mL filtrate wascollected in a 1.5 mL GC vial, and 0.5 mL 0.1 mol/L HCl
solution was also added to achieve an acidic pH.

reactors under continuous stirring conditions and analysed,
for monitoring each reactors performance. Experiments
were conducted successively to determine the optimum
HRT for methane production. Organic loading rate (OLR)
was increased by decreasing the operating HRT. The reactor
was operated at different HRTs with feeding mixture ofpaper waste and food waste. The tested operating conditions
and removal rate in the system are summarized in Table 1.

3.Results and Discussion
The experimental results about organic removal efficiency
obtained at steady-state conditions for the different HRTs.
The TS, VS, carbohydrate and COD removal efficiency wasalmost same at different HRT in thermophilic condition, theall best performance as positive removal was achieved in
HRT＝30 d, TS(77.3%), VS(81.6%) , carbohydrate (91.5%)
and COD(78.9%).Meanwhile, the protein showed negative
removal efficieny.
In the mesophilic fermentation, stable methane
fermentation in high organic loading rate utilizing mixtureof food waste and paper was possible at HRT 20, 30 days.
And the mixing ratio of paper was found to have an
important role in the whole process. The processing was
operating stably if the paper ratio was less than 40%,otherwise the propionic acid began to accumulate, pH wasdecreased, methanogenesis was inhibited, tending toacidification When the ratio of paper was 40%, HRT was
set to 20 days, the process could be operated steadily, but if
HRT was shortened to 10 days, the VFA accumulated
rapidly and biogas production ceased.
In the thermophilic phase, stable operation was possible
even when the waste mixed with food waste and paper (TS)1: 1 ratio, the HRT was shortened from 30 days to 7.5 days,
but the HRT was more shortening, the VFA accumulated, so
the process was failed. Propionic acid tends to accumulate
in both the mesophilic and thermophilic condition when thereaction of the process does not progress properly. Then, it
made other various Volatile Fatty Acid accumulated andinhibition occurred.

2.3 Reactors start-up and operation
A mixture of paper waste and food waste (in the TS ratioof 1:1) was used, based on our previous experiment.
Experiments were carried out in a single-stage continuous
anaerobic process. The anaerobic reactors were cylindricalin shape, made with a double wall, having an operating
volume of 3 L, and were operated at constant temperature(55±1oC) via a heaters and water jackets. The feedstock
was stored in a tank maintained constant temperature at 4oC.

Fig. 1. Schematic diagram of the single-stage systemused in this study for methane production
The continuous operation of the reactor started at HRT of
30 d. Sludge samples were sampled twice a week from65
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G7Safety$
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Use of PMA-PCR for revelation of viable microbial members in anaerobic digesters
Jialing Ni1, Shingo Hatori1, Yu-You Li1, Kengo Kubota1
1
Department of Civil and Environmental Engineering, Graduate School of Engineering, Tohoku University, Sendai,
Miyagi 980-8579, Japan.
E-mail: jialing@dc.tohoku.ac.jp

Abstract
Twelve digester sludge samples were treated with PMA to study actually viable microbial community in 5 sludge fed
digestion systems. The alpha diversity indicated an obvious decrease in numbers of Operational taxonomic unit (OTU), Chao1
index and Shannon index. The microbial community analysis results showed that the obvious decrease of methane-producing
bacteria, and the class Alphaproteobacteria and Betaproteobacteria remarkably decreased by 40.02% - 96.07% and 13.95% 98.71%, respectively, after the PMA treatment in relative abundance. Digester sludge samples treated with PMA showed afarther distance than the original ones from primary sedimentation sludge and excess sludge. This study achievably detected
the PMA-PCR based 16S rRNA gene sequencing method can reflect the microbial structure of microorganisms with activity
in AD samples.

Keywords: Propidium monoazide, 16S rRNA gene sequencing, digester sludge, compromised membrane

PMAxx (Biotium) was added to the DS samples. Afterincubation in dark, light exposure was performed.

1.# Introduction#
Anaerobic digester wildly used in the world nowadays
makes it possible to achieve pollutant removal combinedwith energy generation and sludge reducing. Next
generation sequencing is a very available method that can
not only detect the microorganisms connecting anaerobic
digestion, but also reveal matter decomposing underanaerobic condition, through microbial community structure
analysis. The analysis of 16S rRNA gene extracted fromsamples without special treatment could not represent theactive microbial populations in the certain moment. For
instance, in the situation that some microbial populations
without activity are remained after aerobic treatment inexcess sludge entering into a digester will hardly be proved
whether they did make contribution to the digestion process
or not.
One method to analyze the DNA from viable cells with
intact membrane is a PMA-PCR method. PMA has thecharacteristic that it cannot get into microbial cells with
intact cell membrane (assumed to be live cells). It only
penetrates into cells with compromised membrane (assumed
to be dead cells) and binds to DNA in the cells. Upon the
exposure to bright light, PMA covalently crosslinks DNA,by which dissociation of DNA is prevented even during thethermal treatment during PCR. The cross-linked DNA is notamplified by PCR, so it is not reflected in the community
structure analysis result. Our research is to investigate
microbial community structure in anaerobic sludge digesters
further exploring the actually active microbial community
by using PMA-PCR method.

2.2#16S#rRNA#sequencing#and#analysis#
After DNA extraction, the qPCR analysis was performedby using a LightCycler® 2.0. And PCR was conducted withthe primer set of 341F-806R-mix targeting the V3 -V4region of the 16S rRNA gene. Sequencing was performed
using an Illumina MiSeq platform. Analysis of sequence
data was done using the QIIME software with theGreengenes database. Operational taxonomic unit (OTU)
was made with the 97% sequence identity. The Silva
database (Silva 132 NR99) and the NCBI blast search tool
were also used to confirm the assignment of the taxonomy
of several important OTUs.
3.# Results#and#Discussion#
Since PMA treatment can eliminate the influence of
extracellular and dead cells-derived DNA, quantification ofgene copy number allows to estimate the ratio of viable cellin a certain environment. Detail information about qPCR
results of all the samples is shown in Table1. Ribosomal
RNA gene copy numbers of the DS and DS_PMA samples
55
were 4.3-8.0×10
and 4.1-6.0×10
copies/ngDNA,
respectively. Decreasing rate of gene copy number was
19.1% in average. In our study, through comparing theqPCR results with and without PMA treatment, 17.4% to35.6% of DNA didn’t amplify in qPCR process with PMA
treatment, which can be considered as dead cells, which
don’t have intact membrane. Taking into account theaccuracy limitation of qPCR, it is reasonable to get theconclusion that qPCR results was very close to the results of
Mei and colleagues who pointed out that the involved
microbial residue approximately less than 10% in most of
the full-scale digesters. Therefore, the PMA-PCR based 16S
rRNA gene sequencing method can reflect the microbial
structure of microorganisms with activity in AD samples.
The minimum number of sequence reads in all 46 samples
was 48,919, and randomly picked 35,000 sequence reads

2.# Materials#and#Methods#
2.1 Sample#collection#and#PMA#treatment#
The samples were collected from five different sewage
works in Japan (AKT, NGT, NIT, SEN and TNN sewage
works). In total, 34 sludge samples were collected for thisstudy (12 DS samples, 11 PS samples, and 11 ES samples).

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were used for further analysis in order to keep the same
sequencing depth. The number of OTU and two diversity
indices, Chao1 and Shannon, of DS and DS_PMA samples
as well as PS and ES samples were calculated to estimate
and compare the diversity of all samples. A total of 8,756
OTUs was retrieved from the 46 samples. The numbers of
OTUs ranged 790-1726 for DS, 534-1267 for DS_PMA,
1045-1767 for ES, and 1264-1727 for PS. The reduced
numbers of OTU, Chao1, and Shannon were in the range of
122 to 459, 120 to 594, and 0.27 to 0.89, respectively. The
ES and PS samples showed much more diversity than the
DS and DS_PMA samples.
Fig1 shows the relative abundance of microorganisms ineach sample at the phylum and/or class levels. The phylawith the population less than 1.0% are summarized in“Other”. Proteobacteria, Bacteroidetes, Firmicutes,
Chloroflexi and WWE1 were dominant members.
The changes on the relative abundances of theProteobacteria were remarkable. The relative abundance ofthe phylum Proteobacteria reduced by 49%, 30%, 41%,
44%, and 4% on average in the AKT, NGT, SEN, TNN, andNIT samples, respectively, after the PMA treatment. The
class Alphaproteobacteria and Betaproteobacteria occupied
several percent of the total community without PMA
treatment, but they remarkably decreased their population
by 40% (SEN1) - 96% (TNN1) and 14% (NIT1) - 99%(AKT1), respectively, after the PMA treatment.
Top 25 OTUs whose relative abundance decreased themost in descending order. The OTU 44890 and the OTU
34527 showed 99% and 98% sequence identity, respectively,
to Methanosaeta concilii, known as a acetoclastic
methanogen. OTU 44890 decreased the most after PMA
treatment (6676 sequence reads reduced of all the total
sequence reads belonging to DS for about 40.9%). The
results also concluded that members of Betaproteobacteriathat Rivière and colleagues reported in anaerobic digested
sludge as one of the core groups may not be, on the other
hand, WWE1 group may be belonged to the core groups.
The similarity and differences of each microbialcommunity structure are analyzed by principal coordinates
analysis (PCoA) (Fig2). The digested sludge sample treatedwith PMA shifted the same directions farther away fromprimary sedimentation sludge and excess sludge thandigested sludge sample without PMA treatment. This resultshows that some specific microbial microorganisms derived
from PS or ES was removed from the structure analysis
result of DS by PMA treatment.

G7Safety$
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5.# Acknowledgments#
The authors would like to thank Inter-Graduate School
Doctoral Degree Program on Science for Global Safety forthe funding support.
Table 1 qPCR results of all the samples.
DNA concentration(105 gene copies/ng DNA)
Sampledecreasing
name
DS
DS_PMArate(copies/ng)(copies/ng)(100%)*1
AKT1
8.0±0.64
5.2±0.2736
AKT2
6.7±0.75
5.2±0.52
21
AKT3
4.3±0.51
4.3±0.800
NGT1
5.1±0.87
6.0±0.58-16NGT2
6.1±0.63
4.6±0.1125NIT1
5.7±0.57
4.5±1.0720
NIT2
5.9±0.57
4.6±0.06
22SEN1
5.0±0.38
5.0±0.47
1
SEN2
5.8±0.31
4.3±0.1526SEN3
5.6±0.64
4.7±0.7917SEN4
5.8±0.39
4.2±0.3927TNN1
5.9±0.13
4.1±0.67
31

Fig.1 Relative abundance of digested sludge samples

4.# Conclusions#This study revealed that Combining PMA-PCR with 16S
rRNA sequencing analysis can exclude microbialcommunity involved from primary sedimentation sludge
and excess sludge with compromised membranes, and it ispossible that alive microorganisms actively playing a role in
the anaerobic digestion could be detected by this study. In
addition, the results of qPCR suggested that that near
20-30% of the total prokaryotic microorganisms can beconsidered as inactive microorganisms in most digesters.
And it is also possible to eliminate the toxicity of PMA tomicrobes in environmental samples and the confusion of
biofilm penetration to a degree. OTUs belonging to the
Betaproteobacteria in anaerobic sludge digesters may not
be one of the core groups.

Fig.5 Principal coordinates analysis (PCoA) of prokaryotic
community structures

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Tohoku$University!

Optimization of cultivation of humicreducing bacteria based on ORP/pH feedback
Runda Du1*, Jingmei Sun2, Yu-You Li1
1
Department of Civil and Environmental Engineering, Tohoku University, Sendai, Miyagi 980-8579, Japan2
Department of Environmental Engineering, School of Environmental Science and Engineering, Tianjin University,
Jinnan District, Tianjin 300350, China*E-mail: du.runda.t3@dc.tohoku.ac.jp

Abstract
Humic-reducing bacteria can be used as a kind of ecological remediation agent in the process of water restoration.
Therefore, the research and development of its high-density culture technology are attached to great importance.
In this study, humic-reducing bacteria were cultured with high-cell-density culture using active sludge acclimatedby AQDS and its feeding strategy based on ORP/pH feedback was studied. The optimum nutrient conditions forthe growth of humic-reducing bacteria were explored by three-factor four-level orthogonal experiment and themetabolic reaction process was further analyzed. The results showed that glucose, as carbon source, could cause
acid fermentation and lead to sludge reduction. The reduction process of AQDS significantly improved theobserved sludge yield (Y obs=0.622~0.786), thus it promoted the rapid growth of biomass. In the reactor stage,
daily parameters such as TOC, pH, ORP were monitored to search for the correlations between microbial growthand metabolism in the system and the changes of ORP or pH, the results showed that the increase of HRT andthe addition of agitation could contribute to redox process and the propagation of active sludge. The feeding
control strategy was determined: when ORP was close to -150 mv and the pH was higher than 8.0, the reactor
should to be enriched.

Keywords: humic-reducing, high-cell-density cultivation, ORP

1.! Introduction
Many researches have found that humic-reducing bacteria
widely exist in nature, and they can accelerate reduction of
toxic substances such as Azo, nitro substituted aromaticcompounds and polymerized organic compounds in
wastewater. They also have potential applications in
remediation situation.
To supply sufficient bioagent (e.g. humic-reducing bacteria)
for remediation bio-augmentation, we have to do withpropagation or expanding culture situations. However,monoculture of an anaerobic bacterium is so complicated andslow-growing. Furthermore, bioagent with monoculture aresusceptible to the aborigines and lost their ecological niche
in sediment system. As opposed to monoculture, sludge hasa larger microbial community, higher biodiversity andstronger eco-stability. So, we chose to use active sludge
acclimated by AQDS, and hence, there was a need forbuilding up a high-density culture strategy. Considering that
humic-reducing is closely related to redox, the feeding
strategy was designed based on ORP/pH feedback.
According to existing results, the growth and metabolic
process of humic-reducing bacteria are accompanied by
ORP's sudden jump (this can be explained by Nernst
Equation), and the change of pH is also accompanied by theabrupt change of ORP. This coupled with the specific humicreducing reaction. The microbes thus can be controlled in
specific metabolic phase by clarify the relationship between
the change of ORP/pH and the growth of microbes. Adequate
use of carbon source, nutrients and energy and elimination ofside-effects (e.g. acidic fermentation) are expected by using
ORP/pH feedback strategy.

2.1
Source of sludge
The activated sludge used in this experiment is obtained by
the research group in the early stage. The sludge is taken fromthe sludge of the secondary settling tank of a sewage
treatment plant and then acclimated by AQDS.
2.2
Methods of pre-experiment
An orthogonal experiment was designed with three factors(types and concentrations of carbon source, C/N ratio) and
four levels to find the optimal conditions for biomass
production. The substrate is composed of carbon source, 0.5
g/L NH4Cl, 0.5 g/L KH2(PO4), 0.5 g/L CaCl2, 0.6 g/L
Mg(SO4)2, 10 ml/L concentrate of trace element.
According to the formula, the corresponding concentrationsof nutrients were added to the 250 ml of serum bottles, andthen each bottle was inoculated with 5 g-VSS/L of acclimatedsludge.
2.3
Operation of the reactor
The acclimated sludge was inoculated in five reactors, andaccording to the optimal carbon source and carbon source
concentration obtained by the pre-experiment, 25 L of
substrate was supplied to all the reactor. The substrate iscomposed of 15 g/L Sodium acetate, 0.25 g/L (NH4)2SO4,
0.05 g/L KH2(PO4), 0.5 g/L CaCl2, 0.2 g/L Mg(SO4)2,
0.00875 g/L Lawsone, 0.1 g/L Na2SO3. The specificparameters of the reactor are in the Table 1.
Table 1 Parameters of the reactor
Number1
2
3
4
5
HRT(h)
24 24 84
1
Agitation Y
N
N N
N

2.! Materials and Methods

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77777$with$Perspective$of$Global$Safety$ $ $ $ (Nov.$29 7$Dec.$ ,$2018)

G7Safety$
Tohoku$University!

3.! Results and Discussion
3.1
Analysis of ORP in pre-experiment
As shown in Fig. 1(a-d), ORP of sodium acetate and beef
extract can reach a lower level (below -400 mv) than that of
glucose and yeast extract. In general, ORP of the substrates
with different carbon sources shows similar trends. As thefeed cycles increase, the initial level of ORP after feeding
becomes lower and has smaller range of mutation during
feeding, which may be due to the accumulation of reducing
metabolites. ORP of the substrates with different carbon
sources can decrease and stabilize within a certain range ineach cycle, and the effect of different carbon source loading
rates on the stability interval is not significant. Therefore,
concentration of carbon source is not the key factor to
regulate ORP.

a. Glucose

Fig. 2 MLSSTOC and Yobs(observed sludge
yield )

3.3
Reactor phase
When acclimated activated sludge containing humicreducing bacteria uses sodium acetate as carbon source for
growth and metabolism, the change of ORP and pH show akind of correlation that pH increases with the decrease of
ORP, and they stabilize around a certain value or in a certain
interval before supplementing the carbon source. The
lengthening of HRT and the agitation are beneficial to theredox reaction and the proliferation of activated sludge.

b. Sodium acetate

c. Beef Extractd. Yeast Extract
Fig. 1(a-d) ORP in different substrates of preexperiment phase
The carbon source in the system is consumed along with the
AQDS being reduced and the reduction of ORP. For glucose,
according to the Nernst equation:
12
6
RT &$,* C AH 2QDS )' ,* CH 2CO3 )'#!
E=E .
ln
nF $*+ C AQDS '( *+ CC6 H12O6 '(!
%"Fig. 3 pH and ORP in system of reactor phase0

4.! Conclusions
The lower ORP is, the conversion of the carbon source andthe reduction of AQDS are more complete. That is to say, thereduction of ORP reflects the degree of carbon source
consumption and AQDS reduction, which can be used as abasis for preliminary analysis of the growth state of humicreducing bacteria. It can be also seen that the effect ofC[AH2QDS]/C[AQDS] on ORP is significantly greater thanthat of carbon source, which is consistent with the conclusion
above: ORP change and stability are less related to carbon
source loading rate.

3.2

4.1
pre-experiment phase(1) Glucose as carbon source produces acid fermentationthat causes sludge reduction.
(2) The reduction of AQDS is the main factor in the
decline of ORP, and the impact of carbon source loading
rate on ORP changes is small.
(3) 5 g-COD/L yeast extract and 10 g-COD/L sodium
acetate are suitable as carbon source for high-cell-density
cultivation. Considering economic factors, sodium acetate
is selected as the carbon source, and its concentration is
not less than 10 g- COD/L.

Proliferation and observed yield of sludge in

pre-experiment phase
From Fig. 2, as the glucose loading rate rises, the sludge hasa tendency to decrease because of the acidic fermentation of
the acclimated sludge when using glucose. The accumulationof organic acids makes damage to DNA and protein, whichleads to cell decline. The concentration of MLSS also
decreases when the yeast extract reaches 20 g/L because
yeast extract is decomposed by microbes into carbohydrate.

4.2
reactor phase(1) As ORP in the system decreases, pH increases
indicating that the metabolism of sodium acetate coupledwith AQDS is a reaction process that produces alkalinity.
(2) Determine the threshold of feeding as: ORP=-150 mv
or pH=8.0(3) Lengthening the HRT and increasing the hydraulicdisturbance intensity can further promote the proliferationof sludge.

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The effects of pre-acidification on the granuler sludge perperties in the anaerobic treatment ofstarch wastewater○Bo Feng1*, Jing Wu2, Bo Jiang2 & Yu-You Li2

1 Graduate School of Environmental Studies, Tohoku University, 6-6-06 Aoba, Sendai 980-8579, Japan2

Department of Civil and Environmental Engineering, Graduate School of Engineering, Tohoku University, 6 -6-06
Aza-Aoba, Aramaki, Aoba-ku, Sendai, Miyagi 980-8579, Japan.
*E-mail: feng.bo.r3@dc.tohoku.jp

Abstract

In this study, two different anaerobic digestions (AD) processes for the treatment of starch wastewater (SW) werecompared to determine the effect of adding a pre-acidification unit to this promising cost-saving process known for its
bioenergy advantages. A single-phase UASB reactor (the SP process) and a modified pre-acidification tank/EGSB
reactor combined process (the TP process) were continuously fed with SW under organic loading rates (OLR) from 1 to
12g-COD/L/d by shortening the hydraulic retention time (HRT) from 24 to 2h. The SW treatment performances of bothprocesses were compared in terms of their capability to remove organic nutrients, the morphological properties of the
granular sludge, and their stability. Results showed that the soluble chemical oxygen demand (SCOD) removal
efficiencies were higher than 80% in both processes. However, granule floatation and disintegration were noticed in the
SP process when the HRT as shortened to 6h at an OLR of 4g-COD/L/d. These results indicate that the use of a preacidification unit (the TP process) is an effective strategy to enhance sludge stability in the AD process in treating SW.

Keywords: Starch wastewater; Anaerobic treatment; Pre-acidification; EPS; Granular physicochemical
properties
hydraulic retention time (HRT) to observe the organics
removal and biogas production under the elevated organic
loading rate (OLR). Lu employed a single-phase (SP) UASB
process to treat low-strength SW, and noted that when the
OLR reached 6g-COD/L/d with an HRT of 4h, the increasein the extracellular polymeric substance (EPS) content in the
granular sludge triggered granules floatation and reactor
foaming. This suggests that the treatment of SW in a UASB
reactor may involve uncertain sludge physicochemical
properties (PCPs).
The organics conversion of carbohydrates in the AD process
broadly includes two steps, acidogenensis andmethanogenesis. The biokinetic and physiologic
characteristics of the related-microorganisms of the two steps
are different. The pre-acidification unit, a primary part of thetwo-phase (TP) AD process, is where acidogenesis isseparated from methanogenesis, to ease the rate-limitation of
methanogenesis in the bioreactors. In the pre-acidification
unit, complex organic compounds are mainly converted tosmall molecule organics easily biodegradable substances formethanogenesis, such as butyric, propionic and acetic acids,
all classified as volatile fatty acids (VFAs). Earlier studies
have reported excellent treatment performance of the UASB
reactor for short-chain fatty acids, including considerable
nutrient removal feasibility, the fully developed granulemicrostructure and satisfactory granular settling properties.
The pre-acidification/UASB process has been reported tosuccessfully improve the AD performance in various waste
streams, e.g., coffee-grounds, cheese whey, brewery andchicken manure wastes.
Hence, we considered the possibility that the adverse effectsof SW on granular sludge may be overcome by introducing a
pre-acidification unit before EGSB treatment.

1. Introduction
With the total starch consumption in 2011 at approximately
70 million tons, world-wide starch production maintains an
upward trend. In China, the world’s largest producers ofsweet potatoes, around 1.5 million ton of sweet potato wasused in starch production in 2011. Notably, the starch residue
is discarded at many points in the production chain. For
example, during the roughing process, in excess of 10 cubic
meters of starch wastewater (SW) is typically discharged in
the processing of one ton of sweet potato root. The organic
content of the raw SW drained from the starch workshop ishigh and its pH value is low. The large-scale discharge ofsuch waste streams, once large-scale discharged into natural
waters without any treatment, can lead to a range of
ecological problems.
Awareness of the environmental hazards of waste streams
has led to the development of various treatment strategies.
Among the alternatives, biological treatment is one of themost recommended technologies. Starch is non-toxic, with arelatively high BOD/COD ratio of 0.4-0.7, and isbiodegradable. Anaerobic digestion (AD), is a biologicalprocess which converts organic compounds into different
products under the oxygen-free condition. The biogas
produced by AD process contains 50-75% methane and 510% hydrogen, and is a potential biofuel. The Up-flow
Anaerobic Sludge Blanket (UASB) technology, one of the
AD processes, is employed in 80% of the anaerobic
wastewater treatment plants around the world. A modified
version of the UASB is the Expanded Granular Sludge
Blanket (EGSB) technology: this allows the effluent to beinternally circulated to improve contact among the granules
with the substrate and provides an alkalinity buffer for thereactor.
Much of the focus of investigations into the performance of
the UASB treatment of SW has been on shortening the7071

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granular sludge shows
properties (PCPs).

2. Materials and Methods
2.1 Wastewater and seed sludge inoculation
The SW employed in this study was synthesized according to
the diluted real SW from a paper mill industry in Japan, andcontained 1000mg/L of COD from modified (soluble) starch(Wako, purity: 99.0%). Supplements of the essential
elements and 1000-3500mg/L of NaHCO3 which was used
to supplement alkalinity. Mesophilic anaerobic granularsludge was withdrawn from a full-scale UASB reactor in a
brewery wastewater treatment plant, Japan. For the preacidification unit of TP process, 5.5L of the substrate was
mixed with 0.5L of digested sludge from a domestic
wastewater treatment plant, in Japan. 1M of hydrochloric
acid (HCl) was used to adjust the pH of the mixed sludge to
5.5, an appropriate value for carbohydrate-based
acidogenesis.
2.2 Lab-scale reactors and the operational conditions
A schematic diagram of the reactor setup including theaccessories is provided in Fig.1. The apparatus used in thelong-term experiment comprised a UASB reactor (SP) and a
CSTR-EGSB process (TP). the working volume was 6L andthe vertical height was 0.8m. Water baths (NTT-20S,
EYELA) were equipped to maintain the temperature of bothprocesses at 35±1℃. The recycle ratio of the EGSB in TP
process was 1.0 to replenish reactor alkalinity. A plastictank with an effective volume of 200L was employed tostore the substrate. A condenser (Yamato, BE201) was
inserted into the plastic tank to refrigerate the substrate at
4°C. Peristaltic pumps (Masterflex, EW-07528-10) wereused for substrate delivery and the recirculation of theeffluent in the EGSB reactor. Two wet gas flow meters(SHINAGAWA W-NK-0.5) were used to measure the daily
production of biogas. The substrate was continuously fed toboth processes. The two processes were started up at an
HRT of 24h (SU stage), and the stepwise withdrawal
shortened the HRT, thereby increasing the OLR.
3.

Tohoku University
satisfactory

physicochemical

3.2 Temporal variation of granular sludge PCPs
along the reactor height
Longitudinal diameter distributions along both reactors
were generally consistent when the HRT was higher than 12h.
During these periods, the granular size distribution fitted wellto the Gaussian distribution model (the correlation
coefficients (R2) were 0.7351-0.9999). Liu applied anelectric field in a UASB reactor to enhance the anaerobic
granulation, and reported similar statistical characteristics in
the healthy and mature anaerobic granules. When the HRT
conditions were higher than 12h, more than 25% of the
granules in the upper part of the granular sludge had adiameter range of 1.0-2.0mm, in the middle part, it wasaround 40%, and around and it was 50% at the bottom part.
The longitudinal distributions of the median particle size
(extended meaning of mathematical expectation (XC))
stepwise panned right along the X-axis from the "Up" to"Down" part of the two reactors, suggesting that the densityof the larger granules made them more buoyant. The granules
in both reactors showed a compact structure with a smooth
particle surface. According to the granular distribution in the
UASB reactor, granules with a diameter size at the range of
2.8-4.0mm were abundant in the "Up" and "Down" part of
the reactor when the HRT was shortened to 6h, then the ratios
continuously increased up to 34.7% and 40.8% at the HRT of
3h (Fig.4). On the other hand, the granular sludge sampledfrom each part of the UASB reactor, a sharp increase in
granules with a diameter size range of 0.35-0.5mm was foundwhen the HRT was shortened from 12 to 3h. During these
HRT conditions, the granule distribution in the UASB reactor(SP) was clearly polarized, statistically disobeying theGaussian distribution (R2 < 0.1). From the perspective ofprocess stability, granule floatation and foaming wereobserved in the UASB reactor (SP) when the HRT was
shortened to 6h and the floatation and foaming became
aggravated when the HRT was further shortened. In contrast,
no significant changes were noted in the granule distribution
and process appearance in the EGSB (TP) when the HRT was
shortened from 12 to 3h.
4. Conclusions(1) The SP process can be successfully used in thetreatment of low-strength SW with an OLR far below 4gCOD/L/d.
(2) Granules floatation and disintegration became aserious concern in the SP process when the HRT was 6hwith an OLR of 4g-COD/L/d.
(3) The potential for the application of the TP process inthe anaerobic treatment of high-strength SW is significant.

Results and Discussion

3.1 Overall treatment performance of TP and SP
processes
In this study, two different anaerobic digestions (AD)processes for the treatment of starch wastewater (SW) werecompared to determine the effect of adding a preacidification unit to this promising cost-saving process
known for its bioenergy advantages. Results showed that thesoluble chemical oxygen demand (SCOD) removal
efficiencies were higher than 80% in both processes.
However, granule floatation and disintegration were noticed
in the SP process when the HRT as shortened to 6h at an OLR
of 4g-COD/L/d. In-depth analysis of the elemental
composition and content of the extracellular polymeric
substances (EPS) in the granular sludge indicated an increasein the protein content in the loosely-bound EPS (LB-EPS)
due to the paste-like membrane structure attached to the
granules. Sludge disintegration in the SP process may havebeen triggered by the electrostatic repulsion between thenegatively charged protein content. In the TP process, the7172The 6th International Symposium on Water Environment Systems----- with Perspective of Global Safety(Nov. 29th - Dec. 30st, 2018)

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Tohoku University

Development of urban resource recovery system by methane fermentation and disposal
wastewater treatment○Ming CONG1*, Yu-You Li1
1 Graduate School of Environmental Studies, Tohoku University, 6-6-06 Aoba, Sendai 980-8579, Japan*E-mail: soumei0615@gmail.com

Abstract
In this research, we examined the establishment of unit urban resource recovery system integrating highly efficientmethane fermentation and disposal wastewater treatment which enables introduction to small-scale businessestablishments. Methane fermentation process and active sludge process were combined as a unit system. Two unit
systems were operated over 420 days in different organic loading rate (OLR) and operating temperature forcomparison between mesophilic condition (35±1℃) and thermophilic condition (55±1℃). Siphon-driven selfagitated anaerobic reactors were used for methane fermentation. Scum problem occurred with OLR of 7.3 kgCOD/m3/d in mesophilic condition. The maximum methane production rate was obtained of 4.2 L/L/d with OLR of
14.7 kg-COD/m3/d in thermophilic condition. According to the results, combined system had good performance inmethane fermentation and wastewater treatment. In addition, it was also revealed that thermophilic condition got
better performance than mesophilic condition.
Keywords: Methane fermentation, Food waste, Biogas, Wastewater treatment, Energy recovery

1.

Introduction
It is pointed out that management of food waste (FW) is
becoming increasingly important due to the rapid progress ofurbanization and the rapid population increase. The annual
amount of FW generated in the food industry is about 19
million tons, in the food manufacturing industry, therecycling rate in the industry is 95%, the food wholesaleindustry 58%, the food retailing industry 45% and only 24%
in the industry. There are many usual treatment methods suchas landfill method, incineration method, compost method,anaerobic digestion method. Although landfilling was wellknown as a conventional method, problems such as lack offacilities and adverse effects on the environment werepointed out. In the incineration method, expensive costs,
humidity control of and exhaust gas regulation wererestricted. Therefore, methane fermentation as a solution of
bio-gasification method is expected which has two
advantages in biomass reducing and biological energy
production.
In addition, methane fermentation effluent treatment wasalso a problem for small methane fermentation plant. Since
few cases are set up in rural areas where the fermentation
liquor can be reduced as liquid fertilizer. The cost ofwastewater treatment and management was come out.
It has been suggested as an effective way of centralized
anaerobic FW treatment that FW is crushed first by foodwaste disposers and sent to a wastewater treatment plant withan anerobic digestion plant though sewerage systems. It
could be an efficient way to transportation, order and storage.
In this study we found the solution of those problems which
was the development of urban resource recovery system bymethane fermentation and disposal wastewater treatment.
2.

Materials and Methods

2.1

Experimental diagram

The experiment apparatus was composed of two substratetank and a Siphon-Driven Self-agitated Anaerobic Reactor(SDSAR) with 10L effective volume and a Active sludge
reactor with 5L effective volume. A disposer was used as
setting tank to separate precipitate and supernatant.

2.2
Food waste sample collection
The raw and cooked FW was collected from the canteen of
the National Institute of Environmental Studies, Japan. A
disposal machine (Cuisinart, DLC-NXJ2PS) was used todisintegrate the raw FW. Adds 12 L water to 1 kg FW for thelaboratory FW grinding. To maintain a solid content about 5%
for direct methane fermentation, this crushed FW was settled
in a settlement tank with an effective volume of 15 L for 24
h. Then, the settled FW was transferred and stored in thesubstrate tank. To prevent the trace elements deficiencies in
the reactors, Fe, Co and Ni were added artificially. The traceelements concentration in the substrate was as follows: 100

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mg-Fe/L, 10mg-Co/L, and 10 mg-Ni/L, respectively. In thisstudy, no extra buffer was added into the substrate.

3.

Results and Discussion

3.1
Results of Biogas production
The treatment performance of methane production in two
reactors is shown in Fig.1. At OLR of 3.0 kg-COD/m3/d(HRT 20 d), the methane production rate was under 1 L/L/d
for both reactors. The mesophilic reactor performed slightly
better than the thermophilic one at methane production under
this condition. This could be explained as the thermophilic
bacteria need more time (over a month) to develop redox
population compared with mesophilic bacteria. Since stable
methane production was obtained in the reactors, the HRT
was changed from 20 to 10 d from day 106. The methane
production in two reactors increased to around 2 L/L/d with
HRT decrease. However, after day 160, at OLR of 7.3 kgCOD/m3/d, scum accumulated in the mesophilic reactor andcaused problems with blocking and gas production reduction.
Fig.2 The performance of effluent from active sludge
reactors with BOD removal rate and nitrogen removal
rate.

4.
1.

Fig.1 The comparison of biogas production rate and COD
removal rate in Thermophilic condition and Mesophilic
condition.

3.2
Results of active sludge rector
The treatment performance of active sludge reactors isshown in Fig.2. The effluent performance of both reactors
was lower than drainage standard until day 146. According
to the HRT shortening in both two reactors. The
concentration of DO and MLSS became lower. BOD
concentration of effluent was over 1200mg/L. Because of theinstability of the properties of the fermented liquid of theprevious term, the dilution method was changed from 170
days to stabilize the BOD volume load (fermentation solution:
supernatant water: water = 1: 3: 1). From Phase 4, thefermentation solution dilution method returned to 1: 4, andfrom 330 days to 400 days, the AOD BOD concentration fell
below the standard value. From the results of BOD, thetemperature was better than the medium temperature. The
average values of high temperature and medium temperature
were 243 ± 301 and 276 ± 338 mg/L, respectively.

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Conclusions
The effects of OLR and operating temperature on
the performance of SDSAR for the food waste
treatment were investigated. It is observed that with
HRT above 15 d and OLR below 4.8 kg-COD/m3/d,
relatively high COD removal in the range of 84.5–
92.3% was obtained in both reactors. It is suggested
that the SDSAR could successfully avoid VFA
accumulation compared with other anaerobic
digesters. However, the limits of the loading capacityof the mesophilic SDSAR were observed when OLR
was further increased to 7.3 kg-COD/m3/d byshortening HRT. In contrast, the thermophilic reactor
can be operated at OLR as high as 14.4 kgCOD/m3/d with satisfactory COD removal andbiogas production. Based on the results, it issuggested that thermophilic SDSAR is a promisingtechnology for the on-site FW treatment.
2.
Methane fermentation by Siphon-driven selfagitated anaerobic reactor achieves that COD
removal rate over 80% and methane conversion rateover 70%. It showed the same performance as thesame anaerobic treatment tank of the same type thatprocessed garbage as reported.
3.
As a result of continuous experiments of treatment,
it was possible to sustainably attain good quality oftreated water sufficiently lower than sewage
exclusion criteria.
4.
According to the results of aerobic treatment
removal of phosphorus was different from nitrogen,and efficient removal was not possible. Removal ofphosphorus is considered to be difficult only with an
aerobic tank.

The 6th International Symposium on Water Environment Systems----- with Perspective of Global Safety(Nov. 29th - Nov. 30th, 2018)

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Co-digestion of Food Waste and Sewage Sludge in Anaerobic Membrane Bioreactor○Yemei Li1*, Hui Cheng1 & Yu-You Li1,2

1Department of Civil and Environmental Engineering, Graduate School of Engineering, Tohoku University,6-6-06 Aoba,
2

Aramaki-Aza, Sendai, Miyagi 980-8579, Japan.
Department of Frontier Science for Advanced Environment, Graduate School of Environmental Sciences, Tohoku
University, 6-6-20 Aoba, Aramaki-Aza, Sendai, Miyagi 980-8579, Japan.
*E-mail: li.yemei.s5@dc.tohoku.ac.jp

Abstract
The increase of food waste which generates during the food transfer chain catchs more and more attention becauseof the shortage of food and the improvement of living conditions. Sewage sludge, the major by-product from theconventional activated sludge process which is widely used for the removal of organics and nutrients in municipal
and industrial wastewater plants. The huge volume and complex composition of sewage sludge makes it the maindrawback of the process. Anaerobic digestion is thought to be the most suitable method to treat these two kinds ofwaste. To improve the digestion efficiency, co-digestion was proposed. In this study, the co-digestion of food waste
and sewage sludge in AnMBR system at different temperatures was studied. The results showed an excellent
performance gained in AnMBR system at both mesophilic and thermophilic conditions. The mesophilic reactor had
a better and more stable performance in the removal of organic matters than the thermophilic one.
Keywords: Food waste, sewage sludge, co-digestion, AnMBR

Because of the disposal limitation by the reason of strict
environmental regulations, together with the increase of theamount of sewage sludge resulting from the development ofmodern wastewater treatment, more and more attention has
been focused on the full use and treatment of sludge.
Normally, sewage sludge is treated biologically to stabilize
and improve final dewatering. AD is the most commonlyused and well-studied method to do this.
Although, AD is regarded as the most suitable way to treat
food waste and sewage sludge, there still some disadvantages
exist in the process, such as the high suspended solids andsodium concentration contained in food waste and low
organic content in sewage sludge. These negative factors
result in an improvable digestion process who need longer
retention time, be sensitive to feed and operation conditions,and has reactor limitation. One option to enhance theperformance of anaerobic digestion is to improve biogas
production via co-digestion of multiple substrates. Codigestion can enhance digestion performance and enrich key
active microbial populations via increase the organic loading
rate, dilute concentrations of potentially toxic compounds,
improve microbial activity and enhance the nutrient balance.
Previous researches found that co-digestion can overall
increase biogas production.
In this study, the effective of temperature and food waste to
sewage sludge ratio on the performance including biogas
production and organic removal rate of co-digestion of foodwaste and sewage sludge were studied.

1. Introduction
With the development of population and living conditions,food wastage, which include food loss and food waste,increases rapidly. It is estimated by the Food and Agriculture
Organization of the United Nations (FAO) that over one thirdof food produced was lost or wasted along the food value
chain in 2009. According to FAO’s report, cost of societal
impacts of food wastage, including the financial value of lost
food and indirect consequences of degraded environmental
resources and loss of social wellbeing, is 1224.2 billion USD
per year globally. Reduce, reuse, recycle or recovery anddisposal are mitigation measures that being used to face thesituation. The recycle and disposal measures are thought tobe the least environmentally friendly. However, for foodwaste, which means any food being produced for humanconsumption, but which leaves the food supply chain,landfilling, incineration, composting and anaerobic digestion
are the most common treatments. Among all these options,anaerobic digestion (AD) is regarded as the most promising
pathway for this kind of waste which containing highmoisture and biodegradable organics.
Sewage sludge is made up by biosolids which convert fromchemical oxygen demand initially presented in the influent.
The sludge is the major by-product of the conventional
activated sludge process which is widely used for the removalof organics and nutrients in municipal and industrial
wastewater plants. Sewage sludge usually represents 1-2% of
the treated wastewater volume. Though the process is highefficiency, cost effectiveness, flexibility and ease ofoperation, a big generation of sludge is one of its major
drawbacks. Furthermore, sewage sludge contains a myriad of
toxic substance which creates odors and hygiene concerns.
Improper use and disposal of it will cause severe
environmental impacts and health hazard to the public.

2. Materials and Methods
2.1
Substrate Preparation
The substrate used in this study was prepared according toan earlier research which was about the composition of foodwaste generated in Japan. The food waste was first ground by

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a cutting pump. Then it was diluted with tap water andmaintained in the substrate tank at the temperature of 4°C.
The sewage sludge, including primary sludge and excess
sludge, used in this study was generated from a wastewater
treatment plant located in Tagajo city area. The sludge wasalso kept in tank at 4°C until be used. The mixture of foodwaste and sewage sludge was based on the total solid of thesethree feedstocks. The researched replace ratios of sewage
sludge to food waste were 0%, 25%, and 75%. For the solo
food waste as digestion substrate, considering its shortage oftrace elements, supplements of the cardinal elements wereadded. The seed sludge was also taken from the same
wastewater treatment plant mentioned previously.

Tohoku University

organic matters in mesophilic and thermophilic reactor,
nearly 100% of total removal efficiency showed an excellent
performance of AnMBR system in both mesophilic andthermophilic conditions.

2.2
Reactor System and Operation
The reactor used in this study is called anaerobic membranebioreactor or AnMBR for short. The AnMBR system wasmade up by a continuous stirred-tank reactor (CSTR) and ahollow fiber membrane unit. The effective volume of thesystem was 15 L. The membrane was made bypolytetrafluoroethylene, with a mean pore size of 0.2 µm andan effective filtration area of 0.1 m2. The procedure ofdigestion was described as follow. The substrate was pumped
into CSTR from the substrate tank which was maintained at
4°C by a peristaltic pump. The HRT of the system was holdat 15 d. Then the sludge in CSTR was pumped in tomembrane unit also by a peristaltic pump and the sludge inmembrane unit flow back to the CSTR on account of gravity.
This process made the CSTR and membrane unit a cycle
system. The biogas generated in the CSTR was used as
aeration source in membrane unit. To research the effect oftemperature, there was two AnMBR systems were used. The
temperature of these two systems were maintained at 35°C
and 55°C.

Fig. 1. The concentrations of TS, VS, COD, protein andpolysaccharide in substrate and effluent, and the organic
matters removal rate in mesophilic and thermophilic
reactor. (The concentrations of (a) TS and VS, (b) COD,(c) protein, (d) polysaccharide in substrate; the effluent
concentrations and removal rate of (e) TS and VS, (f)COD, (g) protein, (h) polysaccharide in mesophilic
reactor; the effluent concentrations and removal rate of(i) TS and VS, (j) COD, (k) protein, (l) polysaccharide inthermophilic reactor.)

2.3
Analysis Methods
The volume of biogas generated was recorded by a wet gas
meter, and environment temperature and pressure were also
taken into consider. The composition of biogas was measured
by gas chromatograph (Shimadzu GC-8A, Japan). TS, VS,
COD, protein and polysaccharide were measured according
to a modified Japan Standard Testing Method for wastewater.
The suspended samples were the suspended liquid gained
from the centrifugation of 15 mL samples at 8,000 rpm for
15 min. The results of these samples were marked as S while
the results of original samples were called T. The removal
rate (RR) of organic matters were calculated as Eq. (1).
RR =G-Safety

A comparison between the digestion performance ofmesophilic and thermophilic systems also can be observed
from Fig. 1(e) – 1(h) and Fig. 1(i) – 1(l). For all measured
indexes, the mesophilic reactor had a better performance than
the thermophilic one, especially for COD, protein andpolysaccharide. Furthermore, there was no obvious tendency
of system performance with the change of the food waste to
sewage sludge ratio which showed a stable performance of
AnMBR system at mesophilic condition. However, in thethermophilic system, the tendency of removal efficiency
decreased with the decrease of the occupancy volume of foodwaste. Particularly, for protein, the total removal rate wasmuch higher than the suspended one means that there would
be some insoluble protein be used by microorganism atthermophilic condition.

(𝐶𝑖𝑛𝑓 − 𝐶𝑒𝑓𝑓 )× 100%
𝐶𝑖𝑛𝑓

where 𝐶𝑖𝑛𝑓 and 𝐶𝑒𝑓𝑓 were the concentrations of organic
matters in the influent and effluent, respectively.

4. Conclusions(1) Both mesophilic and thermophilic system had an
excellent co-digestion performance of food waste andsewage sludge.
(2) The mesophilic system had a stable performance withthe change of co-digestion substrate while thethermophilic system changed.

3. Results and Discussion
Fig. 1 showed the concentrations of TS, VS, COD, protein
and polysaccharide in substrate and effluent, and the organic
matters removal rate in mesophilic and thermophilic reactor.
Fig. 1(a) - 1(d) showed the concentrations of TS, VS, COD,
protein and polysaccharide in substrate. Except protein, theconcentrations of organic matters decreased with thereplacement of sewage sludge. Both total and suspended
concentrations expressed the same tendency. From 1(e)-1(l),which showed the effluent concentration and removal rate of75
76

G-Safety

The 6th International Symposium on Water Environment Systems----- with Perspective of Global Safety(Nov. 29th - Nov. 30th, 2018)

Tohoku University

The potential appearance of free-chlorine resistant norovirus
after disinfection process in wastewater○Andri Taruna Rachmadi1, Masaaki Kitajima2, Kozo Watanabe3, Sakiko Yaegashi3,4, Joeselle Serrana3, Arata
Nakamura1, Toyoko Nakagomi5, Osamu Nakagomi5, Kazuhiko Katayama6, Satoshi Okabe2, Daisuke Sano2
1 Department of Civil and Environmental Engineering, Tohoku University, Miyagi, Japan

2

Division of Environmental Engineering, Faculty of Engineering, Hokkaido University, Japan3
Department of Civil and Environmental Engineering, Ehime University, Ehime, Japan
4
Department of Civil and Environmental Engineering, University of Yamanashi, Yamanashi, Japan
5
Department of Hygiene and Molecular Epidemiology, Nagasaki University, Japan
6
Laboratory of Viral Infection I, Department of Infection Control and Immunology, Kitasato Institute for Life Sciences,
Kitasato University, Tokyo, Japan*E-mail: rachmadi.andri.taruna.a7@tohoku.ac.jp

Abstract
Human norovirus is present in a high number at wastewater treatment plant (WWTP) influent and become a healthhazard, hence its inactivation mainly in the disinfection step is needed before release to aquatic environment.
However, the high genomic diversity of norovirus owing to the high mutation rate raises a concern of the emergenceof higher resistance population to water disinfection. This study aims to observe if free chlorine, one of the common
water disinfectants, will act as selective pressure on norovirus evolution and give them higher resistance. Murine
norovirus S7-PP3 was used as human norovirus surrogate. Two independent cycle experiments were performed
with ten-time repetition of free chlorine exposure and propagation in host cells (RAW 264.7), followed by a capsid
gene analysis using next generation sequencing. As a control, a cycle experiment with 10,000-fold dilution insteadof free chlorine exposure was conducted. The chlorine-treated populations showed higher resistant to free chlorine
and included higher number of synonymous and nonsynonymous mutations compared to the control population. A
nonsynonymous mutation at nucleotide (nt) position of 7280 was observed only in the chlorine-treated populations
from both trials. The result of principal coordinate analysis indicated that the chlorine-treated populations at the 5th
and 10th cycles were clustered separately from the control populations at the identical cycle numbers, although theoriginal population and all the populations at the 1st cycle were clustered together. In addition, all clones isolated
from the chlorine-treated population with a mutation at nt T7280C[VP2:F200S] had higher resistant to free chlorine
than clones from the control population. These results suggest that the free chlorine treatment has the effect of
selection pressure on murine norovirus evolution, and the mutation at nt T7280C[VP2:F200S] determines theresistant of murine norovirus as human norovirus surrogate to free chlorine.

Keywords: Human norovirus, wastewater, disinfection, free chlorine, selective pressure

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