The 7th International Symposium
on Water Environment Systems---with Perspective of Global Safety(November 14th – 16th, 2019)
Department of Civil and Environmental Engineering
Graduate School of Engineering
Tohoku University

Sponsors
International Joint Graduate Program in Resilienceand Safety Studies of Tohoku University
http://gp-rss.tohoku.ac.jp/

Department of Civil and Environmental Engineering,
Graduate School of Engineering, Tohoku University
http://www.eng.tohoku.ac.jp/

ORGANIZERSDr. 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, Yoshiya TOUGE
yoshiya.touge.a6@tohoku.ac.jp
Assistant Professor, Yu QIN
yu.qin.e8@tohoku.ac.jp
DC Student, Yemei LI
li.yemei.s5@dc.tohoku.ac.jp
DC Student, Ying SONG
song.ying.q2@dc.tohoku.ac.jp
DC Student, Yuanyuan REN
ren.yuan.yuan.t7@dc.tohoku.ac.jp

ScheduleNov. 14th, 201915:00~17:30

Registration

Venue
Graduate School of Engineering, Aobayama campus, Tohoku University
Lectures and oral presentations
Room 110, Engineering Laboratory Complex building (総合研究棟) (C10)
Posters exhibition
Room 201, Engineering Laboratory Complex building (総合研究棟) (C10)Net-work party
Center hall Aoba restaurant (工学部中央棟)

Engineering Laboratory Complex building(総合研究棟)

Access to Aobayama Campus (Subway)

Map of East-west line subway(地下鉄 東西線) in Sendai
Please take this subway line and get off on the stop Aobayama (青葉山) .
Tips
If you start from Sendai Station, please make sure you are entering
East-west line subway (地下鉄 東西線) rather than South-north line
subway (地下鉄 南北線) nor JR station.

Nov. 15th, 2019
The 7th International Symposium on Water Environment Systems
Place: Room 101, Engineering Laboratory Complex building, Tohoku University
8:55 ~ 9:00 Opening address
Section I Invited Keynote Speech
9:00 ~ 9:30 Anaerobic treatment of a low pH high strength dissolved organic wastewaterin a membrane bioreactor
George A. EKAMA
1
University of Cape Town
9:30 ~ 10:00 Moving inventions to innovations in wastewater and sludge treatment
Guang-Hao CHEN
2
The Hong Kong University of Science and Technology
10:00 ~ 10:05 Coffee break
Section II Water, Wastewater and Waste Management
10:05 ~ 10:20 Source water odor in one reservoir in hot and humid areas of southern China:
diagnosis and possible mitigation measures
3
Chao RONG, Jianwei YU, Xiaobo Han, Jinsong ZHANG
Harbin Institute of Technology Shenzhen Graduate School & Shenzhen Water
Affairs (Group) Co.
10:20 ~ 10:35 Human health risk of Legionella pneumophila infection caused by thegroundwater usage in a decentralized drinking water system
5
Arief Nurul UMAM, Mohan AMARASIRI, Daisuke SANO
Tohoku University
10:35 ~ 10:50 Applicability of anaerobic dynamic membrane bioreactor (AnDMBR) for
domestic wastewater treatment at short HRTs
Yisong HU, Yuan YANG, Xiaochang C. WANG & Yu-You LI
7Xi’an University of Architecture and Technology & Tohoku University
10:50 ~ 10:55 Coffee break
10:55 ~ 11:10 Startup and performance of a novel one-stage partial nitritation-anammox
system for reject water treatment
9
Yunzhi QIAN, Huaji MA, Yongzhi CHI, Hongying YUAN, Yanmei DING
Tohoku University
11:10 ~ 11:25 Effects of phosphate and Ca/P ratio on the Anammox-hydroxyapatite (HAP)
crystallization process
11
Lan LIN, Yanlong ZHANG, Tong OUYANG, Yu-You LI
Tohoku University & Xiamen UniversityP3

11:25 ~ 11:40 Sulfate in anaerobic co-digester accelerates methane production from foodwaste and waste activated sludge
Feixiang Zan, Tianwei Hao
13
The Hong Kong University of Science and Technology
11:40 ~ 11:45 Coffee break
11:45 ~ 12:00 Trace metal element supplementation impact in methane fermentation offish processing wastewater
15
Eli Hendrik SANJAYA, Yu-You LI
Tohoku University & State University of Malang (Universitas Negeri Malang)
12:00 ~ 12:15 Experimental study on filtrate of thermal hydrolysis of sludge as an additional
carbon source for denitrification
17
Lu HAN, Yun HAN, Yuan GAO
Xi' an University of Architecture and Technology &Tohoku University
12:15 ~ 12:30 Co-digestion of cow and sheep manure: Performance evaluation and relative
microbial activity
19
Yu Li, Jing Zhao, Gert-Jan Euverink
University of Groningen
12:30 ~ 12:45 Anaerobic co-digestion of paper waste and sewage sludge: The effects ofpaper waste ratio
21
Aijun ZHU, Yu Qin, Jing WU, Yu-You LI
Tohoku University
12:45 ~ 12:55 Group photo
13:00 ~ 15:00 Poster Exhibition
Place: Room 201, Engineering Laboratory Complex building, Tohoku University
・ Estimation of vulnerable spots for overtopping by using 3-D point cloud data
generated by UAV-SfM approach
Masakazu Hashimoto, Shosuke Sato, Ken Ichikawa, Susumu Naradate, Kaori Amaya,
Keiji Sato, Arata Nasuno
Tohoku University
・ Understanding flood hazard and crop yield loss relationship usinghydrodynamic and remote sensing model
Vempi Satriya, Daisuke Komori
34
Tohoku University
・ Assessing the impact of human-induced land-use change on natural water
cycle at watershed scale based on land surface model
36
Linghao Meng, Daisuke Komori
Tohoku University
・ Estimation of the flood damage caused by Climate change and effect of theadaptation
Tao Yamamoto, So Kazama, Yoshiya Touge, Tsuyoshi Tada, Takeshi Yamashita
Tohoku University
38

P4

・ Analysis of Flood Inundation Area in Naruse River Basin by RRI Model Toward
Consideration of Paddy Field Dams
40
Chai Yi Kai, Yoshiya Touge, So Kazama
Tohoku University
・ Introduction of Collaborative Fields Projects in Eastern Africa
Toshikazu Tanaka, Yoshiya Touge, Jacqueline Muthoni Mbugua
Tohoku University
・ Hydrological Analysis for seasonal change of fire event in Edo era in Miyagi
Prefecture
Runa Inoue, Yoko Yasuda, Yoshiya Touge
Tohoku University
・ Stepwise regulation of endogenous free ammonia to improve short-chain
fatty acids production from waste activated sludge alkaline fermentation
Min YE, Yu-You LI
42
Tohoku University
・ Gradual deterioration in methanogenic degradation of N, Ndimethylformamide under the anaerobic condition
Tianjie WANG, Zhe KONG, Yu-You LI
44
Tohoku University
・ Antibacterial effect of food waste hydrolysate on Escherichia Coli
46
Yuanyuan REN, Qunhui WANG, Yu-You LI
Tohoku University & University of Science and Technology Beijing
・ Anammox-dependent nitrogen removal process using up-flow fixed-bed
bioreactor
48
Ying SONG, Yu-You LI
Tohoku University
・ Anaerobic digestion of milk waste and sewage sludge by using hollow fiber
anaerobic membrane reactor
Ziang HE, Xueqin LU ,Chenglei XIE, Yu-You LI
50
Tohoku University
・ Effect of thermal pretreatment on digestion of sewage slduge in ananaerobic membrane bioreactor
52
Guangze GUO, Yemei LI, Yu-You LI
Tohoku University
・ Impact of HRT on the performance of AnMBR treating municipal sewage
Runda Du, Jiayuan JI, Yujie CHEN, Yu-You LI
54
Tohoku University

P5

Section III Global Climate Assessment
15:00 ~ 15:30 Manifold Analysis of Pacific Islands Climate Data
23
Ming-Che HU, Yi-Hsuan Shih, Tai-Yi Liu
National Taiwan University
15:30 ~ 16:00 A preliminary study on development flood early warning system in Southeast
Sulawesi Province
25
Nurul Fajar Januriyadi
Universitas Pertamina
16:00 ~ 16:25 Application of a large ensemble climate simulation for flood risk assessment
Satoshi Watanabe
University of Tokyo
16:25 ~ 16:50 Data Assimilation Research for Global Weather Prediction: Past
Achievements and Future Perspectives
Shunji Kotsuki
Chiba University
16:50 ~ 17:05 Coffee break
17:05 ~ 17:30 Spatial Representativity of AMeDAS Gauge Rainfall over Radar/RaingaugeAnalysed Precipitation28
Masafumi Yamada
Kyoto University
17:30 ~ 17:50 Estimating Irrigated Area through a Combined Use of MODIS and Land
Surface Model in the Amu Darya Delta
30
Jacqueline Muthoni Mbugua, Yoshiya Touge, So Kazama
Tohoku University
17:50 ~ 18:10 Estimating fire severity map based on representative plots in Kamaishi 2017
forest fireGrace Puyang Emang, Yoshiya Touge, So Kazama
32
Tohoku University
18:10 ~ 18:20 Closing speech
18:30 ~ 20:00 Dinner party

P6

Nov. 16th, 2019Technical Tour: Energy recovery & New technology

Place: Sendai Senen Sewage Treatment Plant
Biogas typically refers to a gas produced by the anaerobic digestion of organic
matter including manure, sewage sludge, municipal solid waste and otherbiodegradable waste. The biogas for electricity system of Senen sewage
treatment plant was implemented in 2018 with 7 electric generator installed.
The total electric generation and yearly generation is 350 kW and 1,880,000 kWh
respectively, which could supply electricity consumption of 407 generations(4618 kWh/Year/household). The emission of CO2 can be reduced by 1040tCO2/year.

・The biogas for electricity system in Senen sewage treatment plant
AnMBR and One-stage Anammox is a new technology to develop an energypositive innovative sewage treatment system integrating an anaerobic
membrane bioreactor (AnMBR) and anaerobic ammonium oxidation(Anammox) process. The pilot plant in Senen sewage treatment plant has aworking volumes of 20 L for the AnMBR and 7 L for One-stage Anammox reactor
and completed with a capacity of 20 m3/d after a 140-day operation.

・Pilot plant of AnMBR and One-stage Anammox in Senen sewage treatment
plantAnaerobic treatment of a low pH high strength dissolved organic
wastewater in a membrane bioreactor○ Ekama GA1
1

University of Cape Town, South Africa.

Abstract
An Anaerobic Membrane Bio-Reactor (AnMBR) was developed for the treatment of FischerTropsch reaction water (FTRW) originating in Sasol’s coal to fuel synthesis process. The laboratoryscale AnMBR used A4-size submerged flat panel ultra filtration membranes for 100% solids-liquidseparation. Membrane scour to reduce fouling was provided by biogas recirculation. The FTRW has
a high strength (18000 mgCOD/L) and comprises mostly C2 to C6 short chain fatty acids (SCFA).
Although the pH of the FTRW is low (3.8), only a low alkalinity (NaOH) dose is required toneutralize the low residual (effluent) VFA to maintain a reactor pH of 7.1.
The AnMBR was operated at a steady state organic loading rate (OLR) = 15 kgCOD/m 3/d for aperiod of 800 days and yielded a total effluent COD of only 35 mgCOD/L. The alkalinity requirement
was only 0.067 kgNaOH/kgCOD removed and the MLSS concentration could be increased up to
30gTSS/L resulting in an organic loading rate (OLR) of 30 kgCOD/m 3 reactor/d. The sludge
production of the AnMBR was 0.021 kgTSS/kgCOD removed.
Compared with aerobic treatment of FTRW, the advantages of anaerobic treatment are not only very
low sludge production and energy consumption and high quality water recovery, but also, more
importantly, the CH4 produced can be added to the producer gas fed to synthesis process, whichsignificantly reduces the carbon footprint of the coal to fuel synthesis process.
Biography
Professor George Ekama, who has been at the University of Cape Town (UCT) for 40 years, is wellknown for his work on activated sludge such as biological nutrient removal, secondary settling tankperformance, filamentous bulking and kinetic modelling, some of which was adopted into the IWA
activated sludge models (ASM1&2). He has published over 150 journal papers, 10 books and 25 PhD
and 40 MSc students qualified under his supervision. His current research interests are (1) developing
integrated chemical, physical and biological processes complete CHONPS element mass balanced
three phase (aqueous-gas-solid) steady state and dynamic kinetic simulation models for plant widemunicipal and industrial aerobic and anaerobic wastewater treatment modelling, (2) impact of source
separation of urine, enhanced primary separation and discharge of food waste to sewer on biologicalwastewater treatment, nutrient removal, anaerobic digestion and energy recovery. Although retired
since 2018, he continues to be active at UCT, Hong Kong University of Science and Technology(HKUST) and the UNESCO-IHE in the Netherlands teaching post-graduate courses and supervising
PhD and MSc students.

1

Moving inventions to innovations in wastewater and sludge treatment○ Guang-Hao CHEN1
1

The Hong Kong University of Science and Technology, Hong Kong, China.

Abstract
This talk shares our experiences in development of a couple of key novel technologies (Sulfate
reduction Autotrophic denitrification Nitrification Integrated (SANI) process and Sulfated
Polysaccharides (SP) production from sludge) in sewage treatment from ideas, concept-proofs, andscale-up trials into potential applications. Challenges, approaches, funding and collaboration
opportunities, project risk control and market exploration are also discussed.
Biography
GH Chen obtained his PhD in Environmental Engineering from Kyoto University in 1990 and has
joined the Hong Kong University of Science and Technology (HKUST) since 1995. Currently he isa chair professor of Civil and Environmental Engineering at HKUST, Director of Water Technology
Center of HKUST, Head of the CNERC for control and treatment of heavy metal pollution (HongKong Branch), and an Associated Director of the Institute for the Environment of HKUST. GH wasselected Distinguished Fellow of the International Water Association (IWA) (DFIWA) in 2015,
received four international awards including the Global Honor Award of IWA Project Innovation
Awards (PIA) in 2012, a winner of the first Chinese National Innovation Pioneer Awards in 2018; abronze winner of IWA PIA of Breakthroughs in Research Development in 2018, and 2018 Gold Prizeof Hong Kong Green Innovation Awards. He has published nearly 200 journal papers, including 74
papers published in Water Research.

2

Source water odor in one reservoir in hot and humid areas of southern
China: diagnosis and possible mitigation measures○ Chao RONG1,3*, Jianwei YU 2, Xiaobo Han3, Jinsong ZHANG3
1

Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China.
Key Laboratory of Drinking Water Science and Technology, Research Center for Eco‑Environmental
Sciences, Chinese Academy of Sciences, Beijing 100085, China.
3
Shenzhen Water Affairs (Group) Co., Ltd., Shenzhen 518048, China.
*E-mail: rong.chao.p7@dc.tohoku.ac.jp.
2

Abstract
Taste and odor (T&O) are an important esthetic index to indicate the quality of drinking water and it is a direct
hint for users to judge the drinking water whether is safe. In this study, aiming at a major water source reservoir in
hot and humid areas in southern China, which encountered seasonable odor problems caused by 2-MIB in recent
years, an integrated approach including GC/MS, algal enumeration and quantitative real-time polymerase chain
reaction (qPCR) was adopted to investigate the odor problem. The results indicated that Pseudanabaena sp. is themain 2-MIB producer in the reservoir, which was confirmed as a benthic filamentous algae. The release of 2-MIB
from the Pseudanabaena sp. cells is affected by temperature and light. The risk of sudden increase of 2-MIB will
be reduced by raising the depth of water in the reservoir. CODMn and DOC also have great influence on
Pseudanabaena sp. growth, further measures to reduce the CODMn and DOC input should be performed.
Keywords: Drinking water; Earthy-musty odor; Pseudanabaena sp.; 2-MIB; qPCR.
1 Introduction
T&O problem occurrence in drinking water was widelyreported worldwide. Although no researches have shownthat the presence of some odorants in the drinking water
will cause harm to human health, the unpleasant smell of
drinking water will undoubtedly affect the quality of
consumers’ life and the impression of water supply
enterprise.
Due to the low odor threshold concentration (OTC) of theodorants, for instance, 2-MIB (2-methylisoborneol) andgeosmin were reported as 10 ng/L and 8 ng/L, respectively,
and the limited removal efficiency with conventional water
treatment process, it is difficult for water treatment plant toadopt applicable control measures once encountering T&O
episodes in drinking water. Previous research has provedthat most T&O occurrence in reservoir or lake source water
is linked to some cyanobacterial metabolites, such as 2-MIB
and geosmin. As a result, identifying the odorant-producer
and take effective measures in the water resource isconsidered the key point to reduce the T&O problem indrinking water. However, it should be noted that thisprocess is difficult since there are many algal species in thereservoir, and not all phytoplankton produce thesecompounds, such as 2-MIB and geosmin, < 50 of the morethan 2000 species classified to date have been directly
confirmed as producers. In addition, the mechanism of algae
growth, synthesis and release of odorants in algae cells arecomplex, as it affected by many environmental factors suchas water temperature, precipitation, light, and nutrients.
Fortunately, in recent years, the genes involved in thesynthesis of odorants have studied a lot, and detection ofodorants producing algae with qPCR method has been
widely used. Wang et al. have revealed genes involved incyanobacteria and first studied the correlation between thegene (mic) and 2-MIB concentration (10–60 fg 2-MIB per
mic copy). This provides a new way to identify theodorants-produced algae and must be more accurate thanprevious method.
Shiyan reservoir (SY) is a representative water resource in
hot and humid regions of China and encountered seasonable
T&O problems, especially in April to July which is one
typical high temperature and rainy season. Our previous
research has confirmed that 2-MIB is the major odorant andblamed to the T&O problem. In this study, the further
research has conducted to the occurrence procedure andcause. By comparing to one nearby reservoir with no odor
occurrence, the odor-producing algae, key environmental
affecting factors and possible control measures for reducing
odor occurrence risk in SY reservoir were systematicallyinvestigated. The results of this study will be helpful forfurther improvement of the drinking water quality and water
management suffering from odor problems.

2 Materials and methods
2.1 Field sites and sampling
Samples were taken from the intake (0.5m below thesurface) of two reservoirs from October 2016 to May 2018,
and total of 66 samples were collected. Samples werecollected weekly in the spring and summer (April to
September), and approximately monthly at other times ofthe period. The samples from October 28, 2016, May 8,2017 (odor event) and September 26, 2017 were used forodorant-producer identification analysis. During the April toJuly, 2018, additional sampling sites (1–6#) were added in
SY. Sample sites 1–2# were close to the water inlet of thereservoir and the water depth is only about 3 meters. For
other sample sites, the water depth is > 10 m.
2.2 Analysis Methods
2.2.1 Algal enumeration
The water sample was added 5% Lugol’ s iodine and static
settlement by 48 h, then pre-concentrated 100×and kept in3

dark until cell counting. The algal cell density wasdetermined by the Uterml technique using a
Sedgewick-Rafter counting chamber under a microscope(Nikon Eclipse 50i) with phase contrast and bright field
illumination. A magnification of 160 × was used toenumerate the cells. Triplicates of 1 mL concentratesamples were collected separately and counted.
2.2.2 Real-time PCR
The genomic DNA was extracted from Pseudanabaena sp.
(FACHB1277) which is 2-MIB producer. Field samples
were filtered through 0.22 μm polycarbonate filter (GTTP
type, Millipore, USA) before genomic DNA extraction. The
Fast DNA®SPIN KIT for Soil (MP Biomedicals, USA) kit
was used and operated according to the manufacturer’s
instructions. The primer pair MIB-R(f/r)(MIB-Rf 5′-CGA
CAGCTTCTACAYCYCCATGAC-3′, MIB-Rr 5′-CGC
CGCAATCTGTAGCACCAT-3′) was used to amplify themic fragments. The PCR instrument used was C1000™
Thermal Cycler (Bio-Rad, USA). PCR amplification was
carried out in 1 × DynaZyme II buffer (Thermo Scientific,USA) with 0.2 mM of each primer, 200 mM of dNTPs, 0.4
U of DynaZyme II, and 20–50 ng of genomic DNA as
template. The PCR protocol was 94 °C for 3 min, 35 cyclesof 94 °C for 30 s, 59 °C for 30 s, 72 °C for 60 s, and 72 °C
for 5 min. Then, the amplification product is subjected to a
purification operation with SanPrep KIT (Sangon Biotech,
China) and performed according to the manufacturer’s
instructions.

3 Results and discussion
During the odor event, the total amount of algae in SY was
as high as 100 million, which was significantly higher thanthe other two times. The relative abundance of somecyanobacteria species, especially Pseudanabaena sp.,increased significantly. In previous studies, Pseudanabaena
sp. has been identified as one of the 2-MIB producers in theaquatic ecosystem.

Fig. 1. The variation of phytoplankton in SZ and SY
before, after, and during the odor event by genus
The relationship between algae biovolume represented bychlorophyll-a and 2-MIB indicates was 0.766 (P < 0.01),
when the Pseudanabaena sp. is the dominant species.
Table 1 correlation analysis of chlorophyll-a and 2-MIB
of SY after data selected
Chl.a
2-MIB
Pearson Correlation
1
0.766**
Chl.a
Sig.(2-tailed)
0.000
N
19
19
Pearson Correlation
0.766**
1
2-MIB
Sig.(2-tailed)
0.000
N
19
19

**correlation is significant at the 0.01 level (2-tailed).
The relationship between the number of mic and 2-MIB
concentration in the filed simples is shown in Fig. 2(a. The
results indicated that the number of mic genes was closely
correlated with 2-MIB concentration by linear regression(R2 = 0.746, P < 0.001), and show that one mic represent
about 10 fg 2-MIB.
Principal compound analysis (PCA) was used to analyze
the difference of physio-chemical variables in SZ and SY,
and the result proved that CODMn and DOC are the key
factors cause of the difference in phytoplankton.

Fig. 2. Comprehensive analysis result of mic,
environmental factors and 2-MIB concentration: (a.
Relationship between 2-MIB concentration and mic. (b.
PCA plot based on the physio-chemical variables in SZ
and SY. (c. 2-MIB distribution in surface and bottom ofdifferent sites in SY. (d. mic distribution in surface andbottom of different sites in SY
The concentration of 2-MIB at 4–6# in the surface layer ismuch higher than 1–3# close to the inlet, while the 2-MIB
in the bottom layer presents the opposite phenomenon. The
distribution of mic genes is significantly different from
2-MIB, showing the opposite situation in 3–6# (Fig. 2(d).
This may be attributed to the fact that the Pseudanabaena
sp. is one typical benthic alga, which prefers to grow near
the sediment to obtain enough nutrients. increase of waterlevel may also cause nutrient dilution and decrease of watertemperature, which is not only inhibit the growth of algae,
but also detrimental to the production and release of 2-MIB
in the cell.

Conclusions
Pseudanabaena sp. is the main 2-MIB producer in Shiyan
reservoir. CODMn and DOC had higher effects on
Pseudanabaena sp. growth, reduce the CODMn and DOC
input which might be a useful measure for controlling the
growth of Pseudanabaena sp. Pseudanabaena sp. is abenthic filamentous algae, and the risk of sudden increase of
2-MIB will be reduced by raising the depth of water toreduce the temperature and light in the reservoir.

Reference[1] RONG C. et al, Source water odor in one reservoir in
hot and humid areas of southern China: occurrence,
diagnosis and possible mitigation measures, Environ Sci
Eur, 30:45 (2018) doi: 10.1186/s12302-018-0175-8.

4Human health risk of Legionella pneumophila infection caused by thegroundwater usage in a decentralized drinking water system○ Arief Nurul UMAM1, Mohan AMARASIRI3, Daisuke SANO1,2,3
1

Department of Environmental Studies, Tohoku University, Sendai 980-8579, Japan.
Department of Frontier Science for Advanced Environment, Tohoku University, Sendai 980-8579, Japan.
3
Department of Civil and Environmental Engineering, Graduate School of Engineering, Tohoku University,
Sendai 980-8579, Japan*
E-mail: arief.nurul.umam.s3@dc.tohoku.ac.jp
2

Abstract
Daily usage of poorly-treated water poses human health risks owing to the presence of harmful
microorganisms. This study evaluated the health risks caused by Legionella pneumophila in untreated groundwaterused as source of a decentralized drinking water system. Quantitative Microbial Risk Assessment (QMRA) was
performed for two exposure scenarios; toilet flushing, and showering. The result indicated that showering scenario
had a higher health risk with the median loss of disability-adjusted life years per person per year (DALYpppy loss)
with 1.19 x 10-5, compared to toilet flushing scenario with 1.30 x 10-8 DALYpppy loss. This estimated disease
burden was mitigated by the introduction of ultrafiltration membrane to the system, and the DALYpppy loss weredecreased significantly below 10-6, which is recommended by WHO as a tolerable disease burden.
Keywords: QMRA; Legionella pneumophila; groundwater usage; DALY
1 Introduction
United Nation’s Sustainable Development Goals(SDGs) target number 6.1, that explicitly calls for fullcoverage of safely managed drinking water for all, still has a
tough challenge, especially in rural areas. Up until 2017, it
was only approximately 53 % of the total population in rural
areas across the world were covered with safely managed
drinking water systems[1]. The reason for this low coverage
percentage is the cost of a reliable centralized water supply
system is often too high for the population to afford. People
live in a rural or remote area usually rely on decentralized
water supply (DWS), which refers to small scale purificationand distribution systems of water in a single household or asmall community, and uses locally available water sources
such as groundwater, river water, spring water, etc.
DWS still has challenges in providing water that freeof physical, chemical, and microbiological contaminants
since environmental waters, such as groundwater and riverwater, are prone to many natural and anthropogenic harmful
contaminants. Legionella pneumophila, a rod-shaped gramnegative bacterium, is one that threatens to the usage of DWS.
This bacterium infects lung tissues and causes a severe
pneumonia-like disease called Legionnaires. It can be foundin diverse environments and naturally occur in freshwater.
Unlike traditional fecal-oral route pathogens, L. pneumophila
can disperse into aerosolized waterdrops, and be inhaled by
the victim[2]. L. pneumophila then enter and multiply in thealveolar macrophages that may cause the destruction of thepulmonary tissues.
In this research, the risk of infection of L. pneumophilafrom the direct usage of groundwater/river water is evaluatedin two scenarios, showering and toilet flushing. Quantitative
Microbial Risk Assessment (QMRA) model was built andapplied for showering and toilet flushing’s exposure routes.
The usage of membrane filtration as a countermeasure thatcan potentially reduce the concentration of the L.
pneumophila was also incorporated into the study.

2 Materials and methods
QMRA for L. pneumophila exposure resulting fromriver water usage was performed in the four steps described
below :
Hazard identification
Data sets of L. pneumophila in the rural ground water
was taken from Stojek & Dutkiewicz (2006)[3] thatcharacterize L. pneumophila concentration in 107 potablewater samples collected from Lublin region, Poland. Amongthese samples, 32 samples were taken from wells situated inrural areas. These 32 L. pneumophila concentration data sets
were then fitted into log normal distribution using fitdistrplus
package in R, representing L. pneumophila probability
distribution in the groundwater from rural area.
Exposure assessment
Two exposure scenarios were evaluated for aerosol
contaminated with L. pneumophila inhalation during
showering and toilet flushing modelled by equation 1[4].
Each scenario was modelled based on the inhalation ofaerosols of various diameters that are large enough to hold L.
pneumophila bacteria but small enough to deposit at thealveoli. The effect of ultrafiltration membrane log removal
value (LRV) also studied on each exposure routes.
𝑑𝑜𝑠𝑒 =
𝐶𝑙𝑒𝑔 10−𝑘 𝐵𝑡 ∑𝑛𝑖=1 𝐶𝑎𝑒𝑟,𝑖 𝑉𝑎𝑒𝑟,𝑖 ∑𝑛𝑖=1 𝐹𝑖 𝐷𝑖(1),

where Cleg = the concentration of L. pneumophila inwater, Caer, j= the concentration of aerosols of diameter i,where i=1:10 µm, Vaer, i is the volume of aerosol for size i,
k=LRV, B = breathing rate (m3 / min), t = exposure duration(min), Fi = the fraction of L. pneumophila that partitions to
the applicable size diameter aerosols, and Di = alveolar
deposition efficiency of size i diameter aerosols.
Dose response
Dose response analysis was performed using
exponential dose-response equation [2]:5𝑃𝑖𝑛𝑓,𝑑𝑎𝑖𝑙𝑦 = 1 − 𝑒 −𝑟𝑑(2),

a tolerable disease burden. The median of DALY for shower

Figure 1 Monte carlo simulation result: per exposure risks , annual risks, and DALY loss per person per year
where Pinf, daily = daily probability of infection, d= dose, and r
is a parameter of the exponential dose response model. Monte
carlo simulation with 10,000 iteration, was performed foreach calculation.
Risk Characterization
Annual risk was calculated as equation [3]:𝑛𝑓𝑗

𝑃𝑖𝑛𝑓,𝑎𝑛𝑛 = 1 − ∏2 (1 − 𝑃𝑖𝑛𝑓,𝑑𝑎𝑖𝑙𝑦 )(3),where Pinf, ann= annual risk of infection, n = the yearlyfrequency, and f = the daily frequency of the activity j.
Additionally, the annual risks were converted to the disability
adjusted life years (DALY) metric using the following
equation[4]:𝐷𝐴𝐿𝑌 =

𝐷𝐴𝐿𝑌
𝑃
𝑖𝑛𝑓𝑒𝑐𝑡𝑖𝑜𝑛 𝑖𝑛𝑓,𝑎𝑛𝑛(4).

All risk computations were performed in R using mc2d
package and 100,000 monte carlo iterations.
3 Results and discussion
Figure 1 shows the per exposure risk, annual risk, andthe loss of DALY per person per year (DALYpppy) from L.
pneumophila infection for the initial concentration calculated
from Stojek & Dutkiewicz (2006), in which the rural
groundwater concentration of L. pneumophila data wereindicated. The showering scenario showed higher risk than
toilet flushing scenario both in per exposure and annual riskof infection. The median of per exposure and annual risk forshowering scenario were 3.37 x 10-8 and 1.23 x 10-5,
respectively. While the median per exposure and annual risk
for toilet flushing scenario were 3.66 x 10-11 and 1.34 x 10-8.
The much higher risk from showering is possible because
during the showering event, the aerosols concentration ishigher than during the toilet flushing.
This high risk of L. pneumophila infection in showerin
scenario, bring consequences to its DALYpppy loss. The dat
a showed that the median of showering scenario’s DALYpppy loss has exceeded 10-6, which is recommend by WHO as

ing scenario was 10-4.92 DALYpppy.
The application of membrane ultrafiltration led to thedecreasing of L. pneumophila risk of infection. Bothscenarios showed that the DALYpppy loss reduced to underthe 10-6 with the application of membrane ultrafiltration, byfiltering out the L. pneumophila concentration in the source
water.
4 Conclusions
In this study, L. pneumophila risk from the usage ofgroundwater in showering and toilet flushing scenarios at adecentralized drinking water system was modeled using
QMRA approach. The results indicated that showering gavea significant disease burden (higher than 10-6 DALYpppyloss). The introduction of ultrafiltration membrane into thesystem mitigated the disease burden, indicating that theadditional membrane filtration led to promising result for
reducing L. pneumophila concentration in the source water,thus significantly decrease the risk of infection.
Reference[1]“The WHO/UNICEF JMP global database includes
estimates of progress in household drinking water,
sanitation, and Hygiene since 2000.” [Online].
Available: https://washdata.org/data. [Accessed: 30Aug-2019].
[2]
T. W. Armstrong and C. N. Haas, “Quantitative
microbial risk assessment model for Legionnaires’
disease: Assessment of human exposures forselected spa outbreaks,” J. Occup. Environ. Hyg.,
vol. 4, no. 9, pp. 634–646, 2007.
[3]
N. M. Stojek and J. Dutkiewicz, “Legionella andother Gram-negative bacteria in potable water fromvarious rural and urban sources,” Ann Agric Env.
Med, vol. 13, p. :323-335, 2006.
[4]
K. A. Hamilton et al., “Risk-Based Critical
Concentrations of Legionella pneumophila for
Indoor Residential Water Uses,” Environ. Sci.
Technol., vol. 53, no. 8, pp. 4528–4541, 2019.

6

Applicability of anaerobic dynamic membrane bioreactor (AnDMBR) for
domestic wastewater treatment at short HRTs
○ Yisong HU1,2*, Yuan YANG1, Xiaochang C. WANG1 & Yu-You LI2
School of Environmental and Municipal Engineering, Xi’an University of Architecture and Technology,
Xi’an 710055, P.R. China
2Department of Civil and Environmental Engineering, Tohoku University, Miyagi 980-8579, Japan*
E-mail: yshu86@163.com.

1

Abstract
An upflow anaerobic dynamic membrane bioreactor (AnDMBR) was applied for real domestic wastewater
treatment at room temperature and short hydraulic retention time (HRT = 8 h, 4 h, 2 h, and 1 h). Following
continuous operation for 93 days with stepwise decreased HRT, stable average chemical oxygen demand (COD)
removal was achieved (between 77.3% and 70.6%) when HRT was reduced from 8 h to 4 h, then 2 h with flux
varying from 22.5 to 90 L/m2h. At these three HRTs, the rate of increase in trans-membrane pressure (TMP) was
0.4, 0.38, and 0.57 kPa/d, and average CH4 production was 0.12, 0.10, and 0.08 L/g CODremoved, respectively.
Further decreasing the HRT to 1 h resulted in less COD being removed (60.4%) and lower CH4 production (0.05
L/g CODremoved) as well as a faster rate of TMP increase (2.11 kPa/d). Various analytical methods were applied tocharacterize the morphology and composition of the dynamic membrane (DM) layers. With reduced HRT,
increased accumulation of organic substances was noted in the DM layer, especially when the HRT was shortened
to 1 h. AnDMBR was applicable to wastewater treatment at room temperature with short HRTs, 2 h could be the
HRT limit to maintain stable operation.
Keywords: AnDMBR; wastewater treatment; methane production; hydraulic retention time
1 Introduction
In the AnMBRs, the continuous attachment of foulants on
the UF/MF membrane surface is a common phenomenon
resulting in cake layer fouling. This problem accounts forover 80% of the total filtration resistance. Meanwhile, thecake layer could function as a “secondary membrane” orfilter to enhance pollutant removal, possibly due to thephysical rejection and biodegradation effects of active
biomass. Thus, researchers have attempted to strengthencake layer formation on coarse pore materials (such as
non-woven cloths and meshes) to replace conventional
UF/MF membrane during wastewater or waste solids
treatment. The successful formation of a heterogeneous
cake layer, which is commonly termed “dynamic membrane”(DM) has a robust structure and stable filterability and isobserved both in anaerobic dynamic membrane bioreactors(AnDMBRs) and aerobic dynamic membrane bioreactors(AeDMBRs). Both systems possess several uniqueadvantages such as high membrane flux, low membranecost, and easy membrane cleaning [1]. Here, the DM cantransform one of the most critical disadvantages ofmembrane fouling, into a competitive advantage.
It is known that real municipal wastewater is generally inthe complex wastewater category due to its large amount ofparticulate substances and moderate biodegradability. Thus,
challenges encountered for anaerobic municipal wastewater
treatment at temperate area. For real municipal wastewater
treatment in low-temperature areas, utilizing the AnDMBR
at room temperature could be an appropriate alternative.
However, studies on the application of the emerging
AnDMBR process in real municipal wastewater treatment
are lacking, and its potential has not been fully understood
until recently. According to one review regarding theapplication of AnDMBR, when treating municipal

wastewater, the shortest HRT reported was 2.6 h, with otherstudies reporting HRTs of 8 h or longer.
Thus, the main objective of this work is to investigate theapplication of an upflow AnDMBR for real domestic
wastewater treatment at short HRTs (decreasing from 8 h to1 h) at room temperature (20-25 °C), with a focus ontreatment performance and DM filterability during along-term operation period (93 d) [2]. Moreover, thecharacteristics of the DM layer, including the morphology,
component substances, and retention function, werediscussed. No such investigation has yet assessed theimpacts of extreme conditions (relatively short HRT, lowtemperature, and high particulate content wastewater) on
AnDMBR performance

2 Materials and methods
2.1 Experimental setup and operation
The AnDMBR set-up was made of plexiglass with a total
working volume of 3.6 L and comprised an upflow
bioreactor and a submerged DM module located at the topof the bioreactor. The nylon mesh (75 μm pore size) served
as the supporting material for DM formation with a total
filtration area of 0.02 m2. Real domestic wastewater was fed
into the bioreactor using a peristaltic, which was connected
to a water level sensor to maintain a constant water level inthe bioreactor. Permeate was collected using a second
peristaltic pump. To achieve the target HRTs, the membrane
flux was changed by regulating the flow rate of the effluent
pump. An on-line pressure sensor located on the permeateline recorded the trans-membrane pressure (TMP). The
volume of the produced biogas was measured by a wet-type
gas flowmeter.
The AnDMBR was inoculated with sludge from a local
full-scale anaerobic digester The feed wastewater wascollected from a local WWTP in Xi’an, China. The

7

3 Results and discussion
Fig. 1(a) shows that increasing filtration flux from 22.5 to
180 L/m2h is adopted to achieve stepwise decrease of HRT
from 8 h to 1 h. The time course of TMP is shown in Fig.
1(b) at different HRTs. TMP increased steadily when HRTs
were 8 h, 4 h and 2 h, with increasing rate of 0.4, 0.38 and0.57 kPa/d, respectively. TMP increasing rate was muchhigher during HRT of 1 h (2.11 kPa/d). Apparently, HRTs
at the 2-8 h TMP increasing rates were acceptable and notaffected by the high filtration flux (22.5-90 L/m2h).
However, at extremely short HRT (1 h) and high flux (180L/m2h) with a quick TMP increase coupled with an evenunsustainable flux appeared.
As shown in Fig. 1(c), at HRTs of 8 h, 4 h and 2 h, theeffluent turbidity maintained within a relatively stable range
with the average values as 21.4, 19.2 and 24.4 NTU,
respectively, this was when influent turbidity was 139.4
NTU on average. However, at HRT of 1 h, the effluent
turbidity increased to 37.9 NTU on average, indicating a
reduction of filtration performance. The DM layers at theend of operation at HRT of 2 h and 1 h were chosen toinvestigate the effects of short HRTs on the DM layer.
The potential reasons were presented as follows. It is
worth noting that at HRT of 1 h the high flux (180 L/m 2h)
was seldom reported to date in the AnDMBRs and also in
the AnMBRs. Increased upflow velocity (1.7 m/h) at HRT
of 1 h resulted in turbulence of the sludge blanket, more
small flocs, colloids and biopolymers were presented in the
supernatant, which can be verified by supernatant turbidity(67 and 102 NTU at HRT of 2 h and 1 h on average) andsupernatant COD concentrations. On the other hand, thedrag force was much higher to maintain high flux, which
may accelerate the deposition of flocs, colloids andbiopolymers on support material. This process induced theformation of a thicker and denser cake layer,
As shown in Fig. 2, the black gray cake layers were bothformed on the support material at two HRTs. In addition,
SEM images were taken to determine the morphology of the
DM layer. It can be seen that the virgin nylon mesh wassmooth and the pores were clear. At HRT of 2 h and 1 h, theDM layers were covered by microbial flocs or biopolymers,
and seemed to be dense, rough and uneven. Meanwhile, theSEM images revealed showed that the DM layer at HRT of
1 h appears to be denser, and more compact when comparedwith that at HRT of 2 h.

HRT=8 hHRT=4 h

HRT=2 h

HRT=1 hFlux (L/m2·h)

200
(a)

New DM module

150
100
50
0
40

TMP (kPa)(b)30
y=0.4x+2.55
R2=0.8720

y=0.38x+3.66
R2=0.91y=0.57x-17.15
R2=0.94y=2.11x-162.13
R2=0.93100400

Turbidity (NTU)

AnDMBR was operated at 20-25 °C covering four phases.
At the first phase, the HRT was 8 h, then it was shortened to
4 h, 2 h, and 1 h at the subsequent phases, with fluxes of
22.5, 45, 90 and 180 L/m2h, respectively. During the fourstages, the OLR was 0.82, 1.63, 3.01, and 6.8 kg COD/m3·d.
2.2 Analytical methods
Various analytical methods are adopted and listed below:
particle size distribution (PSD) analysis, scanning electron
microscopy (SEM) - energy diffusive X-ray (EDX) analysis,
fluorescent excitation–emission matrix (EEM) analysis,
Fourier transform infrared (FTIR) analysis, specificmethanogenic activity (SMA) analysis as well as others,
such as VFAs, EPS, COD/SCOD, MLSS/MLVSS and SO42-,
and biogas composition.

(c)

Influent

Effluent

300
200
100
0
0

20

40

Time (d)

60

80

100

Fig. 1. Profiles of (a) flux, (b) TMP and (c) effluent
turbidity in the AnDMBR at different HRTs.

Fig. 2. Photos and SEM images of virgin mesh and DM
layers at HRTs of 2 h and 1 h.

4 Conclusions(1) When HRT no less than 2 h satisfactory COD removal
and appreciable CH4 production could be guaranteed.
(2) Further shortening HRT to 1h affected the process
performance as COD removal rate dropping to 60%,
methane production decreasing to 0.05 L CH4/g COD and
TMP increasing rapidly (2.11 kPa/d). Enhanced
accumulation of foulants in the DM layer resulted in severer
membrane fouling issue.
(3) AnDMBR can be a promising alternative forwastewater treatment under extremely environment andoperation conditions.

Reference[1] Y.S. Hu, X.C. Wang, H.H. Ngo, Q.Y. Sun, Y. Yang,
Anaerobic dynamic membrane bioreactor (AnDMBR) for
wastewater treatment: A review, Bioresour. Technol., 247(2018) 1107-1118.
[2] Y. Yang, Y. Zang, Y.S. Hu, X.C. Wang, H.H. Ngo,
Upflow anaerobic dynamic membrane bioreactor(AnDMBR) for wastewater treatment at room temperatureand short HRTs: Process characteristics and practicalapplicability, Chem. Eng. J., (2019) 123186.

8

Startup and performance of a novel one-stage partial
nitritation-anammox system for reject water treatment○ Yunzhi QIAN1*, Huaji MA2, Yongzhi CHI2 , Hongying YUAN2 & Yanmei DING2
1

Graduate School of Environmental Studies, Tohoku University, Miyagi 980-8579, Japan.

2School of Environmental and Municipal Engineering, Tianjin Chengjian University, Tianjin 300384, China.
*E-mail: qian.yunzhi.q8@dc.tohoku.ac.jp

Abstract
A novel internal circulation contact oxidation membrane bioreactor (ICCOMBR) was constructed to investigate
the nitrogen removal of reject water. The nitrosation process, partial nitrosation process and one-stage partial
nitrosation-anammox (PN/A) process were established gradually. The stable nitrite accumulation rate (NAR) of
87.5% was achieved with nitrogen loading rate (NLR) increased from 0.1 to 0.64gN/(L·d). The transition from
nitrosation process to partial nitrosation process could be achieved by reducing the aeration rate (AR) from
1.65L/min to 0.8L/min, and the ratio of NO 2-/NH 4+ in the effluent was 1.15±0.04. With the dissolve oxygen(DO) concentration of 0.50mg/L and the NLR of 0.4gN/(L·d), the one-stage PN/A process was built in 27 days
after inoculated anammox granular sludge. The one-stage PN/A process was successfully operated under
0.4-0.6gN/(L·d), 0.50-0.55mgDO/L, and 82% nitrogen removal effectively.
Keywords: One-stage partial nitrosation-anammox; Reject water; Partial nitrosation; Biofilm carriers.

1 Introduction
The wastewater produced by sludge anaerobic digestion iscalled reject water. T he flow rate of reject water is about 2%
of WWTP influent and the nitrogen load accounts for about
15 to 40% of the WWTP. Besides, the characteristics ofhigh-strength ammonia concentration (500-2000mg/L) and
low C/N (≤1) lead to difficulties in treating reject water withnitrification-denitrification process.
For the low operating cost and no requirement of externalcarbon sources, partial nitrosation/anammox (PN/A) has
been the emerging environment-friendly and energy-saving
process for biological nitrogen removal, especially for highstrength ammonia wastewater such as reject water. The onestage PN/A system is more potential for application due tospace saving. However, only the ammonia-oxidizing
bacteria (AOB) of partial nitrosation process and the
anammox bacteria of anammox process are coexist
efficiently in one reactor can the one-stage PN/A system
could achieve the stable nitrogen removal performance.
Besides, the nitrite oxidizing bacteria (NOB) usually coexist
in the microbial community and compete nitrite withanammox bacteria.
To solve the above challenges, dissolved oxygen (DO)concentration control is used in one-stage PN/A
system. DO concentration below 1.0mg/L is widely usedin the startup and stable operational of one-stage PN/A
system to restrain ammonia over oxidation and avoid
inhibition of anammox activity at higher DO level.
Additionally, contact oxidation reactor was wildly used tostartup and operate the one-stage PN/A system[1]. In
these reactors, the microorganisms rely on the biofilm
carriers to form a two-layer biofilm. Among them, theanammox bacteria live in an anaerobic environment andthe AOB lives in an aerobic environment. However, there
are problems that the microorganisms could lost with
effluent before successful forming biofilm or the

inadequate biomass of anammox bacteria in inoculation
sludge, resulting in prolonged start-up time of onestage PN/A system. Membrane bioreactor(MBR)
is suitable for slow-growth-rate microorganisms suchas anammox bacteria and AOB due to the completebiomass retention. Therefore, by coupling suitable biofillers and membrane separation technology, this could
more potential to achieve the fast start-up and stable
operation of one-stage PN/A system.
In this study, a novel internal circulation contact oxidation
membrane bioreactor (ICCOMBR) was setup to evaluate thestart-up characteristics and nitrogen removal performance of
one-stage PN/A system. The stable operation of nitrosation
process and the mechanism of transformation from
nitrosation process to partial nitrification process werestudied.

2 Materials and Methods
2.1 Biofilm carriers
The biofilm carriers used in this study was made of highdensity polyethylene (HDPE) modified material, which hasan H×D=10mm×7mm, the specific surface area of
1000m2/m3, and the specific gravity is 0.96~0.98.
2.2 Reactor setup and operation
The ICCOMBR system was equipped with an 8.5L
ICCOMBR reactor, a substrate tank and a pretreatment tank.
The ICCOMBR reactor has an H/D of 12:1. Operation
temperature was kept at 35℃ with a water bath heater.
Influent was pumped by a peristaltic pump. Effluent
recirculation was pumped by a peristaltic pump to dilute thesubstrate concentration in the reactor. The U-type
microfiltration membrane module was made ofpolyvinylidene fluoride (PVDF) with a membrane pore sizeof 0.22μm and a membrane effective surface area of 0.1
m2 .The bio-filler with a packing ratio of 84.5% was fixedwith nylon thread.

9

NAR of 87.6±3.5% while NOB was successfully inhibited ina low level of 0.05±0.02gNO3--N/L/d. Fig.2 (d) - (f) showed
the startup and operation of partial nitrosation process. After
the AR was adjusted from 1.65L/min to 0.8L/min, the partial
nitrosation process was startup in 5 days and the effluent
ratio of NO2--N/NH 4+-N was kept in a stable level of 1.15
±0.04. It suggested that the partial nitrosation process can befast started by reducing the AR to about 50% of thenitrosation process. AR was the key control parameter forthe transition from nitrosation process to partial nitrosation
process. Fig.3 (g) - (i) showed the startup and operation of
one-stage PN/A process. The one-stage PN/A process wasbuilt in 27 days after inoculated anammox granular sludge
and was successfully operated under nitrogen loading rate of
0.4-0.6gN/(L·d), DO of 0.50-0.55mg/L, and with a nitrogen
removal effectively of 82%.

Fig. 1. Schematic diagram of the ICCOMBR reactor

2.3 Influent and seed sludge
Reject water was generated from digestive sludge
dewatering treatment, which was treated with struvite
precipitation for phosphorus recovery and pretreated bypolyaluminium
chloride(PAC)
coagulationandsedimentation. The molar ratio of HCO3-/NH4+ in rejectwater was 2:1 by adding NaHCO3. The main compositionsof reject water were NH4+-N 584±140mg/L, TP 0.56
±0.16mg/L, COD 232±47mg/L and SS 47±8mg/L,
respectively.
Activated sludge was inoculated to start the nitrosation
process, with SS was 3.3g/L and VSS/SS was 0.46.
The anammox granular sludge was taken from an UASB
reactor treat high strength ammonia synthetic wastewater,
with the SS was 56 g/L and VSS/SS was 0.44.
2.4 Analysis Methods
Influent and effluent samples were measured daily andprocessed immediately for analysis. The samples were
filtered through 0.45μm filter prior to measurements.
Ammonia, nitrite and nitrate were analyzed according tostandard methods[2] .
Nitrite accumulation rate (NAR), ammonia removal
efficiency (ARE), volume nitrosation rate (v1) and volume
nitrification rate (v2) were calculated as Eq. (1)-(4).
NO2--Neff
×100%
(1)
NAR=
NO2 -Neff +NO3 -Neff
ARE=
v1=
v2=

NH4+-Ninf -NH4+-N eff
NH4+-Ninf

×100%

NO2--Neff ×Q- NO 2--Ninf×Q
V×1000NO3 -Neff ×Q- NO 3--Ninf×Q
V×1000(2)(3)

Fig. 2. The startup and operation performance of the
ICCOMBR reactor. ((a)the nitrogen concentrations,(b)NAR and ARE, and (c) v1 and v2 of nitrosation
process; (d)effluent substrate ratio, (e)DO and AR, and(f) v1 and v2 of partial nitrosation process; (g) the
nitrogen concentrations and NLR, (h)DO and AR, and(i)TN removal effeiency of one-stage PN/A process.

4 Conclusions(1) The nitrosation process could achieve fast startup andstable operation while NOB was successfully inhibitedwhen influent NH4+-N concentration was higher than
250mg/L.
(2) The partial nitrosation process can be fast started by
reducing the AR to about 50% of the nitrosation process.
(3) The one-stage PN/A process could successfully operated
under NLR of 0.4-0.6gN/(L·d) and nitrogen removal
effectively of 82%.

Reference(4)

Where Q and V are inflow rate and reactor volume,
respectively.

3 Results and Discussion
Fig. 2 showed the startup and operation performance of the
ICCOMBR reactor. Fig.2 (a) - (c) showed the startup
and operation of nitrosation process. After influent
NH4+-N concentration higher than 250mg/L, thenitrosation process could achieve the stable operation with

[1] Liu, Y., Niu, Q.G., Wang, S.P., Ji, J.Y., Zhang, Y.,
Yang, M., Hojo, T., Li, Y.Y. 2017. Upgrading of thesymbiosis of Nitrosomanas and anammox bacteria in a novel
single-stage partial nitritation-anammox system: Nitrogen
removal potential and Microbial characterization. Bioresource
Technology, 244, 463-472[2] APHA. 2005. Standard Methods for the Examination of
Water and Wastewater. American Water Works Associationand Water Environment Federation, Washington, DC,USA.

10

Effects of phosphate and Ca/P ratio on the Anammox-hydroxyapatite(HAP) crystallization process○ Lan LIN1,2,*, Yanlong ZHANG2, Tong OUYANG2 & Yu-You LI3
1

Department of Civil and Environmental Engineering, Tohoku University, Miyagi 980-8579, Japan.

2College of the Environment & Ecology, Xiamen University, Xiang’an District, Xiamen, Fujian 361102, China.
3

Department of Frontier Science for Advanced Environment, Tohoku University, Miyagi 980-8579, Japan.
*
E-mail: lnkhl06@163.com.

Abstract
Based on the requirements for advanced treatment and resource recovery of nitrogen and phosphorus pollutants
in wastewater, the coupled anammox and hydroxyapatite (HAP) crystallization process was studied with an aim ofachieving high efficiency and low energy consumption during simultaneous nitrogen and phosphorus removal. In
this study, the effects of phosphate and calcium-phosphorus (Ca/P) ratio on the coupled anammox-HAP systemwere investigated through batch tests. The inhibition effects of phosphate and free HAP were analyzed by themodified 2-P logistic model to obtain the threshold concentrations of inhibitors. And a reference formula ofcalcium dosage was obtained to provide theoretical basis for practical engineering application of this system.
Keywords: Anammox; hydroxyapatite crystallization; phosphate; Ca/P ratio; calcium dosage.
1 Introduction
Excess nitrogen and phosphorus have been released into
waterbodies due to excessive use of fertilizer in agriculture.
And the worldwide phosphorus reserves have been sharply
reduced, and apparently a global shortage situation will be
unavoidable in the near future. Therefore, recycling andreusing released phosphorus resources is a possible solution
for eutrophication.
The anammox process was discovered to be an advanced
and promising technology for nitrogen removal, which cangreatly cut down the energy consumption and sludge
production in contrast to the traditional biological process.
However, phosphorus cannot be removed from wastewaterby a single anammox process. The traditional phosphorus
removal process mainly uses the bioenrichment andchemical precipitation methods, while the former has low
removal efficiency and the latter is not suitable for treatmentof wastewater with a low phosphorus concentration and also
costly.
The anammox biological process was coupled withhydroxyapatite (HAP) crystallization and combined into an
anammox-HAP crystallization process in this study. The
HAP was produced through the effects of both biologicallyinduced mineralization and biologically controlled
mineralization via anammox. The produced HAP was
combined with the anammox microorganism and finally
acted as a carrier of the anammox biofilm. Since HAP has
the advantages of excellent bio-compatibility and highcrystallinity, this structure can enhance the mechanicalstability and sedimentation performance of sludge granules
and improve the ability of sludge to withstand compression.
In addition, it offers a way to recover phosphorus fromwastewater in the form of HAP, which is also an essential
product in industry.
Based on this, it is of significance to investigate theeffects of phosphate and calcium-phosphorus (Ca/P) ratioon the coupled anammox-HAP system. Since higher
phosphate concentrations can inhibit anammox activity andthe Ca/P ratio is an important factor in the HAP
crystallization process. Furthermore, the phosphorus

concentration varies in a range in practical water treatment,
thus the optimal amount of calcium added also differs.
In this study, an anammox attached film expanded bed(AAFEB) reactor was used to start up the anammox-HAP
crystallization process. Through batch tests, the effects ofphosphate concentration and Ca/P ratio on the specificanammox activity (SAA) and phosphorus removal
performance were investigated. And the data were fitted bymodels to analyze and discuss the results.

2 Materials and methods
2.1 Batch Tests
The effects of phosphate and Ca/P ratio were investigatedthrough the determination of the SAA and phosphorus
removal efficiency. The phosphorus removal efficiency was
obtained by analyzing filtered samples using ammonium
molybdate spectrophotometry method.
The steps of the SAA test were as follows. The tests wereconducted in serum flasks with a working volume of 150
mL and incubated in a thermostatic shaker (110 rpm) at
35 °C. The substrate was basically consistent with the
influent of AAFEB reactor system, which had total nitrogencontent of 200 mg N/L, a NO2--N to NH4+-N ratio of 1.32:1
and an initial pH value of 7.8. The substrate was also
sparged with nitrogen gas to obtain anoxic conditions.
Sludge granules were taken from the middle of AAFEB
reactor and washed with nutrient solution to remove
substrate residue. By recording the amount of gas produced,
a curve of gas production over time was obtained. The
modified Gompertz equation model (Eq. (1)) was used to fit
the data:(1)
P  P0  exp- exp[ Rmax  e  ( λ-t ) /P0  1]
where P is the cumulative nitrogen production (mL), P 0 is
the theoretical maximum nitrogen gas production (mL),
Rmax is the maximum nitrogen production rate, e is aconstant (= 2.718281828), t is the reaction time, and λ is thelag phase time (h). The P0, Rmax and λ parameters are
obtained by fitting, and the SAA is calculated using these
fitted parameters.

11

2.2 Model Description
The modified 2-P logistic model is used to fit the
inhibitory effect, and the threshold concentrations ofinhibitor are defined as the IC10 (at which the inhibition rate
is 10%), IC50 and IC90. The modified 2-P logistic model canbe expressed as:
100(2)
I  100 

1 ( X / X 0 )P

where I is the inhibition ratio (%), X is the inhibitor
concentration, X0 is the half maximal inhibitory
concentration (IC50), and P is a constant.
The Monod equation model is a kinetic model used todescribe the relationship between the cell growth rate andsubstrate concentration. It was used in this study to fit theeffect of Ca/P ratio on the phosphorus removal rate. The
Monod equation used in the batch test can be expressed as:(3)
P  Pm  r /(k r  r )where P is the phosphorus removal efficiency (%), P m is
the maximum phosphorus removal efficiency (%), r is the
Ca/P ratio, and kr is half of the Ca/P ratio corresponding to
the Pm.

concentrations, the rate of increase of phosphorus removal
was slow. When the concentration increased, the Ca/P ratio
had a more significant effect on phosphorus removal, andthe phosphorus removal rate tended to be constant in theend. In addition, the initial phosphorus concentration had agreat influence on its removal rate. For a specified Ca/P
ratio, a higher phosphorus concentration would give rise tohigher phosphorus removal efficiency.
The Monod equation model was used to fit the data points
in Fig. 1(e), and the Pm was substituted into the equation to
obtain the corresponding optimal Ca/P ratio (calcium
dosage). A quadratic equation of the calcium dosage versus
the phosphorus concentration was then obtained by
nonpolynomial fitting (Fig. 1(f)). Thus, the reference
formula of calcium dosage was:DCa = 1.79E-05 CP2 + 0.00163 CP + 0.312(4)where DCa is the calcium dosage (g/L), and CP is thephosphorus concentration (mg/L) in the wastewater.

3 Results and discussion
3.1 Effects of Phosphate Concentration
Fig. 1(a)-1(b) shows the SAA variation with the phosphateconcentration and the inhibition curve fitted by the modified
2-P logistic model. The SAA value decreased to 0.0676±0.0046 when the phosphate concentration reached 600
mg/L and further decreased to 0.0069±0.0042 when thephosphate concentration was 1500 mg/L. Higher phosphateconcentrations inhibited anammox activity and the the IC10,
IC50 and IC90 of phosphate for the system were 375, 651,
and 1221 mg P/L, respectively.
3.2 Inhibition Due to Free HAPIn this study, a phosphorus concentration of 600 mg/L,
which is similar to the IC50 of phosphate inhibition (651
mg/L), and a Ca/P ratio range of 0~1.0 were selected toinvestigate the effect of the calcium dosage. The SAA
results (Fig. 1(c)) show that when the Ca/P was higher than0.1, an increase in the Ca dosage deteriorated rather thanalleviated the inhibition induced by high phosphate.
In addition, the phosphorus concentrations after reaction at
Ca/P ratios of 0.5, 0.7, and 1.0 (295.14, 236.20 and 164.16
mg/L, respectively) were lower than the IC10 (375 mg/L).
Therefore, under these Ca/P ratios, the inhibitory effect ofhigh phosphate was considered to be eliminated. Meanwhile,
many white crystals formed in the serum flasks, and morewhite crystals formed under higher Ca/P ratios. XRD
characterization revealed that the white crystals were HAP.
Therefore, the reaction system may be inhibited by thesegenerated free HAP crystals. The inhibition curve of free
HAP was obtained by the modified 2-P logistic model (Fig.
1(d)) and the IC10, IC50 and IC90 values of free HAP were
6.34, 8.74, and 12.05 g/L, respectively.
3.3 Reference Formula of Calcium Dosage
Different initial phosphorus concentrations (5.7, 11.4, 40,
and 600 mg/L) were used to investigate the effect ofdifferent Ca/P ratios on phosphorus removal (Fig. 1(e)).
With the increase in Ca/P ratios at the low phosphorus

Fig. 1. SAA for different phosphate concentrations (a)and different Ca/P ratios (c), inhibition rate fitting curveof phosphate (b) and free HAP (d); phosphorus removal
results for different initial concentrations (e) andcalcium dosage curve (f).

4 Conclusions(1) Higher phosphate concentration had an adverse effecton the coupled anammox-HAP system and higher Ca/P ratio
could inhibit this system through the formation of free HAP.
(2) The inhibition thresholds of phosphate and free HAPon the coupled system and a reference formula of thecalcium dosage were obtained.

Reference[1] Zhang, Y.L. et al, Long-term operation performanceand variation of substrate tolerance ability in an anammox
attached film expanded bed (AAFEB) reactor, Bioresour.
Technol., 211 (2016a) 31-40.

12

Sulfate in anaerobic co-digester accelerates methane production fromfood waste and waste activated sludge○ Feixiang Zan1, Tianwei Hao2*
1

Department of Civil and Environmental Engineering, The Hong Kong University of Science andTechnology, Clear Water Bay, Hong Kong, China
2
Department of Civil and Environmental Engineering, Faculty of Science and Technology, University of
Macau, Macau, China*E-mail: thao@connect.ust.hk.

Abstract
The presence of sulfate in food waste (FW) and waste activated sludge (WAS) threatens the anaerobic
co-digestion for methane production. In this study, methane production from the anaerobic co-digestion of FW
and WAS at sulfate concentrations from 50 to 400 mg S/L was not affected, but instead deteriorated at 200–300
mg S/L. However, a model-based kinetic analysis reveals that sulfate can significantly promote the conversion of
rapidly biodegradable substrates. From a point of thermodynamic view, the presence of sulfate can stimulate
sulfate reducing bacteria acting as acetogens to convert propionate to acetate, providing an alternative metabolic
pathway for methanogenesis. In the anaerobic co-digestion, regulation of sulfate at a proper concentration can be apotential strategy to improve the efficiency of methane production. However, more research is needed to optimize
the sulfate concentration and substrate types in the anaerobic co-digester.
Keywords: Sulfate; Anaerobic co-digestion; Food waste; Waste activated sludge.
1 Introduction
Anaerobic digestion (AD) is a versatile biotechnology
capable of stabilizing organic waste while simultaneously
recovering energy in the form of methane. A high organic
substrate such as FW can be effectively converted into
bioenergy through the AD process for energy recovery andsolids reduction. However, the mono-digestion of FW canbe inhibited by the improper ratio of carbon-to-nitrogen(C/N) and the imbalanced nutrients. To overcome thedisadvantages of mono-digestion, the co-digestion of FW
and waste activated sludge (WAS) is done in view of
optimizing the C/N ratio and improving the nutrientsbalance. However, the sulfur content in FW, from 0.2% to0.6% of the dry matter, is usually overlooked in theanaerobic
co-digestion.
Besides,sewagesulfateconcentrations of 20 to 300 mg S/L can inevitably end up in
the anaerobic digesters through the seawater intrusion,
application of sulfate-containing flocculants and/or
discharge of sulfate-laden industrial wastewater. Sulfate
reducing bacteria (SRB) thrive in the presence of sulfate andimpact negatively on methane production. SRB competewith methanogens or fermentative bacteria for carbon
sources and electrons, while the produced sulfide by SRB
can inhibit both methanogens and fermentative bacteria.
Other than the competition, an active syntrophic
relationship between methanogens and SRB have been
extensively identified. In this study, it was hypothesized thatthe sulfate concentration could be the key driver for diverse
SRB-related anaerobic metabolisms in the anaerobic
co-digestion of FW and WAS; sulfate could potentiallydetermine the functional role of SRB in AD.
Hence, the main objective of this study is to investigate therole of sulfate at a proper concentration in the anaerobic
co-digestion of FW and WAS. Biochemical methane
potential (BMP) tests were conducted to evaluate themethane production from the co-digestion of FW and WAS
at different sulfate concentrations. A model-based kinetic

analysis was used to evaluate the hydrolysis rates andmethane production potential of the substrates at various
co-digesters sulfate concentrations.

2 Materials and methods
The WAS was collected from a secondary sedimentation
tank in a local saline wastewater treatment plant (WWTP) in
Shatin of Hong Kong with an average solids retention timeof about 15 days. The total solids (TS), volatile solids (TS)and pH were 15.85 ± 0.14 g/L, 9.60 ± 0.07 g/L and 6.53 ±0.02, respectively. Inoculum for BMP tests were obtained
from a mesophilic anaerobic digester (AD) at the same
WWTP with 30.15 ± 0.27 g TS/L, 16.20 ± 0.20 g VS/L and
pH of 6.60 ± 0.02. The FW was prepared according toprevious study1. The FW slurry was characterized with
30.83 ± 0.32 g TS/L, 30.50 ± 0.49 g VS/L and pH of 6.04 ±0.02. To assess the impacts of different sulfate
concentrations on the co-digestion of FW and WAS, BMP
tests were conducted with an Automatic Methane Potential
Test unit (AMPTS II, Bioprocess Control, Sweden) with areactor volume of 500 mL. All tests were carried out in
duplicate and last for 30 days.
To investigate the impacts of sulfate on the reaction rates
and biodegradability in the anaerobic co-digestion of FW
and WAS, two kinetic models were applied to fit the
cumulative methane production data from the BMP tests.
First, one substrate-based first-order kinetic model Eq. (1)was used2, and the model parameters were calculated byminimizing the least square difference between the observed
and predicted values.
B(t)=Bu∙(1-exp(-khyd∙t))(1)

Where, B(t) represents the cumulative methane production(L CH4/kg VSadded); Bu is the ultimate biochemicalmethane potential (L CH4/kg VSadded); khyd is the hydrolysis
rate (1/d); and t is the length of the test duration (d).

13

250

Cumulative methane production(L CH 4/kg VSadded)

Methane production from the co-digestion of FW andWAS at different sulfate concentrations are shown in Fig. 1.
The presence of sulfate at 200 and 300 mg S/L in thereactors resulted in a considerable drop in methane
production. The nonlinear inhibition of sulfate or sulfide
was observed and the underlying reason why methane
production deteriorated at sulfate concentrations of 200–300
mg S/L or remained unaffected at 400 mg S/L is unclear at
this stage. Sulfide production by sulfate reduction during thetests was monitored. The presence of sulfide can inhibit
different microorganisms. This can be attributed to the
diffusion of sulfide into the cell membrane and deactivationof native protein in the form of hydrogen sulfide. Moreover,there is a potential competition for electron donor between
SRB and MA in the system, which can divert the electron
flow from methane production to sulfate reduction. Moreresearch must be conducted to verify this observation.
Although the addition of sulfate of 50 to 400 mg S/L did not
improve the total methane production, over 80% of the
maximum accumulated methane production was achieved
within the first ten days by adding sulfate concentrations of
50 to 400 mg S/L, suggesting that the rate of methane
production can be increased by the presence of sulfate.
Sulfate at a proper concentration can be beneficial formethane production given that it accelerates the substratedegradation through the mediation of SRB serving asacetogens.
The simulated methane production curves using
one-substrate model are illustrated in Fig. 2, which show
agreement with the experimental results of methane
production (R2>0.98 in all studied cases). The highest
estimated values of Bu were obtained without sulfate
addition (i.e. sulfate is 0 mg S/L) in the co-digestion of FW
and WAS. There were slight decreases in Bu at the sulfate
concentrations of 50, 100 and 400 mg S/L, whereas
significant decreases were obtained at the sulfate
concentrations of 200 and 300 mg S/L. Also, it is clear thatthe estimated values of khyd were significantly improved by
70% up to 144% at sulfate concentrations greater than 50
mg S/L. The hydrolysis rates of substrates increased withthe presence of sulfate at different concentrations as shownby the one-substrate kinetic model. From a point ofthermodynamic view, the presence of sulfate can stimulate
sulfate reducing bacteria acting as acetogens to convert
propionate to acetate, which provides an alternative
metabolic pathway for methanogenesis.
In the presence of sulfate, propionate is first converted toacetate by a group of SRB. The produced acetate can beeither directly used for acetotrophic methanogens or
indirectly converted to hydrogen and CO2 forhydrogenotrophic methanogens. Further research into
microbial communities is deemed necessary to confirm the
key functional microorganisms in the degradation pathways
with the addition of sulfate. The proper addition of sulfate in
the anaerobic co-digestion of FW and WAS can improve thehydrolysis rates of rapidly biodegradable substrates andaccelerate the methane production. The kinetic analysis
reveals the substantial increases in khyd with sulfate atdifferent concentrations through the one-substrate model.
The regulation of sulfate concentration in the anaerobic

200

150

100
Sulfate=0 mg S/L
Sulfate=50 mg S/L
Sulfate=100 mg S/L
Sulfate=200 mg S/L
Sulfate=300 mg S/L
Sulfate=400 mg S/L

50

0
0

5

10

15

20

25

30

35

Day

Fig. 1. Cumulative methane production from theco-digestion of FW and WAS with different sulfate
concentrations (0, 50, 100, 200, 300, and 400 mg S/L).
250

a-One-substrate model

Cumulative methane production(L CH4/kg VSadded)

3 Results and discussion

digester can be a potential strategy to optimize methane
production with the assistance of SRB. However,identifying the substrate types and effective sulfate
concentration control strategy are necessary to sustain the
optimum productivity of the anaerobic digester.

200

150

100

Sulfate=0 mg S/L
Sulfate=50 mg S/L
Sulfate=100 mg S/L50

Sulfate=200 mg S/L
Sulfate=300 mg S/L
Sulfate=400 mg S/L

0
0

5

10

15

20

25

30

35

Day

Fig. 2. Observed and predicted methane production(from FW and WAS co-digestion) in the BMP tests (thesymbols represent the experimental measurements andthe lines represent the model fits) using one-substratemodel.

4 Conclusions
This study demonstrated that the presence of sulfate canaccelerate the methane production by improving thehydrolysis rate, at sulfate concentrations of 50 to 400 mg
S/L. The kinetic analysis reveals that sulfate at proper
concentrations can increase the hydrolysis rates.

Reference[1] Zan, F., Dai, J., Hong, Y., Wong, M., Jiang, F., Chen,
G., 2018. The characteristics of household food waste inHong Kong and their implications for sewage quality andenergy recovery. Waste Manag. 74, 63–73.
[2] Rao, M.S., Singh, S.P., Singh, A. K., Sodha, M.S.,
2000. Bioenergy conversion studies of the organic fractionof MSW: assessment of ultimate bioenergy production
potential of municipal garbage. Appl. Energy 66, 75–87.

14

Trace metal element supplementation impact in methane fermentation offish processing wastewater○ 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
Trace metal elements are an important and essential element needed for enzyme complex synthesize and also as
growing factor. Fe, Ni, Co, Zn and Mo/W were reported as essential elements needed to form enzyme/cofactor in
acetogenesis and methagonesis process. It is linear with the fact that in methabolic pathways, methanogenesis is
one of the most enzymatic pathways with the metal-rich content. However, their optimum concentration in theanaerobic digestion system will be different for every reactor and substrate. The effect of trace metal elements in
Fish processing wastewater (FPW) fermentation was studied in both batch experiment and self-agitated anaerobic
baffled reactor (SA-ABR). Based on the four batch experiments, the three best performance of sole element addition
were resulted from Ca2+, Co2+, and Fe2+ elements. On the other hand, the three best metal combinations wereCa2++Mg2++Fe2++Co2++Ni2+, Fe2++Ni2++Co2+, and Ca2++Mg2+. Supplementing 15 mg/L Ca2+ on the SA-ABR
could increase the performance significantly at OLR 3.115 gCOD/L/d. COD, protein, carbohydrates removal andgas production were growing up. Moreover, the VFA concentration was dropped from 4901.74 to 765.63 mg/L at
OLR 3.115. The performance was quite stable at OLR 4.750 gCOD/L/d.
Keywords: trace element; methane fermentation; fish processing wastewater (FPW).
1 Introduction
As one method in organic waste treatment, produce energy,
and offset the global warming all at once, anaerobic
digestion has been used in most advanced countries andfollowed by developing countries [1]. Various reactors suchas up-flow anaerobic sludge blanket reactor (UASB),
completely stirred tank reactor (CSTR), anaerobic baffled
reactor (ABR), membrane (MBR), etc. have been developed
to treat solid and liquid biomass. Moreover, many
researchers also innovated those reactors in order toimprove the performance especially for highly methane
production and organic removal efficiency. For instance, it
was self-agitated anaerobic baffled reactor (SA-ABR)developed by Kobayashi and Li (2011).
The development and innovation of anaerobic digestion isso fast, however, it does not mean without any troubles andobstacles. Stability of reactor performance, removal
efficiency, inhibition, and lack of several nutrients orelements are the most problem in the anaerobic digestion[3]–[5].
Many studies resulted that anaerobic process were not only
requiring main macronutrients such as carbon (C), nitrogen(N), sulphur (S) and phosphorus (P), but also trace metalelements at relatively lower concentration [6]Trace metals are an important and essential element
needed for enzyme complex synthesize and also as thegrowing factor. Iron (Fe), nickel (Ni), cobalt (Co), zink (Zn)and molybdenum (Mo)/ wolfram (W) were reported as
essential elements needed to form enzyme or cofactor in
acetogenesis and methagonesis process [6].
On the other hand, related to the trace metal problem inanaerobic digestion, less trace elements in the reactor is one

problem that sometimes cannot be avoided. Commonly thiscomes from the substrate characteristic itself or caused by
the substrate pretreatment such as dilution. This researchstudied several trace metals supplementing impact to themethane fermentation of FPW in both batch and lab scale of
SA-ABR experiments.

2 Materials and methods
Batch experiment did by using vial bottle 120mL with theworking volume 80mL, sludge 2g VSS, fish wastewater as
substrate was 8 mL at first and second round and 16 mL atthird and fourth round. It was operated at temperature
35±1oC. Supplementing was various for sole andcombination of metal ions. SA-ABR was operated started
from OLR 3.115 gCOD/L/day. Sludge was taken from
SA-ABR which was operated for methane fermentation of
FPW during 460 days operation.

3 Results and discussion
Batch experiment result at the 1st round show that as initial
the gas production in all vials were still very low (below
25mL accumulated in 9 days). The addition of Ca 2+, Mg2+,
Ca2++Mg2+, Mn, and Fe has positive effect for anaerobic
digestion of FPW with the strength of effect in order fromhigh to low is Ca2+ to Fe2+. At the 2nd round, the metals which
resulted positive effect were little bit changed. These wereFe2+, Co2+, Ca2++Mg2++Fe2++Co2++Ni2+, and Ca2+ with
Fe2+ were the best one. At the 3rd and 4th round, the amountof substrate was multiplied into two times. The results at the
3rd
round
show
that
the
combinationofCa2++Mg2++Fe2++Co2++Ni2+ defeated Ca2+, Ca2++Mg2+,
Co2+, Fe2++Co2++Ni2+, and Fe2+. At the last round, the result15was not so much different with the 3rd round result, with theorderofstrengthin
positive
effectwere
Ca2++Mg2++Fe2++Co2++Ni2+, Fe2++Co2++Ni2+, Co2+, Ca2+,
Ca2++Mg2+, and Fe2+.
Based on the all batch experiment, the three best
performance of sole element addition were resulted from Ca,
Co, and Fe elements. On the other hand, the three best metal
combinations
were
Ca2++Mg2++Fe2++Co2++Ni2+,
2+
2+
2+
2+
Fe +Co +Ni , and Ca +Mg2+.
Batch experiment result show that trace metal ion
supplementation was not only resulting positive effect but
also negative effect. Addition of Ca2+, Co2+, Fe2+, and Mg2+
gave the positive effect. Surprisingly result was the thesupplementing Ca2+ had an immediately impact, it could beseen that addition Ca2+ could improve the gas production
and became the best at the 1st round defeated Fe2+ and Co2+.
On the other hand, addition Co2+ and Fe needed more timeto get the best impact to escalate the gas production. Co 2+
and Fe2+ could beat Ca2+ at the 2nd round. It also proved
when the amount of substrate was multiplied into two times
higher than at the 1st and 2nd round. At the 3rd round, Ca2+
came back defeated Co2+ and Fe2+, but Co2+ addition could
be higher than Fe2+ addition. On the other hand, at the 4thround, the effect of addition Fe was still below Ca2+
addition. It might be caused by the concentration of Fe 2+ inthe vial bottle was below the optimum condition.
The metal ion addition of sole Ni2+, Zn2+ and Mn2+ did nothave significant effect to the fermentation performance.
Their gas productions were almost the same with the blank.
However, supplementing Mo6+ could inhibit the anaerobic
digestion. It can be seen in Fig.1, the gas production in thevial which was added Mo6+ was very low, even far below
the blank. It was suggested that on the vial added Mo6+ had
high ammonia concentration which could be inhibiting theanaerobic digestion process since the chemical in stock
solution was (NH4)6Mo7O24·4H2O which is in the water
solution it can release NH4+.
Based on the batch experiment result, the semi continues
SA-ABR was operated started from OLR 3.115 gCOD/L/dwithout supplementing any elements. On the day 63, bothreactor and substrate were added 15 mg/L of Ca 2+. The
performance was significantly increased. It can be seen in
Table 1, COD, protein, carbohydrates removal and gas
production were growing up. Moreover, the VFA
concentration was dropped from 4901.74 to 765.63 mg/L.
This performance was stable even at the higher OLR (4.750
gCOD/L/d).
Table 1. SA-ABR Performance in FPW treatment
OLR gCOD/L/d
3.115
4.75
Added Ca2+
beforeafter
After
COD (%)
66.21
87.46
89.98
Protein (%)
55.22
79.44
85.80
Carbohydrate (%)
50.28
76.83
78.75
Gas Production (L/d) 11.01
14.57
21.4
Methane
76.32
74.32
74.17
Composition (%)
TAN (mg/L)
5334.64 5334.64
5172.81
VFA (mg/L)
4901.74 765.63
527.95
pH
7.88
7.96
7.99
Alkalinity (mg/L)
12066.67 11077.78 10128.57

(a)

(b)

(c)

(d)

Fig. 1. Accumulation of Gas Production During 9 days
Batch Experiment. (a) first round, (b) second
round, (c) third round, and (d) fourth round.

4 Conclusions
Batch experiment resulted the three best performances ofsole element addition were resulted from Ca2+, Co2+, and Fe2+
elements. On the other hand, the three best metal
combinations
were
Ca2++Mg2++Fe2++Co2++Ni2+,
Fe2++Co2++Ni2+, and Ca2++Mg2+. Supplementing 15 mg/L
Ca2+ on the SA-ABR could increase the performance
significantly at OLR 3.115 gCOD/L/d. COD, protein,carbohydrates removal and gas production were growing up.
Moreover, the VFA concentration was dropped from
4901.74 to 765.63 mg/L at OLR 3.115. The performancewas quite stable at OLR 4.750 gCOD/L/d.

Reference[1] H. Qiang, D.-L. Lang, and Y.-Y. Li, “High-solid
mesophilic methane fermentation of food waste with
an emphasis on Iron, Cobalt, and Nickel
requirements,” Bioresour. Technol., vol. 103, no. 1, pp.
21–27, Jan. 2012.
[2] T. Kobayashi and Y.-Y. Li, “Performance andcharacterization of a newly developed self-agitatedanaerobic reactor with biological desulfurization,”
Bioresour. Technol., vol. 102, no. 10, pp. 5580–5588,
May 2011.
[3] Y. Chen, J. J. Cheng, and K. S. Creamer, “Inhibition ofanaerobic digestion process: A review,” Bioresour.
Technol., vol. 99, no. 10, pp. 4044–4064, Jul. 2008.
[4] F. G. Fermoso et al., “Fate of Trace Metals in
Anaerobic Digestion,” Adv. Biochem. Eng. Biotechnol.,vol. 151, pp. 171–195, 2015.
[5] M. Ye et al., “Improving the stability and efficiency ofanaerobic digestion of food waste using additives: A
critical review,” J. Clean. Prod., vol. 192, pp. 316–326,
Aug. 2018.
[6] Y. Y. Choong, I. Norli, A. Z. Abdullah, and M. F.
Yhaya, “Impacts of trace element supplementation on
the performance of anaerobic digestion process: A
critical review,” Bioresour. Technol., vol. 209, pp.
369–379, Jun. 2016.

16

Experimental study on filtrate of thermal hydrolysis of sludge as anadditional carbon source for denitrification○Lu HAN1,3, Yun HAN1,2* &Yuan GAO1
1

School of Municipal and Environmental Engineering, Xi' an University of Architecture and Technology, Xi' an
710055, China.
2
Key Laboratory of Northwest Water Resources, Environment and Ecology, Ministry of Education, Xi' an
710055,China.
3Department of Civil and Environment, Tohoku University, Miyagi 980-8579, Japan*
E-mail: hanyun@xauat. edu. cn.

Abstract
This study presents a strategy to improve the removal efficiency of nitrogen and phosphorus from municipal
sewage by feeding sequencing batch reactor(SBR) with the filtrate of thermal hydrolysis of sludge(FTHS) as
carbon sources. High solid sludge (solid content 10%) was subjected to thermal hydrolysis pretreatment at
different temperatures (140°C, 170°C, 200°C, 230°C, 260°C) for 30 min. Through the analysis of the FTHS, theoptimal temperature was determined to be 170℃. The biodegradability of the FTHS was also analyzed. The
FTHS as an additional carbon source added to the low C/N ratio sewage. The chemical oxygen demand(COD),
ammonia nitrogen(NH4+-N), phosphate of the low C/N ratio influent was 300 mg/L, 80 mg/L and 5 mg/L,
respectively. In addition, the microbial communities in the activated sludge of the treatment system with carbon
source was analyzed. This paper provides an effective way to solve the problem of insufficient organic carbon
source in biological nitrogen and phosphorus removal of low C/N wastewater.
Keywords: Thermal hydrolysis of sludge; Filtrate; Additional carbon source; Low C/N ratio; SBR
1 Introduction
It is well known that exceeded discharge of nutrients(Nand P) to nature water bodies usually causes eutrophication.
At present, most wastewater treatment plants(WWTPs) use
the biological method to remove the nitrogen and phosphorus. However, due to the shortage of carbon source inurban sewage, nitrogen and phosphorus in the effluent can
not reach the preset standard simultaneously. WWTPs
usually increase the influent dissolved chemical oxygen
demand (SCOD) concentration by adding carbon sourcesuch as sodium acetate or methanol. This operation will
result in a significant increase in the operating costs of the
WWTPs.
After thermal hydrolysis, a large amount of insoluble organics in the high solid sludge was converted into solubleorganics and transferred to the hydrolyzate. After the hydrolyzate was dehydrated, the filtrate contains a large amountof organics. At present, thermal hydrolyzate(TH) mainly focuses on dehydration and anaerobic digestion, and there arefew studies on the recycling of carbon, nitrogen andphosphorus. If the FTHS is applied as a carbon source
supplement for biological denitrification, the problem of
insufficient carbon source can be solved. Moreover, therecycling of resources in the TH can be realized. This study
provides a new idea for the efficient treatment of sludge andsolves the problem of treatment and outlet of FTHS.
In this study, the SBR system was used to simulate
anoxic/oxic(A/O) process. The influent of A/O process was
low C/N ratio urban sewage. The feasibility of the FTHS as
an additional carbon source and its difference with sodium
acetate were analyzed.

2 Materials and methods
2.1 Test devices and operation mode

The thermal hydrolysis test device is an autoclave (KCF-5,
Beijing Century Senlang). The effective volume is 5L. The
working temperature is<350℃. The working pressure is<10MPa. During the test, 1.2L of sludge was added to thereactor and thermally hydrolyzed at the set temperature for
30 min. The hydrolyzate was stored in a refrigerator at 4℃
for analysis. The effective volume of the SBR is 5.2L. The
oxic stage used an air pump to charge the reactor with air.
The anoxic stage is stirred by an agitator(JHS-1/60). The
SBR were operated under same conditions with four cycles
per day. Each cycle of the SBRs consisted of 5min of
influent, 160min of anoxic, 160min of oxic, 30min of
settling, 5min of effluent.

2.2 Sludge and sewage source
The thermal hydrolysis sludge was collected from a
WWTP located in Xi`an, China. The WWTP used the A2O
process.The main physicochemical characteristics of the test
sludge are as follows: pH of 7.01, total chemical oxygen
demand(TCOD) of 96.3g/L, suspended chemical oxygen
demand(SCOD) of 1.65g/L, suspended solids(SS) of 99.98
g/L, volatile suspended solids(VSS) of 67.99g/L.
The inoculated sludge of the SBR reactor was also collected from the WWTP. The physicochemical characteristics of
the inoculated sludge are as follows: pH of 7.01, SS of 6.33
g/L, VSS of 3.81g/L.
The influent of the second stage SBR reactor was a dilution of the FTHS. The dilution factor is 100 times. The main
physicochemical characteristics of the influent are as
follows: pH of 7.00, COD of 508.71mg/L, biochemical
oxygen demand (BOD5) of 114.35mg/L, total nitrogen(TN)of 36.10mg/L, NH4+ -N of 13.41mg/L, total phosphorus(TP)of 7.55mg/L, phosphate of 7.33mg/L.
The influent of the third stage SBR reactor was low C/Nratio of municipal sewage. The COD, NH4+-N, phosphate
was 300mg/L, 80mg/L and 5 mg/L, respectively.

17

pH

7
6
5
100
80
60
40
20
0
80
60
40
20
0
80
60
40
20
0

去除率

400
200
400

去除率

30
20
10
200

去除率

15
10
5
100
8
6
4
2
100

80
60
40
20
0

去除率

COD(mg/L)

600

总氮(mg/L)

3.1Determination of optimal thermal hydrolysis
temperature of high solid sludge
Table.1 showed the sludge characteristics before and afterthermal hydrolysis pretreatment.
Table.1.Sludge characteristics before and after thermalhydrolysis pretreatment

进水
出水
去除率

8

氨氮(mg/L)

3 Results and discussion

about 13.58 mg/L, and the removal efficiency is about
63.0%. The effluent concentration of TP is about 3.56 mg/L,
and the removal efficiency is about 54.3%. The average
effluent concentration of phosphate is 3.06 mg/L, and theremoval efficiency is about 59.1%. It can be seen that theremoval rate of organics and phosphorus by the anoxic/oxic
process is very low. According to Huang Qingtao etal.(2017), the removal efficiency of COD, NH4+-N andphosphate was 90%, 91% and 82%, respectively by theanaerobic/oxic/anoxic(A/O/A) process. Therefore, in orderto improve the removal efficiency of nitrogen andphosphorus, the next stage will use A/O/A process.

总磷(mg/L)

2.3 Thermal hydrolysis pretreatment
The sludge were thermally hydrolyzed at temperatures of140, 170, 200, 230, 260℃. The obtained TH was collected
to measure total protein, total carbohydrate, TCOD, TN,andTP. And a certain amount of TH was centrifuged. The
obtained solution was measure dissolved protein, dissolved
carbohydrate, SCOD, dissolved TN, and dissolved TP in thefiltrate.
2.4 Biodegradability of the FTHSThe SBR was employed in the current study. The influentof the SBR is the dilution of the FTHS.
2.5 Analysis methods
pH was measured by a pH meter(PHS-3C). DO was measured by a portable dissolved oxygen analyzer. COD, BOD 5,
TN, NH4+-N, TP, Phosphate, protein and carbohydrate were
measured by the standard methods.

8
6
4
2
0

80
60
40
20
0

0

5

10

15

去除率

Pretreatment
sludgecharacteristics

磷酸盐(mg/L)

Sludge characteristics after
pretreatment at different
temperatures
140℃ 170℃ 200℃ 230℃ 260℃
运行天数（d）
pH
7.01
5.91 5.53 5.44 6.58 6.69
Fig.1.Influent and effluent water quality of the SBR
T g/L
93.39
92.84 89.06 89.79 83.08 84.78
COD
and removal rate of pollutants
S g/L
1.65
35.73 40.71 40.37 40.08 38.31
BOD5 g/L
0.03
11.16 12.33 11.43 11.56 10.74
T g/L
6.52
6.76 6.59 6.75 6.17 6.40
TN
4 Conclusions
S g/L
0.17
3.78 3.56 3.98 4.22 4.70+
(1)The optimal temperature for the thermal hydrolysis of
NH4 -N g/L
0.01
0.61 0.99 0.77 0.76 2.12
the sludge was determined to be 170℃.
T g/L
2.49
2.40 2.37 2.35 2.29 2.41
TP(2)The removal rate of organics and phosphorus in the
S g/L
0.15
0.78 0.82 0.79 0.67 0.53
influent water is very low by the A/O process.
T g/L
46.30
42.25 42.03 33.16 32.15 28.05(3)In order to improve the removal efficiency of nitrogen
Protein
S g/L
0.14
18.80 20.56 11.68 16.87 14.93
and phosphorus, the next test will use the A/O/A process.
Carbohy T g/L
15.49
15.43 14.73 10.81 6.06 6.59
Referencedrate S g/L
0.93
11.74 9.10 1.92 0.79 0.65
SS
g/L
99.97
62.43 55.77 53.36 52.48 50.78 [1] HUANG, Q, T. et al, Influences of extra carbon
VSS
g/L
67.99
37.39 40.20 28.55 26.23 22.25 sources on the removal of nitrogen and phosphate by
AOA -SBR process, Industrial Water Treatment,
Compared with the untreated sludge, the SCOD, BOD5,37(2017)26-29.
TN, etc. in the FTHS were greatly increased. The thermalhydrolysis pretreatment made the organics enter the liquid
phase from the solid phase. Then increasing the SCOD
concentration of the FTHS. The SCOD in the FTHS reacheda maximum at 170℃(40.71 ± 0.11 g/L). In addition,compared with other temperatures, the TN in the FTHS at
170℃ is the least (3.56 g/L). Therefore, the FTHS at 170℃
can ensure that the amount of TN introduced is reduced
while introducing the carbon source.
Detection
Unit
Indexes20

3.2 Biodegradability test of the FTHS
Fig.1 shows the influent and effluent water quality of the
SBR and removal rate of the pollutants. The average pH of
the SBR influent is about 7.0, and the effluent pH is 7.14.
The effluent concentration of COD is about 136.32 mg/L,
and the average removal rate is 73.3%. The effluent
concentration of NH4+-N is about 3.1 mg/L, and the average
removal rate is 77.12%. The effluent concentration of TN is

18

The 7th International Symposium on Water Environment Systems----- with Perspective of Global Safety(Nov. 14th - Nov. 16th, 2019)

GP-RSS
Tohoku University

Co-digestion of cow and sheep manure: Performance evaluation andrelative microbial activity○ Yu Li1*, Jing Zhao1 & Gert-Jan Euverink1
Faculty of Science and Engineering, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The
Netherlands*

E-mail:yu.li@rug.nl

Abstract
This study investigated the feasibility of anaerobic co-digestion of cow manure (CM) with sheep manure (SM) at
varying volatile solid (VS) ratios under mesophilic condition (37˚C) in batch system. Synergistic effects ofco-digesting CM and SM were obtained in the batch experiment, with the most effective degradation of cellulose(56%) and hemicellulose (55%), and thus, the highest cumulative methane yield (210 mL/gVSadded) when CM
and SM were mixed at a ratio of 1:3. Microbial analysis indicated that cellulase activities were observed in SM
instead of CM. Enzymes involved in methane metabolism excreted by Candidatus Cloacimonas andMethanocelleus were found more variable in high SM addition (≥50%) compared with CM alone. Prolonged
positive influence of SM on anaerobic digestion (AD) of CM in continuous experiment was observed as well.
Results implied that compared with mono-digestion of CM, co-digestion of CM and SM contributed to thebiodegradation of lignocellulosic compounds in CM and thus favoured the growth of Candidatus Cloacimonas
and Methanocelleus to boost the methane yield.
Keywords: Anaerobic co-digestion; cow manure; sheep manure; microbial community.
as well as key functional enzymes were determined toelucidate the mechanisms of biogas production during
manure co-digestion.

1 Introduction
Intensive livestock farming has been increasingly
developed over the last decades to satisfy our overwhelming
demand of meat and dairy products. Despite the benefits
these farms bring to human beings, these farms inevitably
contribute to a considerable amount of manure waste
annually. Anaerobic digestion (AD) is regarded as a
cost-effective method for manure disposal because throughthis process, simultaneous waste treatment and productionof biogas can be achieved.
Cow manure (CM) is served as the substrate in AD
worldwide due to its high availability, which is similar in
the Netherlands. However, the use of CM in AD generally
leads to low production of biogas, probably due to a highpercentage of lignocellulosic materials (cellulose,
hemicellulose and lignin). One of the energy-consuming
approaches to improve the degradation of lignocellulosic
materials including management of the biogas process toensure growth of microorganisms with efficient degradation
capabilities. Hence, effort can be done by co-digesting CM
with another substrate which either contains low
lignocellulosic
compounds
orcontains
activefibre-degrading microbes.
Compared with CM, sheep manure (SM) is rarely
mentioned as a substrate in AD. However, based on FAO’s
data, SM can no longer be overlooked since its high amount.
(1.3 billion tons annual). Unlike CM, SM is generally
believed to contain lower lignocellulosic compounds.
Another intriguing point for SM is that it may contain active
microbes which could boost the hydrolysis step in AD.
These merits of SM hint it could be a good idea to mix CM
and SM in AD.
This study investigated the performance of co-digesting
cow and sheep manures at the mesophilic condition. Bothbatch and continuous digestion were comprehensively
evaluated. Microbial community diversity and community

2 Materials and methods
2.1 Subtrate and Inoculum
The CM and SM were taken from a farm. The inoculum
was taken from the anaerobic digester in a wastewater
treatment plant (WWTP). Before use, the inoculum was
acclimated in an incubator at 37 ˚C for 10 days.
2.2 Reactor set-up and operation
The batch AD of CM and SM was carried out at five
CM/SM ratios based on volatile solid (VS): 1:0, 0:1, 1:1,
3:1, 1:3. The experiments were performed in 500 mL glass
bottles capped with butyl rubber stoppers with a working
volume of 400 mL. For each treatment, an initial substrateconcentration of 25 gVS/L was used. The substrate toinocula ratio (ISR) was set 0.5. After adding the properamounts of substrate and inoculum, each bottle was filledwith distilled water to achieve the working volume. The
bottles were then flushed with pure nitrogen gas for 5 min toensure anaerobic conditions. Finally the bottles were placedin a shaking incubator (140 rpm) at 37±1˚C.
A single stage laboratory-scale continuously stirred tankreactor (CSTR) with a working volume 2.4 L wasestablished in this study. The experiment lasted 200 days.
The reactor was fed once a day by a peristaltic pump.
Hydrolytic retention time (HRT) was set 25 days based onbatch experiment, and organic loading rate (OLR) was 0.5 g
VS L-1d-1 from day 0 to day 50 and then adjusted to 1g VS
L-1d-1 afterwards. The temperature was maintained at
37˚C±1˚C by a water bath. The speed of stirring wasconstantly set at 120rpm. The reactor was initiated with
2.4L same seed sludge coming from WWTP as used inbatch experiment, followed by feeding once per day with
CM alone or CM and SM.

1

19

The 7th International Symposium on Water Environment Systems----- with Perspective of Global Safety(Nov. 14th - Nov. 16th, 2019)

GP-RSS
Tohoku University

higher daily methane yield was observed (179mL/g/d),which was in line with batch results (p˂0.05). Besides,
methane content (61%) was also significantly higher thanCM alone (56%), indicating the addition of SM could
enhance both methane production and methane quality(p˂0.05). From day 151 onwards the feeding was changed
back to CM alone, the daily methane yield obtained was
159mL/g/d, increased by 10% compared with initial period.

2.3 Analytical methods
Biogas volume in batch test was recorded by displacing
75% saturated solution of sodium chloride, which wasacidified to pH 2.0. Wet meter was used for continuous
experiment.Biogas composition was analyzed by gas
chromatography (C2V-200 Micro GC Thermal Scientific).
The TS VS were measured based on National Renewable
Energy Laboratory (NREL). For lignocellulosic compounds
measurement, CM and SM were pre-treated according to themethod in NREL. Extractive-free samples were
subsequently used to determine the structural carbohydrates
and lignin with a two-step acid hydrolysis method.
Biological samples were fixed by adding ethanol to a final
concentration of 50% (v/v) pending DNA and RNA
extractions. The fixed samples were sent for sequence
analysis using the BioProphyler approach. High-throughput
sequencing was performed on an Illumina MiSeq. The reads,
maximal 301 bp in size, were processed using in-housedeveloped BioProphyler software. The sequence reads werefirst run through a quality filter named Trimmomatic,followed by a comparison to sequences stored in GenBank
using the BLASTn algorithm. Reads were also compared to
the nr database by using the RAPSEARCH2 algorithm.
Identifications were ranked by tag abundance.

3 Results and discussion
The CMYs of co-digestion of CM and SM at mixing ratiosof 3:1, 1:1, and 1:3 were 175, 200, and 210 mL/gVS added,
respectively (Fig.1(a)-(e)), which showed a higher methane
yield of 11.6% -30.5% than mono-digestion of CM.
The removal of cellulose and hemicellulose in SM alone(56% and 54%, respectively) was significantly higher thanthat in CM (38% and 31% respectively). Moreover, theremoval of cellulose and hemicelluloses in mixtures of CM
and SM was significantly higher than CM alone, ranging
from 43% to 56% and 38% to 55%, respectively.
Enzymes such as cellulase, glycogen phosphorylase andalpha-amylase are known for hydrolysing polysaccharides.
On day 0, compared with CM, SM possessed more kinds ofbacteria which could excrete those enzymes (Table 1(a)).
Besides, it was observed uniquely in SM the activity ofcellulase excreted by Bacteroides sp. and an uncultured
bacteria species which is responsible for cellulose and/or
hemi-cellulose degradation. In addition, glutamate
decarboxylase found in SM was also supposed to be
involvedincellulasepathway.
Coenzyme-B
sulfoethylthiotransferase
excretedby
Methanothrix
soehngenii (Methanosaeta) accounted for 50% or even morein all CM, SM as well as their blends. In addition,
Coenzyme-B
sulfoethylthiotransferase
excretedby
Methanoculleus sp.was also active in high SM addition(CM:SM=1:1 and 1:3), reaching 31% and 29%. Additionly,
phosphoglycerate dehydrogenase and phosphoserine
transaminase excreted by bacteria Candidatus Cloacimonas
acidaminovorans were observed in all CM and SM
scenarios, indicating its functional role in methane
metabolism. Overall, the addition of SM introduced active
enzymes on lignocellulosic compounds degradation andenriched the methane metabolism pathway, especially athigh SM addition.
The daily methane yield at steady stage (day50-day100)
was 146 mL/g/d with the average methane yield 56%. WhenCM was half replaced by SM from day 101 on, significantly

Fig.1. (a)-(e) CMYs of CM and SM as well as theirblends; (f) TS VS cellulose and hemi-cellulose removals;(g) predominant bacteria genus; (h) predominantarchaea genus; Table.1(a). Functional enzymes;
Table.1(b). Performance of continuous reactor
4 Conclusions(1) Synergistic effect of co-digesting of CM and SM was
obtained.
(2) enzymes excreted by Bacteroides, Candidatus
Cloacimonas acidaminovorans and Methanoculleus
contributed to a higher lignocellulostic compounds
degration and higher cumulative methane yield observed inco-digestion of CM and SM than CM alone.
(3) The prolonged positive influence of co-digesting SMwith CM was also observed in long-term continuous AD,
reflected by higher methane production (159 mL/g/d) when
fed back to CM compared with feeding CM at the beginning(146mL/g/d)
Reference[1] Yu et al Co-digestion of cow and sheep manure:
Performance evaluation and relative microbial activity
RENEW ENERG (accepted)

220

Anaerobic co-digestion of paper waste and sewage sludge: The effectsof paper waste ratio
1○Aijun ZHU1*, Yu Qin1, Jing WU1 & Yu-You Li1,2
Department of Civil and Environmental Engineering, Graduate School of Engineering, Tohoku University, 6-606 Aoba, Aramaki-Aza, Aoba-Ku, Sendai, Miyagi 980-8579, Japan;
2
Department 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*E-mail: zhu.aijun.t4@dc.tohoku.ac.jp

Abstract
The aim of this study was to investigate the effects of different paper waste content in substrate on anaerobic
digestion. The single-stage mesophilic anaerobic digestion in lab scale continuously stirred tank reactor (CSTR)
systems fed semi-continuously with sewage sludge containing paper waste were studied to evaluated theirperformance, energy balance and reaction processes. The CSTR were operated under mesophilic(35±1oC) inorder to enhance methanogenesis. The reactor was fed with substrate containing different mixture of paper waste
to improve the methane production and organic removal efficiency. The mixing ratios of sewage sludge and paper
waste was ranged from 4:0, 4:2,4:4,4:6 based on TS. The TS, VS, carbohydrate and COD removal efficiency wasalmost the same at different conditions and protein showed negative removal efficiency.
The experimental results showed the different substrate containing various mixture ratio of paper waste substrate
has good performance at a hydraulic retention time (HRT) of 30 d.
Keywords: co-digestion; paper waste; sewage sludge; mesophilic anaerobic digestion

1. Introduction
Sewage sludge(SS), the byproduct of biological wastewater
treatment processes, is expected to increase continuously inthe next decade, due to increasing population connected to
sewage networks, building new waste water treatment plants(WWTPs) and upgrading existing plants to meet the more
stringent local effluent regulations. Treatment of sewage
sludge via anaerobic digestion has been carried out widely,
and the technology has recently received increased interest in
many countries due to the renewable energy it can produce.
However, despite its high carbohydrate content, theanaerobic treatment of sewage sludge is quite problematicdue to its low bicarbonate alkalinity high COD concentrationand tendency to rapid acidification. biodegradation, the low
C/N ratio of sewage sludge 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. Paper waste(PW) has a C/N ratio ranging from173/1 to greater than 1000/1. Both wastes are produced inlarge quantities and in many places, and much research has
focused on this particular issue. Co-digestion using paper
waste and sewage sludge as substrate may enhance theperformance of the anaerobic digestion (AD) due to bettercarbon and nutrients balance. That is, the use of a cosubstrate that in most cases improves the biogas yields due topositive synergisms established in the digestion medium andthe supply of missing nutrients by the co-digestion. For highsolids digestion of DS, the addition of FW not only improved
system stability but also greatly enhanced volumetric biogas
production.

System performances of co-digestion systems were mainly
determined by the mixing ratios of DS and FW. Biogas
production and volatile solids (VSs) reduction in digestion of
the co-mixture of DS and FW increased linearly with higher
ratios of FW. A kinetic model, which aimed to forecast theperformance of co-digestion and to assist reactor design, wasdeveloped from long term semi continuous experiments.

2. Materials and Methods
2.1 Materials
The sewage sludge was consisted primary sludge of waste
activity sludge 1:1(based on wet mass) from the Senen waste
water treatment plant as substrate. The collected sewage
sludge was ground and homogenized in a blender and stored
at 4°C before feeding.
The paper waste was consisted of office paper, toilet paperand newspaper which are the largest amount of recycling
paper in Japan. The ratio of three paper waste was 1：1：1
based on wet mass, cut by shredder before mixing withsewage sludge for feeding.
Seed sludge was from a mesophilic sewage sludge digesterat the Sendai municipal sewage treatment plant located along
Sendai, Japan.
2.2 Analytical 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

21

Table 1 the operating conditions in this study

work volume
Substr SS
ate
PW
HRT
（d）
Operationtime（d）
OLR
gVS/L/d

Run 1

Run 2

Run 3

Run 4

4%

3L
4%

4%

4%

0

2%

4%

6%

30

30

30

30

1-120

121-182

1.01

1.63

183-253 254-315
2.27

2.96

Fig. 1. Schematic diagram of the single-stage system usedin this study for methane production

Table 2 the stability of the reactor(L/L/d)biogas
L/g-TS
L/g-VS
N2 content (%)
CO2 content (%)
CH4 content (%)
pHalkalinity(g-CaCO3 /L)
ammonia(g /L)
H2S(ppm)

Run 1

Run 2

Run 3

Run 4

0.51±0.24
0.38±0.05
0.46±0.06
0.01±0.00
0.38±0.02
0.61±0.02
7.16±0.05

0.75±0.11
0.37±0.02
0.42±0.02
0.01±0.01
0.43±0.02
0.56±0.02
7.14±0.08

1.38±0.13
0.53±0.02
0.60±0.02
0.01±0.01
0.43±0.06
0.54±0.03
7.11±0.09

1.68±0.24
0.53±0.05
0.61±0.06
0.01±0.01
0.45±0.02
0.54±0.02
7.10±0.06

3.85±0.24

3.62±0.15

3.55±0.16

3.54±0.16

1.21±0.11
543.33±40.41

1.10±0.0
726.67±25.17

0.93±0.11
540±14.14

0.70±0.07
68±5.66

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 and thenfiltered 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, was utilized
to detect VFAs and ethanol. A 0.5 mL filtrate was collected
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.

2.3 Reactors start-up and operation
Experiments were carried out in a single-stage continuous
anaerobic process. On the first day of the experiments, 3.0 L
seed sludge was added to the reactor, which was operated
semi-continuously (twice-a day draw-off and feeding). The
OLR was increased stepwise before normal performances
could be achieved at designed paper waste mixture ratio. The
mixing ratios based on TS substrate for each reactor wereshown in Table 1. As the mixing ratios based on TS were
accurately controlled, reactor was operated for four different
paper mixture ratio.
The continuous operation of the reactor started at HRT of
30d. Sludge samples were sampled twice a week fromreactors under continuous stirring conditions and analysed,
for monitoring each reactors performance. Experiments wereconducted successively to determine the optimum andmaximum paper waste ratio for methane production. Organic
loading rate (OLR) was increased by increasing the paper
waste ratio. The reactor was operated at different feeding
substrate of paper waste and food waste. The tested stability
and gas production in the system are summarized in Table 2.

3.Results and Discussion
In mono high-solids anaerobic digestion of sewage sludge,
ammonia concentration was the highest in all the condition,
reaching 1.2 g/L. Cause of the low OLR(1.01 gVS/L/d), thereactor was running stable. With the paper waste addition,
despite of OLR had increased，the reactor was running stable
until the PW content reaching 8%TS.
The pH ， alkalinity, ammonia concentration weredecreased with the paper waste increasing, maintained stably
under the steady stage. The experimental results aboutorganic removal efficiency obtained at steady-state with thevarious PW content ranged 0% to 6%. The TS, VS,carbohydrate and COD removal efficiency was almost thesame in different conditions. The maximum organic removal
rate was achieved for TS(61.3±1.8%), VS(71.5±1.3%),
carbohydrate (82.78±8.7%) and COD (85.9±5.3%).
Meanwhile, the protein showed negative removal efficieny
during the all periods. The biogas production also increased
with the OLR increased. However, the methane content was
decreased with the paper waste addition, cause of the
carbonhydrate content increasing.
In the mesophilic fermentation, stable methane
fermentation in high organic loading rate utilizing mixture of
sewage sludge and paper waste was possible at OLR 2.96
gVS/L/d. 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 8% TS.

22

The 7th International Symposium on Water Environment Systems‐‐‐‐‐ with Perspective of Global Safety(Nov. 14th ‐ Nov. 16th, 2019)

GP‐RSS
Tohoku University

Manifold Analysis of Pacific Islands Climate Data
○ Ming-Che HU1*, Yi-Hsuan Shih1, Tai-Yi Liu1
1

Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan.
*
E-mail: mchu@ntu.edu.tw

Abstract
Dimensional reduction is an important issue while dealing with complex datasets. Traditional methods, such as
principal component analysis (PCA) or multidimensional scaling (MDS), are usually based on Euclidian distance.
However, this may underestimate the true distance between data points while there exists a curved structure ofdata. In order to solve the problem above, nonlinear dimensional reduction methods based on manifold learning,such as isometric feature mapping (Isomap) or locally linear embedding (LLE), are proposed. In this study,
Isomap is applied to help us understand the structure of climate data in the Pacific Islands. In Isomap, theneighborhood graph is constructed based on the original distance. Next, geodesic distances is approximated by theshortest path calculated by using Dijkstra’s algorithm. Finally, the geodesic distance matrix is imported into
multidimensional scaling (MDS) to create a two-dimension map representing their dissimilarities in a higher
dimension. Results have shown that the first Isomap dimension has a relationship with latitude. The sensitive
analysis of variables has shown that though some countries are spatially close, their temperature or rainfall
distance are much farther. Also, we can observe the change of data structure along time through this research.
Keywords: Multidimensional scaling (MDS); Nonlinear dimensionality reduction; Manifold learning;
Isometric feature mapping (Isomap), Pacific islands climate data
1 Introduction
The analysis of the temporal and spatial pattern ofhydrological data has always been a concerned topic,
especially under the deteriorating situation of climate change,
where extreme weather events have caused changes in
distribution of rainfall and temperature. If we can understandthese trends, it will be quite helpful for us to make strategy
or plan policies of adjustment or adaptation to climate change.
In this study, Section 2 introduces the methodology usedin this, including theory of the linear dimension reduction
method MDS, details of Isomap, and the method, Procrustes
analysis, applied to compare configurations. Section 3
describes the the results of this research, and finally this work
will be concluded in Section 4 and some future directions will
be point out.

2 Methods
In this study, we use Isomap to help us understand thestructure of climate data in Pacific islands. Isomap is anonlinear method consisting two parts, one is to calculate thegeodesic distance on manifold, and second is to map thegeodesic distance into a low dimension space remaining the
global structure. Hence, we will first introduce the linear
dimension reduction method, MDS. Next, how the geodesic
distances are calculated will be presented. Finally, we will
apply a method of comparing configurations.
Multidimensional scaling (MDS) is a linear dimension
reduction method widely used in sociology, marketing andpsychology. The purpose of MDS is to build a lower
dimension space maintaining the dissimilarities between data.
In some cases, we can get similar results using either MDS
or Principal component analysis (PCA), however, thedifference between two methods is that the input of PCA islinear combinations of the dataset, MDS only needs the
distance matrix. When the properties of data are quantitative,

metric MDS can be used. Classical MDS, one of the metric
MDS is introduced in this study.

3 Results and Discussion
In this part of research, we will observe how the climate
data structure varies under different ratios. Different kinds of
distance matrices, including spatial, rainfall and temperature
distance are displayed. In each figure, the darker the grid thelarger the distance between countries. In order to compare
different kinds of distance, every matrix is scaled from zero
to one.
Figures show the results in which only rainfall or
temperature data is considered. The figure illustrates how thestress value changes with the embedding dimension p in bothcases . From the figure, we can observe that it is notinappropriate to set the embedding dimension as two. The
embedding coordinates are plotted, and the first and second
dimension values are listed. Since it is not straight forward toobserve the relationship between countries, the dendrogramof the 24 countries are thus listed. In the dendrogram, thehierarchical relationship between objects is shown in the
diagram. Dendrogram can be considered as a summary of the
distance matrix, the height of the dendrogram indicates theorder in which the clusters were joined.

23

The 7th International Symposium on Water Environment Systems‐‐‐‐‐ with Perspective of Global Safety(a)(Nov. 14th ‐ Nov. 16th, 2019)

GP‐RSS
Tohoku University(c)Fig. 1. Scaled correlation of islands (a) Geographic
distance (b) Rainfall distance (c) Temperature

4 Conclusions(b)

In this research, the embedding dimension p is set to be 2.
From the result, we can find out that at least 70% of variation
can be explained by the first two Isomap dimension.
By projecting the first Isomap dimension on the map, we
can observe that the structure of both rainfall and temperature
have relationship to latitude. The values will first decrease
then increase from north to south Pacific in rainfall cases,
however, the values just decrease in temperature cases.
By applying Procrustes analysis, we can compare different
configurations. In the sensitive analysis of variables, we canconclude that for some countries, they may be short in thespatial distance, however the temperature or rainfall distance
may be much farther. In the other part, we can observe how
the climate data structure varies under climate change.
The distance of temperature and rainfall in this research isdefined as one half of the Pearson distance, however, this isjust one of the method to calculate dissimilarity. In the future,
it is possible to see how the structure of the climate data
structure will perform under different definition of distance.
In this research, we only considered temperature andrainfall. Besides the two data, more variables can be analyzed,
such as wind speed, frequency of typhoons. In addition, we
may focus on some species, specified on the characteristic of
their favorable habitat, or concentrate on other issues.
This research applies a nonlinear dimensional reduction
method based on manifold learning, here we present the low
dimension embedding of climate data. In the future, we may
combine the concept of network theory, topology or grouptheory, and build a temporal spatial prediction model basedon manifold learning. The prediction of the hydrologicalchanges in the Pacific Islands can be a reference for thedecision-making on disaster adaptation or water resource
distribution.

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Tohoku University

A preliminary study on development flood early warning system inSoutheast Sulawesi Province○ N. Fajar JANURIYADI1*
1

Department of Civil Engineering, Pertamina University, Jakarta 12220, Indonesia.
*
E-mail: nurul.fj@universitaspertamina.ac.id.

Abstract
Flood early warning system (FEWS) is one of the alternatives for reducing flood risk, which can allow people toprepare for flooding and to protect assets and property. Lacking information is one of the obstacles to develop aflood early warning system. The study aims to obtain the information of 2019 flood inundation extents and to findthe appropriate rainfall stations number. The multi-temporal Sentinel-1A images were employed to identify theflood inundation extents while kriging with external drift (KED) used to find the appropriate rainfall station numbers.
The study found that the inundation areas of the 2019 flood event estimated 49.9 km2, while the required additional
rainfall stations in the watershed are 8 stations. The results of this study are expected being used as the basic
information to develop FEWS.
Keywords: FEWS; Sentinel-1A; KED.
1 Introduction

2 Data and Methodology

Flooding is one of the major natural disasters that makesuffer the human being life. In 2018, there were 127 flood
events of 315 climate-related and geophysical disaster events
recorded in the EM-DAT, over 34 million people wereaffected by flooding [1]. In Indonesia, flooding contributed
26 % of natural disaster events that occurred in 2018, withthe affected people over 470 thousand or 78 % of total
affected people due to natural disasters. [2]. The latest flood
event occurred at the beginning of June 2019 in Southeast
Sulawesi Province (SSP). The flood event is one of thebiggest floods in SSP, which inundated three regencies. The
flood event has shown the destructive impact that flooding
can have on people and property. More than 1500 houses
inundated and 8000 people become refugees. Furthermore,
many people isolated for a few days due to no sufficient timethat allowing people to be evacuated from areas at risk, andto move vehicles and personal assets to safety.
Flood early warning system (FEWS) could be one of thealternatives to reduce the flood risk. FWES could provide thelead time to reduce the fatalities risk and to allow people toprepare for flooding and to protect assets and property. Floodprevention actions may also be taken to reduce flooding; forexample, by operating river control structures and installing
temporary barriers. FEWS is expected to provide theinformation location, time and magnitude of the flood, theinformation of predicted exposures both people andproperties, and the information on how to respond effectively[3]. This project focuses on providing the information on
location, time and magnitude of the flood and the informationof predicted exposures, by developing a flood forecastingmodel. Lasolo river selected as the study site, because it is
one of the rivers that contributed to the flood in SSP. The firststep of this project is obtaining the information related to thelatest flood event. One of the obstacles to the project islacking data. Therefore, we used some approach to obtain thedata, for example, flood inundation areas of flood derived
from Sentinel-1A dataset. This paper shows the floodinundation areas obtained from the Sentinel-1A dataset andthe information of rain gauge network that design using
kriging external drift method.

2.1 Data
The Sentinel-1A dataset provided from the European Space
Agency (ESA), which performing C-band synthetic apertureradar imaging to acquire imagery regardless of the weather.
Four images of Sentinel-1A IW GRDH (Ground Range
Detected in High resolution) are employed to identify theflood inundation in SSP. One dataset was acquired threemonths before the flood event (March 3rd,2019), while theother datasets were acquired during the flood event (May 28th,
June 9th, and June 21st, 2019).
The information rainfall stations were collected to identify
whether the numbers and location of the stations sufficient or
not. Seven rainfall stations are located in the Lasolo
watershed as shown in Fig. 2 (red dots).

2.2 Inundation Area Identification
This study used the Sentinel-1A dataset to identify the floodinundation extents. The sentinel-1A images include pre- andduring the flood event. The pre-processing of the images is
the removal of speckle noises. The speckle noises removal
was done using a median filter of window size 3*3. The
second step is image processing, the water areas, and nonwater areas are separated from the Sentinel-1A images.
Subsequently, the separated images are compared before andduring flood events [4].

2.3 Kriging with External Drift
An approach to add external information in the interpolation
technique is kriging with external drift (KED). Some studies
also used this method to interpolate the rainfall data [5]–[7].
This study uses the digital elevation model as additional
information. In the case of KED, predictions at new locations
are estimated using Eq. 1 and 2:
𝑤

𝑧̂
𝑤

𝑠

𝑠 .𝑞 𝑠

∙𝑧 𝑠
𝑞 𝑠 ;

1
𝑘

1, … , 𝑝

2

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The 7th International Symposium on Water Environment Systems‐‐‐‐‐ with Perspective of Global Safety(Nov. 14th ‐ Nov. 16th, 2019)

Where 𝑧 is the target variable, 𝑞 are the predictor
variables i.e. values at a new location 𝑠 , 𝑝 is the numberof predictors. The variance of predictor error is estimatedusing Eq. 3.
𝑠𝐶
𝒄 𝟎 𝐓 ∙ 𝜆𝟎
𝐶
3
𝜎Where 𝜎is KED variance, 𝜆𝟎
is the vector ofsolved weights and 𝒄𝟎
is the extended vector ofcovariances at the new location. The square root of the
kriging variance, also named as kriging standard error (KSE),
is used as a gauge network evaluation factor that can reflect
the performance of optimal gauge combination [8].

3 Results and Discussion
The comparison of Sentinel-1A images before and during
the flood event shown in Fig.1. The figure shows the water
body increase during the flood event. The water body areasslightly increase on May 28th, which indicates the flood
began to inundate several areas around the Lasolo river.
Furthermore, the image on June 9th shows a significant
increase in the water body areas in Lasolo River, whichindicates the peak of flood occurred. On June 21st, the image
shows water body areas decrease, which identifies the flood
began to recede. Based on the June 9th image, the total areasof flood inundation was approximately 49.9 km2. Even
though this study succeeds to identify the flood inundation
extents, the distribution of flood depths is not identified yet.
Also, adding the image data from the other dataset (e.g.

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Sentinel-1B) required to improve the information of floodinundation extents.
Fig. 2 shows the KED result of average annual rainfall in
Lasolo watershed and the kriging standard error. Fig. 2a andFig.2b show the KED result and KSE values for the existingstations. The figure shows that the high KSE values in thenorth part or upstream region of watershed, which indicates
those areas require additional rainfall stations. This study
recommends adding eight additional stations on the upstream
region. The additional stations determined based on thehighest KSE values location. Fig. 2c and Fig. 2d show the
KED result and KSE values after adding the stations. The
new KSE values are lower compared to before adding thestations, which indicates adding new stations could reduce
the prediction error on rainfall interpolation.
As explained above, this paper is the introduction step of the
project of the development flood early warning system in
SSP. Additional data analysis is required to obtain moreaccurate results before developing the flood forecastingmodel. The next steps of this project are adding the image
data from the other sources and analyzing the distribution offlood inundation depth. Also, the project will generate theland cover map from historical satellite images data, which
can be used as the input parameters in the flood forecastingmodel.

Fig. 1 Comparison of sentinel-1A images before and during flood event

Fig. 2 Annual average rainfall and kriging standard error before and after adding the rainfall stations

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The 7th International Symposium on Water Environment Systems‐‐‐‐‐ with Perspective of Global Safety(Nov. 14th ‐ Nov. 16th, 2019)

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Tohoku University

4 Conclusions
This paper presented a preliminary study on thedevelopment of a flood early warning system, which consistsof the detection of flood inundation extents and estimation of
the appropriate numbers of rainfall stations. The results show
the flood inundation areas of the 2019 flood event estimated49.9 km2, which is detected by the Sentinel-1A image on June
9th. The study also found that the inappropriate existing
rainfall stations number in the watershed, particularly in the
upstream region. Eight new rainfall stations should be addedin the watershed for obtaining more reliable rainfall
interpolation. The results of this study are expected being
used as the basic information to develop the flood forecastingmodel.

Reference[1] CRED, “Natural Disasters 2018: An opportunity to
prepare,” CRED, Brussels, 2019.
[2] BNPB, “Natural disaster in 2018,” 2019. [Online].
Available: https://bnpb.cloud/dibi/laporan5. [Accessed:
10-Oct-2019].
[3] K. Sene, Flood warning, forecasting and emergency
response. New York: Springer, 2008.
[4] N. Anusha and B. Bharathi, “Flood detection and flood
mapping using multi-temporal synthetic aperture radarand optical data,” Egypt. J. Remote Sens. Space Sci., p.
S1110982318303041, Feb. 2019.
[5] E. Rabiei and U. Haberlandt, “Applying bias correction
for merging rain gauge and radar data,” J. Hydrol., vol.
522, pp. 544–557, Mar. 2015.
[6] A. M. J.-C. Wadoux, D. J. Brus, M. A. Rico-Ramirez,
and G. B. M. Heuvelink, “Sampling design
optimisation for rainfall prediction using a nonstationary geostatistical model,” Adv. Water Resour.,vol. 107, pp. 126–138, Sep. 2017.
[7] A. Verworn and U. Haberlandt, “Spatial interpolationof hourly rainfall – effect of additional information,
variogram inference and storm properties,” Hydrol.
Earth Syst. Sci., vol. 15, no. 2, pp. 569–584, Feb. 2011.
[8] P. Xu et al., “A kriging and entropy-based approach toraingauge network design,” Environ. Res., vol. 161,
pp. 61–75, Feb. 2018.

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The 7th International Symposium on Water Environment Systems‐‐‐‐‐ with Perspective of Global Safety(Nov. 14th ‐ Nov. 16th, 2019)

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Tohoku University

Spatial Representativity of AMeDAS Gauge Rainfall over Radar/Rain
Gauge-Analysed Precipitation○ Masafumi Yamada
1

Disaster Prevention Research Institute, Kyoto University, Uji 611-0011, Japan.
*
E-mail: yamada.m.hydra@gmail.com.

Abstract
In the studies of climate change impact, ensemble climate datasets, e.g. d4PDF [1], are commonly used to know
the probabilistic aspects of these impacts [2]. These ensemble datasets inevitably include the model-output biases,
and in order to overcome these weak points, various bias correction methods have been developed. For Japan area,
Watanabe et al. [3] proposed the bias correction method of d4PDF precipitation, using long-term rain gauge
observation data, and confirmed its performance.
Keywords: d4PDF; AMeDAS; climate change.
1 Introduction
In order to use the point-based rain gauge data, whether it isfrom actual observation or from bias corrected data, for the
analyses taking the 2-D precipitation data as input (e.g. runoff
analysis), it is needed to spatially interpolate the multi-point
rain gauge data. The simplest way is the Thiessen polygon:
dividing the plain into some regions for that within eachregion all cells have the same nearest rain gauge, and thismethod is still common in runoff analysis [2].
Therefore,
the spatial representativity of each rain gauge data over theregion covered by that rain gauge is crucial for the reliabilityof the analyses with the rain gauge data as an input. So far,many researches have discussed the effect of input data on
the outcome of runoff analysis [4]. However, as far as theauthors know, the simple comparative study between the
point-based rain gauge data and spatial precipitation data,
focusing on the spatial representativity of the rain gauge, has
not been undertaken. This study aims to examine how theAMeDAS gauge rainfall data, the most common rain gauge
dataset in Japan, is spatially representative over the 2D
precipitation
dataset,Radar/Raingauge-Analysed
Precipitation (hereafter R/A), and also to quantify the
associated biases.
AMeDAS, acronym of “Automated Meteorological Data
Acquisition System”, is a dense network of climate data
observation points, managed by Japan Meteorological
Agency (JMA). In September 2019, there are around 1300
AMeDAS point all over Japan, and each point covers
approximately 17km grid area.
We collected thenationwide AMeDAS data from 1988 to 2018 as one of the
inputs of our research.
On the other hand, R/A is a reanalysis dataset issued by
Japan Meteorology Agency (JMA) from April 1988, basedon the AMeDAS rain gauge measurement, and MLIT
(Ministry of Land, Infrastructure, Transport and Tourism)
and JMA radar observation. The R/A is developed for theforecast and prevention of disasters triggered by heavy
rainfalls like landslides and flashfloods, and it is now used as
the basic input data for various sort of disaster prevention
calculations, including the flood risk prediction and heavy
rainfall warning. Despite some reports pointing out theover-estimation trend of R/A dataset compared to the rain
gauge data [5], R/A is widely accepted as the basic dataset of
rainfall in Japan. We use R/A as the other input of ourresearch. The spatial resolution of R/A is not consistentover time: ca. 5km from April 1988 to March 2001, ca. 2.5km
from April 2001 to December 2005, and ca. 1km fromJanuary 2006 to December 2018. In order to assure the data
recording period enough long to do the statistical analysis,
we upscaled the R/A after April 2001 so that all data have ca.
5km spatial resolution [6]. The coverage period of 5km
R/A data is from 1998 April to 2018 December, around 31
years. In addition to that, we set the period from January 2006
to December 2018, 14 years, also as the research target withthe finest resolution of 1km.
As the coverage area of each AMeDAS rain gauge point, we
set the 20km*20km grid around each AMeDAS point.
Each covering area contains 16 grids of 5km resolution R/A,or 400 grids of 1km resolution R/A. In order to quantify thespatial representativity of rain gauge data to R/A, we carriedout two analyses below:
1) quantify the distribution of the ratio of AMeDAS rain
gauge data and the R/A precipitation of each cell in the area,for each section of AMeDAS rain gauge data, and2) for each rainfall event, defined by at least 24 hours nonprecipitation period, quantify the difference of area average
total precipitation amount between AMeDAS and R/A withtwo different resolution.

Reference[1] Mizuta, R. and co-authors. Over 5000 years of ensemble
future climate simulations by 60 km global and 20 km
regional atmospheric models. Bull. Amer. Meteor. Soc.,
pp.1383-1398, 2017.
[2] Abe, S., Watanabe, S., Yamada, M., Kotsuki, S.,
Watanuki, A. Impact of observed rainfall and pattern of sea
surface temperature on flood analysis using massive climate
prediction data. J. of Japan Soc. of Civil Eng, Series B1, inpress. (in Jpn with Eng abstract)[3] Watanabe, S., Nakamura, M., and Utsumi, N. The
development of bias corrected hourly precipitation dataset
for AMeDAS stations based on the projections from d4PDF.
J. of Japan Soc. of Civil Engineers, Series B1, Vol.75,
pp.I_1399-I_1404, 2018. (in Jpn with Eng abstract)[4] Fekete, B. M. and Vörösmarty, C. J. Uncertainties inPrecipitation and Their Impacts on Runoff Estimates. J. of
Climate, Vol. 17, pp.294-304, 2003.
[5] Ishizaki, H. and Matsuyama, H. Distribution of theAnnual Precipitation Ratio of Radar/Rain gauge-Analyzed

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Tohoku University

Precipitation to AMeDAS across Japan. SOLA, Vol.14,
pp.192-196, 2018.
[6] Urita S., Saito, H., and Matsuyama, H. Temporal andspatial discontinuity of Radar/Rain gauge-Analysed
Precipitation that appeared in relation to the modification ofits spatial resolution, Hydrological Research Letters, Vol.5,
pp. 37-41, 2011.

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The 7th International Symposium on Water Environment Systems‐‐‐‐‐ with Perspective of Global Safety(Nov. 14th ‐ Nov. 16th, 2019)

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Tohoku University

Estimating Irrigated Area through a Combined Use of MODIS and Land
Surface Model in the Amu Darya Delta○ Jacqueline Muthoni MBUGUA1*, Yoshiya TOUGE1 & So Kazama1
1

Department of Civil and Environmental Engineering, Tohoku University, Miyagi 980-8579, Japan.
*
E-mail: mbugua.jacqueline.muthoni.s8@dc.tohoku.ac.jp.

Abstract
Secondary salinization coupled with the frequent droughts causes the irrigation area to constantly change forfarmers to adapt to cropping environmental problems and the changing annual climate in this region. However,obtaining an accurate report of the actual distribution of the irrigated area has proven to be difficult even to the local
government and agricultural institutes. This study aims to assess the potential of using Land Surface Temperature(LST) from MODIS and a Land Surface Model (LSM) to detect annual changes in irrigated area. 3 indices weredeveloped using LST by MODIS and LSM based on the concept of heat capacity difference between water and soil.
The LSM provides LST for ideal conditions while MODIS provides the actual LST. A combination of the two
enables the elimination of external influence on LST such as rainfall and geological variations which may impact
on the LST. A distributed map of all the 3 indices shows the potential of LST in detecting drought. The irrigation
fraction during a drought year was observed to be lower as compared to that of a normal year. This was true
especially further away from the water source due to water scarcity.
Keywords: Irrigation; MODIS; LSM; Drought; Amu Darya delta.
1 Introduction

2 Methodology

Irrigation water is an important part of the hydrology cycle
and has thus been included in recently developedhydrological models both at the basin and global scales.
These hydrological models are however based on the conceptof fixed land use. The variability of irrigated area is an
important component to be considered as it impacts the heat
and water balance in the basin. Central Asia is a region where
the land use changes dynamically. It is difficult to obtain an
accurate estimation of the irrigated area because collecting
data in a wide region is difficult especially in developing
countries.
Since global land cover products such as the Global Land
Cover Characterization (GLCC) map are static, the land
cover classification provided does not reflect land covervariability which may be caused by a reduction of theirrigated area especially during drought or dry years. In thiscase, the non-irrigated area may incorrectly be classified as
irrigated.
In addition, some of the native plants in this study area havelong roots of between 10 to 30m enabling the plants toflourish. This makes it difficult to identify the area of land
under irrigation using NDVI. Irrigated area in statistical
databases such as FAO AQUASTAT is mentioned as ‘area
equipped for irrigation’. This is the definition of irrigableland and not the actual irrigated area
In this research, a combination of satellite analysis and Land
Surface Modelling approach was performed to detect theirrigated area. A global dataset developed by Doll (2002)
showing the percentage of area equipped for irrigation wasused to identify the irrigable area in the basin. The LST
obtained from both MODIS and a LSM considering
conditions with and without irrigation was used to detect theirrigation effect on heat balance in the basin. The objective of
this study is to assess the annual changes in the irrigated area
so as to facilitate an understanding of changing water demandthrough climatic variables.

Since water has a higher heat capacity than soil, soil withhigh soil moisture does not immediately respond totemperature changes. Moreover, increased evapotranspiration as a result of the presence of irrigation water
further reduces the LST in the day time. LST by the satellite
represents the actual conditions on the ground while the LSM
was used to provide the estimated ideal LST for bothconditions with and without the influence of irrigation. The
mesh was assumed to be fully irrigated when simulating
irrigation conditions and fully non-irrigated while simulating
conditions without irrigation. Since MODIS provides thesurface temperature for the actual conditions on the ground,when attempting to detect irrigation, it is sometimes impacted
upon by precipitation. Additionally, effects on LST due togeological variability cannot be isolated. The purpose of thesimulation of LST using the LSM is to remove the influenceof this climatology and geological variability which results intemperature difference.

2.1 Satellite data
MOD11A1 product with a temporal resolution of 12 hours
and a spatial resolution of 1km was used for LST data.
MODIS collects daily observations from the study area atapproximately 4 pm and 3 am local time. This study used
MODIS data from 2001 when it was first available.

2.2 SiBUC Land Surface Model
Simple Biosphere including Urban Canopy (SiBUC) by
Tanaka (2005) is developed based on the Simple Biosphere(SiB). It considers artificial water operation in irrigated land
and water and heat balance on an urban area. The basic
concept of the irrigation scheme is to maintain soil moistureof the green area above minimum soil moisture defined foreach growing stage of the crop. Irrigation scheme in thismodel was improved based on field investigation to consider
local furrow irrigation rules in the basin (Touge et al. 2015).

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For meteorological input, Japanese 55-year Reanalysis(JRA-55) was used to provide forcing data to the model.
Since all the data is provided at a temporal resolution of
between 3 and 6 hours, hourly data was linearly interpolated
and spatial interpolation was done using Inverse distanceweighting (IDW). Global Satellite Mapping of Precipitation(GSMaP) version 6 data was used for precipitation data. Thisis a real-time radar derived dataset and it was used in order toisolate irrigation effect on soil moisture from that caused byrainfall.

2.3 Irrigation indices
The following Eq.(1-(3) 下の were used to develop LST
based indices in an attempt to detect the irrigated area.
𝑅
𝑅

𝑅

∆𝑆𝑇

∆𝑆𝑇

𝑆𝑇

𝑆𝑇

𝑆𝑇

𝑆𝑇

𝑆𝑇
𝐴𝑣𝑒𝑆𝑇

_

𝑆𝑇

_

(1)

_

(2)

_

_

𝐴𝑣𝑒𝑆𝑇

_

(3)

where, 𝑅 is irrigation index, 𝑆𝑇 is surface temperature,∆𝑆𝑇 is surface temperature difference between day and night,
𝐴𝑣𝑒𝑆𝑇 is long term average of surface temperature. And
suffix of 𝑆𝐴𝑇 means observed by satellite, 𝐿𝑆𝑀_𝐼𝑅 and
𝐿𝑆𝑀_𝑁𝐼 means simulated with full irrigation and without
irrigation, respectively, and 𝑑𝑎𝑦 means daytime.
Eq.(1) 上の was used to remove the climatic influences
and the geological difference that may affect ΔLST in order
to identify the irrigation effect in a mesh. Eq. (2) compares
the irrigation effect observed by MODIS with the ideal

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simulated irrigation effect. Since this is the rate of the actual
irrigation effect from the ideal irrigation effect, it would
reflect the irrigation fraction in the mesh. Eq.(3) compares
the irrigation effect observed by MODIS with the averagesimulated irrigation effect. This is used to detect a drought
event by comparing the irrigation effect in a given year to an
average year.

3 Results and Discussion
Fig.1 below shows the potential of the 3 indices to detect
drought effect on irrigated area during the irrigation season.
During a dry year, the irrigation fraction away from the water
source is reduced due to water scarcity resulting in a higher
LST. The indices were computed from a 10-day average of
LST data.
Fig.1 (a-c) shows a higher value for all the indices in themonth of August 2001 especially in the northern part of thedelta. In 2001, some areas in the north were severely affectedby drought and could not be irrigated. This can be clearlyviewed by the proposed LST indices. R1 shows changes tothe irrigated area due to anthropogenic effect which in thiscase, relates to the reduction of the irrigated area due todrought. R2 seems to be directly related to the irrigation
fraction and therefore has a much clear result on the impactof drought than R1 and R3. It compares an ideal natural
simulated effect of irrigation and the actual irrigation
condition. R3 seems least suitable of the 3 since it compares
the actual condition to average changes which may include
drought conditions in the past. Fig.1 (d-f) shows an average
year where the irrigation fraction is much larger as compared
to 2001.

(a) R1 index 2001(b) R2 index 2001(c) R3 index 2001(d) R1 index 2017(e) R2 index 2017(f) R3 index 2017

Fig.1 Effect of drought on LST in the irrigated area

4 Conclusions

Reference(1) Due to the difference in heat capacity between water andsoil, it was expected that soil with high moisture content will
not respond immediately to temperature changes. Therefore,
3 indices were developed to detect the irrigated area.
(2) Irrigation fraction during a drought year was observed
to be lower as compared to that of a normal year especiallyfurther away from the water source due to water scarcity.

[1] Tanaka, K., Development of the New Land Surface Scheme
SiBUC Commonly Applicable to Basin Water
Management and Numerical Weather Prediction Model,
PhD Thesis, Kyoto University., (2005).
[2] Touge, Y. et al, Developing a Water Circulation Model in
the Aral Sea Basin Based on in Situ Measurements on
Irrigated Farms, Journal of Arid Land Studies 25 (3) (2015)
133–36.

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Tohoku University

Estimating fire severity map based on representative plots in the
Kamaishi 2017 forest fire
○ Grace Puyang Emang1*, Yoshiya TOUGE1 & So KAZAMA1
1

Department of Civil and Environmental Engineering, Tohoku University, Miyagi 980-8579, Japan.
*
E-mail: grace.puyang.emang.r2@dc.tohoku.ac.jp.

Abstract
Fire severity in a forest fire varies within the burned area and this variation will cause different impacts towards
the forest such changes to hydrological processes and carbon dioxide emission. Thus it is important to map the fireseverity to assess the magnitude of such impacts. These fire severity can be observed using satellite, field
investigation or both. Thus in this study, the estimation of fire severity map was studied using 15 representative
plots of four different fire severity levels that were based on the probability of tree mortality. The threshold of eachfire severity level was based on the maximum NDVI difference between pre and post-fire images of each
representative plots. The resulting fire severity map showed a coarse delineation of fire severity indicating thethresholds used were insufficient due to the small number of representative plots used.
Keywords: forest fire; fire severity; NDVI; Landsat 8.
1 Introduction
Fire severity is a term to describe how fire intensity affects
ecosystems where the effects are often varied within andbetween different ecosystems. Some studies have reported
increased in sedimentation concentration observed in burnedwatersheds and higher sediment production rates were foundin high severity forest than a moderate and low severity forest
fire. The increase in sedimentation rate and heavy
precipitation events post-fire will lead to deterioration ofwater quality.
Variation in fire severity existed in a burned area for thesame fire event because it is influenced by various factors
such as fire intensity, heating and duration, soil and plant
dryness, topography and climate. Hence it is vital to estimate
and map the various fire severity in order to quantify andmitigate the effects of a forest fire.
Fire severity can be measured through the loss or change ofabove and below ground organic matter by field observationor satellite images. The fire severity and indices used in moststudies were defined and measured according to the studies
purpose and localised to the study areas which are
insufficient in other study areas because of different
ecosystem and climates. The common satellite index used to
study fire severity is Normalise Burn Ratio (NBR) but
another index which could be viable in detecting fire severity
and related directly to forest growth is Normalise Difference
Vegetation Index (NDVI). The assumption in this study wasthat various fire severity will result in a different probabilityof tree mortality due to various damages on trees so thesetrees are expected to grow differently after the fire wassuppressed and its growth rate should be different from trees
growing in an unburned area. These changes in trees growth
due to various fire severity can potentially be detected using
NDVI.
In this study, fire severity were visually identified during
field investigation by the percentage of scorch crown height
and were then categorised into three levels of fire severity(low, moderate and high severity) to match the probability oftree mortality (low, moderate and high tree mortality) whichwere based on the percentage of scorch crown height. This

study aims to estimate the fire severity map based on
representative plots in the Kamaishi 2017 forest fire.

2 Study Area and Methodology
2.1 Study Area
The study area is located around Takanosu Mountain in
Kamaishi city, Iwate Prefecture and forested mainly withneedleleaf trees (pine and cedar) and sparsely with broadleaf
trees. A fire which broke out on 8 May 2017 and suppressedon 22 May 2017, lasted for 14 days and the estimated total
burned area, as indicated in Fig.1, was 413ha which is greaterthan the total of burned area for the whole of Japan in 2016.
The burned area was extensive due to the strong presence ofwind and low accessibility of vehicle into the burned area
which was surrounded with steep cliffs, Komatsu Bay on theeast and Ozaki Peninsula on the northeast.

Fig.1 Study area drawn in solid line based on burned andunburned areas as indicated by information from Kamaishi
Forestry Association and the distribution of 15 trees used inthis study

32

The 7th International Symposium on Water Environment Systems‐‐‐‐‐ with Perspective of Global Safety(Nov. 14th ‐ Nov. 16th, 2019)

2.2 Ground Truth and Fire Severity Classification
The study area is forested mostly with needleleaf trees sothree needleleaf trees and a broadleaf tree were used in eachfire severity levels. In total, 15 trees were used in this study
as shown in Fig.1 with 12 trees in the burned area and theremaining trees were from unaffected areas.
Fire severity was observed by visually estimating thepercentage of scorch crown height relative to its total crownheight. These percentages were categorised into three levels(low, moderate and high severity) so to match the probabilityof tree mortality: low (scorch crown height less than 30 % ofcrown height), moderate (scorch crown height more than
30 % but less than 80 % of crown height), and high treemortality (scorch crown height more than 80 % of scorch
crown height) as shown in Table 1.
Table 1 Classification of fire severity levels used based on
the probability of tree mortality17).
Fire severity
Percentage of scorch crown height
Low
Less than 30 %Moderate
More than 30 % but less than 80 %High
More than 80 %GP‐RSS
Tohoku University

to grow or the higher NDVI is due to the growth of grass inthe sparsely forested broadleaf areas captured by Landsat 8.

Fig. 2. Delineation of fire severity areas based on NDVI
threshold2.3 Satellite Images
NDVI images free of cloud and snow on 30 April 2017 (prefire) and 24 June 2017 (post-fire) from Landsat 8 were usedin this study and calculated as Eq. (1).
𝑁𝐷𝑉𝐼

𝑁𝐼𝑅
𝑁𝐼𝑅

𝑅
𝑅

…..….(1)where NIR is spectral reflectance of the near infra-red band
(0.85-0.88 micrometres) and R is spectral reflectance of the
red band (0.64-0.67 micrometres).
The NDVI difference between these two images wasdetermined for all 15 trees in the representative plots. The
maximum NDVI difference from each fire severity level wasused as thresholds of severity levels as indicated in Table 2.
Table 2 NDVI thresholds in each fire severity level basedon the maximum NDVI difference of the 15 trees used.
Fire severity
NDVI threshold used in delineating
fire severity area
Unaffectedless than 0
Low
More than 0 but less than 0.03
Moderate
More than 0.03 but less than 0.33
High
More than 0.33

3 Results and Discussion
The resulting fire severity delineated areas in the spatial
distribution map in Fig.2 was coarse, suggesting thethresholds used based on the 15 trees were inappropriate anddetermining the thresholds with limited observation points
are inadequate. Some areas which were forested withbroadleaf trees showed no changes in NDVI suggestingobserving broadleaf trees by NDVI are difficult due to its
canopy being less dense than needleleaf trees.
However, it is possible the prior fire conditions in broadleaf
trees areas could have led to a minimal decrease in NDVI for
broadleaf trees. NDVI of pre-fire image showed the areas of
the broadleaf trees had lower NDVI than other areas but ahigher NDVI in the post-fire image indicating these broadleaf
trees might have not been affected by the fire and continued

4 Conclusions(1) The small number of representative plots were notsufficient in determining the NDVI threshold used toestimate the fire severity map.
(2) Estimation of fire severity map using both fieldobservation and satellite is needed by increasing number ofrepresentative plots to enhance understanding of forest fireeffects towards forest ecosystem and provide insights to therelations of different fire severity with dryness conditions
prior to and after a forest fire.

Reference[1] Keeley, J. E. Fire intensity, fire severity and burnseverity: A brief review and suggested usage, Int. J. Wildl.
Fire, 18, (2009) 116–126.
[2] Silins, U., Stone, M., Emelko, M. B. & Bladon, K. D.
Sediment production following severe wildfire and post-firesalvage logging in the Rocky Mountain headwaters of theOldman River Basin, Alberta Catena, 79 (2009) 189–197.
[3] Benavides-Solorio, J. D. D. & MacDonald, L. H.
Measurement and prediction of post-fire erosion at thehillslope scale, Colorado Front Range, Int. J. Wildl. Fire, 14,(2005) 457–474.
[4] Chatto, K. & Tolhurst, K. A review of the relationshipbetween fireline intensity and the ecological and economic
effects of fire, and methods currently used to collect fire data,
Research Report No. 67, Department of Sustainability andEnvironment, pp. 1-20, 2004.
[5] Ryan, K. C. Techniques for assessing fire damage totrees, Proceedings of the Symposium on Fire -- Its Field
Effects, pp. 1–11, 1982.
[6] Touge, Y., Emang, G. P., Kazama, S., Takahashi, Y. &Sasaki, K. Introduction of the Tohoku Forest Fires on May 2017;case in Kamaishi city of Iwate Prefecture and Kurihara city ofMiyagi Prefecture. 自然災害科学 J. JSNDS, Vol. 36, pp. 361–
370, 2018.

33

The 7th International Symposium on Water Environment Systems‐‐‐‐‐ with Perspective of Global Safety(Nov. 14th ‐ Nov. 16th, 2019)

GP‐RSS
Tohoku University

Understanding flood hazard and crop yield loss relationship usinghydrodynamic and remote sensing model○ Vempi Satriya Adi HENDRAWAN1*, Daisuke KOMORI2
1

2

Graduate School of Environmental Studies, Tohoku University, Miyagi 980-8572, Japan.
Department of Civil and Environmental Engineering, Tohoku University, Miyagi 980-8579, Japan.
*
E-mail: vempi@dc.tohoku.ac.jp.

Abstract
The existing evaluation methods to understand flood impact on agricultural crop are mostly to utilize a damage
function. However, constructing a damage function is time-consuming as it requires ex-post survey data andexperimental analysis based on historical event. Therefore, developing an alternative approach is needed,
particularly for an area where a stage damage function has not been developed yet and agricultural flood impact
data are limited. This study is aimed to develop an alternative approach to understand the relationship between flood
characteristics and associated crop yield loss.
1 Introduction
In most Asian countries, low land rice crop cultivation has
suffered huge losses during wet monsoon season due toextreme river flooding [1]. The existing evaluation methods
to understand flood impact on agricultural crop are mostly toutilize a damage function. However, constructing a damage
function is time-consuming as it requires ex-post survey dataand experimental analysis based on historical event [2].
Therefore, developing an alternative approach is needed,
particularly for an area where a stage damage function has
not been developed yet and agricultural flood impact data arelimited.
This study is aimed to develop an alternative approach tounderstand the relationship between flood characteristics andassociated crop yield loss. The approach proposed by [3] wasadopted to achieve abovementioned purpose. The previous
studies used capability of near real time remote sensing
technology to record vegetation greenness condition to build
empirical crop yield model. Whereas the inundation andflood parameters was simulated by a hydrodynamic model.
A relationship between crop loss and flood parameter thenwas obtained. Moreover, the applicability of those approachfor different climate, geographical condition and different
crop behavior is still unknown. Therefore, another case study
was employed in this study to test the applicability of theapproach and to develop an alternative flood-loss
relationship curve. Timeseries smoothing were also taken
into consideration to overcome image noise disturbances thatwas not considered in the previous study.
To achieve the purpose of this study, some objectives were
defined as follows: 1) To estimate crop yield loss usingsmoothed satellite images, 2) To simulate flood propagationin the crop field, and 3) To understand relationship between
flood characteristic and its associated damage.

2 Materials and Methods
2.1 Case study and flood event
Solo river basin was selected as a case study (Figure 1). Soloriver basin is the largest river basin in java island Indonesia
with approximately 16,100 km2 of the total area. It has rice
crop cultivated in the basin 31% of the total basin area. There
are mainly two paddy-crop pattern system in the river basin[4]: (1) October-January and February-June and (2)December-April and April-June. It is varying spatially andtemporary based on local farmer’s practice and climate
condition.
The flood event occurred in the basin from the 25th
December 2007 to 10th January 2008 was chosen as an eventfor flood simulation. The flood occurred during newlyplanted paddy-crop growth stage in the eastern part of basin
and maturity stage in western part of basin variously. It
damaged about 60,630 ha paddy field [4].

Figure 1 Solo river basin and rice field
2.2 Methodology
This study consists of three main analyses, crop yield loss
estimation, flood characteristics simulation, and obtaining
relationship of flood characteristics and its associated loss
estimation.
1) Crop Yield Loss Estimation
MODIS Vegetation indices (MOD13Q1) product was used
to develop statistical crop yield model to estimate thepotential crop yield. The multiple linear regression model
based on the city level yield as a dependent variable andseveral NDVI and EVI value within the period of the flood
event was performed. The yield loss was obtained by making
a ratio between assumed normal year and flood affected year
2) Flood characteristics simulation
A flood model, Rainfall-Runoff-Inundation (RRI) model
developed by [5] was used to simulate the inundation andoutput flood parameters.
3) Flood-loss relationship

34

The 7th International Symposium on Water Environment Systems‐‐‐‐‐ with Perspective of Global Safety

GP‐RSS(Nov. 14th ‐ Nov. 16th, 2019)

Then the relationship between flood parameters and crop
yield loss then was obtained to understand flood
characteristic affecting crop yield.

Tohoku University

showing that rice plants may suffer considerable yield losses
when water depths are at 20 to 50 cm water depth [1].
Loss Ratio - Depth

3 Results and Discussion
1

Crop yield model was established by using smoothed timeseries images that was performed by TIMESAT [6]. Multiple
linear regression model was used to estimate yield using city
scale NDVI and EVI in both 2007 and 2015 year. Relatively
satisfactory reliability of the model could be achieved withthe coefficient of determinant by 0.65 and 0.63 for 2007 and2015 model respectively (Table 1).

Loss Ratio

3.1 Crop Yield Loss Estimation

0.8
0.6
y = 0.28ln(x) + 0.9
R² = 0.42

0.4
0.2
0
0

0.5

1

1.5

2

Depth (m)Table 1 Yield Regression ModelYear

Regression Equation

R2adj

2007

Y = 4.18 - 8.53*EVI14sep07 +0.65

12.82*EVI16oct07
2015

Y = 4.81 - 11.08*NDVI30sep15 +0.63

15.7*NDVI1nov15
In order to estimate the potential loss, we use the ratio value
between normal and affected year:

𝑅𝑎𝑡𝑖𝑜

𝑌𝑖𝑒𝑙𝑑
𝑌𝑖𝑒𝑙𝑑

From above ratio formula, then the range value from 0 to 1
was considered as a loss ratio, meaning that the yield in 2007
as affected year is lower than 2005 as normal year.

3.2 Flood Characteristic Simulation
From the result, flood characteristics in the basin then wereobtained in both river and slope plain in spatial distribution.
A huge inundation area is shown in the downstream area
where rice crop field is cultivated. The maximum depth
within all inundated pixels is 3.55 m and the maximum
velocity is 0.26 m/s. While maximum duration for > 1-meter
depth inundation is 13.5 days. The spatial distribution of
those parameters was also obtained in the same resolution
with crop yield model. Then those was used to obtain floodloss relationship.

3.3 Flood-Loss Relationship
In order to obtain the relationship, we used interval 1% ofratio to obstruct average each flood parameter value withinthose pixels, rather than using every single pixel. We usedmaximum velocity, duration and depth to represent flood
hazard. While, loss ratio was obtained by following formula.
Higher loss ratio means higher potential loss.

𝐿𝑜𝑠𝑠 𝑅𝑎𝑡𝑖𝑜

1

𝑅𝑎𝑡𝑖𝑜 <1

Loss ratio was obtained by rating 2007 yield and 2015 yield.
Ratio value was used is less than 1 meaning that there wasloss in 2007 yield as mentioned above.
Taking one into discussion, depth-loss relationship showsthat there are two side of loss ratio resulted from the same
flood parameter (i.e. 20 – 40 cm in) (Figure 2). However, it
shows a clear relationship from 40 cm water depth meaningthat more than 40 cm water depth would result a damage on
the rice crop. The results are inline the previous study

Figure 2 Flood-loss relationship

4 Conclusion
This study is aimed to develop an alternative approach tounderstand the relationship between flood characteristics andassociated crop yield loss. This study consists of three main
analyses, crop yield loss estimation, flood characteristics
simulation, and obtaining relationship of flood characteristics
and its associated loss estimation.
From the case study we find that remote sensing-basedmodel could be potentially used to assess the crop yield loss.
Integrating spatial crop yield loss model and flood simulation
could result a new relationship. The results show clear
relationship between flood parameter and the potential loss.
In depth-loss relationship solely, it shows that more than 40
cm water depth would result a damage on the rice crop. The
results are inline the previous study.
For further research, it is needed to build more reliable crop
yield loss model and to validate the loss resulted from thehazard. Field observation seems able to fill the gap between
predicted and observed yield loss. Proposed by a number ofstudies taking sample of Vegetation Indices of crop field isalso promising to get better crop yield model.

References[1] Singh A.et al., 2017. Genetics, Physiological
Mechanisms and Breeding of Flood-Tolerant Rice (Oryza
sativa L.), Plant and Cell Physiology, Volume 58, Issue 2,
February 2017, Pages 185–197.
[2] Brémond, P., Grelot, F. and Agenais, A.L., 2013.
Economic evaluation of flood damage to agriculture–review
and analysis of existing methods. Natural Hazards and Earth
System Sciences, 13(10), pp.2493-2512.
[3] Chen, H. et al., 2017. Integrated remote sensing
imagery and two-dimensional hydraulic modeling approach
for impact evaluation of flood on crop yields. Journal of
Hydrology, 553, pp.262-275.
[4] Shrestha B. B et al., 2016. Assessment of Disaster
Damage due to Flood Hazard in the Solo River Basin ofIndonesia. 7th ICWRER.FAO. 2015. The impact of disasters
on agriculture and food security. Rome.
[5] Sayama T et al., 2012, Rainfall-Runoff-Inundation
analysis of Pakistan flood 2010 at the Kabul river basin,
Hydrological Sciences Journal, 57(2), 298-3.
[6] Eklundh, L. and Per, J., 2017. TIMESAT 3.3 With
Seasonal Trend Decomposition and Parallel Processing
Software Manual.

35

The 7th International Symposium on Water Environment Systems‐‐‐‐‐ with Perspective of Global Safety

GP‐RSS(Nov. 14th ‐ Nov. 16th, 2019)

Tohoku University

Assessing the impact of human-induced land-use change on natural
water cycle at watershed scale based on land surface model----a case study in the Loess Plateau Region, North Central China○ Linghao Meng1*, Daisuke Komori1, 2
1

Department of Frontier Science for Advanced Environment, Tohoku University, Miyagi 980-8579, Japan.
2
Department of Civil and Environmental Engineering, Tohoku University, Miyagi 980-8579, Japan.
*
E-mail: meng.linghao.t8@dc.tohoku.ac.jp.

Abstract
Human activities have become one of the most crucial factors of the natural water cycle. This research is to assess
the impact of human activities, especially land use and vegetation changes on the natural hydrological factors in the
Loess Plateau Region (LPR), a region suffered the most severe soil erosion over the world. From 1999 the water
conservation project, forest and grass land increased significantly to replace most of the field land to more than 25%.
three stations at the watershed are used to analyze water cycle parameter changes. Climate and land use data are
divided into three parts and thus nine cases for simulation. The model calibration shows good accuracy of H08 usedin the region.
Keywords: Loess Plateau Region, land surface model, land use change, water cycle
1 Introduction
Rapidly expanding human activities have profoundly
affected various biophysical and biogeochemical processesof the Earth system over a broad range of scales, andfreshwater systems are now amongst the most extensively
altered ecosystems (Carpenter et al. 2011). Apart fromurbanization, human activities also apparently altered thenatural water cycle, with more or less both positive andnegative impacts simultaneously. So, it is significant toassess the effects of human activities as a way of natureenvironment protection project.
So far, various researches about the relationship between
water and the environment as well as human activities havebeen carried out. For example, Gorman (2015) found climatechange increased the runoff in Mississippi River Basin and
Hanasaki (2017) pointed out that the role that forest plays
depends on specific catchment and so on. Researchers
usually first investigates the consequences caused by the
impact, makes qualitative analysis, and then quantitatively
calculates according to the actual data. However, most arefocus on climate change; for land use changes the dominantpoint lies on urbanization and deforestation, human-induced
afforestation is rarely being researched. Therefore, thisresearch aims using land surface model to physicallysimulate the water cycle changes under human afforestation
at large watershed scale.

2 Materials and Methods
2.1 Data Preparation(Figure 1) in the Loess Plateau Region were obtained from
China Geo-data Sharing Service Platform (CGSSP) and used
to calibrate and validate the models.
LegendStations!

Toudaoguai

River

!

DEM
Value
High : 5210
Lanzhou

Low : 85!

Sanmenxia!

0

75 150

300

450

±

600
Kilometers

LegendMain River

Climate Data

China

We used the downscale daily climate data in 5-arc minuteresolution from three observation datasets provided by H08:
1) the Water and Global Change (WATCH) forcing data for1980 –1989 (hereafter H for history), 2) the Second Global
Soil Wetness Project for 1996 – 2005 (hereafter P for past),
3) the WATCH Forcing Data methodology applied to ERAInterim data for 2005 – 2014 (hereafter C for current). DEM
data and River discharge data from three observation stations

Loess Plateau Region

Figure 1. DEM, location and three stations of LPR
Land use data

The LPR is one of the most severe soil erosion area. Chinese
government started its water and soil conservation project byreducing the area of field crops into forest. Land use data of36

The 7th International Symposium on Water Environment Systems

the total ratio of forest and grassland increased from 53% to
80% while field crops deceased from 33% to 12%.
Figure 3 showed the result of model calibration. The NSEof each station is 0.84, 0.78, 0.88, which means the H08 has
high accuracy when applying to LPR.
Station 1

Urban

Jan-09Jul-08

Oct-08Jan-08Apr-08

Jul-07

Oct-07

Jan-07

Apr-07

Jul-06

Oct-060
Jan-06

Field Crops

500
Apr-06

Water

1000

Jul-05

Forest1500

Oct-05

1985

NSE: 0.84

Simulation

2000

Jan-05±

Bare Land

Observation

Apr-05

LegendDischarge (m3/s)

2500

Jul-09

2000

Tohoku University

Oct-091985 (history), 2000 (past), 2012 (current) were obtained
from CGSSP (Figure 2).

2012

GP‐RSS(Nov. 14th ‐ Nov. 16th, 2019)

Apr-09‐‐‐‐‐ with Perspective of Global Safety

Time

Grassland
800
Kilometers

1000
500
Oct-09Jul-09Apr-09

Jan-09Oct-08

Jul-08Apr-08Jan-08

Oct-07

Jul-07

Apr-07

Jan-07

Jul-06

Oct-06

Jan-06

Apr-06

Oct-05Jul-050Time

Station 3

NSE: 0.88

Oct-09Jul-09Apr-09

Jan-09Oct-08

Jul-08Apr-08Jan-08

Oct-07

Jul-07

Apr-07

Jan-07

Jul-06

Oct-06

Apr-06

Jan-06

Jul-05

3500
3000
2500
2000
1500
1000
500
0

Oct-05

Table 1. Details of nine simulations

1500

Jan-05A land surface model called H08 (Hanasaki, 2008) is used
to analyze water cycle under different land use scenarios in
the LPR. This module is based on a bucket model (Manabe,
1969; Robock et al., 1995) with improvements. The soil
water balance and runoff were expressed as follows:ௗௐ= Rainfall+Qsm-E-Qs-Qsb
ௗ௧
Where Qsm is the snow melt rate; Qs is the surface runoff, Qsb
is the subsurface runoff, τ is a time constant as 100 days ×
86400 (s) and the γ is set at 2 which are global constant(Hanasaki, 2008)
Nine simulation will be conducted in this research as shownin table 1.

2000

Apr-05

2.2 Analysis Methods

NSE: 0.78

2500

Jan-05. Figure 2. Land use in LPR of 1985, 2000, and 2012

Station 2

3000

Apr-05600

Discharge (m3/s)

400Discharge (m3/s)

0 100 200Time

. Figure 3. Model calibration of three stations

4 Conclusions
2.3 Model Calibration
Observed daily discharge of three stations in the LPR named
Landzhou, Toudaoguai, and Sanmenxia (hereafter station 1,
2, and 3) was used for model calibration. Parameters of theH08 model that are susceptible to river discharge and related
to land use (Mateo et al., 2012) are soil depth (SD), bulk
transfer coefficient (CD), time constant for daily maximum
subsurface runoff (τ), and a shape parameter (γ) that is related
to subsurface flow (Hanasaki et al., 2014). The model can becalibrated by changing the values of the parameters based onland use type and evaluated using the Nash-Sutcliffe
Efficiency Coefficient (NSE).
The C-C case of each station is used to calibrate the model.
By changing the 4 parameters to find the optimal match of
the parameter sets. Later on, the H-H and P-P case of eachstation will be used for model validation.

3 Results and Discussion
As shown in figure 2, forest and grassland increased
significantly while crop field dramatically decreased becauseof the Water and Soil Conservation Project. By calculation,(1) Human-induced afforestation have exerted greatchanges in the land use pattern of the LPR. Forest andgrassland have greatly replaced the crop fields which used tobe the dominant land use type.
(2) The simulation of 3 selected stations shows satisfactory
accuracy in calibrating the models, which means it can beapplied to simulate the water cycle in the studied area. Later
another two senarios will be used for model validation.

Reference
AghaKouchak A, Norouzi H, Madani K, Mirchi A, Azarderakhsh M, Nazemi A,
Nasrollahi N, Farahmand A, Mehran A, Hasanzadeh E (2015) Aral Sea
syndrome desiccates Lake Urmia: call for action. J Great Lakes Res
41(1):307–311.
Carpenter SR, Stanley EH, Vander Zanden MJ (2011) State of the world’s
freshwater ecosystems: physical, chemical, and biological changes. Annu Rev
Environ Resour 36(1):75–99.
Ghimire, C. P., van Meerveld, H. I., Zwartendijk, B. W., Ravelona, M., Lahitiana,J., Razakamanarivo, H., ... & Jones, J. (2015). Effects of reforestation on thepartitioning of precipitation into evapotranspiration, overland flow andrecharge in eastern Madagascar.
Hanasaki N., (2008). An integrated model for the assessment of global water
resources–Part 1: Model description and input meteorological forcing.
Hydrology and Earth System Sciences, 12(4), 1007-1025.
Hanasaki N., (2014). A quasi-real-time hydrological simulation of the Chao Phraya
River using meteorological data from the Thai Meteorological Department
Automatic Weather Stations. Hydrological Research Letters, 8(1), 9-14.

37

The 7th International Symposium on Water Environment Systems‐‐‐‐‐ with Perspective of Global Safety(Nov. 14th ‐ Nov. 16th, 2019)

GP‐RSS
Tohoku University

Estimation of the flood damage caused by Climate change
and effect of the adaptation○ Tao Yamamoto1*, So Kazama1 ,Yoshiya Touge1, Tsuyoshi Tada2, & Takeshi Yamashita3
1

Department of Civil and Environmental Engineering, Tohoku University, Miyagi 980-8579, Japan.
Department of Civil and Environmental Engineering,National Defense Academy,Kanagawa 2398686,Japan
3
Information Infrastructure Division, Information Department, Tohoku University,Miyagi, 980-8578,
Japan*
E-mail: tao.yamamoto.r4@dc.tohoku.ac.jp.
2

Abstract
This study shows and compares the effect of the mitigation and the adaptation for the flood caused by climatechange. It was estimated that the flood depth and the damage using 2D non-uniform flow model and the flood
control economy investigation manual. Furthermore, it was estimated that the future flood damage using GCM(Global Climate Model)s and two RCP(Representative Concentration Pathways) scenarios. And we considered theland use control as the adaptation. We consider the reduction if we withdraw the land based on the 100 year return
period flood depth as the effect of the land use control.
Keywords: global warming, land use control, GCM, future climate.
1 Introduction
Due to the impact of climate change associated with global
warming, there are concerns about the increase in various
water disaster risks in Japan. According to Wada et al., daily
precipitation at the end of the 21st century with a recurrence
period of 100 years is predicted to increase by 20%
nationwide compared to the present 1). This suggests anincreasing trend in the number of flood disasters such as
heavy rain in July 2018. Based on these trends, it is necessary
to consider the impact and adaptation measures. As aprevious study about flood disasters, Tezuka et al.
quantitatively evaluated future flood damages in Japan2).
Tanaka et al. estimated the amount of damage caused byflood and storm surge combined disasters throughout Japan3).
However, in these studies, the resolution of the analysis was
about 1km square, and small rivers such as mountainous
areas could not be represented. As for the evaluation methodof adaptation measures, the difference between the twodamages when the flood control level was raised and when it
was raised was evaluated as the effect of adaptation measures.
This method cannot individually evaluate the effects ofvarious adaptation measures. Although land use control canbe cited as an adaptation measure against floods, the effectsof the entire country have not been verified.
Therefore, the purpose of this study is to quantitatively
evaluate the amount of flood damage with higher resolution
data, and to individually evaluate the effects of adaptation
measures using the future climate. We also compare theeffects of adaptation and mitigation measures against floods.

2 Datasets
In order to estimate the amount of damage in the futureclimate, the rate of increase of future precipitation relative tothe current precipitation is calculated. For this, we used thedaily precipitation that was downscaled by the 1km regional
climate scenario / multi-meteorological element version in
Japan4). There are six GCMs: GFDL-CM23, HadGEM2-ES,
MIROC5, MRI-CGCM3, CISRO-Mk3-6-0, and IPSL-

CM5A-LR. There are two scenarios: RCP8.5 and RCP2.6.
The target period was 1981 to 2005(current) and 2031 to2055(near future).
The input data for flood calculation is shown below. The
national land numerical information was used for elevationand land use data. In addition, for daily precipitation data, weused the probabilistic flood-contributing rainfall distribution,
which is a rainfall distribution that generates floods at any
point of time, calculated from the relationship between thecatchment area and runoff coefficient created by Tezuka et al.

3 Method
In this study, we used a two-dimensional unsteady flow
model that has been used in previous studies as an inundation
model. The resolution was increased to about 250m square.
Based on the inundation depth obtained from these, referringto the flood control economic survey manual, the damage rate
corresponding to the inundation depth and the unit price ofdamage according to land use are set, and the damage amountis calculated based on them.
The future climate estimation method is shown. For the
GCM data shown in the data set, we extracted the maximum
daily precipitation for each year at 50 locations nationwide.
The parameter of the generalized extreme value distribution
GEV (Generalized Extreme Value) distribution was
estimated by using the Probability Weight Moment (PWM)
method, and precipitation with a recurrence period of 100
years was calculated. The ratio of precipitation obtained
during the two periods was taken as the rate of future
precipitation increase. Figure 1 shows the distribution of
precipitation increase rates created based on MRI-CGCM3.
By multiplying this with the above-mentioned stochastic
flood-contributing rainfall distribution in the current climate,
the future probabilistic flood-contributing rainfall
distribution was created.
Next, the calculation method of the annual expected
damage(AED) is shown. According to the method of Tezuka
et al., it was assumed that the flood control equipment
withstands floods with a recurrence period of 50 years, and38

The 7th International Symposium on Water Environment Systems‐‐‐‐‐ with Perspective of Global Safety

GP‐RSS(Nov. 14th ‐ Nov. 16th, 2019)floods with a recurrence period of 100 years or more with alow probability of occurrence do not have a significant
impact on the annual expected damage. Based on thisassumption, the AED is calculated by multiplying the section
average damage amount and section probability between therecurrence periods of 100 and 50 years.
Finally, the land use control method is shown. In this study,
land use was changed according to the inundation depth
during floods with a recurrence period of 100 years in thecurrent climate. For example, the land use of house andbuilding land that was flooded for more than 3m was
uniformly changed to the land use of forests that did notaccount for damage. The amount of damage was calculatedusing the land use distribution after the control as an input
value.

4 Result and Discussion
The expected annual damage due to flooding in the wholecountry was estimated to be 1.39 trillion JPY. If theinundation depth is over 3m and the mesh that is residential
and commercial land is withdrawn , and the damage amountof the mesh is not counted (hereinafter referred to as 3m
control), the national expected damage amount is 947 billion
JPY. It decreased by 31.8% from the current expected
damage. Therefore, the effect of land use control is estimatedto be 42.2 billion JPY in annual expected amount. The annual
expected damage was estimated to be 1.33 trillion JPY when
the control inundation depth standard was set to over10m(hereinafter referred to as 10m control, which was 55.7
billion yen less than the current AED.
The amount of damage in the future climate is shown below.
The AED due to flooding in the future in the future climate
was estimated to be 1.43 trillion JPY for the RCP2.6 scenario
and 1.45 trillion JPY for the RCP8.5 scenario. They increasedby 2.7% and 4.1%, respectively, from the current expected
damage. However, the damages of four combinations of
CISRO-Mk3-6-0 and MIROC5 RCP2.6 and GFDL-CM23
and MIROC5 RCP8.5 were reduced.
In addition, when 3m control is performed in the futureclimate, AED is calculated as the average value of 6GCMs,
which is 988 billion JPY in the RCP2.6 scenario and 1.01
trillion JPY in the RCP8.5 scenario. They decreased by
30.7% and 30.3%, respectively, from the expected futuredamage without land use control. AED for the 10m control
was estimated to be 1.37 trillion JPY for the RCP2.6 scenario
and 1.39 trillion JPY for the RCP8.5 scenario. Both werereduced by 4.0% from the expected future damage in theabsence of land use control. Therefore, AED reduction effectin the future climate with land use control is estimated to beabout 438 billion JPY in both RCP scenarios in the 3m
control. In the 10m control, both RCP scenarios are estimatedto be 57.6 billion JPY. The above results are shown in Table
1. From Table 1, the effect of adaptation measures based onland use control is estimated to be about 438 billion JPY for
the 3m control and about 57.6 billion JPY for the 10m control.
The effect of mitigation measures can be regarded as thedifference in the amount of damage when mitigating the RCP
scenario from 8.5 to 2.6. Therefore, the effect of mitigation
measures is considered to be about 19.2 billion JPY. Thisshows the effectiveness of adaptation measures.

Tohoku University

5. Conclusion
1) AED of flood disaster nationwide in current climate is
1.39 trillion JPY.
2) AED of flood disasters in the future climate is 1.43
trillion yen based on the RCP2.6 and 1.45 trillion yen
based on the RCP8.5.
3) If land use is restricted to land with a flood depth of morethan 3m during a flood with 100 years return period, there
is an effect of reducing AED amount of about 438 billion
JPY in both the present and future. In addition, if land use
is restricted to land with a flood depth of more than 10m
during a flood with a 100 years return period in the current
climate, there is an AED reduction effect of approximately
57.6 billion JPY in both the present and future.
4) AED reduction of floods when mitigating global warmingfrom RCP8.5 to RCP2.6 is about 19.2 billion JPY, while
AED reduction due to land use regulations is 57.6 billion
JPY to 438 billion JPY.

Reference
1) 和田一範，村瀬勝彦，冨澤洋介：地球温暖化に伴う降雨特性の変化と洪水・渇水リスクの評価に関する研究，土木学会論文集，No.796/II-72，pp.23-37，2005．
2) S. Tezuka, H. Takiguchi, S. Kazama, A. Sato, S. Kawagoe,
R. Sarukkaliged, Estimation of the effects of climate change
on flood-triggered economic losses in Japan, International
Journal of Disaster Risk Reduction, Vol.9, pp.58-67, 2014.
3) 田中裕夏子，風間聡，小森大輔： 洪水・高潮複合災
害リスク評価， 土木学会論文集 G(環境), Vol.74, No.5,
pp. I_257-I_264, 2018.
4) 西森基貴・石郷岡康史・桑形恒男・遠藤伸彦・飯泉仁
之直・雨貝裕介（2017）：日本における影響評価・適応研究のためのメッシュ気候シナリオデータセット群
について，日本農業気象学会 2018 年全国大会．

RCP2.6

RCP8.5

Fig.1 The distribution of precipitation increase rates
created based on MRI-CGCM3
Table.1
Expected Annual
Damage(trillionJPY)

AEDs of each senarios
current
climate

RCP2.6 2050

RCP8.5 2050

no control

1.39

1.43

1.45

3m control

0.947

0.988

1.01

10m control

1.33

1.37

1.39

39

The 7th International Symposium on Water Environment Systems‐‐‐‐‐ with Perspective of Global Safety(Nov. 14th ‐ Nov. 16th, 2019)

GP‐RSS
Tohoku University

Analysis of Flood Inundation Area in Naruse River Basin by RRI Model
toward Consideration of Paddy Field Dams
○Yikai CHAI*, Yoshiya TOUGE & So KAZAMA
Department of Civil and Environmental Engineering, Tohoku University, Miyagi 980-8579, Japan.
*
E-mail: chai.yikai.r1@dc.tohoku.ac.jp.
Abstract
In recent year, to mitigate flood damage due to increasing of the intensity and frequency of torrential rain events,
the Miyagi prefecture, Japan, has adapted measures using paddy field by installing runoff control devices named
paddy field dam, and utilizing rainwater storage capacity potentially of irrigation ponds. The purpose of this study
is to analysis the flood inundation area caused by typhoon in 1986 in Naruse River Basin toward consideration of
paddy field dams and small irrigation ponds by Rainfall-Runoff-Inundation (RRI), which is a two-dimensionalmodel capable of simulating rainfall-runoff and flood inundation simultaneously. As an input data, 3 arc-second
HydroSHEDS Digital Elevation Model (DEM) and daily observation rainfall data are used. According to reductionof inundation damage, drop in channel water level and decrease in discharge volume using combined flood routing
and hydrologic analyses. The result showed that the paddy field dam has a temporarily effect on the inundation area
and the peak discharge.
Key word: Paddy field dam; RRI Model; Naruse River Basin.

1 Introduction
Flood caused by Typhoon or torrential rains is one of themost devastating natural disasters in the world. In recent year,
flood risks are expected to increase in the year to come.
Although Japan is very small, it is very prone to flooding.
Hence, flood-risk assessments in those regions are crucial.
In order to estimate the flood damage in the area affected byflooding, the inundation area within a short time after thedisaster must be calculated by hydrological model simulation.
In recent years, a variety of flood inundation models havebeen developed and have been applied in river basins all over
the world. One of the models is the Rainfall-RunoffInundation (RRI) model, which can simulate maximum water
depth, river discharge and inundation area, even subsurface
flow. One of the advantages of RRI Model is the predictionof flood events development by considering the effects ofriver discharge and floods inundation. Therefore, the model
is designed to address the problems of simulated flooded
areas in paddy fields and irrigation ponds.
The paddy field dam which is low material installation costand high immediate effect with extensive address, is one of
the flood mitigation measures during torrential rains. By
artificially enhancing the flood control function of the paddy
fields, the rainwater is temporarily stored in the paddy field
during torrential rains, and the flood peak discharge of the
paddy field is controlled to reduce the disaster of the
farmland downstream of the paddy field.
The purpose of this study is to analysis the flood inundation
area caused by typhoon in 1986 in Naruse River Basin toward
consideration of paddy field dams and small irrigation pondsby Rainfall-Runoff-Inundation (RRI). The Naruse River
Basin is choosed as the study area because of its small
footprint and the river bed slope. Except for a few towns
along the Yangtze River, most of the mid-to-downstream
region consists of paddy fields and irrigation ponds. As an
input data, 3 arc-second HydroSHEDS Digital Elevation
Model (DEM) and daily observation rainfall data are used.
According to the maximum water depth and inundation area
obtained by RRI model, it is showed that the paddy field dam
has a temporarily effect on the inundation area and the peakdischarge.

2 Methodology
2.1 Study Area
For the case study, the Naruse River with a length of about
89 km, which is located in Miyagi Prefecture, Japan, is oneof the most important river for agriculture, infrastructure andtransportation of Miyagi Prefecture. The Naruse Riveroriginates from the Funagata Mountain at the junction of
Miyagi and Yamagata Prefecture, and merges with Tagawa
River, Hanagawa river, Tadakawa River and an artificial
river named Shineaigawa. In addition, the Yoshida Riveroriginates from Kurokawa District Yamato town. Finally, theNaruse River and the Yoshida River which is the righttributary, also belong to first-class river, converges into the
Pacific Ocean, and the mouth is located in 38°22′34″N141°10′29″E. The Naruse River Basin area is approximately
1130km2. Geographically, the Naruse River Basin issurrounded by forest and hills in the north and south, andOuu mountain range in the west. The slope of the waterarising from the mountains is significant large, ranging from1/100 to 1/500, and the slope of the plain is only 1/2500 to
1/5000, therefore, if the river bursts its banks, serious
disasters will occur. The basin consists of 8 cities and 3
villages including Osaki City. The population of the basin is
about 180,000 people, and the land use in the basin is about
72% such as mountain, about 22% such as paddy fields andupland areas, and about 6% of urban areas such as residential
areas.

Figure 1. Naruse River Basin and River Bed Slope

2.2RRI Model

40

The 7th International Symposium on Water Environment Systems(Nov. 14th ‐ Nov. 16th, 2019)‐‐‐‐‐ with Perspective of Global Safety

Rainfall-Runoff-Inundation (RRI) Model is a twodimensional model capable of simulating rainfall-runoff andflood inundation simultaneously (Sayama et al., 2012). The
model deals with slopes and river channel respectively, andassumes that both slope and river channel are positioned in
the same grid cell. The channel is discretized as a single line
along the center line of the overlying slope grid cell. The flow
on a grid cell is calculated by the Two-Dimensional diffusion
wave model, and the channel flow is calculated by OneDimensional diffusion wave model. The advantage of the
RRI Model is the simulation also including lateral subsurface
flow, vertical infiltration flow and surface flow in order tobetter representations of the rainfall-runoff and floodinundation process. Considering saturated subsurface flow
and surface flow, a discharge-hydraulic gradient relationshipis used to treat the more important lateral subsurface flow inmountainous regions. On the other hand, the verticalinfiltration flow rate was estimated using the Green-Amptmodel. Based on the different overflow formulas, the flowinteraction between the river channel and slope is estimatedbased on the water-level and the levee-height condition.

GP‐RSS
Tohoku University

h = Height of the water from the local surface
qx, qy = Unit width discharges in x and y directions
r = Rainfall intensity
f = Infiltration rateu, v = Flow velocity in x and y directions
g = Gravitational acceleration
H = Height of the water from the datum
τx, τy = Shear stress in x and y directions
ρw = Density of watern = Manning’s roughness parameter

2.3 Paddy Field Dam Model
Sayama et al. (2012) applied the RRI model to the Niigata
Prefecture Niigata City Yokoe drought drainage basin in
Japan. The runoff analysis construction consists of amountain city module, a paddy field module and a river
channel module, meanwhile, the runoff control measures
play an effective role. In the flood model, the unsteady flow
is calculated from the momentum equation and the continuity
equation. In the river model, based on characteristic of
topographically adjustable cell's and topographicallyadjustable cell's production, the geometric properties of theorganizational unit are used to calculate the flood flow. In theland-use flow model, a new definition was introduced,
namely the introduction of the terminal drains and thecalculation of the flow of rice fields, farmfield area and urban
area.

2.4 Analysis method

Figure 2. Schematic diagram of RRI Model.
The Rainfall-Runoff-Inundation model equations arederived based on the following (1) mass balance Equationand (2) momentum Equations and (3) for gradually varied
unsteady flow:
(1)
(2)
(3)
The second terms of the right side of Equations (2) and (3)are calculated with Manning’s Equations (4) and (5).
(4)

The Naruse River Basin is covered by the GSMaP radarrainfall product, which is available from 2000 onward at an
approximately 1-km spatial and an hourly temporal
resolution. Due to the flooding event that this study used isin 1986, this means that the method to obtain rainfall data
only base on the observation station data. Beacuse only 4observation stations in Naruse River Basin, it is not enoughto simulate accurately, for decrease the uncertainty,
according to the three days rainfall data obtained fromobservation station in Miyagi Prefecture, simulate theprecipitation in entire Miyagi Prefecture by ArcGIS and try
to generate a hourly rainfall time series for longer. As an
input data, 3 arc-second HydroSHEDS Digital Elevation
Model (DEM), flow direction data, and flow accumulation
data modified by ArcGIS, through RRI Model combinedwith paddy field model, reduction of inundation damage,
drop in channel water level and decrease in discharge volume
where is regulated and unregulated by paddy field dam.

(5)

41

Stepwise regulation of endogenous free ammonia to improve
short-chain fatty acids production from waste activated sludge alkaline
fermentation○ Min YE1*, Yu-You Li,1,2
12

Department of Civil and Environmental Engineering, Tohoku University, Miyagi 980-8579, Japan.
Department of Frontier Science for Advanced Environment, Tohoku University, Miyagi 980-8579, Japan.
*
E-mail: 402093078@qq.com

Abstract
Short-chain fatty acids (SCFAs) derived from waste activated sludge (WAS) is a promising alternative as carbon
source for denitrifying processes, while the free ammonia present in the fermentation supernatant requires more
attention. Stepwise regulation of endogenous FA under 24 h accumulation at phase I and removing FA fromsupernatant at phase II were conducted to improve SCFAs production and carbon source availability
simultaneously from WAS alkaline fermentation. The results show that a maximal SCFAs production in the pH=9
+ Ammonia stripping reached 431.4 mg chemical oxygen demand (COD)/g volatile suspended solid (VSS), i.e.,
9.0% and 119.8% higher than that of the pH=9 and the control, respectively. The WAS-derived SCFAs
availability as carbon source also increased by 58.7% than the pH=9.
Keywords: Short-chain fatty acid; free ammonia; waste activated sludge; alkaline fermentation.
1 Introduction
Waste activated sludge (WAS), a by-product from BNR
processes, has drawn more and more attraction. WAS
contains abundant organic matters like proteins (25-62%)
and carbohydrates (7-19%) which could be degraded to
added-value of short-chain fatty acids (SCFAs) via
anaerobic fermentation. It is well known that SCFAs could
be used as the carbon sources to biological nutrient removal(BNR) in wastewater treatment. However, the SCFAs
production is always limited because of a low hydrolysis
efficiency.
Additional pretreatments are always needed to promote
WAS fermentation, such as thermal, ultrasonic andmicrowave-assisted processes. Among them, alkaline
fermentation has been demonstrated to be a promisingstrategy to improve SCFAs production by enhancing WAS
solubilization and/or hydrolysis as well as inhibiting
subsequent SCFAs consumption by methanogens. However,the conventional alkaline fermentation at pH=10 needs
numerous alkaline reagents with high costs. In addition, it isnoted that even though alkaline conditions produced
substantial dissolved organic matters (DOMs) via strongly
abiotic effect, they may concomitantly inhibit the biotic
effect, and thereby hinder SCFAs production maximization.
In fact, WAS alkaline fermentation not only produces
SCFAs but also releases ammonium as a byproduct.
Alkaline fermentation with a very high pH favors mainly
free ammonia (FA) rather than ammonium ion in the
supernatant. FA can easily diffuse from cell membrane into
the cytoplasm and lead to intracellular proton imbalance orpotassium deficiency. On one hand, FA was indicated as an
inhibitory factor for hydrolysis and acidogenesis process.
On the other hand, adding FA was also demonstrated as aneffective pretreatment to WAS disintegration. However, theactual functions of endogenous FA in previous WAS
alkaline fermentation studies was not be confirmed.
Therefore, stepwise regulation of endogenous FA for its
positive function maximization during WAS fermentation at

weak-alkaline condition could be a potential approach toenhance the SCFAs production.
In this study, batch trials of WAS alkaline fermentation
process were conducted to investigated the contributions ofendogenous FA accumulation to WAS disintegration at
early fermentation stage and its removal at later
fermentation stage to SCFAs production promotion.

2 Materials and methods
2.1 Characterization of WAS
The WAS was collected from the second sedimentation
tank of a municipal WWTP. The WAS was concentrated bynatural settling for around 24 h at 4 oC refrigerator prior tousing. The characteristics of WAS are as follows: pH
7.1-7.4, total suspended solid (TSS) 17.9 g/L, volatilesuspended solid (VSS) 12.9 g/L, TCOD 22.3 g/L, SCOD
89.2 mg/L, total carbohydrate 2552.8 mg COD/L, total
protein 11477.3 mg COD/L, SCFAs 60.8 mg COD/L,
ammonium 17.1 mg N/L, total nitrogen (TN) 5.7% in
sludge dry weight.

2.2 Batch trials
The release of endogenous FA and its effect bring about to
WAS solubilization on alkaline condition was carried out insix identical reactors with working volume of 0.2 L. The
reactors were divided into the control (neutral pH), thepH=9 + Ammonia stripping, the pH=9, the FA 100, the FA
300, and the FA 500. Specially, the pH=9 + Ammonia
stripping was conducted with intermittent nitrogen gas
stripping to regulate FA at a low level, which on behalf ofthe sole pH=9 effect to WAS. In addition, the different
initial FA reactors (i.e. FA: 100, 300, 500 mg/L) wereconducted by adding a series NH4HCO3 (NH4+concentrationof 188.9, 566.9, and 944.8 mg/L). The initial FA
concentration was calculated with conditions of pH,
temperature and total ammonia nitrogen concentration(TAN). Each reactor was lasted for 24 h in an air-bathshaker (160 rpm, 35 oC), during which the pH wasmaintained at pH=9 via using 2M HCl and 2M NaOH.

42

Batch WAS fermentation tests were conducted to assess
the removal of endogenous FA to SCFAs production, andcarried out in the working volume of 500 mL reactors. The
reactors were divided into the control (without pH
adjustment), the pH=9 fermentation, and the pH=9 +ammonia stripping. The pH in alkaline fermentation was
controlled via adding 2M HCl and 2M NaOH. The pH=9 +ammonia stripping fermentation included two phases: (1)endogenous FA accumulation during first 24 h; (2) the FA
was reduced by in-situ ammonia stripping until fermentation
ending. The intermittent stripping intensity was controlled
at 12 Lgas/LWAS/min by high purity nitrogen gas. Eachreactor was firstly flushed with nitrogen gas for 5 min toremove oxygen, and then placed in air-bath shaker (160 rpm,
35 oC) for 8 days anaerobic fermentation.

Fig. 1m-1o demonstrated that the WAS-derived SCFAs
availability as carbon source was effectively enhanced by
improving SCFAs production and reducing ammonium. The
available COD in the pH=9 + Ammonia stripping was
5437.9 mg COD/L, which was higher than that of the pH=9(3426.4 mg COD/L) and the control (879.7 mg COD/L).
stepwise regulation endogenous FA could be a potential
application of SCFAs-containing WAS fermentation
supernatant for effective denitrification processes inwastewater treatment plants.

2.3 Analytical methods
Determination of TSS, VSS, TN, ammonium, were carriedout following standard methods. COD was measured via aspectrophotometer (HACH DR890, USA) coupled with a
HACH DRB200 reactor. The pH was detected using a
pH-meter. The proteins and carbohydrates were determined
by Lowry-Folin method and phenol-sulfuric method.
SCFAs concentration and composition were measured using
gas chromatography (Shimadzu, Japan) equipped with a
Stabilwax-DA chromatographic column and a flame
ionization detector (FID).
The FA concentration was determined by total ammonium
nitrogen (TAN) according to the following equation.

where [NH3] and [TAN] are FA and TAN concentrations,
respectively, and T the medium temperature, K.

3 Results and discussion
Fig. 1a-1c shows the effect of endogenous FA production
to WAS solubilization during first 24 h. The FA was fastreleased from 49.3 mg/L (6 h) to 210.6 mg/L (24 h).
Compared to without FA accumulation via in-situ stripping
ammonia from supernatant, the SCOD concentration(6303.8 mg/L) in the pH=9 was increased by 26.0%. In
addition, adding initial FA (100, 300, 500 N mg/L) into
WAS achieved a fast SCOD release rate, but the maximal
SCOD value at 24 h was similar to the pH=9. Therefore,
keeping endogenous FA accumulation at first 24 h could bea useful way to perform FA-induced abiotic effect andpromote WAS solubilization.
The process of WAS alkaline fermentation at pH=9 was
divide into two phases by stepwise regulation of FA: phase I,
endogenous FA pretreatment for 24 h; phase II, removing
endogenous FA from supernatant in later fermentation. Fig.
1d-1e indicated that the maximal SCFAs production in the
pH=9 + ammonia stripping reached up to 431.4 mg COD/Lat day 7, which was higher than the pH=9 (395.7 mg
COD/L) and the control (193.8 mg COD/L). With respect to
SCFAs composition in fermentation supernatants, acetic
acid (accounting for 50-60% of total SCFAs) all constituted
the most abundant component, followed by propionic acids
and butyric acids. While in the pH=9 + Ammonia stripping,
the final FA concentration fell into a range of 5-20 mg N/Lafter a peak accumulation at 24 h. Furthermore, removing
FA was contributed to SCOD, SP, SC increment, in which
FA-induced inhibition to hydrolysis and acidogenesis was
mitigated (as seen in Fig. 1f-1i).

Fig. 1. The SCOD (a), NH4+-N (b), FA (c) release from
WAS with different initial FA during 24 h; the SCFAs
production (d) and components (e), SCOD (f), soluble
carbohydrate (g), soluble protein (h), FA (i), NH4+-N (m),PO43- (n), carbon source availability (o) from WAS
fermentation at pH=9 by regulating endogenous FA.

4 Conclusions(1) The endogenous FA accumulated in supernatant during
first 24 h, could be an effective strategy to WAS
solubilization, compared with adding initial FA.
(2) Stepwise regulation endogenous FA (phase I: FA
accumulation for WAS pretreatment, phase II: removing FA
from WAS to mitigate FA-induced biotic inhibition)
realized a higher SCFAs production and an enhancement ofcarbon source availability.

Reference[1] Wang, X., Li, Y., Zhang, Y., Pan, Y.R., Li, L., Liu, J.,
Butler, D. Stepwise pH control to promote synergy ofchemical and biological processes for augmenting
short-chain fatty acid production from anaerobic sludge
fermentation. Water Res., 155(2019), 193-203.
[2] Liu, Y., Ngo, H.H., Guo, W., Peng, L., Wang, D., Ni, B.
The roles of free ammonia (FA) in biological wastewater
treatment processes: A review. Environ. Int., 123(2019),
10-19.

43

Gradual deterioration in methanogenic degradation of N,
N-dimethylformamide under the anaerobic condition○ Tianjie WANG1*, Zhe KONG1 & Yu-You LI1
1

Laboratory of Environmental Protection Engineering, Department of Civil and Environmental
Engineering, Graduate School of Engineering, Tohoku University, 6-6-06 Aza-Aoba, Aramaki, Aoba
Ward, Sendai, Miyagi 980-8579, Japan*
E-mail: wang.tianjie.s3@dc.tohoku.ac.jp.

Abstract
The stability of the methanogenic degradation of N, N-dimethylformamide (DMF) was evaluated under aconstant DMF concentration of approximately 2000 mg L-1 using a lab-scale up-flow anaerobic sludge blanket(UASB) inoculated with activated sludge. While the UASB realized a nearly 100% degradation of DMF and ahigh methane production for the first two months, both the removal efficiency and methane production continued
to decrease until the end. The characterization of the prokaryotic community reveals that those DMF-hydrolyzing
bacteria (DHB) originating from the activated sludge were responsible for the effective degradation of DMF.
However, even when fed with a constant concentration of DMF, the DHB kept decreasing all the time whilemethane-producing archaea were rapidly cultivated. The variation of prokaryotic community suggests that the
DHB could not proliferate anaerobically without assimilating the intermediate products from the hydrolysis of
DMF, resulting in an unstable DMF-degrading consortium. The cultivation of DHB under the anaerobic conditionof the UASB was therefore difficult.
Keywords: N, N-dimethylformamide; UASB; methanogenic degradation; anaerobic digestion;
DMF-hydrolyzing
1 Introduction
N, N-dimethylformamide (DMF) [(CH3)2NCHO] is widely
implemented in a wide variety of chemical industries as aversatile organic solvent due to its excellent miscibility inwater and many other organic liquids (Nisha et al., 2015;
Sanjeev Kumar et al., 2013; Yang et al., 2014). However,this nitrogenous compound is also toxic and because it
remains refractory and recalcitrant in aquatic environment,
the excessive discharge of DMF-containing wastewater
results in serious environmental problems, likeeutrophication (Rahmaninezhad et al., 2016; Vidhya andThatheyus, 2013; Xiao et al., 2016).
DMF-hydrolyzing bacteria (DHB) provide a hydrolytic
enzyme (known as DMFase) to hydrolyze DMF into
dimethylamine (DMA) and formic acid (HFc): theseintermediates are then fermented into methane bymethylotrophic and hydrogenotrophic methanogens (Konget al., 2019c). The stoichiometric equation of DMF
methanogenic degradation is described below:of the biogas including N2, CH4 and CO2 were measured
using a gas chromatograph (GC-8A, Shimadzu, Japan), andpH was measured by a HM-30R pH meter (DKK-TOA,
Japan). The DMF concentrations were determined using theWaters ACQUITY UPLC H-class system (Milford, USA).
All ions including ammonium (NH4+), DMA ((CH3)2NH2+),
MMA (CH3NH3+) and HFc (OOH-) were determined bycapillary electrophoresis (7100CE, Agilent Technologies,
USA).

2.2 Experimental apparatus and procedure

In this study, a lab-scale UASB reactor was used toinvestigate the long-term behavior and stability of the DMF
methanogenic degradation under a constant concentration of
approximately 2000 mg L-1. The UASB reactor was
inoculated with the activated sludge as the sole seed
inoculum because it is widely acknowledged that activatedsludge lacks archaea (methanogens) (Kobayashi et al.,2009).

The lab-scale UASB used in this study obtains a maximum
volume of 5 L and was kept at the mesophilic condition ataround 35 °C by a thermostatic water circulation system. An
inoculum of 5 L DMF-degrading activated sludge was
inoculated to the UASB as the seed sludge.
DMF-containing wastewater with a concentration of
approximately 2000 mg L-1 was prepared in a 120 L
substrate tank and was dosed with sufficient trace elements.
The hydraulic retention time (HRT) was kept at a constantof 24 h in order to maintain a relative constant organic
loading rate (OLR) around 3 g COD L-1 d-1. The extractionof genome DNA, the sequencing procedure of PCR and
Illumina, and the processing of sequencing data were allconsistent with those reported in previous studies (Kong etal., 2018c, 2019c). The operational taxonomic units (OTUs)
were generated on the basis of 97% similarity. Singleton
OTUs were removed and sequences were randomly selected
to unify the sequence number of each sample to 50,000 as
the standard number.

2 Materials and methods

3 Results and discussion

2.1 Analytical reagents and method

3.1 Rapid start-up and stable methanogenic

Biogas production and gas temperature were recorded by a
wet tip gas meter (Shinagawa, Japan). The components (%)

degradation of DMF(𝐶𝐻3 )2 𝑁𝐶𝐻𝑂 + 2.5𝐻2 𝑂 →1.75𝐶𝐻4 +0.25𝐶𝑂2 + 𝑁𝐻4+ + 𝐻𝐶𝑂3− ; ∆𝐺 0 = 28.01 𝑘𝐽 𝑚𝑜𝑙 −1

(1)

44

As shown in Fig. 2 (b), it took about a week for the UASB
to realize a rapid start-up: the reactor obtained a high DMF
removal efficiency of 81.97% on the first day, and reached98.17% on Day 7. As can be clearly seen in Fig. 2 (a) and(c): The effluent pH reached a high of 8.45 because of thehigh concentration of the alkaline compound DMA, at
786.23 mg L-1, detected on the first day (Ferguson et al.,2000). Besides, the growing trend of the biogas production
rate from merely zero on the first day to a high of 1.02 L L-1
d-1 on Day 7 can be obviously seen in Fig. 2 (d). The
methane content of the biogas also significantly increased
from a low of 14.62% at the very beginning to a high of
84.71% on Day 7, which can be seen in Fig. 2 (e).
For a two months period from Day 7 until Day 56, therelatively stable and remarkably high DMF removal
efficiency of over 95% (even reached 99.99% on somedays). During this period, the effluent DMF concentration
was basically lower than 10 mg L-1, and the pH remained
stable at around 7.50. The thorough mineralization of DMF
also resulted in a stable effluent ammonium concentrationof around 500 mg L-1, which was in good accordance withthe stoichiometric calculation using Eq. (1). As can be seenin Fig. 2 (d) and (e), the majority of DMF was successfully
recovered as biogas with an average production rate of 1.15
L L-1 d-1, in which the bio-methane accounted for nearly
90% (the highest methane content recorded was 91.58%) of
the components with an average production rate of 1.03 L
L-1 d-1. This was also in good accordance withstoichiometric Eq. (1).

calculated as 9.98% on Day 56 in Fig. 2 (a). This suggeststhat only when the abundance of DHB accounted for morethan 10% of the total prokaryotic community could thesystem realize the thorough methanogenic degradation of
DMF under a constant OLR of around 3 g COD L-1 d-1.

4 Conclusions
The performance of DMF methanogenic degradation andthe corresponding variation in microbial community were
investigated in detail, leading to a better awareness of themechanisms and requirements in the anaerobic treatment of
DMF-containing wastewater. These can be summarized asfollows:
1) While the DMF-degrading methanogenic consortium
realized a quick start-up within a week, the performance of
DMF methanogenic degradation gradually weakened eventhough the system was operated at a constant DMF loadingrate.
2) DMF-hydrolyzing bacteria are difficult to cultivate
under the methanogenic condition because they areincapable of assimilating DMA and HFc even though they
are facultative anaerobes.

3.2 Deterioration of DMF methanogenic
degradation
The operation of the UASB began to deteriorate after Day
70. As shown in Fig. 1 (b), the removal efficiency of DMF
continued to drop: By the end of the experiment, the DMF
removal efficiency had dramatically dropped to a low of
32.79%. Due to the insufficient degradation of DMF, theammonium concentration in Fig. 1 (c) also kept decreasingfrom around 500 mg L-1 to 156.66 mg L-1. As shown in Fig.
1 (d), the biogas production rate dramatically dropped from
1.10 L L-1 d-1 to a low of 0.26 L L-1 d-1. However, themethane content in the biogas still remained high, at over
86%, as can be seen in Fig. 1 (e). In order to reveal thereason for this deterioration, the entire prokaryotic
community structure was investigated in detail.
The variation of the total abundance of these
DMF-degrading candidates (or DHB) illustrated in Fig. 2(a) shows an obvious decreasing trend from 34.27% on thefirst day to just 5.37% on the last day. The continuous
decrease in the total abundance of these DMF-degrading
candidates suggests that most of these DHB were unable toproliferate under the anaerobic condition even though someof them were actually facultative anaerobes (Nisha et al.,2015; Vidhya and Thatheyus, 2013). While these bacteria
seemed to successfully survive under the anaerobic
condition of the UASB and they “temporarily” retained
their DMF-hydrolyzing ability or ability to producehydrolytic enzyme, the fact is that these bacteria kept
decaying all the time and the hydrolytic enzymes produced
from these bacteria were left in the reactor, maintaining the
DMF-hydrolyzing activity for about two months.
Meanwhile, the total abundance of the DMF-degrading
candidates in the entire prokaryotic community wasFig. 1. Operational results of the continuous experimentconducted using a lab-scale UASB.

Fig. 2. Variation of the total relative abundance of the
DMF-hydrolyzing bacteria and methanogens

45

Antibacterial effect of food waste hydrolysate on Escherichia Coli
○ Yuanyuan REN1,3*, Qunhui WANG3 & Yu-You LI1,2
1

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;
2
School of Energy and Environmental Engineer, University of Science and Technology Beijing, 30 Xueyuan
Road, Haidian District, Beijing 100083, China
3

Department 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*
E-mail: ren.yuan.yuan.t7@dc.tohoku.ac.jp.

Abstract
With the development of the economy and social progress, people's pursuit of quality of life is getting higher andhigher. According to the survey, the amount of food waste generated is increasing every year. The treatment offood waste mainly includes landfill, incineration and anaerobic digestion. But it takes time from the generation offood waste to the transport to disposal location. During this period, due to the high organic content of food waste,a large number of harmful bacteria breed. Moreover, because of the corruption of food waste, it will affect theurban environment and people's health. Previous studies have shown that the amount of harmful bacteria in foodwaste can be greatly reduced under anaerobic preservation conditions. This study focused on the inhibition ofEscherichia coli by various hydrolysates generated from hydrolysis of food waste during the anaerobicpreservation. The results showed lactic acid and acetic acid have bacteriostatic effect when they exist in amolecular state rather than in an ionic state. And lactic acid, acetic acid and ethanol have a synergistic
antibacterial effect between every two materials.
Keywords: Food waste; hydrolysate; bacteriostatic preservation; Escherichia Coli.
1 Introduction
Food waste (FW) is one of the most important componentsof municipal solid waste, including household food waste,food-processing waste, canteen and restaurant waste. The
stacking of FW has gradually become a global problem. It isestimated that the amount of FW sharply increased from2.78 billion tons to 4.16 billion tons in Asian countries by2025. Especially in China, the growth rate of FW has
increased more than 10% with the acceleration of industrial
development and urbanization processes. At present, FW
regarded as municipal waste is sent to landfills andincineration plants as final disposal points. In some ways,
these processes release some stress from garbage siege; atthe same time, a series of problems are emerging including
the rising cost of waste disposal, the lack of land space,groundwater pollution by leachate, and the emission of toxic
and greenhouse gases. Moreover, FW with highconcentrations of organic matter (volatile solids/total solids[VS/TS]: 0.8–0.9), high moisture content, and goodbiodegradability have been regarded as the most promisinganaerobic substrates (Ren et al., 2018). Large-scale FW
recycling is a current major issue because the collection of
FW is dispersed. Consequently, the collection and transportof FW before being transported to the anaerobic treatment
plant takes time. During this period, FW easily promotes
bacterial growth because it is rich in water and organic
substrates to affect urban environment. if the FW is notsterilized, the pathogenic bacteria represented byEscherichia coli will multiply to affect the health of people.
The bacteriostatic preservation of FW is a key problem in
FW recycling. Therefore, how to effectively inhibit thegrowth of spoilage bacteria during transportation is theprimary problem to be solved in this study. The traditional

solutions mainly include cryopreservation, high temperaturesterilization, dry dehydration, and addition of preservatives
to alleviate the problem of rapid decay of FW, but thesemethods require relatively high costs. Therefore, finding a
cost-effective way to prevent and cure FW is an urgent
problem to be solved. Previous studies have shown that theanaerobic preservation of FW can effectively inhibit the
proliferation of pathogenic bacteria (Zhao et al., 2016). This
study conducted further bacteriostatic effects analysis onvarious acidic substances in anaerobic preservation samples.

2 Materials and methods
2.1 Substrate Preparation
FW was obtained from a student canteen in the Universityof Science and Technology in Beijing. Large bones andpericarps of FW were first removed. The remaining waste
was shredded and then stored at −20 °C. The substrates
were thawed and kept at 4 °C before use.
The Escherichia Coli stored at -70 °C were taken out of therefrigerator and immediately placed in a 38-40 °C water
bath until the icing was completely dissolved. The
appropriate amount of Escherichia Coli was firstly added tothe sterilized LB liquid medium, and the culture wascompleted by incubating at 150 r/min and 37 °C for 24 h.
The activated Escherichia Coli was then added to a new LB
liquid medium with a certain inoculum for subsequentbacteriostatic experiments.
2.2 Experiment methods
The checkerboard assay was used to assess thebacteriostatic preservation effect. The antibacterial materials
and Escherichia Coli were added 100 ul into every hole in
the 96-well sterile microplate, and then the 96-well plate
was placed in a multi-function microplate reader, and46

incubated at 37 °C for 24 h with shaking. The OD600 was
measured every 1 h, and taken out after 24 h. The minimum
inhibitory concentration of the absorbance decreased bymore than 80% compared with the non-medicated blankgroup was the minimum inhibitory concentration MIC80 of
the drug. The interaction was judged by calculating afractional inhibitory concentration (FIC).
To evaluate the interaction of hydrolysate against
Escherichia Coli, the fractional inhibitory concentrationindex (FICI) was calculated for each combination. The
FICIs were calculated using the following equation:
FICI=FICA+FICB=CAcomb/MICAalone+CBcomb/MICBalone
where MICAalone, MICBalone are the MICs values ofcompound A and B when acting alone, and CAcomb, CBcomb
are the concentrations of compound A and B at theisoeffective combinations. The FICI was interpreted asfollows: a FICI ≤ 0.5 demonstrated synergy; a FICI
between 0.5 and 4 indicated no interaction; and a FICI ＞4
showed antagonism.

It is indicated that lactic acid and acetic acid havebacteriostatic effect when they exist in a molecular state,while they have no bacteriostatic effect when they exist in
an ionic state. From the synergistic inhibition experiments,
it can be concluded that the three substances have asynergistic effect between every two materials.

3 Results and discussion
3.1 Materials in the hydrolysate
Fig. 2. Time–kill assay of lactic acid, acetic acid andethanol.

Fig. 1. The concentration of carboxylic acid, lactic acidand ethanol in hydrolysate.
Fig. 1 showed the concentration of carboxylic acid, lactic
acid and ethanol in hydrolysate. The majority of material
was lactic acid, meanwhile the concentration of acetic acidand ethanol can also reach about 5 g/L. After anaerobicpreservation for 4 days, the concentrations of organic acids
and lactic acid in the two groups were 38.77 g/L and 28.88
g/L, respectively. However, the lactic acid concentration in
the anaerobic preservation group can reach the maximum
value on the fifth day. A large amount of lactic acid can begenerated quickly, so that the antibacterial effect can bebetter.
3.2 Antibacterial effect of different materials
The Time–kill assay of lactic acid, acetic acid and ethanol
is shown in Fig. 2. With the increasing concentration ofbacteriostatic substances, the measured OD600 value isgetting smaller and smaller, and the bacteriostatic effect ismore and more obvious. It is considered to haveantibacterial effect when the OD600 is more than 80%
lower than the growth curve after 24h culture. Aftercalculation, the minimum inhibitory concentrations of lactic
acid, acetic acid and ethanol are 2.5g/L, 1.25g/L and 35g/ L,
respectively. While sodium lactate and sodium acetate did
not inhibit the growth of E. coli even at 40 g/L and 20 g/L.

Table 1 the MIC80 and interpretation relationship ofdifferent hydrolysate.
Hydrolysate
FIC Interpretation
MIC80（g/L）
Lactic acid
2.5
Acetic acid
1.25
Ethanol
35
Sodium lactate
SodiumacetateLactic acid +0.625/0.625
0.17synergistic
Acetic acid
Lactic acid +1.25/1.25
0.03
synergistic
Ethanol
Acetic acid +0.625/7.5
0.22
synergistic
Ethanol

4 Conclusions(1) After anaerobic preservation for 4 days, theconcentrations of organic acids and lactic acid in the twogroups achieved 38.77 g/L and 28.88 g/L, respectively.
(2) Lactic acid and acetic acid have obvious bacteriostatic
effect when they exist in a molecular state rather than in anionic state.
(3) Lactic acid, acetic acid and ethanol have a synergistic
antibacterial effect between every two materials.

Reference[1] Ren, Y., Yu, M., Wu, C., Wang, Q., Gao, M., Huang, Q.,
Liu, Y., 2018. A comprehensive review on food waste
anaerobic digestion: Research updates and tendencies.
Bioresource Technology, 247, 1069-1076.
[2] Zhao, N., Yu, M., Wang, Q., Song, N., Che, S., Wu, C.,
Sun, X., 2016. Effect of Ethanol and Lactic Acid
Pre-fermentation on Putrefactive Bacteria Suppression,
Hydrolysis, and Methanogenesis of Food Waste. Energy &Fuels, 30, 2982-2989.

47

Anammox-dependent nitrogen removal process using up-flow fixed-bed
bioreactor○ Ying SONG1*, Yu-You LI2
1

Department of Environmental Engineering, Environmental University, Miyagi 980-8579, Japan.

2Department of Frontier Science for Advanced Environment, Tohoku University, Miyagi 980-8579, Japan.

*E-mail: song.ying.q2@dc.tohoku.ac.jp

Abstract
The treatment of wastewater containing high-strength nitrogen, represented as the wastewater of fermentation,
has been obtaining ever-increasing attention due to far-ranging applications of biogas process. Anammox-based
technologies as a cost-effective and high-capacity process have been considered a promising alternative to treat highammonium concentration wastewater. In this thesis, the fixed-bed biofilm reactor was applied and successfully
started up after inoculation and stably operated with growing nitrogen loading rate (0.74g-N/L/d~2.56±0.03gN/L/d) and nitrogen removal rate(0.39±0.34g-N/L/d~1.99±0.04g-N/L/d) by increasing the substrate concentration
at a constant hydraulic retention time(HRT) of 8h. The total nitrogen(TN) removal efficiency was in the range of
76.12±4.82%~87.98±3.72%. Along with the increasing nitrogen loading rate, the biomass attached to biofilms aswell as the suspended biomass was largely improved because of the interception impact provided by the biofilms.
Spatial distribution of fixed-bed UASB Anammox reactor for nitrogen removal capacity was also
investigated .Nitrogen removal efficiency of bottom was around 90% of total nitrogen removal efficiency, whichindicated that the bottom bacteria possesses outstanding activity.
Keywords: Anammox, Nitrogen removal, fixed-bed biofilm, spatial distribution
1 Introduction

Nitrogen gas+

Effluentgas meter

water seal

Water bath

Anaerobic ammonium oxidation (Anammox) has been
considered as a high-efficient, cost-effective andenvironmental-friendly alternative for nitrogen removal toconventional
nitrogenremovalprocess
asnitrification/denitrification process. The mechanism of
anammox process is producing molecular nitrogen gas withammonium as the electron donor (energy source) and nitrite
as the electron acceptor according to the following reaction:-

NH4 +1.146NO2 +0.07HCO3-+0.057H+

Remaining issues of this process are long start-up period
due to generation time of ca.15 days, mainstream applicationand sensitivity to operational conditions. Also, theproduction of the nitrogen gas poses a side effect on sludge
floating. What’s more, the accumulation of extracellularpolymeric substance (EPS) tends to give rise to sludge
floatation when the Anammox bacteria is inhibited. Fixationand entrapment of anammox biomass are key to efficient
start-up. Hence, in order to retain and enrich the functional
bacterial in anammox reactors, fixed-bed biofilm is designed
to culture Anammox bacteria with the purpose of obtaining
and maintaining high nitrogen removal rates.

2 Materials and methods
A cylindrical UASB reactor are set up for Anammox
cultivation. The volume filling rate and working volume of
the reactor are 5.29% and 3.06L. Inoculum was originatedfrom granule Anammox bacteria which is gently smashed toparticles for the biofilm culturing. Temperatures was set
around 35 ˚C which is optimal for Anammox bacteria.

Reflux→0.986N2+ 0.161NO3+0.071CH1.74O0.31N0.20+2.002H2O

pump

pump
temperature control
substrate tank

Influent

Fig.1 Diagram of experimental set-up

Influent and effluent samples were filtered by 0.45μm
membrane filter and stored in a refrigerator at 4 °C before
analysis. Nitrogen components of NH4+-N, NO2--N and NO3-N were analyzed by a capillary electrophoresis (CE) system(Agilent 7100).

3 Results and discussion
3.1 Nitrogen removal performance
As shown in Fig.2, Anammox reactor was started up at
nitrogen concentration of 250mg-N/L with NLR of 0.7gN/L/d and nitrogen removal efficiency was around 76% since
beginning. Then nitrogen concentration of influent was
gradiently increased to 660mg-N/L with nitrogenconcentration of effluent lowering than 100mg-N/L/d stable
nitrogen removal efficiency of 76-85%. However, the ∆NO₂⁻-N/∆ NH₄⁺-N of effluent was increased from 1.25 to48

1.82 and then decreased to 1.2 while the ∆ NO₃⁻-N/∆ NH₄⁺N was stayed relatively stable during this capacity
enhancement period. This phenomenon might be caused by
the loss of Anammox bacteria because the granule Anammox
bacteria was smashed to little ones before inoculation,
releasing extra ammonium. From 52nd, the nitrogenconcentration of 1000mg-N/L resulted in the slightdeterioration of the performance. Therefore, the nitrogenconcentration of influent was reduced to 750mg-N/L in caseof further inhibition of reactor. After stable operation of 22days, the NLR was increased to 2.5g-N/L/d by increasing
nitrogen concentration of influent to 830mg-N/L and thenitrogen concentration of effluent was at 200mg-N/L/dowing to FA concentration of 20-45mg-N/L or inadequate
growth of Anammox bacteria.

channel of the bed, leading to a gradual decline inperformance, which ultimately would lead to system collapse.
Therefore, the fixed-bed was arranged in the longitudinal
direction in the reactor, and each fixed biofilm was verticallycut 5 circular hole, which could facilitate N2 elimination andrelieves channeling. In the phase IV, clogging and stuck EPS
were witnessed along with the increased NLR, resulting
higher nitrite and ammonium concentration of effluent.

Nitrogen removal
rate(mg-N/d/L)

8.00

BottomMiddleTop 6.5

6.00
3.6

4.00

2.9

2.00
0.08 0.14

0.170.28

NH₄⁺-NNO₂⁻-N

TN

90.3 92.3 91.4

NH₄⁺-N

0.08 0.15

0.00

nitrgen removal
effieciency(%)

120
90NO₂⁻-NTN

60
30
5.2 4.2 4.6

4.5 3.5 4.0

MiddleTop

0
Bottom

Fig.3 Spatial distribution of fixed-bed
UASB Anammox reactor

Fig.2 Performance of Anammox process

3.2 Spatial distribution
reactor for nitrogen removal capacity was also investigated(shown in Fig.3). The reactor was divided into upper, middle
and lower parts, and the effective volume from bottom to top
are 0.865L,1,73L and 0.865L, respectively. The nitrogen
removal efficiency of bottom was around 90% of total
nitrogen removal efficiency, which indicated that the bottomof the reactor was the most functional area. As Fig.3 a shows,the NRR of the bottom reached up to 6.5g-N/L/d comparedto the average value of 1.9g-N/L/d while NRR of the middle
and the top was only 0.17 g-N/L/d and 0.28 g-N/L/d. The
specific activity (from aspect of Heme C color) and qualityof the Anammox bacteria down the reactor were much higher
than that of the middle and the top from visual inspection.
As reported, hydrodynamic problems like clogging andturbulence occur in fixed-bed biofilm reactors, mainly due

to the production of EPS and their accumulation in the

4 Conclusions
Anammox process using two-stage fixed-bed biofilm
configuration has exhibited a commendable performance
during the start-up period. The Anammox reactor was able tostably operated with around 70% total nitrogen removal
efficiency under the slight pressure of FA and FNA. The
acclimated tolerance of FNA and FA of Anammox reactor
assure the resistance of trifling performance of partial
nitritation. Nitrogen removal capacity was most attributed tothe bottom of the reactor where biofilm culturing andsuspended granule were much more enriched.

Reference[1]Cláudio Antonio Andrade LimaI; Rogers RibeiroII;
Eugenio ForestiII; Marcelo ZaiatII,2005. Morphologicalstudy of biomass during the start-up period of a fixed-bed
anaerobic reactor treating domestic sewage. ENGINEERINGAND TECHNOLOGY.48, no5.

49

Anaerobic digestion of milk waste and sewage sludge by using hollow
fiber anaerobic membrane reactor○ Ziang HE1*, Xueqin Lu2 ,Chenglei XIE3&Yu-You Li3
1

Department 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
School of Ecological and Environmental Science, East China Normal University, PR China
3Department of Civil and Environmental Engineering, Tohoku University, 6-6-06 Aramaki Aza Aoba, Aobaku, Sendai, Miyagi, 9808579, Japan*
E-mail: he.zi.ang.p8@dc.tohoku.ac.jp
Abstract
In this study, the performance of a mesophilic (37℃) HF-AnMBR (hollow fiber type anaerobic membranebioreactor) in treating sewage sludge with milk waste for 132 days is studied and its effectiveness is evaluated.
The milk waste used in this study was made of crusher liquid, yogurt and concentrated permeate. The organic
loading rates (OLR) of HF-AnMBR was gradually increased from 0.97g-COD/L/d to 8.82 g-COD/L/d byshortening the hydraulic retention time (HRT) from 30d to 3.5d. Respectively，the COD conversion efficiency
was as high as 96.04%, 97.35%, 98.98% and 96.79% at OLRs of 0.97, 2.94, 6.35 and 8.82 g-COD/L/d.
Meanwhile, membrane fouling is the serious problem for MBR, which affects the performance of reactor, so therelationship between the reversible fouling resistance and irreversible fouling resistance was evaluated by thiskind of substrate under flux of 11 L/m2/h. The long-term stable performance of AnMBR indicates that sewage
sludge with milk waste were suitable to decompose under condition of mesophilic.

Abstract: Milk waste, sewage sludge, co-digestion, AnMBR
1 Introduction
Nowadays, wastewater treatment plants have to face criticalissue with strict regulation of water discharge quality. The
food industry generates a plenty of milk product waste instock breeding. According to the key facts on food loss andfood waste by the Food and Agriculture Organization of theUnited Nations (FAO), 1/3 of food is lost or wasted during
production process and human consumption globally [1]. The
increasing production of food waste like milk product waste
worldwide and new international regulations call for thedevelopment of new technologies to treat this biowaste. Due
to high-organic content (81.9%) of milk waste, many
researchers have investigated the use of the anaerobic
digestion in treatment of milk waste, which are able to treat
efficiently organic wastes, producing at the same timedifferent value-added compounds, such as methane.
However, in anaerobic digestion system, HRT and sludge
retention time (SRT) is the same parameter compare toconventional activated sludge process (CAS). Meanwhile,
due to the low growing rate of archeae. The anaerobic
digestion system needs a large reactor volume to maintaincapability of treatment [2]. In recent years, anaerobic
membrane bioreactor (AnMBR) was invented, which canseparate HRT and SRT by retaining solids within reactor anddischarging permeate through membrane filtration. This
combination of MBR with anaerobic digestion brings a highbiomass of archeae, which contribute to high organic loading
rate (OLR). Meanwhile, applying co-digestion could be oneof methods to improve the biogas yield and stability of theanaerobic digester by providing some ways i.e. adjustment ofmoisture content; supplying trace metal elements;
maintaining an optimal pH, enhance capacity of buffer.
The objectives of this study were to assess the performanceof an AnMBR treating milk waste with sewage sludge in the

co-digestion process as well as the effect of different HRT on
membrane fouling.

2 Materials and methods
2.1 Materials
The co-substrate used in this experiment which was madeof concentrated triple sewage sludge, crusher liquid, yogurt
and concentrated permeate. All the substrates and inoculum
sludge were provided by Tokyo Gas Co Ltd.
Table 1 Characteristics of inoculum sludge
Inoculum sludge
Parameters （mg/L）
pH
7.28-7.50
24.6±0.27
Total COD （g/L）
Total Carbohydrates
1862±99
Total Protein
13617±3426.75
TS（g/L）
21.92
VS（g/L）
22.62
TSS（g/L）
19.21
VSS（g/L）
NH4+-N1316.38

Fig. 1. The schematic diagram of HF-AnMBR.

50

Table 2 The operation conditions and removal rate in reactor
Temperature
Operation time（d）
OLR (g COD L/d)
HRT (d)ITEM
T-COD (g/L)
S-COD (g/L)
T-Protein (g/L)
S-Protein (g/L)
T-Carbohydrates (g/L)
S-Carbohydrates (g/L)
T-P (mg/L)
VS (g/L)
VSS (g/L)
NH4-N (mg/L)

Substrate
29.15
15.77
3.6
0.4
3.6
1.45
385.84
17.54
10.82
124.98

1-13
0.97
30
Permeate
1.15
0.96
0.07
0.04
0.07
0.06
177.060.97
0.54
770.74

37℃
13-31
31-52
52-71
2.94
6.35
8.82
10
5
3.5
Removal rate Substrate Permeate Removal rate Substrate Permeate Removal rate Substrate Permeate Removal rate
96.04%
29.13
0.7
97.35%
31.77
0.31
98.98%
30.86
1.01
96.79%
93.90%
17.85
0.66
97.49%
17.16
0.27
99.12%
16.52
0.98
96.80%
97.96%
5.38
0.3
93.42%
5.41
0.05
99.14%
5.63
0.08
98.52%
90.10%
0.88
0.18
95.89%
0.81
0.04
99.19%
0.58
0.07
98.70%
97.96%
8.12
0.05
99.30%
6.18
0.05
99.13%
7.01
0.03
99.86%
98.17%
6.26
0.04
99.43%
4.51
0.03
99.41%
5.61
0.01
99.73%
54.04%
402.58191.1652.60%
467.73289.3735.20%
407.84100.1475.35%
94.79%
18.65
0.8
95.93%
19.91
0.68
96.39%
20.33
1.38
93.23%
95.09%
8.42
0.28
98.09%
10.35
0.32
96.90%
10.76
0.22
98%
-516.69%251.08610.16-143.01%
200.86222.55-0.84%
200.86222.55-10.79%2.2 Analysis methods
Daily biogas production is measured by wet gas meter, thecomposition of (CH4, CO2 and N2) were measured by a
ShimadzuGC-8A gas chromatograph, and COD,
carbohydrate, protein, ammonium nitrogen, alkalinity, TS,
TSS, VS and VSS were measured according to Japan
Standard Testing Method. Sludge samples were taken fromthe digesters and the substrate tank three times a week. The
total effective volume was 15L.The membrane unit was madeof polytetrafluoroethylene, with a mean pore size of 0.2 μm
and an effective filtration area of 0.1m2

3 Results and discussion

rate of around 0.54 L/L/d, after 11 day, the composition of
methane rose to 62.3%. However, the substrate pump has aproblem about timer setting, so the content of methane showsa little lower. The value of COD removal efficiency wasaround 96% with the OLR was about 0.97 g COD L/d.
After 15 day, the OLR was increased to 2.94 g COD L/d byshorten HRT from 30 day to 10 day. The biogas production
rate achieved a value of 1.15 L/L/d, 112.9% higher than theinitial value, due to the high treatment capacity of theanaerobic biomass.
From the 76th day, reactor became possible to operatecontinuously under HRT of 5 days. From the 45th day to the90th day, gas production rate was 0.39 ± 0.05 L/L/d as HRT
was reduced stepwise and reactor volumetric load increased
from 1,3 to 8.82 kg-COD/m3/d 1.15 ± 0.16 L/L/d, increased
from 2.20 ± 0.41 L/L/d to 2.7 ± 0.22 L/L/d. The methane
content was maintained at 55-58%. When the COD
volumetric load was increased to 8.82 kg-COD/m3/d, themethane content 53.23% tended to decrease gradually.

4 Conclusions
In this study, we conducted continuous experiments onmembrane-type methane fermentation for milk waste andsewage sludge and examined important parameters for plantdesign. The main results are summarized as follows.
Based on the generation rate of waste liquid in the factory,
the HRT was changed step by step to improve the OLR. As aresult, HRT was over 5 days, OLR was 6.35 g COD L/d, thereactor maintains good operation performance. When the
OLR was increased to 9 kg-COD / m3 / d, there was storageof organic acids. From these results, it is considered that thelimit of the AnMBR for maintaining the absence of organicacid accumulation is about 6.35 g COD L/d.

Reference

Fig. 2. Reactor performance.
The treatment performance of AnMBR is shown in Fig.2.
The start-up period (HRT=30d) began with a gas production[1]H. Cheng, Y. Hiro, T. Hojo, Y.Y. Li, Upgrading methane
fermentation of food waste by using a hollow fiber typeanaerobic membrane bioreactor, Bioresour Technol,
267 (2018), pp. 386-394.
[2]W. Qiao, K. Takayanagi, M. Shofie, Q. Niu, H.Q. Yu, Y.
-Y. Li Thermophilic anaerobic digestion of coffee groundswith and without waste activated sludge as co-substrate using
a submerged AnMBR: system amendments and membrane
performance.Bioresour Technol, 150 (2013), pp. 249-258

51

Effect of thermal pretreatment on digestion of sewage sludge in ananaerobic membrane bioreactor○ Guangze GUO1*, Yemei LI2 & Yu-You LI 1,2
1

Graduate School of Environmental Studies, Tohoku University, Miyagi 980-8579, Japan.

2Department of Civil and Environmental Engineering, Tohoku University, Miyagi 980-8579, Japan.
*

E-mail: guo.guangze.t1@dc.tohoku.ac.jp

Abstract
The treatment of sewage sludge has become an important environmental issue all over the world. Anaerobic
digestion is a simple method that converts chemical energy from sewage sludge into biogas like methane as a
substitute for fossil fuels in contemporary society while eliminating pathogens and eliminating odors. According
to the previous study, hydrolysis becomes a rate-limiting step in the anaerobic digestion process due to thecomplex floc structure of the sewage sludge (eg, extracellular polymer) and hard cell walls. In order to accelerate
the hydrolysis process and increase the efficiency of anaerobic digestion, a thermal pretreatment method is applied
to carry out the solubilization reaction to decompose the organic matter in the sludge. This study explored theeffects of thermal treatment on sludge properties, methane production efficiency, and membrane reactor
performance. Current results showed that thermal pretreatment had a more obvious influence on the property ofexcess sludge than on that of primary sludge.
Keywords: Thermal pretreatment; Sewage sludge digestion; AnMBR.
1 Introduction
The generation of renewable energy in the field of sewage
treatment has been allocated great attention. In 2016, theeffective utilization rate of sewage sludge reached morethan 70%, but most of the sewage sludge was recycled andused as building materials. In 2017, only 22% of sewage
sludge was used as biogas and solid fuel. 68% of sewage
sludge is not used as energy. For biogas power generation,
about 20% of the sewage treatment plants in the country
that use anaerobic digestion will generate biogas for power
generation. A sewage treatment plant that does not use
biogas to generate electricity mainly utilizes biogas as an
auxiliary fuel for digester heating and sludge incineration.
Therefore, the effective use of renewable energy in the fieldof sewage sludge treatment is not good enough.
In the anaerobic digestion of sewage sludge, the main
component of the substrate is solid organic matter. The
hydrolysis step is a previous step and is also a critical stepfor determining the rate in anaerobic digestion. Thermal
pretreatment causes the lysis of cells, thus making organics
and nutrients readily available for microbial growth andmetabolic activities. The hydrolysis efficiency and biogas
production by organic dissolution are improved through thisprocess. This system of thermal pretreatment combined withanaerobic digestion is called TP / AD (thermal pretreatment
anaerobic digestion) system. Since heat treatment of sewage
sludge is a simple method and a high pollution dissolution
effect can be obtained, thermal treatment temperature
conditions which have been extensively studied in recent
years are 50℃ to 90℃. However, since the heat treatment
of sewage sludge requires a large amount of energy, the TP/ AD system is not widely implied. Waste heat from thepower plant is able to be used for sludge thermal treatment.
Thus an efficient TP / AD system can be constructed byusing waste heat as a thermal treatment energy source forthe sewage sludge.

The anaerobic membrane bioreactor is a novel biologicaltreatment technology coupled with anaerobic fermentationand membrane processes. The process has the traditional
advantages of anaerobic biological treatment, and thefiltration water retention effect of the membranesignificantly improves the effluent water quality, while
maintaining a high concentration of microorganisms in thereactor, further improving the decomposition andconversion efficiency of organic matter, and effectively
making up for the tradition. Insufficient anaerobic
biological treatment process. In addition, the process cancontrol the hydraulic retention time (HRT) and sludge
residence time (SRT), and extend the SRT to reduce thevolume of the anaerobic fermentation tank.
In this research, the changes of the properties of primarysludge and excess sludge by thermal treatment at four
different temperatures were studied. The volatile solids(VSS), total solids (SS) and dissolved chemical oxygen
demand (SCOD) in the liquid phase were investigated. The
effect of thermal treatment temperature and time on theproperties of the sludge is determined by the increase of
dissolved organic matter and the change of sludge
characteristics.

2 Materials and methods
The sewage sludge used for the experiment was withdrawn
from a municipal wastewater treatment plant in Sendai,
Japan. The sludges were stored at 4℃ in order to maintainfreshness.
The method of primary sludge and excess sludge thermal
pretreatment is to transfer sewage sludge to glass bottles andheat them in a water bath. Sludge was put into eight 120 ml
glass bottles using a 100 ml syringe, and each of bottle wasfilled with 80ml sludge. After that, placing glass bottles in a
50°C water bath and continuously vibrated in order toevenly heat. It was necessary to discharge the gas in thebottle frequently during the heating process. Eight sludge

52

50℃

(a)

60℃

70℃

7.5

80℃

4.0

70℃

6.0

3.0

5.5

50℃

(b)

60℃

70℃

(e)

50℃

60℃

70℃

80℃

(f)

50℃

60℃

70℃

80℃

6.5

VSS (%)

4.0

3.5

6.0

3.0

5.5

2.5

5.0

15

50℃

(c)

60℃

70℃

15

80℃

6000

3000
300

9

6

3

0
30

60

90

120

Time (min)

150

180

60℃

70℃

200
150
100
50

0

30

60

90

120

Time (min)

150

180

50℃

60℃

70℃

80℃

(d)

50℃

60℃

70℃

80℃

9000

6000

3000
40

80℃

250

(c)

12000

30

20

10

0

0

30

60

90

120

150

180

Time (min)SCOD is often used to evaluate the effect of pretreatment.
Fig. 2 showed the trends of SCOD concentration changed
with time and pretreatment temperature during thepretreatment. Similar to that shown in Figure 1, the effect ofpretreatment on SCOD in the primary sludge is far lessaffected by the SCOD in the excess sludge. The change in
SCOD levels of primary sludge was not obvious and the
SCOD increasing rates were below 35%. In contrast, after
the heat treatment of the remaining sludge, the SCOD
growth rate can reach nearly 250%.

(1) Thermal pretreatment has a little influence on theproperty of primary sludge while excess sludge change a lot
after pretreatment undert 50-80℃.
(2) In general, the 70℃ thermal treatment is more effective
than other temperatures.

9

6

3

Reference

0
0

50℃

(b)

15000

4 Conclusions

12

SS reduction rate (%)

12

80℃

Fig. 2. Changes of (a) SCOD, and (b) SCOD
increasing rate of excess sludge and (c) SCOD and(d) SCOD increasing rate of primary sludge under
different period of pretreatment.

7.0

80℃

70℃

9000

80℃

6.5

3.5

4.5

VSS (%)

60℃

7.0

SS (%)

SS (%)

4.5

SS reduction rate (%)

50℃

(d)

60℃

12000

0
5.0

50℃

(a)

S-COD concentration (mg/L)

SCOD concentration (mg/L)

15000

SCOD increasing rate (%)

3 Results and discussion

temperature showed a slight influence on the SS and VSSlevels of primary sludge, while they changed a lot when thetemperature changed in excess sludge pretreatment. Whenthe temperature bellow 70℃, the SS and VSS level
increased with the increase of temperature. However, when
the temperature increased 80℃, the results nearly nochanged. That means that 70℃ is a boundary value forexcess sludge thermal pretreatment.

SCOD increasing rate (%)

samples were heated for 10 minutes, 20 minutes, 30 minutes,
45 minutes, 60 minutes, 90 minutes, 120 minutes and 180
minutes respectively. The treated samples were stored in 4℃
refrigerator. Next, keeping the temperature of water bath at
60°C and 8 bottles of 80 ml of the sludge samples weretaken again and placed in 60°C water bath for heating thesame time as 50°C. In the end, temperature of water was
raised to 70°C and 80°C. Primary sludge and excess sludge
were treated under 70°C and 80°C conditions.
In order to examine the change in sludge properties afterthermal treatment, the SS, VSS, and pH of the sludge were
measured by sewage test standard method. The
measurement of the dissolved substance requires
centrifugation of the sludge at 15,000 rpm for 15 minutes
using a high-speed centrifuge. The obtained supernatant isfiltered through a 0.45 μm membrane. The dissolved COD
and ammonia nitrogen concentrations of the sludge were
determined by the Standard Methods, and the carbohydratecontent was determined by the phenol-sulfuric acid method.
The protein concentration was determined by the Lowry
method.

0

30

60

90

120

150

180

Time (min)Fig. 1. Changes of (a) SS, (b) VSS and (c) SS
reduction rate of excess sludge and (d) SS, (e) VSS
and (f) SS reduction rate of primary sludge under
different period of pretreatment.

[1] Zhen, Guangyin, et al. "Overview of pretreatment
strategies for enhancing sewage sludge disintegration andsubsequent anaerobic digestion: Current advances, full-scale
application and future perspectives." Renewable andSustainable Energy Reviews 69 (2017): 559-577.

Fig.1 showed the changes of SS, VSS of excess sludge andprimary sludge during the three-hour-thermal pretreatment.
As the pretreatment time was extended, the concentrationsof SS and VSS in the sludge gradually reached equilibrium.
Especially for primary sludge, the equilibrium value occurs
essentially within thirty minutes of the pretreatment. The

53

The 7th International Symposium on Water Environment Systems----- with Perspective of Global Safety(Nov. 14th - Nov. 16th, 2019)

GP-RSS
Tohoku University

Impact of HRT on the performance of AnMBR treating municipal sewage○ Runda Du1*, Jiayuan Ji1, Yujie Chen1, Yu-You Li1
1

Department of Civil Engineering and Architecture, Tohoku University, 6-6-06 Aoba, Aramaki, Aoba-ku,
Miyagi 980-8579, Japan.
*E-mail: du.runda.t3@dc.tohoku.ac.jp.

Abstract
The performance of the municipal sewage treatment process anaerobic membrane bioreactor (AnMBR) was
studied through a small reactor with a working capacity of 20 L. In this experiment, the HRT (hydraulic holdingtime) was changed from 12 hours to 4 hours at 25℃ to explain the effect of HRT. Process performance was
evaluated by organic matter removal efficiency, biogas production, sludge production yield, membrane filtration
flux and TMP.
Keywords: AnMBR; municipal sewage; HRT.

1 Introduction
The activated sludge process has evolved into a mature method for wastewater treatment for over 100 years. However, there
are still some issues such as high energy costs for aeration and mass production of waste sludge to be solved. At the same time,anaerobic membrane bioreactor (AnMBR) has been attached to great attention because its high COD removal rate, low energy
demand, low waste sludge generation and even energy recovery (Rong Chen et al., 2017). Although this technology has beensuccessfully applied in the field of industrial wastewater treatment, it has not yet been applied to the treatment of municipal
wastewater containing many complex compositions, the contaminants in which have instabilities and low concentrations(Zhen Lei et al., 2018). In this study, a small-scale AnMBR was used for actual municipal sewage to achieve stable operationand investigate the effects of HRT.

2 Materials and methods
A 20L small-scale AnMBR was installed at a Wastewater Treatment Plant (WWTP), so the original sewage could be
immediately pumped into the reactor. The membrane was placed in AnMBR filled with anaerobic digested sludge as seedsludge. After the acclimation of microorganisms (about 50 days after beginning), the long-term operation of AnMBR suppliedby actual sewage began. The details of operating conditions are shown in Tables 1 and 2.
Table 1 Experiment conditions for AnMBR.
Membrane type
Temperature
Hollow fiber
25.2 ± 0.5 ℃
Membrane pore size
Sewage CODCr
0.4μm
400 ± 200 mg/L
Membrane material
Sewage BOD
PVDF
150 ± 50 mg/L
Reaction volume
20 L
Sewage SS
125 ± 25 mg/L
Table 2 Detail experiment conditions at different HRT.
HRT (h)
24
12
12
6
8
4
Day
Day
Day
Day
Day
Day
Operated period
6-48
49-134 135-145 147-245 246-299
300FLUX(m/d)
0.137
0.274
0.274
0.232
0.174
0.348
Membrane area (m2)
0.146
0.345
Biogas circulation (m3/h)
69.76
118.59116.21
174.31

3 Results and discussion(1) Removal of organic matters & sludge production yield
Figure 1 shows the average removal efficiency of CODCr, BOD and SS under different HRT conditions. The results of
CODCr removal efficiency shows that the values of HRT 8h and 4h were lower, while it is about 90% at 12h and 6h. In termsof BOD removal, the average BOD removal efficiency of HRT 4h is only 87.3%, which is lower than 91% at other HRT
conditions. Since the pore diameter of the membrane is 0.4μm, the removal efficiency of SS reaches 100%.

Fig. 1. Average removal efficiency of CODCr, BOD and SS at different HRT

1

54

The 7th International Symposium on Water Environment Systems----- with Perspective of Global Safety(Nov. 14th - Nov. 16th, 2019)

GP-RSS
Tohoku University

Figure 2 shows the sludge production yield under various HRT conditions. Obviously, as HRT is shortened, sludge
production yield has increased. Even the highest sludge yield (0.143 gVSS/gCODCr) shown in HRT 4h is much lower than
the theoretical value of the activated sludge process of 0.33 gVSS/gCODCr.
Therefore, it can be seen that the removal of organic matters and SS performs well between HRT 12h and 4h, and sludge
production is also low.

Fig. 2. Average sludge production yield at different HRT(2) Biogas production
In long-term experiment, the highest biogas production was 0.119 L-gas/L-waterduring at HRT 8h. Average biogas
production (Figure 3) also reached 0.103 L-gas/L-water (HRT 12h) and 0.099 L-gas/L-water (HRT 6h). HRT decreased
significantly after shortening to 4h. However, in different HRT, the methane gas content was always about 80% of the gas
composition. The gas composition was also composed of about 14% and 6% nitrogen and carbon dioxide, respectively.

Fig. 3. Average biogas production and gas composition at different HRT(3) Membrane performance
The TMP and flux datas shown in Figure 4 shows the membrane properties during long-term operation. For flux < 0.27 m/d(HRT6-12h), TMP stabilized at very low values. Since flux > 0.35, TMP rapidly increased due to sludge generation, although
there is crossflow by the biogas cycle which is typically used as an effective method to mitigate membrane fouling. This
indicates that the concentration of the mixed solution must be tightly controlled to maintain membrane filtration capacity
under short HRT conditions (4h).

Fig. 4. TMP-FLUX during long-term operation

4 Conclusion
The results so far show that AnMBR can be used for municipal sewage treatment in the HRT range of 6 to 12 hours withgood organic removal rates, sludge generation and biogas generation. And the use of HRT for 4 hours or less is notrecommended for sewage treatment by AnMBR.

Reference[1] Rong Chen, Yulun Nie, Jiayuan Ji, Tetsuya Utashiro, Qian Li, Daisuke Komori and Yu-You Li, (2017), Submerged
anaerobic membrane bioreactor (SAnMBR) performance on sewage treatment: removal efficiencies, biogas production andmembrane fouling, Wat. Sci. and Technol., 76(6): 1308-1317.
[2] Zhen Lei, Shuming Yang, Yu-you Li, Wen Wen, Xiaochang C. Wang and Rong Chen, (2018), Application of anaerobic
membrane bioreactors to municipal wastewater treatment at ambient temperature: A review of achievements, challenges, andperspectives. Bioresour. Technol., 267: 756-768.

255