LIME2_C2.7-C2.9_2014

LIME2
Life-cycle Impact assessment Method
based on Endpoint modeling

Chapter 2 (2.7-2.9)
Characterization and Damage
Evaluation Methods
2.7 Ecotoxicity
2.8 Eutrophication
2.9 Indoor air pollution
written and edited by
Dr. Norihiro ITSUBO(Professor, Faculty of Environmental and Information Studies,Tokyo City University)
Dr. Atsushi INABA(Professor, Faculty of Engineering, Kogakuin University)

LIME2_C2.7-C2.9_2014

Chapter 2 (2.7-2.9)

Characterization and Damage Evaluation Methods
Contents
2.7

Ecotoxicity...................................................................................................................... 1
2.7.1 What phenomenon is ecotoxicity? ............................................................................ 1(1)

Causal relationships of ecotoxicity ........................................................................... 1(2)

Endpoints of ecotoxicity ........................................................................................... 2

2.7.2 Characterization of ecotoxicity ................................................................................. 2(1)

Existing characterization factors of ecotoxicity ....................................................... 2(2)

Characterization factors of ecotoxicity under LIME ................................................ 3

2.7.3 Assessment of damage from ecotoxicity .................................................................. 6(1)

Basic policy for calculation of damage factors ......................................................... 6(2)

Damage indices of ecotoxicity.................................................................................. 6(3)

Classification of assessment-covered species and the setting of the number of .......
assessment-covered species (NG) ........................................................................... 10(4)

Calculation for fate analysis of a toxic chemical (FFi, ecomp, fcomp) ................... 11(5)

Calculation of the effect factor of a toxic chemical (EFi, fcomp, G) ....................... 12(6)

Arrangement of the damage function and damage factor of ecotoxicity ................. 29(7)

Characteristics of the damage factor of ecotoxicity ................................................. 29(8)Comparison of damage factors between LIME 1 and LIME 2 ................................ 36

2.7.4 Procedure for impact assessment of ecotoxicity ..................................................... 36
2.8

Eutrophication .............................................................................................................. 39
2.8.1 What phenomenon is eutrophication? .................................................................... 39(1)

Causal relationships of eutrophication ..................................................................... 40(2)

Endpoints of eutrophication ..................................................................................... 43

2.8.2 Eutrophication and characterization ....................................................................... 46(1)

Existing methods for finding the characterization factor of eutrophication ............. 46(2)

Characterization factors of eutrophication under LIME ......................................... 47

2.8.3 Eutrophication damage assessment ........................................................................ 50(1 )Basic policy for calculation of damage factors ....................................................... 50(2)

Process from the generation of a substance of concern to its flowing into a closed
water area...................................................................................................................... 51

LIME2_C2.7-C2.9_2014(3)

Changes in material dynamics in a closed water area and the DO concentration inwater ....................................................................................................................... 51(4)

Biodiversity: damage function to the number of kinds of benthic organisms ........ 58(5)

Primary production: damage function for the current amount of benthic organisms .
................................................................................................................................ 59(6)

Social assets: damage function for fishery production ........................................... 61(7)

Arrangement of damage functions of eutrophication ............................................. 61

2.8-4 Procedures for impact assessment of eutrophication .............................................. 62
2.9

Indoor air pollution ....................................................................................................... 65
2.9.1 What phenomenon is indoor air pollution? ............................................................ 65(1)

Background factors to the increasing appearance of indoor air pollution .............. 65(2)

Causes of indoor air pollution damage ................................................................... 65(3)

Endpoints of indoor air pollution ............................................................................ 66

2.9.2 Characterization and damage assessment of indoor air pollution .......................... 68(1)

Basic policies for calculation of damage factors and analysis of uncertainty ........ 68(2)

Characterization of indoor air pollution (fate analysis and exposure analysis) ...... 71(3)

Health impact: damage function of sick house syndrome ...................................... 77(4)

Health impact: damage function of nose/larynx cancer ......................................... 86(5 )Health impact: damage function of acute poisoning .............................................. 87(6)

Health impact: damage function of respiratory diseases ........................................ 88(7)

Arrangement of damage factors of indoor air pollution ......................................... 91

2.9-3 Procedure for impact assessment of indoor air pollution ....................................... 94
2.9.4 Uncertainty analysis of damage assessment of indoor air pollution....................... 95(1)

Total floor area of single-family house [m2] .......................................................... 97(2)

Relation of formaldehyde concentration and level of mucosa symptoms of sick ....
house syndrome [QEESI/person/year/ppm] ........................................................... 97(3)

Relation of formaldehyde concentration and the seriousness of mental symptoms .
of sick house syndrome [QEESI/person/year/ppm]................................................ 98(4)

Target population .................................................................................................... 99(5)

Amount of damage from mucosa symptoms of sick house syndrome [DALY/case] .
................................................................................................................................ 99(6)

Amount of damage from mental symptoms of sick house syndrome [DALY/case] ..
.............................................................................................................................. 100(7)Result of uncertainty analysis of damage functions of formaldehyde (single-family

LIME2_C2.7-C2.9_2014

house) .............................................................................................................................. 100

LIME2_C2.7-C2.9_2014

Chapter II

Characterization and Damage Evaluation Methods
2.7 Ecotoxicity
Changes between LIME 1 and LIME 2• Although the data on the ecotoxicity of chemicals in ECOTOX of the United States
Environmental Protection Agency (USEPA) were used for LIME 1, because the data
have recently been renewed greatly, the data as of 2007 are used for LIME 2. As aresult, the number of substances included in the list of damage factors increased
greatly, from 89 in LIME 1 to 297 (in 155 groups) in LIME 2.
• Under LIME 1, the data on the ecotoxicity of chemicals harmful to fishes, crustaceans,
and algae as groups of organism species were used and three types of mean values wereused for toxicity data on the other aquatic organisms. Under LIME 2, however, it was
decided to reflect ecotoxicity data in each of the other organism species (amphibians,
shellfishes, crustaceans, etc. (other than crustaceans)).
• The number of endangered species in the Red Data Book (RDB) was used for LIME 1.
For the purpose of LIME 2, the data were renewed to those as of December 2006.
2.7.1 What phenomenon is ecotoxicity?
(1)

Causal relationships of ecotoxicity

When a heavy-metal or organic compound is released from a factory, it, being transformed or
decomposed, diffuses into various environmental media, such as the air, water, and soil,
causing a change in the concentration of chemicals in the environmental media. How theenvironment changes greatly differs according to the type of chemical. Some chemicals
change their concentration so greatly as to influence individual organisms, sometimes causing
a change in the population of species or a change in the regional ecosystem balance and, as aresult, becoming a factor for the extinction of species. Such occurrence of an undesirable
situation for the ecosystem due to the release of a toxic chemical is herein called“ecotoxicity.” Because it is rumored that many chemicals, such as dioxin and polychlorinated biphenyl (PCB), have toxicity, how to protect the ecosystem from these toxic
chemicals has become an extremely important challenge to modern society.
Figure 2.7-1 shows the cause-effect chain of ecotoxicity. The endpoints in the figure areones that are generally regarded as problematic.
When ecotoxicity was assessed under the LCA so far, risk assessment was carried out by theuse of the ratio of the predicted environmental concentration (PEC) estimated by fate analysis
to the predicted no-effect concentration (PNEC) estimated from the no observed effectconcentration (NOEC) of the species in question. Under LCIA, such an approach has beenused for the development of characterization factors as midpoint level assessment. Under
LIME, an ecotoxicity assessment method was created with new endpoints by the method toevaluate the extinction probability by the use of not only characterization factors based on theenvironmental conditions in Japan but also a population dynamics model.
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有害化学物質排出Releaseof a toxic（重金属，有害有機物）
chemical(heavy-metalor organic compound)

環境媒体内
Conveyance/
輸送・分解
decompositio
n inenvironment
al media

環境媒体内
Change in
濃度変化
concentrationinenvironment
al media

Endpoint impacts
エンドポイント影響
Socialassets社会資産

Primary
production
一次生産

Biodiversity
生物多様性

Agricultural
農業生産
production

Terrestrial
陸域一次生産primary
production
Aquatic
水域一次生産primary
production

Terrestrial
陸域生態系

Forestry
林業生産
production
Fishery
漁業生産
production

ecosystem
Aquatic
ecosystem

水域生態系

Figure 2.7-1: Causal relationships of ecotoxicity(2)

Endpoints of ecotoxicityaImpact on biodiversity

If a toxic chemical is released into the environment, the concentration of toxic chemicals in
the environment changes and the soundness of individuals of a certain species may receive
damage. Impact on the dynamic state of the environment and species differs according to
the type of chemical. In the case of some chemicals, even if the amount of released
chemicals is extremely small, some species receive serious damage.
If the soundness of individuals of a certain species receives impact, the population may
change greatly, resulting in extinction. Because, whether in terrestrial or aquatic areas, all
the species in the ecosystem exist by balancing with various other species, a change in the
population of a species or the extinction of a species will break down the balance of thebiodiversity in the whole ecosystem.
b

Impact on primary production and social assets

If a toxic chemical is released into the environment, impact may arise on the primary
production (the number of plankton individuals, production of terrestrial plants), which isimportant for the ecosystem. Moreover, a change in the primary production due to therelease of a chemical has impact on zooplankton, which eat phytoplankton. This leads to theextension of the impact to upper stages of the food chain. As a result, fishery and othersocial assets valuable for human beings receive the impact of the release of the toxic chemical,
such as a reduction in yield and a loss of qualification. However, there has so far been
hardly any information on changes in plant growth volume due to the release of a chemical.
2.7.2 Characterization of ecotoxicity(1)

Existing characterization factors of ecotoxicity

Because areas are divided into aquatic and terrestrial areas, the aquatic ecotoxicity potential(AETP) and the terrestrial ecotoxicity potential (TETP) are used as the characterization factorof ecotoxicity – that is, the ecotoxicity potential (ETP). Although AETP and TETP arecalculated in the same way as the human toxicity potential (HTP), there is no concept ofintake as in the case of human beings. Instead, two factors are taken into consideration – thefate and effect of toxic chemicals in environmental media (water and soil). That is, theconcentration in environmental media as a result of fate analysis corresponds to the exposure
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concentration.
Like the human toxicity characterization factors, the ecotoxicity characterization factors
developed early (Heijungs et al. 1992) did not take into consideration the fate of released
chemicals in the environment. Recently, however, a lot of those that use results of fateanalyses have been developed (Hauschild et al. 1998, Guinée et al. 1996, Huijbregts 1999,
Hertwich 1999, Jolliet et al. 1997).
The general form of ETP is as follows (Equation 2.7-1):

ETPi , ecomp

F

F

i , ecomp, fcomp  E i , fcomp

fcomp

ref , ecomp, fcomp  E ref , fcomp(2.7-1)

fcomp

In this equation, ETPi,ecomp is the ETP when the substance i is released into the environmental
medium ecomp. Fi, ecomp, fcomp (fate factor) is the amount of the released i that reaches fcomp,the final environmental medium just before exposure. Ei, fcomp (effect factor) is the toxic
effect of the exposure of the organisms growing or living in fcomp to i. ref is the basesubstance. Concretely, F is the increment in the concentration in the environmental medium
if a unit amount of the substance i is released. E is usually the reciprocal of the maximum
predicted no-effect concentration (PNEC) of the substance i that meets the species
conservation target in the ecosystem (usually, conservation of 95%). That is, the stronger
the substance’s toxicity, the lower the PNEC (effect occurs even if the amount of the chemical
is small) and the larger the E. Therefore, the higher the exposure efficiency and the stronger
the chemical’s toxicity, the larger the ETP.
(2)

Characterization factors of ecotoxicity under LIMEa

Basic policy for calculation of characterization factors

Under LIME, characterization factors were calculated after research and examination ofconcentration changes in environmental media at the time of release of chemicals and theimpact factors of chemicals on each species. Figure 2.7-2 shows the flowchart of estimationof characterization factors.
b

Fate analysis of toxic chemicals

Like the toxicity assessment of substances harmful to human health (Section 2.6), themultimedia model developed by École Polytechnique Fédérale de Lausanne (EPFL) was
improved and used for fate analysis of released chemicals. This model geographically covers
Japan and the surrounding seas and consists of the following environmental media: the air,water, aquatic bottom sediments, the marine surface layer, deep seawater, marine bottom
sediments, surface soil, rhizospheric soil, unsaturated layer soil, the water-bearing layer,
vegetation regions, and urban regions.
This model can be used for consideringtransportation between environmental media, decomposition within a medium, and theprocess of external movement. By the use of this model, calculation was made about PECi,
ecomp, fcomp the concentration of the substance i in the final environmental medium fcomp if i isreleased to the environmental medium ecomp. The result was used as the fate factor (Fi, ecomp,fcomp in Equation 2.7-1). When characterization factors were calculated, consideration was
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LIME2_C2.7-C2.9_2014

given to three media to which the substance is released (ecomp): the air, water, and soil. In
addition, the final environmental medium, fcomp, is water in the case of the aquatic
ecosystem, while it is soil in the case of the terrestrial ecosystem.
No-effect
concentration of
toxic chemical

Fate & exposure analysis(Estimation of concentration inenvironmental medium as a resultof release of unit amount of toxic
chemical)

Predicted no-effectconcentration(PNEC)

Release of toxic chemical(Baseline + release of unit
amount))
Air
Water
Soil

Water
Multimedia fate model(Covering Japan and surrounding
seas)

Soil

Concentration of toxic substanceafter release(PEC)

Toxicity
potential
PEC/PNEC

Water

Soil

Making it relative on thebasis of benzene (release
into water)

Making it relative on thebasis of benzene (release
to soil)Aquatic ecotoxicity
potential (AETP)

Terrestrial ecotoxicity
potential (TETP)

Figure 2.7-2: Flowchart of calculation of characterization factors for ecotoxicitycEffect factor of the toxic chemical

The effect factor (Ei, fcomp in Equation 2.7-1) is the reciprocal of PNEC. PNEC (PNECaquai,
PNECterri) in the aquatic ecosystem or the territorial ecosystem for each chemical was
obtained from the existing LCIA study cases (Huijbregts 1999) (Table 2.7-1).
dCalculation of characterization factors of ecotoxicity

The resultant PEC was divided by PNEC to find the base value of toxicity assessment. Thebase value was divided by the value calculated in the same way concerning the base chemical
to make the base value relative and find AETP and TETP. Under LIME, the base chemical
was benzene released into water in the case of AETP and benzene released into soil in thecase of TETP. In other words, this is the factor for estimating how many times higher the
impact becomes compared with the impact when the same amount of benzene is released into
the environment (Equations 2.7-2 and 2.7-3).
AETPi , ecomp 

TETPi , ecomp 

4

PEC i , ecomp, water PNECaqua i
PEC benzene, water, water PNECaqua benzene
PEC i , ecomp, soil PNECterr i
PEC benzene, soil, soil PNECterr benzene(2.7-1)(2.7-2)

LIME2_C2.7-C2.9_2014

Table 2-7-1: Ecotoxicity characterization factors developed under LIME(a) AETP (aquatic ecotoxicity potential)

Place for release

Predicted
no-effect
concentration(PNECaqua)[kg/m3]

Predicted effect concentration(PEC) in surface water[(g/m3)/ (t/yr)]
Air

Water

Soil

Aquatic ecotoxicity potential(AETP)(estimated based on the casewhere benzene is released into
water)
Air

Water

Soil

Benzene

2.40E-03

1.38E-12

1.70E-08

2.95E-11

8.12E-05

1.00E+00

1.73E-03

Arsenic

2.40E-05

9.68E-07

2.65E-06

1.81E-06

5.68E+03

1.55E+04

1.06E+04

Trichloroethylene2.40E-03

4.52E-12

4.79E-08

1.18E-10

2.66E-04

2.81E+00

6.92E-03

Dichloromethane2.00E-02

4.45E-11

9.92E-08

3.01E-09

3.13E-04

6.99E-01

2.12E-02

Cadmium

3.40E-07

5.88E-07

1.90E-06

9.08E-07

2.44E+05

7.89E+05

3.76E+05(b) TETP (terrestrial ecotoxicity potential)
Place for release

Predicted
no-effect
concentration(PNECterr)[kg/kg (dwt)]

Predicted effect concentration(PEC) in surface soil[(g/m3)/ (t/yr)]

Terrestrial ecotoxicity potential(TETP)(estimated based on the case where
benzene is released into soil)

Air

Water

Soil

Air

Water

Soil

Benzene

4.19E-06

4.00E-13

1.64E-1
3

4.53E-09

8.83E-05

3.61E-05

1.00E+00

Arsenic

3.60E-07

2.97E-05

2.56E-2
0

1.09E-04

7.64E+04

6.57E-11

2.80E+05

Trichloroethylene5.67E-06

1.93E-12

9.69E-1
3

1.79E-08

3.15E-04

1.58E-04

2.92E+00

Dichloromethane1.70E-05

6.26E-12

3.75E-1
2

9.18E-08

3.40E-04

2.04E-04

4.99E+00

Cadmium

4.60E-07

2.96E-06

0.00E+00

1.08E-05

5.94E+03

0.00E+00

2.17E+04

In Western countries, Guinée (1996) and Hauschild (1998) developed characterization factors
for ecotoxicity. Although these factors are based on the multimedia fate model, they cannot
be used for assessment in Japan, because the environmental conditions set for the model werebased on data in Western countries. Because the geographical characteristics in Japan weretaken into consideration for the fate analysis under LIME, we recommend the ETP developed
under LIME as the characterization factor for ecotoxicity. Tables 2.7-1 (a) (b) show thecharacterization factors of main chemicals.
eCharacteristics of characterization factors of ecotoxicity

First, we explain the characteristics of benzene, the base substance. PNEC related to toxicity
against aquatic creatures is higher than that against terrestrial creatures, by about three ordersof magnitude. On the other hand, PEC in surface water at the time of release into water ishigher than PEC in surface soil at the time of release into soil, by about one order. Becauseof this, the value of PEC/PNEC concerning aquatic ecotoxicity at the time of release into
water is lower than the value concerning terrestrial ecotoxicity at the time of release into soil,
by about two orders. If they are used as base values and the characterization factors arecompared with those in the case of the release of benzene into other environmental media,
AETP at the time of release to soil is lower than that at the time of release into water by about
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LIME2_C2.7-C2.9_2014

three orders, and AETP at the time of release into the air is lower than that at the time ofrelease into water by about five orders. On the other hand, TETP at the time of release into
the air is lower than that at the time of release to soil by about five orders, and TETP at thetime of release into water also is lower by about five orders.
With regard to the other chemicals, AETP of trichloroethylene and dichloromethane is almost
the same as or slightly lower than that of benzene in the case of any types of release. On theother hand, AETP of arsenic and cadmium tends to be higher by about 3 to 5 orders.
Because arsenic and cadmium are heavy metals that are not volatile, PEC becomes higher.
This is thought to have increased AETP.
With regard to TETP of the other chemicals, those of trichloroethylene and dichloromethane
are almost the same as or slightly lower than that of benzene in the case of any types of
release. On the other hand, those of arsenic and cadmium tend to be higher by about 3 to 5
orders in the case of both release into the air and release into soil, but are extremely low in thecase of release into water. Even when trichloroethylene or dichloromethane is released into
water, its concentration increases to some extent because of volatilization and soil deposition.
However, when a heavy metal is released into water, because it does not volatilize, it is hardfor its concentration to increase in soil. This seems to be the reason for the result that TETP
is high in the case of release to the air and soil, while it is low in the case of release to water.
2.7.3 Assessment of damage from ecotoxicity(1)

Basic policy for calculation of damage factors

Table 2.7-2 shows the category endpoints of ecotoxicity and the object of protection about
which the damage function is calculated under LIME. Because at present quantitative
information necessary for assessment cannot be fully obtained concerning social assets andprimary production, the object of protection about which the damage function was calculated
under LIME was limited to biodiversity. In addition, Figure 2.7-3 shows the flowchart ofcalculating the damage function. As in the case of characterization, the geographical range
covered by the damage assessment was limited to Japan.
(2)

Damage indices of ecotoxicityaIndex related to the extinction risk of species: EINESThe damage function of ecotoxicity can be expressed by the Expected Increase in Number ofExtinct Species (EINES) from an increase in released toxic chemicals in Japan. Equation
2.7-4 shows the total damage amount of all the species if a substance i is released into an
environmental medium ecomp.
EINES i , ecomp target species



EINES i , ecomp, species  ( N  R

pi , ecomp, species target speciesGGi , ecomp,G )Ei , ecomp (N  FFG

G

i ,ecomp, fcomp  EFi , fcomp,G )Ei , ecomp  DFi ,ecomp  Ei ,ecomp(2.7-3)

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Table 2.7-2: Category endpoints of ecotoxicity and the target of LIME
Object ofprotection

Social assets

Primary
production

Category endpoint

Target of damage function calculation

Agricultural Decrease in yield of farm products,
production change in qualityQuantitative information is
insufficient. However, theinfluence of intake of chemicals
in farm products on health isassessed by the use of toxic
chemicals.

Forestry
productionQuantitative information is
insufficient.

Decrease in forest increment, change
in quality

Fishery
production

Decrease in fishery resources throughinfluence on fishes and shellfishes

Quantitative information is
insufficient. However, theinfluence of intake of chemicals
in fishes on health is assessed by
the use of toxic chemicals.

Terrestrial
primary
production

Influence on production of terrestrial
plants

Quantitative information is
insufficient.

Aquatic
primary
production

Influence on production ofphytoplankton

Quantitative information is
insufficient.

Terrestrial
ecosystem

Increase in extinction risk throughinfluence on individuals of terrestrialorganisms

Quantitative information is
insufficient.

Aquatic
ecosystem

Increase in extinction risk throughinfluence on individuals of aquaticorganisms

Algae, crustaceans, fishes, andother aquatic organisms in freshwater

Biodiversity

In this equation, target species are all the species covered by the assessment of EINES;
species is each group of organisms; ∆E is the amount of release of the toxic chemical i, and∆p is the increment in the extinction risk due to ∆E; G is the category of species (divided intosome ranks according to the degree of extinction risk); ∆R is the increment in the extinction
risk of a specific species due to a unit release amount of a chemical; FFi, ecomp, fcomp (fatefactor) is the amount that reaches the environmental medium fcomp among the unit releaseamount of i; and EFi,fcomp,G (impact factor) is an increment in the extinction risk when aspecies belonging to the category G is exposed to a unit amount of the chemical i in theenvironmental medium fcomp. NG is the number of species included in the category G (suchas the endangered class I) among a specific group of species (such as fishes or algae). The
damage factor (DFi, ecomp) is the total increment in the extinction risk of each species due toa unit release amount of the chemical i. When this index is used, it is assumed that new
extinction of a species is the same damage as an increment in the extinction risk of ten species
by 10%, which is expressed as 1 EINES.

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LIME2_C2.7-C2.9_2014

Fate andexposure運命
曝 露 分 析analysis(Estimationof 量concentrationin 物
environmental
mediumunit
amount（単 位の有害化 学質 の 排 出 による 環after
境 媒release
体 中 濃of度の推計 ） of toxic
chemical)Releaseof学
toxic
chemical
有害化物質
排出ベBaseline
ー スライン

ベー
スライン
単位量
出
Baseline+＋releaseof 排
unit
amount
大Air
気
水
域
土
壌
Water
Soil

大
気
Air

水域
Water

土
壌
Soil

運 命 Fate
分 析 analysis
モデ ル ： model:
マル チボ
ッ クスモデル（model
日本と周辺海域
を 含andむ 範 囲）
multimedia
fate(coveringJapansurrounding seas)
Concentration
chemical
排 出 後 の 有of害toxic
化学
物 質 濃after度
releaseBaselineベー スライン

Baseline+＋release
of 排
unit
ベー
スライン
単位量
出
amount
Soil
Water
水域
土
壌

Water
水域

Soil
土壌

Expected
単 位Increase
量 の 有in
害Number
化 学 物of
質Extinctの 排 出Speciesによ る(EINES)
from release of a unit amount of toxic chemical

絶 滅 種 数 増 加 （INE S)

Extinction
絶 滅 評 価assessment
モデ ル
model

Chemical
toxicity
化学物質
毒性(Algae, fishes, crustaceans, etc.
(crustaceans,（藻類， 甲 殻other
類 ，than魚類）
crustaceans), amphibians,
shellfishes)

Increasein のextinction
絶滅確率増加（ 藻 類 ，probability
甲殻類 ，魚類 ）(Algae, fishes, crustaceans, etc.
(crustaceans, other than crustaceans),amphibians, shellfishes)

全 体 化 モデmodel
ル
Generalizationin 内
Japan,（(All日regions
本国
全all
地aquatic
域，species)
陸域・水域全生 物種）

Expected Increase in Number
絶Extinct
滅 種 数Species
増 加（(EINES)
IN ES）
of

生 態function
毒 性 の of
Damageecotoxicity
ダメー
ジ関数

Aquatic
陸 域 ・水ecosystem
域生態系
生
物多様性
Biodiversity

Damage
被 害 係factor
数（ D(DF)F）(Biodiversity（生
物 多 様 性 only)のみ）

Figure 2.7-3: Flowchart of calculation of damage function of ecotoxicitybMethod of calculating EINES under LIMEAs shown in Equation 2.7-4, when the damage factor is calculated, it is appropriate to find anincrement in the extinction risk due to exposure to a chemical and accumulate the results.
However, because the assessment of the extinction risks of all the existing species is difficult
due to limited data, analysis was carried out under LIME for each rank of extinction risk of
species written in the Red Data Book (RDB), taking into consideration the availability ofmodels for the assessment of existing extinction risks and data used for the assessment. The
assessment was carried out about the species representative of a category (such as theendangered class I). The result was multiplied by the number of specifies that belong to thatcategory to find the assessment result for each category. This is based on the assumptionthat the assessment result of the extinction risk of the species representative of the category is
the mean value of the assessment results of all the species that constitute the category.
cSpecies covered by assessment

The extinction risk was directly calculated concerning six groups of species: algae,
crustaceans, etc. (crustaceans), fishes, shellfishes, crustaceans, etc. (other than crustaceans)
and amphibians, all of which are aquatic organisms. They include algae, crustaceans, etc.
8

LIME2_C2.7-C2.9_2014(crustaceans) and fishes, because algae, water fleas, and fishes are regarded as important for
the field of ecotoxicity and have so far been covered by research studies and there are richdata on them. On the other hand, shellfishes, crustaceans, etc. (other than crustaceans) andamphibians, which had been grouped into “other aquatic organisms” for assessment under
LIME 1, were separately covered by assessment under LIME 2 because data on them havebeen increasing in response to sharp improvement in the ecotoxicity database and becausesome chemicals have strong toxicity against them.
The characteristics of each aquatic species are described below. With regard to algae,
crustaceans, etc. (crustaceans) and fishes in particular, reasons why they are regarded asimportant in the field of ecotoxicity are added.
1) Algae
Algae are primary producers that grow and increase through photosynthesis, taking nutrient
salts. They exist at the bottom of the food chain. Therefore, if the impact of water pollution
hinders the growth or propagation of algae, the whole food chain receives great impact. Fromthe viewpoint of experiment, because green unicellular algae are characterized by a short
lifecycle, it is possible to assess several-generation impact in a few days. Many assessment
results have been attained so far.
2) Crustaceans, etc. (crustaceans)
Water fleas, which are zooplankton, eat plants and are regarded as important organisms thatexist in the middle of the food chain and connect primary producers with higher organisms.
In addition, because their lifecycle is comparatively short and the first immature flea isreleased seven to ten days after birth, it is easy to use them for reproductive testing. Water
fleas belong to crustaceans and are frequently used for predicting the impact of waterpollution on shrimps and other human food.
3) Fishes
Fishes are located at the highest position in the aquatic ecosystem. Because many toxicity
test data were reported concerning various types of fishes, they are important as index
organisms for environmental pollution.
4) Shellfishes
Mollusks are roughly divided into eight classes. Freshwater shellfishes consist of“gastropod (snail)” and “bivalve.” The mollusks that have the largest number of types are
gastropods. The total number of types of mollusks is said to be more than 100,000, andgastropods account for about 84% of the total number. In ECOTOX of the United States
Environmental Protection Agency (USEPA), the toxicity data on shellfishes are the fourthlargest among those on the assessment-covered six types, following those on algae, fishes,and crustaceans.
5) Crustaceans, etc. (other than crustaceans)
According to the Ministry of the Environment’s RDB, “crustaceans, etc.” consists of six phyla
9LIME2_C2.7-C2.9_2014of animals*1, and land ones and freshwater ones (including brackish water ones in the case of
decapoda) are covered by the assessment. Of them, four types of freshwater phyla –“Porifera,” “Cnidaria,” “Platyhelminthes,” and “Annelida” – are covered by the assessment
concerning “crustaceans, etc. (other than crustaceans) under LIME 2. Although, according
to USEPA’s ECOTOX, the number of toxic data on crustaceans, etc. (other than crustaceans)is the smallest among the covered six types, data on about 70 substances have been obtained
among about 320 substances covered.
6) Amphibians
Amphibians usually spawn in freshwater, and larvae live in water. Although adults do notnecessarily always live in water, because they cannot live apart from water, they may be
influenced by a rise in the concentration of a toxic chemical in water. Therefore, they arecovered by the assessment. According to USEPA’s ECOTOX, the number of toxic data on
amphibians is the fifth largest among the covered six types. However, some chemicals, suchas arsenic and lead, have stronger toxicity against amphibians than against other aquatic
species.
Damage from toxic chemicals was limited to aquatic organisms, mainly because the data used
for the damage assessment were limited to water. Although exclusion of influence onterrestrial organisms may underestimate the assessment results, because the current ecologicalrisk assessment mainly covers aquatic organisms, it can be thought to include the main effectsof ecological risk.
Therefore, the development of a damage factor for the assessment of the impact of a toxic
chemical on biodiversity requires the finding of NG, FFi, ecomp, fcomp, and EFi, fcomp, G in Equation
2.7-4. (3) to (5) below describe the method to calculate these parameters.
(3)
Classification of assessment-covered species and the setting of the number ofassessment-covered species (NG)Information on the extinction of wild species has been collected and disclosed by theInternational Union for Conservation of Nature (IUCN) globally and by the Ministry of theEnvironment in Japan. The Ministry of the Environment has divided the wild species into
the following classes according to probability of extinction: extinct, wild extinct, endangeredclass I, endangered class II, quasi-endangered, and lack of information. The endangeredclasses I and II have the following qualitative requirements. If a species meets either of 1) or
2), it falls under that class.
aEndangered class IA1) The number of mature individuals is less than 50.
2) The probability of extinction is 50% or more in the next 10 years or in the following
3 generations, whichever is longer.

*1

10

The six phyla are Porifera (sponge), Platyhelminthes (jellyfish), Cnidaria (triclad), Class Phylactolaemata(bryozoans), Hirudinea (leech, annelid) of Annelida, and Xiphosurida (horseshoe crab), Arachnida(Pseudoscorpiones, harvestman, tick, spider), Crustasia (wood louse, Gammaridea, shrimp, crab) and
Diplopod (galleyworm) of Arthropoda.

LIME2_C2.7-C2.9_2014b

Endangered class IB1) The number of mature individuals is less than 250.
2) The probability of extinction is 50% or more in the next 20 years or in the following
5 generations, whichever is longer.

cEndangered class II1) The number of mature individuals is less than 1,000.
2) The probability of extinction is 50% or more in the next 100 years.

Table 2.7-3 shows the number of endangered species in Japan that the Ministry of theEnvironment calculated according to these standards and published. Of them, the number of
species for each rank in Table 2.7-4, NG, was used for the assessment of ecotoxicity.
(4)

Calculation for fate analysis of a toxic chemical (FFi, ecomp, fcomp)

By the use of the multimedia fate model used for the characterization, a change in theconcentration of each toxic chemical in water was estimated through analysis of the fate of thechemical in the environment when a unit amount of the chemical was released into theenvironment. Although the multimedia fate model was the same as the model used for theestimation of the amount of exposure to each toxic chemical to human beings, what was usedas output was not the amount of exposure of human beings compared with the amount of
release but a change in the concentration in water compared with the amount of release, andthe index was treated as what indicated the amount of exposure of aquatic organisms. Table
2.7-5 shows the result of fate analysis of main chemicals.

Shellfishes

Vascular plants

89

17

86

13
1

12

3

1

21

29

Total

Spiders,
Crustaceans,
etc.

20
5

0
0

OthersInsects25
0

00Other thanvascularplants

Algae

Brackishwater/
freshwater
fishes

0
1

Birds

2
0

4
0

20 10

Plants30

ReptilesExtinct (EX)Wild extinct (EW)
Endangered type
IA (CR)
Endangered type
IB (EN)
Endangered type
II (VU)
Quasi-endangered(NT)
Lack ofinformation (DD)Amphibians

Rank

MammalsClass

Table 2.7-3: Number of endangered species in Japan
Animals

5
1

32
1

34

185 1621

102
9

564

9

32

29

16 18 11

39

18

82

39

165

621

6

103 1118

16 17 14

18

12

161

40

201

145

24

21

669

9

17

5

87

39

71

52

0

71

357

5

1

480

The source is the Biodiversity Information System (http://www.biodic.go.jp/J-IBIS.html). This
table is based on the results published through December 2006.

11

LIME2_C2.7-C2.9_2014

Table 2.7-4: Setting of the number of species (NG) under LIME
IA†1
IB†1
II
Amphibia
Brackish water/freshwater
fishesCrustaceans,
Crustaceans
etc.
Other
Shellfishes
Algae
Total
Number of species of “otheraquatic organisms” †2Soundspecies

1

9

11

65

29

29

18

300

4.5

4.5

17

100

3

3

14

100

43

43

165

1000

17

17

6

5500

97.5

105.5

231

7065

47

55

190

1165

†1

Only the total number of species belonging to the endangered type I is shown concerning algae, crustaceans,
etc. and shellfishes. Because the breakdown of classes IA and IB is unknown, half of the total number isallotted between the classes.
†2
Number of species of “other aquatic organisms”: the total number of species of amphibia, crustaceans, etc.
(other than crustaceans), shellfishes, etc.

(5)

Calculation of the effect factor of a toxic chemical (EFi, fcomp, G)This section describes how to calculate EFi, fcomp, G, the increment in the number of extinct
specifies according to the amount of exposure to a toxic chemical. First, the basic data to be
used for the model for assessment under LIME are introduced in “a Collection of data on
the toxicity of chemicals and the setting of representative values.” Some models for the
calculation of extinction risk due to exposure to a chemical have been already developed in
the field of conservation biology, etc. Main models are explained in “b Existing models
for the calculation of extinction risk.”
The sensitivity to an increase in the extinction risk due to exposure to a chemical differs
among species. Under LIME, different models and parameters were used according to theclasses related to the extinction of species (such as the endangered type IA).
Table 2.7-5: Results of fate analysis of main chemicals
Concentration in water [(g/m3)/ (t/yr)]
ChemicalBenzeneArsenic
Trichloroethylene
Dichloromethane
Cadmium

Release to the air
SurfaceSoilwater
1.38E-12 4.00E-13
9.68E-07 2.97E-05
4.52E-12 1.93E-12
4.45E-11 6.26E-12
5.88E-07 2.96E-06

Release to water
Release to soil
Surface
Surface
Soil
Soil
water
water
1.70E-08 1.64E-13 2.95E-11 4.53E-09
2.65E-06 2.56E-20 1.81E-06 1.09E-04
4.79E-08 9.69E-13 1.18E-10 1.79E-08
9.92E-08 3.75E-12 3.01E-09 9.18E-08
1.90E-06 0.00E+00 9.08E-07 1.08E-05“c Assessment of EF for sound species” and “d Assessment of EF for the endangered typeII”, “e Assessment of EF for the endangered type IB” and “f Assessment of EF for theendangered type IA” show how to assess EF for the respective classes and what parameters
were used. Moreover, they also show assessment results.
The damage factor can be gained by applying the results of “c” to “f” to Equation 2.7-4.
12

LIME2_C2.7-C2.9_2014aCollection of data on the toxicity of chemicals and the setting of representativevaluesUnder LIME, toxic data from USEPA’s ECOTOX were used to clarify the toxicity ofchemicals against groups of species (algae, crustaceans, etc. (crustaceans), fishes, crustaceans,
etc. (other than crustaceans), shellfishes and amphibians). Data on effective concentration
50% (EC50; the concentration of a chemical or the like that makes the growth increment of thetarget species 50% in the experiment period) [μg/L] were extracted for algae, while data on
lethal concentration 50% (LC50: the concentration of a chemical or the like that makes thedeath rate of the target species 50% in the experiment period) [μg/L] were extracted foranimal groups.
To arrange data, if several sets of data on the toxicity of a chemical exist for the same species,
the geometric average was calculated. Next, for each chemical, the geometric average wascalculated concerning all data for the same group of organisms (algae, crustaceans, etc.
(crustaceans), fishes, crustaceans, etc. (other than crustaceans), shellfishes, and amphibians)
and the result was regarded as the evaluated toxicity value of the group. If toxicity data were
not obtained concerning a group, the geometric average of the toxicity values of species about
which toxicity data were obtained was used. Table 2.7-6 shows the representative values of
EC50 and LC50 of main chemicals.
With regard to species whose extinction risk is high (such as endangered species), toxicity
data usually do not exist, due to their rareness. Therefore, evaluated toxicity data obtained
from experiment data on the toxicity of sound species were used for such species.
b

Existing models for the calculation of extinction risk

Extinction means that the population of a species becomes zero. Therefore, a model forassessing extinction risk needs to express changes in the population. 1) to 3) below explain
models for expressing changes in the population, and 4) below compares these models fromthe viewpoint of application to LIME.
Table 2.7-6: Example of data on ecotoxicity of main chemicals
Algae

Crustaceans,etc.
(crustaceans)

FishesShellfishes

Crustaceans, etc. (otherthan crustaceans)

Amphibians

EC50(μg/L)

LC50(μg/L)

LC50(μg/L)

LC50(μg/L)

LC50(μg/L)

LC50(μg/L)

ChemicalBenzene

7.06E+04

1.38E+03

3.87E+04

3.87E+01

3.24E+04

2.48E+05

Zinc

1.11E+03

5.74E+02

4.67E+03

1.66E+03

2.65E+03

1.00E+04

Trichloroethylene2.98E+05

4.07E+04

6.56E+04

5.60E+04

7.50E+04

4.58E+04

Dichloromethane3.15E+05

3.55E+03

4.00E+05

3.14E+04

2.38E+04

2.88E+04

Cadmium

1.68E+02

9.00E+01

2.18E+03

1.97E+03

2.50E+03

4.00E+01

In the case of benzene, for example, because the value of LC 50 of mollusks (shellfishes) is lower than that ofother species by nearly 2 orders of magnitude, benzene is highly toxic especially against shellfishes. In thisway, LIME 2 also evaluates ecological effects, which could not be accurately evaluated from the average value
for algae, crustaceans, and fishes.

13

LIME2_C2.7-C2.9_2014

1) Canonical model
Iwasa et al. (1997) developed the “canonical model,” a basic equation that expresses changes
in the population and includes essential elements, as an analysis model for changes in the
population. The canonical model takes into consideration the following three elements (thefollowing A, B, and C correspond to A, B, and C in Equation 2.7-5):
A: Multiplication through propagation (see Column 2.7-1)
B: Change in the population according to environmental stochasticity (see Column
2.7-1)
A change in the population according to whether the environmental conditions
around organisms, such as temperature and precipitation, are favorable orunfavorable; this change occurs independently from a change in the populationaccording to demographic stochasticity.
C: Change in the population according to demographic stochasticity
This is a change in the gender ratio, the average number of births, or the survival
rate due to a limited population. This includes cases where only male individuals
exist by chance or where children continue to die by chance. This change occurs
independently from a change in the population according to environmental
stochasticity. If the population is large, this change has smaller impact than theother elements. However, if the population is small, its impact is not ignorable.
This model can be expressed by the following equation:dNN r ( N ) N 1     e Z e (t )  N  Z d (t ) 
dt
K


A

B

N

(2.7-4)C

N (t): Population in the generationt

Ze (t): Random number that expresses

K: Carrying capacity

Zd (t): Random number that expresses

environmental change
demographic changer (N): Increase rate of an individual ∘: Stratonovich integration
σe: Degree of environmental change : Ito integration

2) Tanaka’s calculation method
This model is applicable to cases where the population changes nearly steadily, such as withsound species. Of the elements that the canonical model takes into consideration, this modeltakes into consideration A and B.
It is all right to assume that sound species are in a steady state, because the population is
sufficiently large. In this case, the extinction probability does not change according to thepoint of time t.
If the probability that a species becomes extinct within a year is 10-6, the cumulative
extinction probability is as shown in Figure 2.7-4.

14

LIME2_C2.7-C2.9_2014

Lande (1993) pointed out that, when the population is steady, the average extinction time
gradually becomes close to the exponential function of carrying capacity. Moreover, Lande(1998) found a scaling law of the influence of environmental stochasticity on the average
extinction time T and formulated the following equation from the law (see Column 2.7-2):

T  cNr
2 1v(2.7-5)

N is the initial population. r and υ are the average and dispersion of the intrinsic natural
growth rate, respectively. c is a constant. Equation 2.7-6 can be transformed as follows,
replacing N with the carrying capacity K: r 
log T  log c   2  1 log K v (2.7-6)This shows that, because of exposure to a toxic chemical, if r or K decreases or υ increases, T
decreases.

Cumulative extinction probability

If comparison is made in the degree of sensitivity of extinction probability to a change in eachelement, r and υ directly influence log T. However, because K has influence in the form oflog K, the influence of a change in the carrying capacity on the extinction time is relatively
small. Therefore, under LIME, the influence of a change in the carrying capacity on theextinction time was ignored, and the influence of a change in the intrinsic natural growth rate
and the environmental variability were taken into consideration. Below, explanations are
given to “the relation between the amount of exposure to a chemical and the intrinsic natural
growth rate,” “the relation between the amount of exposure to a chemical and changes in theincrease rate due to environmental stochasticity” and “estimation of extinction probability.”

1
0.8
0.6
0.4
0.2
0
0

2000004000006000008000001000000

Point of time t
Figure 2.7-4: The cumulative extinction probability until the point of time t on the assumption thatthe population changes steadily and the extinction probability within a year is 10-6

15

LIME2_C2.7-C2.9_2014

Column 2.7-1
Meanings of carrying capacity, changes in the increase rate according to environmental
stochasticity, and the intrinsic natural growth rate
Under LIME, the extinction risk of a species is evaluated by the use of carrying capacity,
environmental changes, and the intrinsic natural growth rate as parameters. This column
roughly explains these parameters (for details, see Washitani et al. (1996)).
When the population N is changing at the average rate of change r per individual and per timet, the change can be expressed as follows:
dN
 rN
dt

The change rate r is the difference between the birth rate and the death rate. If r > 0, thepopulation increases. If r < 0, the population decreases. If r > 0, the population increases
geometrically with the passage of time. In reality, however, the number remains at a certain
level. This is because when the population increases, the effect of restraining the increase(which is called the “density effect”) works. For example, the living space or the amount offood is limited. This relation can be expressed by the following equation:dNr
 rN  N 2
dt
K

In this equation, the second item of the right side corresponds to the density effect. In thecase of this relation, when the population reaches K, it becomes stable (Figure 2.7-A). This
K is called “carrying capacity.”
In addition, although r differs according to species and group, it changes also according tovarious factors. If a change in r is taken into account, the equation above can be changed tothe equation below.
dNr r0  xt N  N 2
dt
K

r0 is a characteristic of the species and refers to the change rate if there is no influence onchanges in the change rate, which is called the “intrinsic natural growth rate.” x(t) is apositive or negative function that temporarily and stochastically changes at the average valueof 0. If the population is large, the species’ extinction risk is low even if the increase rate (r0+ x(t)) changes more or less. However, if the population is small, the species may become
extinct if the increase rate continues to be negative for several years. x(t) incorporates
demographic stochasticity, catastroph, genetic stochasticity, etc. It also incorporates
environmental stochasticity. This refers to a change in the increase rate of a group ofindividuals due to an environmental factor, such as temperature or precipitation.
Population
個体数
K

ｔ
時間
Time

Figure 2.7-A: Changes in the population if the density effect is taken into account
The population increases with the passage of time. However, as the number is nearer to the carrying
capacity K, the density effect becomes stronger, and changes in the number remain under the level of K.

16

LIME2_C2.7-C2.9_2014

Column 2.7-2Lande’s scaling law
Lande’s scaling law can be expressed as Equation 2.7-6 under the condition that the
population is sufficiently large and is in a steady state. This indicates that, as the intrinsic
natural growth rate and the population become higher and the dispersion of the increase rate
due to an environmental change becomes smaller, the extinction probability becomes smaller.
If the population is sufficiently large and is in a steady state, the average extinction time
(temporary scale) can be approximately expressed by the exponential function of the carrying
capacity (environmental scale). Therefore, this equation is not applicable to unsteady cases,
such as a continuous decrease in the population. Because of this, LIME limits theapplication of this equation to sound species.
[Relation between the amount of exposure to chemicals and the intrinsic natural growthrate]
Tanaka (Tanaka et al. 2000) carried out statistical fitting of experimental data on several typesof chemicals and found that there is the causal relationship between the amount of exposure tochemicals and the intrinsic natural growth rate, as shown in Equation 2.7-8 (Figure 2.7-5). α
is defined as the concentration at which the intrinsic natural growth rate is 0.
xrx  r0 {1  ( ) }


(2.7-7)x
r  rx  r0  r0 ( ) rx/r1

  1.82(2.7-8)x/αFigure 2.7-5: Relation between the exposure concentration and the intrinsic natural growth rate ofspecies(Tanaka et al. 2001b)

17

LIME2_C2.7-C2.9_2014

In(LC50)

Figure 2.7-6: Relation between the acute toxicity value and the value of α (Tanaka et al. 2001b)

In this equation, r0 is the initial value of the intrinsic natural growth rate, r(x) is the value atthe time of exposure, and x is the exposure concentration. β is the parameter that expresses
the slope when response increases as the concentration increases. α is the parameter that
determines the density at which toxicity emerges, and is peculiar to each substance andspecies. Although α can be calculated by the equation below, this equation also has beenobtained from statistical fitting of experimental data. Figure 2.7-6 shows the result of fittingof experimental data. In this way, α is highly correlative to LC50.

ln()  ln(LC50 )  0.843  ln(1.562)(2.7-9)[Relation between the amount of exposure to chemicals and a change in the increase rate
due to environmental stochasticity]
This section describes the relation between exposure to chemicals and a change in theincrease rate due to environmental stochasticity. Changes in the amount of exposure tochemicals in the general environment were estimated from the items of “Outline of Bottom
Sediment Monitoring Results in FY1999” in “Chemicals and the Environment (FY2010
Edition)” published by the Ministry of the Environment. As a result, it was found that thevariation coefficient for changes in the amount of exposure is 0.58 on the assumption that thevariation coefficient is constant. Because the variation coefficient is assumed to be constant,
if the amount of exposure to chemicals (representative value) increases, a change in theamount of exposure (standard deviation) increases accordingly (Figure 2.7-7). By applying
this to Equation 2.7-8, the representative value of the intrinsic natural growth rate and theamount of change as a result of a change in the amount of exposure to a chemical were
estimated.

18

LIME2_C2.7-C2.9_2014v( x)   r20   r ( x) 2  v0   r ( x) 2

 r ( x) |(2.7-10)rx
x
1x|  x  r0  ( )  1   x  r0  ( ) 
x




(2.7-11)ν(x): Dispersion of the increase rate due to environmental
stochasticity if the amount of exposure is x
rx
r0 1.2
1
Change in intrinsic
natural growth rate

0.8

rx
x
 1 ( )
r0


0.6

0.4
Change in amount of
exposure(Increase in proportion toamount of exposure)

0.2

0

x
0

0.2

0.4

0.6

0.8

1

1.2 

Figure 2.7-7: Changes in the amount of exposure to chemicals and the intrinsic natural growth rate
due to environmental stochasticity
If the amount of exposure to chemicals increases, the amount of change in the representative value of the amountof exposure becomes larger. In proportion to this, the amount of change in the intrinsic natural growth rate
becomes larger.

σr (x): Standard deviation of a change in the intrinsic natural growth rate (if the amountof exposure is x)
σr0:Standard deviation of a change in the intrinsic natural growth rate (if the amountof exposure is 0)ν0:Dispersion of the increase rate due to environmental stochasticity if the amount of
exposure is 0γ:
Variation coefficient = 0.58
Figure 2.7-8 shows differences in the extinction probability according to the variation
coefficient if environmental variation is taken into consideration in Tanaka’s calculation
method. Parameters are set in the same way as in the case of the endangered type II
crustaceans described below (Table 2.7-9). The initial value of the intrinsic natural growthrate r0 is 1.5, the initial value of environmental dispersion ν0 is 3.0, the carrying capacity K is1000, and the extinction probability when the exposure concentration is α is 1.0. This figure
shows that, given environmental variability, the extinction probability increases even if theamount of exposure is the same.
19

LIME2_C2.7-C2.9_2014

1.E+00
0

0.2

0.4

0.6

0.8

1

Variation
変動係数＝０coefficient=0

Extinction
絶滅確率
probability

1.E-01

変動係数＝０．４

Variation
変動係数＝０．８
coefficient=0.4

変動係数＝１．２
Variationcoefficient=0.8
変動係数＝１．６

1.E-02

Variation
変動係数＝２．０
coefficient=1.2
Variationcoefficient=1.6
Variationcoefficient=2.0

1.E-03

Exposure暴露濃度／α
concentration/aFigure 2.7-8: Change in the relation between exposure concentration and the extinction probability
when environmental stochasticity is taken into account (based on Tanaka’s calculation method)
If environmental stochasticity is introduced, the extinction probability increases even if the amount of exposureis the same.

[Estimation of extinction probability]
The extinction probability p is defined as the reciprocal of the extinction time T. For
example, if T is 10 (years), p is 0.1, which means that the probability of extinction during thenext one year is 0.1.
1
(2.7-12)p
T
The following relation can be obtained from the scaling law (Equation 2.7-6):
1

p c N r 
 2 1 v (2.7-13)3) Lande-Orzack model
Lande and Orzack found an analytical solution irrespective of the influence of demographic
fluctuation when the population continues to decrease and the density effect can be ignored.
The following is the analytical solution:

2n 0 r   n  rt 
 n0  rt  G(t | n0 )    0
  exp{  2 }1      t   t 


(2.7-14)y  2 1/ 2 yexp z 2 / 2dz

(2.7-15)

The variables have the following meanings:t:n0:
G (t|n0):
r:
σ2:
20

time (unit: year)
natural logarithm of the population N0 when t is 0
probability of extinction within t years when the natural logarithm of the
population is n0
average of intrinsic natural growth rate
dispersion of intrinsic natural growth rate

LIME2_C2.7-C2.9_2014

Because there are no referable quantitative data concerning the relation between the intrinsic
natural growth rate r and the amount of exposure x, the possibility of extinction within a year
is regarded as 1 when x is LC50, on the assumption that the relation between the two is linear(Figure 2.7-9).
ｒ

0

LC50

x

ｒ0

-lnN0

Figure 2.7-9: Assumed relation between r and x (linear interpolation function)

The following is the equation obtained from this assumption:r ( x)  r0  ln N 0  r0x
LC50

(2.7-16)

As shown below, the Lande-Orzack model also takes into consideration changes in theincrease rate due to the amount of exposure to chemicals and environmental stochasticity.

 ( x)   r20   r ( x) 2   0   r ( x) 2
 r ( x)  ln N 0  r0
r ( x)
x 
x
x
LC50

(2.7-17)(2.7-18)ν(x):Dispersion of the increase rate due to environmental stochasticity when theamount of exposure is x
σr (x): Variable standard deviation of the intrinsic natural growth rate (if the amount of
exposure is x)
σr0:
Variable standard deviation of the intrinsic natural growth rate (if the amount of
exposure is 0)σr:
Dispersion of the increase rate due to environmental stochasticity when theamount of exposure is 0γ:
Variation coefficient = 0.58
4) Comparison of characteristics of existing models
Although both Tanaka’s calculation method and the Lande-Orzack model are based on the
canonical model, they add some limited aspects to consider, in order to clarify the relation
between extinction probability and its factors. This is because the canonical model cannot
be solved analytically. Table 2.7-7 compare the three models. Under LIME, EF was
21

LIME2_C2.7-C2.9_2014

calculated by the use of Tanaka’s calculation method for sound species and the endangered
type II, the population of which is thought to be almost constant, and by the use of theLande-Orzack model for the endangered classes IA and IB, the population of which is thought
to be decreasing.
Table 2.7-7: Comparison of three models
Canonical
Tanaka’s calculation
model
methodConsideration of propagation
potential
Consideration of environmental
stochasticity
Consideration of demographic
stochasticity
Consideration of carrying capacity(density effect)
Treatment of non-steady state
Expressed in the form of functionof extinction probability?
Treatment under LIME
(Target group of species)

0.2





































Not used

Used for assessmentof sound species andendangered type II

Used for assessmentof endangeredclasses IA and IB

0.4

0.6

Extinction
絶滅確率
probability

1.E+00
1.E-01 0
1.E-02
1.E-03
1.E-04
1.E-05
1.E-06
1.E-07
1.E-08
1.E-09
1.E-101.E-11
1.E-12
1.E-13
1.E-14
1.E-15Lande-Orzack model

0.8

1

Algae
藻類
Crustacea
甲殻類
ns

魚類

FishesExposure暴露濃度／α
concentration/aFigure 2.7-10: Relation between exposure concentration and extinction probability (sound species)
Sound species: when the variation coefficient of the intrinsic natural growth rate by environmental
stochasticity is 0.58cAssessment of EF for sound species

The parameters used for the calculation of the extinction risk of sound species were set as
follows:
22

LIME2_C2.7-C2.9_2014

1) Proportionality factor (indicated as c)
The proportionality factor was set so that the extinction probability would become 1 when the
exposure concentration is α (α is defined as the degree of concentration at which the intrinsic
natural growth rate becomes 0 under LIME).
2) Intrinsic natural growth rate (the initial value is indicated as r0)
The initial value r0 of the intrinsic natural growth rate of algae, crustaceans, and fishes r(x)was set based on exiting literature (Tanaka et al. 2000, Tanaka et al. 2001a, Tanaka et al.
2001b, Tanaka 2000) and results of interviews with experts (Table 2.7-8). The geometricaverage value for crustaceans and fishes was used for the other species. The relation
expressed by Equation 2.7-8 was used for changes in the amount of exposure x.
3) Dispersion of the increase rate due to environmental stochasticity (the initial value isindicated as ν0)
The initial value ν0 of the dispersion of the increase rate due to environmental stochasticity
concerning algae, crustaceans, and fishes υ(x) was set based on literature (Tanaka 2000,
Weinberger et al. 1982, Hakoyama et al. 2000) and experts’ advice (Table 2.7-8). The
geometric average value for crustaceans and fishes was used for the other species. The
relations expressed by Equations 2.7-11 and 2.7-12 were used for a changes due to theamount of exposure x.
Table 2.7-8: List of parameters used for assessment of sound species
Crustace
Crustaceans,ans, etc. Fishe Shellfis
etc. (otherAlgae(crustace
s
hes
than
ans)
crustaceans)

Initial value of intrinsic
natural growth rate r0

41.2

1.5

0.171

0.506

Initial value of dispersionof increase rate due toenvironmental change v 0

82.4

3.0

0.342

1.01

Carrying capacity K

1015

1012

106

109

Amphi
bians

4) Carrying capacity (indicated as K)
The value obtained from outdoor data on water fleas in Lake Kasumigaura was used forcrustaceans, and the carrying capacities for algae and fishes were calculated based on thevalue and by reference to the ratio specified in Lande (1993) (Table 2.7-8). The geometricaverage value for crustaceans and fishes was used for the other species.Figure 2.7-10 showsthe extinction probability of each species calculated by application of these parameters to
Tanaka’s calculation method.This result can be interpreted as the D-R relationship for risk
assessment, and makes it possible to calculate EF. That is, EF is the slope of the curve of
extinction probability for the current exposure concentration.
In the case of sound species, because the population is very large, if the amount of exposure
23

LIME2_C2.7-C2.9_2014

to a chemical is almost zero, the extinction probability becomes very small. In addition, if
the amount of exposure is less than α, an increase in the extinction probability due to exposure
to the chemical becomes relatively small (for example, even if the exposure concentration
increases from zero to 0.2 α, the extinction risk of algae increases by only about 1.0 × 10-14).
d

Assessment of EF for endangered type II

The following parameters were used for the assessment of the extinction risk of endangered
type II.
1) Proportionality factor (indicated as c)
The proportionality factor was set so that the extinction probability would become 1 when the
exposure concentration disperses at an increase rate with the probability of α.
2) Intrinsic natural growth rate (the initial value is indicated as r0)
The initial value r0 of the intrinsic natural growth rate of algae, crustaceans, and fishes r(x)was set based on existing literature (Tanaka et al. 2000, Tanaka et al. 2001a, Tanaka et al.
2001b, Tanaka 2000) and results of interviews with experts (Table 2.7-9). The geometricaverage value for crustaceans and fishes was used for the other species. The relation
expressed by Equation 2.7-8 was used for changes in the amount of exposure.
Table 2.7-9: List of parameters used for assessment of endangered type II
Crustacea
Crustaceans,ns, etc.
Shellfish
Algae
Fishesetc. (other than(crustacea
escrustaceans)ns)

Initial value of intrinsic
natural growth rate r0

41.2

1.5

0.171

0.506

Initial value of dispersion ofincrease rate due toenvironmental change ν0

82.4

3.0

0.342

1.01

Carrying capacity K

1000

1000

1000

1000

Amphi
bians

3) Dispersion of the increase rate due to environmental stochasticity (the initial value isindicated as ν0)
The initial value ν0 of the dispersion of the increase rate due to environmental stochasticity
concerning algae, crustaceans, and fishes ν(x) was set based on literature (Tanaka at al. 2000,
Tanaka et al. 2001a, Tanaka et al. 2001b) and results of interviews with experts (Table 2.7-9).
The geometric average value for crustaceans and fishes was used for the other species. The
relations expressed by Equations 2.7-11 and 2.7-12 were used for changes according to theamount of exposure.
4) Carrying capacity (indicated as K)
K was set at 1000 by the use of the threshold value of the population condition (mature
population should be less than 1000) among the quantitative requirements specified in the24

LIME2_C2.7-C2.9_2014

Ministry of the Environment’s category definitions (Table 2.7-9). Figure 2.7-11 shows theextinction probability of each species calculated by application of these parameters to
Tanaka’s calculation method. In this case, because the population is smaller than that forsound species, the extinction risk when the exposure concentration is 0 is higher than that forsound species. In addition, when the exposure concentration is well smaller than α, theincrement in the extinction probability due to an increase in the amount of exposure is larger
than that for sound species. The slope of the extinction probability at the current exposure
concentration is EF.
1.E+00
0.2

0.4

0.6

0.8

probability

絶滅確率
Extinction01

1.E-01Algae
藻類
Crustacea
甲殻類
ns

魚類

Fishes

1.E-02

1.E-03

Exposure暴露濃度／α
concentration/aFigure 2.7-11: Relation between exposure concentration and extinction probability(endangered type II)
Endangered type II: when the variation coefficient of environmental changes is 0.58eAssessment of EF for the endangered type IB

The following parameters were used for the assessment of the extinction risk of theendangered type IB.
1) Intrinsic natural growth rate (the initial value is indicated as r0)
In the state of no exposure (the initial state), the initial value of the intrinsic natural growthrate r(x) was set by the use of the threshold value of the decrease rate condition (thepopulation will decrease by more than 20% in the next five years) among the qualitativerequirements specified in the Ministry of the Environment’s category definitions (r0 = -0.045(Table 2.7-10)). The relation expressed by Equation 2.7-17 was used for changes according
to the amount of exposure (Table 2.7-10).
2) Dispersion of the increase rate due to environmental stochasticity (the initial value isindicated as ν0)
There are no referential quantitative data. The initial value ν0 of the dispersion of theincrease rate due to environmental stochasticity ν(x) was set so as to meet the threshold valueof the extinction probability condition (the probability that the species will become extinct
within 20 years is less than 20%) among the quantitative requirements specified in the
Ministry of the Environment’s category definitions (G (20|n0) = 0.2; that is, ν0 = 0.72 (Table
2.7-10)). The relations expressed by Equations 2.7-18 and 2.7-19 were used for changes
according to the amount of exposure.
25

LIME2_C2.7-C2.9_2014

Table 2.7-10: List of parameters used for assessment of the endangered type IB
Crustace
Crustaceans,ans, etc.
Shellfietc. (other
Algae
FishesAmphibians(crustace
shes
than
ans)
crustaceans)

Initial value of intrinsic natural
growth rate r0-0.045Initial value of dispersion ofincrease rate due toenvironmental stochasticity σ20

0.72

Carrying capacity K

2503) Carrying capacity (indicated as K)
The threshold value of the population condition (mature population should be less than 250)
among the quantitative requirements specified in the Ministry of the Environment’s category
definitions was used (K = 250 (Table 2.7-10)).
4) Period for calculation of extinction probability (indicated as t)
The period was temporarily set at 50 years (which will finally be converted to risk per year).
These parameters were assigned in the Lande-Orzack model to calculate the extinction
probability. Figures 2.7-12 and 2.7-13 show the time-series cumulative extinction
probability if the amount of extinction is 0, and the relation between the exposure
concentration and the extinction probability when 50 years pass.
Because the increase rate is negative and the initial population is small in the case of thespecies belonging to endangered type IB, the extinction probability is high even if there is no
exposure to a chemical (the probability that extinction occurs within 50 years is about 50%).
An increase in the amount of exposure to chemicals caused an increase in the extinction risk,
with the result that the extinction risk has become almost 100% due to about 10% exposure to
LC50. In addition, difference in extinction probability according to whether there isconsideration for environmental changes is small. After this result was converted into theextinction risk per year to find the slope at the current exposure concentration, EF was
calculated.
1

Cumulative
累積絶滅確率probability
extinction

0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1

10

100

1000

10000

Time時間t [years]

Figure 2.7-12: Endangered type IB’s cumulative extinction probability until the point of time t (ifthere is no exposure to a chemical)

26

LIME2_C2.7-C2.9_2014

Extinction probability
50年以内の絶滅確率
within 50 years

1
0.9
0.8
0.7
0.6

Variation
変動係数＝０coefficient=0
Variation
変動係数＝５．０
coefficient=5.0

0.5
0.4
0.3
0.2
0.1
0
0

0.1

0.2

0.3

0.4

0.5

暴露濃度／LC50
Exposure
concentration /LC50

Figure 2.7-13: Relation of exposure concentration and extinction probability concerning endangered
type IB(when 50 years pass)f

Assessment of EF for endangered type IA

The following parameters were used for the assessment of the extinction risk of endangered
type IA.
1) Intrinsic natural growth rate (the initial value is indicated as r0)
In the state of no exposure (the initial state), the initial value of the intrinsic natural growthrate r(x) was set by the use of the threshold value of the decrease rate condition (thepopulation will decrease by more than 50% in the next 10 years) among the qualitativerequirements specified in the Ministry of the Environment’s category definitions (r0 = -0.096(Table 2.7-11)). The relation expressed by Equation 2.7-17 was used for changes according
to the amount of exposure.
2) Dispersion of the increase rate due to environmental stochasticity (the initial value isindicated as ν0)
There are no referential quantitative data. The initial value ν0 of the dispersion of theincrease rate due to environmental stochasticity ν(x) was set so as to meet the threshold valueof the extinction probability condition (the probability that the species will become extinct
within 3 years is less than 25%) among the quantitative requirements specified in the Ministryof the Environment’s category definitions (G (3|n0) = 0.25; that is, ν0 = 2.5 (Table 2.7-11)).
The relations expressed by Equations 2.7-18 and 2.7-19 were used for changes according to
the amount of exposure.
3) Carrying capacity (indicated as K)
The threshold value of the population condition (mature population should be less than 50)
among the quantitative requirements specified in the Ministry of the Environment’s category
definitions was used (K = 50 (Table 2.7-11)).
4) Period for calculation of extinction probability (indicated as t)
The period was temporarily set at 50 years (which, however, will finally be converted to risk
per year).
27

LIME2_C2.7-C2.9_2014

Table 2.7-11: List of parameters used for assessment of endangered type IA
Crustace
Crustaceans,
Alga ans, etc.
Shellfietc. (otherFishesAmphibianse(crustace
shes
than
ans)
crustaceans)

Initial value of intrinsic natural
growth rate r0-0.096

Initial value of dispersion ofincrease rate due to environmental
stochasticity σ20

2.5

Carrying capacity K

50

1Cumulative
extinction probability

0.9
0.8

0.7
ｦ
・ 0.6
m
・
ﾅ
・
・ 0.5
ﾏ
・
ﾝ
・ 0.4
0.3
0.2
0.1
0
1

10

100

1000

10000

Time 時間t (years)

Figure 2.7-14: The endangered type IA’s cumulative extinction probability until the point of time t (ifthere is no exposure to a chemical)

Extinction probability
50年以内の絶滅確率
within 50 years

1
0.9
0.8
0.7
0.6

変動係数＝０
Variationcoefficient=0

0.5
0.4

Variationcoefficient=5.0
変動係数＝５．０

0.3
0.2
0.1
0
0

0.1

0.2

0.3

0.4

0.5

暴露濃度／LC50
Exposure
concentration/LC50

Figure 2.7-15: Relation of exposure concentration and extinction probability concerning endangered
type IA (when 50 years pass)

These parameters were assigned in the Lande-Orzack model to calculate the extinction
probability. Figures 2.7-14 and 2.7-15 show the time-series cumulative extinction
probability if the amount of extinction is 0, and the relation between the exposure
concentration and the extinction probability when 50 years pass.
28

LIME2_C2.7-C2.9_2014

Like endangered type IB, endangered type IA already have very high extinction probability(50 years after, about 80%) even if they are not exposed to chemicals, and the result showedthat exposure to about 10% of LC50 will lead to extinction almost without fail.
After this result was converted into the extinction risk per year to find the slope at the current
exposure concentration, EF was calculated for each chemical.
(6)

Arrangement of the damage function and damage factor of ecotoxicity

The calculation results and parameters gained so far were entered in Equation 2.7-4 to find thedamage factor of ecotoxicity. With regard to some chemicals, Table 2.7-12 shows theparameters used for the calculation of the damage function and the calculation results.
(7)

Characteristics of the damage factor of ecotoxicity

The following are characteristics of the damage factors of the five chemical substances shownin Table 2.7-12:
1) Benzene
If the damage factor is compared in terms of the place for release, it is greater in the following
order, from highest to lowest: release to water, release to soil, and release to the air. This isbecause of high efficiency in raising the concentration in surface water.
If EINES is compared among species, EINES of “other species” (the total for shellfishes,
crustaceans (other than crustaceans), and amphibians) is the highest. This is because LC50 ofshellfishes is lower than the other species’ toxicity, and shellfishes have strong toxicity. If
EINES is compared among endangered classes, EINES of endangered classes IA and IB ishigher concerning all the species, while EINES of endangered type II and sound species is 0.
2) Arsenic
If the damage factor is compared in terms of the place for release, it is greater in the following
order, from highest to lowest: release to water, release to soil, and release to the air. This isbecause of high efficiency in raising the concentration in surface water. However, comparedwith benzene, the damage factor is larger and the differences among the places for release aresmaller.
If EINES is compared among species, EINES of algae is highest. This is because EC50 ofalgae is lower than the other species’ toxicity, and algae have strong toxicity. If EINES iscompared among endangered classes, EINES of endangered classes IA and IB is higher andoccupies most of the damage factor concerning all the species. Although EINES ofendangered type II and sound species is higher than that of benzene, its contribution to thedamage factor is small.
3) Trichloroethylene
If the damage factor is compared in terms of the place for release, it is greater in the following
order, from highest to lowest: release to water, release to soil, and release to the air. This isbecause of high efficiency in raising the concentration in surface water. However, comparedwith benzene, the damage factor is smaller.
29

LIME2_C2.7-C2.9_2014

If EINES is compared among species, EINES of “other species” is the highest. This isbecause LC50 of trichloroethylene has strong toxicity against amphibians and the total EINES
for shellfishes, crustaceans (other than crustaceans), and amphibians has been entered. If
EINES is compared among endangered classes, like benzene, EINES of endangered classes
IA and IB is higher concerning all species, while EINES of endangered type II and the soundspecies is 0.
4) Dichloromethane
If the damage factor is compared in terms of the place for release, it is greater in the following
order, from highest to lowest: release to water, release to soil, and release to the air. This isbecause of high efficiency in raising the concentration in surface water. However, comparedwith benzene, the damage factor is smaller.
If EINES is compared among species, EINES of “other species” is the highest. This isbecause LC50 of dichloromethane is the smallest for crustaceans, etc. (crustaceans) and hasstrong toxicity against them and the total EINES for the three other types of species has beenentered. If EINES is compared among endangered classes, like benzene, EINES ofendangered classes IA and IB is higher concerning all species, while EINES of endangered
type II and sound species is 0.
5) Cadmium
If the damage factor is compared in terms of the place for release, it is greater in the following
order, from highest to lowest: release to water, release to soil, and release to the air. This isbecause of high efficiency in raising the concentration in surface water. However, comparedwith benzene, the damage factor is larger and the differences among the places where thechemical is released are smaller.
If EINES is compared among species, EINES of “other species” is highest. This is because
LC50 of amphibians is the lowest and has strong toxicity. Moreover, the toxicity against
crustaceans and algae is stronger than that against other species. If EINES is comparedamong endangered classes, EINES of endangered classes IA and IB is higher and occupies
most of the damage factor concerning all species. Although EINES of endangered type II
and sound species is higher than that of benzene, its contribution to the damage factor issmall.

30

LIME2（EINES/kg）

LIME2_C2.7-C2.9_2014

アルキル水銀
Alkyl

1E+00
スチレン
Styrene

1E-03
1E-06
1E-09

mercury

アクリルアミド
Acrylamide
ベンゼン
Benzeneペンタクロロニト
Pentachloroロベンゼン
nitrobenzene

Air
大気

1E-12
1E-15
1E-18

ペンタクロロフェ
Pentachloroノール
phenolWater
水域
アセトアルデヒド
Acetaldehyde

土壌
Soil

Ethyleneglycolエチレングリコールモ
monoethylether
ノエチルエーテル

1E-21
1E-24
1E-27
1E-27 1E-24 1E-21 1E-18 1E-15 1E-12 1E-09 1E-06 1E-03 1E+00
LIME1（EINES/kg）

Figure 2.7-16: Comparison between LIME 1 and LIME 2 (ecotoxicity)
The figure shows the chemicals whose damage factor greatly rose at the time of release to water.

31

LIME2_C2.7-C2.9_2014

Water

Soil

Algae

Air

Toxicity(Average ofEC50 or LC50
[μg/L]

7.06E+04

Division

Extinction
probability inordinary(current)time

No. of species

ChemicalEfficiency in raising concentration insurface water by place for release[(g/m3)/ (t/yr)]Species

Table 2.7-12: Parameters used for calculation of damage factors and calculation results (extracts concerning some chemicals)
Expected Increase in Number of Extinct Species (EINES)
due to additional release of 1[kg/yr]
Additional release to the air Additional release to water(1species)

Crustaceans
1.70E-08

2.95E-11
Fishes

1.38E-12

3.87E+04

Other†
2.65E-06

1.81E-06

Algae

9.68E-07(1species)(Wholeenvironment
in Japan)
5.31E-16

1.65E-02

0.00E+00

0.00E+00

1.69E-14

2.88E-13

3.12E-17

17

9.75E-03

0.00E+00

0.00E+00

7.88E-14

1.34E-12

1.35E-16

2.30E-15

Endangered (II)6

1.00E-03

0.00E+00

0.00E+00

0.00E+00

0.00E+00

0.00E+00

0.00E+00

Sound species

5500

1.00E-15

0.00E+00

0.00E+00

0.00E+00

0.00E+00

0.00E+00

0.00E+00

0.00E+00

1.63E-12

2.83E-15

Endangered (IA)

4.5

1.65E-02

6.59E-17

9.89E-17

8.67E-13

1.30E-12

1.50E-15

Endangered (IB)

4.5

9.75E-03

3.26E-16

4.89E-16

4.04E-12

6.05E-12

6.98E-15

1.05E-14

Endangered (II)17

1.00E-03

0.00E+00

0.00E+00

0.00E+00

0.00E+00

0.00E+00

0.00E+00

Sound species

100

1.00E-12

0.00E+00

0.00E+00

0.00E+00

0.00E+00

0.00E+00

0.00E+00

5.88E+16

7.35E-12

2.25E-15

1.27E-14

Endangered (IA)29

1.65E-02

0.00E+00

0.00E+00

3.10E-14

8.98E-13

5.20E-17

Endangered (IB)29

9.75E-03

1.39E-17

4.02E-16

1.44E-13

4.18E-12

2.43E-16

7.04E-15

Endangered (II)18

1.00E-03

0.00E+00

0.00E+00

0.00E+00

0.00E+00

0.00E+00

0.00E+00

Sound species

300

1.00E-06

0.00E+00

0.00E+00

0.00E+00

0.00E+00

0.00E+00

0.00E+00

4.02E+16

5.07E-12

1.51E-15

8.55E-15

Endangered (IA)47

2.32E-15

1.08E-13

2.86E-11

1.33E-09

4.96E-14

2.30E-12

Endangered (IB)55

1.02E-14

5.03E-13

1.25E-10

6.19E-09

2.17E-13

1.07E-11

Endangered (II)190

0.00E+00

0.00E+00

0.00E+00

0.00E+00

0.00E+00

0.00E+00

Sound species

1165

0.00E+00

0.00E+00

0.00E+00

0.00E+00

0.00E+00

0.00E+00

Subtotal

Arsenic(Wholeenvironment
in Japan)17

Subtotal
3.87E+01
3.24E+04
2.48E+05(1species)

Endangered (IB)Subtotal
Benzene(Wholeenvironment
in Japan)

Endangered (IA)Subtotal

1.38E+03

Additional release to soil

Total (DF EcoTox(Safe, X, R))
Endangered (IA)Endangered (IB)3.66E+01 Endangered (II)Sound species
Subtotal

17
17
6
5500

1.65E-02
9.75E-03
1.00E-03
1.00E-15

1.86E-09
8.65E-09
1.40E-16
6.98E-28

6.11E+13

7.52E-09

6.12E-13
3.16E-08
1.47E-07
8.38E-16
3.84E-24

7.54E-09
8.65E-08
4.02E-07
5.20E-15
2.38E-23

1.79E-07

5.09E-09
2.37E-08
8.66E-16
4.33E-27

4.89E-07

1.30E-11
3.48E-09
1.62E-08
4.35E-16
2.17E-27

1.31E-11
5.92E-08
2.75E-07
2.61E-15
1.20E-23
3.35E-0732

WaterSoil

9.68E-07

2.65E-06

1.81E-06

4.52E-12

4.79E-08

1.18E-10

FishesTrichloroethene

Crustaceans

Algae

Other†

Arsenic

FishesCrustaceans

Air

Toxicity(Average ofEC50 or LC50
[μg/L]

1.14E+03

2.20E+03

4.00E+03
7.97E+02
4.00E+01

Division

Endangered (IA)Endangered (IB)Endangered (II)Sound species
SubtotalEndangered (IA)Endangered (IB)Endangered (II)Sound species
SubtotalEndangered (IA)Endangered (IB)Endangered (II)Sound species
Subtotal

Total (DF EcoTox(Safe, X, R))
Endangered (IA)Endangered (IB)2.98E+05 Endangered (II)Sound species
SubtotalEndangered (IA)Endangered (IB)4.07E+04 Endangered (II)Sound species
SubtotalEndangered (IA)Endangered (IB)6.56E+04 Endangered (II)Sound species
Subtotal

No. of species

ChemicalEfficiency in raising concentration insurface water by place for release[(g/m3)/ (t/yr)]Species

LIME2_C2.7-C2.9_2014

Extinction
probability inordinary(current)
time

Expected Increase in Number of Extinct Species (EINES)
due to additional release of 1[kg/yr]
Additional release to the air Additional release to water(1species)

Additional release to soil

(Wholeenvironment
in Japan)(1species)(Wholeenvironment
in Japan)(1species)(Wholeenvironment
in Japan)

4.5
4.5
17
100

1.65E-02
9.77E-03
1.00E-03
1.00E-12

5.95E-11
2.77E-10
0.00E+00
3.03E-27

8.92E-11
4.15E-10
0.00E+00
3.03E-25

1.63E-10
7.57E-10
3.69E-18
1.53E-26

2.44E-10
1.13E-09
4.79E-17
1.53E-24

1.11E-10
5.18E-10
2.17E-18
9.09E-27

1.67E-10
7.77E-10
2.82E-17
9.09E-25

29
29
18
300

1.65E-02
9.76E-03
1.00E-03
1.00E-06

3.09E-11
1.44E-10
0.00E+00
0.00E+00

5.04E-10
8.95E-10
4.17E-09
0.00E+00
0.00E+00

8.45E-11
3.93E-10
0.00E+00
0.00E+00

1.38E-09
2.45E-09
1.14E-08
0.00E+00
0.00E+00

5.78E-11
2.69E-10
0.00E+00
0.00E+00

9.44E-10
1.68E-09
7.80E-09
0.00E+00
0.00E+00

5.78E-11
1.53E-09
7.49E-18
1.98E-23

5.06E-09
2.69E-09
7.58E-08
1.33E-15
2.30E-20

1.58E-10
4.19E-09
4.73E-17
1.27E-22

1.38E-08
7.35E-09
2.07E-07
8.42E-15
1.48E-19

1.08E-10
2.87E-09
2.38E-17
6.17E-23

9.48E-09
5.03E-09
1.42E-07
4.23E-15
7.19E-20

47
55
190
1165

7.85E-08

2.15E-07
1.13E-14
5.25E-14
0.00E+00
0.00E+00

7.19E-07
1.92E-13
8.93E-13
0.00E+00
0.00E+00

1.47E-07
2.78E-17
1.25E-16
0.00E+00
0.00E+00

4.92E-07
4.72E-16
2.12E-15
0.00E+00
0.00E+00

17
17
6
5500

1.65E-02
9.75E-03
1.00E-03
1.00E-15

0.00E+00
0.00E+00
0.00E+00
0.00E+00

2.63E-07
0.00E+00
0.00E+00
0.00E+00
0.00E+00

4.5
4.5
17
100

1.65E-02
9.75E-03
1.00E-03
1.00E-12

0.00E+00
3.47E-17
0.00E+00
0.00E+00

0.00E+00
0.00E+00
5.20E-17
0.00E+00
0.00E+00

8.28E-14
3.85E-13
0.00E+00
0.00E+00

1.09E-12
1.24E-13
5.78E-13
0.00E+00
0.00E+00

2.01E-16
9.49E-16
0.00E+00
0.00E+00

2.60E-15
3.02E-16
1.42E-15
0.00E+00
0.00E+00

29
29
18
300

1.65E-02
9.75E-03
1.00E-03
1.00E-06

0.00E+00
2.08E-17
0.00E+00
0.00E+00

5.20E-17
0.00E+00
6.04E-16
0.00E+00
0.00E+00

5.13E-14
2.39E-13
0.00E+00
0.00E+00

7.02E-13
1.49E-12
6.92E-12
0.00E+00
0.00E+00

1.28E-16
5.86E-16
0.00E+00
0.00E+00

1.73E-15
3.72E-15
1.70E-14
0.00E+00
0.00E+00

6.04E-16

8.4E-12

2.07E-1433

4.52E-12

4.79E-08

Soil

1.18E-10

Crustaceans

Algae

Trichloroethene

Water

9.92E-08

3.01E-09

Fishes

4.45E-11

Other†

Dichloromethane

Toxicity(Average ofEC50 or LC50
[μg/L]

Division

5.60E+04
7.50E+04
4.58E+04

Endangered (IA)Endangered (IB)Endangered (II)Sound species
Subtotal

Total (DF EcoTox(Safe, X, R))
Endangered (IA)Endangered (IB)3.15E+05 Endangered (II)Sound species
SubtotalEndangered (IA)Endangered (IB)3.55E+03 Endangered (II)Sound species
SubtotalEndangered (IA)Endangered (IB)4.00E+05 Endangered (II)Sound species
SubtotalEndangered (IA)
3.14E+04 Endangered (IB)2.38E+04 Endangered (II)2.88E+04 Sound species
Subtotal
Total (DF EcoTox(Safe, X, R))

No. of species

Air

Other†

ChemicalEfficiency in raising concentration insurface water by place for release[(g/m3)/ (t/yr)]Species

LIME2_C2.7-C2.9_2014

Extinction
probability inordinary(current)
time

47
55
190
1165

Expected Increase in Number of Extinct Species (EINES)
due to additional release of 1[kg/yr]
Additional release to the air Additional release to water(1species)
0.00E+00
2.86E-17
0.00E+00
0.00E+00

Additional release to soil

(Wholeenvironment
in Japan)(1species)(Wholeenvironment
in Japan)(1species)(Wholeenvironment
in Japan)

0.00E+00
1.41E-15
0.00E+00
0.00E+00

6.01E-14
3.18E-13
0.00E+00
0.00E+00

2.79E-12
1.57E-11
0.00E+00
0.00E+00

1.45E-16
7.76E-16
0.00E+00
0.00E+00

6.77E-15
3.84E-14
0.00E+00
0.00E+00

1.41E-15

1.85E-11
2.21E-14
1.03E-13
0.00E+00
0.00E+00

2.87E-11
3.76E-13
1.75E-12
0.00E+00
0.00E+00

4.52E-14
6.70E-16
3.12E-15
0.00E+00
0.00E+00

7.02E-14
1.14E-14
5.31E-14
0.00E+00
0.00E+00

17
17
6
5500

1.65E-02
9.75E-03
1.00E-03
1.00E-15

0.00E+00
4.16E-17
0.00E+00
0.00E+00

2.07E-15
0.00E+00
7.08E-16
0.00E+00
0.00E+00

4.5
4.5
17
100

1.65E-02
9.75E-03
1.00E-03
1.00E-12

8.78E-16
4.09E-15
0.00E+00
0.00E+00

7.08E-16
1.32E-15
6.14E-15
0.00E+00
0.00E+00

1.96E-12
9.14E-12
0.00E+00
0.00E+00

2.12E-12
2.95E-12
1.37E-11
0.00E+00
0.00E+00

5.97E-14
2.78E-13
0.00E+00
0.00E+00

6.45E-14
8.95E-14
4.16E-13
0.00E+00
0.00E+00

29
29
18
300

1.65E-02
9.75E-03
1.00E-03
1.00E-06

0.00E+00
3.47E-17
0.00E+00
0.00E+00

7.45E-15
0.00E+00
1.01E-15
0.00E+00
0.00E+00

1.74E-14
8.10E-14
0.00E+00
0.00E+00

1.66E-11
5.05E-13
2.35E-12
0.00E+00
0.00E+00

5.27E-16
2.45E-15
0.00E+00
0.00E+00

5.06E-13
1.53E-14
7.11E-14
0.00E+00
0.00E+00

1.00E-16
5.26E-16
0.00E+00
0.00E+00

1.01E-15
4.67E-15
2.60E-14
0.00E+00
0.00E+00

2.29E-13
1.18E-12
0.00E+00
0.00E+00

2.85E-12
1.07E-11
5.86E-11
0.00E+00
0.00E+00

6.96E-15
3.60E-14
0.00E+00
0.00E+00

8.64E-14
3.24E-13
1.78E-12
0.00E+00
0.00E+00

47
55
190
1165

3.07E-14

6.93E-11

2.10E-12

3.99E-14

9.09E-11

2.76E-1234

WaterSoil

Crustaceans

Algae

Air

5.88E-07

1.90E-06

1.68E+02

9.00E+01

9.08E-07

Other†

FishesCadmium

Toxicity(Average ofEC50 or LC50
[μg/L]

2.18E+03

1.97E+03
2.50E+03
4.00E+01

Division

Endangered (IA)Endangered (IB)Endangered (II)Sound species
SubtotalEndangered (IA)Endangered (IB)Endangered (II)Sound species
SubtotalEndangered (IA)Endangered (IB)Endangered (II)Sound species
SubtotalEndangered (IA)Endangered (IB)Endangered (II)Sound species
Subtotal

Total (DF EcoTox(Safe, X, R))†

No. of species

ChemicalEfficiency in raising concentration insurface water by place for release[(g/m3)/ (t/yr)]Species

LIME2_C2.7-C2.9_2014

Extinction
probability inordinary(current)
time

Expected Increase in Number of Extinct Species (EINES)
due to additional release of 1[kg/yr]
Additional release to the air Additional release to water(1species)

Additional release to soil

(Wholeenvironment
in Japan)(1species)(Wholeenvironment
in Japan)(1species)(Wholeenvironment
in Japan)

17
17
6
5500

1.65E-02
9.78E-03
1.00E-03
1.00E-15

2.46E-10
1.15E-09
6.29E-18
3.06E-29

4.19E-09
1.95E-08
3.77E-17
1.68E-25

7.98E-10
3.71E-09
4.68E-17
2.34E-28

1.36E-08
6.31E-08
2.81E-16
1.29E-24

3.81E-10
1.77E-09
1.21E-17
6.13E-29

6.47E-09
3.01E-08
7.29E-17
3.37E-25

4.5
4.5
17
100

1.65E-02
9.83E-03
1.00E-03
1.00E-12

4.59E-10
2.14E-09
1.47E-17
5.82E-26

2.37E-08
6.89E-10
3.20E-09
1.92E-16
5.82E-24

1.49E-09
6.91E-09
1.20E-16
4.78E-25

7.67E-08
2.23E-09
1.04E-08
1.56E-15
4.78E-23

7.09E-10
3.30E-09
3.14E-17
1.26E-25

3.66E-08
1.06E-09
4.95E-09
4.09E-16
1.26E-23

29
29
18
300

1.65E-02
9.75E-03
1.00E-03
1.00E-06

1.89E-11
8.80E-11
0.00E+00
0.00E+00

3.89E-09
5.49E-10
2.55E-09
0.00E+00
0.00E+00

6.12E-11
2.85E-10
0.00E+00
0.00E+00

1.26E-08
1.78E-09
8.26E-09
0.00E+00
0.00E+00

2.92E-11
1.36E-10
0.00E+00
0.00E+00

6.01E-09
8.48E-10
3.94E-09
0.00E+00
0.00E+00

4.27E-11
9.63E-10
2.99E-18
7.72E-24

3.10E-09
1.99E-09
4.77E-08
5.32E-16
8.99E-21

1.38E-10
3.12E-09
2.56E-17
7.06E-23

1.00E-08
6.42E-09
1.54E-07
4.55E-15
8.21E-20

6.60E-11
1.49E-09
6.65E-18
1.72E-23

4.79E-09
3.07E-09
7.36E-08
1.18E-15
2.00E-20

47
55
190
1165

4.96E-08

1.61E-07

7.67E-08

8.03E-08

2.60E-07

1.24E-07

The spaces for toxicity values (LC50 average) concerning “other” species show the values of LC50 for shellfishes, crustaceans (other than crustaceans), and amphibians. With regard to the procedure for calculationof the Expected Increase in Number of Extinct Species (EINES), the extinction probability was first assessed from the increase in the concentration and the toxicity value for each of the three groups of species.
After the result was multiplied by the number of species belonging to each group of species, the total was calculated. The spaces for “whole environment in Japan” show the total of EINES of the three groups ofspecies. The spaces for “1 species” shows the total divided by the total number of species.

35

LIME2_C2.7-C2.9_2014(8)Comparison of damage factors between LIME 1 and LIME 2

Figure 2.7-16 shows the results of comparison between the newly calculated damage factors
under LIME 2 and the existing ones under LIME 1. Although there is generally no great
difference between LIME 1 and LIME, damage factors greatly rose concerning somechemicals. This is because LIME 2 greatly reflects the impact on the species other thanalgae, fishes, and crustaceans. If chemicals have strong toxicity especially against the
species other than algae, fishes, and crustaceans (amphibians, shell, etc.), although LIME 1
could not accurately assess toxicity because the average toxicity for algae, fishes, andcrustaceans was applied, LIME 2 can accurately assess the toxicity against amphibians andshell.
The following are chemicals whose damage factors sharply rose (by aone-or-more-digit percentage).
[Chemicals whose damage factors sharply rose (examples)]• Alkyl mercury• Styrene• Acrylamide• Benzene• Acetaldehyde• Pentachloronitrobenzene• Ethyleneglycolmonoethylether• Pentachlorophenol
2.7.4 Procedure for impact assessment of ecotoxicity
Concrete procedures for characterization of ecotoxicity and assessment of damage are as
described below.
Users can select characterization, damage assessment, or integration according to purpose anduse it for LCA.
In the characterization, the impact of the release of a toxic chemical to the ecosystem isassessed by separating the terrestrial ecosystem from the aquatic ecosystem. The
characterization results CITerr and CIAqua can be obtained from the inventory Inv (X, R) to R,
the place for release of the toxic chemical X, and the characterization factors CFTerr (X, R) and
CFAqua (X, R).
CI Terr  CFX

CI Aqua 

Terr

 CF
X

( X , R)  Inv ( X , R)

(2.7-19)( X , R)  Inv ( X , R)

(2.7-20)

R

Aqua

R

Because the characterization factors CFTerr (X, R) and CFAqua (X, R) differ according to R, theplace for release of the toxic chemical, it is necessary to divide the inventory data by R (air,water, or soil).
With regard to the characterization factors CFTerr (X, R) and CFAqua (X, R), it isrecommendable that LIME should use the characterization factors TETP and AETP obtained
from the comparison between fate analysis results based on the environmental conditions inJapan and the maximum permissible amount of toxic chemicals for conservation of species.
CITerr and CIAqua are regarded as the total amount of released toxic chemicals converted into
the amount of benzene released to soil, the typical causative chemical, and the total amount of
released toxic chemicals converted into the amount of benzene released to water, respectively.
36

LIME2_C2.7-C2.9_2014

The damage assessment is carried out concerning the aquatic ecosystem (algae, crustaceans,
etc. (crustaceans), fishes, shellfishes, crustaceans, etc. (other than crustaceans) andamphibians). The damage assessment result DI (Safe) can be obtained from Inv (X, R) of
toxic chemicals and the damage factor DFEcoTox (Safe, X, R) of the object of protection Safe.
DI (Safe) 

 DF
X

EcoTox( Safe，X , R)  Inv ( X , R)

(2.7-21)R

As in the case of characterization factors, the damage factor DFEcoTox (Safe, X, R) differs
according to R, the place for release of the toxic chemical, it is necessary to divide theinventory data by R.
DI (Safe) means the amount of potential damage against the object of protection Safe due tothe release of a toxic chemical. This enables damage assessment concerning biodiversity.
If the object of protection is the same, it is possible to compare or integrate it with the amountof damage arising through different impact categories – for example, the impact of landtransformation on biodiversity.
The integration factor into which impact on biodiversity is economically converted or
non-dimensionalized, the integration factor IFEcoTox (X, R), is used for integration. The
integration index SI can be obtained from each chemical’s Inv (X, R) and integration factor
IFEcoTox (X, R). The results can be compared directly with or added to assessment results inother impact categories.
SI  IFX

EcoTox(X,R)  Inv(X, R)(2.7-22)R

The characterization factor CFTerr (X, R) has been attached hereto as A1.
The
characterization factor CFAqua (X, R) and the damage factor DFEcoTox (Safe, X, R) have been
attached hereto as A2. The integration factor IFEcoTox (X, R) has been attached hereto as A3.

37

LIME2_C2.7-C2.9_2014

References for Section 2.7]
Guinée, et al. (1996): Int. J. LCA, 1 : 133 - 138
Guinée J, Heijungs R, van Oers L, van de Meent D, Vermeire, Rikken M (1996): LCA Impact Assessment of
Toxic Releases, CML, RIVM, VROM The Hague
Hakoyama H, Iwasa Y (2000): Journal of Theoretical Biology, 204: 337 - 359
Hauschild, W (1998): Environmental Assessment of Products. Volume 2: Scientific background. Chapman &Hall, London
Heijungs, et al. (1992): Environmental Life Cycle Assessment of Products. Guide and Backgrounds. CML,
Leiden University, Leiden
Hertwich (1999): Toxic Equivalency: Addressing Human Health Effects in Life Cycle Impact Assessment.
Thesis, University of California, Berkeley
Huijbregts (1999): Priority Assessment of Toxic Substances in LCA. Development and Application of theMulti-media Fate, Exposure and Effect Model USES-LCA. IVAM environmental research, University of
Amsterdam, Amsterdam
Jolliet O, Crettaz P (1997): Critical surface-time 95 A life cycle impact assessment methodology including fateand exposure, Swiss Federal Institute of Technology, Institute of Soil and Water Management, LausanneLande R (1993): Risks of Population Extinction from Demographic and Environmental Stochasticity andRandom Catastrophes, American Naturalist, 142 (6): 911 - 927
Lande R (1998): Anthropogenic, Ecological, and Genetic Factors in Extinction and Conservation, Researches on
Population Ecology, 40 (3): 259 - 269
Lande R, Orzack SH (1988): Extinction dynamics of age-saturated populations in a fluctuating environment,
Proc. Natl. Acad. Sci. USA, 85: 7418 - 7421
Tanaka Y, Nakanishi J (2000): Environmental Toxicology and Chemistry, 19: 2856 - 2862
Tanaka Y, Nakanishi J (2001a): Environmental Toxicology and Chemistry, 20: 1857 - 1865
Tanaka Y, Nakanishi J (2001b): Model Selection and Parameterization of the Concentration-Response Functions
for Population-Level Effects, Environmental Toxicology and Chemistry, 20 (8): 1857 - 1865
USEPA: ECOTOX. http://www.epa.gov/cgi-bin/ecotox_search
Weinberger P, Rea M (1982): Effects of Aminocarb and Its Formulation Adjuncts on the Growth of Chlorella
Pyrenoidosa Chic, Environmental and Experimental Botany, 22 (4): 491 - 496
Keidanren (2000): Report on the Results of the Third Keidanren PRTR (Pollutant Release and Transfer Register)
Research
Iwasa Y (1997): Population Stochasticity and Extinction Risk Assessment – Biodiversity and Conservation,
Shokabo Publishing: 106 - 114
Tanaka Y (2000): Ecological Risk Analysis of Chemicals – Development into Population Level Impact Analysis,
Third International Workshop on Risk Assessment and Risk Management of Chemicals: 34 - 43
Fuji Research Institute (1999): Research Report on Annual Total Environmental Impact Values (Standard Values)
Washitani I, Yahara T (1996): Introduction to Conservation Ecology – from Genes to Landscapes, Bun-ichi Sogo
Shuppan

38

LIME2_C2.7-C2.9_2014

2.8 Eutrophication
2.8.1 What phenomenon is eutrophication?
The term “eutrophication” is often used to mean an increase in the amount of nutrient salts(mainly nitrogen and phosphorus) flowing into closed water areas, such as inner bays andlakes. Nutrient salts, such as nitrogen and phosphorus, are essential elements for organisms.
Usually, the photosynthesis of algae, which is represented by phytoplankton in water areas, islimited by shortage of nutrient salts. Because of this, an increase in the concentration ofnutrient salts in water promotes the photosynthesis of algae – that is, the primary productionof organic substances. For example, there is a thesis that defines the state of eutrophicationby the speed of supply of organic carbon into the ecosystem, using the speed of the primary
production of algae in water, which is easier to measure than the inflow amount of nutrient
salts (substance of concern) into water (Table 2.8-1) (Nixon 1995).
At the initial stage, eutrophication in water usually means that an increase in the concentrationof nutrient salts results in a rise in the speed of primary production, enriching the productionof the organisms in the whole ecosystem, including fishery production, through the food chain
within the ecosystem. However, if the degree of eutrophication becomes higher, various
unfavorable effects arise, including hindrance to the sound circulation of substances in theaquatic ecosystem. Because of this, the term “eutrophication” is usually used as an indicatorof not only progress in the nutritional condition in water but also such effects as progress inorganic pollution and occurrence of a suboxic phenomenon. In other words, because theeffects of eutrophication greatly differ according to water condition, eutrophication may
increase fishery production or have unfavorable effects on the aquatic ecosystem.
Table 2.8-1: Trophic levels in the sea (Nixon 1995)
Organic carbon supply (primary production)
Trophic levelgCm-2y-1
Oligotrophic
100
Mesotrophic
Eutrophic
Hypertrophic

100-300
301-500> 500

Column 2.8-1
Eutrophication in Europe
Although eutrophication is regarded as an issue related to water in Japan, the term is also used
for the phenomenon of an increase in the amount of nitrogen included in soil in Europe. Soilenvironments where a shortage of nitrogen limits the growth of plants generally exist. An
increase in the amount of nitrogen is drawing attention because it has effects on the plants andalgae struggling for existence under a limited amount of nitrogen, such as causing a change inthe species composition, increasing productivity, decreasing the diversity of species, orchanging tolerance to frost, drought, and other stresses (UNEP/RIVM 1999).
In Japan also, there is the problem that an increase in the amount of nitrogen in soil causes
health damage due to groundwater pollution related to nitrate-nitrogen and nitrite-nitrogen.
In July 2001, the Ministry of the Environment added ammonia, ammonium compounds,
nitrous acid compounds, and nitric compounds to the toxic substances and placed them under
the regulation of underground seepage. In Japan, however, an increase in the amount of
nitrogen in soil or groundwater is hardly treated as a problem related to eutrophication.
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LIME2_C2.7-C2.9_2014(1)

Causal relationships of eutrophicationa

Generation of substances of concern

The sources of substances of concern related to eutrophication (nutrient salts and organic
substances) can be divided into daily, industrial, livestock, and agricultural water pollutants,
as well as water pollutants flowing from urban areas. Generally, there are three types ofchannels through which substances of concern flow into closed water areas: 1) rivers; 2)
direct release from a coast to a closed water area; and 3) the air. In Japan, 1) and 2) occupy alarge percentage in light of quantity (Water Pollution Control Division, Water Quality Bureau,
Ministry of the Environment 1996). Especially in inner bays where the population andindustries concentrate in basins, the amount of substances of concern is great and waterpollution due to the inflow of organic substances and eutrophication due to the inflow ofnitrogen and phosphorus have great impact on coastal environments. If the circulation ofsubstances is considered from a somewhat wider viewpoint, huge amounts of nitrogen andphosphorus brought in from the outside (domestic and overseas areas) as food, industrial
materials, and fertilizer finally flow into seas after being used for human activities, except for
those that accumulate in terrestrial areas, are transferred to other basins or are exported tooverseas.
As measures for reducing the amount of generated substances of concern and the amount of
those flowing into water areas, the regulation of drainage and the improvement of seweragefacilities have been steadily promoted under the Water Pollution Control Act. However,further efforts are still needed for improving water pollution due to the inflow of organic
substance and water pollution caused by the inflow of nitrogen and phosphorus, about which
the biochemical oxygen demand (BOD) and the chemical oxygen demand (COD) are used asindices. Because of this, in the wide closed sea areas where it is difficult to fulfill theenvironmental standards only by the regulation of the concentration of wastewater, total
volume control has been established to reduce the total volume of pollutants flowing into theareas with consideration for not only the concentration but also the volume of wastewater.
At present, the seventh total volume control, whose target year is FY2014, is underconsideration.
Table 2.8-2 shows the amount of generated eutrophication substances:
Table 2.8-2: Amount of generated eutrophication substances (UNEP/RIVM 1999)[tons/day]
COD

NitrogenPhosphorus

FY1979FY1994

FY1979FY1994

FY1979FY1994

Tokyo BayIse Bay

477
307

286
246

365
197

281
174

41.4
26.0

23.0
18.9

Seto Inland Sea

1010

746

709

736

66.0

42.6

b

Physical process

The impact of eutrophication is closely connected with the flow in the water area, thedevelopment of density stratification, formation of a front (a border between different
seawaters), and mixing of water masses.
For example, in a water area, if low-density water (light water) is formed above high-density
40

LIME2_C2.7-C2.9_2014

water (heavy water) (this is called “stratification”), the water area becomes stable in light ofdensity and vertical mixing becomes difficult. This formation of stratification strongly
influences eutrophication damage.
The density of seawater depends on water temperature, salinity, and pressure. Because achange in the density due to pressure is small in a shallow coastal area, the density depends onwater temperature and salinity. In addition, the strength of stratification in a coastal area
depends on the balance between the stability and instability of the density. The factors forstability are phenomena that facilitate a decrease in the density near the surface, such as the
inflow of fresh water and the heating of the sea surface, while the factors for instability
include a disturbance of seawater caused by tides or wind waves (Figure 2.8-1). The
stratification of salt and water temperature, which can be seen in almost all the coastal areas inJapan, is divided into the upper layer for the photosynthesis process (organic substanceproduction process) and the lower layer for the process of mineralizing organic substances(process of consuming oxygen) (Fenchel 1992). Suboxic water mass, the most serious
problem among the effects of eutrophication, is not produced only as a result of a rise in theconcentration of nutrient salt in a water area. It also occurs under physical conditions wherea stationary water mass with little vertical mixing is formed.
Moreover, one of the characteristics of inner bays, where eutrophication is likely to occur, is aflow called estuary circulation. Eutrophication is likely to occur at the mouth of a river,which is characterized by mixing freshwater with seawater and is called an estuary.

Column 2.8-2
Eutrophication substances via the air
Although the amount of substances of concern via the air is not so large in Japan, the supplyof nitrogen via the air occupies 20% to 50% of the total load of nitrogen from the outside ofwater areas (Paerl et al. 1994) and therefore is regarded as important. On the other hand,with regard to phosphorus, substances of concern supplied via the air to seas can be ignoredcompared with the other supply sources.
Nitrogen compounds that serve as nutrient salts in the air can be classified into the following
groups:• NHx group: gaseous ammonia (NH3) and particulate ammonia (NH4+ aerosol)• NOv group: Gaseous N2O5, HNO4, NO, NO2, HNO3, HNO2 and PAN, and particulate
NO3- aerosol and NO3 radical• Organic compounds: The concentration of organic nitrogen in the air is very low and there
is not much information on their origin.
The supply of nitrogen via the air has two processes: wet deposition, which is caused byrainfall, and dry deposition, which is caused by a disturbance of the air on a sea or land
surface. There are two main differences between these two processes: while nitrogen moves
from the air to the sea at all the layers where snow or rain falls in the air in the process of wet
deposition, nitrogen moves from the air only in the layer very near a sea or land surface;
moreover, while wet deposition occurs only when rain falls, dry deposition occurs at any time
(Asman et al. 1996).
Because there is a difference between freshwater and seawater in density, a special flow flows
toward the bay entrance, and the flow gradually takes in water in the lower layer and becomes
saltier. As a supplement flow, water in the lower layer flows toward the mouth of called
estuary circulation occurs at an estuary. The upper layer of low-salt water flowing from a
41

LIME2_C2.7-C2.9_2014

river the river and forms vertical circulation. This flow is called estuary circulation.
Estuary circulation is an important flow that greatly influences the transportation ofsubstances and organisms in the inner bay, such as nutrient salt supplied from the river, and isclosely connected with the impact of eutrophication.
Ocean wind
海上風

Flow of river water
河川水の流入

Wind
吹送流
Solar
日射
Heat
熱交換
insolation

current

潮汐
Tide

exchange

Density
密度流
flow

Estuary
エスチャリー循環

潮汐流
Tide flow

circulation

Figure 2.8-1: Physical process in an inner baycBiochemical process

In a water area where eutrophication has progressed, the transformation of substances fromthe dissolved state to the particulate state occurs actively and consecutively. Inorganic
nutrient salts (nitrogen and phosphorus) and carbon dioxide in the dissolved state change toorganic substances in the particulate state by the photosynthesis of phytoplankton and thenbecome organic again and change to the particulate state through metabolic processes bybacteria and the like. The processes of transforming from the dissolved state to theparticulate state include photosynthesis, assimilation of dissolved organic substances byheterotrophic organisms, and transformation of dissolved substances into colloid particles bysedimentation or condensation. The processes of transforming from the particulate state to
the dissolved state include dissolution of cells, exudation and leakage of dissolved organic
substances from cells of phytoplankton or bacteria, discharge of inorganic dissolved
substances that have changed from organic to inorganic due to the metabolism process byheterotrophic organisms, leakage of dissolved substances at the time of breakage ofphytoplankton cells during the process of congestion, and leakage of dissolved substancesfrom fecal pellets.
Because, in this way, processes differ between dissolved substances and particulate
substances, they create vertically different layers in stratified water. As photosynthesis that
produces particulate substances (the process that produces oxygen at the same time) is limited
by light, it is carried out in the euphotic layer (the upper layer). In the layer below theeuphotic layer (the lower layer), the process of mineralizing submerged phytoplankton andparticulate organic substances (oxygen consumption process) is prominent (Figure 2.8-2).
The stratification of water and the difference in place (water depth) between the oxygen
production process and the oxygen consumption process result in the development of suboxic
water under the pycnocline.

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LIME2_C2.7-C2.9_2014

Solarinsolation

Sea surface

ProductionInflow/outflow

Nutrient
salt

Organic
substance
Mineralizati
onStratification (pycnocline)

Inflow/outflow

Nutrient
salt

Submergence

Mineralization

Figure 2.8-2: Nutrient salt circulation under a stratified condition (Revised version of Richardson(1996))(2)

Endpoints of eutrophication

Figure 2.8-3 shows the cause-effect chain of eutrophication. Inorganic nitrogen andphosphorus and organic substances released into water first cause an increase in the current
amount of phytoplankton and underwater organic substances and a change in the organizationof phytoplankton species, which then create various effects on diversified elements of theecosystem. This change in the ecosystem has impact on human activities that use water,such as fisheries, and also on social assets. The figure shows endpoints that are usuallyregarded as problematic.
Of the endpoints of eutrophication, the most serious problem is various effects caused bysuboxic water (the concentration of dissolved oxygen (DO) becomes lower than the saturated
concentration). Because organisms’ systems basically depend on oxygen even in water, adecrease in DO in water greatly influences the whole aquatic ecosystem.
If eutrophication increases plant photosynthesis (production of organic substances), thesupply of organic substances to the sea bottom increases. Under the above-mentioned
condition stratified by the physical process, oxygen consumption by the decomposition oforganic substances and the process of mineralizing them increases in the bottom layer, and theconcentration of DO in water decreases. If this progresses, the bottom-layer water may be
short of oxygen. Under the condition that no oxygen exists, organic substances may bemineralized in an anaerobic way. This process of mineralization by the use of hydrosulfate
may facilitate the production of hydrogen sulfide harmful to living organisms, forming a matof sulfate reducers on the sea bottom.

43

LIME2_C2.7-C2.9_2014
Releaseof organic
有機物（COD）の排出
substance(COD)

Increase in
水中有機物
aquaticorganicの増加
substances

窒素・リン
Release of
nitrogen/phosphorの排出us

植物ﾌﾟﾗﾝｸﾄﾝ
Increase inの増殖
phytoplankton

Occurrenceof red
赤潮の発生
tide

植物ﾌﾟﾗﾝｸﾄﾝ種
Change in typesof phytoplanktonの変化

Occurrence of
異臭味発生

利utilization
水
Water

Aquatic primary
水域一次生産酸素消費量
Increase in増加
oxygenconsumption

Formation
貧酸素水塊ofsuboxic
water
の形成
mass

Occurrence
青潮など ofblueの発生tide, etc.

Impacton growth
生育・生息
and living
環境への影響environments

unusualsmell/taste

Endpoint
impact
エンドポイント影響
production

Recreation
ﾚｸﾘｴｰｼｮﾝ機能
function

一次影響
Littleprimaryは少ない
impactFishery
production
漁業生産

Human
health
人間健康Socialassets社会資産

Primary
一次生産
production

Numberof types
水生生物
and
population of
種数・個体数aquaticorganisms
Especially, number of
特に底生生物types
and population
種数・個体数of benthic
organisms

底生生物食
Fishesthat eat魚類
benthicorganisms

Biodiversity
生物多様性

Figure 2.8-3: Cause-effect chain of eutrophication

The important processes of supplying oxygen to water are the supply of oxygen from the air
through the sea surface, and the production of oxygen by phytoplankton’s photosynthesis.
Because both oxygen supply processes occur in the sea surface layer, as described above,suboxic water mass develops in the bottom layer if mixing of water in the surface layer withwater in the bottom layer is difficult due to sharp vertical density gradient (density
stratification). The processes of supplying oxygen to the bottom-layer water under the
stratified condition are usually the supply of oxygen from the surface through pycnocline
caused by winds, and the moving of the bottom-layer water that contains oxygen fromsurrounding sea areas. If pycnocline is destroyed, oxygen is supplied from the surface layer
to the bottom layer. Therefore, in a water area, such as Mikawa Bay, where pycnocline iseasily destroyed by ocean wind, the amount of organic substances is large because of
eutrophication and the speed of oxygen consumption at the bottom layer is high, because thesupply of oxygen is frequently replaced with the consumption of oxygen and vice versa, theDO concentration in the bottom layer changes sharply.
Living organisms’ first response to a suboxic phenomenon is escape from the suboxic area, as
often shown by many kinds of fishes. If oxygen cannot be used in bottom-layer water, shells
and other species that have low mobility and low tolerance to lack of oxygen begin to die, thestructure of the association of benthic organisms changes, and the decomposition of organic
substances centers on the metabolism of microscopic organisms. In serious cases, nooxygen exists in the bottom-layer water and most of the benthic organisms die. According
to Denmark’s EPA, many types of fishes and benthic organisms try to escape from the waterarea if the concentration of dissolved oxygen becomes less than 4 mg/L, and animals thathave low mobility and cannot escape die in a short time if it becomes less than 2 mg/L
(Danish Environmental Protection Agency 1998).
The Japan Fisheries Resource
Conservation Association has designated 3 mL/L (about 4.2 mg/L) as the minimum
concentration of oxygen in the bottom layer that does not have adverse effect on the fishing ofbenthic fishes (Japan Fisheries Resource Conservation Association 2000).
Figure 2.8-4 shows observed data about the central part of Mikawa Bay as an example ofchanges in the DO concentration in the bottom layer. The DO concentration shows very
44

LIME2_C2.7-C2.9_2014

sharp seasonal changes, and decreases to less than 2 mg/L every summer. In a temperatezone, water stratification is a seasonal phenomenon and, in autumn, stratification is destroyed
and oxygen is supplied to the bottom layer, with the result that living organisms begin to enter
the layer again. However, when suboxic water mass appears again in the next summer, theliving organisms die again. In water areas where this pattern is repeated, age structure is notformed in the association of benthic organisms, and the number of kinds of organisms that canlive is limited. As a result, serious effects occur on fishery production.
A decrease in the number of benthic organisms due to lack of oxygen has influence on theaquatic ecosystem. Living organisms that play a part in the food chain disappear. Because
benthic organisms play a great part in the physical circulation of bottom sediments, thedisappearance of benthic organisms transforms the physical circulation of sediments – that is,
the process of decomposing organic substances. This frequently worsens the environment
further.
With regard to impact on the other endpoints – water utilization and recreation function –although damage that occurred in various areas has been pointed out as a problem, full
discussions have yet to be held about how to assess the damage.

DO concentration in the bottom layer in the
三河湾湾央底層溶存酸素濃度
central part of Mikawa Bay

1210[mg/L]（mg/l）

8
6
4
2

AprAu 95gDe 95
cAp 95rAu 96gDe 96
cAp 96rAu 97gDe 97
cAp 97rAu 98gDe 98cAp 98rAu 99gDe 99c99

0Figure 2.8-4: DO concentration in the bottom layer in the central part of Mikawa Bay(result of a survey on public water areas in Aichi Prefecture)

Column 2.8-3
Process of mineralizing organic substances around the sea bottom
In the process of mineralizing substances around the sea bottom, the main final electronic
receptors are oxygen (oxic respiration), nitrate salt (denitrification), manganese oxide (IV),iron oxide, sulfate salt (sulfate reduction) and carbonate (methane production) (Canfield1993). Mineralization by the use of oxygen has the highest energy efficiency, and theassociation of benthic organisms first uses oxygen for the mineralization of organic
substances. If molecular oxygen disappears, nitrate salt, which has high energy efficiency, isused next, followed by manganese oxide (IV) and iron oxide. Sulfate reduction and methane
production have lower energy efficiency than these. Organic substances around the sea
bottom are gradually mineralized from processes with high energy efficiency to those withlow energy efficiency.
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LIME2_C2.7-C2.9_2014

Oxic respiration
CH2O  O2  CO2  H2O[free energy yields: -475 DG°(KJ mol-1 of CH2O)]

Denitrification
5CH2O  4NO3  2N2  4HCO3  CO2  3H2O[-448 DG°(KJ mol-1 of CH2O)]

Mn-oxide reduction
CH2O  3CO2  H 2O  2MnO2  2Mn 2   4HCO3

[-349 DG°(KJ mol-1 of CH2O)]

Fe-oxide reduction

CH2O  7CO2  4FeOH 3  4Fe2   8HCO3

[-114 DG°(KJ mol-1 of CH2O)]

Sulfate reduction
2CH2O  SO4 2   H2S  2HCO3

[-77 DG°(KJ mol-1 of CH2O)]

Methane production
2CH2O  2H2O  2CO2  4H2
4H 2  CO2  CH4  2H 2O[-58 DG°(KJ mol-1 of CH2O)]

2.8.2 Eutrophication and characterization
Under LIME, the characterization factor for the midpoint approach and the damage factor forthe endpoint approach were calculated in the impact category of eutrophication. This partdescribes the existing characterization factor.
(1)

Existing methods for finding the characterization factor of eutrophication

There are three methods for finding the characterization factor of eutrophication(eutrophication potential, EP):
1) Calculation from the average composition ratio of biomass (Heijungs et al. 1992,
Wenzel et al. 1997, Kärrman 2001)
2) Inclusion of fate and exposure analysis and regional information (Huijbregts et al.
2000)3) Paying attention to endpoints (Goedkoop et al. 1999)
The method for finding the characterization factor listed as 1) has been widely used for LCA
so far. Each eutrophic substance is weighted based on the average biomass composition
ratio of aquatic organisms (Redfield ratio: C106H263O110N16P). First, the weights of nitrogenand phosphorus are calculated from the average biomass composition ratio, and the weight of
138 moles of oxygen molecules is calculated as COD because the decomposition of a mole ofbiomass requires 138 moles of oxygen molecules. Then, phosphoric salt is standardized as
the base substance to find the characterization factor.

EP  ( viMi )( v PO 3
4

M PO 3 )4(2.8-1)In this equation, νi is the contribution ratio (P:N : COD = 1 : 1/16 : 1/138) of the substance i(P, N, COD) as an eutrophication substance, and Mi is the molecular weight of the substance i.
46

LIME2_C2.7-C2.9_2014

The method listed as 2) calculates the fate factor FF of nitrogen compounds released into the
air in the whole of Europe. FF was calculated for each European country. Based on the
average, FF was calculated also for Western Europe, Eastern Europe, and the Whole ofEurope. FF can be calculated by the following equation:

FFx ,i ,direct,airmarine, Europe ti, j ,xjEurope E x ,i  A j  K j ,marine
E x ,i(2.8-2)In this equation, ti, j, x is the transport coefficient – that is, the ratio of the substance x that was
released from the territory i and deposited in the territory j [mgNm-2kgN-1]. Ex,i is theamount of the released substance x in the territory i [kgN yr-1]. Aj is the area of the grid cell j[km2]. Kj, marine is the ratio of the ocean in the grid cell j [ – ]. The transport coefficient t isfrom the European Monitoring and Evaluation Programme (EMEP).
Based on this result, it was estimated that an average of 16% directly deposit in the ocean in
the whole of Europe (Western Europe: 24%, Eastern Europe: 7.2%).
The method listed as 3) calculates the damage factor, taking into consideration thevulnerability of deposition areas. The disappearance rate of plant species (PDF) is used as
the index for damage to the ecosystem. This is advantageous in that it enables comparison
with other impact categories, such as land use and acidification. However, the method hassome problems. For example, because it is based on data in Holland, it is difficult to expand
it across the whole of Europe. In addition, it only takes into consideration terrestrial impact,
and not impact on aquatic organisms.
(2)

Characterization factors of eutrophication under LIME

Although the characterization factor calculated by the Equation 2.8-1 has been adopted widelyin Europe, it is impossible to consider the characteristic that the impact of eutrophication
greatly differs depending on the condition of water into which a substance of concern isreleased. The following problems exist in relation to eutrophication: there are various
processes related to environmental response to the substance of concern; and theinterrelationships among the physical, biological, and chemical processes that form theecosystem in a closed water area influence damage in an extremely complicated way and havehighly regional characteristics. Therefore, in the impact assessment of eutrophication, it isnecessary to identify damage and assess the impact, taking into consideration the mainprocesses between the generation of a substance of concern and the occurrence of damage andthe interaction among the processes in relation to the environmental characteristics of theplace for release. In addition, with regard to the processes between the generation of a
substance of concern and its inflow into water, if Equation 2.8-1 is applied to theeutrophication substances released into the air (nitrogen oxide (NOx) and ammonia (NH3)), all
the released substances are regarded as substances of concern and it becomes impossible toconsider the fate of substances that are thought to differ among countries and regions. In
addition, the characterization factor about which the fate analysis in Equation 2.8-2 is taken
into consideration is not very useful in Japan because it is based on backgrounds in Europe.
Under LIME, because eutrophication is an impact category where regional characteristics
have strong influence, the development of the following characterization factors was triedwith consideration for regional characteristics in Japan:
47

LIME2_C2.7-C2.9_2014

1) With regard to the eutrophication substances released into the air (nitrogen oxide andammonia), add the fate in the air to the value calculated from the average biomass
composition ratio [eutrophication potential by fate analysis (EPF)].
2) Use the DO concentration as the midpoint for the impact of eutrophication and add
the fate in the air in the case of the substances released into the air [eutrophication
potential by material circulation (EPMC)].
With regard to EPF, the source-receptor relationship (SRR) of NOx and NH3 calculated during
the development of the characterization factors of acidification under this project was used.
SRR of acidification gives the ratio of substances that deposit in Japan to the substances
released into the air in Japan. On the assumption that the remaining substances deposit inwater areas, the characterization factor was calculated on the basis of phosphoric salt by
multiplying it by the Redfield ratio. This is based on the assumption that substances that
deposited in land areas do not flow into water areas. Among the eutrophication substances,
the organic substances’ amount of terrestrial deposition through the air is much lower thantheir amount on the ground. Regarding phosphorus, the amount that is deposited through the
air is small, and the outflow in a dissolved state is low because it is strongly absorbed in soil.
With regard to phosphorus, the outflow of phosphorus that is deposited into the air is low
because, although the substances that are deposited through the air can flow out to water areas
as in the case of groundwater pollution by nitrate-nitrogen, nitrogen controls the plant growthrate in general forests.
With regard to EPMC, for the purpose of the assessment of the DO concentration, which isused for the calculation of the damage function of eutrophication, a change in thebottom-layer DO concentration due to a substance of concern was calculated by the
below-described flow model and material circulation model. The results of the calculation
concerning representative closed sea areas in Japan (Tokyo Bay, Ise Bay, Mikawa Bay, andOsaka Bay) (see Table 2.8-3) were averaged, weighting the amount of each eutrophication
substance released in the existing four closed water areas. As a result, the value of a change
in the DO concentration due to the release of a unit amount of eutrophication substances wasfound. After that, characterization factors were calculated by taking into account SRR
concerning the substances released in the air (NOx and NH3). Table 2.8-4 shows theresultant characterization factors.
With regard to EPF, compared with the value calculated from the average biomass
composition ratio (EP), only the substances released into the air were taken into consideration
for the ratio of substances depositing in water areas, with the result that the impact became
smaller.
The characteristics of the water area to which a substance of concern is released, which arevery important for the impact of eutrophication, were taken into consideration for EPMC.
The comparison between EPF and EPMC shows that the impact of COD was smaller in thecase of EPMC than in the case of EPF, while the impact of nitrogen and phosphorus waslarger, which indicates that the impact of organic substances produced in the target closed
water area by the use of nutrient salt is larger than the impact of organic substances flowing infrom the outside of the water area. Moreover, the comparison between nitrogen andphosphorus shows that the impact of phosphorus is larger in the case of EPMC than in thecase of EPF. This reflects the situation where phosphorus’s control of photosynthesis byphytoplankton, which produces organic substances in water areas, is stronger than nitrogen’s.
Under LIME, the value for which consideration is given to atmospheric fate analysis based on
information about Japan and the material circulation characteristics in the target water area
(EPMC) is recommendable as the characterization factor of eutrophication.
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LIME2_C2.7-C2.9_2014

Table 2.8-3: Results of calculation of a change in the bottom-layer DO concentration due to a change
in the load
Bottom-layer DO concentration [mg/L]

Tokyo Bay
Case of change in N

30%
decrease20%
decrease30%
decrease20%
decrease30%
decrease20%
decrease30%
decrease20%
decrease10%
decrease

Current
condition

10%
increase

20%
increase

30%
increase

20%
increase

30%
increase

20%
increase

30%
increase

20%
increase

30%
increase

Case of change in P
Case of change in NP
Case of change in COD

Bottom-layer DO concentration [mg/L]

Ise Bay
Case of change in N

10%decrease

Current
condition

10%
increase

Case of change in P
Case of change in NP
Case of change in COD

Bottom-layer DO concentration [mg/L]

Mikawa Bay
Case of change in N

10%decrease

Current
condition

10%
increase

Case of change in P
Case of change in NP
Case of change in COD

Bottom-layer DO concentration [mg/L]

Osaka Bay
Case of change in N

10%decrease

Current
condition

10%
increase

Case of change in P
Case of change in NP
Case of change in COD

Table 2.8-4: Characterization factors of eutrophicationEP:

calculated from the average composition ratio of biomass(Heijungs et al. 1992)
EPF:
calculated from the average composition ratio of biomass
with consideration for SRR
EPMC: calculated from decrease in the DO concentration in closed
inner bays in Japan with consideration for SRR

49

LIME2_C2.7-C2.9_2014

2.8.3 Eutrophication damage assessment
This part describes the damage factors (damage functions) calculated for the endpoint
approach.
(1)

Basic policy for calculation of damage factors

The calculation of eutrophication damage requires assuming a specific water area, because, as
described above, the impact of eutrophication greatly differs among water areas according totheir regional characteristics. Under LIME, a damage function was calculated for four
typical closed water areas in Japan – Tokyo Bay, Ise Bay, Mikawa Bay, and Osaka Bay.
Table 2.8-5 shows the category endpoints of eutrophication and the objects of the calculationof the damage functions under LIME. In addition, Figure 2.8-5 shows a flowchart of thecalculation of damage functions.
Although the impact of eutrophication varies, under LIME, attention was paid to lack ofoxygen as an effect of eutrophication, and the number of kinds of benthic species, which are
directly influenced by lack of oxygen the most, and a decrease in the current amount ofbenthic species was dealt with as concrete damage. Moreover, the impact on fisheryproduction was estimated from a decrease in the current amount of benthic species. Because
effects on the other endpoints – recreation function and water utilization function – have notbeen fully discussed yet, they are not dealt with herein.
Under LIME, when these estimations were carried out, the effects were calculated by dividing
a series of processes where complicated interactions are thought to exist into the following
stages, for the sake of convenience: the process from the generation of a substance of concern
to its flowing into a water area; changes in material dynamics in the water area and the DOconcentration in water; and the response of benthic organisms to the change.
Table 2.8-5: Category endpoints of eutrophication and the objects of calculation under LIME

Object ofprotection
Human
healthCategory endpoint
Temporary impact on human health is small.
Waterutilization

Social assets

Recreation
functionFishery
production

Primary
production

Aquatic
ecosystem

Biodiversity

Aquatic
ecosystem

50

Obstacle to water utilization due
to unusual smell/taste caused by
red tide, etc.
Obstacle to hydrophilic function
due to decreasing transparency,
unusual taste, etc.
Damage to fisheries and culture
due to a decrease in fisheryresources through influence ofgeneration of suboxic watermass on benthic organisms
Change in primary production ofphytoplankton (usually, an
increase)
Impact on the structure ofaquatic species

Object of calculation of damage
function－

No object－

Quantitative assessment isdifficult.

－

Quantitative assessment isdifficult.

Fishery production in closed
water areas as a result of lackof oxygenImpact on the current amountof benthic organisms (impact
on higher-order production)
Impact on the number of kindsof aquatic species

LIME2_C2.7-C2.9_2014
Inventory of
富栄養化物質
eutrophication（N，P，COD）
substances (N,インベントリP, COD)

Characterizati
特性化係数
on factor（富栄養化(eutrophicatio
係数EP）
n potential
EP)

Damagefunctionkg(amountof damage at each endpoint from release of 1 kg yr -1 of
ダメージ関数（1yr-1の富栄養化物質排出に対する各エンドポイントへの被害量）
eutrophication substances)（４閉鎖性海域：東京湾，伊勢湾，三河湾，大阪湾ごとに計算）(Estimated for each of 4 closed sea areas: Tokyo Bay, Ise Bay, Mikawa Bay, Osaka
Calculated各富栄養化物質の総排出量が現況，±10％，±20％，±30％について計算
if the total amount of each released eutrophication
substance is at the current level, ±10%, ±Bay)
20%, and ±30%

Category
ｶﾃｺﾞﾘ指標index(ﾐｯﾄﾞﾎﾟｲﾝﾄ)(midpoint)

Total amount of
水域への窒素（N），
released nitrogen (N),
リン（P），あるいは
phosphorus (P), or
CODの総排出量
COD in the waterarea

Currently
observed
底層DO濃度value of
現況観測値
bottom-layerDOconcentration

Fishery
内湾性魚種
production of
漁業生産額
inner bay
fishesDistribution
インベントリof
inventory
in water
の水域配分
area

Damage
被害指標

index(ｴﾝﾄﾞﾎﾟｲﾝﾄ)(endpoint)summer)

物質循環モデルMaterial
circulation
model (vertical（鉛直方向１次元多層）
one-dimensional
水質シミュレーション
multilayer)water quality（夏季を対象）Inter層間
layer
流量
flow
収支
balancesimulation (in summer)

Result of calculation of bottom-layer
底層溶存酸素（DO)濃度計算結果
dissolved oxygen (DO) concentration

Current total
４閉鎖性海域
amount of released
現況富栄養化
eutrophication
物質総排出量substances in 4
closed water areas

Ratio
of current
水域別現況
released amount in
排出量比
each water area

マルチレベルモデル
Multilevel model（３次元多層）(three-dimensional
流動シミュレーション
multilayer) flow
（夏季を対象）
simulation(inRate of decrease in
底生生物現存量
current amount of
減少率
benthicorganisms

Estimatedvalue of
底層DO濃度bottom-layer DO
推計値
concentration

Current
底生生物
amount of
現存量
benthic

Rate of change in bottom-layer
単位量の富栄養化物質排出DO concentration due to a unit
に対する底層DO濃度変化率
amount of released
eutrophication substances

Increasein death
底生生物
rate of benthic
死亡率増加
organisms

Number of kinds of
底生生物種数
benthic organisms

Valueof decrease
漁業生産
in減少額
fishery
production

Decrease in current
底生生物現存量
amount of benthic
減少量
organisms

Decrease in the
底生生物種数
number of kinds of
減少数
benthicorganisms

Fishery
production
漁業生産

Current amount of
底生生物現存量
benthic organisms

Numberof kinds of
底生生物種数
benthic organisms

社会資産Socialassets

一次生産
Primary

生物多様性
Biodiversity

organisms

production

Figure 2.8-5: Flowchart of estimation of eutrophication damage functions(2)
Process from the generation of a substance of concern to its flowing into a closed
water area
In the process from the generation of a substance of concern to its flowing into a closed water
area, the amount of organic substances, for which COD is used as the index, changes
according to production, decomposition, mineralization, etc., and the forms of nitrogen andphosphorus also change in various manners. The ratio of the amount of a substance ofconcern generated in land to the amount of the substance of concern flowing into water iscalled the reaching rate. It is thought that the reaching rate greatly changes according togeographical conditions and weather conditions, such as rainfall. However, there are almost
no data for examination of such changes. Therefore, in this study, the reaching rate adopted
by the Ministry of the Environment is used for COD. With regard to nitrogen andphosphorus, no consideration is given to the reaching rate, because if their forms change, thewhole amount rarely changes like COD, and most of the amount reaches water sooner or later.
According to the Ministry of the Environment’s data, the ratio of the amount of reaching
COD to the amount of generated COD was 80.2% in Tokyo Bay, 94.9% in Ise Bay, and96.4% in Osaka Bay on average for the 14 years from 1979 to 1992.
(3)
Changes in material dynamics in a closed water area and the DO concentrationin waterPrediction of the DO concentration in a closed water area requires expression of material
dynamics with consideration for physical, biological, and chemical processes within theabove-mentioned water areas. Because of this, the flow balance in the inner bays wascalculated by a flow simulation, using a numerical simulation model that combined the “flowmodel” for expressing the physical process with the “material circulation model (water quality
model)” for expressing the biological and chemical processes (Nakata 1993). Based on the
result, the bottom-layer DO concentration was calculated by a material circulation simulation.
51

LIME2_C2.7-C2.9_2014aFlow simulation

The “multilevel model” was adopted as the flow model. It took into consideration the flow
driven by wind above sea level, difference in water density, and tides, all of which areimportant for the flow in an inner bay. This model consists of a group of equations that
describe 1) the movement of fluids in an inner bay and the mouth of a river; 2) continuity of
fluids; 3) changes in the tidal level; 4) dispersion of chlorinity; 5) dispersion of heat; and 6)
relationships among seawater density, chlorinity, and water temperature. The model waspartitioned by 1 km × 1 km horizontally and by intervals of several meters vertically.
With regard to each water area, geographical features, water depth, tides, and weather
conditions were inputted and flow simulation was carried out for the summer season, when
the DO concentration seems to decrease more greatly than in the other seasons. Based on
the result of the simulation, the interlayer flow balance was calculated.
Figure 2.8-6 shows the result of the flow simulation in Tokyo Bay in summer. The figure
reproduces the estuary circulation – that is, water flowing from rivers flows out to the mouthof the bay from the upper layer and flows into the closed-off section of the bay from the lower
layer around the line between Kawasaki/Yokohama and Kisarazu.
bWater quality simulation

The ecosystem material circulation model was adopted as the water quality model. It
expresses the dynamics of carbon, nitrogen, and phosphorus among phytoplankton,
zooplankton, detritus (non-living particulate organic substances), dissolved organic
substances, and nutrient salt in a water area, taking into consideration the relations withoxygen generation and consumption. The water quality model was not partitioned
horizontally, and consideration was given only to the vertically divided layers (box multilayer
model).
The initial value was the observed value of water quality in each layer. A water quality
simulation was carried out by inputting the flow balance between layers and between theinside and outside of each bay through a flow simulation and the load of eutrophication
substances in each water area into the water quality model. The validity of the simulation
was examined by comparison between the simulation results and the observed values.
Recently, there has been developed a model that can assess the interaction between thematerial circulation in neritic waters, including tidelands and seaweed bends, and the material
circulation in inner bay sediments and the inner bay floating material circulation and canassess changes in time (Sohma et al. 2001, Sohma et al. 2002, Sohma et al. 2004). In thisstudy, because of lack of data, consideration was not given to the interaction between thematerial circulation in neritic waters and that in sediments and changes in time.
Figure 2.8-7 shows the result of the water quality simulation in Tokyo Bay together withobserved data during the target calculation period. It was judged from the comparison
between the calculation results and the observed values that although detailed examination isdifficult due to wide fluctuation in observed values, the calculation results show a trend invertical changes and reproduce the current water quality trends.

52

LIME2_C2.7-C2.9_2014

Chiba

Funabashi

Kisarazu
Kawasaki

FuttsuYokohama

Yokosuka

Figure 2.8-6 (1) Flow simulation result (Tokyo Bay, surface layer)
The direction and size of a vector show the direction and speed of the average flow in summer.

c
Relation between the amount of released eutrophication substances and thebottom-layer DO concentration
To calculate the DO concentration in each layer, a simulation was carried out to change theload of nitrogen, phosphorus, and COD to the current level, ±10%, ±20%, and ±30%. Basedon the result, the rate of a change in the bottom-layer DO concentration due to release of aunit amount of eutrophication substances was calculated.
However, conditions for the layer division of the water qualification model differ in reality,
although they are the same for the purpose of calculation. For example, some layers are incontact with the sea bottom, while other layers are not in contact with it, depending on
geographical features. Moreover, because the result of calculation of the DO concentrationby the water quality model is the calculated average value within each layer, it is different
from the actual value at places where layers are in contact with the sea bottom and therefore
the impact of a DO decrease on benthic organisms becomes a problem.
53

LIME2_C2.7-C2.9_2014

Chiba

Funabashi

Kisarazu
Kawasaki

FuttsuYokohama

Yokosuka

Figure 2.8-6 (2) Flow simulation result (Tokyo Bay, bottom layer)
The direction and size of a vector show the direction and speed of the average flow in summer.

Because of this, a change in the bottom-layer DO concentration due to release of
eutrophication substances was calculated by multiplying the currently observed value of the
DO concentration in the water just above the sea bottom by the DO concentration change rate
found from the water quality simulation (result of DO simulation at the time of a change inthe load/result of Do simulation in the case of the current load).

54

LIME2_C2.7-C2.9_2014
東京湾・夏季Tokyo Bay in summer
16.0

3000.014.0

Water depth

東京湾・夏季Tokyo Bay in summer

Water depth

水深水深

東京湾・夏季Tokyo Bay in summer

Water depth

水深

btm

30m

AverageCalculated value

btm

btm

30m

0.5m

btm

30m

25m

20m

15m

10m

0.000

5.0m

0.050

0.000

0.5m

1.000

25m

0.100

20m

0.150

15m

0.200

10m

AverageCalculated value

5.0m

0.250

TP (mg/L)

2.000

Max
Min
平均
計算値

TP(mg/l)

3.000

Max
Min
平均
計算値

Tokyo Bay in summer

0.300

DIP (g-at/L)

DIP(ug-at/l)

4.000

Water depth

0.350

7.000
5.000

AverageCalculated value

東京湾・夏季

8.000
6.000

30m

0.5m

btm

30m

25m

20m

15m

10m

5.0m

0.5m

0

25m

500

10m

AverageCalculated value

5.0m

1000

4.50
4.00
3.50
3.00
2.50
2.00
1.50
1.00
0.50
0.00

TN (mg/L)

1500

Max
Min
平均
計算値

TN(mg/l)2000

DOC (mgC/m3)

DOC(mgC/m3)

3500
2500

Max
Min
平均
計算値

Tokyo Bay in summer

東京湾・夏季4000
3000

AverageCalculated value

Water depth

水深

20m

水深

Max
Min
平均
計算値

btm

0.5m

btm

30m

25m

20m

15m

10m

0.0

5.0m

1.0

0

30m

2.0

200

25m

3.0

20m

4.0

10m

5.0

COD (mg/L)

400

AverageCalculated value

5.0m

600

6.0

Max
Min
平均
計算値

COD(mg/l)

800

POC (mgC/m3)

7.0

0.5m

POC(mgC/m3)

8.0

1400
1000

AverageCalculated value

東京湾・夏季Tokyo Bay in summer

1600
1200

Max
Min
平均
計算値

Water depth

水深東京湾・夏季Tokyo Bay in summer

25m

0.5m

btm

30m

Water depth

15m

水深

25m

20m

15m

0.0

10m

2.0

0.0

5.0m

500.0

20m

4.0

15m

6.0

10m

AverageCalculated value

8.0

15m

1000.010.0

DO (mg/L)

1500.0

12.0

Max
Min
平均
計算値

5.0m

2000.0DO(mg/l)

2500.0

Phytoplankton(mgC/m3)

3500.00.5m

植物プランクトン(mgC/m3)

東京湾・夏季Tokyo Bay in summer

水深 Water depth

東京湾・夏季Tokyo Bay in summer

300.00200.00150.00100.00DIN (g-at/L)

DIN(ug-at/l)

250.00
Max
Min
平均
計算値

AverageCalculated value

50.00

水深

btm

30m

25m

20m

15m

10m

5.0m

0.5m

0.00

Water depth

Figure 2.8-7: Results of water quality simulation in Tokyo Bay and observed values
POC: particulate organic carbon; DOC: dissolved organic carbon; DIP: dissolved inorganic phosphorus; DIN:
dissolved inorganic nitrogen; DO: dissolved oxygen; COD: chemical oxygen demand; TN: total nitrogen; TP:
total phosphorus

55

LIME2_C2.7-C2.9_2014

Column 2.8-4
Flow model
The movement and mixing of water and substances in the sea are expressed by motion
equations and continuity equations that include items that express momentum, heat, andtransport of substances. The multilevel barocline flow model adopted here is widely usedfor various kinds of assessment in coastal areas.
The basic equations for the multilevel barocline flow model are the below-described six
equations concerning state quantities (momentum, flow, mass, heat conservation law, andstate equation). The coordinate system of these equations consists of the x-y axis for meansea level and the z axis for the vertical direction.
Because the vertical scale for an inner bay area is sufficiently smaller than the horizontal
scale, it is assumed that the acceleration and the viscosity in the vertical movement equation
are ignorable compared with gravitational acceleration (hydrostatic approximation). In
addition, because changes in density are sufficiently small, the Boussinesq approximation isassumed, which ignores changes in density other than buoyancy and enables the applicationof a continuity equation for an incompressible fluid.
Motion equation for direction xu g 0 
1 P0  (u2 )  (uv )  (uw)  f 0v  g
 
dz 
z
t
x
y
z
x  x
 x

u

u

u
 ( Nx )  ( N y )  ( Nz )
x
x y
y z
z
Motion equation for direction yv g 0 
1 P0  (uv )  (v 2 )  (vw)  f 0u  g
 
dz 
t
x
y
z
y  z y
 y

v

v

v
 ( Nx )  ( N y )  ( Nz )
x
x y
y z
z
Continuity equationu v w 0
x y zHeat balance equation (temperature diffusion equation)
T




T

T

T
  (uT )  (vT )  ( wT )  (k x)  (k y)  (k z
)
t
x
y
z
x
x y
y z
z
Chlorine balance equation (chlorine diffusion equation)
Cl




Cl

Cl

Cl
  (uCl )  (vCl)  ( wCl )  ( K x)  (K y)  (K z
)
t
x
y
z
x
x
y
y
z
z
State equation   (Cl , T )The following are the variables and signs used in these equations:u, v, w: components of flow speed in directions x, y, z [cm/sec]ζ:displacement between average water surface and free water surface (tide level) [cm]
H: height between average water surface and sea bottom [cm]ρ:fluid density [g/cm3]
fo: Coriolis parameter [sec-1]
56

LIME2_C2.7-C2.9_2014g:gravitational acceleration [cm/sec2]
P0: atmospheric pressure [g/cm·sec2]
T:water temperature [°C]
Cl: chlorinity
Nx, Ny, Nz: eddy viscosity coefficients in directions x, y, z
kx, ky, kz: temperature diffusion coefficients in directions x, y, z
Kx, Ky, Kz: substance diffusion coefficients in directions x, y, z
Column 2.8-5
Floating material circulation model
Because many organisms floating in water are transported by flow or diffused by disturbance,
their distribution greatly depends on the processes of fluid dynamics. In addition, thedistribution of nutrient salts necessary for the propagation of phytoplankton also greatly
depends on the process of diffusive mixing, a process of fluid dynamics. Because theprocesses of fluid dynamics greatly influence the ecosystem, it is necessary to formularize theinteraction between the two systems. To describe temporal changes in the current volume Bof a compartment (a component of the model) at an arbitrary point in a water area (x, y, z), thematerial circulation model shows a material balance equation as follows:

B
B
B
B
(Advection)
 u
v
w
( 移流）
t
x
y
z

B

B

B
 ( K x )  ( K y )  ( Kz )
x
x y
y z
z
B
(Biological/chemical process)( )（生物・化学過程）
t(Diffusion)（拡散）

In this equation,B:current volume of each compartment (see the table below)
u, v, w:
components of flow speed in directions x, y, z
Kx, Ky, Kz: substance diffusion coefficients in directions x, y, zCompartment

Sign

Unit

Phytoplankton
Zooplankton
Particulatematerial
Dissolved
material
Dissolved

organic

Chemicaldemandoxygenorganic

inorganic
nitrogen
Dissolved
inorganic
phosphorus
Dissolved oxygen

57

LIME2_C2.7-C2.9_2014

Insolation
日射
Inflow of a
負荷流入

Inflowof a
負荷流入
substance ofconcern

substance ofconcern

Dissolved
溶存態無機燐

inorganic
phosphorus
inorganic nitrogen

Mineralizati
on

無
化

解

Firing 枯死

Dissolved
organic
溶存態有機物
material

Dung/death
排糞・死亡

Zooplankton
動物プランクトン

Predation

Excretion

Sedimentation

泄

降

吸

光
合
成

植物プランクトン
Phytoplankton排沈呼Photosynthes
is

機

分

RespirationDissolved
溶存態無機窒素

補食

SedimentationDecompositionParticulateorganic
懸濁態有機物
material

沈
降Inorganic
無機態窒素・燐
nitrogen/phosphorus

溶出ElutionBiological and chemical processes have been formularized for each compartment. If the coefficient forthe speed of a change process is v ( ), the coefficient for limitation on the speed is μ ( ), and a factor
function is entered in the parentheses, the change process for phytoplankton, for example, can be expressed
as described below. For detailed formularization of each elementary process, see Nakata K. (1993), etc.
dP dP
by photosynthesis]
 v1 (T)v1(TDIP), DIP
2 ( I) , P
 1 (, DIN)2 ( IP,［光合成による増殖］
P[propagation)  P［光合成による増殖］
1 ()DINdt dt
 3 ( P)3(vP1 ()T )v1(1T( DIN
2 ( I),P)2(P［細胞外分泌］
secretion])  1 ,(DIP
DIN),DIP
I ,P)[extracellular P［細胞外分泌］[predation by zooplankton]
 v3 (T)v3P(T［呼吸］ v4 (Tv, 4P()T,ZP［動物プランクトンによる補食］)  P［呼吸］)  Z［動物プランクトンによる補食］[respiration]
P P
 v5 (T)v5P(T［枯死］ wP wP［沈降］)  P［枯死］［沈降］[firing][sedimentation]z zIn this equation,
P: current amount of phytoplankton
T: Water temperature
DIN, DIP: dissolved inorganic nitrogen, dissolved inorganic phosphorus
I: light intensity
Z: current amount of zooplankton
wp: sedimentation speed(4)

Biodiversity: damage function to the number of kinds of benthic organisms

To quantify the impact of suboxidation on the number of kinds of benthic organisms, theTokyo Metropolitan Government carried out a survey on benthic organisms (from FY1988 to
FY1997). Based on the resultant data, it was examined how the bottom-layer DO
concentration is related with the number of kinds of generated benthic organisms.
Regression analysis was carried out by a quadratic polynomial, using the data on thebottom-layer DO concentration of less than 6.0 mg/L (Figure 2.8-8). The analysis excluded
four tidal lands where the impact of a decrease in the aquatic DO concentration is small
58

LIME2_C2.7-C2.9_2014

because they are exposed to the air at regular intervals. Moreover, consideration was given
to the fact that if the DO concentration is more than 6 mg/L, no living organisms receive
impact (Matsukawa 1992).
y ＝ 0.3746 x2 ＋ 2.0068 x ＋ 0.9831（R2 ＝ 0.4669，n ＝ 184） (2.8-3)

In this equation, y and x are the number of kinds of generated benthic organisms and thebottom-layer DO concentration [mg/L], respectively.
The damage function that indicates the rate of decrease in the number of kinds of benthic
organisms was calculated by assigning the rate of change in the bottom-layer DO
concentration due to release of a unit amount of eutrophication substances to Equation 2.8-3.
50
45

of kinds [/0.15m2]
Number 種類数（/0.15m2）

40
35
30
25
20
15

2

= 0.3746x+ 2.0068x
0.9831y = y0.3746
x2 + 2,0068
x + +0.9831
R2 = 0.4669
n = 184
R2 = 0.4669
n = 184

10
5
0
0

1

2

3

4

5

6

7

底層DO（mg/l）
Bottom-layer DO [mg/L]

Figure 2.8-8: Relation between bottom-layer DO and the number of kinds of benthic organisms
according to the Tokyo Metropolitan Government’s data
Data for May and September during the decade from 1988 to 1997

(5)
Primary production: damage function for the current amount of benthic
organisms
The relation between the release of eutrophication substances and the damage to the current
amount of benthic organisms was estimated as the damage function for biological production.
As the basis for quantifying the impact of suboxidation on the current amount of benthic
organisms, a case in Holland (Baretta et al. 1988) was selected where the DO saturation (theratio of the actual amount of dissolved oxygen to the potential amount) and the death speed ofeach category of benthic organisms were formularized.

W

B  mor  ( 1  min( 1.0 , ROX qox ) )(2.8-4)59

LIME2_C2.7-C2.9_2014

In this equation, W, B, mor, ROX, and qox are the amount of dead benthic organisms, theamount of existing benthic organisms, the relative death speed of benthic organisms, the DOsaturation, and the level of Do saturation under which death occurs, respectively.
Under LIME, the model for which Suzuki et al. formularized qox (Suzuki et al. 1998) wasused. Macrobenthos, meiobenthos, and aerobic layer bacteria were taken into considerationas categories of benthic organisms.

qox  0.7  ( T / ks ) a( 1  ( T / ks ) a )

(2.8-5)In this equation, T, ks, and a are the water temperature [°C], the half-value constant (=25°C),
and the index around the half-value constant, respectively. The value a for each category is
20 for macrobenthos, 5 for meiobenthos, and 10 for aerobic layer bacteria. Moreover, mor is
1.0 day-1 for all categories (If the DO concentration is zero, this means that all organisms diein one day).
The damage function was calculated by assigning the rate of change in the bottom-layer DO
concentration due to release of a unit amount of eutrophication substances to Equations 2.8-4
and 2.8-5 to find the rate of decrease in the current amount of benthic organisms of eachcategory and summing up the products of the results and the current amount of benthic
organisms of each category.

+R20.2164= 0.9993
R2 = 0.9993

10
5
0
-20

-5

0

20

40

N
P
N+P
線形 (N+P)
Line
shape
(N+P)

-10
-15
負荷量変化率（％）

Disappearance rate in the
底生生物種類数消失率（％）
of kinds of benthic
numberorganisms (%)

Change rate in load
(%)

-40

0.06

yy == 0.0016x
0.0016+x0.0003

0.04

R =1
+ 0.0003
R2 = 1

2

0.02
0
-20

-0.02

0

20

-0.04
-0.06
負荷量変化率（％）
Change rate in load
(%)

40

COD
線形 (COD)
Line
shape
(COD)

Decrease rate in wet weight of
減少率（％）
底生生 物湿重量
organisms
benthic
(%)

-40

yy =
0.3842+x0.2164= 0.3842x

15

Decrease rate in wet weight

Decrease rate in wet weight ofbenthic organisms (%)
底生生物湿重量減少率（％）

Disappearance rate in the
底生生
benthic
kinds of消失率（％）
of物種類数
numberorganisms (%)

Disappearance rate in the number of
kinds

-40

yy == 2.5342x
2.5342+x0.0541

100
80
60
40
20
0
-20
20
-20 0
-40
-60
-80
-100
負荷量変化率（％）

2= 0.9286+R0.0541

R2 = 0.9286

NP
N+P
線形 (N+P)
Line
shape

40

(N+P)

Change rate in load(%)

y y= =0.0181
x + 0.0028
0.0181x0.8+ 0.0028
R2 = 1

0.6

R2 = 1

0.4
0.2

COD

0
-40

-20

-0.2 02040

線形 (COD)
Line
shape
(COD)

-0.4
-0.6
負荷量変化率（％）
Change rate in load(%)

Figure 2.8-9: Changes in the number of kinds and wet weight of benthic organisms according to loadchange (Tokyo Bay)
N: change in nitrogen only; P: change in phosphorus only;
N+P: simultaneous change in nitrogen and phosphorus; COD: change in COD only

Figure 2.8-9 shows the results of calculation in Tokyo Bay concerning changes in the numberof kinds and wet weight of benthic organisms on the assumption that the load of nitrogen,phosphorus, and COD is at the current level, 10%, 20%, and 30%. The results of the60

LIME2_C2.7-C2.9_2014

calculation show that an increase in the load results in a decrease in both the number of kinds
and the current amount of benthic organisms and that the inflow of phosphorus has stronger
impact than the inflow of nitrogen.
(6)

Social assets: damage function for fishery production

Of the fishery species caught in Tokyo Bay, Ise Bay, Mikawa Bay, and Osaka Bay, damage tobenthic organisms and fishes for which inner bays are important habitats was calculated.
It was assumed that the impact of lack of oxygen on benthic organisms is almost the same as
the impact on fishes that strongly depend on the inner bay environment, because the impact
strongly influences the ecosystem in the whole inner bay. That is, the degree of damage tofishery production due to release of a unit amount of eutrophication substances was regarded
as the same as the decrease rate calculated by the damage function for the current amount ofbenthic organisms.
The target of calculation of damage function was fishery species caught in the inner bays.
However, fishes that temporarily flow into an inner bay and eat plankton, such as sardines, areexcluded from the target, in view that they do not depend on the inner environment much andare unlikely to receive the impact of eutrophication. The following fishery species seem toreceive the impact:
flatfish, sea bream, striped mullet, sea bass, conger, greenling, sand borer, gizzard
shad, grunt, goby, synodontidae, conger pike, Spanish mackerel, sand lance, puffer,deep-sea smelt, halfbeak, shrimp, crab, shellfish, squid, octopus, sea cucumber,squilla, and other fishery species
With regard to these fishery species, information on catches and fishery values was obtained
from the fishery statistical data (catches, fishery value, and unit price of each kind of fish) of
the Kanto, Chubu, and Kinki Agricultural Administration Offices. Because statistical dataon catches are divided not according to water area but according to area to which fisherybusinesses that caught aquatic organisms belong (catch accounting for production unit), it was
assumed, based on data about each fishery business or municipality, that aquatic organisms
were caught in surrounding areas.
The damage function for fishery production was calculated by multiplying the damage
function, which indicates the decrease rate in the current amount of benthic organisms due torelease of a unit amount of eutrophication substances, by catches.
(7)

Arrangement of damage functions of eutrophication

Damage functions were obtained for each of the four closed sea areas. The damage
functions of eutrophication are results of weighting the damage functions for the four areas by
the current amount of release of each eutrophication substance in the four areas. Figure
2.8-10 shows the damage functions for fishery production (social assets). With regard to thesubstances released into the air, the SSR of the NOx and NH3 were calculated for thedevelopment of the characterization factors of acidification under LIME. With regard to
biodiversity and primary production, because they were consistent with the other impact
categories in terms of the calculated number of kinds and current amount of benthic
organisms, they are not reflected in the damage functions of eutrophication.

61

LIME2_C2.7-C2.9_2014

Damage per load of 1 kg [× 10-6%]
Disappearance rate in the number of kinds of benthic
Decrease rate in the wet weight of benthic
organisms
Mikawa Osaka Bay
Ise Bay
Tokyo BayIse BayspeciesMikawa Osaka Bay Tokyo Bay
Bay
Bay

Model calculation results
Damage functions of eutrophication (fishery production)
yen/kgEutrophication-contributing
substance

Total annual fishery production of target species likely toreceive damage from eutrophication (millions of yen)
Tokyo
Bay

Ise Bay

Mikawa Bay

Osaka
Bay

Estimated from data of each Regional Agricultural
Administration Office

Estimated damage in each water area

Weighting the results for the four water areas by
the current ratio of generated load

Figure 2.8-10: Damage functions of eutrophication (fishery production)
The source-receptor relationship (SRR) of NOx and NH3 calculated during the development of
the characterization factors of acidification was applied to substances released to the air.

2.8-4 Procedures for impact assessment of eutrophication
The concrete procedures for characterization of eutrophication and assessment of damage areas follows. With regard to characterization, Equation 2.8 is used to find the category index
CI from the inventory Inv (X) of each eutrophication substance X and CFEutrophication (X):CI Eutrophication  CF

Eutrophication( X )  Inv ( X )(2.8-6)

X

Several suggestions have so far been made concerning CFEutrophication (X). Under LIME,
among them, the characterization factor EPMC was recommended, which is based on theenvironmental conditions in Japan and gives consideration to atmospheric fate analysis andaquatic material circulation characteristics.
The index for the endpoint approach – that is, the damage index DI (Safe) – can be obtained
from Inv (X) of each eutrophication substance X and the DF Eutrophication (Safe, X) for eachobject of protection Safe (only social assets by fishery production herein) (the Equation 2.8-7).
62

LIME2_C2.7-C2.9_2014

Moreover, the integrated index SI can be obtained by the use of the integration IFEutrophication(X) into which the impact on social assets is economically converted or non-dimensionalized(the Equation 2.8-8).
DI ( Safe) 
SI 

 IF

 DF

Eutrophication( Safe，X )  Inv ( X )(2.8-7)X

Eutrophication( X )  Inv ( X )(2.8-8)X

The characterization factor CFEutrophication (X), the damage factor DF Eutrophication(Safe, X), and the integration factor IFEutrophication (X) have been attached hereto as
A1, A2, and A3, respectively.

[References for Section 2.8]
Asman WAH, Larsen SE (1996): Atmospheric Processes, in Eutrophication in Coastal Marine Ecosystems,
Coastal and Estuarine Studies, American Geophysical Union, 52: 21 - 50
Baretta J, Ruardij P (1988): Ecological Studies 71, Estuary, Springer ― Verlag, p.353
Canfield DE (1993): Organic matter oxidation in marine sediments, (in Wollast R, Mackensie FT, Chou L (ed.)),Interactions of C, N, P and S Biogeochemical Cycles and Glocal Change, Berlin Heidelberg, Springer ― Verlag,
pp.333 - 363
Danish Environmental Protection Agency (1998): The Danish Marine Environment: Has Action Improved its
State?, p.120
Fenchel T (ed.) (1992): Plankton Dynamics and Carbon and Nutrient Flow in Kattegat (in Danish), Havforskning
fra Miljostyrelsen, Danish Environmental Protection Agency, Copenhagen 10: 240
Goedkoop M, Spriensma R (1999): The Eco-indicator 99, A Damage Oriented Method for Life Cycle Impact
Assessment, Methodology Report
Heijungs R, et al. (1992): Environmental Life Cycle Assessment of Products. Guide and Backgrounds. CML,
Leiden University, Leiden
Huijbregts MAJ，Seppala J (2000): Towards Region ― Specific, European Fate Factors for Airbone Nitrogen
Compounds Causing Aquatic Eutrophication, Int. J. LCA, 5 (2): 65 - 67
Karrman E (2001): Int. J. LCA
Nixon SW (1995): Coastal marine eutrophication: a definition, social causes, and future concerns, Ophelia, 41:
199 - 220
Paerl HW, Fogel ML (1994): Isotopic characterization of atmospheric nitrogen inputs as sources of enhanced
primary production in coastal Atlantic Ocean waters, Mar. Biol
Richardson K (1996): Carbon Flow in the Water Column Case Study: The Southern Kattegat, in Eutrophicationin Coastal Marine Ecosystems, Coastal and Estuarine Studies, American Geophysical Union, 52: 95 - 114

63

LIME2_C2.7-C2.9_2014

Sohma A, Sekiguchi Y, Yamada H, Sato T, Nakata K (2001): A New Coastal Marine Ecosystem Model Study
Coupled with Hydrodynamic and Tidal Flat Ecosystem Effect, Marine Pollution Bulletin, 43 (7 - 12): 187 - 208
Sohma A, Sekiguchi Y, Nakata K (2004): Modeling and evaluating the ecosystem of sea ― grass beds, shallow
waters without sea ― grass, and an oxygen― depleted offshore area, Journal of Marine Systems, in press
UNEP/RIVM (1999): Bouwman AF and Van Vuuren DP: Global assessment of acidification and eutrophicationof natural ecosystems. UNEP/DEIA&EW/TR.99 ― 6 and RIVM 402001012
Wenzel H et al. (1997): Environmental Assessment of Products Volume 1: Methodology, Tools, and Case Studies
in Product Development, Chapman & Hall, London
Water Pollution Control Division, Water Quality Bureau, Ministry of the Environment (1996): Water
Environments in Inner Bays and Inland Seas, Gyosei
Suzuki T, Aoyama H, Kai M (1998): Formularization of the Death Rate of Ruditapes phillipinarum due toSuboxidation in Mikawa Bay, J. Adc. Mar. Sci. Tech. Soci, 4 (1)Sohma A, Nakata K (2002): Prediction and Assessment of the Seaweed Bed Growth Environment and the
Seaweed Bed Function Related to the Development of Shallow Bottoms by the Use of an Ecosystem Model,
Anthology of Lecture Essays at the FY2002 Autumn Meeting of the Advanced Marine Science and Technology
Society: 45 - 54
Nakata K (1993): Simulation of Inland Sea Environments, Simulation of Ocean Environments, Hakua Shobo
Japan Fisheries Resource Conservation Association (2000): Standards for Fishery Water (2000 Edition)
Matsukawa Y (1992): Fishing Carrying Capacity in Tokyo Bay and Mikawa Bay, Kouseisha-Kouseikaku

64

LIME2_C2.7-C2.9_2014

2.9

Indoor air pollution

2.9.1 What phenomenon is indoor air pollution?
(1)

Background factors to the increasing appearance of indoor air pollution

There are three main background factors to indoor air pollution. One of them is problems inhouses. Japanese houses have traditionally been devised to be ventilated well, because
importance has been placed on the summer season. Therefore, the amount of ventilation isextremely large, resulting in almost no indoor air pollution or sick house problem. Recently,
however, the improvement of indoor environments and energy conservation has required
super-insulated houses. As a result, although indoor environments have been sharply
improved, drafts have decreased, and wood, paper, and soil have been replaced with new
construction materials. This has resulted in the production of a large quantity of pollutants.
In addition, because of a decrease in the amount of ventilation, the pollutants produced inopen combustion appliances and mold caused by moisture have become tangible problems.
The second background factor is lifestyle changes. If a house is next to another house or
faces a noisy road, it is difficult to open windows even in the summer season. In addition,
chemicals are often used for living ware.
The third background factor is problems in human bodies. While in the past few people
suffered hay fever or allergies, recently many people have suffered them. It is said that suchdiseases occur if the amount of pollutants accumulated in the body through respiration or foodexceeds a certain level. Because, as described above, a large amount of chemicals has beenbrought into our lives, every time people breathe or have a meal, they accumulate pollutantsin their bodies. With the worsening of air pollution, the amount of chemical pollutants
accumulated in bodies has been increasing. Moreover, when houses are built, people inhale
a lot of chemical pollutants, which may trigger the occurrence of disease. In addition,
pollutants accumulated in human bodies are thought to be concentrated between generations.
If so, a child born from a mother who has accumulated pollutants in her body has a certain
amount of pollutants even at the birth. The number of people suffering allergies has recently
increased as a result of an increasing number of people who have become sensitive tochemicals because they had already accumulated pollutants at birth or have accumulated a lotof pollutants due to their proneness to pollution by chemicals around them.
(2)

Causes of indoor air pollution damage

The main causes of indoor air pollution are not only volatile organic compounds,
formaldehyde, and other toxic chemicals generated from construction materials and furniture
but also carbon dioxide (CO2) generated from human bodies, nitrogen oxides (NOx) generated
from open combustion appliances, sulfur oxides (SOx), and cigarette smoke. In rooms, there
are various other sources of pollution that pollute indoor air.
People take in substances through various activities, such as eating and drinking. The
amount of indoor air taken in through respiration is the largest and accounts for nearly 60% of
the total amount of substances taken in. The amount of indoor air taken in is
overwhelmingly larger than the amount of food and water taken in. That is, it can be said
that indoor air has great influence on human health. However, while people are sensitive to
muddy water and rotten food, they tend to be indifferent to dirty air. Because, unlike the65LIME2_C2.7-C2.9_2014

case of water, changes in the properties of air cannot be seen, people tend to be carelesstoward worsening of air quality, which is said to be one of the serious causes of indoor air
pollution damage.
(3)

Endpoints of indoor air pollution

The object of protection from indoor air pollution is limited to “human health.” Althoughdamage to some “social assets” can be imagined – for example, a decrease in the monetary
value of a building where indoor air pollution occurs – because such damage is not general, it
is not examined herein.
As there are various indoor air pollutants, various types of health damage occur due toexposure to these pollutants, such as sick house syndrome, allergies, and respiratory diseases.
With regard to some pollutants, the cause-effect relationship with health damage has still notbeen clarified. In this study, an assessment method was developed concerning healthdamage that is caused by main pollutants and about which sufficient quantitative information
can be obtained. Figure 2.9-1 shows the substances and health damage covered by theassessment.
Duringresidence
Formaldehyde

TolueneSick house
syndrome
Nose/larynx cancer
Sick
syndrome
Poisoning
symptoms

Duringconstruction(Highconcentration
exposure)
Nose/larynx
cancerhouse
Poisoning
symptoms

Respiratory
diseaseRespiratory
diseaseRespiratory
diseaseFigure 2.9-1: Target indoor pollutants and health damage (endpoints)a

Health damage by formaldehyde

Although formaldehyde exists in the natural world, such as in food, it is mainly used as glue
for construction materials and causes health damage through indoor diffusion. Conspicuous
symptoms caused by exposure to formaldehyde are usually acute irritation in the eyes, thenose, and the pharynx. In addition, formaldehyde is suspected causing cancer and is known
to give pathological and cellular genetic influence to the mucous membrane of the nasal
cavities. With the spread of the sick house problem, sick house syndrome, which has
symptoms such as dizziness, nausea, and eye and nose pain, has become known to more
people. There are similarities between sick house syndrome and chemical hypersensitivity.
However, sick house syndrome is different from chemical hypersensitivity in that symptoms,
such as throat irritation or pain, headache, and difficulty in concentration, completely
disappear as a result of leaving the house in question. Although a person suffers symptoms
when he is living in a room, his physical condition becomes better outside of the room66

LIME2_C2.7-C2.9_2014

because he does not make contact with the causative pollutant – this is sick house syndrome,
which is caused by indoor environmental pollution. The symptoms of sick house syndrome
greatly differ from person to person. Usually, however, people have the following
symptoms: 1) headache; 2) reduction in concentration, memory, or thinking power; 3)
discomfort in the eyes; 4) dizziness and nausea; 5) nasal mucus, nosebleed, difficulty inrespiration, or asthmatic attack; 6) migraine; 7) skin itching; 8) bowel movement disturbance;9) menstruation disorder; 10) muscle or joint ache; 11) fatigue or wariness; and 12)
depression, excitement, and sleep disorder. These symptoms can be roughly divided into
mucosa symptoms and mental symptoms.
b

Health damage by toluene

Because information on health damage by toluene greatly differs between treatises on
occupational exposure, there are many reports on health damage by high-concentration
exposure. Toluene diffuses when used as glue for housing materials or as solvent for paint.
It is easily absorbed in the human body and stimulates the eyes, the throat, the trachea, etc.
In the case of relatively low-concentration or short-time exposure, people suffer sick house
syndrome or chemical hypersensitivity, both of which involve mild mental disorder. It has
been pointed out that chronic high-concentration exposure has influence on the central
nervous system. If exposure is extremely high-concentration, blackout or death may occur.
The evidence proving cancer-causing property is insufficient, and toluene is said to beunlikely to cause cancer.
c

Health damage by NOx

NOx is generated when nitrogen and oxygen in the air react to combustion. It is generated
indoors by an open combustion appliance or cigarette smoke and causes respiratory healthdamage, such as bronchitis or pneumonia. In addition, because it stimulates the respiratory
tract, asthma patients may respond to lower-concentration exposure than healthy people.
d

Health damage by SOx

Absorption of SOx generated by oil or coal combustion may cause a respiratory disease.
Like the symptoms in the case of NOx, it is known that a respiratory disease, such asbronchitis or pneumonia, may occur.
e

Health damage by SPM

Suspended particulate matter (SPM) is generated indoors mainly by cigarette smoke. SPMs
may flow into a room from an outside factory or an automobile. The occurrence of healthdamage depends on the components, concentration, and diameter of particles. SPMs mainly
cause a respiratory disease. Although there are substances that cause cancer in theirchemical structure, such as cigarette smoke and radon, respiratory diseases caused by general
SPMs are covered herein. If the diameter is large, SPMs easily deposit in the upper
respiratory tract and have effect on the pharynx. In addition, they have effect on health byreacting to allergens. SPMs with a small diameter reach the pulmonary alveoli and depositin them. Because SPMs that deposit in the pulmonary alveoli exist for several hundred days,
they may cause various disorders because of their chemical and physical properties. In
addition, the smaller the diameter, the higher the ratio of SPMs that deposit in the pulmonary
alveoli and the more likely they are to cause serious health damage.
67

LIME2_C2.7-C2.9_2014

2.9.2 Characterization and damage assessment of indoor air pollution(1)

Basic policies for calculation of damage factors and analysis of uncertaintyaSelection of indoor air pollutants

We decided to deal mainly with the indoor pollutants that fulfill the following requirements:
1) The probability of existing indoors is relatively high.
2) The substance causes serious health damage.
3) Quantitative and highly reliable data can be collected.
4) Necessity for a quantitative assessment method is socially high.
Sick house syndrome is an important object of research, and formaldehyde is the worst among
the substances that cause sick house syndrome. However, the research is still at the stage ofdevelopment, and few data are available data. With regard to toluene and volatile organic
compounds (VOC) also, the Ministry of Health, Labour and Industry has fixed standard
values for indoor concentration as in the case of formaldehyde. However, because
quantitative data on VOC are fewer and the rate of VOC existing indoors is lower than in thecases of formaldehyde and toluene, this study covers only formaldehyde and toluene. In
addition to chemicals generated from construction materials, there are pollutants generated
from products that residents bring in rooms. In Japan especially, NOx and SOx are generated
from open combustion appliances used in winter and may cause respiratory diseases. In
Japan, open combustion appliances have been used in houses for many years. In addition,
because of economic efficiency, many families are consuming oil, which generates SOx.
Because NOx and SOx have been regulated severely from the viewpoint of air pollution,
research on them has been carried out for many years and sufficient quantitative data can becollected. Because of this, NOx and SOx are covered by this study. It is reported that NOx
and SOx react to water outdoors and change into nitric acid and sulfuric acid, secondary
pollutants, causing serious health damage. Indoors, however, they hardly react with waterand change into such pollutants. Therefore, secondary pollutants are not covered by thisstudy. One of the main causes of indoor air pollution is smoking, which causes countless
SPMs indoors. Such respiratory diseases due to particles have been reported and researchedfrequently. Because, like NOx and SOx, SPMs are regulated outdoors also, it is easy tocollect a lot of quantitative data. Because of this, SPMs also were included in the objects ofthis study. Because impact on heath damage differs according to the diameter of SPM, PM
10 (with a diameter of 10 μm) and PM 2.5 (with a diameter of 2.5μm) were used for the
research.
In addition to the sick house problem, concern for health damage due to exposure to toxic
chemicals at the stage of construction of houses has grown into a social problem. Although
the number of people suffering occupational diseases due to chemicals has been decreasing, aconsiderable number of people still suffer them every year. According to the result ofspecial health checkup of workers handling chemicals, the number of people who show
symptoms of such diseases is increasing. During the research, two models were prepared
concerning the sick house problem and the stage of construction of houses. Damage
assessment was conducted for each of the models. The target chemicals were formaldehyde
and toluene, both of which were known to become highly concentrated during construction.

68

LIME2_C2.7-C2.9_2014b

Selection of category endpoints (symptoms)

Formaldehyde is known to cause the symptoms of sick house syndrome, such as dizziness,headache, and nausea. In the case of sick house syndrome in particular, because symptoms
differ greatly among people, the symptoms of sick house syndrome were roughly divided into
mucosa symptoms and mental symptoms according to medical specialists’ knowledge.
Moreover, formaldehyde is suspected of causing cancer by worldwide environmental
protection organizations, such as the U.S. Environmental Protection Agency (EPA). It is
pointed out that high-concentration exposure or long-term exposure – especially, chronic
exposure during construction – may cause nose or larynx cancer. Because of this, nose andlarynx cancers are covered by this study.
Toluene also is known to cause the symptoms of sick house syndrome, such as headache andnausea. Because, like the symptoms of sick house syndrome caused by formaldehyde, thosecaused by toluene can be roughly divided into mucosa syndromes and mental syndromes,
these two types of symptoms were covered by the assessment. In addition, it has been
medically recognized that high-concentration or chronic exposure to toluene during
construction causes serious poisoning symptoms.
It is medically clear that NOx and SOx cause respiratory disease. Because of this,
hospitalization and acute death due to respiratory disease were covered by the assessment.
Because SPMs are used as a general term for various pollutants, they are related to many
diseases because of the chemical structure of the substance itself. Although thecancer-causing property of cigarette smoking is frequently discussed, it is necessary to limit
discussions to “cigarette smoking.” Because general SPMs are covered herein, we decided
that this study should include respiratory diseases whose causal relationship with SPMs has
been medically recognized.
cFlowchart of damage assessment of indoor air pollutants

Each of the items necessary for calculation for damage assessment is examined according to
the flow shown in Figure 2.9-2.
[Equation for calculation of damage function of indoor air pollution]

DF Indoorairpollution  C  DR  P  DS

(2.9-1)DFIndoorairpollution: damage function of indoor air pollution [DALY/kg]
ΔC: increment in the indoor concentration to the open air due to release of a unit
amount of indoor pollutant [g/m3]  (first step)
DR: dose-response relationship [case/person/year/(g/m3)]  (second step)
P: number of target patients
DS : degree of damage from disease (amount of damage per case) [DALY/case]∆C (increment in the indoor concentration) in Equation 2.9-1 is examined according to thefate and exposure analysis as the first step in Figure 2.9-2. Based on the result, thecharacterization of indoor air pollution is also examined. In addition, DR, P, and Ds arecalculated through examination of the “dose-response relationship” in the damage/effectanalysis as the second step, the examination of the “number of target patients,” and theexamination of the “degree of damage from disease” respectively. This can be summarized
as in Table 2.9-3.
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LIME2_C2.7-C2.9_2014

Table 2.9-1: Indoor air pollutants and category endpoints [during residence]
Formaldehyde

Suspended particulate matter (SPM)(Right column: condition for each symptom）

NOx

1

Sick house syndrome
Mucosa symptoms

1

Respiratory disease
Acute death1

Respiratory disease
Acute death2

Sick house syndrome
Mental symptoms

2

Hospitalization for
Respiratory disease2

Respiratory disease
Chronic death3

Nose/larynx cancer

3

Hospitalization for Respiratory
disease4

Use of bronchodilator

Asthma sufferer

5

Cough

Asthma sufferer

6

Stridor

Asthma sufferer

7

Chronic bronchitis

8

Chronic coughToluene1
2
3

SOx

Sick house syndrome
Mucosa symptoms

1

Sick house syndrome
Mental symptoms

2

Respiratory disease
Acute death
Hospitalization for
Respiratory disease9

Day of movement restriction

10

Emergency room hospitalization
due to chronic closed lung disease

11

Emergency room hospitalization
due to asthma

12

Preschooler’s emergency room
hospitalization due topharyngitis/bronchitis

Poisoning symptoms

30 year of age
or olderTable 2.9-2: Indoor air pollutants and category endpoints [during construction]
Formaldehyde

Toluene1

Sick house syndrome mucosa symptoms

1

Sick house syndrome mucosa symptoms

2

Sick house syndrome mental symptoms

2

Sick house syndrome mental symptoms

3

Nose/larynx cancer

3

Poisoning symptoms

Identification of pollutant
Determination of damage form
(symptom)
(1st
step)
fate-exposure
analysis
Relation between the amount ofgenerated indoor pollutant andthe indoor concentration/theamountof exposure
Releaseof a unit amount ofpollutant

Concentration of indoor
pollutant

Amount of human
exposure(2nd step) damage/effectanalysis
Relation between exposure
concentration and disease
Dose-response relationship(Relation between exposure
concentration and incidence)

Number of target
patients
Degree of damage from
diseaseDamage function of health damage[DALY/kg]

Figure 2.9-2: Flowchart of calculation of damage function of indoor air pollution(Shaded parts are elements to which uncertainty analysis is applied)70

LIME2_C2.7-C2.9_2014

Table 2.9-3: Relation between the flowchart of calculation of damage function and each item
Item corresponding toCalculation flowchart
Item to be calculated
damage function equation
Increment in indoor concentration due to1) Fate analysis
ΔC
release of a unit amount of pollutant
Predicted daily intake, characterization－
2) Exposure analysis
factor
Dose-response relationshipDR3) Damage analysis4) Impact analysisdNumber of target patients
Degree of damage from disease

P
DS

Uncertainty analysis of indoor air pollution

Limited data were used for damage assessment of indoor air pollution. If a judgment wasneeded, consultation was held with experts.
Because of this, when a parameter(representative value) was fixed, an average value or an appropriate value was selected fromamong uncertain values that contain individual variability in data (amount of intake,metabolic capacity, and sensitivity) and measurement errors. It can be said that the resultant
damage function also contains such uncertainties. Although damage assessment requires
estimation of safety (division by a safety coefficient or an uncertainty coefficient), because
excessive estimation may result in overestimation, consideration was given to assuming theuncertainty of each parameter and showing the degree of uncertainty as a whole.
(2)

Characterization of indoor air pollution (fate analysis and exposure analysis)aModeling of indoor air pollution

First, general indoor air pollution was modeled. The general house and living environment
for Japanese, including construction, were modeled as described below.
[Housing model]
In the field of architecture, “Architectural Institute of Japan’s Standard Specifications for
Wooden Houses” are usually used for houses, while the Institute for Building Environment
and Energy Conservation’s “New Energy Saving and Standards for Housing” are usually used
for apartment houses. In this study, however, importance was placed also on highly reliable
models and representation of Japanese average houses. Because of this, a survey was carriedout about the distribution of total floor areas of houses in Japan (Architectural Institute ofJapan, housing macro model). According to the result, the average total floor area for ahouse is 129.03 m2, almost the same as 125.86 m2, the total floor area specified in theArchitectural Institute of Japan’s Standard Specifications. Therefore, it will be no problem if
the Architectural Institute of Japan’s Standard Specifications is used as the representativemodel of houses in Japan. On the other hand, the average total floor area for an apartment
house in Japan is 47.06 m2 (National Census 2000). If compared with 80.99 m2, the total
floor area of the house model by the Institute for Building Environment and Energy
Conservation, it is found that the size of the house model is far larger than the size of an
actual apartment house. The national average total floor area for an apartment house, 47.06
m2, was adopted herein and was combined with the floor height examined separately todetermine a representative value for room volume. Because both the Architectural Instituteof Japan’s Standard Specifications and the Institute for Building Environment and Energy
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LIME2_C2.7-C2.9_2014

Conservation specify that the floor height is 2.4 m, it was assumed that the floor height of theapartment house model is 2.4 m.
[Frequency of ventilation]
The frequency of ventilation expresses the number of times of the indoor air’s entire
replacement in a certain time. As a sick house measure, the Building Standards Act specifies
that the frequency of ventilation for a house should be 0.5 times/h. This means that it isnecessary to secure an amount of fresh air that makes it possible to replace half of the amountof indoor air in one hour.
With regard to the frequency of ventilation for general houses, we could not find any studies
in Japan that show a value appropriate as the representative value. Although actually
measured values can be found in some studies, because they cover only a certain region orhouses where no one lives, they do not have sufficiently decisive grounds to show arepresentative value in Japan. Because of this, after ventilation was divided into three types– 1) opening ventilation, 2) machinery ventilation, and 3) open window ventilation – weestimated a value for each type and then estimated the average frequency of ventilation. As
a result, the average frequency of ventilation for a house was found to be 1.07 times/h. For
the purpose of calculation, the fraction was cut off and 1.0 time/h was adopted.
Table 2.9-4: Characteristics of single-family and apartment house models
Type of house
Single-family
Apartment

House area [m2]Floor height [m]Frequency of ventilation [times/h]

125.86
2.4
1.0

47.06
2.4
0.7

Generally, apartment houses are more airtight than single-family houses. As a result of aconsultation with specialists engaged in construction, the frequency of ventilation for anapartment house was fixed at 0.7 times/h, compared with 1.0 time/h, the representative value
for a single-family house.
Because sufficient data on the frequency of ventilation during construction could not be found,it was assumed that the frequency of ventilation is 1.0 time/h for single-family houses and 0.7
times/h for apartment houses. Although windows and the like are usually open during
interior finishing, it was assumed herein that the minimum essential ventilation is maintained.
b

Fate analysis

Results of fate and exposure analysis in relation to the human body’s inhalation of pollutants
vary according to type of pollutant, form of room, ventilation method, interior furniturearrangement, positioning of people indoors, human postures and human activities (static ormoving), duration of staying in the room, etc. It is difficult to analyze the properties
immediately and comprehensively. Representative pollutants, rooms, ventilation, humanposition, human posture and duration of residence, etc, were modeled and general properties
were examined. In addition, with regard to the exposure of the human body to an indoor
pollutant, first the properties of indoor air pollution were clarified by the use of computational
fluid dynamics (CFD). Formaldehyde was used as the pollutant, because rich data have been
accumulated data on it. A 6-tatami room, which is popular in Japan, was assumed as shownin Figure 2.9-3. The room capacity was about 22 m3. Because only natural ventilation was
72

LIME2_C2.7-C2.9_2014

assumed to observe indoor air properties, the frequency of ventilation was set at 0.5 times/h,
although this is different from the above-described assumption model (usually, ventilation iscarried out through window opening and machinery ventilation in addition to naturalventilation). Opening ventilation was adopted: fresh air flows into the room through thebaseboards on all sides of the floor and flows out from all sides of the ceiling. The room
temperature is fixed at 23 degrees centigrade and there is no heat exchange with the outside
on the surface of each wall. Formaldehyde, the pollutant, is generated from the floor, thewalls, and the ceiling in the model room in a unified way. Interior material with a diffusion
grade of F☆☆☆ (20 μg/(m2·h) herein) is used for the floor, and interior material with adiffusion grade of F☆☆☆☆ (5 μg/(m2·h) herein) is used for the walls and the ceiling. The
amount of formaldehyde generated from the interior materials is 8.4 mg/day.
対称面
Symmetry
plane

Air排気（Uout)
exhaust(Uout)

床
Floor
2.4

給気（Uin)Air supply
(Uin)
2.6

3.5

単位[m]
Unit
[m]

Figure 2.9-3: Indoor model for CFD analysis

Although the amount of human respiration is usually fixed at 15 m3/day for the purpose of risk
analysis, because it varies according to the human body’s activities, it was fixed at 8 L/min(11.5 m3/day) during sitting and at 6 L/min (8.6 m3/day) during lying (sleeping). With
regard to the duration of residence, it was assumed that people lived in their house for 75% of
the lives (18 hours per day), and the number of sleeping hours was fixed at 7.5 according to asurvey on the actual condition (Data book 2000 National Time Use Survey). The position
for breathing was lying down during sleeping (height of face: 30 cm above the floor) andsitting during the hours other than sleeping hours (10.5 hours) (height of face: 120 cm above
the floor). Table 2.9-5 shows a summary of the numerical analysis. It was assumed that
fresh air flowed into the room at a wind velocity of 0.25 m/s through the baseboards on all
sides of the floor. The strength of disturbance was 5%. Each wall’s coordinates y+ were0.02 or less.
After the flow-field analysis, boundary conditions were given to the surface of formaldehyde
diffusion to analyze the field of formaldehyde diffusion. It was assumed that formaldehyde
diffused into the room was exhausted from all sides of the ceiling by ventilation without
adsorption on the walls and the ceiling. In addition, it was assumed that fresh air flowed into
the room through the baseboards and contained formaldehyde with a concentration of 0
μg/m3.
Figures 2.9-4 and 2.9-5 show the results of analysis of wind velocity vectors on the symmetry
plane in the model room and analysis of the place of formaldehyde diffusion.
Although Figure 2.9-4 shows that the wind velocity (Uin) around the inflow openings of fresh
air on all sides of the floor is 0.25 m/s, which is a little high, the wind velocity in most parts of
the room is low. Therefore, the room is extremely calm on the whole. The velocity ofwinds along the walls is about 1 × 10-2 m/s. The velocity of downward winds around thecenter of the room is less than 5 × 10-3 m/s.
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LIME2_C2.7-C2.9_2014

Table 2.9-5: Summary of numerical analysis
Room size

3.5 × 2.4 × 2.6 m

Number of meshes

About 170,000

Frequency of ventilation

0.5 times/hWind velocity

Uin = 0.25 m/s

Room temperature

23C (isothermal)

Turbulence model

Low Re type k-ε model (Abe-Nagano model)

Difference scheme

Primary upwind difference (motion equation,scalar equation)

Entrance boundary

Uin=0.25 m/s, lin=1/7･Lin,
k=3/2(Uin×0.05)2, ε=Cμ・k3/2/lin,Cμ=0.09, Lin=0.001 m

Exit boundary

Uout: kout by mass conservation, εout: free slip

Amount of diffused formaldehyde*

Floor: 20μg/(m2•h,)Wall/ceiling: 5μg/(m2•h)

Diffusion coefficient of formaldehyde in the air Da=1.53×10-5 m2/s (23C)* The highest amount of diffusion at each diffusion class is used.

ExhaustWind velocity along wall

Floor
Inflow of fresh
surfaceair (Uin)Wind velocity
along floor
510-3m/s or lessFigure 2.9-4: Wind velocity vectors
Averageconcentration in
room

Point
A

Point
B

Figure 2.9-5: Formaldehyde concentration distribution
The numbers in the figure indicate concentration [μg/m3]

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LIME2_C2.7-C2.9_2014

According to Figure 2.9-5, although formaldehyde is distributed all over the room almost
equally, the concentration is the highest near the floor. Although formaldehyde moves anddiffuses in the room because of an indoor air current, its concentration tends to become highnear the floor because the wind velocity is extremely low there and the amount offormaldehyde generated from the floor. The average indoor concentration was 30 μg/m3.
cExposure analysis

Usually, pollutants enter the human body through the mouth, inhalation, and the skin. This
study covers only inhalation. According to existing research (Shu et al. 2001; Kasahara et al.
2002), the human body inhales air from the area below the mouth, because of an upward air
current caused by human heat generation. In this study, it was assumed that the human bodyinhales the average concentration of formaldehyde in the area up to 120 cm above the floor
when sitting (Point A in Figure 2.9-5) and in the area up to 30 cm above the floor when lying
down (Point B in Figure 2.9-5). As shown in Figure 2.9-5, if formaldehyde of 8.4 mg/day is
generated in the room, the average concentration of formaldehyde is 30 μg/m3 in theinhalation area when sitting and 34 μg/m3 in the inhalation area when lying down. If therelation between the amount of generated formaldehyde and the indoor concentration is
proportional in the room and 1 kg of formaldehyde is generated per year (= 2.74 g/day), theconcentration of formaldehyde is 9.79 × 103 μg/m3 in the inhalation area when sitting and 1.11
× 104 μg/m3 in the inhalation area when lying down.
The result of CFD analysis found that the wind velocity in most of the room for the analysis
model was low and the room was extremely calm and that, in such as case, the concentrationof chemicals was almost the same all over the room. Because of this, in the case of analysis
models similar to this model, it is all right to assume uniform diffusion in the room andcalculate simply by the use of the indoor concentration uniform diffusion equation (Equation
2.9-2) used for architectural environment engineering. The following is the result of
calculation by the use of the indoor concentration uniform diffusion equation (Equation 2.9-2)
concerning an analysis model similar to an actual CFD analysis model:[Indoor concentration uniform diffusion equation]
C  C0 

C:
C0 :
M:
Q:

M
Q

(2.9-2)indoor concentration [mg/m3]
outdoor concentration [mg/m3]
amount of generated pollutant [mg/h]
amount of ventilation [m3/h] (= frequency of ventilation (no. of times/h)  room
capacity V [m3])According to Equation 2.9-2, if C0, the initial indoor concentration for CFD analysis, is = 0
mg/m3, the calculation result becomes as follows:C  Initial indoor concentrat ion 0mg/m3 

8.4 mg, total amount of generated formaldehy de per day
262.08m3 , total amount of ventilation per day ( 0.5times/h 21.8 m 3  24 h/day)

 0.03205mg/m3  32g/m3

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LIME2_C2.7-C2.9_2014

The results of the estimation of indoor concentration distribution as a result of the CFD
analysis were the average concentration of formaldehyde 30 μg/m3 and 34 μg/m3 in theinhalation area when sitting and when lying down, respectively. This is almost the same as
32 μg/m3, the result of calculation of indoor uniform diffusion. The calculations below will
be carried out by the use of the indoor concentration uniform diffusion equation.
dCalculation of an increment in the concentration due to release of a unit amountof a pollutant∆C in the Equation 2.9-1, the equation for the calculation of the damage function, is anincrement in the indoor concentration due to release of a unit amount of a pollutant. It canbe calculated by the use of the “indoor concentration uniform diffusion equation,” based on
the result of indoor concentration analysis by CFD. For the purpose of calculation of anincrement, based on the Equation 2.9-2, ∆C can be expressed as follows:C  C  C0 

MQ

(2.9-3)

Because this depends only on the amount of generation and the amount of ventilation, if
calculation is carried out by assuming a unit amount of release (1 kg), the calculation result
becomes the same irrespective of the type of substance if the house model and the frequency
of ventilation are the same. In this study, because the house model is the same for bothliving and construction, consideration is given only to the difference between single-family
houses and apartment houses.
Increment in indoor concentration ∆C [single-family house]= Annual unit amount of released pollutant (1 kg) ÷ annual amount of ventilation [m3]
= 1 kg/year ÷ (1.0 times/h × 302 m3 × 24 h/day × 365 days/year)= 3.78×10-7 kg/m3 =3.78×102 μg/m3(Room capacity (single-family house): 302 m3 (Architectural Institute of Japan’s Standard
model), frequency of ventilation: 1.0 time/h)Increment in indoor concentration ΔC [apartment house]= Annual unit amount of released pollutant (1 kg) ÷ annual amount of ventilation [m3]
= 1 kg/year ÷ (0.7 times/h × 111 m3 × 24 h/day × 365 days/year)= 1.47×10-6 kg/m3 =1.47×103 μg/m3(Room capacity (single-family house): 111 m3 (national average in Japan), frequency ofventilation: 0.7 times/h)eCharacterization

The characterization of indoor air pollution is calculated in the same way as that of human
toxicity and air pollution. First, predicted daily intake (PDI) is calculated from the indoor
concentration at the time of a unit amount of release (1kg/year) obtained from “c Exposure
analysis.” The result is divided by acceptable daily intake (ADI). The substances about
which the standard indoor concentration value has been specified in the Building Standards
Act are examined. In this study, calculation is carried out concerning formaldehyde andtoluene as examples. The results are divided by DPI/ADI of formaldehyde to find thecharacterization factor.

76

LIME2_C2.7-C2.9_2014[Predicted daily intake (PDI)]As shown in Equation 2.9-4, PDI of an indoor chemical can be expressed by dividing the
product of the indoor chemical concentration in the inhaled air and the amount of respirationby the body weight. Outdoor inhalation is not included in the object of calculation.
Because a daily air intake of 15 m3 and a weight of 50 kg are generally used for risk analysis,
they are used.
Predicted daily intake of indoor pollutant [μg/kg bw/day]=Indoor pollutant concentration [μg/m3] × intake [m3/day]Weight [kg bw](2.9-4)

PDI was calculated by the use of an increase in the indoor concentration for single-family
houses. 1 of Table 2.9-1 shows the calculation results. Because indoor air pollution
depends only on the amount of generation and the amount of ventilation, the value is the same,irrespective of the type of substance.
Table 2.9-6: Calculation results of characterization factors of indoor pollutants

1 Predicted daily intake (PDI)
3.78×102 [g/m3] 15 [m3/day] ÷ 50 [kg bw] = 1.13102 [g/kg bw/day](*bw: body weight)
2 Acceptable daily intake (ADI)Formaldehyde ADI = 100 [g/m3] 15 [m3/day] ÷ 50 [kg bw] = 30 [g/kg bw/day]Toluene ADI= 260 [g/m3] 15 [m3/day] ÷ 50 [kg bw] = 78 [g/kg bw/day]
3 PDI/ADIFormaldehyde= 1.13102 [g/kg bw/day] ÷30 [g/kg bw/day] = 3.78
Toluene= 1.13102 [g/kg bw/day] ÷78 [g/kg bw/day] = 1.45
4 Characterization factor
Formaldehyde= 3.78÷3.78 = 1 (standard)
Toluene= 1.45÷3.78 = 0.38

[Acceptable daily intake (ADI)]
With regard to formaldehyde and toluene, the standard indoor concentration has been fixedunder the Building Standards Act. The standard indoor concentration is 100 μg/m3 forformaldehyde and 260 μg/m3 for toluene. Like the calculation of PDI, assuming that theamount of intake is 15 m3/day and the weight is 50 kg, the calculation is carried out as in 2 ofTable 2.9-6.
In this way, after PDI/ADI of each substance is calculated, the characterization factor is
calculated based on formaldehyde (PDA/ADI of each substance ÷ PDA/ADI offormaldehyde).
(3)

Health impact: damage function of sick house syndrome

With regard to sick house syndrome, there is no research on dose-response relationships in theworld. Sakabe (2007), a co-researcher who is a medical expert in sick house syndrome,
pointed out that it is difficult to determine a dose-response relationship simply concerning
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LIME2_C2.7-C2.9_2014

sick house syndrome, which is characterized by great differences among individuals. This isbecause if the same dose is given, some people show response, while other people do not.
An ordinary dose-response relationship indicates the response rate to the amount of exposure(or concentration). Because of this, in many cases, if the dose increases, the response rate
also increases. The calculation method examines the relationship after comparing
differences in the response rate when different doses are given to several test groups.
Because of this, usually, animal experience is employed and the result is extrapolated to findhuman beings’ dose-response relationship.
Symptoms of sick house syndrome are often light ones, such as dizziness or abnormality inthe nose, or ones peculiar to human beings. In addition, because it is difficult to reproduce
disease by experiment – for example, disease occurs only in a room – experiment by the useof animals is very difficult. For the same reason, experiment on human beings also isdifficult. As a result, we found it necessary to examine the dose-response relationshipconcerning sick house syndrome by ourselves.
a“QEESI,” a diagnosis sheet for sick house syndrome

In the case of sick house syndrome, “QEESI (Quick Environmental Exposure Sensitivity
Inventory)” is used to inquire patients about their health conditions to check symptoms. In
addition, because the situation in which exposure occurred is very important for investigating
the cause, we request patients to measure the concentration of pollution and bring the result tous (upon request, a health center is ready to measure it). The Kitasato Institute Hospital, towhich the co-researcher Sakabe belongs, has a lot of such data. We herein examine thedose-response relationship based on these valuable data by ourselves.
b

Seriousness of symptoms and the incidence rate of disease

QEESI consists of five questions. One of them concerns the seriousness of symptoms.
Focusing on them, we decided to examine the relation between the indoor concentration andthe seriousness of symptoms. In this case, however, because of the definition of the
response rate (the incidence rate), the dose-response relationship has becomes useless. Thisis because QEESI is used only for sick house syndrome patients and therefore the incidence
rate is 100%. In addition, the dose-response relationship of ordinary chemicals does notcontain information to the effect that with an increase in the concentration, symptoms becomeheavier. The basic idea is simple: as the concentration increases, the number of people who
respond to it increases. Worsening of the health conditions of patients is not examined
concerning the dose-response relationship. Because of this, we decided to review thestructure from the viewpoint of the whole flow of risk assessment.
cIncidence rate of sick house syndrome

Because the incidence rate of sick house syndrome does not depend on the degree ofconcentration, it is difficult to express it as the number of incidences per degree ofconcentration. Because of this, we examined whether the incidence rate could be replacedwith another means. As a result, we decided to use the ratio of sick house syndrome patientsto the total population of Japan as the incidence rate of sick house syndrome. This makes it
possible to estimate the incidence of sick house syndrome for average Japanese. Although
no large-scale survey on the number of sick house syndrome patients has been conducted in78

LIME2_C2.7-C2.9_2014

Japan, Prof. Uchiyama, of Kyoto University, reported in FY2003 that the ratio of chemical
hypersensitivity patients in Japan is 0.74% of the population of Japan (based on a survey ofabout 4,000 people). The medical expert Sakabe estimated that this value included thenumber of sick house syndrome patients and chemical hypersensitivity patients. Because
this study concerns sick house syndrome, chemical hypersensitivity patients should be
excluded. Because of this, the value of 0.74% was adjusted with consideration for the ratio
between chemical hypersensitivity patients and sick house syndrome patients. Of theoutpatients of the Kitasato Institute Hospital, 40% to 50% suffer sick house syndrome andabout 14% suffer chemical hypersensitivity. Based on this, we fixed the ratio of sick house
syndrome patients at 0.56% (= 0.74% × (45 / (45 + 14)).

Flow under LIME
(risk assessment)
Equation for
calculation ofdamage function

Equation for
calculation ofdamage function
for sick house
syndrome

Fate/exposure analysis

DamageImpactIncrement in indoor
concentration due torelease of a unit amount ofpollutant ∆Canalysis
Dose-response
relationship DR

Number of analysis Degree of
patients (in 1damage fromhouse) P
disease Ds[Concentration/
generated amount][Incidence rate(= cases/persons)/
concentration]

DR of sick[persons]

Incidence rate of sickhouse syndrome

house
syndrome[Concentration/generated amount] [QEESI/concentration][No. of patients (=cases)/total population in Japan] [persons]Degree of damage incomparison withseriousness of sick house
syndrome Ds* Case is a unit that expresses one case of disease* QEESI is a unit that expresses the degree of seriousness in sick house syndrome

Figure 2.9-6: Equation for calculation of damage function for sick house syndromedDevice of equation for calculation of damage function

The dose-response relationship of sick house syndrome is expressed by the degree ofseriousness in symptoms in comparison with the concentration by the use of QEESI (QEESI
is used as a unit herein). Therefore, the structure of the calculation equation as a whole wasreviewed as shown in Figure 2.9-6. Because the dose-response relationship is a datum on
the incidence rate of 100% limited to sick house syndrome patients, the ratio of patients to the
population as examined in 3) above was regarded as the incidence rate and was used as themultiplier for the equation. In addition, because the degree of seriousness of symptoms
QEESI is used also for the degree of damage from disease in impact analysis (the last Ds), it
is necessary to adjust the degree of damage to QEESI. By this method, the equation for
calculation of damage function for sick house syndrome was newly defined as shown in thelower part of Figure 2.9-6.
eDose-response relationship of sick house syndrome

Although the sick house syndrome patients of the Kitasato Institute Hospital request healthcenters to measure the indoor concentration in their houses, the method to indicate theconcentration differs among measuring agencies. Because of this, the data dealt with hereinconsist of detailed and rough data. To give preference to utilization of all the data, we
divided degrees of concentration into three levels, such as below and above the standard
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LIME2_C2.7-C2.9_2014

indoor concentration value. Medical inspections sometimes adopt the “concept of
doubling.” For example, because the standard indoor concentration value is 0.08 ppm in thecase of formaldehyde, inspection is carried out at 0.04 ppm, half the concentration, and at
0.16 ppm, double the concentration. We adopted this method and divided concentration by
doubling the standard value. In addition, because 0.25 ppm is used as the threshold of
poisoning due to high-concentration exposure, this value was regarded as the maximum
concentration (if a person is exposed to higher concentration, not sick house syndrome but
acute poisoning is applied). As a result, the method to express the concentration offormaldehyde and toluene is as shown in Table 2.9-7.
Table 2.9-7: Division of concentration to three levels concerning formaldehyde and toluene
Standard
DoubleBoundary value with
Chemicalvalue
standard value
acute poisoning

Formaldehyde

0.08 ppm

0.16 ppm

0.25 ppm

Toluene0.07 ppm

0.14 ppm

0.28 ppm

Because sick house syndrome is roughly divided into 1) mucosa symptoms and 2) mental
symptoms, QEESI data also are arranged as follows:
Of the five main items of QEESI, the item questioning the “level of symptoms” consists of 10questions about 10 body parts. The degree of seriousness is divided into ten grades (0 for no
symptom; the higher the grade, the higher the degree of seriousness). 1) Mucosa symptoms
are those related to a) tracheas and mucosa, b) the skin, and c) digestive organs. 2) Mental
symptoms are symptoms related to a) cognition, b) affection, and c) nervous mechanism.
QEESI makes it possible to grasp immediately what symptom emerges in what part of the
body. Figure 2.9-7 shows how the degree of seriousness of symptoms is grasped by QEESI.
The highest score for the ten questions concerning 1) mucosa symptoms and 2) mental
symptoms is 30 (10  3 symptoms). In addition, QEESI data only cover the following
persons: those who have clearly suffered the disease by exposure in their houses (excluding
those who live in newly built houses or houses rebuilt within three years or who have received
occupational exposure); those who do not have another chronic disease (to exclude disorders
by causes other than sick house syndrome); and those aged 15 years and over (to exclude
answers from young people because the data are based on answers from individuals). Figure
2.9-8 shows the relation between the Kitasato Institute Hospital’s concentration data and theseriousness of symptoms of sick house syndrome (QEESI score).
Regression analysis of these data was carried out to prepare a relation equation. First,
consideration was given to setting a threshold. The Building Standards Act has set thestandard indoor concentration value as the value at which a person will not suffer sick house
syndrome if exposed for his or her lifetime. Because of this, if the actual value is less than
the standard value, no person suffers sick house syndrome. That is, it is desirable to set thethreshold at the standard concentration value. According to actual data on patients, however,some people suffered sick house syndrome even at less than the standard value. To copewith this, regarding the Kitasato Institute Hospital’s data, relation equations were created forthe case where a threshold is set and for the case where no threshold is set, and the degree offitting (prediction accuracy of the data of the relation equations) was judged by another index.

80

LIME2_C2.7-C2.9_2014

Level of symptom(1) Mucosa symptoms(2) Mental symptoms(head)(cognition)(Tracheas
andmucosa)(Cardinalandrespiratory)

Maximum score: 10(affection)5: Mid-level symptom
10: Symptom so serious that the patientcannot move

(Digestiv
e organs)(Urinaryand sexorgans)0: None(nervousmechanism)
First medical examination(Muscle)(Skin)

In normal conditionQEESI score

Figure 2.9-7: “Level of symptom” by QEESI (to check each symptom and the seriousness)

Mucosasymptoms
Mentalsymptoms

Correlation
analysis

Mucosasymptoms

Mentalsymptoms

Significant

Significant

Mid-level

Mid-level

No. of data
Correlation
coefficient r
P valueSignificance
test
Strength ofcorrelation

Indoor formaldehyde concentration [ppm]

QEESI score

Figure 2.9-8 (1): Indoor formaldehyde and seriousness of symptoms of sick house syndrome (QEESI score)

Mucosasymptoms
Mentalsymptoms

Correlation
analysis

Mucosasymptoms

Mentalsymptoms

Significant

Significant

StrongStrongNo. of data
Correlation
coefficient r
P valueSignificance
test
Strength ofcorrelation

Indoor toluene concentration [ppm]Figure 2.9-8 (2): Indoor toluene and seriousness of symptoms of sick house syndrome (QEESI score)81

LIME2_C2.7-C2.9_2014

The Akaike Information Criteria (AIC) was used for the judgment. The purpose of this index
is to evaluate the accuracy of statistical models. It is frequently called AIC. Because thejudgment was made on the equation that passes through the origin (no threshold) and on theequation that does not pass through the origin (a threshold exists; y has a negative value
section), AIC suitable for the judgment was used. However, AIC was used not for judging thedegree of fitting but for comparing several equations. The following is the AIC Equation: ( y  yˆ ) 2 
AIC  n  ln   2k  1n(2.1-4)[in the case of a linear relation equation (equation derived by the least-square
method)]

k:
n:
y:
^
y:

number of parameters
number of data
observed value
predicted value

Because the standard concentration value is used as the threshold, the threshold was fixed at
0.08 ppm for formaldehyde and 0.07 ppm for toluene. Figures 2.9-9 and 2.9-10 show
relation equations for toluene and sick house syndrome, respectively. The figures show that
AIC of the equation without a threshold that passes through the origin is smaller than AIC of
the equation with a threshold in both equations (regression equations). It was found fromcomparison of both equations that (1) the equation without a threshold can accurately
predict data on patients.
Based on this result, we decided to adopt the dose-response relationship equation without athreshold. The inclination of (1) the equation without a threshold in Figures 2.9-9 and2.9-10 is multiplied by the above-described incidence of sick house syndrome to find thedose-response relation (Tables 2.9-8 and 2.9-9).
Tables 2.9-8 and 2.9-9 show that the dose-response relationship of formaldehyde is almost thesame as that of toluene. It is known that the risk of toluene is usually lower than the risk offormaldehyde. The reason why the dose-response relationship of toluene is higher is that
there are persons who have suffered from exposure to both formaldehyde and toluene, withthe result that toluene-related results increase due to the influence of formaldehyde.
Although it is essential to investigate the influence of each substance separately, this isdifficult with the patients data used for this study. Because this study is characterized by theuse of data on actual patients, we respect and use the results gained from the study.

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LIME2_C2.7-C2.9_2014

Mucosa symptoms
Number of data: 97
Correlation coefficient:
0.40

QEESI score

Seriousness of symptoms ofsick house syndrome [score]High(2) Regression equation
with threshold of 0.08
ppm

(1) Without
threshold(1) Regression
equation
without
thresholdRegression
equation(2) With
threshold(0.08 ppm)

(thesmaller the
better)Low
Indoor formaldehyde concentration[ppm]

Figure 2.9-9 (1): Regression equation of indoor formaldehyde concentration and mucosa symptoms
of sick house syndrome
Mucosa symptoms
Number of data: 97
Correlation coefficient:
0.52

QEESI score

Seriousness of symptoms ofsick house syndrome [score]High(2) Regression equation
with threshold of 0.08
ppm
(1) Regression
equation
without
threshold(1) Without
thresholdRegression
equation(2) With
threshold(0.08 ppm)

(thesmaller the
better)Low
Indoor formaldehyde concentration[ppm]

Figure 2.9-9 (2): Regression equation of indoor formaldehyde concentration and mental symptoms ofsick house syndrome
Mucosa symptoms
Number of data: 10
Correlation coefficient:
0.66

QEESI score

Seriousness of symptoms ofsick house syndrome [score]

High

Low

(2) Regression
equation withthreshold of 0.07
ppm

(1) Regression equation
without threshold(1) Without
thresholdRegression
equation(2) With
threshold(0.07 ppm)

(thesmaller the
better)Indoor toluene concentration [ppm]Figure 2.9-10 (1): Regression equation of indoor toluene concentration and mucosa symptoms of sickhouse syndromeQEESI score

Seriousness of symptoms ofsick house syndrome [score]High

Mucosa symptoms
Number of data: 11
Correlation coefficient:
0.72(2) Regression
equation withthreshold of 0.07
ppm

(1) Without
thresholdRegression
equation(1) Regression
equation without
threshold(2) With
threshold(0.07 ppm)

(thesmaller the
better)Low
Indoor toluene concentration [ppm]Figure 2.9-10 (2): Regression equation of indoor toluene concentration and mental symptoms of sickhouse syndrome83

LIME2_C2.7-C2.9_2014

Table 2.9-8: Dose-response relationships DR of indoor formaldehyde and sick house syndrome
DR relationship of formaldehyde [QEESI /ppm](With consideration for the incidence of 0.56%)(1) Mucosa symptoms
66.98×0.0056＝3.78×10-1(2) Mental symptoms
71.96×0.0056＝4.06×10-1
Table 2.9-9: Dose-response relationships DR of indoor toluene and sick house syndrome
Dose-response relationship of toluene [QEESI /ppm](With consideration for the incidence of 0.56%)(1) Mucosa symptoms
53.16×0.0056＝3.00×10-1(2) Mental symptoms
57.88×0.0056＝3.27×10-1

fImpact analysis of sick house syndrome

We will examine the number of target persons. An actual house has rooms with a strong
impact of indoor air pollution and rooms with little impact of it. The residents move fromroom to room with the passage of time. For the purpose of accuracy, it may be necessary totake into consideration the number of persons in each room and the time of their staying ineach room. However, if the residents stay somewhere in the house, a calculation method canbe devised for one house as a unit. This method was adopted for the study. Although not
all the residents stay home 24 hours a day, as the number of persons who stay home daily weadopted the average number of household members, 2.58, according to National Census
FY2005. That is, the average number of household members was used as the number of
persons exposed to indoor air pollution. This value was used irrespective of whether thehouse is a single-family house or an apartment house.
Next, we will calculate DALY of sick house syndrome.
Under LIME, DALY,
disability-adjusted life-years, is used as an index of human health damage. DALY can becalculated by the sum of years lived with a disability (YLD) and years of life lost (YLL).
Because there seems no existing research on DALY of sick house syndrome, a new calculation
method was developed. Sick house syndrome rarely causes death directly and it is difficult
to prove a causal relationship. Because of this, consideration was given only to the sum ofyears lived with a disability YLD. YLD can be calculated by multiplying the degree ofseriousness of a disability by the duration of the disability.
When YLD is calculated, it is necessary to calculate for (1) mucosa symptoms, (2) mental
symptoms, and the degree of seriousness and duration of each disability. The degree ofseriousness of a disability is estimated from similar symptoms, based on the method ofclassification that divides the degree into six levels according to typical symptoms andtroubles. The six-level classification is as shown in Table 2.9-10. Table 2.9-11 shows thedegrees of seriousness in symptoms similar to the mucosa symptoms and mental symptoms ofsick house syndrome. Based on this, medical experts determined the degree of seriousness
in disability caused by sick house disease as shown in Table 2.9-12.
The representative value of duration of disability was fixed at two years, because it takes
about two years to cure ordinary sick house syndrome (Table 2.9-13). Moreover, to assign
the value in the equation for sick house syndrome (Figure 2.9-7), DALY Ds for each case84

LIME2_C2.7-C2.9_2014[DALY/case] was substituted with DALY for each point of QESSI [DALY/QEESI] by
dividing DALY Ds for each case by the average QEESI for each case [QEESI/case]. The
average score of patients’ data used in this study was used as the average QEESI for each
case.
Level

Table 2.9-10: Classification of level of disability (6 levels)
Seriousness
Health condition

1

0.096

2

0.220

3

0.400

4

0.600

5

0.810

6

0.920

Disability exists in at least one activity in one of the fields of amusement,
education, reproduction, and work.
Disability exists in most of the activities in one of the fields ofamusement, education, reproduction, and work.
Disability exists in activities in two or more of the fields of amusement,
education, reproduction, and work.
Disability exists in most of the activities in most of the fields ofamusement, education, reproduction, and work.
Assistance is necessary for daily life, such as preparation of meals,
shopping, and housework.
Assistance is necessary for basic activities, such as meal, sanitation, andevacuation.

Table 2.9-11: Symptoms similar to those of sick house syndrome and the level of seriousness
Symptoms similar to mucosa symptoms
Symptoms similar to mental symptoms
Seriousness of
Seriousness of
Disease/symptoms
Disease/symptoms
disability
disability
Asthma0.099

Peptic ulcer

0.115

Liver cirrhosis

0.330

Appendicitis

0.463

Depressive
disorderPanic disorder
Drug-relateddisorderPoisoning

0.600
0.173
0.251
0.609

Table 2.9-12: Seriousness of disability caused by sick house syndrome
Average
Maximum
Mucosa symptoms
Mental syndromes

0.096
0.220

0.220
0.400

Table 2.9-13: DALY DS of disability caused by sick house syndrome(1) Mucosa(2) Mental
symptoms
symptoms
0.096(a) Seriousness of disability(b) Duration of disability(c) (= (a)×(b)) DALY DS [DALY/case](d) Average QEESI by case [QEESI/case](e) (= (c)/(d)) DLAY for each point of
QEESI [DALY/QEESI]

0.220
2 (years)

0.192

0.440

Formaldehyde

11.19

11.56

Toluene

11.00

10.91

Formaldehyde

1.72  10-2

3.81  10-2

Toluene1.75  10-2

4.03  10-2

85

LIME2_C2.7-C2.9_2014(4)

Health impact: damage function of nose/larynx canceraDose-response relationship of nose/larynx cancer

Although various models were devised concerning carcinogenesis, EPA’s unit risk is wellknown in the world and is used for many epidemiological studies. The unit risk expresses
carcinogenesis probability when a person is exposed to a carcinogenic material in his or her
lifetime as the value per unit concentration in the medium. Under LIME, it was already used
for the assessment of the carcinogenic risk when a chemical is generated outdoors. In thisstudy, by following it, we calculated a dose-response relationship when formaldehyde is
generated indoors. Because the unit risk expresses the response rate when ordinary life ismaintained for one’s lifetime, it is necessary to divide it by the number of years in the lifetime
to calculate the dose-response relationship per year. In addition, when the dose-response
relationship in the working environment during construction is examined, it is necessary toconvert exposure during ordinary life into exposure in the working environment. We
calculated this by estimating the conversion coefficient from an average life expectancy of 75
years and 45 working years, which are used for ordinary risk assessment. Tables 2.9-14 and2.9-15 show the dose-response relationship during residence and during construction,
respectively.
Table 2.9-14: Dose-response relationship DR between indoor formaldehyde and nose/larynx cancer[during residence]

Dose-response relationship DR
1.30  10-2 (case/(mg/m3)/ lifetime years) ÷ 75 (years) = 1.73  10-4 (case/(mg/m3))
Unit risk: 1.30  10-2 (case/(mg/m3)/lifetime years)
Lifetime years: 75 (years)
Table 2.9-15: Dose-response relationship DR between indoor formaldehyde and nose/larynx cancer[during construction]

Dose-response relationship DR
1.30  10-2 (case/(mg/m3)/lifetime years) ÷ 75 (years)  conversion coefficient 0.13 = 2.25 
10-5(case/(mg/m3))-2
3
Unit risk: 1.30  10 (case/(mg/m )/lifetime years)
Lifetime years: 75 (years), working years: 45 (years)
Conversion coefficient from ordinary exposure to occupational exposure =8 (h)/24 (h)  5 (days)/7 (days)  48 (weeks)/52 (weeks)  45 (years)/75 (years) = 0.13b

Impact analysis of nose/larynx cancer

The average number of residing people is 2.58, the same number as in the case of sick house
syndrome. With regard to construction, the number of persons who suffer damage fromoccupational exposure to an indoor air pollutant is calculated. The total number of workers
engaged in house construction is about 1.88 million (in 2000) and the number of cases where
the construction of a house begins is about 1.23 million. Therefore, the number of persons
who receive occupational exposure at the construction site of a house is 1.53.
Under LIME 1, in the method to assess health damage from toxic chemicals, the value of
DALY was examined concerning nose/larynx cancer caused by formaldehyde. Therefore, thealready-calculated DALY Ds of nose/larynx cancer caused by formaldehyde (YLL = 4.5; YLD= 0.4; DALY = 4.9) were used.
86

LIME2_C2.7-C2.9_2014(5)

Health impact: damage function of acute poisoninga

Dose-response relationship of toluene and acute poisoning

With regard to poisoning caused by toluene, by reference to Nakanishi et al. (2005), thealready-calculated concentration increment ∆C was used to calculate the value of thedose-response relationship. Nakanishi et al. (2005) examined the relation between the
exposure concentration and the incidence rate based on epidemiological data. Because thisrelation concerns general exposure, it is necessary to change the relation to that for
construction. Therefore, we considered the conversion coefficient from general exposure tooccupational exposure (similar to Table 2.9-15) and defined the dose-response relationship of
poisoning caused by toluene (Tables 2.9-16 and 2.9-17). In addition, because therelationship depends on ∆C (indoor concentration increment), the difference between asingle-family house and an apartment house was taken into consideration.
Table 2.9-16: Dose-response relationship DR between indoor toluene and poisoning[during residence]

Dose-response relationship DR [single-family house]
Response = {1/[1＋exp(－(－3.09722)－0.00909961×Dose)－0.043} = 3.65×10-4 (case/(mg/m3))
Response is an increment in the incidence, and Dose is the exposure concentration for asingle-family house. ( = ΔC = 3.78×10-1(mg/m3))
Dose-response relationship DR [apartment house]
Response = {1/[1＋exp(－(－3.09722)－0.00909961×Dose)－0.043} = 7.77×10-4 (case/(mg/m3))
Response is an increment in the incidence, and Dose is the exposure concentration for anapartment house. ( = ΔC = 1.471(mg/m3))
Table 2.9-17: Dose-response relationship DR between indoor toluene and poisoning[during construction]

Dose-response relationship DR [single-family house]
Response = {1/[1＋exp(－(－3.09722)－0.00909961×Dose)－0.043} × conversion coefficient
0.13 = 4.81×10-5 (case/(mg/m3))
Response is an increment in the incidence, and Dose is the exposure concentration for asingle-family house. (= ΔC = 3.78×10-1(mg/m3))
Dose-response relationship DR [apartment house]
Response = {1/[1＋exp(－(－3.09722)－0.00909961×Dose)－0.043} × conversion coefficient
0.13= 1.02×10-5 (case/(mg/m3))
Response is an increment in the incidence, and Dose is the exposure concentration for anapartment house. ( = ΔC = 1.47(mg/m3))b

Impact analysis of acute poisoning

The average number of residing people is 2.58, the same number as in the case of sick house
syndrome. With regard to construction, the number of persons who receive occupational
exposure at the construction site of a house is 1.53 as examined above.
87

LIME2_C2.7-C2.9_2014

For the purpose of this study, acute poisoning is mainly caused by inhalation of toluene.
Because acute poisoning caused by toluene rarely results in death, YLL is omitted and only
YLD was examined. YLD can be calculated by multiplying the level of seriousness of thedisability by the duration of the disability. The level of seriousness was calculated from adecrease in the quality of life (QOL) due to poisoning. QOL is 0 in the state of death and 1
in the state of ordinary health (or complete health). The object of assessment is scored 0 to 1
in terms of health condition. Because the level of seriousness of disability is equivalent to 1
minus QOL, the level was calculated at 0.08 by the use of the value of QOL, 0.92, whichcorresponds to a decline in the muscular strength of the limbs, hearing ability, and power ofconcentration according to Nakanishi et al. (2005). The duration of disability due topoisoning was fixed at 2 years, the ordinary duration of disease based on medical judgment.
As a result, DALY of poisoning was calculated at 0.16.
(6)

Health impact: damage function of respiratory diseasesaDose-response relationship of respiratory diseases

Respiratory diseases were divided into hospitalization and acute death. Because the healthimpact caused by a substance after exposure in the inside of the body is the same irrespectiveof exposure method and channel if the substance is the same, a similar dose-response
relationship was cited from ExternE (Holland et al. 1999), epidemiological data examined
concerning air pollution under LIME 1. The dose-response relationship is a relationshipbetween the substance and the health impact and is common irrespective of whether indoors
or outdoors.
With regard to respiratory hospitalization, the incidence rate per unit of NOx or SOx
concentration is shown in ExternE (Holland et al. 1999). With regard to acute death, thedose-response coefficient obtained on the assumption that the current death rate increases
according to the concentration of a pollutant is applied. This coefficient is expressed as therate of increase in the death rate due to an increase in the unit of NOx concentration[risk/riskbaseline/(μg/m3)]. The death rate after an increase in the unit of NOx can be calculated
by multiplying the current death rate [riskbaseline/year] by the coefficient. Table 2.9-18 shows
the dose-response relationships of NOx and SOx.
The epidemiological data ExternE (Holland et al. 1999) is applied also to SPM. Table
2.9-19 shows the dose-response relationship of SPM. The death rate per SPM concentration
unit is used for acute death and chronic death, while the incidence rate per SPM concentration
unit is used for the dose-response relationships of the other respiratory diseases. Because theincidence rate may differ between adults and children, depending on symptoms, different
dose-response relationships should be used in such cases.
b

Impact analysis of respiratory diseases

The average number of residing people is 2.58, the same number as in the case of sick house
syndrome. With regard to SPM, however, the difference in the dose-response relationshipbetween adults and children and the diseases suffered only by asthmatics were assessed.
This required consideration of the ratio between adults and children and the ratio ofasthmatics to make them target populations. The ratios were calculated based on the resultof examination of statistical data, and multiplication was performed. Table 2.9-20 shows thecalculated ratios and Table 2.9-21 shows the calculated target populations for each disease.
88

LIME2_C2.7-C2.9_2014

DALY Ds of respiratory diseases was examined based on Hofstetter (1998). Table 2.9-22
shows DALY Ds of all the respiratory diseases assessed in this study.
Table 2.9-18: Dose-response relationships DR of NOx and SOx
NOx

Respiratory
Incidence rate per unit of concentration
hospitalization [case/person/year/(μg/m3)](i) Rate of increase in death rate perunit of concentration(ii) Current death rate
Acute death(i) × (ii)
Death rate per unit of concentration[case/person/year/(μg/m3)]SOx

1.40010-6

2.04010-6

3.40010-4

7.20010-4

814.710-3

7.41810-3

2.52210-6

5.34110-6

Table 2.9-19: Dose-response relationship DR of SPM and respiratory diseases
Death rate or incidence per DR concentration unit
3
)]

PM10
1
2
3

Acute death
Chronic death
Respiratory hospitalization(Adult)Use of4
bronchodilator(Child)(Adult)5
Cough(Child)(Adult)6Stridor(Child)(Adult)7
Chronic bronchitis(Child)8Chronic cough9
Day of movement restriction
10 Chronic occlusive lung disease
11
Asthma12 Inflammation in pharynx/bronchi
of preschool child

PM2.5
-6

2.97×10
7.42×10-6
2.07×10-6
1.63×10-1
7.80×10-2
1.68×10-1
1.33×10-1
6.10×10-2
1.03×10-1
4.90×10-5
1.61×10-3
2.07×10-3
2.50×10-2
7.20×10-6
6.50×10-6

5.05×10-6
4.40×10-5
3.46×10-6
2.72×10-1
1.29×10-1
2.80×10-1
2.23×10-1
1.01×10-1
1.72×10-1
7.80×10-5
2.69×10-3
3.46×10-3
4.20×10-2
1.20×10-5
1.08×10-5

2.91×10-5

4.86×10-5

Table 2.9-20: Ratio of persons with a respiratory disease-related condition to the target population
Condition
Ratio to the whole population

Adult (aged 20 and over)

0.795

Child (aged 0 to 19)

0.205

Aged 30 and over

0.652

Asthma (adult) patient

5.03  10-3

Asthma (child) patient

3.57  10-3

89

LIME2_C2.7-C2.9_2014

Table 2.9-21: Target population for respiratory diseases
Target population

1
2
3
4
5
6
7

Acute death
Chronic death

2.58

Aged 30 and over

1.68

Respiratory hospitalization
Use ofbronchodilator
Cough
Stridor

2.58(Adult) (asthmatic)

0.00130(Child) (asthmatic)

0.00920(Adult) (asthmatic)

0.00130(Child) (asthmatic)

0.00920(Adult) (asthmatic)

0.00130(Child) (asthmatic)

0.00920(Adult)2.05(Child)0.530

Chronic bronchitis

8

Chronic cough(Child)0.530

9Day of movement
restriction(Adult)2.05

10

Chronic occlusive lung disease

2.58

11

Asthma2.58

12

Inflammation in pharynx/bronchi
of preschool child

2.58

Table 2.9-22: DALY Ds of respiratory diseases
Respiratory disease
DALY DS per death or incidence

1

Acute death

0.75

2

Chronic death

6.6

3

Respiratory hospitalization

0.011

4

Use ofbronchodilator(Adult)0.00027(Child)0.00027(Adult)0.00014(Child)0.00014(Adult)0.00014(Child)0.00014(Adult)2(Child)

0.025

5
6
7

90

Cough
Stridor
Chronic bronchitis

8

Chronic cough

0.025

9

Day of movement restriction

0.00027

10Chronic occlusive lung disease

0.00082

11

Asthma0.00082

12

Inflammation inpharynx/bronchi
of preschool child

0.00082

LIME2_C2.7-C2.9_2014(7)

Arrangement of damage factors of indoor air pollution

All the items necessary for the equation for calculation of damage function were examined as
described above. Equation 2.9-1 shown in the basic policies at the beginning, the equation
for calculation of damage function of indoor air pollution, and the equation for calculation ofdamage function of sick house syndrome are calculated by the new equations suggested in
Figure 2.9-6. Table 2.9-23 (during residence) and Table 2.9-24 (during construction) show
calculated values and calculation results for each pollutant and disease.
Table 2.9-23 (1): Damage functions of indoor air pollution [during residence] (formaldehyde)
Formaldehyde

House

Healthcondition(Disease)

Sick house
syndrome
mucosasymptoms
Singlefamily

Apartment(F)
Calculation of(G)
increase in
Doseconcentration due
response
to release of unit relationshipamount ofDR
pollutant ΔC
3.06×10-1
[ppm]

(H)
(I)
No. ofDegree oftarget damage frompatients
diseasePDALY DS
1.72×10-2[DALY/QEESI]

3.78×10-1[QEESI/ppm](F)×(G)×(H)×(
I)
Damagefunction foreach disease[DALY/kg]

Damagefunctionof sick
house
syndrome

Damagefunction

5.12×10-3
1.73×10-2

Sick house
syndromementalsymptoms

3.06×10-1[ppm]

4.06×10-1[QEESI/ppm]

Nose/larynx
cancer

3.78×10-1[mg/m3]Sick house
syndrome
mucosasymptoms

1.19
[ppm]

-2

3.81×10[DALY/QEE
SI]

1.22×10-2

1.73×10-4[case/person/
year/(mg/m3)]

4.9[DALY/case]

8.28×10-4

3.78×10-1[QEESI/ppm]

1.72×10-2[DALY/QEESI]

1.98×10-2

2.58

1.81×10-2-

6.71×10-2

Sick house
syndromementalsymptoms

1.19
[ppm]

4.06×10-1[QEESI/ppm]

Nose/larynx
cancer1.47[mg/m3]

1.73×10-4[case/person/
year/(mg/m3)]

2.58

-2

3.81×10[DALY/QEE
SI]

4.73×10-2

4.9[DALY/case]

3.21×10-3

7.03×10-2-

Table 2.9-23 (2): Damage functions of indoor air pollution [during residence] (Toluene, NOX, SOX)
TolueneHouse

Healthcondition(Disease)

Singlefamily

Sick house
syndrome
mucosasymptoms(F)
Calculation of(G)
increase in
Doseconcentration due
response
to release of unit relationshipamount ofDR
pollutant ΔC
3.06×10-1[ppm]

3.00×10-1[QEESI/ppm](H)
No. oftargetpatients
P(I)
Degree ofdamage from
diseaseDALY DS(F)×(G)×(H)×(I)
Damagefunction foreach disease[DALY/kg]

Damagefunctionof sick
house
syndrome

Damagefunction

2.58

1.75×10-2[DALY/QEESI]

4.13×10-3

1.45×10-2

1.46×10-2

91

LIME2_C2.7-C2.9_2014

Apartment

Sick house
syndromementalsymptoms

3.06×10-1[ppm]

3.27×10-1[QEESI/ppm]

4.03×10-2[DALY/QEESI]

1.04×10-2

Nose/larynx
cancer

3.78×10-1[mg/m3]

3.65×10-4[case/person/
year/(mg/m3)]

0.16[DALY/case]

5.69×10-4

Sick house
syndrome
mucosasymptoms

1.19
[ppm]

3.00×10-1[QEESI/ppm]

1.75×10-2[DALY/QEESI]

1.60×10-2-

5.63×10-2

Sick house
syndromementalsymptoms

1.19
[ppm]

3.27×10-1[QEESI/ppm]

Nose/larynx
cancer1.47[mg/m3]

7.77×10-4[case/person/
year/(mg/m3)]

2.58

-2

4.03×10[DALY/QEESI]

4.03×10-2

0.16[DALY/case]

4.70×10-4

5.68×10-2

-

NOX
(G)
(F)
DoseCalculation of
response
increase inrelationship
Health condition concentration
House
DR(Disease)due to release of[case/person
unit amount of/pollutant ΔC
3]
3)]
Respiratory disease
2.52×10-6
Acute deathSingle3.78×102
family Respiratory disease
1.40×10-6
hospitalization
Respiratory
disease2.52×10-6
Acute death
Apart1.47×102
ment
Respiratory
disease1.40×10-6
hospitalization(H)
No. oftargetpatients
P(I)
Degree of damage
from disease
DALY DS[DALY/case](F)×(G)×(H)×(I)Damage function
for each disease[DALY/kg]

0.75

1.84×10-3

0.011

-5

1.50×100.75

7.15×10-3

2.58

Damagefunction

1.86×10-3

7.21×10-3

2.58
0.011

5.82×10-5(I)
Degree of damage
from disease
DALY DS[DALY/case](F)×(G)×(H)×(I)Damage function
for each disease[DALY/kg]

0.75

3.90×10-3

0.011

-5

2.19×100.75

1.51×10-3

0.011

-5

SOX
(G)
(F)
DoseCalculation of
response
increase inrelationship
Health condition concentration
House
DR(Disease)due to release of[case/person
unit amount of/pollutant ΔC
3]
3)]
Respiratory disease
5.34×10-6
Acute deathSingle3.78×102
family Respiratory disease
2.04×10-6
hospitalization
Respiratory
disease
5.34×10-6
Acute death
Apart1.47×102
ment
Respiratory
disease2.04×10-6
hospitalization

92(H)
No. oftargetpatientsP

2.58

Damagefunction

3.93×10-3

1.52×10-3

2.58
8.49×10

LIME2_C2.7-C2.9_2014

Table 2.9-23 (3): Damage functions of indoor air pollution [during construction] (SPM)
SPM
(F)
(G)
Calculation of
Doseincrease in
response relationship
DR
Health condition concentrationHouse
due to release of[case/person/(Disease)
3
unit amount of)]pollutant ΔC
PM10
PM2.5
3
]

Singlefamily

Apartment(H)
No. oftargetpatients
P(I)(F)×(G)×(H)×(I)
Degree ofDamage function
Damagedamagefor each disease
function
from[DALY/kg]
diseaseDALY DS[DALY/
PM10/PM
PM10
PM2.5
case]
2.5
0.75
2.17×10-3 3.69×10-3

Acute death

2.97×10-6 5.05×10-6

2.58

Chronic death
Respiratory
hospitalization
Use ofbronchodilator(adult)Use ofbronchodilator(child)Cough (adult)

7.42×10-6 4.40×10-5

1.68

6.6

3.11×10-2 1.85×10-1

2.07×10-6 3.46×10-6

2.58

0.011

2.22×10-5 3.71×10-5

1.63×10-1 2.72×10-1 1.30×10-3

0.00027

2.16×10-5 3.60×10-5

7.80×10-2 1.29×10-1 9.20×10-3

0.00027

7.32×10-5 1.21×10-4

1.68×10-1 2.80×10-1 1.30×10-3

0.00014

1.15×10-5 1.92×10-5

Cough (child)

1.33×10-1 2.23×10-1 9.20×10-3-1

Chronic death
Respiratory
hospitalization
Use ofbronchodilator(adult)Use ofbronchodilator(child)Cough (adult)
Cough (child)
Stridor (adult)
Stridor (child)
Chronic bronchitis(adult)Chronic bronchitis(child)Chronic cough(child)Day of motion
restriction (adult)

0.00014

6.47×10-5 1.09×10-4-3

1.30×100.00014

4.19×10-6 6.94×10-6

1.03×10-1 1.72×10-1 9.20×10-3

0.000145.01×10-5 8.37×10-5

2

7.59×10-2 1.21×10-1

1.61×10-3 2.69×10-3 5.30×10-1

0.025

8.06×10-3 1.35×10-2

2.07×10-3 3.46×10-3 5.30×10-1

0.025

1.04×10-2 1.73×10-2

2.50×10-2 4.20×10-2

2.05

0.00027

5.23×10-3 8.79×10-3

7.20×10-6 1.20×10-5

2.58

0.00082

5.76×10-6 9.59×10-6

6.50×10-6 1.08×10-5

2.58

0.00082

5.16×10-6 8.63×10-6

2.91×10-5 4.86×10-5

2.58

0.00082

2.33×10-5 3.89×10-5

2.97×10-6 5.05×10-6-2Stridor (adult)
Stridor (child)
Chronic bronchitis(adult)Chronic bronchitis(child)Chronic cough(child)Day of motion
restriction (adult)
Chronic occlusive
lung disease
AsthmaInflammation inpharynx/bronchi
of preschool child
Acute death

PM10
1.33×10

6.10×10
3.78×102-1

1.01×10

4.90×10-5 7.80×10-5

2.05

2.58

0.75

8.42×10-3 1.43×10-2-5

4.40×10

1.68

6.6

1.21×10-1 7.16×10-1

2.07×10-6 3.46×10-6

2.58

0.011

8.61×10-5 1.44×10-4

1.63×10-1 2.72×10-1 1.30×10-3

0.00027

8.38×10-5 1.40×10-4

7.80×10-2 1.29×10-1 9.20×10-3

0.00027

2.84×10-4 4.70×10-4

1.68×10-1 2.80×10-1 1.30×10-3

0.00014

4.48×10-5 7.46×10-5

1.33×10-1 2.23×10-1 9.20×10-3

0.00014

2.51×10-4 4.21×10-4

6.10×10-2 1.01×10-1 1.30×10-3

0.00014

1.63×10-5 2.69×10-5-6

7.42×10PM2.5
3.49×10-1PM105.16×10-1

1.47×102-1

1.72×10

9.20×100.00014

1.94×10-4 3.25×10-4

4.90×10-5 7.80×10-5

2.05

2

2.95×10-1 4.69×10-1

1.61×10-3 2.69×10-3 5.30×10-1

0.025

3.12×10-2 5.22×10-2

2.07×10-3 3.46×10-3 5.30×10-1

0.025

4.02×10-2 6.71×10-2

2.50×10-2 4.20×10-2

0.00027

2.03×10-2 3.41×10-2

1.03×10

-1

-3

2.05

PM2.5
1.35

93

LIME2_C2.7-C2.9_2014
Chronic occlusive
lung disease
AsthmaInflammation inpharynx/bronchi
of preschool child

7.20×10-6 1.20×10-5

2.58

0.00082

2.23×10-5 3.72×10-5

6.50×10-6 1.08×10-5

2.58

0.00082

2.00×10-5 3.35×10-5

2.91×10-5 4.86×10-5

2.58

0.00082

9.02×10-5 1.51×10-4

Table 2.9-24: Damage functions of indoor air pollution [during construction]
Formaldehyde

House

Healthcondition(Disease)(G)
Doseresponse
relationship(F)
Calculation ofincrease inconcentration due torelease of unit
amount of pollutant
ΔC [mg/m3][case/person/
year/mg/m3)]

DR

(I)
(H)
Degree of damage
No. of target
from disease
patientsP

DALY DS[DALY/case](F)×(G)×(H)×(I)Damagefunction[DALY/kg]

Singlefamily

Nose/larynx
cancer

3.78×10-1

2.29×10-5

1.53

4.9

6.49×10-5

Apartment

Nose/larynx
cancer

1.47

2.29×10-5

1.53

4.9

2.52×10-4

Healthcondition(Disease)(F)
Calculation ofincrease inconcentration due torelease of unit
amount of pollutant
ΔC [mg/m3](G)
Doseresponse
relationship[case/person/
year/mg/m3)]

Toluene

House

DR

(I)
(H)
Degree of damage
No. of target
from disease
patientsP

DALY DS[DALY/case](F)×(G)×(H)×(I)Damagefunction[DALY/kg]

Singlefamily

Poisoning

3.78×10-1

4.81×10-5

1.53

0.16

4.45×10-6

Apartment

Poisoning

1.47

1.02×10-4

1.53

0.16

3.68×10-5

2.9-3 Procedure for impact assessment of indoor air pollution
The procedure for damage assessment of indoor air pollution can be described concretely as
follows. First, assumptions are made about whether during residence or during construction
and whether a single-family house or an apartment house. If damage is assessed about theperiod between construction and residence, the results of assessment of damage during
construction and during residence are totaled. After assumptions are made, an equation forassessment of a pollutant is selected. For example, if the impact of formaldehyde is assessed
during residence in a single-family house, the damage function 1.81  10-2 [DALY/kg] for[during residence] – (formaldehyde) – single-family house in Table 2.9-23 (1) is used. If theamount of generated formaldehyde is found, it is possible to express potential damage fromindoor air pollution as a value of DALY.
To know damage from sick house syndrome only, use the damage function 1.73  10-2
DALY/kg for sick house in the column of damage function for each disease in the same table.
The same is applicable to other substances and symptoms.
For example, if the construction period for a single-family house is 3 months andformaldehyde of 1 kg/year is released from construction materials for the two years after theconstruction, the generated amount is 0.25 kg during construction and 2 kg during residence.
94

LIME2_C2.7-C2.9_2014

Such damage can be calculated by the following equations:1) Table 2.9-24 [construction] - formaldehyde – damage function for single-family
house  6.49  10-5 [DALY/kg]
6.49  10-5  0.25 ＝ 1.63  10-5 [DALY]
2) Table 2.9-23 [residence] - formaldehyde – damage function for single-family house 1.81  10-2 [DALY/kg]
1.81  10-2  2 ＝ 3.62  10-2 [DALY]
3) Add 1) with 2).
1.63  10-5＋3.62  10-2 ≒ 3.62  10-2 [DALY]
4) The damage from the generation of formaldehyde from this constriction material is3.62  10-2 [DALY].
2.9.4 Uncertainty analysis of damage assessment of indoor air pollution
The parameters used for the calculation of typical damage functions of formaldehyde can bearranged as described herein (Table 2.9-25).
How to prepare probability distributions for the fluctuation of these parameters was firstexamined (Table 2.9-26). Quantitative data for all the probability distributions could beobtained through the calculation of damage functions. However, judging that a probability
distribution cannot be given to every parameter and that great impact is given unlessuncertainty is high, probability distributions were given by placing priority on items sharedwith other substances.
Table 2.9-25: Parameters taken into consideration in the calculation of Equation 2.9-1
Analysis
Itemprocess
Increase Total floor
inarea
concentration
ΔC

Highness

Data used
Single-family housestandard specifications(referring also to
single-family houseaverage floor area bytype of family)
Apartment houseaverage floor area bytype of family
1) Single-family housestandard specifications(referring also to IBEC
apartment model)

Data source(Original source)1) Architectural Institute ofJapan, single-family housestandard specifications
2) Architectural Institute ofJapan, housing macro model(National Census 2000, Housing
and Land Statistics)

Number of
Causes fororiginal data uncertainty
1 (235)
Unevenness
in Japan(difference
betweenurban andrural areas,
etc.)
235

1) Architectural Institute of1
Japan, single-family housestandard specifications
2) IBEC, New Energy-Saving
Standards and Guidelines forHousesFrequency Simplified prediction
IBEC, New Energy-Saving
1ofsheet for relation
Standards and Guidelines forventilation between airtightness and Houses
amount of leaked air(Yoshino H: Plan of Air Tightness
and Ventilation Equipment for aSingle-Family House)
Corresponding opening IBEC, New Energy-Saving
12
areaStandards and Guidelines for(3 regions (Survey of actually
Houses4 insulation
measured airtightness)(Yoshino H: Trend Survey on Air standards)

Errors inrepresentativ
e values

Errors inmodel
Incomplete
information

Data inlimited years
and regions95

LIME2_C2.7-C2.9_2014

Temperature, windvelocity

Model
Dose-resp
onse
relationsh
ip DR
(sick
house
syndrome)(Cancer)

TargetpopulationP

Total
DALY DSfor eachdisease96

Tightness of Houses andStandards)
Meteorological Agency, data in2005

6 (3 regions Data inlimited years 2)and regions
National sharing ofArchitectural Institute of Japan,
24
Data inhousing insulation
housing macro model (National (3 regions  limited years
standards
Census 2000, housing and land
4 insulation and regions,
statistics)
standards  incomplete
information
2
single-family houses orapartmenthouses)Frequency of machinery Society of Heating, Air
4
Ambiguity,
ventilation
Conditioning and Sanity
incomplete
Engineers, ventilation standards
information
Heating and coolingAir-conditioning load calculation 3 (byIncomplete
periods(SMASH) default dataregion)information
Time of residence
Society of Heating, Air
6 (by type
Ambiguity,
Conditioning and Sanityof family)
incomplete
Engineers, SHASE schedule
information
Frequency of ventilation Empirical value
1
Unknown
through open window
Indoor instant uniform
Empirical equation
1
Errors indiffusion equation
model
Relation between indoor Kitasato Institute Hospital’s data 97 (byUncertainty
concentration andon patients
disease)of modelseriousness of symptoms
Incidence rate
National survey
1
Incomplete
Kitasato Institute Hospital’s data 2 (ratio
information
on patients
between
SHS andMCS)
Unit risk
US EPA unit risk
1
Differencesamong
species,lower
concentrationAverage household
National Census 2005
47
Unevenness
population(prefectures in Japan)
Working population
Ministry of Internal Affairs and1
Unevenness
Communications: Input-Output
among years
Table, Data Report (2) in 2000
Employment Table
Amount of damage per 1) Medical experts‘ judgment
14
Unknown,case2) Table of level of disorderuncertainty3) Data from existing researchof model,incomplete
information,
ambiguity
Average QEESI
Kitasato Institute Hospital’s data 97 (byUnevenness
on patients
disease)among
patients

LIME2_C2.7-C2.9_2014

Table 2.9-26: Uncertainty of damage functions of indoor air pollution: details of setting of probability
distributions
No.

Item

Unit

Total floor area of
1 single-family
m2
house

Parameter(standard
deviation,
etc.)Standard:
Student t 126.46
19.96
Distributio (midpoint
Latitude:
n
)
1.077
Value
Probability(represent
distribution
ative)

Relation between
QEESI/
Standard
formaldehyde
Normal66.98
2
person/ye
deviation:
concentration anddistribution (average)
ar/ppm3.28
mucosa symptoms
Relation between
QEESI/
Standard
formaldehyde
Normal71.96
3
person/ye
deviation:
concentration anddistribution (average)
ar/ppm3.70
mental symptoms
4 Target population Person

Beta
2.58
α:14.32
distribution (average) β:6.05

Data used
Architectural
Institute ofJapan, housing
macro model
Kitasato
Institute
Hospital’s dataon patients
Kitasato
Institute
Hospital’s dataon patients

How to set parameter

Based on distribution of
all data on the left
Regression analysis ofdata on the left andregarding standard error
as standard deviation
Regression analysis ofdata on the left andregarding standard error
as standard deviation

National Census Based on distribution of2005
all data on the left

Amount of damage
Upper limit Value of medical
DALY/ca Normal
0.096
Setting maximum value
5 from mucosa
of 95% (2σ): specialist’ssedistribution (average)
at upper limit of 95%
symptoms
0.220
diagnosis
Amount of damage
Upper limit Value of medical
DALY/ca Normal
0.220
Setting maximum value
6 from mentalof 95% (2σ): specialist’ssedistribution (average)
at upper limit of 95%
symptoms
0.400
diagnosis(1)

Total floor area of single-family house [m2]Although damage functions are calculated by the use of the Architectural Institute of Japan’s
standard specifications model, the national distribution of total floor areas of single-family
houses written in the housing macro model (Architectural Institute of Japan) was referred tofor the estimation of probability distribution. Because the Architectural Institute of Japan’s
standard specifications model was selected by reference to the national distribution, thenational distribution was used without any change and was defined from the viewpoint ofconformity with the most suitable distribution by Crystal Ball (Figure 2.9-11).
Student t
distribution

Average129.03Standard deviation

28.40

Student t distribution
Probability

Indication of amountof statistics

Figure 2.9-11: Probability distribution of total floor areas of single-family houses(2)
Relation of formaldehyde concentration and level of mucosa symptoms of sickhouse syndrome [QEESI/person/year/ppm]
The relation was estimated by the use of the Kitasato Institute Hospital’s data on patients.
Because uneven data were integrated into an equation, the equation seems to contain many
97

LIME2_C2.7-C2.9_2014

uncertainties, such as personal difference (sensitivity and metabolism) and errors in measureof concentration. We examined how to define the degree of the uncertainties. Under LIME
1, regression analysis of observed data was carried out to find the dose-response relationshipof global warming, and the standard error in the slope of the resultant equation was used as
the standard deviation to define the normal probability distribution. In this study, therefore,
the same method was used to define the probability distribution. Figure 2.9-12 shows theresult of the regression analysis (Excel Sheet).
As a result of the regression analysis, the standard error in the slope (concentration
coefficient) was found to be 3.28. Based on this, a normal distribution with an average of
67.0, the slope, and with a standard deviation of 3.28 was assumed (Figure 2.9-13).

CoefficientStandard
deviation

Value of P

Lower limit of Upper limit of95%
95%

Concentration
Assumed to be a normal distribution with standard deviation of 3.28

Figure 2.9-12: Result of regression analysis (coefficient and standard deviation)

Normaldistribution

Average

66.98

Standard deviation

3.28

Normal distribution
Probability

Indication of amountof statistics

Figure 2.9-13: Probability distribution of relation between formaldehyde and the seriousness of
mucosa symptoms(3)
Relation of formaldehyde concentration and the seriousness of mental
symptoms of sick house syndrome [QEESI/person/year/ppm]
In the same way, probability distribution was defined concerning formaldehyde concentrationand mental symptoms of sick house syndrome. As a result of the regression analysis
described in (2), normal distribution was assumed by using the standard error as the standard
deviation (Figure 2.9-14).
Normaldistribution

Average

71.96

Standard deviation

3.70

Normal distribution
Probability

Indication of amountof statistics

Figure 2.9-14: Probability distribution of relation between formaldehyde and the seriousness ofmental symptoms

98LIME2_C2.7-C2.9_2014(4)

Target population

With regard to the target population, the average was calculated from the National Census
2005’s data on the average number of household members in each prefecture. Because ofthis, the distribution was defined from the viewpoint of conformity with the most suitable
distribution by Crystal Ball (Figure 2.9-15).
Beta
distribution

Average

2.72

Standard deviation

0.20

Bata distribution
Probability

Indication of amountof statistics

Figure 2.5-15: Probability distribution of target population(5)
Amount of damage from mucosa symptoms of sick house syndrome[DALY/case]
Although a new value was generally discussed by a panel of two or more doctors, because it
was difficult to hold such a panel this time, one medical specialist determined a value fromexisting research. Uncertainties predicted concerning this item include one doctor’s
prejudiced judgment and fluctuation in the seriousness of symptoms themselves. This study
used the average and maximum values of the seriousness of disorder used for thedetermination. Assuming that there were fluctuations between the average value as thecenter and the maximum value, we dealt with this by the 95% upper limit of normal
distribution. That is, the larger of 2σ (σ standard deviation) added to the average μ was usedas the maximum value. Figure 2.9-16 shows the probability distribution. The average andmaximum values were set at 0.096 and 0.220, respectively as the 95% upper limit.
Normaldistribution

Average

9.60  10-2

Standard deviation

7.54  10-2

Normal distribution
Probability

Indication of amountof statistics

Figure 2.9-16: Probability distribution of the amount of damage from mucosa symptoms of sickhouse syndrome

Normaldistribution

Average

2.20  10-1

Standard deviation

1.09  10-1

Diffusion

1.20  10-2

Skewness

0.00

Kurtosis

3.00

Variation coefficient

0.04974

Normal distribution
Probability

Indication of amountof statistics

Figure 2.9-17: Probability distribution of the amount of damage from mental symptoms of sick house
syndrome

99

LIME2_C2.7-C2.9_2014(6)

Amount of damage from mental symptoms of sick house syndrome [DALY/case]In the same way, 0.220, probability distribution was established based on a normal
distribution in which the average value of the seriousness of disorder from mental symptoms
determined by medical specialists, and 0.400, the maximum value of the seriousness, were set
as the 95% upper limit (Figure 2.9-17).

Probability

Frequency

Damage function of single-family house during residence

Conformity: distributionof maximum values

Predicted
valuesIndication of amount ofstatistics

Conformity:
distribution of
maximum values

Predicted
value

Average

1.96  10-2

1.96  10-2

Median1.78  10-2

1.79  10-2Mode1.47  10-2'---

Standard deviation

1.08  10-2

1.08  10-2

Diffusion

1.17  10-4

1.18  10-4

Skewness

1.14

1.27

Kurtosis

5.40

6.04

Variation coefficient

0.55240.5544Lower limit-∞-9.22  10-3

Upper limit+∞

8.54  10-2

Average standard error'---

1.53  10-4

Figure 2.9-18: Prediction graph of formaldehyde during residence (single-family house)(7)
Result of uncertainty analysis of damage functions of formaldehyde(single-family house)
Simulation was performed 5,000 times by the use of Crystal Ball, calculation software(Figures 2.9-18 and 2.9-19).
The results show that the maximum value distribution of damage functions is for
single-family houses during residence. Because the standard deviation and the variation
coefficient are considerably small, the impact of the distribution seems low.

100

LIME2_C2.7-C2.9_2014

Sensitivity graph: Damage functions for single-family houses during residence
9. Amount of damage from mental symptoms
8. Amount of damage from mucosa symptoms
3. Frequency of ventilation [times/h]
1. Total floor area (single-family house) [m2]7. Target population P
5. DR relationship between formaldehyde and mental symptoms
6. DR relationship between formaldehyde and nose/larynx cancer
4. DR relationship between formaldehyde and mucosa symptoms
10. Amount of damage from nose/larynx cancer

Figure 2.9-19: Graph of sensitivity to formaldehyde during residence (single-family house)[References for Section 2.9]
Woodcock Ashley, Custovic Adnan (1998): ABC of allergies, Avoiding exposure to indoor allergens
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