JLCA NEWS ENGLISH EDITION SEP.2013

No.16

JLCA
NEWS LETTER

Life-Cycle Assessment Society of Japan

LIME2

Life-cycle Impact assessment Method based on Endpoint modeling

Chapter 2 :Characterization and Damage Evaluation Methods
2.4 Urban Air Pollution
2.5 Photochemical Oxidant
2.6 Toxic Chemicals (Human Toxicity)

LIME2_C2.4-C2.6_2013

LIME2
Life-cycle Impact assessment Method
based on Endpoint modeling

Chapter 2
Characterization and Damage
Evaluation Methods
2.4 Urban Air Pollution
2.5 Photochemical Oxidant
2.6 Toxic Chemicals (Human Toxicity)
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_C2.4-C2.6_2013

Chapter Ⅱ

Characterization and Damage Evaluation Methods
Contents
2.4

Urban Air Pollution ........................................................................................................ 1
2.4.1 What phenomenon is urban air pollution? ................................................................ 1(1)

Cause of urban air pollution ..................................................................................... 3(2 )Endpoints for urban air pollution .............................................................................. 5

2.4.2 Characterization of urban air pollution ..................................................................... 5
2.4.3 Damage assessment and uncertainty assessment of urban air pollution ................... 6(1)

Basic policies for damage assessment and uncertainty assessment .......................... 6(2)

Pollution by primary pollutants (NO2, SO2, primary particles) .............................. 9(3)

Pollution by secondary pollutants (nitrate salt and sulfate salt) ............................. 21(4)

Uncertainty assessment ........................................................................................... 29

2.4.4 Procedure for impact assessment of urban air pollution ......................................... 37(1)

Characterization ...................................................................................................... 37(2)

Damage assessment, weighting .............................................................................. 37

2.5

Photochemical Oxidant ................................................................................................ 40
2.5.1 What phenomenon is photochemical oxidant? ....................................................... 40(1)

Causation of photochemical oxidant ...................................................................... 40(2)

Endpoints of photochemical oxidant ...................................................................... 44

2.5.2 Characterization of photochemical oxidant ............................................................ 45(1)

Existing characterization factors for photochemical oxidant ................................. 45(2)

Characterization factor for photochemical oxidant under LIME............................ 46

2.5.3 Damage assessment of photochemical oxidant ...................................................... 46(1)

Basic policies for calculation of damage functions and factors.............................. 46(2)

Correlation of the amount of VOCs emissions with an increase in the ozone
concentration in the atmosphere ................................................................................... 51(3)

Damage functions and factors for human health .................................................... 57(4)

Damage functions and factors for social assets ...................................................... 60(5)

Primary production: Damage functions and factors for terrestrial NPP ................. 67

2.5.4 Procedure for the impact assessment of photochemical oxidant ............................ 68

LIME2_C2.4-C2.6_2013

2.6

Toxic Chemicals (Human Toxicity).............................................................................. 73
2.6.1 What phenomenon is the human toxicity of toxic chemicals? ............................... 73(1)

What is the human toxicity of toxic chemicals? ..................................................... 73(2 )Endpoints of human toxicity of toxic chemicals .................................................... 74

2.6.2 Characterization factor for toxic chemicals ............................................................ 76(1 )Existing characterization factor for toxic chemicals .............................................. 76(2)

Characterization factor of toxic chemicals under LIME ........................................ 79

2.6.3 Damage assessment of toxic chemicals .................................................................. 83(1)

Basic policies for calculation of damage factors .................................................... 83(2)

Fate analysis of toxic chemicals ............................................................................. 85(3 )Impact factor for toxic chemicals ........................................................................... 85(4)

Human health: cancer damage analysis .................................................................. 86(5)

Human health: damage analysis of chronic diseases .............................................. 88(6)

Damage factor for toxic chemicals ......................................................................... 92(7)

Comparison between LIME 1 and LIME 2 in damage factors ............................... 95

2.6-4 Procedure for impact assessment of a toxic chemical ............................................ 98

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LIME2_C2.4-C2.6_2013

Chapter II

Characterization and Damage Evaluation Methods
2.4

Urban Air Pollution

Changes in LIME2• Characterization factors not calculated under LIME1 were calculated.
• With regard to some substances, values were reviewed concerning “the increasing rate ofthe death rate and the disease rate at each endpoint per unit of pollutant concentration.”• Uncertainty assessment of damage factors was carried out.
2.4.1 What phenomenon is urban air pollution?
The atmosphere contains various substances. The volume of vapor (H2O) greatly differs
according to place and time. However, the composition of the substances other than vapor isalmost the same up to 80 km above the ground. Concretely, nitrogen (N2) and oxygen (O2)
occupy more than 98%, followed by carbon dioxide (CO2), argon (Ar), etc. (see “Invariant
components” in Table 2.4-1).
On the other hand, the concentrations of ozone (O3), sulfur dioxide (SO2), and othersubstances, whose volumes are slight, vary depending on the condition of the source andhuman activities (see “Variant components” in Table 2.4-1). If a trace of such a substancereaches a level of concentration harmful to human beings and organisms, this is called air
pollution (Kawamura et al. 1988).
Air pollution has become more serious as human beings’ urban activities and industrial
activities have become brisker – especially, as consumption of fossil fuels has increased.
For example, in the middle of the 20th century, serious air pollution caused much suffereringand many deaths all over the world. The smog in London in December 1952 was especially
famous. Emissions of SO2 and smoke dust derived from coal burning for manufacturing,heating, and cooking caused smog so serious as to obstruct the view, with the result that about
4,000 persons – mainly, infants and elderly persons – were dead.
In Japan, before World War II, sulfur oxide (SOx) emitted from refining in Ashio and othercopper mines caused damage to surrounding forests and farm products. Around World War
II, burning of coal for manufacturing and heating produced smoke dust, causing air pollution.
From the second half of the 1950s, the energy source shifted from coal to petroleum, andmany petrochemical complexes were constructed in seaside areas. When petroleum, whichcontains a lot of sulfur, was burnt, SOx was emitted from petroleum and caused asthma in
Yokkaichi City. From around 1970, nitrogen oxide (NOx) and volatile organic compound(VOCs) emitted from automobiles and factories produced photochemical oxidant (maincomponent is O3) and caused photochemical smog.

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LIME2_C2.4-C2.6_2013

Table 2.4-1: Composition of the atmosphere
Substance

VolumeVolumeInvariantcomponents

Photoreaction(Summer)(Winter)

Factory, volcano, etc.
Variant
components

Factory, automobile, etc.
Acidification of organic CH4
Manufacturing

Indetermination
Within 10– 4 [g m–3]

Sea salt
Manufacturing

Trace

Manufacturing

Trace

Evaporation(Source) Kawamura et al. (1988); data were altered.

After that, air pollution by SO2 greatly improved because of the government’s tightening of
regulations and the industrial world’s introduction of measures. On the other hand, with theprogress of motorization, pollution of roadsides by nitrogen dioxide (NO2) and diesel
emission particles (DEP) have been regarded as problems. Another problem is that air
pollution has occurred also by various hazardous chemical substances, such as nitrate salt andsulfate salt produced during long-distance transportation of SO2 and NOx as well as benzene,organochloride compounds (trichloroethylene, tetrachloroethylene, etc.), dioxin, etc.
As described above, the sources of air pollutants and the substances that cause air pollution
have changed with changes in industries and energy sources, urbanization, and changes inlifestyle. Although some air pollution problems have been solved through efforts by thegovernment, industries, citizens, etc., serious problems still remain. Therefore, it isnecessary to prevent the impact of various substances that originate from various sources ofemissions.
Under LIME, these air pollution problems were dealt with in “urban air pollution,” the impact
category covered by this section. However, O3 is covered by the impact category“photochemical oxidant” (see Section 2.5). Various hazardous chemical substances arecovered by the impact categories “hazardous chemical substances” and “eco-toxicity” (seeSections 2.6 and 2.7).
Table 2.4-2 shows the above-described types of air pollution and the measures for theabove-described impact categories under LIME. In the table, “primary pollutants” means
pollutants directly emitted from factories, automobiles, etc. In addition, “secondary
pollutants” means pollutants produced from primary pollutants through chemical reaction.

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LIME2_C2.4-C2.6_2013

Table 2.4-2: Types of air pollution and impact categories under LIME
Targets of LIME
Types of air pollution
Pollution by
primarypollutants

Pollution bysecondarypollutants

Other

Emittedsubstances

Atmosphericsubstances

Urban air pollution(this section)

Pollution by emission of NO2
Pollution by emission of SO2
Pollution by emission of primary particles*

Primary particles Primary particles

Photochemical oxidant(Section 2.5)

Pollution by O3 due to primary pollutant
emissions (VOCs and NOx)
Pollution by nitrate salt due to primarypollutant emissions (NO2)nitratePollution by sulfate salt due to primarypollutant emissions (SO2)
Pollution due to emission of hazardous
chemical substances

Impact categories

Urban air pollution(this section)

SulfateHazardous
chemical
substances

Hazardous
chemical
substances

Hazardous chemical
substances (Section 2.6)
Eco-toxicity (Section 2.7)

† Particles directly emitted from sources of emissions, such as smoke dust and diesel emission particles(1)

Cause of urban air pollution

After the emission of a primary pollutant, it is transported by the wind (advection), spread out,
and chemically changed into a secondary pollutant. In addition, a part of it is removed fromthe atmosphere through deposition. If human beings and organisms are exposed to thesepollutants, they may receive harmful effect, depending on the amount of exposure.
Figure 2.4-1 shows the causation of such urban air pollution (however, for O3, see Section
2.5).
The first half of the causation will be described herein, while the second half will be describednext in (2).
The main physical and chemical phenomena (advection, diffusion, chemical change,
deposition, etc.) of air pollution differ according to type and, as a result, the scale of the spaceover which air pollution spreads.
Primary pollutants spread over the source of emissions and the surroundings throughadvection and diffusion by the wind.
On the other hand, secondary pollutants are formed though chemical changes, etc. under the
influence of insolation, humidity, etc. Because a chemical change takes time, theconcentration of secondary pollutants does not necessarily become high around the source ofemissions, and the pollution area spreads more widely.
Below, the phenomenon and space scale of air pollution will be described concerning bothprimary pollutants and secondary pollutants.
aPollution by primary pollutants

Primary pollutants include NOx, SO2, and particulate matter.
NOx is produced through oxidization of burning nitrogen compounds and nitrogen in theatmosphere during the incineration process at factories, automobiles, etc. There are two
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LIME2_C2.4-C2.6_2013

types of NOx: nitrogen dioxide (NO2) and nitrogen monoxide (NO). Although Most of NOx
is NO at the time of emission, NO is relatively quickly oxidized into NO2.
SO2 is created through oxidation of burning sulfur during the incineration process at factories,
etc.
Primary particles include soot created during the incineration process at factories, etc. and
DEP created during the combustion of diesel engines (there are also secondary particles,
which will be described in b).
After the emission, the above-mentioned primary particles move by the wind and spreadhorizontally and vertically. When the surface of the ground becomes warmer because ofinsolation, an air parcel rises, it becomes easier for convection to occur (it becomes easier tobe unstable), and it become easier for substances to be mixed with upper and lower substancesand diffuse.
As a result of such advection and diffusion, pollution extends from a local scale around thesource of emission (a scale of up to about 200 m) to an urban scale (a scale of up to about 20
km).

Pollution by primary pollutants (NO2, SO2, primary particles): local scale (up to about 200 m)
Conspicuous on urban scale (up to about 20 km)
Endpoint impact
Human health

Advection, diffusion
Emission of primary pollutants(NO2, SO2, primary particles)

Increase in primary pollutant concentration in theatmosphere(NO2, SO2, primary particles)

Exposure
Respiratory
diseasePollution by secondary pollutants (nitrate salt, sulfate salt): mesoscale (up to about 200 km)
Conspicuous on regional scale (up to about 2,000 km)
Advection, diffusion
Emission of primary pollutants(NO2)

Oxidation by OH radical
Absorption into water drops
Increase in nitrate salt concentration in theatmosphere(NH4NO3, etc.)

Exposure

Endpoint impact
Human healthRespiratory disease

Removal from the atmosphere
due to deposition

Advection, diffusion
Emission of primary pollutants(SO2)

Oxidation by OH radical
Absorption into water drops, oxidization

Endpoint impact
Human health

Increase in sulfate salt concentration in theatmosphere((NH4)2SO4)

Exposure

Removal from the atmosphere
due to deposition

Figure 2.4-1: Cause of urban air pollution(1) and (2) in the figure correspond to headings in the main text.
(Source: prepared with reference for Ohara et al. (1997); Kasahara (1994))

4

Respiratory disease

LIME2_C2.4-C2.6_2013b

Pollution by secondary pollutants

Secondary pollutants include O3 produced from VOCs and NOx, and nitrate and sulfate salt
produced from NOx and SO2. Nitrate and sulfate are described herein (for O3, see Section
2.5).
Although NOx and SO2 are gaseous substances, they are oxidized by OH radical (for themeaning of radical, see Column 2.5-2 “What is a radical”), pass through various processes(chemical reaction with other pollutants, solution in drops of water, etc.), and are changed into
particle nitrate (such as NH4NO3) and particle sulfate (such as (NH4)2SO4) respectively(particles created from emitted substances through chemical changes or phase changes in thisway are called secondary particles).
Such pollution by nitrate and sulfate extends to mesoscale (up to about 200 km) and regional
scale (up to about 2,000 km). The extent of pollution by nitrate is wider than that by sulfate .
This is because the oxidation speed of SO2 by OH radical is about one-tenth that of NO2, SO2
remains gaseous for a long time, and is oxidized into sulfate salt after long-distancetransportation.
Nitrate salt and sulfate salt cause oxidization when depositing from the atmosphere to soil andwater areas. Because this phenomenon is treated separately in the impact category“acidification,” it is excluded from this impact category.

(2)

Endpoints for urban air pollution

After air pollutants invade a human being’s respiratory system (nasal cavity - pharynx trachea - bronchi - small bronchi - lung alveoli), they cause deposition, absorption, and injury
at various parts according to physical and chemical characteristics.
aGaseous substances (NO2, SO2)

SO2, a gaseous and highly water-soluble substance, is absorbed in walls of bronchial tubes
and causes spasmodic contraction of the respiratory tract. On the other hand, NO2, having
relatively low water solubility, reaches small bronchi and lung alveoli and causes
inflammation of mucous membranes.
bParticle substances (primary particles, nitrate salt, and sulfate salt)

It has been pointed out that, although most particles with a diameter of 10 μm or more depositin the nasal cavity and the pharynx, particles with a smaller diameter deposit deeper in the
trachea and the lungs, causing asthma and other medical problems.
2.4.2

Characterization of urban air pollution

Urban air pollution characterization factor (UAF) was calculated in the same way of thinking
as the characterization factors for hazardous chemical substances and eco-toxicity (see 2.6.2
(2)).

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LIME2_C2.4-C2.6_2013

1) With regard to each air pollutant, calculate an increase in the concentration at the time ofemission of a unit amount – that is, the concentration factor [(μg/m3) / (kg/yr)]. Divide thisby the environmental standard value in Japan.
2) Calculate 1) also with regard to the standard substance (SO2 herein).
3) Divide the result of 1) by 2) to obtain a ratio to the standard substance.
NOx and SO2 were adopted as air pollutants, and secondary pollutants transmuted from air
pollutants (nitrate salt, sulfate salt) were adopted as substances to which human bodies are
exposed.
With regard to calculation of the concentration factor, an increment in the concentration of thesecondary pollutants created during the emission of a unit amount of air pollutants wascalculated for each prefecture, and increments were totaled in each zone (see 2.4.3 (3)). The
daily average threshold of hourly values (0.04 ppm for both NOx and SO2) was used as theenvironmental standard for air pollution.
2.4.3 Damage assessment and uncertainty assessment of urban air pollution(1)

Basic policies for damage assessment and uncertainty assessment

The existing LCA methods that have adopted the endpoint approach for the impact categories
of urban air pollution are Eco-indicator 99 (Goedkoop et al. 2000) and EPS (Steen 1999) inEurope. An assessment method based on the endpoint approach was adopted also for LIME.
That is, category endpoints were selected for each type of air pollution, and damage
assessment was carried out to calculate damage functions and factors. Moreover, final
damage factors were calculated through uncertainty assessment.
The policies for the damage assessment and the uncertainty assessment are as follows:aDamage assessment

Damage factors were calculated from damage assessment.
The types of air pollution and the zones used for the calculation were as follows (see Figure
2.4-2):
With regard to the types of air pollution, as mentioned in 2.4.1, main physicochemical
phenomena and space scale differ by types of air pollution. Therefore, under LIME, damage
factors were calculated for primary pollutants and secondary pollutants.
NO2, SO2, and primary particles were used as primary pollutants (as shown in Table 2.4-2).
The diameters of the primary particles were fixed at PM10 and PM2.5. This is for thefollowing reason: if the diameter is less than 10 μm as described before, primary particles
reach deeper parts of the respiratory system and it is easy for them to have influence; in
addition, although particles with a diameter of 10 μm are regarded as suspended particulate
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LIME2_C2.4-C2.6_2013

matters under the environmental standards in Japan, environmental standards have been
established even for particles with a diameter of less than 2.5 μm in the US (Note: however,the definition of particle diameter differs between Japan and the US).
Moreover, two types of sources of emission of the primary pollutants were used: chimneys
and automobiles. This is because the pattern of concentration differs due to the differences
between the two in the height of the source of emission and the pattern of emissions during a
day.
In addition, the types of secondary pollutants are nitrate salt produced from NO2 and sulfate
salt produced from SO2 (as shown in Table 2.4-2).
After the calculation of damage factors for primary pollutants and secondary pollutants, withregard to NO2 and SO2, the damage factors for primary pollutants are added to the damage
factors for secondary pollutants (nitrate salt, sulfate salt) (see Figure 2.4-2).
With regard to zones, air pollution is greatly influenced by local weather conditions.
Therefore, under LIME, damage factors were calculated for each of the zones (Hokkaido,
Tohoku, Kanto, Chubu, Kansai, Chugoku/Shikoku, Kyushu/Okinawa) and on average inJapan (see Figure 2.4-2).
The calculation was carried out as follows:First, the damage factors for primary and secondary pollutants were calculated according
causal channels (see Figure 2.4.1) as follows (see Figure 2.4-3):
Step 1:The relation between the amount of primary pollutant emissions and an increase in theatmospheric concentration of primary (or secondary) pollutants was quantified.
Step 2:The relation between the increase in the concentration in 1) and the amount of damage
was quantified at each endpoint. Endpoints at which the relation between the concentrationof air pollutants and the death/disease rate was assessed quantitatively by epidemiologic
surveys were selected as endpoints for human health (see Table 2.4-3). Endpoints forprimary production were selected not from the impact category of urban air pollution but fromthe impact category of “acidification.”
Step 3:The damage function for each endpoint was calculated by combining Steps 1 and 2.
Step 4:The damage functions for the endpoints in Step 3 were totaled by the area of protection
to obtain damage factors.
Details will be described in 2.4.3 (2) for primary pollutants and 2.4.3 (3) for secondary
pollutants.
Next, the obtained damage factors for primary and secondary pollutants were totaled (asshown in Figure 2.4-2).
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LIME2_C2.4-C2.6_2013

Damage factor on average in Japan
Damage factor for Kyushu/Okinawa
Damage factor for Chugoku/Shikoku
Damage factor for Kansai
Damage factor for Chubu
Damage factor for Kanto
Damage factor for Tohoku
Damage factor for Hokkaido

Pollution by NO2
due to NO2emissions

Damage factors
due to NO2emissions

Pollution by SO2
due to SO2emissions

Damage factors
due to SO2emissions

Pollution by
PM2.5 due toPM2.5 emissions

Damage factors
due to PM2.5
emissions

Pollution by
PM10 due toPM10 emissions

Damage factors
due to PM10
emissions

Chimney
Automobile

Chimney

2.4.3(2)Pollution by
primary pollutants

Urban airpollution

Chimney
Automobile

Chimney
Automobile

Figure 2.4-2: Objects of calculation of damage factors for urban air pollution(The numbers in italics correspond to the Pollution
headingsbyof the main text.)
2.4.3(3)Pollution bysecondarypollutants

nitrate due to NO2emissions

Pollution by
sulfate due to SO2emissionsFigure 2.4-2: Objects of calculation for damage factors of urban air pollution
Numbers in italics correspond to headings in the main text.

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LIME2_C2.4-C2.6_2013

Area of protection

Table 2.4-3: Category endpoints as objects of calculation for damage functions and factors

Category endpoint

Pollution by primarypollutants

Pollution by primarypollutants• Pollution by NO2 due toNO2 emissions• Pollution by PM2.5 due
to PM2.5 emissions• Pollution by SO2 due toSO2 emissions• Pollution by PM10 due
to PM10 emissions

Objects of calculation for damage
functions

Pollution bysecondary pollutants

Objects ofcalculation fordamagefactors• Pollution by nitrate salt
due to NO2 emissions

Human healthRespiratory disease• Pollution by sulfate salt
due to SO2 emissions• Acute death

Increase in no. of acute deaths(DALY-converted)• Chronic death

Increase in no. of chronic deaths(DALY-converted)• Use of bronchodilator

Increase in no. of sufferers((DALY-converted)• Cough

Increase in no. of sufferers(DALY-converted)• Lower Respiratory Symptoms

Increase in no. of sufferers(DALY-converted)• Chronic bronchitis

Increase in no. of sufferers(DALY-converted)• Chronic coughIncrease in no. of sufferers(DALY-converted)• Days of behavioral restriction

Increase in no. of sufferers(DALY-converted)• Hospitalization for respiratory
system restriction

Increase in no. of sufferers(DALY-converted)• Entry into emergency room alization
for respiratory system restriction

Increase in no. of sufferers(DALY-converted)• Entry into emergency room
(Asthma)

Increase in no. of sufferers(DALY-converted)• Entry into emergency room

Increase in no. of sufferers(DALY-converted)(Inflammation of pharynx/bronchi ofpreschooler)
Primary
productionb

Terrestrial
ecosystem

Fall in growth due to direct
impact to plants

To be considered in the impact category ofacidification

Uncertainty assessment

Four types of damage factors were obtained by the procedure described in (a) (damage factors
due to NO2 emissions, SO2 emissions, PM2.5 emissions, and PM10 emissions). Uncertainty
assessment was carried out for each damage factor, and the obtained statistical data wereadopted as final damage factors.
Details will be described in 2.4.3 (4).
(2)

Pollution by primary pollutants (NO2, SO2, primary particles)

Explanations are given according to Steps 1 to 4 in Figure 2.4-3.
aStep 1: Correlation of the amount of primary pollutant emissions with anincrease in the concentration of primary pollutants
Simulation was carried out by the use of an atmospheric model to calculate an “increase in theconcentration of primary pollutants due to a unit amount of primary pollutant emissions”(∆C/∆E).
9

LIME2_C2.4-C2.6_2013(a)Cases used for calculation

The cases specified in Table 2.4-4 were used for the calculation.
Table 2.4-4: Case used for calculation of an “increase in the concentration of primary pollutants due
to a unit amount of primary pollutant emissions” (∆C/∆E)

Zon

Time

Source ofemission(b)

Case
7 zones (Hokkaido, Tohoku,
Kanto,
Chubu,
Kansai,
Chugoku/Shikoku,
Kyushu/Okinawa)• Because the annual average concentration has been
assumed as the “concentration” in the “death/disease rate
at each endpoint per unit of primary pollutant
concentration” (described in b below)

1 type (annual average)2
types
automobile)(chimney

Reason for selecting the case• To represent various weather conditions in Japanand• Because there are difference between chimneys andautomobiles in the height of emission and the dailychanging pattern of the amount of emission (see 2.4.3 (2)a (b). However, only chimneys were used for SO2.

Atmospheric model used for calculation

The concentration of primary pollutants (NO2, SO2, and primary particles) is high around thesource of emission and becomes lower with increasing distance from the source, due toadvection and diffusion. This distribution is influenced by the height of the source, thedirection and velocity of the wind, the stability of the atmosphere, etc. To make such a
polluting mechanism, a plume model and a puff model were selected for LIME (see Column
2.4-1).
(c)

Calculation procedure

A simulation was carried out by the use of revised equations for the plume model and the puff
model in (b) above and by the application of weather conditions in each zone. Because theplume model and the puff model assume a linear relation between the amount of emission andthe concentration, an “increase in the concentration of primary pollutants due to a unit amountof primary pollutant emissions” (∆C/∆E) was calculated by dividing the concentration
obtained from the simulation by the amount of emissions.
Although ∆C/∆E covers the annual average concentration (see Table 2.4-4), because therelation between weather conditions, such as wind velocity, and concentration is non-linear in
the plume model and the puff model, the concentration may not be reproduced accurately if
only one case of annual average weather conditions is simulated. To cope with this, under
LIME, first the wind direction and velocity and the atmospheric stability were classified into
several patterns, and a simulation was carried out for each pattern to calculate ∆C/∆E. Next,
the calculated ∆C/∆E was weighted with the annual appearance frequency for each pattern tocalculate the annual average ∆C/∆E (see Figure 2.4-4).

10

Step 4
Calculation of damage
factors
Calculate “the amount ofdamage to human healthdue to a unit amount ofprimary pollutant
emissions” (∆DALY/∆E).

Step 3Calculation of damage functions
Calculate “DALY loss at each
endpoint due to a unit amount ofprimary pollutant emissions”(∆DALYeach endpoint/∆E).

DALY perdeath

Population

Endpoint[Chronic death]

Same asto theleft

Damage factor (DF)[Human health]Area of protection[Human health]

DALY perdeath

Population
Same asto theleft

Endpoint
Disease for [use of bronchodilator]

DALY loss

Multiplication

No. ofsufferers

Multiplication

Disease rate

Increase in atmosphericconcentration ofprimary/secondary pollutants

Types of endpoints depend on types of air pollutants.

Endpoint[Acute death]

DALY loss

Multiplication

No. of deaths

Multiplication

Death rate

Emission of a unit
amount of primarypollutants

Damage function (amount of damage to each endpoint due to 1 kg yr-1 of primary pollutant emissions)Figure 2.4-3: Flowchart of calculation of damage functions and factors for urban air
pollution (outline)

Step 2
Correlation of an increase in theconcentration of primary/secondary
pollutants with DALY loss at each endpoint
Calculate “DALY loss at each endpoint per unitof primary and secondary pollutant
concentration” (∆DALYeach endpoint/∆C).

Step 1
Correlation of the amount of primary pollutant
emissions with an increase in the concentrationof primary/secondary pollutants
Calculate an “increase in the concentration ofprimary/secondary pollutants due to a unit amountof primary pollutant emissions” (∆C/∆E).

LIME2_C2.4-C2.6_201311

LIME2_C2.4-C2.6_2013

Column 2.4-1
Plume model and puff model
The plume model and the puff model are simple models used widely for the calculation ofconcentration of air pollutants around the source of emission for the purpose of environment
assessment.
The plume model and the puff model express the concentration downwindunder the lee fromthe source of emission by the use of a normal distribution. Consideration is given to
advection and diffusion, but not to chemical changes (although there are revised equations
that take into account chemical changes, they are not minute).
Originally, they were models that assumed a normal distribution for not only the downwind
direction but also the crosswind direction. From a long-term perspective, because it can beassumed that the probability of wind direction is fixed within a classification of winddirection, the long-term average concentration in the crosswind direction does not change
within the classification. To predict such as long-term average concentration, revised
equations for the plume model and the puff model were proposed.1) 2) The concentration
assumed for an “increase in the concentration of primary pollutants due to a unit amount ofprimary pollutant emissions” is expressed as the annual average (see Table 2.4-4), and arevised equation was adopted for LIME.
The use of the revised equations for the plume model and the puff model differs depending on
wind conditions: windy (1 [m/s] or more); weak wind (0.5 [m/s] to 0.9 [m/s]; no wind (0[m/s] to 0.4 [m/s]) (see Table 2.4-A).
The equations for the plume model and the puff model include an equation that takes into
consideration the deposition of particles according to particle diameter. Under LIME,
although an attempt was made to use this equation for primary particles (PM2.5, PM10), theresult of the calculation was not so different from that of the equations that do not take thedeposition into consideration. Because of this, finally, equations that do not take thedeposition into consideration were used for NO2, SO2, and primary particles (PM2.5, PM10).

12

LIME2_C2.4-C2.6_2013

Table 2.4-A: Revised equations for the plume model and the puff model used asatmospheric models1) 2)
Basic equation
Use the following equation into which Holland (1953) revised the plume model for the point source:

In a windy
condition (1 [m/s]or more)R is a downwind distance from the source of emission [m].
z is a vertical distance from the source of emission [m].
(The original point is located at the point source.)
concentration of substance [kg/m3]; q: substance emission speed [kg/s]; U: velocity[m/s]vertical diffusion parameter [m]; He: effective chimney height [m]σz, the vertical diffusion parameter in the above equation, was calculated based on the
Pasquill-Gifford chart (Gifford 1961).
Use the following equation into which Muto (1979) revised the puff model for the point source:

In a weak windcondition (0.5 to
0.9 [m/s])α, γ: Use the result of revision based on the diffusion parameter [m/s] and the Turner chart (Turner1964).

In no windcondition (0 to 0.4[m/s])

Use the following equation:

1) Committee on Suspended Particulate Matters (1997)
2) Environmental Research and Control Center, Committee on Nitrogen Oxide (2000)
Wind direction, wind velocity, and atmospheric stability are classified into the total L pattern, the total M pattern,
and the total N pattern, respectively.
Each pattern’s “increase in the concentrationof primary pollutants due to a unit amount of Each pattern’s annual
primary pollutant emissions”
appearance frequency
Wind direction pattern 1, wind velocity pattern 1,
atmospheric stability pattern 1
Annual average ofWind direction pattern 1, wind velocity pattern 1,“increase in theatmospheric stability pattern 2
concentration of

Total L × M ×
N patterns

Wind direction pattern 1, wind velocity pattern
m, atmospheric stability pattern nprimary pollutants
due to a unit amountof primary pollutant
emissions”

Wind direction pattern L, wind velocity pattern
M, atmospheric stability pattern N

Figure 2.4-4: Procedure for calculation of an “increase in the concentration of primary pollutants due to aunit amount of primary pollutant emissions” (∆C/∆E) concerning annual average concentration

13

LIME2_C2.4-C2.6_2013(d)Calculation conditions

The scope of calculation, the amount of emissions, and the height of calculation ofconcentration were set as shown in Table 2.4-5.
With regard to weather, as described in (c) above, wind direction and velocity andatmospheric stability were classified into several patterns. Table 2.4-6 shows theclassification standards.
Weather patterns were classified according to the combination of wind direction and velocity
and atmospheric stability specified in the table. Because, as a matter of course, thelikelihood of appearance of each weather pattern differs from zone to zone, the annual
appearance frequency for each weather pattern was calculated for each of the seven zones
specified in “a Cases used for calculation.” Data from terrestrial weather observatories
around highly populated cities in each zone were used for calculation (see Table 2.4-7).
(e)Calculation results(i)Concentration distribution

The concentration distribution around sources of primary pollutant emissions was calculatedby the above-described method.
Because, as described in (b) above, the same equations (the revised equations for the plumemodel and the puff model) were used for all the primary pollutants (NO2, SO2, primaryparticles), the calculation results are the same irrespective of the kind of substance.
Of the calculation results in the seven zones – Hokkaido, Tohoku, Kanto, Chubu, Kansai,
Chugoku/Shikoku, and Kyushu/Okinawa – the results in Kanto (calculated under the weather
conditions in Tokyo; see Table 2.4-7) are shown in Figure 2.4-5. The following are thecharacteristics of the results:[Emission from chimneys]
The following two points are characteristics common to all the zones:•
Generally, concentration is highest around sources of emissions and becomes lower
with increasing distance from them.
•
However, when sources of emissions were surveyed in detail, it was found that thedecreasing rate of concentration was lower or the concentration rose slightly at a distance of
50 to 100 m from a source of emission.
The reasons for the latter characteristic are as follows:
When there is no wind, the puff model for the time of no wind is applied for the calculation.
In this case, the nearer a source of emission, the higher the concentration. On the other hand,when it is windy and when there is weak wind, the plume model and the puff model for thetime of weak wind are respectively applied for calculation. In these cases, at a slight
distance from a source of emission, a vertical extent reaches the ground and the highest
14

LIME2_C2.4-C2.6_2013

concentration appears.
Because the calculation results of concentration were an
accumulation of the results at the times of no wind and weak wind and at the windy time, theconcentration distribution has the characteristics of them all.
[Emission from automobiles]
Because the height of emissions from automobiles is lower than emissions from chimneys,
concentration is high around sources of emissions and becomes considerably lower withincreasing distance from the sources.
Table 2.4-5: Conditions for calculation of an “increase in the concentration of primary pollutants due
to a unit amount of primary pollutant emissions” (∆C/∆E)
Conditions

Content

Scope ofcalculation

Concentration was calculated until 20 km (radius of 10 km) from the source of emission so that air pollution ona local scale and that on an urban scale (see Figure 2.4-1) could be reproduced.

Amount ofemissions

Daily changes in the amount of emissions from chimneys and automobiles were fixed with reference for asurvey on daily changes in emission speed.1) The height of emissions was fixed at 20 m for chimney (themode of a statistical data2)) and 1 m for automobiles.

Height for calculation ofconcentration

Fixed at 1.5 m from the ground with consideration for the height at which human beings breathe

1) FY1999 Survey on the Relation between Environmental Impact and Damage – Preparation of a
Pollution Map (March 2000)
2) FY1997 Report on the Results of Operations Entrusted by the Environment Agency: Comprehensive
Survey on the Amount of Air Pollutant Emissions (1998)

Table 2.4-6: Standards for weather pattern classification
Weather element

Classification

Wind direction

Wind direction was divided into the 16 directions that can be obtained from terrestrial weather observation (Table 2.4-7).

Wind velocity

Wind velocity was divided as shown in the following table so as to be1)
consistent2) with the classification for the application of the plume model2)
and the puff model and the classificationof wind velocity used for the judgment of atmospheric stability by Pasquill (described below). 1) 2)
Wind velocity [m/s]No windWeak wind
Windy

Atmosphericstability

Atmospheric stability was divided into A, A-B, B, B-C, C, C-D, D, E, F, and G based on the following table 2) in which theatmospheric stability by Pasquill was revised.
Wind
velocity(U)[m/s]

Daytime: amount of insolation cloud coverNighttime: cloud cover
Main cloud

Upper cloud

Cloud cover

Middle/lower cloud1) Committee on Suspended Particulate Matters (1997)
2) Environmental Research and Control Center, Committee on Nitrogen Oxide (2000)

15

LIME2_C2.4-C2.6_2013

Table 2.4-7: Observation points of terrestrial weather data used
Observation points of terrestrial weather data used

Zone
Hokkaido

Sapporo District Meteorological Observatory

TohokuSendai District Meteorological Observatory

Kanto

Tokyo District Meteorological Observatory

Chubu

Nagoya Local Meteorological Observatory

Kansai

Osaka District Meteorological Observatory

Chugoku/Shikoku

Hiroshima Local Meteorological Observatory

Kyushu/Okinawa

Fukuoka District Meteorological Observatory

濃度 中心付近の拡大図
Concentration: enlargementof surroundings of the source of emissions
1.20E-03[μg/m3]
Concentration
濃度[ug/m3]

1.00E-03
8.00E-04
6.00E-04
4.00E-04
2.00E-04
0.00E+00
0

200

400

600
800
1,000
1,200
1,400
Distance from thesource of emissions<x [m]>
排出源からの距離<x:[m]>

1,600

1,800

2,000

[Emission from a chimney]
Concentration: enlargementof surroundings of the source of emissions
濃度 中心付近の拡大図

3[μg/mConcentration]m3 ]濃度[ug/m3]

3.00E+00
2.50E+00
2.00E+00
1.50E+00
1.00E+00
5.00E-01
0.00E+00
0

50

100
150
200
Distance from 排出源からの距離
the source of emissions<x [m]><x:[m]>

250

300[Emission from an automobile]
Figure 2.4-5: Annual average concentration due to 1 kg yr-1 of primary pollutant emissions: Tokyo

16

LIME2_C2.4-C2.6_2013(ii)
Increase in the concentration of primary pollutants due to a unit amount of primarypollutant emissions (∆C/∆E)
A concentration distribution can be obtained from the plume model and the puff modelaccording to the distance from the source of emission (see (i)). Because of this, when an“increase in the concentration of primary pollutants due to a unit amount of primary pollutant
emissions” (∆C/∆E) was calculated, the products of concentration and areas were summed
within a calculation scope of 20 km (radius of 10 km) described in (d). Concretely, theconcentration at each calculation point (every 2 m in this case) was multiplied by an inside
range of 2 m, including the point, and the resultant products were totaled (Equation 2.4-1).

    2 n     2n  1  C (n)

500 n

2

2

(2.4-1）n 1n:point that is 1 at a distance of 2 m from the source of emission and increments by
1 with every 2 m distance.
C (n): annual average concentration of primary pollutants at the nth point [μg/m3]
The sum of products obtained in this way was divided by the amount of emissions to calculate∆C/∆E.
Table 2.4-8 shows the calculation results in each zone.
Table 2.4-8: “Increase in the concentration of primary pollutants due to a unit amount of primarypollutant emissions” (∆C/∆E)[ug m-3 m2 kg-1 yr]

Zone

Chimney

Automobile

Hokkaido
Tohoku
Kanto
Chubu
Kansai
Chugoku/Shikoku
Kyushu/Okinawab
Step 2: Correlation of an increase in the atmospheric concentration of primarypollutants with DALY loss at each endpoint“DALY loss at each endpoint per unit of primary pollutant concentration” (∆DALY each endpoint /∆C) was calculated by Step 2 as shown in Figure 2.4-3.
Figure 2.4-6 shows the calculation flowchart. “(a) Death/Disease rate at each endpoint perunit of primary pollutant concentration” was multiplied by “(b) Population density in eachzone” and “(c) DALY of 1 death/1 disease at each endpoint.”
The following is an explanation of (a) to (c) in Figure 2.4-6:
17

LIME2_C2.4-C2.6_2013(a) Disease rate at each
endpoint per unit of primarypollutant concentration(a) Death rate at each endpoint
per unit of primary pollutant
concentration
Death rate
Disease rate(b) Population density ineach zone (person m-2)

Multiplication(b) Population density ineach zone (person m-2)

Multiplication

Population
density

Population
densityNo. of deaths(c) DALY per death at each
endpoint (by use of
Hofstetter (1998))

Multiplication

DALY perdeath

No. ofsufferers
Multiplication

DALY perdiseaseDALYloss(c) DALY per disease at each
endpoint (by use of
Hofstetter (1998))

DALY
loss

Endpoint[Chronic death] or [acute death]

Endpoint
Each disease, such as [use of bronchodilator]

Figure 2.4-6: Flowchart of calculation of “DALY loss at each endpoint per unit of primary pollutant
concentration” (∆DALYeach endpoint / ∆C)(a), (b), and (c) in the chart correspond to headings in the main text.

(a)Death/disease rate at each endpoint per unit of primary pollutant concentration

With regard to the “death/disease rate at each endpoint per unit of primary pollutant
concentration,” the results of an epidemiological survey used for ExternE (European
Commission 1999) were used for setting the rate (see Table 2.4-9). However, with regard tochronic death due to PM2.5 or PM10, reexamination was carried out after LIME1, and a rate
was fixed based on a new document (Pope et al. 2002). Table 2.4-10 shows values set as
described above.
The target population differs among the endpoints (for example, entire population, adult, child,and asthma sufferer). In addition, these “death/disease rates at each endpoint per unit ofprimary pollutant concentration” are based on the assumption that “concentration” is annual
average concentration. Because of this, annual average concentration was used when (a)“death/disease rates at each endpoint per unit of primary pollutant concentration” wascalculated in a above (see Table 2.4-4).
Table 2.4-9: Method of setting a “death/disease rate at each endpoint per unit of primary pollutant
concentration”
Item

Method of setting(a) Death rate at each endpoint
per unit of primarypollutant concentration

Death rate in Japan by multiplying “increase in death rate at each
endpoint per unit of primary pollutant concentration” under
ExternE by the current situation(a) Disease rate at each
endpoint per unit of primarypollutant concentration

Disease rate at each endpoint per unit of primary pollutant
concentration under ExternE(a) in the Table corresponds to Figure 2.4-6.

18

(2002)

Entire population
Entire population
Entire population

Entry into emergency room (asthma)

Entry into emergency room (inflammation of pharynx/bronchi ofpreschooler)(a) and (c) in the table correspond to Figure 2.4-6(Sources) Extern E (European Commission 1999); Pope et al. (2002); the source for the shaded parts is Hofstetter (1998).

Entire population

Hospitalization for respiratory system

Child
Adult

Days of behavioral restriction

Entry into emergency room (chronic obstructive pulmonary disease)Child

Asthma and child

Lower respiratory symptoms (stridor): child

Chronic coughAsthma and adult

Lower respiratory symptoms (stridor): adult

Chronic bronchitis: child

Asthma and child

Cough: child

Adult

Asthma and adult

Cough: adult

Chronic bronchitis: adult

Asthma and child

0.00593 3.4E-7

Asthma and adult0.001 4.0E-6

Use of bronchodilator: child(2002)

Use of bronchodilator: adult

Chronic death

Targetpopulation

Entire population(c) DALY of 1 Geometric
death/1 disease diffusion
at eachGeometric endpoint
diffusion[DALY/case]

Aged 30 and over

Geometric
diffusion“Increase in death rate at each endpoint per unit of PM10/PM2.5 concentration” Risk=case person–1 yr–1“(a) Disease rate at each endpoint per unit of PM10/PM2.5concentration” [case person–1 yr–1 μg–1 m3]

Acute deathType of respiratory disease(Endpoint)

Entire population

Targetpopulation

Hospitalization for respiratory system(c) DALY of 1 Geometric
death/1 disease diffusion
at eachGeometric endpoint
diffusion[DALY/case]

Entire population

Geometric
diffusion“(a) Disease rate at each endpoint per unit of NO2/SO2 concentration” [case person–1 yr–1 μg–1 m3]“Increase in death rate at each endpoint per unit of NO 2/SO2 concentration” [(Risk/Riskbaseline) μg–1m3], Risk=case person–1 yr–1

Acute deathType of respiratory disease(Endpoint)

Table 2.4-10: “The increase in death rate and the disease rate at each endpoint per unit of primary pollutant”
concentration”

LIME2_C2.4-C2.6_2013

19

LIME2_C2.4-C2.6_2013(b)

Population density in each zone

The population density was fixed for the target population for each endpoint in (a).
Population density was adopted rather than population because the dimension of thecalculated “death/disease rates at each endpoint per unit of primary pollutant concentration” isthe product of the concentration and the area of the ground surface.
(c)

DALY of 1 death/1 disease at each endpoint

Values in Hofsterrer (1998) were used (see Table 2.4-10).
c

Steps 3 and 4: Calculation of the damage function and the damage factor

Steps 3 and 4 in Figure 2.4-3 were used for calculating the damage function and the damage
factor. The concrete procedure is as follows:

Step 3: The “increase in the concentration of primary pollutants due to a unit amount ofprimary pollutant emissions” (∆C/∆E) in Step 1 was multiplied by the “DALY loss ateach endpoint per unit of primary pollutant concentration” (∆DALY each endpoint/∆C) in Step
2 to obtain the damage function at each endpoint (∆DALYeach endpoint/∆E).



Step 4: The damage functions in Step 3 at all the endpoints for respiratory disease weretotaled to obtain the damage factor (∆DALY/∆E).

Table 2.4-11 shows examples of the calculated damage functions and factors (pollution due toNO2 emissions, Tokyo). Table 2.4-12 shows the average prefectural damage factor in eachzone and the national average.
Table 2.4-11: The damage function (∆DALYeach endpoint/∆E) and the damage factor (∆DALY/∆E) for
human health concerning pollution by NO2 due to NO2 emissions: Tokyo
Tokyo
Type of source of emissions

Chimney

Automobile(1)
Acute death(a) Death rate per unit of NO2 concentration** (entire population)[case person–1 yr–1 μg–1 m3](b) Population density [person m–2](c) DALY for acute death [DALY case–1]

Endpoint(2)(3)

Hospitalizati
on forrespiratory
system( =(1)×(2))(a) Disease rate per unit of NO2 concentration (entire population)[case person–1 yr–1 μg–1 m3](b) Population density [person m–2](c) DALY for hospitalization for respiratory system [DALY case –1]

(2)
(3)
(4)

( =(1)×(2))(3)(1) to (4) in the table correspond to Figure 2.4-3; (a) to (c) correspond to Figure 2.4-6.
†1
An increase in NO2 concentration due to a unit amount of NO2 emissions†2“(a) Acute death rate per unit of NO2 concentration” is calculated by “the increase rate of acute deaths per unit of NO2 emissions ×
the current death rate” (see 2.4.3 (2) b (a)).
†3
DALY loss at each endpoint per unit of NO2 emissions

20

LIME2_C2.4-C2.6_2013

Table 2.4-12: The damage factor for human health concerning pollution due to primary pollutant
emissions (ΔDALY /ΔE) [ΔDALY kg-1]: the average prefectural damage factor in each zone and thenational average

Chimney

Automobile

Pollution by SO2
due to SO2emissions
Chimney

1.803E-07

7.514E-07

3.808E-07

4.377E-05

1.825E-04

1.462E-05

6.094E-05

4.329E-07
5.077E-06
8.414E-07
3.339E-06

1.942E-06
2.406E-05
3.978E-06
1.435E-05

9.143E-07
1.072E-05
1.777E-06
7.054E-06

1.052E-04
1.231E-03
2.030E-04
7.975E-04

4.721E-04
5.833E-03
9.598E-04
3.428E-03

3.510E-05
4.118E-04
6.798E-05
2.687E-04

1.575E-04
1.951E-03
3.214E-04
1.155E-03

6.222E-07

2.847E-06

1.314E-06

1.518E-04

6.948E-04

5.058E-05

2.314E-04

9.652E-07

3.909E-06

2.039E-06

2.302E-04

9.320E-04

7.744E-05

3.136E-04

1.637E-06

7.406E-06

3.458E-06

3.946E-04

1.786E-03

1.323E-04

5.987E-04

Pollution by NO2 due toNO2 emissions
Average in
Hokkaido
Average in Tohoku
Average in Kanto
Average in Chubu
Average in Kansai
Average in
Chugoku/ Shikoku
Average in Kyushu/
Okinawa
National average

Pollution by PM2.5 due
to PM2.5 emissions

Pollution by PM10 due
to PM10 emissions

Chimney

Automobile

Chimney

Automobile(Note) Prefectures’ damage factors were averaged in each zone.

(3)

Pollution by secondary pollutants (nitrate salt and sulfate salt)

The following is an explanation according to Steps 1 to 4 in Figure 2.4-3.
aStep 1: Correlation of the amount of primary pollutant emissions and anincrease in the concentration of nitrate/sulfate
An “increase in nitrate/sulfate concentration due to a unit amount of NO2/SO2 emissions”(∆C/∆E) was calculated based on the result of simulation by the use of an air quality model.
(a)Cases used for calculation

Table 2.4-13 shows the cases used for the calculation.
The “source zone” and the “receptor zone” in the table are types of zones used in the
below-described source-receptor matrix formulated by Ikeda and Hagimoto (see Figure
2.4-7).
(b)

Air quality model used for calculation

NO2 and SO2 are converted into nitrate and sulfate respectively through various channels
during long-distant transportation (acidification, chemical reaction with other pollutants, etc.),
and they are partially removed from the atmosphere by deposition. To reflect such polluting
mechanism, the OPU model formulated by Ikeda (2001) was selected as an air quality model
under LIME (see Column 2.4-2).
(c)Calculation procedure

As shown in Figure 2.4-7, a simulation was carried out around Japan, formulated by Ikeda
and Hagimoto, with using (b) Air quality model used for calculation. As a result, the annual
average concentration of nitrate and sulfate in each grid and the average concentration in eachzone were calculated. The source-receptor matrix is a table that shows the relation between
the nitrate and sulfate concentration in each zone (= receptor zone) and NO2/SO2 emissions ineach source zone.

21

LIME2_C2.4-C2.6_2013

Under LIME, an “increase in nitrate/sulfate salt concentration due to a unit amount of
NO2/SO2 emissions” was calculated from the source-receptor matrix. Concretely, because
the OPU model assumes that the relation between the amount of emissions and concentration
is linear, the NO3– concentration in each receptor zone was divided by the amount of NO2emissions in the source zone, and the SO42– concentration in each receptor zone was divided
by the amount of SO2 emissions in the source zone.
(d)Conditions for calculation

The conditions for calculation were set as shown in Table 2.4-14.
Table 2.4-13: Cases used for calculation of an “increase in nitrate/sulfate salt due to a unit amount of
NO2/SO2 emissions” (∆C/∆E)
Case
Zone

6 source zone  6receptor zone
(See Figure 2.4-7)

Time

1 type(Annual average)

Reason for selecting the case
• See the main text.
• Because the annual average concentration has been
assumed as the “concentration” in the “death/disease
rate at each endpoint per unit of nitrate/sulfate”

Figure 2.4-7: Division of zones for source-receptor matrix(Source) Ikeda (2001)

Table 2.4-14: Conditions for calculation of an “increase in nitrate/sulfate concentration due to a unit
amount of NO2/SO2 emissions” (∆C/∆E)
Condition
Mesh width, layer
thickness

22

Content
1 grid was fixed at 80 km × 80 km. The vertical direction was divided into sevenlayers: 1st layer between 0 to 100 m; 2nd layer between 100 to 300 m; 3rd layer
between 300 to 500 m; 4th layer between 500 to 1,000 m; 5th layer between 1,000 to
2,000 m; 6th layer between 2,000 to 3,000 m; 7th layer between 3,000 and 5,000 m.

Amount ofemissions

Values in 1991 calculated by Higashino (1997) were used as the amounts of
NO2/SO2 emissions.

Initial andboundary values

The initial values for nitrate salt and sulfate were fixed at almost 0.

LIME2_C2.4-C2.6_2013

Column 2.4-2Column 2.4-2: Ikeda’s OPU modelNorth America, Europe, and East Asia have the problem that air pollutants emitted fromindustrial districts are transported beyond borders and generate photochemical oxidant andcause oxidization.
The OPU model formulated by Ikeda (2001) is a model for predicting how emitted air
pollutants are transported over a long distance and change all over Japan and East Asia.
Ikeda used this model to assess the effect of measures for controlling the amount of air
pollutant emissions.
This model is a three-dimensional grid model. The transportation, diffusion, chemicalchanges, atmospheric removal, and other phenomena of air pollutants are calculated.
Table 2.4-B shows equations for the calculation.
Table 2.4-B: Equations for the OPU model (Ikeda 2001) used as an air
quality model
Basic equations
Use the following equations:

SO2, particulate
sulfate ion(SO42–), cloudwater sulfate ion
concentration(cloud- SO42–)

Atmospheric SO2 gas concentration [g/m3]

Atmospheric SO42– concentration [g/m3]

Cloud water SO42– concentration (clod– SO42–) [g/m3]
Reaction speed coefficient from SO2 to SO42– [1/h]Absorption speed coefficient of SO2 to cloud water [1/h]Absorption speed coefficient of SO42– to cloud water [1/h]Dry deposition speed coefficient of SO2 [1/h]Dry deposition speed coefficient of SO42– [1/h]Wet deposition (washout) speed coefficient of SO2 [1/h]Wet deposition speed coefficient of SO42– [1/h]Rainwater absorption (rainout) speed coefficient of SO42– [1/h]Use the following equations:

NO, NO2,
Nitric acid gas(HNO3), PAN,
particulate
sulfate ion(NO3–)

3

Atmospheric NO gas concentration [g/m3]

Atmospheric NO2 gas concentration [g/m ]Atmospheric PAN gas concentration [g/m3]

Atmospheric HNO3 gas concentration [g/m3]

Atmospheric particulate NO3– ion concentration [g/m3]
Molar ratio between NO gas and NO2 gas
Reaction speed coefficient from NO2 gas to other substances [1/h]Reaction speed coefficient from PAN gas to other substances (daytime) [1/h]Reaction speed coefficient from PAN gas to other substances (nighttime) [1/h]Dry deposition speed coefficient of NO gas and NO2 gas [1/h]Dry deposition speed coefficient of PAN gas [1/h]Dry deposition speed coefficient of HNO3 gas [1/h]Dry deposition speed coefficient of particulate NO3– ion [1/h]Wet deposition speed coefficient of NO gas and NO2 gas [1/h]Wet deposition speed coefficient of PAN gas [1/h]Wet deposition speed coefficient of HNO3 gas [1/h]Wet deposition speed coefficient of particulate NO3– ion [1/h]23

LIME2_C2.4-C2.6_2013

Table 2.4-15: Source-receptor matrix of NO2 converted emissions and NO3– concentration
Average concentration until a vertical height of 100 m
NO3– average concentration at receptor [μg m-3]
NO2 converted emissions(Source) Personal communication between Ikeda and Hagimoto

Table 2.4-16: Source-receptor matrix of SO2 converted emissions and SO42– concentration
Average concentration until a vertical height of 100 m
SO42– average concentration at receptor [μg m-3]
SO2 converted emissions(Source) Personal communication between Ikeda and Hagimoto(e)

Calculation results(i)Concentration distribution

Ikeda and Hagimoto’s source-receptor matrix shows the comparison between the average
concentration of NO3– and SO42– in each receptor zone and the amount of NO2/SO2 emissions
in each source zone (see Tables 2.4-15 and 2.4-16).
The matrix shows that the concentration in the zone where the emission occurred is thehighest, and the high concentration area has extended to the surroundings.
(ii)

Increase in nitrate/sulfate concentration due to a unit amount of NO2/SO2 emissions

Under LIME, the concentration in each receptor zone in (i) is divided by the amount ofemissions in each source zone to calculate an “increase in the concentration of NO3–/SO42–
due to a unit amount of NO2/SO2 emissions” (see Table 2.4-17 and Table 2.4-18).
As shown in these tables, compared with the NO3– concentration due to the emission of 1 [kgyr–1] of NO2, the SO42– concentration has spread further due to the emission of 1 [kg yr–1] of
SO42–. This indicates that the time necessary for SO2’s change into sulfate salt is longer than
the time necessary for NO2’s change into nitrate and that SO2 changes into sulfate after beingtransported to a more distant place.

24

LIME2_C2.4-C2.6_2013

Table 2.4-17: “Increase in NO3– concentration due to a unit amount of NO2 emissions”(∆C/∆E) [μg m-3 kg-1 yr]

Table 2.4-18: “Increase in SO42– concentration due to a unit amount of SO2 emissions”(∆C/∆E) [μg m-3 kg-1 yr](a) Disease rate per unit ofnitrate/sulfate salt
concentration(a) Death rate per unit ofnitrate/sulfate salt
concentration
Death rate(b) Population in eachzone (person)

Multiplication

Diseaserate(b) Population in eachzone (person)

Multiplication

Population
Population

No. ofdeaths(c) DALY per death ateach endpoint (byuse of Hofstetter(1998))

Multiplication

DALY per
death
DALY
loss

No. ofsufferers
Multiplication

DALY perdisease(c) DALY per disease ateach endpoint (byuse of Hofstetter(1998))DALY
loss

Endpoint[Chronic death] or [acute death]

Endpoint
Each disease, such as [use of bronchodilator]

Figure 2.4-8: Flowchart of calculation of “DALY loss at each endpoint per unit of nitrate/sulfate salt
concentration” (∆DALYeach endpoint /∆C)(a), (b), and (c) in the chart correspond to headings in the main text.

25

LIME2_C2.4-C2.6_2013

Table 2.4-19: “The increasing rate of the death rate and the disease rate at each endpoint per unit of nitrate/sulfate salt concentration”
Type of respiratory disease (endpoint)

Acute death
Chronic death“Increasing death rate at each endpoint per unit of nitrate/sulfate salt concentration”[(Risk/Riskbaseline) μg–1m3], Risk=case person–1 yr–1“(a) Disease rate at each endpoint per unit of nitrate/sulfate salt concentration”[case person–1 yr–1μg–1m3]
Geometric
Nitrate/sulfate †diffusion
σ2
Spix et al. (1996),Verhorf et al. (2002)
0.00068
16.0
0.00643
16.0（Nitrate）Pope et al. (1995)
0.00769（Sulfate）Pope et al. (2002)
1.1e ｰ 6
Dusseldorp et al. (1995)
0.272
16.0
Roemer et al. (1993)
0.129
16.0
Dusseldorp et al. (1995)
0.280
6.0
Pope et al. (1992)
0.223
6.0
Dusseldorp et al. (1995)
0.101
6.0
Roemer et al. (1993)
0.172
6.0
Abbey et al. (1995)
7.800E-05
16.0
Dockery et al. (1989)
2.690E-03
6.0
Dockery et al. (1989)
3.460E-03
16.0
Ostro (1987)
0.042
16.0
Dab et al. (1996)
3.460E-06
6.0

Use of bronchodilator: adult
Use of bronchodilator: child
Cough: adult
Cough: child
Lower respiratory symptoms (stridor): adult
Lower respiratory symptoms (stridor): child
Chronic bronchitis: adult
Chronic bronchitis: child
Chronic coughDays of behavioral restriction
Hospitalization for respiratory system
Entry into emergency room (chronic obstructive pulmonary
Sunyer et al. (1993)
disease)Entry into emergency room (asthma)
Schwarts (1993), Bates et al. (1990)
Entry into emergency room (inflammation of pharynx/bronchi
Schwarts et al. (1991)
of preschooler)(a) and (c) in the table correspond to Figure 2.4-8.
† Except for Pope et al. (2002), values for PM2.5 under ExternE were applied.
(Sources) Extern E (European Commission 1999); Pope et al. (2002); the source for the shaded parts is Hofstetter (1998).

(c) DALY of 1 death/1 Geometricdisease at eachdiffusion
endpoint
σ2[DALY/case]Target population

0.75

5.0

Entire population

6.6

3.0

Aged 30 and over

0.000270.000270.000140.000140.000140.00014
2
0.025
0.025
0.00027
0.011

2.0
2.0
2.0
2.0
2.0
2.0
3.0
3.0
3.0
2.0
2.0

Asthma/adult
Asthma/ child
Asthma/ adult
Asthma/ child
Asthma/ adult
Asthma/child
Adult
Child
Child
Adult
Entire population

1.200E-05

36.0

0.00082

3.0

Entire population

1.080E-05

36.0

0.00082

3.0

Entire population

4.860E-05

36.0

0.00082

3.0

Entire population

26

LIME2_C2.4-C2.6_2013

Table 2.4-20: The damage function (∆DALYeach endpoint/∆E) and the damage factor (∆DALY/∆E) for
human health concerning pollution by nitrate salt due to NO2 emissions: pollution by nitrate in Zone
3 due to NO2 emissions in Zone 4

(1)

†1

(a) Death rate per unit of nitrate salt concentration†2(Entire population) [case person-1 yr-1 μg-1 m3]

Acute death(b) Entire population (person)(c) DALY for acute death [DALY case-1]†3

Endpoint(2)(3)

Chronic death(= (1)(2))(3)(a) Disease rate per unit of nitrate salt concentration(Asthmatic adult) [case person-1 yr-1 μg-1 m3]

Use of bronchodilator: adult(b) Population of asthmatic adults(c) DALY for use of bronchodilator [DALY case-1]
(2)

†3

(3)

Use of bronchodilator: child(3)

Cough: adult(3)

Cough: child(3)

Lower respiratory symptoms(Stridor): adult(3)

Lower respiratory symptoms(Stridor): child(3)

Chronic bronchitis: adult(3)

Chronic bronchitis: child(3)

Chronic cough(3)

Days of behavioral restriction(3)

Hospitalization for respirator system(3)(= (1)(2))

Entry into emergency room (chronic (3)obstructive pulmonary disease)
Entry into emergency room (asthma)(3)

XXXroom
Entry into emergency(3)(inflammation of pharynx/bronchi ofpreschooler)
(4)

(3)

(1) to (4) in the table correspond to Figure 2.4-3; (a) to (c) correspond to Figure 2.4-8.
†1 An increase in NO3– concentration due to 1kg yr–1 of NO2 emissions†2 “(a) Acute death rate per unit of nitrate salt concentration” is calculated by “the increase rate of acute deaths per unit of nitrate salt
concentration × current death rate.” The same applies to chronic death (see 2.4.3 (3) b (a)).
With regard to (a), the values in Table 2.4-19 are converted into values per unit of NO3– concentration (NH4NO3 is used as nitrate
salt and converted by the use of the mass ratio with NO3–). This is because (1) in the table expresses an increase in NO3–
concentration due to 1 kg yr–1 of NO2 emissions.
†3 DALY loss at each endpoint per unit of NO3– concentration

27

LIME2_C2.4-C2.6_2013b
Step 2: Correlation of an increase in the concentration of nitrate/sulfate withDALY loss at each endpoint
According to Step 2 in Figure 2.4-3, “DALY at each endpoint per unit of nitrate/sulfate
concentration” (∆DALYeach endpoint/∆C) was calculated.
The calculation flowchart is as shown in Figure 2.4-8, and is the same as in the case of the
primary pollutants in 2.4.3 (2).
The following is an explanation of (a) to (c) in Figure 2.4-8.
(a)Death/disease rates at each endpoint per unit of nitrate/sulfate salt concentration

Like pollution by primary pollutants (see 2.4.3 (2)), the rates were fixed by the use of theresults of the epidemiological survey used for ExternE (European Commission 1999) (seeTable 2.4-19). However, with regard to chronic death by sulfate salt, reexamination was
carried out after LIME1, and a rate was fixed based on a new document (Pope et al. 2002).
(b)

Population in each zone

A population is fixed for the target demographic group at each endpoint in (a) (see Table
2.4-19).
(c)

DALY of 1 death/1disease at each endpoint

Like the pollution by primary pollutants in 2.4.3 (2), values by Hofstetter (1998) were used(see Table 2.4-19).
c

Steps 3 and 4: Calculation of damage functions and damage factors

Steps 3 and 4 in Figure 2.4-3 were used for calculating damage functions and damage factors.
The concrete procedure is as follows:•Steps 3: The “increase in the concentration of nitrate/sulfate due to a unit amount of
NO2/SO2 emissions” (∆C/∆E) in Step 1 was multiplied by the “DALY at each endpoint perunit of the concentration of nitrate/sulfate” (∆DALYeach endpoint/∆C) in Step 2 to obtain thedamage function at each endpoint (∆DALYeach endpoint/∆E).
•Step 4: The damage functions in Step 3 at all the endpoints for respiratory disease weretotaled to obtain the damage factor (∆DALY/∆E).
Table 2.4-20 shows examples of the calculated damage functions and factors (pollution bynitrate in Zone 3 when NO2 is emitted in Zone 4). Zones 3 and 4 are located as shown in
Figure 2.4-7.
In addition, table 2.4-21 shows the average prefectural damage factor in each zone and thenational average.

28

LIME2_C2.4-C2.6_2013

Table 2.4-21: The damage factor for human health concerning pollution by nitrate/sulfate salt due toNO2/SO2 emissions (∆DALY/∆E) [∆DALY kg–1]
The average prefectural damage factor in each zone and the national average
Zone
Average in Hokkaido
Average in Tohoku
Average in Kanto
Average in Chubu
Average in Kansai
Average in
Chugoku/Shikoku
Average inKyushu/Okinawa
National average

Pollution by nitrate salt due toemission of 1 kg of NO2
1.148E-06
9.112E-06
1.991E-05
1.732E-05
1.627E-05

Pollution by sulfate salt due toemission of 1 kg of SO2
1.088E-05
8.878E-05
1.479E-04
1.668E-04
1.807E-04

1.541E-05

1.576E-04

1.128E-05

4.582E-05

1.292E-05

1.141E-04(Note) Prefectures’ damage factors were averaged in each zone.

(4)

Uncertainty assessment

The damage factors for primary and secondary pollutants obtained in (2) and (3) were totaled
to calculate four types of damage factors (damage factors for NO2 emissions, SO2 emissions,PM2.5 emissions, and PM10 emissions). Uncertainty assessment of each of these factors
was carried out and the obtained statistical data (median, etc.) were used as the final damage
factors.
The method for uncertainty assessment was as follows:
・With regard to both primary pollutants and secondary pollutants, the calculation of damage
factors had two steps: “Step 1: correlation of the amount of primary pollutant emissions withan increase in the concentration of primary and secondary pollutants” and “Step 2: correlationof an increase in the atmospheric concentration of primary and secondary pollutants withDALY loss at each endpoint.” During each of the steps, uncertainty factors were extracted
and expressed as a distribution (see Table 2.4-22).
・Although the damage factor for each zone was calculated for the seven zones (Hokkaido,
Tohoku, Kanto, Chubu, Kansai, Chugoku/Shikoku, Kyushu/Okinawa), because emission
zones are often unknown from inventory, the uncertainty factor was used. A distribution
was expressed on the assumption that emissions in each of the prefectures in each zone could
occur with the same probability.
To examine the distribution for each of the four types of damage factors (damage factors forNO2 emissions, SO2 emissions, PM2.5 emissions, and PM10 emissions), a Monte Carlosimulation was carried out by applying random numbers of a normal distribution to the causeof uncertainty and the zones in Table 2.4-22. Figure 2.4-10 shows examples of the obtained
distributions, and Table 2.4-24 shows the statistical data.
In addition, to analyze the cause of deviation of damage factor values and the contributions toit, Spearman rank-correlation coefficients were calculated. Table 2.4-25 shows examples.
Moreover, Figure 2.4-11 shows comparison between LIME1 and LIME2 in damage factor.
29

LIME2_C2.4-C2.6_2013

Table 2.4-22: Method for uncertainty assessment of damage factors for urban air pollution

Damage factors
for NO2emissions• Chimney• Automobile

Damage factors
for SO2emissions• Chimney• Automobile
Damage factors
for PM2.5
emissions• Chimney• Automobile
Damage factors
for PM10
emissions• Chimney• Automobile

2.4.3 (2)Pollution by primary pollutants
2.4.3 (2) a
Step 1: Correlation of the amount ofprimary pollutant emissions with anincrease in the concentration of primarypollutants
Assessment
Uncertainty factor
methodThe “increase in the
Uncertainty wasconcentration of
expressed by aprimary pollutants
standard
due to a unit amount deviation ofof primary pollutant
values according
emissions” in Table
to the weather
2.4-8 is an annual
patterns
average. In reality,
mentioned in thethe value differs
left column
according to weather (Table 2.4-23).
patterns. The
existence of such
weather patterns is
the uncertainty
factor.

2.4.3 (2) b
Step 2: Correlation of an increase in theamount of primary pollutants in theatmosphere with DALY loss at each
endpoint
Assessment
Uncertainty factor
methodUncertainty was
Uncertainty wasconsidered in
expressed byrelation to “(a) thegeometric standard
increasing rate ofdeviation in (a)the death rate andand (c) in the left
the disease rate per column byunit of primary
Hofstetter (1998)
pollutant(see Table 2.4-10).
concentration” and“(c) DALY foreach respiratory
disease,”components of“relation between
an increase in theatmosphericconcentration ofprimary pollutants
and DALY at each
endpoint.”-

2.4.3 (3)Pollution by secondary pollutants
2.4.3 (3) a
Step 1: Correlation of the amount ofprimary pollutant emissions with anincrease in the concentration of secondary
pollutants

2.4.3 (3) b
Step 2: Correlation of an increase in theconcentration of secondary pollutants inthe atmosphere with DALY loss at each
endpoint
Uncertainty
Assessment methodfactor
The same as in
The same as in thethe case ofcase of pollution by
pollution by
primary pollutants
primary(see Table 2.4-19)
pollutants

Uncertainty factor

Assessment method

The uncertainty
factor is whether
the model has
reproduced air
pollution
concentration.

Uncertainty was
expressed by thedifference between
the calculated values
obtained through theverification of thecurrent situation
reproducibility of the
OPU model (Ikeda
2001; see Figure
2.4-9) and theobserved values(standard variation:2.20).

-

-

-

-

-

-

-

-

(Numbers in italics correspond to headings of the main text.)

30

LIME2_C2.4-C2.6_2013

Table 2.4-23: Standard deviation of factors for an increase in primary pollutants according toweather patterns
Zone
Hokkaido
Tohoku
Kanto
Chubu
Kansai
Chugoku/Shikoku
Kyushu/Okinawa

ChimneyStandard deviation
9.18E+2
9.40E+2
9.58E+2
8.60E+2
9.18E+2
8.97E+2
9.44E+2

Automobile
Standard deviation
5.48E+3
5.74E+3
6.07E+3
5.02E+3
5.48E+3
5.31E+3
5.31E+3

2SO442– concentration(January)SO
濃度（１月）

SO4 2concentration (May)SO
4 濃度（５月）
2–value [μg/m3]
Calculated
計算値[ug/m3]

計算値[ug/m3]

Calculated value [μg/m3]

20
15
10
5
0

20
15
10
5
0

0

5
10
15
Observed
value [μg/m3]
観測値[ug/m3]

20

0

5
10
15
観測値[ug/m3]

20

Observed value [μg/m3]

Figure 2.4-9: Verification of the current situation reproducibility of the OPU model(Source) Ikeda (2001)

31

LIME2_C2.4-C2.6_2013

50000 times

Frequency distribution

Indicated value

Frequency

Probability

Urban air pollution NOx point source (Kanto)

2.00E-0.4

Chimney

50000 times

Frequency distribution

Indicated value

Frequency

Probability

Urban air pollution NOx line source (Kanto)

3.00E-03
Automobile
Figure 2.4-10: Damage factor distribution obtained from uncertainty assessment of urban air pollution:
NO2, Kanto

32

LIME2_C2.4-C2.6_2013

Chimney

Table 2.4-24: Statistical data of damage factors for urban air pollution

SO2

Chimney/Automobile

Automobile

NO2

Zone

No. ofCalculation

Representativevalue(Median)mean valueStandard
deviation

dispersion

Kurtosis

Coefficientof variance

10-percentil
e value

90-percentil
e valueJapan
Hokkaido
Tohoku
Kanto
Chubu
Kansai
Chugoku/Shikoku
Kyushu/Okinawa
Tochigi
Tokyo
Japan
Hokkaido
Tohoku
Kanto
Chubu
Kansai
Chugoku/Shikoku
Kyushu/Okinawa
Tochigi
Tokyo
Japan
Hokkaido
Tohoku
Kanto
Chubu
Kansai
Chugoku/Shikoku
Kyushu/Okinawa
Tochigi
Tokyo

50000
50000
50000
50000
50000
50000
50000
50000
50000
50000
50000
50000
50000
50000
50000
50000
50000
50000
50000
50000
50000
50000
50000
50000
50000
50000
50000
50000
50000
50000

1.20E-05
1.21E-06
7.73E-06
2.10E-05
1.44E-05
1.60E-05
1.21E-05
1.06E-05
1.64E-05
3.62E-05
2.13E-05
1.84E-06
9.12E-06
4.15E-05
2.08E-05
2.85E-05
1.68E-05
1.49E-05
2.21E-05
8.18E-05

2.75E-05
2.63E-06
1.62E-05
5.06E-05
2.88E-05
3.94E-05
2.24E-05
2.21E-05
3.03E-05
1.31E-04
1.18E-04
6.72E-06
2.50E-05
2.38E-04
6.22E-05
1.72E-04
4.13E-05
4.97E-05
5.26E-05
5.12E-04

1.07E-04
8.42E-06
3.97E-05
2.02E-04
1.06E-04
1.75E-04
4.53E-05
6.33E-05
6.12E-05
7.57E-04
1.12E-03
3.93E-05
1.11E-04
1.95E-03
3.74E-04
1.37E-03
2.45E-04
3.04E-04
1.98E-04
3.56E-03

1.14E-08
7.09E-11
1.58E-09
4.07E-08
1.13E-08
3.07E-08
2.05E-09
4.00E-09
3.75E-09
5.73E-07
1.25E-06
1.55E-09
1.24E-08
3.81E-06
1.40E-07
1.87E-06
6.02E-08
9.25E-08
3.92E-08
1.27E-05

2.33E+03
2.19E+03
1.79E+03
1.94E+03
9.24E+03
2.72E+03
4.28E+02
1.25E+03
6.12E+02
2.66E+03
6.78E+03
1.63E+03
1.76E+03
5.47E+03
4.66E+03
2.01E+03
9.26E+03
5.95E+03
1.34E+03
3.81E+03

3.88E+00
3.21E+00
2.45E+00
3.99E+00
3.69E+00
4.45E+00
2.02E+00
2.86E+00
2.02E+00
5.78E+00
9.47E+00
5.85E+00
4.45E+00
8.22E+00
6.01E+00
7.94E+00
5.94E+00
6.12E+00
3.76E+00
6.96E+00

2.70E-06
4.06E-07
2.19E-06
6.93E-06
5.03E-06
5.35E-06
4.19E-06
3.68E-06
5.85E-06
1.03E-05
4.81E-06
5.38E-07
2.59E-06
1.05E-05
6.61E-06
7.65E-06
5.51E-06
4.57E-06
7.32E-06
1.71E-05

5.15E-05
5.01E-06
3.34E-05
9.05E-05
5.53E-05
6.85E-05
4.39E-05
4.16E-05
5.99E-05
2.14E-04
1.55E-04
1.06E-05
4.53E-05
3.43E-04
1.02E-04
2.26E-04
7.21E-05
7.81E-05
9.54E-05
8.11E-04

standard
margin oferror deviation
4.77E-07
3.77E-08
1.78E-07
9.02E-07
4.75E-07
7.84E-07
2.03E-07
2.83E-07
2.74E-07
3.38E-06
5.00E-06
1.76E-07
4.98E-07
8.73E-06
1.67E-06
6.12E-06
1.10E-06
1.36E-06
8.85E-07
1.59E-05

1.49E-04
1.84E-05
1.48E-04
2.32E-04
2.62E-04
2.96E-04
2.29E-04
7.95E-05
2.17E-04
2.95E-04

2.64E-04
2.76E-05
2.49E-04
3.86E-04
3.91E-04
4.46E-04
3.55E-04
1.19E-04
3.54E-04
5.52E-04

4.91E-04
4.02E-05
4.30E-04
7.90E-04
5.89E-04
6.99E-04
5.84E-04
1.81E-04
5.95E-04
1.35E-03

2.41E-07
1.62E-09
1.85E-07
6.24E-07
3.47E-07
4.88E-07
3.41E-07
3.28E-08
3.54E-07
1.83E-06

9.80E+02
2.80E+02
7.97E+02
2.50E+03
1.71E+03
1.29E+03
1.39E+03
1.00E+03
7.26E+02
9.94E+02

1.86E+00
1.45E+00
1.72E+00
2.05E+00
1.51E+00
1.57E+00
1.65E+00
1.52E+00
1.68E+00
2.45E+00

2.19E-05
7.55E-06
5.40E-05
8.21E-05
1.06E-04
1.20E-04
6.53E-05
3.24E-05
7.70E-05
1.02E-04

5.76E-04
5.17E-05
4.97E-04
7.57E-04
7.45E-04
8.46E-04
7.14E-04
2.24E-04
7.01E-04
1.05E-03

2.20E-06
1.80E-07
1.92E-06
3.53E-06
2.64E-06
3.13E-06
2.61E-06
8.11E-07
2.66E-06
6.05E-06

33

LIME2_C2.4-C2.6_2013

No. ofCalculation

Representativevalue(Median)mean valueStandard
deviation

dispersion

Kurtosis

Coefficientof variance

10-percentil
e value

90-percentil
e value

Japan

50000

1.93E-04

5.77E-04

2.46E-03

6.04E-06

8.54E+03

4.26E+00

4.00E-05

1.12E-03

standard
margin oferror deviation
1.10E-05

Hokkaido

50000

4.70E-05

7.93E-05

1.33E-04

1.78E-08

4.47E+02

1.68E+00

1.48E-05

1.61E-04

5.96E-07

Tohoku50000

1.16E-04

2.17E-04

4.11E-04

1.69E-07

4.52E+02

1.90E+00

3.26E-05

4.54E-04

1.84E-06

Kanto

50000

7.43E-04

1.76E-03

4.86E-03

2.36E-05

1.35E+03

2.75E+00

1.64E-04

3.77E-03

2.17E-05

Chubu

50000

2.10E-04

4.91E-04

1.17E-03

1.37E-06

9.20E+02

2.39E+00

5.30E-05

1.08E-03

5.24E-06

Kansai50000

3.88E-04

1.45E-03

4.20E-03

1.77E-05

4.77E+02

2.91E+00

8.50E-05

3.43E-03

1.88E-05

Chugoku/Shikoku

50000

1.80E-04

3.14E-04

5.69E-04

3.24E-07

4.06E+02

1.82E+00

5.38E-05

6.37E-04

2.55E-06

Kyushu/Okinawa

50000

2.89E-04

5.41E-04

1.07E-03

1.14E-06

4.75E+02

1.97E+00

7.52E-05

1.14E-03

4.77E-06

Tochigi

50000

2.28E-04

3.76E-04

5.81E-04

3.37E-07

1.89E+02

1.55E+00

7.40E-05

7.58E-04

2.60E-06

Tokyo

50000

3.96E-03

6.65E-03

1.13E-02

1.28E-04

3.09E+02

1.70E+00

1.27E-03

1.34E-02

5.06E-05

Japan

50000

1.33E-03

6.18E-03

2.06E-02

4.24E-04

1.17E+03

3.33E+00

1.92E-04

1.43E-02

9.21E-05

Hokkaido

50000

1.69E-04

3.34E-04

7.65E-04

5.86E-07

2.38E+03

2.29E+00

4.32E-05

6.97E-04

3.42E-06

Tohoku50000

4.40E-04

9.07E-04

2.09E-03

4.38E-06

1.49E+03

2.31E+00

1.08E-04

1.94E-03

9.35E-06

Kanto

50000

5.26E-03

1.38E-02

3.64E-02

1.33E-03

1.11E+03

2.64E+00

7.50E-04

3.13E-02

1.63E-04

Chubu

50000

9.84E-04

2.64E-03

7.37E-03

5.43E-05

1.96E+03

2.79E+00

2.02E-04

5.90E-03

3.29E-05

Kansai50000

2.55E-03

9.70E-03

2.70E-02

7.31E-04

2.95E+02

2.79E+00

4.04E-04

2.29E-02

1.21E-04

Chugoku/Shikoku

50000

6.90E-04

1.36E-03

2.94E-03

8.63E-06

1.19E+03

2.15E+00

1.69E-04

2.87E-03

1.31E-05

Kyushu/Okinawa

50000

9.16E-04

2.24E-03

5.72E-03

3.27E-05

2.48E+03

2.55E+00

1.80E-04

4.98E-03

2.56E-05

Tochigi

50000

9.86E-04

1.82E-03

3.80E-03

1.44E-05

1.43E+03

2.09E+00

2.73E-04

3.76E-03

1.70E-05

Tokyo

50000

1.70E-02

3.14E-02

6.19E-02

3.83E-03

4.57E+02

1.97E+00

4.72E-03

6.48E-02

2.77E-04

Japan

50000

2.38E-05

4.94E-05

1.56E-04

2.42E-08

8.99E+03

3.15E+00

6.99E-06

9.98E-05

6.96E-07

Hokkaido

50000

2.38E-05

4.94E-05

1.56E-04

2.42E-08

8.99E+03

3.15E+00

6.99E-06

9.98E-05

6.96E-07

Tohoku50000

5.40E-05

1.09E-04

2.70E-04

7.29E-08

1.49E+03

2.47E+00

1.60E-05

2.19E-04

1.21E-06

Kanto

50000

1.81E-04

3.66E-04

1.01E-03

1.03E-06

4.89E+03

2.77E+00

5.47E-05

7.37E-04

4.53E-06

Chubu

50000

6.99E-05

1.40E-04

3.23E-04

1.04E-07

1.14E+03

2.31E+00

2.02E-05

2.84E-04

1.44E-06

Kansai50000

9.41E-05

1.92E-04

4.32E-04

1.87E-07

5.05E+02

2.25E+00

2.70E-05

3.92E-04

1.93E-06

Chugoku/Shikoku

50000

5.74E-05

1.16E-04

3.07E-04

9.40E-08

6.71E+03

2.65E+00

1.69E-05

2.34E-04

1.37E-06

Kyushu/Okinawa

50000

3.22E-04

6.60E-04

1.53E-03

2.33E-06

8.57E+02

2.31E+00

9.36E-05

1.34E-03

6.82E-06

Tochigi

50000

1.16E-04

2.32E-04

5.12E-04

2.63E-07

9.08E+02

2.21E+00

3.49E-05

4.74E-04

2.29E-06

Tokyo

50000

1.05E-02

1.09E-04

3.68E+03

2.56E+00

5.95E-04

8.19E-03

4.67E-05

PM
10

AutomobilePM
2.5

ChimneyPM
2.5

ChimneyZone

2.01E-03

4.08E-03

34

LIME2_C2.4-C2.6_2013

No. ofCalculation

Representativevalue(Median)mean valueStandard
deviation

dispersion

Kurtosis

Coefficientof variancet

10-percentil
e value

90-percentil
e value

Japan

50000

8.70E-05

2.06E-04

6.98E-04

4.87E-07

9.77E+03

3.39E+00

2.05E-05

4.21E-04

standard
margin oferror deviation
3.12E-06

Hokkaido

50000

8.70E-05

2.06E-04

6.98E-04

4.87E-07

9.77E+03

3.39E+00

2.05E-05

4.21E-04

3.12E-06

Tohoku50000

2.19E-04

4.88E-04

1.49E-03

2.21E-06

3.83E+03

3.05E+00

5.41E-05

1.00E-03

6.65E-06

Kanto

50000

7.86E-04

1.72E-03

4.36E-03

1.90E-05

7.74E+02

2.54E+00

2.00E-04

3.48E-03

1.95E-05

Chubu

50000

2.79E-04

6.34E-04

2.95E-03

8.70E-06

2.11E+04

4.65E+00

6.96E-05

1.28E-03

1.32E-05

Kansai

50000

4.12E-04

9.13E-04

3.05E-03

9.30E-06

1.31E+04

3.34E+00

1.04E-04

1.87E-03

1.36E-05

Chugoku/Shikoku

50000

2.40E-04

5.28E-04

1.30E-03

1.68E-06

4.72E+02

2.46E+00

6.02E-05

1.09E-03

5.80E-06

Kyushu/Okinawa

50000

1.15E-03

2.70E-03

8.34E-03

6.96E-05

2.12E+03

3.09E+00

2.75E-04

5.53E-03

3.73E-05

Tochigi

50000

4.98E-04

1.10E-03

2.62E-03

6.87E-06

8.96E+02

2.38E+00

1.31E-04

2.28E-03

1.17E-05

Tokyo

50000

8.51E-03

1.92E-02

4.76E-02

2.27E-03

4.53E+02

2.48E+00

2.19E-03

3.96E-02

2.13E-04

PM
10

Automobile

Zone

35

LIME2_C2.4-C2.6_2013

Table 2.4-25: Rank correlation coefficients of damage factors for air pollution (example)
NO2: chimney

Rank correlation
coefficient
0.541
0.429
0.207
0.204
0.202
0.151
0.102

Uncertainty item
Disease rate per unit of nitrate concentration (chronic bronchitis: adult)
Disease rate per unit of NO2 concentration (acute death)
DALY (acute death)
Disease rate per unit of nitrate salt concentration (chronic death)
DALY (chronic bronchitis: adult)
NOx emission zone: point source (Kanto)
Disease rate per unit of nitrate salt concentration (chronic cough)NO2: automobile

Rank correlation
coefficient
0.623
0.303
0.297
0.279
0.119
0.100

Uncertainty item
Disease rate per unit of NO2 concentration (acute death)NOx emission zone: line source (Kanto)
Disease rate per unit of nitrate salt concentration (chronic bronchitis: adult)
DALY (acute death)
DALY (chronic bronchitis: adult)
Disease rate per unit of nitrate salt concentration (chronic death)

0.04

LIME2[DALY kg-1]

0.03

0.02

0.01

0.00
0

0.01

0.02

0.03

0.04

-1

LIME1[DALY kg ]Figure 2.4-11: Comparison between LIME1 and LIME2 in damage factor
The median obtained from uncertainty analysis is used as the damage factor under LIME2(see Table 2.4-24).

36

LIME2_C2.4-C2.6_2013

2.4.4

Procedure for impact assessment of urban air pollution

LCA users can select what meets their purpose from among characterization, damage
assessment, and weighting and use it for LCA, etc.
(1)

Characterization

The result of characterization CIAirPollution (X) can be obtained from the inventory Inv (X) of theatmospheric emissions of the primary pollutant X (NO2 or SO2) and the characterization factor
CFAirPollution (X) (see Equation 2.4-2).
CI AirPollution   IF AirPollution  X   Inv X (2.4-2)X

The characterization factor CIAirPollution (X) is shown in Appendix A1.
(2)

Damage assessment, weightingaDamage assessment

The result of damage assessment DI (Safe) can be obtained from Inv (X, Region, Source) of
urban air pollutants and the damage factor for each area of protection Safe DFAirPollution (Safe,
X, Region, Source) (see Equation 2.4-3). DI (Safe) means the amount of potential damage toSafe, the area of protection from air pollutant emissions.
DI ( Safe)  X  DF

AirPollution( Safe，X , Region, Source)  Inv ( X , Region, Source)

Region Source(2.4-3)
When an LCA user carries out damage assessment for urban air pollution, inventory data must
be matched with the form of damage factor as follows:<Division by the source of emissions and the particle diameter>
・The sources of emissions of NOx and PM should be divided into chimneys and automobiles.
・Inventory should be calculated in the form of PM2.5 or PM10. An inventory database may
be presented in the form of soot or dust. In such a case, it is necessary to convert theinventory data into PM2.5 or PM10 before applying the data to LIME.
<Division by zone>
・It is desirable to divide inventory data by emission zone. However, if it is impossible todivide inventory data by emission zone, assessment can be made by the use of the averagedamage factor in Japan DFAirPollution (Safe, X, Average, Source) (see Equation 2.4-4).
DI ( Safe)    DF AirPollution ( Safe，
X , Average, Source)  Inv ( X , Source)
X Source(2.4-4）37

LIME2_C2.4-C2.6_2013b

Integration

As in the case of a, in this impact category “urban air pollution,” damage assessment can be
carried out for human health. If an area of protection is common to two or more impact
categories, comparison and integration are possible.
When integration is carried out, the integration factor IFAirPollution (X) is used after economic
conversion or non-dimensionalization of the impact on human health. The single index SI
can be obtained from each pollutant’s Inv (X) and the integration factor IFAirPollution (X) (seeEquation 2.4-5). The obtained result can be compared directly or added to assessment
results for other impact categories.

SI   IF AirPollution (X)  Inv(X)(2.4-5）

X

Appendix A2 shows the damage factors DFAirPollution (Safe, X, Region, Source) and
DFAirPollution (Safe, X, Average, Source). Appendix A3 shows the integration factor
IFAirPollution (X).

Acknowledgement
For the purpose of the development of this LCIA method, the source-receptor matrix fornitrate and sulfate became available through the kindness of Mr. Yuko Ikeda, Professor
Emeritus of Osaka Prefecture University, and Mr. Hagimoto (Osaka Prefecture University atthat time). We are very grateful for their kindness.

[References for Section 2.4]
Busch, Buschbom RL, Cannon WC, Lauhala KE, Miller FJ, Graham JA, Smith LG (1986): Effects ofammonium nitrate aerosol exposure on ling structure of normal and elastase – impaired rats and guinea pigs,
Environ. Res., 39: 237 - 252
European Commission (1999): ExternE: Externalities of Energy, Vol. 7 Methodology 1998 update, Office for
Official Publications of the European Communities
Gifford FA (1961): Use of Routine Meteorological Observations for Estimating Atmospheric Dispersion,
Nuclear Safety, 2 (4)Goedkoop, Spriensma (2000): The Eco─indicator 99, A Damage Oriented Method for Life Cycle Impact
Assessment, Methodology Report 2nd edition
Hofstetter P (1998): Perspectives in Life Cycle Impact Assessment; A Structured Approach to Combine Modelsof the Technosphere, Ecosphere, and Valuesphere, Kluwers Academic Publishers
Holland (1953): A Meteorological Survey of the Oak Ridge Area, Atomic Energy Comm. Report ORO─99,
Washington D.C.
Loscutoff SM, Cannon WC, Buschbom RL, Busch RH, Kirkland BW (1985): Pulmonary function inelastase-treated guinea pigs and rats exposed to ammonium sulfate or ammonium nitrate aerosols, Environ. Res.,
36:170 - 180

38

LIME2_C2.4-C2.6_2013

Pepelko WE, Mattox JK and Cohen AL (1980): Toxicology of ammonium sulfate in the lung. Bull. Environm.
Contam. Toxicol. 24, pp.156-160.
Pope, CA, Burnett, RT, Thun, MJ, Calle, EE, Krewski, D, Ito, K & Thurston, GD (2002): Lung cancer,
cardiopulmonary mortality, and long-term exposure to fine particulate air pollution, Journal of the American
Medical Association, vol. 287, no. 9, pp. 1132-1141.
Steen B (1999): A Systematic Approach to Environmental Priority Strategies in Product Development (EPS)
Version 2000 - Models and Data of the Default Method, CPM Report 1999:5, Chalmers University ofTechnology, Göteborg, p. 312
Turner (1964): A diffusion Model for an Urban Area, J. of Appl. Met., 3 (1)Ikeda Y (2001): Establishment of Comprehensive Measures for Control of the Amount of Air Pollutant
Emissions in the Whole East Asia, Report on Research Results for FY1997 to FY2000 Scientific Research
Subsidies (Basic Research (B) (1)) (Mar. 2001)
Ohara T, Uno I (1997): Numerical Simulation of Mesoscale Air Pollution, Journal of Japan Society for
Atmospheric Environment, 32 (3): A39 - A57
Kasahara M (1994): Behavior of Particles in the Atmosphere – Formation, Transformation, Removal, Journal ofJapan Society for Atmospheric Environment, 29 (6): A93 - A101
Kawamura T, Iwaki H (ed.) (1988): 2 Atmospheric Environment, Environmental Science I, Natural Environment
System, Asakura Publishing
Environmental Agency, Air Quality Bureau, Air Pollution Control Bureau (supervision), Committee on
Suspended Particulate Matters (1997): Suspended Particulate Matter Pollution Prediction Manual, Toyokan
Publishing
Environmental Research and Control Center, Committee on Nitrogen Oxide [new edition] (2000): Nitrogen
Oxide Control Manual
Higashino H (1997): Research on Emission and Transportation of Air Pollutants in East Asia, doctoral thesis at
Osaka Prefecture University
Muto N (1979): Long-term average puff model for urban pollution prediction, Journal of Japan Society for
Atmospheric Environment, 14 (8): 340 - 352
1999 Survey on the Relation between Environmental Impact and Damage – Preparation of a Pollution Map(March 2003)
1997 Report on the Results of Operations Entrusted by the Environment Agency: Comprehensive Survey on theAmount of Air Pollutant Emissions (1998)

39

LIME2_C2.4-C2.6_2013

2.5

Photochemical Oxidant

2.5.1

What phenomenon is photochemical oxidant?

Although photochemical oxidant is a part of urban air pollution described in the preceding
section (2.4), under LIME it is treated as an independent impact category.
Photochemical oxidant is a main component of photochemical smog, which occurs mainly insummer, and gives impact on the human respiratory system and plant leaves.
Photochemical smog first occurred in Los Angeles around 1940 and has become a problem
since around 1970 in Japan. It has become a serious air pollution problem not only inadvanced countries but also in major cities of developing countries, such as Mexico City. In
Japan, during the 1970s, there was a year when photochemical smog warnings were issued ona total of 328 days. Although the number of such days has recently decreased, it has still
been around 100 every year.
The main components of photochemical oxidant are ozone (O3) and peroxyacetyl nitrate(PAN), with ozone occupying the majority. From the outset, ozone has existed in the natural
world. If there are volatile organic compounds (VOCs) and nitrogen oxide (NOx) in theatmosphere, a large quantity of ozone will be produced from them through photochemical
reaction and have impact on organisms.
(1)

Causation of photochemical oxidant

After emissions, VOCs and NOx (both are called ozone “precursor substances”) cause
photochemical reaction by ultraviolet rays and produce ozone. If human beings and otherorganisms are exposed to ozone, they may receive harmful impact according to the amount of
exposure. Figure 2.5-1 shows the causation of ozone. The first half of the causation will
be described herein, while the second half will be described in (2) below.

Pollution of a secondary pollutant (ozone) is conspicuous on local scale (up to about 200 m) and on an
urban scale (up to about 20 km).
Endpoint impact
Primary pollutant
emissions (NOx)Primary pollutant
emissions (VOCs)

Advection, diffusion
Photochemical
reaction

Advection, diffusion

Contents of (1)HumanhealthSocial assets

Primary
production

Respiratory
diseaseFarm
production

Terrestrial
primary
production

Exposure
Increase in the concentrationof atmosphaeric ozone (O3)

Woodproduction

Contents of (2) below

Figure 2.5-1: Cause effect chain of photochemical oxidant(1) and (2) in the figure correspond to headings in the main text.

40

LIME2_C2.4-C2.6_2013aEmission of ozone precursor substances

Because, among ozone precursor substances, NOx has already described in 2.4.1 (1) a, an
explanation herein is given about VOCs.
VOCs are organic compounds that mostly move as gas in the atmosphere when being emitted
into the environment (Sakurai 2000). The number of types is more than 100 and there are an
extremely wide range of types (see Table 2.5-1).
Column 2.5-1
Tropospheric ozone and stratospheric ozone
The ozone covered by this impact category is the tropospheric ozone existing between thesurface of the ground and about 12 km above the ground.
“Ozone layer depletion” (Section 2.1) covers the destruction of ozone existing in thestratosphere (about 12 to 50 km above the ground), the layer just above the troposphere (seeFigure 2.5-A).
Tropospheric ozone and the stratospheric ozone are the same chemical substance.
However, while stratospheric ozone absorbs harmful ultraviolet rays from the sun andenables organisms to live, tropospheric ozone gives harmful impact to organisms as
described in this section.

Altitude [km]]A problem arises aboutozone destruction.

Stratospheric ozone

A problem arises aboutphotochemical smog (whose main
component is ozone).

Ozone from photochemical smog

Tropospheric ozone

O3 concentration [1012 molec cm-3]

Figure 2.5-A: Photochemical oxidant ozone and ozone in the destructed ozone layer(Source) Prepared based on Imamura (2001)

41

LIME2_C2.4-C2.6_2013

Table 2.5-1: Examples of VOCs
Concrete examples
Alkanes (hydrocarbons of methane series)
Methane, ethane, propane, isopentane, etc.
Alkenes (hydrocarbons of ethylene series)
Ethylene, propylene, isoprene, 1,3-butadiene, etc.
Alkynes (hydrocarbons of acetylene series) Acetylene, etc.
Aromatic series
Benzene, toluene, o-xylen, m-xylen, p-xylen, ethyl benzene, etc.
Aldehydes
Formaldehyde, acetaldehyde, etc.
KetonesAcetone, methyl ethyl ketone, etc.
EthersDimethyl ether, diethyl ether, etc.
Alcohols
Methyl alcohol, ethyl alcohol, butyl alcohol, etc.
(Sources) Prepared by Mizuho Information and Research Institute with reference to National Institute forEnvironmental Studies (2001) and Andersson-Skold et al. (1992).

There are two processes of occurrence of VOCs: artificial processes and natural processes(plants).
Artificial processes include evaporation from fuels, solvents, paints, and other petrochemical
products, and emission during burning. Concrete sources are oil refineries, oil tank facilities,
oil stations (evaporation from fuels), factories (evaporation from solvents, burning of fuels),
outdoor painting (evaporation from paints, etc.), automobiles (burning of and evaporationfrom fuels), etc.
bCreation of ozone from the ozone precursor substancesThis part describes the process of ozone creation, the influencing factors, and thecharacteristics of ozone concentration.
a)Process of creation of ozone

Ozone has existed in the natural world from the outset and has repeated formation anddisappearance as described in 1) to 3) below.
1)
Photodissociation of NO2 by ultraviolet rays
Atmospheric NO2 is dissociated into NO and O (oxygen atom) by ultraviolet rays.

NO2  hv  NO  O
2)Creation of ozone
The oxygen atom in 1) is combined with O2 (oxygen molecule) and creates ozone.
O  O 2  M  O3  M

3)Consumption of ozone by NO oxidation (NO2 production)
The ozone produced in 2) disappears through oxidation of NO.
NO  O3  NO2  O2

If only NOx is present in the atmosphere, ozone does not increase, because the balance
between the production and disappearance of ozone is maintained through the cycle describedin 1) to 3) above. However, if VOCs are present in the atmosphere, a large quantity of ozone
is typically produced by the processes described in 4) and 5) below (see Figure 2.-5-2).

42

LIME2_C2.4-C2.6_2013

4)

Resolution of VOCs and production of radicals

VOCs are resolved by attacks from ozone and OH radicals and produce radicals.
Because radicals are very highly reactive, a chain reaction occurs and results in the productionof various additional radicals (hydroperoxyl radical (HO2) ( is a sign that indicates a radical),alkyl radical (R), alkoxy radical (RO), alkylperoxy radical (RO2), etc.).
The number of carbons in VOCs decreases through the creation of the above-mentioned
radicals. This cycle is repeated until VOCs disappear.

hv: ultraviolet rays

O2

hv1) to 5) correspond to the same
numbers in the main text.

1)

VOCs OH･
4)

NO2

NO
5)

OH･

O3

2)

HO2･

Carbonyl
product (s)

4)

O2

RO2･
NO
2)

O3

RO･

5)

NO2
1)

hv

O2
２

Figure 2.5-2: Pattern diagram of the process of ozone creation through resolution of VOCs and oxidationof NO(Sources) Prepared based on USEPA (1971), Jenkin et al. (1999), and Wakamatsu et al. (2001)

5)
Oxidation of NO by radicals
NO is oxidized by the radicals produced through process 4) above and becomes NO2.
Unlike process 3), this process 5) produces NO2 without consuming ozone. NO2 producesozone in the reaction 1) and 2) above. Because of this, the production of ozone isaccelerated.
Column 2.5-2What is a radical?
“A molecule contains an even number of electrons, pairs of which distribute around theatomic nucleus and contribute to the chemical binding of molecules. If the number ofelectrons is odd, one electron does not form a pair – that is, it is an unpaired electron. If
a chemical combination has an unpaired electron, it is called a “free radical,” or simply a“radical.” [……] Because radicals are unstable and responsive, they promptly
respond to each other or to stable molecules and cease to be radicals. [……]”(Source) Ito et al. (2000)
43

LIME2_C2.4-C2.6_2013b)

Actors that influence ozone creation

Ozone creation is influenced by the status of pollution by VOCs and NOx. With regard to
weather conditions, it is easier for ozone to be created when insolation is strong and thetemperature is high (see Table 2.5-2).
Table 2.5-2: Factors that influence ozone creation
FactorWhat influence does the factor cause?
• Because the speed of response to OH radicals and degraded products
differ among types of VOCs, they differ in the easiness of creating
Type of VOCs
ozone.
Status ofair
Concentration • The easiness of creating ozone differs according to the concentration
ratios of NOx and VOCs in the atmosphere. As the case may be, thepollution
ratios of NOxemission of NOx or VOCs may reduce ozone concentration (Shinozaki
and VOCs in
et al. (1984a, 1984b), etc.).
the atmosphere• If insolation or temperature becomes higher, many responses related toozone creation increase in speed.
• Atmospheric movement facilitates the diffusion of NOx and VOCs.
Concretely, when the sun rises, vertical mixture becomes active
because of convection and the stable layer near the ground isdestroyed, with the result that the so-called mixed layer grows. In the
Weathermixed layer, it is easier for NOx and VOCs to be mixed uniformly.
• As a result of growth of the mixed layer, stratospheric ozone is taken
in from the upper layer. This is because stratospheric ozone may reachthe ground during the period between the end of winter and the
beginning of spring because of the jet stream, the passing of a cold
front, and the passing of migratory anticyclone (Reference:
Wakamatsu et al. (2001)) (Utsunomiya et al. (1994)).

As described above, the process of ozone creation is very complicated. Because of this, therelation betrween NOx and VOCs emissions and ozone concentration is not linear. In
addition, because weather conditions and air pollution differ, easiness of ozone creationdiffers among zones.
c)

Characteristics of ozone concentration

If there is a change in the amount of precursor substance emissions or the amount of
insolation during a day, the ozone concentration also changes. The typical change pattern is
as follows: the concentration is low early in the morning and, with the passage of time,
gradually rises because of an increasing amount of precursor substance emissions fromfactories and automobiles and an increase in insolation; the concentration reaches a peak in
the afternoon and decreases afterwards.
After precursor substance emissions, ozone is created through reaction that lasts several hours
to about one day. Because of this, ozone concentration does not necessarily rise near sourcesof emissions, and pollution extends on a mesoscale (up to 200 km) (Ohara et al. 1997).
(2)

Endpoints of photochemical oxidantaHuman healthBecause ozone is strongly oxidative and highly responsive, it has harmful impact on thehuman body. If ozone is absorbed in the respiratory system, it is likely to reach deep parts of
the lungs. It causes symptoms such as a stimulus to the nose or the throat, asthma, chronic
44

LIME2_C2.4-C2.6_2013

bronchitis, and a decline in lung function.
membrane of the eyes.
b

In addition, it causes a stimulus to the mucous

PlantsOzone brings about various types of damage to plants. Damage can be roughly divided into
visible damage and invisible damage.
Visible damage is leaves’ whitening, browning, and blackening. If damage worsens frompigment disorder to cellular necrosis, the function of leaves greatly declines.
In addition, invisible damage occurs when ozone is taken in from pores on leaves andinfluences respiration, photosynthesis, transpiration, enzymatic activity, etc. Even if visible
damage does not appear, invisible damage may appear.
These types of damage hinder the growth of plants.
2.5.2

Characterization of photochemical oxidant(1)

Existing characterization factors for photochemical oxidant

Indices for the power to form oxidant have been used as characterization factors forphotochemical oxidant, such as the photochemical oxidant creation potential (POCP)(UNECE1990; Derwent et al. 1991) and the incremental hydrocarbon reactivity (IR) (Carter
et al. 1989).
Of them, POCP is an index developed for the assessment of VOCs emission scenarios.
POCP is calculated by dividing the ozone concentration at the time of a change in the amountof emission of a certain type of VOC by the ozone concentration at the time of a change in theamount of ethylene emissions.

POCPi  ( ai / bi )( aC2 H 4 / bC2 H 4 )(2.5-1)In this equation, a is an increase in the oxidant concentration at the time of emission ofsubstance i or ethylene (C2H4), and b is the amount of emissions of substance i or ethylene.
POCP, which was developed in 1990, was adopted for the UN Economic Commission for
Europe’s Convention on Long-range Transboundary Air Pollution. It has been widely usedin the field of LCA, too (Heijungs et al. 1992). Although POCP was an average-type index
at that time, Derwent et al. adopted a marginal-type index for assessing an increment in theozone concentration at the time of additional emissions of ozone precursor substances to thecurrent emission scenario since 1996. There are also other POCPs developed by researchersother than Derwent et al.
On the other hand, because IR indicates a change in the ozone concentration at the time ofemission of a certain type of VOC, it does not effect standardization from ethylene and otherspecific substances like POCP. Because of this, IR cannot be used as a characterization
factor as it is.

IR i  [O3 ]m i(2.5-2)45

LIME2_C2.4-C2.6_2013

In this equation, ∆[O3] is an increase in the ozone concentration with an increase in theamount of emissions of the type i of VOC, and ∆mi is an increase in the amount of emissionsof the type i of VOC. IR is a marginal-type index that indicates to what extent a change in
the amount of emission of a certain type of VOC influences a change in the ozone
concentration under the baseline scenario.
POCP and IR have been calculated for the whole of Europe and urban areas in the US,
respectively.
(2)

Characterization factor for photochemical oxidant under LIME

When the damage factor DF is calculated in the following Section 2.5.3, “the increase in theozone concentration due to a unit amount of VOCs emissions” (∆C/∆E) will be calculated(see 2.5.3 (2)). The characterization factor for photochemical oxidant is calculated by
dividing each VOC’s ∆C/∆E by ethylene’s ∆C/∆E.
Under LIME, this characterization factor is called the “ozone conversion equivalency factor”(OCEF).
As described in Section 2.5.3, photochemical oxidant is a highly regional environmental
problem. Unlike POCP and IR in (1) above, OCEF under LIME is a characterization factor
that reflects weather and air pollution in Japan and is calculated for each zone in Japan.
In addition, while POCP and IR are calculated under some weather and air pollution
conditions (for example, average conditions in the summer season, conditions whereby theozone concentration becomes maximum, etc.), OCEF are obtained by averaging OCEFs undervarious conditions concerning weather in each zone, etc (see Section 2.5.3). Because of this,it can be said that OCEF is a characterization factor that fully represents the conditions ineach zone.
2.5.3

Damage assessment of photochemical oxidant(1)

Basic policies for calculation of damage functions and factors

Eco-indicator 99 (Goedkoop et al. 2000) and the Environmental Priority Strategies (EPS)(Steen 1999) in Europe have adopted an endpoint approach for the impact category ofphotochemical oxidant by the existing LCA method. LIME also has adopted an endpoint
approach for the assessment method.
Photochemical oxidant is a very regional environmental problem because it is greatly
influenced by regional weather and air pollution conditions. Because of this, under LIME,
damage functions and factors are calculated for each zone.
The object of calculation and the calculation method are described as follows:a
Object of calculation
The calculation covers the following category endpoints:
1)
Category endpoints of human healthEndpoints were selected where the relation between the air pollutant concentration and thedeath/disease rates has been assessed quantitatively based on epidemiological surveys, etc.
46

LIME2_C2.4-C2.6_20132)
Category endpoints of social assets
Decreases in the values of farm production and wood production due to ozone were selected.
3)
Category endpoint of primary production
A decrease in terrestrial net primary productivity (NPP) due to ozone was selected.
Table 2.5-3 shows details of these category endpoints. The amount of damage calculated foreach endpoint in the table is used as the damage function. The total of damage functions ineach area of protection is the damage factor.
Table 2.5-3: Category endpoints covered by the calculation of damage functions and factors
Area ofprotection

Category endpoint

Object of calculation of damage function• Acute death

Increase in the number of acute deaths(DALY-converted))
Increase in the number of sufferers(DALY-converted)
Increase in the number of sufferers(DALY-converted)
Increase in the number of sufferers(DALY-converted)
Increase in the number of sufferers(DALY-converted)
Increase in the number of sufferers(DALY-converted)• Asthma spasm
HumanhealthRespiratory
disease• Day of slight behavioral
restriction• Hospitalization for
respiratory system• Symptom days

SocialassetsPrimary
production

b

Agri.
production
Woodproduction
Terrestrial
eco-system• Entry into emergency
room (asthma)• Impact of damage to farm
Decrease in the value of farm production
products

Object ofcalculation ofdamage factor• Impact of damage to trees Decrease in the value of wood production• Impact of damage toplants

Decrease in terrestrial NPPAssessment method

Damage functions and factors were calculated according to the causal channels (see Figure2.5-1) by the following Steps 1 to 4 (see Figures 2.5-3 to 2.5-5).
Step 1: The relation between the amount of VOCs emissions and an increase in the ozone
concentration in the atmosphere was quantified.
Step 2: The relation between the increase in the concentration in Step 1 and the amount ofdamage at each endpoint was quantified.
Step 3: The damage function at each endpoint was calculated by combination of Steps 1 and2.
Step 4: The total of damage functions in Step 3 for each area of protection was used as thedamage factor.
Of these steps, Step 1 will be explained in (2) below. Steps 2 to 4 will be explained in (3) to(5) below concerning each area of protection – human health, social assets, and primary
production.

47

48
Step 2
Correlation of an increasein the ozone concentrationin the atmosphere withDALY loss at each
endpoint
Calculate “DALY loss ateach endpoint per unit ofozone concentration”(∆DALYeach endpoint/∆C).
DALY perdiseasePopulation

Endpoint[Acute death]

DALY loss

Multiplication

No. of deaths

Multiplication

Death rate

Unit amount of
VOCs emissions

DALY perdiseasePopulation
Same asleft

Damage factor (DF)[Human health]Area of proection[Human health]Endpoint
Each disease such as [asthma spasm]

DALY loss

Multiplication

No. ofsufferers

Multiplication

Disease rate

Increase in atmosphericozone concentration

Damage function (amount of damage to each endpoint due to 1 kg yr–1 of primary pollutant emissions)Figure 2.5-3: Flowchart of calculation of damage functions and damage factors for photochemical oxidant (area of protection:
human health)

Step 4
Calculation of damage
factors
Calculate “the amount ofdamage to human healthdue to a unit amount of
VOCs emissions”(∆DALY/∆E).

Step 3Calculation of damage
functions
Calculate “DALY loss ateach endpoint due to a unit
amount of VOCs emissions”(∆DALYeach endpoint/∆E).

Step 1
Correlation of the amountof VOCs emissions with anincrease in the ozone
concentration in theatmosphere
Calculate “the increase in
the ozone concentration due
to a unit amount of VOCs
emissions” (∆C/∆E).

LIME2_C2.4-C2.6_2013

Step 4
Calculation ofdamage factors
Calculate “theamount of damage
to social assets due
to a unit amount of
VOCs emissions”(∆P/∆E).

Step 2
Correlation of theincrease in the ozone
concentration in theatmosphere with thedecrease in the valueof farm or wood
production
Calculate “thedecrease in the valueof farm or wood
production per unit ofozone concentration”(∆Pagriculture/∆C;∆Pforestry/∆C).

Damage factor (DF)[Social assets]

Area of protection[Social assets]

Endpoint[Decrease in value of wood production]

Decrease in value ofwood production

Same asleft

AdditionPrice of wood
product A

Multiplication

Decrease inproduction of wood
product A

Production ofwood product A

Multiplication

Decrease rate ofproduction of wood
product A

AdditionPrice of farm
product B

Production of farm
product B

Multiplication

Decrease inproduction of farm
product B

Multiplication

Same asleft

Decrease in value offarm production

Price of farm
product A

Production offarm product A

Decrease rate ofproduction of farm
product B

Increase in atmosphericozone concentration

Endpoint[Decrease in value of farm products]

Multiplication

Decrease inproduction of farm
product A

Multiplication

Decrease rate ofproduction of farm
product A

Unit amount of
VOCs emission

Damage function (amount of damage to each endpoint due to 1 kg yr–1 of primary pollutant emissions)Figure 2.5-4: Flowchart of calculation of damage functions and damage factors for photochemical oxidant (area ofprotection: social assets)

Step 3Calculation ofdamage functions
Calculate “thedecrease in the valueof farm production or
the value of wood
production due to aunit amount of VOCs
emissions”(∆Pagriculture/∆E;∆Pforestry/∆E).

Step 1Same as Step 1 inFigure 2.5-3

LIME2_C2.4-C2.6_201349

50
Step 2
Correlation of the increasein the ozone concentrationin the atmosphere with thedecrease in NPP
Calculate “the decrease in
NPP per unit of ozone
concentration” (∆NPP/∆C).

Increase in atmosphericozone concentration

Damage factor (DF)[Primary production]

Area of protection[Primary production]

Endpoint[Decrease in NPP]

Decrease in NPP

Addition

NPP ofvegetation B

Multiplication

Decrease rate of
NPP of vegetation B

NPP ofvegetation A

Multiplication

Decrease rate of NPP
of vegetation A

Unit amount of VOCs
emissions

Same as left

Same as left

Damage function (amount of damage to NPP due to 1 kg yr–1 of primary pollutant emissions)Figure 2.5-5: Flowchart of calculation of damage functions and damage factors for photochemical oxidant (area of protection:
primary production)

Step 4
Calculation of damage
factors
Calculate “the amount ofdamage to primary
production due to a unit
amount of VOCs
emissions” (∆NPP/∆E).

Step 3Calculation of damage
functions
Calculate “the decrease in
NPP due to a unit amountof VOCs emissions”(∆NPP/∆E).

Step 1Same as Step 1 in Figure
2.5-3

LIME2_C2.4-C2.6_2013

LIME2_C2.4-C2.6_2013(2)
Correlation of the amount of VOCs emissions with an increase in the ozone
concentration in the atmosphere
As Step 1 in Figures 2.5-3 to 2.5-5, a simulation was carried out by the use of an air quality
model to calculate an “increase in the ozone concentration due to a unit amount of VOCs
emissions” (∆C/∆E).
a

Object of assessment

The calculation covered the case shown in Table 2.5-4.
Table 2.5-4: Case used for calculation of “the increase in the ozone concentration due to a unit
amount of VOCs emissions” (∆C/∆E)
Zone

Time
Ozone
concentration
index

b

Case
Reasons for selecting the case
7 zones• To represent various weather conditions and air pollution(Hokkaido, Tohoku, Kanto,
situations in Japan
Chubu, Kansai,
Chugoku/Shikoku,
Kyushu/Okinawa)
Summer (June to August)• Because summer is the season when insolation and temperature
are high and creation of ozone is the most likely
Daytime 7-hour average• Because the “concentration” in the “the death/disease rates atconcentrationeach endpoint per unit of ozone concentration” is based on theDaytime 12-hour averageassumption of the daytime 6-hour average concentration
concentration(described below in 2.5.3 (3))• Because the “concentration” in the “the decrease rate of farm
production per unit of ozone concentration” is based on theassumption of the daytime 7-hour average concentration(described below in 2.5.3 (4))• Because the “concentration” in the “the decrease rate of wood
production per unit of ozone concentration” is based on theassumption of the daytime 12-hour average concentration(described below in 2.5.3 (4))• Because the “concentration” in the “the decrease rate of NPP
per unit of ozone concentration” is based on the assumption of
the daytime 7-hour average concentration and the daytime
12-hour average concentration (described below in 2.5.3 (5))

Air quality model used for calculation

As described above, ozone creation is very complicated, because many kinds of pollutants,
such as VOCs and NOx, are involved and insolation and temperature have impact. To reflect
such a polluting mechanism, a model that Uno et al. (1992) developed by correcting thephotochemical box model (PBM) developed by the US Environmental Protection Agency(EPA) (Schere et al. 1984) was selected for LIME (see Column 2.5-3).
cAssessment procedure

Simulation was carried out by the use of the model in b and by the application of weather
conditions in each zone.
Because, as described above, the relation between the amount of VOCs emissions and theamount of created ozone is nonlinear, the division of the concentration obtained from asimulation by the amount of emissions is inappropriate for the calculation of “the increase in
the ozone concentration due to a unit amount of VOCs emissions” (∆C/∆E). Because of this,51

LIME2_C2.4-C2.6_2013

under LIME, a simulation (base case) was carried out with the current amount of VOCs
emissions first, and then a simulation (sensitivity analysis) was carried out with a small
increase in the amount of emissions of one of the eight components of CBM-IV (seeColumn 2.5-3). Lastly, the difference between the concentrations obtained from the twosimulations was divided by the difference in the amount of emissions to calculate ∆C/∆E (seeFigure 2.5-6).
Although what can be gained from a base case simulation is the ozone concentration
attributable to the current amount of VOCs emissions, the air quality model was verified bycomparison with actually observed values.
Although ∆C/∆E in summer is calculated this time (see Table 2.5-4), because the creation ofozone receives complicated impact from weather conditions and air pollution, theconcentration may not be reproduced accurately if a simulation is carried out concerning only
one case of average summer weather and air pollution conditions.
Under LIME, with regard to the Kanto and Kansai zones, which are urban areas where theformation of ozone is conspicuous, weather conditions and air pollution situations in summer
are classified into several patterns, for each of which ∆C/∆E was calculated in detail. That is,∆C/∆E was calculated for each pattern by the method specified in Figure 2.5-6 and weighted
with the frequency of appearance of each pattern to obtain the average value in summer (seeFigure 2.5-7).
On the other hand, with regard to the zones other than Kanto and Kansai, ∆C/∆E wascalculated concerning one case of average summer weather and air pollution conditions.

To be carried out for each component of CBM-IV
a: Current amount of VOCs emissions

a’: Increase in the amount of emissions of a specific VOC component1) Base case simulation

2) Simulation for sensitivity analysis of the VOC componentb: Current ozone concentrationb’: Ozone concentration under a’3) Increase in ozone concentration due to emission of a unit amount of the VOC component (ΔC/ΔE) = (b’-b) ÷ (a’-a)Figure 2.5-6: Procedure for calculation of “the increase in the ozone concentration due to a unit amount of
VOCs emissions” (∆C/∆E)

Column 2.5-3
PBM model (Schere et al. (1984) corrected by Uno et al. (1992))

PBM is a model that indicates the photochemical reaction of VOCs and NOx on an urban
scale or on a mesoscale.
PBM approximates altitudes up to the mixed layer in the calculation area by one box. It is
assumed that, within the mixed layer, substances are well mixed vertically through convection
and are distributed homogenously within the mixed layer.
PBM takes into account the following phenomena: emission of substances, dilution of52

LIME2_C2.4-C2.6_2013

substances by the wind, the growth of the mixed layer and resultant intake of substances fromthe upper layer, and various chemical reactions (see Figure 2.5-B).
Table 2.5-A shows the basic equation for the model.
Uno et al. (1992) developed the chemical reaction model by improving CBM-IV(Carbon-Bond Mechanism – IV) (Gery et al. 1989).
Intake of substances from the
上空からの物質取り込み
upper layer

Growth of the
混合層の成長mixed layer

風WindChemical

reaction
化学反応
Emission
排出Inflow by
移流による
advection
流入Outflowby
移流による
advection
流出

Figure 2.5-B: Conceptual diagram of PBM(Source) Prepared based on Shere et al. (1984)

Table 2.5-A: Basic equation of PBM
How to introduce the basic
equation

Basic equation

 Ci
 C i  z  C i Qi
 U
 Ri C1 , C 2 ,    C i ,    C n t x t  zz

TemporalchangeAdvection of
物
質濃度の 風によるin concentration of substance by
時間変化
物質の移流
substance
wind

Intake of substance
混合層成長によfrom upper layer asる上空からの物
result of growth of
質の取込み
mixed layer

Emission of
物質の排出
substance

Occurrence and
化学反応による物
disappearance of
質の発生・消滅
substance throughchemical reaction

Ci: Average concentration of substance i in the area [ppm]
U: Average velocity in the area [m/s]z: Altitude of mixed layer [m]Qi: Intensity of emission of substance i [ppm – m/S]
Ri (C1, C2, …, Ci, …Cn): speed of occurrence/disappearance of substance i throughchemical reaction [ppm/s]

On the assumptions described
below, integrate the substanceconservation equation in andbelow the mixed layer.
• Substancesarealways
mixed
well
and
the
concentrationof
each
substance is homogeneous in
the box.
• Sources of emissions are
uniformly distributed on thesurface of the ground.
• Intake of air from outside of
the box is caused byadvection by the windhorizontally and by growthof the mixed layer vertically.

Table 2.5-B: Correspondence in volume between each component of VOC and 8 components of
CBM-IV (example)
Each VOC
PAR
OLE
ETH
component
ETHYLENE
1.0
PROPENE
1.0
1.0
FORMALDEHYDE
M-XYLENEETHYL ALCOHOL
0.4
Some components are extracted from USEPA (1989).

TOL

XYL

FORM

ALD2

ISOP

1.0
1.0

53

LIME2_C2.4-C2.6_2013

Because, as described above, the number of VOC components is more than 100, if each theirchemical reactions them are modeled separately, the calculation would consume enormous
quantities of data and time. To avoid this, VOC components are expressed by the following
eight components (carbon bond) in CBM-IV (see Table 2.5-B).
•
•
•
•
•
•

Single bond carbon atoms (PAR)
Ethylene (ETH)
Double bond carbon atoms other than ethylene (OLE)
Reactive aromatic ring (toluene TOL and Xylen XYL)
Carbonyl group (formaldehyde FORM and other aldehydes ALD2)
Terpenes originated from plants (isoprene ISOP)

Chemical reaction equations are described not by each component of VOCs but by the eightcomponents in Table 2.5-B. That is, the reactions of more than 100 VOC components are
combined together into the reactions of the eight components, thereby saving calculationtime.
The model takes into account 33 types of components, including the eight components andNO2. The total number of chemical reaction equations is 82.
The PBM explained above includes all the physical and chemical processes necessary for theprediction of pollutants and, if a polluting phenomenon occurs under the mixed layer, cansimulate the characteristics well (Uno et al. 1994). In addition, calculation time is reducedby making the calculation area one box as described above and by combining together the
VOCs components into eight, whereby it becomes possible to carry out many simulations asshown in Figure 2.5-7 below.

The amount of insolation, maximum temperature, NO x concentration, and the ratio between VOCs concentration and
日射量は全 I パターン、最高気温は全 J パターン、NOx 濃度は全 L パターン、VOC/NOx 濃度比は全 M パターンに分類
NOx concentration are classified into the total I pattern, the total J pattern, the total L pattern, and the total M pattern,
respectively.

“Increase in theozone concentration
各パターンのdue to「VOCs
a unit amountof VOCsの単位排出量にemissions” of each pattern

対するオゾン濃度増加」

日射量パターン
1、最高気温パターン
1、NOx
濃度パターン
1 、VOC/NOx
濃度比パターン
1
Insolation amountpattern 1, maximum
temperaturepattern
1, NOx concentration
pattern

Frequency of appearanceof each pattern
各パターンの出現頻度

ΔC 1,1,1,1/ΔE

× F1,1,1,1

ΔC 1,1,1,2/ΔE

× F1,1,1,2

・・・

× ・・・

Summer average“increase in the
夏季平均でのozone concentration「VOCｓの単位排出量に対するdue to a unit amount
オゾン濃度増加」
of VOCs emissions”

・・・

× ・・・

ΔC average/ΔE

ΔC i,j,l,m/ΔE

× F i,j,l,m

・・・

× ・・・

・・・

× ・・・

ΔC I,J,L,M/ΔE

× F I,J,L,M

1, VOCs/NOx concentration ratio pattern 1
日射量パターン
1、最高気温パターン
1、NOx
濃度パターン
1 1,、VOC/NOx
濃度比パターン
Insolation
amountpattern 1, maximum
temperaturepattern
NOx concentration
pattern2
1, VOCs/NOx concentration ratio pattern 2

Total
I×J×L
全 I×J×L×M
× Mパターン
pattern

・
・
・
・
・
・
・
Insolation amount pattern i, maximum
temperature pattern j, NOx concentration pattern l,
日射量パターンi、最高気温パターン j、NOx 濃度パターン l 、VOC/NOx 濃度比パターン m
VOCs/NOx concentration ratio pattern m
・
・
・
・
・
・
Insolation amount pattern I, maximum
temperature pattern J, NOx concentration pattern
・
日射量パターン
I、最高気温パターン
J、NOx
L,
VOCs/NOx concentration
ratio pattern
M濃度パターン L、VOC/NOx 濃度比パターン M

Figure 2.5-7: Procedure for calculation of “the increase in the ozone concentration due to a unit amount of
VOCs emissions” (∆C/∆E) concerning the summer average concentration

54

LIME2_C2.4-C2.6_2013dConditions for calculation

Conditions for calculation were set as described in Table 2.5-5.
Table 2.5-5: Conditions for calculation of “the increase in the ozone concentration due to a unit
amount of VOCs emissions” (∆C/∆E)
Conditions for calculation
Condition
for amountof emissions

Amount ofemissions bysource and time

Initialconditions

VOCs, NOx, O3

Conditions
for airboundaries
Conditions
for lateralboundaries

VOCs, NOx
O3
VOCs, NOx
O3

Conditions for weather(Insolation, temperature, windvelocity)eContents of setting
Kanto: Environmental Agency (1998)
Kansai: Kondo et al. (1999)
Other than Kanto and Kansai: “FY2009 Survey on Environmental Impact and Damage – Preparation of a
Pollution Map” (2000)
The concentration at 5:00 a.m., the time of beginning of calculation, was set from each zone’s observed
values of concentration by time (environment data file (National Institute for Environmental Studies)). The
following are the observation points used (observation points where observed values of ozone concentration
by time existed were selected so that the air quality model could be verified through comparison between
calculated values and observed values):
Kanto (whole Kanto zone), Kansai (all of Osaka and Hyogo Prefectures), Hokkaido (National Sapporo),
Tohoku (National Sendai), Chubu (Aichi Prefecture), Chugoku/Shikoku (National Kurashiki),
Kyushu/Okinawa (National Kita Kyushu)
1/10 of the initial values
Concentration at each time was set from ozone observation data at Happo-One (Nagano Research Institutefor Health and Pollution (now Nagano Environmental Conservation Research Institute), National Institutefor Environmental Studies).
1/10 of the initial values
Conditions for lateral boundaries for O3 + 0.9 × conditions for lateral boundaries for NO2 (=PObg) =background O3 concentration 0.004 ppm (Ohara et al. 1997)
Japan Meteorological Agency’s terrestrial weather observation data
The following are observatories used (selected from among those near to the observation points selected for“Initial conditions” in this table):
Kanto (whole Kanto zone), Kansai (Kobe Marine Observatory, Osaka District MeteorologicalObservatory), Hokkaido (Sapporo District Meteorological Observatory), Tohoku (Sendai District
Meteorological Observatory), Chubu (Nagoya District Meteorological Observatory), Chugoku/Shikoku(Okayama District Meteorological Observatory), Kyushu/Okinawa (Fukuoka District MeteorologicalObservatory)

Calculation results

By the above-described method, the base case simulation in Figure 2.5-6 was carried out tocalculate the ozone concentration that corresponds to the current amount of VOCs emissions.
In addition, a sensitivity analysis simulation was carried out and was compared with the basecase simulation to calculate “the increase in the ozone concentration due to a unit amount of
VOCs emissions” (∆C/∆E).
Of results in Hokkaido, Tohoku, Kanto, Chubu, Kansai, Chugoku/Shikoku, andKyushu/Okinawa, the results in Kanto are as follows:(a)

Results of the base case simulation

Weather conditions and air pollution situations in summer were divided into 25 patterns (seeFigure 2.5-7) and a simulation was carried out for each of the patterns.
The concentration obtained from the simulation was compared with the value observed on the
day related to each pattern. The comparison was made concerning 1) daily changes in theconcentration and 2) the daytime 7-hour and 12-hour average concentrations.
With regard to 1) daily changes in the concentration, Figure 2.5-8 shows the pattern thatappears the most frequently among the 25 patterns. On the whole, the calculated values have55

LIME2_C2.4-C2.6_2013

reproduced actual daily changes in the ozone concentration well.
With regard to 2) the daytime 7-hour and 12-hour average concentrations, comparison wasmade between the calculated values and the observed values concerning all 25 patterns to
calculate correlation coefficients.
Table 2.5-6 shows the results in relation to correlation coefficients. Because the coefficientsfor the daytime 7-hour and 12-hour average concentrations exceeded 0.8, it was judged thatthe air quality model’s capability to reproduce the current situation was high.
140

オゾン濃度
[ppb]
concentration
Ozone

120
100

Calculated
計算値
value

80
60

Observed
value
観測値_

40
20
0

5

10

15

20

Time

[Weather pattern, air pollution pattern]
時刻 ［時］

2

Amount of insolation = (20.0MJ/m or more), maximum temperature = (30.0 deg or more)［気象パターン・大気汚染パターン］
NOx concentration = (0.040 ppm or more),concentration ratio of VOCs/NOx = (7.5 - 10.0)
日射量=( 20.0 MJ/m2) 最高気温=( 30.0 deg)NOx 濃度=( 0.040 ppm) VOCs／NOx 濃度比=( 7.5 - 10.0)

Figure 2.5-8: Daily changes in the ozone concentration obtained from base case simulation: Kanto
Observed value: observation points in Kanto in the National Institute for Environmental Studies’ environmental
database file (air environmental time values)

Table 2.5-6: Correlation coefficients between the base case simulation of ozone concentrationand observed values: Kanto[Summer] (25 patterns)
Daytime 7-hour average concentration

Correlation coefficient

0.859

Daytime 12-hour average concentration

Correlation coefficient

0.838

(b)
Increase in the ozone concentration due to a unit amount of VOCs emissions(∆C/∆E)A sensitivity analysis simulation was carried out by increasing a unit of emissions of each of
the eight components of CBM-IV. “Increase in the ozone concentration due to the emissionof a unit amount” (∆C/∆E) of the eight components of CBM-IV was calculated. Using this∆C/∆E and the correlation between the eight components of CBM-IV and each VOCs
component (see Table 2.5-B in Column 2.5-3), the ∆C/∆E of each VOCs component wascalculated.
Table 2.5-7 shows examples of the calculation results (Kanto).

56

LIME2_C2.4-C2.6_2013

Table 2.5-7: “Increase in the ozone concentration due to the emission of a unit amount of each
VOC component” (∆C/∆E): Kanto, summer[ppm kg-1 yr]
Daytime 7-hour average
concentration

Daytime 12-hour average
concentration(3)

ETHYLENE
PROPENE
FORMALDEHYDE
M-XYLENEETHYL ALCOHOLETHYLENE
PROPENE
FORMALDEHYDE
M-XYLENEETHYL ALCOHOL

4.19E-11
6.32E-11
3.23E-11
1.77E-11
1.04E-12
2.92E-11
4.61E-11
2.55E-11
1.51E-11
7.92E-13

Damage functions and factors for human healthWith regard to damage functions and factors for human health, an explanation is given for
Steps 2 to 4 in Figure 2.5-3.
aStep 2: Correlation of an increase in the ozone concentration in the atmospherewith DALY loss at each endpoint
By Step 2 in Figure 2.5-3, “DALY loss at each endpoint per unit of ozone concentration”(∆DALYeach endpoint/∆C) was calculated.
Figure 2.5-9 shows the flowchart of calculation, and it is as in the case of pollution by
primary pollutants in 2.4.3 (2).
The following is an explanation about (a) to (c) of Figure 2.5-9.
(a)Death/disease rates at each endpoint per unit of ozone concentration

Like pollution by the primary pollutants in 2.4.3 (2), rates were set as shown in Table 2.5-8 by
the use of the epidemiologic survey results used for ExternE (EC 1999).
Table 2.5-9 shows “the increase rate of the death rate and the disease rate at each endpoint perunit of ozone concentration” under ExternE, which was used for the setting in the above table.
The “concentration” in “the increase rate of the death rate and the disease rate at each
endpoint per unit of ozone concentration” under ExternE is the daytime 6-hour average
concentration. Because of this, when “an increase in the ozone concentration due to a unit
amount of VOCs emissions” (∆C/∆E) was calculated in 2.5.3 (2), the daytime 7-hour average
concentration was used as an approximate value to the daytime 6-hour average concentration(see Table 2.5-4).
(b)Population in each zone

Population was set for the target population group at each endpoint in (a) (see Table 2.5-9).
(c)

DALY per death/disease at each endpoint
57

LIME2_C2.4-C2.6_2013

As in the case of pollution by primary pollutants in 2.4.3 (2), the values from Hofstetter(1998) were used (see Table 2.5-9).
b

Steps 3 and 4: Calculation of damage functions and factors

Steps 3 and 4 in Figure 2.5-3 were carried out as follows:•
Step 3: “The increase in the ozone concentration due to a unit amount of VOCs
emissions” (∆C/∆E) in 2.5.3 (2) was multiplied by “DALY loss at each endpoint per unit ofozone concentration” (∆DALYeach endpoint/∆C) to obtain the damage function for each
endpoint (∆DALYeach endpoint/∆E).
• Step 4: The damage functions calculated as described in Step 3 were added up at all the
endpoints of respiratory disease to obtain the damage factor (∆DALY/∆E).
Table 2.5-10 shows examples (ethylene, Kanto) of the calculated damage functions andfactors.
In addition, Table 2.5-11 shows the average regional damage factor in each zone and thenational average.

(a) Acute death rate per unitof ozone concentration

Death rate

Multiplication(a) Disease rate at each
endpoint per unit of ozone
concentration

Diseaserate
Multiplication

Population

No. ofdeathsMultiplication
DALY perdeath(b) Population in eachzone (person)(c) DALY per acutedeath (by use of
Hofstetter (1998))

DALY loss

Population(b) Population in eachzone (person)

No. ofsufferers
Multiplication
DALY perdisease(c) DALY per disease ateach endpoint (by use of
Hofstetter (1998))

DALY loss
Endpoint[Acute death]

Endpoint
Each disease such as [asthma spasm]

Figure 2.5-9: Flowchart of calculation of “DALY loss at each endpoint per unit of ozone
concentration” (∆DALYeach endpoint/∆C)
Table 2.5-8: Method of setting “death/disease rates at each endpoint per unit of ozone concentration”
Item
Setting method(a) Death rate at each endpoint
The following equation was used for the calculation:
per unit of ozone concentration“Increasing rate of the death rate at each endpoint per unit of ozone
concentration” under ExternE × current death rate in Japan(a) Disease rate at each endpoint “Disease rate at each endpoint per unit of ozone concentration”
per unit of ozone concentration
under ExternE was used as it was.
(a) in the table corresponds to Figure 2.5-9.

58

LIME2_C2.4-C2.6_2013

Table 2.5-9: The increase rate of the acute death rate and the disease rate per unit of ozone
concentration
Type of respiratory
disease(Endpoint)

Acute death
Asthma spasm“Increase rate of the acute death rate per unit of ozone(c) DALY
Geometri Target
concentration”
perc
population
death/disease diffusion[(Risk/Riskbaseline) ppm-1], Risk＝case person-1 yr-1
at eachσ2“(a) Disease rate at each endpoint per unit of ozone-1-1-1
endpoint
concentration” [case person yr ppm ]Ozone
Geometric [DALY/case]diffusion
σ2[DALY/case]Sunyer et al.(1996)
1.178E+00
16.0
0.75
5.0 Entire pop.
Whittemore et al.(1989)
8.565E+00
36.0
0.00027
2.0 Asthma

Day of slight
Ostro et al.(1989)
1.949E+01
16.0
behavioral
restriction
Hospitalization for Pounce de Leon et al.(1996)
7.067E-03
6.0
respiratory system
Symptom days
Krupnick et al.(1990)
6.588E+01
6.0
Entry into
Cody et al.(1992), Bates et 2.635E-02
36.0
emergency roomal.(1990)(asthma)(a) and (c) in the table correspond to Figure 2.5-9.
(Source) ExternE (EC1999); Hofstetter (1998) is used for shadowed parts.

0.00014

2.0 Adult

0.011

2.0 Entire pop.

0.000140.00082

2.0 Entire pop.
3.0 Entire pop.

Table 2.5-10: Damage function (∆DALYeach endpoint/∆E) and damage factor (∆DALY/∆E) for humanhealth due to ozone pollution by ethylene emissions: Kanto

Endpoint(1) ΔC/ΔE†1 [ppm kg-1 yr][Acute death][Asthma spasm]<Daytime 7-hour average concentration>(a) Death rate per unit of ozone concentration †2 (entire
population)[case person-1 yr-1 ppm -1](b) Population [person](c) DALY of acute death [DALY case-1](2) ΔDALYeach endpoint/ΔC †3 [DALY yr-1 ppm -1] (=(a)×(b)×(c))(3) ΔDALYeach endpoint /ΔE [DALY kg-1 ] (=(1)×(2))(a) Disease rate per unit of ozone concentration (asthma
sufferers)[case person-1 yr-1 ppm -1](b) Number of asthma sufferers [person](c) DALY of asthma spasm [DALY case-1](2) ΔDALYeach endpoint/ΔC†3 [DALY yr-1 ppm -1] (=(a)×(b)×(c))(3) ΔDALYeach endpoint /ΔE [DALY kg-1 ] (＝(1)×(2))[Day of slight(3) ΔDALYeach endpoint /ΔE
behavioral restriction][Hospitalization for(3) ΔDALYeach endpoint /ΔE
respiratory system][Symptom days](3) ΔDALYeach endpoint /ΔE[Entry into emergency(3) ΔDALYeach endpoint /ΔE
room (asthma)]-1(4) ΔDALY /ΔE [DALY kg ] (=Σ (3))

[DALY kg-1 ]Kanto
4.191E-11
8.739E-03

39,520,058
7.500E-01
2.590E+05
1.086E-05
8.565E+00

51,3762.700E-04
1.188E+02
4.980E-09
3.843E-06[DALY kg-1 ]1.288E-07[DALY kg-1 ]1.528E-05
3.580E-08-1[DALY kg ]3.015E-05(1) to (4) in the table correspond to Figure 2.5-3; (a) to (c) correspond to Figure 2.5-9.
†1
Increase in the ozone concentration due to a unit amount of ethylene emissions†2“(a) Acute death rate per unit of ozone concentration” is calculated by “the increase rate of the acute death rate
per unit of ozone concentration × the current death rate” (see 2.5.3 (3) a (a))†3
DALY loss at each endpoint per unit of ozone concentration

59

LIME2_C2.4-C2.6_2013

Table 2.5-11: Damage factor for human health due to ozone pollution by ethylene emissions(∆DALY/∆E) [DALY kg–1]
Average regional damage factor in each zone and national average
Average in Hokkaido
Average in Tohoku
Average in Kanto
Average in Chubu
Average in Kansai
Average in Chugoku/Shikoku
Average in Kyushu/Okinawa
National average(4)3.106E-06
4.392E-06
3.015E-05
3.867E-06
2.543E-05
6.196E-06
4.394E-05
1.673E-05

Damage functions and factors for social assets

As shown in Table 2.5-3, the endpoints of social assets include farm production and wood
production.
In a) below, a damage function was calculated by Steps 2 and 3 in Figure 2.5-4 concerning theendpoint of farm production. In the following b), a damage function was calculated also
concerning the endpoint of wood production.
In the last c), Step 4 in the figure was carried out to add up the damage functions at both
endpoints and obtain the damage factor.
aDamage function for farm productiona)Step 2: Correlation between the increase in the ozone concentration in theatmosphere and the value of decrease in farm production
Step 2 in Figure 2.5-4 was carried out to calculate “the value of decrease in farm production
per unit of ozone concentration” (∆Pagriculture/∆C).
Figure 2.5-10 shows the flowchart of calculation. “(a) Decrease rate of production of eachfarm product per unit of ozone concentration” was multiplied by “(b) Production of each farm
product” and “(c) Price of each farm product.” The results were added up by type of farm
product.
The following is an explanation about (a) to (c) in Figure 2.5-10.
(a)Decrease rate of production of each farm product per unit of ozone concentration

The rate was fixed as follows:
Decrease rate of production per unit increase in ozone concentration= Decrease rate of yield of each farm product per unit increase in ozone concentration= (Relative yield at current ozone concentration – relative yield at time of unit increase inozone concentration) ÷ relative yield at current ozone concentration
The yield in the equation was calculated by the use of the equation obtained throughexperiments by Kobayashi et al. (1995) and Lesser at al. (1990) (see Table 2.5-12). Theseequations for calculation of yield are based on the daytime 7-hour average ozone
concentration. Because of this, when “the increase in the ozone concentration due to a unit
60

LIME2_C2.4-C2.6_2013

amount of VOCs emissions” was calculated in 2.5.3.(2), the daytime 7-hour average ozone
concentration was used (see Table 2.5-4).
(b)Production of each farm productIt was set by the use of the yield data in the “Statistics on Crops” (Statistics and Information
Department of Ministry of Agriculture, Forestry and Fisheries).
(c)

Price of each farm product

The price was set by the use of the “Statistical Yearbook of Ministry of Agriculture, Forestry
and Fisheries” (Statistics and Information Department of Ministry of Agriculture, Forestry
and Fisheries). The selected prices were not wholesale prices, retail prices, or other prices
downstream in the distribution channel but producers’ prices upstream. This is because thedownstream of the distribution channel is far from the endpoint, and it was estimated that the
influence of factors other than the natural environment, the target of LCA, would become
greater.

Decrease rate ofproduction of farm
product A
Multiplication

Decrease rate ofproduction of farm
product B
Multiplication

Multiplication

Production offarm product B

Production offarm product A

Decrease inproduction of farm
product A

Same asleft

Decrease inproduction of farm
product B
Multiplication
Price of farm
product A

Price of farm
product B
Addition(a) Decrease rate of production of eachfarm product per unit of ozone
concentration(By use of Lesser et al. (1990);
Kobayashi et al. (1995))(b) Production of each farm product(By use of Statistics on Crops (Statistics
and Information Department of
Ministry of Agriculture, Forestry and
Fisheries))(c) Price of each farm product(By use of “76th Statistical Yearbook of
Ministry of Agriculture, Forestry and
Fisheries” (Statistics and Information
Department of Ministry of Agriculture,
Forestry and Fisheries), etc.)

Value of decrease in farm production

Endpoint[Value of decrease in farm production]
Figure 2.5-10: Flowchart of calculation of “the value of decrease in farm production per unit of ozone
concentration” (∆Pagriculture/∆C)

Table 2.5-12: Yield equation used for “the decrease rate of production of each farm product per unitof ozone concentration”
Basic equation
Kobayashi et al. (1995)

Lesser et al. (1990)

Crop
Rice

7 kinds, suchas alfalfa

Equation
RY ＝ exp [C(O3-20)]
RY: Relative yield on the assumption that the standard ozone
concentration is 20 ppb
C=-0.001822
O3[ppb] Daytime 7-hour average concentration
RY ＝ exp{-(O3/β)m}/ exp{-(0.02/β)m}
RY: Relative yield on the assumption that the standard ozone
concentration is 0.02 ppm
O3 [ppm] Daytime 7-hour average concentrationβ, m: Weibull model parameter (see Table 2.5-13)

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LIME2_C2.4-C2.6_2013

Table 2.5-13: Weibull model parameter for each farm product in Lesser at al. (1990)

Alfalfa
Corn
CottonForageKidney Bean
SoybeanWinter Wheat
Lettuce
PeanutSorghum
TobaccoTomatoTurnipWeibull Model (see Table 2.5-12)m
β (ppm)
0.178
2.07
0.124
2.83
0.111
2.06
0.139
1.95
0.279
1.35
0.107
1.58
0.136
2.56
0.120
9.76
0.109
2.27
0.314
2.07
0.145
1.66
0.204
1.67
0.093
2.70

Table 2.5-14: Damage factors for farm production due to ozone pollution by ethylene emissions(∆Pagriculture/∆E): Kanto
ΔC/ΔE† [ppm kg-1 yr] ＜daytime 7-hour average concentration＞[Paddy rice](a) Decrease rate of production per unit of ozone concentration[ppm–1](b) Production [kg yr–1](c) Price [yen kg–1](2) Value of decrease in production per unit of ozone concentration[yen yr–1 ppm–1] (= (a) × (b) × (c))(3)’ Value of decrease in production due to 1 kg of ethylene emissions[yen kg–1] (= (1) × (2))[Wheat](3)’ Value of decrease in production due to 1 kg of ethylene emissions[yen kg–1][Soybean](3)’ Value of decrease in production due to 1 kg of ethylene emissions[yen kg–1][Peanut](3)’ Value of decrease in production due to 1 kg of ethylene emissions[yen kg–1][Corn](3)’ Value of decrease in production due to 1 kg of ethylene emissions[yen kg–1][Tomato](3)’ Value of decrease in production due to 1 kg of ethylene emissions[yen kg–1][Immature corn](3)’ Value of decrease in production due to 1 kg of ethylene emissions[yen kg–1][Lettuce](3)’ Value of decrease in production due to 1 kg of ethylene emissions[yen kg–1][Leaf tobacco](3)’ Value of decrease in production due to 1 kg of ethylene emissions[yen kg–1][Early harvested(3)’ Value of decrease in production due to 1 kg of ethylene emissions
corn][yen kg–1](3)ΔPagriculture/ΔE [Yen kg-1] (＝Σ (3)’)(1) to (3) in the table correspond to Figure 2.5-4; (a) to (c) correspond to Figure 2.5-10.
†
Increase in ozone concentration due to a unit amount of ethylene emissions(1)b)

Step 3: Calculation of damage function

Step 3 in Figure 2.5-4 was carried out to calculate the damage function.
62

Kanto
4.191E-11
1.82E+00
1464250000
243
649467538422
2.72E+01
7.13E+00
3.30E+00
6.94E+00
0.00E+00
4.13E+00
3.88E-01
6.42E+00
2.92E+00
5.79E+00
6.42E+01

LIME2_C2.4-C2.6_2013“The increase in the ozone concentration due to a unit amount of VOCs emissions” (∆C/∆E)
in 2.5.3.(2) was multiplied by “the value of decrease in the production of each farm produce
per unit of ozone concentration” (∆Pagriculture/∆C) in a) above, to calculate the damage function(∆Pagriculture/∆E).
Table 2.5-14 shows the results. Finally, the farm products covered by the calculation of thedamage function (∆Pagriculture/∆E) were the farm products about which all data on “(a)Decreasing rate of production of each farm product per unit of ozone concentration,” “(b)Production of each farm product,” and “(c) Price of each farm product” in Figure 2.5-10 werecollected.
bDamage function for wood production

Wood is processed from the material (logs) to wood products, pulp, chips, etc. LIME
covered the material at the upstream stage of the processing. This is for the same reason as
in the case of the farm products in a above.
Wood is used as materials (for sawing, pulp/chips, plywood, etc.), fuel wood and charcoal,wood for cultivating mushrooms, etc. Of them, wood for sawing and pulp/chips, which
occupies a large percentage of supply and demand, was used for LIME.
a)
Step 2: Correlation of the increase in the atmospheric ozone concentration with thevalue of decrease in wood production
Step 2 in Figure 2.5-4 was carried out to calculate “the value of decrease in wood production
per unit of ozone concentration.”

Decrease rate ofproduction of wood A

Decrease rate ofproduction of wood B

Multiplication
Productionof wood A

Decrease inproduction of wood A

Multiplication
Productionof wood B

Decrease inproduction of wood B

Multiplication
Price ofwood A

Multiplication
Price ofwood B
Addition(a) Decrease rate of production in eachtype of wood per unit of ozone
concentration(By use of Matsumura et al.
(1996))
Same as
left(b) Production of each type of wood
(By use of “Report on Supply and
Demand of Lumber” (Statistics andInformation Department of Ministry ofAgriculture, Forestry and Fisheries))(c) Price of each type of wood
(By use of “Report on Supply and
Demand of Lumber” (Statistics andInformation Department of Ministry ofAgriculture, Forestry and Fisheries))

Value of decrease in wood production

Endpoint[Value of increase in wood production]
Figure 2.5-11: Flowchart of calculation of “the value of decrease in wood production per unit of ozone
concentration”

63

LIME2_C2.4-C2.6_2013

Figure 2.5-11 shows the flowchart of calculation. “(a) Decrease rate of production in eachtype of wood per unit of ozone concentration” was multiplied by “(b) Production of each typeof wood” and “(c) Price of each type of wood” and the results for all the types of wood wereadded up.
The following is an explanation about (a) to (c) in Figure 2.5-11.
(a)Decrease rate of production of each type of wood per unit of ozone concentration

The decrease rate was set as follows on the assumption that a decrease in the growth of trees
leads to a decrease in wood production:
Decrease rate of production of each type of wood per unit increase in ozone concentration= decrease rate of the dry growth rate of each type of tree per unit increase in ozone
concentration= (dry growth rate at current ozone concentration – dry growth rate per a unit increase inozone concentration) ÷ dry growth rate at current ozone concentration
The dry growth rate in the equation was calculated by the calculation equation prepared basedon experimental data from Matsumura et al. (1996) (see Table 2.5-15). The preparedequation for calculation of dry growth rate is based on the assumed daytime 12-hour averageozone concentration. Because of this, when “the increase in the ozone concentration due to aunit amount of VOCs emissions” was calculated in 2.5.3 (2), the daytime 12-hour average
concentration was used (see Table 2.5-4).
The dry growth rate for cedar in Table 2.5-15 was used for conifer wood. The dry growthrate for zelkova in the table was used for broadleaf wood.
Table 2.5-15: Equation for calculation of the dry growth rate used for “the decrease rate of the dry
growth rate of each tree per unit of ozone concentration”
Basic equation
Matsumura et al. (1996)

Crop
Cedar

Zelkova

Equation
Y＝-0.0926X＋46.765 (R2=0.9738)
Y ：Dry growth rate [g/yr]
X [ppb]: Daytime 12-hour average concentration
Y＝-0.6777X＋164.09 (R2=0.9762)
X and Y are the same as those for cedar.

The above equation was obtained as follows: first, among studies that quantitatively assessed the impact ofozone on the growth of trees constituting forests (Matsumura et al. (1996); Matsumura et al. (1998); Kono et al.
(1999)), the data from Matsumura et al. (1996) were selected because they enabled the calculation of the growthrate and showed a trend for the growth rate to decrease consistently as the ozone concentration becomes higher
within the extent of ozone concentration observable in the environment; next, the data were used to obtain the
regression equation for the prediction of the dry growth rate from the ozone concentration.

(b)

Production of each type of wood

Data on production of materials were prepared based on the “Report on Supply and Demandof Lumber” (Statistics and Information Department of Ministry of Agriculture, Forestry and
Fisheries).

64

LIME2_C2.4-C2.6_2013(c)Price of each type of wood

The prices of sawing materials and pulp materials were set by the use of the “Statistical
Yearbook of Ministry of Agriculture, Forestry and Fisheries” (Statistics and Information
Department of Ministry of Agriculture, Forestry and Fisheries) and the “Report on Supply and
Demand of Lumber” (Statistics and Information Department of Ministry of Agriculture,
Forestry and Fisheries). The price of chip materials was set by the use of “Lumber Prices”(Statistics and Information Department of Ministry of Agriculture, Forestry and Fisheries).
Step 3: Calculation of damage function
Step 3 in Figure 2.5-4 was carried out to calculate the damage function.
“The increase in the ozone concentration due to a unit amount of VOCs emissions” (see 2.5.3(2)) was multiplied by “the value of decrease in production of each type of wood per unit ofozone concentration” (see a) above) to calculate the damage function for each endpoint.
Table 2.5-16 shows the results.
Table 2.5-16: Damage function for wood production due to ozone pollution by ethylene
emissions (∆Pforestry/∆E): Kanto(1) ΔC/ΔE†[ppm kg-1 yr] <daytime 12-hour average concentration>Red(a) Decrease rate of production per unit of ozone concentration [ppm–1]
pine,
Sawing material(b) Production [m3 yr–1]
black(c) Price [yen m–3]
pine(2) Value of decrease in production per unit of ozone concentration[yen yr–1 ppm–1] (= (a) × (b) × (c))(3)’ Value of decrease in production due to 1 kg of ethylene emissions[yen kg–1] (= (1) × (2))
Wood chip(b) Production [m3 yr–1]
material(c) Price [yen m–3]
(2) Production per unit of ozone concentration [yen yr –1 ppm–1] (= (a)× (b) × (c))(3)’Value of decrease in production due to 1 kg of ethylene emissions[yen kg–1] (= (1) × (2))
Pulp material(b) Production [m3 yr–1](c) Price [yen m–3]
(2) Value of decrease in production per unit of ozone concentration[yen yr–1 ppm–1] (= (a) × (b) × (c))(3)’ Value of decrease in production due to 1 kg of ethylene emissions[yen kg–1] (= (1) × (2))Cedar(a) Decrease rate of production per unit of ozone concentration [ppm–1]
Sawing material(3)’ Value of decrease in production due to 1 kg of ethylene
emissions [yen kg–1]
Wood chip(3)’ Value of decrease in production due to 1 kg of ethylenematerial
emissions [yen kg–1]
Pulp material(3)’ Value of decrease in production due to 1 kg of ethylene
emissions [yen kg–1]
Japanes (a) Decrease rate of production per unit of ozone concentration [ppm–1]e
Sawing material(3)’ Value of decrease in production due to 1 kg of ethylene
cypress
emissions [yen kg–1]
Wood chip(3)’ Value of decrease in production due to 1 kg of ethylenematerial
emissions [yen kg–1]
Pulp material(3)’ Value of decrease in production due to 1 kg of ethylene
emissions [yen kg–1]

Kanto
2.916E-11
4.167E+00
19,39920,2001,632,747,93
14.762E-02
11,532
4,950
237,832,761
6.936E-03
6,787
6,583
186,161,358
5.429E-03
4.167E+00
1.186E+00
2.188E-02
9.866E-04
4.167E+00
9.193E-01
3.535E-03
6.814E-04

65

LIME2_C2.4-C2.6_2013(a) Decrease rate of production per unit of ozone concentration [ppm–1]
Sawing material(3)’ Value of decrease in production due to 1 kg of ethylene
emissions [yen kg–1]
Wood chip(3)’ Value of decrease in production due to 1 kg of ethylenematerial
emissions [yen kg–1]
Pulp material(3)’ Value of decrease in production due to 1 kg of ethylene
emissions [yen kg–1]
Conifer (a) Decrease rate of production per unit of ozone concentration [ppm–1]
Other
Sawing material(3)’ Value of decrease in production due to 1 kg of ethylene
emissions [yen kg–1]
Wood chip(3)’ Value of decrease in production due to 1 kg of ethylenematerial
emissions [yen kg–1]
Pulp material(3)’ Value of decrease in production due to 1 kg of ethylene
emissions [yen kg–1]
Oak(a) Decrease rate of production per unit of ozone concentration [ppm–1]
Sawing material(3)’ Value of decrease in production due to 1 kg of ethylene
emissions [yen kg–1]
Wood chip(3)’ Value of decrease in production due to 1 kg of ethylenematerial
emissions [yen kg–1]
Pulp material(3)’ Value of decrease in production due to 1 kg of ethylene
emissions [yen kg–1]
Beech(a) Decrease rate of production per unit of ozone concentration [ppm–1]
Sawing material(3)’ Value of decrease in production due to 1 kg of ethylene
emissions [yen kg–1]
Wood chip(3)’ Value of decrease in production due to 1 kg of ethylenematerial
emissions [yen kg–1]
Pulp material(3)’ Value of decrease in production due to 1 kg of ethylene
emissions [yen kg–1]
Broad(a) Decrease rate of production per unit of ozone concentration [ppm–1]
leaf tree Sawing material(3)’ Value of decrease in production due to 1 kg of ethylene
Otheremissions [yen kg–1]
Wood chip(3)’ Value of decrease in production due to 1 kg of ethylenematerial
emissions [yen kg–1]
Pulp material(3)’ Value of decrease in production due to 1 kg of ethylene
emissions [yen kg–1]-1(3) ΔPforestry/ΔE [Yen kg ] (＝Σ (3)’)(1) to (3) in the table correspond to Figure 2.5-4; (a) to (c) correspond to Figure 2.5-11.
†
Increase in the ozone concentration due to a unit amount of ethylene emissions
Larch

4.167E+00
2.190E-02
2.045E-03
4.250E-05
4.167E+00
3.449E-02
2.857E-03
0.000E+00
7.973E+00
1.436E-02
2.000E-03
6.377E-04
7.973E+00
3.384E-03
5.167E-04
1.613E-04
7.973E+00
9.794E-02
1.778E-01
1.781E-03
2.552E+00

Table 2.5-17: Damage factor for social assets due to ozone pollution by ethylene emissions (∆P/∆E)[Yen kg–1]
Average regional damage factor in each zone and national average
Damage factor for farm
Damage factor for wood Damage factor for social
products (ΔPagriculture/ΔE) production (ΔPforestry/ΔE)
assets (ΔP/ΔE)
Average in Hokkaido
2.506E+01
2.916E+00
2.798E+01
Average in Tohoku
3.817E+01
3.638E+00
4.181E+01
Average in Kanto
6.425E+01
2.552E+00
6.680E+01
Average in Chubu
1.512E+01
1.582E+00
1.670E+01
Average in Kansai
1.034E+01
2.632E+00
1.297E+01
Average in
2.219E+01
5.538E+00
2.773E+01
Chugoku/Shikoku
Average in
2.268E+02
4.062E+01
2.674E+02
Kyushu/Okinawa
National average
5.741E+01
8.498E+00
6.591E+01

66

LIME2_C2.4-C2.6_2013cCalculation of damage factors for social production

The damage factors for farm products and wood production calculated in a and b above(∆Pagriculture/∆E; ∆Pforestry/∆E) were added up to obtain the damage factor for social assets(∆P/∆E).
Table 2.5-17 shows the average regional damage factor in each zone and the national average.
(5)

Primary production: Damage functions and factors for terrestrial NPP

As shown in Table 2.5-3, the endpoint of primary production is terrestrial NPP.
The calculation of the damage function and damage factor for terrestrial NPP can be
explained according to Steps 2 to 4 in Figure 2.5-5.
aStep 2: Correlation of the increase in the atmospheric ozone concentration withthe amount of decrease in NPP
Step 2 in Figure 2.5-5 was carried out to calculate “the amount of decrease in NPP per a unitof ozone concentration” (∆NPP/∆C).
Figure 2.5-12 shows the flowchart of calculation. “(a) Decrease rate of NPP of each type ofvegetation per unit of ozone concentration” was multiplied by “(b) NPP of each type ofvegetation.” After that, the results for all types of vegetation were added up.
(a) Decrease rate inNPP of each type of vegetation
per unit of ozone concentration(by use of Matsumura et al. (1996); Lesser etal. (1990); Kobayashi et al. (1995))

Decrease rate of NPP
of vegetation A

Decrease rate of NPP
of vegetation B

Multiplication

Multiplication

NPP of vegetation A

Same asleft

NPP of vegetation B(b) NPP in each type of vegetation(Current NPP obtained from “FY2000
Survey on the Relation between
Environmental Impact and Damage(Land Use, Resources Exhaustion,
Waste)”)

Addition

Amount of decrease of NPP

Figure 2.5-12: Flowchart of calculation of “the amount of decrease in NPP per unit of ozone
concentration” (∆NPP/∆C)

The following is an explanation of (a) and (b) in Figure 2.5-12:(a)Decrease rate of NPP of each type of vegetation per unit of ozone concentration

Types of vegetation were classified according to the “FY2000 Survey on the Relation between
Environmental Impact and Damage (Land Use, Consumption Exhaustion, Waste)” (2001) asshown in Table 2.5-18.
67

LIME2_C2.4-C2.6_2013

Table 2.5-18: Classification of vegetation

Evergreen broadleaf forest, Beech forest, Birch forest, Oak forest, Natural conifer forest,
Pine forest, Cedar and cypress forest, Deciduous conifer forest, Alpine scrub forest,
Subtropical scrub forest, Montane evergreen scrub forest, Montane deciduous scrub forest,
Bamboo forest, Evergreen orchard, Tea plantation, Deciduous orchard, Mulberry field, Dry
field, Paddy field, Fallow field, Bamboo grass field, Rice field, Amphibious field, Aquaticfield, special field, Large plant group, Small plant group, Artificial plant, Urban green
space, etc.
With regard to the decreasing rate of NPP of each type of vegetation, “the decrease rate of thedry growth rate of each type of wood per unit of ozone concentration” (see 2.5.3 (4) b) wasused for the vegetation of trees. In addition, “the decrease rate of production of each farm
product per unit of ozone concentration” (see 2.5.3 (4) a) was used for the types of vegetation
other than trees.
(b)

NPP of each type of vegetation

It was decided that the current NPP obtained from the “FY2000 Survey on the Relation
between Environmental Impact and Damage (Land Use, Resources Consumption, Waste)”(2001) should be used.
b

Steps 3 and 4: Calculation of damage function and damage factor

Steps 3 and 4 were carried out as shown in Figure 2.5-3.
In Step 3, “the increase in the ozone concentration due to a unit amount of VOCs emissions”(∆C/∆E) in 2.5.3 (2) was multiplied by “the amount of decrease in NPP per unit of ozone
concentration” (∆NPP/∆C) in a above to obtain the damage function (∆NPP/∆E). Because
the number of endpoints is one, it is used as the damage factor in Step 4 as it is.
Table 2.5-19 shows the average prefectural damage factor in each zone and the national
average.
Table 2.5-19: Damage factor for primary production due to ozone pollution by ethylene
emissions (∆NPP/∆E) [ton kg–1]
Average regional damage factor in each zone and the national average
Average in Hokkaido
6.842E-03
Average in Tohoku
4.783E-03
Average in Kanto
6.819E-03
Average in Chubu
2.743E-03
Average in Kansai
2.893E-03
Average in Chugoku/Shikoku
6.466E-03
Average in Kyushu/Okinawa
3.009E-02
National average
8.662E-03

2.5.4

Procedure for the impact assessment of photochemical oxidant

Concrete procedures for the characterization and impact assessment of photochemical oxidant
are as follows:

68

LIME2_C2.4-C2.6_2013

Users can select what meets their purpose from among characterization, damage assessment,
and weighting and use it for LCA.
With regard to characterization, the characterization result CIPhotoOxidant can be obtained fromthe inventory of the photochemical oxidant formation substance X Inv (X, Region) and thecharacterization factor CFPhotoOxident (X, Region) (see Equation 2.5-3). CIPhotoOxidant isregarded as the total amount of emissions of ethylene (C2H4), a typical VOC, into which eachprecursor VOC to photochemical oxidant is converted.
CI PhotoOxidant  X CFPhotoOxidant( X , Region)  Inv ( X , Region) (2.5-3)

RegionThere are various lists of characterization factors CFPhotoOxidant (X, Region). Under LIME,
OCEF, which is described in 2.5.2 (2), is recommended as a characterization factor that isbased on the weather conditions in Japan and enables assessment with consideration for theweather conditions in each zone.
Because the formation of photochemical oxidant greatly differs according to the weather
conditions in emission areas, Japan was divided into seven zones, and OCEF was presented
for each zone (CFPhotoOxidant (X, Region)).
Therefore, if inventory data are expressed by zone, characterization can be carried out by zone.
If there is no inventory classified by zone, calculation can be carried out by the use of thenational average characterization factor CFPhotoOxidant (X, Average) (see Equation 2.5-4).
CI PhotoOxidant   CF PhotoOxidant ( X , Average)  Inv ( X )(2.5-4)

X

With regard to damage assessment, the damage assessment result DI (Safe) can be obtained
from Inv (X, Region), the inventory of the precursor substance of photochemical oxidant, and
DFPhotoOxidant (Safe, X, Region), the damage factor by area of protection Safe (see Equation
2.5-5).
DI (Safe)   DFX

PhotoOxida nt(Safe，X,Region)  Inv (X,Region)(2.5-5)

RegionDI (Safe) means the amount of latent damage to each area of protection Safe due to emissionof a photochemical oxidant formation substance.
Because, like the characterization factor, the damage factor DF has been obtained for eachzone, if inventory data are classified by zone, it is possible to carry out damage assessmentwith consideration for differences in regional conditions. If inventory has no information onemission areas, damage assessment can be carried out by the use of the national averagedamage factor DFPhotoOxidant (Safe, X, Average) (see Equation 2.5-6).
DI ( Safe)  DF

PhotoOxida nt( Safe，X , Average)  Inv ( X )(2.5-6)X

In this impact category of “photochemical oxidant,” damage assessment can be carried out
concerning human health, social assets, and primary production. If there is an area ofprotection common to two or more impact categories, comparison and integration are
69

LIME2_C2.4-C2.6_2013

possible.
In the case of integration, IFphotoOxidant (X) is used as a factor that integrates human health,social assets, and primary production. The single index SI can be obtained from Inv (X) of
each photochemical oxidant formation substance and the integration factor IFPhotoOxidant (X).
The result can be compared directly with or added to assessment results in other impact
categories (see Equation 2.5-7).
SI 

 IF

PhotoOxida nt(X) Inv(X)(2.5-7)

X

Appendix A1 shows the characterization factors CFPhotoOxidant (X, Region) and CFPhotoOxidant (X,Average). Appendix A2 shows the damage factors DFPhotoOxidant (Safe, X, Region) and
DFPhotoOxidant (Safe, X, Average). Appendix A3 shows the integration factor IFPhotoOxidant (X).

Acknowledgment
For the purpose of the development of this LCIA method, ozone observation data at
Happo-One were provided through the kindness of Takao Katsuno, team leader of the Nagano
Environmental Conservation Research Institute, and Kentaro Murano, Research Director of
the National Institute for Environmental Studies. We express our heartfelt thanks for theirkindness.

[References for Section 2.5]
Andersson ─ Skold Y,Grennfelt P, Pleijel K (1992): Photochemical Ozone Creation Potentials: A study ofDifferent Concepts, J. Air Waste Manage. Assoc., 42( 9): 1152 - 1158
Carter WPL, Atkinson R (1989): Computer modeling study of incremental hydrocarbon reactivity, envir.
Sci.Technol, 23 : 864 - 880
Derwent RG, Jenkin ME (1991): Hydrocarbons and the long ─ range transport of ozone and pan across Europe,
Atomos. Environ, 25: 1661 - 1678 D
EC (1999): ExternE: Externalities of Energy, Vol.7 Methodology 1998 update, Office for Official Publications of
the European Communities
Gery MW, Whitten GZ, Killus JP, Dodge MC (1989): A photochemical kinetics mechanism for urban andregional scale computer modeling J. Geophy. Res., 94 (D10): 12925 - 12956
Goedkoop M, Spriensma R (2000): The Eco ─ indicator 99, A Damage Oriented Method for Life Cycle Impact
Assessment, Methodology Report 2nd edition
Heijungs R, Guinée JB, Huppes G, Lankreijer RM, Udo de Haes HA, Wegener Sleeswijk A, Ansems AMM,
Eggels PG, Diun R van, Goede HP dé (1992): Environmental Life Cycle Assessment of Products; Backgrounds& Guide, Leiden
Hofstetter P (1998): Perspectives in Life Cycle Impact Assessment; A Structured Approach to Combine Modelsof the Technosphere, Ecosphere and Valuesphere, Kluwers Academic Publishers
Jenkin ME, Hayman GD (1999): Photochemical ozone creation potentials for oxygenated volatile organic
compounds: sensitivity to variations in kinetic and mechanistic parameters, Atomos. Environ., 33: 1275 - 1293D

70

LIME2_C2.4-C2.6_2013

Kobayashi K, Okada M, Nouchi I (1995): Effects of ozone on dry matter partitioning and yield of Japanesecultivars of rice (Oryza sativa L.): Agriculture, Ecosystems & Environment, 53 : 109 - 122
Lesser VM, Rawlings JO, Spruill SE, Somerville MC (1990): Ozone Effects on Agricultural Crops : Statistical
Methodologies and Estimated Dose ─ Response Relationships, Crop Science, 30: 148 - 155
Schere KL, Demerjian KL (1984): User’s guide for the photochemical box model (PBM), EPA ─ 600/8 ─ 84
─022a
Steen B (1999): A Systematic Approach to Environmental Priority Strategies in Product Development (EPS)
Version 2000-Models and Data of the Default Method, CPM Report 1999: 5, Chalmers University of Technology,
Göteborg, p.312
UNECE (1990): Draft technical annex on classification of volatile organic compounds based on theirphotochemical ozone creation potential, United Nations Economic Commission for Europe, Geneva
USEPA (1971): Requirements for preparation, adoption, and submittal of implementation plans, Fed. Regist, 36:
15486 - 15506, August 14
USEPA (1989): User’s Manual for OZIPM – 4 (ozone isopleth plotting with optional mechanisms), Vol. I
Ito H, Iwamura H, Saito T, Watanabe N (2000): Small Encyclopedia of Chemical Substances, Iwanami Junior
ShinshoImamura T (2001): Basic Knowledge on Ozone and the Ozone Layer, News of the National Institute forEnvironmental Studies, 20 (3)Utsunomiya A, Doi T, Mizoguchi T (1994): Changes in the Ozone Concentration in Unpolluted Mountainous
Areas, Journal of Japan Society of Air Pollution, 29 (6): 332 - 339
Uno I, Wakamatsu S (1992): Analysis of Winter High-Concentration NO2 Pollution by the Photochemical Box
Model, Journal of Japan Society of Air Pollution, 27 (5): 246 - 257
Uno I, Mitsumoto S (1994): Numerical Simulation of Air Pollution, Journal of Japan Society of Air Pollution, 29(3): A43 - A54
Ohara T, Uno I (1997): Numerical Simulation of Mesoscale Air Pollution, Journal of Japan Society for
Atmospheric Environment, 32 (3): A39 - A57
Operations Entrusted to the Institute of Behavioral Sciences by the Environment Agency (1998): FY1997 Survey
for Development of an Acid Rain Forecast Method
Kono Y, Matsumura H (1999): Multiple Impact of Ozone and Artificial Acid Rain on the Growth of Cedar,
Japanese Cypress, and Sawara Cypress, Journal of Japan Society for Atmospheric Environment, 34 (2), 74 - 85
National Institute for Environmental Studies (2001): Clarification of the Dynamic State of VOC in Urban Areas
and Research on Assessment of Impact on Air Quality (FY1998 to FY2000), Report of Special Research from
National Institute for Environmental Studies (SR-42-2001)
Kondo A, Yamaguchi K, Nishikawa E, Hara T, Okazaki R (1999): Examination of Impact of Emissions from
Ships on the Air Pollution Concentration in the Osaka Bay Area, Journal of Kansai Shipbuilding Association,(231): 101 - 109
Sakurai T (2000): Tunnel Survey on VOCs Emitted from Automobiles, News of the National Institute forEnvironmental Studies, 19 (1)Shinozaki M, Yoshida T, Katatani N, Mizuno T (1984a): Examination of Impact of Intake of Emitted NOx on
Atmospheric O3 Formation Reaction, Environmental Pollution, 19 (3): 113 - 120
Shinozaki M, Yoshida T, Katatani N, Mizuno T (1984b): Examination of Impact of Intake of Emitted NMHC on

71

LIME2_C2.4-C2.6_2013

Atmospheric O3 Formation Reaction, Environmental Pollution, 19 (6): 327 - 334
FY1999 Survey on the Relation between Environmental Impact and Damage – Preparation of Pollution Map(2000)
FY2000 Survey on the Relation between Environmental Impact and Damage (Land Use, Resources Exhaustion,
Waste) (2001)
Matsumura H, Aoki H, Kono Y, Izuta T, Totsuka T (1996): Impact of Ozone on the Dry Growth and Gas
Exchange Rates of Cedar, Japanese Cypress, and Zelkova Seedlings, Journal of Japan Society for Atmospheric
Environment, 31 (6): 247 - 261
Matsumura H, Kobayashi T, Kono Y (1998): Single and Multiple Impact of Ozone and Artificial Acid Rain on
the Dry Growth and Gas Exchange Rates of Cedar, Abies Homolepis, and Zelkova Seedlings, Journal of Japan
Society for Atmospheric Environment, 33 (1): 16 - 35
Wakamatsu S, Shinozaki M (2001): Extensive Air Pollution – Mechanism and Impact on Plants, Popular Science
199, Shokabo

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2.6

Toxic Chemicals (Human Toxicity)

Changes under LIME 2• Under LIME2, damage functions were renewed because the International Agency for
Research on Cancer (IARC) reviewed the carcinogenesis risk of some substances since
LIME 1.
• Although the substances covered by the Pollutant Release and Transfer Register(PRTR) under the Law Concerning Reporting, etc. of Releases to the Environment ofSpecific Chemical Substances and Promoting Improvements in Their Management
were assessed under LIME1, the database on the Integrated Risk Information System(IRIS) of the Environmental Protection Agency (EPA) specifies the unit carcinogenesis
risk of other substances. As a result of an increase in the number of covered
substances by the use of the database, the number of substances in the damage factor
list increased from 135 under LIME 1 to 168 under LIME 2.
• Under LIME 1, the D-R factor for chronic diseases was estimated from the noobservable adverse effect level (NOAEL) and the lowest observed adverse effect level(LOAEL). However, with regard to heavy metals, because quantitative informationon the D-R relationship was obtained from ample examples of epidemiological surveys
and risk assessment documents, the damage functions for chronic disease from heavy
metals were renewed based on the information.
2.6.1

What phenomenon is the human toxicity of toxic chemicals?

(1)

What is the human toxicity of toxic chemicals?

At present, more than 100,000 types of chemicals are produced and used for various purposes
all over the world, greatly contributing to the realization of healthy, safe, and rich lives. On
the other hand, some chemicals have intrinsic “hazards.” They have a “toxic risk” to humanbeings and the ecosystem if human beings and other organisms are exposed to them throughair, water, food, etc. Under LIME, chemicals that may have toxic impact on human healthare called “toxic chemicals with human toxicity.” Human toxicity appears when humanbeings take toxic chemicals into their bodies under the circumstances where they are exposedto them. However, how human toxicity appears differs according to type of chemical (suchas cancer-causing substances and asthma-causing substances). Moreover, because theintensity of human toxicity differs according to the degree of toxicity of chemicals and thedegree of exposure, exposure may lead to slight sickness or fatal cases.
Under LIME, “impact on human health in the case of emission of toxic chemicals with human
toxicity into general environments, such as the atmosphere, water areas, and soil” wasassessed. The following are important points concerning the development of LIME.
•
Because, as described in the beginning, there are various types of chemicals, it isextremely difficult to grasp the toxicity of and exposure to all substances quantitatively.
Therefore, LIME only covers the substances whose impact can be estimated quantitatively
from the existing information.

73

LIME2_C2.4-C2.6_2013•
With regard to the exposure route of toxic substances, it was assumed that exposure
occurred through general environments, such as the atmosphere, water areas, and soil.
Although there are cases where human beings are exposed to toxic substances directly
through working environments and the use of products, such cases were excluded.
•
The “hazard” of toxic substances is classified into the hazard that may have impact if
the threshold level is exceeded (such as acute toxicity and developmental toxicity) and thehazard that has no threshold level and may have impact even if the substance is taken in slightamounts (such as carcinogenesis). If there is a threshold level, whether or not impact exists
differs below and above the level. Because the purpose was to obtain a factor that indicates“risk increment per unit amount of emissions,” an index for the hazard without a thresholdlevel was estimated.
•
Although chemicals move through environmental media, such as the atmosphere, thehydrosphere, and the biosphere, movability differs among substances (such as highly
vaporizable substances and highly soluble substances). In addition, chemicals are changed
through decomposition and oxidation-reduction reaction. Because of this, a box model thattakes into consideration tis complicated material balance and changes (Figure 2.6-1) was used
to estimate the fate of chemicals in the environment ( “fate” means chemicals’ emission into
environmental media, movement through them, transformation/decomposition, and final
distribution in each medium).
•
The routes through which chemicals are taken into human bodies are roughlyclassified into transbronchial (intake by the respiratory system through respiration), oral
(intake by the digestive system through food and drinking water), and endemic (penetration
through the skin). The transtracheal route and the oral route were assessed this time.

大気
Atmospher
e

土Soil
壌

淡水
Fresh
water
底質
Sediment

Figure 2.6-1: Concept of the box model
The box model expresses the environmental media (air, water, soil, etc.) in the spatial sphere(region, continent, etc.) as compartments, and it is assumed that various conditions arehomogeneous within each compartment. Based on the material balance between compartments(transportation between media) and the material balance within a compartment (emission anddecomposition), the substance concentration in each compartment is estimated from theequilibrium condition or the initial value to a certain point of time. There are also a model that
divides an environmental medium into two or more compartments and a model that takes into
consideration material balance with the outside of the system.

(2)

Endpoints of human toxicity of toxic chemicals

The toxic chemicals dealt with herein are those covered by the Pollutant Release and Transfer
Register (PRTR) under the Law Concerning Reporting, etc. of Releases to the Environment ofSpecific Chemical Substances and Promoting Improvements in Their Management (type-1
designated chemicals), and the area of protection was limited to human health (impact on the74

LIME2_C2.4-C2.6_2013

ecosystem is not assessed herein, but will be assessed in Section 2.7).
First, endpoints for human toxicity should be defined from among human diseases.
Under PRTR, carcinogenicity, mutagenicity, oral chronic toxicity, inhalant chronic toxicity,
reproductive/developmental toxicity, and sensitizing property were assessed as toxicity items
against human health. Substances that have any of these types of toxicity have been selected
for assessment (I. (1) of Table 2.6-1). It is said that about 95% of the 354 chemical
substances selected as Type-1 designated chemicals may have toxicity against human health(under the cabinet order revised in 2008, the number of substances was increased to 462 in
April 2010).
Table 2.6-1: Assessment items and criteria for selection of type-1 designated chemicals under PRTR

I. Toxicity

Assessment content(1) Risk of having
damage to humanhealth(2) Inhabitation or
growth of animals
and plants(3) Damage tohuman healththrough ozone
layer destruction“Continuous
existence in aconsiderably wide
local environment”

Item
Carcinogenicity
Mutagenicity
Oral chronic toxicity
Inhalant chronic toxicity
Reproductive/developmental
toxicity (including
teratogenesis)
Sensitizing property
Acceptable concentration in
working environment
Eco-toxicity against aquaticorganisms (alga, water flea,
fishes)Ozone depleting substances

Criterion by item
Class 1 or 2
Existence of reliable data
Class 1 or 2 or 3
Class 1 or 2 or 3
Class 1 or 2 or 3

Criterion

Existence of reliable data
Class 1 or 2 or 3

Chemicals
classified into
any of them

Class 1 or 2 or 3

Substances specified inMontreal Protocol

10 or more tons of annual
Substances
manufacture/ importdesignated in I.
(Cumulative amount ofand falling
manufacture/import in theunder eithercase of ozone depleting
substances)
Detection
Detection from two or
more zones during the past
decade• Prepared based on “Designation of Type-1 Designated Chemicals and Type-2 Designated Chemicals under the
Law Concerning Reporting, etc. of Releases to the Environment of Specific Chemical Substances and Promoting
Improvements in Their Management (Report)” (February 2000).
II. Amount of
exposureAmount ofmanufacture/import• With regard to the selection of type-2 designated chemicals, although the items and criteria for toxicity
assessment are the same as in the case of type-1 designated chemicals, there are differences in the criteria for theamount of exposure.
• “Class 1” and “Class 2”: “Classes” have been defined according to the intensity of toxicity for each assessment
item in “Concrete Criteria for Selection of Chemicals Covered by PRTR and MSDS,” an annex to “Designationof Type-1 Designated Chemicals and Type-2 Designated Chemicals under the Law Concerning Reporting, etc. of
Releases to the Environment of Specific Chemical Substances and Promoting Improvements in Their
Management (Report).”For example, with regard to carcinogenicity, the criteria for classification into each class are as follows:• Class 1: Substances classified into a category equivalent to the assessment “carcinogenic to human beings”
by any of the institutes (IARC, EPA, EU, NTP, ACGIH, Japan Society for Occupational Health)75

LIME2_C2.4-C2.6_2013• Class 2: Substances classified into 2A or 2B by IARC as substances “highly suspected of being
carcinogenic to human beings” or substances classified into a category equivalent to “highly suspected ofbeing carcinogenic to human beings” by other two or more institutes

Risk assessment requires quantitative values concerning dose-response (D-R). Whensubstances were selected under PRTR, quantitative values were used as a criterion for the
existence of toxicity concerning only carcinogenicity and oral/inhalant chronic toxicity among
the toxicity items to be assessed. With regard to the other assessment items, information
sufficient for quantitative assessment has not been obtained. For example, quantitative
information on “positive” or “negative” is used for judging sensitizing property. As forinhalant chronic toxicity, because the quality of data on substances is not integrated, it was
excluded from assessment.
Therefore, carcinogenicity and oral chronic toxicity were selected as the endpoints of human
toxicity to be assessed under LIME (however, with regard to heavy metals newly assessed
under LIME 2, because a D-R factor was obtained based on epidemiological research cases,
inhalant chronic toxicity was assessed. See 2.6.3).
Figure 2.6-2 shows the causality of human toxicity. Toxic chemicals emitted into theenvironment were transformed or decomposed, moving through environmental media, such as
the air, water, and soil. Toxic chemicals in the air are taken into human bodies through air
inhalation, and toxic substances in water areas and soil are taken into human bodies throughoral intake of drinking water and food. These induce illness, such as cancer and chronic
diseases.
Emission of toxic
substances(Heavy metal/organic
compound)Transportation
betweenenvironmental
media/
decomposition
within media

Concentration
change within
environmental
media

Change in theamount of exposure
from inhaled air orthings taken orally

Change in response toeach disease by achange in the amount of
exposure
Impact on endpoint:
human healthCarcinogenicity
Chronic disease

Figure 2.6-2: Causation of human toxicity

2.6.2

Characterization factor for toxic chemicals(1)

Existing characterization factor for toxic chemicals

The characterization factor for toxic substances is called “human toxicity potential” (HTP).
Generally, HTP is calculated with consideration for three processes: fate, exposure/intake, andeffect. Many studies on LCA point out that the fate of emitted toxic substances in theenvironment – especially, “transportation between media” and “decomposition within media”– is important for calculation of a characterization factor (CML 2001; Heijungs et al. 1992;
Hauschild et al. 1998; Guinée et al. 1996; Jolliet et al. 1997; Lindfors et al. 1995; Udo deHaes et al. 1996). An early characterization factor developed in 1992 (Heijungs et al. 1992)
did not take into consideration the fate of emitted chemicals in the environment. At present,
however, three types of HTP, which differ from each other in how the fate in the environmentis treated, have been developed:
1) HTP that semi-quantitatively expresses transportation between media and76

LIME2_C2.4-C2.6_2013

decomposition within media (Hauschild et al. 1998)
2) HTP that expresses transportation between media and decomposition within
media by a numerical model (Guinée et al. 1996, Huijbregts 1999, Hertwich
1999)3) HTP that expresses transportation between media and decomposition within
media by a numerical model and a rule of thumb (Jolliet et al. 1997)
Moreover, in many cases, HTP has been defined as shown in the equation below. However,the names of items differ among cases.

HTPi , ecomp

F

F

i , ecomp , fcomp

 T i , fcomp , r  I r  E i , r

fcomp rref , ecomp , fcomp

 T ref , fcomp , r  I r  E ref , r(2.6-1)

fcomp rIn this equation:
HTPi, ecomp:Fi, ecomp, fcomp:Ti, fcomp, r:
Ir:
Ei,r:
ref:

HTP when the toxic chemical i is emitted into the compartment ecomp
Fate factor – ratio of i emitted into ecomp and having reached the final
compartment fcomp just before exposure
Transportation factor – ratio of i distributed to r, two or more exposure
routes between fcomp and human beings (air inhalation, oral intake ofdrinking water and food, etc.)
Intake factor – ratio of i taken in through r by human beings (such as
the ratio of the amount of human beings’ inhalation to the amount ofair)
Effect factor – ratio of the effect of exposure of i on human toxicity
through r. Ei,r is often given as the reciprocal of the acceptable daily
intake (ADI).
Reference substance – the substance that is selected as the reference
substance differs among cases.

The numerator of Equation 2.6-1 quantitatively expresses the impact of the emission of a unit
amount of a toxic chemical on human toxicity and is called “toxicity potential.” Thecharacterization factor for human toxicity can be obtained by dividing the toxicity potential ofeach toxic chemical by the toxicity potential of the reference substance (the denominator of
Equation 2.6-1).
The following is an overview of two cases where the characterization factor for human
toxicity was obtained by expressing transportation between media and decomposition within
media by a numerical model (Guinée et al. 1996, Huijbregts 1999).
Guinée et al. (1996) regarded four elements – the air, water, agricultural soil, and industrial
soil – as the environmental media and regarded six routes – the air, seafood, drinking water,
grain, beef, and dairy products – as the routes of human exposure. The largest differencewith the method so far (Hauschild et al. 1998) is that consideration was given to
decomposition within environmental media and immobilization (sedimentation on the sea
bottom had been neglected because it takes a lot of time to move to another medium), and77

LIME2_C2.4-C2.6_2013

they were expressed as numerals concerning each substance so that fate analysis could bemodeled more realistically and comprehensively. The potential daily intake (PDI) of a toxic
chemical is expressed as a total estimate of daily intake for each exposure route and is divided
by ADI to obtain toxicity potential.
The reference substance for obtaining thecharacterization factor is 1.4-dichlorobenzene (air emission). HTP was calculated by thefollowing equation:
HTPi , ecomp 

PDI i , ecomp  E i
PDI 1, 4 dichlorobenzene , air  E 1, 4dichlorobenzene(2.6-2)In this equation:
HTPi, ecomp:
PDIi, ecomp:Ei:

HTP when the toxic chemical i is constantly emitted into thecompartment ecomp at a rate of 1,000 kg/day
PDI when i is emitted to ecomp
Effect factor – the reciprocal of ADI of i, which is fixed irrespective of
exposure route

Guinée et al. (1996) calculated HTP on the assumption that a toxic chemical is constantly
emitted. However, this is unsuitable for impact assessment when an additional amount of a
substance over the baseline emission is emitted. Moreover, although transportation between
media was expressed by a numerical model, they did not take into full consideration exposure
routes and intake.
Huijbregts (1999) calculated a characterization factor for human toxicity, improving themethod developed by Guinée et al (1996). The following are main improvements:
1) It became possible to carry out fate analysis of toxic chemicals emitted
inconstantly.
2) An assessment period was adopted for fate analysis so that HTP of substances
likely to remain for a long time (such as heavy metal chemicals) would beweighted comparatively highly.
3) Fate analysis was modeled globally and a nested model was adopted so that space
sizes – regional, continental, and global – could be taken into consideration.
4) The temperature dependencies of steam pressure, solubility, Henry’s constant, anddecomposition speed were taken into consideration.
5) Water areas were classified into freshwaters and salt waters in the case of regionaland global sizes.
HTP is calculated by the following equation:

HTPi , ecomp  PDI PDIr78

sr

i , ecomp , r , s E i, r  N ss1, 4  dichlorobenzene , air , r , s

 E 1, 4 dichlorobenzene , r  N s(2.6-3)

LIME2_C2.4-C2.6_2013

In this equation:
HTPi, ecomp:

HTP when the toxic chemical i is constantly emitted into thecompartment ecomp
PDIi, ecomp,r,s: PDI at a space size of s through the exposure route r when i is emitted
to ecomp – estimation of PDI for each of four types of assessment
periods (20 years, 100 years, 500 years, and an indefinite period after
emission)
Ei,r:Effect factor – the reciprocal of ADI of i through the exposure route r
Ns:
Weighting factor of the space size s set from population density
Although the method developed by Huijbregts (1999) could more realistically model the fateanalysis of toxic chemicals, it has a problem of having considerable uncertainty. This ismainly because of many model parameters. With regard to heavy metal chemicals
especially, HTP greatly differs according to the assessment period. However, pointing out
many problems, Leiden University’s Institute of Environmental Sciences (CML) (2001) has
recommended Huijbregts (1999)’s global HTP with an indefinite assessment period as thefirst choice for the characterization factor.
(2)

Characterization factor of toxic chemicals under LIME

Under LIME also, HTP was developed by the use of a numerical model to fate analysis.
Figure 2.6-3 shows a flowchart of the calculation of the characterization factor. Proceduresfor calculating the characterization factor can be roughly divided into the estimation of PDI
by fate and exposure analysis and the estimation of the impact factor.
Carcinogene
sis risk

Diseasethresholdlevel

Human limited value(HLV)

Potential
daily intake(PDI)

Toxicity potential(PDI/HLV)
Relativization on the basisof benzene (air emission)
Human toxicity
potential (HTP)

Fate and exposure analysis(Amount of a human being’s exposure
to 1kg/year of toxic chemical
emissions)
Unit amount of toxic
emissions Soil
Atmoschemical
Water
phere
areas
Toxic chemical
concentration
SeawatAtmos
Water in eachmedium
er
phere
areas
Groun
Soil
PlantsdwaterExposure concentrationof toxicDrinki
chemicalsPaddy
Atmos
phere
rice
ng
Fresh
Meat
Sea
water
water
fish
Soil
Vegetafish
bles
Amount of intake
InhalatIntakeion

Figure 2.6-3: Flowchart of calculation of human toxicity potentialaFate and exposure analysis

Under LIME, the fate analysis of toxic chemicals in the air was carried out by the use of themultimedia fate model developed by École Polytechnique Fédérale de Lausanne (EPFL).
When the fate analysis was carried out, consideration was given to conditions in Japan in
79

LIME2_C2.4-C2.6_2013

relation to geographical features, population, the amount of intake by type of food, etc. The
following are main characteristics of the model:• The geographical extent of the model is Japan and surrounding sea areas.
• The compartments are the atmospheric boundary layer (lower tropospheric layer),surface water (rivers, freshwater), surface water (lakes, freshwater), freshwater
substratum, marine surface layer, deep seawater, marine substratum, topsoil,
rhizosphere soil, unsaturated layer, vegetation, and urban areas.
• The model takes into consideration transportation between media and decomposition
within media.
• The compartments where toxic chemicals are artificially emitted are the terrestrial
atmospheric boundary layer, surface water, and topsoil (Figure 2.6-4).
• As human exposure routes, oral intake of air inhalation, drinking water, seafood,farm products, beef, and dairy products was taken into consideration (Figure 2.6-5).
By the use of this model, the amount of intake through inhalation and the mouth in the causeof the emission of a unit amount of a substance into a compartment was calculated as PDI[mg/kg/day] (the unit means the amount of intake per kilogram of weight per day).
Upper
troposphereUppertroposphere

Atmosphericboundary
layer
VegetationAtmosphericboundary
layer

Artificial
emission

Topsoil

Urban
area

Rhizosphere soil

Surfacewater(freshwater)

Marinesurfacelayer

Freshwater
substratum

Deepseawater

Unsaturated
layer

Marine substratum

Soil

Urban area

Surfacewater

Terrestrial area (Japan: 377,836 km2)

Marine area(within 12 miles: 430,000 km2)

Artificial emission – a: atmosphere; w: freshwater; s: soil
Decomposition withincompartment between
Transportationcompartments

Terrestrial
Marinearea
areaThis model is based on theassumption that the terrestrial area
is encircled by the marine area.

Figure 2.6-4: Pattern diagram of the fate analysis model under LIME

80

LIME2_C2.4-C2.6_2013

Atmospheric boundary layer

Atmospheric boundary layer

CattleVegetation

Topsoil

Surface water(freshwater)

Urban area
Average for
topsoil andrhizosphere soil

Drinking Rice
water

Beef,
Vegetables, milk
fruitsMarine surface layer
Freshwater fish

Sea fishFish

IntakeRhizosphere soil

Paddy rice

Accidental
intakeFreshwater
substratum
Fish

Deep seawater

Unsaturated layer

Groundwater

Human exposure

Drinking
water

Soil

Urban area

Terrestrial area (Japan: 377,836 km2)

Surface water

Marine substratum

Marine area (within 12 miles: 430,000 km2)

Route of human beings’ exposure
Route of cattle’s exposure

Figure 2.6-5: Exposure routes to be considered by the fate analysis model under LIMEb

Impact factor for toxic chemicals

The maximum limit until which exposure concentration (inhalation) or intake (oral intake) is
unlikely to have impact on human health (the limit is called the “human limited value”(HLV)) was used for the calculation of an impact factor, which was supposed to be thereciprocal of HLV.
If, like chronic diseases, a threshold level of pathogenesis exists concerning exposure
concentration or amount, the threshold level is supposed to be HLV. The ADI value
calculated from the no observed adverse effect level (NOAEL) was used as HVL (Equations
2.6-4 to 2.4-7).
NOAELh = NOAELa / UF a→h / UF sub Chronic→Chronic(2.6-4)

ADI = NOAELh / SF

(2.6-5)Einh = 1 / HLV = 1 / ADIinh(2.6-6)Eoral = 1 / HLV = 1 / ADIoral(2.6-7)In this equation:
NOAELa:NOAELh:UFa→h:NOAEL obtained from animal testing
Human NOAEL
Uncertainty factor concerning extrapolation from animals tohuman beings (species difference)
UFsub Chronic→Chronic: Uncertainty factor concerning extrapolation from sub-chronic tochronic
SF:
Safety factor concerning individual difference81LIME2_C2.4-C2.6_2013

Einh, Eoral:
ADIinh, ADIoral:

Effect factor of inhalation and oral intake of a toxic chemical
ADI [mg/kg/day] of inhalation and oral intake of the chemical

On the other hand, if there is no threshold level of pathogenesis such as cancer, the amount of
intake of a toxic chemical in the case of an increase in the lifetime carcinogenesis risk by 10–6
was supposed to be HLV and was calculated from the unit risk (Equations 2.6-8 and 2.6-9).
Einh = 1 /( 10-6 / URinh )(2.6-8)Eoral = 1 /( 10-6 / URoral )(2.6-9)In this equation:
URinh, URoral:cUnit risk of inhalation and oral intake of the substance [risk/(mg/kg/day)] (for how to obtain each value, see 2.6.3 (3))

Calculation of characterization factors for toxic chemicals

Toxicity potential is the total of the products of PDI of inhalation and oral intake of each toxic
chemical and the effect factor (reciprocal of HLV). The total was divided by the value
calculated in the same way concerning the reference substance to obtain HTP.
Under LIME, benzene emitted into the air was chosen as the reference substance, and thecharacterization factor was calculated for oral chronic toxicity and carcinogenesis each
Cancer( HTPi Chronic,ecomp , HTPi ,ecomp ) (Equations 2.6-10 and 2.6-11).

Chronic
i ,comp

HTP



 PDI

i ,comp , r

 Ei ,rr

 PDI

benzene , air , r

 Ebenzene ,r



 PDI
 PDI

r

HTPi Cancer
,comp



/ ADI i ,rbenzene , air , r/ ADI benzene ,r(2.6-10)

r

 PDI
r

i ,comp ,r Ei ,r PDI benzene ,air ,r  Ebenzene ,rri ,comp , r

r



 PDI

i ,comp ,r/( 106 / URi ,r )r

 PDI benzene ,air ,r /(106 / URbenzene ,r )r

(2.6-11)
Table 2.6-2 shows some of the calculated characterization factors. If there is sufficient
information about the chronic toxicity of a substance, the substance is excluded fromassessment (the substance is indicated by “–”). Note that, because the denominator differs
between the chronic toxicity and the characterization factor, they cannot be compared simply.
With regard to the characterization factors for carcinogenesis, the substances that show large
characterization factors are hexavalent chromium compound (air emission), ethylene oxide(water emission), and acrylamide (water emission). Although it has been recognized thathexavalent chromium compound has the possibility of causing cancer through inhalation, it
has not been well recognized that it has the possibility of causing cancer through oral intake.
Therefore, the characterization factor for air emission, which causes a large quantity of
inhalation exposure, is large, while the characterization factors for water emission and soil
emission are small. On the other hand, it has been recognized that acrylamide and ethylene
82

LIME2_C2.4-C2.6_2013

oxide are carcinogenic both through inhalation and through the oral route, and the result
showed a high characterization factor for water emissions whereby the amount of exposure is
the largest.
With regard to the characterization factors for chronic toxicity, acrylamide (water emission),benezene (water emission), and methacrylic acid (water emission) showed especially large
values. Because the amount of oral exposure is the largest in the case of water emission, theresult showed that the characterization factors for water emission is high.
Table 2.6-2: Characterization factors for human toxicity (partial)

Substance
Acrylamide
Ethyl acrylate
Acrylonitrile
Acetaldehyde
AnilineEthyl acrylate
Hexavalent chromium
compoundLead
BenzeneMethacrylic acidCharacterization factors for
carcinogenesis(Air(Water(Soilemission)emission)emission)
5.52E+02
2.24E+03
1.98E+00
2.31E+00
1.12E+01
1.48E-01
2.96E+01
5.67E+02
4.08E+00
2.11E-01
4.88E-02
8.09E-03
7.39E-01
9.54E+00
4.63E-02
7.35E+01
4.10E+03
3.20E+01

Characterization factors for chronic
toxicity(Air(Water(Soilemission) emission)
emission)
6.68E+00 2.66E+01
2.35E-02
1.17E-02
4.74E-01
1.91E-03-

5.14E+03

3.45E-15

3.44E-14

3.08E-01

9.99E-01

5.30E-01

7.71E+01
1.00E+00-

2.14E+02
2.95E-01-

1.38E+02
2.63E-01-

2.62E+00
1.00E+00
7.52E-01

7.52E+00
1.30E+01
8.66E+00

4.86E+00
2.30E-01
4.81E-02

2.6.3

Damage assessment of toxic chemicals(1)

Basic policies for calculation of damage factors

Table 2.6-3 shows the category endpoints of human toxicity and the objects of calculation ofdamage factors under LIME. Although toxic chemicals cause various diseases, as describedabove, the number of assessment items that enable the obtainment of quantitative information
is limited. Therefore, under LIME, the objects of assessment were limited to carcinogenesis
and oral chronic diseases. However, with regard to the heavy metals newly reassessed under
LIME 2,1 because quantitative information can be obtained based on epidemiological surveys,
inhalant chronic diseases were included in the objects of assessment. Main policies for andcharacteristics of the calculation of damage factors are as follows:• As in the case of characterization factors, the geographical extent of damage factors
is Japan and surrounding sea areas.
• With regard to carcinogenesis, 16 types of cancer, including lung cancer and uterus
cancer, were covered.
• With regard to oral chronic diseases, among the non-communicable diseases (NCDs),
39 diseases, excluding congenital anomaly and oral diseases, were defined as oral
chronic diseases.2 Except for heavy metals, the average for all the diseases was1

2

Seven types of heavy metals were included in the objects of calculation: lead, cadmium, mercury, hexavalent chromium, arsenic, nickel,
and antimony.
It is hard to think that oral diseases are caused by exposure to chemicals. In addition, because DALY for a disease is very small and theincidence is very large (75% of the total incidence of non-communicable diseases), the average DALY for a chronic disease is very small
if oral diseases are taken into consideration. For these reasons, oral diseases were excluded. In addition, because diseases that have83

LIME2_C2.4-C2.6_2013

used for the calculation of “DALY per disease.” With regard to heavy metals, thevalue calculated for each category of the International Classification of Diseases in
ICD-10 codes was used.
• With regard to inhalant chronic diseases, because quantitative and unified datacannot be obtained, they were not included in the objects of assessment under LIME
1. However, they were included in LIME 2 because quantitative information
concerning heavy metals could be obtained based on rich epidemiological surveys.
• Among the PRTR type-1 designated chemicals, the number of chemicals selectedpartially by reason of carcinogenesis is 74, while the number of chemicals selectedpartially by oral chronic toxicity is 151 (before the revision of the relevant cabinet
order in 2008).
Figure 2.6-6 shows the flowchart of the calculation of the damage function and the damage
factor.
Table 2.6-3: Category endpoints and damage functions included in the objects of calculation
concerning human toxicity
Area ofprotection

Objects of calculation of damage
functionsCategory endpoint
Carcinogenesis

Oral chronic
diseasesHuman
healthInhalant
chronic
diseases
Other diseases

Cancer caused by intake ofcarcinogenic toxic chemicals
Diseases caused when a toxic
chemical is repeatedly taken intoa human body through intake offood and drinking water for along period
Diseases caused when a toxic
chemical is repeatedly taken intoa human body through inhalation
for a long period
Other diseases caused throughintake of a toxic chemical

Fate 運命曝露分析
and exposure analysis（1 kg yr-1の有害化学物質の(Amount of human exposure to 1
排出に対する人間の曝露量）
kg/yr of toxic chemical emissions)

16 types of cancer

Setting of “chronic diseases”
with consideration for 39
types of diseases

Setting of “chronic diseases”
with consideration for 39
types of diseases* Limited to heavy metals
Quantitative information ispoor.

単位量の有害化学物質の曝露による疾病リスク
Disease risk due to exposure to a unit amount of a toxic chemical

Exposure to a unit amount of a toxic chemical
単位量の有害化学物質の曝露（吸入，経口摂取）(inhalation, oral intake)

図6-4参照See Figures
2.6-4 and 2.6-5.
ユニットリスク
Unit risk

人間健康のDamagefunction for
ダメージ関数
human health

被害係数（DF）
Damagefactor (DF)
（人間健康のみ）(Humanhealth only)

曝露発症関係
Relation

罹患数
Incidence

日本
Population
人口of Japanbetweenexposure
and
罹患数
Incidencedisease
onset

総DALY
Total
DALY

疾病別
DALY by
DALY
disease

総DALY
Total
DALY

発ガン
Carcinogenes
is

呼吸器系疾患Respiratory

Human 人間健康
healthdiseaseFigure 2.6-6: Flowchart of calculation of damage function and factor for human toxicity

not been caused by toxic chemicals may be included in the 39 types of diseases, it is necessary to examine this closely.

84

LIME2_C2.4-C2.6_2013(2)

Fate analysis of toxic chemicals

Fate analysis results obtained from the calculation of the characterization factor was used.
(3)

Impact factor for toxic chemicals

The intensity of human toxicity in the area of protection of human health is quantitatively
indicated by disability-adjusted life years (DALY). DALY, which was proposed by Murray
et al. (1996a), indicates the amount of damage as loss of life expectancy. DALY is
calculated by the following equation, based on YLL, which is the number of years lost due todeath from disease, and YLD, which is the number of years lost due to survival with disability.
DALY  YLL  YLD(2.6-12)

YLL and YLD are calculated from disease information, such as epidemiological statistical
data and disability weight. With regard to such disease information, sequences to “Global
Health Statistics” (GHS) (Murray et al. 1996a) and “Global Burden of Diseases” (GBD)(Murray et al. 1996b) are available on the website of the World Health Organization (WHO)and include epidemiological statistical data that can be used for the calculation.
If r is the discount rate, YLL and YLD can be calculated according to age and sex as in thefollowing equation (the resultant values are not weighted by age):1  exp rLage,sex (if r=0, YLLage,sex  Lage,sex )r
YLDage,sex  Tdur ,sex Rtreat DWtreated ,age  (1  Rtreat ) DWuntreated ,age 

YLLage,sex (2.6-13)(2.6-14)

Therefore, the total loss of life expectancy at each age group, DALYage [DALYs], can becalculated by the following equation:

DALYage, sex  Ndeath, age, sexYLLage, sex  Ninc, age, sexYLDage, sex(2.6-15)

Therefore, the average DALY per incidence [DALYs/incidence] was calculated by thefollowing equation:

 DALYDALY 
 N

age, sex

sex age(2.6-16)

inc, age, sex

sex age

Table 2.6-4 summarizes the meanings of the variables in the equations and the concrete
sources of values.
Under this method, to calculate DALY suitable for the conditions in Japan, available
information on diseases in Japan was used. If such information was unavailable, GBD, GHS,
and other statistical information (such as WHO) were used, selecting data on conditions
similar to those in Japan (such as data in advanced countries) (Table 2.6-4).

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LIME2_C2.4-C2.6_2013

Table 2.6-4: Variables used for the calculation of DALY and sources of values
Variable

Name of variable

Unit

Source (cancer)

Rinc,age,sex

Age-specificdisease rate

[incidence/100000
persons]

Cancer statistics in Japan(National Cancer Center)

Rdeath,age,sex

Age-specificdeath rate (ASDR)[deaths/100000
persons]

Cancer statistics in Japan(National Cancer Center)

DWuntreated,age

Age-specificdisease duration
Disability weight(treated)
Disability weight(untreated)

RtreatTreated rate

Page,sex

Age-specificpopulation[person]Lage,sex

Average life
expectancy[years]Tdur,age,sex
DWtreated,age

Ninc,age,sex
Ndeath,age,sex

Number ofincidents
Number of deaths[years]NO unitNO unitNO unitGlobal Health Statistics’classification for EME
Global Burden of Disease’sclassification for EME
Global Burden of Disease’sclassification for EME
Global Burden of Disease’sclassification for EME(Necessary only in the case of
DWtreated, age ≠ DWuntreated, age)
Ministry of Health, Labour andWelfare’s Vital Statistics
FY2002 (Ministry of Health,Labour and Welfare’s
Information and Statistics
Department 2001)
Interpolation of WHO’s Life
Table of Japanese Men and
Women

Source(chronic disease)
Global Health
Statistics’classification for
EME
Global Health
Statistics’classification for
EME
Same as left
Same as left
Same as left

Same as left

Global Burden of
Disease’sclassification for
EME

Same as left

[person]= Page,sex * Rinc,age,sex/100000

Same as left

[person]= Page,sex * Rdeath,age,sex/100000

Same as left

DALY and the average DALY (applied to cases where body parts could not be identified) was
calculated concerning cancers about which the unit risk of carcinogenesis could be obtained –mouth and oropharynx cancer, lung cancer, oesophagus cancer, stomach cancer, spleen cancer,
liver/gallbladder cancer, rectum cancer, colon cancer, breast cancer, uterus cancer, ovary
cancer, prostate cancer, bladder cancer, leukemia, lymphoma/myeloma, and skin cancer(Table 2.6-5). Although cancer and chronic disease are lifetime risks, the discount ratio for
future impact r was fixed at 0.
On the other hand, with regard to chronic disease, except for heavy metals, DALY wascalculated for the 36 types of diseases (8 categories) in Table 2.6-6. The resultant DALYs
were averaged with the weight of incidence to obtain DALY for chronic disease. With
regard to heavy metals, DALY in Japan was estimated for each of the 16 large categories of
ICD-10 based on WHO’s DALY for each country and patient statistics.
(4)

Human health: cancer damage analysis

Under LIME, the incidence of cancer during the lifetime, which increases with a unit amountof toxic chemical emissions, is expressed as a unit risk (UR [risk/(mg/kg/day)]) (WHO 1987).
The inhalation unit risk (IUR) and the oral slope factor (OSF) were obtained from thedatabase (USEPA, IRIS Database) of the Integrated Risk Information System (IRIS) of theEnvironmental Protection Agency (EPA) and existing studies (Hofstetter 1998; Crettaz et al.
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LIME2_C2.4-C2.6_2013

Table 2.6-5: DALY for cancer
Name of disease
Oesophagus cancer
Stomach cancer
Colon cancer
Rectum cancer
Liver cancer
Pancreas cancer
Lung cancer
Breast cancer
Uterus cancer
Ovary cancer
Prostate cancer
Leukemia
Mouse and oropharynx cancer
Melanoma and other skin cancers
Bladder cancer
Lymphomas and multiple myeloma
CancerDALYs/incidence[DALYs/inc.]12.757
8.087
7.138
7.413
10.28316.19912.5878.282
7.033
15.2795.271
18.992
8.078
4.368
3.586
11.535
9.339

Incidences in Japan[inc.]
1.54E+04
1.13E+05
6.43E+04
3.49E+04
6.07E+04
2.07E+04
7.02E+04
3.43E+04
1.83E+04
7.05E+03
1.85E+04
8.47E+03
9.51E+03
7.75E+03
1.48E+04
1.63E+04
5.14E+05

DALYs (Japan)[DALYs]
1.96E+05
9.11E+05
4.59E+05
2.59E+05
6.24E+05
3.36E+05
8.84E+05
2.84E+05
1.29E+05
1.08E+05
9.77E+04
1.61E+05
7.68E+04
3.39E+04
5.32E+04
1.88E+05
4.80E+06

Table 2.6-6: DALY for chronic diseases
Category of chronic
diseaseNumber of subcategories(typical diseases)

Nervous/psychiatric
symptom

4 (leg gangrene, retinopathy,
amputation, etc.)
11 (Parkinson’s disease, epilepsy,
melancholia, etc.)

Sensory disease

2 (glaucoma, cataract)

Cardiocirculatory
disease8 (myocardiosis, angina, congested
heart failure, etc.)
2 (asthma, chronic obstructive
pulmonary disease)
3 (digestive ulcer, cirrhosis,
appendicitis, etc.)
3 (nephritis, benign prostatic
hyperplasia, etc.)
3 (rheumatoid arthritis,
osteoarthritis, etc.)－

Diabetes

Respiratory disease
Digestive disease
Genital/urinary
diseaseMusculoskeletal
diseaseChronic disease

DALY[DALY/
incidence]

Incidencesin EME[inc.]DALYs(EME)[DALYs]

1.384

4.40E+06

6.08E+06

0.563

4.46E+07

2.51E+07

1.441

1.92E+05

2.77E+05

11.096

4.31E+06

4.79E+07

1.742

4.26E+06

7.43E+06

1.999

2.03E+06

4.05E+06

0.682

2.74E+06

1.87E+06

2.781

2.63E+06

7.32E+06

1.535

6.51E+07

1.00E+08

Of the 39 types of diseases, the table shows the 36 types about which DALY could be obtained.

lungs through inhalation and indicates the incidence of cancer per unit concentration (1μg/m3) of the toxic chemicals contained in the inhaled air. IUR is multiplied by the daily
amount of inhalation per kg of weight to obtain URinh [risk/ (mg/kg/day)]. OSF covers the
toxic chemicals included in water, food, and other things taken orally into the digestivesystem and indicates the incidence of cancer per mg of intake per kg of weight per day. Thiswas expressed as URoral [risk/ (mg/kg/day)].
If the unit risk cannot be obtained, supplement was made based on the carcinogenic class of
PRTR. Concretely, if substances whose unit risk is already known and whose carcinogenic
class is the same exist in the chemical group to which the type of chemical in question
belongs (the type number under PRTR is the same as the cabinet order number), the averageof their unit risks was applied.
87

LIME2_C2.4-C2.6_2013

In addition, because the carcinogenic risk list of the International Agency for Research on
Cancer (IARC) was partially updated after LIME 1, the carcinogenic classes of the related
substances were changed and the average of the unit risks was recalculated for supplement
under LIME 2. Moreover, because unit risks of chemicals not covered by LIME 1 were
established in EPA’s chemical database IRIS, the number of substances to be assessed wasincreased.
(5)

Human health: damage analysis of chronic diseases

Under LIME, the probability of suffering a chronic disease due to exposure to a unit amountof a toxic chemical was expressed by the D-R factor obtained based on epidemiologicalsurveys. The D-R factor indicates the relation between the amount of intake and theincidence rate. Although chronic diseases are not defined clearly, they are defined as the
non-communicable diseases other than congenital anomaly and oral diseases for the purpose
thereof. The damage factor for chronic diseases is calculated by two methods. One ofthem was used for LIME 1; it is the method whereby the damage factor is calculated throughestimation of the D-R factor from NOAEL and the lowest observed adverse effect level
concerning most chemicals whose detailed epidemiological information is hard to obtain.
The other method was used for heavy metals under LIME 2; it is the method whereby thedamage factor is calculated by reference to the D-R factor based on rich epidemiologicalsurveys and risk assessment documents. The following are explanations for the methods:aChemicals other than heavy metals

Only the oral chronic diseases are dealt with herein. With regard to the inhalant chronic
diseases, because substances differ in uncertainty factor, they cannot be dealt with uniformly
and are excluded here. The D-R factor for chronic diseases due to oral intake of a toxic
substance of 1 mg/kg/day was estimated from EPA’s IRIS Database (USEPA, IRIS Database)and existing studies (Hofstetter 1998; Crettaz et al. 2004a; Crettaz et al. 2004b).
Under PRTR, the following are used as criteria for judging whether a chemical has oral
chronic toxicity:
1) Water quality standard
2) ADI, an index of oral toxicity of agricultural chemicals
3) NOAEL of repeated oral administration4) LOAEL of repeated oral administration
These qualitatively indicate the intensity of toxicity and can be inferred to have a connection
with the D-R factor. Because of this, a method for calculating a D-R factor was developed
and applied from these indices.
The standard value and the index value were converted into a D-R factor by the following
five-step procedure:1)88

Calculation of NOAEL/LOAELLIME2_C2.4-C2.6_2013

If an index value of NOAEL/LOAEL for animals serves as a criterion, the value is used. If
the water quality standard or the agricultural chemical standard serves as a criterion, NOAEL
for animals is estimated from the standard value or the ADI of the agricultural chemical(Equations 2.6-17 and 2.6-18).
• ADI of agricultural chemical
NOAELa = ADI/SF(2.6-17)• Standard value of water quality
NOAELa = WQC/UF × VDW / BW × r(2.6-18)

In this equation:
NOAELa:
SF:
WQC:
UF:
VDW:
BW:
r:
2)

NOAEL for animals
Safety factor for agricultural chemical standard (100)WHO’s or Japan’s standard value of water quality
Uncertainty factor (100)Volume of drinking water (2 L/day)Weight of human body (WHO: 60 kg; Japan: 50 kg)
Contribution rate of drinking water (WHO and Japan: 10%)

Correction of species difference

NOAEL (LOAEL) for animals is converted into NOAEL (LOAEL) for human beings withconsideration for the species difference between human beings and animals. The factor forthe conversion of species difference differs among documents; there is no uniform factor.
The factor has been fixed at 10 for the purpose hereof.
3)

Revision of exposure duration

NOAEL (LOAEL) for sub-chronic toxicity is converted to NOAEL (LOAEL) for chronic
toxicity so that the results of short-term and mid/long-term experiments can be applied tolifelong exposure. Concretely, the empirical value obtained by Lewis et al. (1990) was
adopted (correction factor: 3.3).
4)

Estimation of ED10h by correlating equation

ED10h is estimated from NOAEL (LOAEL). ED10h is 10% effect level [mg/kg/day].
Crettaz et al. (2004a, 2004b) obtained correlating equations, ED10h = 1.6・NOAEL and ED10h= 0.3・LOAEL, from chemicals about which NOAEL/LOAEL and ED10h were found. Theseequations were adopted herein.
5)

Calculation of D-R factor

D-R factor is calculated from ED10h, as expressed by the following equation:89

LIME2_C2.4-C2.6_2013

DR 

0.1
ED10 h  Thr(2.6-19)

Although Thr is the threshold level, as shown in Figure 2.6-7, interpretation can be made in
two ways according to the existence of a threshold level. Under this method, irrespective of
background dose (concentration), it was decided that a D-R factor should be calculated,assuming that Thr = 0, taking into consideration the purpose of obtaining a factor thatindicates a risk increment at every range of low dose (concentration).

D-R coefficient
without threshold level

D-R coefficient withwiththresholdthresholdlevel level

Figure 2.6-7: Difference in interpretation of D-R coefficient according to existence of threshold
level

b

Heavy metals

Epidemiological documents concerning dose-response relationships were examined for eachtype of heavy metal, and a slope of the straight line (hereinafter referred to as the “D-R
factor”) was calculated on the assumption that the dose-response curve can be approximatedto a straight line. Not only oral chronic diseases but also inhalant chronic diseases were
included in the objects of calculation.
1)

Heavy metals covered

Lead, cadmium, mercury, hexavalent chromium, arsenic, nickel, and antimony
2)

Documents examined

Under LIME 2, examination was carried out not about each treatise on an epidemiologicalsurvey of heavy metals but about various risk assessment documents. The following is thelist of assessment documents:• International Program on Chemical Safety (IPCS) INCHEM: Environmental HealthCriteria Monographs (EHCs)• Agency for Toxic Substances and Disease Registry (ATSDR): Toxicological Profile• Environmental Protection Agency (EPA): IRIS
• National Institute of Advanced Industrial Science and Technology, Research Center90

LIME2_C2.4-C2.6_2013

for Chemical Risk Management: Risk Assessment Documents• Ministry of the Environment: Environmental Risk Assessment of Chemicals• New Energy and Industrial Technology Development Organization (NEDO),
National Institute of Technology and Evaluation: Initial Risk Assessments
3)

Arrangement of D-R factors

The D-R factors obtained from the documents were arranged by type of disease and by
exposure route. If D-R factors were different between the sexes and between adults andchildren, the information about them was arranged as well (Table 2.6-7).
Table 2.6-7: D-R factors for heavy metals used for LIME (partial extracts)
Substance

Lead

Disease covered bydamage analysis
Disease

Mercury

Inorganic
mercury
Methylmercury

TargetD-R factor

Inhalation, oral
intakeAdult

7.75E-03 (1/(μg/100mL))

a

Child

3.33E-03 (1/(μg/100mL))b

Reduction in IQ points

Inhalation, oral
intakeChild (up to 1
year)

1.74E-01(points/(μg/100m
L))b

Disorder ofreproductionNaturalabortion

Inhalation, oral
intakePregnant
woman

3.60E-03 (1/(μg/100mL))a, cIschemicheart
diseaseInhalation, oral
intakeHypertension
2.07E-05
incidence

Inhalation, oral
intakeOral intake

Hypertension
3.12E-06 (1/(μg/100mL))
incidenceMan
1.37E-03 (1/(μg/day))

Oral intake

Woman

1.97E-03 (1/(μg/day))b

Inhalation-

3.82E-03 (1/(μg/day))b, eInhalation-

1.43E-03 (1/(μg/day))b, eInhalation-

1.53E-04 (1/(μg/day))b

Inhalation-

4.60E-05 (1/(μg/day))b

Pregnant
woman
Child

3.31E-04 (1/(μg/day))d

3.08E-04 (1/(μg/day))dApoplexy

Hexavalentchromium

Exposure route

Sour
ce

AnemiaHypertensionCadmium

Damage analysis result

Renal tubular disorder
Septonasal
Renal
ulcer
tubular
Septonasal
disorderperforation
Limb sensory organ
disorderAutonomic function
disorderDevelopmental nerve
disorderAbnormal perception

Oral intake
Oral intake(1/(μg/100mL))ddb

The unit of the denominator of the D-R factor for lead indicates not the amount of exposure but an increase in serum leadconcentration. As for the other substances, the denominator indicates the amount of exposure per day.

[Sources]
a Agency for Toxic Substances and Disease Registry (ATSDR): Toxicological Profile Information Sheetb IPCS INCHEM: Environmental Health Criteria Monographs（EHCs）
c Borja-Aburto VH, Hertz-Picciotto I, Lopez MR, et al. 1999. Blood lead levels measured prospectively andrisk of spontaneous abortion. Am J Epidemiol 150: 590-597.
d the Empire State Electric Energy Research Corporation (ESEERCO): NEW YORK STATEENVIRONMENTAL EXTERNALITIES COST STUDY, 1995
e LINDBERG E, HEDENSTIERNA G, (Swedish National Board of Occupational Health and safety) (1983)
Chrome plating: Symptoms, findings in the upper airways, and effects on lung function. Arch Environ Health,38 (6 ): 367-374

91

LIME2_C2.4-C2.6_2013(6)

Damage factor for toxic chemicals

A damage factor for human toxicity is a factor that indicates an increase in the amount ofdamage to human health due to additional emission of a toxic chemical. Two damage
factors are examined herein concerning carcinogenesis and chronic diseases. Under LIME,
it is assumed that the amount of damage increases linearly with emission of a chemical.
Because of this, calculation of a damage function means calculation of its inclination.
aCalculation of the carcinogenic damage function

As shown in Equation 2.6-20, the number of sufferers from each type of cancer was estimatedfrom PDI (daily amount of intake per kg of weight) of inhalation and oral intake gained fromthe result of fate analysis, the unit risk of carcinogenesis (Inhalation: URinh; oral intake:
URoral), and the population of Japan. After that, the number is converted to DALY to obtain
the damage function for carcinogenesis (Equation 2.6-20).
Canci , ecomp  ( PDI i , ecomp , inh   UR inh,i , can  DALYcan
can PDI i , ecomp , oral   UR oral ,i , can  DALYcan )  PopJapan

(2.6-20)can

In this equation:
Canci, ecomp:
PDI i, ecomp, r:
URinh, i, can:URoral, i, can:DALY can:
Pop Japan:b

Factor for the carcinogenic damage function when toxic chemical i isemitted into compartment ecomp [DALYs]
PDI through the exposure route r (oral or inhalant) of toxic chemicali emitted into compartment ecomp [mg/kg/day]
Unit risk of a type of cancer can though inhalation of a toxic
chemical i [risk/ (mg/kg/day)] (conversion based on (IURi,can [risk/(μg/m3)] and the daily amount of inhalation per 1 kg)
Unit risk of a type of cancer can through oral intake of a toxic
chemical i [risk/ (mg/kg/day)] (equal to OSFi,cam)
DALY of a type of cancer can [DALYs/incidence]
Population of Japan (persons)Calculation of damage functions for chronic diseases

As shown in Equation 2.6-21, damage functions for chronic diseases were calculated by theuse of PDI (daily amount of intake per kg of weight) of oral intake obtained from the fateanalysis result, D-R factors for chronic diseases, DALY for chronic diseases estimated fromthe incidence of each disease classified as chronic disease; and the population of Japanbeyond the threshold level.
<Other than heavy metals>

Chronici , ecomp  PDI i , ecomp , oral  DRi  DALYchronic   Pop Japan92(2.6-21)

LIME2_C2.4-C2.6_2013<Heavy metals>
Chronici , ecomp  { PDI i , ecomp , inh  ( DR i ,inh  DALYchronic ) PDI i , ecomp , oral  ( DR i ,oral  DALYchronic )} PopJapan
In this equation,
Chronici, ecomp:DRi,oral:DRi,inh:
DALY chronic:
Pop Japan:

Factor of the damage function for chronic diseases when toxic
chemical i is emitted into compartment ecomp [DALYs]
Risk of suffering a chronic disease through oral intake of toxic
chemical i [risk/ (mg/kg/day)]
Risk of suffering a chronic disease through inhalant exposure of
toxic chemical i [risk/ (mg/kg/day)]DALY for chronic disease [DALYs/incidence]
Population of Japan (persons)[Method of estimating the population that has the risk of suffering a chronic disease]
In the case of a chronic disease, if the amount of exposure exceeds the threshold level, there isa risk of suffering it.
With regard to substances other than heavy metals, because, as described above, the purpose
was to obtain a factor that indicates a risk increment to every extent of low dose(concentration), a D-R factor was fixed on the assumption that Thr = 0 without consideration
for the threshold level. Because of this, the population that has the risk of suffering achronic disease is the population of Japan itself.

Threshold level concerning child
IQ decline

Cumulative distribution of theratio of exposed population

Cumulative distribution of the ratio of
population whose exposure exceeds thethreshold level

Ratio of population
whose exposure exceeds
the threshold level

Figure 2.6-8: Exposed population distribution and the threshold level concerning decline in IQ of infantsless than 1 year old due to exposure to lead (example of estimation by the IEUBK model)

93

LIME2_C2.4-C2.6_2013

On the other hand, with regard to heavy metals, because it was relatively easy to obtain dataon the threshold level for each disease and the daily amount of intake, assessment was carriedout in as much detail as possible. Concretely, by reference to the “Risk Assessment
Documents: Lead” (National Institute of Advanced Industrial Science and Technology,
Research Center for Chemical Risk Management 2006), a probability density distribution was
estimated concerning the daily amount of exposure to heavy metals, collecting information on
the amount of exposure via air, water, food, and soil, and carrying out a Monte Carlosimulation by the use of the statistics software “Crystal Ball” (Kozo Kaikaku Engineering)
and an analysis by the biokinetic model of lead “IEUBK” (USEPA) (Figure 2.6-8). Basedon the estimated probability density distribution, the ratio of the population beyond thethreshold level for each disease was estimated and the population that has the risk of sufferinga chronic disease in Japan was estimated (Equation 2.6-22).
[Population with the risk of suffering a chronic disease in Japan]= [total population of Japan] × [ratio of population whose daily amount of exposure exceeds
the threshold level](2.6-22)c Calculation of the damage factor (DF)
Based on the results of a and b, the damage factor DF was calculated as the total of the factorsof the damage functions for carcinogenesis and oral chronic diseases as shown in the Equation
2.6-23:

DFi , ecomp  Canci , ecomp  Chronici , ecomp(2.6-23)

Table 2.6-8 shows examples of intake efficiency, unit risks (cancer), D-R factors (oral chronic
diseases), and endpoints. Table 2.6-9 shows examples of the damage factor DF for human
toxicity.
Of the substances whose damage factor has been entered, the following substances haverelatively high damage factors: lead, hexavalent chromium compound, and acrylamide (in thecase of air emission); lead, acrylamide, and benzene (in the case of water emission); and lead,hexavalent chromium compound, and ethylene oxide (in the case of soil emission).
Therefore, it is necessary to examine the characteristics of lead, hexavalent chromium,
acylamide, and benzene closely, as follows:
With regard to lead, the contribution of the damage function for chronic toxicity is large in allcases – air, water, and soil emission. When comparison is made among the media to which
the substance is emitted, the damage factor is the highest in the case of water emission. Thisis because, if there is a possibility of suffering a disease due to both inhalant exposure andoral exposure, the amount of oral exposure is larger than the amount of inhalant exposure andtherefore the damage factor is higher in the case of water emission.
With regard to hexavalent chromium, the contribution of the damage function for
carcinogenesis through inhalant exposure is large in the case of air emission, while thecontribution of the damage function for chronic toxicity is large in the case of water and soil
emission. When comparison is made among the media to which the substance is emitted,
the damage factor is the highest in the case of air emission. This is because it has been
pointed out that hexavalent chromium has inhalant carcinogenicity and therefore the damage
94

LIME2_C2.4-C2.6_2013

factor becomes large at the time of air emission, which increases the amount of inhalantemission.
With regard to acrylamide, irrespective of the medium to which the substance is emitted, thecontributions of the damage functions for oral carcinogenesis and oral chronic toxicity are
high. When comparison is made among the media to which the substance is emitted, thedamage factor is the highest in the case of water emission. This is because the amount oforal exposure is higher in the case of water emission.
With regard to benzene, the contribution of the damage function for carcinogenesis throughinhalant exposure is high in the case of air emission, while the contribution of the damage
functions for oral chronic diseases is high in the case of water and soil emission. When
comparison is made among the media to which the substance is emitted, the damage factor isthe largest in the case of water emission. This is because the value of damage function due
to oral exposure is large, and therefore the amount of oral exposure is higher in the case ofwater emission.
(7)

Comparison between LIME 1 and LIME 2 in damage factors

Figure 2.6-9 shows the results of comparison of the damage factors newly calculated under
LIME 2 with those under LIME 1. Damage factors greatly increased concerning some
substances, while they greatly decreased concerning several substances. Generally, there isno great difference between LIME 1 and LIME 2. Some of the substances whose damage
factors greatly increased or decreased are listed below, together with reasons.
1)

Substances whose damage factors greatly increased

Air emission: Cadmium, arsenic, hexavalent chromium compound, etc.
Water and soil emission: cadmium, arsenic, etc.
< Cause >All of them are heavy metals, because epidemiological literature was examined closely tohave the impact of chronic toxicity reflected in assessment results.
2)

Substances whose damage factors greatly decreased

Air emission: bis (2-ethylhexyl) phthalate, methacrylic acid
Water and soil emission: 1,3-butadiene, acetaldehyde, formaldehyde, pentachlorophenol,
nickel, nickel compound, hexavalent chromium compound< Cause >Under LIME 1, to prevent underestimation, damage functions were calculated concerning
both substances with an inhalant carcinogenetic risk and those with an oral carcinogenetic risk.
Under LIME 2, to make the judgment more strictly, damage functions were not calculated
concerning substances that are still not recognized as having an inhalant carcinogenetic risk or
an oral carcinogenetic risk. In addition, with regard to bis (2-ethylhexyl) phthalate, because
the literature value adopted for LIME 1 as the no observed adverse effect level (NOAEL) was
considerably high and the reliability of the value was doubtful, the value entered in IRIS was
newly adopted.
95

LIME2_C2.4-C2.6_2013

Substance

Oral intake efficiency[mg-chem intake/mg-chem
emitted/Japanese pop.][mg-chem intake/mg-chem
emitted/Japanese pop.]

Intakeefficiency†1

Table 2.6-8: Intake efficiency, unit risks (cancer), D-R factors (oral chronic diseases), and endpoints (partial)
Inhalant intake efficiency

IUR[risk/(μg/m3)]†2

Cancer byinhalation

OSF[risk/(mg/kg-day)]†2

Oral carcinogenesisAiremissionWateremission

SoilemissionAiremissionWateremission

Soilemission

Acrylamide

1.08E-06

1.13E-11

7.10E-11

5.32E-06

2.55E-05

2.24E-08

1.30E-03

αAv. for cancer

4.50E+00

αAv. for cancer +uterus cancer +oral/pharynx cancer

Ethyl acrylate

1.43E-06

2.63E-07

8.16E-08

4.40E-09

2.63E-05

3.89E-08

2.74E-05

ε

Av. for cancer

2.10E+02γ

Av. for cancer

5.40E-01

Av. for cancer +melanoma andαother skin cancer +stomach cancer

Acrylonitrile

7.19E-06

2.86E-06

4.41E-07

8.73E-08

5.25E-05

2.19E-07

6.80E-05

αLung cancer

Acetaldehyde

1.03E-06

3.77E-07

6.26E-08

1.87E-08

3.08E-05

4.45E-08

2.20E-06

αAv. for cancer

Aniline

1.56E-06

8.70E-08

2.45E-08

5.30E-07

8.51E-05

3.19E-07

7.40E-06

β

Av. for cancer

5.70E-03

αAv. for cancer

1.08E-05

5.10E-06

8.47E-07

1.41E-07

5.83E-05

3.86E-07

1.00E-04

β

Av. for cancer

3.57E+02

ε

Av. for cancer

7.28E-06

4.90E-24

4.88E-23

2.81E-05

1.15E-04

6.10E-05

1.20E-02

αLung cancer

Benzene

4.40E-06

1.68E-06

8.40E-07

2.07E-09

5.18E-05

1.04E-07

4.14E-06

αLeukemia

Methacrylicacid

3.47E-06

5.08E-08

4.53E-08

5.17E-06

9.95E-05

5.07E-07

Ethylene
oxide
Hexavalentchromiumcompoundα†1 Note on intake efficiency:
α: The intake efficiency of the same metal element was used as a substitute.
†2 About carcinogenic OSF (oral slope factor) and IUR (inhalation unit risk)
α: Cited from the database of IRIS
β: Cited from Hofstetter’s literature
γ: Cited from Crettaz’s literature
δ: The same carcinogenesis class and the same chemical species were used as substitutes.
ε: The same cancer class geometric average under PRTR

D-R factor[risk/(mg/kg-day)]†3

1.24E+02

α

Chronic
disease

6.60E+01

γ

Chronic
disease

Septonasal
perforation

2.39E+02
2.87E-02

αLeukemia

Chronic
toxicity†4

2.89E+01

α

1.03E+01

δChronic
diseaseChronic
disease†3 Sources of original data for calculation of chronic disease D-R factors (other than heavy
metals)α: Water qualification standard (WHO)β: Long-term NOAEL (IRIS)
γ: Long-term LOAEL (IRIS)
δ: Irregular NOAEL (assessment sheet)
ε: Agricultural chemical ADI* With regard to heavy metals, D-R factors have been cited directly from various
epidemiological survey cases.
†4 There are several types of chronic diseases to be assessed concerning heavy metals. Oneof them has been picked up herein.

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LIME2_C2.4-C2.6_2013

Table 2.6-9: Damage factors for human toxicity (partial)
Damage factor for humancarcinogenesis through inhalantexposure

Damage factor for humancarcinogenesis through oral
exposure

Damage factor for chronic
toxicity through oral exposure

Damage factors for carcinogenesis
through inhalant and oral exposureand chronic toxicity through oral
exposure[DALYs/kg][DALYs/kg][DALYs/kg][DALYs/kg]

Substance(Air(Water(Soil(Air(Water(Soil(Air(Water(Soil(Air(Wateremission) emission) emission) emission) emission) emission) emission) emission) emission) emission) emission)(Soilemission)

Acrylamide

2.58E-05

2.70E-10

1.69E-09

3.82E-04

1.83E-03

1.61E-06

Ethyl acrylate

7.19E-07

1.32E-07

4.09E-08

5.63E-10

3.37E-06

Acrylonitrile

1.20E-05

4.79E-06

7.39E-07

6.70E-07

4.03E-04

Acetaldehyde

6.55E-08

1.52E-08

2.52E-09

Aniline

2.11E-07

1.18E-08

3.32E-09

1.84E-08

2.95E-06

1.11E-08

Ethylene oxide

1.98E-05

9.32E-06

1.55E-06

3.06E-06

1.27E-03

8.39E-06

Hexavalentchromiumcompound2.15E-03

1.45E-21

1.44E-20

Lead

3.65E-06

0.00E+00

4.85E-23

2.03E-05

6.65E-05

Benzene

6.32E-07

2.59E-07

1.29E-07

7.36E-10

1.84E-05

Methacrylicacid

6.59E-04

1.07E-03

4.98E-03

4.39E-06

4.97E-09

7.09E-07

3.50E-06

4.59E-08

1.68E-06

1.27E-05

4.08E-04

2.42E-06

6.55E-08

1.52E-08

2.52E-09

5.80E-07

5.92E-05

2.25E-07

2.29E-05

1.28E-03

9.94E-06

3.50E-07

3.15E-03

5.62E-05

2.78E-06

2.11E-07

2.90E-05

1.19E-04

6.29E-05

2.18E-03

1.19E-04

6.29E-05

4.30E-05

1.98E-02

4.75E-02

3.60E-02

1.98E-02

4.76E-02

3.60E-02

3.70E-08

5.98E-08

1.50E-03

3.01E-06

6.92E-07

1.51E-03

3.17E-06

5.33E-05

1.03E-03

5.24E-06

5.33E-05

1.03E-03

5.24E-06

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LIME2_C2.4-C2.6_2013

1E+04
1E+02

Air

Cadmium

Water
1E+00
1E-02

Soil

Arsenic
Hexavalent chromium compound(Calcium chromate)

Pentachlorophenol

LIME 2 (DALY/kg）

1E-04

Bis (2-ethylhexyl) phthalate

1E-06
1E-08

1,3-butadiene

1E-10

Acetaldehyde
1E-12

Formaldehyde

1E-14

Nickel
1E-16

Nickel compound(Nickel sulfide)

1E-18
1E-20

Hexavalent chromium compound(Calcium chromate)Hexavalent chromium compound(Zinc chromate, etc.)

1E-22
1E-22 1E-20 1E-18 1E-16 1E-14 1E-12 1E-10 1E-08 1E-06 1E-04 1E-02 1E+00 1E+02 1E+04

LIME 1 (DALY/kg)

Figure 2.6-9: Comparison between LIME 1 and LIME 2 (toxic chemicals)

2.6-4

Procedure for impact assessment of a toxic chemicalThis part concretely describes the procedure for impact assessment of a toxic chemical,
including characterization, damage assessment, and weighting. Although the toxic chemical
and the medium to which the substance is emitted were so far described as i and r respectively,
they are described as X and R respectively herein to coordinate with the other impact
categories (other sections).
Users can select what is suitable for their purpose from among characterization, damage
assessment, and weighting and use it for LCA.
The characterization results CIHumanTox_Cancer and CIHumanTox_Chronic can be obtained from Inv(X,R), the amount of emission (inventory) of the toxic chemical X to the medium to which thesubstance is emitted (air, water, soil) R, and the characterization factors for cancer and chronic
toxicity CFHumanTox_Cancer(X,R) and CFHumantox_Chronic(X,R) (Equations 2.6-24 and 2.6-25).
CI HumanTox _ Cancer   CF HumanTox _ Cancer ( X , R)  Inv ( X , R)X

CI HumanTox _ Chronic   CF HumanTox _ Chronic ( X , R)  Inv ( X , R)X

(2.6-24)R(2.6-25)

R

Because the characterization factor CFHumanTox (X,R) differs according to R, the medium towhich the substance is emitted, it is necessary to divide inventory data according to it.

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LIME2_C2.4-C2.6_2013

Column 2.6-1
Relation between [damage factor] and [damage factor × amount of emissions]
A damage factor indicates the amount of damage due to a unit amount of chemical
emissions. Given the actual amount of emissions in the whole Japan, how does the amountof damage by each substance change? In this column, the relation between [damage
factor] and [damage factor × PRTR amount of emissions] was analyzed by the use of theaverage of the past five years’ PRTR data on the amount of chemical emissions in the whole
Japan (FY2002 to FY2006). Figure 2.6-A shows the result.
Substances can be classified into three groups according to changes in ranking.
(1) Substances that hold a high rank in both [damage factor] and [damage factor × PRTR
amount of emissions](2) Substances that hold a considerably lower rank in [damage factor × PRTR amount ofemissions] than the rank in [damage factor](3) Substances that hold a considerably higher rank in [damage factor × PRTR amount ofemissions] than the rank in [damage factor]
Substances falling under (1) cause both a large amount of damage per unit amount and alarge amount of damage in the whole of Japan. Substances falling under (2) cause a
relatively small amount of damage due to a relatively small amount of emissions in Japan,
although the amount of damage per unit amount is high. Substances falling under (3) causea large amount of damage due to a large amount of emissions in Japan, although the amountof damage per unit amount is small.
Some of the substances falling under each of the three groups are listed below. Heavy
metals fall under (1), and dioxin falls under (2). In addition, agricultural chemicals fallunder (3) because the amount of emissions not reported is relatively large. However,attention must be paid to the fact that uncertainty is high because the amount of emissions
not reported is an estimate.
[(1) Substances that hold high ranks in both [damage factor] and [damage factor ×
PRTR amount of emissions]]
Lead: [damage factor] 4th place → [damage factor × PRTR amount of emissions] 1st place
Cadmium: [damage factor] 3rd place → [damage factor × PRTR amount of emissions] 7th
place
Arsenic: [damage factor] 8th place → [damage factor × PRTR amount of emissions] 8thplace[(2) Substances that hold a high rank in [damage factor] but a low rank in [damage
factor × PRTR amount of emissions]]
2,3,7,8-Tetrachlorodibenzo-p-dioxin (dioxin group): [damage factor] 1st place → [damage
factor × PRTR amount of emissions] 27th place
Berylium: [damage factor] 12th place → [damage factor × PRTR amount of emissions]72nd place99

LIME2_C2.4-C2.6_2013[(3) Substances that hold a low rank in [damage factor] but a high rank in [damage
factor × PRTR amount of emissions]]
1,3-Dichloropropene: [damage factor] 40th place → [damage factor × PRTR amount ofemissions] 3rd place
2-Thioxo-3,5-dimethyltetrahydro-2H-1,3,5-thiadiazine: [damage factor] 56th place →[damage factor × PRTR amount of emissions] 6th place
Ranking in[amount of emissions × damage factor]【排出量×被害係数】の順位

Group 1
1

21

Ranking in【被害係数】の順位[damage factor]
41

61

81

101

121

1

Group 3
21
41
61
81
101
121

Group 2

Figure 2.6-4A: Relation between [damage factor] and [damage factor × amount of emissions]

There are several methods for calculating a characteristic factor. LIME recommends that
HTP, characterization factors obtained from the ratio between the result of fate and exposure
analysis and the permissible amount of intake based on the environmental conditions in Japan,
be used as a list of characterization factors. Because HTP is a set of factors based on the air
emission of benzene, CIHumanTox can be regarded as the total emissions of toxic chemicals
converted into the amount of air emissions of benzene, the reference substance. Given thatthe meaning of the threshold level differs between carcinogenic substances and chronic
toxicity, it became possible to carry out assessment after distinguishing carcinogenesis fromchronic toxicity.
In addition, the damage assessment result DI (Safe) can be obtained from each toxic
chemical’s Inv (X, R) and the damage factor for Safe, each area of protection, DFHumanTox (Safe,
X, R) (Equation 2.6-26):
DI ( Safe)   DF HumanTox ( Safe，
X , R)  Inv ( X , R)X

(2.6-26)

R

Because, as in the case of CIHumanTox, the damage factor DFHumanTox (Safe, X, R) differs
according to R, the medium to which the toxic chemical is emitted, inventory data must be
divided according to it.
DI (Safe) means the amount of latent damage to the area of protection Safe due to toxic
chemical emissions. It is possible to carry out damage assessment of toxic chemicals against
human health. DI (Safe) can be compared and integrated with the amount of damage to thearea of protection common to different impact categories – that is, the amount of damage tohuman health through the impact categories other than toxic chemicals.
The integration factor converted economically from impact on human health or converted into
zero dimension IFHumanTox (X, R) are used for integration. The single index SI can beobtained from each toxic chemical’s Inv (X, R) and the integration factor IFHumanTox (X, R).
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LIME2_C2.4-C2.6_2013

The result can be compared directly with or added to assessment results in other impact
categories (Equation 2.6-27).
SI 

 IF

HumanTox(X)  Inv(X)(2.6-27)

X

Appendices A1, A2, and A3 show the characterization factor CFHumanTox (X,R), the damage
factor DFHumanTox (Safe, X, R), and the integration factor IFHumanTox (X, R), respectively.

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Murray CLJ et al. (1996a): Global Health Statistics, Harvard University Press, Boston
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Udo de Haes HA et al. (1996): Practical Guidelines for Life Cycle Assessment for the EU Ecolabeling
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