10th Asian Regional Conference of IAEG (2015)

Geochemical risk assessment of highly specified road
Mariko MONMA(1) and Akihisa HOSONO(2)(1) Oyo Corporation, Japan
E-mail:monma-mariko@oyonet.oyo.co.jp(2) Oyo Corporation, Japan
Abstract
In order to determine geochemical safety route for highly specified road in mining
area, natural background of arsenic and heavy metals was evaluated, and geochemical risk
assessment was performed. The study area, including two candidates of the routes (A routeand B route), was divided into 1 km mesh, and one sample from soil layer B was collectedfrom individual mesh. Based on chemical composition of the sample, we carried out
multivariate analysis to evaluate geochemical risks of arsenic and heavy metals. Based on
distribution of arsenic and heavy metals and principal factor of the multivariate analysis, we
could determine the geochemical safety route, here, A route was adequate plan to build thehighly specified road. Base on risk assessment and estimation of total amount of excavatedrocks, 7.8 % of the total excavated rocks should have high risks of heavy metals, and then
7.4% of excavated rocks was recognized as high risk, which was very good corresponded toestimated value.
Keywords: heavy metal, excavated rocks, geochemistry figure, analysis of surface soil
1.

Introduction

The geological feature of this area receives
mineralization over a wide area and includes muchheavy metals.
Hydrothermal alteration is weak in the northernpart of investigation area including the A route and avein deposit excels, but is small for the scale in thisarea. Other than it, a small manganese deposits are
distributed. On the other hand, the southern part is aprominent area of the mineralization, and Kuroko
deposits and vein deposits are distributed around B
route.

In the ground constituting Japan which is a world
eminent volcano country, heavy metals supplied froma deep part under the ground is taken after various
processes. These heavy metals may spread in what is
dug with construction in neighboring environment,
and in late years this is taken up as a heavy metals
problem derived from nature (Hattori et al., 2003;
Hosokawa et al., 2007).
We carried out the route choice of the highly
specified road by geochemical technique from a pointof view that prevented environmental influence with
the natural-origin heavy metals included in the
excavated rocks from tunnels.
2.

3.

Heavy metal risk evaluation by
the geochemical technique

We evaluated by the geochemical map in a heavy
metal risk in this route choice. The geochemical map
is an elemental concentration distribution map(Shiikawa et al., 1975). The geochemical map wasused by discovering a collection of local body of theheavy metals on the surface of the ground to explore
the deposit that there was in the outskirts. Generally,
the investigation area is divided by mesh, and the soil
or riverbed sediment are gathered from therepresentative spot in the mesh and the element
included in them is analyzed and is plotted according
to concentration. It is understood that these analyses
level shows the background concentration of element

Distribution of the stratum of theroad planning routes

Two route plans (A route and B route) wereplanned in a highly specified road (section of
approximately 14km).
The volcanic rock which is main in so-calledgreen tuff, a volcano clastic form rock and asedimentation rock of the Miocene, are distributed
along the plan route. And bench sediment andpyroclastic flow sediment of the Quaternary coatthese.

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10th Asian Regional Conference of IAEG (2015)of interest in the mesh.

interpreted it based on elemental symbiosis as follows.
The 1st factor shows strong correlation with copper,lead, zinc, mercury and arsenic and shows slightly
strong correlation with cadmium. These results
suggest that each element except manganese is
symbiosis. From this, we can estimate that theseelemental distribution is regulated by the distributionof the mineralization zone. The 2nd factor shows only
manganese and strong correlation. From this, it isguessed that the distribution of manganese isunrelated to other elements.
Then, we show the thing which plotted the factor
score of each sample provided by a factor analysis in
Fig. 1. In the point colored in the 1st factor score
distribution map densely, it is thought with the area
where a deposit mainly composed of copper, lead andzinc is distributed over. In that figure, on the east sideof the A route, the center and the east side of the B
route, the high point of the factor score is recognized.
On the other hand, in the point colored in the 2nd
factor score distribution map densely, the possibility
that a rock including manganese primodial is
distributed, or a manganese deposit does existence. Inthat figure, the high point of the factor score isrecognized on the east side of the A route, the centerand the east side of the B route. From theabove-mentioned result, we understood that the 1st
factor score was the area where the area where themesh indicating the positive price was distributed
over was high in content such as the heavy metal. The
1st factor score distribution map shows that only one
place of mesh indicating the factor score becomes the
positive number on the A route, in other hand, on the
B route, such meshes are distributed for the whole of
the route.

3.1 Method of sampling and analysis
Mesh of 1km square was set around each route,and a sample was gathered for 79 mesh in total. The
representative spot was chosen in each mesh, and thesoil of around 1 kg was sampled from there. The soil
is divided into the A to C layer towards a deep part byappearance. The soil was sampled by B- layer said
that the materials such as inorganic elements wereaccumulated most. The sampled soil was brought into
a room and was dried naturally. The enough dry soil
sample was sieved by a 100 mesh sieve (the size ofcavity is 0.147mm) and was used for analysis by theabsorptiometry. The sample was analyzed about
cadmium, lead, arsenic, mercury, copper, manganese,zinc.
3.2 Relative examination between the elements by
the multivariate analysis
A multivariate analysis was carried out about eachelemental concentration to consider the association(symbiosis) between the elements included in the soil
sample. A multivariate analysis included sometechnique, but we carried out principal component
analysis and a factor analysis. We show thecontribution ratio of factor loading to express thecorrelation of principal ingredient and each element,
eigenvalue to express the dispersion of the factor
which we demanded in principal ingredient analysis
and each factor to Table 1. In addition, as for thenumber of the factors, two were adopted because theaccumulation contribution ratio to the 2nd factor
became 77.2%.
Then, about the meaning that the first and 2nd
factor that had the substantial contribution, weTable 1 Factor loadings, eigenvalue and contribution of each factor
Principal components

1

2

3

4

5

6

7

Cd

0.642

- 0.289

0.696

0.033

0.133

0.002

0.034

Cu

0.944

0.034

- 0.114- 0.109- 0.228

0.030

0.174

Pb

0.948

0.008

- 0.0880.074- 0.027- 0.290- 0.047

Zn

0.936

0.041

0.090

- 0.107- 0.2570.129- 0.143Hg

0.809- 0.103- 0.260- 0.395

0.330

0.051

- 0.013As

0.785- 0.017- 0.215

0.554

0.139

0.106

0.002

Mn

0.214

0.955

0.180

- 0.015

0.098

0.004

0.009

Eigenvalue

4.39

1.01

0.66

0.49

0.27

0.12

0.05

Contribution (%)

62.8

14.4

9.4

7.0

3.9

1.7

0.8

Cumulative contribution (%)

62.8

77.2

86.6

93.6

97.5

99.2

100.0

Factorloadings

2

10th Asian Regional Conference of IAEG (2015)

N
W

E

A

B

C

D

E

F

G

H

I

J

K

L

M

N

score corresponded to the mesh indicating thepositive number was considered high in heavy metal
content. Then, we found a ratio of quantity of heavy
metal high content rocks in the quantity of all
excavated rocks and show it in Table 2. A ratio of
heavy metal high content rocks quantity in thequantity of all excavated rocks was 7.8% by the A
route as we showed it in Table 2. On the other hand,
the ratio became 67.0% by the B route. From theseresults, it became clear that there were few areas
where heavy metal treatment needed A route than B
route. It was judged this result to have a superiority of
the A route in environmental conservation andconstruction cost than B route. Based on theabove-mentioned result, A route was finally chosen.

O

S

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

a) 1st factor
N
W

E

A

B

C

D

E

F

G

H

I

J

1km

K

L

M

N

3.3 Inspection of the validity of the route choicebased on the construction results
The A route was finally designed for a routeconsisting of five tunnels. In these tunnels,
classification of the excavated rocks was performed
by geologic confirmation by the prior evaluation andface observation of the heavy metal risk by the
analysis of the advanced bowling core. As a result,the excavated rocks determined that measures werenecessary was 10.2% in the whole. These excavatedrocks having a risk of fluorine that was not surveyed
at the time of the route choice is included in this.
Therefore, the excavated rocks of measures to the
exclusion of it required becomes 7.4%.This is a value
at the same level as 7.8% which is a ratio of quantityof heavy metal high content rocks estimated based ona multivariate analysis result that was shown in
Table-1. The forecast about the quantity of excavatedrocks needing measures is judged to have been properby this result.
Then, we show the area where measures of the
excavated rocks were required in Fig. 2.

O

S

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

b)2 nd factor

1km

Kuroko ore body

1.0≦X
Mine(Kuroko deposit)

0.5≦X＜1.0

Mine(Vein type)

0.0≦X＜0.5

Mine(Manganese deposit)

X＜0.0

Fig.1 Distribution map of factor score
Then, we evaluated the heavy metal content of the
B route by the comparison with the factor score ofwhich nearby the C tunnel located in theneighborhood. Because quantity of elution of copper,lead, iron from a rock targeted for digging exceeded
waste water regulations in the C tunnel, treatment of
excavated rocks was performed. From this, it isjudged when there is the concentration of heavy metal
included in the rock around the C tunnel in the levelthat should be handled. It became clear that the factor
score of the B route showed the value that 1 rank was
higher in than C tunnel to the whole by comparing thefactor score of the B route with the factor score of theneighborhood of this C tunnel. It was expected in the
B route by this result that treatment of excavatedrocks was necessary for extension more than C
tunnel.
Based on the above-mentioned result, all the
excavated rocks of the range where the 1st factor

Area of exceed a standard (selenium,arsenic)
Area of exceed a standard (fluorine)

1km

1.0≦X

0.5≦X＜1.0
0.0≦X＜0.5
X＜0.0

Fig.2 Area of exceed a standard and distributionof 1st factor score3

10th Asian Regional Conference of IAEG (2015)

Table 2 Rate of the heavy metal quantity content rocks in each route（×10,000m3）
A routeB routeAmount of Surplus soil
cutting

Mesh
No.

Includingtotal

Amount of Surplus soil

tunnelMesh

Includinghigh-concentratio

total

n heavy metals

cuttingNo.

high-concentratio

tunnelIncludingtotaln heavy metals

high-concentratio

Includingtotaln heavy metals

high-concentration heavy metals

A-7

28.0

0.0

－

－

B-8

5.8

0.0

5.0

0.0

B-7

93.2

0.0

－

－

C-8

1.3

1.3

12.6

12.6

B-6

2.0

0.0

－

－

D-8

－

－

1.6

1.6

C-6

－

－

18.2

0.0

D-9

－

－

12.6

12.6

1.9

－

0.0

C-5

－

－

0.0

E-9

D-5

16.9

0.0

－

－

F-9

D-4

1.0

0.0

－

－

F-10

4.9
－

16.0
－

－

－

－

－

E-4

2.3

0.0

15.4

0.0

G-10

F-4

16.9

0.0

－

－

H-10

G-5

－

－

－

－

H-11

G-4

13.1

0.0

－

－

I-11

－

H-4

31.7

0.0

3.8

0.0

I-12

－

－

－

－

H-3

－

－

8.8

8.8

J-11

33.9

33.9

－

－

I-3

－

－

13.6

13.6

K-11

12.7

12.7

－

J-3

3.7

0.0

－

－

L-11

8.4

0.0

J-2

0.9

0.0

10.2

0.0

M-11

1.9

1.9

K-2

3.9

0.0

0.6

0.0

N-11

0.1

0.1

－

K-1

－

－

－

N-12

4.5

4.5

－

－

O-12

1.0

1.0

－

－

total

77.8

58.7

72.8

total

213.6

－

0.0

72.5

22.4

1.3

－
0.0

－
2.0

1.3

10.8

－

－

10.8

2.0

－

－

－

－

－

－

－

9.6

0.0

4.6

4.6
－

42.2

The rate of the heavy metal quantity content rocks in the The rate of the heavy metal quantity content rocks in theamount of whole surplus rocks

amount of whole surplus rocks

7.8%

67.0%

The figure shows the fact that measures of the
excavated rocks came to need almost near of the areathat the 1st factor score is high in except the areas
where fluorine which was the outside targeted for an
evaluation exceeded a standard at the time of theroute choice and the western area where selenium,
arsenic exceeded a standard. From this result, it isguessed that the quantity of excavated rocks needingmeasures largely increased when B route is chosen.
In this way, the comparison between analysis result
and construction results shows that the technique of
this heavy metal risk evaluation was proper.

specified road concerned and people concerned
deeply.
References

Hattori,S.,Ohta,T.,Kiya,H. (2003) ： Engineering
Study on Exudatoin of Acid Water from Rock
Mucks –Evaluation Methods of Rocks at the
Hakkouda Tunnel near Mine Area-, Jour. Japan
Soc. Eng. Geol., Vol.43，No.6，pp.359-371．
Hosokawa,T.,Sugai,K.,Yamazaki,M.(2007)：
Identification and Countermeasure of Tunnel
Excavation Muck Leaching Heavy Metal–Yukisawa-Daini-Tunnel, in Nihonkai-Engan
Tohoku Expressway from Oodate to Kosaka
Section, Jour. Japan Soc. Eng. Geol., Vol.47，
No.6，pp.346-353．
Shiikawa,M.,Kuboota,H.,Ono,K.(1975) : Study on
Geochemical Mapping and Technique,Reprint
from Memories of the Faculty of Education Akita
University Natural Science,No.26.

Acknowledgements
On these investigations, analysis, emeritus at lateProfessor Makoto Shiikawa gave much advice andinstruction to me. In addition, Professor Noriyoshi
Tsuchiya gave useful advice to me on making thearticle. I appreciate that I write it down here. In
addition, I thank Ministry of Land, Infrastructure andTransport which is the manager of the highly

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