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drainage area map of Hadano City. Then, to verify the accuracy of data with respect to
the real condition of the study site, they were checked by the concerned officers of
Hadano City and the data were revised based on their advices. Accurate locations of the
monitoring stations were digitally recorded by using Mobile GIS (ArcPad and GPS)
(Fig. 2). Ochiaibashi monitoring station of Kaname River was situated before any rivers
merged with it and Shinsaibashi monitoring station was located after Kuzuha,
Mizunashi and Muro rivers along with its other tributaries merged with it.
Water Sampling and Chemical Analysis
At the allocated monitoring stations (Table 1), water samples were collected once every
two months from May 2009 to March 2010. Water samples (1500 mL each) were
collected manually at each station using polyethylene bottles (1000 mL and 500 mL).
The pre-washed bottles were rinsed thrice with water samples on the site before sample
collection. Water samples were stored in a cooler box and transported to the laboratory.
Chemical oxygen demand (COD) concentration was determined by analyzing the
oxygen demand by potassium permanganate at 100
o
C (COD
Mn
) (JIS K0102 17). Total
nitrogen (TN) concentration was determined by ultraviolet absorption photometry (JIS
K0102 45.2) and total phosphorus (TP) concentration was determined by potassium
peroxydisulfate resolution method (JIS KO102 46.3.1). These analytical methods for the
determination of COD, TN and TP concentrations were based on the testing methods for
industrial wastewater, Japan Industrial Standard (JIS) KO102 published by Japanese
Standards Association in 2009. Electrical conductivity (EC) was measured on-site using
an EC meter (ES-51; Horiba, Tokyo). The EC meter was first calibrated using a
standard solution of potassium chloride. A conversion factor was used to estimate total
dissolved solids (TDS) (mg/L) from EC (µs/cm), which depends on the salts specifically
present in the water. In this study, the conversion factor of 0.7 was considered (Walton,
1989). At the same time, river flow velocity was also measured with a flow velocity
meter (CM-1BN; Toho Dentan, Tokyo) at each station. River flow was calculated by
multiplying the river cross-sectional area by the flow velocity at various points along a
transect across the rivers and tributaries.
Sampling was always done in clear weather condition to prevent any abrupt changes in
measurements, except in January which was influenced by an unpredicted rainfall. To
avoid unsteady conditions, sampling was not conducted within 3 to 4 days after rainfall
events. Sampling and measurements along each individual river were done continuously
from upstream to downstream and at its tributary. Only after this that the sampling was
done in other rivers. There were three working groups sampling in three different rivers
at the same time to shorten the time lag as much as possible. The monitoring sequence
was always in the order: Kaname, Kuzuha, Mizunashi and Muro rivers as they merge
with one another. Then only Ohne and Shijuhase rivers were monitored. It took about
five hours on the average to monitor six rivers in each monitoring day. Thus, the
average time consumed for the monitoring of each river was almost about an hour a day.
Data Collection and Preparation
Data of areas where the sewerage system had been installed until 2009 were acquired
from Hadano City as a sewerage system data in paper format. These data were digitally
prepared in ArcGIS (Fig. 3). Population data were obtained from the national census
Journal of Water and Environment Technology, Vol. 9, No.2, 2011
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data of Japan and the data of each drainage basin were prepared in GIS and were
calculated based on area proportion (Fig. 4). Land use data of 2005 used in this study
were acquired from Kanagawa Prefecture City Planning Basic Survey data. Land use
data consisted of 14 categories (Fig. 5), which were recategorized into 4 categories:
paddy field, cultivated land, forest and urban area. The river, water body and seashore
were not considered. While recategorizing, open space, residence, park, business,
industry, agriculture facility, road and railway were categorized as urban areas. Similarly,
abandoned farm was merged with cultivated land. Paddy field and forest were used as it
is (Table 2).
Fig. 5 - Land use Data
LandUse 2005
Openspace
Residence
Park
Business
Forest
Industry
River, waterbody
Seashore
Paddy field
Cultivated land
Abandoned farm
Agriculture facility
Road
Railway
Fig. 3 - Sewerage System Data
Sewerage System
Sewered Area
Drainage Basin
Hadano Basin
Fig. 4 - Population Data
Population 2009
(Person)
0 - 515
516 - 3287
3288 - 7588
7589 - 13699
13700 - 19755
Drainage Basin
Hadano Basin
Table 2 - Recategorization of Land use
Land Use Category Recategorized Areas
1 Paddy field Paddy field
2 Cultivated land
3 Abandoned farm
4 Forest Forest
5 Openspace
6 Residence
7Park
8Business
9 Industry
10 Agriculture facility
11 Road
12 Railway
13 River, water body River, water body
14 Seas hore Seashore
Cult ivated lan d
Urban area
Journal of Water and Environment Technology, Vol. 9, No.2, 2011
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Land use data of each drainage basin was extracted in GIS and the area of each land use
type was calculated (Fig. 6).Building data with floor area were also acquired from
Kanagawa Prefecture City Planning Basic Survey data. Then, unsewered population
was calculated in GIS by overlaying layers of sewered areas, households with total floor
areas and population data for each basin. On the basis of sewered areas and household
data, households without connection to the sewerage system were distinguished first
(Fig. 7). After that, unsewered population was calculated using unsewered household
buildings and population data, based on area proportion (Fig. 8).
Overlay of sewered area
Sewered Area
Minasebashi Basin
Separation of unsewered
households
Unsewered Household
Sewered Household
Minasebashi Basin
Overlay of sewered area
and households
Household
Sewered Area
Minasebashi Basin
Fig. 6 - Land use proportion of each drainage basin
Fig. 7 - Separation of unsewered households of Minasebashi basin of Mizunashi River
Journal of Water and Environment Technology, Vol. 9, No.2, 2011
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Pollution Load of Individual Basin
As river flows downstream, pollution from upstream basins are also carried to
downstream basins. Thus, pollution observed at downstream basin also includes
pollution loads from its upstream basin in addition to those contributed by the pollution
sources within the basin. However, pollutants headed downstream get self-purified to
some extent because of physical processes like dilution, diffusion and settling; chemical
processes like oxidation, reduction and adsorption; and biological processes like
decomposition and uptake by organisms.
Fig. 9 shows the upstream Chimura, midstream Ishiuchiba and downstream
Neshitabashi sub-basins of Muro River basin. Pollution loads at Chimura sub-basin
include the loads of this basin only. While at Ishiuchiba sub-basin, pollution loads
Unsewered Population
(Person)
0 - 269
270 - 547
548 - 1036
1037 - 1964
1965 - 3417
Drainage Basin
Hadano Basin
Chimura
Ishiuchiba
Neshitabashi
#
Monitoring Station
Chimura
Ishiuchiba
Neshitabashi
River
Fig. 8 - Unsewered population of each drainage basin
Fig. 9 - Upstream, midstream and downstream basins of Muro River
▲
Journal of Water and Environment Technology, Vol. 9, No.2, 2011
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include those from upstream Chimura basin in addition to loads from this basin.
Similarly, at downstream Neshitabashi sub-basin, pollution loads are also contributed by
the upstream basins Chimura and Ishiuchiba, in addition to its own loads. In this study,
as the water quality was monitored at the upstream, midstream and downstream sections
of each river, analysis was done considering the water quality pollution load of
individual basin (P
n
) of each monitoring station, so that the pollution scenario and the
related influencing factors of each individual basin could be analyzed in detail,
reflecting its own characteristics. This was calculated through equation (ii) below by
deducting the pollution loads observed at the upstream basins (Po
n-1
) multiplied by
pollution remnant rate (Rr) after self purification from those observed at downstream
ones (Po
n
).
The general equation for pollution load is given as
1000
QC
P
………… (i)
where P: pollution load (kg/day)
C: concentration of water quality parameter (mg/L)
Q: river flow (m
3
/day)
while the pollution load of individual basin was calculated as (Yoshida and Yasui, 1992;
Modified from Ministry of Construction, 1999)
P
n
= Po
n
– Po
n-1
× Rr ……… (ii)
where P
n
: pollution load of n
th
basin
Po
n
: pollution load observed at the monitoring station of n
th
basin
Po
n-1
: pollution load observed at the upstream monitoring station of n
th
basin
Rr: pollution remnant rate after self-purification
The pollution remnant rate after self-purification was calculated as
Rr = exp (-Kr
•
t) ………… (iii)
where Kr: self-purification coefficient (day
-1
)
t: time for pollution to flow from upstream to downstream station (day)
Meanwhile, t was calculated as
L
v
t
86.4
…………… (iv)
where L: river section length (km)
v: river flow velocity (m/s)
While calculating the pollution load of the individual basin, the influence of ground
water circulation, such as infiltration and inflow of ground water were also taken into
consideration on the basis of river flow survey. In the rivers with highly permeable
geology, water easily infiltrates to the subsurface and as a result river flow is less and in
some sections water does not flow at all. Consequently, pollution loads will not flow to
Journal of Water and Environment Technology, Vol. 9, No.2, 2011
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downstream basins. In these cases, it was considered that upstream basins do not
contribute to pollution in downstream basins. On the other hand, in the drainage basins
that experience the influence of ground water inflow, river flow is higher than its
upstream, thereby diluting the pollution. In these contexts, the dilution factor was
calculated based on the monitored river flow and considered in the analysis for COD
and TP. In case of TN, its inorganic form (NO
3
-
) gets easily transported to ground water
so ground water can contribute to N concentrations in the river water. Likewise, it can
also contribute to TDS concentrations. Therefore, dilution factor was not considered for
TN and TDS. Detailed explanation is presented in the results and discussion section.
Self-Purification Coefficient (Kr)
With reference to the values of several rivers in Japan reported by Nagasawa and
Teraguchi (1971), Yoshida and Yasui (1992), and MLIT (2003), self-purification
coefficients (Kr) were selected from 0.5 to 2.5 with 0.5 interval, i.e. 0.5, 1, 1.5, 2 and
2.5. Pollution loads of individual basins (P
n
) were calculated for each Kr at different
time periods of monitoring, using equation (ii). Then, Kr was chosen by comparing the
calculated pollution loads of individual basins with the pollution loads generated from
nonpoint sources of pollution within the respective basins such as unsewered population
and different land uses, which were calculated by using the unit loads of pollution
reported by the Ministry of Construction, Sewerage and Wastewater Management
Department (Ministry of Construction, 1999) (Table 3). Detailed explanation is
presented in the results and discussion section. As there was no animal husbandry
within the study area, its pollution loads were not taken into account.
River Flow Survey
Water of the Hadano basin, on its way from top of the alluvial fan to its center,
infiltrates to the subsurface, flows as a ground water and springs out at the southern part
or gets stored deep under the ground (Ichikawa, 1978; Hadano City, 2003). Hence, to
comprehend this kind of complex water circulation and its impact on water quality and
quantity, the river flow survey was carried out in February 2010, on the basis of the
water temperature survey carried out in February 2009 (Shrestha
et al
., 2009).
The flow of each river was measured in detail from upstream to downstream at an
interval of 200m to 300m. River flow velocity was measured with a flow velocity meter
(CM-1BN; Toho Dentan, Japan) at each monitoring point. River flow was calculated by
Table 3 - Unit loads of pollution
Source: Ministry of Construction, 1999
COD TN TP
Unsewered Population
(g/capita/day)
17 3 0.9
Paddy Field
(kg/ha/day)
0.565 0.113 0.011
Cultivated Land
(kg/ha/day)
0.073 0.189 0.002
Urban Area
(kg/ha/day)
0.293 0.044 0.005
Forest
(kg/ha/day)
0.100 0.012 0.001
Journal of Water and Environment Technology, Vol. 9, No.2, 2011
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multiplying the river cross-sectional area by the flow velocity at various points along a
transect across the main rivers. Depending upon the circumstances and characteristics of
rivers, river flow was measured at short distances also, such as at sections where the
inflow of spring water and the ground water gushing out from river bed, banks and cliff
walls were observed as well as at those points where river merges with its tributaries.
Accurate locations of monitoring points were digitally recorded by using Mobile GIS
(ArcPad and GPS) (Fig. 10). Measurements were done two to three times at the points
where rivers merge with one another and at the respective rivers before their confluence.
On the basis of these measurements, calibration was done. Furthermore, measurements
were done in clear weather conditions to avoid any unsteady conditions.
Simulation for the Transport of Reactive Nitrogen in the Atmosphere
Reactive nitrogen compounds affecting water system and ecosystem that had been
emitted to the atmosphere as a result of anthropogenic activities are mainly ammonia
and nitrogen oxides. These compounds, which are diffused from their respective sources,
are transported along with wind and again get deposited to the earth surface either
directly as dry deposition (gases and particles) or indirectly as wet deposition after
getting mixed with rain or mist. On the basis of these deposition mechanisms, the
simulation was undertaken using EAGrid2000-JAPAN database (Kannari
et al
., 2004;
Kannari
et al
., 2007) to calculate the distribution of anthropogenic nitrogen deposition
in 1-km mesh for Kanagawa Prefecture.
In this simulation, AIST-MM model (Kondo
et al
., 2001) developed by AIST (National
Institute of Advanced Industrial Science and Technology) was used to calculate the
transport of reactive nitrogen compounds released from their respective sources to the
atmosphere. This model is based on the equations of meteorological models with
hydrostatic and Boussinesq approximations, implying that the atmospheric layer is
thinner as it extends horizontally and the range of temperature change is comparatively
smaller. Elevation data and land use information were also considered in the model to
incorporate the effects of ground geography and land features, respectively. Similarly,
physical parameters such as heat capacity, reflection coefficient and thermal conduction
Muro
Shijuhase
Mizunashi
Kuzuha
Kaname
Ohne
Kaname
Fig. 10 - River system and river flow monitoring points in GIS
Journal of Water and Environment Technology, Vol. 9, No.2, 2011
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of soil layer were incorporated in the model. These information are essential to calculate
the sensible heat flux from the earth surface to the atmosphere that drives the
atmospheric motion.
Transportation of reactive nitrogen compounds was calculated with mass conservation
equation. EAGrid2000-Japan (Kannari
et al
., 2007) was used for the emission inventory
data. In this simulation, transformation of the compounds from gas to particle phases
was simply taken into account with an exponential decay function of time. The
conversion of nitrogen oxide (C
NOx
) to its particle phase (C
NO3
-
) with time is based on
the following equation (Kitabayashi and Yokoyama, 1984; Environment Agency Air
Quality Bureau, 1997)
C
NO3
-
= C
NOx
•
A
N
•
{1-βexp(-K
tN
t)}Pk
NOx
(v)
where A
N
: conversion coefficient from NOx to NO
3
-
β: initialization rate of NOx (= 1)
K
tN
: conversion rate from NOx to NO
3
-
Pk
NOx
: remnant rate of particle material after subtracting the amount that
sublimes to gas phase.
As already mentioned, reactive nitrogen gets deposited to the earth’s surface through
dry and wet depositions. For the calculation of dry deposition, the deposition velocity
was introduced. Usually, deposition velocity is a function of aerodynamic resistance,
surface resistance and residual resistance. Although the first depends on surface
roughness and the last two depends on chemical species, constant deposition velocities
were used for the sake of simplicity for all the compounds.
v
d
= 0.002 (ms
-1
) day-time
v
d
= 0.0007 (ms
-1
) night-time
where v
d
: deposition velocity
For the calculation of wet deposition, the amount of rainfall is necessary. However, the
differences of distribution and amount between those calculated by numerical model
and observation are usually not small. Then, Radar-AMeDAS Analysis rainfall data
available in 1-km mesh was used.
ACdzFw
h
0
A = 17*10
-6
Jo
0.6
(Gas)
A = 17*10
-4
Jo
0.6
(Particles)
where Fw: scavenging amount by rainfall
A: scavenging coefficient
h: thickness of rainfall layer
Jo: rainfall amount in one hour (mmh
-1
)
(vi) (Environment Agency Air Quality Bureau, 1997)
Journal of Water and Environment Technology, Vol. 9, No.2, 2011
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Fig. 11 - Results of atmospheric nitrogen simulation in GIS (month of July)
The simulation results for each month were first converted into GIS layer data in the
mesh form and overlaid with the delineated drainage basins in ArcGIS (Fig. 11). Then
using the clip tool of ArcGIS, meshes with the values of atmospheric N deposition were
extracted for each drainage basin. Atmospheric N deposition load in each basin was
calculated by adding the values of each mesh that lies within the respective basins, for
the months when the water quality was monitored and the average value was taken in
the analysis. By the mapping of atmospheric N deposition loads in GIS, the distribution
of its loads was found to agree with the real situation trend-wise, such as higher
deposition loads along roads, highways, ship passage and urban areas. Calculated NOx
were compared with observed values in Yokohama City. The calculated concentrations
were found lower than those observed. However, the variation trend of their monthly
average values was almost similar (Kondo, 2010).
RESULTS AND DISCUSSION
Survey Results
Water Quality Survey
It was found that the distribution of water quality in different time periods followed
almost similar trends with some fluctuations at the upstream, midstream and
downstream sections of rivers. However, water quality concentrations observed in the
month of January were found comparatively higher in most of the cases because of the
unpredicted rainfall on the monitoring day in January (Fig. 12). Particularly, the water
quality of downstream monitoring stations located at tributaries and service water canals
showed some higher fluctuations with higher concentrations mainly in January, such as
stations 17 (Shimo-ochiai), 23 (Shimo-oduki) and 22 (Neshita-yohsui) in the case of TN
and TP (Fig. 13). COD concentrations at stations 17 and 23 were observed as high as 17
mg/L and 20 mg/L, respectively, in January (Fig. 14). Similarly, it was found that the
highest TN concentration was 19 mg/L at station 22 and the highest TP concentration
0 52.5 km
Monitoring Station
Hadano Basin
Dratnage Basin
River
Atms. N
(g/m2/month)
0.003 - 0.027
0.027 - 0.042
0.042 - 0.058
0.058 - 0.075
0.075 - 0.096
0.096 - 0.120
0.120 - 0.144
0.144 - 0.172
0.172 - 0.211
0.211 - 0.320
Journal of Water and Environment Technology, Vol. 9, No.2, 2011
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was 1 mg/L at station 23 in January. In TDS, not many fluctuations were observed
except at station 23 with a quite higher standard deviation.
Hence, in all cases of COD, TN, TP and TDS, higher fluctuations were mainly observed
at station 23. Water quality was found distinctive as per the peculiarity of corresponding
drainage basins. However, TN concentrations were found consistently higher in
drainage basins which were more urbanized than upstream basins (Figs. 12, 13). This
Fig. 13 - Water quality at each station including mean and standard deviation
(monitoring stations are shown in Fig. 2)
Fig. 12 - Water quality variation of Muro River in different time periods
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