Lee E.

Oh Ho.

Oh Hy.

Seo D.

I-Revues

NOVATECH 2010 
1 
Development of an Automatic Monitoring System for 
the Assessment of Urban Wet-Weather Discharge in 
the DTV (Daedeok Techno Valley), Daejeon, Korea 
Développement d'un système automatique de 
surveillance pour l'évaluation des rejets urbains de 
temps de pluie dans la "techno valley" de Daedeok 
(DTV), Daejeon, Corée 
 
Seo D.; Lee E*.; Oh Hy.**; Oh Ho.*** 
 
  Chungnam National University, Daejeon, Korea (seodi@cnu.ac.kr),  
*      M-Cubic, Inc, Yongsan, Yusung, Daejeon, Korea (lehmmm@empal.com), 
**   KICT, 1190 Simindaero, Ilsan-gu, Goyang, Korea (hjoh@kict.re.kr),  
***  Kyung-Ho Eng & Arch. Inc., Sootaek, Guri, Korea (khbnr@hanmail.net) 
 
RÉSUMÉ:  
Ce travail de recherche est destiné à évaluer la faisabilité d’un système de contrôle automatique de 
débit et de surveillance de la qualité des eaux de surface, ainsi que son utilisation dans tous types 
d’environnements.  
Un essai pilote a été conduit avec l'installation d’un système de contrôle des charges polluantes sur 
un exutoire d’eau de pluie immédiatement en amont d’un bassin de rétention sue une petite rivière à 
Daejeon, en Corée. Des capteurs ont été installés pour mesurer la turbidité, la conductivité, l’oxygène 
dissout, etc. Les débits ont été mesurés au moyen de débitmètres à ultrasons et calcul de l'équation 
de seuil. Un équipement d’échantillonnage automatique et un pluviomètre ont été ajoutés pour 
contrôler la qualité de l’eau et suivre en temps réel le fonctionnement du système. Il a également été 
possible d’établir un graphique de pollution estimant simultanément avec précision les débits et la 
qualité de l’eau. Cette étude va être intégrée au système de gestion et de suivi de la qualité de l’eau.  
MOTS CLÉS 
Eau de pluie, pollution source non-ponctuelle, modèle de bassin versant MGEP (SWMM), 
pollutogramme, contrôle automatique 
 
ABSTRACT 
This study was performed to test feasibility of integrated automatic flow rate and water quality 
monitoring system from urban storm water runoff events.   Rainfall gauge, water quality sensors, water 
sampler and ultra sonic water level meter were used to estimate rainfall, water quality and flow rate, 
respectively on real time basis. A pilot test was performed by installing the monitoring system in a 
storm water outlet before wet-weather detention pond in Daedeok Techno Valley, Daejeon, S. Korea.  
Remotely controlled automatic water sampler was used for laboratory water quality analysis of storm 
water due to detection limitation of currently available water quality sensors in the field. Flow rates 
were measured by using ultra sonic water level meter with a rectangular weir equation.  Flow rates 
and water quality concentrations were verified using intensive field measurements in storm events.  
The SWMM watershed model was applied to analyze cause and effect of flow rate and waste load in 
the study area and thus to support decision making processes. By using this system, it was possible to 
estimate hydrograph and pollutograph simultaneously from in storm events. This system can be used 
to develop water quality management strategies of surface water by accurately monitoring and 
modelling flow and pollutant load from a basin especially in storm events. 
KEYWORDS 
Automatic Storm Water Monitoring, Nonpoint Source Pollution, SWMM Watershed Model, 
Pollutograph, Water Quality Management 
SESSION 3.7 
2 
1. INTRODUCTION 
Environmental conditions of rivers and streams are closely related to hydrological characteristics and 
status of water pollution sources from their basins.   As rapid urbanization of basin area in Korea, 
fractions of impervious area and sewer coverage rate in the basin increase and these also lead 
increase in transport efficiency or delivery ratio of water and pollutants from basin to streams.  
Therefore, it would be appropriate to apply hydrological models in the prediction of delivery rates of 
water and pollutants for the management of surface water environment.   Though storm water plays 
essential role in the above processes, its data are not readily available due to difficulties in the 
measurement in both temporal and spatial scales. 
Due to advancement in modern technologies, automatic water quality monitoring has become more 
feasible than before.  Use of water quality sensors for various parameters such as dissolved oxygen 
concentration, conductivity, pH and turbidity has become popular. Seo et al. (1997a; 1997b) 
introduced automatic water quality sensors for the analysis of stratification characteristics of the lakes.  
They used automatic water quality sensor with depth measurement system.  Their system was 
designed to move a set of water quality sensors along the depth continuously.  Lee et al. (2002) 
applied the above system to verify stratification modelling of Daechung Lake by using CE-QUAL-W2 
model.  Lee et al. (2006a, 2006b) suggested to use water quality monitoring system to identify the 
cause of fish kills thus to overcome the water quality problem in Yudeung stream in Daejeon, Korea. 
Seo and Lee (2009) developed VeMAS system that can send data from water quality sensors for 
surface water via cellular phone technology to a host computer and then made data available on the 
web.  However, there have not been many attempts to monitor flow rate and water quality at the same 
time in the fields.  It is essential to have both water quality and quantity data to estimate pollutant load 
and thus to develop water quality management strategies.  This paper reports the design and 
operations of automatic water flow and water quality monitoring system to accurately estimate 
pollutograph from watershed area due to storm water runoff. 
 
2. METHODS 
 
2.1 Study Site 
As a study site, a small stream, Gwanpyungcheon (will be called as the G-stream from now on) in 
Daejeon, Korea was selected.  The basin area of the stream, Daedeok Techno Valley (DTV) is the 
newly developed town in Daejeon for promoting industrial and economic development of the city by 
hosting industries of highly advanced technologies.  However, in the environmental point of view, the 
basin and its stream are under serious stress and thus basin-wide environmental management plan is 
necessary.  The G-stream is a small stream passing through DTV basin.  Its basin area is 10.85 km2 
and length is 5.35 km.  A block of its basin of 1.85 km2 is connected to a strom water detention pond 
and this location is selected as a site for monitoring system installation as shown in Figure 1.  Pond 
area is 5,000 m2 and maximum storage volume is 9,700 m3.  
 
G Stream
Monitoring
Station
Daejeon
Gabcheon
River
Figure 1-(a) The Location of Daejeon, Korea Figure 1-(b) Location of Monitoring Site, G Stream 
NOVATECH 2010 
3 
2.2 Automatic Monitoring System 
Figure 2 shows picture of the pilot study site and the monitoring station.  Storm water runoff flows into 
detention pond via storm water discharge outlet.  Storm water is stored in the detention pond as 
shown in the Figure 2-(a) and discharged to G-stream through a flap gate operated by water pressure. 
Rainfall gage is installed on the roof of the station as shown Figure 2-(b). 
 
Detention pondFlow out
G-Stream
Flow in
Monitoring
Station
 
Figure 2-(a) Monitoring station, Detention pond, Flow inlet, Flow 
outlet and G-stream.  
Figure 2-(b) Rain Gage on the Roof of Station 
 
Figure 3 shows water flow monitoring system, water intake and inside of monitoring station.  The 
monitoring system is equipped with water quality sensors and automatic samplers. It is designed that 
water sample is pumped to a flowing through water tank inside station where water quality sensors are 
installed and samples are taken to refrigerated container.  In this way, it was possible to protect 
sensors from damaged from foreign object in the storm sewer.  Temperature, Conductivity, Turbidity, 
Dissolved Oxygen, ORP and pH can be measured at site and data can be sent immediately using data 
logger and internet.  However, water quality sensor for commonly used water quality variables such as 
BOD, COD, TN, and TP are not readily available.  To automatically   such important water quality 
variables, it is common to install automatic analysis system in the field.  However, installation and 
operation cost of these equipments are usually very high.  Seo et al. (2009) suggested to use 
automatic water sampler and send samples to laboratories where proper experts and equipments are 
available if more detailed and accurate water quality analysis are required.  
 
Ultra Sonic
Water Level 
Meter
Weir To
Maintain
Water Level
For Flow
Measurement
Removable
Ladder
Water Sample
Intake Hose
DO, pH, Turbidity, 
Conductivity Sensors 
Automatic
Water 
Sampler
Data 
Logger
Rain Gage
On Roof
Figure 3-(a) Flow Monitoring Equipments and Water 
Intake Hose for Water Quality Samples 
Figure 3-(b) Inside Water Quality Monitoring Station. 
 
Flow rate was estimated using the weir equation in eq(1) and Figure 4-(a).  Field measurements were 
SESSION 3.7 
4 
performed to confirm flow rate estimation using this equation as shown in Figure 4-(b) 
 
Q = KBH2/3      (1) 
Where Q = Flow rate (m3/sec), K = coefficient (m/sec); 1.84 for this study,  
B = width of weir (m), H = water depth (m) 
 
Specification of equipment used for automatic monitoring system is as shown in Table 1.  
 
 
Figure 4.  Water Flow Measurement System Using Ultrasound Level Meter (Seo et al., 2009) 
 
Item Range Unit 
DO meter 0 – 20 mg/L 
Turbidity 40 - 4,000  NTU 
Conductivity 0 - 2,000 Micro Siemens 
ORP -1,000 - 1,000 Mv 
Water Level Meter  0 - 3.0 M 
Rain Gage 0.2  Mm 
Automatic Water Sampler Temp. : 4℃, No. of Bottles : 24 Sampling Volume 100mL~ 1,000mL 
Table 1. Specification of equipment used for automatic monitoring system 
3. RESULTS 
Figure 5-(a) and Figure 5-(b) show data from automatic flow rate and water quality sensors in the field.  
Two peaks of flow, 544 m3/sec and 480 m3/sec, were detected in 3 hour interval.  As shown in Fig 5-
(a), it was evident that turbidity and conductivity were increased right after the first peak but increase 
of both variables was not clear after the second peak.  This indicates first flush may washed the most 
debris and dirt from the surface of the basin.  DO concentrations were less than 2 mg/L and became 
lower after the first peak.  Compared to turbidity and conductivity, DO level recovery was slower.  pH 
became lower during the storm and recovered in 5 hours after the second peak.  It was notable to 
observe abrupt changes in turbidity, conductivity, DO and pH at the same time at 18:20.  It is believed 
totally different water might have been introduced at that time, though it was not possible to identify the 
cause of the event.  
Figure 6 shows verification of automatic flow rate estimation using intensive field measurement using 
current meter.  As shown in the figure automatic flow rate measurement using flow level meter seems 
NOVATECH 2010 
5 
to provide reasonable flow rate estimation.   However, when flow rates were too high it was not 
possible to measure flow rate manually.  For these cases, SWMM watershed model (Rossman, 2008) 
was used to estimate flow rate form the basin during the storm events as shown in Figure 7-(a) 
through Figure 7-(d).  The flow rate estimation seems to be reasonable when flow rates were less than 
1 m3/sec.  However when flow rate was greater than 1 m3/sec, there are considerable difference 
between automatic measurements and prediction results.   This may be caused by the flow pattern 
inside detention pond.  Since water level of the pond is operated by still flap gate, it is possible that 
backwater from the pond may affected water depth in the vicinity of ultra sonic water level meter.   
Figure 8 shows automatically mesaured flow rate and some important water quality variables collected 
by auto sampler and subsequently analyzed in the laboratory. To include variabels in a graph TSS 
value was reduced 10 times and TP value enlarged 10 times.  Variation of water concentration values 
were not obvious compared to changes of flow rate.  However, this figure shows that this system can 
be applied successfully to derive pollutograph of any water quality variable of interest. 
As shown iun Figure 9, this system was designed to monitor flow rate and water quality data 
continuously from a remote site using internet on a real time basis.  This system can be easily linked 
with water quality modelling system for decision making and also with water quality management 
actions for timely control of pollutant. 
 
544
200.17
0
50
100
150
200
250
0
100
200
300
400
500
600
0
:0
0
0
:5
0
1
:4
0
2
:3
0
3
:2
0
4
:1
0
5
:0
0
5
:5
0
6
:4
0
7
:3
0
8
:2
0
9
:1
0
10
:0
0
10
:5
0
11
:4
0
12
:3
0
13
:2
0
14
:1
0
15
:0
0
15
:5
0
16
:4
0
17
:3
0
18
:2
0
19
:1
0
20
:0
0
20
:5
0
21
:4
0
22
:3
0
23
:2
0
NTUcms 2009.07.17 Turbidity
Flow Turbidity
544 432.31
0
100
200
300
400
500
0
100
200
300
400
500
600
0:
00
0:
50
1:
40
2:
30
3:
20
4:
10
5:
00
5:
50
6:
40
7:
30
8:
20
9:
10
1
0:
00
1
0:
50
11
:4
0
1
2:
30
1
3:
20
1
4:
10
1
5:
00
1
5:
50
1
6:
40
1
7:
30
1
8:
20
1
9:
10
2
0:
00
2
0:
50
2
1:
40
2
2:
30
2
3:
20
us/cmcms 2009.07.17 Conducvity
Flow Conductivity
544 8.34
0.00
2.00
4.00
6.00
8.00
10.00
0
100
200
300
400
500
600
0
:0
0
0
:5
0
1
:4
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2
:3
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:2
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:1
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:0
0
5
:5
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:4
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:3
0
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:2
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:1
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:0
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1
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:5
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1
1
:4
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:3
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:2
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1
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:1
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:0
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:5
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:4
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:3
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:2
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:1
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:0
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:5
0
2
1
:4
0
2
2
:3
0
2
3
:2
0
mg/Lcms
2009.07.17 DO
Flow Dissolved Oxygen
544 8.52
6.5 
7.0 
7.5 
8.0 
8.5 
9.0 
0
100
200
300
400
500
600
0
:0
0
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:5
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:0
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:5
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:4
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1
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1
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:1
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1
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:0
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:5
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:3
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1
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:2
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1
9
:1
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0
:0
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2
0
:5
0
2
1
:4
0
2
2
:3
0
2
3
:2
0
cms
2009.07.17 pH 
Flow pH
Figure 5-(a) Flow vs Turbidity and Flow vs Conductivity 
Measurements using Sensors 
Figure 5-(b) Fow vs DO and Flow vs pH 
Measurements using Senors 
 
Figure 6. Automatic Flow Rate Measurements and Vefication Using Manual Measurements 
SESSION 3.7 
6 
 
 
 
(a) Maximum Flow Rate, 0.3 m3/sec (b) Maximum Flow Rate, 1.0 m3/sec 
(c) Maximum Flow Rate, 1.5 m3/sec (d) Maximum Flow Rate, 1.8 m3/sec 
Figure 7.  Flow rate measurement and Flow Prediction using SWMM program 
 
0.0 
0.2 
0.4 
0.6 
0.8 
1.0 
1.2 
1.4 
1.6 
0.0 
2.0 
4.0 
6.0 
8.0 
10.0 
12.0 
14.0 
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0
m
3
/s
e
cm
g
/L
time(hr)
TSS/10 BOD COD TN TP*10 Flow
 
Figure 8.  Automatic Flow Rate Measurement and Water Quality Concentrations Using Auto Sampler and 
Laboratory Analysis  (2009. 08. 11) 
 
NOVATECH 2010 
7 
 
Figure 9. Schematic Diagram of Real Time Water Monitoring System 
 
 
4. CONCLUSIONS 
 
Accurate information of flow rate and pollutant concentrations from basin area is the essential factor 
for development of effective water quality management plan.  Using the developed system in this 
study, it is possible to accurately estimate flow rate and water quality concentrations in time and to 
develop hydrograph and pollutograph for a basin.  Flow rate estimations were accurate when flow rate 
was less than 1 m3/sec. It seems to be appropriate to develop multi-step weir equation considering 
hydraulic characteristics of detention pond in the study site to appropriately consider high flow rates. 
While water quality sensors can generate real time measurement of storm events, water samplers can 
be used to collect samples for further detailed water quality analysis in the laboratory.   
SWMM watershed model was successfully applied for the prediction of flow rate and it was possible to 
provide flow information when manual measurement was not available.  The system also equipped 
with real time data sending system and it also is possible to visualize the data through web pages. 
This information can further be used for water quality modelling for various water quality management 
scenarios in the basin.  The prediction results can be used to determine which water and water quality 
management actions can be implemented.  
In conclusion, automatic flow and water quality monitoring system will initiate entire real time 
environment control in basin scale. This system can be used to protect environment in basin and in 
surface water more effectively.  
This system can be regarded as a good sample of fusion technology that applied IT (Information 
technology) and ET (Environment technology) together.  
 
 
5. ACKNOWLEDGEMENT 
This research was financially supported by U-Based Eco Space Development Research Project of 
Ubiquitous and Ecology City R&D Center.  Authors thank for the support. 
 
 
SESSION 3.7 
8 
LIST OF REFERENCES  
Ambrose R. B., Wool T. A. and Martin J. L. (1988). WASP4, A Hydrodynamic and Water Quality 
Model-Model Theory, User's Manual, and Programmer's Guide. USEPA, Athens, GA. 
Lee S. U., Seo D., Kim J. and Oh H.(2002). Automatic Monitoring and CE-QUAL-W2 Modelling of 
Stratification Characteristics in Daechung Lake, Korea, The 22th International Symposium of North 
American Lake Management Society, Anchorage, AK, USA. 
Lee E. H., Seo D., Hwang H., Yoon J. and Choi J. (2006a). Causes of Fish Kill in the Urban Streams I 
- Field Surveys and Laboratory Experiments, J. of Korean Society of Water and Wastewater, Vol. 
20., no. 4, pp 573-584. 
Lee E. H., Seo D. I., Hwang H. D., Yoon J. H. and Choi J. H. (2006b). Causes of Fish Kill in the Urban 
Stream and Prevention Methods II - Application of Automatic Water Quality Monitoring System and 
Water Quality Modeling, J. of Korean Society of Water and Wastewater, 20(4): 585-594. 
Seo D., Lee E. and Lim S. (1997a). Development of Automatic Vertical Moving System for Water 
Quality Sensors with Internal Memory to Monitor Stratification Characteristics of Lakes and 
Reservoirs, The 17th International Conference of North American Lake Management Society, 
Houston, TX, USA 
Seo D., Choi J., Jung S., Cha M. and Song H. (1997b). Analysis of Stratification Characteristics in the 
Daechung Lake by Using Continuous Water Quality Monitoring System, The 17th International 
Conference of North American Lake Management Society, Houston, TX, USA 
Seo D. and Lee E. (2009). Development of Vertically Moving Automatic Water Monitoring System 
(VeMAS) For Lake Water Quality Management, in Atmospheric and Biological Environmental 
Monitoring, Y. Kim et al., ed., Springer-Verlag GmbH.  
Seo D., Lee E. H., Joo M., Oh H. J. and Oh H. S. (2009). Automatic Waste Load Monitoring System 
from Storm water in Daedeok Tech Valley, Daejeon, Korea, 13th International Conference on 
Integrated Diffuse Pollution Management (IWA DIPCON 2009), Seoul, Korea. 
Lee J. W., Choi J. H., Hwang H. and Seo D. (2004). New Generation Automatic Lake Water Quality 
Monitoring System, The 24th International Symposium of North American Lake Management 
Society, Victoria, Canada. 
Rossman L. A. (2008). Storm Water Management Model User’s Manual, Version 5.0, EPA/600/R-
05/040. 


Développement d'un système automatique de surveillance pour l'évaluation des rejets urbains de temps de pluie dans la "techno valley" de Daedeok (DTV), Daejeon, Corée

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Développement d'un système automatique de surveillance pour l'évaluation des rejets urbains de temps de pluie dans la "techno valley" de Daedeok (DTV), Daejeon, Corée

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