Design of on-line river water quality monitoring ... - Springer Link

Environ Monit Assess (2009) 155:63–81 DOI 10.1007/s10661-008-0418-z

Design of on-line river water quality monitoring systems using the entropy theory: a case study Mohammad Karamouz · Amir Khajehzadeh Nokhandan · ˇ Reza Kerachian · Cedo Maksimovic

Received: 27 January 2008 / Accepted: 22 May 2008 / Published online: 29 July 2008 © Springer Science + Business Media B.V. 2008

Abstract The design of a water quality monitoring network is considered as the main component of water quality management including selection of the water quality variables, location of sampling stations and determination of sampling frequencies. In this study, an entropy-based approach is presented for design of an on-line water quality monitoring network for the Karoon River, which is the largest and the most important river in Iran. In the proposed algorithm of design, the number and location of sampling sites and sampling frequencies are determined by minimizing the redundant information, which is quantified using the entropy theory. A water quality simulation model is also used to generate the time series of the

M. Karamouz · R. Kerachian (B) Center of Excellence for Infrastructure Engineering and Management, School of Civil Engineering, University of Tehran, Tehran, Iran e-mail: [email protected] M. Karamouz e-mail: [email protected] A. K. Nokhandan Department of Environment, University of Tehran, Tehran, Iran e-mail: [email protected] ˇ Maksimovic C. Department of Civil and Environmental Engineering, Imperial College, London, UK e-mail: [email protected]

concentration of water quality variables at some potential sites along the river. As several water quality variables are usually considered in the design of water quality monitoring networks, the pair-wise comparison is used to combine the spatial and temporal frequencies calculated for each water quality variable. After selecting the sampling frequencies, different components of a comprehensive monitoring system such as data acquisition, transmission and processing are designed for the study area, and technical characteristics of the on-line and off-line monitoring equipment are presented. Finally, the assessment for the human resources needs, as well as training and quality assurance programs are presented considering the existing resources in the study area. The results show that the proposed approach can be effectively used for the optimal design of the river monitoring systems. Keywords On-line monitoring · Water quality · The entropy theory · The Karoon River

Introduction Water quality monitoring systems are designed to obtain quantitative information about temporal and spatial distribution of water quality variables and thus on the physical, chemical, and biological characteristics of water resources. Due to the

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important role of rivers for supplying water demands and the considerable capital and operational costs of sampling and monitoring systems, the design of an optimal monitoring network as well as economic constraints should be considered. Uslu and Tanriover (1979) analyzed the entropy concept for the delineation of optimum sampling intervals in data collection systems, both in space and time. Harmancioglu (1981) investigated the transfer of information between observations of two stream gauging stations. Optimal design of water quality networks has been studied by many researchers during the past decades. Tirsch and Male (1984) proposed a measure of monitoring precision as a function of sampling location and time frequencies. This measure is defined using the coefficient of determination of a multivariate linear regression model. Harmancioglu and Alpaslan (1992) proposed a statistical procedure based on the entropy theory to address the assessment of both network efficiency and cost-effectiveness. Woldt and Bogardi (1992) and Karamouz et al. (2008) proposed different models for the optimal design of water quality monitoring systems combining geostatistical and Multiple Criteria Decision Making (MCDM) methods. Ozkul et al. (2000) extended the work of Harmancioglu and Alpaslan (1992) to better define the zones with high monitoring data uncertainties along a river. This model can only be used for reducing redundant stations or decreasing the sampling frequency in an existing or a primary monitoring system. Mogheir et al. (2004a), Mogheir and Singh (2002) developed a methodology for design of an optimal groundwater monitoring network using entropy (or information) theory. Because a monitoring system is essentially an information collection system, its technical design requires a quantifiable measure of information which can be achieved through application of the information theory. They applied the entropy (or information) theory to describe the spatial variability of synthetic data that can represent spatially correlated groundwater quality data. The application involves calculating information measures such

Environ Monit Assess (2009) 155:63–81

as transinformation, the information transfer index and the correlation coefficient. These measures are calculated using discrete and analytical approaches. Mogheir et al. (2004b) extended the previous work of Mogheir et al. (2004a). This paper uses the entropy theory to describe the spatial variability of groundwater quality data sets. The application of the entropy theory is illustrated using the chloride observations obtained from a network of groundwater quality monitoring wells in the Gaza Strip, Palestine. Mogheir et al. (2005) evaluated the monitoring cycle in the Gaza Strip using the entropy theory. This article also proposes a flowchart, which is used to evaluate the relation between the objectives, the tasks, the data and the monitoring activities using the entropy theory. In this paper, the entropy-based model developed by Ozkul et al. (2000) is extended for proposing new monitoring stations and revising the sampling frequencies in an existing monitoring system while several water quality variables are considered. In this new approach, water quality data for several potential monitoring sites along the river is generated using a river water quality simulation model. Then, the stations with redundant information are eliminated using the entropy theory. To evaluate the applicability and efficiency of the model, it is applied to the Karoon River system in the southern part of Iran. The results show that the proposed model can be easily used for evaluating and revising the existing monitoring networks. To develop a comprehensive on-line monitoring system, different components of the system such as on-line sensors, data transmission systems, lab equipment and the required human resources are also proposed for the study area.

Entropy theory The entropy (or information) theory, developed by Shannon and Weaver (1949), has recently been applied in many different fields. This theory has also been applied in hydrology and water resources for measuring the information content of random variables and models, evaluating infor-


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mation transfer between hydrological processes, evaluating data acquisition systems, and designing water quality monitoring networks. There are four basic information measures based on entropy, which are marginal, joint, and conditional entropies and transinformation. Shannon and Weaver (1949) were the first to define the marginal entropy, H(X), of a discrete random variable X as: H (x) =

N

p (xi ) log p (xi )

(1)

i=1

where, N represents the number of events xi with probabilities p(xi ) (i = 1, . . . , N). The total entropy of two independent random variables X and Y is equal to the sum of their marginal entropies. H (x, y) = H (x) + H (y)

(2)

when X and Y are stochastically dependent, their joint entropy is less than the total entropy of Eq. 2. Conditional entropy of X given Y represents the uncertainty remaining in X when Y is known, and vice versa: H (x |y ) = H (x, y) − H (y)

(3)

Transinformation (the redundant or mutual information) between X and Y is described as the difference between the total entropy and the joint entropy of dependent X and Y. T (x, y) = H (x) + H (y) − H (x, y)

when the multivariate normal distribution is assumed for f (X1 , X2 . . . , X M ), the joint entropy of X, with X being the vector of M variables, can be expressed as (Ozkul et al. 2000): H (x1 , x2 , . . . , x M ) = (M/2)ln 2π + (1/2)ln |C| + M/2 − Mln(x) (7) where, |C| = determinant of the covariance matrix C; and x = class interval size assumed to be the same for all M variables. Design of water quality monitoring networks is still a controversial issue, for there are difficulties in the selection of temporal and spatial sampling frequencies, the variables to be monitored, the sampling duration, and the objectives of sampling (Harmancioglu et al. 1999). Entropy theory can be used in optimal design of water quality monitoring networks. By adding new water quality sampling station and gathering new information, the uncertainty (entropy) in the quality of the water is reduced. On the other hand, transinformation can show the redundant information obtained from a monitoring system, which is due to spatial and temporal correlation among the values of the water quality variables. Therefore, this index can be effectively used for optimal location of the monitoring stations as well as determining the sampling frequencies.

= H (x) − H (x |y ) = H (y) − H (y |x ) (4) The above expression can be extended to the multivariate case with M variables. If the variables are dependent, their joint entropy can be expressed as (Harmancioglu and Alpaslan 1992): H (Xm |X1 , X2 , . . . , Xm−1 ) = H (X1 , X2 , . . . Xm ) − H (X1 , X2 , . . . , Xm−1 ) (5) H (X1 , X2 , . . . , X M ) = H (X1 ) +

M

H (Xm |X1 , X2 , . . . , Xm−1 )

m=2

(6)

Optimal location of monitoring stations In the proposed methodology, daily or weekly water quality data is required in order to evaluate an existing water quality monitoring network. Required data can be obtained from a primary adhoc monitoring program or an existing water quality monitoring system. In the primary monitoring program, sampling points along the river should provide the required data for calibration and validation of a river water quality simulation model. This program should also include monitoring main pollution sources within the river system. In the proposed model, main steps for selecting water quality monitoring stations are as follows: •

Selecting water quality variables.

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•

• • •

•

•


Figure 1 presents details of selection of the best set of monitoring stations along the river. As shown in this Figure, at each station, to incorporate the transinformation of different water quality variables, a weighted average transinformation is considered. The relative importance weight of water quality variables are calculated using a pair-wise comparison matrix, which is obtained through a group decision making process. The idea of introducing pair-wise comparisons between different criteria is due to the fact that for a decision maker it is easier to make comparisons between a pair at a time rather than assigning weights to the whole set of criteria (Karamouz et al. 2002). As mentioned before, the river water quality at potential monitoring stations is estimated using the river water quality simulation model. The water quality simulation model uses the water quality data obtained using the existing monitoring stations. Therefore, the simulated data can be used in the Entropy analysis. As shown in Fig. 1, the following procedure is applied to select the best combination of stations: •

Selecting the total number of stations (TNS). TNS with higher priorities are selected among the potential stations

Selecting a primary set of monitoring stations along the river. The water quality data obtained from these stations should provide the required data for calibrating and verifying a river water quality simulation model. Gathering data and information related to river pollution sources and the physical characteristics of the river. Simulating temporal and spatial variations of the concentration of water quality variables using the water quality simulation model. Selecting several potential monitoring stations along the river. The river water quality at monitoring stations is evaluated using the river water quality simulation model. Determining the relative weight of the water quality variables using a group decision making method which incorporates pair-wise comparison matrices. Selecting the best potential monitoring stations which provide a minimum value for redundant information in the system.

The marginal entropy of the water quality variable series at each station is computed.

Calculating the marginal entropy for each water quality variable at each primary/ potential monitoring station

Selecting a station with the highest weighted marginal entropy. This station is denoted as the first priority station ( X 1 )

i =1 i = i +1 Calculating the weighted transinformation among station

X 1 to

X i and every other station in the network Selecting station

X i +1 , which provides

minimum weighted transinformation with stations X 1 to X i

i < TNS

Yes

No The stations with 1 to m priority are selected as the optimal locations for the water quality monitoring network. (m is determined by the weighted transinformation content and economic considerations)

Fig. 1 Proposed method for determination of the optimal stations when the sampling frequencies are not considered

•

•

•

The station with the highest value is denoted as the first priority station (X1 ). This is the location with presumably the highest uncertainty in water quality variations that has the greatest potential for attaining more information. So it is perhaps the most suitable station. Then, this station is jointly analyzed with every other station in the network to select the pair with the least transinformation. It is marked as the second priority station (X2 ). The above step is repeated, but this time the pair (X1 , X2 ) is coupled with every other station in the network to select the third station (X3 ) with the least transinformation. The same procedure is continued by successively considering combinations of 3, 4, 5, . . . , j stations already ranked with other stations.


•

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Finally the stations with 1 to m ranks are selected as the better locations for the water quality monitoring network (m is determined by the transinformation content and economical considerations).

Selecting the optimal sampling frequencies In the last section, the methodology of selecting optimal sampling stations was presented without considering the sampling frequency.

Defining the minimum and maximum values for sampling interval ( n , m m = K ′.n , K ′ ∈ N )

i=2 i =i+1

k =1 k =k +1 L = kn

Calculating the weighted transinformation between station X 1 and stations X 2 to X i with time lag L

Percent of redundant information (PRI)= weighted transinformation × 100/weighted marginal entropy at station X 1

Saving PRI and L values

L=m

No

Yes No

L = total number of potential stations

Yes Plotting values of PRI versus L

Selecting the best sampling frequency for all water quality variables considering PRI value and economic considerations

Fig. 2 Main steps in the proposed method for selecting the best combination of monitoring stations and sampling frequencies

However, monitoring stations and sampling frequencies should be selected at the same time considering their effects on the total monitoring cost and reducing the redundant information. In this paper, the spatial-temporal sampling frequencies are selected to minimize the total weighted redundant information. In other words, the best combination of monitoring stations and sampling frequencies are obtained considering the measure of redundant information. An increase in the sampling interval decreases the redundant information between the stations in a given combination, whereas an increase in the number of stations increases the transinformation for a given sampling frequency. One would look for the best combination with respect to time and space for reduction of the total uncertainty in data sampling (Ozkul 1996). To analyze spatial and temporal frequencies, the best combination of monitoring stations has to be selected first, following the procedure outlined in the last section. Then, starting with the highest ranked station, the number of stations is successively increased by adding to the combination the next station on the priority list. For each number of stations, the temporal frequencies are decreased to identify how much information is provided by those stations at different sampling intervals. Finally, changes in information are plotted on the same graph with respect to both the increase in the number of stations and the decrease in temporal frequencies. The objective is to select a space-time combination that produces the least amount of transinformation. Figure 2 presents the main steps in the proposed method for selecting the best combination of monitoring stations and sampling frequencies. The total marginal entropy is determined by the summation of marginal entropy of each water quality variable multiplied by the relative weight of the variable. Similar weighing method is used for calculating the total transinformation.

Case study The study area consists of a 380 km long stretch of the Karoon River from the Gotvand Dam to the Persian Gulf in Khuzestan province. The

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Karoon River water pollution due to increasing water withdrawal from and wastewater discharge to this river has endangered the aquatic life of this ecosystem, which is the largest river in Iran. The current water quality condition of the Karoon River system is alarming and exceeds the minimum water quality standards. If immediate attention and remediation is not provided, the increasing discharge of domestic, agricultural, and industrial wastewater will make it critical. Agricultural and agro-industrial return flows, domestic wastewater of the cities/rural area and industrial wastewaters are the main pollution sources of the surface and groundwater resources in the Karoon River basins. The proposed model is applied to Karoon River to revise the location of existing stations as well as to determine sampling frequency of the water quality variables. The main characteristics of different components of the proposed on-line water quality monitoring system are also presented in the next sections. Considering the pollution sources in the study area, the selected water quality variables for on-line monitoring system are temperature, EC (TDS), turbidity, DO, nitrate, chlorophyll II, PAHs and pH. Some water quality variables, such as TSS, chloride, sulphate, nitrite, COD, heavy metals (Fe, As, Hg, Zn, Pb, Cd, Cr, Cu, Mn), BOD5 , NH+ 4 , NH3 , fecal coliform, fecal streptococcus, Escherichia coli, total coliform, toxic materials are also monitored in some ad-hoc stations manually. The gathered water samples are transferred to proposed central and local laboratories which should be capable to measure a variety of water quality variables. To obtain the required water quality data, existing data from an ad-hoc monitoring system is used. In this system, the river water quality is monitored daily in five stations along the river. In this paper, EC and DO that are frequently violating the standards are considered as water quality variables for primary selection of the stations. The location of stations is then adjusted considering the pollution sources and the water withdrawal points. Finally, 28 potential monitoring stations are selected along the river and the daily time series of EC and DO at these stations are obtained using


Fig. 3 Location of the potential monitoring stations within the Karoon River system

the Qual2k river water quality simulation model (see Chapra and Pelletier 2003 for more details about the model). Figure 3 presents the location of these stations in the study area. As shown in this figure, no station is considered downstream of Darkhoein Station. Because the water quality in this reach is affected by the high tide of the Persian Gulf and the proposed model can not be applied to this river reach. Therefore, selection of monitoring stations in this reach is strictly based on engineering judgment. To evaluate the sensitivity of the results with respect to the relative weight of the water quality variables, the results are presented considering three different relative weights for DO and EC obtained from three groups of


69

experts in the study area. They have expressed their opinion through pair-wise comparison matrices:

Case 1:

EC DO EC 1 2 ⇒ WEC = 0.67 , WDO = 0.33 DO 0.5 1

Case 2:

EC DO

Case 3:

EC DO EC 1 1 ⇒ WEC = 0.5 , WDO = 0.5 DO 1 1

EC DO 1 3 ⇒ WEC = 0.9 , WDO = 0.1 0.33 1

where, WEC and WDO are the relative weights of EC and DO, respectively. For each pair-wise matrix, WEC and WDO are considered to be the elements of the eigen vector of the corresponding pair-wise comparison matrix (Karamouz et al. 2003). The proposed procedure allows sorting the stations in such a way that the first rank (priority) is given to the station with the highest marginal entropy. Then, the next rank goes to a station which provides the least transinformation with the first ranked station. The following ranking will be based on the least redundancy of a station with all the stations ranked before and so on. The minimum redundancy that can be measured based on the minimum transinformation could be considered as a measure of maximum uncertainty. Based on economic considerations, ten stations from 28 potential stations are selected using the proposed methodology. Figure 4 presents the location of these stations in the study area. As shown in Tables 1 and 2, the first ten stations are repeated in all three cases but their ranks depend on the relative weight of the water quality variables which could be sampled. Sampling intervals are also determined considering the measure of transinformation for a combination of “number of stations” and “sampling frequency”. To evaluate the selected network in time and space, the number of stations is selected from 2 to 10 by using the ranking of the ten stations determined in the previous section. The first priority station is taken as the base station where weekly sampling is taken. For each number of stations, transinformations in the form of T(X1,0 , X2,k , X3,k , . . . X M,k ) are computed. Then,

Fig. 4 Location of 14 selected monitoring stations in the Karoon River system

temporal frequencies are changed to investigate how these changes affect the reduction of the highest uncertainty in the basin. In Figs. 5, 6 and 7, the percent of redundant information for different combination of sampling frequencies and number of stations for cases 1, 2 and 3 are shown. The final decision to select among many alternatives depends on the evaluation of monitoring cost. In these figures, for a constant level of transinformation, a number of alternatives exist such that one may evaluate (1) whether to increase the number of stations and decrease the sampling frequency; or (2) decrease the number of stations and increase the temporal frequency. As it can be seen in Figs. 5 to 7, by decreasing the number of stations from 10 to 2, the percent of redundant information is only decreased about 3–7%.

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Table 1 Ranking of the stations based on the maximum uncertainty and minimum redundant information Case 1

Case 2

Case 3

Station

Transinformation measure

Station


Station


Darkhoein Shooshtar Mollasani Zargan 2 (14) Ahvaz Shotait— Bandemizan (3) After Mollasani (9) Shotait (5) Gotvand

0.03 0.03 0.64 0.83 1.19 1.28

0.03 0.03 0.72 0.91 1.27 1.29

Shotait (5) Darkhoein Mollasani Shooshtar Zargan 2 (14) Ahvaz

0.34 0.34 0.66 0.69 0.77 1.05

1.50 1.78 1.99

Darkhoein Shooshtar Mollasani Zargan 2 (14) Ahvaz Shotait— Bandemizan (3) Shotait (5) After Mollasani (9) Gotvand

1.48 1.78 1.94

1.46 1.76 2.38

After Gotvand (1) 20 11 7 23 17 4 10 16 21 2 6 8 15 19 22 12 18 13

2.63 3.00 3.10 3.30 3.35 3.39 3.80 4.10 4.20 4.21 4.25 4.28 4.30 4.40 4.51 4.55 4.60 4.62 4.64

After Gotvand (1) 7 21 13 17 16 6 20 11 8 15 19 22 12 18 4 10 23 2

2.50 3.20 3.50 3.70 3.75 3.90 4.00 4.30 4.32 4.36 4.44 4.48 4.50 4.53 4.60 4.75 4.77 4.82 4.90

After Gotvand (1) After Mollasani (9) Shotait— Bandemizan (3) Gotvand 21 16 10 6 20 11 13 17 7 22 12 8 15 19 23 2 18 4

Therefore, by selecting the best ten stations, the value of the redundant information would be limited and acceptable. Based on Figs. 5 to 7, the minimum value of weighted transinformation will be obtained with a monthly sampling interval. Therefore, in this study, the maximum value for the sampling interval is considered as one month. The sampling frequency of the other water quality variables, which are not considered in the analysis, should be set based on engineering judgments. The selected sampling frequencies for different water quality variables are presented in Table 3. In the following sections, different components of the on-line monitoring system such as data acquisition, transmission and processing are

2.70 3.12 3.15 3.30 3.80 4.00 4.20 4.40 4.45 4.50 4.54 4.60 4.70 4.79 4.80 4.85 4.86 4.87 4.88

designed for the study area and technical characteristics of the on-line and off-line monitoring equipment are presented. Finally, the assessment for the needs of human resources and training and quality assurance programs are presented considering the existing resources in the Khuzestan province.

Data acquisition and local recording options In this study, data obtained through the measurement of selected variables by proposed set-array of sensors will be converted into digital forms. The affordable A/D converters can nowadays easily

Environ Monit Assess (2009) 155:63–81 Table 2 Selected stations in each case

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Station

Rank

Darkhoein Shooshtar Mollasani Zargan 2 Ahvaz Shotait—Bandemizan After Mollasani Shotait Gotvand Dam After Gotvand

handle simultaneous data collection, which can be temporarily stored in the buffer unit located in the control unit. From each individual field station, data transfer will be organized through a telemetric data collection network. Thus, the local storage will only be temporary between two successive data transfers to the central data acquisition unit. A schematic of a field station is shown in Fig. 8 in which two basic options for placing sensors are shown. The options are: •

Case 2

Case 3

1 2 3 4 5 6 7 8 9 10

1 2 3 4 5 6 8 7 9 10

2 4 3 5 6 9 8 1 10 7

supported). Individual cables for power supply and A/D converted signals connect the casing with sensors with the control unit located in the shelter. Option 2 (called through flow): water is pumped (or sucked) to the array of sensors placed in the shelter for an on-line measurement. Option 3: combination of the above two methods in which most of the sensors are arranged in the through flow mode and only some sensors (for example the sensor for water level) are placed either bellow the minimum water

•

•

Option 1: the array of sensors placed in the stream (in protective casing and securely

Case 2

Case 1 8

8

7

7

Percent of redundant information


Case 1

6 5 4 3 2 1

6 5 4 3 2 1 0

0 1

2

3

4

5

6

7

1

2

2 Stations

3 Stations

7 Stations

4 Stations

8 Stations

5 Stations

9 Stations

3

4

5

6

7

Sampling intervals (Week)

Sampling intervals (Week) 6 Stations

10 Stations

Fig. 5 The percent of redundant information for different combinations of the number of stations and sampling intervals (case 1)

2 Stations

3 Stations

7 Stations

4 Stations

8 Stations

5 Stations

9 Stations

6 Stations

10 Stations


72


8

Table 3 Sampling frequency for water quality variables in the proposed monitoring system

7

Water quality variables

Sampling frequency

Temperature EC TSS Turbidity DO BOD COD Chloride Sulphate Nitrate Nitrite Fe, Cd, As, Cr, Cu, Hg, Pb, Mn, Ba, Zn Chlorophyll NH3 NH+4 Fecal streptococcus Escherichia coli TPH Toxicants pH FC,TC

Continuous Monthly Monthly Continuous Monthly Weekly Weekly Monthly Monthly Continuous Biweekly Monthly


Case 3

6 5 4 3 2 1 0

1

2

3

4

5

6

7

Sampling intervals (Week) 2 Stations

3 Stations

7 Stations

4 Stations

8 Stations

5 Stations

9 Stations

6 Stations

10 Stations


Continuous Biweekly Biweekly Weekly Weekly Continuous Monthly Continuous Weekly

level (pressure probe) or above water surface (ultrasonic sensor) The advantage of the option 1 is that the sensors are placed in the “genuine” environment (the stream), thus the measurement should be more accurate in principle. A major disadvantage of this option is that an expensive set of sensors is left in the stream—vulnerable to both vandalism and damage by floating objects (debris). The advantage of the option 2 is that the sensors are better protected in the shelter, which can be placed either on the river bank or on a bridge above the point of measurement. The disadvantage of this option is that both air temperature in the shelter and the water temperature in the inflow pipe may be different than of the ambient water temperature, which might distort the measurement of some variable. Additionally the inflow pipe may clog. In this case, the central data acquisition unit of the Karoon Water Quality Monitoring Center (KWQMC) will most probably be located in Ahvaz City (in the center of the Khuzestan Province). One of the three options explained should be selected considering the condition of the set of sensors and their measurement technology.

Data transmission options Considering the available technologies in Iran, the following methods for data transmission can be used: • • • •

Modem and ground based telephone lines GSM (cellular) communications Radio transmission Satellite

A suitable system should be selected according to the design and operation expenses, the required equipment expenses, the reliability of the operation, the accuracy of data transmission, and the maintenance expenses in the region. If there is a telephone system available in the study area, the modem and ground based telephone lines are the first choice and the satellite system is selected as the backup system. Main characteristics, advantages, and disadvantages of the selected data transmission options are briefly presented in Table 4.


73 Security Equipement

External Communication Unit (Telephone,GPS,Satelite)

House(Shelter) Additional Equipment Data Transmission Local Storage

Sampler

Field Data Control Unit

Sensor Supporting & Approach Structure

Sensors(Option2)

Pump (Option2) Sensors(Option1)

Fig. 8 Two options for placing sensors in an automatic monitoring station (Karamouz and Maksimovi´c 2005)

Equipment for continuous monitoring, sampling, and ancillary infrastructures Despite the advances in sensor technology, the number of variables that can be monitored online is still limited. For a better insight into water quality, the on-line monitoring will have to be reenforced by the more conventional techniques; taking (grabbing) samples manually and analyzing them in the laboratory. Table 5 presents the list

of water quality variables which will be measured (monitored) on-line and off-line. In order to perform all analyses as planned, the laboratory must have four groups of instruments: a. For inorganic substances: titration, atomic absorption spectrometer (AAS) and ion chromatography (IC) b. For organic substances and molecules: mass spectrometer (MS) and high performance liquid chromatograph (HPLC)

Table 4 Technical characteristics and comparison of data transmission options Options

Characteristics

Advantage

Disadvantage

Main system

Modem and ground based telephone lines

Speed: 33.6 (Kb/s) Initial costsa : 100$ Monthly operational costs: negligible

Backup system

Satellite

High speed Initial costs: 3000 $ Monthly operational costs: 200 $

Generally good coverage of the area though in some cases Everywhere availability by a telephone line Readily available

Security of the system if sharing the common lines Cost of the dedicated lines Unavailability of ground telephone lines in some specific region High operational and initial costs Dependence to international servicing companies Dependence to climatologic conditions

a The

prices given are for one unit (automatic monitoring station)

74 Table 5 On-line and off-line variables


Physical variables

Chemical variables

Biological variables

On-line analysis

Off-line analysis

Temperature, turbidity, water level (depth) and electrical conductivity (TDS) pH, DO, NO− 3 and PAH/BTEX

TSS

Chlorophyll

− + COD, Cl− , SO2− 4 , NO2 , NH4 , NH3 , heavy metals (Fe, As, Hg, Zn, Pb, Cd, Cu and Mn) and aromatic hydrocarbons BOD, Fecal coliforms (Escherichia coli), Fecal streptococcus, Total coliforms and bio-toxic materials

Since laboratory analyses will be done at the central laboratory located at KWQMC, the work is proposed to be distributed as follows:

g. Measurements in emergency situation such as natural disasters, h. Staff training i. Others

a. Local laboratories, which are located in Abadan, Dezful, and Shooshtar will deal with pH, temperature, salinity, electro conductivity, ORP, Turbidity, Alkalinity, Anions, and all analyses related to oxygen and etc. b. The central laboratory which is located in Ahvaz City, will deal with heavy metals, anions, organic substances, gases and etc.

Measurements are made at the monitoring site by using calibrated field instruments as close to the sensor as possible and within 5-min intervals. Before site visits, all support field meters should be checked for operation and accuracy. Minimum calibration frequency is detailed by Wilde and Radtke (1998) for each type of meter, and all calibrations are recorded in the corresponding instrument log books. All information related to pre- and post-trip, and other periodic calibrations is recorded in the instrument logbooks. Table 6 presents technical characteristics of two sample portable water quality meters.

Portable equipment for ad-hoc measurements and sensor calibration This set of equipment, of which five complete sets will be supplied has several roles: a. Performing ad-hoc measurements on-the spot for control of the compliance of the automatic sensors b. Control (calibration) of the on-line sensors. c. Performing measurements in the intermediate places, between the automatic stations. d. Control measurements in the same cross section (in the cases when the monitoring station is close to effluent disposal so that fully mixed flow has not been achieved.) e. Control measurement on specific locations such as disposal of municipal or industrial effluents f. Special measurements requested by stakeholders

Laboratory network The laboratories should be capable of analyzing physical parameters (temperature, turbidity and conductivity), inorganic parameters (pH, ammonia, dissolved oxygen, nitrates, nitrites, phosphates, sulfates, etc.), aggregate organic parameters (chemical oxygen demand, total organic carbon), trace metals (including mercury), oil and grease, pesticides, phenolic compounds, and volatile organic compounds. It is noted that some existing equipments is no longer reliable either due to electromechanical problems or their life cycle (aging). Biological and microbiological analyses are to be carried out in the central labs. In addition to analytic equipment, an incubator, hot-air sterilizing


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Table 6 Technical characteristics of two sample portable water quality meters Range Equipment: multiparameter water quality meter pH 0.0 to 14.0 Conductivity 0 to 1 mS, 1 to 10 mS, 10 to 100 mS Salinity 0 to 4.0% Oxygen 0 to 19.99 mg/L Turbidity 0 to 800 NTU Temperature 0 to 50◦ C Type of analysis Electrometric Equipment: dissolved oxygen meter DO 0.0 to 60.0 mg/l Percent saturation 0 to 600.0% Barometric pressure 0 to 1,500 mbar Temperature −5.0 to 50.0◦ C Type of analysis Membrane electrometry

Resolution

Accuracy

0.1/0.01 0.01/0.001 mS, 0.1/0.1 mS, 1/0.1 mS 0.1/0.01% 0.1/0.01 mg/l 10/1 NTU 1/0.1◦ C

±0.05 pH ±1% full-scale ±0.1% ±3% full-scale ±0.3◦ C

0.01 mg/l 0.10% 1 mbar 0.01◦ C

±0.1% ±0.1% ±1% ±0.1◦ C

oven, autoclave, colony counter, pH-equipment, and balance are included. The other small items are media preparation utensils, pipettes, containers, dilution bottles or tubes, Petri dishes, fermentation tubes, and sample bottles. The USEPA or ISO standards that should be observed in dealing with analytical procedure.

b. Biological accumulation i.e. off-line monitoring of bioaccumulation of organisms living in the environment i.e. primarily fish and invertebrates living in the relevant reaches of Karoon River. Additionally benthic community status will be performed in a way that complies with sediment sampling procedure.

Program for monitoring ecological variables (biological assessment)

The program of monitoring will be established by a group of biologist from the Karoon Water Quality Monitoring Center (KWQMC), which will develop at least three bio-clusters as follows:

In addition to BOD5 and chlorophyll, which will be monitored automatically, the program of monitoring the ecological status and biological assessment of the water quality will be performed by the off-line measurements and assessments. This part of the water quality monitoring program will be performed by a combination of the following methods: a. Ecological method (assessment of biological communities), is primarily based on the use of the chlorophyll (fluorometric) data in combination with the other gathered information. Table 7 Human resources needed for public relation unit of the Karoon River Monitoring System

a. Upper (upstream) cluster, where the Karoon River reaches upstream of the Mollasani station. b. Central cluster between the Mollasani station and the Darkhovain station, which is the furthest downstream station and where there are no backwater effects, and c. Lower cluster, in the most downstream reach of the river under the tidal effects (brackish water)

No. of staff needed

Specialty

Position

1 1 1

Public relation and management Computer engineering Public relation/social science

1

Public relation/social science

Manager Coordinator of the website Coordinator of the public awareness programs Contact person with local people

Description/preferred qualification

General Management BS in Chemistry or equal

Work with analytical instruments and complex analyses/ two chemist or equal one biologist

Title

Laboratory head—manager

Analytical engineers

3

1

Number for the central laboratory

1 chemist or equal/each

1/each

Number for the local laboratories

Course 2 on analytical methods and use of instrumentation five working days

Course for Laboratory Management course 1 (5-day)

Type of traininga

Table 8 Proposed programme of training (group A: staff working in the central and local laboratories)

Day 1: general principles of modern environmental management and role and principles of environmental monitoring. Karoon river characteristics and environmental status and concept of the Karoon river monitoring projects. (to be attended also by analytical engineers, four leaders of cluster teams and leaders of technical teams at KWWMC) Day 2: (attended only by five lab leaders) principles of team work, team building, laboratory management and interactions with other sectors in the Karoon river monitoring programme Day 3: primer on analytical instruments in central and local labs, analytical procedures (attended by all analytical engineers from all five labs) Day 4: Good housekeeping, safety at work, handling emergency situations (War game), (Attended by all analytical engineers from all 5 labs) Day 5: Project development, PR principles, communication and reporting skills Day 1 (coincides with the day 3 of the course for laboratory heads): primer on analytical instruments in Central and local labs, analytical procedures Day 2: good housekeeping, safety at work, handling emergency situations (war game), Day 3: advanced analysis of physical and chemical variables using the equipment supplied by the winner in the tender Day 4: analysis of biological and microbiological variables Day 5: reporting skills, simple data processing and data base interactions

Proposed programme of training

76 Environ Monit Assess (2009) 155:63–81

77

Day 1: coincides with the day 2 for the analytical engineers Day 2: cleaning and housekeeping, security procedure

In each of the three clusters, the samples will be taken. A benchmark test will be carried out in order to establish the reliable criteria, indicators and procedure for the assessment of the proposed arrangement.

Human resources (HR) and training needs Qualified human resources are needed for successful operation of a monitoring system. For example, the public relations units can coordinate the following important tasks:

Course 3 for laboratory technicians (attending course 2 is mandatory (including safety at work) + 2-day additional course on lab maintenance and safety on work) 2-day training on safety on work

Days 1–5: the same (joint) programme as for the group of analytical engineers Day 6: maintenance of the instruments Day 7: preparations of samples and interactions with the teams for field sampling


• • • •

1 cleaner

training courses to be held in Ahvaz a All

Administrator Cleaner Security officer Supporting staff

1+1+1

Technician/support to laboratory analysis/ performing simpler analytical tasks/ database update Laboratory technician

3

1/each

•

Development and implementation of public awareness programs on the activities of the monitoring network. Updating the information and news on the website of the monitoring system. Receiving and documentation of pollution reports provided by the people. Follow up on reports and news provided by the local entities. Implementation of a feedback system to the general public in order to provide reports and news to the water quality monitoring system in the region.

The Karoon River is a complex system with respect to point and non-point pollution sources. Water quality monitoring in these systems can only be successful if the public is involved in the monitoring and control processes. NGOs can play a significant role in organizing people to be involved in this process. A public relations unit within the water quality monitoring system can provide an effective coordination among the public, NGOs and the monitoring system. So far, reports have been provided by locals about offsite disposal of some industrial wastewaters in different reaches of the river. The human resources needed for the Karoon River monitoring system and the proposed program of training are presented in Tables 7, 8 and 9.

Telecommunications and processing data acquired from the automatic field stations

Central data processing, GIS development and support, Data analysis and quality check, Data customization for the needs of clients

Technical reporting and publishing

Engineer for telecommunications

Data processing specialists

Graphic designer two technicians

Technical staff in cluster units Cluster manager (CM) Supervision and support to cluster activities

Major tasks

Title

3 (one for each cluster)

1+2+2+2

1

Number

Cluster management course

5-day training course

5-day data processing training course

5-day training course

Type of training

Table 9 Proposed programme of training (group B: technical staff for KWQMC)

Day 1 (together with lab heads): general principles of modern environmental management and role and principles of environmental monitoring. Karoon river characteristics and environmental status and concept of the Karoon river monitoring projects. (to be attended also by analytical engineers, four leaders of cluster teams and leaders of technical teams at KWWMC) Days 2, 3, 4 and 5: the same program (together with CET—Days 1–4) Day 6: commissioning procedure for the whole system

Day 1 (together with lab heads): general principles of modern environmental management and role and principles of environmental monitoring. Days 2–3: training programme on telecommunication equipment, use, trouble shouting, maintenance, repair Day 4: software for data transmission and storage, Day 5: Full scale commissioning and hands-on fine tuning Day 1 (together with lab heads): general principles of modern environmental management and role and principles of environmental monitoring Days 2–3: data processing software, water quality data analysis, database creation and update, data customisation, Day 4: links with GIS packages (ArcGIS), data quality checks Day 5: full scale commissioning and hands-on fine tuning Day 1 (together with lab heads): general principles of modern environmental management and role and principles of environmental monitoring Days 2–3: environmental awareness principles, communication skills, presentation skills, principles of quality graphic design Day 4: internet search for quality environmental publications and hand-son training Day 5: trial production of an outline design of a KWQMC report

Proposed programme of training

78 Environ Monit Assess (2009) 155:63–81

Computer engineering

Coordinator of the public awareness programs Contact person with local people

Staff working in PR Manager

Cluster engineers and technician (CET)

1

1

Public relation

Coordinator of the website

1

1

Public relation and management Public relation

Supervision and maintenance of filed stations

Update on the KQWMC concept

3-day course for PR manager 3-day course for PR officer

Day 1 (together with lab heads): general principles of modern environmental management and role and principles of environmental monitoring. Karoon river characteristics and environmental status and concept of the Karoon river monitoring projects Day 2: introduction to Karoon river monitoring programme, general principles of sensors and monitoring and data communication) Day 3: public presentation, communication skills, reporting skills Day 1 (together with lab heads): general principles of modern environmental management and role and principles of environmental monitoring. Karoon river characteristics and environmental status and concept of the Karoon river monitoring projects Day 2: introduction to Karoon river monitoring programme, general principles of sensors and monitoring and data communication) Day 3: principles of international communication, web search for good examples, trail design

Day 1: introduction to Karoon river monitoring programme, general principles of sensors and monitoring and data communication) Day 2: sensors and local data control and storage units: principles, operation, trouble shooting, maintenance and repair Day 3: data communication (transmission) and principles of data base creation, operation, maintenance, trouble shooting and repair Day 4: automatic and manual data sampling, sampling of species for biological monitoring, sediment sampling, preparation and transport of samples Day 5: start-up of the automatic monitoring and data transfer (full-scale hands-on) initiation of the programme Day 6: fine tuning of the elements that failed to start-up properly

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Summary and conclusions In this paper, the best combination of sampling stations and frequencies in a monitoring network is selected using the entropy theory by considering the maximum uncertainty and minimum redundant information in the system. The proposed model is applied to the Karoon River system in the south- eastern of Iran. After simulating the water quality in this river system, ten stations out of 28 potential stations are selected. Different combinations of number of stations and sampling frequencies are also evaluated using the measure of redundant information called transinformation. The results show that the proposed methodology can be effectively used for monitoring the river system. New stations and sampling frequencies are also identified. After identifying the stations, a new online monitoring system including a data acquisition and communication system as well as data processing and analysis components are designed. It has been proposed that the selected field stations be equipped with a set of sensors. In the last section, the assessment of human resource and training needs are given. Finally it is shown that a combination of analytical tools and methods with technologically fit equipment and sensors have been used in design of the monitoring system that is vital for the region. Acknowledgements This study was partially supported by the World Bank and Iran Department of Environment. The contribution of Dr. B. Zahraie, Ms. N. Mahjouri and Mr. M. Akhbari is hereby acknowledged.

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