Design methodology for optimum dosage air monitoring site selection

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Vol. Il. No. 12. PP. 2583-2590.

ooo44981/83

1983

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DESIGN METHODOLOGY FOR OPTIMUM DOSAGE AIR MONITORING SITE SELECTION KENNETH

E. NOLL and SATORU MITSUTOMI

*Pritzker Department of Environmental Engineering, Illinois Institute of Technology, Chicago, IL 60616, U.S.A. (First receioed 7 February 1983 and infinalform

27 April 1983)

Abstract-An air monitoring site selection procedure has been developed that ranks potential air monitoring sites according to their ability to represent the ambient dosage (i.e. the product of the concentration by exposure time) pattern in a monitoring network. The Dosage Monitoring Survey Design (DMSS) analyzes the dosage impact at grid receptors by dispersion modeling. High-dosage grids become potential monitoring sites. The uniqueness of DMSS is the introduction of a cluster of contiguous grid receptors that exceed a threshold value. One station is assigned to the cluster, thus eliminating redundancies among adjacent high dosage grids. The site selection procedure specifies locations for high-dosage monitoring stations along with the cluster area capabilities of each station. An efficiency term based on the ratio of a station’s dosage measuring capabilities to the total dosage in the network provides a method of ranking stations. An analysis of the design procedure shows that as the threshold concentration decreased the distance from the source

where the maximum dosage was found increased’andthere was a slight increase in station efficiency.Also, as averaging time increased, higher efficiencieswere achieved for individual stations.

INTRODUCTION monitoring may be conducted to meet diverse objectives. Achievement of many of these objectives in a cost-effective way requires the determination of an optimal number of monitoring stations. Determining the total number and location for air monitoring stations is complicated because air pollution phenomena involve not only irregularity of atmospheric movement but also uncertainty of human activities. Furthermore, conflicting objectives often need to be achieved with restricted resources. The design of air monitoring networks are typically based on the prediction of air pollution concentrations using simulation models. Design criteria are then established that provide assessment of ambient air quality for comparison to the predicted values. The last step involves the optimization of the locations and number of stations. Typically, potential monitoring sites are ranked according to their ability to achieve network objectives such as the measurement of high concentrations. Cost-effective siting is then achieved by assigning monitoring stations based on each station’s ability to achieve the network monitoring objectives. Darvy et al. (1974) developed a survey design method that locates monitoring sites near the most severe public health effects. System effectiveness is a function of pollutant concentration, exposure time, population exposed and the age distribution of the exposed population. No11and Miller (1976) developed a compliance network design that identifies the maximum concentration near large point sources. The number of stations and their location are determined Air quality

by statistical techniques using the frequency of occurrence of the maximum. Hougland and Stevens (1976) and Houghland (1977) provided monitor site assignments to maximize monitor coverage of a region within existing equipment limitations. The coverage is a function of the strength of a source, the frequency of occurrence of specified meteorological conditions and the likelihood that maximum concentrations from a source can be measured at a location under specified meteorological conditions. Lee et al. (1978) used a statistical model to assess the likelihood of standards violations. Probabilities of exceeding standards were translated into probabilities of detecting violations of the standards on the basis of annual geometric means and standard deviations. This paper presents a methodology fdr air monitoring site selection that ranks potential air monitoring sites according to their ability to assess the ambient dosage pattern near air pollution sources. Historically, dosage has been defined as the time integral of the concentration, and air pollution effects are empirically and legally related to dosage. The damaging effects of air pollutants are generally quantified in terms of dosage-damage functions (Liu, 1976); their evaluation in relation to ambient air quality standards is an important matter.

DOSAGE MONITORINGSITESELECTION(DMSS)

The dosage monitoring survey design procedure comprises analyses of the dosage impacts over a set time period at a number of grid receptors by dispersion modeling using emission and meteorological data

2583

2584

KENNETH E.

NOLL and

(Turner and Novak, 1978). A threshold concentration which should not be exceeded is selected for a set averaging time (i.e. 1 h). A group of contiguous grid receptors that exceed this value during the set averaging time are identified as a cluster. Each cluster is assigned a dosage that is representative of that cluster. Grid receptors that occur in a high number of clusters are identified as high-dosage areas and become potential dosage monitoring sites. Dosage monitoring sites are ranked according to efficiency and monitoring stations are assigned based on efficiency. Highefficiency stations are then associated with an expanded area identified as the dosage monitoring area for the station. A monitoring station located in the area theoretically would measure the highest dosage for the year in the assigned area. The uniqueness of the DMSS approach is the introduction of an averaging time cluster and a station efficiency term based on a ratio of dosages. The use of RAM allows for flexibility in the grid array selection and the number of sources to be considered in the monitoring network, but other simulation models could be used. The procedure is outlined in the following steps: (1) Characterize grid receptors by diffusion modeling (RAM) using an array of grids and a set averaging time for dosage calculation in each grid. (2) Locate clusters of receptors associated with the averaging time (i.e. 1 h, 3 h, 24 h) for which the concentrations (dosage) exceed a given threshold. Each identified receptor can be considered a potential monitoring site. (3) Calculate the dosage assigned to each cluster as the sum of concentrations for each receptor in the cluster (area dosage). Each receptor or potential monitoring site in the cluster is assigned the area dosage characteristic of that cluster. (4) Each potential monitoring site is assigned a station dosage that is the sum of all area dosages assigned to that site for the total averaging period (usually 1 y). (5) The sum of all cluster area dosages represents the total dosage for the entire grid system over the time period. The total area for the grid system is determined by the number and size of grids selected in the RAM program. (6) A station efficiency is now determined based on the ratio of station dosage (No. 4) to total dosage (NO. 5). (7) The dosage monitoring area for each station is assigned as equal to the area represented by all of the clusters (union of clusters) in which the station is contained. Figure 1 provides a flow diagram of the dosage monitoring procedure and includes: (1) the input requirements; (2) the output from RAM; (3) the selection of the station design criteria; (4) the calculation procedure for determining station efficiency and coverage area and (5) priority selection procedures. These

SATORU MITSUTOMI

are discussed in order in the following sections, which an illustrated example is provided.

after

input requirements

The four initial inputs required by DMSS are shown in Fig. 1 and can be divided into two parts: (1) hourly data for running RAM (meteorological and source data) and (2) data regarding DMSS design features (dosage and receptor constraints). Inputs to RAM must be chosen with consideration for design criteria of the DMSS model. The area grid to be evaluated must be selected that corresponds to the receptors to be monitored. Grid squares of equal area are selected with a permanent receptor assigned to the corners of each square. It is assumed that each receptor will accurately represent an area of one grid-size, with the receptor as a point located in the center of that grid. The receptors are assigned a number value for identification. This receptor array provides potential monitoring sites to be considered by DMSS. The second monitoring station design control input to RAM is the time requirements that serve as dosage constraints. First, a total time period over which the source is to be evaluated is selected. This period is typically 1 y. Next, an averaging time over which concentrations will be evaluated is determined. Average times are numbered in order of occurrence. A 1-h averaging time over a 1-y period would yield 8760 averaging times. This averaging time is crucial to dosage determination. Dosage is defined as concentration multiplied by time. When this definition is incorporated into the use of RAM, dosage becomes the concentration RAM assigns a receptor over a given averaging time. Typical averaging time values are: 1,3, 8 or 24 h. Limitations of RAM allow only averaging times that are equal to or less than 24 h. There are several design parameters that are required input to the DMSS program. The most important is the selection of a threshold concentration. Only dosages above this threshold will be considered by DMSS as potential dosages to be monitored. The area coverage of each receptor is initially assigned by RAM. Dosage monitoring design calculations The site selection procedure is based on the concepts ofcluster, area dosage, station dosage, total dosage and station efficiency. They are presented in Fig. 1 in the two boxes titled ‘Characteristics of monitoring site’ and ‘Calculation of station efficiency’. Each of these calculations are described below. Cluster formation A group of contiguous receptors that exceed the threshold for a given averaging time are designated a cluster (Fig. 2). Each cluster is therefore associated with a specific averaging time. Each receptor in the cluster can now be considered a potential monitoring site. Mathematically a cluster is defined as follows:

2585

Design methodology for optimum dosage air monitoring site selection NET MTA Hourly hteoro1oglcal Ddtr: wlndswed Mind Dlrcctlon Strblljty Class Mixing Height

RXAGE COWSTPAINTS

L-

Parameter: AveragIng Ttm Total Tia:Perlod Oosagc: cont. owr the selected we. ttm(

RAN for Period-1yr selected avcrag1ng mdffied out vut fn terms of dosage Dosage by averaging time by receptor Array

lOENTtFICATlONOF AIR HlHtTORIffi SITE DESIGN FUTURES

CALCULATION OF STATtOW EFFICIENCY

I IASSIGH STATION COVERAGE

PRlORITV NMITOR 1. 2. 3.

I

4. 5.

I

SELECTIO((

Select station with highest efficlcncy Eliminate clusters covered by selected site Recaluculate ststlon dosages without eliminated cluster Pick next most efflcfcnt station Continue process until dcslred no. monftorinqsite a* sell?cted

of

I

Fig I. A flow diagram of the dosage monitoring program.

where Q, = the set of all potential monitoring sites for which concentrations exceed a particular threshold during time period t, M = potential monitoring sites, m = index over the monitoring sites, t = index over the averaging times, Co = the threshold value and = concentration at the mth monitoring site c dL&g the tth period. Area dosage

All potential monitoring sites in the cluster would detect a high-dosage pattern, as shown in Fig. 2, with approximately equal probability. Therefore, it is assumed that any potential monitoring site (M) in the cluster is representative of the dosage in that particular

cluster of monitoring sites. An area dosage for any cluster is defined as the sum of concentrations of each receptor in that particular cluster, and is assigned to each receptor contained in the cluster:

where A, = area dosage that is a summation of the concentrations at all the potential monitoring sites (M) contained in the cluster Q,. Station dosage

Each monitoring site may be involved in more than one cluster (Fig. 3). The effectiveness of a monitoring

2SXh

KENNETH E. NOLL. and SATORI: MITSUWMI

Mathematically. this can be expressed as follws:

The above procedure provides for expanding the area coverage of a monitoring station beyond that of the original grid size identified in RAM. The station selected will register the highest dosage in the cxpanded area during the period to be analyze&

Fig. 2.

Formation

of cluster.

rc

Fig. 3.

Potential monitoring site involved in several clusters.

site can be described in terms of the number of clusters to which it belongs. Specifically, station dosage is the summation of the area dosages for all clusters that contain the station:

s, = c

A,,

where S, = station dosage, the sum of area dosages to which m is a member. Tofal dosage

A second dosage summation of interest is the total pollutant dosage accummulated over the entire period evaluated. This is merely a sum of all the area dosages observed: A=

CA,,

where A = total dosage. Eficienc) Station efficiency is defined in terms of the number of high-dosage readings occurring at a monitoring site. A station is considered efficient if the ratio of its station dosage to total dosage is high: E, = &IA. where E, = the efficiency of monitoring site m. In addition to ranking monitors according to efficiency, the procedure has the result of defining the area that an efficient station can cover. The original grid area is extended to in&de all sites in any cluster coverage). (station that station containing

After all of the above terms have been calculated the actual site selection process is as follows: (I) The monitoring site with the highest station efficiency is selected. (2) All clusters associated with this site are eliminated and new station dosages and therefore station efficiencies are calculated for the remaining sites. (3) The second most efficient site is selected based on these new calculations. (4) This process continues until the desired amount of total network efficiency is achieved. Network or total efficiency is the cumulative efficiency of all sites selected

up to that

point.

The efficiency of the first two stations selected would be the sum of their Individual efficiencies.

EXAMPLE

SITE SELEC-I‘IDN

USING

DMSS

To illustrate the use of DMSS. consider a 25receptor grid and 8 clusters. (See Fig. 4). Table I summarizes the calculations made by DMSS in selecting potential monitoring sites. Only potential monitoring sites have been listed. Clusters are in the vertical column on the left, monitoring sites are along the top. The numbers in the table refer to concentrations calculated at each site during the averaging time for that particular cluster. A void indicates that the potential monitoring site does not appear in that cluster. The column on the far right shows the area dosage for each cluster (total of concentrations). Cluster 1 contains sites 8 and 13; therefore its area dosage is the sum of 450 and 410. which is 860. The bottom of this right-hand column gives the total of all area dosages, which is total dosage for the example period (8830). Below the eight clusters is thecalculated station dosages for each potential monitoring site (Sm). For example, potential monitoring site No. 12 belongs to 3 clusters (4,5 and 6). The station dosage assigned to site 12 is the sum of the area dosages of these 3 clusters (930 + 400 + 1010 = 2340). The bottom row gives the station efficiency of 234018830 = 0.26 (station dosage/total dosage). In the calculation of network efficiency the model would: (1) pick site 13 as the most efficient with a station efficiency of 0.53; (2) eliminate clusters involving site 13 (this eliminates clusters 1, 2, 3 and 4); and (3) recalculate station dosages and therefore station efficiencies without clusters 1.2, 3 and 4. For example.

Design methodology for optimum dosage air monitoring site selection

2587

Table 1. Calculation of efficiency

Q,

1 2 3 4 5 6 7 8 Sm F”,

2

6

7

8

9

Potential monitor sites 11 12

(m)

13

450

410 580 410 470

420

480 680 420 3740 0.43

730 1930 0.22

460 400 530

860 0.10

1230 0.14

1930 0.22

Table 2. Network efficiency for example calculation

13 6 12

18

23

550

490

1620 0.18

1620 0.18

400

520 430 850 0.10

Fig. 4. Overlapping cluster.

Monitoring site

17

Efficiency

Total efficiency

0.53 0.43 0.04

53 96 100

the station dosage of site 12 will not be (400+ 1010 = 1410) since cluster 4 has been eliminated. The efficiency of site 6 remains the same since it is not contained in the eliminated clusters. It is the second site chosen with an efficiency of 0.43. Cluster 5 still remains; therefore site 12 will be selected to cover this area. See Table 2. Site 13 will cover a region comprised of all stations contained in clusters 1,2,3 and 4, which are stations 23, l&17,13,9,8 and 12. Station 6covers: 6,7,2,11 and 12. Note that station 12 is covered three times in order to get full coverage of cluster 5. Receptors not listed above do not need coverage since they never exceed the threshold concentration. APPLICATION

DMSS has been applied to a power plant located in Northern Illinois. The air monitoring dosage network is based on 1 y of meteorological data and emission

2340 0.26

4640 0.53

1230 0.14

A, 860 1620 1230 930 400 1010 1930 850 8830 1.00

data from the power plant. The power plant consists of two 8%MW units (units 5 and 6) discharging through a common 152-m stack. The emission data in Table 3 represents operation at full capacity. The annual average emission values are based on the annual capacity factors shown. The standard deviation of the hourly emission data is small, so that the hourly emission values are approximately the same as the annual average emission values. The meteorological data used as input to the atmospheric dispersion model, RAM, covers a l-y period from 1 March 1977 through 28 February 1978. The data includes: ambient temperature, wind speed and wind direction from the Peoria Airport Weather Records; atmospheric stability calculated by the Pasquill and Turner Stability Class; and mixing height acquired by monostatic acoustic radar. The site selection program requires the selection of a grid receptor array and design criteria. For this application example the grid receptor array and design criteria are determined as follows: the receptor array selected was a rectangular 10 x 15km grid area with the 15-km side in the N-S direction. The source is located 1 km S and 4 km E of the SW corner of the grid. A permanent receptor is at the corner at each lkm square. This provides for 150 receptors their SO, concentrations, which were calculated and stored as input to the site selection program. The following survey design criteria were employed: (1) averaging time = 1 h; (2) total time period = 1 y; Table 3. Source emission data Parameters capacity Stack height Stack diameter Stack exit velocity: short-term annual average Emission rate: short-term annual average Annual capacity factor Average fuel sulfur content

Quantity 1700 MW 152.4 m 10.4 m 34.3 ms-’ 27.4 ms-’ 13134 gs-’ 9194 gs-’ 70% 3.52 %

KENNETH E. NOLL and SATORU MITSUTOMI

This particular network of 15 monitors will cover the entire grid area with an efficiency of 90.37 7;. The total station area covered by each monitoring site is given in Table 5. Although the station selection procedure is based on

(3) threshold concentration = 400 pg m - 3; and (4) No. of monitoring stations = 15. Table 4 shows the results from DMSS. The most efficient station is No. 55, with an efficiency of 16.68. The second is No. 43, with an efficiency is 29.79. Table 4. Monitoring

No.

Monitor location

1

55

2 3 4 5 6 I 8 9 10 11 12 13 14 15

43 78 38 145 11 6 69 113 37 111 42 30 21 44

stations

assigned

by DMSS

Station

Network

efficiency

efficiency

16.68 13.11 12.54 8.12 6.83 6.53 5.93 5.60 4.48 2.71 2.19 1.95 1.34 1.21 1.14

16.68 29.79 42.33 so.45 57.28 63.82 69.15 15.35 79.83 82.54 84.74 86.69 88.02 89.23 90.37

t 100 t

Fig. 5. The effects of choice of threshold monitoring site location.

dosage

Table 5. Station coverage for selected stations Station

55 5 105

15 115

25

25

45

55

65

15

85

95

13 44

14 51

23 52

24 53

32 61

33 62

34 63

41 71

42 72

26 17 130

21 78 140

36 88 150

37 89

46 98

4-l 99

57 109

58 119

67 120

16 70

17

27

28

38

39

49

50

59

85

95

105

115

125

135

145

3

4

11

12

13

21

8 30

16 37

17 38

18 48

19

20

26

2-l

29 48

58

59

69

79

80

90

100

73 123

74 132

83 133

84 142

93 143

94

103

112

113

16 61

26 60

21 69

36 17

31 78

46 88

41 89

48

57

62

12

82

91

101

111

13 52

14 61

23 62

24 71

32

33

41

42

43

I

8

18

19

20

29

30

3

4

11

12

13

21

22

24

33

34

43

44

53

54

43 4 43 82 78 16 68 129 Station

38 6 60 145

Station 15

11 1

Station

6 6 28

Station

69 37

Station

113 64 122

Station

3-l 6 50

Station

111 53

Station

42 4 51 30

Station 6

21 2 44

Station 14

on

Design methodology for optimum dosage air monitoring site selection

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In*mN__d___

Q\coP-\DmbmNhl8 0000000000

2589

the total station dosage (Table 6), a table of individual station dosage does not take into account the dosages due to cluster membership that is associated with the total station dosage. It is only a summation of the actual dosage recorded at each station. Station 55 had the highest individual dosage and the highest station dosage. Station 5 had the second highest individual dosage but does not have a high station dosage. After Station 55 is selected then the next selection gives it an efficiency of 0. (Since Station 5 belongs to several clusters with 55, its dosage and efficiency were reduced from 14089 to 1348 in the selection process.) Station 43 is then chosen second, based on a higher station dosage.

SENSITIVITY

ANALYSIS

To better understand the behavior of DMSS a sensitivity analysis was conducted that utilized the same source and meteorological data that were used in the previous section. This analysis examined the changes in station location and efficiency caused by variations in the design parameters of threshold and averaging time as follows: (1) threshold effect on station location; (2) averaging time effect on network efficiency; and (3) threshold effect on network efficiency. Figure 5 shows the change in location for the most efficient dosage monitoring station as a function of the threshold dosage that is selected as the initial design criteria. As the threshold concentration decreases, the distance from the source where the maximum dosage is found increases. A bar associated with each site indicates the grid size of 1 km. The actual maximum

Averaging Time

01 0

”

1

2

”

3

4

” 5

6

’ 7

0

1

hr

0

3

hr

0

24

hr

“““‘I 8 9

10 I, 12

13 14 1s

NO. OF STATIONS

Fig. 6. Network efficiency vs no. of stations required for several averaging times (total dosage is 3OO@gm-” for each averaging time).

KENrxrH E. NOLL and SATORU MITSIJTOMI

2590 100

r

m -3 h-l) for each averaging time was used tn the figure, giving a threshold concentration of 300 for I h. 100 for 3 h and 12 for 24 h. The figure shows that higher efficiencies are achieved for individual stations as the averaging time is increased. Figure 7 shows the effect of change in threshold concentrations based on l-h averages. There is only a slight increase in slope as lower thresholds are selected and these differences diminish as the number of stations selected increases.

REFERENCES Darvy W. P., Ossenbruggen P. J., Gregory C. J. et al. (1974) Optimization of urban air monitoring networks. Proc. ASCE

100, 577-589.

Houghland E. S. and Stephens N. T. (1976) Air pollutant monitoring siting by analytical techniques. J. Air Polh. Control Ass. 26, 51-53. NO. OF STATIONS

Fig. 7. Network efficiency vs no. of stations required for several threshold concentrations (averaging time 1 h).

dosage may be found anywhere within this l-km region. Figure 6 shows the effect of changes in averaging time on network efficiency. A constant dosage (300 pg

Houghland E. S. (1977) Air pollutant monitor network design using mathematical programming. Ph.D. thesis. Virginia Polytechnic Institute and State University, Virginia. Lee T. S., Graves R. J. and McGinnis (1978) A procedure for air monitoring instrumentation location. Mgmr Sri. 24, 1451-1459. Liu B. (1976) Phq’sical and Economic Damage Func!iom for Air Pollutants. EPA 600/5-76-011. Noll K. E. and Miller T. L. (1976) Design of air monitoring surveys near large power planfs. Power Generarim (Edited by Noll K. E. and Davis W. J., pp. 121- 122. Turner D. B. and Novak J. H. (1978) User’s Guidrftir RAM. EPA 600/8-78-016.