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An objective air monitoring site selection methodology for large point sources
Armosphertc
Environment
Vol.II,pp.1051-1059. Pergamon Press 1977. Printed
m Great
Britain
AN OBJECTIVE AIR MONITORING SITE SELECTION METHODOLOGY FOR LARGE POINT SOURCES KENNETH *Pritzker
Department
E. NOLL,* TERRY
and R. K. RAUFER$
L. MILLER,? JAY E.No~cof
of Environmental Engineering, Illinois Institute of Technology, tEnviro-measure, Inc., Knoxville, TN, U.S.A. SETA, Inc.. Oak Brook, IL. U.S.A. (First received
14 December
1976 and in jifinal
,form27
April
Chicago,
IL 60616, U.S.A.
1977)
Abstract-Quantitative methods for determining the total number and location of air monitoring sites to meet specific air sampling objectives have proven difficult to identify by the traditional application of diffusion models and historical meteorological data. Objective criteria for site selection are important to the design of cost-effective air monitoring surveys, obtaining required data results in the minimum time period, and trade-off decisions between fixed and mobile monitoring options. The objective technique reported here utilizes statistical methods to determine the number and location of sensors from the ratio of areas defined by (1) the range of available historical meteorological conditions producing maximum concentrations and (2) a predetermined concentration measurement tolerance range around the maximum concentration which represents the maximum within 10 or 2076 of the predicted value. The desired confidence level for measuring a specified air pollution maximum value is also required. The application of the method to the design of an air monitoring network to support an intermittent control system for two power plants in central Illinois confirms the practicality of the method. The final monitoring network resulted from a consideration of numerous combinations of fixed and mobile monitoring options. The sites having the highest probability of occurrence and the greatest predicted concentrations were selected for fixed, continuous monitoring. Where high concentrations occur with a relatively low frequency. a mobile monitor which relies on meteorological forecasting can be dispatched through a control center to predetermined, fixed, sites is cost-effective in reducing the requirement for a large number of permanent monitors.
1.INTRODUCTION
and air monitoring site selection in particular has traditionally been approached in a qualitative way. This has resulted because of the complex nature of air quality due to the uneven distribution of sources and the variability in atmospheric mixing from place to place and from time to time. Decisions concerning the minimum number of sampling stations to meet the objectives of a study and the siting of individual sampling stations in order to obtain “representative” readings are generally based on available air quality and meteorological data and on the results of dispersion models. In recent years, air monitoring surveys have become more complex and require comprehensive planning to assure that objectives can be attained in the shortest possible time and at the least cost. One of the manifestations of these developments is the need for the de’velopment of quantitative procedures for air monitoring site selection to assure that specific objectives can be attained. This paper provides a quantitative procedure for the selection of air monitoring sites which utilizes dispersion models, historical meteorological data and probability calculations. New terms such as potential monitoring zone, tolerance range, station area and station coverage ratio are defined and a description of their use in the design procedure is provided. The application of the methodology to both the coning and trapping meteorological regimes contained in Air quality
monitoring
in general
1051
most simulation models for point sources is then provided. Finally, the concept of mobile monitoring stations dispatched to fixed predetermined locations is discussed.
II.
SITE SELECTION
PROCEDURE
The first priority in the effective selection of air monitoring sites is to obtain an understanding of the spatial distribution of pollution concentration under different source and meteorological conditions. This is accomplished by utilizing diffusion models @lade, 1968; Turner, 1970). The design method presented here requires the calculation, of the distance from a source that maximum concentrations occur under different meteorological conditions. These locations then become potential monitoring sites. Historical meteorological data and statistical methods are then utilized to determine the likelihood that an air monitoring station located where each of the maximum pollution concentrations are predicted to occur could measure the predicted maximum value with some confidence level. The analysis requires a dispersion model appropriate to the point source under consideration. The model is assumed to be error-free for the purpose of this analysis. After initial monitoring data have been evaluated, systematic modeling errors can be removed by calibration factors. Terrain effects have not been included in the analysis. Their inclusion, however, would not change the procedure.
I052
KENNETH E. Nou..
T~.KKY L. MILLER, JAY E. NORW and R. K. K~\LFEK
The objective network design can be described in the following steps: 1. Identify the location of the maximum concentration using atmospheric dispersion models. 2. Generate the joint frequency of occurrence of meteorological conditions producing the maximum. 3. Determine the ground area defined by the range of these meteorological conditions by using atmospheric dispersion models (Potential Monitoring Zone). 3. Determine the ground level station coverage area, i.e. an area defined by a given tolerance range around the maximum concentration. 5. Using statistical techniques determine the number and locations of monitors from the ratio of areas defined in 3 and 4 above, and the desired cootidence level for tneasuring the air quality standard violations. The methodology includes (a) determining the size of a representative sampling area that can be covered by a single station (coverage ratio), (b) determining the minimum number of sites needed to ensure a desired number of observations of C,,, (a statistical approach), and (c) selecting the optimum number and location of stations required to meet monitoring objectives with a high probability of success (Miller
and Nell, 1976: Nell and Miller, 1977). The general methodology is developed below:
ideally. a monitoring network should be designed using a long-term historical base of meteorological data which is compatible with the requirements of the simulation model which is utilized to predict maximum concentrations. The coning plume models require only the historical meteorological data contained in the National Weather Service STAR (STAR Program, 1972) data summary. These are the threeway frequency of occurrence with wind speed class, direction and stability classes. It has been shown (Carpenter et ul., 1970) that the plume “trapping” regime is more critical in estimating high conccntrat~ons caused by large power plants. Worst-case trapping occurs when the atmospheric mixing lid is equal to the final effective plume height. Thus a fourth significant meteorological variable-mixing height is required for the monitoring network design. Di~usion calculations do not accurately predict the maximum concentration, therefore, a more realistic solution to the models for air monitoring site selection purposes, may be to calculate maximum concen-
Fig. I. Illustration of the coverage area of a single station downwind of a point source. Shaded area represents the area within the potential monitoring zone not covered by the station. The ratio of the coverage area to the total area of the potential zone equals the coverage ratio.
1053
Air monitoring site selection methodology trations and distances for a range of conditions for which historical meteorological data is available. For example, rather than determine where the maximum concentration occurs for neutral stability at a wind speed of 5.7 m s-r, it would be more realistic to determine maximum concentrations and distances for wind speeds of from 4 to 7 m s-r. The frequency of occurrence of the range of wind speeds can then be determined from historical meteorological summaries such as STAR. Which directions should be monitored can be determined from historical wind direction/frequency data and a consideration of potential receptors in each direction. Potential monitoring zone A potential monitoring zone is the area defined by the available meteorological data (STAR classes) within which maximum concentrations can occur as found by dispersion modeling of the range of meteorological parameters (wind speed, wind direction, mixing height). A typical potential monitoring zone has distinct boundaries (Fig. 1) and resembles a slice from a “dough-nut”. The front and the back of the boundary corresponds to the upper and lower boundaries of the wind speed classes in the STAR program. The
zone becomes complex when mixing height is also included as a variable and there is interaction of several sources. When the potential monitoring zone boundaries have been established, then the area that it represents on the ground can be calculated.
Tolerance range and station area Perfect resolution of a predicted concentration peak is not necessary because of model inaccuracy and the range of meteorological variables that can generate a peak. Thus the ability to resolve peak concentrations can be considered a variable that can be changed for each application. Location of a monitoring station within an area that represents the maximum concentration within 10 or 20 per cent of the predicted value (Tolerance Range) are not unreasonable criteria. Selection of the tolerance range involves a trade-off between monitor coverage and cost (number of monitors). Having the ability to monitor maximum concentration exactly (with a tolerance range of zero) would be worthwhile, except then an infinite number of monitors would be required. Further, as the expected maximum concentration drops below the applicable air quality standard, the tolerance range becomes less critical and a larger range is acceptable. For example, from curves of normalized concentration, C U/Q, vs distance downwind, x (Turner, 1970) (Fig. 2) it is possible to determine the distance toward and away from the source within which the concentrations deviate from C,,, by some tolerance range (such as 10 per cent). Figure 3 shows the distance from the source, XI,, within which concentrations are within lJ3 per cent of C,,,,, when the peak occurs at X for different stability classes. In addition to the downwind distance determined by the tolerance range, it is also necessary to know the crosswind extent. The distance, YrO, within which concentrations are within 10 per cent of C,,, can be calculated from the equation (1 - 0.1) = exp - Zr_ Y which is derived from the general Gaussian equation (Hino, 1968). This reduces to Y,,, = 0.92 cry(x).
ii..
1
10
DISTANCE, km
Fig. 2. xu/Q with distance for various heights of emission (H) and limits to vertical dispersion (L), B stability. [From Turner (1970)].
plume
(2)
Values of Y,, for various distances downwind, and stability classes are presented in Fig. 4 (a, are Pasquill-Gifford values). Values of X,, and Yr, determine the size and shape of the station coverage area under C,,, conditions. Values of XI, tend to exceed Yr, by an order of magnitude for most stabilities. This results in an area which is elliptical in shape, (an ellipse is used here to approximate what is actually a “teardrop” shape) (Fig. 1). The number and location of monitoring sites can
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Wi)‘oC,
‘lIWl1
E. NOLL.
33NVU13101
TEKKY L.
MILLER.
%Ol QNIMNMOQ
311
JAY
E. NORCO and R. K.
01 33NVlSla
KALI~EK
Air monitoring site selection methodology
FREQUENCY OF OCCURRENCE
OF METEOROLORICAL
EVENT,NUMRERS
I055
OF EVENTS PER YEAR, N
Fig. 5. Coverage ratio required to observe C,,,, n times, with 99 per cent confidence, vs the number of occurrences, N, of meteorological phenomena causing C,,,.
now be determined from (a) the size of the monitoring station area under C,,, conditions, (b) the size of the potential zone requiring monitoring, and (c) the frequency of occurrence of meteorological conditions causing C,,, values in each zone. The cover ratio for each potential monitoring site is determined by dividing the monitoring station coverage area by the area of the potential monitoring zone. If XI0 is less than the radial dimension of the zone, then the proportion of the zone covered by a single monitoring station can be calculated from
CR, =
x10 YlO
The number of hours per year (N) for a defined set of meteorological conditions, the confidence level selected, and the number of times per year that the desired concentration must be successfully monitored (n) provides the total coverage ratio required. The number of monitors required to fulfill the objective is equal to the total coverage ratio required divided by the coverage ratio of a single station. To calculate the probability that one station will measure the maximum concentration n times when the meteorological conditions causing C,,, occur N times per year requires use of the binomial distribution function (SAS, 1972).
4(XIo - X?,)’ P=
where CR, is the coverage ratio for one station (without regard for wind direction), X,, is inside radius of circular zone, and Xzo is outside radius of circular zone. To calculate coverage ratio for a single wind direction, multiply the CR, from Equation (3) by the number of wind sectors the “dough-nut” shaped zone is divided into. Conjdence
level and total number of stations
The confidence level is defined as the probability that one station will measure the maximum concentration n times when the meteorological conditions causing the maximum occurs N times per year. The number of stations required to monitor each maximum concentration can be determined by utilizing general statistical solutions as presented in Fig. 5. A.E.
1l/l 1-D
N! n!(N - n)!
CR;(l
- CR#-“.
(4)
For large values of N, the probability, P, or coverage ratio, CR,, is best determined by using the normal approximation to the binomial distribution and solving for the statistic, Z. The probability associated with various Z values can be determined from standard statistical tables. Z is calculated using the equation
Z=-_
-n - NP [NP(l
- P)]l’*’
(5)
One solution of equation (5) is given in Fig. 5. This figure can be used to determine the total coverage ratio, CRT, required to allow n successful measurements of C,,, with 99% confidence given the frequency of occurrence, N, each year of the meteorological event producing C,,,.
KENNETH E. NOLL, TERRY L. MILLER. JA\ E. NOR(Y) and
IO56
After solving the total coverage ratio required, the number of stations needed in the crosswind direction can be calculated. IV, = CR,JCR,.
(6)
where N, is number of stations needed, CR, is coverage ratio of each station. and CRT is total coverage ratio required to achieve desired results.
111. APPLICATION
TO DESIGN
The highest concentrations near large point sources often occur for the plume “trapping” regime (Carpenter, 1971). The maximum concentrations found by diffusion models have a wind speed, wind direction, stability class and associated mixing height. The wind speed associated with each maximum concentration is defined by a wind speed class. as in the STAR data, bounded by an upper and lower wind speed. For trapping, a worst-case mixing height is associated with the upper and lower wind speed, and together with the specified stability class and wind direction ranges, the number of hours per year (N) that the given combination of parameters occurs can be interpolated from historical meteorological frequency data. Wind directions for maximum concentrations
R. K. RAI
1,K
give the other meteorological parameter. designated as the wind direction class with the largest !V-\#alue. As has already been stated. a potential monitoring zone is an area defined by the available meteorological data (STAR classes) within which maximum concentrations can occur. Discussion of the potential monitoring zone for trapping conditions will be aided by the definition of names for the different portions of its boundaries. In F‘lg. I. the curved boundary nearest the emission source i\ culled the zone’s “front” The other curved boundary is the “back” and the‘ two straight boundaries arc the “sides”. The wind direction is from the front to the back of the ~onc. The front and back of a potential monitoring /one created by a source arc &lined by circles drawn around the source. with radii equal to the downwind distances to the appropriate maximum concentrations. For trapping conditions. with a gi\cn stability class, four of these diatanccs exist. They represent the four combinations of the two houndalMind speeds with the two boundary mixing heights used to determine the N-value for Ihe specific monitoring candidate concentration. Of these four circles. the inner and outermost circles arc used as the front and back boundaries of the potential monitoring LWL’. Front and back boundaries for unlimited mixing conditions are defined in ;I Gmilar manner. In this
1.W
SC
1.0
5.0
10.0
50.0
100.0
N ~OCWRHHCES PLR YEAR) Fig. 6. Cover ratio vs frequency of occurrence for various confidence lecela. These curves Indicate the cover ratio required (CR,) to successfully monitor a maximum concentration once per year (n = I b if the appropriate meteorological conditions occur N times per year (for various confidence levels).
Air monitoring site selection methodology
1057
POWERTON UNITS Sand6
POTENTIAL MONITJRING ZONE
STATION @VERA=
AREA
MAXIMUM CONCENTRATION LINE
scale 1
2
kilormtem
Fig. 7. Example monitoring candidate. Wind speed (U): 3.3-5.4 m s-l. Wind direction (0): 11.25-33.75 Stability: B. Mixing height (L): 73&1083 m. Station coverage area: 1.98 km’. Potential monitoring zone area: 7.16 km*. Station coverage ratio: 0.28.
case, only two circles exist because, for a given wind speed, the downwind distance to the maximum concentration is not affected by changes in mixing height when the mixing height is higher than the coningtrapping transition. When the radii of the front and back boundaries are known, then the arcs they define can be plotted, along with the side boundaries, which are determined by the extremes of the wind direction range. The potential monitoring zone is now defined and its area can be determined. The potential monitoring zone created by the interaction of several sources is more difficult to define. The coordinates of the maximum concentrations resulting from combining various lower, central and upper values of each of the three parameter ranges, (wind speed, wind direction and mixing height), are found by dispersion modeling. These coordinates locate the endpoints and midpoints of line segments which form portions of the potential zone boundary. The largest potential zone that can be defined by the line segments is used, and its area is measured with a planimeter. This methodology was applied to a multiple source
problem in which the coverage area for each potential monitoring station was determined by plotting an isopleth around the maximum concentration which enclosed all concentrations within a tolerance range of 20 per cent of the maximum con~ntration(Nol1 et al., 1976). This plot was su~rimpos~ upon the associated potential monitoring zone, and the effective station coverage area (the portion of the station coverage area contained within the potential monitoring zone) was measured with a planimeter. The cover for each monitoring candidate was determined by dividing the station coverage area by the area of the potential monitoring zone. The number of stations required to monitor each maximum concentration was determined by using Fig. 6. This figure is similar to Fig. 5, except that the number of times a maximum concentration must be successfully monitored (n) has been selected as 1. The number of hours per year (N) for a meteorological condition likely to cause high concentrations is found on the horizontal scale of Fig. 6. Using a 90 per cent confidence line, the total coverage ratio required (CR,) is determined from the vertical scale. The number of monitors required for a given maxi-
KENNETH
10%
E.
NOLL, TERRY
L.
MILLEX, JAY t.
mum concentration is equal to the total coverage ratio required divided by the coverage ratio of a single station, or CRT/CR,. The methodology can now be demonstrated by outlining the analysis for a typical monitoring candidate with the following results from dispersion modeling for a large power plant located in the midwestern United States: Regime: Maximum concentration: Wind speed : Stability: Mixing height: Downwind distance from source to maximum concentration :
worst-case 973pgm-’ 514m s-r B 738 m
trapping
4.4 km.
The wind speed of 5.4 m s- ’ is contained in the third STAR wind speed class, which included wind speeds of 3.3-5.4 m s- *. Single source modeling at 3.3 and 4.4ms-’ produced maximum concentrations of 779 and 889pgmd3, respectively. This indicates that the concentration associated with the 5.4 m s ’ wind speed is the maximum concentration for this wind speed range, and hence the monitor location is based upon this condition. The worst-case trapping mixing height associated with the 3.3 m s -’ wind speed is 1083 m. Therefore, determination of the N-value for this candidate is based on these parameter ranges: Wind speed : Stability: Mixing height:
3.3 5.4 m s ’ B 738-1083 m.
The meteorological frequency data showed that these conditions occurred most often in the northnortheast wind direction class (I I .25-33.75 ). The applicable meteorological frequency data and resulting N-value for the example candidate are: Raw data Mixing height Gross N (hy-$1 Cm) 700- 800 80&900 9WlOOO 100&l 250
1 2 2 0
Interpolated data Mixing height Net N (hy~‘) (m) 738HOO SO&900 90&1000 1000-1083
0.6 2.0 2.0 0.0
738~ 1083
4.6
The four downwind distances, which are required to locate the front and back boundaries of the potential monitoring zone are: Mixing height Cm)
73x 1083
_______ Downwind distance (km) 5.4mC 3.3 m s ’ wind speed wind speed
I .4 6.2
4.4 5.2
Nowo
and K. h
KAI~I.I:K
These boundaries. along with the side boundaries which are determined by the wind direction range, are shown in Fig. 7. The station cov’erage area boundary. a 77X ~lg m ’ concentration isopleth representing 80 per cent of the maximum concentration. is also shown. The station cokerage md potential monitoring zone areas are also indicated in Fig. 7. along with the station coverage ratio. Figure 6 is now used to determine the total covcragc ratio required based on an .V-value of 4.6 Ii y ’ and ;I W,, confidence level. The resulting total covcragc ratio is 0.53. which requires 0.530.X or I .9 stations (rounded up to 2) to successfully monitor the example maximum concentrations. The final monitormg network design resulted from a consideration of numerous combinations of fixed and mobile monitoring options for conditions leading to high concentrations and the frequency of occurrence of such concentrations. The resulting network included thirteen fixed and two mobile monitors. The sites having the highest probability of occurrence and the greatest predicted concentrations were selected for fixed. continuous monitoring. In man) casts. high concentrations whtch occur relatively infrequently (such as A stability trapping conditions) could occurs’ with nearly equal probability in any direction. It is highly improbable that one fixed monitor would detect this type of condition. In fact, it was estimated that 13 monitors would have to be located in ;I ring quite close to the power plant in this example just to see the .J stability trapping conditionh. One mobile monitor- which relies on meteorological forecasting and is dispatched through a control center has the same capability as the 13 fixed monitor‘;. The cost-effectiveness of this option is very significant. The concept 01‘mobile monitoring at fixed sites is etfectivc in mitigating the need for great numbers of permanent monitors and can also (I) increase temporarily the monitoring density in an area potentially subject to ambient SO2 excursions, (2) increase the data base available for calibrating computer models. (3) modify monitor location patterns in preparation for relocation of fixed sites, and (4) extend the upwind data base at times when downwind concentrations arc not critical.
An objcctrve air monitoring network design methodology has been developed for large point sources utilizing “coning” and “trapping” regimes from dispersion models. The application of the methodology can be approached as follows: (a) The survey design method is run for several hundred meteorological and source emission combinations until all maxima are determined. The single station coverage ratio for selected peaks is determined based upon a given tolerance range.
Air monitoring site selection methodology (b) Meteorological frequency data are used to determine the likely directions for each condition and the number of occurrences per year. (c) The potential monitoring zones for selected situations of high concentration and high meteorological occurrence (N > 1) are determined. (d) The total coverage area is determined based upon a specific confidence level and the number of successful measurements required for each potential monitoring zone. (e) The total number of stations for each potential zone is determined by dividing the total coverage area required by the single station coverage area. (f) The final monitoring network results from a trade-off of all monitoring options determined above and the benefits of fixed and mobile monitoring sites. In many cases, high concentrations which occur infrequently will not show a preferred direction. This makes it highly unlikely that one fixed station would measure this condition. However, one mobile monitor (dispatched, based upon meteorological forecasting) could measure the peak and be cost-effective because it would have the same capability as many fixed monitors. The application of the methodology produces the following general results: (1) As the joint frequency of occurrence of meteorological conditions producing a maximum decreases, then the required total coverage ratio increases (and number of monitors increases) unless the specific confidence level is lowered. Following the determination of N-values for the various maximum concentrations, those with N-values less than or equal to one hour per year are generally eliminated or measured with mobile stations. (2) When four-way frequency distributions of historical meteorological data are used (wind speed, direction, stability class and mixing height) instead of three-way (wind speed, direction and stability class) then the number of historical meteorological occurrences generally decrease.
I059
(3) As the tolerance range for measurement of peak concentrations is increased, then the single station coverage ratio increases and the number of monitors required decreases. (4) As the averaging time for which the maximum concentration is desired increases, then the single station coverage ratio increases and the number of monitoring stations required decreases. (5) One mobile monitor can have the same capability as many fixed monitors when the objective is to measure concentrations which occur infrequently (such as A stability trapping conditions).
REFERENCES
Carpenter S. B., Montgomery T. L., Leavitt J. M., Colbaugh W. C. and Thomas F. W. (1971) Principal plume dispersion models: TVA power plants. J. Air. Pollur. Contra/. Ass. 21(8), 940-972. Hino M. (1968) Maximum ground-level concentration and sampling time. Atmospheric Environment 2, 149-165. Miller T. L. and No11 K. E. (1976) Design of air monitoring surveys near large power plants. In Power Generation: Air Pollution Monitoring and Control (Edited by No11 K. E. and Davis W. T., pp. 121-142. Ann Arbor Science Pub]., Ann Arbor. No11 K. E. and ETA Inc. (1976) The Sulfur Dioxide Monitoring Network Design for the Powerton-Edward Supplementary Control System, Final Report to Commonwealth Edison and Central Illinois Light Co., 48 pp. Noll K. E. and Miller T. L. (1977) Air Monitoring Suroey Design, 296 pp. Ann Arbor Science Publ., Ann Arbor. SAS (Statistical Analysis System) (1972) Designed by Anthony Barr and James Goodnight, Dept. of Statistics, North Carolina State University, User Manual published by Student Supply Stores, NCSU, Raleigh, N.C. Slade D. H. (ed.) (1968) Meteorology and Atomic Energy-1968, pp. 977105. U.S. Atomic Energy Commission, Oak Ridge. STAR Program (1972) Seasonal and annual wind distribution by Pasquill stability class (6). National Climatic Center, Federal Bldg., Asheville, NC. Turner D. B. (1970) Workbook of atmospheric dispersion estimates. U.S. EPA Publ. AP-26, pp. 5-30.
Environment
Vol.II,pp.1051-1059. Pergamon Press 1977. Printed
m Great
Britain
AN OBJECTIVE AIR MONITORING SITE SELECTION METHODOLOGY FOR LARGE POINT SOURCES KENNETH *Pritzker
Department
E. NOLL,* TERRY
and R. K. RAUFER$
L. MILLER,? JAY E.No~cof
of Environmental Engineering, Illinois Institute of Technology, tEnviro-measure, Inc., Knoxville, TN, U.S.A. SETA, Inc.. Oak Brook, IL. U.S.A. (First received
14 December
1976 and in jifinal
,form27
April
Chicago,
IL 60616, U.S.A.
1977)
Abstract-Quantitative methods for determining the total number and location of air monitoring sites to meet specific air sampling objectives have proven difficult to identify by the traditional application of diffusion models and historical meteorological data. Objective criteria for site selection are important to the design of cost-effective air monitoring surveys, obtaining required data results in the minimum time period, and trade-off decisions between fixed and mobile monitoring options. The objective technique reported here utilizes statistical methods to determine the number and location of sensors from the ratio of areas defined by (1) the range of available historical meteorological conditions producing maximum concentrations and (2) a predetermined concentration measurement tolerance range around the maximum concentration which represents the maximum within 10 or 2076 of the predicted value. The desired confidence level for measuring a specified air pollution maximum value is also required. The application of the method to the design of an air monitoring network to support an intermittent control system for two power plants in central Illinois confirms the practicality of the method. The final monitoring network resulted from a consideration of numerous combinations of fixed and mobile monitoring options. The sites having the highest probability of occurrence and the greatest predicted concentrations were selected for fixed, continuous monitoring. Where high concentrations occur with a relatively low frequency. a mobile monitor which relies on meteorological forecasting can be dispatched through a control center to predetermined, fixed, sites is cost-effective in reducing the requirement for a large number of permanent monitors.
1.INTRODUCTION
and air monitoring site selection in particular has traditionally been approached in a qualitative way. This has resulted because of the complex nature of air quality due to the uneven distribution of sources and the variability in atmospheric mixing from place to place and from time to time. Decisions concerning the minimum number of sampling stations to meet the objectives of a study and the siting of individual sampling stations in order to obtain “representative” readings are generally based on available air quality and meteorological data and on the results of dispersion models. In recent years, air monitoring surveys have become more complex and require comprehensive planning to assure that objectives can be attained in the shortest possible time and at the least cost. One of the manifestations of these developments is the need for the de’velopment of quantitative procedures for air monitoring site selection to assure that specific objectives can be attained. This paper provides a quantitative procedure for the selection of air monitoring sites which utilizes dispersion models, historical meteorological data and probability calculations. New terms such as potential monitoring zone, tolerance range, station area and station coverage ratio are defined and a description of their use in the design procedure is provided. The application of the methodology to both the coning and trapping meteorological regimes contained in Air quality
monitoring
in general
1051
most simulation models for point sources is then provided. Finally, the concept of mobile monitoring stations dispatched to fixed predetermined locations is discussed.
II.
SITE SELECTION
PROCEDURE
The first priority in the effective selection of air monitoring sites is to obtain an understanding of the spatial distribution of pollution concentration under different source and meteorological conditions. This is accomplished by utilizing diffusion models @lade, 1968; Turner, 1970). The design method presented here requires the calculation, of the distance from a source that maximum concentrations occur under different meteorological conditions. These locations then become potential monitoring sites. Historical meteorological data and statistical methods are then utilized to determine the likelihood that an air monitoring station located where each of the maximum pollution concentrations are predicted to occur could measure the predicted maximum value with some confidence level. The analysis requires a dispersion model appropriate to the point source under consideration. The model is assumed to be error-free for the purpose of this analysis. After initial monitoring data have been evaluated, systematic modeling errors can be removed by calibration factors. Terrain effects have not been included in the analysis. Their inclusion, however, would not change the procedure.
I052
KENNETH E. Nou..
T~.KKY L. MILLER, JAY E. NORW and R. K. K~\LFEK
The objective network design can be described in the following steps: 1. Identify the location of the maximum concentration using atmospheric dispersion models. 2. Generate the joint frequency of occurrence of meteorological conditions producing the maximum. 3. Determine the ground area defined by the range of these meteorological conditions by using atmospheric dispersion models (Potential Monitoring Zone). 3. Determine the ground level station coverage area, i.e. an area defined by a given tolerance range around the maximum concentration. 5. Using statistical techniques determine the number and locations of monitors from the ratio of areas defined in 3 and 4 above, and the desired cootidence level for tneasuring the air quality standard violations. The methodology includes (a) determining the size of a representative sampling area that can be covered by a single station (coverage ratio), (b) determining the minimum number of sites needed to ensure a desired number of observations of C,,, (a statistical approach), and (c) selecting the optimum number and location of stations required to meet monitoring objectives with a high probability of success (Miller
and Nell, 1976: Nell and Miller, 1977). The general methodology is developed below:
ideally. a monitoring network should be designed using a long-term historical base of meteorological data which is compatible with the requirements of the simulation model which is utilized to predict maximum concentrations. The coning plume models require only the historical meteorological data contained in the National Weather Service STAR (STAR Program, 1972) data summary. These are the threeway frequency of occurrence with wind speed class, direction and stability classes. It has been shown (Carpenter et ul., 1970) that the plume “trapping” regime is more critical in estimating high conccntrat~ons caused by large power plants. Worst-case trapping occurs when the atmospheric mixing lid is equal to the final effective plume height. Thus a fourth significant meteorological variable-mixing height is required for the monitoring network design. Di~usion calculations do not accurately predict the maximum concentration, therefore, a more realistic solution to the models for air monitoring site selection purposes, may be to calculate maximum concen-
Fig. I. Illustration of the coverage area of a single station downwind of a point source. Shaded area represents the area within the potential monitoring zone not covered by the station. The ratio of the coverage area to the total area of the potential zone equals the coverage ratio.
1053
Air monitoring site selection methodology trations and distances for a range of conditions for which historical meteorological data is available. For example, rather than determine where the maximum concentration occurs for neutral stability at a wind speed of 5.7 m s-r, it would be more realistic to determine maximum concentrations and distances for wind speeds of from 4 to 7 m s-r. The frequency of occurrence of the range of wind speeds can then be determined from historical meteorological summaries such as STAR. Which directions should be monitored can be determined from historical wind direction/frequency data and a consideration of potential receptors in each direction. Potential monitoring zone A potential monitoring zone is the area defined by the available meteorological data (STAR classes) within which maximum concentrations can occur as found by dispersion modeling of the range of meteorological parameters (wind speed, wind direction, mixing height). A typical potential monitoring zone has distinct boundaries (Fig. 1) and resembles a slice from a “dough-nut”. The front and the back of the boundary corresponds to the upper and lower boundaries of the wind speed classes in the STAR program. The
zone becomes complex when mixing height is also included as a variable and there is interaction of several sources. When the potential monitoring zone boundaries have been established, then the area that it represents on the ground can be calculated.
Tolerance range and station area Perfect resolution of a predicted concentration peak is not necessary because of model inaccuracy and the range of meteorological variables that can generate a peak. Thus the ability to resolve peak concentrations can be considered a variable that can be changed for each application. Location of a monitoring station within an area that represents the maximum concentration within 10 or 20 per cent of the predicted value (Tolerance Range) are not unreasonable criteria. Selection of the tolerance range involves a trade-off between monitor coverage and cost (number of monitors). Having the ability to monitor maximum concentration exactly (with a tolerance range of zero) would be worthwhile, except then an infinite number of monitors would be required. Further, as the expected maximum concentration drops below the applicable air quality standard, the tolerance range becomes less critical and a larger range is acceptable. For example, from curves of normalized concentration, C U/Q, vs distance downwind, x (Turner, 1970) (Fig. 2) it is possible to determine the distance toward and away from the source within which the concentrations deviate from C,,, by some tolerance range (such as 10 per cent). Figure 3 shows the distance from the source, XI,, within which concentrations are within lJ3 per cent of C,,,,, when the peak occurs at X for different stability classes. In addition to the downwind distance determined by the tolerance range, it is also necessary to know the crosswind extent. The distance, YrO, within which concentrations are within 10 per cent of C,,, can be calculated from the equation (1 - 0.1) = exp - Zr_ Y which is derived from the general Gaussian equation (Hino, 1968). This reduces to Y,,, = 0.92 cry(x).
ii..
1
10
DISTANCE, km
Fig. 2. xu/Q with distance for various heights of emission (H) and limits to vertical dispersion (L), B stability. [From Turner (1970)].
plume
(2)
Values of Y,, for various distances downwind, and stability classes are presented in Fig. 4 (a, are Pasquill-Gifford values). Values of X,, and Yr, determine the size and shape of the station coverage area under C,,, conditions. Values of XI, tend to exceed Yr, by an order of magnitude for most stabilities. This results in an area which is elliptical in shape, (an ellipse is used here to approximate what is actually a “teardrop” shape) (Fig. 1). The number and location of monitoring sites can
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Air monitoring site selection methodology
FREQUENCY OF OCCURRENCE
OF METEOROLORICAL
EVENT,NUMRERS
I055
OF EVENTS PER YEAR, N
Fig. 5. Coverage ratio required to observe C,,,, n times, with 99 per cent confidence, vs the number of occurrences, N, of meteorological phenomena causing C,,,.
now be determined from (a) the size of the monitoring station area under C,,, conditions, (b) the size of the potential zone requiring monitoring, and (c) the frequency of occurrence of meteorological conditions causing C,,, values in each zone. The cover ratio for each potential monitoring site is determined by dividing the monitoring station coverage area by the area of the potential monitoring zone. If XI0 is less than the radial dimension of the zone, then the proportion of the zone covered by a single monitoring station can be calculated from
CR, =
x10 YlO
The number of hours per year (N) for a defined set of meteorological conditions, the confidence level selected, and the number of times per year that the desired concentration must be successfully monitored (n) provides the total coverage ratio required. The number of monitors required to fulfill the objective is equal to the total coverage ratio required divided by the coverage ratio of a single station. To calculate the probability that one station will measure the maximum concentration n times when the meteorological conditions causing C,,, occur N times per year requires use of the binomial distribution function (SAS, 1972).
4(XIo - X?,)’ P=
where CR, is the coverage ratio for one station (without regard for wind direction), X,, is inside radius of circular zone, and Xzo is outside radius of circular zone. To calculate coverage ratio for a single wind direction, multiply the CR, from Equation (3) by the number of wind sectors the “dough-nut” shaped zone is divided into. Conjdence
level and total number of stations
The confidence level is defined as the probability that one station will measure the maximum concentration n times when the meteorological conditions causing the maximum occurs N times per year. The number of stations required to monitor each maximum concentration can be determined by utilizing general statistical solutions as presented in Fig. 5. A.E.
1l/l 1-D
N! n!(N - n)!
CR;(l
- CR#-“.
(4)
For large values of N, the probability, P, or coverage ratio, CR,, is best determined by using the normal approximation to the binomial distribution and solving for the statistic, Z. The probability associated with various Z values can be determined from standard statistical tables. Z is calculated using the equation
Z=-_
-n - NP [NP(l
- P)]l’*’
(5)
One solution of equation (5) is given in Fig. 5. This figure can be used to determine the total coverage ratio, CRT, required to allow n successful measurements of C,,, with 99% confidence given the frequency of occurrence, N, each year of the meteorological event producing C,,,.
KENNETH E. NOLL, TERRY L. MILLER. JA\ E. NOR(Y) and
IO56
After solving the total coverage ratio required, the number of stations needed in the crosswind direction can be calculated. IV, = CR,JCR,.
(6)
where N, is number of stations needed, CR, is coverage ratio of each station. and CRT is total coverage ratio required to achieve desired results.
111. APPLICATION
TO DESIGN
The highest concentrations near large point sources often occur for the plume “trapping” regime (Carpenter, 1971). The maximum concentrations found by diffusion models have a wind speed, wind direction, stability class and associated mixing height. The wind speed associated with each maximum concentration is defined by a wind speed class. as in the STAR data, bounded by an upper and lower wind speed. For trapping, a worst-case mixing height is associated with the upper and lower wind speed, and together with the specified stability class and wind direction ranges, the number of hours per year (N) that the given combination of parameters occurs can be interpolated from historical meteorological frequency data. Wind directions for maximum concentrations
R. K. RAI
1,K
give the other meteorological parameter. designated as the wind direction class with the largest !V-\#alue. As has already been stated. a potential monitoring zone is an area defined by the available meteorological data (STAR classes) within which maximum concentrations can occur. Discussion of the potential monitoring zone for trapping conditions will be aided by the definition of names for the different portions of its boundaries. In F‘lg. I. the curved boundary nearest the emission source i\ culled the zone’s “front” The other curved boundary is the “back” and the‘ two straight boundaries arc the “sides”. The wind direction is from the front to the back of the ~onc. The front and back of a potential monitoring /one created by a source arc &lined by circles drawn around the source. with radii equal to the downwind distances to the appropriate maximum concentrations. For trapping conditions. with a gi\cn stability class, four of these diatanccs exist. They represent the four combinations of the two houndalMind speeds with the two boundary mixing heights used to determine the N-value for Ihe specific monitoring candidate concentration. Of these four circles. the inner and outermost circles arc used as the front and back boundaries of the potential monitoring LWL’. Front and back boundaries for unlimited mixing conditions are defined in ;I Gmilar manner. In this
1.W
SC
1.0
5.0
10.0
50.0
100.0
N ~OCWRHHCES PLR YEAR) Fig. 6. Cover ratio vs frequency of occurrence for various confidence lecela. These curves Indicate the cover ratio required (CR,) to successfully monitor a maximum concentration once per year (n = I b if the appropriate meteorological conditions occur N times per year (for various confidence levels).
Air monitoring site selection methodology
1057
POWERTON UNITS Sand6
POTENTIAL MONITJRING ZONE
STATION @VERA=
AREA
MAXIMUM CONCENTRATION LINE
scale 1
2
kilormtem
Fig. 7. Example monitoring candidate. Wind speed (U): 3.3-5.4 m s-l. Wind direction (0): 11.25-33.75 Stability: B. Mixing height (L): 73&1083 m. Station coverage area: 1.98 km’. Potential monitoring zone area: 7.16 km*. Station coverage ratio: 0.28.
case, only two circles exist because, for a given wind speed, the downwind distance to the maximum concentration is not affected by changes in mixing height when the mixing height is higher than the coningtrapping transition. When the radii of the front and back boundaries are known, then the arcs they define can be plotted, along with the side boundaries, which are determined by the extremes of the wind direction range. The potential monitoring zone is now defined and its area can be determined. The potential monitoring zone created by the interaction of several sources is more difficult to define. The coordinates of the maximum concentrations resulting from combining various lower, central and upper values of each of the three parameter ranges, (wind speed, wind direction and mixing height), are found by dispersion modeling. These coordinates locate the endpoints and midpoints of line segments which form portions of the potential zone boundary. The largest potential zone that can be defined by the line segments is used, and its area is measured with a planimeter. This methodology was applied to a multiple source
problem in which the coverage area for each potential monitoring station was determined by plotting an isopleth around the maximum concentration which enclosed all concentrations within a tolerance range of 20 per cent of the maximum con~ntration(Nol1 et al., 1976). This plot was su~rimpos~ upon the associated potential monitoring zone, and the effective station coverage area (the portion of the station coverage area contained within the potential monitoring zone) was measured with a planimeter. The cover for each monitoring candidate was determined by dividing the station coverage area by the area of the potential monitoring zone. The number of stations required to monitor each maximum concentration was determined by using Fig. 6. This figure is similar to Fig. 5, except that the number of times a maximum concentration must be successfully monitored (n) has been selected as 1. The number of hours per year (N) for a meteorological condition likely to cause high concentrations is found on the horizontal scale of Fig. 6. Using a 90 per cent confidence line, the total coverage ratio required (CR,) is determined from the vertical scale. The number of monitors required for a given maxi-
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mum concentration is equal to the total coverage ratio required divided by the coverage ratio of a single station, or CRT/CR,. The methodology can now be demonstrated by outlining the analysis for a typical monitoring candidate with the following results from dispersion modeling for a large power plant located in the midwestern United States: Regime: Maximum concentration: Wind speed : Stability: Mixing height: Downwind distance from source to maximum concentration :
worst-case 973pgm-’ 514m s-r B 738 m
trapping
4.4 km.
The wind speed of 5.4 m s- ’ is contained in the third STAR wind speed class, which included wind speeds of 3.3-5.4 m s- *. Single source modeling at 3.3 and 4.4ms-’ produced maximum concentrations of 779 and 889pgmd3, respectively. This indicates that the concentration associated with the 5.4 m s ’ wind speed is the maximum concentration for this wind speed range, and hence the monitor location is based upon this condition. The worst-case trapping mixing height associated with the 3.3 m s -’ wind speed is 1083 m. Therefore, determination of the N-value for this candidate is based on these parameter ranges: Wind speed : Stability: Mixing height:
3.3 5.4 m s ’ B 738-1083 m.
The meteorological frequency data showed that these conditions occurred most often in the northnortheast wind direction class (I I .25-33.75 ). The applicable meteorological frequency data and resulting N-value for the example candidate are: Raw data Mixing height Gross N (hy-$1 Cm) 700- 800 80&900 9WlOOO 100&l 250
1 2 2 0
Interpolated data Mixing height Net N (hy~‘) (m) 738HOO SO&900 90&1000 1000-1083
0.6 2.0 2.0 0.0
738~ 1083
4.6
The four downwind distances, which are required to locate the front and back boundaries of the potential monitoring zone are: Mixing height Cm)
73x 1083
_______ Downwind distance (km) 5.4mC 3.3 m s ’ wind speed wind speed
I .4 6.2
4.4 5.2
Nowo
and K. h
KAI~I.I:K
These boundaries. along with the side boundaries which are determined by the wind direction range, are shown in Fig. 7. The station cov’erage area boundary. a 77X ~lg m ’ concentration isopleth representing 80 per cent of the maximum concentration. is also shown. The station cokerage md potential monitoring zone areas are also indicated in Fig. 7. along with the station coverage ratio. Figure 6 is now used to determine the total covcragc ratio required based on an .V-value of 4.6 Ii y ’ and ;I W,, confidence level. The resulting total covcragc ratio is 0.53. which requires 0.530.X or I .9 stations (rounded up to 2) to successfully monitor the example maximum concentrations. The final monitormg network design resulted from a consideration of numerous combinations of fixed and mobile monitoring options for conditions leading to high concentrations and the frequency of occurrence of such concentrations. The resulting network included thirteen fixed and two mobile monitors. The sites having the highest probability of occurrence and the greatest predicted concentrations were selected for fixed. continuous monitoring. In man) casts. high concentrations whtch occur relatively infrequently (such as A stability trapping conditions) could occurs’ with nearly equal probability in any direction. It is highly improbable that one fixed monitor would detect this type of condition. In fact, it was estimated that 13 monitors would have to be located in ;I ring quite close to the power plant in this example just to see the .J stability trapping conditionh. One mobile monitor- which relies on meteorological forecasting and is dispatched through a control center has the same capability as the 13 fixed monitor‘;. The cost-effectiveness of this option is very significant. The concept 01‘mobile monitoring at fixed sites is etfectivc in mitigating the need for great numbers of permanent monitors and can also (I) increase temporarily the monitoring density in an area potentially subject to ambient SO2 excursions, (2) increase the data base available for calibrating computer models. (3) modify monitor location patterns in preparation for relocation of fixed sites, and (4) extend the upwind data base at times when downwind concentrations arc not critical.
An objcctrve air monitoring network design methodology has been developed for large point sources utilizing “coning” and “trapping” regimes from dispersion models. The application of the methodology can be approached as follows: (a) The survey design method is run for several hundred meteorological and source emission combinations until all maxima are determined. The single station coverage ratio for selected peaks is determined based upon a given tolerance range.
Air monitoring site selection methodology (b) Meteorological frequency data are used to determine the likely directions for each condition and the number of occurrences per year. (c) The potential monitoring zones for selected situations of high concentration and high meteorological occurrence (N > 1) are determined. (d) The total coverage area is determined based upon a specific confidence level and the number of successful measurements required for each potential monitoring zone. (e) The total number of stations for each potential zone is determined by dividing the total coverage area required by the single station coverage area. (f) The final monitoring network results from a trade-off of all monitoring options determined above and the benefits of fixed and mobile monitoring sites. In many cases, high concentrations which occur infrequently will not show a preferred direction. This makes it highly unlikely that one fixed station would measure this condition. However, one mobile monitor (dispatched, based upon meteorological forecasting) could measure the peak and be cost-effective because it would have the same capability as many fixed monitors. The application of the methodology produces the following general results: (1) As the joint frequency of occurrence of meteorological conditions producing a maximum decreases, then the required total coverage ratio increases (and number of monitors increases) unless the specific confidence level is lowered. Following the determination of N-values for the various maximum concentrations, those with N-values less than or equal to one hour per year are generally eliminated or measured with mobile stations. (2) When four-way frequency distributions of historical meteorological data are used (wind speed, direction, stability class and mixing height) instead of three-way (wind speed, direction and stability class) then the number of historical meteorological occurrences generally decrease.
I059
(3) As the tolerance range for measurement of peak concentrations is increased, then the single station coverage ratio increases and the number of monitors required decreases. (4) As the averaging time for which the maximum concentration is desired increases, then the single station coverage ratio increases and the number of monitoring stations required decreases. (5) One mobile monitor can have the same capability as many fixed monitors when the objective is to measure concentrations which occur infrequently (such as A stability trapping conditions).
REFERENCES
Carpenter S. B., Montgomery T. L., Leavitt J. M., Colbaugh W. C. and Thomas F. W. (1971) Principal plume dispersion models: TVA power plants. J. Air. Pollur. Contra/. Ass. 21(8), 940-972. Hino M. (1968) Maximum ground-level concentration and sampling time. Atmospheric Environment 2, 149-165. Miller T. L. and No11 K. E. (1976) Design of air monitoring surveys near large power plants. In Power Generation: Air Pollution Monitoring and Control (Edited by No11 K. E. and Davis W. T., pp. 121-142. Ann Arbor Science Pub]., Ann Arbor. No11 K. E. and ETA Inc. (1976) The Sulfur Dioxide Monitoring Network Design for the Powerton-Edward Supplementary Control System, Final Report to Commonwealth Edison and Central Illinois Light Co., 48 pp. Noll K. E. and Miller T. L. (1977) Air Monitoring Suroey Design, 296 pp. Ann Arbor Science Publ., Ann Arbor. SAS (Statistical Analysis System) (1972) Designed by Anthony Barr and James Goodnight, Dept. of Statistics, North Carolina State University, User Manual published by Student Supply Stores, NCSU, Raleigh, N.C. Slade D. H. (ed.) (1968) Meteorology and Atomic Energy-1968, pp. 977105. U.S. Atomic Energy Commission, Oak Ridge. STAR Program (1972) Seasonal and annual wind distribution by Pasquill stability class (6). National Climatic Center, Federal Bldg., Asheville, NC. Turner D. B. (1970) Workbook of atmospheric dispersion estimates. U.S. EPA Publ. AP-26, pp. 5-30.