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Total SO2 emission control strategies for the management of air pollution in ulsan industrial complex
Atmospheric Printed
thironmenr m Great
Vol.
21.
No.
3. pp
469-471,
ooo4-6981/87
1987.
Pergamon
Britain.
13.00+0.00 Journals
Ltd
TOTAL SO2 EMISSION CONTROL STRATEGIES FOR THE MANAGEMENT OF AIR POLLUTION IN ULSAN INDUSTRIAL COMPLEX* YOUNG KEE JANG and JUNG WK KIMt Graduate School of Environmental Studies, Seoul National University, Shinlim-Dong, Seoul, Korea (First received 28 June 1985 and receioed for publicarion 7 August 1986) Abstract-Since emission regulations in Korea concentrate mainly on the limitation of pollutant concentration in the stack gas, it is difficult lo achieve adesirable air quality in a heavily industralized city like Ulsan. To ensure a suitable air quality in the future, a total emission control method is proposed with a stack height formula of H = 10.6 q”,5. where H is the stack height (m) and q is the SO1 emission rate (m3 h-’ reduced 10 OOC).The total emission permitted can be allocated lo industries (1) at an uniform reduction rate, (2)bytheformulaQ = a a.9*5, where Q is the emission allowed (g s _ ’ ).o is a constant, and Q, is the emission before control (gs-I), or (3) by using a linear programming technique. The above three approaches were evaluated using the TCM 2 air quality model. In order to achieve the air quality goal set for the area, the first approach requires 38.7 % reduction of SO2 emission from industries, the second 53.3%, and the third 4.3 %. The linear programming method is found to be very economical, but there are some administrative difficulties in enforcement.
INTRODUCTION
The Environmental Preservation Law of Korea requires that the total mass of emission be controlled in areas where it is difficult to achieve the air quality standards by means of the equal emission standards because of the high density of pollution sources. The emission standards mainly concern the concentrations of pollutants in exhaust gases, thus to ensure a desirable air quality in a heavily industrialized city like Ulsan, the total emission control method needs to be employed. The city is expected to grow further, but expansion of industries or establishment of new plants will be restricted under this control system unless the existing industries share the burden of keeping emissions within the total emission standards. In this study, a procedure to estimate the permissible total emission for SO2 is presented and the emission allocation methods are evaluated using a Gaussian diffusion model, TCM 2. AIR QUALITY PREDICTION
AND STACK HEIGHT
CONTROL
The input data required were an emission inventory and meteorological parameters. The emission inventory covered industrial sources, domestic and commercial heating, and automobiles. All industrial stacks were counted as point source, and domestic and commercial heating as area source. The SO2 level due to automobiles was estimated using ANL/HIWAY model (Concaildi et al., 1976) and was added to the results from the TCM 2 model which computed the concentration due to point and area sources. The contribution of automobiles to the overall pollution level was not very significant and the ANL/HIWAY model was used without validation. The meteorological data for wind direction and wind speed were obtained from the observatory station in Ulsan, and the stability was classified according to Turner (1964). The mixing height was obtained from the sounding measurements. The annual average SO1 levels from nine monitoring stations are compared with the computed concentrations, as shown in Fig. 1. The relationship between the computed SO* level and the observed value may be formulated as Y = 0.84 X + 5.71
Validation
o/the
Gaussian model, TCM
2
The TCM 2 model (Texas Air Control Board, 1980) was used as a tool for the cause-effect analysis in predicting the annual average SO1 level in the area.
where Y is the predicted value (ppb) and X is the observed value (ppb). The correlation coefficient is 0.81. Thus it is judged that the TCM 2 is a useful tool in predicting air quality in the area. Prediction
Paper presented at the First International Conference on Atmospheric Sciencesand Applications to Air Quality, held in Seoul, Korea. 20-24 May 1985. t To whom correspondence should be addressed l
469
(1)
o/future
air qua/it)
The residential area of the city is not fully developed at present and the city has no plan to expand its boundary in future, mainly because of geographical
470
YOUNG KEE JANG and JUNG WK KIM
restrictions. It is therefore expected that the emission will be more concentrated within the rather than spread out as the city grows. The emission density from area sources in 1991
Fig. 1. Comparison of compuwf and observed annual average SO2 concentrations.
SO1 area SO2 was
estimated by multiplying the present emission density by the increase factor predicted in the Government energy supply plan. Big industries have already secured enough building lots for future expansion and open space available for new plants is very limited. Therefore it is expected that the increase in fuel consumption predicted in the Government plan will be mostly divided between the existing industries. Thus the total increase in emission was distributed to existing industries based on the assumption that all industries grow at a same growth rate, establishment of new plants being neglected. However it was not allocated to power plants which did not have definite plans for expansion. The total SO2 emission from industries in 1991 was estimated to be 6065gsCt. The overall SO2 concentration in 1991 predicted is shown in Fig. 2. The annual mean SO2 concentration is expected to reach up to 90ppb which obviously necessitates total emission control in the area. An annual mean SO2 level of 30 ppb is set for the goal of the total emission control in this study.
Fig, 2. Expected annual average SO2 concentrations in 1991.
Total SOI emission control strategies for the management of air pollution Stack height conrrol
The Gaussian plume dispersion equation shows that the ground level concentration is a function of effective stack height. Therefore, without the control of stack height, total emission control cannot be effective. The minimum stack height was calculated so as not to violate a daily SO2 concentration level of 150 ppb at a wind speed of 2.5 m s- ’ and a stability class A. At lower wind speed the stability increased and subsequently the maximum ground level concentration decreased in Ulsan. The effective stack heights calculated by Briggs formula (1969) turned out to be roughly proportional to the actual stack heights in Ulsan. Thus the stack height equation is expressed in actual stack height rather than in effective stack height for convenience in enforcement as follows. H = 10.6q”.5
(2)
where H = minimum
stack height (m, < 200 m) and q = emission rate of SO, (m3 h-’ reduced to 0°C).
/
,-.I
,-4
471
The annual average SO2 level after raising stacks according to Equation (2) is shown in Fig. 3. By controlling stack height the maximum concentration could be lowered from 90 ppb to 46 ppb. In Equation (2) the minimum stack height was determined so that the ground level concentration by a single source did not exceed the short term air quality goal. However, at the worst point a single source contributed only one-third of the total concentration. Therefore to meet the daily SO* level of 150 ppb, a single source should not contribute more than 50 ppb at the worst meteorological condition. To satisfy this criterion, the stack should be further raised according to the equation H = 18.6 q’.‘.
(3)
When Equation (3) was applied, the maximum annual average SO1 concentration turned out to be 40 ppb compared with 46 ppb given by Equation (2). The site of maximum concentration drifted to the nearby residential area from the heart of the industrial area. However it turned out to be costlier to reduce
!
\
i i. ! / \
5Km I
Fig. 3. Annual average SO1 concentrations
with stack heights H = 10.6 q”.‘.
YOUNGKEEJANGand JUNGWK KIM
412
40 ppb to 30 ppb in the residential area than 46 ppb to 30ppb in the industrial area by means of the total emission control against industrial sources. Therefore, Equation (2) was selected for stack height control in Ulsan. Even though Equation (2) itself cannot guarantee the short term air quality goal, it is expected that when the total mass of emission in the area is reduced the short term air quality will also be markedly improved. ALLOCATIONOF TOTALEMISSION Uniform
reduction
rate approach
Since the ground level concentration is proportional to the emission amount, the air quality goal, annual average SO, level of 30ppb, can be achieved by reducing emissions as follows on the condition that the stack heights remain unchanged.
QAa,
(4)
B
where Q = allowable emission, C, = air quality goal, Cs = ground level concentration, Qo = emission before control.
and
However, there are uncontrollable emission sources such as space heating and automobiles. Then the emission reduction rate can be expressed as
G -c, *= ]---.--- -
(5)
where r = emission reduction rate, and C, = concentration due to uncontrollable
sources.
Then the maximum reduction rate in the area can be uniformly applied to all industrial sources to achieve the air quality goal. The maximum value of r does not necessarily occur at the maximum concentration point. In this case study, the reduction rate for the industrial sources was computed to be 38.7%. This process is shown in Table 1. The total emission allowed for industriesamounted to3718 gs-‘.Theannualaverage SO2 level after the total emission control is shown in Fig. 4, which shows that this method satisfies the air quality goal. The approach can be implemented simply by reducing the sulfur content of fuel by 38.7%. Graduated
reduction
rate approach
A few big industries occupy most of the total emission and their contribution to the ground level concentration is very significant in Ulsan. This approach emphasizes on controlling a few big sources and on alleviating burdens on small sources as expressed by the equation,
Q=aQob
(6)
where a and h(,< I) are constants. This approach is
Total SO2 emission control strategies for the ~nagement of air pollution
473
5Km ,:,:
Fig. 4. Annual average SO2 concentrations after IONI emission control with uniform reduction rate approach.
similar to the Japanese policy on allocating emission with regard to fuel consumption (Environment Agency of Japan, 1975). The constants u and b were determined as follows in this study. At receptor points which violated the air quality goal the reduction amount to achieve the goal was allocated to industries in proportion to their contribution to the ground level concentration. Plotting a (In Q) vs (In &) graph, we obtained the value of b, 0.925, from the slope ofthe best fit line. Then the annual average SO, concentration was computed using TCM 2 for emissions Q = @‘.9*5,and from the results CI was determined as the minimum value of (C, -C&C, -C,) in the receptors which violated the goal. The value of a was estimated to be 0.73 as shown in Table 2. Thus Q i=
The annual
0.73
average
Qz ‘t2s.
WI
SO2 level after
reducing
emissions according to Equations (6a) satisfies the air cttt;ility go:1135 shown in I‘ig. 5. With this mclhod llrc lotal
emission
allowed
for industries
amounted
IO
2832 gs- ‘, which means 53.3 % reduction of industrial emission compared to 38.7% for the previous method. This approach does not seem very economical, but the air quality in neighbouring areas including residential areas and the center improved significantly. The pollution impacts from industrial sources were confined within the industrial area with this method. Linear prffgramming
approach
tn the previous two methods emission reduction rate was calculated for the worst point and this rate was applied to all emission sources in the control area; therefore some industries might be over-controlled if the air quality goal was the final objective of the emission reduction. The most economical way of controlling the total emission will be in minimizing the total amount of reduction for receptors which violate the goal. This can be accomplished by using the linear programming technique (Fronza and MeJJi, 1984; Environment Agency of Japan, 1975). 1x1 A( ‘, Jcnoic lhc dillcrcncc between ihe ground level concentration at receptor i and the air quality goal,
474
YOUNGKEEJANG and
JUNG WK KJM
Table 2. Computation of allowable emission for the graduated reduction rate approach
G-C”
5
G
Ground level ~ncentration
Concentration due to non~ntrollable sources
c -c, AIlowa6k emission
TM coordinates
230
231
232
233
234
230
231
232
233
234
230
231
232
233
225 224 223 222 221
29 27 23 24 30
27 35 34 37 29
30 34 31 38 28
23 35 39 31 29
18 2f 26 26 28
14.0 8.8 3.9 2.5 1.1
8.8 7.0 4.2 2.8 1.2
3.5 3.5 3.9 2.5 1.1
1.8 3.5 4.6 2.5 1.1
1.1 2.5 3.2 2.5 I.8
-
0.82 0.87 0.80 -
0.87 0.96 0.77 -
0.84 10.731 0.96 -
234 -
Fig. 5. AnnuaI average SO2 concentrations after total emission control with graduated reduction rate approach.
Gi the concentration at receptor i, Pij the contribution rate of source j to receptor i, and R, the reduction rate to be imposed on source j to meet the goal. Then the following expression is obtained. G,P,,R,+GiPi,R,+
...
+G,PijRj>ACi
(7)
This can
be expressed in a matrix form as folfows.
iz”:::‘:]
x [ ijl
or P,,R,fPi2R,+
. . . + P,,R,
3 ACi/Gi.
(7a)
1) 82 .
. * *‘ii
B
[IE’)E~ . (8)
Total SO2 emission control strategies for the management of air pollution
If we assume that the sulfur content of the fuel used in Ulsan can be lowered from 1.6 % at present to 0.8 % by 1991, which means a 50% reduction, R, cannot exceed 0.5. Let P denote the matrix [ Pij], R [Rj], C [AC,/G,],
475
and E, the emission from source j, then the most costeffective way of emission control is solved from the following linear program. (9) Min Z = E, R I
Table 3. Emission allocated to each industry from linear programming
Source No. I 2 3 4 5 6 7 8 9 10 11 12 13 14 I5 I6 17 18 19 20 21’ Total l
Emission before control (gs-‘) 3869.1 401.1 282.5 217.3 206.7 104.8 96.5 82.6 15.9 56.7 48.9 45.4 44.6 41.5 38.3 37.0 32.2 31.5 26.2 25.1 294.2 6064.7
Source No. 21 includes
Number of stacks 3 1 1 1 3 1 2
1 1 1 3 I
1 2
1 2
1 1 1 I 97 126
Stack height (m) 200 200 200 175 95 120 80 100 100 85 45 80 75 55 70 40 65 60 40 40
Stack diameter (m) 5.1 3.3 4.1 4.0 2.0 2.6 1.7 2.1 2.7 1.4 1.2 2.1 I.5 1.4 0.7 1.2 1.2 2.6 1.2 2.3
Heal emission (kcals-‘) 136.5 14.4 9.2 8.7 5.8 3.3 3.9 3.3 7.4 2.4 5.2
1.9 2.5 3.8 1.2 4.8 1.3 1.8 1.1 0.6
Reduction rate (%)
Emission after reduction (gs-‘)
0 0 0
3869.7 407.1 282.5 217.3 206.7 104.8 96.5 82.5 15.9 56.7 24.4 28.1 44.6 20.8 21.6 18.5 32.2 31.5 22.3 12.5 147.1 5803.4
0 0
0 0 0 0
0 50.0 38.2 0 50.0 43.6 50.0 0 0 14.7 50.0 50.0 4.3
all other sources,
Fig. 6. Locations of industrial sources which require emission reduction IO).
YOUNG KEE JANG
476
and
subject to restrictions PxR>C
(10)
0 Q Rj < OS.
(11)
The reduction rates computed for individual sources from the linear program are shown in Table 3, and the locations of sources in Fig. 6. Small industries open or close business frequently and it is almost impossible to compute allowable emission for each source whenever such a change occurs, and nearly all of the small sources around the problem area with emission rates less than 20 gs-’ showed 50% reduction of emission. Therefore a SO% reduction was assigned to all sources with emission less than 20 g s-’ to simplify administrative difficulties in implementation. It turned out that most of the sources with emission less than 50 g s- ’ needed 50 % reduction while larger sources required no reduction at all, which shows that reduction from small sources is more cost-effective than controlling a few large sources. This is the opposite to the policy of the second approach. With this approach the total allowable emission
Fig. 7. Annual
average
SO2 concentrations programming
JUNG WK KIM
amounted to 5803 g s- ’ which means 4.3 y0 reduction of industrial emission compared to 38.7 % and 53.3 % reduction for the first and the second approach, respectively. Among 117 industrial sources in Ulsan Industrial Complex, 10 big sources which emit more than 50 gs-’ of SO2 contribute 89 % of total industrial emission, and the largest 20 sources 95 %. Therefore if we can possibly achieve the goal by reducing emissions from small sources, it evidently will be significantly economical. The air quality with this method is shown in Fig. 7, which is quite comparable to that with the tirst approach. In implementing this method the cost for procuring the low S oil can be secured from taxes or surcharges on all users, since it is not fair to impose a heavier burden on small industries. The low S fuel can then be allocated to industries as necessary.
CONCLUSIONS
A procedure for planning the total emission control was presented and three approaches of emission
after toktl approach.
emission
control
with
lrnear
Total SO, emission control strategies for the management of air pollution
allocation were evaluated using TCM 2 model. The model was quite effective in predicting air quality in Ulsan. Stack height should be controlled before implementing the total emission control. The stack height proposed is H = 10.6 q”.’ where H is the stack height (m)and q is the SO1 emission rate (m-’ h-’ reduced to O’C). Stacks higher than the equation resulted in a higher pollution level in distant residential areas and consequently required more reduction of emission for industries. The total allowable
emission can be allo-
cated to industries at an uniform reduction rate, at a graduated reduction rate with a formula Q = 4 @.925 where Q is the atlowable emission, a is a constant, and Q, is the emission before control, and by a linear
programming technique. The first approach required 38.7% reduction of SO, emission from industries to achieve the air quality goal of the area, the second 53.3%, and the third 4.3%. The linear programming technique is found to be very economical, but ad-
ministrative difficulties in implementation overcome.
471
should be
REFERENCES
BriggsG. A. (1969) Plume rise. A.E.C. Critical Review Series. TID-25075, Atomic Energy Commission, Washington, _ _ G. A., Cohen A. S. and King R. F. (1976) ANUHIWAY: an air pollution evaluation model for
Coziildi
roadways. Argonne N’ational Laboratory. Argonne, Illinois. Environment Agency, Government of Japan (1975) Tom/ Emission Control Manual (Jap.), pp. 10&l 16. Environment Agency, Tokyo. Fronza G. and Me& P. (1984) Assignment of emission abatement levels by stochastic programming. Armospheric Encironment IS, 53 t-535. Texas Air Control Board (1980) User’s Guide fo rhe Texas C/imurologica/ Model, EPA/DF-8 l/O0 1b, National Technical Information Service, Springfield, Virginia. Turner D. B. (1964) A dilTusion model for an urban area. J. appl. Mel. 3, 83-91.
thironmenr m Great
Vol.
21.
No.
3. pp
469-471,
ooo4-6981/87
1987.
Pergamon
Britain.
13.00+0.00 Journals
Ltd
TOTAL SO2 EMISSION CONTROL STRATEGIES FOR THE MANAGEMENT OF AIR POLLUTION IN ULSAN INDUSTRIAL COMPLEX* YOUNG KEE JANG and JUNG WK KIMt Graduate School of Environmental Studies, Seoul National University, Shinlim-Dong, Seoul, Korea (First received 28 June 1985 and receioed for publicarion 7 August 1986) Abstract-Since emission regulations in Korea concentrate mainly on the limitation of pollutant concentration in the stack gas, it is difficult lo achieve adesirable air quality in a heavily industralized city like Ulsan. To ensure a suitable air quality in the future, a total emission control method is proposed with a stack height formula of H = 10.6 q”,5. where H is the stack height (m) and q is the SO1 emission rate (m3 h-’ reduced 10 OOC).The total emission permitted can be allocated lo industries (1) at an uniform reduction rate, (2)bytheformulaQ = a a.9*5, where Q is the emission allowed (g s _ ’ ).o is a constant, and Q, is the emission before control (gs-I), or (3) by using a linear programming technique. The above three approaches were evaluated using the TCM 2 air quality model. In order to achieve the air quality goal set for the area, the first approach requires 38.7 % reduction of SO2 emission from industries, the second 53.3%, and the third 4.3 %. The linear programming method is found to be very economical, but there are some administrative difficulties in enforcement.
INTRODUCTION
The Environmental Preservation Law of Korea requires that the total mass of emission be controlled in areas where it is difficult to achieve the air quality standards by means of the equal emission standards because of the high density of pollution sources. The emission standards mainly concern the concentrations of pollutants in exhaust gases, thus to ensure a desirable air quality in a heavily industrialized city like Ulsan, the total emission control method needs to be employed. The city is expected to grow further, but expansion of industries or establishment of new plants will be restricted under this control system unless the existing industries share the burden of keeping emissions within the total emission standards. In this study, a procedure to estimate the permissible total emission for SO2 is presented and the emission allocation methods are evaluated using a Gaussian diffusion model, TCM 2. AIR QUALITY PREDICTION
AND STACK HEIGHT
CONTROL
The input data required were an emission inventory and meteorological parameters. The emission inventory covered industrial sources, domestic and commercial heating, and automobiles. All industrial stacks were counted as point source, and domestic and commercial heating as area source. The SO2 level due to automobiles was estimated using ANL/HIWAY model (Concaildi et al., 1976) and was added to the results from the TCM 2 model which computed the concentration due to point and area sources. The contribution of automobiles to the overall pollution level was not very significant and the ANL/HIWAY model was used without validation. The meteorological data for wind direction and wind speed were obtained from the observatory station in Ulsan, and the stability was classified according to Turner (1964). The mixing height was obtained from the sounding measurements. The annual average SO1 levels from nine monitoring stations are compared with the computed concentrations, as shown in Fig. 1. The relationship between the computed SO* level and the observed value may be formulated as Y = 0.84 X + 5.71
Validation
o/the
Gaussian model, TCM
2
The TCM 2 model (Texas Air Control Board, 1980) was used as a tool for the cause-effect analysis in predicting the annual average SO1 level in the area.
where Y is the predicted value (ppb) and X is the observed value (ppb). The correlation coefficient is 0.81. Thus it is judged that the TCM 2 is a useful tool in predicting air quality in the area. Prediction
Paper presented at the First International Conference on Atmospheric Sciencesand Applications to Air Quality, held in Seoul, Korea. 20-24 May 1985. t To whom correspondence should be addressed l
469
(1)
o/future
air qua/it)
The residential area of the city is not fully developed at present and the city has no plan to expand its boundary in future, mainly because of geographical
470
YOUNG KEE JANG and JUNG WK KIM
restrictions. It is therefore expected that the emission will be more concentrated within the rather than spread out as the city grows. The emission density from area sources in 1991
Fig. 1. Comparison of compuwf and observed annual average SO2 concentrations.
SO1 area SO2 was
estimated by multiplying the present emission density by the increase factor predicted in the Government energy supply plan. Big industries have already secured enough building lots for future expansion and open space available for new plants is very limited. Therefore it is expected that the increase in fuel consumption predicted in the Government plan will be mostly divided between the existing industries. Thus the total increase in emission was distributed to existing industries based on the assumption that all industries grow at a same growth rate, establishment of new plants being neglected. However it was not allocated to power plants which did not have definite plans for expansion. The total SO2 emission from industries in 1991 was estimated to be 6065gsCt. The overall SO2 concentration in 1991 predicted is shown in Fig. 2. The annual mean SO2 concentration is expected to reach up to 90ppb which obviously necessitates total emission control in the area. An annual mean SO2 level of 30 ppb is set for the goal of the total emission control in this study.
Fig, 2. Expected annual average SO2 concentrations in 1991.
Total SOI emission control strategies for the management of air pollution Stack height conrrol
The Gaussian plume dispersion equation shows that the ground level concentration is a function of effective stack height. Therefore, without the control of stack height, total emission control cannot be effective. The minimum stack height was calculated so as not to violate a daily SO2 concentration level of 150 ppb at a wind speed of 2.5 m s- ’ and a stability class A. At lower wind speed the stability increased and subsequently the maximum ground level concentration decreased in Ulsan. The effective stack heights calculated by Briggs formula (1969) turned out to be roughly proportional to the actual stack heights in Ulsan. Thus the stack height equation is expressed in actual stack height rather than in effective stack height for convenience in enforcement as follows. H = 10.6q”.5
(2)
where H = minimum
stack height (m, < 200 m) and q = emission rate of SO, (m3 h-’ reduced to 0°C).
/
,-.I
,-4
471
The annual average SO2 level after raising stacks according to Equation (2) is shown in Fig. 3. By controlling stack height the maximum concentration could be lowered from 90 ppb to 46 ppb. In Equation (2) the minimum stack height was determined so that the ground level concentration by a single source did not exceed the short term air quality goal. However, at the worst point a single source contributed only one-third of the total concentration. Therefore to meet the daily SO* level of 150 ppb, a single source should not contribute more than 50 ppb at the worst meteorological condition. To satisfy this criterion, the stack should be further raised according to the equation H = 18.6 q’.‘.
(3)
When Equation (3) was applied, the maximum annual average SO1 concentration turned out to be 40 ppb compared with 46 ppb given by Equation (2). The site of maximum concentration drifted to the nearby residential area from the heart of the industrial area. However it turned out to be costlier to reduce
!
\
i i. ! / \
5Km I
Fig. 3. Annual average SO1 concentrations
with stack heights H = 10.6 q”.‘.
YOUNGKEEJANGand JUNGWK KIM
412
40 ppb to 30 ppb in the residential area than 46 ppb to 30ppb in the industrial area by means of the total emission control against industrial sources. Therefore, Equation (2) was selected for stack height control in Ulsan. Even though Equation (2) itself cannot guarantee the short term air quality goal, it is expected that when the total mass of emission in the area is reduced the short term air quality will also be markedly improved. ALLOCATIONOF TOTALEMISSION Uniform
reduction
rate approach
Since the ground level concentration is proportional to the emission amount, the air quality goal, annual average SO, level of 30ppb, can be achieved by reducing emissions as follows on the condition that the stack heights remain unchanged.
QAa,
(4)
B
where Q = allowable emission, C, = air quality goal, Cs = ground level concentration, Qo = emission before control.
and
However, there are uncontrollable emission sources such as space heating and automobiles. Then the emission reduction rate can be expressed as
G -c, *= ]---.--- -
(5)
where r = emission reduction rate, and C, = concentration due to uncontrollable
sources.
Then the maximum reduction rate in the area can be uniformly applied to all industrial sources to achieve the air quality goal. The maximum value of r does not necessarily occur at the maximum concentration point. In this case study, the reduction rate for the industrial sources was computed to be 38.7%. This process is shown in Table 1. The total emission allowed for industriesamounted to3718 gs-‘.Theannualaverage SO2 level after the total emission control is shown in Fig. 4, which shows that this method satisfies the air quality goal. The approach can be implemented simply by reducing the sulfur content of fuel by 38.7%. Graduated
reduction
rate approach
A few big industries occupy most of the total emission and their contribution to the ground level concentration is very significant in Ulsan. This approach emphasizes on controlling a few big sources and on alleviating burdens on small sources as expressed by the equation,
Q=aQob
(6)
where a and h(,< I) are constants. This approach is
Total SO2 emission control strategies for the ~nagement of air pollution
473
5Km ,:,:
Fig. 4. Annual average SO2 concentrations after IONI emission control with uniform reduction rate approach.
similar to the Japanese policy on allocating emission with regard to fuel consumption (Environment Agency of Japan, 1975). The constants u and b were determined as follows in this study. At receptor points which violated the air quality goal the reduction amount to achieve the goal was allocated to industries in proportion to their contribution to the ground level concentration. Plotting a (In Q) vs (In &) graph, we obtained the value of b, 0.925, from the slope ofthe best fit line. Then the annual average SO, concentration was computed using TCM 2 for emissions Q = @‘.9*5,and from the results CI was determined as the minimum value of (C, -C&C, -C,) in the receptors which violated the goal. The value of a was estimated to be 0.73 as shown in Table 2. Thus Q i=
The annual
0.73
average
Qz ‘t2s.
WI
SO2 level after
reducing
emissions according to Equations (6a) satisfies the air cttt;ility go:1135 shown in I‘ig. 5. With this mclhod llrc lotal
emission
allowed
for industries
amounted
IO
2832 gs- ‘, which means 53.3 % reduction of industrial emission compared to 38.7% for the previous method. This approach does not seem very economical, but the air quality in neighbouring areas including residential areas and the center improved significantly. The pollution impacts from industrial sources were confined within the industrial area with this method. Linear prffgramming
approach
tn the previous two methods emission reduction rate was calculated for the worst point and this rate was applied to all emission sources in the control area; therefore some industries might be over-controlled if the air quality goal was the final objective of the emission reduction. The most economical way of controlling the total emission will be in minimizing the total amount of reduction for receptors which violate the goal. This can be accomplished by using the linear programming technique (Fronza and MeJJi, 1984; Environment Agency of Japan, 1975). 1x1 A( ‘, Jcnoic lhc dillcrcncc between ihe ground level concentration at receptor i and the air quality goal,
474
YOUNGKEEJANG and
JUNG WK KJM
Table 2. Computation of allowable emission for the graduated reduction rate approach
G-C”
5
G
Ground level ~ncentration
Concentration due to non~ntrollable sources
c -c, AIlowa6k emission
TM coordinates
230
231
232
233
234
230
231
232
233
234
230
231
232
233
225 224 223 222 221
29 27 23 24 30
27 35 34 37 29
30 34 31 38 28
23 35 39 31 29
18 2f 26 26 28
14.0 8.8 3.9 2.5 1.1
8.8 7.0 4.2 2.8 1.2
3.5 3.5 3.9 2.5 1.1
1.8 3.5 4.6 2.5 1.1
1.1 2.5 3.2 2.5 I.8
-
0.82 0.87 0.80 -
0.87 0.96 0.77 -
0.84 10.731 0.96 -
234 -
Fig. 5. AnnuaI average SO2 concentrations after total emission control with graduated reduction rate approach.
Gi the concentration at receptor i, Pij the contribution rate of source j to receptor i, and R, the reduction rate to be imposed on source j to meet the goal. Then the following expression is obtained. G,P,,R,+GiPi,R,+
...
+G,PijRj>ACi
(7)
This can
be expressed in a matrix form as folfows.
iz”:::‘:]
x [ ijl
or P,,R,fPi2R,+
. . . + P,,R,
3 ACi/Gi.
(7a)
1) 82 .
. * *‘ii
B
[IE’)E~ . (8)
Total SO2 emission control strategies for the management of air pollution
If we assume that the sulfur content of the fuel used in Ulsan can be lowered from 1.6 % at present to 0.8 % by 1991, which means a 50% reduction, R, cannot exceed 0.5. Let P denote the matrix [ Pij], R [Rj], C [AC,/G,],
475
and E, the emission from source j, then the most costeffective way of emission control is solved from the following linear program. (9) Min Z = E, R I
Table 3. Emission allocated to each industry from linear programming
Source No. I 2 3 4 5 6 7 8 9 10 11 12 13 14 I5 I6 17 18 19 20 21’ Total l
Emission before control (gs-‘) 3869.1 401.1 282.5 217.3 206.7 104.8 96.5 82.6 15.9 56.7 48.9 45.4 44.6 41.5 38.3 37.0 32.2 31.5 26.2 25.1 294.2 6064.7
Source No. 21 includes
Number of stacks 3 1 1 1 3 1 2
1 1 1 3 I
1 2
1 2
1 1 1 I 97 126
Stack height (m) 200 200 200 175 95 120 80 100 100 85 45 80 75 55 70 40 65 60 40 40
Stack diameter (m) 5.1 3.3 4.1 4.0 2.0 2.6 1.7 2.1 2.7 1.4 1.2 2.1 I.5 1.4 0.7 1.2 1.2 2.6 1.2 2.3
Heal emission (kcals-‘) 136.5 14.4 9.2 8.7 5.8 3.3 3.9 3.3 7.4 2.4 5.2
1.9 2.5 3.8 1.2 4.8 1.3 1.8 1.1 0.6
Reduction rate (%)
Emission after reduction (gs-‘)
0 0 0
3869.7 407.1 282.5 217.3 206.7 104.8 96.5 82.5 15.9 56.7 24.4 28.1 44.6 20.8 21.6 18.5 32.2 31.5 22.3 12.5 147.1 5803.4
0 0
0 0 0 0
0 50.0 38.2 0 50.0 43.6 50.0 0 0 14.7 50.0 50.0 4.3
all other sources,
Fig. 6. Locations of industrial sources which require emission reduction IO).
YOUNG KEE JANG
476
and
subject to restrictions PxR>C
(10)
0 Q Rj < OS.
(11)
The reduction rates computed for individual sources from the linear program are shown in Table 3, and the locations of sources in Fig. 6. Small industries open or close business frequently and it is almost impossible to compute allowable emission for each source whenever such a change occurs, and nearly all of the small sources around the problem area with emission rates less than 20 gs-’ showed 50% reduction of emission. Therefore a SO% reduction was assigned to all sources with emission less than 20 g s-’ to simplify administrative difficulties in implementation. It turned out that most of the sources with emission less than 50 g s- ’ needed 50 % reduction while larger sources required no reduction at all, which shows that reduction from small sources is more cost-effective than controlling a few large sources. This is the opposite to the policy of the second approach. With this approach the total allowable emission
Fig. 7. Annual
average
SO2 concentrations programming
JUNG WK KIM
amounted to 5803 g s- ’ which means 4.3 y0 reduction of industrial emission compared to 38.7 % and 53.3 % reduction for the first and the second approach, respectively. Among 117 industrial sources in Ulsan Industrial Complex, 10 big sources which emit more than 50 gs-’ of SO2 contribute 89 % of total industrial emission, and the largest 20 sources 95 %. Therefore if we can possibly achieve the goal by reducing emissions from small sources, it evidently will be significantly economical. The air quality with this method is shown in Fig. 7, which is quite comparable to that with the tirst approach. In implementing this method the cost for procuring the low S oil can be secured from taxes or surcharges on all users, since it is not fair to impose a heavier burden on small industries. The low S fuel can then be allocated to industries as necessary.
CONCLUSIONS
A procedure for planning the total emission control was presented and three approaches of emission
after toktl approach.
emission
control
with
lrnear
Total SO, emission control strategies for the management of air pollution
allocation were evaluated using TCM 2 model. The model was quite effective in predicting air quality in Ulsan. Stack height should be controlled before implementing the total emission control. The stack height proposed is H = 10.6 q”.’ where H is the stack height (m)and q is the SO1 emission rate (m-’ h-’ reduced to O’C). Stacks higher than the equation resulted in a higher pollution level in distant residential areas and consequently required more reduction of emission for industries. The total allowable
emission can be allo-
cated to industries at an uniform reduction rate, at a graduated reduction rate with a formula Q = 4 @.925 where Q is the atlowable emission, a is a constant, and Q, is the emission before control, and by a linear
programming technique. The first approach required 38.7% reduction of SO, emission from industries to achieve the air quality goal of the area, the second 53.3%, and the third 4.3%. The linear programming technique is found to be very economical, but ad-
ministrative difficulties in implementation overcome.
471
should be
REFERENCES
BriggsG. A. (1969) Plume rise. A.E.C. Critical Review Series. TID-25075, Atomic Energy Commission, Washington, _ _ G. A., Cohen A. S. and King R. F. (1976) ANUHIWAY: an air pollution evaluation model for
Coziildi
roadways. Argonne N’ational Laboratory. Argonne, Illinois. Environment Agency, Government of Japan (1975) Tom/ Emission Control Manual (Jap.), pp. 10&l 16. Environment Agency, Tokyo. Fronza G. and Me& P. (1984) Assignment of emission abatement levels by stochastic programming. Armospheric Encironment IS, 53 t-535. Texas Air Control Board (1980) User’s Guide fo rhe Texas C/imurologica/ Model, EPA/DF-8 l/O0 1b, National Technical Information Service, Springfield, Virginia. Turner D. B. (1964) A dilTusion model for an urban area. J. appl. Mel. 3, 83-91.