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Impact of an industrial complex on the ambient air quality: Case study using a dispersion model
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Atmospheric Environment 39 (2005) 5395–5407 www.elsevier.com/locate/atmosenv
Impact of an industrial complex on the ambient air quality: Case study using a dispersion model T.V.B.P.S. Rama Krishna, M.K. Reddy, R.C. Reddy, R.N. Singh NEERI Zonal Laboratory, IICT Campus, Uppal Road, Hyderabad 500 007, India Received 21 February 2005; received in revised form 10 May 2005; accepted 28 May 2005
Abstract The Industrial Source Complex Short Term (ISCST-3) model has been used to study the impact of an industrial complex, located at Jeedimetla in the outskirts of Hyderabad city, India, on the ambient air quality. The emissions of SO2 from 38 elevated point sources and 11 area sources along with the meteorological data for 2 months (April and May 2000) representing the summer season and for 1 month (January 2001) representing the winter season have been used for computing the ground level concentrations of SO2. The 8- and 24-h averaged model-predicted concentrations have been compared with corresponding observed concentrations at three receptors in April 2000 and at three receptors in May 2000 where ambient air quality is monitored during the study period. A total of 90 pairs of the predicted and observed concentrations have been used for model validation by computing different statistical errors and through Quantile–Quantile (Q–Q) plot. The results show that the model-predicted concentrations are in good agreement with observed values and the model performance is found to be satisfactory. The spatial distribution of SO2 concentrations over the study area is examined in the summer and winter months and found that the levels of SO2 are within the limits in comparison to the National Ambient Air Quality Standards except near the industrial area. r 2005 Elsevier Ltd. All rights reserved. Keywords: Air quality; Industrial sources; Observed concentrations; Statistical errors; Spatial distribution; Model evaluation
1. Introduction The deterioration of air quality in urban areas may be attributed to the rapid industrialization and increased growth of vehicular traffic. The ambient air quality has deteriorated to such an extent that it adversely affects the health and welfare of human beings. Extensive research has established that air pollutants affect the health of humans and animals, damage vegetation and materials, reduce visibility and solar radiation and affect weather and climate (Arya, 1999). The air quality Corresponding author. Fax: +91 40 27160639.
E-mail address: [email protected] (T.V.B.P.S. Rama Krishna).
models are used widely to assess the ambient air quality of desired region due to different sources as regular monitoring of pollutants both temporally and spatially is not always feasible due to high cost and experimental difficulties involved. The dispersion of different pollutants using air quality models of Gaussian-type have been studied for different cities, e.g. Keihin area (Okamoto and Shiozawa, 1978), Delhi (Singh et al., 1990; Goyal and Rama Krishna, 2002; Goyal et al., 2003), Agra (Goyal et al., 1994). Several air quality modelling studies were made in the past using the Industrial Source Complex Short Term (ISCST) model (Goyal et al., 1995; Lorber et al., 2000; Sivacoumar et al., 2001; Manju et al., 2002; Yegnan et al., 2002; Sax and Isakov, 2003). Rama Krishna et al.
1352-2310/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2005.06.003
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(2004) have studied the assimilative capacity and dispersion of gaseous pollutants namely sulphur dioxide and oxides of nitrogen due to industrial sources in Visakhapatnam bowl area using a Gaussian plume model and ISCST-3 model. These studies reveal the importance of application of mathematical models for air quality management studies due to different sources in urban areas. The impact of an industrial complex located at Jeedimetla in the outskirts of Hyderabad city, on the ambient air quality has been examined using the ISCST3 model in the present study. The temporal and spatial distributions of a primary pollutant namely sulphur dioxide have been analysed in the study region.
area sources. The industrial stacks that are connected to process and operation (confined sources) are considered as elevated point sources whereas unconfined sources of process/operation are taken as area sources. The area of process/operation is considered for computing the emission rate/source strength of these area sources. The stack characteristics of elevated point sources such as stack height, internal diameter, exit velocity and exit temperature are considered along with emissions (in g s1) of SO2. The location (X, Y) on the Cartesian grid of these point sources is also taken. The location (X, Y) of area sources on the Cartesian grid along with emissions (in g m2 s1) of SO2 is considered in the present study. The characteristics of the elevated point sources and area sources used in the simulations are given in Tables 1a and b, respectively.
2. Study area 3.2. Meteorological data The industrial complex considered in the present study located (171320 N; 781270 E) at Jeedimetla in the outskirts of Hyderabad city in Andhra Pradesh, India, is one of the major industrial centres and is referred as Jeedimetla Industrial Development Area (JIDA) in the discussion further. JIDA is situated at a distance of 20 km in the northwest direction from Hyderabad city and spread over an area of 4.26 km2 consisting of five phases (Fig. 1). There are several small and medium scale units of different types of industries in operation in this area. JIDA hosts several industries viz., chemical, drugs and pharmaceuticals, food, plastics, paints and polymers, paper and eclectic industries. These industries release different pollutants into the air environment such as sulphur dioxide (SO2), oxides of nitrogen, suspended particulate matter, carbon monoxide, volatile organic compounds, ammonia, chlorine and hydrogen chloride vapours. The emissions from these industries cause major concern in deteriorating the ambient air quality. Thus it is necessary to study the impact of JIDA on the ambient air quality in and around the surrounding industrial region (Fig. 2). The dispersion of a common pollutant SO2 that is being emitted from different industrial sources is examined in this study.
3. Data The emissions of primary pollutant namely SO2 emitted from different industrial sources and their source characteristics, meteorological and ambient air quality data (NEERI, 2001) are described in this section. 3.1. Emissions data There are about 50 industrial sources located in JIDA that are considered in the present study. Of these 38 are considered as elevated point sources and 11 are taken as
The meteorological data for 14 days in April (11–24 April 2000) and 12 days in May (15–26 May 2000) representing the summer season and for 12 days in January (10–21 January 2001) representing the winter season are taken for the numerical simulations. The meteorological data comprises the 3-h wind speed, wind direction, temperature, cloud cover and solar insolation. These data are obtained from the India Meteorological Department, Hyderabad for Airport station. Atmospheric stability (Pasquill classification of six stabilities, A–F; A-extremely unstable, B-moderately unstable, C-slightly unstable, D-neutral, E-slightly stable and F-moderately stable) is compiled from the wind speed, cloud cover and solar insolation following Turner’s (1969) table. The mixing height is defined as the height above the surface through which relatively rigorous vertical mixing occurs (Manju et al., 2002) and it is determined using the Holzworth (1967) technique. Thus in the present study, the meteorological data of 3-h (0200, 0500, 0800, 1100, 1400, 1700, 2000 and 2300 h) wind speed, wind direction, temperature, atmospheric stability and mixing height are used. A fair estimate of the dispersion of pollutants in the atmosphere is possible based on the frequency distribution of wind direction as well as wind speed (Manju et al., 2002). Fig. 3 illustrates the wind roses for the study period in April 2000, May 2000 and January 2001. The prevailing wind direction in April is ESE, SE, SSE and NW (Fig. 3a). The winds are found to be stronger in ESE, SSE and NW directions. The calm winds occur about 42% in April. The predominant wind direction in May is WNW and NW (Fig. 3b) and the winds are found to be stronger. The calm wind conditions occur about 19% in May. The winds in January are dominant in ESE, SE and SSE directions (Fig. 3c) and are found to be stronger. The calm winds occur about 29% in January representing the winter season.
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Fig. 1. Map of Jeedimetla industrial development area in the study region.
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Fig. 2. Location of ambient air quality monitoring stations () in the study area. Hatched regions are the five phases of the Jeedimetla industrial development area. is the reference point (0,0) in the present study.
The diurnal variation of wind speed and mixing height are shown in Fig. 4 for the study period in April 2000, May 2000 and January 2001. The wind speed is found to be ranging from 1.75–3.5 m s1 in April (Fig. 4a), from 2.4–5 m s1 in May (Fig. 4b) and from 1.9–5.6 m s1 in January (Fig. 4c), respectively. The mixing heights are available for some days in April and it is assumed that the mixing heights are same for those days in May representing summer season due to non-availability of mixing height data in the later month. The mixing height shows a typical diurnal trend over land. Mixing height in April and May is found to be increasing from morning (300 m at 0500 h) and reaches a maximum around noon
(1000 m at 1400 h) and decreases further towards night (Figs. 4a and b). Similar variation of mixing height is observed in January (Fig. 4c) with minimum mixing height of 125 m at 0500 h and maximum mixing height of 900 m at 1400 h. High pollution potential occurs when the mixing height iso500 m and the mean wind speed does not exceed 4 m s1 during night and morning hours (Stack Pole, 1967; Gross, 1970). Based on these criteria it is noticed that high pollution potential may occur during nighttime and early morning during the study period in April and May 2000 and during late night and early morning hours in January 2001 implying weak dispersion of pollutants.
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Table 1a Characteristics of elevated point sources S.No
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
Source ID
PT1 PT2 PT3 PT4 PT5 PT6 PT7 PT9 PT11 PT12 PT13 PT14 PT15 PT16 PT18 PT19 PT20 PT21 PT22 PT23 PT24 PT25 PT26 PT27 PT28 PT29 PT30 PT32 PT33 PT34 PT35 PT36 PT37 PT38 PT39 PT40 PT41 PT42
Stack height (m)
19.0 15.0 16.0 30.0 14.0 19.0 18.0 10.0 15.0 13.0 30.0 32.0 21.3 42.0 22.0 10.0 15.0 10.0 13.0 15.0 10.0 12.0 5.0 20.0 10.0 14.0 32.0 15.0 17.0 19.8 18.0 19.8 19.8 32.0 10.0 30.0 13.0 9.0
Stack internal diameter (m)
0.30 0.44 0.18 0.40 0.40 0.30 0.71 0.15 0.40 0.30 0.40 0.60 1.00 0.90 0.50 0.23 0.50 0.30 0.36 0.30 0.30 0.11 0.10 0.62 0.20 0.24 0.75 0.60 0.73 0.51 0.32 0.50 0.22 0.68 0.30 1.10 0.30 0.60
3.3. Ambient air quality data The Andhra Pradesh State Pollution Control Board (APPCB), Hyderabad, India, has monitored the ambient levels of different pollutants at different monitoring stations/receptors in and around the study area (Fig. 2) during the study period in April and May 2000. The 8-h averaged values of SO2 monitored at these receptors are used for validating the model-predicted SO2 concentrations in the present study. These observed concentrations are averaged over 8 h, i.e. 1400–2200 h; 2200–0600 h; 0600–1400 h on each day. Thus there are three 8-h averaged observations in each day. The
Emission rate (g s1)
Exit gas Velocity (m s1)
Temperature (K)
4.00 10.00 15.00 18.00 8.00 4.00 8.50 15.00 5.57 3.68 9.71 10.96 11.06 11.92 7.48 10.00 15.00 7.07 14.50 2.83 14.15 10.00 15.00 6.50 15.00 15.00 6.54 4.72 6.08 15.00 15.00 14.52 15.00 10.00 5.00 10.00 3.84 5.00
473 370 500 488 489 473 500 500 500 488 482 503 351 577 379 486 430 413 482 403 548 423 573 467 573 488 458 530 413 471 430 449 440 423 398 354 423 503
0.12 0.85 0.15 2.82 0.30 0.12 0.40 0.07 0.15 0.09 0.41 3.22 0.27 4.90 0.12 0.07 0.42 0.12 0.64 0.04 0.11 0.01 0.03 0.24 0.01 0.22 0.75 0.44 0.45 0.47 0.27 0.42 0.14 1.55 0.06 3.90 0.03 0.08
location (X, Y) of the receptors is taken into account on the Cartesian grid of the study area.
4. Dispersion model The ISCST-3 model developed by US Environmental Protection Agency (EPA) is used to compute the ground level concentrations of the pollutant. This model has the capability to handle polar or Cartesians co-ordinates, simulate point, area and volume sources, considers wet and dry deposition, makes terrain adjustments, considers building downwash (ISC-3, 1995a). The details of
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Table 1b Characteristics of area sources S.No
Source ID
Length of side Emission rate of area (m) (g m2 s1)
1 2 3 4 5 6 7 8 9 10 11
AS5 AS12 AS18 AS30 AS33 AS34 AS35 AS36 AS37 AS39 AS40
20 20 20 20 50 30 25 30 20 10 60
0.00375 0.00250 0.00175 0.00400 0.00032 0.00016 0.00016 0.00021 0.00038 0.00060 0.00139
the model are given below briefly for completeness of the presentation. The ISCST-3 model for continuous elevated point sources uses the steady-state Gaussian plume equation (ISC-3, 1995b) given by " # QKVD y2 C¼ exp 2 , (1) 2pusy sz 2sy where Q is the source strength or emission rate of pollutant (g s1), u is the mean wind speed (m s1), y is the cross wind distance (m), sy and sz are the dispersion parameters (m) in the horizontal and vertical directions, respectively, K is the scaling coefficient to convert calculated concentration to desired units, V is the term for vertical distribution of Gaussian plume and D is the decay term accounting for the pollutant removal by physical or chemical process. The expression for V in Eq. (1) is given by " " # # zr he 2 zr þ he 2 V ¼ exp 0:5 þ exp 0:5 sz sz ( " # " 2 # 2 1 X H1 H2 exp 0:5 þ exp 0:5 þ s sz z i¼1 " " 2 # 2 #) H3 H4 þ exp 0:5 , ð2Þ þ exp 0:5 sz sz where he ¼ hs þ Dh; H 1 ¼ zr þ ð2izi he Þ H 2 ¼ zr þ ð2izi þ he Þ H 3 ¼ zr ð2izi þ he Þ H 4 ¼ zr ð2izi he Þ;
(3)
in which zr is the receptor height above ground or flag pole (m), he is the effective stack height (m), hs is the
physical stack height (m), Dh is the plume rise (m), zi is the mixing height (m). Eq. (2) is the vertical term without dry deposition. The infinite series term in Eq. (2) accounts for the effects of the restriction on vertical plume growth at the top of the mixing layer. The method of image sources is used to account for multiple reflections of the plume from the ground surface and the top of the mixed layer. If the effective stack height (he) exceeds the mixing height, the plume is assumed to fully penetrate the elevated inversion and the ground level concentrations are set to zero. The vertical term (Eq. 2) changes the form of the vertical concentration distribution from Gaussian to rectangular (i.e. a uniform concentration distribution within the surface mixing layer) at long down wind distances (ISC-3, 1995b). The expression for D in Eq. (1) is given by 8 < exp c x for c40; us D¼ (4) : 1 for c ¼ 0; where c is the decay coefficient (s1) (a value of zero means decay is not considered), x is the downwind distance (m). If T1/2 is the half life in seconds then c can be obtained by c ¼ 0:693=T 1=2 (ISC-3, 1995b). The ISCST-3 model employs Briggs formulae to compute plume rise, Pasquill-Gifford curves for parameterising the horizontal and vertical dispersion parameters for rural background and empirical relations (Briggs, 1973) for urban background and it includes buoyancy-induced dispersion. This model has an option to use rural or urban background. Wind profile law is used to estimate the wind speed at stack height (ISC-3, 1995b).
5. Computational methodology The SO2 concentrations due to emissions from 38 elevated point sources and 11 area sources are computed using the ISCST-3 model. The stack location, emission rates of SO2 and stack characteristics such as stack height, internal diameter, exit velocity and exit temperature of 38 elevated point sources along with the emission rates of SO2 from 11 area sources were given as emissions data in the model. The 3-h wind speed, wind direction, mixing height, atmospheric stability and ambient temperature were given as meteorological input to the model. The location of receptors at which the concentrations were computed was given as input to the model. The urban option is used in running the ISCST-3 model. The 3-h concentrations of SO2 were computed using the model in April and May 2000 representing the summer season and in January 2001 representing the
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Fig. 3. Wind roses for the study period in (a) April 2000, (b) May 2000 and (c) January 2001 at Hyderabad. Wind speed is in knots.
winter season. The 8-h averages of computed concentrations in April and May 2000 were obtained from the 3-h values at each receptor in the following manner. The 3-h concentrations on each day were obtained for 0200, 0500, 0800, 1100, 1400, 1700, 2000 and 2300 h from the model. The average of computed concentrations obtained for 1400, 1700 and 2000 h is the 8-h average corresponding to 1400–2200 h of observed 8-h concentration. The average of computed concentrations obtained for 2300, 0200 and 0500 h is the 8-h average corresponding to 2200–0600 h of observed 8-h concen-
tration. The average of computed concentrations obtained for 0500, 0800 and 1100 h is the 8-h average corresponding to 0600–1400 h of observed 8-h concentration. This criterion for averaging has been assumed for the comparison of concentrations. Thus the 8-h averages of predicted concentrations obtained from the ISCST-3 model were compared with those 8-h observed concentrations at three receptors in April 2000 and at three receptors in May 2000, respectively. The 24-h average values of predicted concentrations were obtained by taking the average of all the 3-h values during
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0
0 0 2 4 6 8 10 12 14 16 18 20 22 24 Time (LST) 1200
6 5 4 3 2 1 0
800 400 0
Mixing Height (m)
0 2 4 6 8 10 12 14 16 18 20 22 24 Time (LST) 6 5 4 3 2 1 0
1000 750 500 250 0
6.1. Comparison of 8-h averaged concentrations The comparison of SO2 concentrations computed by the ISCST-3 model with the observed values at three receptors in April 2000 and at three receptors in May 2000 is shown in Figs. 5 and 6, respectively. The time indicated on the X-axis in Figs. 5 and 6 are the 8-h periods starting on different dates. The 8-h averaged concentrations depicted in Fig. 5 are given for 19–20 April 2000 at HAL colony and JETL Pump House and for 11–13 April 2000 at Kalavathi Nagar. The modelpredicted values are found to be reasonably close to those observed values except in few hours at HAL colony (Fig. 5a) and at JETL Pump House (Fig. 5b). The model-computed concentrations are found to be matching well with the observed values at Kalavathi Nagar (Fig. 5c). It is also noticed that the predicted concentrations follow the observed concentration pattern at all three receptors except in few hours at HAL colony where the model-predicted values are high. The maximum values predicted by the model are found to occur in the stable conditions during nighttime where the dispersion will be low. Fig. 6 illustrates the comparison between the ISCST-3 model-predicted SO2 concentrations and observations at
0 2 4 6 8 10 12 14 16 18 20 22 24 Time (LST)
the entire study period 11–24 April 2000, 15–26 May 2000 and 10–21 January 2001, respectively.
6. Results and discussion The ISCST-3 model has been used to predict the ground level concentrations of SO2 due to 38 elevated point sources and 11 area sources located in JIDA. The 8- and 24-h averaged concentrations obtained from the model as described in the earlier section are compared with the corresponding observed concentrations at three receptors in April 2000 and at three receptors in May 2000 where ambient air quality data is monitored during the study period. Similar comparison is not given for January 2001, as monitored air quality data is not available in this month. The model validation has also been carried out by computing several statistical errors and through Quantile–Quantile (Q–Q) plot. Finally, spatial distribution of SO2 concentrations in and around JIDA is examined in April and May 2000 representing summer season and in January 2001 representing winter season.
Concentration (µg m-3)
19-20 April 2000 30 20 10 0 1
2
3
5
6
5
6
19-20 April 2000
18 12 6 0 1
2
3
4 Time
(b)
11-13 April 2000
18 12 6 0 1
(c)
4 Time
(a)
Concentration (µg m-3)
Fig. 4. Diurnal variation of wind speed (———) and mixing height (- - - - - - -) during the study period in (a) April 2000, (b) May 2000 and (c) January 2001.
Concentration (µg m-3)
Wind Speed (m s-1)
400
1
(b)
Wind Speed (m s-1)
800
2
(a)
(c)
1200
Mixing Height (m)
Wind Speed (m s-1)
4
Mixing Height (m)
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2
3
4
5
6
7
8
9
Time
Fig. 5. Comparison of SO2 concentrations predicted by ISCST3 model with observed concentrations at (a) HAL colony, (b) JETL Pump House and (c) Kalavathi Nagar in the study region in April 2000. ISCST-3 ———; Observed JJJJ.
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6.2. Comparison of 24-h average concentrations
20 0 1
2
3
4
5 6 Time
Concentration (µg m-3)
(a)
8
9
10
30 15 0 1
Concentration (µg m-3)
7
15-17 May 2000
45
2
3
4
5
6
Time
(b)
16-19 May 2000 8 4 0 1
(c)
simulated by the model under unstable conditions when rigorous mixing takes place.
15-18 May 2000
60
5403
2
3
4
5 Time
6
7
8
9
Fig. 6. Comparison of SO2 concentrations predicted by ISCST3 model with observed concentrations at (a) Ramireddy Nagar, (b) SR Naik Nagar and (c) Qutabullapur in the study region in May 2000. ISCST-3 ———; Observed JJJJ.
three receptors in May 2000. The 8-h averaged concentrations are given for 15–18 May 2000 at Ramireddy Nagar; for 15–17 May 2000 at SR Naik Nagar and for 16–19 May 2000 at Qutabullapur. It can be seen from Fig. 6(a) that the model-computed values are close to those observed values except for the three peaks by the model at Ramireddy Nagar. The peak values are found to occur in stable conditions during nighttime. However, the model-predicted values are in good agreement with the observed concentrations at SR Naik Nagar (Fig. 6b) and at Qutabullapur (Fig. 6c). It is also found that the model-predicted values show similar trend with those observations. The comparison is presented only for selected days during the entire study period for the brevity of presentation. The monitoring station Ramireddy Nagar is located far away from the industrial complex compared to other stations (Fig. 2). The concentrations of the pollutants may be measured rather exactly at the source sites, but they become more dispersed and less dense as the distance increases from the emission points (Yildirim et al., 2002). The pollutants released from industrial stacks may not get carried to the receptor due to the obstruction of buildings and hence the low values of monitored values. The dispersion of the pollutant is well
The comparison of 24-h averaged concentrations computed by the ISCST-3 model and corresponding observations at all receptors is given in Table 2. The values in Table 2 represents the 24-h average values averaged over the entire study period 11–24 April 2000 and 15–26 May 2000, respectively. It is noticed from Table 2 that the predicted average is found to be matching with the observed average at JETL Pump House. The predicted average is found to be approximately 1.5 times the observed average at HAL colony and Kalavathi Nagar. However, the predicted average values at Ramireddy Nagar and at SR Naik Nagar are found to be approximately twice that of the observed average, whereas at Qutabullapur the predicted average is reasonably close to the observed average. The concentrations predicted by the model in May are higher than in April. This may be due to the influence of pollutant decay considered by the model. The ISCST-3 model uses a decay half-life of 4 h (c ¼ 0:0000481 s1 ) for SO2 when used in urban mode. The decay term D in Eq. (1) contributes more when wind speed is more. It can be seen from Figs. 3 and 4 that calm winds are more in April than in May and also wind speeds are low in April than in May. This may be the reason for higher concentrations computed by the model in May than in April. 6.3. Statistical analysis The model performance should be evaluated to ensure the modelling results are appropriate (Venkatram, 1981). The model results have also been validated through computing different statistical errors namely Table 2 Comparison of SO2 concentrations predicted by ISCST-3 model with observed values at different receptors during the study period in April and May 2000 Predicteda (mg m3)
Observeda (mg m3)
(a) April HAL colony JETL Pump House Kalavathi Nagar
11.47 9.03 11.3
7.64 9.73 8.1
(b) May Ramireddy Nagar SR Naik Nagar Qutabullapur
14.4 14.6 3.95
7.2 8.5 5.3
Name of the receptor
a
24 hourly averages.
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Normalized Mean Square Error (NMSE), Fraction of two (FA2), Fractional Bias (FB) and Index of Agreement (IOA) (Arya, 1999; Rama Krishna et al., 2004). A total of 90 pairs of computed and observed concentrations are used for computing the above statistical errors and the estimated errors are given in Table 3. The values of NMSE from Table 3 suggest that the model results are close to observed values. The values of FA2 imply slight overprediction of SO2 concentrations by the model. The FB indicates how well the computation produces the average values around the average values of observed variable. The ideal value of this measure is zero, but it can range from 2 to 2; and absolute value 0.67 corresponds to a prediction within a factor of two of the observations (Arya, 1999). The value of FB indicates better performance of the model. The values representing IOA suggests that the model predicts satisfactorily. The model performance has been examined through Q–Q plot (Fig. 7) in which the ranked model predictions are plotted against ranked observations. If the distribution of the model predictions and observations were
Table 3 Statistical errors Name of the error
Error
Ideal value
NMSE FA2 FB IOA
1.44 1.3 0.26 0.2
Least value 1 0 1
40
Observed (µg m-3)
30
20
10
0 0
10
20 Predicted (µg
30 m-3)
Fig. 7. Q–Q plot with respect to SO2 concentrations.
40
identical, the points would lie on the one-to-one line (Venkatram, 1999). It is observed from Fig. 7 that the ISCST-3 model shows both underprediction and overprediction compared to observed values. However, it is found that the model performs satisfactorily as approximately 60% of the points are lying reasonably close to the one-to-one line (Fig. 7). Based on the comparison of 8- and 24-hrly concentrations, it may be concluded that the performance of the model is reasonably good, as majority of the predicted values are within a factor of two of the observed values (Hanna et al., 1982). The Q–Q plot and the computed statistical errors also show that the model’s performance is satisfactory. 6.4. Spatial distribution of pollutant concentration The spatial distribution of the SO2 concentrations has been examined over an area of 10 km 10 km surrounding the JIDA. The ISCST-3 model is used to predict the ground level concentrations of SO2 over the study region with 250 m grid spacing near JIDA up to 5 km and the grid distance is taken as 500 m beyond 5 km up to 10 km region. The isopleths of the 24-h averaged predicted SO2 concentrations over the study region are shown in Fig. 8. It is observed from Fig. 8 that the SO2 concentrations are higher nearby the industrial area in both summer (April and May 2000) and winter (January 2001) seasons. The maximum concentrations of SO2 are found to be 160 mg m3 in April (Fig. 8a), 130 mg m3 in May (Fig. 8b) and 120 mg m3 in January (Fig. 8c), respectively. The concentrations are less in winter month because of stronger winds in January 2001 compared to those in April and May 2000 (Fig. 4). The SO2 concentrations obtained form the ISCST-3 model near the vicinity of JIDA have slightly exceeded the National Ambient Air Quality Standards (NAAQS) in April and May 2000 representing summer season, whereas the SO2 concentrations are within NAAQS in January 2001 representing winter season. The NAAQS for industrial areas for 24-h averaged SO2 is 120 mg m3 (CPCB, 1994). However, the SO2 concentrations are within NAAQS in the rest of the region. It is also noticed that the isopleths of SO2 are found to have distributed to large distances and the concentrations are found to be negligible beyond 4–5 km region. Majority of the industrial units in JIDA are having stacks with height less than 30 m. Because of these stacks comparable to adjacent building heights, the pollutants may not reach farther distances and the pollution is limited to the industrial region mostly. Based on the above study it may be concluded that the impact of JIDA on the ambient air quality is significant only in the industrial zone of JIDA and near by areas. The pollutants are not carried to longer distances. From the validation of the model-computed values, the
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Fig. 8. Spatial distribution of SO2 concentrations obtained from ISCST-3 model during the study period in (a) April 2000, (b) May 2000 and (c) January 2001 in and around JIDA.
performance of the ISCST-3 model is found to be satisfactory. The dispersion modelling is an important tool in predicting the temporal and spatial distribution of pollutants to assess the impact of industrial sources on the ambient air quality. Similar studies may be carried out for different industrial complexes with the availability of data to examine the impact of the emissions of different pollutants from such industrial sources on the ambient air quality, which will help in
delineating an air quality management plan for industrial complexes.
7. Conclusions The ISCST-3 model has been used to study the impact of an industrial complex located at Jeedimetla in the outskirts of Hyderabad city, India, on the ambient air
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quality. The emissions data of 38 elevated point sources and 11 area sources for SO2 have been considered. The 3-h meteorological data for the period 11–24 April 2000 and 15–26 May 2000 representing the summer season and for the period 10–21 January 2001 representing the winter season have been used for predicting the ground level concentrations of SO2. The 8- and 24-h averaged model-predicted concentrations have been compared with the corresponding observed concentrations at three receptors in April 2000 and at three receptors in May 2000 where the ambient air quality is monitored in the study area. The model validation has been carried out by computing several statistical errors and through Q–Q plot using 90 pairs of predicted and observed concentrations. It is observed that the ISCST-3 modelpredicted pollutant concentrations are relatively close to those observed values and the model performance is found to be satisfactory. The isopleths of the 24-h averaged ground level concentrations of SO2 have been plotted over the study area in April and May 2000 representing the summer season and in January 2001 representing the winter season. It is found that the levels of SO2 are within the limits in comparison to the NAAQS except near the industrial area. It may be concluded that the impact of the JIDA on the ambient air quality is significant only in the industrial zone of JIDA and near by areas as concentrations are found to be negligible beyond 4–5 km region.
Acknowledgements The authors would like to thank Dr. S. Devotta, Director, National Environmental Engineering Research Institute, Nagpur, India, for his encouragement to carry out this work. The authors wish to acknowledge the Andhra Pradesh Pollution Control Board, Hyderabad, India, for the financial support. The authors wish to thank the anonymous reviewers for their valuable suggestions.
References Arya, S.P., 1999. Air Pollution Meteorology and Dispersion. Oxford University Press, Oxford. Briggs, G.A., 1973. Diffusion estimation for small emissions. Contribution No. 79, Atmospheric Turbulence and Diffusion Laboratory, Oak Ridge, TN. CPCB, 1994. National Ambient Air Quality Standards notification Delhi 11th April 1994 under section 16(2)(h) of the Air Act 1981. Central Pollution Control Board, New Delhi, India. Goyal, P., Rama Krishna, T.V.B.P.S., 2002. Dispersion of pollutants in convective low wind: a case study of Delhi. Atmospheric Environment 36, 2071–2079.
Goyal, P., Singh, M.P., Bandyopadhyay, T.K., 1994. Environmental studies of SO2, SPM and NOx over Agra with various methods of treating calms. Atmospheric Environment 28, 3113–3123. Goyal, P., Singh, M.P., Bandyopadhyay, T.K., Rama Krishna, T.V.B.P.S., 1995. Comparative study of line source models for estimating lead levels due to vehicular traffic in Delhi. Environmental Software 10, 289–299. Goyal, P., Rama Krishna, T.V.B.P.S., Sankalp, Anand., 2003. Assimilative capacity and dispersion of pollutants in Delhi. Proceedings of the Indian National Science Academy, PartA Physical Sciences 69, 775–784. Gross, E., 1970. The National Air Pollution Forecast Programme, ESSA Technical Memo. WBTM-NMC 47, US Department of Commerce. Hanna, S.R., Briggs, G.A., Hosker Jr., R.P., 1982. Handbook on Atmospheric Diffusion. US Department of Energy, Technical Information Centre, Oak Ridge, TN. Holzworth, G.C., 1967. Mixing depths, wind speed and air pollution potential for selected locations in the United States. Journal of Applied Meteorology 6, 1039–1044. ISC-3, 1995a. User’s guide for Industrial Source Complex (ISC 3) Dispersion Models, vol. 1 (User’s instructions). EPA-454/ B-95-003a; USEPA; Research Triangle Park, NC. ISC-3, 1995b. User’s Guide for Industrial Source Complex (ISC 3) Dispersion Models, vol. 2 (Description of model algorithms). EPA-454/B-95-003a; USEPA; Research Triangle Park, NC. Lorber, M., Eschenroeder, A., Robinson, R., 2000. Testing the US EPA’s ISCST—Version 3 Model on dioxins: a comparison of predicted and observed air and soil concentration. Atmospheric Environment 34, 3995–4010. Manju, N., Balakrishnan, R., Mani, N., 2002. Assimilative capacity and pollutant dispersion studies for the industrial zone of Manali. Atmospheric Environment 36, 3461–3471. NEERI, 2001. Evaluation of Emission Standards and Air Quality Management—IDA (Jeedimetla). National Environmental Engineering Research Institute, Nagpur, India. Okamoto, S., Shiozawa, K., 1978. Validation of an air pollution model for the Keihin area. Atmospheric Environment 12, 2139–2149. Rama Krishna, T.V.B.P.S., Reddy, M.K., Reddy, R.C., Singh, R.N., 2004. Assimilative capacity and dispersion of pollutants due to industrial sources in Visakhapatnam bowl area. Atmospheric Environment 38, 6775–6787. Sax, T., Isakov, V., 2003. A case study for assessing uncertainty in local-scale regulatory air quality modelling applications. Atmospheric Environment 37, 3481–3489. Singh, M.P., Goyal, P., Panwar, T.S., Agarwal, P., Nigam, S., Bagchi, N., 1990. Predicted and observed concentrations of SO2, SPM and NOx over Delhi. Atmospheric Environment 24A, 783–788. Sivacoumar, R., Bhanarkar, A.D., Goyal, S.K., Gadkari, S.K., Aggarwal, A.L., 2001. Air pollution modeling for an industrial complex and model performance evaluation. Environmental Pollution 111, 471–477. Stack Pole, J.D., 1967. The Air Pollution Potential Forecast Programme, ESSA Tech., Memo, WBTM-NMC 43.
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sion Modelling for Regulatory Applications, 11–14 October 1999, Rouen, France. Yegnan, A., Williamson, D.G., Graettinger, A.J., 2002. Uncertainty analysis in air dispersion modelling. Environmental Modelling & Software 17, 639–649. Yildirim, Y., Demircioglu, N., Kobya, M., Bayramoglu, M., 2002. A mathematical modeling of sulphur dioxide pollution in Erzurum city. Environmental Pollution 118, 411–417.
Atmospheric Environment 39 (2005) 5395–5407 www.elsevier.com/locate/atmosenv
Impact of an industrial complex on the ambient air quality: Case study using a dispersion model T.V.B.P.S. Rama Krishna, M.K. Reddy, R.C. Reddy, R.N. Singh NEERI Zonal Laboratory, IICT Campus, Uppal Road, Hyderabad 500 007, India Received 21 February 2005; received in revised form 10 May 2005; accepted 28 May 2005
Abstract The Industrial Source Complex Short Term (ISCST-3) model has been used to study the impact of an industrial complex, located at Jeedimetla in the outskirts of Hyderabad city, India, on the ambient air quality. The emissions of SO2 from 38 elevated point sources and 11 area sources along with the meteorological data for 2 months (April and May 2000) representing the summer season and for 1 month (January 2001) representing the winter season have been used for computing the ground level concentrations of SO2. The 8- and 24-h averaged model-predicted concentrations have been compared with corresponding observed concentrations at three receptors in April 2000 and at three receptors in May 2000 where ambient air quality is monitored during the study period. A total of 90 pairs of the predicted and observed concentrations have been used for model validation by computing different statistical errors and through Quantile–Quantile (Q–Q) plot. The results show that the model-predicted concentrations are in good agreement with observed values and the model performance is found to be satisfactory. The spatial distribution of SO2 concentrations over the study area is examined in the summer and winter months and found that the levels of SO2 are within the limits in comparison to the National Ambient Air Quality Standards except near the industrial area. r 2005 Elsevier Ltd. All rights reserved. Keywords: Air quality; Industrial sources; Observed concentrations; Statistical errors; Spatial distribution; Model evaluation
1. Introduction The deterioration of air quality in urban areas may be attributed to the rapid industrialization and increased growth of vehicular traffic. The ambient air quality has deteriorated to such an extent that it adversely affects the health and welfare of human beings. Extensive research has established that air pollutants affect the health of humans and animals, damage vegetation and materials, reduce visibility and solar radiation and affect weather and climate (Arya, 1999). The air quality Corresponding author. Fax: +91 40 27160639.
E-mail address: [email protected] (T.V.B.P.S. Rama Krishna).
models are used widely to assess the ambient air quality of desired region due to different sources as regular monitoring of pollutants both temporally and spatially is not always feasible due to high cost and experimental difficulties involved. The dispersion of different pollutants using air quality models of Gaussian-type have been studied for different cities, e.g. Keihin area (Okamoto and Shiozawa, 1978), Delhi (Singh et al., 1990; Goyal and Rama Krishna, 2002; Goyal et al., 2003), Agra (Goyal et al., 1994). Several air quality modelling studies were made in the past using the Industrial Source Complex Short Term (ISCST) model (Goyal et al., 1995; Lorber et al., 2000; Sivacoumar et al., 2001; Manju et al., 2002; Yegnan et al., 2002; Sax and Isakov, 2003). Rama Krishna et al.
1352-2310/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2005.06.003
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(2004) have studied the assimilative capacity and dispersion of gaseous pollutants namely sulphur dioxide and oxides of nitrogen due to industrial sources in Visakhapatnam bowl area using a Gaussian plume model and ISCST-3 model. These studies reveal the importance of application of mathematical models for air quality management studies due to different sources in urban areas. The impact of an industrial complex located at Jeedimetla in the outskirts of Hyderabad city, on the ambient air quality has been examined using the ISCST3 model in the present study. The temporal and spatial distributions of a primary pollutant namely sulphur dioxide have been analysed in the study region.
area sources. The industrial stacks that are connected to process and operation (confined sources) are considered as elevated point sources whereas unconfined sources of process/operation are taken as area sources. The area of process/operation is considered for computing the emission rate/source strength of these area sources. The stack characteristics of elevated point sources such as stack height, internal diameter, exit velocity and exit temperature are considered along with emissions (in g s1) of SO2. The location (X, Y) on the Cartesian grid of these point sources is also taken. The location (X, Y) of area sources on the Cartesian grid along with emissions (in g m2 s1) of SO2 is considered in the present study. The characteristics of the elevated point sources and area sources used in the simulations are given in Tables 1a and b, respectively.
2. Study area 3.2. Meteorological data The industrial complex considered in the present study located (171320 N; 781270 E) at Jeedimetla in the outskirts of Hyderabad city in Andhra Pradesh, India, is one of the major industrial centres and is referred as Jeedimetla Industrial Development Area (JIDA) in the discussion further. JIDA is situated at a distance of 20 km in the northwest direction from Hyderabad city and spread over an area of 4.26 km2 consisting of five phases (Fig. 1). There are several small and medium scale units of different types of industries in operation in this area. JIDA hosts several industries viz., chemical, drugs and pharmaceuticals, food, plastics, paints and polymers, paper and eclectic industries. These industries release different pollutants into the air environment such as sulphur dioxide (SO2), oxides of nitrogen, suspended particulate matter, carbon monoxide, volatile organic compounds, ammonia, chlorine and hydrogen chloride vapours. The emissions from these industries cause major concern in deteriorating the ambient air quality. Thus it is necessary to study the impact of JIDA on the ambient air quality in and around the surrounding industrial region (Fig. 2). The dispersion of a common pollutant SO2 that is being emitted from different industrial sources is examined in this study.
3. Data The emissions of primary pollutant namely SO2 emitted from different industrial sources and their source characteristics, meteorological and ambient air quality data (NEERI, 2001) are described in this section. 3.1. Emissions data There are about 50 industrial sources located in JIDA that are considered in the present study. Of these 38 are considered as elevated point sources and 11 are taken as
The meteorological data for 14 days in April (11–24 April 2000) and 12 days in May (15–26 May 2000) representing the summer season and for 12 days in January (10–21 January 2001) representing the winter season are taken for the numerical simulations. The meteorological data comprises the 3-h wind speed, wind direction, temperature, cloud cover and solar insolation. These data are obtained from the India Meteorological Department, Hyderabad for Airport station. Atmospheric stability (Pasquill classification of six stabilities, A–F; A-extremely unstable, B-moderately unstable, C-slightly unstable, D-neutral, E-slightly stable and F-moderately stable) is compiled from the wind speed, cloud cover and solar insolation following Turner’s (1969) table. The mixing height is defined as the height above the surface through which relatively rigorous vertical mixing occurs (Manju et al., 2002) and it is determined using the Holzworth (1967) technique. Thus in the present study, the meteorological data of 3-h (0200, 0500, 0800, 1100, 1400, 1700, 2000 and 2300 h) wind speed, wind direction, temperature, atmospheric stability and mixing height are used. A fair estimate of the dispersion of pollutants in the atmosphere is possible based on the frequency distribution of wind direction as well as wind speed (Manju et al., 2002). Fig. 3 illustrates the wind roses for the study period in April 2000, May 2000 and January 2001. The prevailing wind direction in April is ESE, SE, SSE and NW (Fig. 3a). The winds are found to be stronger in ESE, SSE and NW directions. The calm winds occur about 42% in April. The predominant wind direction in May is WNW and NW (Fig. 3b) and the winds are found to be stronger. The calm wind conditions occur about 19% in May. The winds in January are dominant in ESE, SE and SSE directions (Fig. 3c) and are found to be stronger. The calm winds occur about 29% in January representing the winter season.
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Fig. 1. Map of Jeedimetla industrial development area in the study region.
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Fig. 2. Location of ambient air quality monitoring stations () in the study area. Hatched regions are the five phases of the Jeedimetla industrial development area. is the reference point (0,0) in the present study.
The diurnal variation of wind speed and mixing height are shown in Fig. 4 for the study period in April 2000, May 2000 and January 2001. The wind speed is found to be ranging from 1.75–3.5 m s1 in April (Fig. 4a), from 2.4–5 m s1 in May (Fig. 4b) and from 1.9–5.6 m s1 in January (Fig. 4c), respectively. The mixing heights are available for some days in April and it is assumed that the mixing heights are same for those days in May representing summer season due to non-availability of mixing height data in the later month. The mixing height shows a typical diurnal trend over land. Mixing height in April and May is found to be increasing from morning (300 m at 0500 h) and reaches a maximum around noon
(1000 m at 1400 h) and decreases further towards night (Figs. 4a and b). Similar variation of mixing height is observed in January (Fig. 4c) with minimum mixing height of 125 m at 0500 h and maximum mixing height of 900 m at 1400 h. High pollution potential occurs when the mixing height iso500 m and the mean wind speed does not exceed 4 m s1 during night and morning hours (Stack Pole, 1967; Gross, 1970). Based on these criteria it is noticed that high pollution potential may occur during nighttime and early morning during the study period in April and May 2000 and during late night and early morning hours in January 2001 implying weak dispersion of pollutants.
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Table 1a Characteristics of elevated point sources S.No
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
Source ID
PT1 PT2 PT3 PT4 PT5 PT6 PT7 PT9 PT11 PT12 PT13 PT14 PT15 PT16 PT18 PT19 PT20 PT21 PT22 PT23 PT24 PT25 PT26 PT27 PT28 PT29 PT30 PT32 PT33 PT34 PT35 PT36 PT37 PT38 PT39 PT40 PT41 PT42
Stack height (m)
19.0 15.0 16.0 30.0 14.0 19.0 18.0 10.0 15.0 13.0 30.0 32.0 21.3 42.0 22.0 10.0 15.0 10.0 13.0 15.0 10.0 12.0 5.0 20.0 10.0 14.0 32.0 15.0 17.0 19.8 18.0 19.8 19.8 32.0 10.0 30.0 13.0 9.0
Stack internal diameter (m)
0.30 0.44 0.18 0.40 0.40 0.30 0.71 0.15 0.40 0.30 0.40 0.60 1.00 0.90 0.50 0.23 0.50 0.30 0.36 0.30 0.30 0.11 0.10 0.62 0.20 0.24 0.75 0.60 0.73 0.51 0.32 0.50 0.22 0.68 0.30 1.10 0.30 0.60
3.3. Ambient air quality data The Andhra Pradesh State Pollution Control Board (APPCB), Hyderabad, India, has monitored the ambient levels of different pollutants at different monitoring stations/receptors in and around the study area (Fig. 2) during the study period in April and May 2000. The 8-h averaged values of SO2 monitored at these receptors are used for validating the model-predicted SO2 concentrations in the present study. These observed concentrations are averaged over 8 h, i.e. 1400–2200 h; 2200–0600 h; 0600–1400 h on each day. Thus there are three 8-h averaged observations in each day. The
Emission rate (g s1)
Exit gas Velocity (m s1)
Temperature (K)
4.00 10.00 15.00 18.00 8.00 4.00 8.50 15.00 5.57 3.68 9.71 10.96 11.06 11.92 7.48 10.00 15.00 7.07 14.50 2.83 14.15 10.00 15.00 6.50 15.00 15.00 6.54 4.72 6.08 15.00 15.00 14.52 15.00 10.00 5.00 10.00 3.84 5.00
473 370 500 488 489 473 500 500 500 488 482 503 351 577 379 486 430 413 482 403 548 423 573 467 573 488 458 530 413 471 430 449 440 423 398 354 423 503
0.12 0.85 0.15 2.82 0.30 0.12 0.40 0.07 0.15 0.09 0.41 3.22 0.27 4.90 0.12 0.07 0.42 0.12 0.64 0.04 0.11 0.01 0.03 0.24 0.01 0.22 0.75 0.44 0.45 0.47 0.27 0.42 0.14 1.55 0.06 3.90 0.03 0.08
location (X, Y) of the receptors is taken into account on the Cartesian grid of the study area.
4. Dispersion model The ISCST-3 model developed by US Environmental Protection Agency (EPA) is used to compute the ground level concentrations of the pollutant. This model has the capability to handle polar or Cartesians co-ordinates, simulate point, area and volume sources, considers wet and dry deposition, makes terrain adjustments, considers building downwash (ISC-3, 1995a). The details of
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Table 1b Characteristics of area sources S.No
Source ID
Length of side Emission rate of area (m) (g m2 s1)
1 2 3 4 5 6 7 8 9 10 11
AS5 AS12 AS18 AS30 AS33 AS34 AS35 AS36 AS37 AS39 AS40
20 20 20 20 50 30 25 30 20 10 60
0.00375 0.00250 0.00175 0.00400 0.00032 0.00016 0.00016 0.00021 0.00038 0.00060 0.00139
the model are given below briefly for completeness of the presentation. The ISCST-3 model for continuous elevated point sources uses the steady-state Gaussian plume equation (ISC-3, 1995b) given by " # QKVD y2 C¼ exp 2 , (1) 2pusy sz 2sy where Q is the source strength or emission rate of pollutant (g s1), u is the mean wind speed (m s1), y is the cross wind distance (m), sy and sz are the dispersion parameters (m) in the horizontal and vertical directions, respectively, K is the scaling coefficient to convert calculated concentration to desired units, V is the term for vertical distribution of Gaussian plume and D is the decay term accounting for the pollutant removal by physical or chemical process. The expression for V in Eq. (1) is given by " " # # zr he 2 zr þ he 2 V ¼ exp 0:5 þ exp 0:5 sz sz ( " # " 2 # 2 1 X H1 H2 exp 0:5 þ exp 0:5 þ s sz z i¼1 " " 2 # 2 #) H3 H4 þ exp 0:5 , ð2Þ þ exp 0:5 sz sz where he ¼ hs þ Dh; H 1 ¼ zr þ ð2izi he Þ H 2 ¼ zr þ ð2izi þ he Þ H 3 ¼ zr ð2izi þ he Þ H 4 ¼ zr ð2izi he Þ;
(3)
in which zr is the receptor height above ground or flag pole (m), he is the effective stack height (m), hs is the
physical stack height (m), Dh is the plume rise (m), zi is the mixing height (m). Eq. (2) is the vertical term without dry deposition. The infinite series term in Eq. (2) accounts for the effects of the restriction on vertical plume growth at the top of the mixing layer. The method of image sources is used to account for multiple reflections of the plume from the ground surface and the top of the mixed layer. If the effective stack height (he) exceeds the mixing height, the plume is assumed to fully penetrate the elevated inversion and the ground level concentrations are set to zero. The vertical term (Eq. 2) changes the form of the vertical concentration distribution from Gaussian to rectangular (i.e. a uniform concentration distribution within the surface mixing layer) at long down wind distances (ISC-3, 1995b). The expression for D in Eq. (1) is given by 8 < exp c x for c40; us D¼ (4) : 1 for c ¼ 0; where c is the decay coefficient (s1) (a value of zero means decay is not considered), x is the downwind distance (m). If T1/2 is the half life in seconds then c can be obtained by c ¼ 0:693=T 1=2 (ISC-3, 1995b). The ISCST-3 model employs Briggs formulae to compute plume rise, Pasquill-Gifford curves for parameterising the horizontal and vertical dispersion parameters for rural background and empirical relations (Briggs, 1973) for urban background and it includes buoyancy-induced dispersion. This model has an option to use rural or urban background. Wind profile law is used to estimate the wind speed at stack height (ISC-3, 1995b).
5. Computational methodology The SO2 concentrations due to emissions from 38 elevated point sources and 11 area sources are computed using the ISCST-3 model. The stack location, emission rates of SO2 and stack characteristics such as stack height, internal diameter, exit velocity and exit temperature of 38 elevated point sources along with the emission rates of SO2 from 11 area sources were given as emissions data in the model. The 3-h wind speed, wind direction, mixing height, atmospheric stability and ambient temperature were given as meteorological input to the model. The location of receptors at which the concentrations were computed was given as input to the model. The urban option is used in running the ISCST-3 model. The 3-h concentrations of SO2 were computed using the model in April and May 2000 representing the summer season and in January 2001 representing the
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Fig. 3. Wind roses for the study period in (a) April 2000, (b) May 2000 and (c) January 2001 at Hyderabad. Wind speed is in knots.
winter season. The 8-h averages of computed concentrations in April and May 2000 were obtained from the 3-h values at each receptor in the following manner. The 3-h concentrations on each day were obtained for 0200, 0500, 0800, 1100, 1400, 1700, 2000 and 2300 h from the model. The average of computed concentrations obtained for 1400, 1700 and 2000 h is the 8-h average corresponding to 1400–2200 h of observed 8-h concentration. The average of computed concentrations obtained for 2300, 0200 and 0500 h is the 8-h average corresponding to 2200–0600 h of observed 8-h concen-
tration. The average of computed concentrations obtained for 0500, 0800 and 1100 h is the 8-h average corresponding to 0600–1400 h of observed 8-h concentration. This criterion for averaging has been assumed for the comparison of concentrations. Thus the 8-h averages of predicted concentrations obtained from the ISCST-3 model were compared with those 8-h observed concentrations at three receptors in April 2000 and at three receptors in May 2000, respectively. The 24-h average values of predicted concentrations were obtained by taking the average of all the 3-h values during
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0
0 0 2 4 6 8 10 12 14 16 18 20 22 24 Time (LST) 1200
6 5 4 3 2 1 0
800 400 0
Mixing Height (m)
0 2 4 6 8 10 12 14 16 18 20 22 24 Time (LST) 6 5 4 3 2 1 0
1000 750 500 250 0
6.1. Comparison of 8-h averaged concentrations The comparison of SO2 concentrations computed by the ISCST-3 model with the observed values at three receptors in April 2000 and at three receptors in May 2000 is shown in Figs. 5 and 6, respectively. The time indicated on the X-axis in Figs. 5 and 6 are the 8-h periods starting on different dates. The 8-h averaged concentrations depicted in Fig. 5 are given for 19–20 April 2000 at HAL colony and JETL Pump House and for 11–13 April 2000 at Kalavathi Nagar. The modelpredicted values are found to be reasonably close to those observed values except in few hours at HAL colony (Fig. 5a) and at JETL Pump House (Fig. 5b). The model-computed concentrations are found to be matching well with the observed values at Kalavathi Nagar (Fig. 5c). It is also noticed that the predicted concentrations follow the observed concentration pattern at all three receptors except in few hours at HAL colony where the model-predicted values are high. The maximum values predicted by the model are found to occur in the stable conditions during nighttime where the dispersion will be low. Fig. 6 illustrates the comparison between the ISCST-3 model-predicted SO2 concentrations and observations at
0 2 4 6 8 10 12 14 16 18 20 22 24 Time (LST)
the entire study period 11–24 April 2000, 15–26 May 2000 and 10–21 January 2001, respectively.
6. Results and discussion The ISCST-3 model has been used to predict the ground level concentrations of SO2 due to 38 elevated point sources and 11 area sources located in JIDA. The 8- and 24-h averaged concentrations obtained from the model as described in the earlier section are compared with the corresponding observed concentrations at three receptors in April 2000 and at three receptors in May 2000 where ambient air quality data is monitored during the study period. Similar comparison is not given for January 2001, as monitored air quality data is not available in this month. The model validation has also been carried out by computing several statistical errors and through Quantile–Quantile (Q–Q) plot. Finally, spatial distribution of SO2 concentrations in and around JIDA is examined in April and May 2000 representing summer season and in January 2001 representing winter season.
Concentration (µg m-3)
19-20 April 2000 30 20 10 0 1
2
3
5
6
5
6
19-20 April 2000
18 12 6 0 1
2
3
4 Time
(b)
11-13 April 2000
18 12 6 0 1
(c)
4 Time
(a)
Concentration (µg m-3)
Fig. 4. Diurnal variation of wind speed (———) and mixing height (- - - - - - -) during the study period in (a) April 2000, (b) May 2000 and (c) January 2001.
Concentration (µg m-3)
Wind Speed (m s-1)
400
1
(b)
Wind Speed (m s-1)
800
2
(a)
(c)
1200
Mixing Height (m)
Wind Speed (m s-1)
4
Mixing Height (m)
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2
3
4
5
6
7
8
9
Time
Fig. 5. Comparison of SO2 concentrations predicted by ISCST3 model with observed concentrations at (a) HAL colony, (b) JETL Pump House and (c) Kalavathi Nagar in the study region in April 2000. ISCST-3 ———; Observed JJJJ.
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6.2. Comparison of 24-h average concentrations
20 0 1
2
3
4
5 6 Time
Concentration (µg m-3)
(a)
8
9
10
30 15 0 1
Concentration (µg m-3)
7
15-17 May 2000
45
2
3
4
5
6
Time
(b)
16-19 May 2000 8 4 0 1
(c)
simulated by the model under unstable conditions when rigorous mixing takes place.
15-18 May 2000
60
5403
2
3
4
5 Time
6
7
8
9
Fig. 6. Comparison of SO2 concentrations predicted by ISCST3 model with observed concentrations at (a) Ramireddy Nagar, (b) SR Naik Nagar and (c) Qutabullapur in the study region in May 2000. ISCST-3 ———; Observed JJJJ.
three receptors in May 2000. The 8-h averaged concentrations are given for 15–18 May 2000 at Ramireddy Nagar; for 15–17 May 2000 at SR Naik Nagar and for 16–19 May 2000 at Qutabullapur. It can be seen from Fig. 6(a) that the model-computed values are close to those observed values except for the three peaks by the model at Ramireddy Nagar. The peak values are found to occur in stable conditions during nighttime. However, the model-predicted values are in good agreement with the observed concentrations at SR Naik Nagar (Fig. 6b) and at Qutabullapur (Fig. 6c). It is also found that the model-predicted values show similar trend with those observations. The comparison is presented only for selected days during the entire study period for the brevity of presentation. The monitoring station Ramireddy Nagar is located far away from the industrial complex compared to other stations (Fig. 2). The concentrations of the pollutants may be measured rather exactly at the source sites, but they become more dispersed and less dense as the distance increases from the emission points (Yildirim et al., 2002). The pollutants released from industrial stacks may not get carried to the receptor due to the obstruction of buildings and hence the low values of monitored values. The dispersion of the pollutant is well
The comparison of 24-h averaged concentrations computed by the ISCST-3 model and corresponding observations at all receptors is given in Table 2. The values in Table 2 represents the 24-h average values averaged over the entire study period 11–24 April 2000 and 15–26 May 2000, respectively. It is noticed from Table 2 that the predicted average is found to be matching with the observed average at JETL Pump House. The predicted average is found to be approximately 1.5 times the observed average at HAL colony and Kalavathi Nagar. However, the predicted average values at Ramireddy Nagar and at SR Naik Nagar are found to be approximately twice that of the observed average, whereas at Qutabullapur the predicted average is reasonably close to the observed average. The concentrations predicted by the model in May are higher than in April. This may be due to the influence of pollutant decay considered by the model. The ISCST-3 model uses a decay half-life of 4 h (c ¼ 0:0000481 s1 ) for SO2 when used in urban mode. The decay term D in Eq. (1) contributes more when wind speed is more. It can be seen from Figs. 3 and 4 that calm winds are more in April than in May and also wind speeds are low in April than in May. This may be the reason for higher concentrations computed by the model in May than in April. 6.3. Statistical analysis The model performance should be evaluated to ensure the modelling results are appropriate (Venkatram, 1981). The model results have also been validated through computing different statistical errors namely Table 2 Comparison of SO2 concentrations predicted by ISCST-3 model with observed values at different receptors during the study period in April and May 2000 Predicteda (mg m3)
Observeda (mg m3)
(a) April HAL colony JETL Pump House Kalavathi Nagar
11.47 9.03 11.3
7.64 9.73 8.1
(b) May Ramireddy Nagar SR Naik Nagar Qutabullapur
14.4 14.6 3.95
7.2 8.5 5.3
Name of the receptor
a
24 hourly averages.
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Normalized Mean Square Error (NMSE), Fraction of two (FA2), Fractional Bias (FB) and Index of Agreement (IOA) (Arya, 1999; Rama Krishna et al., 2004). A total of 90 pairs of computed and observed concentrations are used for computing the above statistical errors and the estimated errors are given in Table 3. The values of NMSE from Table 3 suggest that the model results are close to observed values. The values of FA2 imply slight overprediction of SO2 concentrations by the model. The FB indicates how well the computation produces the average values around the average values of observed variable. The ideal value of this measure is zero, but it can range from 2 to 2; and absolute value 0.67 corresponds to a prediction within a factor of two of the observations (Arya, 1999). The value of FB indicates better performance of the model. The values representing IOA suggests that the model predicts satisfactorily. The model performance has been examined through Q–Q plot (Fig. 7) in which the ranked model predictions are plotted against ranked observations. If the distribution of the model predictions and observations were
Table 3 Statistical errors Name of the error
Error
Ideal value
NMSE FA2 FB IOA
1.44 1.3 0.26 0.2
Least value 1 0 1
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Observed (µg m-3)
30
20
10
0 0
10
20 Predicted (µg
30 m-3)
Fig. 7. Q–Q plot with respect to SO2 concentrations.
40
identical, the points would lie on the one-to-one line (Venkatram, 1999). It is observed from Fig. 7 that the ISCST-3 model shows both underprediction and overprediction compared to observed values. However, it is found that the model performs satisfactorily as approximately 60% of the points are lying reasonably close to the one-to-one line (Fig. 7). Based on the comparison of 8- and 24-hrly concentrations, it may be concluded that the performance of the model is reasonably good, as majority of the predicted values are within a factor of two of the observed values (Hanna et al., 1982). The Q–Q plot and the computed statistical errors also show that the model’s performance is satisfactory. 6.4. Spatial distribution of pollutant concentration The spatial distribution of the SO2 concentrations has been examined over an area of 10 km 10 km surrounding the JIDA. The ISCST-3 model is used to predict the ground level concentrations of SO2 over the study region with 250 m grid spacing near JIDA up to 5 km and the grid distance is taken as 500 m beyond 5 km up to 10 km region. The isopleths of the 24-h averaged predicted SO2 concentrations over the study region are shown in Fig. 8. It is observed from Fig. 8 that the SO2 concentrations are higher nearby the industrial area in both summer (April and May 2000) and winter (January 2001) seasons. The maximum concentrations of SO2 are found to be 160 mg m3 in April (Fig. 8a), 130 mg m3 in May (Fig. 8b) and 120 mg m3 in January (Fig. 8c), respectively. The concentrations are less in winter month because of stronger winds in January 2001 compared to those in April and May 2000 (Fig. 4). The SO2 concentrations obtained form the ISCST-3 model near the vicinity of JIDA have slightly exceeded the National Ambient Air Quality Standards (NAAQS) in April and May 2000 representing summer season, whereas the SO2 concentrations are within NAAQS in January 2001 representing winter season. The NAAQS for industrial areas for 24-h averaged SO2 is 120 mg m3 (CPCB, 1994). However, the SO2 concentrations are within NAAQS in the rest of the region. It is also noticed that the isopleths of SO2 are found to have distributed to large distances and the concentrations are found to be negligible beyond 4–5 km region. Majority of the industrial units in JIDA are having stacks with height less than 30 m. Because of these stacks comparable to adjacent building heights, the pollutants may not reach farther distances and the pollution is limited to the industrial region mostly. Based on the above study it may be concluded that the impact of JIDA on the ambient air quality is significant only in the industrial zone of JIDA and near by areas. The pollutants are not carried to longer distances. From the validation of the model-computed values, the
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Fig. 8. Spatial distribution of SO2 concentrations obtained from ISCST-3 model during the study period in (a) April 2000, (b) May 2000 and (c) January 2001 in and around JIDA.
performance of the ISCST-3 model is found to be satisfactory. The dispersion modelling is an important tool in predicting the temporal and spatial distribution of pollutants to assess the impact of industrial sources on the ambient air quality. Similar studies may be carried out for different industrial complexes with the availability of data to examine the impact of the emissions of different pollutants from such industrial sources on the ambient air quality, which will help in
delineating an air quality management plan for industrial complexes.
7. Conclusions The ISCST-3 model has been used to study the impact of an industrial complex located at Jeedimetla in the outskirts of Hyderabad city, India, on the ambient air
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quality. The emissions data of 38 elevated point sources and 11 area sources for SO2 have been considered. The 3-h meteorological data for the period 11–24 April 2000 and 15–26 May 2000 representing the summer season and for the period 10–21 January 2001 representing the winter season have been used for predicting the ground level concentrations of SO2. The 8- and 24-h averaged model-predicted concentrations have been compared with the corresponding observed concentrations at three receptors in April 2000 and at three receptors in May 2000 where the ambient air quality is monitored in the study area. The model validation has been carried out by computing several statistical errors and through Q–Q plot using 90 pairs of predicted and observed concentrations. It is observed that the ISCST-3 modelpredicted pollutant concentrations are relatively close to those observed values and the model performance is found to be satisfactory. The isopleths of the 24-h averaged ground level concentrations of SO2 have been plotted over the study area in April and May 2000 representing the summer season and in January 2001 representing the winter season. It is found that the levels of SO2 are within the limits in comparison to the NAAQS except near the industrial area. It may be concluded that the impact of the JIDA on the ambient air quality is significant only in the industrial zone of JIDA and near by areas as concentrations are found to be negligible beyond 4–5 km region.
Acknowledgements The authors would like to thank Dr. S. Devotta, Director, National Environmental Engineering Research Institute, Nagpur, India, for his encouragement to carry out this work. The authors wish to acknowledge the Andhra Pradesh Pollution Control Board, Hyderabad, India, for the financial support. The authors wish to thank the anonymous reviewers for their valuable suggestions.
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