MULTIPLE POINT SOURCE DISPERSION ANALYSIS OF CO, …from the Pembangkit Listrik Tenaga Uap – Steam...

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International Journal of Civil Engineering and Technology (IJCIET)

Volume 9, Issue 1, January 2018, pp. 837–846, Article ID: IJCIET_09_01_081

Available online at http://http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=9&IType=1

ISSN Print: 0976-6308 and ISSN Online: 0976-6316

© IAEME Publication Scopus Indexed

MULTIPLE POINT SOURCE DISPERSION

ANALYSIS OF CO, NO2, PM10 AND SO2 FROM

PAITON POWER PLANT USING CALPUFF

Arie Dipareza Syafei, Reska Putri Tansida Anung Wuri

Department of Environmental Engineering,

Faculty of Civil, Environmental and Geo Engineering,

Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia

ABSTRACT

The flue gas from burning coal consists of SO2, NO2, CO, and particulate matter

(PM10). When this flue gas leaves the stack it is dispersed vertically and horizontally.

This dispersion can cause air pollution inside or outside of the power plant area. This

research investigated the dispersion of CO, NO2, PM10, and SO2 emitted from the

Paiton power plant complex in Indonesia. Some scenarios involved individual CO,

NO2, PM10, and SO2 emission rates in minimum, average, and maximum conditions.

The dispersion was in each case was modeled using a CALPUFF model. The results

show that the dispersion of the air pollutants are still considered safe and will not

harm humans, animals, or plants even when the power plant emits its maximum

emission load.

Key words: Air dispersion, Multiple Point Source, Coal Power Plant, and CALPUFF.

Cite this Article: Arie Dipareza Syafei, Reska Putri Tansida Anung Wuri, Multiple

Point Source Dispersion Analysis of CO, NO2, PM10 and SO2 from Paiton Power Plant

Using CALPUFF. International Journal of Civil Engineering and Technology, 9(1),

2018, pp. 837-846.

http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=9&IType=1

1. INTRODUCTION

A coal power plant is a power plant that utilizes fossil fuels like coal and oil to produce high

pressure steam. The steam drives turbines at high speed to produce electricity (World Coal

Institute, 2009) [6]. The Paiton power plant complex in Indonesia contains eight plants. The

amount of electricity that is channeled from Paiton to an interconnected system in Java,

Maduram, and Bali averages 5606.18 GWh annually (PT PJB, 2018) [2]. Paiton is one of two

coal-fired power plants in Indonesia whose primary fuel is coal, and it requires approximately

12.144 million tons of coal per year to operate.

The use of coal and oil as fuel for a power plant can produce emissions of CO, CO2, NO2,

and SO2 gas, all of which cause air pollution (Finahari, 2007) [1]. When it comes out of the

stack, the flue gas is dispersed. Dispersion is the process of spreading the gasses from the

source to a specific region (Vesilind, 1994) [5]. The CALPUFF model is a multi-layer, non-



steady-state puff dispersion model designed to model the dispersion of particles and gases

using space and time varying meteorology (Scire et all, 2000) [3].

In this paper, we calculated the CO, NO2, SO2, and particulate matter (PM10) emissions

from the Pembangkit Listrik Tenaga Uap – Steam Powered Generator Plant (PLTU) Paiton

complex, which included point and area sources and used CALPUFF models to predict the

concentration distribution of the pollutants. Furthermore, the resulting data were compared to

national standard concentrations for CO, NO2, SO2 and PM10. It is hoped that the results of

this study will help to improve the air quality in the study area.

1.1. Model Description

CALPUFF is a non-steady-state, multi-layer Langrangian puff model used for estimating

deposition or concentration patterns for multiple air pollutants by considering the effect of

space-time varying meteorological conditions. This model assumes that the emission of a

stack can be subdivided into a series of pollutant puffs that have a Gaussian concentration

profile in all directions. Each puff follows wind speed and direction independently of the

other puffs. Puff models are more accurate than plume models. Puff models perform well for

distances of up to at least 50 km and are routinely used for distances up to 200 km.

The CALPUFF modeling system includes three main components, which are CALMET,

CALPUFF, and CALPOST. CALMET is a meteorological model which includes a diagnostic

wind field generator containing objective analysis and parameterized treatments of slope

flows, kinematics terrain effects, terrain blocking effects, a divergence minimization

procedure, and a micro-meteorological model for overland and overwater boundary layers.

CALPUFF simulates dispersion and transformation processes and contains modules for

complex terrain effects, overwater transport, coastal interaction effects, building downwash,

wet and dry removal, and simple chemical transformations. CALPOST is a post-processing

program with an option for computing time-averaged concentrations and deposition fluxes

predicted by the CALPUFF model, which are used to summarize the results of the simulation

(Scire et all, 2000) [3].

1.2. Emission Source Data

The emission source used in this modeling consists of the emissions from six stacks of an

eight-unit, electricity-generating boiler using coal and oil as its fuel. Emission load data from

boiler activity was obtained from CEMS (Continuous Emission Monitoring System). A

CEMS is the only equipment necessary for determining a gas or particulate matter

concentration or emission rate. It does so by using pollutant analyzer measurements and a

conversion equation, graph, or computer program to produce results in the units of the

applicable emission limitation or standard (US EPA, 2016) [4].

1.3. Geophysical and Meteorological Data of the Study Area

This study take place at the Paiton power plant complex located approximately 35 km to the

east of Surabaya City, the capital of East Java (7°42’51” S, 113°34’57”E), about halfway

between Probolinggo and Situbondo. This coal power plant complex consists of eight units

with a combined electrical capacity of 29.231 MW, which supplies the electricity for all of

Java and Bali. The eight units are operated by four different companies, so they have different

types of exhaust gas controlling procedures. Figure 1 shows the location for each power plant

stack in the Paiton complex.

Multiple Point Source Dispersion Analysis of CO, NO2, PM10 and SO2 from Paiton Power Plant

Using CALPUFF


Figure 1 Stack Locations at the Paiton Power Plant Complex

Other data required for modeling are geophysical, which includes land use and terrain

elevation data, and meteorological, which consists of surface and upper air data. Data from

the terrain data processor SRTM3 (global coverage ~90 m) and land coverage data from the

Global Land Cover Characterization (GLCC) with global coverage ~1 km are also used. All

these data were obtained from the National Aeronautics and Space Administration (NASA)

website. The domain grid used for modeling goes out 40 km in every direction from a

centered reference point at (7°44’7” S, 113°35’7” E). This grid covers parts of Probolinggo,

Lumajang, Bondowoso, and Situbondo. Figure 2 shows the area modelled.

Figure 2 Modeling Domain



Figure 3 Wind Rose from Juanda Monitoring Station May 2015

Figure 4 Wind Rose from Ngurah Rai Monitoring Station May 2015


Using CALPUFF


For meteorological data, surface data was collected from the two closest surface stations

at Juanda and Ngurah Rai. The Juanda surface station is located at Surabaya City on 7°21’55”

S, 112°45’54” E, and Ngurah Rai is located at Badung Regency on 8°43’58,99” S,

115°9’56,72” E. Upper air data was obtained only from Juanda. The Paiton power plant,

itself, is located mid-way between the two surface stations. Surface data consists of wind

speed, wind direction, temperature, humidity, and air pressure and was obtained from a fixed

point, usually at 10 m of height. This data can be obtained online from the National Oceanic

and Atmospheric Administration (NOAA) data center website. The upper air data was

obtained from the Juanda Station, only.

Figures 1 and 2 show wind rose data from the Juanda and Ngurah Rai monitoring stations,

respectively, during May 2015. The figures show that the wind blew mostly from east to west

at both monitoring stations. At the Juanda station, 18% of the wind blowing to the east had a

speed of 3.6 – 5.7 m/s, and 20% had a speed of 5.7 – 8.8 m/s. At the Ngurah Rai station, 10%

of the wind blowing to the east had a speed of 2.1 – 3.6 m/s, and 15% of had a speed of 3.6 –

5.7 m/s. These two data sets will be used to generate a diagnostic meteorological field around

the modeled area using the CALMET pre-processor.

2. RESULTS AND DISCUSSION

In this dispersion analysis, three scenarios are created for each pollutant via emission load

variation. These scenarios are created to discover how varied emission loads affect the

pollutant’s dispersion. Note the all of these scenarios use the same stack characteristics.

Scenario 1 used a maximum value for emission load, Scenario 2 used an average value, and

Scenario 3 used a minimum value. The maximum emission load of each pollutant did not

exceed the emission threshold based on Regulation of Ministry of Environment No. 21 Year

2008. Tables 1, 2, and 3 show emission load for each scenario for each emission source.

Table 1 Maximum Emission Load May 2015

Emission Source CO (g/s) NO2 (g/s) PM10 (g/s) SO2 (g/s)

Unit 1 191.25 6.19 4.09 380.39

Unit 2 95.11 47.75 6.92 381.97

Unit 3 313.53 10.15 6.70 623.59

Unit 5&6 22.77 332.83 3.39 139.83

Unit 7&8 377.83 12.24 8.07 751.47

Unit 9 293.58 9.51 6.27 583.91

Table 2 Average Emission Load May 2015


Unit 1 57.87 5.63 3.67 363.59

Unit 2 8.97 36.60 5.89 86.98

Unit 3 94.87 9.22 6.02 596.06

Unit 5&6 6.01 238.49 2.16 28.84

Unit 7&8 114.32 11.12 7.25 718.28

Unit 9 88.83 8.64 5.63 558.12

Table 3 Minimum Emission Load May 2015


Unit 1 3.25 4.96 3.24 313.42

Unit 2 0.32 16.67 5.75 0.84

Unit 3 5.33 8.13 5.31 513.81

Unit 5&6 0.82 212.84 0.93 17.89

Unit 7&8 6.43 9.80 6.40 619.17




Unit 9 4.99 7.61 4.97 481.11

Figure 5 Emission Dispersion

Pattern of CO from Scenario 1

Figure 6 Emission Dispersion

Pattern of NO2 from Scenario 1

Figure 7 Emission Dispersion Pattern of

PM10 from Scenario 1


SO2 from Scenario 1

Figure 9 Emission Dispersion Pattern of CO

from Scenario 2


NO2 from Scenario 2




SO2 from Scenario 2


Using CALPUFF


Scenario 1 covers dispersion when the power plant emits an maximum emission load. The

maximum concentration for CO is 79.7 µg/m3, for NO2 , it is 38.9 µg/m

3, PM10 has 2.21

µg/m3, and SO2 is at 183 µg/m

3. With that maximum emission load, the maximum

concentration in the modeled area still did not exceed the threshold for ambient air based on

Government Regulation No. 41 Year 1999 about Control of Air Pollution. This means that the

dispersion will not harm humans, animals, or plants, nor will it disrupt the aesthetics of the

environment.

Dispersion when the power plant emits an average emission load is covered in Scenario 2.

In this scenario, the maximum concentrations are lower, i.e., they are 23.3 µg/m3

for CO,

28.2 µg/m3

for NO2, 1.97 µg/m3 for PM10, and 125 µg/m

3 for SO2. When average conditions

occur, the concentration of pollutants in the ambient air around power plant is safe because

no thresholds have been exceeded for any of the parameters modeled.

Scenario 3 shows how emissions affect the environment around the Paiton power plant

when it emits a minimum amount. With this minimum emission, there is no place with

maximum concentration that exceeds the threshold for ambient air quality. The maximum

concentration for CO is 1.35 µg/m3, NO2 24.4 µg/m

3, PM10 1.68 µg/m

3, and SO2 122 µg/m

3.

This mean that the effect of the minimum emission dispersion is safe and not harmful for

humans, animals, or plants.

The dispersion patterns are similar, but have differences as well. The differences occur

due to emission load variation. This emission load variation creates different pollutant

concentrations in each scenario, but with a similar pattern. The pollutants disperse mostly to

the west side of the complex. Wind conditions create an important rule for air dispersion since

the pollutants follow the wind direction and are affected by the wind speed. Mostly, the wind


CO from Scenario 3


NO2 from Scenario 3




SO2 from Scenario 3



blows from east to west, as seen previously. Pollutant concentrations slowly increase with

distance until the peak is reached, and then the concentrations slowly decrease. Simulation

results show that maximum concentrations for all scenarios occur at the same point, (780693

m, 9146597 m) or 7°42’47”S, 113°32’40”E, with a ground elevation of 14 m above sea level.

This is located 4.3 km away from the complex on the south-west side. It is a coastal area near

the sea, and there are no residential areas on that site.

Next, a validation model was created to examine how the results of CALPUFF compare to

the actual conditions in the ambient air. Data for actual conditions from three monitoring

points located on the west side of the power plant are obtained. The three monitoring points

are Ash Disposal (782655 m, 9146114 m), Meteorology station (782229 m, 9146366 m), and

Guest House (783840 m, 9146573 m). The validation models are created by plugging actual

emission rates into the models that were developed, then the results of that model are

compared to ambient air monitoring results at those three sites. Tables 4, 5, 6, and 7 show the

comparison of real air quality and air quality modeled by CALPUFF.

Table 4 CO Concentration Comparison of Monitoring and Model Result

Monitoring

Point

Monitoring

Date

Monitoring

Time

CO Concentration

from Monitoring

(µg/m3)

CO Concentration

from CALPUFF

(µg/m3)

Ash Disposal 28 Mei 2015 11.05-12.05 345

0.6 - 1 Ash Disposal 28 Mei 2015 16.00-17.00 345

Ash Disposal 28 Mei 2015 17.45-18.45 345

Meteorology

station 27 Mei 2015 21.05-22.05

805

0.1 - 0.4 Meteorology

station 28 Mei 2015 06.30-07.30

345

Meteorology

station 28 Mei 2015 13.40-14.40

1150

Guest House 28 Mei 2015 10.45-11.45 1150

1 - 4 Guest House 28 Mei 2015 12.30-13.30 1150

Guest House 28 Mei 2015 20.45-21.45 805

Table 5 NO2 Concentration Comparison of Monitoring and Model Result

Monitoring

Point

Monitoring

Date

Monitoring

Time

NO2 Concentration

from Monitoring

(µg/m3)

NO2 Concentration

from CALPUFF

(µg/m3)

Ash Disposal 28 Mei 2015 11.05-12.05 4

1-2 Ash Disposal 28 Mei 2015 16.00-17.00 4.7

Ash Disposal 28 Mei 2015 17.45-18.45 3.7

Meteorology

station 27 Mei 2015 21.05-22.05 2.3

0.1-0.2 Meteorology

station 28 Mei 2015 06.30-07.30 30.1

Meteorology

station 28 Mei 2015 13.40-14.40 4.5

Guest House 28 Mei 2015 10.45-11.45 3.6

2-4 Guest House 28 Mei 2015 12.30-13.30 4.9

Guest House 28 Mei 2015 20.45-21.45 12.8


Using CALPUFF


Table 6 SO2 Concentration Comparison of Monitoring and Model Result

Monitoring

Point

Monitoring

Date

Monitoring

Time

SO2 Concentration

from Monitoring

(µg/m3)

SO2 Concentration

from CALPUFF

(µg/m3)

Ash Disposal 28 Mei 2015 11.05-12.05 <0.5

5-6 Ash Disposal 28 Mei 2015 16.00-17.00 6.8

Ash Disposal 28 Mei 2015 17.45-18.45 <0.5

Meteorology

station 27 Mei 2015 21.05-22.05 <0.5

1-2 Meteorology

station 28 Mei 2015 06.30-07.30 <0.5

Meteorology

station 28 Mei 2015 13.40-14.40 <0.5

Guest House 28 Mei 2015 10.45-11.45 1

10-20 Guest House 28 Mei 2015 12.30-13.30 <0.5

Guest House 28 Mei 2015 20.45-21.45 <0.5

Table 7 PM10 Concentration Comparison of Monitoring and Model Result

Monitoring

Point

Monitoring

Date

Monitoring

Time

PM10 Concentration

from Monitoring

(µg/m3)

PM10 Concentration

from CALPUFF

(µg/m3)

Ash Disposal 28 Mei 2015 11.05-12.05 0.0788

0.1-0.4 Ash Disposal 28 Mei 2015 16.00-17.00 0.1162

Ash Disposal 28 Mei 2015 17.45-18.45 0.0136

Meteorology

station 27 Mei 2015 21.05-22.05 0.1519

0.01-0.04 Meteorology

station 28 Mei 2015 06.30-07.30 0.1725

Meteorology

station 28 Mei 2015 13.40-14.40 0.1201

Guest House 28 Mei 2015 10.45-11.45 0.0446

0.1-0.4 Guest House 28 Mei 2015 12.30-13.30 0.0413

Guest House 28 Mei 2015 20.45-21.45 0.0972

Table 8 Maximum Concentration of Each Scenario

Gas Maximum Concentration (µg/m

3) Threshold of

ambient (µg/m3) Scenario 1 Scenario 2 Scenario 3

CO 79.7 23.3 1.35 10,000

NO2 38.9 28.2 24.4 150

PM10 2.21 1.97 1.68 150

SO2 183 125 122 365

3. CONCLUSIONS

Air emission from the power plant stacks dispersed to the west side, following the dominant

wind direction. The patterns of dispersion for all scenarios are similar because there are no

differences in the meteorological data applied to those scenarios. The emission concentrations

in ambient air slowly increase until reaching a peak concentration, then decrease as they travel

further.

Scenario 1 is the worst scenario of all, since the concentrations are highest. The

maximum concentration for each scenario is shown in Table 8. This research shows that even

with the maximum emission load, the maximum concentrations of all pollutants dispersed still

do not exceed thresholds for ambient air based on PP No. 41 Year 1999. This means that the



activity at the power plant complex will not harm people, plants, or animals, nor will it disrupt

the aesthetic around its location.

However, air pollution monitoring and control still must be done because the emissions on

that site stem not only from power plant boiler activity, but also from other sources, such as

transportation and other residential activities. Emission load also can increase during plant

expansion or when another power generator is added.

REFERENCES

[1] Finahari, I. N. dkk. Gas CO2 dan Radioaktif dari PLTU Batubara. Jurnal Pengembangan

Energi Nuklir, 9(1), 2007.

[2] PT PJB UP Paiton. Informasi Umum Power Plant. PT PJB UP Paiton, 2018.

http://www.ptpjb.com/index.php/id/paiton/up-paiton (accessed on January 15th, 2018)

[3] Scire, J.S., Strimaitis. D.G., and Yamartino, R.J. A User’s Guide for The CALPUFF

Dispersion Model. Concord : Earth Tech, Inc., 2000.

[4] US EPA. EMC: Continuous Emission Monitoring Systems Information and Guidelines,

2016. https://www.epa.gov/emc/emc-continuous-emission-monitoring-systems (accessed

on Dec 15th 2016)

[5] Vesilind, P. A. dkk. Environmental Engineering 3th Edition. Boston : B utterworth-

Heinemann, 1994.

[6] World Coal Institute. Sumber Daya Batubara. Tinjauan Lengkap Mengenai Batu

Bara,2009.

MULTIPLE POINT SOURCE DISPERSION ANALYSIS OF CO, …from the Pembangkit Listrik Tenaga Uap – Steam...

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