cement plant

Journal of Environmental Sciences 2011, 23(6) 931–940

Application of the AERMOD modeling system for environmental impactassessment of NO2 emissions from a cement complex

Kanyanee Seangkiatiyuth1, Vanisa Surapipith2, Kraichat Tantrakarnapa3,Anchaleeporn W. Lothongkum1,∗

1. Department of Chemical Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand.E-mail: [email protected]

2. Air Quality and Noise Management Bureau, Pollution Control Department, Ministry of Natural Resources and Environment, Bangkok 10400,Thailand. E-mail: [email protected]

3. Department of Environmental Health Sciences, Faculty of Public Health, Mahidol University, Bangkok 10400, Thailand

Received 02 July 2010; revised 19 January 2011; accepted 18 February 2011

AbstractWe applied the model of American Meteorological Society-Environmental Protection Agency Regulatory Model (AERMOD) as a

tool for the analysis of nitrogen dioxide (NO2) emissions from a cement complex as a part of the environmental impact assessment. The

dispersion of NO2 from four cement plants within the selected cement complex were investigated both by measurement and AERMOD

simulation in dry and wet seasons. Simulated values of NO2 emissions were compared with those obtained during a 7-day continuous

measurement campaign at 12 receptors. It was predicted that NO2 concentration peaks were found more within 1 to 5 km, where the

measurement and simulation were in good agreement, than at the receptors 5 km further away from the reference point. The Quantile-

Quantile plots of NO2 concentrations in dry season were mostly fitted to the middle line compared to those in wet season. This can be

attributed to high NO2 wet deposition. The results show that for both the measurement and the simulation using the AERMOD, NO2

concentrations do not exceed the NO2 concentration limit set by the National Ambient Air Quality Standards (NAAQS) of Thailand.

This indicates that NO2 emissions from the cement complex have no significant impact on nearby communities. It can be concluded

that the AERMOD can provide useful information to identify high pollution impact areas for the EIA guidelines.

Key words: AERMOD; environmental impact assessment; Gaussian model; air pollutants; NO2; cement plant

DOI: 10.1016/S1001-0742(10)60499-8

Citation: Seangkiatiyuth K, Surapipith V, Tantrakarnapa K, Lothongkum A W, 2011. Application of the AERMOD modeling system

for environmental impact assessment of NO2 emissions from a cement complex. Journal of Environmental Sciences, 23(6): 931–

940

Introduction

Chronic exposure to air pollutants is a worldwide prob-

lem. The World Health Organization (WHO) announced

that every year approximately 2.7 millions deaths can be

attributed through air pollution. Over the past decades,

long-term exposure of humans to nonlethal air pollutants

and the effects of air pollutants on global and regional

atmospheric cycles have been studied intensively. Espe-

cially, ozone (O3), total suspended particulates (TSP),

particulate matter (PM), nitrogen dioxide, sulfur dioxide,

carbon monoxide, lead and other toxins have been the

special focus of investigations due to their health impact

(Kirk-Othmer, 2007).

Thailand is a main grey cement manufacturer and ex-

porter. The annual production of 46 Mega-tons of clinker

and 56 Mega-tons of cement is met by 18 cement plants

and 31 kilns that are operated by nine manufacturers (TC-

* Corresponding author. E-mail: [email protected]

MA, 2008). In the vicinity of the selected cement complex

in this study, the emissions of nitrogen oxides (NOx)

can be attributed to two major sources, i.e., the cement

burning process at high temperatures about 1500°C and

emissions from vehicle transportation. These emissions are

typically composed of a mixture of nitric oxide (NO) and

nitrogen dioxide (NO2) (Wark et al., 1998; Alsop, 2005).

Generally, the amount of NOx in the flue gas from cement

kilns increases with the combustion temperature, the main

component being NO. At ambient temperature and excess

oxygen, NO is subsequently oxidized to NO2 which is a

precursor to nitric acid. NO2 is toxic by inhalation and

causes irritation to human’s eye, nose and throat (Wark et

al., 1998). It is a meso-scale pollutant with a lifetime in the

range of 1–3 days (Perkins, 1974). NOx disperses widely

and can react with O3 and volatile organic compounds

(VOCs) to secondary PM. It is an atmospheric pollutant of

primary concern in industrial nations that has been targeted

for reduction and control in the United States by long-

932 Journal of Environmental Sciences 2011, 23(6) 931–940 / Kanyanee Seangkiatiyuth et al. Vol. 23

standing regulation setting since 1971 for both a primary

standard (to protect health) and a secondary standard (to

protect the public welfare) at 0.053 parts per million (53

ppb), averaged annually. Most recently US EPA (2010)

imposes air quality threshold by that 1 hour average NO2

values should not exceed 100 ppb.

To meet these legislative goals and minimize nega-

tive health impacts, it is important to understand how

an atmospheric pollutant is dispersed in the atmosphere.

When a gas is released from a source it is carried away

by the wind and dispersed in the air. Accordingly, its

maximum concentration can be found at the point of

release. Due to turbulent mixing of the releasing gas with

air the downwind concentrations are comparatively low.

Gas dispersion is affected by a number of parameters, for

example, wind speed and direction, atmospheric stability,

ground conditions (buildings, mountains, trees, water),

and height of the release above ground level (Crowl and

Louvar, 2002).

In order to limit the environmental impact in the vicinity

of cement plants, the Ministry of Natural Resources and

Environment of Thailand requires the cement manufactur-

ers to submit an environmental impact assessment (EIA)

report twice a year stating both negative and positive

activities regarding the ongoing and future projects as well

as significant activities. In addition, the EIA report requires

statements about the emissions of air pollutants such as

TSP, particulate matter with diameter less than 10 μm

(PM10), NO2, SO2 and CH4. Other pollutants may be re-

ported depending on particular conditions of each cement

manufacturer. Additional reports may be required if the

cement manufacturers modify or expand their processes or

change their types of fuel. The EIA, therefore, has proven

to be a very useful management to limit the environmental

impacts, and to respond to related concerns of communities

and agencies.

One of the problems cement manufacturers face when

complying an EIA report is that frequently target areas are

not accessible for monitoring equipment, in these cases

computer simulations are required to estimate concentra-

tions in those areas. The simulations have to be compared

with measurements in the accessible areas. Computer

based dispersion models can simulate these effects very

well. Dispersion software programs based on Gaussian

plume equation have been widely applied to estimate the

dispersions of various pollutants. The American Meteorol-

ogy Society-Environmental Protection Agency Regulatory

Model (AERMOD), a software package based on Gaussian

plume equation, is recommended for air quality simula-

tions by the US EPA (2005). It has been accepted by

the Office of Natural Resources and Environmental Policy

and Planning (ONEP), the Ministry of Natural Resources

and Environment of Thailand to be used for EIA evalua-

tion. As the AERMOD can incorporate various complex

algorithms and concepts, it has been applied to evaluate

the dispersion of a number of pollutants, including PM10,

hydrogen cyanide (HCN), SO2, sulfur hexafluoride (SF6),

VOCs (Venkatram et al., 2001, 2004, 2009; Bhardwaj et

al., 2005; Orloff et al., 2006; Zou et al., 2009, 2010).

Besides these pollutants, the dispersion of heavy metals,

such as hexavalent chromium and total gaseous mercury

(TGM) can also be simulated with the AERMOD (Sax

and Isakov, 2003; Mazur et al., 2009). Where upper me-

teorological data are not available, the AERMOD can be

coupled with meteorological models such as the Weather

Research and Forecasting (WRF), Regional Atmospheric

Modeling System (RAMS), the Fifth-Generation National

Center Atmospheric Research (NCAR)/Pennsylvania State

University Mesoscale Model (MM5), and Eta Models

(Caputo et al., 2003; Kesarkar, 2007; Isakov et al., 2007).

Moreover, the AERMOD can be used with regional mod-

els, e.g., Community Multi-scale Air Quality (CMAQ) and

HYbrid Single-Particle Lagrangian Integrated Trajectory

(HYSPLIT) for complex case applications (Stein et al.,

2007). Because the AERMOD has no reaction module it

cannot simulate the conversion of NO to NO2 in the air

directly.

While the AERMOD is extensively used in the US

and Europe, in Thailand the reliability of the simulation

results, particularly the trajectory of NO2 emission by

the AERMOD, is still in debate. The reason is that the

AERMOD has been developed and optimized based on the

climatic and geological conditions in the US and Europe.

This current work applied the AERMOD to analyze NO2

emissions from a selected cement complex consisting of

four plants with a combined production representing 43%

of total capacity in the country.

1 Methodology

1.1 Study areas

As shown in Fig. 1, the selected cement complex is

located about 108 km northeast of Bangkok in lime stone

mountain area next to the highway of heavy transportation.

It comprises of four major cement plants with the total of

14 cement stacks. The four plants are located within 25 km

radius. One cement plant in the center of the cluster was

designated as the reference point for the receptor distances

and identified as cement plant 1. The area of surrounding

communities is 801.1 km2 with a population density of

79 people/km2 (DOPA, 2010). Most of the population is

farmers. The east side of the study areas is high plains

and plateaus near a National Park, which consists of

complex mountains of high peaks about 800 to 1300 m

above sea level. The regional climate is dominated by

northeast and southwest monsoons with different rainfall

and temperature characteristics. The northeast monsoon

(dry season) from the central of the Asia continent brings

relatively cool and dry air around the middle of November

to April. The southwest monsoon (wet season) from the

Indian Ocean is characterized by periods of intense rain

from May to October with some thunderstorm convection

activity. The average rain fall in the study areas is about

1600 mm with 80% during the wet season. The average

temperatures in the dry season range from 13–36°C and in

the wet season from 19–37°C.

No. 6 Application of the AERMOD modeling system for environmental impact assessment of NO2 emissions from a cement complex 933

Thailand

Saraburi province Muak Lek

Wang

Muang

Kaeng

Khoi Don Phut

Chaloem

Phra Kiat

Phra Phutthabat

Nong Khae

Wihan

Daeng

Mueang

Saraburi

Nong

Saeng

Sao

Hai

Ban Mo

Nong Don

710 720 730 7401610

1615

1620

1625

1630 Cement

plant 4

Cement

Plant 3

Cement

Cement

UTM X (km)

UT

M Y

(km

) 11 12

10

3

5

7

8

9

6 2

4

1

Main road

Laos

Malaysia

Cambodia

Andam

an S

ea

Indian Ocean

Gulf of Thailand

Bangkok

Saraburi

●

B station

● ●

●

P station

N station

K station

N

Myan

mar

Nakhon Ratchasima Province

Receptor Distance from the

reference point (km)

1 1.47

2 2.35

3 2.47

4 3.44

5 3.58

6 3.64

7 3.90

8 4.87

9 6.07

10 6.65

11 7.38

12 9.12

Cement plants

Receptors

● Meteorological station

Plant 2

Plant 1

Fig. 1 Study area location and 12 receptors of the cement complex.

1.2 NO2 emissions and ambient air measurement

At ambient temperature, an equilibrium calculation of

NO2 concentration was greater than that of NO, and thus

NOx emission is considered as NO2 emission. According

to the regulation, the cement complex source owners are

required to report their emissions annually. As described

in the EIA reports, the NOx emissions (mainly NO2)

were detected at 14 stacks of the cement plants by the

US EPA Method 7 (CFR, 1993). The stack sampling was

carried out by environmental specialists. The present study

considers the case of maximum NO2 emission for the

highest production scenario of 24-hr operation. The total

NO2 emission rate sampling from 14 stacks was 1238

g/sec.

The NO2 concentrations in ambient air at 12 receptors

were measured continuously for 7 days according to the

typical routine EIA by the Chemiluminescence Method in

March, 2007 (dry season) and in October and November,

2007 (wet season). The 12 receptor sites were located

in the residential areas, 1 to 10 km from the reference

point, as a part of the manufacturers’ mandate to monitor

potential health effects (Fig. 1). Some of the receptors

were located between the cement plants. No receptor

was located near the cement plant 4 because it had low

production with only 1 stack and was therefore considered

negligible. In this respect, a single chemiluminescence

instrument was rotated among the receptor sites to record

1-hr averaged data, occupying each receptor site for a

one-week interval.


Table 1 Locations and details of the meteorological stations

Meteorological Collected Meteorological Distance from the Direction to the

stations data by parameters reference point (km) reference point

K PCD Surface air data: G, P1, RH, T 21 SW

10 m: WD, WS1 (hourly basis data)

N PCD Surface air data: G, P1, RH, T 24 WNW

10 m: WD, WS1 (hourly basis data)

P TMD Surface air data: P2, RH, T 25 E

10 m: WD, WS2 CH, CV (3-hr basis data)

B TMD Surface air data: P2, RH, T 120 SSW

10 m: WD, WS2, CH, CV (3-hr basis data)

upper air data: DWPT, H, P2, RH, T, WD, WS2

G (W/m2): global radiation; P1 (mmHg), P2 (hPa): pressure; RH (%): relative humidity; T (°C): dry bulb temperature; WD: wind direction (degree from

the North); WS1 (m/sec), WS2 (knot): wind speed; CH (m): ceiling height; CV (tenths): cloud cover; DWPT (°C): dew point temperature; H (m): height

above sea level;.

1.3 Meteorological measurement

Table 1 shows the locations and details of the meteoro-

logical stations. Stations K and N were the meteorological

and air quality monitoring stations of the Pollution Control

Department (PCD), and had no cloud cover or ceiling

height information available. Station P was the meteo-

rological station of the Thai Meteorological Department

(TMD) located near a National Park. The surface wind

speeds and directions from K, N and P stations at 10 m

above the ground were used in the meteorological analysis

to evaluate the primary impact areas due to NO2 emissions

from the cement complex. The wind speeds and directions

from stations K and N were 1-hr averaged data, and 3-hr

averaged data for P.

To run the AERMOD modeling system, the surface

meteorological data were received from station K but cloud

cover and ceiling height were obtained from station P.

The upper meteorological data from radiosonde ascents,

necessary for accurate simulation of the wind fields, were

received from station B of the TMD. Although station B

was located in Bangkok over 100 km southwest from the

cement complex, it was the nearest radiosonde balloon

launching site.

1.4 AERMOD modeling

The AERMOD was developed from the Industrial

Sources Complex Short Term Model (ISCST3) by incor-

poration more complex algorithms and concepts, i.e., plan-

etary boundary layer (PBL) theory and advanced methods

for complex terrains. As with ISCST3, the AERMOD is

considered accurate for dispersion modeling at distances

not exceeding 50 km from the emission source (US EPA,

2005). The model is composed of three parts: AERMOD

Meteorological Preprocessor (AERMET), AERMOD Ter-

rain Preprocessor (AERMAP) and AERMOD Gaussian

Plume Model with the PBL modules. The sequences of

model operations are shown in Fig. 2. The AERMET

processes the hourly surface and upper meteorological

data. The surface parameter coefficients for the AERMET

module (specifying land-used types and surface roughness

for boundary layer dynamics) were set to ‘summer’ con-

ditions for both the wet and dry season model simulations

of the climate in Thailand; i.e., the closest analogue for

the seasonal conditions for the U.S. and Canada where the

Data input Source data Geological

dataMeteorological

data

Meteorological

modeling

Dispersion

modeling

AERMAP

AERMET

Preprocessing

AERMOD dispersion model

Postprocessing WRPLOT

viewPost view

Fig. 2 Data flow in the AERMOD modeling.

model was developed. The second module, AERMAP, is

used for processing the terrain data in conjunction with a

layout of receptors and emission sources to be used for the

AERMOD control files.

The AERMOD modeling system used in this work was

run with a commercial interface, ISC-AERMOD View

(Version 4.6.2) (Lakes Environmental Software, Waterloo,

Ontario, Canada). The simulation was carried out under a

complex terrain with grid spacing of 0.5 km for the domain

UTM X (701 to 740.5 km) and Y (1602 to 1641.5 km).

Although atmospheric reactions for removing NOx were

in the model, wet and dry deposition loss parameteriza-

tions were included as a loss mechanism. The AERMOD

guidelines (US EPA, 2009, 2010) recommend to evaluate

NOx concentrations as follows: Tier 1 estimates ambient

NOx concentration by assuming full conversion of NO to

NO2 based on the application of an appropriate refined

modeling technique under Section 4.2.2 of Appendix W;

Tier 2 multiplies Tier 1 result by empirically-derived NO2/

NOx ratio, with 0.75 as the annual national default ratio;

Tier 3 is the detailed screening methods considering a case-

by-case basis with the Ozone Limiting Method (OLM) for

point sources.

Both surface and upper meteorological data inputs for


the AERMOD were obtained from station measurements.

In this case, the method based on Tier 1 was used be-

cause NO was oxidized to NO2 at ambient temperature.

Therefore, instead of using NOx as the input data in

the AERMOD, the NO2 emissions were input direct-

ly. Finally, 1-hr averaged NO2 concentrations from the

measurement and simulation were compared. The NO2

concentrations at 12 receptors were simulated by discrete

grid mode in the AERMOD. The coordinates of NO2

emission sources, meteorological stations and receptors

were read from the orthographical image (scale 1:25,000),

which was an aerial photography purchased from the Land

Development Department, Ministry of Agriculture and

Cooperatives. The terrain data for the AERMAP were

extracted from the GTOPO30, which was available online

at http://www.src.com.

It is known that a substantial constraint of using the

Gaussian equations accurately to simulate gas dispersion

is due to a low wind speed or calm wind less than 0.5

m/sec (Schnelle and Dey, 2000), which is not significant to

mathematical evaluation by the AERMOD. To overcome

this problem, the performance of the AERMOD in pre-

dicting the trajectory of NO2 emissions is evaluated by the

Quantile-Quantile (Q-Q) plots. The Q-Q plots are created

by first ranking the hourly measured and simulated concen-

trations, and then pairing them by rank. The ranked vectors

are then plotted as a conventional scatter diagram. A line

of unit slope shows where the measured and modeled

concentrations are equal. The lines of half and double slope

indicate under and over prediction respectively. The middle

line of the Q-Q plot shows equal concentration of the

measurement and simulation; the other two lines (above

and below the middle line) mark double levels and half

levels of the measurement and simulation, representing

over and under prediction, respectively (Wilks, 2006; Zou

et al., 2010). Perfect agreement between the measured and

modeled concentrations is indicated by all data plotting

on the unit slope. Possible model transport problems are

indicated the lowest and highest model concentrations

falling off the line. Offsets of the median values indicate

a more serious problem where atmospheric reactions or

deposition mechanisms may not be included in the model.

2 Results and discussion

2.1 Meteorological analysis

The wind fields recorded at the meteorological stations

showed the patterns that reflected large scale monsoonal

circulations and daily mountain-valley breezes, modified

by local topographic conditions. Figure 3 shows windrose

plots constructed from data recorded at K, N and P stations

shown separately for the dry and wet seasons. Generally,

the daily climate is influenced by seasonal winds (both

northeast and southwest monsoons) and mountain-valley

breezes. However, because the winds recorded at meteoro-

logical stations were surface winds influenced by boundary

layer effects, their windrose patterns did not correspond

closely to the direction of the seasonal monsoons. For

example in Fig. 3, during the northeast monsoon (dry

season) the station measurements were dominated by the

easterly winds, not the expected northeast winds, indicat-

ing consistency with an Ekman boundary circulation. In

addition, at K station, which was nearest to the residential

areas, southerly and westerly winds were observed, due

to flow modification by local buildings: an urban canyon

effect. The maximum wind speeds at K, N and P stations

were 5.5, 5.8 and 12.3 m/sec, while intervals when there

was no recorded wind (i.e., below the instrument detection

limit) were 7.1%, 4.4% and 42.7% of the complete time

series, respectively. For the wet season, the westerly and

south-westerly winds dominated N and P stations, whereas

at K station was dominated by the south-easterly wind,

again due to distortions of the wind field by nearby

buildings. For the wet season, the maximum wind speeds

at K, N, and P stations were 6.6, 5.8 and 15.4 m/sec,

respectively, while the percentage of time that the winds

were below the instrument detection limit were 6.9%,

17.7% and 47.8%. Station P, located near low mountains,

showed a pronounced mountain-breeze effect with a larger

fraction of time when the winds were below the instrument

detection limit.

The wind analysis therefore indicates that the flow over

the study region is complicated, subject to local distortion

effects, and not optimally structured for the AERMOD

to simulate a uniform plume. The combination of data

from all meteorological stations was therefore used for

the AERMOD program. The performance of the model

to describe the impact of NO2 emissions from the cement

plants was considered at the best available ensemble data.

2.2 Evaluation of NO2 concentrations by measurementand AERMOD simulation

From Fig. 1, it can be seen that of the 12 receptors in

this investigation, four are located near the road: 1, 2, 5

and 6. Thus, the time-series plots of NO2 concentrations

from these receptors, shown in Fig. 4, were particularly

influenced by road traffic. The relevant NO2 concentration

peaks showed recurring cycles and appeared periodically

in accordance with transportation activities. For example,

high NO2 peaks of a 7-day continuous measurement were

found at night time (around 19:00–3:00) from flow of big

trucks.

Typically, NO2 deposition reactions occur in both wet

and dry environments. Bai et al. (2006) found that NO2

deposition rate was 1.6 times higher in a wet environment

compared with a dry environment, suggesting that signifi-

cant concentrations of NO2 would be detected especially

in the dry season. The 1-hr averaged concentrations of

NO2 monitored at 12 receptors were 2–135 μg/m3 in the

dry season, and were 0–105 μg/m3 in the wet season.

The maximum NO2 concentration in the dry season was

slightly higher than the wet season. The NO2 time-series

plots in Fig. 5 do not show a pronounced effect of season

on the atmospheric mixing ratios, and this may be due to

the small seasonal variation of temperatures between the

wet and dry seasons.

The simulation results by the AERMOD modeling sys-


4.0-6.0

2.0-3.0

Wind speed (m/sec)

≥ 6.0

3.0-4.0

1.0-2.0

0.5-1.0

(a) Dry season

N station

N station

Calm 4.41%

Calm 17.70%

(b) Wet season

Kao Noi

(a) Dry season

Calm 6.86%

K station

(b) Wet season

Calm 7.14%

K station

Dry season

Calm 42.68%

Wet season

P station P station

Calm 47.83%

N

Cement plant 1

Cement plant 2

Cement plant 4

Cement plant 3

N station

P station

K station

UTM Y (km)

UTM X (km)

Height above

sea level (m) 12

1011

3 5

7

8

9

1 4 6 2

Cement plants

Receptors

● Meteorological station

1640

1635

1630

1625

1620

1615

1610

1605

1600

1595

700

710

720

730

740

750

1000

500

2%

4%

6%

8%

10%

4%

8%

12%

16%

20%

10%

5%

15%

20%

25%

2%

4%

6%

8%

10%

3%

6%

9%

12%

15%

3%

6%

9%

12%

15%

North

East

South

West

North

East

South

West

North

East

South

West

North

East

South

West

North

East

South

West

North

East

South

West

Fig. 3 Windroses of the three meteorological stations in the study domain.

tem were next compared with the monitoring results by

the receptors. At the receptors 2 and 6 about 1 to 5 km

from the reference point, the simulation results for the

dry season showed peaks of higher NO2 concentration

compared with the receptor 11 located 7.5 km away from

the reference point. The monitoring and simulation results

showed almost the same trend. In case of wet season,

NO2 concentration peaks were found at the receptor 6

but a few at the receptors 2 and 11. According to safety

thresholds for 1-hr average, NO2 concentrations issued

by the NAAQS (320 μg/m3; PCD, 1995) and by the

Guidelines of WHO (200 μg/m3; WHO, 2005), most of

NO2 emissions from the cement complex, as indicated both

by measurement and the AERMOD simulation, showed

no significant impact on nearby communities. However,

it is worth noting that the NO2 emissions and monitoring

depend on several factors, i.e., wind speed and direction,

humidity, ambient temperature, ceiling height and moni-

toring location, including wet and dry depositions of NO2

and the reactions of NO2 with O3 and VOCs to secondary

PM. Not all of these factors were effectively simulated

by the model, and therefore the model simulation of the

measurements may not be totally accurate. The simulation

results at receptors greater than 5 km away from the

reference point (e.g., the receptor 11 in the target area)

were especially problematic, and it would be uncertain if

the US EPA distance threshold of 50 km was applicable

for the complex terrain and wind fields of this tropical

setting. In short, the current application of the AERMOD

is limited due to the following reasons: (1) there is no

module for NO2 deposition reactions; (2) model output is

considered on a 1-hr average time scale instead of longer


150

120

90

60

30

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150

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2 c

once

ntr

atio

n (

μg/m

3)

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2 c

once

ntr

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μg/m

3)

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2 c

once

ntr

atio

n (

μg/m

3)

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2 c

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n (

μg/m

3)

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Receptor 1

Receptor 5 Receptor 6

Receptor 2

Local time at 10-16 (Sat-Fri) Mar 2007 Local time at 09-15 (Fri-Thurs) Mar 2007

Local time at 02-08 (Fri-Thurs) Mar 2007 Local time at 02-08 (Fri-Thurs) Mar 2007

Fig. 4 Examples of NO2 concentrations measured in dry season. Time labeled on X-axis is local time.

time; (30 scales of 24-hr or more (the AERMOD is highly

sensitive to different time scales as reported by Bhardwaj,

2005 and Zou et al., 2010); (4) the NO2 dispersion is

calculated based on the input emission of the AERMOD

which does not change with time (US EPA, 2009); and (5)

there are significant intervals of time when the wind speed

is lower than 1 m/sec and too low to effectively simulate

the transport of a pollution plume.

2.3 AERMOD performance evaluation

The Q-Q plots of modeled and measured NO2 mixing

ratios at the locations of the receptor sites are shown in

Fig. 6. The Q-Q plots at the receptors 1 to 9 (i.e., the sites

located within 6 km of the cement plants) in the dry season

were mostly fitted to the middle line compared to those of

the wet season. From Fig. 6, the Q-Q plots highlighted the

higher performance of AERMOD in the NO2 simulation

for the dry season in comparison with the wet season due

to enhanced deposition reactions in the wet environment.

High humidity and low temperature in the atmosphere

in the wet season resulted in low NO2 dispersion due to

uptake by vegetative and ground sources. However, it was

found that most of the receptors were under-predicted. This

reflects the fact that emission from sources other than the

cement stacks plays higher role at those receptors.

2.4 Evaluation of NO2 impact areas by the AERMOD

As seen in Table 2, the maximum concentration of NO2

by the AERMOD in dry season (562 μg/m3) and wet

season (548 μg/m3) were much higher than the legislation

of 320 μg/m3 (0.17 ppm) issued by the Ministry of Natural

Resources and Environment, Thailand (PCD, 1995). These

maximum modeled concentrations occurred at short time

intervals of approximately 144 and 62 hr in the dry and

wet season case studies, and impact areas of 123 and

227 km2 within the model domain, respectively. There

was general agreement between the AERMOD and the

measured values as part of the Q-Q analysis. However,

there were some difficulties in the model simulations of

extreme NO2 concentrations, and the maximum AERMOD

concentrations were not necessarily supported by mea-

sured data. The simulation results agreed with the Q-Q

plots. However, because the maximum concentration of

NO2 monitoring at the receptors was 1-hr average in 7-day

continuous measurement, and because of the constraints

of receptor locations due to accessibility, therefore, it was

possible that the maximum NO2 concentration was not

likely detected during the measurement period.

3 Conclusions

For the environmental impact assessment, it is high-

ly recommended to simultaneously confirm the results

between the measurement and simulation. In this case

study, a 7-day continuous measurement of NO2 emissions

showed no significant impact on the environment but it was

clearly observed by AERMOD simulation. It is most likely

attributed to the limitation of a short-time measurement.

The simulation results can help the policy makers to

identify the areas of high pollution exposure risk for the

EIA guidelines. However, in this work it is found that the

AERMOD program is limited in prediction air pollutants

at the distance 5 km further away from the reference point,


250

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Fig. 5 Examples of NO2 concentrations in dry and wet seasons. Solid line by measurements and dashed line by AERMOD; time labeled on X-axis is

local time.

Table 2 The maximum 1-hr averaged NO2 concentrations in dry and wet seasons predicted by the AERMOD

Scenario NO2 maximum Local station time UTM X, Y (km) Impact area Details of impact areas

concentration (μg/m3) (km2)*

Dry season 562 26th December 2007 at 9.00 a.m. 722.5, 1618.5 123 Agricultural area between

cement plants 1 and 3

Wet season 548 8th October 2007 at 8.00 a.m. 727.5, 1617.0 227 Agricultural area near cement plant 1

and beside the main road

* Impact area is the area with the exposure of NO2 concentration higher than 320 μg/m3.

particularly in wet season. It is noteworthy to be aware that

the AERMOD is a dispersion model without the reaction

module while NO2 deposition reactions certainly occur in

wet and dry environments. For a more precise estimation of

NO2 concentrations, the AERMOD model incorporating

with the reaction module is required. Finally, in order to

estimate more precise impact from NO2 emissions it is

recommended to have permanent monitoring stations as

well as to compare the measurement results with the simu-

lation results from more than 1 model, e.g., the AERMOD

including NO2 reaction module, California PuffDispersion

Model (CALPUFF), etc. Nevertheless, this study shows

that the AERMOD model can be applied to environmental

impact assessment management.


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Dry season Dry season

Wet seasonWet season

R2 = 0.87

R2 = 0.91

R2 = 0.50R

2 = 0.47

Fig. 6 Examples of Q-Q plots of hourly NO2 concentrations in dry and wet seasons: slope = 1 (solid line); slope = 2, 0.5 (dashed lines for the

factor-of-two, model acceptable limit, within the over and under estimation).

Acknowledgments

The authors deeply express sincere thanks to the Royal

Golden Jubilee Ph.D program (IUG50K0021), Thailand

Research Fund (TRF) for the financial support. Appreci-

ations also go to the Air Quality and Noise Management

Bureau, Pollution Control Department, the Ministry of

Natural Resources and Environment; the Thai Meteorolog-

ical Department; Faculty of Engineering, King Mongkut’s

Institute of Technology Ladkrabang, Thailand, Dr. Antho-

ny James Kettle, SUNY-Oswego, New York, USA; and our

cement complex partner.

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cement plant

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