Predicting PM10 concentrations in Al-Kut City using a fixed box model

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International Journal of Civil Engineering and Technology (IJCIET)
Volume 7, Issue 2, March-April 2016, pp. 159–170, Article ID: IJCIET_07_02_013
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APPLYING FIXED BOX MODEL TO
PREDICT THE CONCENTRATIONS OF
(PM10) IN A PART OF AL-KUT CITY, WASIT
PROVINCE (IRAQ)
Ali Abdul Khaliq Kamal
Building and Construction Engineering, Environmental Department,
Master Student at University of Technology Baghdad, Iraq
Prof. Dr. Abdul Razzak T. Ziboon
University of Technology Baghdad, Iraq
Dr. Zainab Bahaa Mohammed
University of Technology Baghdad, Iraq
ABSTRACT
This paper offers the applying of Fixed Box Model to predict the
concentration of particulate matter of 10 micrometers (PM10) one of the air
pollutants that most commonly affects people's health. The input parameters
(area source capacity of PM10, wind speed, mixing height, size of area source)
were estimated based on the area source emission inventory results including:
road source, mobile source, construction source, industry source and
household domestic source in a part of AL-Kut District. This emission
inventory project was carried out during five months period from November
2015 to March 2016.
The aim of this study was to present that fixed box model east-to-use for
evaluating the presence of air pollution over AL-Kut city.
The calculated results from the model were such closer to the results
founded by using portable equipment for the study area.
Cite this Article: Ali Abdul Khaliq Kamal, Prof. Dr. Abdul Razzak T. Ziboon
and Dr. Zainab Bahaa Mohammed, Applying Fixed Box Model To Predict
The Concentrations of (Pm10) In A Part of Al-Kut City, Wasit Province
(Iraq), International Journal of Civil Engineering and Technology, 7(2), 2016,
pp. 159–170.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=7&IType=2

Ali Abdul Khaliq Kamal, Prof. Dr. Abdul Razzak T. Ziboon and Dr. Zainab Bahaa
Mohammed
1. INTRODUCTION
The use of comprehensive air quality models started in the late 1970s [1] and since
then, their development has increased rapidly, together with the fast increase in
computational resources. Today, the scientific community develops more, more
complex, and computationally expensive numerical models, and their results made
available to the environmental authorities dealing with the development of air quality
plans and regulations. Models may serve as very useful tools for indirect estimation of
human exposure. As already stated, it is not possible to perform monitoring in all the
various environments that the population meets. Lifetime exposure cannot be measure
directly, and for this kind of study, modeling is the only option. Furthermore, data
from air quality models can supplement the monitoring data for performing mapping
of pollution concentrations in the various microenvironments in which monitoring is
not performed. For model tools to be useful in exposure studies, they need to be well
tested and they need to describe the dominating physical and chemical processes in
the atmosphere at the given location [2]. Present-day numerical air quality models
seen as important tools for the assessment and forecast of air pollutant concentrations
and depositions, contributing to the development of effective strategies for the control
and reduction of air pollutant emissions [3].
The forecasting of air quality is one of the topics of air quality research today due
to urban air pollution and specifically pollution episodes i.e. high pollutant
concentrations causing adverse health effects and even premature deaths among
sensitive groups such as asthmatics and elderly people [4]. The impact of air pollution
on urban environments has become an important research issue [5], leading to
numerous modeling studies related to the influence of buildings and other urban
structures on pollutant accumulation and dissipation patterns. A wide variety of
operational warning systems based on empirical, causal, statistical and hybrid models
have been developed in order to start preventive action before and during episodes
[6].
Modeling approaches to predict pollutant concentrations based on emission
sources and environmental conditions are commonly used tools in air pollution and
climate studies [7]. The forecasting of air quality is one of the topics of air quality
research today due to urban air pollution and specifically pollution episodes i.e. high
pollutant concentrations causing adverse health effects and even premature deaths
among sensitive groups such as asthmatics and elderly people [4].
Air quality models predict air quality in terms of the concentration of specified
pollutants in the air at a certain place. All air quality models need two kinds of input:
1. information about the input pollutants found in the air from one or more sources;
and 2. information about factors that influence the dispersion of pollutants through the
air such as wind speed and direction, presence of high buildings, presence of hills
around the city, etc. The models use all of this information to mathematically
calculate and simulate how pollutants will spread, giving estimates of specific
concentrations at specific places. Some models are very simple, while others are more
complex, including such data as ground level elevation and chemical reactions taking
place in the atmosphere that change the concentration of pollutants in the air. There
are many approaches to modeling, each approach having its strengths and
weaknesses.
Using different models or, even better, combining modeling with other assessment
techniques, significantly improves the reliability of a model [8]. Fixed-box model is a
low cost air pollution modeling method for roughly and quickly estimate the pollutant

Applying Fixed Box Model To Predict The Concentrations of (Pm10) In A Part of Al-Kut
City, Wasit Province (Iraq)
concentration in urban atmosphere [9]. Air quality models now used extensively for
the purpose of air quality management. Whilst such a model can be used in an
operational context, i.e., to predict air pollutant concentrations in advance, more
commonly they are used to evaluate pollution control strategies in advance of
implementation, so as to ensure maximum cost-effectiveness [10].
2. OBJECTIVES
The main objective of this work is to present a fixed-box model method easy-to-use
for evaluating the presence of air pollutants in AL-Kut city. This study was to
compute air pollution concentration in the city using the general material balance
equation.
3. THEORETICAL BACKGROUND OF THE FIXED BOX
MODEL
Area source is the set of emission sources in a large area unit such as the toxic vapor
dispersion from transportation activities, manufactures, households’ cooking
activities, dust from coal and sand banks, etc. If an area source created from some
point sources, which are not very large, mathematical models can used to calculate for
each point source and then their results will aggregated to infer the concentrations of
pollutants at the investigated points. In addition, the surface area can divided into a set
of parallel road sources. Calculating formulas will used for each road source and their
results will aggregated. Besides the above methods, we can also use a fixed box
model in order to estimate the level of atmospheric pollution caused by area sources
(districts, cities, mine areas, etc.). The advantage of the fixed box model in
comparison with the dispersion models of point sources and line sources is able to
solve a non-steady state problem. Its content is as follows:
 Assuming that the air block at the studied area has a parallelepiped shape with length
L (m), width d (m), and height H (m) which is often the height of atmospheric
turbulent mixing layer.
 Capacity of area source is Ms (mg/m2.s).
 The wind whose direction is perpendicular to the width has the average speed U
(m/s).
 The wind takes a pollution flow that has the concentration Cv (mg/m3).
 The concentration inside the parallelepiped is equal C (mg/m3) as shown in Figure
(1) below.

Mohammed
Suppose that pollutants do not diffuse through tow boundary slices parallel with
wind direction as well as with top and bottom slices, it will create the identical
average concentration of pollutants in the air box.
According to the law of mass balance, we must have:
( / ) = + −
It means that:
The variation speed of pollutants in the box = total of pollution level inside the box –
pollution level going out of the box.
If the time span is infinite (t→ ∞) the variation of pollutant reaches the stable
equilibrium:
/ =0
(Steady state), from the equation (6-1), we have:
= ( . / . )+
4. STUDY AREA
Am-Halana is the first apartment complex in Wasit province that located in the
southeast from the center of Al-Kut city, at AL-Hawraa region, on the side of Tigris
River. This apartment complex includes (504) residence or flat with total area of
(182400 m2).
Figure (1) Parallelepiped characterizing the air
block and symbols used in Fixed Box Model at the
studied area

This area has been chosen as model for large cities, to controlling the necessary
parameters that needs for the method of fixed box model during the study period.
Figure (2) shows the location of the study area.
Figure (2) Location of Study Area of Am-Halana Apartment complex and AL-Kut city
5. CALCULATING THE INPUT PARAMETERS OF THE MODEL
BASED ON THE REAL DATA OF EMISSION INVENTORY IN
AM-HALANA AREA
5.1 Dimension of the box
The study area are rectangular with length L (maximum): 480 m and width d
(maximum): 380 m.
5.2 Calculating the capacity of area source Ms of PM10
Area sources in this case are the emission sources such as road sources (10 roads),
mobile sources (emission from vehicles on 10 roads), construction sources (4 areas of
civil construction), and household domestic sources (300 households).
5.2.1 Calculating the emission capacity of PM10 from road sources
Emission of PM10 from a type of vehicles on road calculated according to the
following formula:
MPM10 = (Ef x Sum of VKT x365)/1000000 (ton/year) (1)
VKT: (Vehicles Kilometers Traveled) is the number of km which vehicle travels
in one day, Ef: is the road emission coefficient of PM10 emitted by each vehicle
(g/km).

Mohammed
In which:
Ef=k (sL/2)0.65(W/3)1.5(1-P/4N) (AP42, EPA, 1999)
K: coefficient considering the dimension of PM (g/km).
SL: quantity of alluvia on the road surface (g/m2) varying from (0 – 300 g/m2
).
W: average weight of vehicle (ton).
P: total rainy days in year.
N: number of days in year, N=365 days.
The concretely calculated parameters are in the following table.
Table (1) Parameters of vehicle and parameters related to road source.
Vehicle
type
4-16 places
Over 24
places
Lorry
Container
truck
Bus Motorbike
W (ton) 3 5 5 25 10 0.12
k (PM10) sL P N
4.6 30 51 365
Figure (3) Vehicles Classifications

The emission capacity of PM10 from road sources (calculating for all of 10 roads
in The Study Area) estimated as follows: 265.757 (tons/year).
5.2.2 Calculating the emission capacity of PM10 from mobile sources
Emission of PM10 from exhaust of vehicle calculated according to the formula (1), in
which:
Ef: is emission coefficient of PM10 caused by exhaust of each type of vehicle,
given in Table (2) below.
Table (2) Emission coefficient of PM10 from mobile sources (AP42)
The emission capacity of PM10 from mobile sources (calculating for all of 10
roads in The Study Area) estimated as follows: 0.411 (tons/year).
5.2.3 Calculating the emission capacity of PM10 from construction sources
Emission of PM10 from construction activities calculated according to the
following formula:
MPM10 = S x t x Ef (ton/year) (2)
S: The area of construction surface (m2
).t: duration of construction (month/year).
Ef: emission coefficient = 0.025 kg/m2
.month (AP42)
The emission capacity of PM10 from construction activities (including the
calculating for four civil construction in The Study Area) estimated as follows: 1.245
(ton/year).
5.2.4 Calculating the emission capacity of PM10 from domestic cooking activities
Emission of PM10 from domestic cooking activities calculated according to the
following formula:
MPM10 = Mass of fuel used x Emission coefficient Ef (ton/year) (3)
Emission coefficient from gas burning Ef = 0.0001 kg/ton.
The emission capacity of PM10 from domestic cooking activities in The Study
Area (300 households) was 0.01095 (tons/year).
5.2.5 Calculating the emission capacity of PM10 from point sources
The formula of calculating the emission capacity of PM10 from small point sources:
MPM10 = Fuel amount x Emission coefficient Ef x ash coefficient of coal (ton/year) (4)
Vehicle types
Emission coefficient of PM10
(g/km)
4-16 places 0.10
24 places and over 0.15
Lorry 0.23
Container truck 3.28
Bus 1.97
Motorbike 0.10

Mohammed
The emission capacity of PM10 from small point sources in The Study Area was
zero (ton/year).
6. ADJUSTMENT OF THE CALCULATED RESULTS OF
EMISSION CAPACITY IN THE STUDIED AREA
At present, there is no standard emission coefficient for the above types of studied
sources in the Study Area, United States emission coefficients (according to the
document AP-42, EPA and PUNE project, India, 2004) (PREIS 2004) were used
during the calculating process. Therefore, it is necessary to adjust the calculated
results in order to obtain a relative accuracy. The adjustment principle based on the
surveys of each specific source to correct and estimate the emission capacity M for
the studied area; the results showed in Table (3).
Table (3) Estimation of emission capacities of PM10, from emission sources in the Study Area
with the adjustment coefficient d
Emission sources
Calculated capacity M*
according to document AP-42
and PUNE (ton/year)
Adjustment
coefficient (d)
Adjusted
capacity
(ton/year)
M = M* (d+1)
Civil construction 1.245 0.3 1.6185
Domestic cooking 0.01095 0.15 0.0125
Road source 265.757 0.35 358.7719
Mobile source 0.411 0.15 0.4726
Total 267.4239 360.8755
7. CALCULATION SCENARIOS
7.1. The input parameters of fixed box model based on the Table (3)
 Emission capacity of area source Ms = 0.0627 mg/m2.s.
 The length of the box = 480 m.
 The width of the box = 380 m.
 Mixing height: H1= 120 m; H2 = 200 m.
Mixing height is the height H in the atmospheric boundary layer where the
turbulent coefficient Az = Kzζ = const with z > H; of which, Kz is the vertical
turbulent coefficient, ζ is the average density of the studied atmospheric layer.
The researches (Le Dinh Quang, Pham Ngoc Ho 2006) showed that Profile of Az
(or turbulent coefficient Kz) in the atmospheric border layer has a linear dependence
on the distance of vertical movement of turbulent cycle lz= χz (χ- Karman constant ≈
0.4) in the equilibrium condition to the height H=lzmax, corresponding with the

height Zmax varying from (300-500m). Therefore, the height H can be estimated
varies from (120-200m).
 Wind speed and concentration of PM10 accompanying the wind shown in Table (4)
below.
Table (4) Average monthly wind speed and the concentration of PM10 accompanying the
wind (Cv)
No. Month Average Wind speed (m/s) Cv (mg/m3
)
1 November 2015 3.2 0.015
2 December 2015 4 0.013
3 January 2016 4.8 0.006
4 February 2016 3.5 0.011
5 March 2016 5.1 0.016
7.2. Methodology and Calculated results
In order to check the calculated result of the model with the measured data, the
measured data of PM10 at five points in the Study Area during five months from
November 2015 to March 2016 have been used, the locations of measuring points as
shown in Figure (4) below.
Figure (4) locations of measured concentration of PM10

Mohammed
The methodology of sampling was 4 times per day at (8 am, 12 pm, 4 pm, and 8
pm). The duration of sampling is (30 min) for each time. The measured data were
averaged for each time, after that they were averaged for all of 4 times to get the
specific values for 24h; and the value of calculated concentration C is considered as
the average concentration of 24h which is taken to compare, the results are indicated
in the Table (5).
Table (5) Comparison between the calculated result of the model and the measured result
(mg/m3
)
Month
Measured
concentration
of PM10
(mg/m3
)
Calculated
concentration
of the model at
H=120 m
Relative
error (%)
Calculated
concentration
of the model at
H=200 m
Relative
error (%)
November
2015 0.085 0.0783 7.8 0.0470 44.7
December
2015 0.071 0.067 11.7 0.0376 47.1
January
2016 0.055 0.0522 5.1 0.0313 43.1
February
2016 0.079 0.0716 8.1 0.0429 45.7
March 2016
0.51 0.0491 3.7 0.0295 42.2
Table (5) above shows that, the calculated result is closer to the measured result
when (H1 < H2). The calculated results of the model at (H = 120m) where
approximately with relative error below (10%). While the calculated results of the
model at (H = 200m) where all with relative error exceeding (40%). Therefore, the
estimation of mixing height suitable with the actual conditions plays an important
role.
The calculated results from the model that were smaller than the measured results
corresponds with the physical significance because the measured concentration at the
monitoring point is (C = Co + Cv). Of which: (Co) is the calculated concentration of
emission from area sources and (Cv) is the concentration generated by wind flow,
which takes pollutants from other areas into the box (this element has not been
considered yet in the research).
The calculated results from the model also indicate that there has been a scientific
basic for the adjustment in emission inventory and the adjustment has obtained an
acceptable accurate level.
Pollutant’s concentration (Co) is in reverse ratio to wind speed (U) and mixing
height (H) and corresponding to the fixed box length (L) and area source capacity
(Ms).

Compared with the NAAQS and EPA standards (24h average), the concentrations
of PM10 calculated from box model is within the acceptable limits (150 μg/m3
) or
(0.15 mg/m3
).
The initial result may have significance for studying the application of this fixed
box model on evaluating the air quality in districts of AL-Kut city in particular and
urban areas of Iraq in general.
8. CONCLUSION
This work investigates methodologies for evaluating the performance of dispersion air
quality models. Dispersion models are used to predict the fate of gases and aerosols
after they are released into the atmosphere. The Fixed Box Air Quality Model was
used for studying regional air pollution problem in AL-Kut city. This work has
described a detailed model for studying the urban air pollution.
Mathematical models are needed to optimize air quality monitoring, provide
estimates for monitoring purposes, study different street geometries, and finally test
prospect emission. Depending on their mathematical principles, they may be more or
less suitable for a number of applications. However, validation of the results from this
study for urban air pollution would be highly beneficial. The same approach would
work fit indoors air pollution.
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