GSTF International Journal of Engineering Technology (JET) Vol.1 No.1, 2012
Annual variations of air pollution in Jahra,
Kuwait
Aisha Al-Baroud, Fatemah Al-Baroud, Mohamed Al-Sahali, Hisham Ettouney
neural based modeling methodology applicable to site specific
short and medium term ozone concentration forecasting. In
this study, the type of special user technique called novel
modeling technique which utilizes two feed forward artificial
neural network (FFNN) was improved in order to pick up the
performance of time series predictions. Data from two
locations in Kuwait were collected and recorded data for one
year. A two FFNN model is used to predict the actual data.
Results revealed that newly developed model makes the
predication more accurately over the usual method. Finally, the
most important point established after going through this study
was that the shorter range modeling formed better results.
Abdul-wahab et al. [2] presented a statistical model that is able
to forecast ozone level precursor concentration and
metrological parameters during the day light hours in Shuaiba
industrial area in Kuwait. Recorded data for one year starting
from December 1994 using an air pollution mobile monitoring
station was used to by this model. This study used stepwise
multiple regression modeling determines the practical
relationship between ozone level and the various independent
variables. The model explained that the precursor
concentrations are positively correlated with concentration,
wind speed and solar radiation but inversely correlated with
ozone concentration. The ozone temperature relationship was
presented in detail. The relationship between temperature and
ozone concentration was found to be positively correlated at
temperature less than 27ºC but negatively correlated at
temperature above 27 ºC
Ettouny et al. [3] assessed the air pollution in Kuwait
through the modeling of emission and the dispersion of SO2,
NOx and CO. The air pollution emission in Kuwait was
estimated in hourly average for motor vehicle, power plant, oil
fields and oil refiners. These estimates have come as a function
of the number of motor vehicles and average annual distance
travelled per car, the power rating of each power plant and the
production capacities of the oil fields and refineries. This study
used Industrial Source Complex Short Term (ISCST) model to
imitate the dispersion of SO2, NOx and CO in 2003.Predictions
of this model were based on the comparison of measured data
at two locations in Kuwait. The error ranges for annual
average and annual maximum in (ISCST) model predictions
were ( -11.0% to +26%)and (-5% to 16.7%) respectively . The
prediction of SO2 was found to contain the largest error in
Umm Alhyman.
Abdul- wahab et al. [4] used a special model called
Abstract—A study of air pollution in Jahra residential area has
been conducted over a period of five years on a 24 hours basis.
Measurements of air pollutants and meteorological parameters
were taken at 5 minutes intervals at Jahra from 2000-2004. The
measured pollutants included sulfur dioxide (SO2), nitrogen oxide
(NOx), particulate matter (PM), carbon monoxide (CO), ozone
(O3), methane hydrocarbons (MHC) and non-methane
hydrocarbons (NMHC)). Meteorological parameters monitored
simultaneously included ambient temperature and solar intensity.
Hourly averages were calculated from the measured data. Air
pollution emissions in Jahra were estimated at the following
locations: Ali Salem Air Base, Kuwait International Airport,
Doha power stations, Jahra industrial area 1 and 2. Motor
vehicles emissions were modeled as line source represented by
known highways in the Jahra area. Comparison of measured data
for CO, NOx, and SO2 were made against US-EPA
(Environmental Protection Agency standards). Analysis of this
data show that some pollutants are within or below the US-EPA
standards, however, other pollutants such as NOx and SO2
exceeded these limits. Higher NOx and SO2 values were caused by
increase in the density of population, motor vehicles, power
generation, and industrial activities.
Index Terms—field measurements; meteorological parameters,
air pollution, AERMOD dispersion model.
I. INTRODUCTION
A
ir pollution continues to increase in Kuwait because of
the expansion of many industrial activities, including oil
extraction and refining, petrochemicals, water desalination,
electric power generation, transportation, etc. Air pollution
assessment and motoring in Kuwait have been the focus of
several studies [1-10]. This study continues previous
measurements and analysis of air pollution in Kuwait. The
following review gives insights on study areas and results
obtained in the previous literature studies of air pollution in
Ettouney et al [1] studied the development and validation of
F. A. Author is with Laboratory Control, Department of technical affairs,
Ministry of Defense, Kuwait, (e-mail: [email protected]).
S. B. Author, is with Laboratory Control, Department of technical affairs,
Ministry of Defense, Kuwait, (e-mail: [email protected]).
T. C. Author is with the Department of Chemical Engineering, Kuwait
University, Kuwait, (P.O. Box 5969, Safat 13060; e-mail:
[email protected])).
F.D.Author is with the Department of Chemical Engineering, Kuwait
University, Kuwait, (P.O. Box 5969, Safat 13060; e-mail
[email protected]
74
© 2012 GSTF
GSTF International Journal of Engineering Technology (JET) Vol.1 No.1, 2012
Industrial Source Complex Short Term (ISCST) in Mina Al –
Fahal (MAF) refinery in Muscat to measure the atmospheric
levels of SO2 and then to compare outcome value with the
international standard limits .This study was used to validate
the (ISCST) model by comparing calculated and measured
concentrations. This study established the effect of wind
regimes on the dispersion of SO2 and the spatial distribution of
SO2 over the modeled area. The result showed that the levels
of SO2 were below the ambient air quality standard.
Abdul – wahab et al [5] by using the Industrial Source
Complex Short Term (ISCST) as a model in order to predict
the temporal and spatial spreading of a single pollutant from
large number of emission, We could create more scientific
studies towards the environment, As well as many sources in
vastly industrial area with using Sulfur dioxide as the model
for industrial pollutant. All important meteorological
parameters were used in the model The measurements were
taken throughout the day, within every 5 minute during 12
months and then became average on monthly basis. This study
also played a role in making a comparison between average
pollutants concentrations on a monthly basis, and experimental
values which was found at single monitoring station positioned
within (SIA).The final results showed that there were excellent
quantities agreements between predicted data and actual
concentrations for seven months of the year. The model for
these measurements was the SO2 emission rates for about the
year rather than on a monthly basis. The (ISCST) model output
was used to supervise SO2 concentration for SIA and its
nearby environment .This model was used for several
purposes.
Ettouney et al. [8] evaluated the air pollution data from two
monitoring stations in Kuwait (Jahra & Umm Al Hyman) from
(2001 – 2004). This study used measurements to cover major
pollutants such as CO, CO2, methanated and non-methanated
hydrocarbons (MHC and NMHC), NOx, SO2, O3 and
particulate matter (PM). The Assessed also included variation
in meteorological parameters such as solar intensity,
temperature, and wind speed and wind direction.
Measurements were taken at 5 minute intervals and were
processed to obtain annual hourly averages and annual hourly
maxima. After going through this study, the measured data are
found to be below the international standards except for
particulate matter. Also, it displayed constant growths in
nitrogen oxide concentration during the year.
Abdul-wahab et al. [9] studied the behavior of pollutants in
comparison with the wind speed and direction. This study was
taken over a period of one year on measurements of air
pollution in Shuaiba Industrial Area (SIA) of Kuwait. The
pollutants studied consist of methane, non-methane
hydrocarbons (NMHC) , carbon monoxide , carbon dioxide ,
nitrogen oxides (NO, NO2 and NOx), sulphur dioxide , ozone
and suspended dust .Meteorological parameters monitored
include wind speed and direction , air temperature, relative
humidity , solar radiation and barometric pressure. A mobile
laboratory was used to accumulate air quality data. They
calculated diurnal variations of concentrations of primary
pollutants such as (NO, SO2, NMHC, CO and suspended dust)
and secondary pollutants (O3, and NO2, SO2 and NOx) with
two maxima for primary pollutants and single maxima for
secondary pollutants. The results for distribution of pollutants
except for CO, O3 and relative humidity established that the
mean concentration is extremely low at low wind speed (<5
m/s). Abdul-wahab et al [10] studied the analysis of ozone
pollution in the Shuaiba industrial area of Kuwait. This study
was carried out over a period of 12 months, from December
1994 to November 1995. Shuaiba, which is a well known
industrial area of Kuwait, contains petroleum refinery,
complex of petrochemical plants, cement plant, chlorine and
soda factory, desalination plant, power station, and shipping
port for oil distributing and exporting resons. Measurements of
18 pollutants and meteorological parameters were used by a
mobile station. Measurements were made at 5 minute intervals
and stored on a data station. Concentration of Ozone, nitrogen
oxides, and non-methane hydrocarbons were the parameters
studied in the analysis. The meteorological parameters include
such as air temperature, wind speed and direction, and solar
radiation were used in the examination.. The results reported
that the maxima of the hourly –averages for ozone
concentration coincided with the minima for the
concentrations of nitrogen oxides and non-methane
hydrocarbons. Low rates of ozone formation was found in very
hot summer season and mild winter season .However, highest
rates of ozone formation was observed in spring early summer
and fall periods. The aim of this paper is to analyze air
pollution patterns during (2000- 2004) in Jahra, Kuwait. Also,
the meteorological parameters which include wind speed and
direction were analyzed for the same study period. Finally,
comparison was made between measured /and predicted
concentrations as well as US-EPA limits. It should be noted
that only the data of 2002 are displayed because of limitations
on the manuscript size. However, tables that include
comparison of measured and predicted data as well as
comparison against EPA limits for all years are given.
II. JAHRA RESIDENTIAL AREA AND MEASUREMENTS
Fig. 1 shows map of the Jahra area. The measurement location
is identified with a white triangle and is located in the middle
of the Jahra residential area. Other symbols on the map include
major emission sources, which include two airports
(designated by a star symbol), three industrial zones
(designated by circles), one power plant (designated by a
square), and the main highway roads (designated by the blue
line). Measurements were taken at 5 minutes interval. This
gives a total of 105,120 data points per year. The collected
data was processed to generate hourly averages, which gave
8,760 data points per year.
III. METROLOGICAL PARAMETER
Meteorological parameters monitored simultaneously
include, ambient temperature and solar intensity. Variation in
solar radiation, Fig.2, shows the high intensity of radiation
75
© 2012 GSTF
GSTF International Journal of Engineering Technology (JET) Vol.1 No.1, 2012
during the summer time, with values close to 800 W/m2. Fig.3
reveals the variation of temperature with time for 24 hours
duration for the 12 months in Jahra. The graph exhibits a is
decreasing trend in temperature from 1.00 to 7.00 am and then
an increasing trend from 7.00 am to 6.00 pm and finally a
decreasing trend from 6.00 pm to mid night.
variations in O3 concentration through 12 months for a period
of one year in Jahra. The graph shows increasing trend for O 3
concentration from 24.00 am to 7.00 am around 0.06 ppm and
also increasing trend from 10.00 am to 8.00 pm around 0.09
ppm and finally decreasing trend from 8.00 pm to mid night.
Fig. 12 shows variations in MHC concentration through 12
months for a period of 1 year in Jahra. The graph shows the
static trend for the MHC concentration in the range of (1.5 –
2.6 ppm). Fig.13 represents the graph that shows the variations
in NMHC concentration with time for 24 hours duration for 12
months in Jahra. The graph shows a static trend for the NMHC
concentration in the range of (0 – 0.8 ppm)
IV. ISCST3 SIMULATIONS
The AERMOD software was used to prediction pollutant
dispersion over the study area and during the period 20002004. The simulations were made for CO, SO2, and NOx.
Emission rates for each of the sources included in the study are
shown in Table I.
The highest concentrations for SO2 were found from Doha
power plant but the highest concentrations for CO and NOx
were found around Jahra highways. The pollutant
concentrations around the industrial areas and airports were
much lower than those for the power plant or the highways.
Fig 4 shows the AERMOD contours for CO for the period one
year. Similarly, the contours of NOx and SO2 are shown in Fig
5 and 6 respectively. In all figures similar patterns are obtained
for the pollutant contours. In this regard, the analysis shows
existence of high concentrations around the source lines of
traffic. From tables (II -VI) show the predicted concentration
is closed to the measured concentration for all the pollutants in
5 years.
VI. COMPARISON AGAINST EPA LIMITS
From table below, it shows continuous increase in NOx
levels. The results show that the measured 98% of the 1-hr
maximum are larger than the EPA limits. This is caused by
absence of local regulations in Kuwait on motor vehicle
emissions and use of catalytic converters in motor vehicles. On
the other hand, most of the motor vehicles in the US must pass
emission criteria on hydrocarbons, NOx, etc. Therefore, NOx
emissions from motor vehicle exceed the EPA limits.
Examining, the annual averages for measured NOx and EPA
limits show that measured data gives lower values than the
EPA criteria. This is caused by averaging of less frequent large
values that occur during the rush hours and more frequent
smaller values that occur during the night hours Examining the
measured SO2 concentrations show that the 99% of 1-hr
maximum and the maximum of the 24-hr average are either
close to or large than the EPA criteria. This behavior is
explained in terms of fluctuations in use of crude oil versus
fuel oil in power plants. It is known the Kuwait crude oil
contains a high sulfur percentage. On the other hand, the fuel
oil contains a much smaller percentage of sulfur compounds.
The measured annual averages are lower than EPA criteria.
This is explained in terms of averaging of more frequent low
measurements and less frequent high measurements throughout
the year.
V. ASSESSMENT OF MEASURED AIR POLLUTANTS
Measured pollutants can be divided into primary and
secondary pollutants. Primary pollutants are found in the same
chemical/physical form as when it was emitted from its source
(SO2, NOx, CO, some VOCs, and some PM). On the other
hand, the secondary pollutants are found in the air as a result
of physical/chemical transformations of primary pollutants
(photochemical pollutants: ground–level ozone, some PM,
some VOCs).Fig. 7 shows variations in SO2 concentration in
Jahra through 12 months for a period of one year. SO 2 is a
primary pollutant and its levels are related to human and
industrial activities. A careful study of the graph shows that the
concentration of SO2 peaks at 11.00 am with various in
concentration from (0.15 to 0.2) ppm. Fig. 8 shows variations
in NOx concentration in Jahra through 12 months for a period
of one year. NOx levels are related to power generation and
construction activities. The graph shows that the concentration
of NO peaks at 7.00 am. and at 8.00 pm. Fig 9 shows the
variations in PM in Jahra through 12 months for a period of
one year. The graphs show static trends for 12 months with
concentration around 2500 µg/m3.Fig. 10 represents the graph
that shows the variations in CO concentration with time for 24
hours duration for 12 months in Jahra. CO is a primary
pollutant and its levels are related to transportation, process
emissions, waste disposal, power generation and construction
activities. A careful study of the graph shows that the
concentration of CO peaks at 7.00 am. and at 8.00 pm. It is
also observed that the concentration of CO Fig. 11 shows
VII. CONCLUSION
A five years study of variations in air pollutants and
meteorological parameters were performed in Jahra residential
area. Measurements of 7 pollutants and meteorological
parameters were taken at 5 minutes intervals at the location
(from year 2000 to 2004) which resulted in approximately
105,120 data points per year. The pollutants studied include
SO2, NOx, PM, CO, O3, MHC and NMHC. Meteorological
parameters monitored simultaneously include, ambient
temperature and solar intensity Assessment of air pollution
patterns showed the following trends:
-The highest concentrations of SO2 were found between (10
am to 15 pm) throughout the duration of measurements. This is
due to the increase in power plants capacities to accommodate
increase in power consumption in office buildings as well as
residential areas.
76
© 2012 GSTF
GSTF International Journal of Engineering Technology (JET) Vol.1 No.1, 2012
2000
[10] S. Abdul-Wahab , W. Bouhamra , H. Ettouney , Bev Sowerby , and
B.D. Crittenden, Analysis of ozone pollution in the Shuaiba industrial area in
Kuwait. Int. J. Environ. Stud. 57 (2000), pp. 207-224.
-The variations in NOx and CO concentrations were found
to have two maxima at 7:00 am and at 8:00 pm. These
maximum values are related to the morning and early evening
rush hours.
-Hourly averages of the particulate matter distribution did
not showed no maximums of minimums; however, the
concentrations of the particulate matter vary over a wide range
of 0-2500 µg/m3.This reflects the dessert nature of Kuwait.
-The MHC concentrations varied over a narrow range of
(1.5 – 2.6 ppm). This pattern may indicate that (MHC) is
primary/stable pollutant that does not react or forms other
species.
The AERMOD software was used to assess the pollutants such
as CO, NOx, and SO2 around Jahra. In all cases similar
patterns are obtained for the pollutant contours. In this regard,
the analysis shows existence of high concentrations around the
source lines of traffic. In addition, lower concentrations exist
for all pollutants at large distances from the emission sources.
The measured concentration for each pollutant was compared
with EPA standards, which are based on the maximum
measured concentration for the following averages: 1-hr, 8
hours, and 24 hours. This comparison was applied for the
measured data for CO, SO2 and NOx. The measured 1-hr
maximum for CO is much less than the EPA criteria but the
measured data for the 8 hour average is close to EPA limits.
The measured data of NOx of 98% of the 1-hr maximum are
larger than the EPA limits. The measured SO2 concentrations
of 99% of 1-hr maximum and the maximum of the 24-hr
average are either close to or large than the EPA criteria
Ali salem air base
Doha (west
& east )
plants
Line 1
Industrial area 1
Industrial area 2
Line 3
Line 2
2000
Kuwait international
airport
Fig. 1. Map of Jahra showing location measurement (triangle) and emission
sources.
2002
2002
Fig. 2. Variation in Solar Radiation in 2002 in Jahra.
2004
ACKNOWLEDGMENT
This is to acknowledge the support of the Department of
Technical Affairs in the Ministry of Defense, Kuwait
REFERENCES
[1] G Reem S. Ettouney a; Sabah Abdul-Wahab b; Amal S. Elkilani,
Emissions inventory, ISCST, and neural network modelling of air pollution in
Kuwait, Internal Jour of Environ Studies.66(2009),pp.181-194
[2] S. Abdul-Wahab, W. Bouhamra, H. Ettouney, Bev Sowerby, and B.D.
Crittenden, Predicting ozone levels: A statistical model for predicting ozone
levels, Environ. Sci. Pollut. Res. Int. 3 (1996), pp. 195-204.
[3] R.S. Ettouney, J.G. Zaki, M.A. El-Rifai, and H.M. Ettouney, An
assessment of the air pollution data from two monitoring station in Kuwait,
Toxicol. Environ. Chem. 92 (2010), pp. 655-668.
[4] S. Abdul-Wahab, S.M. Al-Alawi, and A. El-Zawahry, patterns of so2
emissions a refinery case study 17 (2002) 563–570.
[5] S Abdul- Wahab, W.Bouhamra, H Ettouney, B Sowerby and B.Crittenden,
of Air Pollution around Heavily Industrialised Areas: Use of the Industrial
Source Complex Short-Term Model with Emissions from a Large Number of
Sources,1999.
[6] A. Monteiro.; A.I. Miranda; C. Borrego ; R. Vautard Air Quality
Assessment for Portugal. Science of the Total Environment 373 (2007)22–31.
[7] I.Lagzi, R.Meszarosb, L.Horvathc, A. Tomlind;T.Weidingerb, T.Tura´
nyia,F.Acsb,L.Haszprac,Modeling Ozone Fluxes Over Hungary. Atmospheric
Environment 38 6211–6222M.
[8] R. S. Ettouney, J.G. Zaki, An Assessment of the Air Pollution Data from
Two Monitoring Stations in Kuwait,2004.
[9] Mahmoud A. El-Rifai*, Hisham. M. Ettouney S. Abdul-Wahab , W.
Bouhamra , H. Ettouney , Bev Sowerby , and B.D. Crittenden, Analysis of air
pollution at Shuaiba industrial area in Kuwait, Toxicol. Environ. Chem. 78
(2000) 213-232.
Fig. 3. Variation in Temperature in 2002 in Jahra.
2004
Fig. 4. Predicted contour map for 2002 year for hourly average of CO
Concentration ( µg/m3).
77
© 2012 GSTF
GSTF International Journal of Engineering Technology (JET) Vol.1 No.1, 2012
2000
2000
2001
Fig. 5. Predicted contour map for 2002 year for hourly average of NO x
Concentration (µg/m3).
Fig. 9 Variation in PM concentration in 2002 in Jahra.
2002
2000
2001
2002
Fig. 10 Variation in CO concentration in 2002 in Jahra.
Fig. 6. Predicted contour map for 2002 year for hourly average of SO 2
Concentration (µg/m3).
2002
2003
2004
Fig.11.Variation in O3 concentration in 2002 in Jahra.
Fig.7.Variation in SO2 concentration in 2002 in Jahra.
2002
2003
2004
2004
Fig.12 Variation in MHC concentration in 2002 in Jahra.
Fig. 8 Variation in NOx concentration in 2002 in Jahra.
2004
78
© 2012 GSTF
Fig.13.Variation in NMHC concentration in 2002 in Jahra.
GSTF International Journal of Engineering Technology (JET) Vol.1 No.1, 2012
TABLE I
EMISSIONS RATE OF ALL SOURCES OF SO2, NOX AND CO
CONCENTRATION AROUND JAHRA RECEPTOR IN 2002
All
SO2
NOx
CO
Sources
Doha
476
128.4
0
Emission
(g/s)
Area 1
5.47E-06
6.71E-07
0
Emission
(g/m2.s)
Area 2
3.29E-06
4.02E-07
0
Emission
2
(g/m .s)
Airport
7.60E-08
1.04E-06
2.18E-07
Emissions
(g/m2.s)
Air Base
2.95E-08
4.05E-07
8.52E-08
Emission
(g/m2.s)
Line 1
115
310
5070
Emission
(g/s)
Line
82
221
2403
2Emissio
n (g/s)
Line
115
310
5845
3Emissio
n (g/s)
TABLE IV
Comparison of measured and predicted concentrations ofCO, NOx and
SO2 in Jahra for 2002 year
Measured
Predicted
Year
Temperature
Concentration
Concentratio
2002
( °C )
( ppm)
n (ppm)
CO
1.089
1.089
27.9
NOx
0.045
0.043
27.9
SO2
0.012
0.0117
27.9
TABLE V
Comparison of measured and predicted concentrations of CO, NOx and
SO2 in Jahra for 2003 year
Measured
Predicted
Year
Temperature
Concentration
Concentratio
2003
( °C )
( ppm)
n (ppm)
CO
0.977
1.00
27.5
NOx
0.0487
0.0488
27.5
SO2
0.0068
0.007
27.5
TABLE VI
Comparison of measured and predicted concentrations of CO, NOx and
SO2 in Jahra for 2004 year
TABLE II
Comparison of measured and predicted concentrations of CO, NOx and
SO2 in Jahra for 2000 year
Measured
Predicted
Year
Temperature
Concentratio
Concentratio
2000
( °C )
n ( ppm)
n (ppm)
CO
1.200
1.205
29.2
NOx
0.0340
0.0353
29.2
SO2
0.012
0.0113
Year
2004
Measured
Concentration
( ppm)
Predicted
Concentratio
n (ppm)
Temperature
( °C )
CO
NOx
SO2
0.941
0.0592
0.0076
0.941
0.0596
0.0071
27.9
27.9
27.9
29.2
TABLE III
Comparison of measured and predicted concentrations of CO, NOx and
SO2 in Jahra for 2001 year
Year
2001
Measured
Concentration
( ppm)
Predicted
Concentratio
n (ppm)
Temperature
( °C )
CO
NOx
SO2
1.504
0.0357
0.0133
1.544
0.0350
0.125
28
28
28
TABLE VII
COMPARISON OF AVERAGES BETWEEN MEASURED DATA IN JAHRA AND EPA CRITERIA, FOR 5 YEARS
Measured
EPAa
Average
Pollutants
Criteria
Times
2000
2001
2002
2003
2004
1-hr
CO
7.979
9.688
8.538
8.155
7.554
35
maximum
CO
4.86
8.58
6.16
6.34
5.64
9
8-hr
98% of 1
NO
0.233
0.318307
0.295
0.343
0.351
0.15
hour
maximum
NO
0.034
0.0357
0.045
0.0487
0.0592
0.0795
Annual
99% of 1
SO2
0.188
0.1709
0.0902
0.0931
0.112
0.14
hour
maximum
Maximum
SO2
0.083
0.0709
0.0496
0.033
0.0533
0.075
of 24-hr
average
SO2
0.012
0.0133
0.012
0.0068
0.0076
0.03
Annual
79
© 2012 GSTF