Archives of

1. Introduction
ineral dust strongly interacts with the
climate system through direct and
indirect impacts [1]. Mineral dust
emissions into the atmosphere negatively
impact human health, causing
or aggravating allergies, respiratory
diseases, and eyes infections [2]. Mineral dust is discussed
concerning dust storms and serious environmental
problems in arid and semi-arid regions [3]. What is
known as the dust today is due to the movement of air
ambient forming soils suspended in the air affected by
storms and air currents [4]. The speeds over 7 m/s at an
altitude of 10 m play an essential role in storm conditions
and dust formation. A dust storm develops when
a low-pressure system moves toward a desert area [5].
Particle size and concentration, atmospheric persistence,
and altitude in different dust storms of various sources
are not the same [6]. The horizontal visibility reduces
by increasing dust [7]. Horizontal visibility is among the
events recorded by observers at meteorological stations
[8]. Various atmospheric factors, including dust, reduce
horizontal visibility and atmospheric turbidity [9]. The
reported horizontal visibility varies according to the intensity
of the dust. However, this report depends on the
observer’s personal experience and does not indicate the
amount of dust [10]. Knowing the concentration and intensity
of dust will effectively understand its nature and
control strategies [11].
Furthermore, it is a criterion for comparing the results
of air pollution models. Shao presents a valid classification
for dust types based on horizontal visibility, 2015
[12], based on wind dust, blowing dust, dust storm, and
severe dust storms. Multiple studies investigated the relationship
between horizontal visibility and particulate
matter and its importance. The results of various studies,
such as Shao et al. [13], Camino et al. [14], and Baddock
et al. [6], indicated the importance of the particulate
matter in atmospheric parameters significantly influence
climate and habitats. Camino et al. (2015) found
an empirical equation between horizontal visibility and
particulate matter concentrations in North Africa and
compared them with other empirical equations in the
same region. Bertahina et al. [11] simulated the scattering
of dust particles by comparing observational
dust data with a gray model with a coefficient of determination
greater than 0.8. Dayan [15], in his synoptic
analysis and classification of the prevailing air for dust
patterns in Palestine, stated that a significant difference
was between the seasonal routes of these patterns. He
outlined the main routes for Israeli dust from 5 routes
in northwestern Europe, Eastern Europe, Jordan, Saudi
Arabia, and the coasts of North Africa. D’Almeida [16]
outlined a well-described relationship between horizontal
visibility and particulate matter based on data from
a solar pyranometer. He hypothesized that horizontal
visibility and turbidity parameters are often related. Zoljoodi
et al. [17] found a relationship between increasing
drought and dust storms in west of Iran from 1996 to
2011, highlighting the significance of studying dust in
Iran’s western and southwestern regions. Ahvaz City has
extended boundaries with Iraq from the west. Due to its
proximity to the deserts of Iraq, Kuwait, and Saudi Arabia.
Besides, this city is affected by the deserts of those
areas and experiences several dust events annually [18].
In this article, focusing on particulate matter pollution
concentration in Ahvaz City in southwestern Iran, the
best empirical equation between horizontal visibility and
the extent of particulate matter is determined.
2. Materials and Methods
This study was conducted in Ahvaz City in southwestern
Iran (Ahvaz). Figure 1 shows an image taken from
the MODIS sensor of the Aqua satellite. It demonstrates
dust routes that cover areas in Iraq, the Persian Gulf, and
southwestern Iran on January 21, 2018; periods of dust
occurrence in the present study.
This was a descriptive cross-sectional study. The
obtained data were analyzed in 3 groups from the beginning
of April to the end of March of the following
year (2016, 2017, & 2018). This study requires meteorological
and environmental data (particulate matter).
The meteorological data included 3-hour horizontal visibility
data recorded in m. PM10 concentration data were
obtained from the Environment Organization. All data
were filtered for less than 10 km (only dust data were
extracted). After preparing the given data, using the following
empirical equations, PM10 level was determined
by horizontal visibility:
After calculating PM10 using Table 1 relationships, the
values were compared with observational PM10. A linear
regression test was used to investigate the correlation
between the two. In this study, regression analysis was
used to determine the relationship between empirical
equations and the relationship between horizontal visibility
and PM10 concentration in Ahvaz aimed to find
the best connection to estimate the amount of particulate
matter. The lowest, highest, and mean values were also
compared. Finally, particulate matter was classified on
the World Wide Web report [12].
M
Mobarak Hassan E, et al. Comparison of Computational Concentration of PM10 With Actual Concentration. Arch Hyg Sci. 2021; 10(4):323-332.
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3. Results
The data relating to the main variables, including wind
speed and dust concentration based on satellite and field
of visibility data, have been used at 3 time periods of
2016, 2017, and 2018. The data relating to horizontal
visibility in the city Ahvaz at these three intervals are
shown in Figures 2, 3, and 4. Given that the data are recorded
hourly, there are 8760 records each year.
Climatic data (wind speed and direction) and environmental
data (PM10 concentration and horizontal visibility)
were calculated using D’Almeida, Dayan, Chung,
Jugder, et al., and Camino et al. indicators. The method
of calculating each of the empirical equations is presented
in Table 1. The calculations were made only for days
when the horizontal visibility was less than 10,000 m.
Horizontal visibility time series, observational and computational
PM10 in Ahvaz in 2016 is shown in Figure 5.
The obtained results indicated that the lowest horizontal
visibility of 500 m was recorded on March 3, 2016. The
value of PM10 on this day is 1103.3 mg/m³. The highest
D’Almeida, Dayan, Chung, Jugder, et al., and Camino et
al. indicators were measured as 3798.9, 1535.1, 1451.2,
831.6, and 976.9, respectively (Figure 6). The correlation
coefficient results suggested that the highest level of
correlation was between the Camino et al. index and particulate
matter (r=0.72). D’Almeida and Jugder et al.’s
indicators present a high correlation with the particulate
matter with correlation coefficients of 0.7 and 0.71. The
correlation estimation and calculation of empirical dust
indicators are shown in Table 2.
Table 1. Empirical equations between horizontal visibility and PM10*
Region Visibility PM10 Conditions Empirical Equation References
West Africa 200 m to 10 km 30 to 700 PM10= 914.06×V-0.72+19.03 D’Almeida [16]
West Asia
Only dust and vis <5 km
Vis (100m)
Time 06 to 15 UTC
- PM10= 505In(V×10)+2264 Dayan [15]
East Asia - - PM10= 1120×EXP(-0.2733V) Chung [19]
Asia - 50 μg PM10= 485.67×7V-0.72 Jugder [20]
- - - PM10= 1772.24×7V-1.1 Camino [14]
*V: Represents the horizontal visibility in m, and PM10 represents surface dust concentration in micrograms per cubic meter.
Figure 1. Geographic location of Ahvaz City and Khuzestan Province (Aqua satellite image)
Mobarak Hassan E, et al. Comparison of Computational Concentration of PM10 With Actual Concentration. Arch Hyg Sci. 2021; 10(4):323-332.
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Figure 2. Horizontal visibility in Ahvaz (2016)
Figure 3. Horizontal visibility in Ahvaz, 2017
Figure 4. Horizontal visibility in Ahvaz (2018)
Mobarak Hassan E, et al. Comparison of Computational Concentration of PM10 With Actual Concentration. Arch Hyg Sci. 2021; 10(4):323-332.
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Investigating the 2017 data revealed that the lowest
horizontal visibility of 300 m was recorded on October
15, 2017 (Figure 7). The extent of PM10 on this day is
1119. 4 mg/m³. The highest D’Almeida, Dayan, Chung,
Table 2. Calculation of statistical indicators of horizontal visibility and PM10 observational and computational in Ahvaz, 2016
Index No. Mean±SD Min Max PM10 Correlation
Visibility 108 - 0.5 8 -0.52
PM10_Ob 108 377.7±169.3 20.2 1103.3 ---
Camino et al 108 633.4±695.7 179.9 3798.9 0.72
D’Almeida 108 440.7±279.1 219.3 1535.1 0.70
Dayan- 108 456.4±366.5 51.1 1451.2 0.63
Jugder et al 108 215.9±153.9 96.7 831.6 0.71
Chung 108 410.0±255.4 125.8 976.9 0.60
Figure 5. Horizontal visibility time series, observational and computational PM10 in Ahvaz, 2016
Figure 6. A: Relationship between horizontal visibility and PM10 observational and computational; and B: Distribution of PM10
observational and computational in Ahvaz in 2016
Mobarak Hassan E, et al. Comparison of Computational Concentration of PM10 With Actual Concentration. Arch Hyg Sci. 2021; 10(4):323-332.
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Table 3. Calculating the statistical indicators of horizontal visibility and PM10 observational and computational in Ahvaz, 2017
Index No. Mean±SD Min Max R
Visibility 87 - 0.3 8 -0.62
PM10_Ob 87 354.1±161.9 42.4 1119.4 -
Camino et al 87 632.0±1097.7 179.9 6663.3 0.86
D’Almeida 87 419.1±362.7 219.3 2220.3 0.87
Dayan 87 394.4±372.3 51.1 1709.2 0.80
Jugder et al 87 205.3±204.5 96.7 1236.2 0.87
Chung 87 359.4±241.3 125.8 1031.8 0.74
Figure 7. Horizontal visibility time series and PM10 observational and computational in Ahvaz, 2017
Figure 8. A: Relationship between horizontal visibility and PM10 observational and computational; and B: Distribution of PM10
observational and computational in Ahvaz, 2017
Mobarak Hassan E, et al. Comparison of Computational Concentration of PM10 With Actual Concentration. Arch Hyg Sci. 2021; 10(4):323-332.
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Jugder, et al., and Camino et al. indicators were 2220.3,
1709.2, 1031.8, 1236.2, and 6663.3, respectively (Figure
8). D’Almeida and Jugder et al. empirical indicators with
correlation coefficients of 0.87 provided the strongest
correlation with PM10. Also, Camino et al.’s empirical index
with a correlation coefficient of 0.86 addresses a high
correlation with particulate matter above ten microns.
Dayan and Chung’s indicators also correlated with the
particulate matter with correlation coefficients 0.8 and
0.74. The initial data in 2017 had the lowest level of missing
data, compared to 2016 and 2018 data (Tables 3 & 4).
In 2018, the lowest horizontal visibility of 100 m was
recorded on March 16, 2018, and February 19, 2018
(Figure 9). The highest D’Almeida, Dayan, Chung, Jugder,
et al., and Camino et al. indicators are 4927.8, 2264,
1089.8, 2899.6, and 22311.1, respectively (Figure 10).
The concentration of PM10 on this day has been reported
to be 3267 mg/m³. The highest correlation coefficient
was for Dayan empirical index (r=0.625).
The results of the Shao classification for dust events in
Ahvaz in 2016, 2017, and 2018 revealed that the most
severe dust storm in 2018 had 2 events. The total number
of dust events in Ahvaz in 2018 was equal to 2627 events
(Table 5). The classification of Shao results for the highest
and lowest indicators calculated in each category is
presented in Table 5.
Figure 9. Horizontal visibility time series and PM10 observational and computational in Ahvaz, 2018
Figure 10. A: relationship between horizontal visibility and PM10 observational and computational; and B: distribution of PM10
observational and computational in Ahvaz, 2018
Mobarak Hassan E, et al. Comparison of Computational Concentration of PM10 With Actual Concentration. Arch Hyg Sci. 2021; 10(4):323-332.
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4. Discussion
Numerous empirical equations have been estimated
with horizontal visibility and the concentration of particulate
matter less than 10 μm in diameter (PM10) [19-21].
The study results indicated that in the data of 2016, a
total of 1834 events, 2017 with 2111 events, and 2018,
a total of 2627 3-hour data with a field of visibility of
fewer than 10000 m had been recorded. D’Almeida
Dayan, Chung, Jugder, et al., and Camino et al. indicators
were calculated based on the established relationships.
R² suggested that empirical equation calculations
for the 2017 data were more accurate. The coefficients
of determination for D’Almeida, Dayan, Chung, Jugder,
et al., and Camino et al. indicators were calculated to be
0.87, 0.806, 0.745, 0.873, and 0.866, respectively. The
mean R² for D’Almeida, Dayan, Chung, Jugder, et al.,
and Camino et al. indicators were measured as 0.548667,
0.431333, 0.479667, 0.58333, and 0.539, respectively.
These results indicated that Jugder et al.’s [20] index with
R² equal to 0.87 is the best index for estimating PM10 in Ahvaz.
It should be noted that these results do not signify superiority
of this relationship over other empirical equations
but provide the best output for the climate of the city Ahvaz
city based on environmental and meteorological criteria.
Such a relationship makes it possible to estimate empirical
equations to determine the amount of particulate matter and
dust in the atmosphere. Pahlavan et al. (2015) provided the
following equation (y=4049e-6E-04x) for Ilam Province.
The correlation coefficient between computational and observational
values in this study was estimated to be 0.75.
The Jugder et al. index with R² of 0.548667 had the best
estimate in the present study. Dehghan et al. [23] introduced
Gaussian function as the most appropriate function of the
fit index. In this study, the linear regression method was
used. Camino et al. found an empirical equation between
horizontal visibility and particulate matter concentrations
in North Africa and compared them with other empirical
equations in the same region [14]. Bertahina et al. (2012)
simulated dust scattering by comparing fine-grained observational
data with a gray model with a coefficient of determination
greater than 0.8. The results of a study done by
Ebrahimi et al. [24] showed that approximately 45 percent
of Iran’s area has suffered from the increase of dust level in
the atmosphere from 1998 to 2018.
Table 4. Calculating the statistical indicators of horizontal visibility and PM10 observational and computational in Ahvaz, 2018
Indexes No. Mean±SD Min. Max. R
Visibility 118 - - 0.5 8 -0.53
PM10_Ob 118 518.5 616.0 57.1 3267.0 -
Camino et al. 118 524.3 564.5 179.9 22311.1 0.587
D’Almeida 118 397.2 228.8 219.3 4927.8 0.622
Dayan et al. 118 411.5 300.9 51.1 2264 0.625
Jugder et al. 118 191.8 126.1 96.7 2899.6 0.619
Chung 118 377.9 209.9 125.8 1089.8 0.620
Table 5. Classification of Ahvaz dust events based on Shao index
Year
Shao Classification 2016 2017 2018
Severe dust storm 0 0 2
Dust storm 31 25 35
Blowing dust 345 639 1248
Dust haze 1455 1441 1337
Total 1831 2105 2622
Mobarak Hassan E, et al. Comparison of Computational Concentration of PM10 With Actual Concentration. Arch Hyg Sci. 2021; 10(4):323-332.
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5. Conclusion
The results of the study show that according to Shao
classification, dust events in Ahvaz during 2016 to 2018
had an upward trend. Considering that Iran’s western
and southwestern provinces have had numerous problems
with dust storms in recent years, we can use empirical
relationships to understand the dust storms better.
As we know, different variables effectively affect the
occurrence of dust storms. The best indicator should be
identified in any region, such as Ahvaz. It is suggested to
hold studies like the present case in other areas involved
with dust storms to determine the best indicator for estimating
the relationship between horizontal visibility and
PM. Using empirical equations can lead to faster estimating
the concentration of particulate matter and dust.
Ethical Considerations
Compliance with ethical guidelines
There were no ethical considerations to be considered
in this research.
Funding
This research did not receive any grant from funding
agencies in the public, commercial, or non-profit sectors.
Authors' contributions
Conceptualization, acquisition, resources, and supervision:
All authors; Methodology: Elham Mobarak Hassan;
Investigation: Manoush Asadi; Writing - original draft: Ali
Shafie and Maedeh Rouzkhosh; Writing - review and editing:
Reza Ziaie Rad and Reza Sakipour; Data collection:
All authors; Data analysis: Ali Shafie, Elham Mobarak
Hassan and Manoush Asadi.
Conflict of interest
The authors declare t no conflict of interest.
Acknowledgments
We wish to thank the help provided by the Environment
Department of the Islamic Azad University of Ahwaz.
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