Archives of

1. Introduction
In 2011, the World Health Organization (WHO) ranked countries in terms of the state of particulate matter globally. According to this report, Iran was ranked eighth with regard to particulate matter pollution. In addition, several cities in Iran were among the most polluted cities in the world between 2003 and 2014, and Ahvaz was one of these cities. Further, according to the World Bank statistics, the amount of air pollution damage in the large cities of Iran was ten billion dollars in 2010 and 2012, accounting for 20% of the gross domestic product. Air pollution is not only a health-threatening factor but also a factor that holds countries back. Air pollution will decrease the quality of life by increasing the death rate and occurrence of many diseases [1]. Among air pollutants, particulate matters have harmful effects [2]. They can cause a wide variety of health effects such as bronchitis, asthma, lung cancer, and cardiovascular diseases [3]. The International Agency for Research on Cancer experts classified free air aerosols, regardless of their size or chemical composition, as group 1 carcinogenic substances for humans [4]. Particulate matters are among the sources of the production of particulate matter, which can be of natural and artificial types [5]. Approximately
40% of particles with a size between 1 and 2 μm remain
in the bronchi and air sacs. Particles with a size of 0.25-1
micron remain for a longer time in the respiratory tract
due to Brownian motion [6]. PM2.5 is a particulate matter
in the atmosphere that has an aerodynamic diameter
equal to or less than 2.5 μm. These particles are composed
of complex compounds such as organic and elemental
carbon, mineral dust, rare elements, and water, which
cause respiratory and cardiovascular diseases [7]. The
average PM2.5 concentration varies based on the location
of air pollution measurement stations, season, and
population density. Therefore, it has different effects on
people’s health based on time and place. Various studies
have been conducted on the health effects of particulate
matter on humans. Geravandi et al investigated the health
effects of contact with PM less than 10 μm in the air of
Ahvaz. The results demonstrated that the total number
of deaths and cardiovascular deaths due to the presence
of PM less than 10 μm was higher than the standard [8].
Studies by Martins et al [9], Minguillón et al [10], and
Burnett et al [11] have shown that exposure to particulate
matter has increased the number of respiratory and
cardiovascular diseases, and the number of deaths
related to particulate matters. Studies by Burnett et al
[11] and Lim et al [12] estimated the number of deaths
attributed to PM2.5 to be 3.2 million people worldwide
in 2010. According to the above-mentioned data, it is
necessary to evaluate different effects of PM2.5 in the air
on human health. For this purpose, existing models can
be used, which are mostly statistical-epidemiological. In
these models, air quality data in concentration intervals
are combined with epidemiological parameters such as
relative risk, base incidence, and attributable component,
and the result is displayed as mortality [13]. AirQ + is one
of such models that after a revision, has been published by
the WHO European Center for Environment and Health
to facilitate health effect assessments, in which data
related to the call-response relationship of population
exposure data are combined, and the limits of expected
health effects are estimated accordingly. This specialized
software enables the user to evaluate the potential
effects of contact with a specific pollutant on humans in
a certain place (a certain area of the city) and a certain
period of time [13]. Quantifying the health effects of
air pollution is an important guide for decision-makers.
Using quantification, the amount of the health effects of
air pollution is estimated, and the priority of air pollution
control is determined compared to other risk factors
[14]. According to the latest revision of the national
standard of open-air quality from the point of view of
the Environmental Protection Agency, the maximum
allowed annual and daily amount of PM is 15 μg/m3 and
35 μg/m3 [15]. Due to the fact that different responsible
organizations have presented various statistics and
numbers in the field of deaths caused by air pollution
with PM2.5, and given that the amount of health effects
caused by air pollution with this pollutant has not been
scientifically investigated, this study sought to zone the
concentration of PM2.5 and predict the total number of
deaths of people over 30 due to all causes in Ahvaz, Iran
during 2008-2017.
2. Materials and Methods
This is an analytical-descriptive ecological study based
on estimation modeling. AirQ + is used to predict the total
number of deaths of people over 30 attributed to PM2.5
pollution in Ahvaz from 2008 to 2017. This model is a
valid reliable tool for estimating the short-term effects
of air pollutants, which is prepared and released by the
WHO. In this research, the momentary concentrations
of PM2.5 in the air of Ahvaz between 2008 and 2017 were
initially obtained by referring to the Environmental
Protection Organization. The air pollution measurement
stations in this study included Naderi station (X:3467020
Y:279614), Environment Department (X:3466228
Y:275764), University Square (X:3466599 Y:277372), and
Meteorology station (X:3469931 Y:285413). Each of the
stations measured the PM2.5 concentration between 18
and 24 times during the day, and the total number of data
collected from these stations was 350,400. Table 1 presents
the number of the population at risk for people over 30.
Ahvaz with an area of 18,650 hectares is considered one
of the vast cities of Iran (the fourth largest city in Iran)
and is located at 31° 20ʹ North and 48° 40ʹ East (16). The
geographical location of the study area (Ahvaz) is shown
in Figure 1.
To perform statistical analyzes and use raw data, the
validity of these data should be checked, and the criteria
mentioned by the WHO are used for this purpose. Some
of these criteria are the ratio between the number of valid
data for two seasons (hot and cold seasons) should not be
more than 2, and there should be at least 75% valid data
to obtain one-hour average values of data with a shorter
average time. After removing the invalid data, other data
were inserted into the software. Then, primary (removal,
pollutant sheeting, and time equalization for average
estimation) and secondary (writing code, calculating
averages, and correcting conditions) processing were
Table 1. Population Over 30 at Risk During 2008-2017 in Ahvaz
Population at Risk
Year 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Population 545640 550460 544340 560034 564446 570857 574731 578295 581881 585185
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218 Arch Hyg Sci. Volume 11, Number 3, 2022
performed using Excel. Furthermore, relative risk and
baseline incidence parameters are required, and these
parameters vary for different pollutants and effects
[17,18]. Relative risk is often defined as an increase in
mortality and morbidity over the baseline with a specific
increase in the concentration of particulate matter, one
of the special indicators used in reports. The attributable
component or the attributable ratio is the part of the
health outcome that can be considered to be related to the
exposure of a specific population during a certain period
of time. In this way, according to the amount of RR and the
occurrence of that outcome, it is possible to calculate the
attributed component using the following formula, where
AP is equal to a proportion of the population exposed to
the pollutant during the specified study period. Moreover,
RR[c] denotes the relative risk of health effects on the
target population in the contact category “c”, and P[c]
equals the studied population. Calculations are based on
formulas 1 to 4.
AP = SUM {[RR (c) - 1]×P(c)}
SUM[RR (c)×P(c)] (1)
RRX = eX-X0 (2)
BE = B × AP (3)
NE = BE × N (4)
After data collection, the Kolmogorov-Smirnov test
was used . Using SPSS, the variance was considered to
compare the average concentration of PM2.5 in Ahvaz.
Additionally, to calculate the attributed component,
spatio-temporal fluctuations were determined according
to the station’s location, and then the health effects of
PM2.5 were determined in the ten-year period in Ahvaz.
In the last part, using ArcGIS 8.10 with the IDW method,
a spatial profile was drawn based on the air quality index
(AQI) in relation to the PM2.5 concentration changes in
different points, and in this way, the difference between
the selected points in Ahvaz was determined in terms of
pollution. Finally, a combination of EXCEL, LANDCAD,
and ARCGIS was employed to digitize the intensity of
pollution caused by different analyzed parameters.
3. Results
The average daily concentration of PM2.5 in the studied
period is depicted in Figure 2. The results represented
that the highest daily concentration of PM2.5 during a 10-
year period in Ahvaz was 234.19 μg/m3 in 2009, while
the lowest concentration was 18.15 μg/m3 in 2017. Based
on the results of the t-test at the 95% confidence level,
the concentration of PM2.5 had a significant difference
between the days of the year in a 10-year period (p).
Based on the results of Figure 3, the distribution of
PM2.5 in Ahvaz in 2008 showed that 65% and 35% of the
city are highly unhealthy and unhealthy, respectively.
The eastern and western parts of Ahvaz are unhealthy
and extremely unhealthy, respectively. In 2009, 45% and
55% of the city were very unhealthy and unhealthy. The
northwestern, southwestern, and western parts of Ahvaz
were unhealthy, and the southern, southeastern, central,
and eastern parts of this city were extremely unhealthy.
Based on the findings, 12% and 88% of the city was very
unhealthy and unhealthy in 2010. In addition, 17% and
83% of the city in 2011, as well as 5% and 95% of the city
in 2012 were very unhealthy and unhealthy, as well as
highly unhealthy and unhealthy, respectively.
Figure 1. Location of Study Stations in Ahvaz.
Figure 2. Average Daily Concentration of PM2.5 in Ahvaz in 2008-2017 .
Arch Hyg Sci. Volume 11, Number 3, 2022 219
Concentration of PM2.5 and Prediction of Total Death Rates of People over 30
In 2013, 32% and 78% of the city were unhealthy and
very unhealthy, respectively. That year, the west, northwest,
east, and north-east parts of Ahvaz were very
unhealthy. The other parts were unhealthy. In 2014, 4%
and 96% of the city was very unhealthy and unhealthy.
The results also revealed that 11% and 89% of the city was
very unhealthy and unhealthy in 2015. In 2016, 43% and
57% were very unhealthy and unhealthy. Moreover, 2%
and 98% were very unhealthy and unhealthy in 2017. All
parts of Ahvaz were unhealthy, except for a small central
part of this city, which was very unhealthy.
In Table 2, the relative risk indices, the attributed
component, and the number of cases attributed to PM2.5
have been calculated for the total death from all causes in
the base incidence of 55.806 people in 105 during 2008-
2017. According to the results, it was between 21.1 and
39.1 on average in 2008-2017. The highest and lowest
levels of relative risk were 39.1 in 2009 and 21.1 in 2014,
respectively. In expressing the concept of relative risk, it
should be noted that zero does not play a role in relative risk,
and the base is 1. Accordingly, higher than 1 is considered
a risk factor, and smaller than 1 is regarded as a preventive
or protective factor. The total number of attributed cases
is proportional to the attributed component. Based on the
findings, 21.47% of deaths from all causes in Ahvaz in
2008 were attributed to PM2.5, which was 945 cases. This
number of deaths was in the population at risk of 545 640
people. In a population of 100 000, this number was 173
people. The highest attributed component was 6.30% in
2010, while the lowest amount was 5.17% in 2014. The
calculations of the number of attributed cases per 100 000
people at risk showed that the highest rate was 246.82 in
2010 and the lowest rate was 17.141 per 100 000 people
in 2014. According to this estimate, the total number of
cases attributed to the total population was 1344 and 811
in 2010 and 2014, respectively.
4. Discussion
The first available report about the trend of hourly changes
in air pollutants in Tehran is related to the reports of the
Japan International Cooperation Agency known as JICA.
In this report, the trend of hourly changes in air pollutants
in Tehran in two stations, Fatemi and Bazar, affiliated to
Air Quality Control Company Tehran Municipality was
set for one year between October 1995 and September
1996 [19]. The results of this study revealed that the
average amount of PM2.5 in Ahvaz during 2008-2017
was beyond the recommended limits of the WHO.
Figure 3. PM2.5 Dispersion Map in Ahvaz Measuring Stations During 2008-2017 (based on AQI index); Investigating (A) PM2.5 in 2008, (B) PM2.5 in 2009, (C)
PM2.5 in 2010, (D) PM2.5 in 2011, € PM2.5 in 2012, (F) PM2.5 in 2013, (G) PM2.5 in 2014, (H) PM2.5 in 2015, (K) PM2.5 in 2016, and (L) PM2.5 in 2017.
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220 Arch Hyg Sci. Volume 11, Number 3, 2022
Shahram Mohaghegh et al showed that the peak hours of
pollution for PM2.5 were from 20:00 to 23:00 from 2004
to 2008 in Tehran. In addition, in the comparison of the
daily average concentration of pollutants in winter and
summer, it was found that the concentration of pollutants
was higher in winter, and ozone pollutants and PM2.5
were higher in summer [20]. The hourly changes in the
concentration of PM2.5 in Toronto were almost the same
as in Tehran [21]. In both cities, the hourly changes of this
pollutant were small, and the peak hours of concentration
were less visible. As expected, the hourly changes in the
ozone pollutant concentration were also similar in all
three cities, and this pollutant was at its maximum in the
hot afternoon hours [20]. Amarloei et al reported that
the maximum and minimum daily average PM2.5 in the
one-year study period were 4.151 μg/m3 and 4.2 μg/m3
in April and late October, respectively. The highest and
lowest monthly average concentrations of PM2.5 were
6.44 μg/m3 and 2.10 μg/m3 in June and October 2012,
respectively. The highest concentration was at 2:30, 13-
15, and 18-21, and the annual average concentration of
PM2.5 was 1.7 times the annual clean air standard [22]. In
a similar study conducted by Shahsavani et al in Ahvaz
in (April-October) 2010, the average concentration
of PM10, PM2.5, and PM1 in the entire study period was
07.407, 5.69, and 37.02 μg/m3, respectively, and the
maximum hourly concentration for these three pollutants
was 6.5337, 9.910, 3.495 μg/m3 in June, respectively [23].
Further, the trend of changes in the average concentration
of PM10, PM2.5, and PM1 represented the three peaks at
noon [13], evening [19], and night [23]. In another study
by Goudarzi et al in Ahvaz (from November 2011 to May
2012), the average concentration of PM10, PM2.5, and PM1
in the entire study period was 9.595, 9.114, and 5.34 μg/m3,
respectively, and the maximum hourly concentration was
1.4730, 4.774 and 2.164 μg/m3 in February, respectively
[24]. These values, similar to the current study, were
beyond the recommended limits of the WHO. In a study
in 2013-2015, Karimi et al concluded that the level of
significance for PM2.5 concentrations on different days
of the week in the Kruskal-Wallis test was greater than
0.01. Therefore, there was no significant difference in
PM2.5 concentrations on different days of the week, but
the differences in the PM2.5 concentration in different
years, seasons, months, and hours were significant, which
is consistent with our results. Dunn’s test shows these
differences. There is a significant difference in terms of
the PM2.5 concentration between 2014, 2015, and 2016.
This difference is significant between different seasons,
except for spring and autumn. For different months,
there is a significant difference in terms of the PM2.5
concentration (e.g., between April and November). Based
on the results, a significant difference was found in the
PM2.5 concentration between different hours [25]. These
results are in line with those of this study. The occurrence
of high concentrations of PM2.5 in the afternoon hours
Table 2. Comparison of relative risk, attributed component, and cases attributed to PM2.5 in death from all causes (adults 30 + ) in Ahvaz during 2008-2017
Study year Population at risk
Incidence of base per
100 000 people
Relative risk
Attributed component Number of attributed cases per
100 000 population at risk
Total number of
(%) attributed cases
2008 545640 806.55
1.27 21.47 173 945
(1.17-1.37) (14.58-2741) (118-221) (642-1206)
2009 550460 806.55
1.39 28.34 228.55 1258
(1.24-1.55) (19.53-35.7) (157.49-287.96) (867-1585)
2010 544340 806.55
1.44 30.6 246.82 1344
(1.26-1.62) (21.19-38.38) (170.94-309.58) (931-1685)
2011 560034 806.55
1.28 22.07 177.97 997
(1.17-1.39) (15-28.14) (121-236.97) (678-1271)
2012 564446 806.55
1.32 24.77 199.76 1128
(1.20-1.45) (16.94-31.42) (136.59-253.44) (771-1431)
2013 570857 806.55
1.26 20.8 167.78 958
(1.16-1.36) (14.11-26.59) (113.78-214.48) (650-1224)
2014 574731 806.55
1.21 17.5 141.17 811
(1.13-1.29) (11.79-22.51) (95.09-181.56) (547-1043)
2015 578295 806.55
1.25 20.14 162.48 940
(1.15-1.34) (13.64-25.78) (110.03-207.95) (636-1203)
2016 581881 806.55
1.25 20.37 164.33 956
(1.16-1.35) (13.8-26.07) (111.34-210.231) (648-1223)
2017 585185 806.55
1.25 20.42 16.4/73 964
(1.16-1.35) (13.84-26.13) (111.62-210.731) (653-1233)
Arch Hyg Sci. Volume 11, Number 3, 2022 221
Concentration of PM2.5 and Prediction of Total Death Rates of People over 30
can be attributed to unstable weather conditions and the
increase in wind speed in the afternoon. Similar studies
confirm this issue [26,27]. Esmaili reported that the lowest
concentration of PM2.5 pollutant (29 μg/m3) was recorded
on Fridays, and Tuesdays and Thursdays were recorded
as the highest pollutant concentrations. Furthermore, the
minimum and maximum air pollution in Mashhad during
the statistical period were at 16:00 and 22:00, respectively
[28]. In a study in Khorramabad in 2013-2017, Jafarzadeh
Haghighi Fard et al showed that the maximum and
minimum monthly concentrations of PM2.5 and PM10 were
in June and November, respectively, and the maximum
and minimum seasonal concentrations of PM2.5 and PM10
were in summer and autumn, respectively. The maximum
and minimum annual concentrations of PM10 and PM2.5
were assigned to 2014 and 2015, as well as 2014 and
2016, respectively. Moreover, the results of statistical tests
demonstrated that the concentration of PM10 and PM2.5
were not only affected by relative humidity, temperature,
precipitation, and wind speed, but also by their polluting
sources, whose production has increased or decreased in
the studied period [29]. In a study in Ilam during 2005-
2012, Alainejad et al found that based on the multiple
linear regression model, the relationship between climatic
components and particulate matters was estimated at a
medium level (R = 0.589); in addition, the temperature
had a significant relationship with the concentration of
particulate matters (P < 0.05). Other variables had no
significant effect on particulate matters (P ≥ 0.05) [30].
In the 20-year statistical pattern of dust events in Ilam
province, Bahiraei et al (31) also announced that the
dust phenomenon occurs in the western regions of the
country due to the location of the low-pressure core over
the Mediterranean Sea and its landing on the deserts of
Iraq, Syria, and Arabia in June and July. The results of
this study also confirmed the existence of a significant
relationship between horizontal vision and particulate
matter. The results of linear regression fitting between
variables in 2015-2016 estimated the R index to be 0.884,
confirming the existence of a significant relationship
between particulate matter and air temperature (P ≤ 0.05).
Likewise, Motesadi Zarandi et al indicated that the south
and southwest regions are polluted areas in terms of PM2.5,
while the northern and eastern regions and then the
central regions have experienced high traffic, and traffic is
not the main cause of PM2.5 [32]. Asrari and Paydar found
that seasonal changes in the maximum concentration of
particulate matter occurred in autumn and particularly
in October. Unhealthy days increased in 2011-2013 and
then decreased in 2014 and 2017 and increased again
in 2016 . Additionally, their results revealed that there
was a significant difference in PM2.5 changes with year,
season, and month, and it was inversely related to climatic
parameters such as temperature, rainfall, and wind speed.
Dispersion maps also represented that the east and, to
some extent, the center of the city is more polluted than
other areas [33], which corroborates with the findings of
this study. The results of the survey of PM2.5 pollutants in
Ahvaz showed that during 2008-2017, 23.6% and 4.76%
of Ahvaz was very unhealthy and unhealthy, respectively.
Based on the results, the average concentration of PM2.5
was 17.50 μg/m3 in 2017, while it was 10 μg/m3 according
to the WHO guidelines for open air. Therefore, it was 5.02
higher than the WHO standard.
The comparison of quantification results for all cases of
death from all causes of exposure to PM2.5 in Ahvaz during
2008-2017 is shown using AirQ + . Based on the results
of comparing the relative risk indices, the attributed
component, and the number of cases attributed to PM2.5
for death from all causes in the basic incidence of 55 806
people in 105 in the population over 30 in the ten years of
Ahvaz, the highest and lowest relative risk was 1.44 and
1.23 in 2010 and 2014, respectively. The highest and lowest
percentage of deaths attributed to PM2.5 was 30.6% and
17.5% in 2010 and 2014. The findings indicated that the
highest and lowest number of cases per 100 000 population
at risk of death from all causes attributed to PM2.5 was 247
and 141 people in 2010 and 2013, respectively, and the
highest and lowest total number of deaths from all causes
attributed to PM2.5 was 1344 and 811 people in 2010 and
2014, respectively. The results of the correlation test
between PM2.5 and the total mortality index over 30 from
all causes showed that there was a correlation at the 99%
confidence level. In the study conducted by Faridi et al
on the long-term trends and health effects of PM2.5 and
O3 in Tehran (2006-2015), the outcome of mortality from
all causes for people over 30 was decreasing, and out of
5300 people in 2006 (first year) reached 3775 people in
2015 (10th year). The main reason for the decrease in the
trend of mortality attributed to all causes of PM2.5 in this
study was mostly due to the decrease in basic mortality
[34]. Hadei et al investigated the distribution and number
of deaths due to heart diseases and sudden deaths due
to chronic exposure to PM2.5 in 10 Iranian cities (2013-
2015) with the AirQ + model. They found that the
mortality rate due to heart diseases attributed to the PM2.5
concentration increased, and the southern and western
cities (including Ahvaz) demonstrated a high number of
deaths per 100 000 people [35]. Hopke et al reported that
PM2.5 concentrations increased between 2014 and 2016.
In the study by Hopke et al, the total number of natural
deaths due to short-term exposure to fine particles was
338 [36]. The effects of particulate matter on human
health were among the most important subjects studied
in recent years, and the importance of these studies is
greater in areas such as the southwestern regions of
Iran, which are more involved in this phenomenon. The
mortality rate in today’s population is affected by public
health components. Pollutants such as PM2.5 are directly
related to the health of the population. To achieve high
Zallaghi et al
222 Arch Hyg Sci. Volume 11, Number 3, 2022
levels of health, identifying the nature of health and the
factors affecting it will play the most important role. If the
factors threatening health and their importance are not
thoroughly studied, the measures taken to promote public
health will not have the necessary efficiency. Estimates
show that in 2017, an average of 800 people died on a daily
basis around the world due to exposure to particulate
matter. Most of these people were residents of developing
countries [37]. However, this is not the only effect caused
by exposure to particulate matter.
The results of various studies revealed that exposure to
these matters leads to a decrease in the lifespan of people.
In 2021, Cheng et al, using a nonlinear logistic distribution
model, announced that 2.5% of deaths caused by PM2.5 and
5.33% of deaths related to PM10 occurred at concentrations
lower than the WHO recommended limits [38]. Rovira
et al estimated between 0.5% and 7% of all deaths in
Tarragona County to be attributed to PM2.5 [39]. The results
of Yang et al showed that a total of 861 494 respiratory
diseases and 586 962 chronic obstructive pulmonary
diseases caused death in 96 Chinese cities during 2013-
2016 [40]. Noferesti et al sought to predict the mortality
rate due to particulate matters using AirQ and health risk
assessment in Sanandaj. The data relating to the amount
of particulate matter in the statistical years 2012-2013 was
obtained from the Sanandaj Environmental Department.
In the field survey, the particles were measured in 17
points of Sanandaj in different months for one year with
the help of a portable particle measuring device. Then,
AIRQ was used to quantify the effect of air pollutants.
The investigation of air pollution by particulate matter
in Sanandaj confirmed that on most days of the year, the
region had low pollution, and unhealthy and dangerous
days were less. It included less than two months, mainly
from June to August. The average particulate matters in
different months also demonstrated the highest amount
in the first 5 months, mainly in June. This was despite the
fact that the number of patients referred to medical centers
due to air pollution was the highest in winter and April.
The highest air pollution was measured in the northeast
of the city (zone one), which was due to the presence of
repair centers, passenger terminals, and industrial estates.
In examining the number of patients referred to medical
centers due to air pollution in Sanandaj and comparing it
with the prediction of the model, the results of the model
represented less statistics. Nonetheless, the number of
deaths predicted in the model indicated more statistics
than the statistics in the region. In terms of determining
the health risk of the population of the region, the highest
risk was predicted for region 1 and in the age group 20-
44 years old, the main reason for which was the greater
exposure of these people to pollution. [41]. Luan et al
examined the relationship between ambient air pollution
and years of life lost in Beijing and collected the mortality
data between January 1, 2008, and December 31, 2012,
from the National Mortality Surveillance System of China
to estimate the illness burden caused by air pollution. The
distributed lag nonlinear model was used to analyze it
and adjusted for long-term trends and climate-damaging
factors. A total of 386 695 deaths were included in the
study. The results showed that 185 360 and 37 812 deaths
were caused by cardiovascular and respiratory diseases,
respectively [42]. Yarahmadi et al also evaluated mortality
due to long-term exposure to particulate matter in the
ambient air of Tehran. The purpose of the study was to
investigate overall mortality, lung cancer, and chronic
obstructive pulmonary disease attributed to long-term
exposure to PM2.5 among adults over 30 in Tehran from
March 2013 to March 2016 using AirQ + . AirQ + was
employed to estimate the number of deaths caused by
PM2.5 concentrations greater than 10 μg/m2. Air quality
data were obtained from the Department of Environment
and Tehran Air Quality Control Company. The results
revealed that the annual average concentration of PM2.5
in 2015-2016 decreased by 13% compared to 2013-2014.
The annual average number of natural deaths, chronic
obstructive pulmonary disease, and lung cancer due to
long-term exposure to PM2.5 in adults over 30 was 5073,
158, and 142, respectively. The results of all three health
endpoints indicated that mortality caused by PM2.5
decreased annually from 2013 to 2016, and the decrease
in mortality was related to the corresponding decrease
in the PM2.5 concentration [43]. The results of our study
corroborate those of Yarahmadi et al. Therefore, the
mortality rate decreased with a decrease in pollutant
concentration. Additionally, Limaye et al studied the
application of exposure functions to an integrated
response to PM2.5 pollution in India. In this research, these
functions were used to estimate the risks of mortality
from causes related to exposure to ambient PM2.5 in the
population in India until 2030 using the predictions of
the greenhouse gas and air pollution interactions and
synergies. The indices of loss of life expectancy and
national deaths caused by PM2.5 were calculated in the
mentioned study. The results showed that PM2.5 in India
will reach an annual average of 74 μg/m3 in 2030, which is
almost eight times the air quality guidelines of the WHO
[44]. Piersanti et al conducted a study entitled “Italian
National Air Pollution Control Program: Air Quality,
Health Effects, and Cost Assessment”. In their research,
the scenarios formulated in 2030 in the Italian national
air pollution control program were discussed, and 2010
was considered as the reference year. Two scenarios “with
measures” and “with additional measures” demonstrated
a significant reduction in the concentration of pollutants,
namely, PM2.5, NO2, and O3. The scenarios here were
also employed to provide an integrated approach to
calculating the effect of the program on health effects
(mortality) and associated costs. Avoidable attributable
and associated costs are reported here at the national
Arch Hyg Sci. Volume 11, Number 3, 2022 223
Concentration of PM2.5 and Prediction of Total Death Rates of People over 30
and regional levels, providing a significant framework
for evaluating air pollution reduction measures with an
integrated approach. Therefore, the proposed method
may be developed and applied to assess the overall
positive (environmental, health, and economic) benefits
determined by air pollution control programs or other
integrated policies that determine air quality, energy, and
climate goals [45]. Similarly, Sarizadeh et al investigated
the correlation of changes in urban air pollution with the
death rate of cardiovascular and respiratory patients in
Ahvaz between 2008 and 2017. Based on the results of
the Poisson regression analysis, a significant relationship
was observed between the average concentration of PM
and the death rate of patients diagnosed with respiratory
problems [46], which matches the results of our study.
5. Conclusion
Examining the results of the dispersion maps created in
this system will advance us in providing management
solutions to reduce and control pollutants at the level
of large cities, which will ultimately create good quality
air. For this purpose, it is necessary to provide accurate
and complete data and create more measuring stations
around the traffic centers. In all the reviewed years, the
value of PM2.5 was higher than the WHO standard (10 μg/
m3), showing that it was the source of air pollution in this
city. The results of this study confirmed the great impact
of PM2.5 on public health in Ahvaz. The results showed
that during the ten years of the study, there were 10 201
cases corresponding to 64.22% of all deaths attributed to
all causes of PM2.5. Considering the presence of pollution
round the clock, the results were a wake-up call for the
general public and the authorities to look at the problem
of air pollution not as a temporary, but as a comprehensive
problem that affects everyone in society and to take
effective measures to stop the process of air pollution.
Therefore, appropriate measures and policies should be
determined to reduce the concentration of PM2.5 in the
air of Ahvaz to decrease the health effects on people over
30 in this city. In general, by conducting such studies, by
increasing the level of technical knowledge in the field of
spatio-temporal changes of particulate matters and their
effect on public health, it is possible to adopt management
strategies and solutions to deal with such phenomena and
reduce its adverse effects and promote public health.
5.1. Limitations
This study, similar to other studies, had limitations that
knowledging about them will help in using the results of
this study. Among the limitations of this study is that it is
assumed that the concentrations measured at the sampling
points (monitoring stations) represent the average level
of exposure of the people living in Ahvaz. Another
limitation is related to relative risk values obtained from
other studies in different communities. However, despite
the mentioned limitations, this method is one of the most
reliable ones used to evaluate the health effects attributed
to air pollutants, designed and presented by the WHO.
Acknowledgment
The authors would like to express their gratitude to the Islamic Azad
University of Ahvaz, Khuzestan Province Environmental Protection
Organizations, Khuzestan Meteorology, and Health and Deputy of
Health for their cooperation in obtaining the required data.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the
publication of this manuscript. Furthermore, the ethical issues have
been completely observed by the authors, including plagiarism,
informed consent, misconduct, data fabrication and/or falsification,
double publication and/or submission, and redundancy.
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