Atmospheric Concentrations, Seasonal Variations and Health Risk Assessment of PM 2.5 , PM 10 , and SO 2 in Tehran Metropolis, Iran

doi:10.21203/rs.3.rs-3441505/v1

This study assessed seasonal and annual variations as well as the health risks associated with exposure to PM_2.5, PM₁₀, and SO₂ in the ambient air of Tehran from 2019 to 2021. The findings revealed that the average annual concentration of PM_2.5, PM₁₀, and SO₂ varied from 28.24 to 32.34 µg/m³, 69.57 to 82.22 µg/m³, and 14.94 to 17.98 µg/m³, respectively. The amounts of PM_2.5 and SO₂ were the greatest in the west and southwest, while PM₁₀ was the most abundant in the east and northeast which were above WHO guidelines. In exposure duration scenarios of 8 and 12 hours, the mean hazard quotient (HQ) for PM_2.5 and PM₁₀ was >1, suggesting an unacceptable risk to human health. There was no risk to human health according to the mean HQ for SO₂ at all exposure periods of 3, 8, and 12 hours. Further evidence that exposure time plays a significant part in health hazards was provided by the fact that the mean HQ values of exposure to PM_2.5 and PM₁₀ in exposure times of 3 hours were both <1. The Sobol sensitivity analysis revealed that the PM_2.5 concentration in HQ was the most sensitive indicator of the populace.

Health risk assessment

Particulate matters

Sulfur dioxide

Sobol sensitivity analysis

non-carcinogenic risk

air pollution

Tehran

Today, rapid industrialization and urbanization release a variety of pollutants into the atmosphere. Therefore, air pollution is considered to be the most important environmental problem in the world (Mohammadi-Moghadam et al., 2020). It is estimated that air pollution causes 8.9 million deaths worldwide, accounting for 7.6% of total annual death rates and resulting in 103.1 million lost healthy life years (Hassan Bhat et al., 2021). Air pollution is the presence of substances in the air in concentrations that have adverse effects on people, animals, and plants. These substances may be mixtures of solid and liquid particles (particulate matters such as PM_2.5 and PM₁₀), gases (such as NO₂, NO, SO₂, O₃), biological aerosols, or atmospheric agents biological aerosols, or atmospheric agents that can be dispersed, transported, or deformed at any time (Khafaie et al., 2016; Malakootian et al., 2022). The scale of the problem is expanding rapidly due to the high urbanization rate (Bayat et al., 2019). The situation is worsening in developing countries with more urbanization, industrialization, and a rapidly growing population (Sunarsih et al., 2019). Iran is a developing country facing air pollution problems. In many Iranian cities, air pollution has reached dangerous levels, with concentrations of some pollutants in large cities in Iran three times higher than national standards and world health organization (WHO) air quality guidelines (Hadian et al., 2020).

Particulate matter (PM) is considered one of the most harmful air pollutants released from biological and anthropogenic sources or formed by atmospheric reactions (Khaniabadi et al., 2019). PM is a subset of air pollution and represents a complex mixture of suspended particles in the air with varying composition, shape, size, and optical properties depending on their origin (Sánchez-Piñero et al., 2021). The two main PMs that are considered as criteria for air pollutants are PM_2.5 and PM₁₀.

PM_2.5 is defined as fine particles having a diameter of 2.5 microns or less, which can only be observed with electron microscope. These are mostly generated through different types of combustion processes. such as power plants, automobiles, burning wood for homes, and some industrial processes (Wambebe & Duan, 2020). Various toxic substances in PM_2.5 can pass through nose hair filtering, reach the end of the respiratory tract with airflow, accumulate by diffusion, and damage other body parts by air exchange in the lungs (Omidi Khaniabadi et al., 2019). Different epidemiological studies have found strong associations between long-term PM_2.5 exposure and premature mortality due to ischemic heart disease (IHD), stroke, chronic obstructive pulmonary disease (COPD), and lung cancer (LC). the effects of PM_2.5 exposure also include morbidity, including chronic respiratory conditions like bronchitis and asthma, and cataracts (Sharma et al., 2020).

PM₁₀ are coarse particles containing PM_2.5 and particles up to 10 microns in diameter. These particles are breathable that can be generated from vehicle dust on the roads and grinding operations (Wambebe & Duan, 2020). Increased PM₁₀ concentration has been reported to increase non-accidental mortality (Hassan Bhat et al., 2021). Inhaling PM₁₀ can irritate and infect the lungs. Long-term exposure to PM₁₀ may result in pulmonary, cardiovascular, and lung cancer (Hwang et al., 2017).

Sulfur dioxide (SO₂) is one of the most important pollutants in the atmosphere which is produced by vehicles and burning oil and coal in industries (Ebrahimi & Qaderi, 2021). SO₂ can contribute to visibility degradation, haze formation, and acid rain in the atmosphere. In addition, high levels of SO₂ exposure may lead to increased cardiorespiratory mortality and morbidity (Wu et al., 2020). It was shown that exposure to SO₂ was associated with respiratory symptoms, such as wheezing and shortness of breath, total respiratory mortality, increased risk of asthma, and a Worsening of existing respiratory disease (Saygın et al., 2017).

Environmental health risk assessment is the process of recognizing potential risks to public health caused by exposure to environmental toxicants (Gusti, 2019). Risk assessment can aid in identifying the risky pollutants' priority and choosing effective control methods that reduce people's exposure to pollutants and mitigate their adverse health impacts. In addition, risk assessment studies can determine the proportion of each exposure route to a specific environmental pollutant (Rajabi et al., 2022). This set of data can then be utilized to develop standards, which is a significant and valuable part of risk assessment studies. This is due to the fact that the distribution of pollutants through exposure routes varies around the world. (Kermani, Jonidi Jafari, et al., 2021a).

Atmospheric concentrations, seasonal variations, and health risk assessment of PM_2.5, PM₁₀, and SO₂ with appropriate approaches are essential to evaluating the effectiveness of the implemented air pollution control measures. Air quality policy and decision-makers can use these factors as a useful tool to modify and revise air pollution controls strategies to reduce pollutant concentrations and their health effects. Therefore, this study was designed to investigate the seasonal variations of PM_2.5, PM_10, and SO₂ concentration in the ambient air of Tehran and determine the health risks of these pollutants.

2.1. Study area

Tehran, the capital of Iran, is a fast-growing metropolis facing serious environmental and health problems due to air pollution (Torbatian et al., 2020). The center of the city is on latitude 35°41′ N and longitude 51°26′ E. Tehran is located at an altitude of 1000 to 1800 meters above sea level, on the southern slope of the Alborz Mountain range. This city has a mellow and mild climate with hot summers. The maximum temperature is 29°C and the minimum temperature is 0.1°C. Tehran is surrounded by high mountains on both sides and has a population of 9.5 million and is characterized by high levels of particulate matter and gaseous pollutants that adversely affect the environment and human health (Ramyar et al., 2019; Taksibi et al., 2020). Tehran’s wind direction is from the northwest to the southeast. As a result, the wind transports air pollution from various parts of the city, including cities in Tehran's northwest (such as Karaj), to the megacity's south and southeast (Delavar et al., 2019).

2.2. Data collection and analysis

Several air quality monitoring stations (AQMs) are currently in operation in Tehran to raise public awareness about air quality. These AQMSs monitor the levels of PMs, nitrogen dioxide, ozone, sulfur dioxide, and carbon monoxide and show them to the public on Pollution Indicator Boards. Data on ambient PM_2.5, PM₁₀, and SO₂ concentrations were obtained from the 23 AQMs in Tehran. Figure 1 shows the location of some AQMs sites. The average seasonal concentration of PM_2.5, PM₁₀, and SO₂ from 2019 to 2021 was done by averaging all available data across the monitoring stations.

2.3. Health risk assessment

The non-carcinogenic risk is determined by the exposure risk value, while the carcinogenic risk is determined by the lifetime carcinogenic risk caused by human exposure to carcinogenic compounds. The non-carcinogenic health risk of PM_2.5, PM₁₀, and SO₂ was assessed based on the United States Environmental Protection Agency methodology (EPA) (Liu et al., 2020). In this study, three scenarios were considered with three exposure times of 3, 8, and 12 hours.

The process for estimating health risk involves the following three steps: (1) Human exposure to the toxic effects of PM₁₀, PM_2.5, and SO₂ mainly takes place via inhalation, (2) determining Reference Dose (RfD), and non-carcinogenic risk assessment. Therefore, only the exposure concentration (EC) via inhalation was calculated by the following equation (Zhang et al., 2018):

EC = $\frac{C\times IR \times ET \times EF \times ED}{BW\times AT}$ (Eq. 1)

Where EC is the average daily dose of the pollutant (µg/kg.day), C is the concentration of the pollutant in the atmosphere (µg/m³), IR is inhalation rate (m³/day), ET is exposure time or event (hr./day), EF is exposure frequency (days/year), ED is exposure duration (years), BW is the average body weight of the receptor over the exposure period (Kg), and AT is averaging time (days).

RfD is an estimate of continuous inhalation exposure that is unlikely to have a negative impact on a person's health over their lifetime. It used the WHO annual mean air quality guideline (AQG) levels of PM_2.5, PM₁₀, and SO₂ as the reference concentration (RfC) to estimate the RfD of these pollutants due to the lack of information about their RfD. therefore the values of 5 µg/m³, 15 µg/m³, and 40 µg/m³ (Organization, 2021) were used as RfC of PM_2.5, PM₁₀, and SO₂, respectively, then RfD values were calculated by the following equation (Yunesian et al., 2019):

RfD= $\frac{RfC \left(inhalation reference concentration \raisebox{1ex}{$\mu g$}\!\left/ \!\raisebox{-1ex}{${m}^{3}$}\right.\right) \times Assumed inhalation rate \left(\raisebox{1ex}{${m}^{3}$}\!\left/ \!\raisebox{-1ex}{$day$}\right.\right) \times 1 }{BW \left(Kg\right)}$ (Eq. 2)

After the EC values were calculated, the non-carcinogenic risk was determined for each pollutant by calculating the hazard quotient.

HQ = $\raisebox{1ex}{$EC$}\!\left/ \!\raisebox{-1ex}{$RfD$}\right.$ (Eq. 3)

Where HQ is the hazard quotient and RfD is the reference dose $(\mu$g/m³). An HQ value > 1 indicates that there is a greater chance of non-carcinogenic effects (Chalvatzaki et al., 2019; Matooane & Diab, 2003; Wu et al., 2019). A summary of the values used for Health risk assessment calculation is presented in Table 1.

Table 1

Parameters description for non-carcinogenic health risk assessment
Parameter	Description	Value	Unit	Ref.
C	Concentration	-	µg/m³	This study
IR	Inhalation rate	20	m³/day	(Mbazima, 2022)
ET	Exposure time	3	hr.	This study
		8
		12
EF	Exposure frequency	350	days	(Matooane & Diab, 2003)
ED	Exposure duration	30	years	(Matooane & Diab, 2003)
BW	Body weight	68.1	Kg	(Mbazima, 2022)
AT	averaging time	30	years	(Matooane & Diab, 2003)
RfC	Reference concentration	5 µg/m³ for PM_2.5		(Organization, 2021)
		15 µg/m³ for PM₁₀
		40 µg/m³ for SO₂

2.4. Sobol sensitivity analysis

The Sobol sensitivity analysis with the Monte Carlo approach was used to determine the significant input parameters along with their influence on the variance of the exposure results (Karunanidhi et al., 2021). The Sobol Sensitivity Indices (SIS) indicate the proportion of the partial variable compared to the total variable. The first SI term is known as the First Order Sensitivity Index (FOSI), and it describes the influence of a single variable on the variation in model outputs. The second term is known as the Second Order Sensitivity Index (SOSI), which describes the influence of variables interaction. Finally, the Total Order Sensitivity Index (TOSI) is utilized to calculate the variable's overall influence on the final variance. A sensitivity index greater than 0.1 (very sensitive), 0.01–0.1 (sensitive), and less than 0.01 (insensitive) represent those input variables that are notably relevant, conspicuous, and unresponsive, respectively (Rajabi et al., 2022). In this study, Sobol sensitivity analysis was performed using the R-platform version 4.1.2 (‘EnvStats’, ‘sensobol’, ‘Enviro-PRA’ packages).

2.5. Spatial distribution

The Arc-GIS program (10.8.1 version) was used to show the spatial distribution of PM_2.5, PM₁₀, and SO₂ concentrations in the ambient air of Tehran. Inverse Distance Weighted (IDW) was used for pollution concentration interpolation. IDW is one of the most basic and often-used interpolation methods (Kermani, Jonidi Jafari, et al., 2021b).

3.1. pollutants concentration

Table 2 summarizes the average concentrations of PM_2.5, PM_10, and SO₂ during the period of study, also average annual PM_2.5, PM_10, and SO₂ concentrations in 2019, 2020, and 2021 were shown in Figs. 2, 3, and 4, respectively. The annual average mass concentration of PM_2.5 was 30.17 ± 8.2 µg/m³ in 2019, 28.24 ± 6.52 µg/m³ in 2020, and 32.34 ± 8.37 µg/m³ in 2021, which is much higher than the limit of annual concentration of PM_2.5 according to the WHO air quality guidelines (5 µg/m³) (Organization, 2021). The reason may be due to local pollutants surrounding the area, rising in the use of motor vehicles, the usage of low-quality fuels, the use of old, poorly inspected, and maintained automobiles, and weak control of vehicle exhaust emissions (Heger & Sarraf, 2018; Yunesian et al., 2019). Regarding the regional transmission characteristic of PMs, which is influenced by wind patterns and the location of emission sources, the desert region to Tehran's south, southeast, and west has an impact on the level of pollutant concentration (Bai et al., 2021; Khan et al., 2016).

Table 2

Average concentration of PM_2.5, PM_10, and SO₂ in 2019–2021
Year	Season	PM_2.5 (µg/m³)	PM₁₀(µg/m³)	SO₂ (µg/m³)
2019	Winter	27.78	68.47	6.46
	Spring	19.81	54.64	3.78
	Summer	34.58	83.18	11.11
	Fall	38.5	82.58	6.11
2020	Winter	33.62	73.70	6.72
	Spring	20.11	54.23	3.66
	Summer	25.81	76.12	5.64
	Fall	33.41	74.23	6.8
2021	Winter	38.41	82.17	8.27
	Spring	23.11	69.29	4.68
	Summer	27.17	82.06	5.11
	Fall	40.68	95.35	8.62

Although geographic conditions influence pollutant dispersion, considering that PM_2.5 content is mainly influenced by two factors, that is emissions from combustion sources and the creation of secondary particles in the atmosphere, high levels of PM_2.5, particularly at traffic stations, can be attributed to moving emission sources (Heydari et al., 2019). According to research by Heger et al. (2018) mobile sources account for the majority (about 70%) of PM emissions (vehicles). The remaining emissions are produced from non-transportation sources including energy conversion (20% from power plants and refineries), industry (7%), household and commercial (2%), and gas terminals (1%). According to other research, ambient PM_2.5 air pollution causes more than 4,000 premature deaths in Tehran each year. Also, high PM_2.5 concentrations lead to an increase in emergency room visits, particularly for respiratory problems. (Heger & Sarraf, 2018). Any policy plan that targets the efficient reduction of PM_2.5 in ambient air and a reduction in the health burden, needs to balance emission limits in all of these sectors and sources. Focusing on a single source will not result in effective improvements, and it will most likely waste economic resources (Purohit et al., 2019). To reduce PM_2.5 pollution, a variety of air pollution control policies can be implemented, such as enhancing industrial emission standards, the rectification of coal-fired boilers, planning to roll out old industrial facilities, supporting clean fuels in the domestic sector, enhancing vehicle emission standards, and so on (Zhang & Geng, 2019). The Iranian government should work to develop strict vehicle emission standards to reduce car emissions. Also, the quality of the gasoline used in vehicles needs to be improved (Yunesian et al., 2019).

The annual average mass concentration of PM₁₀ was 72.22 ± 11.73 µg/m³ in 2019, 69.57 ± 10.27 µg/m³ in 2020, and 82.22 ± 10.64 µg/m³ in 2021 which is higher than the limit of annual concentration of PM₁₀ according to the WHO air quality guidelines (15 µg/m³) (Organization, 2021). PM₁₀ was shown to be a serious air pollutant in Tehran. It is well known that PM₁₀ contributes to greenhouse gas emissions that warm the climate (Bozkurt et al., 2018). The main anthropogenic sources of this pollution are fossil fuel and biomass combustion, motor vehicles, and industrial activities (Farhadi et al., 2022). In a study, results showed that the effect of clean-up activities such as the Natural Gas Vehicle Supply (NGVS) program and emission control retrofits, which were supposed to result in zero emissions of fine particles, did not result in an overall reduction in PM₁₀ levels (Kim & Shon, 2011). Another study found that more than 90% of the dust-related PM₁₀ concentrations in Tehran during the investigated dust events were caused by deserts in Iraq and Syria (Givehchi et al., 2013).

The annual average mass concentration of SO₂ was 17.98 ± 8 µg/m³ in 2019, 14.94 ± 3.8 µg/m³ in 2020, and 17.47 ± 5.4 µg/m³ in 2021 which is less than the limit of annual concentration of SO₂ according to the WHO air quality guidelines (40 µg/m³). The energy production sector, which represents the usage of fossil fuels, is the main source of SO_x in the ambient air of Tehran (Hadei et al., 2017). The sulfur content of diesel fuel used by mobile and roadway sources has been reduced by more than 98% since the end of 2016, (Sulfur content reduced from 4000 ppm to almost 75 ppm) resulting in considerable reductions in SO₂ production and emissions from mobile sources (Khuzestani et al., 2023). Government choices can influence fuel consumption and urban green space by raising public awareness and people's intentions. A study by Ebrahimi et al. (2021) showed that fuel consumption and urban green space can be changed by government decisions, raising public awareness and people’s intentions. According to this study the reduction of gasoline and gas oil consumption and the increase of green space area reduces SO₂ pollutant concentrations up to 2.096, 1.617, and 2.265 percent, respectively, extremely effective at decreasing pollution caused by these pollutants. If all three of these adjustments occur together, the concentration of this pollutant will be reduced by around 5.9%, mitigating many of the difficulties created by the increase in SO₂ concentration. Source apportionment analysis in Tehran revealed that sulfate might make up to 40% of total PM_2.5 contributions made in the city, therefore SO₂ is still a concern for Tehran's air quality (Khuzestani et al., 2023).

During the period of study, the average concentrations of PM_2.5 and SO₂ were highest in winter with average concentrations of 36.58 and 21.72 µg/m³, respectively. The average concentrations of PM₁₀ were highest in the fall with average concentrations of 84 µg/m³. The average concentrations of PM_2.5, PM₁₀, and SO₂ were lowest in spring with average concentrations of 21 µg/m³, 59.3 µgm³, and 10.52 µg/m³, respectively. Seasonal variations of PM_2.5, PM_10, and SO₂ during the period of study are shown in Fig. 5. The highest concentrations of pollutants in winter and fall are associated with increased use of fossil fuels for heating, increased traffic density, and a combination of unfavorable weather conditions such as stagnant weather, higher haze, reduced sunny days, temperature inversion, and lower boundary layer (Bai et al., 2021; Bozkurt et al., 2018). This finding indicates that the residential sector, and in particular heating systems, may significantly worsen air quality during the colder months of the year. (Ghaffarpasand et al., 2020). Furthermore, the mountain ranges in the north of Tehran stop the flow of the humid wind and prevent the polluted air to leave the city. Thus, during winter, the lack of wind and cold air traps polluted air within the city (Naddafi et al., 2012). Also, the average concentration of PM₁₀ (82.75 µg/m³) was high in summer because the middle east dust storm was responsible for the excessive concentration of PM₁₀ during summer in Tehran (Yousefian et al., 2020). Lower spring concentrations of pollutants may be related to unstable weather conditions and wet deposition during the new year holiday (Ali-Taleshi et al., 2021). The results of this study were consistent with research by Faridi et al. (2018) which found that the most polluted seasons were identified in winter and summer, and were least polluted in spring for PM2.5 in Iran.

3.2. Health risk assessment

‌In this study, only the inhalation pathway was analyzed because it is an important pathway for exposure to PM_2.5, PM₁₀, and SO₂ outdoors. The results obtained from the risk assessment of PM_2.5, PM₁₀, and SO₂ via inhalation are shown in Table 3. The mean HQ values via inhalation exposure to PM_2.5 were 0.72, 1.93, and 2.9 for Exposure time scenarios of 3, 8, and 12 hours respectively. The mean HQ values via inhalation exposure to PM₁₀ were 0.6, 1.6, and 2.4 for Exposure time scenarios of 3, 8, and 12 hours, respectively. HQ values greater than 1 indicate unacceptable exposure levels with significant chronic non-cancer risks for the target organs, and therefore more attention and research should be paid to the non-carcinogenic risks of these pollutants in Tehran's ambient air. The results of this health risk assessment are sensitive to exposure time. An increase in exposure time to 8 hr and even to 12 hr did result in a major change in HQ values. Therefore, there are potential risks related to air pollution for outdoor job workers who spend hours every day outside in ambient air. PM air pollution is widely known to be harmful to human health. Studies have found a strong exposure-response relationship between PM_2.5 and both long and short-term effects, which are largely caused in the sick, elderly, or children. There is also an additional concern due to environmental and atmospheric effects, which extend the exposure period to months rather than hours (De Oliveira et al., 2012). In comparative risk assessments conducted exposure to fine particulate matter (PM_2.5) was recognized as the greatest health risk of air pollution when estimating the global burden of illness. PM_2.5 particles penetrate deep into cells and respiratory systems because of their small size of fewer than 2.5 micrometers, so they may cause a variety of adverse health effects (Purohit et al., 2019). Heavy metals may threaten human health by inhalation of PM_2.5 and PM₁₀ (Khan et al., 2016). Even though heavy metals make up only a small fraction of PM_2.5 and PM₁₀, they are carcinogenic and low biodegradable (Li et al., 2016). Similar results have been found in the ambient atmosphere of the Brazilian Amazon region with mean HQ values of 2.07 for PM_2.5, which is indicative of non-carcinogenic risk (De Oliveira et al., 2012). Similarly, Heydari et al. (2019) found that HQ values for PM_2.5 were > 1 in the outdoor air of waterpipe cafés in Tehran, indicating an unacceptable risk to human health. Also in Yu et al. (2019) study on preschool children, the hazard quotient resulting from PM_2.5 exposure indicated a significant health risk for preschool children (93.74% greater than 1).

The mean HQ values via inhalation exposure to SO₂ for all Exposure time scenarios of 3, 8, and 12 hours were less than 1 which were 0.04, 0.12, and 0.18, respectively, indicating no non-cancer risks via the inhalation exposure pathway for SO₂. This is due to the concentration of SO₂ being less than Air quality standards in the indoor air in Tehran. Furthermore, a 98% reduction in the sulfur content of diesel fuel used by mobile and highway sources has led to significant decreases in SO₂ concentrations in Tehran's ambient air since the end of 2016. As one study shows the annual concentration for SO₂ in Tehran City, from 2005 to 2014 exceeded on standard level (Kermani et al., 2017), confirming that the reduction in the sulfur content of diesel fuel successfully decreased SO₂ concentration since the end of 2016. Similar results have been found in the Matooane et al. (2003) study, in which, the risk level of SO₂ was low (Hazard Quotient < 1) in South Durban; and only under the worst-case scenario (exposure 24 hr./day), there was a significant risk of developing health effects. Moreover, in research by Thongthammachart et al. (2017) both short-term and long-term exposure to SO₂ and NO₂ from a newly developing coal power plant in Thailand was less than 1. Also in research by Fouladi-Fard et al. (2022) on the effect of power plant fuel change on the air pollution (SO₂ and NO_X) of surrounding regions in Qom, Iran, similar findings have been observed that the SO₂ hazard quotients (HQ) values for all age groups were less than 1.

The highest average PM_2.5 and PM₁₀ levels were observed in District 19 of Tehran, with concentrations of 47.5 µg/m³ and 109.75 µg/m³, respectively. So, the HQ values of PM_2.5 and PM₁₀ for the Exposure time of 12 hours were 4.55 and 3.5, respectively. Therefore, there is a need for serious attention in District 19 of Tehran in terms of air pollution. Also, the HQ value of pm_2.5 was more than 1 for the exposure time of 3hr in District 19 of Tehran, so, it seems unhealthy to stay outside for more than 3 hours in this area. The highest concentration of SO₂ was in District 10 of Tehran with an average concentration of 20.94 µg/m³ and its HQ value for the exposure time of 12 hours was 0.25.

Table 3

Non-carcinogenic risks of PM_2.5, PM_10, and SO₂ via inhalation in Tehran (2019–2021)
	Concentration (µg/m³)		EC (µg/kg.day)			HQ
	Concentration (µg/m³)		3h	8h	12h	3h	8h	12h
PM_2.5	average	30.25	1.06	2.83	4.25	0.72	1.93	2.9
PM_2.5	max	47.5	1.67	4.45	6.68	1.13	3.03	4.55
PM₁₀	average	75.24	2.64	7.06	10.59	0.6	1.6	2.4
PM₁₀	max	109.75	3.86	10.3	15.45	0.87	2.3	3.5
SO₂	average	15.46	0.54	1.45	2.17	0.04	0.12	0.18
SO₂	max	20.94	0.73	1.96	2.9	0.06	0.16	0.25

3.3. Spatial distribution of pollutants

Figure 6 depicts the spatial distribution of the sampling area's typical yearly concentrations of SO₂, PM_2.5, and PM₁₀ in Tehran. The greatest PM_2.5 and SO₂ concentrations were found in the west and southwest of Tehran, as seen in Fig. 6.This area is located in an industrial area that emits pollutants from manufacturers like petroleum-based and gas refineries, electronics manufacturing facilities, cement and grinder industries, machinery repair shops, packaging companies, and companies that make plastic pipes, all of which have a negative impact on the area's air quality (Kermani, Asadgol, et al., 2021; Raeisi et al., 2016; Wu et al., 2014). On the other hand, Tehran City experiences predominant northwest to southeast wind rose, which has ultimately resulted in the concentration of PM_2.5 and SO₂ in these regions. Additionally, one of the other influencing variables linked to the rise in PM_2.5 and SO₂ concentration is the proliferation of industries in these regions (Ielpo et al., 2019). car exhaust fumes are partially responsible for the high PM₁₀ concentrations in the east and northeast, which are situated in heavily trafficked regions. Therefore, the spread and dispersion of pollutants in various natural matrices are influenced by human actions. (Gholampour et al., 2016). Additionally, There are parks and forest areas in the east of Tehran, so, plant pollen can increase the concentration of PM₁₀ in these areas According to research of Talebi et al (2008), Isfahan's high-traffic areas have seen the greatest concentrations of PM₁₀ and its associated heavy metals.

3.4. Sobol sensitivity analysis

In order to assess the most pertinent input variables pertaining to the non-carcinogenic health risks associated with PM_2.5, PM₁₀, and SO₂ pollution in the ambient air of Tehran in the populace of the area, the Sobol sensitivity analysis was used. Table 1 contains a summary of the variables that make up this model. The inhalation model required six input variables, as shown in the table, including the concentration of the pollutants in the ambient air (PM_2.5, PM₁₀, and SO₂), body weight (BW), exposure frequency (EF), and inhalation rate (IR). The Sobol sensitivity analysis findings (Fig. 7) demonstrated that the PM_2.5 content was the population's most useful measure in HQ (0.626). The factors were arranged in order after the concentration of PM_2.5 as follows: EF (0.307) > BW (0.031) > PM₁₀ (0.011) > IR (0.010) > SO₂. (0.001). The SO₂ value was less than 0.01, suggesting that it was insensitive. PM_2.5 (640) and EF (321) had interactions because the total effect was greater than the first-order effect. The interaction diagram (Fig. 7) demonstrated that a single factor and PM_2.5 had an interaction impact, with PM_2.5-EF (0.012) being the beneficiary. The impact of this association, though, was not apparent between the other factors. Other studies have emphasized the superior effectiveness of non-carcinogenic risk assessment PM_2.5 in populations (Li et al., 2022; Requia et al., 2018).

The annual average mass concentration of PM_2.5 and PM₁₀ were higher than the limited annual concentration of WHO air quality guidelines. The annual average mass concentration of SO₂ was less than the limit of the annual concentration of SO₂ according to the WHO air quality guidelines. The level of health risk for PM_2.5 and PM₁₀ with Exposure time of 8 and 12 hr. poses a risk. The level of health risk for SO₂ for all Exposure time in Tehran City does not pose a risk. Considering that the concentration of PM by spending more than 8 hours can lead to risk for citizens, so they should be outside for less than 8 hours. Overall, spending more than 8 hours outside the home poses issues, and it is recommended that time spent outdoors be limited to fewer than 8 hours, especially for outside jobs. Risk managers need to be done to control the impact of exposure to PM_2.5 and PM₁₀ in Tehran. Tehran's west and southwest were discovered to have the highest concentrations of PM_2.5 and SO₂, while its east and northeast had the highest concentrations of PM₁₀. The PM_2.5 content was found to be the population's most sensitive indicator in HQ, according to the results of the Sobol sensitivity analysis. The government should take control measures to reduce the concentration of PM_2.5 and PM₁₀ in the air of Tehran city and must always periodically monitor the concentration of pollutants in the ambient air so as not to exceed the recommended safe limits.

Acknowledgments

The present article was adopted from proposal number 25599 and ethical cod IR.SUMS.SCHEANUT.REC.1401.082 approved by Shiraz University of Medical Sciences.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Competing Interests

The authors declare that they have no known competing financial or non-financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Authors’ Contributions

Fahimeh Ahmadian: Preparation, Writing original draft. Conceived and designed the experiments; performed the experiments; analyzed and interpreted the data; contributed reagents, materials, analysis tools, or data; wrote the paper.

Saeed Rajabi: Conceived and designed the experiments; performed the experiments; analyzed and interpreted the data; contributed reagents, materials, analysis tools, or data; wrote the paper.

Abooalfazl Azhdarpoor: Conceived and designed the experiments; contributed reagents, materials, analysis tools, or data; wrote the paper.

Data Availability

The datasets obtained in this study are available from the corresponding author on reasonable request.

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No competing interests reported.

Atmospheric Concentrations, Seasonal Variations and Health Risk Assessment of PM 2.5 , PM 10 , and SO 2 in Tehran Metropolis, Iran

Status:

Version 1

Abstract

Figures

1. Introduction

2. Material and Methods

2.1. Study area

2.2. Data collection and analysis

2.3. Health risk assessment

2.4. Sobol sensitivity analysis

2.5. Spatial distribution

3. Results and Discussion

3.1. pollutants concentration

3.2. Health risk assessment

3.3. Spatial distribution of pollutants

3.4. Sobol sensitivity analysis

4. Conclusion

Declarations

References

Additional Declarations

Status:

Version 1