Temporal Trends and Meteorological Associations of Particulate Matter and Gaseous Air Pollutants in Tehran, Iran (2017–2021)

Abstract

Air pollution is a major environmental risk factor that contributes significantly to the global burden of disease, particularly through its impact on respiratory and cardiovascular health. The aim of this study is to investigate the temporal variations of ambient air pollutants and the influence of MPs (MPs) on their concentrations in the metropolitan area of Tehran from 2017 to 2021. Hourly data for PM_2.5, PM₁₀, O₃, NO₂, SO₂, and CO from all air quality monitoring stations were obtained. Effects of MPs for the same period were assessed. The results revealed that Tehran’s residents are continuously exposed to harmful levels of PM_2.5 (5.7 to 6.3 times), PM₁₀ (4.5–5.6 times), and NO₂ (8.7–10.0 times) that are significantly higher than the updated World Health Organization (WHO) air quality guidelines. All other air pollutants (except for O₃) showed the lowest and highest concentrations during summer and winter, respectively. The highest concentration of O₃ was found on weekends (weekend effect), while other ambient air pollutants had higher levels on weekdays (holiday effect). Although other air pollutants exhibited two peaks, in the morning and late evening, the hourly concentration of O₃ reached its maximum level at 3:00 pm. Approximately 51% to 65% of the Air Quality Index (AQI) values were classified as unhealthy for sensitive groups. Throughout the study period, PM_2.5 was identified as the primary pollutant affecting air quality in Tehran. Among MPs, temperature was the most important factor in increasing the concentration of O₃, while the other ambient pollutants decreased under the influence of wind speed. Given the current situation, effective and evidence-based air quality management strategies, like those that have been successfully applied elsewhere, are now a necessity to avoid the public health impact and economic losses from air pollution. Although this research focuses on Tehran as a model case of rapidly developing cities facing severe air quality challenges, the findings and recommendations have broader applicability to similar urban environments worldwide.

1. Introduction

Air pollution is a major environmental health risk factor, particularly in rapidly urbanizing regions. According to the World Health Organization (WHO), approximately 99% of the global population lives in areas where air quality exceeds recommended limits [1]. The Global Burden of Disease report (2016) ranked air pollution as the fifth leading risk factor in the Middle East and the ninth globally [2]. In 2017, WHO attributed approximately seven million deaths to air pollution, with an estimated 3.7 million linked to outdoor ambient air pollution [3]. Long-term exposure to particulate matter (PM) has been associated with increased risks of cardiovascular and respiratory diseases, lung cancer, and premature death [4,5]. Air pollution also contributes to climate change, accelerates global warming, causes acid rain, and negatively affects ecosystems and water resources [4,6]. Iran, as a developing country, faces serious air quality challenges. Major metropolitan areas like Tehran suffer from persistent air pollution due to growing motor vehicle fleets, unregulated emissions, fossil fuel reliance, and frequent Middle Eastern dust storms [7,8,9,10,11]. According to previous studies, the transportation sector is the dominant source of emissions in Tehran, responsible for approximately 98% of CO, 46% of NO_x, 75% of PM_2.5, and 86% of VOCs [12,13]. Shamsipour et al. (2019) showed that from 1990 to 2016, PM concentrations in Iran frequently exceeded WHO guidelines, with mortality rates associated with PM_2.5 exposure rising especially in western provinces [14]. Faridi et al. (2018) evaluated long-term trends in PM_2.5 and O₃ levels in Tehran from 2006 to 2015 and quantified associated health impacts using the WHO AirQ+ model, highlighting significant excess mortality [4]. In addition, several studies have investigated seasonal and spatial patterns of air pollutants in northern Tehran, reporting higher levels of CO, NO₂, and SO₂ in winter, while O₃ and PM concentrations peaked during summer [11,15,16,17,18].

Despite these contributions, existing studies often focus on historical data or health burden estimation, and few have provided up-to-date, multi-temporal analyses of pollutant trends alongside MPs [4,11,19]. Yousefian et al. (2020), for example, applied convergent cross mapping to assess nonlinear relationships between weather and pollution in Tehran, highlighting the value of advanced methods for uncovering atmospheric interactions [11,16]. However, their analysis was limited to data from 2012 to 2017. In contrast, our study extends the analysis to more recent years, using data from 2017 to 2021, a period for which AQI trends and high-resolution (daily/hourly) data remain relatively scarce. To address these gaps, the present study aims to: (i) examine temporal variations in ambient air pollutants (PM_2.5, PM₁₀, NO₂, SO₂, CO, and O₃) at annual, seasonal, daily, and hourly scales; (ii) assess the influence of MPs (temperature, wind speed, and humidity) on pollutant concentrations; and (iii) evaluate changes in AQI levels from 2017 to 2021. The study provides new insights into pollutant dynamics and atmospheric behavior in Tehran, informing evidence-based policy decisions.

2. Method

In this study, hourly concentration data of six criteria ambient air pollutants (O₃, NO₂, CO, SO₂, PM₁₀, PM_2.5) from 1 January 2017 to 31 December 2021 were obtained from the Tehran Air Quality Control Company (Tehran, Iran) website (http://airnow.tehran.ir/home/DataArchive.aspx), accessed on 1 April 2021. Tehran is equipped with 23 outdoor air quality monitoring stations, distributed across the city’s districts (Figure S1). Their spatial distribution and classifications, with 52% identified as traffic monitoring stations, are detailed in the Supplementary Information (Table S1). Data processing was undertaken for stations that recorded more than 70% of hourly data. Outliers were identified and removed from the dataset using the Z-score method [4,11]. Among all stations, 22 were active during 2017–2018, and 21 remained active between 2019 and 2021. To analyze temporal patterns, the cleaned hourly data were aggregated to calculate daily, weekly, monthly, seasonal, and annual mean values for each air pollutant (PM_2.5, PM₁₀, NO₂, SO₂, CO, and O₃₎. Mean values were calculated only when the respective time units (e.g., day, week) also had sufficient valid hourly records (e.g., at least 18 h for a daily mean). These multi-temporal aggregations enabled both short- and long-term trend analysis and supported robust correlation assessments. After applying a data completeness threshold and removing outliers, the resulting dataset provided continuous, year-round coverage with no seasonal gaps for the selected stations. Also, hourly data related to MPs, temperature, wind speed, relative humidity (RH), and visibility for the 5-year period were obtained from the National Meteorological Organization. AQI is a dimensionless numeric index for daily reporting of air quality, the calculation method of which is designed by the U.S. Environmental Protection Agency (U.S.EPA) [20]. Briefly, AQI is a key tool for informing the public about air quality and its health impacts, and for providing guidance on how to reduce exposure to air pollution. This index is calculated for five main air pollutants, namely PMs, NO₂, O₃, CO, and SO₂. For easy understanding, the AQI index is classified into six categories, each category corresponding to different levels of human health (Table S2). According to the AQI, the daily main pollutant is the one that contributes the most to the overall air pollution level on a given day. Equation (1) is used to calculate AQI as follows [21]:

$${AQI}_{ap}={\displaystyle \frac{{AQI}_{uc}-{AQI}_{Ic}}{{BP}_{uc}-{BP}_{Ic}}}(C_{ap}-{BP}_{Ic})+{AQI}_{Ic}$$

(1)

where AQIap is the AQI for pollutant p; Cap is the measured (rounded) concentration for pollutant p; BPuc is the breakpoint that is greater than or equal to Cp; BPlc is the breakpoint that is less than or equal to CP; AQIuc is the AQI value corresponding to BPuc; and AQIlc is the AQI value corresponding to BPlc [21]. In this study, trends in the annual mean concentrations of each air pollutant were analyzed to identify upward or downward patterns. To examine the influence of MPs on pollutant concentrations, Spearman’s non-parametric correlation test was used, with a significance level of p < 0.05. Shapiro–Wilk and Spearman tests were also used to calculate the normality of the data and the correlation coefficient. All the analyzes and graphs have been provided using R (3.0.1) and STATA (14.1).

3. Results and Discussion

3.1. Temporal Trends of Air Pollutants

One of the major measures used to assess the success of air pollution abatement programs globally is the assessment of differences in concentrations of ambient air pollutants [22]. We compared concentrations of pollutants with the World Health Organization-recommended revised Air Quality Guidelines and Iran’s national air quality standards based on the National Ambient Air Quality Standards promulgated by the U.S. EPA [23,24]. This study reveals that the annual mean concentration of the main air pollutant of Tehran, namely PM_2.5, has continuously exceeded WHO guidelines (5 µg. m⁻³) and Iranian national air quality standards (10 µg. m⁻³) during the five consecutive years from 2017 to 2021 [23]. Residents of Tehran repeatedly face annual mean concentrations of air pollutants that are higher than those stated in the updated WHO air quality guidelines: 5.7 to 6.3 for PM_2.5, 4.5 to 5.6 for PM₁₀ (15 µg. m⁻³), and 8.7 to 10.0 for NO₂ (10 µg. m⁻³). As a result of the current situation, air pollution is still one of the most serious threats to the inhabitants of this large urban region. Our findings are consistent with previous studies that have documented elevated concentrations of air pollutants in Tehran. For instance, a study by Yousefian et al. (2020) reported that Tehran residents were consistently exposed to annual mean concentrations of PM_2.5, PM₁₀, and NO₂ approximately 3.0–4.5, 3.5–4.5, and 1.5–2.5 times higher than the WHO’s air quality guideline levels during the period 2012–2017 [11]. Similarly, a more recent study by Abbasi et al. (2024) found that the annual mean concentration of PM_2.5 in Tehran in 2022 was 36.11 µg. m⁻³, significantly exceeding the WHO guideline of 15 µg. m⁻³ [6].

• Annual trends

Figure 1a–e and Table S3 present the annual mean concentrations of six key air pollutants in Tehran from 2017 to 2021. Overall, the mean concentrations of PM_2.5 and PM₁₀ declined from 2017 to 2020; however, this trend reversed in 2021, showing an upward pattern (Figure 1a,b). The highest annual mean concentrations of PM_2.5, PM₁₀, NO₂, SO₂, and CO were recorded in 2017, with values of 31.50 µg. m⁻³, 83.26 µg. m⁻³, 53.34 ppb, 7.89 ppb, and 2.55 ppm, respectively (Table S3). In contrast, the lowest levels of PM_2.5 and PM₁₀ were observed in 2020, which may be attributed to reduced traffic and industrial activity during the COVID-19 pandemic. Previous studies in Tehran have shown significant reductions in traffic-related air pollutants (PM and NO₂) during the COVID-19 period. In agreement with previous research carried out in Tehran, different studies have reported a significant drop in PM_2.5, PM₁₀, NO₂, and CO levels due to the COVID-19 lockdown as a result of decreased transportation and industrial activity, with minimum concentrations usually observed within 2020 [25,26,27]. The annual mean concentration of O₃ remained relatively stable from 2017 to 2020, ranging between 20.21 and 20.93 ppb, before reaching its peak value of 21.57 ppb in 2021. In contrast, NO₂ showed a decreasing trend from its highest level of 53.34 ppb in 2017 to a low of 46.17 ppb in 2018, with minor fluctuations thereafter. SO₂ concentrations also declined over the study period, dropping from 7.89 ppb in 2017 to their lowest point of 5.39 ppb in 2019, followed by a slight increase. CO showed a similar declining pattern, with the highest concentration recorded in 2017 (2.55 ppm) and the lowest in 2021 (1.84 ppm), indicating a gradual improvement in air quality for this pollutant (Table S3, Figure 1). Similar to our results, PM and NO₂ showed an increasing trend during 2021 after the easing of restrictions. On the other hand, O₃ levels showed a generally stable trend during the period examined, while the variations in SO₂ levels may be due to changes in industrial fuel use. These studies also validate the temporal trends found in our research [4,28].

• Seasonal and monthly trends

In this study, seasonal and monthly concentrations of all air pollutants were examined to determine the most polluted seasons and months in the metropolitan area of Tehran over a five-year period. Based on the Persian calendar, four seasons were considered: spring (21 March to 21 June), summer (22 June to 22 September), autumn (23 September to 21 December), and winter (22 December to 20 March). Compared with the warm seasons and months, it can be observed that the mean concentrations of PM_2.5, SO₂, NO₂, and CO pollutants were higher in the cold seasons and months (Figure 2 and Figure 3). The reason for such variations is due to the occurrence of inversion during the coldest seasons and months [4,29,30]. On the other hand, the pollutant PM₁₀ experienced its highest concentration in summer with a value of 83.79 µg. m⁻³, followed by autumn, winter, and spring with values of 83.16 µg. m⁻³, 77.59 µg. m⁻³, and 62.46 µg. m⁻³, respectively (Figure 2b). This could be attributed to the occurrence of dust storms during the summer season [29,31,32,33]. Unlike other gaseous pollutants, the highest concentration of O₃ (ppb) was observed in the summer (30.67) and spring (27.15) seasons (Figure 2c). Figure 3b indicates that O₃ concentrations were increased during June and July. O₃ is commonly referred to as a summertime pollutant and is one of the main components of photochemical smog, belonging to the class of photochemical air oxidants. Since it is formed by the reaction of Nox and VOCs in the presence of sunlight (photochemical reaction), the highest O₃ levels are formed under sunny conditions [4,34,35]. Our results are consistent with the previous published studies which are conducted in metropolitan cities like Tehran, New York City, and 18 cities of China [4,11,36].

Looking at the monthly trend of mean PM_2.5 concentrations in the ambient air of Tehran, we find that this pollutant reaches its highest levels in December, with a mean of approximately 46 µg. m⁻³ (Figure 3a). This increase during the cold months can be attributed to atmospheric cooling and the frequent occurrence of temperature inversion, which traps pollutants near the surface [4,11,29]. In contrast, the lowest concentration of PM_2.5 (21.5 µg. m⁻³) was observed in April. This reduction is likely due to favorable meteorological conditions such as higher temperatures, increased solar radiation, greater atmospheric mixing, and stronger wind speeds, which collectively enhance the vertical and horizontal dispersion of air pollutants (Figure 3a).

The results also indicate that in the periods of May/June, June/July, and November/December, mean PM₁₀ concentrations were among the highest across the year, with values of 93.4 µg. m⁻³, 97.5 µg. m⁻³, and 93.2 µg. m⁻³, respectively (Figure 3a). The elevated PM₁₀ levels in May and June are likely associated with dust storms that typically occur at the onset of the warm season. Meanwhile, the increase in PM₁₀ during December can be similarly attributed to the cooling of the air and inversion layers, which reduce atmospheric dispersion. The lowest monthly mean concentration of PM₁₀ (57.6 µg. m⁻³) was recorded in April, possibly due to improved meteorological conditions during that time, including increased precipitation, higher wind speeds, and reduced atmospheric stability, all of which facilitate pollutant dispersion [4,11] (Figure 3a). Figure 3c shows that CO and SO₂ concentrations peak in winter months (December–February) and decline in spring and early summer (April–June). This trend is mainly due to unfavorable meteorological conditions in winter—such as temperature inversion and stagnant air, which traps pollutants near the surface. Additionally, increased heating and traffic emissions in cold months contribute to higher levels [4,11,29]. In contrast, lower concentrations in spring are linked to favorable weather conditions that enhance dispersion, as well as reduced emissions during the Nowruz holidays (21 March to 3 April) [12].

• Daily and hourly trends

The daily and hourly variations in the concentrations of ambient air pollutants in Tehran from 2017 to 2021 are depicted in Figure 4a,b. A glance at the presented figures demonstrates that not only do PM_2.5 and PM₁₀ exhibit hourly variations, but their daily variations also follow a similar pattern. In the current study, weekdays from Saturday to Thursday are considered as working days (non-holidays), while Friday is considered as a holiday. With the start of the working days in Tehran, vehicles, as the major sources of air pollution, significantly contribute to the increase in pollutant concentrations [19,37]. Factors such as increased activities of Tehran residents and individuals who commute to Tehran daily also contribute to the notable rise in pollutant concentrations [19,20]. Therefore, the daily concentrations of PM_2.5 and PM₁₀ pollutants have shown an increasing trend, reaching their maximum concentrations on Thursdays, which are 57.31 µg. m⁻³ and 94.79 µg. m⁻³, respectively. In fact, the concentrations of PM_2.5 and PM₁₀ accumulate in the atmosphere and reach their peak on Thursdays. As expected, the lowest daily concentrations for these pollutants (27.94 µg. m⁻³ and 71.69 µg. m⁻³) are recorded on Fridays, known as the “weekend effect” [21]. Regarding the hourly variations in particle concentrations, two peaks are observed: one from 7 am to 12 pm and the other from 6 pm to 11 pm. These peaks can be attributed to morning traffic and the early evening period, which coincide with increased traffic of light and heavy vehicles during the night (after 10 pm), construction activities and the transportation of their waste, illegal open waste burning, and the shutdown of pollution control equipment in industries during the night [14,19,22,23].

By comparing the presented figures, the daily mean concentrations of NO₂, SO₂, and CO on weekdays (Saturday to Thursday) remain relatively constant, while the O₃ exhibits an opposite trend compared with the others throughout the week. At the end of the week, a significant increase in O₃ concentration and a sharp decrease in the concentrations of NO₂, SO₂, and CO are observed. The decrease in the latter group is mainly due to reduced traffic compared with weekdays, while the increase in O₃, as a secondary pollutant, is likely attributed to the reduction in O₃ destruction by titration with Nox during the weekend [29,30].

The hourly variations of pollutants, including NO₂, SO₂, and CO, show two peaks and two valleys similar to the pollutants PM_2.5 and PM₁₀. These variations mainly reflect the influence of traffic emissions and weather conditions on the concentrations of NO₂, CO, and SO₂ throughout the day. After the observed peaks during the hours of 8:00–9:00 in the morning and 23:00 at night, the concentrations of NO₂ and CO show a decreasing trend, reaching their lowest levels at around 5:00 in the morning and mid-afternoon. The occurrence of this trend can be attributed to an increase in the depth of the mixing layer, wind speed, solar radiation, and photochemical reactions that contribute to the production of the O₃, accompanied by a reduction in traffic emissions [38,39,40,41]. Regarding the decrease in NO₂ concentrations, an increasing trend has been observed, which correlates with an increase in solar radiation from 10:00 in the morning to 3:00 in the afternoon, during which the highest concentration of this gas is recorded. However, with a decrease in solar radiation intensity from 4:00 in the afternoon and an increase in the concentration of pollutants other than O₃, the concentration of NO₂ is reduced [16,42,43].

• AQI and responsible air pollutants

Figure 5 and Table S4 illustrate the variations of the AQI index and the contribution of each pollutant to the AQI values in Tehran over the study period. Between 2017 and 2021, AQI readings showed hourly variations ranging from 49 to 498, with both the minimum and maximum values recorded in 2020 (Table S5). During the study period, AQI recordings that fell within the “good” category (i.e., less than 50) were basically nonexistent for the whole area of Tehran, and there was only a single observation, an AQI of 49, for the year 2020. Additionally, the highest daily AQI value was observed in 2018, reaching 297 (Table S4). As shown in Figure 5 (left), the majority of days in 2017 and 2018, specifically 238 days each year, were classified as having an “unhealthy for sensitive groups” AQI level. Additionally, during the year 2017, Tehran experienced unhealthy air quality for 106 days (Table S4). It is worth mentioning that the number of unhealthy days for Tehran residents showed a significant decrease from 2017 to 2020, but we have witnessed an approximately twofold increase in 2021 (Table S4). The number of days with very unhealthy and hazardous conditions throughout the study period varied between 0 and 1 days. According to Figure 5 (right), among the ambient air pollutants, only SO₂ did not contribute to the deterioration of air quality in Tehran during the study period of 2017–2021. Additionally, PM_2.5, among the ambient air pollutants, accounted for the highest number of days as the main pollutant in the AQI in Tehran throughout the study period. PM_2.5 was responsible for air quality deterioration in Tehran for approximately 261 to 297 days, accounting for roughly 72% to 81% of the total days in a year. Following PM_2.5, NO₂ was the second main pollutant for ambient air quality in Tehran, with 49 to 91 days, accounting for approximately 13% to 25% of the total days in a year.

3.2. The Association Between Six Criteria Air Pollutants and MPs

The influence of MPs on the concentrations of the six criteria air pollutants in Tehran has been reported in

Table 1. The Spearman correlation coefficient between ambient air pollutants and MPs.

	PM2.5	PM10	O3	NO2	SO2	CO	Temp 1	RH 2	WS 3	Visib 4
PM2.5	1
PM10	0.81 *	1
O3	−0.37 *	0.21 *	1
NO2	0.61 *	0.45 *	0.62 *	1
SO2	0.58 *	0.48 *	−0.34 *	0.62 *	1
CO	0.48 *	0.42 *	−0.55 *	0.68 *	0.52 *	1
Temp	−0.16 *	0.10 *	0.55 *	−0.32 *	−0.25 *	−0.15	1
HR	−0.13 *	−0.11 *	−0.44 *	0.17 *	0.05 *	0.08 *	0.69 *	1
WS	−0.45 *	−0.22 *	0.33 *	−0.32 *	−0.19 *	−0.30 *	0.44 *	−0.38 *	1
Visib	−0.23 *	−0.14 *	0.31 *	−0.30 *	−0.20 *	−0.24 *	0.22 *	−0.18 *	0.17 *	1

¹ Temperature; ² Relative Humidity; ³ Wind Speed; ⁴ Visibilit; * Significance at p < 0.05.

. The maximum temperature during the study years reached 41 °C in 2017, 2018, and 2021, while the minimum temperature was recorded as 8 °C in 2018. The highest wind speed reported was 7.2 m per second in 2021, and the lowest was 0.4 m per second in 2017. The minimum RH was 8.3% in both 2017 and 2021, while the maximum RH was 98.1% in 2018 (Table S6). As shown in Table 1, statistically significant negative correlations (p < 0.05) were observed between wind speed and the concentrations of most criteria air pollutants, including PM_2.5, PM₁₀, NO₂, SO₂, and CO. This suggests that increased wind speed enhances the dispersion and dilution of primary pollutants [44,45]. However, for O₃, a moderate positive correlation with wind speed (r = 0.33) was observed, likely due to the transport of O₃ or its precursors from surrounding areas [4].

Additionally, a strong positive correlation between temperature and O₃ (r = 0.55) was found, supporting the role of solar radiation and photochemical activity in the formation of this secondary pollutant. Visibility was negatively correlated with all pollutants except O₃, indicating that high concentrations of PMs and gaseous pollutants are typically associated with poor air clarity [11,46,47,48].

Overall, consistent with previous studies, our findings confirm that MPs such as wind speed, temperature, and RH are key factors influencing variations in ambient air pollution levels in urban environments [11,45].

This study has several limitations that should be acknowledged. First, spatial gaps in meteorological measurements may bring inaccuracy into estimating atmospheric interactions and pollutant dispersion, particularly over Tehran’s peripheral or mountainous areas with scarce ground data. Second, employing routine monitoring data may bring in complications in measurement accuracy and consistency, which may affect the robustness of trend analysis and temporal correlation. Third, the research fails to clearly identify pollutant sources, limiting the capacity to identify measured concentrations with specific emission sectors (e.g., traffic, industry, domestic heating), which is crucial for policy suggestions to particular sectors. Subsequent research needs to include ground observations and satellite remote sensing information (e.g., aerosol optical depth, surface temperature) to improve spatial resolution and exposure estimates. Advanced modeling platforms—such as chemical transport models with locally calibrated data—would have the potential to give a more realistic representation of pollutant fate. Application of machine learning or data assimilation techniques also would uncover nonlinear relationships between pollutant concentrations and weather variables and improve forecasting accuracy. Specifically, in order to bridge the gap between science and policy, follow-up research would need to examine the health impacts of pollution with exposure–response functions developed for the local population and the disease burden. Scenario simulation of abatement strategies, e.g., traffic restriction, fuel quality management, or expansion of green space, would support pragmatic decision-making by stakeholders. Quantification of the effectiveness of existing policies with empirical data (e.g., before-and-after analyses) would also add to the evidence base for sustainable air quality management in Tehran and other cities.

4. Conclusions

The results of this study indicate that the mean concentration of the most important air pollutants in Tehran, specifically PM_2.5, has consistently exceeded the guidelines set by the WHO and Iranian national standards from 2017 to 2021. Therefore, considering the current situation, air pollution in Tehran remains one of the most significant health hazards for the residents of this metropolis. During our study period, we did not observe any days with AQI values below 50, which is considered “good” in Tehran. Additionally, PM_2.5 accounted for the highest number of days as the main pollutant in the AQI in Tehran during this five-year study period from 2017 to 2021. PM_2.5 has been the primary pollutant in the decline of air quality in Tehran for approximately 261 to 297 days, representing roughly 0.71% to 0.81% of the total days in a year. Following that, NO₂ accounted for 49 to 91 days, approximately 13% to 25% of the total days in a year, as the second major air pollutant in Tehran. Indeed, it is necessary to review and revise the control laws and policies, and stricter regulations should be implemented for more polluted areas, with precise enforcement. The results of this study can contribute to raising awareness among policymakers, the Ministry of Health, and the public regarding periods of the year with higher air pollution levels in Tehran, supporting more effective seasonal air quality management strategies. Therefore, planning and implementing scientific and practical programs to reduce air pollutant concentrations in this city are vital. Experiences from other countries demonstrate the fact that by providing and implementing evidence-based programs, it is possible to gradually reduce air pollutant concentrations and thereby mitigate the health effects and economic damage associated with air pollution in society.