Air Pollution In Tehran Research Articles

Spatial and temporal patterns of urban air pollution in tehran with a focus on PM2.5 and associated pollutants

Understanding the spatial and temporal dynamics of air pollutants is crucial for effective urban air pollution management. This study focuses on the temporal dynamics of air quality monitoring stations (AQMSs) and the association among air pollutants, particularly PM2.5, in Tehran, Iran. Using time series clustering and the Copula model, we analyzed data from 2019 to 2022. We found that the levels and dynamics of O3 and SO2 were similar across most AQMSs and unrelated to geographical positions. CO levels and dynamics were consistent among urban and border AQMSs, with higher concentrations in urban stations. NO2 levels and dynamics varied significantly among northern AQMSs with no relationship geographical positions. PM10 levels and dynamics had a relationship with geographical positions, with western clusters having the highest and northern clusters the lowest concentrations. The dynamics of PM2.5 showed significant relationship among AQMSs in the eastern, southern, and western regions, but not in the north. We also observed that PM10 and O3 levels were higher in warm seasons, whereas CO, SO2, NO2, and PM2.5 levels were higher in cold seasons. Most pollutants, except O3, peaked during traffic hours. Notably, the significant increase in PM2.5 since spring 2021 was primarily due to PM10. Policymakers should focus on these spatial and temporal variations to improve urban air quality and public health outcomes through targeted interventions.

Phytoremediation Potential of Urban Trees in Mitigating Air Pollution in Tehran

Phytoremediation Potential of Urban Trees in Mitigating Air Pollution in Tehran

How international conflicts and global crises can intertwine and affect the sources and levels of air pollution in urban areas.

This paper analyses the intertwined impacts of the 2018 US sanctions on Iran and the COVID-19 pandemic (as examples of unplanned international conflicts and global crises) on the source and extent of air pollution in Tehran, the capital of Iran. The impacts are parametrized using the levels of criteria air pollutants (CAP) for 5years (2015-2020), which were previously deweathered using the promising machine learning technique of Random Forest (RF). The absolute principal component scores-multiple linear regression (APCS-MLR) method and the bivariate polar plot (BPP) technique are used here to analyze the source apportionment profile of the city for the business as usual (BAU; 2015 to 2018), sanctions (2019), and COVID-19 and sanctions (2020) intervals. The results show the severe impact of the 2018 US sanctions on Tehran's air quality (AQ); O3, NO2, CO, PM2.5, and PM10 levels increased by 117%, 55%, 20%, 35%, and 10%, respectively, while SO2 levels decreased by 30%. The sanctions also triggered a number of events, such as the disruption of the high-grade fuel supply chain and the Mazut crisis, which directly or indirectly accelerated the photochemical production of local tropospheric ozone to some extent. Sanctions also disrupted Tehran's AQ response to the pandemic, with CAP levels decreasing by only 2-7% during the pandemic. The ozone and PM10 BPPs show that the source apportionment profile of the city is dominated by local anthropogenic emission sources, especially urban transport, after the sanctions and the pandemic. Results also show that the impact of soft wars, such as the US sanctions against Iran, on urban air quality degradation is much stronger than that of hard wars, such as the Russia-Ukraine war.

The possible emission sources and atmospheric photochemical processes of air pollutants in Tehran, Iran: the role of micrometeorological factors on the air quality

The possible emission sources and atmospheric photochemical processes of air pollutants in Tehran, Iran: the role of micrometeorological factors on the air quality

The Relationships between Air Pollution, Warming, and Health in Tehran Metropolis, Iran, during 2015–2019

Introduction: Since global warming and air pollution was caused by human activities have increased dramatically in recent decades, studies have been conducted to determine how environmental parameters and air pollutants interact and, subsequently, how these pollutants affect ecosystems and human health. Accordingly, this study aimed to investigate the relationship between temperature and air pollutants in Tehran, Iran, in order to determine whether warming is associated with an increase in air pollutants.
 Materials and Methods: During 2015-2019, Iran Air Quality Monitoring Station (AQMS) and the Landsat 8 satellite were employed to retrieve data on pollutants such as PM, CO, O3, NO2, SO2, and air quality index (AQI), as well as climate-related metrics including ambient temperature and land surface temperature (LST). Pearson correlation coefficient and linear regression model were used to analyze the data.
 Results: A positive correlation was found between temperature variables and PM10 (ρ = 0.29, p = 0.001) and O3 (ρ= 0.55, p = 0.001) pollutants. PM10 and O3 levels were evaluated by 0.46 (95% Confidence Interval (CI) = (0.25, 0.67), p = 0.001) and 1.13 (95% CI = 0.89, 1.37), p = 0.001) units for each one-unit rise in temperature.
 Conclusion: Given the fact that temperature predominantly evidenced a significant synergistic association with PM10 and O3, it was determined that there was no substantial positive association between all pollutants and warming.

TEHRAN AIR POLLUTION MODELING USING LONG-SHORT TERM MEMORY ALGORITHM: AN UNCERTAINTY ANALYSIS

Abstract. Air pollution is a major environmental issue in urban areas, and accurate forecasting of particles 10 μm or smaller (PM10) level is essential for smart public health policies and environmental management in Tehran, Iran. In this study, we evaluated the performance and uncertainty of long short-term memory (LSTM) model, along with two spatial interpolation methods including ordinary kriging (OK) and inverse distance weighting (IDW) for mapping the forecasted daily air pollution in Tehran. We used root mean square error (RMSE) and mean square error (MSE) to evaluate the prediction power of the LSTM model. In addition, prediction intervals (PIs), and Mean and standard deviation (STD) were employed to assess the uncertainty of the process. For this research, the air pollution data in 19 Tehran air pollution monitoring stations and temperature, humidity, wind speed and direction as influential factors were taken into account. The results showed that the OK had better RMSE and STD in the test (32.48 ± 9.8 μg/m3) and predicted data (56.6 ± 13.3 μg/m3) compared with those of the IDW in the test (47.7 ± 22.43 μg/m3) and predicted set (62.18 ± 26.1 μg/m3). However, in PIs, IDW ([0, 0.7] μg/m3) compared with the OK ([0, 0.5] μg/m3) had better performance. The LSTM model achieved in the predicted values an RMSE of 8.6 μg/m3 and a standard deviation of 9.8 μg/m3 and PIs between [2.7 ± 4.8, 14.9 ± 15] μg/m3.

Developing particle-based models to predict solar energy attenuation using long-term daily remote and local measurements

Iran has an annual average of 2.8–5.4 kW h/m2d of radiation and has a high capacity for extracting electricity from its solar resources. Tehran, the capital of Iran, is one of the most polluted cities in the world in terms of atmospheric aerosols. Due to the rising air pollution in Tehran, the existing research is outdated. An analysis of the loss of electricity generation due to particulates can significantly affect the feasibility of a photovoltaic power plant in Tehran. Several factors affect the electricity generation of photovoltaic systems. The most critical is solar radiation. The amount of solar radiation transmitted and, ultimately, the amount of electricity generated depends on several atmospheric factors. One of the most important factors is the concentration of suspended particles of different sizes. In the present work, linear models based on observed suspended particle concentrations, including PM10 and PM2.5, have been proposed for Tehran from 2014 to 2020 to anticipate the aerosol attenuation index due to aerosols. Based on the correlation coefficient values (R), in the first and last months of the year, November, December, and January, the models performed better to predict the aerosol attenuation index based on PM2.5. The R values were, in order, 0.1553, 0.2926, and 0.1341. As remote measurements, the NASA CERES syn 1-deg product parameters and, as ground observations, Surface Solar Radiation (SSR) and PM10 and PM2.5 concentrations were used to estimate the impacts of aerosols on radiation. With the help of the CERES syn 1-deg product, it is declared that, on average, 8.30% of the total radiation received was wasted due to the presence of aerosols. Considering observed SSR, CERES syn 1-deg product performance was validated, with RMSE and MBD values of 14.09% and 10.89%, respectively.

Air pollution and topography in Tehran

Tehran is one of the most polluted cities in the world with 48 days of air pollution exceeding the admissible threshold (AQI > 150) for 3 months of the 15 years studied. This period coincides with the time when Tehran’s inversion reaches its maximum stability. The purpose of this study was to determine the height of air pollution in Tehran in the days when pollution exceeds the permissible limit. Continuing to study the pressure and temperature conditions of these days, we then considered the geographical and topographic conditions, and finally identified the best of these cells for potential theoretical air turbulence. The results of this study, based on the Harmonic Analysis method and based on Tehran temperature and pressure data over a 15-year period (2003–2017), show that the highest elevation of Tehran inversion does not exceed 1800 m on polluted days. Only within 6 days of those beyond the admissible threshold, temperature and pressure cells with the highest Newtonian mass are formed. The center of these cells formed with a compressive difference of 32 mg in November, 7 mg in January, 11 mg in December, and temperature difference of 1.1° in November, 4.4° in January, and 1.9° in December. Generally, we considered the formed cells by the temperature and pressure difference and the gradient between them, as well as the difference in height between the cells and their location. This information, combined with the local winds causing the differences in temperature and pressure, allows us to elucidate conditions for creating air turbulence in Tehran and mitigating the amount and degree of air pollution.

The relationship between atmospheric temperature inversion and urban air pollution characteristics: a case study of Tehran, Iran

This study conducts a comprehensive evaluation of air pollution in Tehran, focusing on its origins and potential remedial measures. Data from the Mehrabad station in Tehran were meticulously analyzed to scrutinize the spatiotemporal dynamics over a three-year period. Leveraging the Universal Radio Sound Observation (RAOB) program, we probed atmospheric attributes up to 750 m on specific critical dates: January 22, 2014; January 25, 2015; and November 27, 2016, which represent peak pollution events. Employing mathematical models, we scrutinized air temperature inversion profiles and applied the Heffter method to gauge the impact of atmospheric inversions on Tehran's air quality. Our investigation unequivocally identified January 25, 2015, as the most polluted date, unveiling precise atmospheric conditions during a radiation inversion, encompassing altitudes, temperatures, humidity, wind speed, and inversion depth. This research significantly advances our comprehension of the intricate interplay between atmospheric inversions and air pollution. It provides critical insights that can inform the development of targeted strategies to mitigate air quality issues not only in Tehran but also in analogous urban settings worldwide.

Investigating patterns of air pollution in metropolises using remote sensing and neural networks during the COVID-19 pandemic

The purpose of this study is to determine the amount of air pollution in Tehran, Isfahan, Semnan, Mashhad, Golestan, and Shiraz during the Corona era and before. For this purpose, Sentinel satellite images were used to investigate the concentration of Methane (CH4), Carbon Monoxide (CO), Carbon Dioxide (CO2), Nitrogen Dioxide (NO2), Ozone (O3), Sulfur Dioxide (SO2), aerosol pollutants in In the era before and during Corona. Furthermore, greenhouse effect-prone areas were determined in this study. In the following, the state of air inversion in the studied area was determined by taking the temperature on the surface of the earth and in the upper atmosphere, as well as the wind speed into account. In this research, the prediction of air temperature for the year 2040 was conducted using the Markov and Cellular Automaton (CA)-Markov methods, considering the impact of air pollution on the air temperature of metropolises. Additionally, the Radial Basis Function (RBF) and Multilayer Perceptron (MLP) methods have been developed to determine the relationship between pollutants, areas prone to air inversions, and temperature values. According to the results, pollution caused by pollutants has decreased in the Corona era. According to the results, there is more pollution in Tehran and Isfahan metropolises. In addition, the results showed that air inversions in Tehran is the highest. Additionally, the results showed a high correlation between temperature and pollution levels (R2 = 0.87). Thermal indices in the studied area indicate that Isfahan and Tehran, with high values of Surface Urban Heat Island (SUHI) and being in the 6th class of thermal comfort (Urban Thermal Field Variance Index (UTFVI)), are affected by thermal pollution. The results showed that parts of southern Tehran province, southern Semnan and northeastern Isfahan will have higher temperatures in 2040 (class 5 and 6). Finally, the results of the neural network method showed that the MLP method with R2 = 0.90 is more accurate than the RBF method in predicting pollution amounts. This study significantly contributes by introducing innovative advancements through the application of RBF and MLP methods to assess air pollution levels during the COVID-19 and pre-pandemic periods, while also investigating the intricate relationships among greenhouse gases, air inversion, air temperature, and pollutant indices within the atmosphere. The utilization of these methods notably enhances the accuracy and reliability of pollution predictions, amplifying the originality and significance of this research.

Using house sparrow in biomonitoring lead in Tehran air pollution

Using house sparrow in biomonitoring lead in Tehran air pollution

A novel Energy Resources Allocation Management model for air pollution reduction.

Although air pollution has been reduced in various industrial and crowded cities during the COVID-19 pandemic, curbing the high concentration of the crisis of air pollution in the megacity of Tehran is still a challenging issue. Thus, identifying the major factors that play significant roles in increasing contaminant concentration is vital. This study aimed to propose a mathematical model to reduce air pollution in a way that does not require citizen participation, limitation on energy usage, alternative energies, any policies on fuel-burn style, extra cost, or time to ensure that consumers have access to energy adequately. In this study, we proposed a novel framework, denoted as the Energy Resources Allocation Management (ERAM) model, to reduce air pollution. The ERAM is designed to optimize the allocation of various energies to the recipients. To do so, the ERAM model is simulated based on the magnitude of fuel demand consumption, the rate of air pollution emission generated by each energy per unit per consumer, and the air pollution contribution produced by each user. To evaluate the reflectiveness and illustrate the feasibility of the model, a real-world case study, i.e., Tehran, was employed. The air pollution emission factors in Tehran territory were identified by considering both mobile sources, e.g., motorcycles, cars, and heavy-duty vehicles, and stationary sources, e.g., energy conversion stations, industries, and household and commercial sectors, which are the main contributors to particulate matter and nitrogen dioxide. An elaborate view of the results indicates that the ERAM model on fuel distribution could remarkably reduce Tehran's air pollution concentration by up to 14%.

Integrated value model for sustainability assessment of residential solar energy systems towards minimizing urban air pollution in Tehran

Renewable energy applications are lucrative alternatives to minimize urban environmental impacts. Solar energy, the most abundant, inexhaustible, and cleanest of all renewable sources, provides an opportunity to transform buildings from energy consumers into active energy producers. Nevertheless, photovoltaic (PV) and hybrid photovoltaic/thermal (PV/T) are considered the most viable alternatives for urban settlements. This study, as part of a broader research project, develops a new model to evaluate solar systems' air pollution mitigation capacity and assist decision-makers in adopting the most suitable solution. The approach is based on the integrated value model for sustainability assessment (MIVES), combined with the analytic hierarchy process (AHP) and sensitivity analysis. This multi-objective tool is applied to residential buildings in Tehran, a megacity example with unused rooftops, solar energy harvest potential, and air pollution reduction needs. Results reveal one square meter of PV and PV/T enables avoiding 211 and 488 kg CO2 emissions annually, as well as 1.2 and 1.9 g PM pollutants, respectively. Although PV achieves higher sustainability indexes as a better socio-economic alternative, PV/T can be a robust solution when stakeholders are more sensitive to environmental requirements and air pollution decrement potential. The critical obstacle to PV/T deployment is the lack of financial incentives. However, allocating 38 % of solar electricity feed-in tariffs to solar thermal energy could solve this issue. Compared to green roofs, solar systems stand out with CO2 saving and energy production potential. Researchers expect future solar collectors’ improvements, such as lower resource consumption, thus, becoming more environmentally friendly and cost-effective solutions.

Assessing the Impacts of Changes in the Currency Exchange Rate on Air Pollution in Tehran: A Sectoral Review

The 2022 winter was the most polluted season of Tehran in the past five years. Tehran is rated as one of the world’s most polluted cities in World Air Quality Index (AQI) Ranking. Air pollution in Iran’s big cities is the consequence of several economic, technological, management, and socio-cultural factors. But how important is the currency exchange rate? Since the Iranian revolution in 1979, the country has been subjected to sanctions imposed by a varied of international entities, namely the United States. The economic and political consequences of the exchange rate have so far been the central topics of interest to researchers, journalists, and officials. Although multiple factors affect the level of air pollution in cities, this study narrows down its focus on the 2018-2020 period, during which changing in Dollar to Rial have burdened environmental strategies in Iran. National and public entities do not officially reveal the trade volume between Iran and the international community, importing and exporting transactions, and foreign and domestic investments. Therefore, this descriptive-analytical paper examines the experts’ and official debate over the topic in the major national and international newspapers, news agencies, and interviews. Findings reveal political debate and economic consequences, namely a decrease in the Iranian Rial exchange rate against the U.S. dollar, have had undeniable impacts on the national and local actions related to five key areas of public transit, motorcycles, automobiles, petrol, and refineries and power plants, which are five significant pollution factors in Tehran.

Source identification, seasonal and spatial variations of airborne dust trace elements pollution in Tehran, the capital of Iran

One of the main challenges in highly populated cities such as Tehran is air pollution, which originates mainly from rapid and irregular urbanization and industrialization. This study was carried out to investigate trace elements contamination in airborne dust of Tehran, the capital of Iran. Dust samples were collected from 44 different sites on a monthly basis from March 2016 to March 2017. Results of trace elements pollution using three factors of enrichment factor, contamination factor and pollution load index, revealed that the pollution has spread in all seasons and its highest amount was observed in the cold seasons, the results of these indices also showed Pb, Zn, Cd and As play an important role in air pollution in Tehran. Three main groups were identified in the sources analysis study: (1) Co and Fe, (2) As, (3) Ni, Cr, Cd, Pb and Zn. The first group does not seem to be much influenced by human activities and originates mostly from geogenic sources; but the second and third groups are mostly from anthropogenic sources. Traffic, fossil fuel combustion and industrial activities are among the most important human factors that have a significant impact on the amount of Trace elements in the dust.

Estimation of short-lived climate forced sulfur dioxide in Tehran, Iran, using machine learning analysis

This paper presents a time-series analysis of SO2 air concentration and the effects of particulates (either PM2.5 and PM10) concentrations and meteorological conditions (relative humidity and wind speed) on SO2 trend in Tehran for the period from 2011 to 2020. The source data were obtained from 21 monitoring stations of Air Quality Control Company and meteorological stations in Tehran. To predict the status of future concentration of SO2, PM2.5 and PM10, a Box–Jenkins ARIMA approach was used to model the monthly time series. Considering the whole period of ten years, a somewhat downward trend was noted for SO2 air concentration, even though a slight rising trend was observed in 2020 year. Monthly sulfur dioxide concentrations showed the lowest value in June and the highest value in January. Seasonal concentrations were lowest in spring and highest in winter. Then, in the ArcGIS software, the IDW method was used to obtain air pollution zoning maps. As a result, the highest average concentration of SO2 occurred in the north and southwest of Tehran. In the last step, Relations between the SO2 concentration and particulate matters and relative humidity and wind speed were calculated statistically using the daily average data. We finally concluded that the combined effect of particulate matters and relative humidity with the increasing role of Sulfur dioxide overcomes the decreasing role of wind speed. This study can contribute to a better understanding of the SO2 air pollution in Tehran affected by meteorological conditions and the rapid urbanization and industrialization, followed by the possible combustion of fuel oil in power plants and health problems.

High-resolution modeling of gaseous air pollutants over Tehran and validation with surface and satellite data

This study addresses the question of how skillful regional air quality modeling can be when using downscaled globally-available emission inventories. This paper applies global datasets to prepare fine-resolution priori emission inventories for an urban area. Emissions Database for Global Atmospheric Research (EDGAR), Gridded Population of the World, and output from the Fossil Fuel Data Assimilation System are used to downscale the spatiotemporal resolution of global emission inventories to the finer scale. The resultant high-resolution inventory is taken as input for an off-line run of the Community Multi-Scale Air Quality (CMAQ) modeling system to simulate the concentrations of air pollutants in Tehran during August 2018, November 2018, February 2019, and May 2019. These runs are forced with meteorology from the Weather Research and Forecasting (WRF) model. Retrievals of atmospheric composition from the TROPOspheric Monitoring Instrument (TROPOMI) and surface measurements (split into ‘road’ or ‘city’ type stations) are used to compare with the modeled concentrations of NO2, CO, and O3 to assess the capability of the applied modeling framework and emission inventories in concentration estimations.Comparison of modeled NO2 concentration with NO2 tropospheric column shows that the model captures the spatial and temporal distribution with Pearson correlation above 0.7 and 0.6, respectively and absolute bias under 0.08 ppb. The offline WRF-CMAQ simulations overestimate surface measurements of NO2 and CO and underestimate O3. The model captures the diurnal variations for NO2, CO, and O3 with a correlation between 0.7 and 0.91, 0.6–0.8, and 0.5–0.98 and absolute bias less than 69 ppb, 1.15 ppm, and 12.5 ppb, respectively. The overall performance of the observing and modeling system is sufficient for a credible inversion of surface emissions, which is the intended purpose of this modeling set-up.

Long term exposure to ambient air particulate matter and mortality effects in Megacity of Tehran, Iran: 2012–2017

Ambient particulate matter (PM) is the major environmental concern in Tehran, Iran. Exposure to such particles is a serious global health concern and substantial evidence indicates that exposure to particulate matter contributes to cardiovascular and respiratory disease. Here, we provide a detailed health impact of ambient air pollution in Tehran by linking PM pollution with mortality caused by cardiovascular and respiratory disease. Air quality data of PM obtained from Tehran Air Quality Monitoring Stations (TAQMSs) during 2012–2017. Data mining methods conducted by Spatio-temporal outlier detection and “Air Pollution and Health: a European Approach (APHEA) project” was applied to impute missing values. Time-series data were analyzed to estimate the associations between PM pollution and cause-specific mortality at different lags in Tehran. Based on descriptive analysis, PM2.5 is expected to be a major influencing factor in the increased cardiovascular-related death (CVD) and respiratory dysfunction related death (RD) deaths. The cross-correlation analysis showed the same source for PM2.5 and PM10 in the ambient air of Tehran. Also, the results indicated delays of about 7–15 days from the increased concentrations of PMs and the observed rising in CVD and RD death. Trend analysis showed a more decreasing trend for PM2.5 pollutions than PM10 pollution and a slightly increasing trend for cause-specific deaths during 2012–2017. Long term exposure to high PM levels increases the CVD and RD deaths and control strategies along with control regulations should be considered to control PM.

Emergency hospital admissions for cardiovascular diseases attributed to air pollution in Tehran during 2016-2019.

The burden of five main air pollutants, including CO, O3, NO2, SO2, and PM2.5, on the emergency department visits (EDVs) during January 2016-December 2019 due to all cardiovascular diseases was assessed in Tehran by using a time-series model. The pollutants data were collected from Iran Department of Environment including 10 air pollution monitoring stations for the period of our study. Cumulative relative risk and attributable number/fraction were calculated for each pollutants by a Quasi-Poisson time-series regression and distributed lag non-linear model (DLNM). The maximum lag was set to 14 days because harvesting effect is more likely happened during few days. We used percentile 25 as reference value in order to calculate cumulative relative risk and attributable fraction. About 69,000 patients with cardiovascular symptoms have been admitted into the hospital during 4 years. The cumulative relative risk during the 14 days was 1.13 (1.01, 1.26), 1.15 (1.02, 1.29), and 1.08 (1.01, 1.18) for CO, NO2, and PM2.5, respectively. The numbers attributed to all values of CO were more than others; about 3800 EDVs were significantly attributed to CO, of which over 3000 were significantly attributed to high values of the pollutant. Low values of all pollutants were, not surprisingly, responsible for low number of EDVs. PM2.5, CO, and NO2 were responsible to considerable attributable number of EDVs. Our study emphasizes the need for local authorities to establish a program to reduce the air pollution in Tehran.

Association of systemic inflammation and coagulation biomarkers with source-specific PM2.5 mass concentrations among young and elderly subjects in central Tehran

ABSTRACT In this study, we investigated the association between short-term exposure to different sources of fine particulate matter (PM2.5) and biomarkers of coagulation and inflammation in two different panels of elderly and healthy young individuals in central Tehran. Five biomarkers, including white blood cells (WBC), high sensitive C-reactive protein (hsCRP), tumor necrosis factor-soluble receptor-II (sTNF-RII), interleukin-6 (IL-6), and von Willebrand factor (vWF) were analyzed in the blood samples drawn every 8 weeks from the subjects between May 2012 and May 2013. The studied populations consisted of 44 elderly individuals at a retirement home as well as 40 young adults residing at a school dormitory. Positive Matrix Factorization (PMF)-resolved source-specific PM2.5 mass concentrations and biomarker levels were used as the input to the linear mixed-effects regression model to evaluate the impact of exposure to previously identified PM sources at retirement home and school dormitory in two time lag configurations: lag 1–3 (1–3 days before the blood sampling), and lag 4–6 (4–6 days before the blood sampling). Our analysis of the elderly revealed positive associations of all biomarkers (except hsCRP) with particles of secondary origin in both time lags, further corroborating the toxicity of secondary aerosols formed by photochemical processing in central Tehran. Moreover, industrial emissions, and road dust particles were positively associated with WBC, sTNF-RII, and IL-6 among seniors, while vehicular emissions exhibited positive associations with all biomarkers in either first- or second-time lag. In contrast, most of the PM2.5 sources showed insignificant associations with biomarkers of inflammation in the panel of healthy young subjects. Therefore, findings from this study indicated that various PM2.5 sources increase the levels of inflammation and coagulation biomarkers, although the strength and significance of these associations vary depending on the type of PM sources, demographic characteristics, and differ across the different time lags. Implications: Tehran, the capital of Iran with a population of more than 9 million people, has been facing serious air pollution challenges as a result of extensive vehicular, and industrial activities in the previous years. Among various air pollutants in Tehran, fine particulate matters (PM2.5, particles with aerodynamic diameters < 2.5 µm) are known as one of the most important critical pollutants, causing several adverse health impacts including lung cancer, respiratory, cardiovascular, and cardiopulmonary diseases. Therefore, a number of studies in the area have tried to investigate the adverse health impacts of exposure to PM2.5. However, no studies have ever been conducted in Tehran to examine the association between specific PM2.5 sources and biomarkers of coagulation and systemic inflammation as indicators of cardiovascular disorders. Indeed, this is the first study in the area investigating the association of source-specific PM2.5 with biomarkers of inflammation including white blood cells (WBC), high sensitive C-reactive protein (hsCRP), tumor necrosis factor-soluble receptor-II (sTNF-RII), interleukin-6 (IL-6), and von Willebrand factor (vWF). Our results have important implications for policy makers in identifying the most toxic sources of PM2.5, and in turn designing schemes for mitigating adverse health impacts of air pollution in Tehran.