Traces of black carbon sources before and after the Covid-19 outbreak in Tehran, Iran.

The concentration of black carbon was measured in four sites of the industrial and high-traffic metropolis of Tehran with different land uses. Then, the contribution of biomass and fossil fuels in the emission of this pollutant was modeled using the Aethalometer model. The possible locations of important sources of black carbon dissemination were projected using PSCF and CWT models, and the results were compared in the two periods before and after the Covid-19 outbreak. Temporal variations of black carbon illustrated that BC concentration decreased in the period after the onset of the pandemic in all studied areas, and this decline was more explicit in the traffic intersection of the city. Diurnal changes of BC concentration indicated the significant impact of the application of the law banning night traffic of motor vehicles in reducing the BC concentration in this period, and probably the reduction of HDDV traffic has played the most important role in this reduction. The results related to the share of BC sources indicated that black carbon emissions are affected by an average of about 80% of fossil fuel combustion and wood combustion interferes with about 20% of BC emissions. Finally, speculations were made about the possible sources of BC emission and its urban scale transport using PSCF and CWT models, which indicated the superiority of the CWT model in terms of source segregation. The results of this analysis were further utilized to surmise black carbon emission sources based on the land use of receptor points.

Real-world and bottom-up methodology for emission inventory development and scenario design in medium-sized cities.

Efficient management of air quality requires a comprehensive emission inventory to support decision-making on air quality improvement. This article presents a comprehensive framework for detailed emission inventory development in cities with low-quality basic data, which examines the emission of primary criteria pollutants (CO, NOx, SO2, PM2.5, PM10, and VOC) from mobile sources, residential, commercial, and public services, fuel stations, transport terminals, energy conversion sections, and industries. This research was applied to Tabriz in Northwest Iran, one of the polluted medium-sized cities with a population of 1.77 million. Results show the city daily emission per capita is 569.8 g of CO, 68.6 g of NOx, 38.6 g of VOC, 17.6 g of SOx, and 3.7 g of PM. Vehicular emissions accounted for 98% of CO, 91% of VOCs, 61% of NOx, and 56% of PM; meaning alternative policy strategies in vehicles would reduce emissions rapidly. Fifteen applicable and effective scenarios in transport and one concerning stationary sources were proposed and reduction potential of them was evaluated. Effectiveness of the public transport improvement and replacement of old passenger cars were founded the key scenarios. These two alternatives decrease 14 and 2 tons of SO2 and 6797 and 2394 tons of NOx annually with the cost of $99.5 MM and $366.5 MM, respectively. The findings of this study provides the choice of travel method by each citizen is a function of cost, speed, comfort and safety of travel; therefore, all the requirements of any scenarios must be fully considered in the implementation step.

Tropospheric Ozone in Tehran, Iran, during the last 20 years.

Air pollution and its effects on human health and the environment are one of the main concerns in urban areas. This study focuses on the distribution and changes in the concentrations of ozone and its precursors (i.e., NO, NO2 and CO) in Tehran for the 20-year period from 2001 to 2020. The effects of precursors and meteorological conditions (temperature, wind speed, dew point, humidity and rainfall) on ozone were investigated using data from 22 stations of the Air Quality Control Company (AQCC) and meteorological stations. Regression models were applied to evaluate the dependence of ozone concentration on its precursors and meteorological parameters based on monthly average values. Finally, the monthly and annual levels of surface ozone and total column ozone were compared during the study period. The results show that the average ozone concentration in Tehran varied substantially between 2001 and 2008, and decreased after 2008 when stringent air quality control measures were implemented. The highest average concentration of ozone occurred in the southwest of Tehran. Although mobile and resident sources play an important role in the release of precursors, the results also indicate a significant effect of meteorological conditions on the changes in ozone concentration. This study is an effective step toward a better understanding of ozone changes in Tehran under the changing influence of precursors and meteorological conditions.

Changes in short-lived climate pollutants during the COVID-19 pandemic in Tehran, Iran.

This study investigates the changes of short-lived climate pollutants and other air pollutants during the COVID-19 pandemic in Tehran, Iran. Concentrations of air pollutants were obtained from 21 monitoring stations for the period from 5 January 2019 to 5 August 2019, representing normal conditions unaffected by COVID-19, and the period 5 January 2020 to 5 August 2020, i.e., during the COVID-19 crisis. We concentrated our analysis on three time windows (23 February 2020 to 15 March 2020, 18 March 2020 to 3 April 2020, and 5 April 2020 to 17 April 2020) during the lockdown when different sets of measures were taken to limit the spread of COVID-19. In comparison to the period not affected by COVID-19 measures, mean concentrations of pollutants were increased during the first lockdown period; when the number of COVID-19 patients increased sharply compared to the other periods, the mean surface concentrations of NO2, SO2, and CO were decreased and concentrations of other pollutants (i.e., O3, PM10, and PM2.5) were increased during the second lockdown period compared to the corresponding period in 2019. In the third period, the mean concentrations were decreased compared to the corresponding period in 2019. For the full period, decreases in mean concentrations of O3, NO2, SO2, CO, and PM10 and increases in PM2.5 were observed during the COVID-19 crisis, compared to 2019. Overall, the strongest reductions, 12% and 6%, respectively, were observed for CO and NO2, pointing to reduced emissions from traffic as a result of lockdown measures. The concentrations of other pollutants changed little, suggesting that the lockdown measures did not result in strong changes in the emissions from stationary sources.

Assessment of air pollution removal by urban trees based on the i-Tree Eco Model: The case of Tehran, Iran.

Air quality concerns have become increasingly serious in metropolises such as Tehran (Iran) in recent years. This study aims to assess the contribution of urban trees in Tehran toward mitigating air pollution and to evaluate the economic value of this ecosystem service using the i-Tree Eco model. To accomplish this objective, we utilized Tehran's original land use map, identifying five distinct land use categories: commercial and industrial, parks and urban forests, residential areas, roads and transportation, and urban services. Field data necessary for this analysis were collected from 316 designated plots, each with a radius of 11.3 m, and subsequently analyzed using the i-Tree Eco model. The locations of these plots were determined using the stratified sampling method. The results illustrate that Tehran's urban trees removed 1286.4 tons of pollutants in 2020. Specifically, the annual rates of air pollution removal were found to be 134.8 tons for CO; 299.7 tons for NO2; 270.3 tons for O3; 0.7 tons for PM2.5; 489.4 tons for PM10 (particulate matter with a diameter size between 2.5 and 10 µm); and 91.5 tons for SO2, with an associated monetary value of US$1 536 619. However, despite this significant removal capacity, the impact remains relatively small compared with the total amount of pollution emitted in 2020, accounting for only 0.17%. This is attributed to the high emissions rate and low per capita green space in the city. These findings could serve as a foundation for future research and urban planning initiatives aimed at enhancing green spaces in urban areas, thereby promoting sustainable urban development. Integr Environ Assess Manag 2024;00:1-11. © 2024 SETAC.

The relative contributions of traffic and non-traffic sources in ultrafine particle formations in Tehran mega city.

Emissions of ultrafine particles (UFPs; diameter < 100 nm) are strongly associated with traffic-related emissions and are a growing global concern in urban environments. The aim of this study was to investigate the variations of particle number concentration (PNC) with a diameter > 10 nm at nine stations and understand the major sources of UFPs (primary vs. secondary) in Tehran megacity. The study was carried out in Tehran in 2020. NOx and PNC were reported from a total of nine urban site locations in Tehran and BC concentrations were examined at two monitoring stations. Data from all stations showed diurnal changes with peak morning and evening rush hours. The hourly PNC was correlated with NOx. PNCs in Tehran were higher compared to those of many cities reported in the literature. The highest concentrations were at District 19 station (traffic) and the lowest was at Punak station (residential) such that the average PNC varied from 8.4 × 103 to 5.7 × 104 cm-3. In Ray and Sharif stations, the average contributions of primary and secondary sources of PNC were 67 and 33%, respectively. Overall, we conclude that a decrease in primary emission leads to a decrease in the total concentration of aerosols, despite an increase in the formation of new particles by photo nucleation.

Current Status and Future Forecast of Short-lived Climate-Forced Ozone in Tehran, Iran, derived from Ground-Based and Satellite Observations.

In this study, the distribution and alterations of ozone concentrations in Tehran, Iran, in 2021 were investigated. The impacts of precursors (i.e., CO, NO2, and NO) on ozone were examined using the data collected over 12 months (i.e., January 2021 to December 2021) from 21 stations of the Air Quality Control Company (AQCC). The results of monthly heat mapping of tropospheric ozone concentrations indicated the lowest value in December and the highest value in July. The lowest and highest seasonal concentrations were in winter and summer, respectively. Moreover, there was a negative correlation between ozone and its precursors. The Inverse Distance Weighting (IDW) method was then implemented to obtain air pollution zoning maps. Then, ozone concentration modeled by the IDW method was compared with the average monthly change of total column density of ozone derived from Sentinel-5 satellite data in the Google Earth Engine (GEE) cloud platform. A good agreement was discovered despite the harsh circumstances that both ground-based and satellite measurements were subjected to. The results obtained from both datasets showed that the west of the city of Tehran had the highest averaged O3 concentration. In this study, the status of the concentration of ozone precursors and tropospheric ozone in 2022 was also predicted. For this purpose, the Box-Jenkins Seasonal Autoregressive Integrated Moving Average (SARIMA) approach was implemented to predict the monthly air quality parameters. Overall, it was observed that the SARIMA approach was an efficient tool for forecasting air quality. Finally, the results showed that the trends of ozone obtained from terrestrial and satellite observations throughout 2021 were slightly different due to the contribution of the tropospheric ozone precursor concentration and meteorology conditions.

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.

The data on the dispersion modeling of traffic-related PM10 and CO emissions using CALINE3; A case study in Tehran, Iran.

CALINE3 model predicts the dispersion of pollutants released from roadways in the receptor places at a certain radius from the source. This model was used to evaluate the dispersion of particulate matter < 2.5 µm (PM10) and carbon monoxide (CO) emitted from Yadegar-e-Emam Expressway (YEE) as one of the most congested highways in Tehran. The hourly concentrations of PM10 and CO, and the count and speed of vehicles were obtained from Tehran׳s Air Quality Control Company (TAQCC). Wind speed and direction, the height of mixing zone, air temperature, relative humidity, and stability class were acquired from IRAN Meteorological Organization (IRIMO). The emission factors (EF) of vehicles were acquired from those proposed for UK. The dispersion of PM10 and CO was predicted over the nearby area, and the modeled concentrations were estimated for a specific point, where an air quality monitoring station was working. The major portion of PM10 and CO released by vehicles in YEE was dispersed to the east. The comparison between the modeled and measured concentrations revealed that CALINE3 underestimates the concentrations of PM10 and CO by about 50%.

Identification of dust storm origin in South -West of Iran.

BACKGROUND: Deserts are the main sources of emitted dust, and are highly responsive to wind erosion. Low content of soil moisture and lack of vegetation cover lead to fine particle's release. One of the semi-arid bare lands in Iran, located in the South-West of Iran in Khoozestan province, was selected to investigate Sand and Dust storm potential. METHODS: This paper focused on the metrological parameters of the sampling site, their changes and the relationship between these changes and dust storm occurrence, estimation of Reconaissance Drought Index, the Atterberg limits of soil samples and their relation with soil erosion ability, the chemical composition, size distribution of soil and airborne dust samples, and estimation of vertical mass flux by COMSALT through considering the effect of saffman force and interparticle cohesion forces during warm period (April-September) in 2010. The chemical compositions are measured with X-ray fluorescence, Atomic absorption spectrophotometer and X-ray diffraction. The particle size distribution analysis was conducted by using Laser particle size and sieve techniques. RESULTS: There was a strong negative correlation between dust storm occurrence and annual and seasonal rainfall and relative humidity. Positive strong correlation between annual and seasonal maximum temperature and dust storm frequency was seen. Estimation of RDIst in the studied period showed an extremely dry condition. Using the results of particle size distribution and soil consistency, the weak structure of soil was represented. X-ray diffraction analyses of soil and dust samples showed that soil mineralogy was dominated mainly by Quartz and calcite. X-ray fluorescence analyses of samples indicated that the most important major oxide compositions of the soil and airborne dust samples were SiO2, Al2O3, CaO, MgO, Na2O, and Fe2O3, demonstrating similar percentages for soil and dust samples. Estimation of Enrichment Factors for all studied trace elements in soil samples showed Br, Cl, Mo, S, Zn, and Hg with EF values higher than 10. CONCLUSION: The findings, showed the possible correlation between the degree of anthropogenic soil pollutants, and the remains of Iraq-Iran war. The results expressed sand and dust storm emission potential in this area, was illustrated with measured vertical mass fluxes by COMSALT.

Air Quality Analysis by Using Fuzzy Inference System and Fuzzy C-mean Clustering in Tehran, Iran from 2009-2013.

BACKGROUND: Since the industrial revolution, the rate of industrialization and urbanization has increased dramatically. Regarding this issue, specific regions mostly located in developing countries have been confronted with serious problems, particularly environmental problems among which air pollution is of high importance. METHODS: Eleven parameters, including CO, SO2, PM10, PM2.5, O3, NO2, benzene, toluene, ethyl-benzene, xylene, and 1,3-butadiene, have been accounted over a period of two years (2011-2012) from five monitoring stations located at Tehran, Iran, were assessed by using fuzzy inference system and fuzzy c-mean clustering. RESULTS: These tools showed that the quality of criteria pollutants between the year 2011 and 2012 did not as much effect the public health as the other pollutants did. CONCLUSION: Using the air EPA AQI, the quality of air, and also the managerial plans required to improve the quality can be misled.