Air pollution trends in Tehran and their anthropogenic drivers

Journal Pre-proof Air pollution trends in Tehran and their anthropogenic drivers Sara Torbatian, Ali Hoshyaripour, Hossein Shahbazi, Vahid Hosseini PII:

S1309-1042(19)30503-3

DOI:

https://doi.org/10.1016/j.apr.2019.11.015

Reference:

APR 689

To appear in:

Atmospheric Pollution Research

Received Date: 1 August 2019 Revised Date:

9 November 2019

Accepted Date: 9 November 2019

Please cite this article as: Torbatian, S., Hoshyaripour, A., Shahbazi, H., Hosseini, V., Air pollution trends in Tehran and their anthropogenic drivers Atmospheric Pollution Research, https:// doi.org/10.1016/j.apr.2019.11.015. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.

Air pollution trends in Tehran and their anthropogenic drivers Sara Torbatian1, Ali Hoshyaripour2, Hossein Shahbazi3, Vahid Hosseini3 1 Air Quality Control Company, Tehran, Iran 2 Institute of Meteorology and Climate Research (IMK), Karlsruhe Institute of Technology (KIT), Germany 3 Mechanical Engineering Department, Sharif University of Technology, Tehran, Iran

* Corresponding author: Vahid Hosseini, Ph.D. Associate Professor of Mechanical Engineering Department Sharif University of Technology, Tehran, I.R. Iran Email address: [email protected] Tel: +98-21-6616-5585 Fax: +98-21-6600-0021

Abstract An assessment of trends in main air pollutant concentrations (including CO, SO 2, NO2, O3, PM2.5, and Asbestos) is conducted for the years 2005-2016 for the city of Tehran, Iran. The focus has been on the contribution of anthropogenic emissions to the observed trend, particularly the role of related mitigation plans implemented in the period of interest. A significant decreasing trend (about 50%) was observed in CO and SO2 concentrations during the considered time interval, which was explained by improvement plans in fuel quality and vehicle emission standards. While a substantial elevation (more than 50%) in NO 2 levels was detected over the study period, variant trends were observed during different periods that appear to be driven by multiple factors including the increase in the number of vehicles and fuel consumption, changes in residential heating fuel from heavy oil to natural gas, etc. The analysis revealed that the O3 formation in Tehran’s atmosphere was converted from NOx-limited to NOx-saturated regime around the years 2012-2013. The overall analysis showed a slight decreasing trend (about 30%) in PM 2.5 concentrations between the years 2011-2015; however, a rise in PM2.5 levels was observed between the years 2015-2016. Apart from the contribution of anthropogenic drivers, general drought in Iran has caused a considerable increase in natural dust in total PM2.5 mass. Finally, a decline in annual mean airborne asbestos fibers (more than 60%) was indicated by the current study during the years 2012 and 2015. Key words: Trend Analysis, Air Pollution, Driving Factor, Control action

1. Introduction Recent industrialization and urbanization in Iran have notably degraded the air quality (AQ), especially in urban areas. Studies have shown that the number of total deaths attributed to air pollution in Iran increased by about 27% during the years 1990-2013 1

(World Bank, 2016). Besides, air pollution is no longer considered as only a threat to human health, but also a threat to the quality of life and productivity within cities (e.g. economic disruption affecting transportation, construction, school access, tourism, etc.) (World Bank, 2016). Tehran, the capital of Iran, is a fast-growing megacity that is facing serious environmental problems and health issues due to air pollution (Sotoudeheian and Arhami, 2014). The developmental changes Tehran has gone through in the last few decades (including changes in the industry and transportation sectors, population growth, etc.) have had major impacts on air pollutant levels (Hosseini and Shahbazi, 2016). For several years, Tehran has experienced severe air pollution episodes during the winter months, especially in December and January, which has led to school closures and high rates of hospital admissions due to respiratory problems (Hassani et al., 2016; Shahbazi et al., 2017; Shahbazi et al., 2019). Atmospheric stability, which usually dominates in the region during the cold months, under certain circumstances leads to strong temperature inversions with low mixing heights, resulting in the accumulation of pollutants over the city (Ashrafi et al., 2012; Saghafi et al., 2014). To mitigate air pollution and its health and socio-economic hazards, emission reduction regulations and mechanisms have been designed and implemented in Tehran over the last decade. For example, imposing pollutant emission limits on vehicles and prohibiting the use of carburetors on all new vehicles have led to a substantial decrease in carbon monoxide concentrations over the past few years (Hosseini and Shahbazi, 2016). The considerable reduction in the fuel sulfur content has been attributed to the control measures taken for the improvement of the quality of gasoline and diesel fuel in the city of Tehran during the past few years (World Bank, 2018). However, developments in industrial and transportation sectors have caused a significant increase in the concentration of fine particulate matter (especially PM2.5), which has exceeded the national ambient air quality standards for annual means during the last six years. According to the latest annual air quality report (Tehran Annual Air Quality Report, 2018), almost 90% of days with unfavorable AQ conditions (Air Quality Index > 100, EPA, 2003c) in Tehran during the last year were associated with high hourly mean fine particulate matter (PM2.5) concentrations (between 130-360 µg/m3). The annual population exposure to PM2.5 (averaged over 2008-2013) in Iran has been estimated to be 2

about 42 μg/m3 (median value), which exceeds the WHO annual standard limit (35 μg/m3) (WHO, 2016). Studying the historical trend of air pollutants can be helpful for understanding the pattern of their changes over time with respect to natural (e.g. meteorology) and anthropogenic factors (e.g. emissions), which in turn is related to economic, social and political developments. Thus, this can be a valuable asset in predicting future variations of AQ and the effectiveness of the relevant policies. An objective of the trend analysis of pollutant concentrations includes understanding emission sources and changes in order to take effective measures to control urban air pollution (Zhang et al., 2011). Moreover, trend analysis of pollutant concentrations is used in some regulatory efforts to determine whether their emission strategies are efficient enough for AQ improvement (IMPROVE, 2011; Guerriro et al., 2014). In addition, trend studies can be used as a valuable tool to assess the capability of the current chemical models in reproducing the inter-annual variability of atmospheric pollutants over the past decade (Colette et al., 2011). Such trends can be obtained from in-situ (e.g. AQ monitoring stations) and remote sensing (e.g. satellites) observations. Lamsal et al. (2015) observed a consistent correspondence between nitrogen dioxide (NO2) column trends derived by satellite measurements and in situ observations. Recent studies investigated long–term AQ trends by using satellite data over Middle Eastern cities (Lelieveld et al., 2015; Duncan et al., 2016; Barkley et al., 2017). It was shown that annual NO2 and sulfur dioxide (SO2) concentrations increased during the years 2005-2010 in major cities of the region, followed by decreasing concentrations between the years 2010-2014 (Lelieveld et al., 2015). The observed trends are attributed mainly to the economic status and conflicts in the region (Lelieveld et al., 2015). These studies provide invaluable information about the drivers of AQ trends on a regional scale but cannot resolve the effects of local activities and policies. These local trends could be better examined by means of in-situ measurements. The relationship between economic development, industrial activities, and mitigation plans with the observed trends in air pollutants has been corroborated by several studies (Castellanos et al., 2012; Reuter et al., 2014; and Cuevas et al., 2014). Since the lifetime of some hazardous pollutants (e.g. NOx) is quite short (a few days to weeks), the local emission of such pollutants directly affects the intensity of the pollution rate (Lelieveld et al., 2015). Therefore, investigating their anthropogenic emission sources could be helpful in explaining the observed trend. 3

In addition, the adverse health impacts associated with different air pollutants emphasize the necessity for conducting trend studies. For example, CO can enter the body via the lungs, and it has the potential to reduce the oxygen-carrying capacity of blood, thereby affecting the circulatory system and oxygen transport to the organs and tissues (WHO, 2000). According to the WHO report (2013), SO2 has been identified as a major threat to the respiratory system and has adverse health effects on lung function. Also, it is considered one of the main contributors to PM formation, which is associated with serious health effects (Guerreiro et al., 2014). Moreover, NO2 plays a key role in the formation of tropospheric ozone and aerosol nitrate, and, as a result, it contributes to severe health impacts (such as reduced lung function) and climate change. The adverse health impacts associated with high levels of tropospheric ozone include increased premature mortality and high rates of hospital admissions due to respiratory problems such as asthma, chronic obstructive pulmonary disease, allergic rhinitis, etc. (Bell et al., 2007). In addition, particulate matters (PM) have been identified as the most complex pollutants, depending on their size, that can penetrate the respiratory system and cause severe health problems, high rates of hospital admissions and premature deaths (WHO, 2013). Also, the importance of monitoring and controlling asbestos concentrations has been stressed by different environmental assessments worldwide. Several Studies have shown the severe health effects that asbestos exposure may cause, including a rise in the risk of lung cancer, mesothelioma, etc. (ATSDR, 2015; Goldberg and Luce et al., 2009). Here, we examine the temporal variations observed by AQ stations to conduct a comprehensive assessment of Tehran’s air quality levels during the last decade. Moreover, regulatory factors including implemented policies, emission controls and legislations contributing to the observed trend of air pollutants are discussed. Although the historical variations of some pollutants such as NO2 over Tehran have been evaluated through the use of satellite data in other studies, this work is the first of its kind to examine the changes in some main pollutants such as PM2.5, O3, and CO. The main objectives of this study are 1) to examine AQ trends over the city of Tehran from 2005 to 2016 and 2) to diagnose the potential drivers of the observed trends. In section 2, the study area, observational data and the analysis methods are explained. The results are discussed in section 3 and conclusions are given in section 4. 4

2. Methodology 2.1. Study Area and data The city of Tehran is occupied by more than 8.8 million inhabitants, making it the third largest megacity in the Middle East region and the primary urban center of Iran (Amini et al., 2016). The city is located between 35°34’ N to 35°59’ N latitude and 51°5’ E to 51°53’ E longitude. Surrounded by mountain ranges from the North to the Northeastern side, the capital is located in a valley with an altitude of 1000-1800 meters above the mean sea level (Naddafi et al., 2012). The topographical conditions of the city, industrial activities, high rates of population growth, and vehicle use have all contributed to the increase of severe pollution episodes each year (Sotoudeheian and Arhami, 2014; Kamali et al., 2015). The evaluation of the variations in the major air pollutants (including CO, NO2, O3, SO2, PM2.5, and asbestos) is examined using ambient air measurements recorded by the Air Quality Control Company of Tehran (AQCC) monitoring network for the years 2005 – 2016. Fig. 1 shows the distribution of air quality monitoring stations of AQCC across the city of Tehran (21 active AQ sites).

Fig. 1. Map of AQCC air quality monitoring sites (updated in 2017)

Currently, the Tehran AQ monitoring network is comprised of 9 urban stations and 12 traffic sites. The AQ monitoring stations are categorized based on the type of environment in which they are situated (Shahbazi et al., 2018). Such classification provides helpful information about the source of a particular pollutant or the type of land use (Local Air Quality Management, 2009). The description of the types of air monitoring stations considered in this study is as follows:

5

For traffic stations, the emissions from nearby traffic, including roads and highways, mainly influence the pollution levels of such stations. Traffic sites should measure air quality, which is representative of more than 200 m2 of their surrounding area. An urban station is located in an urban area that could be considered a representative of the typical population exposure to pollutant levels in the city. An urban site sampling should be located at a distance of 10-200 meters (depending on the type of surrounding area) from a busy road. 2.2. Historic evolution of air quality mitigation in Tehran Energy consumption and transportation are major contributors to the emission of urban air pollutions (Barkley et al., 2017); therefore, studying their evolution can lead us to find a link between the production and pollution rate. Shahbazi et al., (2016) showed that vehicle emissions constitute the main source of pollutants in the city of Tehran (about 85% of the total sources). On average, more than 35% of daily commutes (more than 6 million daily trips) are made by private passenger cars. With a rapid rise in the total number of light duty vehicles (more than 3 million) and motorcycles (more than 800,000) during the considered twelve years (2005-2016), the air quality conditions have been mostly influenced by emissions from the transportation sector (Hosseini and Shahbazi, 2016). According to Shahbazi et al., (2016), the total number of vehicles for the year 2013-2014 in Tehran was about 4.24 million, which is comprised of about 72% light-duty passenger cars and 18% motorcycles. The rest of the fleet in Tehran is categorized into taxis, buses, minibuses, pickups, and trucks. In addition, the average age of the vehicle fleet is also an important issue that plays an indisputable role in the degradation of Tehran’s air quality. Only 43% of passenger vehicles, which is the dominant component in Tehran’s vehicle fleet, are less than five years old, and this ratio is much less for other categories of Tehran’s fleet (Shahbazi et al., 2016). Also, carburetor-equipped vehicles consist of 9.37%, 4.76%, and 22.29% of passenger cars, taxis, and pickups, respectively. Despite all the pollution sources, the adaptation of the European emission standards was considered one of the main solutions to the air pollution crisis in Iran. The historic evolution of different emission certificates, which have been implemented in Iran since

6

2000, is shown in Fig. 2. It should be noted that the timeline is based on the dates on which the policies were put into practice rather than the time of their approval. 2.3. Trend Analysis For the trend analysis, the assessment was conducted over stations that meet the requirements of 50% of data availability for each year. In addition, trend analysis was performed for those sites with complete data for the entire time interval or 80% of the years or more (i.e., 10 years out of 12). Since the monitoring of PM2.5 started in 2010, the trend for this pollutant was computed based on available data for 6 out the 7 years. Also, based on the sampling and analysis results conducted by the AQCC, the changes in asbestos fibers were investigated for the years 2010 – 2016. To understand the trends and to quantify the variations in observations through a more reliable approach, two main functions were applied, including the smooth Trend and Theil-Sen function. The Theil-Sen method (Theil, 1950; Sen, 1968) was used to compute the trend of measured air pollutants. By using such a statistical method, the estimation of trends was completed via taking the median (50th percentile) of all slopes between every possible data pair with the concentration data as the dependent variable and the year as the independent variable (IMPROVE, 2011). The Theil regression is recognized as a nonparametric substitute to the parametric ordinary least squares regression (Unified guidance, EPA 2009). A major benefit of Theil regression is that the results are less biased by the outliers (IMPROVE, 2011). In addition, the capability to compute more accurate confidence intervals even with non-normal data and heteroscedasticity (nonconstant error variance) is another advantage of using the Theil-Sen estimator (Carslaw et al., 2015). The smooth trend line is derived based on the Generalized Additive Models (GAMs) (Carslaw et al., 2007), which fits a smooth line to the monthly mean pollutant concentrations. The algorithms used in such models estimate an optimized fit for the observed variations that is not too smooth to exclude the real effects of the variations and not too fluctuant to consider outliers (Carslaw et al., 2007). For this study, the trend of pollutants was estimated without considering the types of stations (urban and traffic stations). Indeed, due to rapid urbanization in Tehran, the type of the stations was gone through constant changes during the study period. Therefore, any assumptions about the types of stations might lead to inconsistencies in the analysis. 7

The trend analysis presented here was carried out using the Openair "Theil-Sen" and "Smooth Trend" statistical tool (Carslaw et al., 2015). For this study, the trend of pollutants was estimated based on monthly mean pollutant concentrations derived from hourly mean data. In order to disentangle the effects of emission, transport (meteorological conditions), photochemistry and seasonal variations on the trends of Tehran air quality during the considered time interval, the database was categorized as follows. The seasons were separated and then classified into two categories as day-time and night-time. Day-time includes the warmest part of the day with high photochemistry rates and deep mixing heights that facilitate the vertical dispersion of pollutants. On the other hand, night-time covers the coldest part of the 24-hour period with nocturnal atmospheric stability and shallow mixing heights that limit pollutants' diffusion. In the absence of data on the boundary layer height the averaged wind speed of the night-time was considered as the measure of atmospheric stability. The threshold of wind speed to determine the nocturnal stability was assumed as 1.5 m/s (Sesana et al., 2003). Due to seasonal changes, the length of the day and night in Tehran varies throughout one year. For consistency in the method, the considered hours were selected based on a time-interval of a 24-hour period that is not affected by seasonal changes. Table 1 shows the data categories and their specifications. Table 1. The data classification for each 24-hour time period Category #1: Day, Local “Day” (photochemistry active, the hottest 9 hours of each day: 09:00 – 18:00 (of each 24 h)

Category #3: Night, Local “Night” (photochemistry inactive, remainder of each 24hr period: 21:00 – 06:00 (of each 24 h)

“Local” (mainly local pollution, stable previous night)

“Local” (mainly local pollution, stable at night)

Category #2: Day, Remote “Day” (photochemistry active, the hottest 9 hours of each day: 09:00 – 18:00 (of each 24 h)

Category #4: Night, Remote “Night” (photochemistry inactive, remainder of each 24hr period: 21:00 – 06:00 (of each 24 h)

“Remote” (significant remote pollution contribution, near neutral or windy previous night)

“Remote” (significant remote pollution contribution, near neutral or windy at night)

For this study, only the results derived for the seasonal analysis of NO2 concentrations are discussed. The estimated trend values for other pollutants (including O3, PM2.5, and CO) are provided in the supplementary material (Tables A.1-A.4). 3. Results and discussions 3.1 Overall trends 8

The normalized time series of annual mean concentrations of the criteria air pollutants (CO, NO2, O3, SO2, and PM2.5) over Tehran from 2005 to 2016 are shown in Fig. 2 (a). The relative changes in the annual mean concentration of pollutants have been calculated by considering 2005 as the base year for NO2 and CO. Due to the lack of data availability for other pollutants in 2005, the year 2006 was taken as the base year for O3 and SO2. Also, for the same reason, the year 2010 was appointed as the base year for PM2.5. The overall trends show that the concentrations of NO2 have increased by more than 50% over the study period, while CO, SO2, O3 and PM2.5 concentrations have decreased by about 50%, 50%, 40% and 35%, respectively. However, there are significant fluctuations in the trends over certain periods that are quantitatively shown in Table 2. These are further analyzed in the following. Table 2. Long-term (2005-2016) trends of criteria pollutant concentrations (units/year) calculated using the Theil-Sen Regression method for three different time intervals between 2005 and 2016. Time Interval 2005 - 2008 2009 - 2012 2013 - 2016 Pollutant Confidence Confidence Confidence Slope Slope Slope Interval Interval Interval -1.09*** [-1.33, -0.85] -0.2** [-0.35, -0.05] -0.24*** [-0.31, -0.19] CO (ppm/year) 10.4*** [7.98, 14.25] -2.75* [-4.19, -0.42] -2.13*** [-2.71, -1.39] SO2 (ppb/year) *** *** 10.35 [9, 12.5] -3.25 [-5.7, -1.43] 9.48*** [7.25, 11.72] NO2 (ppb/year) *** ** 16.11 [13.25, 20.06] -2.1 [-4.43, -0.68] -3.52*** [-5.2, -1.8] O3 (ppb/year) -3.17+ [-6.45, 0.76] -1.97* [-3.69, -0.06] PM2.5 (µgm-3/year) The statistical trend significance is indicated by the symbols next to each trend value as follows: *** ~ p < 0.001, ** ~ p < 0.01, * ~ p < 0.05 and + ~ p < 0.1

Table 3. Description of domestic and international policies implemented during the years 2005-2016 Policy No.

Year of Implementation

Description

Implications

Policy-1 Policy-2 Policy-3

2005 2005 2006

Implementation of the Euro-2 emission standard on light-duty vehicles Implementation of the Euro-2 emission standard on heavy-duty vehicles Imposition of sanctions on Iran by the United Nations Security Council

CO CO SO2, NO2

Policy-4 Policy-5

2007 2010

Implementation of the Euro-1 emission standard on motorcycles Implementation of the Euro-3 emission standard on heavy-duty vehicles

Policy-6

2010

Implementation of the Euro-2 emission standard on motorcycles

CO NO2 CO, NO2

Policy-7 Policy-8

2010 2012

Extension of the United Nations Security Council imposed sanctions Implementation of the Euro-4 emission standard on light-duty vehicles

CO, SO2, NO2 CO, NO2

Policy-9

2012

Ban on the production and importation of all kinds of asbestos

Policy-10 Policy-11 Policy-12

2012 2013 2014

Implementation of the traffic restriction program (even and odd scheme) Reform measures taken to improve the quality of Diesel and Gasoline fuel Implementation of the Euro-3 emission standard on motorcycles

Asbestos CO, PM2.5, NO2 CO, SO2, NO2 PM2.5

9

(a)

(b)

(c)

Fig. 2. (a).The normalized time series of annual mean concentration of pollutants in Tehran over the period 2005-2016 (the base year was determined based on the first year in which data was available). (b).The timeline of regulations that can affect air pollutant emissions. Here is a brief explanation of the controlling measures pointed out in the Figure: LDV & HDV: The implementation of European emission standards for Light-duty & Heavy-duty vehicles Motorcycles: The implementation of European emission standards for motorcycles Sanctions: The imposition of sanctions on Iran by the United Nations Security Council Traffic policy: The implementation of traffic schemes in some central districts of Tehran that restrict the daily traveling of passenger cars for a specific time interval during the day Fuel policy: The implementation of reform measures by Iran’s national oil refining company (c).The normalized time series of factors that may have contributed to the changes in the air quality of Tehran over the same time period. The dashed line represents the estimated values that were derived based on the linearity assumption.

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3.2. Carbon Monoxide (CO) Emissions of carbon monoxide are mostly due to the incomplete combustion of fossil fuels and biofuels. The relatively long lifetime of CO (about three months) provides enough time to go through some chemical reactions, leading it to gradually oxidize into carbon dioxide (CO2), and under certain circumstances, O3 is formed during this process (Guerriro et al., 2014). Fig. 3 shows the long-term trend of in situ CO concentrations (2005-2016) based on the mean measurements of five stations (Aghdasyeh, Shahre Ray, Poonak, Rose Park, and Golbarg). The trend line is displayed as a solid red line, with 95% confidence intervals shown as shaded areas. A sharp downward trend (-1.09 ppm/year) of monthly mean CO concentrations was observed between the years 2005-2008, which is likely the result of implementing the Euro-2 emission standard. It should be noted that during the year 2005 and prior to that, CO was considered as the criteria air pollutant that exceeded the annual standard limit, and mitigation strategies implemented afterwards have led to a significant decline in CO sources (Fig. 2 (b)). According to Shahbazi et al., (2016), the national light-duty vehicle fleet in Iran is mainly constituted of gasoline-only vehicles, which account for more than 90% of the total fleet, and the rest includes dual-fueled gasoline–natural gas vehicles. During the year 20132014, the Euro-2 emission standards were applied to a significant number of passenger cars (more than 50%), taxis, pickups, and motorcycles, while only 20% of personal vehicles met the emission requirements for Euro-4 (Shahbazi et al., 2016). In addition, the prohibition on the production of carburetor equipped vehicles and the mandatory use of fuel injection systems were the major control measures carried out between the years 2005-2013 (Hosseini and Shahbazi, 2016) and were considered as the main contributors to the consistent decrease in CO concentrations during the studied years. However, the observed decreasing trend has flattened off in recent years (-0.24 ppm/year). Fig. 2 (c) indicates that the total number of registered vehicles in Tehran increased by about 4 times during the studied period. Since the gasoline-fueled vehicles contribute significantly to CO production (Shahbazi et al., 2016), the high rise in the number of light-duty vehicles was mostly linked to the steady trend observed at the considered monitoring stations. It should be taken into account that the carburetor 11

equipped vehicles have not been entirely removed from the fleet and still play an important role in CO production.

Policy-1 & 2

Policy-4

Policy-8

Policy-6

Policy-11

Fig. 3. Long-term (2005-2016) trends of CO concentrations calculated using the smooth trend method based on the mean measurements of 5 stations.

3.3. Sulphur dioxide (SO2) Sulphur dioxide is produced when fuels containing small amounts of sulphuric substances (such as coal, diesel, and oil) are burned. As illustrated in Fig. 4, the monthly mean SO2 concentrations as measured at three monitoring sites (Aghdasyeh, Rose Park, and Masoudyeh), went through several changes during the considered time interval.

An upward trend in SO2 concentrations was

observed between the years 2005 and 2008 (+10.4 ppb/year). According to Fig. 2, the increasing trend in some factors such as the number of heavy-duty vehicles (by about 50%) and population during this time period contributed to the observed rising trend in SO2 levels. Some of the changes in pollutant levels have been driven by political developments. The imposition of sanctions on Iran in 2006 had several adverse impacts including on the availability of updated technology for refining fuels or methods for improving fuel quality (Hosseini and Shahbazi, 2016). According to Fig. 2, the significant increasing trend in some pollutants such as SO2 and NO2 during the years 2005-2008 was partially influenced by the concurrent political challenges. With the extension of the sanctions in 2010, limited access to efficient fuel technology remained and caused Tehran to face several challenges, the most significant of which were environmental issues. 12

In the past, heating oil was widely used for residential heating in Tehran, leading to relatively high concentrations of sulphur dioxide; however, as natural gas has become more widely available, sulphur dioxide concentrations have declined significantly (Hosseini and Shahbazi, 2016). This is depicted in Fig. 4, which shows a decreasing trend for monthly mean SO2 concentrations for the 2009-2012 time period (-2.75 ppb/year). In addition to the controlling initiatives undertaken in Tehran, several efforts have been made to improve the quality of fuel (including gasoline and diesel fuels). The elimination of lead in the production of gasoline was enforced in 2002, leading to a substantial reduction in air pollution in the following years (World Bank, 2018). According to the latest fuel analysis report published in Tehran, the sulphur levels in gasoline and diesel fuels have shown a decreasing trend in the period 2011-2016, leading to a decline in SO2 concentrations of about 30% (Fig. 2). Furthermore, continuous reform measures were implemented by the national Iranian oil refining company in 2013, resulting in a relative decline in the sulfur contents of gasoline and diesel fuels (Fuel Quality Report, 2015). As is indicated by Fig. 2 and Table 2, the observed reduction in SO2 levels (about -2.13 ppb/year) during 2013-2016 could be explained as the consequences of the mentioned mitigation strategies. Moreover, the significant increase in the natural gas demand for residential heating systems between the years 2010-2016 (Azadi et al., 2017) is also consistent with the gradual drop in SO2 levels during the corresponding time period. As indicated by Azadi et al., (2017), the residential demand constituted the highest contribution to the Iranian natural gas consumption (around 29%) between 1990 and 2015.

13

Policy-3

Policy-11

Policy-7

Fig. 4. Long-term (2005-2016) trends of SO2 concentrations calculated using the smooth trend method based on the mean measurements of 3 stations.

3.4. Nitrogen Dioxide (NO2) Nitrogen dioxide is mostly emitted via combustions including diesel and gasoline fuel engines, residential heating, and industrial activities. According to Lelieveld et al. (2015), fossil fuel combustion is the main source of NO2 in the Middle East region. The transport sector is the main contributor to NOx production in Tehran, accounting for 41% of the total sources estimated for the base year of 2013 (Shahbazi et al., 2016). The second main contributor to NO2 emissions is the energy sector, accounting for about 21% of the total. Fig. 5 shows the long-term trend (2005-2016) of NO2 concentrations based on the measurements of four stations (Aghdasyeh, Shahre Ray, Poonak, and Rose Park). According to this Figure, NO2 concentrations have gone through drastic variations during the different periods. There was a significant rise in NO2 levels during 2005-2009 (+10.35 ppb/year), which is consistent with the increasing trend in tropospheric vertical column densities of NO2 with a spatial resolution of 13 x 24 km2 captured by the Ozone Monitoring Instrument (Lelieved et al., 2015). Data retrieved from the GOME and SCHIMACHY satellite measurements also exhibited a significant upward trend (+4.5% per year) in NO2 column density over Tehran until 2008 (Konovalov et al., 2010). Moreover, the analysis of ground-level NO2 concentrations conducted by Motesaddi Zarandi et al. (2015) confirmed an elevation in NO2 levels during the years 2005–2008 based on the records of in situ monitoring stations. This could be explained by the 14

continuous increase of vehicles and motorcycles in this period as shown in Fig. 2 (c), which are the main sources of NOx emissions in Tehran (Shahbazi et al., 2016). Also, as pointed out by Leliveld et al. (2015), political changes on the international level were considered the potential drivers of the changes in NO2 levels during this time period. This time interval is, however, followed by an extreme reversal trend in the years 20092010, which was also reported in previous studies (Motesaddi Zarandi et al. (2015), Leliveld et al 2015). Indeed, a substantial drop (by 50%) in NO2 levels occurred between November– December 2008 and January 2009. Such an extreme downward trend was also seen in some other pollutants (e.g. SO2 and O3) almost during the same time interval. Leliveld et al. (2015) observed the drop by year 2010 and attributed it to the extension of the sanctions imposed by the United Nations Security Council (UNSC) in 2010, leading to reversal changes in the Gross Domestic Product (GDP) and, thus, emissions. But it could be seen that the decreasing trend had already been observed in 2009. Thus, the downward trend cannot be attributed to the UNSC sanctions only. Moreover, according to the annual energy report, a decreasing trend was found in some types of fuel consumption (including gasoline or kerosene consumption) during the same considered months. Since the consumption record is based on national data, there is uncertainty in concluding that the observed drop in fuel consumption is the major contributor to the observed decline in pollutant levels of Tehran between the years 2008–2009.

Policy-3

Policy-5, 6 & 7

Policy-8

Policy-11

Fig. 5. Long-term (2005-2016) trends of NO2 concentrations calculated using the smooth trend method based on the mean measurements of 4 stations.

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After the continuous decrease between the years 2009 and 2012 (-3.25 ppb/year), the NO2 concentrations increased substantially in 2013–2016 (+9.48 ppb/year), which is concurrent with the UNSC sanctions, enhanced EURO standards for vehicles and motorcycles as well as traffic regulation through the even-odd scheme (see Table 2 and Fig. 2). The increase in the number of vehicles and fuel consumption may have contributed to the observed growth in NO2 levels. For this reason, the consumption of some main fuel types (including gasoline and premium gasoline) were studied for the period 2011– 2016 to investigate the possible causes of the observed NO2 concentrations. The strong correlation between the total gasoline consumption and NO2 concentration is also corroborated by Fig. 6 (a), indicating the direct effect that the continued increase in the number of vehicles has on NO2 concentrations. The seasonal variation in the observed correlation is also presented in Fig. 6 (b), indicating that the highest correlation exists in spring. This is as expected, as in spring, NO2 is less affected by meteorological conditions (e.g. inversion and the stable boundary layer are dominant in autumn and winter) and chemical mechanisms (e.g. photolysis, which is dominant in summer). Thus, NO2 levels shows a higher correlation with the emission sources (gasoline consumption). When the temperature drops in cold months (especially in November, December, and January), due to the increase in the gas consumption, gas distribution to the power plants located near the city of Tehran gets cut off. Due to the shortage in the gas needed for operation, these power plants replace heavy and low-quality fuel oil with the needed gas, and as a result more NO2 is produced. It has been reported that the lack of available gas sometimes causes the power plants to close off their desulfurization units, leading them to produce more SO2 and NO2. These explanations would be considered as one of the causes of the observed increase in NO2 concentrations during the cold months in almost every year (Shahbazi et al., 2016).

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(a)

(b)

Fig. 6. Scatterplots of total gasoline consumption and NO2 concentrations on an annual (a) and seasonal basis (b) for the city of Tehran during 2011-2016.Trends were computed for four seasons, including Spring (MAM) March, April, and May; Summer (JJA) June, July, and August; Fall (SON) September, October, and November; Winter (DJF) December, January, and February.

3.5. Ozone (O3) The complexity in the interaction of O3 with atmospheric chemistry and aerosols has made it challenging to understand its trends (Guerreiro et al., 2014). Meteorological conditions as well as the availability of precursor gases, such as NOx, contribute to the formation of such secondary pollutants and have caused large inter-annual variability in O3 concentrations. The long-term trend of monthly mean O3 concentrations for the average of five monitoring sites (Aghdasyeh, Shahre Ray, Masoudyeh, Rose Park, and Poonak), which is presented in Fig. 7, shows a variant pattern during different time intervals. The 17

significantly increasing trend (+16.11 ppb/year) in O3 concentrations during the first period (between 2005 and 2008) was consistent with the changes in some precursor gases that play a major role in its production (e.g. NO2). As indicated by similar studies, the variations in NO2 levels have major impacts on the changes in ozone concentrations (Motesaddi Zarandi et al., 2015). However, the downward trend of O3 concentrations (-2.1 ppb/year) did not follow the same pattern as the one for NO2 during the years 2009-2012. Similar studies indicate a discrepancy between the trends in anthropogenic emissions of precursors, which contribute to O3 formation and its concentration, which can be explained by several factors such as meteorological conditions, uncertainties in measurements, smog events, and the statistical parameters considered for the trend study (Guerreiro et al., 2014; Colette et al., 2011; Wilson et al., 2012). Based on Figures 5 and 7 as well as Table 2, it seems that the NO2 and O3 concentrations tend to have the same trend until 2012, but, thereafter, they go in opposite directions (increasing NOx is concurrent with decreasing O3). This could be explained by the fact that ozone production in the troposphere strongly depends on NOx and VOC abundances (Seinfeld and Pandis, 2006). It is established that an optimum VOC:NOx ratio exists at which a maximum amount of ozone is produced (Seinfeld and Pandis, 2006). For ratios larger than this optimum ratio, NOx increases lead to the increased production of ozone. This seems to be the case in Tehran until the year 2012, where the O3 variation follows the NOx trend linearly. Conversely, for ratios less than this optimum ratio, NOx increases lead to ozone decreases, which can explain the observed trends of O3 in Tehran between 2013-2016. Therefore, it seems that in 2012-2013, the O3 formation in Tehran’s atmosphere is converted from NOx-limited to NOx-saturated.

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NOx-limited

NOx-saturated

Fig. 7. Long-term (2005-2016) trends of O3 concentrations calculated using the smooth trend method based on the mean measurements of 5 stations.

3.6. PM2.5 There are a variety of sources that can emit PM directly into the air or can lead to the formation of such pollutants via chemical reactions by producing gas phase precursors such as NOx. The size and chemical composition of PM are variant which can be determined by several factors such as emission sources and atmospheric conditions. According to Shahbazi et al., (2016), mobile sources were the most dominant contributor to PM emissions in the city of Tehran in the year 2013, accounting for 70% of the total sources. In addition to anthropogenic sources, natural phenomena such as dust storms can carry high amounts of PM from local and regional deserts toward the city of Tehran especially during warm months (Ashrafi et al., 2014; Arhami et al., 2017; Shahsavani et al., 2012). The latest annual AQ report indicates that the annual PM2.5 concentrations exceeded the standard limit at 93% of traffic stations and 85% of urban sites during March 2017-March 2018. Fig. 8 quantifies the downward trend in PM2.5 pollution measured at three monitoring stations (Aghdasyeh, Setad Bohran, and Zone 4) across Tehran, all of which have been monitoring this pollutant since 2011 or earlier. Even though a slight decreasing trend (on average -2 µgm-3/year) is observed in PM2.5 concentrations, this pollutant has been assigned as the criteria pollutant for more than 85% of the days in Tehran during the last 5 years. Moreover, the observed decreasing trend correlates with the SO2 downward trend, which contributes to the formation of secondary PM2.5 (Fig. 9). 19

Policy-8, Policy-10, Policy-11

Policy-12

Fig. 8. Long-term (2005-2016) trends of PM2.5 concentrations calculated using the smooth trend method based on the mean measurements of 3 stations.

A mild increasing trend of PM2.5 occurred between the years 2015-2016 against the implementation of fuel regulations on diesel to reduce SO2 emissions. Thus, this increasing trend was at least not associated with sulfate aerosols. One explanation could be the fact that the magnitude and frequency of atmospheric stagnation conditions have increased in recent years, leading to high-polluted episodes during cold months (www.airnow.tehran.ir).

Fig. 9. The correlation between the annual mean PM2.5 and SO2 concentrations during the years 2011-2016.

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To combat air pollution, especially during highly polluted episodes, a traffic restriction program was implemented in the central district of Tehran for a specific time interval (between 6:30 a.m. and 7:30 p.m.) in 2005, in which unauthorized vehicle categories (except motorcycles and public transport vehicles) were banned from travelling in and out of the considered area (World Bank, 2018). This scheme was revised in 2012, and an odd-even traffic rationing policy was developed. Under this scheme, a larger area located in the central part of Tehran was considered as a traffic restriction zone for personal cars based on the last digit of their license plate (Shahbazi et al., 2017). Furthermore, in October 2015, permanent enforcement of this restriction program was approved by authorities. According to Shahbazi et al., (2017), the estimations indicate that such a scheme would have a less significant impact on NO2 levels (maximum decrease by 1.7%) in comparison with CO concentrations (maximum reduction of 3.5%). Due to the controversial impacts of this scheme, in 2016, the development of a low emission zone (LEZ) was proposed as a replacement for the current traffic scheme. The Tehran LEZ imposes restrictions on the entrance and exit of light-duty vehicles according to their emission standard, age, and inspection and maintenance approval (Shahbazi et al., 2017). This policy has not been implemented completely, and the considered zone is still the same as the odd-even restriction plan.

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Fig. 10. Comparison of the annual mean concentrations of PM2.5 (µg/m3) with the variations in the annual fuel consumption (including fuel oil, diesel, and kerosene (m3)) for the years 2010-2016.

An examination of the variations in the annual averaged fuel consumption data could explain some of the observed changes in the annual mean PM2.5 levels (Fig. 10). According to this Figure, the observed decline (about a 7% reduction) in the pollutant level between the years 2010-2011 appears to be consistent with the reduction in the use of fuel oil and kerosene during the corresponding time interval. On the other hand, the increasing trend in the consumption of fuel oil, kerosene, and diesel for the years 20112012 had an impact on the slight increase in PM2.5 concentrations for the same time period. In addition, the overall decreasing trend in the consumption of kerosene and fuel oil during the years 2012-2015 could have contributed to the observed decline in the production of PM2.5. A significantly steeper downward trend was observed in PM2.5 levels around the year 2013, which is concurrent with the decline in the use of diesel fuel. Another source of particles could be the recent rise in the occurrence of dust events in warm months, which has negatively affected the air quality status at the regional and national level. In recent years, some of the wetlands and lakes have been drained due to the arid climate that dominates in the region, subsequently resulting in the observed increase in the levels of natural dust (Hosseini and Shahbazi, 2016). However, with the lack of further data and measurements, it is not possible to precisely identify the role of primary and secondary aerosols in the observed trend.

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3.7. Asbestos Asbestos is a group of mineral fibers that exist in a variety of materials such as floor tiles, roofing sheets, vehicle braking systems, etc., and can be released into the air via different processes such as cutting or sanding these materials (Khadem et al., 2018). Due to the adverse health impacts associated with asbestos fibers, these airborne pollutants began to be monitored by the AQCC in 2010. Samples of asbestos fibers were collected from a variety of districts in Tehran on a periodic basis. The annual mean concentration of asbestos fibers is illustrated in Fig. 11. A steep decline in airborne asbestos fibers occurred during the years 2012 and 2015. The observed decreasing trend coincides with the implementation of the prohibition of the import and use of asbestos products in Iran.

Policy-9

Fig. 11. Long-term (2005-2016) trends of asbestos fibres concentrations calculated using the smooth trend method based on the annual mean values.

3.8. Seasonal variability Due to the variations in atmospheric conditions and emissions in different seasons, it is necessary to analyze the trends with respect to such variations. This assessment was conducted according to the method explained in section 2 and the results of NO2 are shown in Fig. 12. Detailed trend values of this pollutant are presented in the supplementary material (Table A.1).

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The seasonal NO2 trends shown in Fig. 12 are in agreement with the overall trends displayed in Fig. 5 (decreasing during 2009-2012, and increasing during 2005-2008 and 2013-2016). However, the contributions of the emissions and atmospheric conditions in NO2 trends in different seasons vary during each time period. For instance, in spring, autumn and winter of the 2005-2008 period, the increase in NO2 is mainly due to nighttime emissions and accumulations, while in 2013-2016, daytime emissions show a dominant impact. In summer, the differences between the drivers of the trends in 20052008 and 2013-2016 periods are less pronounced. In both periods, the nighttime emissions are the dominant causes of the trends in summer. This can be explained by the fact that the daytime fleet decreases due to school holidays but the nighttime heavy-duty fleet remains unchanged. In the cold seasons (which is when the pollution episodes take place), the nocturnal stability (category NL) was the major driver of the trends in 20052008. But in 2013-2016, the daytime emissions also became a major driver of the trends. This means that the most recent NO2 trends in cold seasons are mainly controlled by emissions and not meteorological conditions. 30

25

25

20

20

15

NOx trend [ppb/yr]

NOx trend [ppb/yr]

a) Spring 30

2005-2008 2009-2012

10

2013-2016 5 0

b) Summer

15

2005-2008 2009-2012

10

2013-2016 5 0

-5

-5 DL

DR

NL

NR

DL

NL

NR

d) Winter

30

35

25

30

20 15

NOx trend [ppb/yr]

NOx trend [ppb/yr]

c) Autumn

DR

2005-2008 2009-2012

10

2013-2016 5

25 20 2005-2008 15

2009-2012

10

2013-2016

5

0

0

-5

-5 DL

DR

NL

NR

DL

DR

NL

NR

Fig. 12. Variability of the NO2 trend and its drivers in different seasons. Categories are defined according to Table 1 and are shown as follows: DL: Day-Local; DR: Day-Remote; NL: Night-Local; NR: NightRemote.

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4. Conclusion This study analyzed the trends of major air pollutants (CO, SO2, NO2, O3, PM2.5, and asbestos) in Tehran, Iran for the years 2005-2016. The data obtained from air quality monitoring stations was processed to acquire a homogeneous database with a comparable measuring process and reduced uncertainty. According to the results, a substantial decreasing trend was observed for CO and SO2 during the considered time interval despite the continuous increase in the number of vehicles and population. These reductions seem to be the result of the mitigation plans already implemented in Tehran (such as the implementation of European emission standards on vehicles, the ban on the production of carburetor-equipped vehicles, the removal of lead from gasoline fuels, etc.). On the other hand, NO2 and O3 concentrations had fluctuations during different periods, which were explained by potential drivers (growth in the number of vehicles and motorcycles, implementation of UNSC sanctions, increase in fuel consumption, etc.). The observed trend in NO2 until 2013 coincides with the previous trend analysis based on satellite measurements. However, the variation in O3 concentrations was not completely consistent with the one detected for NO2, which can be described by the transition of Tehran's atmosphere from NOx-limited to NOxsaturated regime around the years 2012-2013, which affected the processes involved in O3 formation. Even though PM2.5 has been recognized as the criteria pollutant for more than 80% of polluted days in Tehran, a slight decline in PM2.5 levels was observed between the years 2011-2015. The controlling measures such as the improvement plans for fuel quality or vehicle engine efficiency and the implementation of traffic restriction schemes for some central districts might contribute to the observed decline in PM2.5 concentrations or its precursor gases. However, an elevation in PM2.5 levels was detected between the years 2015-2016, which could be driven by a variety of factors including the frequent occurrence of dust storms during warm months, an increase in the dominance of stable atmospheric conditions during cold months, the formation of highly polluted episodes, the increase in the number of vehicles and fuel consumption, etc. The upward trend in NO2, O3, and PM2.5 presented in this work is evidence of the seriousness of the degradation of Tehran's air quality. Most importantly, these recent 25

trends are mainly controlled by local emissions (and not meteorological conditions). This means that Tehran's air quality is becoming more sensitive to emission control plans compared to the past (e.g. the 2005-2008 period). Thus, the critical condition of the air quality of Tehran requires authorities' attention to seek comprehensive emission control strategies for short-term and long-term plans. Acknowledgement

Authors would like to thank the Air Quality Control Company of Tehran (AQCC) and the National Iranian oil products Distribution Company (NIOPDC) for providing the historical air quality data for the study.

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Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Highlights •

A long-term trend analysis was conducted for the main air pollutants of Tehran city

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Developments in industrial and transport sectors have contributed to observed trends

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A Substantial decline in CO and SO2 concentrations was observed during 2005-2016

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Variant trends in NO2 concentrations were driven by multiple anthropogenic factors