Determination of the transport routes of and the areas potentially affected by SO2 emanating from Khatoonabad Copper Smelter (KCS), Kerman province, Iran using HYSPLIT

Accepted Manuscript Determination of the transport routes of and the areas potentially affected by SO2 emanating from Khatoonabad copper smelter (KCS), Kerman province, Iran using HYSPLIT Hesam Salmabadi, Mohsen Saeedi PII:

S1309-1042(18)30261-7

DOI:

10.1016/j.apr.2018.08.008

Reference:

APR 430

To appear in:

Atmospheric Pollution Research

Received Date: 7 May 2018 Revised Date:

8 August 2018

Accepted Date: 31 August 2018

Please cite this article as: Salmabadi, H., Saeedi, M., Determination of the transport routes of and the areas potentially affected by SO2 emanating from Khatoonabad copper smelter (KCS), Kerman province, Iran using HYSPLIT, Atmospheric Pollution Research (2018), doi: 10.1016/j.apr.2018.08.008. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. 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.

ACCEPTED MANUSCRIPT Determination of the transport routes of and the areas potentially affected by SO2 emanating from Khatoonabad copper smelter (KCS), Kerman Province, Iran using HYSPLIT

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Abstract

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Forward trajectories were calculated from 1 January 2005 to 30 December 2016 using HYSPLIT to determine seasonal variation of potential transport pathways of SO 2 emanating from KCS and identify the most affected area around it. Then, the air parcels of each forward trajectory were divided into three height levels: 0-100m above the ground level (agl.), 100-1000m agl., 1000-5000m agl. Finally, Kernel density analysis was conducted through a GIS tool to plot forward air parcels density map. These show that the potential transport routes vary seasonally as well as the potential distances. Transport distance is approximately two times higher in winter than in summer, and it is equal for spring and autumn. The forward air parcels trajectory calculations have shown that there are two main corridors for transporting SO2 emanating from KCS towards to the Northeast and south direction of this copper smelter. SO2 mainly transport through both corridors in spring and autumn and affect Rafsanjan and Sirjan cities and Bahrame Goor Protected Area (PA). In summer, Air parcels convey SO2 mainly in South direction and influence Bahram-e Goor PA and Darab city. The northeastern corridor is highly active in winter, and within this route, Rafsanjan City and Chah Koume PA are potentially affected. Shahr-e Babak is placed near the source and is highly affected in all seasons except winter. Considering the wide spatial extent of SO 2 transport, besides KCS’ long-term production of SO2 imply its significant negative impact on human health and the environment. Keywords: Transport pathway, Khatoonabad, Copper smelter, Forward trajectory, Sulfur dioxide

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1. Introduction

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Sulfur dioxide is one of the conventional primary atmospheric pollutants whose reverse effects on both human (e.g., cause cardiovascular abnormalities, respiratory illness, aggravate heart and lung diseases), and terrestrial and aquatic ecosystems (e.g., vegetation deterioration) are well recognized (Afif et al., 2008; Klimont et al., 2013; Lu et al., 2013; Lu et al., 2010; Smith et al., 2011). Sulfate Aerosol and sulfuric acid are two main secondary pollutants which are produced from SO 2 through chemical reactions in the atmosphere (Fioletov et al., 2015; Fioletov et al., 2013; Lu et al., 2013). Sulfate aerosol has a greater lifetime than SO2 and can be carried out long-range distances (more than 100km) in the troposphere (Krotkov et al., 2006; Wang et al., 2013). It has a significant impact on climate by upsetting energy equilibrium of the earth surface and atmosphere system directly and changing cloud formation indirectly (Jiang et al., 2012; Krotkov et al., 2015; Wang et al., 2013). Sulfuric acid is another important atmospheric constituent that causes acid rain. Acid rain can harm the biosphere and a wide range of aquatic and terrestrial plants and animals (Krotkov et al., 2015; Smith et al., 2011). Transport distances of pollutants in the atmosphere is directly related to their lifetime (Moran et al., 2013). Sulfur dioxide has a lifetime of 24 hours (Fioletov et al., 2015; Fioletov et al., 2016; Lu et al., 2013; McCormick et al., 2014; Ramaswami et al., 2005) and under favorable atmospheric condition can be carried out to the long-range distances with airflows affecting downwind regions (Serbula et al., 2017). Long range transport is defined as the transportation of air pollutants via airflows for a distance greater than 100 kilometers (Moran et al., 2013). Therefore, SO2 is known as a local, regional and even global concern (Lu et al., 2013; Lu et al., 2010; Smith et al., 2011) and its spatial extent influence has been a subject of interest in many studies (Gundermann and Hutchinson, 1995; Hatakeyama et al., 2011; Jaffe et al., 1999; Lu et al., 2010; Mahura et al., 2012; Park et al., 2016; Saturno et al., 2017; Toutoubalina and Rees, 1999; Tu et al., 2004). Afif et al. (2008) and Kallos et al. (1998) have shown that SO 2 produced in Europe can influence northern Africa and Beirut in Lebanon. Some studies showed that volcanic sources in Africa contribute to high levels of SO2 recorded at India (Mallik et al., 2013) and Amazon rainforest in

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ACCEPTED MANUSCRIPT southern America (Saturno et al., 2017). Many researchers have implied that Japan, Korean peninsula, North Pacific, even North America can be influenced by SO2 produced in East Asia and specifically in China (Hatakeyama et al., 2011; Jaffe et al., 1999; Lu et al., 2010; Park et al., 2016; Tu et al., 2004).

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The annual global emission of SO2 is estimated to be 105,000 kt and statistics show that Asia with the proportion of 52% is the largest source of SO 2 in the world (Klimont et al., 2013). China, India, and the Middle East are the major SO2 contributor in this continental and produce approximately 30,000, -1 9000, 6000 kt yr , respectively (Fioletov et al., 2016; Klimont et al., 2013; Krotkov et al., 2008; Smith et al., 2011). Sulfur dioxide can be emitted into the atmosphere through natural or anthropogenic sources with a proportion of 30% and 70% respectively (Fioletov et al., 2016). Volcanoes are the main natural sources while oil and gas industry, power plants and smelters are the anthropogenic ones (Beirle et al., 2014; Fioletov and McLinden, 2016; Jiang et al., 2012; Lu et al., 2010; Nikolić et al., 2010; Park et al., 2016). Among the main sources of SO2, smelting processes contribute to ~10% of the total SO 2 production in the world (Fioletov et al., 2016). It has been already corroborated that the smelting process is one of the most polluter activity which leads to air, soil and water quality loss (Dudka and Adriano, 1997; González-Castanedo et al., 2014). Copper smelters emit ~2300 kt of sulfur dioxide every year (Fioletov et al., 2016) and are considered as a main anthropogenic source. Hitherto, it was also underlined that discharging SO2 into the atmosphere is the most serious form of pollution for copper smelter industries (Carn et al., 2007; Chen et al., 2016; Dudka and Adriano, 1997; Fernández-Camacho et al., 2010; Nikolić et al., 2010). Many toxic metals, most notably As, Cu, Cd, and Pb, are emitted by copper smelter to the atmosphere as well as SO2 (Dudka and Adriano, 1997; Fernández-Camacho et al., 2010; Nikolić et al., 2010). These heavy metals are not biodegradable and have a lot of adverse effects on human health (e.g., causing growth retardation in children, kidney disease, and cancer) (Saeedi et al., 2012). Since the significant impact of the copper smelters is explicitly understood numerous studies have been done in this context (Daggupaty et al., 2006; Gundermann and Hutchinson, 1995; Hansen et al., 2017; Kozlov et al., 1995; Kozlov, 2005; Mahura et al., 2012; Toutoubalina and Rees, 1999; Wong et al., 2006; Zverev, 2009).

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Copper smelters have a well-known negative influence on air quality of urban and rural area in the far distances and their aerial pollutants can affect a significant radius up to 200km (Daggupaty et al., 2006; Gundermann and Hutchinson, 1995; Hansen et al., 2017; Kozlov et al., 1995; Kozlov, 2005; Steinnes et al., 1997; Toutoubalina and Rees, 1999; Wong et al., 2006; Zverev, 2009). Jorquera (2002) showed that the main contributor to the enhanced SO 2 concentration level in Santiago, Chili is a copper -1 smelter with an emission rate of 300 kt yr located 80 km south of the city. By using HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) trajectory calculations, Worobiec et al. (2008) reported that a copper smelter 50 km away from Czerniawa, in the southwest of Poland, caused a high level of SO2 occurred on July 18, 2006. 24-hour trajectories calculated by HYSPLIT from a rural area east of Phoenix, Arizona, showed that at the days when the receptor experienced high levels of SO 2 concentration, air parcels are originated from the southern areas where three smelters are located 40, 70, and 130 km away from the receptor, respectively (Coury and Dillner, 2007). In this study, a long-term simulation of continuous emissions from Khatoonabad copper smelter, Kerman province, Iran was performed using HYSPLIT model. For the first time, One-day forward trajectories were calculated from 1 January 2005 to 30 December 2016 every 3 hours (8 times a day) to determine transport pathways, and travel distances of aerial pollutants liberated from KCS. Then, most possible affected cities, Protected Area (PA), Wildlife Refuges (WR) and National Parks (NP) around KCS were identified. It should be noted that the trajectory calculations can be related to any aerial pollutants emanating from KCS, but this study focus is mainly on the sulfur dioxide due to the significant amount of discharged SO2 from KCS. 2. Study Area Iran is liberating ~1800 kt of SO2 every year (Fioletov et al., 2016; JRC/PBL, 2011) and is considered as one the main SO2 producers in Asia. Fig. 1, is Ozone Monitoring Instrument (OMI) time

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average map of SO2 column amount in Planetary Boundary Layer (PBL) over 2005 – 2016 coupled with the scattering of major SO2 emission sources in Iran. According to Fig. 1, Main sources are placed over the Persian Gulf and are related to the oil and gas industry. Many other enhanced SO 2 levels are distinguished in Fig. 1, which are mainly related to power plants and smelting factories. Although there are limited smelter industries in Iran, they have a great contribution to SO 2 emissions and are responsible for about 30% of SO2 emitted in Iran (JRC/PBL, 2011). Khatoonabad copper smelter in the southeast of Iran is explicitly recognized in Fig. 1 as a great source for SO2 in Iran.

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Fig. 1. Location of Iran’s major SO2 emission Sources couple with the time-averaged map of SO2 column amount in Planetary boundary layer (PBL) over 2005-2016 acquired from Ozone Monitoring Instrument (OMI).

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Khatoonabad copper smelter lies in the southeast of Iran at Kerman province (Fig. 2). It is located at 0 0 30.08 N and 55.40 E with an attitude of 1780m above sea level. KCS is surrounded by two separated mountains on the northern and eastern sides and connected to a vast plain in the other directions. There are many prominent areas around this smelter including residential zones, wildlife refuges, protected areas and national parks which SO2 emanated from KCS has the potential to influence. Geographical location of these areas is depicted in Fig. 2. Accordingly, Shahr-e Babak is the nearest city to KCS which located 25km west of it. Other important cities around KCS are Rafsanjan, Zarand, Kerman, Bardsir, Sirjan, and Darab. KCS is recognized as a chief SO2 emission origin which has been operated since 1995 and is designed to produce 80,000 tons of copper annually using a flash smelting approach (Keshavarzi et al., 2015). Among all technologies which copper smelters can employe, flash furnace produces an off-gas stream that has a relatively high SO2 content. It is estimated that the SO2 content in the flash furnace off-gas is 10% to 77% (EPA, 1995). Based on Carn et al. (2007) assumption 2 tons of SO2 will liberate to the atmosphere per 1 ton of copper production if no capture technology exists. Hence it drives that at least 160 kt of sulfur dioxide discharges into the atmosphere annually from KCS because it does not use an efficient cleaning gas technology. Besides SO2, toxic metals such as Cu and As also are produced in this kind of smelters

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with a significant rate. Winds transport these pollutants to the surrounding area and cause severe issues for both human and environment which culminate in large economic losses.

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Fig. 2. Geographical location of Study Area

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3. Methodology

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Air parcels movement is directly responsible for chemical compounds transport in the atmosphere (Hemond and Fechner, 2014), considering this principle, potential transport pathways of SO 2 originating from KCS and the most affected areas were determined using HYSPLIT model. HYSPLIT is a hybrid model between Eulerian and Lagrangian approaches which calculates advection, dispersion, and deposition in a Lagrangian coordinate while pollutant’s concentration is computed in the Eulerian framework (Draxler and Hess, 1997, 1998; Rolph et al., 2017). Specifically, one of the most significant applications of Lagrangian models is to calculate transport pathways of atmospheric pollutants using forward trajectory calculations (Bowman et al., 2013). Many researchers have used HYSPLIT model to identify potential transport routes of SO2 (Afif et al., 2008; Hatakeyama et al., 2014; Murray et al., 2010; Park et al., 2016; Tu et al., 2004), dust (Bhattachan et al., 2012; Ge et al., 2016; McGowan and Clark, 2008; Uno et al., 2008), fire ash (Begum et al., 2011; Liu et al., 2009), radionuclides (Long et al., 2012), and other pollutants (de Weger et al., 2016; Langford et al., 2010; Šikoparija et al., 2013) in the atmosphere. Although trajectory calculations have a GDAS1.0 meteorological dataset from ARL (Air Resources Laboratory) was used to drive HYSPLIT model to calculate forward air parcel trajectories starting daily at 00:00, 03:00, 06:00, 09:00, 12:00, 15:00, 18:00, and 21:00 (UTC) from KCS. The starting height for trajectory calculations was 100 m above ground level, and each trajectory was tracked for 24 hours which is the lifetime of SO2 in the atmosphere, that way our computed trajectories represent potential transport routes of SO 2. The model was run seasonally to compute forward air parcel trajectories from 1 January 2005 (which GDAS1.0 dataset archive is available) to 30 December 2016. Using a python script the x, y, z data in HYSPLIT’s output files were extracted and converted to CSV value. The x and y values represent longitude and latitude while z values are air parcels height above the ground level. Air parcels were divided into three levels based on their z value (0-

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100 m agl., 100-1000 m agl., and 1000-5000 m agl.) and were exported into a GIS software to analyze the trajectory paths. According to the method described with details by McGowan and Clark (2008) and Ge et al. (2016) the transport routes were calculated for each level separately in different seasons using 2 kernel density tool. The created plot’s unit is the number of air parcel trajectories hit per 1 m then the 7 density of each raster cell was multiplied by 10 to convert the unit of the map into number of air parcels 2 hit per 10 km . Finally, the density raster files were reclassified with 1-5, 5-25, 25-50, 50-80, >80 (air 2 parcels per 10 km ) intervals and then mapped.

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Cluster analysis is a multivariate statistical technique which is used to classify air parcels into homogeneous groups and has widely been used to identify transport patterns of air parcels previously (Afif et al., 2008; Brankov et al., 1998; Liu et al., 2013; Wang et al., 2010). In this study, cluster analysis was conducted with HYSPLIT model, and air parcels were allocated to the distinct clusters based on their location and speed to explore major trajectories route in different seasons and estimate the air parcels proportion in each of pathways. HYSPLIT model uses Total Spatial Variance (TSV) method to perform cluster analysis (Draxler et al., 2018; Su et al., 2015). To execute clustering process, HYSPLIT computes the Spatial Variance (SV), the Cluster Spatial Variance (CSV), and the Total Spatial Variance (TSV). SV is the squared distances between a trajectory endpoint and the mean of the corresponding trajectories’ endpoint in that cluster. Therefore, SV is computed between each endpoint (k) along the trajectory (j) within its cluster (i): 2

SVi; j = Σk (P j;k - Mi;k) ,

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Where the summation is taken over each of endpoints in a trajectory and P is the position vectors for the individual trajectory and M is the position vectors for the mean trajectory of cluster i. Then, CSV is calculated for each cluster (i), by summation of SV of all trajectories within that cluster: CSVi = Σj SVi; j ,

(2)

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Finally, the sum of CSV for all cluster will result in the TSV: TSV = Σi CSVi ,

(3)

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In the first step, each trajectory is considered as an individual cluster, in other words, the number of trajectories and clusters is the same (i=j). In each iteration, two clusters are merged, and the number of clusters is reduced by one. Hence, there are i-1 cluster in the second iteration and 1 cluster in the last iteration.

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The TSV is calculated separately in each iteration. At the beginning of the clustering process, the TSV augments significantly, then gradually reduces and increase again in the final steps of calculation. The final increase in TSV is happened owing to merging disparate clusters, highlighting that the paired clusters are no longer similar. The optimum number of clusters is just before the final rise (Draxler et al., 2018; Su et al., 2015). Aura is a sun-synchronized satellite which started to work in July 2004 (Lu et al., 2013; Parkinson et al., 2006). Ozone Monitoring Instrument (OMI) is the most important sensor among Aura’s four sensors, and it is designed to continue Backscatter UltraViolet (BUV) sensor of Nimbus-4 (Parkinson et al., 2006). OMI can measure daily NO2, SO2, O3, and aerosols on the global scale (Lu et al., 2013). In this study, level-3 SO2 data has been collected from GIOVANNI (Geospatial Interacvtive Online Visualization And aNalysis Infrastracture) online software. This online software has been developed by The National Aeronautics and Space Administration (NASA) Goddard Earth Science Data and Information Services Center (GES DISC) and is accessible at http://disc.sci.gsfc.nasa.gov/giovanni. Level-3 SO2 data contain column concentration in the Planetary Boundary Layer (PBL) with a spatial resolution of 0.25 * 0.25 degree and available since October 2004. The level-3 SO2 data were acquired from GIOVANNI between 1 January 2005 and 31 December 2016 seasonally. For further processing, these data were exported to a GIS software, and seasonal mean concentration of SO2 was calculated from 2005 to 2016. The present research used the OMI data to validate the simulation results.

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ACCEPTED MANUSCRIPT 4. Validating the simulation

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As discussed in the methodology, it is postulated that each of the calculated air parcels originating from KCS contains a particular mass of SO2. Therefore, there should be a correlation between the trend of air parcels density fluctuation and SO2 concentration fluctuation trend. For example, if the averaged air parcels density at a specific receptor is maximum in the summer, it is expected that the averaged concentration of SO2 for that receptor is maximum in the summer as well. Hence, we have compared the seasonal fluctuation of the averaged air parcels density with the seasonal fluctuation of the averaged SO 2 concentration during 2005-2016 in order to evaluate the mentioned assumption. To that end, level-3 sulfur dioxide OMI data acquired from 2005 to 2016 were used. Since the OMI calculates the concentration of SO2 in PBL(Jiang et al., 2012), air parcels density is calculated in PBL layer by superposing air parcels density in 0-100 and 100-1000 m layers for all seasons. PBL is a layer with a variable height range from 10-50 m on clear nights to 2000 m in the summer (Leelőssy et al., 2014) and it can be considered fully mixed with a fixed attitude of 1000 m (Eliassen, 1978; Jiang et al., 2012). It should be noted that to execute this kind of validation it is necessary to choose receptors that only are affected by KCS. Hence, among the highly affected receptors, Shahr-e Babak city and Bahram-e Goor PA were chosen. Shahr-e Babak city is close enough to KCS and is mainly affected by sulfur dioxide from it, therefore, this makes Shahr-e Babak a proper choice to perform the validation calculation. Bahram-e Goor PA is far away from megacities and other major sources of SO2 so it is anticipated that mostly KCS can influence it. Sirjan and Rafsanjan cities were excluded from this examination because KCS is not the only contributor to their SO2 levels and urban activities and contiguous industries, most notably Sarcheshmeh copper complex are also affect them. Fig. 3 shows the seasonal average of OMI sulfur dioxide concentration in Shahr-e Babak city and Bahram-e Goor PA between 2005 and 2016 compared with their air parcels density in PBL. The results show a strong agreement between modeling results and satellite data. As it is depicted in the Fig. 3 the air parcels density and SO2 concentration are maximum in the summer and are minimum in winter at the Bahram-e Goor PA and Shahr-e Babak city. Hence, observation from OMI may support the results in the present study.

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Fig. 3 Seasonal fluctuation of the averaged SO2 concentration and air parcels density at PBL between 2005 and 2016 in Bahram-e Goor PA and Shahr-e Babak.

5. Results and Discussion 5.1. Potential transport pathways and affected areas in spring (21 March – 21 June)

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Fig. 4 depicts air parcels density maps for spring 2005 to 2016. Based on Fig. 4a the highest densities of air parcels mainly impact area around the source with a northeast-southwest orientation (Fig. 4a). SO2 are transported mostly to the southwest below 100 m, and also it is rarely with low density to the northeast (Fig. 4b). In the 100-1000 m agl. level the highest densities of air parcels are centered on the source with a northeast-southwest direction, however, northeasterly air parcels are more frequent (Fig. 4c). In this season, the air parcels tend to move to the northeast predominantly as the altitude increases (Fig. 4d).

Fig. 4. Air parcel trajectories density for spring 2005 to 2016: (a) all air parcel trajectories (b) 0 – 100m agl., (c) 100-1000m agl. and (d) 1000-5000m agl. For a more quantitative comparison, all air parcel trajectories density were extracted from its corresponding plot (Fig. 4a) in each given receptors (Supplementary material-Fig1). Accordingly, Rafsanjan city located 90km northeast of KCS has the maximum density of air parcels in spring. Bahrame Goor PA and Sirjan with 125 and 90 km distance, respectively, are also affected significantly. Previous

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Cluster analysis of trajectories from KCS in spring (Supplementary material-Fig2) shows that the majority of air parcels travel to the northeast of KCS with the proportion of 60% and significantly affect Rafsanjan. The other air parcels move toward the southeast and southwest of KCS (21% and 19%, respectively). Potential transport pathways and affected areas in summer (22 June– 22 September)

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Trajectories density maps for summer 2005 to 2016 show apparent south orientation routes for SO 2 transportation (Fig. 5). In all three levels, air flows from KCS convey SO2 to the south and can extend to o 27 N (Fig.5b, c, d). With the increase in height, the domain of transport pathways enlarges and it can potentially influence more areas. The spatial coverage of air parcels in 1000-5000 m agl. level (Fig. 5d) is approximately equal to spring (Fig. 4d).

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Fig. 5. Air parcel trajectories density for summer 2005 to 2016. Air parcels density over Bahram-e Goor PA is significantly high in summer (Supplementary materialFig.3). According to Fig. 5b, this receptor is mostly affected by 0-100 m air parcels. Trajectories can also influence Darab city in the south direction of KCS. In comparison to spring, summertime air parcels can transport SO2 into Shahr-e Babak with a higher rate. Clustering analysis for summertime air parcels (Supplementary material-Fig.4) shows that potential transport distances have reduced compared to spring. There are three main routes (south, west, northeast) with corresponding proportions of 51%, 30% and 19%, through which SO 2 can be transported within 24 hours. The majority of air parcels move to the south of KCS and affect Bahram-e Goor PA and Darab city. 30% of trajectories move to the west of KCS and locate around it which indicate the potential for SO2 to influence Shahr-e Babak significantly.

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The potential transport routes of airflows from KCS in autumn 2005 to 2016 (Fig. 6) shows a pattern similar to spring presented in Fig. 4. The summary plot of all trajectories is depicted in Fig. 6a. Fig. 6 shows that air parcels spread mostly to northeast and south of KCS, however, highest air parcels densities centered on KCS and affected areas around it in a southwest-northeast orientation. Air parcels in 0-100 m layer are clearly moved to the south of the source. Although trajectory densities of < 20 (air 2 parcels per 10 km ) in this layer also extend to the northeast of KCS and affect a vast area. Above this layer, at 100-1000 m layer, a significant part of air parcels can potentially impact areas in south direction, but main orientation is toward the northeast of the source. With the increase of height air parcels mostly tend to convey SO2 into the northeast.

Fig. 6. Air parcel trajectories density for autumn for 2005 to 2016.

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Most receptors located in the northeast of the source are affected in autumn, and the density reduces as the distance increases (Supplementary material-Fig. 5). It also shows that trajectories density in Rafsanjan is maximum in autumn and it is mainly affected by air parcels at higher layers (Fig. 6c). Air parcels in the northeast direction also can impact Chah-Koume PA, Zarand city, and Kuh Banann PA. In autumn Shahr-e Babak is less affected compared to summer. SO 2 has the potential to enter into this city in lower levels (Fig. 6b, c). Trajectories also have the potential to affect Sirjan city and Bahram-e Goor PA on the southern side of the KCS.

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Based on cluster analysis of autumn air parcels (Supplementary material-Fig. 6), potential transport distances is approximately equal to spring, and it is greater than in summer. 58% of trajectories spread to northeast and impact Rafsanjan city, Chah-Koume PA as described hitherto. 27% of air parcels have a low speed and affect regions on the east side of KCS. The remaining 15% of air parcels are transported to the south and affect Bahram-e Goor PA and Sirjan city.

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5.4. Potential transport pathways and affected areas in winter (23 September– 21 December)

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Potential routes of SO2 transportation with winter air flows between 2005 and 2016 are shown in Fig. 7. All trajectories density plot shows a more dominant northeastern orientation in winter (Fig. 7a). Although 2 air parcels with a density below 5 per 10 km are capable of reaching the south of KCS in 0-100m layer, highest densities convey SO2 to the northeast of the source (Fig 7a). A significant number of air parcels in 100-1000m layer are skewed to the northeast and impact areas in this direction. With the increase of altitude, trajectories’ northeastward movement prevails so that in 1000-5000m layer, all air parcels are placed in the northeast of the source. Based on Fig. 4d, 5d, and 6d, the area covered with 1000-5000m air parcels are smallest in winter compared to the other seasons.

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Fig. 7. Air parcel trajectories density for winter 2005 to 2016. Like autumn and spring, the maximum air parcels density occurs over Rafsanjan city (Supplementary material-Fig. 7). SO2 potentially enters this city below 1000 m in winter. Trajectories also can influence Chah-Koumeh PA, Zarand city and Kuh Banann PA on the northeast side of KCS. In comparison to other seasons, Shahr-e Babk city is less affected because of the prevailing northeastward air flows. The main potential transport directions for air parcels in winter are northeast and south (Supplementary material-Fig. 8). 92% of trajectories are spread to the northeast, which is greater than the proportion of other seasons and highly influence Rafsanjan city, Chah-Koume PA, Zarand city, and Kuh Banann PA.

5.5. The inter-annual analysis in seasonality of potential transport pathways of SO2 emanating from KCS and the most affected areas

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In order to consider the inter-annual variations of potential transport routes of SO2, cluster analysis was performed on seasonal trajectories and air parcels density maps were also generated for each season of

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the study period (2005 to 2016). Based on Fig. 8 and Fig. 9 the transport motif of aerial pollution from KCS is changing season to season each year. Generally, spring and autumn air parcels disperse in an approximately same direction and affect identical areas. On the other hand, summertime air parcels’ dispersion pattern is the very antithesis of wintertime air parcels’ dispersal motif. But the general air parcels distribution’s pattern does not occur

observed at the some of the studied seasons.

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in all seasons of the study period and few anomalies, which are elaborated in the ensuing paragraphs, is

Based on Fig. 8 and Fig. 9, generally higher density of air parcels in all spring seasons are placed around

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the KCS with a northeast-southeast orientation except for 2005, 2006, and 2013. In spring 2005 and 2006 the preponderance of air parcels spread to the south and in spring 2013 air parcels mainly move to the northeast side of KCS. In the other years, the springtime air parcels are equally distributed to the southeast and northeast of KCS. Fig. 8 and Fig. 9 also highlights clear zonal transportation of SO2 originated from KCS in summer seasons. According to the Inter-annual analysis of variation in seasonality of transport pathways of SO2 in summer, air flows predominantly move to the south side of KCS in all the study period except 2009. Because of the prevailing southward orientation of air flows in summer

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seasons during 2005 and 2016, the most affected areas which are placed in the southern zones of KCS don’t change very much from summer to another summer. Air parcels in autumn, like spring, generally

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spread to the northeast and southwest of KCS in the most of the years (Fig. 8 and Fig. 9). The analysis shows that the consistency of transport patterns in autumn seasons during the study period (2005 – 2016) is not as much as spring and summer. Based on the Fig. 8 and Fig. 9, in the autumn 2005 and

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2010, unlike the other years, air flows mostly convey SO2 to the south side of KCS, and there is a significant reduction in highly affected area in autumn 2015 and 2016.

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Results indicate that the major portion of air parcels spread to the northeast side of KCS in all the winter seasons during the study period (2005-2016). The main differences in air parcels dispersion’s pattern are due to the influence area. In 2005 and 2012 the area which is affected by a high air parcels density is maximum while there is a significant reduction in the highly affected zone’s extent in 2016.

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Fig. 8. The inter-annual variation of potential transport pathways of SO 2 from KCS and affected areas between 2005 and 2010.

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Fig. 9. The inter-annual variation of potential transport pathways of SO2 from KCS between 2011 and 2016.

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5.6. Transport corridors of SO2 concerning topography

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Topography and land use have a significant role on synoptical patterns changes (Carvalho et al., 2006)) and strongly influence air pollutants transportation in the atmosphere (Meixner and Eugster, 1999) by changing the direction and speed of the air flows (Ahrens, 2012; Hedegaard and Larsen, 1983; Maharani et al., 2009). Therefore, beyond considerations of atmospheric condition, the topography is among the factors that should be taken into account (Ge et al., 2016). Based on the Fig. 2, KCS is placed in complex terrain with an attitude of 1780 m which is encompassed by two separated mountains in the north and west, and it is connected to vast plains from other directions.

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Air parcels mostly tend to move to the south at the lower levels because of northern and western mountains which act as a hamper and prevent them from spreading in those directions. Air parcels which travel to the southwest, specifically in summer, when facing the mountains located southwest of KCS near Bahram-e Goor PA deflect their direction and continue their route to reach Bahram-e Goor PA (Fig. 5a). Remaining air parcels at lower levels traveling along the slope of mountains or move through the existing gap between two separate mountains and extend northeast. There are not any serious obstacles around the source for air parcels so they can potentially influence area in other direction. Trajectories in the upper level are unobstructed therefore regarding the wind flows direction they can spread easily to any side of KCS.

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For the first time, this study determines the major corridors for transporting SO2 emanating from KCS based on forward air parcel trajectories calculations. Northeast and south channels are the two main corridors for air flows to convey SO2 emanating from KCS. Transportation direction of SO2 from KCS varies seasonally. Air parcels in spring and autumn move mostly toward northeast and south directions and influence a vast area through this channels while SO2 has the potential to affect many receptors within the south corridor in summer and northeast corridor in winter.

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This study reveals that a large amount of SO2 originating from KCS can be transmitted long distances (more than 100 km) within its lifetime in the atmosphere by air parcels and cause serious problems for humans, animals and plants living in cities, wildlife refuges and protected areas specially Shahr-e Babak, Rafsanjan, and Bahram-e Goor PA. Because of the vast impact regions, a further study which considers dispersion and deposition of SO2 coupled with advection are required.

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Conclusion

Identification of potential transport routes of pollutants from a source to determine potentially affected areas around it is a valuable avenue for planning and regulate mitigation rules. In this research, for the first time, forward air parcel trajectories between 2005 and 2016 were calculated by using HYSPLIT model to create a 12-year climatology of transport pathways of air flows from KCS. These computations determined potential transport routes of SO2 which is continuously released into the atmosphere from -1 KCS with a rate of 160 kt yr influencing a vast area. Potential transport distance of SO2 was minimum in summer and maximum in winter. This length is approximately equal for both spring and autumn. Air parcels penetrate higher levels with a significant rate in spring and summer while in autumn and winter they mostly move to lower levels. Results showed that air parcels movement pattern changes at different height levels and also varies by season. Therefore, KCS can conceivably influence different receptors significantly. Accordingly, Shahr-e Babak in the vicinity of the source is highly affected in all seasons except winter. Trajectories mostly enter this city at lower levels. In spring, air parcels density in Rafsanjan city, Bahram-e Goor PA, and Sirjan city is more than the other receptors, respectively. Summertime trajectories which have a prevailing southward orientation

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Forward Trajectory calculations showed that within 24 hours SO 2 could be transported regions downwind more than 250km. Main transport corridors of SO2 are also identified via trajectory computations. The results reveal that in spring and autumn SO2 is carried out in northeast and south channels while transportation mainly occurs through the south corridor in summer and northeast corridor in winter. It is worth to mention that the topography of the region has a great influence on transport routes. Acknowledgment

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The authors gratefully acknowledge the Atmospheric Research Center of Iran University of Science and Technology for its ongoing support. We also would like to express our thanks to Mr. Kamiyab Taghizadeh for his valuable contributions to this paper.

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Forward Trajectory calculations were performed using HYSPLIT model to investigate the potential pathways of SO2 The potential affected regions influenced by aerial pollutants originating from Khatoonabad copper smelter (KCS) in Iran were determined. Inter-annual analysis in seasonality of potential transport pathways of SO2 and the most affected areas were investigated. To corroborate the simulations, seasonal fluctuations of the averaged air parcels density were compared to the averaged SO2 concentration from the satellite observations.

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