Elemental and magnetic analyses, source identification, and oxidative potential of airborne, passive, and street dust particles in Asaluyeh County, Iran

Journal Pre-proof Elemental and magnetic analyses, source identification, and oxidative potential of airborne, passive, and street dust particles in Asaluyeh County, Iran

Sajjad Abbasi, Behnam Keshavarzi, Farid Moore, Philip K. Hopke, Frank J. Kelly, Ana Oliete Dominguez PII:

S0048-9697(19)36128-5

DOI:

https://doi.org/10.1016/j.scitotenv.2019.136132

Reference:

STOTEN 136132

To appear in:

Science of the Total Environment

Received date:

29 September 2019

Revised date:

27 November 2019

Accepted date:

13 December 2019

Please cite this article as: S. Abbasi, B. Keshavarzi, F. Moore, et al., Elemental and magnetic analyses, source identification, and oxidative potential of airborne, passive, and street dust particles in Asaluyeh County, Iran, Science of the Total Environment (2019), https://doi.org/10.1016/j.scitotenv.2019.136132

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© 2019 Published by Elsevier.

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Elemental and magnetic analyses, source identification, and oxidative potential of airborne, passive, and street dust particles in Asaluyeh County, Iran a

a

a

b, c

Sajjad Abbasi , Behnam Keshavarzi , Farid Moore , Philip K. Hopke

, Frank J. Kellyd, Ana

Oliete Dominguezd

Department of Earth Sciences, College of Science, Shiraz University, Shiraz, 71454, Iran

b

Department of Puablic Health Sciences, University of Rochester Medical Center, Rochester, NY, United States of

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America

Center for Air Resources Engineering and Science, Clarkson University, Potsdam, NY, United States of America

d

MRC-PHE Centre for Environment and Health, King's College London, 150 Stamford Street, London, SE1 9NH,

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c

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UK

Positive Matrix Factorization (PMF) indicated that Sb, Zn, Pb, Mo, Cu, and As mainly

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

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Highlight

come from anthropogenic sources. 

Magnetic parameters can be used for finding PTEs concentration and source identification.



The mean REEs, main elements oxide, and HYSPLIT results indicated that the Asaluyeh soil are similar to passive dust.



Backward Generalized Estimating Equations (GEE) modeling results display a significant positive relationship between geogenic material and Oxidative Potential (OP).



The magnetic parameters can be used for OP estimation. 1

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ABSTRACT One of the most important environmental issues in arid and semi-arid regions is deposition of dust particles. In this study, airborne, passive, and street dust samples were collected in Asaluyeh County, in August 2017, September 2017, and February 2018. The PM2.5 and PM10

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concentrations for the sampling period ranged between 19.7 – 76.0 µg/m3 and 47.16 – 348 µg/m3

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with an average of 46.4 µg/m3 and 143 µg/m3, respectively. Monthly dust deposition rates ranged from 5.2 to 26.1 g/m2 with an average of 17.85 g/m2. Positive Matrix Factorization

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(PMF) applied to the dust compositional data indicated that Sb, Zn, Pb, Mo, Cu, and As come

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from anthropogenic sources while Al, Fe, Ti, Mn, Ni, Cr, and Co originate mostly from geogenic

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sources. The PMF results indicated that the geogenic material was the major source of passive and airborne dust samples. Elemental compositions were similar for passive dust and local

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surface soil. Frequency-dependent magnetic susceptibility (χIf and χfd%) showed that the local

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soil is entisol. Isothermal remanent magnetization (IRM-100mT/IRM1T) versus saturation IRM

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(SIRM) demonstrated that the background sample contains ferrimagnetic minerals, but with increasing SIRM, the concentration of soft magnetic magnetite-like phases increases and the magnetic particles are larger. Mrs/Ms versus Bcr/Bc indicated that the magnetic particles sizes were probably between 120 to 1000 nm. Eu values and the mean Eu/Eu* and La/Al values clearly show that the airborne dust is most affected by oil industries, while passive dust samples primarily originated from local surface soils. These assumptions were confirmed by Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model results. The samples display a moderate level of oxidation towards ascorbic acid (OPAA) and glutathione (OPGSH). Regarding the passive and airborne dust samples, backward Generalized Estimating Equations (GEE) 2

Journal Pre-proof modeling results display a significant positive relationship between geogenic material and oxidative potential (OP). It includes many redox-active transition metals. Alternatively, the street dust OP is strongly related to geogenic and industrial sources and OP AA is marginally related to urban sources. It was shown that measured magnetic parameters can be used for OP estimation.

Keywords: Dust, Elemental Composition, Rare Earth Elements, Oxidative Potential, Sources,

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Magnetic

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Journal Pre-proof Abbreviations words:

Definition

AA

Ascorbic acid

C0

0-hr particle-free control, blank

C4

4-hr particle-free control

DTNB

5,5'-Dithiobis-(2-nitrobenzoic acid)

EDTA

Ethylenediaminetetraacetic acid

GSH

Reduced glutathione

GSSG

Oxidized glutathione

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Abbreviation

Total glutathione (oxidized + reduced)

HPLC

High performance liquid chromatography

OP

Oxidative potential

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GSX

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OPTOTAL/µg RTLF

+ve –ve

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TSP

Uric acid King’s College London Positive

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KCL

Respiratory tract lining fluid

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UA

OP(AA+GSH)/µg

Negative Total suspended particles

PM

Particulate Matter

PM2.5

Aerodynamic diameter less than 2.5 μm

PM10

Aerodynamic diameter less than 10 μm

GEE

Generalized Estimating Equations

MPW

Magnetic particles weight

χlf

Low-frequency magnetic susceptibility

χhf

High-frequency magnetic susceptibility

χfd

Frequency-dependent magnetic susceptibility

Bc

Coercivity 4

Journal Pre-proof Coercivity of remanence

Ms

Saturation magnetization

Mr

Saturation remanence

IRM

Isothermal remanent magnetization

SIRM

Saturation isothermal remanent magnetization

WHO

World Health Organization

EPA

Environmental Protection Agency

PAH

Polycyclic Aromatic Hydrocarbon

SEM

Scanning Electron Microscopic

PTE

potentially toxic element

REE

Rare Earth Element

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Bcr

PMF

Positive Matrix Factorization

Hybrid Single Particle Lagrangian Integrated Trajectory

Introduction

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HYSPLIT

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Dust storms play an important role in the earth system (Goudie and Middleton, 2006; Ravi et al.,

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2011; Shao et al., 2011). Some areas are major dust generators for the atmosphere. There are a number of relevant reviews on Saharan dust (Zhao et al., 2018), Chinese Loess Plateau (Chen et al., 2007; Li et al., 2018; Che and Li, 2013), American Loess deposits (Yang et al., 2017). Annually in Asia, 800 teragrams (Tg) of particulate matter are transported by dust events (Zhang 1995). The value for Saharan dust is about one billion tons (Kwon et al., 2002). Appraisals of the relative strengths of dust emissions in various parts of the world are variable. However, generally, they show the significance of, 1) Australia, 2) Arabia, 3) China and Central Asia, and 4) the Sahara. The Sahara makes up over half and Central Asia and China contribute about 20%

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Journal Pre-proof of the global emissions.

The Americas and Southern Africa are generally minor sources,

together accounting for less than 5% of the global total (Miller et al., 2004; Tanaka and Chiba, 2006). Although dust sources in Iran are few, local sources are important and inflict serious consequences on the affected Iranian cities. Also, dust storms frequently occur on the alluvial plains of southern Kuwait and Iraq (Thalib and Al-Taiar, 2012). In Iran, airborne dust derived from the deserts of Syria, Iraq, and Saudi Arabia (Givehchi et al., 2013; Goudie, 2014). In

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addition to the sampling of airborne dust, passive dust collections can also be used for further

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investigation of local source impacts. Passive samplers collect particles by gravity, diffusion, and

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convective diffusion onto a glass coverslip that is then examined with multiple analytical

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methods (Leith et al., 2012). Since ambient aerosol particles are generally heterogeneous in terms of size, shape, and density, it is difficult to determine contributions of each particulate

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transport mechanism including the diffusion, gravitational settling, and inertial or electrostatic

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depositions (Yamamoto et al., 2006). Therefore, passive dust samples can help to find the relationship between street dust and airborne dust particles. The characteristics of passive dust

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in the area.

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are intermediate between street dust and airborne dust particles, and can assess the cycle of dust

Rare earth elements (REEs) are widely used in the provenance characterization of various crustal material (Taylor and McLennan, 1985; McLennan, 1989; McLennan et al., 1993; Yang et al., 2007; Muhs et al., 2008). The REEs are the 15 transition elements starting with lanthanum (Z = 57) and ending with lutetium (Z = 71) with these elements having similar physicochemical characteristics. The behavior of REEs in the natural environment is influenced by several factors, including the predominant weathering processes, the parent rock environment, soil parameters (organic matter content, alkalinity, and redox conditions), climate and, which may facilitate

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Journal Pre-proof mobility among various environmental compartments (Allajbeu et al., 2016). Therefore, one can use REEs or other exclusive properties in the environment (such as main elements oxide) to assess likely source regions. Environmental magnetism involves a wide range of research topics (Evans and Heller, 2003). Recently, magnetic techniques have been used for investigation of anthropogenic contaminants, tracing and discriminating pollution sources, and reconstruction of pollution history (Li et al.,

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2011; Blundell et al., 2009; Xia et al., 2011; Marx et al., 2010; Muxworthy et al., 2001; Shaltout

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et al., 2013). The magnetic techniques can be used for measuring very small amounts of

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magnetic particles in bulk samples (equivalent to the µg/kg in chemical analyses) (Wang et al.,

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2015). Several studies have demonstrated that magnetic measurements may serve as an

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inexpensive, fast and nondestructive tool for contamination monitoring and screening (Lecoanet, Léveque and Ambrosi, 2003; Blundell et al., 2009). Magnetic characteristics can be used to

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identify the type of magnetic minerals and changes in their concentration, grain size or grain

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shape, therefore all of them being useful for source identification. Environmental magnetic techniques have been widely used to investigate the temporal and degree evolution of

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anthropogenic contamination related to human activities (Horng et al., 2009). Previous studies have indicated that urban airborne ferrimagnetic particulates are usually dominated by anthropogenic sources, such as iron and steel industries (Hu et al., 2008; Zhang et al., 2011), metal smelters (Jordanova et al., 2013), burning of fossil fuels (Flanders, 1994; Basavaiah et al., 2012), and vehicle emissions (Goddu et al., 2004; Maher et al., 2008). In relation to this, identification of linkages between magnetic characteristics and potentially toxic elements (PTEs) concentrations, and recognition of the special morphology of anthropogenic magnetic particles were performed. Different carriers of contaminants were investigated, such as atmospheric

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Journal Pre-proof particles (Muxworthy et al., 2001; Xia et al., 2008), street dusts (Kim et al., 2009; Xia et al., 2011), soils (Blaha et al., 2008; Blundell et al., 2009), river sediments (Zhang et al., 2011; Wang, 2013), peat bog (Hutchinson and Armitage, 2009; Marx et al., 2010), tree rings (Zhang et al., 2008) and tree leaves (Hofman et al., 2013; Shaltout et al., 2013), and have been proven to be suitable targets for the application of magnetic proxy methods to detect PTEs pollution. Dust exposure can affect human health considering their particulate properties including

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chemical composition, shape, and size (Harrison and Yin, 2000). Many of the observed health

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endpoints may result, at least in part, from oxidative stress initiated by the formation of reactive

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oxygen species (ROS) through the interaction of PM with epithelial cells and macrophages (Li et

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al., 2003; Nel, 2005; Hopke, 2015). As a result, ROS is presently thought to play a serious role in

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adverse health effects induced by ambient air particles and to be a useful health-relevant metric in addition to the commonly used mass-based metric (Borm et al., 2007). Mineralogical and

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chemical composition, surface, size, and biologically properties of PM affect the oxidative

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potential (Janssen et al., 2014). Organic chemical species and transition metals such as copper, iron, and vanadium in low concentrations are likely to contribute significantly to oxidative

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potential and subsequent toxicity by initiating ROS formation both directly and indirectly through redox-mediated mechanisms (Kelly et al., 2011). Therefore investigating PTEs in dust is a necessary step in evaluating their potential health effects. The main purpose of this study is to (1) determine the distribution and relative pollution levels of PTEs in street dust, passive dust and airborne dust of Asaluyeh County; (2) find the relationship between PTEs concentration and magnetic parameters; (3) identify possible sources of PTEs using positive matrix factorization (PMF) and general estimating equations (GEE) models and

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Journal Pre-proof also magnetic parameters; (4) identify possible sources of passive dust and airborne dust using REEs and air parcel back trajectories; and (5) assess the oxidative potential of inhalable dust.

Material and Methods Study area

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Asaluyeh County is located in Bushehr province, south Iran, which is in the mid-latitudinal belt

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of arid and semi-arid regions (Figure 1). The average annual rainfall and temperature in the county are 35 mm and 25.9°C, respectively. In general, long-term average rainfall in winter

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season is higher than in fall. Asaluyeh County is bordered by mountains to the northeast and the

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Persian Gulf to the southwest, the dominant wind direction (southwesterly) ensures that the air is

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rather poorly ventilated and that the urban area experiences air quality issues (Abbasi et al., 2019). The dominant annual wind direction is from the northwest with a maximum wind speed

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of 21 m/s (75.6 km/h). The annual average humidity is approximately 60% with increases up to

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100%. The Asaluyeh region is the largest gas field in the world and is shared with Iran and

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Qatar. The Iranian portion accounts for 60% of Iran’s total gas reserves and 10% of the worlds’. A number of gas-fired power plants, petrochemical plants, and semi-heavy industries are active in Asaluyeh, including instrumentation industries, electrical and electronic industries, and chemical industries. Since Asaluyeh is an industrial region that currently the highest rank in gas and oil production in Iran. Also in the region, PTEs consumption is high for refining and industrial units (Tolosa et al., 2005; Alipour et al., 2014). Hashemi and Taheri (2013) reported that the industrial waste gases from emission stacks and stationary flares depending on the concentration and number of resources in the region are major local air pollution sources.

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Figure 1. Location of the study area and measurement stations; A) based on Table 1 for PTEs, REEs and OP, B) magnetic parameter

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Sampling and analyses In the current study, 41 airborne dust samples (4 TSP, 17 PM10, and 3 PM2.5) were collected using an ECHO PM ambient filter sampler (TECORA, Italy) with PTFE filters (2 μm pore size and 46.2 mm diameter with a support ring; Tisch Scientific, USA). 24 samples were separated for PTEs and REEs analyses. Airborne dust samples were collected from August 6th to 31st, 2017

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and January 28th to February 6th, 2018. Two sampling stations were used for airborne dust

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sampling: (i) An urban area in the center of Asaluyeh county (PD5), and (ii) the second station

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was installed in an industrial area within the Nuri petrochemical unit (PD6) (Figure 1A).

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Weather conditions during sampling periods are summarized in Figures S1 to S7. The samplers

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were placed at a height of 3–4 m from the surface in accordance with the United States Environmental Protection Agency (USEPA) reference methods for PM 2.5, PM10, and TSP (CFR

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2013). The sampling flow rate was 16.67 L min-1. After sampling for 24 h, the filters were placed

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in plastic Petri dishes previously washed with deionized water and covered with Al foil. The PTFE filters were preconditioned in a desiccator at 25 ºC and 25% relative humidity for 24 h and

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then weighted using an electronic analytical microbalance (Model LIBROR AEL-40SM; SHIMADZU Co., Kyoto, Japan) with a 1 µg sensitivity (USEPA 1999) before and after sampling. From the full set of samples, 24 were selected for analysis and sent to the Labwest Laboratory in Australia for PTEs and REEs analyses. The filter samples were processed by microwave-assisted HF-multiacid digestion. Their Multi-Element Analysis with Rare-Earths (MMA) technique was used to provide total recovery for all but the most refractory minerals providing determination of 61 elements including rare earth elements by a combination of ICPMS and ICP-OES.

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Journal Pre-proof A total of 15 street dust samples were collected in August 2017 (5 samples), September 2017 (5 samples) and February 2018 (5 samples). The sampling methodology is already described by Abbasi et al. (2017), Abbasi et al. (2018), and Keshavarzi et al. (2018) (Figure 1 and Table 1). The sampling points were selected considering wind direction and locations of industrial areas, the street network, traffic loads, and population density. Street dust was sampled by sweeping with a brush into a plastic dustpan. Each sample is a composite of subsamples collected within a

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5-m radius of each sampling point. The samples were kept in polyethylene bags. They were then

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screened using a 63-µm sieve and stored in clean and labeled polythene bags prior to chemical

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analysis. A 0.5 g aliquot of each sample was digested in aqua regia. The PTE concentrations

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were determined using ICP-MS in a certified commercial laboratory (Acme Analytical

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Laboratories, Ltd).

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Table 1. Characteristics of sampling sites Site Label Passive dust Street dust

Airborne dust

Land use

-

-

Background

PA1

PD2 PD3 PD4 PD5

PA2 PA3 PA4 Broken; lost sample

StA1 StA2 StA3 StA4

A1

Urban Urban Background Urban

PD6

Broken; lost sample

StA5

A2

Industrial

PD1

Broken; lost sample

-

-

Background

PD2

PS2

StS1

-

Urban

PD3 PD4

PS3 PS4

StS2 StS3

-

Urban Background

PD5

PS5

StS4

A1

Urban

PD6

PS6

StS5

A2

Industrial

PD1

Broken; lost sample

-

-

Background

PD2 PD3

PF2 PF3

StF1 StF2

-

Urban Urban

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PD1

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Date

August (2017)

September (2017)

February (2018)

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Broken; lost sample PF5

StF3 StF4

A1

Background Urban

PD6

PF6

StF5

A2

Industrial

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Additionally, 13 passive dust samples were collected in August (2017), September (2017) and February (2018). A 1 m2 dry flat surface similar to that described by Menendez et al. (2007) was

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used as the sampling tool. Dry collectors (Goossens and Offer, 1994) have been suggested for

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sampling dust to be analyzed for its chemical composition (Hojati et al., 2012). The collection

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sampler consisted of a glass surface (100 × 100 cm) covered with a 2 mm PVC mesh to form a

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rough surface to trap saltating particles. The traps were placed on the roof of buildings ~ 3–4 m above the ground level at 6 locations (~ 20-50 m above the sea level): Background (PD1 and

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PD4 where PD1 was placed on a mountain at 722.6 meters height); Urban areas: (PD2, PD3, and

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PD5); and Industrial area: (PD6) (Figure 1A). Dust samples were collected during sampling periods of 1 month in August and September 2017 (summer season) and January to February

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2018 (winter season). During this period, some traps were broken. Thus, only 13 samples were collected (Table 1). The dust samples were recovered by scraping the material adhered to the glass traps using a rubber spatula. All traps were washed before the next collection using distilled water. The passive dust sent for PTEs analysis using ICP-MS in the same commercial lab as the street dust samples. In addition, aliquots of the passive dust samples were also analyzed for REEs. For all PTEs analyses of airborne dust, passive dust, and street dust, quality control (QC) and quality assurance (QA) was achieved by the analysis of standard reference material (STD 13

Journal Pre-proof OREAS45EA), blank samples, and analytical duplicates. The recovery percentages were Al (99.11%), As (89.99%), Cu (98.84%), Cr (94.56%), Cd (88.23%), Fe (93.12%), Hg (114.53%), Mn (95.12%), Mo (97.87%), Ni (97.92%), Pb (98.66%), Sb (103.21%), Ti (99.12%), and Zn (95.17%) demonstrating good agreement between the measured and certified values.

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Magnetic measurements

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After air-drying in the laboratory, 100 g of street dust and background soil samples were

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weighted and according to land uses, 20 samples were selected (Figure 1B). Magnetic particles were extracted using a strong magnet and were weighted via an electronic analytical

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microbalance (Model LIBROR AEL-40SM; SHIMADZU Co., Kyoto, Japan) with 1 µg

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sensitivity to provide the magnetic particle weight (MPW). Finally, the magnetic particles were

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placed in a petri dish and sent for PTE analysis by ICP-MS. An additional 30 g of each bulk sample was packed in plastic boxes and used for the magnetic

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measurements. Low- and high-frequency magnetic susceptibility measurements at 470 Hz and

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4.7 kHz, respectively, were performed using a Bartington Instruments MS2B magnetometer. The frequency-dependent susceptibility was calculated by using the difference between the measurements at the two frequencies (χfd

=

χIf - χhf), which may also be expressed as a

percentage of the low-frequency susceptibility, i.e. [χfd%

= ((χIf

- χhf) / χIf) × 100]. For QC/QA

each sample was analyzed twice and the results were very similar (83.5% similarity). Isothermal remanent magnetization (IRM) was induced via a vibrating sample magnetometer (VSM), and the IRM produced at 1000 mT was taken as saturation IRM (SIRM). Stepwise backfield remagnetization of the SIRM was performed, and the results at reverse field strengths of 20 and 14

Journal Pre-proof 300 mT were used to calculate the HIRM (HIRM = [(SIRM / IRM-300mT)/2]/mass), the SOFT (SOFT = [(SIRM-IRM-20mT)/2]/mass), and the S ratio as according to S = (-IRM-300mT/SIRM). Variations in the relative concentration of low- and high-coercivity phases revealed by the S ratio are nonlinear and the interpretation of the S ratio is not unique (Heslop, 2009). Quantitative interpretation, therefore, requires constraints from other parameters. Liu et al. (2007) proposed the L ratio (L = (SIRM + IRM-300mT)/(SIRM + IRM-100mT)), using reverse fields of 100 and 300

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mT, to determine how the hardness of a hematite sample affected the HIRM and the S ratio.

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Only when the L ratio is stable, can the HIRM and S ratios be interpreted conventionally.

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Variable L ratio values indicate an influence of the magnetic grain size (coercivity) distribution

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of the high-coercivity component. The vibrating sample magnetometer was also used to obtain magnetic hysteresis loops (maximum field 1 T), from which saturation magnetization (Ms),

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saturation remanence (Mrs), and coercivity (Bc) were determined. Together with the coercivity

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of remanence (Bcr) from backfield curves, the hysteresis parameters were analyzed by the Day

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diagram (Day et al., 1977; Dunlop, 2002).

Oxidative Potential (OP) analyses Eight of the PM2.5, PM10, and TSP samples were selected for oxidative potential measurements (Table S1). Filters were stored at 4ºC in plastic Petri dishes sealed with parafilm and sent to Dr. Frank Kelly’s laboratory at KCL for the OP analyses. Additionally, nine passive dust and seven street dust samples were also selected, stored in plastic Petri dishes sealed with parafilm at 4ºC and then sent to KCL for OP analysis (Table S1). The methods used in this study are based on the approach described by Zielinski et al. (1999) and the abbreviations used are shown in the abbreviation section. 15

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Processing of filters and PM mass at KCL The PM samples were supplied as either loose dust or on Teflon filters. The Teflon filters were conditioned for 48 hours in a controlled atmosphere (17-21°C, 40-50% humidity) and weighed (Mettler Toldeo UMX2 microbalance) before extraction. For the extraction, the filters were

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placed into 50 mL plastic centrifuge tube (sterile, cell-culture grade) and the PM extracted using

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the combined action of several small volumes of methanol and a sonicating water bath at 40 oC (Gulliver et al., 2018). The methanol extracts were combined and evaporated under a stream of

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nitrogen gas. The extracted filters were allowed to dry, reconditioned, and reweighed to

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determine the mass of extracted PM.

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All samples were resuspended at a concentration of 150 µg/mL in 5% methanol in ultrapure chelex100 resin-treated water, pH 7.0, with vigorous mixing and sonication (sonicating probe at

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an amplitude of 15 micron for 30 seconds). The resuspension solutions were then further diluted

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to 55.56 µg/mL with chelex-treated water pH 7.0 and stored at -70oC.

Determination of PM oxidative activity RTLF (respiratory tract lining fluid) exposure of PM suspensions Aliquots of 450 μl of the 55.56 µg/mL PM suspension were dispensed into three 1.5 mL incubation tubes (triplicate analysis) and pre-incubated at 37°C for 10 min prior to the addition of 50 μl of synthetic RTLF solution. RTLF stock solution contains 2 mM of ascorbic acid (AA), urate (UA) and reduced glutathione (GSH), resulting in a final starting concentration of 200 µM per antioxidant and 50 µg/ml PM. Samples were incubated for 4 hours at 37 oC with constant 16

Journal Pre-proof mixing. Immediately following the 4-hour incubation the micro-tubes were centrifuged at 13,000 rpm for 1 hour at 4oC, followed by dispensing aliquots into 100 mM sodium phosphate buffer pH 7.5 (for GSH analysis) and 5% w/v meta-phosphoric acid (for AA and UA analysis). All tubes were immediately stored at -70oC and analyzed within 1 week of storage. In-house controls of (a) particle-free control blanks (C0 and C4), (b) negative (MONARCH® 120 Carbon Black [M120]) and (c) positive (NIST SRM1648a purchased from the US National

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Institute for Standards and Technology) PM, were incubated in parallel to samples, to control for

To eliminate as much background antioxidant oxidation as

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in the RTLF exposure model.

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background antioxidant oxidation and delivery of expected oxidation by the –ve and +ve controls

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possible from the model system, HPLC-grade water that had been treated previously with

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Determination of glutathione

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Chelex-100 resin (Sigma, UK) was used throughout for preparation of stocks and dilutions.

This assay employs the technique of the GSSG reductase-DTNB linked assay based on the

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method of Baker et al. (1990). To begin, 16.7 µL of the centrifuged RTLF-exposed liquid was

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added to 983.3 µL of cold 100 mM sodium phosphate buffer pH 7.5 containing 1 mM EDTA. Aliquots of 50 µL of this diluted sample was then analyzed (in duplicate) in parallel with glutathione standards for total both total (GSX) glutathione and (following derivatization with 2vinyl pyridine) for oxidized (GSSG) glutathione. The reduced (GSH) glutathione is obtained by subtraction of the GSSG from the GSX. The %CV of analysis was less than 10% with an experimental minimum detection limit of 9 µmoles GSSG/L. The microplate reader used is a Spectramax190 (Molecular Devices, UK) operated with their SoftMaxPro v4.8 software.

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Journal Pre-proof Determination of AA and UA This assay employs high-performance liquid chromatography (HPLC) with an electrochemical detector based on the method of Iriyama et al. (1984) with modifications. To begin, 50 µL of the centrifuged RTLF-exposed liquid was added to 450 µL of cold 5.6% meta-phosphoric acid in 0.7ml amber HPLC vial. Aliquots of 20 µL of acidified sample were injected onto a 150 x 4.6 mm 5 µm SphereClone ODS(2) column (Phenomenex, UK) and eluted with a 0.2 mol/L

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K2HPO4-H3PO4 (pH 2.1) mobile phase containing 0.25 mmol/L octanesulfonic acid. The final

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concentrations for AA and UA were calculated with external AA/UA standards run

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simultaneously. The %CV of analysis was less than 10%, with an experimental minimum

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detection limit for ascorbic acid of 7 µmol/L and uric acid of 9 µmol/L. The HPLC used was from Gilson Scientific UK along with the Unipoint v5.1 software. All chemicals used are of the

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Statistical analyses

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Company (UK) or VWR (UK).

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highest grade possible, usually HPLC-grade, and purchased from either the Sigma Chemical

Descriptive statistics, Mann-Whitney U test, and Backward GEE were applied using SPSS version

19.0

software.

Mann-Whitney

U

test

was

used

to

investigate

whether

two independent samples were selected from populations having the same distribution. A specific advantage of GEE is its ability to robustly estimate variances of the regression coefficient for data exhibiting high correlation between repeated measurements (Abbasi and Keshavarzi, et al., 2019). PMF (Hopke, 2016) was used to determine the sources of the PTEs. For more information, these statistical methods are already described in our previous study (Abbasi and Keshavarzi, 2019; Abbasi et al., 2018; Abbasi et al., 2019). 18

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HYSPLIT model The HYSPLIT model was used for computing air parcel back trajectories (Draxler and Hess, 1998; Stein et al., 2015). The model calculated the trajectories using the GDAS (1 degree, global, 2006-present, starting heights are 0, 10 and 500 meter, duration is from 8/August/2017 to

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1/February/2018) meteorological dataset. The model was configured to compute forward and

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backward trajectories starting daily.

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Results Airborne dust

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The PM2.5 and PM10 mass concentrations are shown in Figure 2 ranging between 19.7 – 76.0

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µg/m3 and 47.16 – 348 µg/m3 with average values of 46.4 µg/m3 and 143 µg/m3, respectively. TSP concentrations ranged from 126 to 173 µg/m3 with an average of 150 µg/m3. Based on

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WHO and USEPA, standard values for PM10 concentration are 60 and 100 µg/m3 and for PM2.5

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concentration are 25 and 50 µg/m3, respectively. Therefore, the dust concentrations of most measured days were higher than the WHO and USEPA standards. These results are similar to those reported by other studies. For example, Shahsavani et al., (2012) reported that the overall mean values of PM10 and PM2.5 in Ahvaz (Iran) were 319.6 ± 407.07 and 69.5 ± 83.2 µg/m3, respectively. Also, another study on Ahvaz during the 30 days of sampling indicated PM 10 concentration ranged from 44.5 to 4848.8 µg/m3 with a mean value of 422.8 µg/m3 (Najmeddin and Keshavarzi, 2018). Dust events in Iraq, Saudi Arabia, and Kuwait increased PM10 concentrations to greater than 3000 µg/m3 (Draxler et al., 2001). In the other studies, the average

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Journal Pre-proof annual PM10 concentration in Iranian cities, Arak (78 μg/m3), Ahvaz (385 μg/m3), Shiraz (102 μg/m3), Tehran (90 μg/m3), Tabriz (85 μg/m3), Kermanshah (116 μg/m3), Khorramabad (136.48 µg/m3), and Yazd (103 μg/m3) were reported (Shahsavani et al. 2012; Mokhtari et al. 2015, Fazelinia et al. 2013, Nourmoradi et al., 2016, Gholampour et al. 2014). The mean PM2.5 concentration in Tehran was 24.3 μg/m3 (Kakooei and Kakooei, 2007). Marzouni et al. (2016) reported annual average values for PM10 in Kermanshah of 148 μg/m3 and 116 μg/m3 in 2011

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and 2012, respectively, exceeding the national air quality standard. Another study showed that

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the annual 24 h average concentration of PM10 in Ilam for multiple years exceeded the WHO

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guideline with average and maximum concentrations of PM10 in 2012 and 2013 that were ~1.9

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and 29.33 times the WHO guideline, respectively. In 2014, the annual mean and maximum values were 1.12 and 8 times (Nikoonahad et al., 2017). Nourmoradi et al. (2016) found that the

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highest and lowest seasonal mean PM10 concentrations were observed during the summer and

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winter, respectively.

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Journal Pre-proof Figure 2. Airborne dust concentration in different days (Blue: TSP; Black: PM10; Red: PM2.5 – Downward diagonal: Industrial areas; Solid: Urban areas)

The results of the Mann-Whitney test found no significant differences existed between the airborne dust concentrations between the two sites in the industrial and urban areas. Thus, the

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same factors affected the dust concentrations at both locations. From the differences in daily

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weather conditions, it can be seen that increasing wind speed or changing wind direction

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increases the dust concentration. For example, higher dust concentrations were observed on the

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29 December and 7, 18, 19, 23 and 24 August 2017. These higher values may be due to high

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wind speeds or wind direction changes or both.

The areal densities of the passive dust samples are shown in Figure 3. The sample masses

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collected in August and September 2017 (summer) were significantly higher in all sampling sites

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than in February 2018 (winter). This difference is attributed to the influence of the dry season on dust deposition. Most dust storms in central Iran occurred when precipitation rates were low

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(from May to July) (Middleton, 1986). In the study, the mean dust deposition rate for the PA1 sample that was collected on a mountain height of 722.6 meters is lower than other stations (Figure 3). Generally, at lower heights, nearer to human activities, with lower wind speeds, and affected by local flow obstacles (buildings), dust deposition rates increase. The mean dust accumulation in the northeastern sector of the Canary Islands was reported to decrease with altitude (Menendez et al., 2007). Dust deposition rates in same seasons in southern Iran are higher than the Zagros Mountains and central Iran (Hojati et al., 2012) but lower than those reported for Riyadh, Saudi Arabia (Al-Tayeb and Jarrar, 1993).

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Figure 3. Dust deposition rate (g/m2/month) in different samples

According to the SEM results (Figure S8), dust grains deposited during the winter were smaller

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than those in the summer samples. Passive dust samples in industrial areas were also smaller than in the urban areas (Figure S8). These differences may be due to the impact of greater human activity in the industrial areas and lower airborne dust in the winter season. The background sample on the mountain also had smaller dust grains. The grain size of the passive dust reflects three factors: (1) the size distribution of particles in the source area, (2) distance from the source, (3) wind speed and local turburlence, and (4) timing and frequency of precipitation events (McTainsh et al., 1997; Singer et al., 2003; Ding et al., 1995; Chen and Li, 2011). Regional sources and short-distance transport produces deposits mainly in the coarse size ranges, while 22

Journal Pre-proof long-distance transport of dust produces fine deposits. Singer et al. (2003) using particle size of dust, deduced that dust was transported from long-range to medium distances. Given that coarser particles deposit from the suspension most rapidly, the dust deposits tend to get finer with

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distance from the source region (Goudie and Middleton, 2006).

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Journal Pre-proof Dust source identification In the current study, dust source identification was performed based on REE, main elements oxide, and statistical models. The main elements in airborne dust, passive dust, street dust (in summer and winter seasons), and surface soil were investigated (Figure 4). Due to the presence of calcareous formations in Asaluyeh County, it was expected that a high percentage of dust particles formed from calcium oxides, which is seen in the samples. The K2O/Al2O3 ratio can be

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used as a marker for the original compounds (Nagarajan et al., 2007). The percentages K 2O and

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Al2O3 in street dust (both of summer and winter), passive dust, and surface soil samples are

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similar. However, the percentages of K2O and Al2O3 in airborne dust samples are between

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limestone and sandstone averages. This ratio varies from 0 to 0.3 in dust samples, indicating the dominance of illite (Eby, 2004). The relatively high concentration of Na2O in dust can be due to

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halite and salinity of the soils in the region. According to XRD results (Abbasi et al., 2018) the

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presence of these minerals in near-sea samples was confirmed.

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Journal Pre-proof Figure 4. Percentage of K2O and Al2O3 in the surface soil, street dust, Passive dust and airborne dust (Kabata-Pendias and Mukherjee, 2007; Taylor and McLennan, 1985; Rudnick and Gao, 2003)

The variations in REE compositions among the samples were assessed using the convention of concentrations normalized to upper continental crust (UCC) using post-Archean Australian

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average shale (Gromet et al. 1984). These REE patterns can assist in interpreting the

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interrelationships between transported atmospheric dust and local soil samples, REEs

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concentration was measured in local soil, passive dust, and airborne dust samples. From

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examining the oxides of the main elements, street dust was found to originate from local surface soil. After the normalization of REEs to their values in the UCC, the relationship between the

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samples was examined. REE results in the local surface soil and limestone showed a similar

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trend. Comparison of the results indicates that the main source of the local soil is limestone (Figure 5). Also, REEs results of passive dust samples are quite similar and indicate passive dust

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originating from local soil. However minor differences were observed among the airborne dust

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samples. The PM2.5 results are quite similar to each other but do not match with the local surface soil. On the other hand, PM10 and TSP results are similar and match with the local surface soil except for a few samples that include S4 (7 August 2017), S11 and S12 (18 August 2017) for PM10 and S7 (9 August 2017) for TSP (Figure 5).

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Figure 5. REEs results a) Average airborne dust compared to reference material patterns; b)

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passive dust samples; c) PM2.5; d) all of samples e) PM10; f) TSP (Kabata-Pendias and

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Mukherjee, 2007; Taylor and McLennan, 1985; Rudnick and Gao, 2003)

In topsoil, light REEs (LREEs) are usually more abundant than heavy REEs (HREEs). They are also more abundant in the parent crustal materials (Tyler, 2004). An abundance of LREEs was observed in all dust and soil samples in this study. The LREE/HREE values of airborne dust varied from 0.5 to 4.23 (mean = 2.7) and for passive dust ranged from 3.3 to 4 (mean = 3.6). Some airborne dust samples, such as S7, S5, S6, and S10, demonstrated the lowest LREE/HREE 26

Journal Pre-proof values i.e., 0.5, 0.77, 0.76, and 0.64, respectively. The mean LREE/HREE value in the local soil is 3.53. Tang et al. (2013) reported that LREE/HREE values in atmospheric dust near Beijing in China ranged from 7.49 to 10.74. Also, our results are lower than the value for the Hunshandake sands (7.61), and Chinese loesses in Luochuan (7.5–8.2) (Gallet et al. 1996). The values of UCCnormalized GdN/YbN, LaN/SmN, and LaN/YbN ratios also provide insights into REE fractionation patterns. The LaN/YbN values of passive dust and soil in Asaluyeh county are similar to Beijing

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and vary from 1 to 1.5 (Tang et al., 2013). Passive dust showed similar LaN/SmN and GdN/YbN

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values, which are close to corresponding soil values (Figure 6). Also, airborne dust samples

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indicate similar ratios except for S7, S5, S6, and S10. The ratios in the passive and airborne dust

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were different. These differences suggest that passive dust and some airborne dust samples come from different sources with passive dust derived mostly from local soil. The results of LaN/YbN

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versus LaN are similar to LaN/SmN and GdN/YbN (Figure 6). These issues indicate that the dust

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particles (especially passive dust) and soils have undergone similar weathering processes.

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However, these ratios for airborne dust are different from other dust particles.

Figure 6. Distributions of LaN/SmN, GdN/YbN and LaN/YbN in the passive dust, airborne dust, and mean local soil 27

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REEs with the exception of Eu and Ce occur in the trivalent state. For Eu and Ce, the oxidation state depends on the redox conditions, complexation, and adsorption behaviors (Han et al. 2009; Tang et al., 2013). Such conditions can lead to Eu anomalies in which the Eu concentration in a mineral is either enriched or depleted relative to some standard value. Eu-anomaly (Eu/Eu*)

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values are calculated as: (1)

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Eu/Eu* = 2 (Eusample / EuN) / ((Smsample / SmN) + (Gdsample / GdN))

Eu-anomaly values for passive dust samples varied from 0.96 to 1.59 and for airborne dust

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ranged from 0.71 to 2.65 (Figure 7). These values show that the airborne dust has a slightly more

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positive Eu anomalies than in passive dust. The mean Eu/Eu* value of soils in Asaluyeh County

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is 1.06, which is closer to the mean value of passive dust (1.24) than to the airborne dust (1.56). Some prior studies suggested that weathering processes have no appreciable impacts on Eu/Eu*

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values and that Eu anomalies were more likely inherited from the source materials (Prego et al.

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2009). Thus, discordant Eu/Eu* anomalies at different sites may reflect separate, local source provenances (Tang et al., 2013) along contributions from human activities. The REE pattern of by coal-fired plant particles are similar to crustal material. However, the patterns for oil-fired plants and refineries are strongly depleted in heavy REE's (Olmez and Gordon, 1985). Generally, concentrations of REE's in crude oil are low. Although the catalyst is repeatedly regenerated and recovered, an estimated 2000 tons of catalyst material was lost per day by U.S. oil refineries (Fox, 1985). Some catalyst escapes from refinery stacks and some is apparently incorporated into the petroleum products. These losses account for the estimated 7600 tons of REE's used by the industry yearly (43 percent of the total U.S. use) (Moore, 1980). The unusual REE pattern of 28

Journal Pre-proof refinery emissions and of affected petroleum products results not from fractionation during a process but rather from the composition of the REE source material. In Houston, where many refineries are located, Sm was not measured but the La/Al ratio in the fine fraction was 0.012, whereas the crustal ratio is 0.00056 (Johnson et al., 1984). This ratio in airborne dust and passive dust of Asaluyeh County are 0.004 and 0.000021, respectively. This clearly shows that airborne

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dust is most affected by the oil industry.

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Journal Pre-proof Figure 7. Eu/Eu* anomalies in different samples of passive and airborne dust (Mean Eu/Eu* for local soil = 1.06)

PTEs pollution in airborne dust

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Descriptive statistics of the PTEs concentration in three sizes of TSP, PM10, and PM2.5 during

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summer and winter seasons in urban and industrial areas are presented in Table S2 to S4. The

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results indicate that the relative concentrations of most elements are enriched in PM2.5 compared

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to PM10, and TSP. The concentration of As, Mo, Cu, Pb, Zn, and Sb are enriched in winter compared to summer whereas Al, Co, Cr, Fe, Mn, Ni, and Ti represent more of the mass in

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summer than in winter season. Zn and Mo are more concentrated in the industrial zone. In the

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PMF analysis, the input data were dominated by PM10 samples that the results generally reflect the influence of crustal and mechanically generated particles. Only 2 source profiles were

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required to provide acceptable distributions of the scaled residuals. The results showed a

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geogenic factor with high concentrations of Al, Mn, Co, Cr, Ti, Fe, and Ni and an anthropogenic factor that included Mo, Cu, Pb, Zn, As and Sb (Figure 8). Although anthropogenic sources would have been expected to predominate for elements like V and Ni in PM2.5 samples, the much larger number of PM10 and TSP samples precluded the resolution of such sources. Source contributions are shown in Figure S9 and show the dominance of the geogenic material in all but a few samples.

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PTE in passive dust

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Figure 8. PMF models on PTEs in airborne, passive, and street dusts

The results of Shapiro-Wilk and Kolmogorov-Smirnov tests indicated that the distributions were non-normal (Table S5). In general, the lowest concentration of Mo, Cu, Zn, Ni, Mn, Fe, As, Cd, Sb, Cr, Ti, and Co was found in the PD11 station, which is related to the urban areas in Asaluyeh in August/September. Higher concentration of Mo, Pb, Zn, Ni, Co, Fe, As, Cd, Sb, Cr, Ti, and Cu were observed in sample PD13 collected in winter in the industrial area. Almost all elements have minimum and maximum concentrations in PD11 and PD13 stations, respectively. Based on the scaled residuals obtained in the PMFanalysis, the elements were classified into two factors

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Journal Pre-proof (Figure 8), geogenic and anthropogenic. The geogenic factor included Al, Ti, Cr, Mn, Fe, Co, and Ni. The anthropogenic factor included Mo, Cu, Pb, Zn, As and Sb. The passive dust samples were also dominated by geogenic source contributions (Figure S9).

PTE in street dust PTEs results in Agust, September, and February are similar, approximately. Also, in the study,

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the EF and statistical methods results are similar to our previous study and to avoid repeating

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those results, readers are referred to Abbasi et al. (2018). The PMF required 3 sources, geogenic,

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urban, and industrial, to provide good distributions of the scaled residuals. Pb, Cu, and Sb have

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high factor loading values and probably originate from urban sources (Figure 8). Brake dust is a significant source of Cu and Sb used in brakes to control heat transfer (Adachi and Tainosho

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2004; Sternbeck et al. 2002; Huang et al. 2009). Thus, Cu and Sb are emitted by road traffic

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(Sternbeck et al. 2002; Bem et al. 2003; Kabata-Pendias and Mukherjee 2007). Also, Pb has emitted gasoline combustion because the element is used as an antiknock agent in the fossil foils

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(Manno et al. 2006). The second factor included Zn and Mo. This factor represents the industrial

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source, including petrochemical and refinery units. Zn may have been derived from mechanical abrasion of tires, and combustion of lubricating oils (Salim and Madany 1993; Jiries et al. 2001; Arslan 2001). Mo is used in petroleum industries and in the production of high-temperature greases. Finally, a geogenic factor included Al, Co, Cr, Fe, Mn, Ti, and Ni derived mainly from local soil. The geogenic factor represented a smaller fraction of the mass for the street dust with the industrial sources often dominating the source contributions (Figure S9). In addition, similar results were observed in the Asaluyeh sediments (Abbasi et al., 2019).

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Journal Pre-proof Magnetic parameters The magnetic parameters values for the street dust samples, including magnetic particles weight MPW, χIf, χhf, χfd%, coercivity (Bc), initial slope of hysteresis curves, magnetization (Ms), and magnetic retentivity (Mr) are given in Table 2. Low-frequency magnetic susceptibility (χIf) is most commonly used to represent the total contribution of ferrimagnetic minerals (Liu et al.,

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2012). χIf values ranged from 75.7×10 -8 (D9 station) to 272.1×10-8 (D10 station) and from

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87.05×10-8 to 5770.2×10-8 in urban and industrial areas, respectively. Compared to other studies,

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the χIf value in the industrial area of Asaluyeh County was higher than other cities such as

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Karamay, Urumqi, Lanzhou, Yinchuan, Shizuishan, Xuzhou and Wuhai in China (Wang et al., 2014; Wang 2012; Wang, 2016) and West Midlands and Liverpool in the United Kingdom (Xie

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et al., 1999; Shilton et al., 2005; Robertson et al., 2003). This observation suggests a relatively

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significant enrichment of the anthropogenic magnetic particulates in Asaluyeh County. However, χIf values in Asaluyeh County (550.85×10-8 m3 kg-1) were lower than some polluted cities such as

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Zhuzhou (663×10-8 m3 kg-1) and near to Hezhang (527×10-8 m3 kg-1) in China (Zhu et al., 2013).

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Table 2 shows that the average χfd% is 0.97% and the highest values are 2.48% (D7 station) and 1.81% (D43 station), both of which were located in industrial areas. In general, the highest χfd% was found in industrial areas and then in urban areas, and also the least was observed in the background samples (Figure S10). The same zoning maps were observed for both MPW and χfd%. Generally, all values were very low and therefore fine particles in the superparamagnetic range do not play an important role. According to Figure S10, the industrial area samples have a high magnetic concentration (with a little difference). Soil formation is characterized by an enhanced content of sub-micron superparamagnetic (SP) magnetite and maghemite grains that is

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Journal Pre-proof indicated by an increased χfd% (Liu et al., 2012). In the current study, χ fd% of the background (and other) samples were below 3%, suggesting that there were few SP particles within them and indicating that the anthropogenic contaminants contain fewer SP particles. Given that χfd% is less than 3% in this study, it can be seen that most of the magnetic particles are single domain (SD) and multi-domain (MD), and SP particles have a small contribution to magnetic susceptibility. The reason for the weak correlation between χIf and χfd% (Table S6 and S7), can be attributed to

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the local soil which is entisol. According to REEs results, the source of most street dust is the

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local soil. It should be noted that the entisol soils are young in terms of geological age, and a

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poor maturing process has occurred in this type of soil.

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Table 2. The magnetic parameters in the street dust samples

%Xfd

Initial Slope (A m²/(kg T))

Magnetization (Ms) (A m²/kg)

Retentivity (Mr) (A m²/kg)

Mean

0.042

550.845

541.218

0.969

5.685

2.256

19.514

2.249

Median

0.027

212.825

210.500

0.988

0.008

1.307

0.192

0.019

Mode

.0012a

75.7a

75.7a

0

.0058a

.12a

.0116a

.0018a

Std. Deviation

0.04

1250.66

1219.44

0.60

21.99

2.47

74.29

8.59

Variance

0.001

1564140

1487031

0.36

483.41

6.082

5519.677

73.756

Skewness

1.368

4.227

4.219

0.463

3.873

1.423

3.873

3.873

Kurtosis

0.737

18.385

18.333

0.994

15

0.852

14.999

14.999

Minimum

0.0012

75.7

75.7

0

0.006

0.123

0.0116

0.0018

Maximum

0.13

5770.20

5627.25

2.48

85.16

7.90

288.07

33.29

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(gr)

Statistics

Xhf (10-8) (m3 kg-1)

Coercivity (Bc) (T)

Xlf (10-8) (m3 kg-1)

MPW

a. Multiple modes exist. The smallest value is shown.

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Saturation isothermal remanent magnetization (SIRM) values in less polluted areas commonly show small χ values that are prone to high relative errors (Hanesch et al., 2003). Our study area is polluted, thus SIRM and χ are equally suitable for magnetic concentration mapping. SIRM is largely driven by ferrimagnetic and canted-antiferromagnetic minerals without being influenced

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by paramagnetic and diamagnetic materials (Thompson and Oldfield, 1986). In the current study,

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the results approximately suggest that more SP minerals exist in the low magnetic samples. However, few or no such minerals were present in the high magnetic samples. The generally

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very low χfd% indicates the dominating concentration of human-induced pollution in the region. The S-ratio reflects the relative proportion of soft magnetic components to hard magnetic

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components. The S-ratio was calculated as IRM-100mT/IRM1T. The S-100 is used for the

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separation of antiferromagnetism and ferrimagnetic minerals. When this coefficient is close to one, ferri-magnetite-like minerals (such as magnetite and maghemite) are dominant, and if it is

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near to zero, there is a weak magnetic response and the relative contribution of hematite-like

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minerals (such as hematite and goethite) is higher. The results are similar to the West Midlands (UK), Liverpool (UK), and Xuzhou (China) (Shilton et al., 2005; Robertson et al., 2003; Wang, 2016). Figure 9 shows that the background sample contained ferrimagnetic minerals, but with increasing SIRM in other samples, the concentration of soft magnetic magnetite-like phases increases and the magnetic particles are larger. The SIRM for Asaluyeh County (Figure 9) was higher than Wuhan (China) (11898×10 -5 A m2/kg), Zhuzhou (China) (7400×10 -5 A m2/kg), Liverpool (UK) (3433×10-5 A m2/kg) and West Midlands (UK) (4746.1×10-5 A m2/kg) (Yang et al., 2010; Wang et al., 2012; Xie et al., 1999; Shilton et al., 2005). The green circle in Figure 9

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Journal Pre-proof (urban street dust samples) indicates that hematite-like minerals are dominant, while the yellow circle (industrial street dust samples) shows that more concentration of magnetite-like minerals (especially D7) are present. In Figure 9, other samples that were near background values, such as D9 and D13, were in a places that local soil effect on the street dust particles and consequently, the results can be similar to local background. The hysteresis loops of these samples are characterized by ―narrow‖ loops closing below approximately 100 mT and showing low

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coercivity. This result indicates that low coercivity ferrimagnetic phases are dominant in the

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street dust samples. Also, the different saturation magnetizations demonstrates different magnetic

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phases concentrations with the highest and lowest concentration in sample D11 and background

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samples, respectively. The King and Day plots indicate that magnetic particles in the street dust samples were dominated by the pseudo-single domain (PSD) and MD, further implying that

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ferrimagnetic minerals originated from anthropogenic sources of industrial areas (such as gas

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flares in petrochemical units) and urban areas (for example exhaust automobiles) (Figure 9). Also, the results indicate that magnetic particles size is between 120 to 1000 nm (Figure 10). In

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summary, magnetic characteristics revealed that the PSD - MD ferrimagnetic minerals

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(magnetite) were the dominant magnetic phase in the street dust samples from the urban and industrial areas. Coarse grains are mainly from anthropogenic activities, such as abrasion and combustion (Maher, 2011). The correlation coefficients between magnetic parameters and PTEs show a strong and positive relationship among χIf, MPW, and PTEs include Mo, Cu, Pb, Zn, Co, Mn, As, Cd, Sb, Fe (Table S8).

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Figure 9. S-100 versus SIRM of the street dust samples

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Oxidative Potential (OP)

Starting with a concentration of 200 µmol/L of antioxidant (C 0), the remaining antioxidant

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concentrations after 4 hours incubation of the in-house controls were as expected for the used

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RTLF model (Table S9). The –ve control PM (M120, 50µg/mL) displayed no reactivity with the

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antioxidants, whereas the +ve control PM (NIST1648a, 50µg/mL) displayed 30% consumption of AA and 18% consumption of GSH with respect to C4. The field filter blanks displayed no reactivity towards AA. However, a 15% reduction of GSH was observed. The samples displayed a moderate level of oxidation towards AA and GSH per unit of PM mass (in the same range as the positive control NIST1648a) and negligible oxidation of urate. Prior reports on a variety of PM samples have indicated that the urate is not usually susceptible to oxidization by PM (Szigeti et al., 2014; Künzli et al., 2006; Soltani et al., 2017). According to Figure 9, the highest oxidative potential is attributed to street dust followed by passive dust and airborne dust, respectively. This result may be related to the chemical composition and size of 37

Journal Pre-proof dust particles. Glutathione consumed by passive dust was higher than for the airborne dust. In general, the consumption of ascorbic acid was higher than glutathione. The OP of almost all airborne dust samples from the urban area was higher than from the industrial area (Figure 11). For example, S12 (TSP), S15 (PM 2.5), S19 (PM2.5), S25 (PM10) (Table S1) from the urban area have high OP values. The same pattern was repeated by the passive dust (such as PS5) and street dust samples (such as SA4). To better investigate the

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relationship between the OP measurements and contaminants in Asaluyeh County, the PD1 site,

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which was located in a height of 722.6 meters above the sea level, was used as the background

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station. This site had the lowest OP values.

Figure 11. OPAA/µg and OPGSH/µg data for airborne dust, passive dust, and street dust 38

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The backward GEE model was used to investigate the relationship between elements and OPTOTAL/µg. At first, the GEE method was used to eliminate meaningless variables. As shown in Table 3, only Pb, Zn, Sb, Al, Sc as well as PAHs, which previously reported by Abbasi and Keshavarzi (2018), have significant correlations with OP. However, the relationships between Al and Sc with OP were negative. Consequently, backward GEE analysis was used for Pb, Zn, Sb,

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Al, and Sc, and their effect on antioxidant consumption (Table 3). Al, Sb, and Zn showed

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significant relationships with OP (p <0.05). Among these elements, only Sb has a high and

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positive beta (β) suggesting that Sb is effective in inducing antioxidant consumption.

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Regarding the GEE model of OP with resolved source contributions calculated by the PMF

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model, passive and airborne dust OPAA values indicate a significant correlation with geogenic (β = 119.268) and anthropogenic (β = 57.36) sources (Table S10). Furthermore, street dust OP AA

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and OPGSH are significantly correlated with natural and anthropogenic sources. The β values

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reveal higher influence of industrial sources on street dust OP AA and OPGSH.

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Journal Pre-proof Table 3. Backward GEE matrix indicating PTEs, PAHs and TPH effect on OPTOTAL/µg measurements Hypothesis

Test

β

Std. Error

Lower

Upper

Wald Chi-Square

df

p

Mo

107

56

-3.4

217

3.61

1

0.057

Cu

119

316

-500

737

0.141

1

0.707

Pb

99

23

54

145

18.19

1

0

Zn

2254

658

964

3545

11.72

1

0.001

Ni

31

29

-26

89

1.17

1

0.279

Co

11.0

9.5

-7.6

30

1.34

1

0.247

Mn

-140

123

-381

102

1.29

1

0.257

Fe

16801

13647

-9948

43549

1.52

1

0.218

As

3.7

2.6

-1.34

8.747

2.07

1

0.15

Cd

0.18

0.22

-0.25

0.61

0.67

1

0.415

Sb

3.151

0.7533

1.674

4.627

17.497

1

0

Cr

17.948

21.1506

-23.507

59.402

0.72

1

0.396

Ti

-19.887

78.4386

-173.624

133.85

0.064

1

0.8

Al

-4930

1261

-7402

-2458

15.3

1

0

Sc

-1.59

0.47

-2.52

-0.67

11.4

1

0.001

Hg

0.052

0.052

-0.05

0.153

1.01

1

0.316

TPH*

-5.83-9

1.53E-8

-3.57E-8

2.41E-8

0.15

1

.703

PAHs*

3.72E-5

1.52E-5

5.92E-5

11.0

1

.001

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Parameter

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95% Wald Confidence Interval

1.12E-5

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* The PAHs and TPH data were reported in the our previous study (Abbasi and Keshavarzi, 2019).

Discussion Airborne dust concentrations in Asaluyeh County were higher than WHO and USEPA healthbased standards. The highest airborne dust concentrations were observed on 29th of December and 7th, 18th, 19th, 23rd and 24th of August 2017. The differences in airborne dust concentrations overtime was dependent on factors such as wind speed and direction, precipitation, external sources including traffic load, especially in urban areas, and industrial activity such as the gas

40

Journal Pre-proof conversion processes (Kulshrestha et al., 2009). The atmospheric data shown in Figures S1 to S7 suggest that increasing wind speeds or specific wind directions resulted in increased airborne dust concentrations. Asaluyeh County is located along the Persian Gulf coast and hence the soil texture is mostly sandy. Therefore, there is a potential for local production of airborne dust from fine-grained surface soil, especially where there is little vegetation cover. The air parcel back trajectory results (Figure S11) show that wind for almost all sampling days passed over local

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areas. The REEs results indicated that the airborne dust primarily originated from local surface

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soils. On some days such as 17th August 2017, the source of airborne dust is from other regions

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in southern Iran and probably from Saudi Arabia since the wind speed was high (Figures S1 to

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S7; S12). The REEs results show that the airborne dust on this day originated from a region with different REE patterns confirming this conclusion (Figure 5). From the HYSPLIT results (Figure

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S11), transport during the last week of January and the first week of February 2017 was mostly

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from the north and west of the sampling sites. Given that wind speed and the low temperature, it may be concluded that the airborne dust source was local. A fact that is also confirmed by the

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REEs results (Figures 5, and S1 to S7; S9).

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Based on the REEs results, it was found that surface soils were derived from weathering of calcareous formations. The passive dust samples primarily originated from the surficial soils. PM10 and TSP displayed similar REE patterns that generally matched the local soil distribution. However, the REE patterns for several airborne dust samples including S4 (7 th August 2017), S11 (12th August 2017), and S12 (18th August 2017) for PM10 and S7 (9th August 2017) for TSP (Figure 5) are different from the local soils (Figure 5). Thus, they likely were influenced by transported dust (Figure S1 to S7, S10). The PM2.5 REE patterns were quite similar to each other but do not match either the local soils or the passive dust compositions. Thus, the PM2.5 samples

41

Journal Pre-proof were derived from different sources including transport from other regions and anthropogenic sources such as the catalytic crackers at the local refinery (Olmez and Gordon, 1985). The concentrations of anthropogenic elements in PM2.5 represent a larger fraction of the PM mass than in the PM10 and TSP samples. The PMF results determined that the geogenic material, Al, Fe, Ti, Mn, Ni, Cr, and Co, was the major source for the passive and airborne dust samples (Figure S9). Sb, Zn, Pb, Mo, Cu, and As

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come from anthropogenic sources. Passive dust samples PS5 and PF6 contained higher

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anthropogenic than geogenic material. However, industrial and urban sources (traffic) are major

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contributors to the street dust (Figure S9). The industrial emissions included flares and ducted

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emissions from stacks at elevated heights. Street dust seems to be more affected by local urban

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sources such as traffic. Comparison among street dust samples collected in summer 2016 (Abbasi et al., 2018), August and September 2017, February 2018 along with the passive dust

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samples indicated that the compositional changes with time were much greater for the street dust

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samples than for the passive dust. Since the passive dust-traps were installed 3 to 4 meters above

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the ground, they were similar in composition to airborne dust. Our magnetic findings confirm these results and indicate that particles size ranged from 120 nm to 1000 nm (Liu et al., 2012). Based on the results from the hysteresis parameters (Daydiagram), small magnetic particles are dominant in the industrial and especially urban areas (Figure 10). Magnetic concentration (χ) and PTEs show a strong and positive relationship including Mo, Cu, Pb, Zn, Co, Mn, As, Cd, Sb, Fe (Table S8). For confirmation, one sample of separated magnetic particles analyzed by ICP-MS method and the result showed a high concentration of anthropogenic elements, for example, the concentration of Cu, Zn, Mo, Fe, Sb, Mn, Cr, and Pb are 2554, 3488, 511.9, >10%, >0.01%, 1927, 1730 and 325 mg kg -1, while Al 42

Journal Pre-proof and Sc have a usual concentration that includes 12208 and 2.9 mg kg -1. Thus, PM2.5 originates probably from anthropogenic sources and for this reason, REEs results are different from local background and surface soils. Alternatively, the fractional concentration of anthropogenic elements in PM2.5 are greater than in PM10 and TSP. HIRM is used as a measure of the mass concentration of high coercivity magnetic minerals, e.g., hematite and goethite. Alternatively, the S-ratio is a measure of the relative abundance of high-coercivity minerals in a mixture with

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ferrimagnetic minerals (e.g., magnetite, maghemite). In the current study, the S ratio approached

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unity indicating that ferrimagnetic minerals were dominant. Therefore, high-temperature

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industrial or combustion processes produced both anthropogenic atmospheric pollutants like

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PTEs as well as magnetic particles (Hanesch et al., 2003; Machemer, 2004). Because they have similar discharge and transport paths, and because the PTEs can be directly absorbed in

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ferro(i)magnetic phases, these two classes of materials are often closely related and show similar

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spatial distributions (Gautameta et al., 2005; Spiteri et al., 2005).

43

Journal Pre-proof Figure 10. Mrs/Ms versus Bcr/Bc in the street dust samples, plotted in the Day-diagram (Day et al., 1977)

The results presented in Table 3 are not easily interpreted since some elements with significant

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relationships with OP such as Pb and Sb are not strongly redox-active while redox-active

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transition elements like Fe, Mn, Cu, and Ni do not show significant capabilities for ROS formation. However, the results revealed that Zn and PAHs in the Asaluyeh County affect the

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dust OP (Table 3). Prior studies have reported the transition metals (Fe, Cu, Ni, Cr, and V) and

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organic compounds (such as PAHs) in airborne particles can catalyze the formation of ROS even

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at very low concentrations (Knaapen et al., 2002; Ghio et al., 2002) and soluble Zn in PM is responsible for plasmid DNA damage (Shao et al., 2006; Richards et al., 1989). The OP values

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were also regressed against the source contributions for the three dust sample types and the

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results are presented in Table S10. In the regression results, the passive and airborne dust were

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analyzed together (because the passive and airborne dust samples showed very similar profiles), while the street dust samples were analyzed separately. Since there should be no OP if the concentration was zero, the regressions were thought to be forced through the origin. For the passive and airborne dust samples, the geogenic material had a significant positive relationship with OPAA. The geogenic materials include many redox-active transition metals. Alternatively, the street dust OP (both of OPAA and OPGSH) would have been strongly related to the geogenic and industrial sources. OPAA and OPGSH were marginally related to industrial sources. These materials included a number of the redox-active metals that were substantially enhanced in the street dust by the presence of contributions from the anthropogenic sources. 44

Journal Pre-proof Magnetic particles are generally not a specific threat to human health. However, because of their close relationship with hazardous pollutants, they can be used as proxies to assess heavy metals contamination (Jordanova et al., 2003) and to target pollution sources (Hansard et al., 2012). In this study, magnetic particles had a significant and strong correlation with PTEs concentrations and our results revealed high concentrations of Cu, Zn, Mo, Fe, Sb, Mn, Cr, and Pb in magnetic particles. Thus, magnetic parameters can be used for OP values estimation. For confirmation,

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one sample of separated magnetic particles was analyzed by ICP-MS and the result showed a

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high concentration of anthropogenic elements. For example, the concentration of Cu, Zn, Mo,

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Fe, Sb, Mn, Cr, and Pb are 2554, 3488, 511.9, >10%, >0.01%, 1927, 1730 and 325 mg kg -1,

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respectively. These high concentrations of redox-active transition metals exist in the magnetic particles, and consequently, magnetic parameters (such as MPW) can estimate the OP values.

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The magnetic particles examined in this study have usually high concentrations of geogenic

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Conclusions

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elements such as Al (12208 mg kg-1) and Sc (2.9 mg kg-1).

In this study, three types of dust, airborne, passively deposited, and street dust, have been characterized for a series of possibly toxic elements and oxidative potential. Sb, Zn, Pb, Mo, Cu, and As were found to originate mostly from anthropogenic sources while the source of Al, Fe, Ti, Mn, Ni, Cr, and Co proved to be geogenic. The major source for the passive and airborne dust samples was geogenic. Street dust and passive dust samples primarily originated from local surface soils. Industrial emissions including fixed flares and ducted emissions from stacks at elevated heights primarily affected the airborne dust compositions. Industrial and urban sources (traffic) represented a major contributor to the street dust samples. Airborne dust samples 45

Journal Pre-proof generally originate from local surface soil except when the wind speed and direction are different and likely influenced by transported dust and anthropogenic sources. Also, magnetic methods provide an an additional tool and confirmed the results. The magnetic methods results indicated that magnetic particles in the street dust samples were dominated by the PSD and MD, further implying that ferrimagnetic minerals originated from anthropogenic sources in the industrial areas (such as gas flares in petrochemical units) and urban areas (for example exhaust

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automobiles). Also, the results revealed that magnetic particles sizes were between 120 to 1000

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nm. The ability of these materials to oxidize biologically relevant compounds, ascorbic acid, and

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glutathione, were measured as the oxidative potential. In general, crustal species such as Al were

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negatively related to OP while many transition elements (Fe, Cu, Ni, Cr, and V) were related to OP formation in street dust probably derived from industrial emissions and/or local traffic

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emissions. It can also be concluded that magnetic parameters can be used for OP values

Acknowledgments

ur

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estimation.

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The authors wish to express their gratitude to the Research Committee and Medical Geology Center of Shiraz University for logistic and technical assistance. Thanks are extended to Cabot Corporation (USA) for its generous donation of the MONARCH® 120 Carbon Black (M120). Also, it was an honor to work with Professor Erwin Apple who takes his time for editing our manuscript.

46

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