A Dissertation Submitted to the Faculty of The

Metal and Metalloid Contaminants in Atmospheric Aerosols from Mining Operations

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Authors Csavina, Janae Lynn

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METAL AND METALLOID CONTAMINANTS IN ATMOSPHERIC AEROSOLS FROM MINING OPERATIONS

Janae Csavina

______

A Dissertation Submitted to the Faculty of the

DEPARTMENT OF CHEMICAL AND ENVIRONMENTAL ENGINEERING

In Partial Fulfillment of the Requirements For the Degree of

DOCTOR OF PHILOSOPHY

WITH A MAJOR IN ENVIRONMENTAL ENGINEERING

In the Graduate College

THE UNIVERSITY OF ARIZONA

2012

THE UNIVERSITY OF ARIZONA GRADUATE COLLEGE

As members of the Dissertation Committee, we certify that we have read the dissertation prepared by Janae Csavina entitled Metal and Metalloid Contaminants in Atmospheric

Aerosols from Mining Operations and recommend that it be accepted as fulfilling the dissertation requirement for the Degree of Doctor of Philosophy.

______Date: 8/7/2012 A. Eduardo Sáez

______Date: 8/7/2012 Eric A. Betterton

______Date: 8/7/2012 Wendell P. Ela

______Date: 8/7/2012 Raina M. Maier

Final approval and acceptance of this dissertation is contingent upon the candidate’s submission of the final copies of the dissertation to the Graduate College.

I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fulfilling the dissertation requirement.

______Date: 8/7/2012 Dissertation Director: A. Eduardo Sáez

______Date: 8/7/2012 Dissertation Director: Eric A. Betterton

STATEMENT BY AUTHOR

This dissertation has been submitted in partial fulfillment of requirements for an advanced degree at the University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library.

Brief quotations from this dissertation are allowable without special permission, provided that accurate acknowledgment of source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the head of the major department or the Dean of the Graduate College when in his or her judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from the author.

SIGNED: Janae Csavina

ACKNOWLEDGEMENTS

I would first like to thank my advisers Dr. Sáez and Dr. Betterton. You have given me invaluable guidance throughout my PhD yet given me the freedom necessary to become a researcher. Your support throughout this process has been the cornerstone for my achievements. I owe any future successes as a researcher and academic to you as I have grown into the scientist I am today because your mentorship. I am beyond grateful for having you both as my advisers.

I’d also like to thank Prof. Mark Taylor for his mentorship in my Australia research. First of all, I am grateful for the opportunity to work with you and assistance in the grants we wrote. Secondly, thank you for the support (both financial and guidance) of the research. You have broadened my abilities both as a researcher and communicator that will no doubt aid in my future academic pursuits. Finally, thank you for your hospitality to both Mike and myself that made our stay in Australia more enjoyable.

I am also grateful to Dr. Ela and Dr. Maier for being a part of my dissertation committee. Dr. Ela, I hope to one day emulate you as a teacher as I learned so much and enjoyed the Advanced Water Treatment class; I wish I had more opportunities to have you as a teacher. Dr. Maier, I have enjoyed your times of mentorship through the SRP meetings and conferences; you are an inspiring woman.

Thank you also to Dr. Sorooshian for utilizing our samples for a hygroscopicity study and enrichment factors analysis from Hayden. I am honored to be a co-author on this study.

It would be impossible to name all the people who made this research possible. Some key people I’d like to thank include Omar Felix, Kyle Rine, Jason Field, Peter Saliba, Paul Rheinheimer, Anna Wonaschütz, Andrea Landazuri, Brian Barbaris, Homa Shayan, and Mackenzie Russell. A special thanks to Mike Kopplin who did much of the ICP-MS analysis, Steven Hernandez for the SEM-EDS work, and Paloma Beamer for allowing us to use the ultra-microbalance. Additionally, thank you to all the graduate students in the ChEE department that made up a support community for getting through this PhD program; those friendships are important and will never be forgotten. I am especially grateful to Lucia Rodriguez who has always been there for me through the fun and not so fun times of this journey.

Finally, many thanks are afforded to my husband Mike whose support and willingness to move across the country and world made all this possible. My family has also been instrumental in their support and giving me confidence to pursue this career. Of course, I am also grateful to all the teachers/mentors in my life that have led me to where I am today. I am humbled by the amount of support I have in my life, and for all that, I am grateful.

TABLE OF CONTENTS

ABSTRACT ...... 9

CHAPTER 1 - INTRODUCTION ...... 11

CHAPTER 2 - PRESENT STUDY ...... 16

REFERENCES ...... 24

APPENDIX A: A REVIEW ON THE IMPORTANCE OF METALS AND METALLOIDS IN ATMOSPHERIC DUST AND AEROSOL FROM MINING OPERATIONS ...... 27

A.1 Abstract ...... 27

A.2 Introduction ...... 28

A.3 Mechanisms and Implications of Atmospheric Particle Emissions ...... 38

A.4 Mining and Smelting Operations & Environmental Assessment ...... 44

A.4.1 Background ...... 44

A.4.2 Dust and Aerosol Monitoring ...... 49

A.4.3 Contaminant Analysis ...... 52

A.4.4 Modeling ...... 55

A.4.5 Case Studies ...... 58

A.6 Health and Environmental Impacts ...... 66

A.7 Research Priorities and Insights ...... 75

A.8 Acknowledgments ...... 79

A.9 References ...... 80

TABLE OF CONTENTS - Continued

APPENDIX B: METAL AND METALLOID CONTAMINANTS IN ATMOSPHERIC AEROSOLS FROM MINING OPERATIONS...... 105

B.1 Abstract ...... 105

B.2 Introduction ...... 106

B.3 Materials and Methods ...... 108

B.3.1 Sampling...... 108

B.3.2 Sample Extraction ...... 111

B.3.3 Sample Analysis ...... 112

B.4 Results and Discussion ...... 113

B.5 Concluding Remarks ...... 134

B.6 Acknowledgements ...... 135

B.7 References ...... 135

APPENDIX C: SIZE-RESOLVED AEROSOL CONTAMINANTS ASSOCIATED WITH COPPER AND LEAD SMELTING EMISSIONS IN AUSTRALIA AND ARIZONA: IMPLICATIONS FOR MORE EFFECTIVE EMISSIONS MANAGEMENT AND HUMAN HEALTH RISKS...... 139

C.1 Abstract ...... 139

C.2 Introduction ...... 141

C.3 Materials and Methods ...... 144

C.4 Results and Discussion ...... 146

C.5 Conclusions ...... 157

C.6 Acknowledgements ...... 157

TABLE OF CONTENTS - Continued

C.7 References ...... 159

APPENDIX D: EFFECT OF WIND SPEED AND RELATIVE HUMIDITY ON ATMOERPHIC DUST IN ARID TO SEMI-ARID CLIMATES ...... 163

D.1 Abstract ...... 163

D.2 Introduction ...... 164

D.3 Materials and Methods ...... 165

D.3.1 Green Valley Study ...... 165

D.3.2 Juárez Study ...... 168

D.4 Results and discussion ...... 170

D.4.1 Green Valley location comparison ...... 170

D.4.2 Green Valley wind event comparison ...... 172

D.4.3 Green Valley annual analysis ...... 176

D.4.4 Juárez PM 10 study ...... 179

D.4.5 Supporting Literature ...... 182

D.5 Conclusion ...... 184

D.6 Additional Data ...... 184

D.7 Acknowledgements ...... 185

D.8 References ...... 185

TABLE OF CONTENTS - Continued

APPENDIX E: HYGROSCOPIC AND CHEMICAL PROPOERTIES OF AEROSOLS COLLECTED NEAR A COPPER SMELTER: IMPLICATIONS FOR PUBLIC AND ENVIRONMENTAL HEALTH ...... 190

E.1 Abstract...... 190

E.2 Introduction ...... 191

E.3 Methods ...... 193

E.4 Results and Discussion ...... 197

E.4.1 Composition ...... 197

E.4.2 Enrichment Factors at Sample Site...... 202

E.4.3 Aerosol Hygroscopicity ...... 204

E.4.4 Regional Transport ...... 207

E.5 Conclusions ...... 209

E.6 References ...... 210

APPENDIX F: SUPPLEMENTARY MATERIALS ...... 214

ABSTRACT

Mining operations, including crushing, grinding, smelting, refining, and tailings

management, are a significant source of airborne metal and metalloid contaminants such

as As, Pb, Cd and other potentially toxic elements. Dust particles emitted from mining

operations can accumulate in surrounding soils, natural waters and vegetation at

relatively high concentrations through wind and water transport. Human exposure to the dust can occur through inhalation and, especially in the case of children, incidental dust

ingestion, particularly during the early years when children are likely to exhibit pica.

Furthermore, smelting operations release metals and metalloids in the form of fumes and

ultra-fine particulate matter, which disperses more readily than coarser soil dusts. Of

specific concern, these fine particulates can be transported to the lungs, allowing

contaminants to be transferred into the blood stream. The main aim of this research is to assess the role of atmospheric aerosol and dust in the transport of metal and metalloid contaminants from mining operations to assess the deleterious impacts of these emissions to ecology and human health.

In a field campaign, ambient particulates from five mining sites and four reference sites were examined utilizing micro-orifice deposit impactors (MOUDI), total suspended particulate (TSP) collectors, a scanning mobility particle sizer (SMPS), and Dusttrak optical particle counters for an understanding of the fate and transport of atmospheric aerosols. One of the major findings from size-resolved chemical characterization at three mining sites showed that the majority of the contaminant concentrations were found in the fine size fraction (<1 m). Further, metal and metalloids (e.g. As, Cd, and Pb)

around smelting activities are significantly enriched in both the coarse and fine size fraction when compared to reference sites. Additionally, with dust events being a growing concern because of predicted climate change and mine tailings being a significant source for dust, high wind conditions around mine tailings were studied for dust generation. Relative humidity was found to play an important predicting role in atmospheric dust concentration. More generally, findings indicate mining activities remain a serious threat to human health and ecology despite the regulations in place to protect from their pollution.

CHAPTER 1 - INTRODUCTION

Dust and aerosols are produced from mining operations and may contain elevated levels of contaminants, including the toxic elements Pb and As, which form the focus of this dissertation (Csavina et al., 2011; de la Campa et al., 2012; Gray and Eppinger, 2012;

Taylor et al., 2010). Mining operations are known to have contributed to negative ecological and human health effects, including elevated childhood blood Pb levels within the surrounding communities (Goix et al., 2012; Munksgaard et al., 2010; Queensland

Health, 2008; Simon et al., 2007). However, the specific physicochemical mechanisms governing transport and exposure remain poorly understood. Additionally, the role of mining activities in the fate and transport of environmental contaminants may become

increasingly important in the coming decades as land use intensifies and climate change

increases drought occurrence in arid and semi-arid regions, both of which can

substantially increase the potential for dust emissions (IPCC (International Panel for

Climate Change), 2007).

The physical and chemical properties and size distribution of inhaled aerosols are

necessary to completely assess risks associated with contaminant exposure (Spear et al.,

1998). The size of the particle determines the efficiency and region of deposition in the

respiratory tract (Park and Wexler, 2008). Coarse particles (>3 m), such as those

resulting from crushing and grinding of ore, deposit in the upper respiratory system and

are swallowed and eliminated through the digestive system. In contrast, fine particles (<1

m), such as those originating from smelting operations, are respired deep into the lungs

where they are transported directly to the blood stream and may also have a high

bioavailability due to the higher surface to volume ratios (Krombach et al., 1997; Valiulis et al., 2008). Particle size is also a critical characteristic for transport distance and building penetration within the adjoining environment: fine particles can travel further in the environment with an average residence time of ten days as compared to residence time for coarser particles of seconds to hours (Hinds, 1999). Therefore, determining the chemical composition in dust from mining operations as a function of particle size is crucial in quantifying the potential deleterious effects on human health and the environment, which is the main objective of this dissertation.

This dissertation is divided into three major sections: the introduction describes the author’s unique contribution to the field of work; the present study summarizes the timeline, methods, results and conclusions of the research; and the appendix contains six publications and supplementary material written or co-written by the author.

A brief description of the published or intended to be published work by the author and the author’s specific contributions to the research are given below.

Appendix A: A Review on the Importance of Metals and Metalloids in Atmospheric

Dust and Aerosol from Mining Operations

Literature is extensively reviewed to provide a foundation for the research detailed in the latter sections of the dissertation. The survey provides a background for mechanisms and implications of atmospheric particle emissions, dust and aerosol emissions related to mining and smelting operations, current technologies for dust and aerosol monitoring, techniques used to analyze for contaminants, modeling methods for particulate transport,

and human health and environmental implications of particulate emissions from mining operations. Case studies are reviewed to detail current research activities and literature available. The results highlight future research priorities for which this research hopes to fill some of the gaps. The author of this dissertation performed the literature search and analysis, as well as writing the majority of the review.

Appendix B: Metal and Metalloid Contaminants in Atmospheric Aerosols from

Mining Operations

One of the knowledge gaps in research highlighted by the literature review in Appendix

A is the lack of understanding of the size distribution, especially in the fine size fraction, of contaminant emissions from mining. This study explores the size-resolved chemical characterization of atmospheric aerosols around mining and smelting activities in

Hayden, AZ. Source apportionment was also explored using various correlation and analytical techniques.

The author developed methods, performed the research and analysis, and led the writing for this publication, except for the scanning mobility particle sizer (SMPS) results and the correlation analysis.

Appendix C: Australia and Hayden Comparison Study

To complement the study presented in Appendix B, this study provides a comparison of multiple smelting sites and from two different countries. For this paper, source apportionment was more fully developed at the smelting site in Hayden, AZ.

Additionally, examination of size resolved chemical characterization at two smelting sites in Australia further confirms the size distribution of particulate that results from this industry.

This work was performed as part of the Endeavour Research Award granted to the author of this dissertation by the Australian Government.

Appendix D: Green Valley Study

Mine tailings are a significant source for windblown dust and often contain contaminants.

Dust events are a growing concern due to recent haboob and dust storms causing low visibility traffic condition occurrences in the southwestern US. This research was an effort to study maximum dust loadings around mine tailings as a result to high wind events along with the predicting factors for dust generation.

The author helped in the development of this study, including the selection of sites, instruments to use in the field, and events to study.

Appendix E: Hygroscopic and Chemical Properties of Aerosols Collected near a

Copper Smelter: Implications for Public and Environmental Health

To the authors’ knowledge, this study was the first to report on simultaneous size- resolved hygroscopic and chemical properties of atmospheric aerosols around mining activities. Hygroscopic properties have important implications on the fate of aerosols both in the environment and human respiratory system. Further, chemical composition of

mining site emissions compared to reference sites and a no-smelter operations scenario determined enrichment of contaminants according to particle size.

The author’s contribution was the collection of samples, contaminant chemical analysis and determination of enrichment factors.

Appendix F: Supplementary Materials

This appendix contains results that were not considered publication material due to the lack of significant findings or work in progress. However, the results were included as they may be relevant to future research.

CHAPTER 2 - PRESENT STUDY

This section summarizes the author’s research methods, findings and recommendations for future research. The reader will be directed to appropriate appendices for more detailed methods, results and conclusions.

An extensive review of literature on contaminant transport of dust and aerosol emissions from mining operations (Appendix A) found a lack of size fractionated characterization of the emissions, particularly around smelting operations where refining of ores can create extreme conditions resulting in emissions of fine particulates. In order to address this gap in research, a micro-orifice uniform deposit impactor (MOUDI) was deployed to two mining sites in Arizona for size-fractionated aerosol collection. One field site was an active Cu smelter operated by ASARCO in Hayden, AZ, where crushing, grinding, smelting and windblown mine tailings laden with As and Pb are sources for aerosols and dust. The roof of a nearby high school located approximately 1.2 km from mining activities was selected for instrument set up because of the accessibility and child exposure interest. The other field site was the Iron King Mine Humbolt Smelter

Superfund site, an inactive mine designated by the US EPA in 2008 to be on the National

Priority List because of excessively high concentrations of As, Pb, and Cd in the soil and contains an old inactive smelter where windblown dust is the main source of contaminant transport. A site approximately 200 m east of the mine tailings was selected for aerosol collection. Additional comparison sites include Green Valley, located within one mile of mine tailings containing no As or Pb, main target contaminants of this study; Mount

Lemmon, which represents an environment not impacted by anthropogenic sources;

Tucson for a typical urban environment; and Wilcox near a natural dry lake bed (playa).

All site monitored aerosols with both TSP and MOUDI instruments except Wilcox which

only TSP samples were collected.

Utilizing studies from Harper et al. (1983) and Marple et al. (1991), standard operating

procedures were created for the substrate cleaning, handling, and metals extraction.

While the metals extraction method utilizing aqua regia was suitable for As, Cd, Pb

ICP-MS analysis, it was determined that HCl interfered with Cr measurements. Cr is currently not considered for part of the metals analysis due to the low concentrations reported by the EPA, but in future, if analyzing for Cr in the samples, using only HNO3 as the acid extraction would produce successful ICP-MS results. At the time, it was more important to stay consistent with procedure for this study. Gravimetric analyses of

MOUDI substrates using an ultra-microbalance were modeled from the EPA Class I equivalent methods. Static from low relative humidity was found to be an issue with procedure. While controls were consistent, a more ideal experimental set-up would be in a temperature and humidity controlled chamber.

Results for a year of sampling at the Hayden site were summarized in a publication attached in Appendix B. MOUDI results for As, Cd and Pb concentrations followed a bimodal distribution with particle size, with maxima centered at approximately 0.3 and

7.0 m diameter due to two local sources of the smelting process for the fine particulate

and windblown contaminated dust for the coarse. As mentioned previously, MOUDI

observations were also taken at Green Valley and Tucson. When a compilation of 59

Hayden results were compared to these two reference sites, both coarse and fine

particulate size fractions were found to be significantly enriched in As, Cd, and Pb.

However, when using the reference of a period when smelting activities were known to

be non-operational in Hayden, only the fine size fraction was enriched for when the

smelter was active. This suggests smelting emissions are the primary cause for

contaminant enrichment in the fine size fraction of particulate and the local soils are

contaminated with these anthropogenic emissions. Detailed enrichment factor results can

be found in Appendix E.

One of the major results from the publication in Appendix B was that approximately 70%

of the concentration of these contaminants resides in the fine size fraction (<1 m). This result has major implications to both health and environmental effects. Because of the small size fraction, the aerosols will remain in the atmosphere much longer than the coarser particles: 10 days compared to hours, respectively (Harper et al., 1983).

Additionally, these fine particles have a higher bioavailability due to volume to surface ratio and can be respired to the lungs (Spear et al., 1998). In the lungs, contaminants are transported to the bloodstream via macrophages resulting in a higher contaminant dose when compared to coarse particles which deposit in the upper respiratory tract and processed through the digestive system (Donaldson et al., 2001; Krombach et al., 1997;

Park and Wexler, 2008).

The deposition region for inhaled particles in the respiratory system is also influenced by

water uptake propensity (hygroscopicity) (Valiulis et al., 2008). The hygroscopic

properties of the particles collected in Hayden were explored in the manuscript presented

in Appendix E, which reports that the mode of particles containing the maximum

concentration of contaminants (0.1-0.32 m) overlapped the most hygroscopic mode at a relative humidity of 90%. This size of particle is predicted by models to have low deposition efficiency for “natural” aerosols. The increased water uptake propensity when compared to natural particles may increase deposition in moist conditions such as those seen in the human respiratory tract and may impact aerosol interactions in the environment such as cloud microphysics and fate and transport.

Whether ingested or inhaled, metal and metalloid emissions from mining operations can have serious deleterious consequences to health. For instance, As, Cd, and Cr are known carcinogens and can cause a decrease in nervous and mental function (ATSDR -Agency for Toxic Substances and Disease Registry, 2011). The neurotoxic impacts of Pb and high blood Pb levels in children around mining operations are well established in scientific literature (Munksgaard et al., 2010; Needleman, 2004; Soto-Jimenez and

Flegal, 2011; Taylor et al., 2010; Woolf et al., 2007). There is also some evidence that

As and Pb provide a synergistic adverse neurotoxic effect on cognitive function and behavior (Calderón et al., 2001; Wasserman et al., 2007; Wright et al., 2006).

Iron King results obtained in this investigation have not been published yet, but can be seen as supplementary material in Appendix F, Figure 1. Contaminant concentrations in the ultra-fine particle mode were unexplained and were very inconsistently present among various sampling periods. Various studies have explored the possible generation of arsine or other volatile As compounds by biological activity in contaminated soils

(Mestrot et al., 2011; Turpeinen et al., 2002), but studies performed at the University of

Arizona greenhouse by Dr. Maier’s group have not shown this to be the case.

While MOUDI observations provide a good determination for fine particles (

PM 10 observations (Figure 4, Appendix C). However, in Iron King where windblown dust is the primary source of particles, TSP measurements show higher average concentration for As and Pb compared to the MOUDI, which could indicate high concentrations of coarse particles (Figure 4, Appendix F).

After annualized climatology of the ambient contaminant concentrations were performed in Hayden, MOUDI operations were modified according to weather patterns and timing scenarios to observe changes in contaminant loadings. In June 2010, a program was created for the weather station to control MOUDI operations according to wind direction and speed. The results from this analysis were included in Appendix C and surprisingly

showed little correlation to wind patterns. It is thought that the lack of correlation between contaminant concentration and wind direction is due to irregular wind patterns in the area that originate from the complex topography of the site.

Additionally, in Appendix C, results are reported from a study carried out in Australia in

March and April 2012. Two mining towns: Mount Isa, Queensland, and Port Pirie, South

Australia, were examined for size-fractionated contaminant composition in ambient aerosols. At Mount Isa, Cu, Zn, Pb and Ag mining and smelting results in the emission of significant quantities of airborne contaminants. Pb and Zn smelting in Port Pirie is also associated with significant atmospheric pollution. Set up and methods were the same as the study outlined in Appendix B. Similarly to Hayden, the majority of the contaminant concentrations resided in the fine size fraction of particulate, stressing the potential for human health risks and the need for emissions management.

With the growing concern about dust events and a secondary motivation to determine maximum dust loadings around mine tailings, a study of high wind events was undertaken in Green Valley during spring 2011. Green Valley, as mentioned previously, has a significant stretch of metal contaminant-free mine tailings. However, active extraction of ore still occurs in the region, which made for a unique study location.

Utilizing a Kestrel for weather observations, Dusttraks for optical monitoring of dust concentrations at various particulate size fractions, and TSP collectors, nine wind events were monitored at six sites spread throughout the Green Valley region. See Appendix D

for detailed methods and results of this study. It was found that low relative humidity along with high winds play an important role in predicting dust generation.

Taken together, the results of this dissertation point to future research needs on dust and aerosol emissions from mining operations:

1. Smelting operations emit large quantities of contaminants in the fine size fraction.

Many of these operations have bag houses or electrostatic precipitators (ESPs) as air pollution controls. These technologies are most efficient at removing coarse particles while having much lower removal efficiency on fine particles. If a technology were created to artificially enhance growth rate of the fine particles before reaching the currently in-place air pollution control, higher contaminant removal would be achieved.

With the consideration of increased hygroscopicity of fine particles, temperature controlled humidification of the stack stream is a possible method to achieve growth of the particle.

2. More research needs to be done in the area of human respiratory deposition efficiency

and effects of particles associated with mining emissions. The enhanced hygroscopic

properties of these particles may cause current human inhalation models of natural

particles to incorrectly predict deposition efficiency. Additionally, high SO 2 concentrations are associated with smelting emissions (Appendix E), which may impact how contaminants associated with these aerosols affect the human respiratory system.

3. Pollution is easiest to treat at the source. Yet, the US EPA, for example, only regulates six atmospheric contaminants compared to ~90 regulations on drinking water.

Additionally, there is no current standard for As in the environment. Smelting communities are impacted by high concentrations of Pb and As (3% and 0.2% by gravimetric analysis in Hayden) which may have a uniquely enhanced negative impact on human health due to fine size fraction. Regulators should take this into account, particularly for these communities, to provide every citizen with equivalent protection in air quality.

REFERENCES

ATSDR (Agency for Toxic Substances and Disease Registry). Agency for Toxic Substances and Disease Registry. 2011, Atlanta, 2011.

Cabada JC, Rees S, Takahama S, Khlystov A, Pandis SN, Davidson CI, et al. Mass size distributions and size resolved chemical composition of fine particulate matter at the Pittsburgh supersite. Atmospheric Environment 2004; 38: 3127-3141.

Calderón J, Navarro M, Jimenez-Capdeville M, Santos-Diaz M, Golden A, Rodriguez- Leyva I, et al. Exposure to Arsenic and Lead and Neuropsychological Development in Mexican Children. Environmental Research 2001; 85: 69-76.

Csavina J, Landázuri A, Wonaschütz A, Rine K, Rheinheimer P, Barbaris B, et al. Metal and Metalloid Contaminants in Atmospheric Aerosols from Mining Operations. Water Air and Soil Pollution 2011; 221: 145-157. de la Campa AMS, de la Rosa JD, Fernandez-Caliani JC, Gonzalez-Castanedo Y. Impact of abandoned mine waste on atmospheric respirable particulate matter in the historic mining district of Rio Tinto (Iberian Pyrite Belt). Environmental Research 2012; 111: 1018-1023.

Donaldson K, Stone V, Seaton A, MacNee W. Ambient particle inhalation and the cardiovascular system: Potential mechanisms. Us Dept Health Human Sciences Public Health Science, 2001, pp. 523-527.

Goix S, Point D, Oliva P, Polve M, Duprey JL, Mazurek H, et al. Influence of source distribution and geochemical composition of aerosols on children exposure in the large polymetallic mining region of the Bolivian Altiplano. Science of The Total Environment 2012; 412: 170-184.

Gray JE, Eppinger RG. Distribution of Cu, Co, As, and Fe in mine waste, sediment, soil, and water in and around mineral deposits and mines of the Idaho Cobalt Belt, USA. Applied Geochemistry 2012; 27: 1053-1062.

Harper SL, Walling JF, Holland DM, Pranger LJ. Simplex optimization of multielement ultrasonic extraction of atmospheric particulates. Analytical Chemistry 1983; 55: 1553-1557.

IPCC (International Pannel for Climate Change). Working Group II Contribution to the Intergovernmental Panel on Climate Change Fourth Assessment Report. Climate Change 2007: Climate Change Impacts, Adaptation and Vulnerability. Cambridge University Press, New York, 2007.

Krombach F, Munzing S, Allmeling AM, Gerlach JT, Behr J, Dorger M. Cell size of alveolar macrophages: An interspecies comparison. Environmental Health Perspectives 1997; 105: 1261-1263.

Marple VA, Rubow KL, Behm SM. A microorifice uniform deposit impactor (MOUDI) - description, calibration, and use. Aerosol Science and Technology 1991; 14: 434- 446.

Mestrot A, Feldmann J, Krupp EM, Hossain MS, Roman-Ross G, Meharg AA. Field Fluxes and Speciation of Arsines Emanating from Soils. Environmental Science & Technology 2011; 45: 1798-1804.

Munksgaard NC, Taylor MP, Mackay A. Recognising and responding to the obvious: the source of lead pollution at Mount Isa and the likely health impacts. Medical Journal of Australia 2010; 193: 131-132.

Needleman H. Lead poisoning. Annual review of medicine 2004; 55: 209-22.

Park SS, Wexler AS. Size-dependent deposition of particles in the human lung at steady- state breathing. Journal of Aerosol Science 2008; 39: 266-276.

Queensland Health . Mount Isa Community Lead Screening Program 2006-2007: A Report into the Results of a Blood-lead Screening Program of 1-4 year Old Children in Mount Isa, Queensland, Environmental Health Services of the Tropical Population Health Network, Northern Area Health Service, Queensland Health . 2011, 2008.

Simon DL, Maynard EJ, Thomas KD. Living in a sea of lead-changes in blood-and hand lead of infants living near a smelter. Journal of Exposure Analysis and Environmental Epidemiology 2007; 17: 248-259.

Soto-Jimenez MF, Flegal AR. Childhood lead poisoning from the smelter in Torreon, Mexico. Environmental Research 2011; 111: 590-596.

Spear TM, Svee W, Vincent JH, Stanisich N. Chemical speciation of lead dust associated with primary lead smelting. Environmental Health Perspectives 1998; 106: 565- 571.

Taylor MP, Mackay AK, Hudson-Edwards KA, Holz E. Soil Cd, Cu, Pb and Zn contaminants around Mount Isa city, Queensland, Australia: Potential sources and risks to human health. Applied Geochemistry 2010; 25: 841-855.

Turpeinen R, Pantsar-Kallio M, Kairesalo T. Role of microbes in controlling the speciation of arsenic and production of arsines in contaminated soils. Science of The Total Environment 2002; 285: 133-145.

Valiulis D, Sakalys J, Plauskaite K. Heavy metal penetration into the human respiratory tract in vilnius. Lithuanian Journal of Physics 2008; 48: 349-355.

Wasserman GA, Liu X, Parvez F, Ahsan H, Factor-Litvak P, Kline J, et al. Water Arsenic Exposure and Intellectual Function in 6-Year-Old Children in Araihazar, Bangladesh. Environmental Health Perspectives 2007; 115: 285-289.

Woolf AD, Goldman R, Bellinger DC. Update on the clinical management of childhood lead poisoning. Pediatric Clinics of North America 2007; 54: 271-+.

Wright RO, Amarasiriwardena C, Woolf AD, Jim R, Bellinger DC. Neuropsychological correlates of hair arsenic, manganese, and cadmium levels in school-age children residing near a hazardous waste site. Neurotoxicology 2006; 27: 210-6.

APPENDIX A: A REVIEW ON THE IMPORTANCE OF METALS AND METALLOIDS IN ATMOSPHERIC DUST AND AEROSOL FROM MINING OPERATIONS

Janae Csavina a, Jason Field b, Mark P. Taylor c, Song Gao d, Andrea Landázuri a, Eric A. Betterton a,e , A. Eduardo Sáez a a Department of Chemical and Environmental Engineering, The University of Arizona, Tucson, AZ 85721, United States b School of Natural Resources and the Environment, The University of Arizona, Tucson, AZ 85721, United States c Environmental Science, Faculty of Science, Macquarie University, North Ryde, Sydney NSW 2109, Australia d Farquhar College of Arts and Sciences, Nova Southeastern University, Ft Lauderdale, FL 33314, United States e Department of Atmospheric Sciences, The University of Arizona, Tucson, AZ 85721, United States

This article was published in Science of the Total Environment (Csavina et al. 2012).

A.1 Abstract

Contaminants can be transported rapidly and over relatively long distances by atmospheric dust and aerosol relative to other media such as water, soil and biota; yet few studies have explicitly evaluated the environmental implications of this pathway, making it a fundamental but understudied transport mechanism. Although there are numerous natural and anthropogenic activities that can increase dust and aerosol emissions and contaminant levels in the environment, mining operations are notable with respect to the quantity of particulates generated, the global extent of area impacted, and the toxicity of contaminants associated with the emissions. Here we review (i) the environmental fate and transport of metals and metalloids in dust and aerosol from mining operations, (ii) current methodologies used to assess contaminant concentrations and particulate emissions, and (iii) the potential health and environmental risks associated with airborne

contaminants from mining operations. The review evaluates future research priorities based on the available literature and suggest that there is a particular need to measure and understand the generation, fate and transport of airborne particulates from mining operations, specifically the finer particle fraction. More generally, our findings suggest that mining operations play an important but underappreciated role in the generation of contaminated atmospheric dust and aerosol and the transport of metal and metalloid contaminants, and highlight the need for further research in this area. The role of mining activities in the fate and transport of environmental contaminants may become increasingly important in the coming decades, as climate change and land use are projected to intensify, both of which can substantially increase the potential for dust emissions and transport.

A.2 Introduction

Contaminants that are persistent in the environment pose a potential risk to human health worldwide. Although natural processes can produce a number of environmental contaminants, significant amounts of contaminants are generated by anthropogenic activities such as agricultural practices, industrial manufacturing, and mining operations

(Barrie et al., 1992; Lacerda, 1997; Kolpin et al., 1998; Ritter et al., 2002; Driscoll et al.,

2003; Volkamer et al., 2006). Major transport pathways for contaminants in the environment are: air, water, soils, and biota. Transport of contaminants by air may occur by direct transfer of volatilized species or by particles. Atmospheric suspended particles

(usually in the particle size range < 60 µm), including aerosol and dust (collectively

referred here as atmospheric particulates), can play an important role in the transport of environmental contaminants, particularly those that have low volatility and low aqueous solubility and remain attached to soil particles. Transport by atmospheric particulates is an important pathway by which contaminants can be redistributed in the environment either from point sources, such as mine smelters, or by distributed sources, such as large industrial centers or urban environments. This transport mechanism will likely become more important with increased land-use activity and projected climate change (Pelletier,

2006). For example, dust storm frequency and intensity have increased in recent decades throughout many parts of the world such as Africa, Australia, and China, largely due to increased human activities and climate (Middleton et al., 1986; Tegen and Fung, 1995).

Large dust events, such as the one shown in Figure 1, have the potential to transport large amounts of contaminants rapidly over long distances and large aerial extent relative to other transport pathways (water, soil and biota) and thus represent a unique risk to human health and the environment (Griffin et al., 2001; Park et al., 2004; Pope and Dockery,

2006).

Figure 1. Schematic derived from satellite remote sensing images of 6-day transport of atmospheric dust from the Gobi Desert to the U.S. Pacific Coast during a massive dust storm in April 1998 (reprinted from Wilkening et al., 2000).

Contaminant transport by atmospheric particulates is of global concern because air masses containing large amounts of dust and aerosol frequently cross continental and international boundaries and often have adverse environmental consequences in downwind depositional areas (Barrie et al., 1992; Perry et al., 1999; Chu et al., 2003;

Park et al., 2004; Gallon et al., 2011; Figure 2). Relative to the other major transport pathways (water, soil and biota), this mechanism has greatest potential to transport nonvolatile contaminants at regional and global scales because air masses generally are not confined to an appreciable extent by topographic boundaries or other barriers that might impede transport, as is the case with water, soil, and biota (Kolpin et al., 1998;

Kersting et al., 1999; McGechan and Lewis, 2002; Mulligan and Yong, 2004; Braune et al., 2005). Additionally, atmospheric particulates have the greatest potential to transport contaminants rapidly (hours to days) through the environment since air masses move at much greater velocity than surface water, groundwater, and most biological vectors.

Figure 2. Illustration of environmental contaminant transport media, their transport times and spatial extent and their relative number of scientific peer -reviewed papers (low < 1,000 studies; high > 10,000 studies).

Atmospheric dust and aerosol can also play an important role in the transport of contaminants over longer time periods (years to decades) and at smaller spatial scales

(meters to kilometers). All of the major environmental transport pathways (i.e., water, air, soil and biota) can be important to consider when assessin g the potential for contaminant transport over longer time periods (i.e., years to decades) and at local scales

(i.e., meters). For example, although most transport processes in soil are inherently slow and limited in spatial extent (Weber et al., 1991; Sheppard, 2005) , over sufficient time large amounts of contaminants can be transported though the soil, resulting in elevated

risks to human health and the local environment. Contaminant transport by biological

vectors (biota) is generally less confined by topographic barriers than transport processes

in soil and thus has a greater potential to redistribute contaminants at spatial scales that

extend beyond the local environment (Arthur and Markham, 1982; Hope, 1993; Braune et

al., 2005). Water and fluvial sediment are also major transport pathways for the

mobilization and redistribution of contaminants in the environment (Weber et al., 1991;

Kolpin et al., 1998; McGechan and Lewis, 2002). Contaminant transport by water and

sediments in terrestrial systems is typically confined in spatial extent by watershed

boundaries, and most contaminants and associated risks are generally concentrated in

riparian areas, aquatic systems, and shallow groundwater (e.g., Kolpin et al., 1998).

When considering all of the major transport pathways in the context of their potential risk

to human health and the environment, contaminant transport by air and atmospheric

particulates is perhaps most notable due to the potential speed, distance, and aerial extent

in which contaminants can be transported in the environment. Despite the potential

importance of this mechanism, most studies that assess issues related to contaminant

transport in the environment focus almost entirely on transport in soil and water (Figure

2). For example, in the case of contaminant transport from mining operations, there are nearly 10,000 peer-reviewed studies that explicitly focus on environmental problems associated with contaminant transport in water; about half as many studies that focus on contaminant transport in soil; and only a few hundred studies that focus on contaminant transport by atmospheric particulates. More specifically, very few studies explicitly focus on the transport of metals and metalloids in dust and aerosol from mining

operations, despite the potential environmental and health risks associated with this

specific transport pathway and these particular contaminants (Brotons et al., 2010; Taylor

et al., 2010; Csavina et al., 2011). Data on contaminant concentrations in bulk

atmospheric particulates are regularly gathered by government agencies and industrial

operators in various countries at sites with suspected pollution sources. In addition,

occupational samplers are used to assess worker exposure to contaminants of concern.

These data are sometimes made publicly available and are used to establish possible

environmental risks by comparing them with emissions and environmental standards.

The potential speed and distance through which contaminants are transported by biota are

typically much less than those associated with transport by other pathways, making

contaminant transport by biota more of a localized environmental problem (Arthur and

Markham, 1982; Hope, 1993). Contaminant transport in soil and water is also usually

more of a localized environmental problem compared to contaminant transport by air,

which is more of a worldwide phenomenon that has important global implications. For

example, it has been estimated that approximately 60 % of all atmospheric arsenic (As)

initially originates as a point source contaminant from mining operations and then is

subsequently transported and dispersed globally by atmospheric particulates (Chilvers

and Peterson, 1987). This mechanism is an underappreciated and potentially understudied problem, especially with respect to other transport pathways (mainly water in the case of As). Nevertheless, atmospheric transport of metal and metalloid

contaminants has important health and environmental implications from local to global scales and, therefore, should be further studied.

Atmospheric dust can be generated by natural processes associated with wind erosion, such as abrasion and deflation, as well as by anthropogenic activities and disturbances

(e.g., Ravi et al., 2011). Most natural and anthropogenic sources of atmospheric dust are found in arid and semiarid regions, which account for approximately 40 % of the global land area and are inhabited by one-third of the world’s population (UNSO, 1997). These environments are particularly susceptible to wind erosion and dust emissions because vegetation cover is typically sparse, surface moisture content is generally low, and soil particle cohesion is often inherently weak due to their moisture and low organic matter content (Pye, 1987; Sivakumar, 2007). Natural sources of atmospheric mineral dust are typically associated with low levels of chemical contaminants (e.g., Reheis et al., 2009) and include dune fields and playas, desertified lands, deserts and shrublands, rangelands and grasslands, as well as forests (Tegen and Fung, 1995; Goudie and Middleton, 2006;

Breshears et al., 2009). Anthropogenic sources of atmospheric mineral dust and aerosol are typically associated with higher levels of chemical contaminants (e.g., Vega et al.,

2001) and include croplands and agricultural systems, feedlots and pastures, dirt roads, construction activities, landfills, mining operations, and mine tailings (Chow et al., 1992;

Chow et al., 1994; Nordstrom and Hotta, 2004; Triantafyllou et al., 2006; Csavina et al.,

2011).

When considering the major sources of atmospheric particulates in arid and semiarid environments, forests and grasslands typically have the lowest potential emissions followed by shrublands and deserts (Breshears et al., 2009; Okin et al., 2009; Figure 3).

Because most natural sources of atmospheric particulates typically all have low concentrations of associated contaminants (Zobeck and Fryrear, 1986; van Pelt and

Zobeck, 2007; Reheis et al., 2009), they typically pose a low potential risk to human health and the environment. However, disturbances that reduce or remove vegetation cover, such as fire and prolonged drought, can result in an increased potential for particulate emissions and contaminant transport (Whicker et al., 2006; Breshears et al.,

2012; Ravi et al., 2011). When considering anthropogenic sources of mineral atmospheric particulates, mining operations potentially pose the greatest risk to human health and the environment because these sources are globally extensive, generate large quantities of particulates nearly continuously, and have high potential for toxic contaminants associated with the emissions (Thornton, 1996; Porcella et al., 1997;

Lacerda, 1997; Chakradhar, 2004; Neuman et al., 2009; Brotons et al., 2010; Csavina et al., 2011). Agricultural practices are also globally extensive and can generate large quantities of particulates, but, unlike mining operations, agricultural dust is typically generated only in response to strong wind events and only when fields are bare or fallow

(Fryrear, 1986; Stout and Zobeck, 1996). Properly managed croplands and agricultural systems are therefore not likely to generate significant quantities of atmospheric particulates throughout most the year when fields are covered by crops. In addition, croplands and agricultural systems likely have lower concentrations of toxic chemicals

associated with particulate emissions than most types of mining operations and therefore may pose less potential risk to human health and the environment.

Natural Dune Fields Mining Construction Anthropogenic

& Playas Operations High

Dirt Roads & ATVs Active Mine Feedlots & Tailings Desertified Abandoned Lands Croplands Natural Desserts & Disturbances Croplands & Shrublands Remediated Agricultural Mine Tailings Rangelands & Intensively Systems Managed environment and the Grasslands Active Ecosystems Landfills Potential risk to to Potential humanhealth risk

Potential Potential emissions particulate Capped Forests Landfills Low High Low

Low High Potential contaminant concentration

Figure 3. Natural and anthropogenic sources of dust associated with relative amounts of emissions, contaminant concentration, and risk to human health and the environment.

Although there are numerous natural and anthropogenic sources of atmospheric particulates, mining operations pose the greatest potential risk to human health and the environment (Figure 3). In a recent comprehensive assessment of the worst environmental pollution problems, activities associated with mining operations, including artisanal gold mining; metal smelting and processing; industrial mining; and uranium mining, were identified as four of the world’s top ten pollution problems (Ericson et al.,

2008). In addition, a recent assessment on the global health impacts of contaminants in the environment identified Hg, Pb, As, Cr, pesticides, and radionuclides as the six most

toxic pollutants threatening human health (McCartor and Becker, 2010). Dust and

aerosol emissions associated with mining operations are commonly associated with

significantly elevated levels of one or more of these contaminants, including Hg, Pb, As,

and Cr (Meza-Figueroa et al., 2009; Brotons et al., 2010; Csavina et al., 2011, Corriveau

et al., 2011). Furthermore, contaminants commonly associated with particulates from

mining operations are usually most concentrated in the finer particle size fraction (< 2

µm), which travels greater distance in the environment and poses a greater potential risks

to human health than coarser particles (e.g., Ravi et al., 2011). As climate is projected to

become hotter and drier and population and land-use intensity are projected to increase

for many arid and semiarid regions worldwide (CCSP, 2008; IPCC, 2007; Seager et al.,

2007; UNSO, 1997), the potential for atmospheric particulate emissions and associated

contaminant transport from both natural and anthropogenic sources will likely increase in many parts of the world.

To address some of key emerging environmental issues related to contaminant transport by atmospheric particulates, this work provides a critical review of the literature on (i) the fate and transport of contaminants in dust and aerosols from mining operations, (ii) the current methodologies for evaluating atmospheric particulate emissions and associated contaminant concentrations, and (iii) the potential health and environmental risks associated with airborne contaminants from mining operations. More generally, this review provides an overview on the mechanisms and environmental implications of dust

and aerosol generation and evaluate the potential importance of contaminant transport

relative to other transport pathways (soil, water, and biota.)

A.3 Mechanisms and Implications of Atmospheric Particle Emissions

Wind erosion is the largest source of tropospheric aerosols (e.g., Tegen et al., 1996) and

affects all major components of the biosphere (Ravi et al., 2011). Windblown dust and

aerosol can be transported in the environment by three distinct mechanisms that are

roughly differentiated based on the particle size: surface creep, saltation, and suspension

(Bagnold, 1941). All three processes redistribute soil, nutrients, and contaminants in the

environment at different spatial scales ranging from local to global (Goudie and

Middleton, 2006; Li et al., 2007; Field et al., 2010). Large particles are transported by

surface creep (> 2,000 m) and saltation (60-2,000 m) and account for the majority of mass movement at the local scale (Stout and Zobeck, 1996; Ravi et al., 2011). Smaller silt- and clay-sized particles from the soil (< 60 m) are transported by suspension and are available for long-range transport at regional, continental, and global scales

(Chadwick et al., 1999; Prospero et al., 2002). Additionally, aerosol produced by condensation of hot vapors (such as those emitted by smelters) have particle sizes that are typically < 60 m and also are, therefore, susceptible to long-range transport. Most wind- driven particle transport from soils occurs close to the soil surface and decreases sharply with height, with surface creep and saltation accounting for the majority of total transport

(Shao et al., 1993). Surface creep is characterized by particles that are too large to be lifted from the ground by aerodynamic forces and thus roll or ‘creep’ across the soil

surface. Saltation is characterized by particles that are lifted from the ground but are too large to be dispersed into the atmosphere and therefore bounce along the soil surface.

Creep and saltation processes play an important role in wind erosion and the generation of dust emissions. The kinetic energy associated with the bombardment of large particles

(> 60 m) impacting the soil surface through creep and saltation may be sufficient to overcome the cohesive forces that bind fine particles together, which typically renders them unavailable for wind-driven transport (Shao et al., 1993). Creep and saltation processes can also increase dust emissions by the production and subsequent suspension of dust-sized particles through abrasion of saltating particles (Smith et al., 1991; Wright,

2001; Hagen, 2001; Mackie et al., 2006; Bullard et al., 2007). Because soil organic matter and nutrients (e.g., nitrogen, phosphorus), as well as many environmental contaminants (e.g., As, Pb), are often associated with small particles (Li et al., 2007;

Csavina et al., 2011, Corriveau et al., 2011), the production and subsequent entrainment of dust-size particles in the atmosphere is an important mechanism of environmental transport that has global implications (Goudie and Middleton, 2006; Ravi et al., 2011).

Wind erosion and atmospheric particulate emissions are influenced by a variety of factors including climatic conditions, land-use, vegetation cover, and soil characteristics (Shao and Lu, 2000; Toy et al., 2002; Zobeck et al., 2003; Breshears et al., 2009; Okin et al.,

2006; Ravi et al., 2011; Figure 4). Particle entrainment occurs when the wind shear velocity at the soil surface exceeds the shear strength of the soil aggregates by a certain

minimum value, referred to as the threshold friction velocity or threshold shear velocity

(Gillette et al., 1980; Shao, 2008). Many environmental factors, such as atmospheric

humidity and surface soil moisture, can affect the minimum threshold friction velocity at

which particles begin to detach from the soil surface and become available for transport

by wind. Because the soil moisture in many arid and semiarid regions is typically in

equilibrium with atmospheric moisture, relative humidity has a large influence on soil

interparticle forces, which in turn influence the threshold friction velocity, resulting in a

complex relation between relative humidity, particle size and soil erodibility (Ravi et al.,

2004). Other environmental factors, such as land-use and vegetation cover, can also have

a large effect on the threshold friction velocity and the potential for atmospheric

particulate emissions. Vegetation cover and other non-erodible surface elements protect the soil surface by absorbing a fraction of the wind momentum flux thereby reducing the shear stress on soil particles (Stockton and Gillette, 1990). The effectiveness of vegetation in protecting the soil surface from erosion depends upon the type, cover, and arrangement of vegetation (Ravi et al., 2011). For example, in relatively undisturbed systems where land use is low, wind erosion and dust emissions are likely to be relatively low when vegetation cover is dense (> 40 %) but can increase rapidly as vegetation cover approaches intermediate levels of density (16-40 %), which results in turbulence (i.e., wake interference flow) near the soil surface, greatly increasing the potential for dust emissions (Breshears et al., 2009; Wolfe and Nickling, 1993). Soil properties, including surface roughness, clay content, particle size distribution, organic matter, and physical and biological soil crusts, also play a key role in determining the potential for wind

erosion and atmospheric particle emissions under a given set of climatic conditions and vegetation cover (Gillette, 1979; Belnap and Lange , 2003; Okin et al., 2006) .

Figure 4. Factors influencing dust emissions by wind erosion and the environmental implica tions of dust emissions (reprinted from Ravi et al., 2011).

Atmospheric particle emissions affect all major components of the biosphere and provide important biogeochemical linkages between the atmosphere, hydrosphere, and pedosphere (Schlesinger et al., 1990; Syvitski, 2003; Munson et al., 2011) .

Understanding the role of aeolian processes in the biosphere is critical for managing the world’s arid and semiarid regions and for addressing emerging challenges associated with environmental transport, land-use intensification, and climate change (Field et al., 2010;

Ravi et al., 2011) . Land degradation and desertification by accelerated wind erosion is a

major environmental problem worldwide (Schlesinger et al., 1990; Bridges and Oldeman,

1999) and will likely remain so throughout the 21 st century (Lal, 2001; Valentin et al.,

2005). Globally, there are approximately 430 million ha of land surface susceptible to wind erosion (Middleton and Thomas, 1997). Global estimates of soil erosion are 20-100 greater than average rates of soil renewal (Cuff and Goudie, 2009), which is particularly true in arid environments, as it takes hundreds to thousands of years to form only a few centimeters of soil (Pillans, 1997). Increased human activity is largely responsible for observed increases in wind erosion and global dust emissions that have been documented in the past (Middleton et al., 1986). Anthropogenic activities such as cultivation, livestock grazing, deforestation and vegetation shifts are estimated to account for more than half of the global atmospheric particle load and are responsible for increased inputs of aeolian sediment to mountains and marine systems worldwide (Tegen and Fung, 1995;

Neff et al., 2008; Painter et al., 2010).

Wind erosion and associated particle emissions have important environmental and ecological consequences, including the loss and redistribution of soil nutrients (e.g.,

Zobeck and Fryrear, 1986), pollution of air masses and water bodies (e.g., Griffin et al.,

2001), and direct and indirect effects on global radiation budgets and cloud condensation nuclei (e.g., Kaufman et al., 2002; Figure 4). Wind erosion has a large effect on the loss and redistribution of soil nutrients and plays a key role in exacerbating land degradation and the desertification process (Schlesinger et al., 1990; Lal, 2001; Li et al., 2007; Okin et al., 2009). This is because the erosive force of wind preferentially removes the finer

silt- and clay-sized particles, which contain most of the cation-exchange capacity and

water-holding capacity of the soil (Toy et al., 2002). Over time, many of these fine

particles can eventually be lost from the system due to wind erosion (Gillette, 1979),

resulting in local depletion of soil fertility (Zobeck and Fryrear, 1986; Li et al., 2007).

Large quantities of nutrient-rich particles are deposited in terrestrial and marine systems

throughout the world with important implications for global biogeochemical cycles (Okin

et al., 2004; Mahowald et al., 2009). Wind erosion can also transport environmental

contaminants such as metals and metalloids, pesticides, and biological pathogens, all of

which are typically associated with finer atmospheric particles and tropospheric aerosols

(Pope et al., 1996; Griffin et al., 2001; Csavina et al., 2011). Thus, anthropogenic

contaminants such as metals and metalloids in particles that have deposited on soils in the

vicinity of mining and other industrial operations can be suspended as atmospheric

particulates from soils that have become susceptible to wind erosion.

Tropospheric aerosols generated by wind erosion can have both direct and indirect effects

on Earth’s radiation budget (e.g., Tegen et al., 1996). Atmospheric mineral dust can

absorb and reflect solar radiation and thus has direct effects on global radiation budgets

by reducing the amount of radiation reaching the Earth’s surface (Andreae, 2001). Non-

absorbing particulates, such as desert dust, typically result in atmospheric cooling unless

iron concentration in the dust is enough to absorb visible and near-infrared wavelengths, which could result in atmospheric warming (Sokolik and Toon, 1996; Ramanathan et al.,

2001). Atmospheric particulates also have direct and indirect effects on the nucleation

and optical properties of clouds; an increase in local dust and aerosol emissions can result in the suppression of precipitation at landscape and regional scales (e.g., Rosenfeld et al.,

2001). Atmospheric particulates act as cloud condensation nuclei and can reduce precipitation because they result in a partitioning of atmospheric moisture into a greater number of droplets within the cloud (Twomey effect) and can also result in cooling of the land surface, which causes a decrease in convection and evapotranspiration (Albrecht,

1989; Rosenfeld et al., 2001). Particles can also have an indirect effect on climate through the snow-albedo feedback (Painter et al., 2007; Steltzer et al., 2009).

Atmospheric particle deposition can decrease snow albedo, causing a darker surface that absorbs solar radiation, triggering earlier and faster snowmelt, which can potentially result in less available water supplies, especially in areas where water shortages occurs.

The direct and indirect effects of atmospheric particulates on radiation budgets and cloud properties can collectively lead to a weaker hydrological cycle and reduced water availability in many arid regions throughout the world (Hartmann, 1994; Ramanathan et al., 2001; Painter et al., 2007; Hui et al., 2008).

A.4 Mining and Smelting Operations & Environmental Assessment

A.4.1 Background

Modern day mining operations include excavating, crushing, grinding, separation, smelting, refining and tailings management (Figure 5). All processes produce large quantities of dust and aerosol, including the transportation of ore with haul trucks and

trains (Reed and Westman, 2005). Though the vast majority of mining operations produce coarse dusts, high temperature processes produce fume and fine particulates potentially laden with metals and metalloids that are present in the ore.

Figure 5. Coarse particulate emissions from (a) crushing and grinding and (d) wind erosion of mine tailings; fine particulate dust emissions from (b) smelting and (c) slag dumps. a. (SBM Machinery, 2010) b. (Listavia International, 2011) d. (Courtesy Blenda Machado, Mexico).

Dust and aerosol particles emitted from mining operations may mobilize dangerously high levels of metals and metalloids including the neurotoxic elements Pb and As, which can then accumulate in soils, natural waters and vegetation. It has been estimated that 40

% of the total atmospheric emissions of As arise from smelting operations (Alloway and

Ayres, 1997). Accumulated contaminants in local soils and mine tailings can be further dispersed by wind erosion. As will be discussed in Section A.5, high atmospheric levels of metal and metalloid-bearing atmospheric particulates can have a substantial impact on the environment and human health (Berico et al., 1997; Soukup et al., 2000; Ghio and

Devlin, 2001; WHO, 2003). The magnitude of this impact is dependent both on contaminant concentration and the size of the particle.

Atmospheric dust and aerosols occur in three main size ranges: the ultrafine (or Aitken), accumulative and coarse modes, as is discussed in detail by Seinfeld and Pandis (2006)

(Figure 6). All three modes are of relevance to mining-related emissions: Ultra-fine particles are often generated from hot vapors, including smelting and slag dumps (Figure

5 b and c), and are so small that they rapidly diffuse, coagulate and grow into the accumulation mode with characteristic residence times in the atmosphere of minutes to hours. At the other end of the spectrum, coarse particles are generated by mechanical action, including crushing and grinding of ore and wind erosion of mine tailings (Figure 5 a and d), and are large enough to rapidly sediment out of the atmosphere in minutes to hours. Particles emitted from mine tailings due to wind erosion are usually in the coarse range. However, they potentially can have a wide size distribution as well as a range of metal and metalloid concentrations. In particular, efflorescent deposits, generated by evaporating water that flows to the surface of the tailings by capillarity, may contain metal and metalloid concentrations that are much higher than the surrounding tailings topsoil due to presence of crystallized soluble contaminant salts (Meza-Figueroa et al.,

2009, Hayes et al., 2012). Dry, efflorescent particles are particularly susceptible to wind

erosion. In the middle of the size spectrum, accumulation mode particles are too large to

diffuse/coagulate at a significant rate and yet are too small to sediment by gravity so they

accumulate in the atmosphere. Accumulation mode particles are highly dependent on the water cycle for the rainout/washout of the particles and therefore can have a global impact, with an average residence time of 10 days in the atmosphere (Hinds, 1999). Arid and semiarid climates can have dust storms that can transport even coarse particles tens or even hundreds of miles as discussed above. Drier climates also tend to prolong the transport of particles.

Figure 6. Idealized description of processes that affect atmospheric aerosol formation in three size ranges or modes – ultrafine (or Aitken), accumulation, and coarse (reprinted from Seinfeld and Pandis, 2006).

Particle size also affects dust and aerosol deposition efficiency in the human respiratory system upon inhalation. Coarse particles, such as those resulting from ore crushing and grinding, deposit in the upper respiratory system and are swallowed and go through the digestive system where contaminants may be absorbed, depending on their bioavailability. In contrast, fine particles, such as those originating from smelting operations, are respired deep into the lungs where they can be transported directly to the blood stream (Krombach et al., 1997; Park and Wexler, 2008; Valiulis et al., 2008). In addition, dermal deposition and incidental ingestion are exposure mechanisms that are

related to particle size. Therefore, determining the chemical composition in dust from

mining operations as a function of particle size is crucial in quantifying the potential

deleterious effects on human health and the environment.

A.4.2 Dust and Aerosol Monitoring

Various types of aerosol and dust samplers have been used in studies to characterize

atmospheric particulates originating in mining operations. Example studies presented in

this section and Sections A.4.3 and A.4.4 are not necessarily related to mining operations

due to the relative lack of research in this area, but provide an example application that

can be used for such investigations.

Bulk particle collection (without size fractionation) allows for the quantification of the

total atmospheric particulates and contaminants in the ambient air. Some studies simply

collect fallout or settleable particulate matter (>10-20 m) by open collection containers

(e.g., Munksgaard and Parry, 1998; Deb et al., 2002). Other studies investigate atmospheric particulates by the deposition onto top soil, which requires a consistent method of soil collection (e.g., Taylor et al., 2010). Additionally, for the understanding of saltation from soils or wind erosion, passive samplers are often used, including a

Modified Wilson and Cook (MWAC) and Big Spring Number Eight catcher (BSNE)

(Fryrear, 1985; Goossens et al., 2000; Goossens and Offer, 2000). High volume samplers such as total suspended particle (TSP) samplers have been used to collect particles with

size up to 250 m (Benin et al., 1999; Shaheen et al., 2005; Garcia et al., 2008). These measurements can be used to understand the magnitude of the contaminant environmental hazards and for comparative measurements with other analyses.

Concentrations of particulate matter (PM) with aerodynamic diameters less than 10 m

(PM 10 ) and 2.5 m (PM 2.5 ) are measured using dichotomous samplers with size-selective inlets. These concentrations are widely used for regulatory work due to the health concerns associated with these particles (Lv et al., 2006; Moreno et al., 2007; Kon et al.,

2007; Tursic et al., 2008). Recent epidemiological studies indicate that smaller size fractions may be most intimately involved in causing hazards to human health; therefore, studies with PM 2.5 measurements may be relevant (Shaheen et al., 2005; Moreno et al.,

2006; Querol et al., 2007).

Particle counters and mobility particle sizers are also used to collect atmospheric particulate samples (Neuman et al., 2009). The particle number concentration as a function of diameter can be measured using a scanning mobility particle sizer (SMPS) system (Wang and Flagan, 1990; Csavina et al., 2011). Optical particle counters measure mass concentration of aerosols and can be set to respond to different particle diameters with a selective inlet (< PM 1, < PM 2.5 , and < PM 10 ) (e.g., Chan et al., 2005). Locating these at various horizontal and vertical positions can provide measurements of horizontal and vertical dust and aerosol flux transport (e.g., Gillies et al., 2005).

For size-fractionated dust and aerosol collection, multi-stage cascade impactors have

been used (Querol et al., 2000; Tursic et al., 2008). Size-fractioned samples can be

individually analyzed both physically (e.g., microscopically, gravimetrically) and

chemically. The mass distributions of individual chemical species can be determined by

analyzing the chemical composition of particles collected in each size range, as described

in the next section. Spear et al. (1998) utilized an Andersen cascade impactor to study Pb

concentrations around a Pb smelter. Corriveau et al. (2011) used a Proton Induced X-ray

Emission Spectroscopy Cascade Impactor (PCI) sampler to assess As contamination from

a mine-tailing site. Micro-orifice uniform deposit impactors (MOUDI) have been used in

several studies (Querol et al., 2000; Allen et al., 2001; Tursic et al., 2008; Csavina et al.,

2011). In one model (MSP model #110), the nozzle plates rotate relative to the impaction plates to achieve near uniform particle deposition on the collecting substrates over the impaction area. Marple et al. (1991) described in detail the calibration and use of MOUDI samplers. The sharp cut-points (particle diameters where 50 % collection efficiency is achieved) for the size fractions are: 0.056, 0.10, 0.16, 0.32, 0.56, 1.0, 1.8, 3.2, 5.6, 10 and

18 µm, respectively. An after-filter is placed after the 10 th (smallest particle diameter) stage to collect all particles smaller than 0.056 µm in aerodynamic diameter, whereas a size-selective inlet collects particles larger than 18 µm by impaction. At the typical operating flow rate (30 L/min), the internal wall loss is very low.

Previous field investigations have shown that metals and metalloids, including As, Pb and

Cr, have been detected worldwide in clouds, fog and snow (e.g., Tatsumoto and

Patterson, 1963; Schemenauer and Cereceda, 1992; Barbaris and Betterton, 1996; Cherif et al., 1998; Rattigan et al., 2002; Mancinelli et al., 2005; Hutchings et al., 2009), with mining activities as possible sources. The effect of mining and smelting emissions in the

19 th and 20 th centuries from the Australian sites of Broken Hill and Port Pirie on Pb emissions on remote southern hemisphere pristine environments has been detailed in several studies on Antarctic ice cores (e.g., Vallelonga et al., 2003; Burn-Nunes et al.,

2011.)

Other sampling techniques exist beyond those reviewed here; however, these are widely used methods in other areas and have best application for the field. Most advantageous is having multiple samplers to provide a comparison measurement to ensure no bias in sampling methods. Additionally, a holistic approach with multiple forms of analyses discussed in the next section provides for the best understanding of ambient aerosol source and transport vectors.

A.4.3 Contaminant Analysis

Digestion is typically used to extract contaminants of concern from collected dust and aerosols into the aqueous phase. The contaminant and analytical method chosen will determine the type of digestion to use. For example, for ion chromatography work, deionized water is typically used to preserve the composition of aerosols. Acid digestion, with HF and HNO 3, HNO 3 and HClO 4, or HNO 3 and HCl, is used to extract metal and metalloid species from dust and aerosol samples (Harper et al., 1983; Shaheen et al.,

2005; Moreno et al., 2007; Garcia et al., 2008). Extraction is sometimes enhanced by

heating and/or agitation. The aqueous extracts can then be subjected to a suite of

chemical analysis, including inductively coupled plasma – mass spectrometry (ICP-MS)

(Querol et al., 2000; Allen et al., 2001; Herner et al., 2006), inductively coupled plasma –

atomic emission spectrometry (ICP-AES) (Lv et al., 2006; Li et al., 2007), and atomic

absorption spectrophotometry (AA) (Benin et al., 1999; Shaheen et al., 2005; Garcia et

al., 2008). Depending on the sampler and analysis to be done, various sampler substrates

can be used, such as cellulose membrane filters (Querol et al., 2000; Pekney et al., 2006),

quartz fiber filters (Lv et al., 2006; Querol et al., 2007), glass fiber filters (Shaheen et al.,

2005), aluminum foil substrates (Csavina et al., 2011) and polycarbonate filters (Moreno

et al., 2006; Csavina et al., 2011).

For water soluble ions, ion chromatography (IC) and capillary electrophoresis (CE) have

been used to measure chloride, sulfate and nitrate (Querol et al., 2000; Lv et al., 2006;

Tursic et al., 2008). For soluble cation measurements, colorimetry-flow injection analysis

(FIA) and ICP-AES have been used (Querol et al., 2000; Lv et al., 2006).

Gravimetric analysis often requires an ultra-microbalance with a filter attachment for

mass quantities that are small (< 100 g). This is often the case for size-fractionated samples. For larger quantities of mass such as those found with TSP and PM 10 samplers, a balance with the sensitivity of 1 mg is often sufficient.

X-ray diffraction has been used to examine the mineralogy of non-soluble materials in dust (Spear et al., 1998; Querol et al., 2000; Moreno et al., 2007). Moreno et al. (2007) identified silica, kaolinite, alunite, jarosite, illite and iron oxides in PM 10 particles in the vicinity of an ore processing plant near a gold mine in Southeastern Spain. In comparison, after a spill of mining heavy metals in Southwestern Spain, Querol et al.

(2000) identified pyrite as the main component, along with clay, quartz, calcite, gypsum, and plagioclase. X-ray absorption spectroscopy has also been used to characterize As- laden mine tailings (Paktunc et al., 2003; Slowey et al., 2007).

Morphological and chemical characterization of particles by scanning electron microcopy

(SEM) and field emission SEM (FESEM), have been used to examine the microscopic structures of aerosol particles (Csavina et al., 2011). Using polycarbonate filters, Querol et al., (2007) identified silica, Al-Si clay, sulfates (with K and Al), as well as K, Fe and

Ti. Three metalloids, As, Sb, and Te, were also identified, possibly derived from mine tailings.

Isotope concentrations can provide an understanding of the geological nature of elements in ores (Day et al., 2010). They are often used for source apportionment when isotope concentrations in dust and aerosols are compared to the mined ore body. Pb is often associated with mined ores, and therefore Pb isotope ratios are commonly used for source apportionment (e.g., Munksgaard and Parry, 1998; Hsu et al., 2006; Taylor et al., 2010).

Pb isotopes can be analyzed by thermal ionization mass spectrometry or ICP-MS (Cheng

and Hu, 2010). For example, the Burn-Nunes et al. (2011) study of East Antarctica Law

Dome ice cores confirmed that the isotopic Pb signature of samples dated to the late 19 th century was identical to the Pb emitted from Broken Hill and Port Pirie, Australia, at that time.

In addition to isotope analysis, source apportionment can also be done utilizing chemical concentrations. Some examples of this include measurements of enhanced contaminant concentration according to wind patterns consistent with the source plume (Fernandez-

Camacho et al., 2010), ratios of metal concentrations of the source compared to background (Beavington et al., 2004; Bi et al., 2006), or enrichment factors (Meza-

Figueroa et al., 2009). Modeling discussed in the next section can also be used for source apportionment by trajectory of dust emissions.

A.4.4 Modeling

Dust and aerosol suspension and dispersion can be best investigated by an integration of field measurements, physical approaches and computer modeling. The pioneer work developed by Bagnold (1941) and investigations by Chepil and others (Chepil, 1962;

Woodruff and Siddoway, 1965) served as starting point to numerous models (Goudie and

Middleton, 2006). Existing models use mathematical equations that are continuously adapted to physical and/or empirical approaches to describe the factors and processes that are involved in wind erosion. A comprehensive numerical model of steady-state saltation

(COMSALT) was one of the first physically-based models that could reproduce a wide

range of experimental observations using a minimum of empirical equations (Kok and

Renno, 2009). The model takes into account gravity, drag, particle spin, fluid shear,

turbulence and a parameterization of the splashing of surface particles by impacting

saltating particles; the latter is considered a stochastic process. The key issue is to

identify the concepts behind the parameterization of soil (i.e. understand the transport

mechanisms that contribute to dust emissions), vegetation, and land management effects

on the susceptibility of landscapes to wind erosion (Webb and McGowan, 2009). A

disadvantage of empirical models is that most rely upon field-measured inputs, which are

not available at broad spatial scales (Woodruff and Siddoway, 1965; Fryrear et al., 1998;

Webb and McGowan, 2009). On the other hand, physically based models typically do

not account for temporal variations in soil erodibility (Webb and McGowan, 2009).

Currently, there are a limited number of integrated dust models that can provide forecasts

in both time and space, such as the Dust Regional Atmospheric Model (DREAM).

The DREAM model is a deterministic numerical model that simulates the emission,

transport and deposition of dust and aerosol with predicted atmospheric conditions, and is

coupled with the Weather Research and Forecasting (WRF) model (Nickovic et al.,

2001). WRF is a state-of-the art weather forecasting model that can be run at horizontal

resolutions of 1-10 km (Skamarock et al., 2005). The DREAM model is validated with

in-situ PM 2.5 and PM 10 data usually from USEPA monitoring stations, satellite images of the particle plumes, and ground-based optical sensing of vertical profiles and visibility or

other light extinction monitoring instruments. In addition to this, the DREAM model performance has been tested for various dust storm events in various places using gridded analysis or forecasting fields from various sources for initial and boundary conditions

(Nickovic et al., 2001).

A current strategy to model wind and particle flows from areas susceptible to wind erosion involves the use of computational fluid dynamic models (CFD), which can be run at relatively high resolutions (< 1 m) (Holmes, 2006; Diego et al., 2009). Unlike most currently used regulatory air quality models, CFD simulations are able to treat topographical details such as terrain variations and building structures in urban areas, as well as local aerodynamics and turbulence. Although CFD can provide high resolutions, it can become computationally expensive. Feng and Ning (2010) used CFD and the Owen

(1964) model to determine saltation fluxes. FLUENT CFD was used to reproduce wind patterns over a complex with microtopography (60 m × 60 m) in the Chihuahuan desert that were previously obtained by the Quick Urban & Industrial Complex (QUIC) field model developed by Bowker et al. (2006). Wind profiles using FLUENT CFD agreed well with measured wind speeds. The authors also simulated sand fluxes at several positions of a dune brick in Minquin, China under two different wind directions. The

Owen (1964) model was linked to FLUENT’s CFD codes to simulate sand fluxes. More recently, FLUENT CFD software has become an approved air pollution tool by The EPA

National Exposure Research Laboratory. In a different study, Torno et al. (2011) calculated dust emissions using CFD and realistic topography obtained from Light

Detection and Ranging (LIDAR) techniques, which provides high resolution topographic

data, and wind events from a fixed direction correlated well with wind speed

measurements. Badr and Harion (2005, 2007) used CFD to model fugitive dust emissions

from stockpiles. Their calculations showed that effects of terrain geometry on wind flow

patterns play an important role in erosion and particle transport assessment.

To date, there have been few attempts to model atmospheric particulate emission from

mine tailings piles. Kon et al. (2007) developed a wind erosion model designed to predict

dust emission rates of flat dry tailings prone to wind erosion, taking into account

fluctuations in wind velocity. The performance of the wind erosion model was assessed

using wind tunnel and field data reported in the literature. It was found that analytical

saltation models derived for agricultural lands or desert dunes can be applied to industrial

minerals that are crushed and have a density different from that of quartz. Field

experiments undertaken by Kon et al. (2007) on the tailings dumps of Mantos Blancos

copper mine, which is located in Atacama Desert in Chile, demonstrated that the model

predicts satisfactorily the occurrence and the magnitude of wind erosion events on mine

tailings.

A.4.5 Case Studies

In many cases, towns are built around mines in remote areas and so the source of contaminated atmospheric particulates is easy to discern. However, in those cases where mining occurs along with other industries, source apportionment can become more

difficult. For example, Barcan (2002) performed a study in the Severonickel Smelter

Complex in Monchegorsk, Russia, in which samples were obtained directly from the smelter stack. An important finding from their study was that the fine particle (< 2.5 µm) fractions were enriched in Pb, Zn, and As oxides. Santacatalina et al. (2010) surveyed

Southeast Spain for fugitives emissions of particulate matter and used PM 10 data in a

Positive Matrix Factorization (PMF) receptor model to identify the main sources of mineral, road traffic, secondary, sulfate, petroleum coke, sea spray and industry, with x- ray diffraction (XRD) and scanning electron microscopy-energy dispersive x-ray spectroscopy (SEM-EDX) techniques. Similarly, Allen et al. (2001) performed principal component analysis (PCA) on metal concentrations from MOUDI size-fractionated samples collected in the United Kingdom. Their study identified main components of sources to be road traffic, utility and industrial combustion, resuspension and industrial activities.

Munksgaard and Parry (1998) were able to show differences in pre- and post-mining Pb isotope ratios and ore-derived Pb versus local soil-derived Pb in a mining town. Bi et al.

(2006) examined Pb isotope ratios to distinguish the difference between flue gas particles and waste from a zinc smelter in western Guizhou, China. Depending on the types of sources that can be impacting the samples collection and the accessibility to equipment may make one analysis better than others.

Meza-Fiegueroa et al. (2009) analyzed soils in Sonora, Mexico around the Pilares mine

Nacozari tailings site for metal and metalloid content, which showed that contaminant dispersion took place primarily by surface run off, but enrichment factors for Hg, Cu, As,

Zn and Pb in residential soils arose from wind erosion. Benin et al. (1999) reported that the impacts of smelting and refining were particularly acute adjacent to the Torreón site in Mexico where a large active nonferrous smelter is located. As shown in Figure 7, concentrations of As, Pb, and Cd in roadside dust decreased with distance from the stack.

Differences in the decay shape of curve might be a consequence of differences in the particle size distribution for the three species. In addition, metal and metalloid concentrations were significantly higher to the south and west of the site, but lower to the east. This was attributed to either the effect of prevailing wind or other factors such as transportation, unloading and distribution of ore within the complex.

Figure 7. Plots of contaminant concentrations in roadside dust versus distance from the smelter stack (reference point), approximately the emissions of the industrial complex in Torreon, Mexico. (A) Arsenic; (B) cadmium; and (C) lead (reprinted from Benin et al., 1999).

Earlier studies have shown that distance from the source and meteorological factors play important roles in exposure to potentially toxic metals and metalloids. For example, a

study by Landrigan et al. (1975) showed that Pb, Cd, Zn and As in airborne particulates

near a large ore smelter in El Paso fell rapidly with distance and reached background

values 4 - 5 km from the smelter.

A recent study of soil and outdoor and indoor dust around the Torreón smelter by Soto-

Jiménez and Flegal (2011) showed that contaminant concentrations were greatest near the

smelter, with the highest levels corresponding to the prevailing wind direction. Soil and

dust Pb concentrations were orders of magnitude above background concentrations of

7.3–33.3 g g -1. Atmospheric Pb deposition rates in the city was also significantly

elevated (130 to 1350 g m-2 d-1), with values greatest at distances less than 1 km from the smelter. Emission sources were not only evident in environmental samples but in Pb isotope measures of neighboring children blood, which were indistinguishable from the smelted Pb-ores and the environmental samples.

The effect of prevailing winds in redistributing contaminants in the vicinity of mining and smelting operations is not a new observation, with other studies demonstrating that distance from the source and meteorological factors play significant roles in the soil accumulation of metals and metalloids (e.g., Cartwright et al., 1977; van Alphen, 1999;

Sterckeman et al., 2000; Stafilov et al., 2010; Fernandez-Camacho et al., 2010; Taylor et al., 2010; Sánchez de la Campa et al., 2011; Ojelede et al., 2012). Moreover, dispersion plumes have been shown to correlate with smelter stack height and smelter capacity (e.g.,

Knight and Henderson, 2006).

Utilizing an atmospheric dispersion model and spatial modeling of urban surface soil concentrations, Taylor et al. (2010) found that elevated soil metals were a consequence of mining emissions from the Xstrata Mount Isa Mines lease in Queensland, Australia, where elevated concentrations of Cd, Cu, Pb, and Zn were observed. At a goldmine in

Ghana, Amasa (1975) analyzed soils samples for As to account for As concentrations in human hair samples, which were as high as 1,940 and 268 ppm for mine workers and citizens, respectively. Soil samples were found to be 147, 67.2, and 96.5 ppm at 300 yards, 1.5 mi, and 9 mi away from the smelting stack, respectively. In Humberside, UK,

Rawlins et al. (2006) measured Pb and Sn in an area of land around a former smelter and showed that significant amounts of metals were deposited up to 24 km from the smelter by the prevailing wind with an estimated 2500 and 830 ton of excess Pb and Sn, respectively, deposited in the area. Soil sampling allows for a quick method to discovering the contaminants present due to mining operations.

TSP results from Beavington et al. (2004) in Port Kembla, New South Wales, Australia, showed more than 74 % reduction in atmospheric metal concentration around a smelter from 1978 to 2001-2002 due to improved environmental emissions control. However, even with improvements, the air quality still showed intermediate to high enrichment in metals and metalloids (Cd, Se, Cu, Pb, An, Br, As and Ni).

Fall-out was assessed by Deb et al. (2002) in atmospheric particulates from an urban area in central India. They found the total annual flux of As to be as high as 1.12 kg km -2 yr -1

with the largest emission of atmospheric As from pyrometallurgical processes employed

in the production of non-ferrous metals such as Pb, Cu and Zn. Munksgard and Parry

(1998) were able to show that Pb fall-out from settling atmospheric particulates increased

significantly at a rate consistent with proximity to the McArthur River Mine, Northern

Territory, Australia. A specific collector was designed by Brotons et al. (2010) to

measure mining waste in southeast Spain transported by wind into dust traps placed at

three heights and from eight wind directions. The results showed that nearby towns,

farming areas and beaches would be affected by high levels of wind-eroded dust carrying

high concentrations of metals, especially Zn and Pb. Still, without size-fractionated

measurements, it is difficult to make any inferences to how these particles will transport

in the human respiratory system and environment.

Fernandez-Camacho et al. (2010) used PM 10 and PM 2.5 to assess emissions from a copper smelter in the city of Huelva, Southwest Spain. Though the smelter accounted for only 8

% of the bulk mass of PM 10 , the majority of the mass was attributed to the metals and metalloids of environmental interest (As, Se, Bi, Cu, Zn). Additionally, they found that

85 % of the total PM 10 As was in the PM 2.5 size fraction. In southwestern Spain, Querol et al. (2000) studied suspended particles derived from heavy metal mining wastes. For Cr,

Cu, Mn, Ni and Zn, the mass in the PM 2.5 fraction comprised 17 to 36 % of that in the

TSP, whereas this fraction increased to 59 to 70 % for the mass in the PM 10 fraction. In comparison, for As, Cd, Co, Pb, Sb, Se and Tl, the mass fraction in the TSP was 20-48 % for PM 2.5 and 80-93 % for PM 10 . These generally higher percentages suggest that the

second group of contaminants exist preferentially in the finer fractions of particles due to

their physicochemical properties. Further, the two studies differed in that Fernandez-

Camacho et al.’s was near a smelter while Querol et al.’s was only around mine waste.

The high temperatures of the smelting process produce fine metal and metalloids

particulates that result in relatively high concentrations of metal and metalloids in the fine

size fraction.

Few size-resolved studies have been published for mine-related dust and aerosol. Both

Csavina et al. (2011) and Spear et al. (1998) showed that the majority of atmospheric

metals and metalloids around smelting operations are in the fine to ultrafine size fraction.

Spear et al. (1998) used sequential chemical extractions to show that the solubility of

combined bulk smelter particulate Pb compounds was < 7 %. However, analysis of

coarse airborne dust from the blast and furnace processes showed that 65 % of the total

Pb was exchangeable. Figure 8 illustrates the size distribution for As, Cd, and Pb concentrations in atmospheric particulate around a Cu smelter in Arizona, US, studied by

Csavina et al. (2011). These results show that approximately 75 % of metals and

metalloids exist in the PM range below 1 m. Spear et al. (1998) used an Andersen impactor to investigate the chemical speciation of Pb-laden dust associated with a Pb smelter in Montana, US. This study similarly found the largest percentage of Pb in the fine size fraction, which also had higher percentages of bio-available Pb. Corriveau et al.

(2011) used a PCI sampler to analyze atmospheric particulate near an As-rich abandoned mine tailings site in Nova Scotia, Canada. They found that As speciation in atmospheric

particulates corresponded to soil collected from the tailings. Even though As

concentrations were the highest in coarse size fractions (> 8 m), they found significant

As presence in finer particulates (< 2 m).

3.5 12 )

-3 3.0 10 Pb ) -3 Cd 2.5 As 8

2.0 6 1.5

4 1.0

2 AverageConcentration Pb (ng m 0.5 AverageAsand Concentration Cd (ng m

0.0 0 AF 0.054 0.1 0.18 0.32 0.55 1.0 1.8 3.1 6.2 9.9 18 Cutpoint Diameter ( m)

Figure 8. Annual averaged Pb, As and Cd concentrations from MOUDI observations at the Hayden site for the period December 2008 through November 2009. Data represent average concentrations over thirty six 96-hour sampling periods; AF denotes after filter sample; error bars represent standard deviation of measurements (reprinted from Csavina et al., 2011).

Transport of metals and metalloids from mining operations also can occur over large spatial scales. Andrade et al. (2006) studied Cu concentrations in a marine environment off the coast of Northern Chile in Chañaral Bay contaminated by mining activities,

particularly by windblown mine tailings which remain untreated. Seawater samples showed dissolved Cu concentrations up to 500 nM.

A.6 Health and Environmental Impacts

Contaminated dust and aerosol from mining operations can contribute to transport and accumulation of contaminants in soil, water and biota. Interactions between transported particles and repository media may affect speciation, mineralogy and bioavailability of contaminants. For example, Beaulieu and Savage (2005) determined that arsenic oxide particles released from a smelter were further oxidized, dissolved and the As then adsorbed onto soil minerals and colloids upon deposition.

Ingestion of contaminated soils from mining operations emissions or inhalation of emitted particulates may result in contaminant doses that could have deleterious consequences. Some metals and metalloids are known carcinogens (e.g. As, Cd, Cr) and can reduce mental and nervous system function, cause lower energy levels, damage vital organs (ATSDR, 2011), and cause DNA damage (Yáñez et al., 2003). Long-term exposure to these contaminants may mimic degenerative diseases such as Alzheimer’s disease, Parkinson's disease, muscular dystrophy, and multiple sclerosis (IOSHIC, 1999).

The neurotoxic effects of Pb have been well established in the scientific literature: high levels are known to cause a range of effects ranging from comas, seizures and death

(Woolf et al., 2007). Due to their increased uptake of contaminants when compared to adults, children are particularly at risk of neurotoxic effects such as learning or behavioral

problems including speech, intelligence, attention, behavior, and mental processing problems (Woolf et al., 2007).

Given that most As and Pb exposures are potentially avoidable and owing to their serious childhood environmental health risk factors, special attention is often given to the problem of these elements in human environments. Lubin et al. (2008) found a direct correlation between lung cancer and inhaled As in copper-smelter workers. Multiple studies of Pb exposure and, to a lesser extent, As, have shown these two contaminants are debilitating neurotoxins that cause adverse effects on children’s cognitive function and behavior (Calderón et al., 2001; Wright et al., 2006; Rosado et al., 2007; Wasserman et al., 2007; Munksgaard et al., 2010). In particular, childhood Pb poisoning at blood Pb levels (PbB) > 0.1 g/cm 3 is a common occurrence in smelting and mining towns

(Wichmann et al., 1997; Tong et al., 2000; Needleman, 2004; Munksgaard et al., 2010;

Taylor et al., 2010; Soto-Jimenez and Flegal, 2011), whose communities face a disproportionate exposure risk, with marked losses in intelligence quotient (Baghurst et al., 1992) and also consequentially later childhood emotional and behavior problems

(Burns et al., 1999). Laidlaw and Filippelli (2008) presented evidence that atmospheric resuspension of Pb-enriched soil in urban environments represents a persistent source of

Pb poisoning in children.

The pervasive effect of environmental Pb emissions from all industrial sources on human exposure was demonstrated by Flegal and Smith (1992) who showed that the natural PbB

level in the prehistoric human was as low as 0.16 ng/cm 3. This value is some 625 times

lower than the current National Health and Medical Research Council (NHMRC, 2009),

Centers for Disease Control (CDC, 1991) and World Health Organization (WHO, 1995)

blood Pb level of concern (0.10 g/cm 3) in humans. Recent research has indicated that levels well below the currently accepted PbB intervention may result in significant impairment in terms of neurocognitive functioning (Lanphear et al., 2000; Dietrich et al.,

2004; Lanphear et al., 2005a; Woolf et al., 2007; Jusko et al., 2008). Specifically, exposure to low levels of Pb has been linked with decreased Intelligence Quotient (IQ) and academic achievement, as well as a range of socio-behavioural problems such as attention deficit hyperactivity disorder (ADHD), learning difficulties, oppositional/conduct disorders, and delinquency (Lanphear et al., 2000; Tong et al.,

2000; Braun et al., 2006; Gilbert and Weiss, 2006; Bellinger, 2008; Wright et al., 2008).

These are serious mental health issues that are associated with significant life interference and distress. Furthermore, there is evidence to suggest that childhood disorders do not tend to remit with age but continue into adulthood (Rutter, 1989; Keller et al., 1992;

Schwartz, 1994; Wright et al., 2008).

There is significant evidence that there is no safe threshold for the adverse consequences of Pb exposure (Schwartz, 1994), supporting NHMRC’s (2009) recent statement that there are no benefits of human exposure to Pb and that all demonstrated effects of such exposure are adverse. Indeed, Lanphear et al.’s (2005a) log-linear modeling of pooled data from multiple studies revealed that not only did there appear to be no lower effects

threshold but that the slope of effect of Pb exposure is greater in the range below 0.10

g/cm 3. These patterns of exposure are not unique to environmental Pb. Numerous other environmental contaminants have shown toxicity at very low levels (e.g. As, dioxins, methyl mercury, polychlorinated biphenyls,). Further, many environmental contaminants have a dose-response curve that shows greater impacts per increment of exposure at lower levels than at higher levels (Stayner et al., 2003; Lanphear et al., 2005b).

Consequently, there is increasing pressure to lower or totally eradicate the use of known environmental toxicants because no safe level of exposure can be established and in many cases exposures are preventable (Lanphear et al., 2005b). The effect of Pb exposure and its inherent risk was summarized by the US EPA after their 2008 decision to lower the Pb-in-air guideline for 1.5 g/m 3 to 0.15 g/m 3 (USEPA, 2008).

Particulate matter itself, irrespective of chemical compositions, is regulated under the US

National Ambient Air Quality Standards (NAAQS) PM 10 and PM 2.5 limited to 150 and 35

g/m 3, respectively, for a 24-hour period (USEPA, 2011). Further toxicity, potential human exposure and US regulations and guidelines have been summarized by ATSDR

(2011).

Dust and soil ingestion through hand-to-mouth activity is an exposure pathway for humans, especially in the case of children who can exhibit pica behavior (Filippelli and

Laidlaw, 2010). Calabrese et al. (1997) showed that the levels of metals and metalloids were elevated in blood and/or urinary samples of children living in contaminated areas,

when only an estimated 200 mg soil/day were ingested. Pica can result in 5-50 g soil per episode ingested, with one episode being sufficient to leave children at or above the acute human lethal dose (Calabrese et al., 1997). Of particular importance is the fact that enhanced presence of As and Pb in smaller particle size fractions (< 63 m), which preferentially adhere to skin over large particles, might lead to contaminant uptakes that surpass expected levels from overall soil concentration (Bergstrom et al., 2011).

Carrizales et al. (2006) reported elevated concentrations of Pb and As in the blood of children living near a copper smelter in San Luis Potosí, Mexico. These concentrations correlated with the relatively high contaminant content in the soil.

The physical and chemical properties and size distribution of the inhaled aerosols are necessary to assess more completely risks associated with contaminant exposure (Spear et al., 1998). Absorption of the contaminant from the respiratory and/or gastrointestinal tract is influenced by particle size, the site of deposition, and bioavailability, which is often related to solubility. In particular, the size of particles can be used as a predictor for the efficiency and region of deposition in the respiratory tract. Using a multi-breath inhalation simulation, Figure 9 shows the predicted deposited particle fraction as a function of particle size (Park and Wexler, 2008). Here, the respiratory system is divided into an extrathoracic (the nasal and oral passages, pharynx, and larynx), a conducting

(bronchial generations 0–16), and a pulmonary region (generations 17–23). For the ultrafine fraction particles (< 0.1 m), diffusive mobility increases and so does the fraction deposited. Accumulation mode particles (0.1-1 m) have the lowest deposited

fraction in all the regions due to a combination of relatively low diffusivity and high inertia. They are transported to the lungs since they are too large to undergo significant diffusion and too small to be substantially affected by sedimentation (Hinds, 1999;

Schulz et al., 1996) but are relatively easily exhaled too. Coarse particles are already removed in the upper respiratory airways due to inertial deposition in the extrathoratic region (Park and Wexler, 2008).

100%

90% Total Deposited

80% Extrathoratic Pulmonary 70% Conducting 60%

50%

40%

30% Depositionfraction (%)

20%

10%

0% 0.01 0.1 1 10 Aerodynamic Diameter ( m)

Figure 9. Total multi-breath deposited particle fraction (solid lines) and regional deposition fraction in the extrathoratic, conducting, and pulmonary airways (dotted lines) (adapted from Park and Wexler, 2008).

Particle size also affects the bioavailability of the contaminant. From a study on the bioavailability of Pb according to size fraction from ambient aerosols around a Pb

smelter, Spear et al. (1998) found that not only was the highest percentage of Pb concentration in the fine size fraction, but also it contained the largest percentage of bioavailable Pb. Therefore, finer size fraction particles that deposit in the lungs and are transported to the blood stream via macrophages have a higher bioavailability (Krombach et al., 1997) and have been shown to contain the largest concentrations of metals and metalloids from smelter emissions (Spear et al., 1998; Csavina et al., 2011).

Similar to the relationship between airborne contaminant level and distance, numerous studies have found an inverse relationship between contaminant (e,g., Pb, As) levels in blood/urine and the distance of home or school environment (for biological sampling) from metal smelters and other mining operations. Based on prior studies, Benin et al.

(1999) predicted that the blood Pb levels in children at all three sites studied were likely to be well above the 10 g/dL threshold of concern for children set by the Center for

Disease Control and Prevention. As a result, they suggested that metal contamination in these areas is sufficient to pose a health hazard to the human population. Hwang et al.

(1997) measured As in children’s urine samples, soil samples and indoor floor dust near a former copper smelter in Montana. Arsenic concentration in soil, as well as in urine, was found to be significantly related to the proximity to the smelter site and wind direction, as seen in Figure 10. Goix et al. (2011) reported a correlation between schoolchildren’s hair metal and metalloid content (Ag, As, Cu, Pb and Sb) and smelting activities in a region in the Bolivian Altiplano.

Figure 10. Arsenic levels in soil of bare areas in yards, in interior floor dust and speciated urinary arsenic by proximity index of residence to smelter site (GM±GSE) (reprinted from Hwang et al., 1997).

Many studies have found negative effects from metals and metalloids on plant matter

(e.g., Chardonnens et al., 1999; Ernst and Nelissen, 2000; Alonso et al., 2002; Kim et al.,

2003; Yousefi et al., 2011). The delayed and reduced reproduction of plants may have substantial impacts at the population, community and ecosystem level (Ryser and Sauder,

2006). Vegetation has also been shown to accumulate metals and metalloids (Müller and

Anke, 1994; Hooda et al., 1997; Cobb et al., 2000; Mattina et al., 2003; Hough et al.,

2004; Zhou et al., 2005). Studies have shown an important remobilization of legacy metals pollution from forest fires that combust the soils and vegetation that contain

industrial-sourced contaminants (Biswas et al., 2007; Obrist et al., 2008; Odigie and

Flegal, 2011). While living organisms generally need trace amounts of a variety of elements for good health, large amounts of those same elements may cause chronic or acute toxicity (e.g., Trepka et al., 1997; Benin et al., 1999; Küpper et al., 2002). Athar and Ahmad (2002) found that metals in soils brought about significant reduction in plant growth and grain yield of wheat, with Cd being the most toxic metal followed by Cu, Ni,

Zn, Pb and Cr. Similarly, Ryser and Sauder (2006) found that lower leaf production and plant mass were correlated with high contaminant levels. Bi et al. (2009) observed elevated concentrations of Pb and Cd in maize plants impacted by smelting emissions.

Metals and metalloids taken up by plants may enter the food chain in significant amounts

(Wolnik et al., 1983; Nasreddine and Parent-Massin, 2002; Intawongse and Dean, 2006).

In examining the impacts of the Australian Port Pirie mine on the neighboring environment, Merry (1981) showed that wheat plants and grain concentrations of Cd, Pb and Zn exceeded background values mostly within 30 km of the smelter, depending on wind direction. The greatest impact occurred toward the south east, in the direction of the prevailing winds. Indeed, the levels of Pb and Cd contamination were so high that the wheat required blending with uncontaminated wheat before it was suitable for export quality standards. In a study on cattle from a low level environmental contamination of metals and metalloids, Alonso et al. (2002) found correlations of Cd, Pb, and As and trace elements in the tissue and blood of the cattle most likely as the result of contaminated soil and plant ingestion.

A.7 Research Priorities and Insights

Table 1 summarizes case studies discussed in this review. Dust and aerosol emissions

from mining operations have received much recent attention in a wide variety of

locations. Table 1 also highlights the focus that has been given to four particular locations

of US, Australia, Spain, and UK, despite the global scale of the issue. Studies for bulk

collection show the scale of the problem of contamination from mining operations but do

not fully unravel the environmental health hazards associated with the contamination.

For this, size-resolved chemical analysis of the emissions from mining operations is

needed, yet from Table 1 it is evident that only a handful of studies have looked into size- fractioned aerosols. Additionally, very few studies take a holistic approach at understanding dust from the mining operations, including source apportionment. This knowledge, when fully understood and correctly parameterized, can be incorporated into existing models (e.g., Yin et al., 2005; Yin et al., 2007) to forecast dust generation, contaminant transport by windblown dust and accurately assess human health risk associated with contaminants on airborne particulate.

Table 1. Case studies according to sample collection methods, location and year published.

Collection Complete Size Fractioned PMx Methods impactors PM2.5, PM10 Spear et al. 1998 (United States) Spear et al. 1998 (United States) Allen et al. 2001 (United Kingdom) Querol et al. 2000 (Spain) Csavina et al. 2011 (United States) Allen et al. 2001 (United Kingdom) Fernandez-Camacho et al. 2010 (Spain) Santacatalina et al. 2010 (Spain) Csavina et al. 2011 (United States) Collection Ambient Dust Bulk Collection Methods TSP, passive samplers soils, roadside, dust swabs Munksgard and Parry 1998 (Australia) Amasa 1975 (Ghana) van Alphen 1999 (Australia) Cartwright et al. 1977 (Australia) Querol et al. 2000 (Spain) Spear et al. 1998 (United States) Allen et al. 2001 (United Kingdom) Munksgard and Parry 1998 (Australia) Barcan 2002 (Russia) Benin et al. 1999 (Mexico) Deb et al. 2002 (India) Querol et al. 2000 (Spain) Beavington et al. 2004 (Australia) Sterckeman et al. 2000 (France) Brotons et al. 2010 (Spain) Deb et al. 2002 (India) Csavina et al. 2011 (United States) Rawlins et al. 2005 (United Kingdom) Bi et al. 2006 (China) Andrade et al. 2006 (Chile) Knight and Henderson 2006 (Canada) Meza-Fiegueroa et al. 2009 (Mexico) Brotons et al. 2010 (Spain) Taylor et al. 2010 (Australia) Soto-Jiménez and Flegal 2011 (Mexico)

Figure 11 shows impact of contaminants from mining operations in atmospheric particulate, taking into account relative metal and metalloid concentrations, deposition efficiency and location of deposition in the human respiratory system, as well as the distance traveled in the environment. The fine particle fraction (< 1 m) can travel further and may have higher contaminant concentrations around smelting operations. As discussed in section A.6, the deposition efficiency is lower in the 0.1-1 m size range.

However, the inhaled fine particles deposit in the lungs as opposed to the mouth and nose, which are then transported to the blood stream and therefore have a high dose associated with them. The coarse particles have higher deposition efficiency in the upper respiratory system, which might imply uptake in the gastric system, and therefore could be associated with lower doses. Further, fine particulates have a higher relative surface area which results in higher dissolution rates and thus greater bioavailability. Despite the human health hazards associated with fine particulate, regulations are focused on coarse particles.

78 High Low

3 PM 2.5 : 35 g/m 3 PM 10 : 150 g/m

Pb : 150 ng/m 3 Regulations

Figure 11. A schematic representation of the deleterious impact of contaminants from emissions of mining operations on humans due to metal dose, taking into account relative metal concentrations around mining operations, deposition efficiency and location of deposition in the human respiratory system, and the distance traveled in the environment for the scope of human impact with US air quality regulations. Future research should prioritize understanding the fine particle size fraction in atmospheric particulates, as well as take a holistic approach to understanding contaminant sources across the particle size spectrum. Smelter emissions, slag piles, ore transport, mine tailings and other ore-handling operations should be considered as potential sources of contaminated dust and aerosol near mining and refining sites. In particular, generation of contaminated fine size particles from gaseous emissions and wind erosion of contaminated soils, including efflorescence regions in mine tailings, should be viewed as

a priority in establishing potential effects to human and ecosystem health. Although most studies do tend to focus on a specific aspect of the problem, such as soil contamination, few focus on the small particle size range. Research should utilize methods that are suitable for assessing the fine particle size fraction, such as impactors with metal and metalloid analysis similar to the work developed by Allen et al. (2001), Spear et al.

(1998) and Csavina et al. (2011). Studies should also better integrate field studies with modeling to predict environmental health risks associated with mining operations.

A.8 Acknowledgments

This work was supported by grant number P42 ES04940 from the National Institute of Environmental Health Sciences (NIEHS), National Institutes of Health (NIH). The views of authors do not necessarily represent those of the NIEHS, NIH.

A.9 References

Albrecht BA. Aerosols, cloud microphysics, and fractional cloudiness. Science 1989; 245: 1227-30.

Allen AG, Nemitz E, Shi JP, Harrison RM, Greenwood JC. Size distributions of trace metals in atmospheric aerosols in the United Kingdom. Atmos Environ 2001; 35: 4581- 91.

Alloway BJ, Ayres DC. Chemical principles of environmental pollution. New York: Blackie Academic & Professional; 1997.

Alonso ML, Benedito JL, Miranda M, Castillo C, Hernandez J, Shore RF. Interactions between toxic and essential trace metals in cattle from a region with low levels of pollution. Arch Environ Con Tox 2002; 42: 165-72.

Amasa SK. Arsenic pollution at Obuasi goldmine, town, and surrounding countryside. Environ Health Persp 1975; 12: 131-5.

Andrade S, Moffett J, Correa JA. Distribution of dissolved species and suspended particulate copper in an intertidal ecosystem affected by copper mine tailings in Northern Chile. Mar Chem 2006; 101: 203-12.

Andreae MO. The dark side of aerosols. Nature 2001; 409: 671-2.

Arthur WJ, Markham OD. Radionuclide export and elimination by coyotes at two radioactive waste disposal areas in southeastern Idaho. Health Phys 1982; 43: 493-00.

Athar R, Ahmad M. Heavy metal toxicity: Effect on plant growth and metal uptake by wheat, and on free living Azotobacter. Water Air Soil Poll 2002; 138: 165-80.

ATSDR (Agency for Toxic Substances and Disease Registry.) Atlanta; 2011.

Badr D, Harion JL. Numerical modeling of flow over stockpiles: Implications on dust emissions. Atmos Environ 2005; 39: 5576-84.

Badr D, Harion JL. Effect of aggregate storage piles configuration on dust emissions. Atmos Environ 2007; 41: 360-8.

Baghurst PA, McMichael AJ, Wigg NR, Vimpani GV, Robertson EF, Roberts RJ, et al. Environmental exposure to lead and children's intelligence at the age of seven years. The Port Pirie cohort study. New Engl J Med 1992; 327: 1279-84.

Bagnold RA. The physics of blown sand and desert dunes. London: Chapman and Hall; 1941.

Barbaris B, Betterton EA. Initial snow chemistry survey of the Mogollon Rim in Arizona. Atmos Environ 1996; 30: 3093-103.

Barcan V. Nature and origin of multicomponent aerial emissions of the copper-nickel smelter complex. Environ Int 2002; 28: 451-6.

Barrie LA, Gregor D, Hargrave B, Lake R, Muir D, Shearer R, et al. Artic contaminants - sources, occurrence and pathways. Sci Total Environ 1992; 122: 1-74.

Beaulieu BT, Savage KS. Arsenate adsorption structures on aluminum oxide and phyllosilicate mineral surfaces in smelter-impacted soils. Environ Sci Technol 2005; 39: 3571-9.

Beavington F, Cawse PA, Wakenshaw A. Comparative studies of atmospheric trace elements: improvements in air quality near a copper smelter. Sci Total Environ 2004; 332: 39-49.

Bellinger DC. Lead neurotoxicity and socioeconomic status: Conceptual and analytical issues. Neurotox 2008; 29: 828-32.

Belnap J, Lange OL. Structure and functioning of biological soil crusts: A synthesis. In: Belnap J, Lange OL, editors. Biological soil crusts: Structure, function and management. Berlin: Springer-Verlag; 2003, pp. 471-479.

Benin AL, Sargent JD, Dalton M, Roda S. High concentrations of heavy metals in neighborhoods near ore smelters in northern Mexico. Environ Health Persp 1999; 107: 279-84.

Bergstrom C, Shirai J, Kissel J. Particle size distributions, size concentration relationships, and adherence to hands of selected geologic media derived from mining, smelting, and quarrying activities. Sci Total Environ 2011; 409: 4247-56.

Berico M, Luciani A, Formignani M. Atmospheric aerosol in an urban area - measurements of TSP and PM10 standards and pulmonary deposition assessments. Atmos Environ 1997; 31: 3659-65.

Bi XY, Feng XB, Yang YG, Qiu GL, Li GH, Li FL, et al. Environmental contamination of heavy metals from zinc smelting areas in Hezhang County, western Guizhou, China. Environ Int 2006; 32: 883-90.

Bi XY, Feng XB, Yang YG, Li X, Shin GPY, Li F, et al. Allocation and source attribution of lead and Cd in maize (Zea mays L.) impacted by smelting emissions. Environ Pollut 2009; 157: 834-9.

Biswas A, Blum JD, Klaue B, Keeler GJ. Release of mercury from Rocky Mountain forest fires . Global Biogeochem Cy 2007; 21: GB1002.

Bowker GE, Gillette DA, Bergametti G, Marticorena B. Modeling flow patterns in a small vegetated area in the northern Chihuahuan Desert using QUIC (Quick Urban & Industrial Complex). Environ Fluid Mech 2006; 6: 359-84.

Braun JM, Kahn RS, Froehlich T, Auinger P, Lanphear BP. Exposures to environmental toxicants and attention deficit hyperactivity disorder in US children. Environ Health Persp 2006; 114: 1904-9.

Braune B, Outridge P, Fisk A, Muir D, Helm P, Hobbs K, et al. Persistent organic pollutants and mercury in marine biota of the Canadian Arctic: An overview of spatial and temporal trends. Sci Total Environ 2005; 351-352: 4-56.

Breshears DD, Kirchner TB, Whicker JJ, Field JP, Allen CD. Modeling aeolian transport in response to succession, disturbance and future climate: Dynamic long-term risk assessment for contaminant redistribution. Aeol Res 2012; 3: 445-57.

Breshears DD, Whicker JJ, Zou CB, Field JP, Allen CD. A conceptual framework for dryland aeolian sediment transport along the grassland-forest continuum: Effects of woody plant canopy cover and disturbance. Geomorphol 2009; 105: 28-38.

Bridges EM, Oldeman LR. Global assessment of human-induced soil degradation. Arid Soil Res Rehab 1999; 13: 319-25.

Brotons JM, Diaz AR, Sarria FA, Serrato FB. Wind erosion on mining waste in southeast Spain. Land Degrad Dev 2010; 21: 196-209.

Bullard JE, McTainsh GH, Pudmenzky C. Factors affecting the nature and rate of dust production from natural dune sands. Sedimentology 2007; 54: 169-82.

Burn-Nunes LJ, Loss RD, Burton GR, Edwards R, Rosman KJR, Vallelonga P, et al. Seasonal variability in the input of lead, barium and indium to Law Dome, Antarctica. Geochim Cosmochim Acta 2011; 75: 1-20.

Burns JM, Baghurst PA, Sawyer MG, McMichael AJ, Tong SL. Lifetime low-level exposure to environmental lead and children's emotional and behavioral development at ages 11-13 years. The Port Pirie Cohort Study. Am J Epidemiol 1999; 149: 740-9.

Calabrese EJ, Stanek EJ, James RC, Roberts SM. Soil ingestion: A concern for acute toxicity in children. Environ Health Persp 1997; 105: 1354-8.

Calderón J, Navarro M, Jimenez-Capdeville M, Santos-Diaz M, Golden A, Rodriguez- Leyva I, et al. Exposure to arsenic and lead and neuropsychological development in Mexican children. Environ Res 2001; 85: 69-76.

Carrizales L, Razo I, Téllez-Hernández JI, Torres-Nerio R, Torres A, Batres LF, et al. Exposure to arsenic and lead of children living near a copper-smelter in San Luis Potosi, Mexico: Importance of soil contamination for exposure of children. Environ Res 2006; 101: 1-10.

Cartwright B, Merry RH, Tiller KG. Heavy metal contamination of soils around a lead smelter at Port Pirie, South Australia. Aust J Soil Res 1977; 15: 69-81.

CCSP (Climate Change Science Program). The effects of climate change on agriculture, land resources, water resources, and biodiversity in the United States. Washington, DC; 2008.

CDC (Centers for Disease Control). Strategic plan for the elimination of childhood lead poisoning. Atlanta; 1991.

Chadwick OA, Derry LA, Vitousek PM, Huebert BJ, Hedin LO. Changing sources of nutrients during four million years of ecosystem development. Nature 1999; 397: 491-7.

Chakradhar B. Fugitive dust emissions from mining areas. J Environ Sys 2004; 31: 279- 88.

Chan CY, Xu XD, Li YS, Wong KH, Ding GA, Chan LY, et al. Characteristics of vertical profiles and sources of PM2.5, PM10 and carbonaceous species in Beijing. Atmos Environ 2005; 39: 5113-24.

Chardonnens AN, Bookum WM, Vellinga S, Schat H, Verkleij JAC, Ernst WHO. Allocation patterns of zinc and cadmium in heavy metal tolerant and sensitive Silene vulgaris. J Plant Physiol 1999; 155: 778-87.

Cheng H, Hu Y. Lead (Pb) isotopic fingerprinting and its applications in lead pollution studies in China: A review. Environ Pollut 2010; 158: 1134-46.

Chepil WS, Siddoway FH, Armbrust DV. Climatic factor for estimating wind erodibility of fram fields. J Soil Water Conserv 1962; 17: 162-5.

Cherif S, Millet M, Sanusi A, Herckes P, Wortham H. Protocol for analysis of trace metals and other ions in filtered and unfiltered fogwater. Environ Pollut 1998; 103: 301- 8.

Chilvers DC, Peterson PJ. Global cycling of arsenic. In: Hutchinson TC, Meema KM, editors. Lead, mercury, cadmium, and arsenic in the environment. Chichester: Wiley; 1987, pp. 279–302.

Chow JC, Watson JG, Lowenthal DH, Solomon PA. PM10 source apportionment in California's San Joaquin Valley. Atmos Eviron A 1992; 26: 3335-54.

Chow JC, Watson JG, Fujita EM, Lu ZQ, Lawson DR, Ashbaugh LL. Temporal and spatial variations of PM(2.5) and PM(10) aerosol in the southern California air-quality study. Atmos Environ 1994; 28: 2061-80.

Chu DA, Kaufman YJ, Zibordi G, Chern JD, Mao J, Li C, et al. Global monitoring of air pollution over land from the Earth Observing System-Terra Moderate Resolution Imaging Spectroradiometer (MODIS) J Geophys Res 2003; 108: ACH 4.

Cobb GP, Sands K, Waters M, Wixson BG, Dorward-King E. Accumulation of heavy metals by vegetables grown in mine wastes. Environ Toxicol Chem 2000; 19: 600-7.

Corriveau MC, Jamieson HE, Parsons MB, Campbell JL, Lanzirotti A. Direct characterization of airborne particles associated with arsenic-rich mine tailings: Particle size, mineralogy and texture. Appl Geochem 2011; 26, 1639-48.

Csavina J, Landázuri A, Wonaschütz A, Rine K, Rheinheimer P, Barbaris B, et al. Metal and Metalloid Contaminants in Atmospheric Aerosols from Mining Operations. Water Air Soil Pollut 2011: 221: 145-57.

Cuff DJ, Goudie AS. The Oxford companion to global change. New York: Oxford University Press; 2009.

Day JMD, Pearson DG, Macpherson CG, Lowry D, Carracedo JC. Evidence for distinct proportions of subducted oceanic crust and lithosphere in HIMU-type mantle beneath El Hierro and La Palma, Canary Islands. Geochim Cosmochim Acta 2010; 74: 6565-89.

Deb MK, Thakur M, Mishra RK, Bodhankar N. Assessment of atmospheric arsenic level in airborne dust particulates of an urban city of Central India. Water Air Soil Pollu 2002; 140: 57-71.

Diego I, Pelegry A, Torno S, Toraño J, Menendez M. Simultaneous CFD evaluation of wind flow and dust emission in open storage piles. Appl Math Mod 2009; 33: 3197–207.

Dietrich KN, Ware JH, Salganik M, Radcliffe J, Rogan WJ, Rhoads GG, et al. Effect of chelation therapy on the neuropsychological and behavioral development of lead-exposed children after school entry. Pediatrics 2004; 114: 19-26.

Driscoll CT, Whitall D, Aber J, Boyer E, Castro M, Cronan C, et al. Nitrogen pollution in the Northeastern United States: Sources, effects, and management options. Bioscience 2003; 53: 357-74.

Ericson B, Hanrahan D, Kong V. The World’s worst pollution problems: Top ten of the toxic twenty. New York: Blacksmith Institute; 2008.

Ernst WHO, Nelissen HJM. Life-cycle phases of a zinc- and cadmium-resistant ecotype of Silene vulgaris in risk assessment of polymetallic mine soils. Environ Pollut 2000; 107: 329-38.

Feng S, Ning H. Computational simulations of blown sand fluxes over the surfaces of complex microtopography. Environ Model Software 2010; 25: 362-7.

Fernandez-Camacho R, de la Rosa J, de la Campa AMS, Gonzalez-Castanedo Y, Alastuey A, Querol X, et al. Geochemical characterization of Cu-smelter emission plumes with impact in an urban area of SW Spain. Atmos Res 2010; 96: 590-601.

Field JP, Belnap J, Breshears DD, Neff JC, Okin GS, Whicker JJ, et al. The ecology of dust. Front Ecol Environ 2010; 8: 423-30.

Filippelli GM, Laidlaw MAS. The elephant in the playground. Confronting lead- contaminated soils as an important source of lead burdens to urban populations. Perspect Biol Med 2010; 53: 31-45.

Flegal AR, Smith DR. Lead levels in preindustrial humans. New Engl J Med 1992; 326: 1293-4.

Fryrear DW. Soil cover and wind erosion. T ASAE 1985; 28: 781-4.

Fryrear DW. Wind erosion and its effects on plant-growth. Ann Arid Zone 1986; 25: 245- 54.

Fryrear DW, Saleh A, Bilbro JD. A single event wind erosion model. T ASAE 1998; 41: 1369-74.

Gallon C, Ranville MA, Conaway CH, Landing WM, Buck CS, Morton PL, et al. Asian industrial lead inputs to the North Pacific evidenced by lead concentrations and isotopic compositions in surface waters and aerosols. Environ Sci Technol 2011; 45: 9874-82.

Garcia R, Torres MC, Baez A. Determination of trace elements in total suspended particles at the Southwest of Mexico City from 2003 to 2004. Chem Ecol 2008; 24: 157- 67.

Ghio AJ, Devlin RB. Inflammatory lung injury after bronchial instillation of air pollution particles. Am J Resp Crit Care Med 2001; 164: 704-8.

Gilbert SG, Weiss B. A rationale for lowering the blood lead action level from 10 to 2 mu g/dL. Neurotoxicol 2006; 27: 693-701.

Gillette DA. Environmental factors affecting dust emission by wind erosion. In: Morales C, editor. Saharan Dust. New York: Wiley; 1979, pp. 71-94.

Gillette DA, Adams J, Endo A, Smith D, Kihl R. Threshold velocities for input of soil particles into the air by dessert soils. J Geophy Res Oceans Atmos 1980; 85: 5621-30.

Gillies JA, Etyemezian V, Kuhns H, Nikolic D, Gillette DA. Effect of vehicle characteristics on unpaved road dust emissions. Atmos Environ 2005; 39: 2341-7.

Goossens D, Offer Z, London G. Wind tunnel and field calibration of five aeolian sand traps. Geomorphol 2000; 35: 233-52.

Goossens D, Offer ZY. Wind tunnel and field calibration of six aeolian dust samplers. Atmos Environ 2000; 34: 1043-57.

Goudie A, Middleton N. Desert dust in the global system. Berlin: Springer; 2006.

Griffin D, Kellogg C, Shinn E. Dust in the wind: Long range transport of dust in the atmosphere and its implications for global public and ecosystem Health. Global Change Human Health 2001; 2: 20-33.

Hagen LJ. Processes of soil erosion by wind. Ann Arid Zone 2001; 40: 233-50.

Harper SL, Walling JF, Holland DM, Pranger LJ. Simplex optimization of multielement ultrasonic extraction of atmospheric particulates. Anal Chem 1983; 55: 1553-7.

Hartmann DL. Global Physical Climatology. San Diego: Academic Press; 1994.

Hayes S, Webb SM, Bargar J, O’Day PA, Maier RM, Chorover J. Geochemical weathering increases lead bioaccessibility in semi-arid mine tailings. Environ Sci Technol 2012; in press. DOI: 10.1021/es300603s.

Herner JD, Green PG, Kleeman MJ. Measuring the trace elemental composition of size- resolved airborne particles. Environ Sci Technol 2006; 40: 1925-33.

Hinds WC. Aerosol Science and Technology. New York: John Wiley & Sons; 1999.

Holmes L. A review of dispersion modelling and its application to the dispersion of particles: An overview of different dispersion models available. Atmos Environ 2006; 40: 5902-28.

Hooda PS, McNulty D, Alloway BJ, Aitken MN. Plant availability of heavy metals in soils previously amended with heavy applications of sewage sludge. J Sci Food Agric 1997; 73: 446-54.

Hope B. Estimating contaminant transport by biological vectors. Chemosphere 1993; 27: 817-24.

Hough RL, Breward N, Young SD, Crout NMJ, Tye AM, Moir AM, et al. Assessing potential risk of heavy metal exposure from consumption of home-produced vegetables by urban populations. Environ Health Persp 2004; 112: 215-21.

Hsu SC, Liu SC, Jeng WL, Chou CCK, Hsu RT, Huang YT, et al. Lead isotope ratios in ambient aerosols from Taipei, Taiwan: Identifying long-range transport of airborne Pb from the Yangtze Delta. Atmos Environ 2006; 40: 5393-404.

Hui WJ, Cook BI, Ravi S, Fuentes JD, D'Odorico P. Dust-rainfall feedbacks in the West African sahel. Water Resour Res 2008; 44: W05202.

Hutchings L, Roberts MR, Verheye HM. Marine environmental monitoring programmes in South Africa: a review. South African J Sci 2009; 105: 94-102.

Hwang YH, Bornschein RL, Grote J, Menrath W, Roda S. Environmental arsenic exposure of children around a former copper smelter site. Environ Res 1997; 72: 72-81.

Intawongse M, Dean JR. Uptake of heavy metals by vegetable plants grown on contaminated soil and their bioavailability in the human gastrointestinal tract. Food Addit Contam 2006; 23: 36-48.

IOSHIC (International Occupational Safety and Health Information Centre). Metals. Geneva; 1999.

IPCC (International Pannel for Climate Change). Working group II contribution to the intergovernmental panel on climate change fourth assessment report. In: Climate change 2007: Climate change impacts, adaptation and vulnerability. New York: Cambridge University Press; 2007.

Jusko TA, Henderson CR, Lanphear BP, Cory-Slechta DA, Parsons PJ, Canfield RL. Blood lead concentrations < 10 mu g/dL and child intelligence at 6 years of age. Environ Health Persp 2008; 116: 243-8.

Kaufman YJ, Tanre D, Boucher O. A satellite view of aerosols in the climate system. Nature 2002; 419: 215-23.

Keller MB, Lavori PW, Wunder J, Beardslee WR, Schwartz CE, Roth J. Chronic course of anxiety disorders in children and adolescents. J Am Acad Child Psy 1992; 31: 595-9.

Kersting AB, Efurd DW, Finnegan DL, Rokop DJ, Smith DK, Thompson JL. Migration of plutonium in ground water at the Nevada Test Site. Nature. 1999; 397: 56.

Kim CG, Bell JNB, Power SA. Effects of soil cadmium on Pinus sylvestris L seedlings. Plant Soil 2003; 257: 443-9.

Knight RD, Henderson PJ. Smelter dust in humus around Rouyn-Noranda, Quebec. Geochem Explor Environ Anal 2006; 6: 203-14.

Kok JF, Renno NO. A comprehensive numerical model of steady state saltation (COMSALT). J Geophy Res Atmos 2009; 114: D17204.

Kolpin DW, Barbash JE, Gilliom RJ. Occurrence of pesticides in shallow groundwater of the United States: Initial results from the National water-quality assessment program. Environ Sci Technol 1998; 32: 558-66.

Kon LC, Durucan S, Korre A. The development and application of a wind erosion model for the assessment of fugitive dust emissions from mine tailings dumps. Int J Mining Reclam Environ 2007; 21: 198-218.

Krombach F, Munzing S, Allmeling AM, Gerlach JT, Behr J, Dorger M. Cell size of alveolar macrophages: An interspecies comparison. Environ Health Persp 1997; 105: 1261-3.

Küpper H, Šetlík I, Spiller M, Küpper FC, Prášil O. Heavy metal-induced inhibition of photosynthesis: Targets of in vivo heavy metal chlorophyll formation. J Phycol 2002; 38: 429-41.

Lacerda LD. Global mercury emissions from gold and silver mining. Water Air Soil Pollut 1997; 97: 209-21.

Laidlaw MAS, Filippelli GM. Resuspension of urban soils as a persistent source of lead poisoning in children: A review and new directions. Appl Geochem 2008; 23: 2021-39.

Lal R. Soil degradation by erosion. Land Degrad Devel 2001; 12: 519-39.

Landrigan PJ, Gehlbach SH, Rosenblum BF, Shoults JM, Candelaria RM, Barthel WF, et al. Epidemic lead absorption near an ore smelter - role of particulate lead. New Engl J Med 1975; 292: 123-9.

Lanphear BP, Dietrich K, Auinger P, Cox C. Cognitive deficits associated with blood lead concentrations < 10 mu g/dL in US children and adolescents. Pub Health Rep 2000; 115: 521-9.

Lanphear BP, Hornung R, Khoury J, Yolton K, Baghurstl P, Bellinger DC, et al. Low- level environmental lead exposure and children's intellectual function: An international pooled analysis. Environ Health Persp 2005a; 113: 894-9.

Lanphear BP, Vorhees CV, Bellinger DC. Protecting children from environmental toxins - Toxicity testing of pesticides and industrial chemicals is a crucial step. Plos Med 2005b; 2: 203-8.

Li J, Okin GS, Alvarez L, Epstein H. Quantitative effects of vegetation cover on wind erosion and soil nutrient loss in a desert grassland of southern New Mexico, USA. Biogeochem 2007; 85: 317-32.

Listavia International. Smelting Industry. G3M Solutions Ltd; 2011.

Lubin JH, Moore LE, Fraumeni JF, Cantor KP. Respiratory cancer and inhaled inorganic arsenic in copper smelters workers: A linear relationship with cumulative exposure that increases with concentration. Environ Health Persp 2008; 116: 1661-5.

Lv W, Wang YX, Querol X, Zhuang XG, Alastuey A, Lopez A, et al. Geochemical and statistical analysis of trace metals in atmospheric particulates in Wuhan, central China. Environ Geol 2006; 51: 121-32.

Mackie DS, Peat JM, McTainsh GH, Boyd PW, Hunter KA. Soil abrasion and eolian dust production: Implications for iron partitioning and solubility. Geochem Geophy Geosys 2006; 7: Q12Q03.

Mahowald NM, Engelstaedter S, Luo C, Sealy A, Artaxo P, Benitez-Nelson C, et al. Atmospheric iron deposition: Global distribution, variability, and human perturbations. Annu Rev Marine Sci 2009; 1: 245-78.

Mancinelli V, Decesari S, Facchini MC, Fuzzi S, Mangani F. Partitioning of metals between the aqueous phase and suspended insoluble material in fog droplets. Ann Chim 2005; 95: 275-90.

Marple VA, Rubow KL, Behm SM. A microorifice uniform deposit impactor (MOUDI) - description, calibration, and use. Aerosol Sci Tech 1991; 14: 434-46.

Mattina MI, Lannucci-Berger W, Musante C, White JC. Concurrent plant uptake of heavy metals and persistent organic pollutants from soil. Environ Pollut 2003; 124: 375- 8.

McCartor A, Becker D. Top six toxic threats. In: World´s worst pollution problems report 2010. New York: Blacksmith Institute; 2010.

McGechan M, Lewis D. Transport of particulate and colloid-sorbed contaminants through soil, Part 1: General principles. Biosyst Eng 2002; 83: 255-73.

Merry R. Contamination of wheat crops around a lead-zinc smelter. Environ Pollut B 1981; 2: 37-48.

Meza-Figueroa D, Maier RM, de la O-Villanueva M, Gomez-Alvarez A, Moreno- Zazueta A, Rivera J, et al. The impact of unconfined mine tailings in residential areas from a mining town in a semi-arid environment: Nacozari, Sonora, Mexico. Chemosphere 2009; 77: 140-7.

Middleton N, Goudie A, Wells G. Aeolian geomorphology. Boston: Allen and Unwin; 1986.

Middleton N, Thomas D. World atlas of desertification. London: Arnold Publishing; 1997.

Moreno T, Oldroyd A, McDonald I, Gibbons W. Preferential fractionation of trace metals-metalloids into PM10 resuspended from contaminated gold mine tailings at Rodalquilar, Spain. Water Air Soil Pollut 2007; 179: 93-105.

Moreno T, Querol X, Alastuey A, Viana M, Salvador P, de la Campa AS, et al. Variations in atmospheric PM trace metal content in Spanish towns: Illustrating the chemical complexity of the inorganic urban aerosol cocktail. Atmos Environ 2006; 40: 6791-803.

Müller M, Anke M. Distribution of cadmium in the food chain (soil-plant-human) of a cadmium exposed area and the health risks of the general population. Sci Total Environ 1994; 156: 151-8.

Mulligan CN, Yong RN. Natural attenuation of contaminated soils. Environ Int 2004; 30: 587-601.

Munksgaard NC, Parry DL. Lead isotope ratios determined by ICP-MS: Monitoring of mining-derived metal particulates in atmospheric fallout, Northern Territory, Australia. Sci Total Environ 1998; 217: 113-25.

Munksgaard NC, Taylor MP, Mackay A. Recognising and responding to the obvious: the source of lead pollution at Mount Isa and the likely health impacts. Med J Aust 2010; 193: 131-2.

Munson SM, Belnap J, Okin GS. Responses of wind erosion to climate-induced vegetation changes on the Colorado Plateau. PNAS 2011; 108: 3854-9.

Nasreddine L, Parent-Massin D. Food contamination by metals and pesticides in the European Union. Should we worry? Toxicol Lett 2002; 127: 1-3.

Needleman H. Lead poisoning. Annu Rev Med 2004; 55: 209-22.

Neff JC, Ballantyne AP, Farmer GL, Mahowald NM, Conroy JL, Landry CC, et al. Increasing eolian dust deposition in the western United States linked to human activity. Nature Geosci 2008; 1: 189-195.

Neuman CM, Boulton JW, Sanderson S. Wind tunnel simulation of environmental controls on fugitive dust emissions from mine tailings. Atmos Environ 2009; 43: 520-9.

NHMRC (National Health and Medical Research Council). Blood lead levels for Australians. Canberra; 2009.

Nickovic S, Kallos G, Papadopoulos A, Kakaliagou O. A model for prediction of desert dust cycle in the atmosphere. J Geophys Res 2001; 106: 18113-29.

Nordstrom KF, Hotta S. Wind erosion from cropland in the USA: a review of problems, solutions and prospects. Geoderma 2004; 121: 157.

Obrist D, Moosmuller H, Schurmann R, Chen LWA, Kreidenweis SM. Particulate-phase and gaseous elemental mercury emissions during biomass combustion: Controlling factors and correlation with particulate matter emissions. Environ Sci Technol 2008; 42: 721-7.

Odigie KO, Flegal AR. Pyrogenic remobilization of historic industrial lead depositions. Environ Sci Technol 2011; 45: 6290-5.

Ojelede ME, Annegarn HJ, Kneen MA. Evaluation of aeolian emissions from gold mine tailings on the Witwatersrand. Aeolian Res 2012; 3: 477-86.

Okin GS, Gillette DA, Herrick JE. Multi-scale controls on and consequences of aeolian processes in landscape change in arid and semi-arid environments. J Arid Environ 2006; 65: 253-75.

Okin GS, Mahowald N, Chadwick OA, Artaxo P. Impact of desert dust on the biogeochemistry of phosphorus in terrestrial ecosystems. Global Biogeochem Cy 2004; 18: GB2005.

Okin GS, Parsons AJ, Wainwright J, Herrick JE, Bestelmeyer BT, Peters DC, et al. Do changes in connectivity explain desertification? Bioscience 2009; 59: 237-44.

Owen PR. Saltation of uniform grains in air. J Fluid Mech 1964; 20: 225-42.

Painter TH, Barrett AP, Landry CC, Neff JC, Cassidy MP, Lawrence CR, et al. Impact of disturbed desert soils on duration of mountain snow cover. Geophys Res Lett 2007; 34: L12502.

Painter TH, Deems JS, Belnap J, Hamlet AF, Landry CC, Udall B. Response of Colorado River runoff to dust radiative forcing in snow. PNAS 2010; 107: 17125-30.

Paktunc D, Foster A, Laflamme G. Speciation and characterization of arsenic in Ketza River mine tailings using x-ray absorption spectroscopy. Environ Sci Technol 2003; 37: 2067-74.

Park RJ, Jacob DJ, Field BD, Yantosca RM, Chin M. Natural and transboundary pollution influences on sulfate-nitrate-ammonium aerosols in the United States: Implications for policy. J Geophys Res 2004; 109: D15204.

Park SS, Wexler AS. Size-dependent deposition of particles in the human lung at steady- state breathing. J Aerosol Sci 2008; 39: 266-76.

Pekney NJ, Davidson CI, Bein KJ, Wexler AS, Johnston MV. Identification of sources of atmospheric PM at the Pittsburgh Supersite, Part I: Single particle analysis and filter- based positive matrix factorization. Atmos Environ 2006; 40: S411-3.

Pelletier JD. Sensitivity of playa windblown-dust emissions to climatic and anthropogenic change. J Arid Environ 2006; 66: 62-75.

Perry KD, Cahill TA, Schnell RC, Harris JM. Long-range transport of anthropogenic aerosols to the National Oceanic and Atmospheric Administration baseline station at Mauna Loa Observatory, Hawaii. J Geophys Res 1999; 104: 18521-33.

Pillans B. Soil development at snail's pace: evidence from a 6 Ma soil chronosequence on basalt in north Queensland, Australia. Geoderma 1997; 80: 117-28.

Pope CA, Dockery DW. Health effects of fine particulate air pollution: lines that connect. J Air Waste Manag Assoc 2006; 56: 709-42.

Pope P, VanEeckhout E, Rofer C. Waste site characterization through digital analysis of historical aerial photographs. Photogram Eng Remote Sens 1996; 62: 1387-94.

Porcella DB, Ramel C, Jernelov A. Global Mercury Pollution and the Role of Gold Mining: An Overview. Water Air Soil Pollut 1997; 97: 205-7.

Prospero JM, Ginoux P, Torres O, Nicholson SE, Gill TE. Environmental characterization of global sources of atmospheric soil dust identified with the Nimbus 7 Total Ozone Mapping Spectrometer (TOMS) absorbing aerosol product. Rev Geophys 2002; 40: 1002.

Pye K. Aeolian Dust and Dust Deposits. Boca Raton: Academic Press; 1987.

Querol X, Alastuey A, Lopez-Soler A, Plana F. Levels and chemistry of atmospheric particulates induced by a spill of heavy metal mining wastes in the Donana area, Southwest Spain. Atmos Environ 2000; 34: 239-53.

Querol X, Viana M, Alastuey A, Amato F, Moreno T, Castillo S, et al. Source origin of trace elements in PM from regional background, urban and industrial sites of Spain. Atmos Environ 2007; 41: 7219-31.

Ramanathan V, Crutzen PJ, Kiehl JT, Rosenfeld D. Atmosphere - aerosols, climate, and the hydrological cycle. Science 2001; 294: 2119-24.

Rattigan OV, Mirza MI, Ghauri BM, Khan AR, Swami K, Yang K, et al. Aerosol sulfate and trace elements in urban fog. Energy Fuels 2002; 16: 640-6.

Ravi S, D'Odorico P, Breshears DD, Field JP, Goudie A, Huxman TE, et al. Aeolian processes and the biosphere: Interactions and feedback loops. Rev Geophys 2011; 49: RG3001.

Ravi S, D'Odorico P, Over TM, Zobeck TM. On the effect of air humidity on soil susceptibility to wind erosion: The case of air-dry soils. Geophysical Research Letters 2004; 31: 1-4.

Rawlins BG, Lark RM, Webster R, O'Donnell KE. The use of soil survey data to determine the magnitude and extent of historic metal deposition related to atmospheric smelter emissions across Humberside, UK. Environ Pollut 2006; 143: 416-26.

Reed WR, Westman EC. A model for predicting the dispersion of dust from a haul truck. Int J Surf Mining Reclam Environ 2005; 19: 66-74.

Reheis MC, Budahn JR, Lamothe PJ, Reynolds RL. Compositions of modern dust and surface sediments in the Desert Southwest, United States. J Geophys Res Earth Surf 2009; 114: F01028.

Ritter L, Solomon K, Sibley P, Hall K, Keen P, Mattu G, et al. Sources, pathways, and relative risks of contaminants in surface water and groundwater: A perspective prepared for the Walkerton Inquiry. J Toxicol Environ Health A 2002; 65: 1-142.

Rosado JL, Ronquillo D, Kordas K, Rojas O, Alatorre J, Lopez P, et al. Arsenic Exposure and Cognitive Performance in Mexican Schoolchildren. Environ Health Persp 2007; 115: 1371-5.

Rosenfeld D, Rudich Y, Lahav R. Desert dust suppressing precipitation: A possible desertification feedback loop. PNAS 2001; 98: 5975-80.

Rutter M. Pathways from childhood to adult life. J Child Psycol Psyc1989; 30: 23-51.

Ryser P, Sauder WR. Effects of heavy-metal-contaminated soil on growth, phenology and biomass turnover of Hieracium piloselloides. Environ Pollut 2006; 140: 52-61.

Sánchez de la Campa AM, de la Rosa J, Fernández-Caliani JC, González-Castanedo Y. Impact of abandoned mine waste on atmospheric respirable particulate matter in the historic mining district of Rio Tinto (Iberian Pyrite Belt). Environ Res 2011; 111: 1018- 23.

Santacatalina M, Reche C, Minguillon MC, Escrig A, Sanfelix V, Carratala A, et al. Impact of fugitive emissions in ambient PM levels and composition: A case study in Southeast Spain. Sci Total Environ 2010; 408: 4999-5009.

SBM Machinery. Ore Grinder. SBM Processing Equipment; 2010.

Schemenauer RS, Cereceda P. Monsoon cloudwater chemistry on the Arabian Peninsula. Atmos Environ A 1992; 26: 1583-7.

Schlesinger WH, Reynolds JF, Cunningham GL, Huenneke LF, Jarrell WM, Virginia RA, et al. Biological feedbacks in global desertification. Science 1990; 247: 1043-8.

Schulz H, Schulz A, Heyder J. Influence of intrinsic particle properties on the assessment of convective gas transport by aerosol bolus technique. Exp Lung Res 1996; 22: 393-407.

Schwartz J. Air-pollution and daily mortality - A review and meta analysis. Environ Res 1994; 64: 36-52.

Seager R, Ting MF, Held I, Kushnir Y, Lu J, Vecchi G, et al. Model projections of an imminent transition to a more arid climate in southwestern North America. Science 2007; 316: 1181-4.

Seinfeld JH, Pandis SN. Atmospheric chemistry and physics: From air pollution to climate change. New York: Wiley; 2006.

Shaheen N, Shah MH, Jaffar M. A study of airborne selected metals and particle size distribution in relation to climatic variables and their source identification. Water Air Soil Pollut 2005; 164: 275-94.

Shao Y. Physics and Modeling of Wind Erosion. Dordrecht: Springer; 2008.

Shao Y, Raupach MR, Findlater PA. Effect of saltation bombardment on the entrainment of dust by wind. J Geophys Res Atmos 1993; 98: 12719-26.

Shao YP, Lu H. A simple expression for wind erosion threshold friction velocity. J Geophy Res Atmos 2000; 105: 22437-43.

Sheppard SC. Assessment of long-term fate of metals in soils: Inferences from analogues. Can J Soil Sci 2005; 85: 1-18.

Sivakumar MVK. Interactions between climate and desertification. Agric Forest Meteorol 2007; 142: 143-55.

Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Wang W, et al. A description of the advanced research WRF Version 2. Ft. Belvoir: Defense Technical Information Center; 2005.

Slowey AJ, Johnson SB, Newville M, Brown GE. Speciation and colloid transport of arsenic from mine tailings. Appl Geochem 2007; 22: 1884-98.

Smith BJ, Wright JS, Whalley WB. Simulated aeolean abrasion of Pannonian sands and its implications for the origins of Hungarian Loess. Earth Surf Proc Land 1991; 16: 745- 52.

Sokolik IN, Toon OB. Direct radiative forcing by anthropogenic airborne mineral aerosols. Nature 1996; 381: 681-3.

Soto-Jimenez MF, Flegal AR. Childhood lead poisoning from the smelter in Torreon, Mexico. Environ Res 2011; 111: 590-6.

Soukup JM, Ghio AJ, Becker S. Soluble components of Utah Valley particulate pollution alter alveolar macrophage function in vivo and in vitro. Inhal Toxicol 2000; 12: 401-14.

Spear TM, Svee W, Vincent JH, Stanisich N. Chemical speciation of lead dust associated with primary lead smelting. Environ Health Persp 1998; 106: 565-71.

Stafilov T, Sajn R, Pancevski Z, Boev B, Frontasyeva MV, Strelkova LP. Heavy metal contamination of topsoils around a lead and zinc smelter in the Republic of Macedonia. J Haz Mat 2010; 175: 896-914.

Stayner L, Steenland K, Dosemeci M, Hertz-Picciotto I. Attenuation of exposure- response curves in occupational cohort studies at high exposure levels. Scand J Work Environ Health 2003; 29: 317-24.

100

Steltzer H, Landry C, Painter TH, Anderson J, Ayres E. Biological consequences of earlier snowmelt from desert dust deposition in alpine landscapes. PNAS 2009; 106: 11629-34.

Sterckeman T, Douay F, Proix N, Fourrier H. Vertical distribution of Cd, Pb and Zn in soils near smelters in the North of France. Environ Pollut 2000; 107: 377-89.

Stockton PH, Gillette DA. Field measurement of the sheltering effect of vegetation on erodible land surfaces. Land Degrad Devel 1990; 2: 77-85.

Stout JE, Zobeck TM. The Wolfforth field experiment: A wind erosion study. Soil Sci 1996; 161: 616.

Syvitski JPM. Supply and flux of sediment along hydrological pathways: research for the 21st century. Global Planet Change 2003; 39: 1-11.

Tatsumoto M, Patterson CC. Concentrations of common lead in some Atlantic and Mediteraranean waters and in snow. Nature 1963; 199: 350-2.

Tegen I, Fung I. Contribution to the atmospheric mineral aerosol load from land-surface modification. J Geophys Res Atmos 1995; 100: 18707-26.

Tegen I, Lacis AA, Fung I. The influence on climate forcing of mineral aerosols from disturbed soils. Nature 1996; 380: 419-22.

Thornton I. Impacts of mining on the environment; some local, regional and global issues. J Int Assoc Geochem Cosmochem 1996; 11: 355-61.

Tong S, von Schirnding YE, Prapamontol T. Environmental lead exposure: a public health problem of global dimensions. Bull WHO 2000; 78: 1068-77.

101

Torno S, Torano J, Menendez M, Gent M, Allende C. Prediction of particulate air pollution from a landfill site using CFD and LIDAR techniques. Environ Fluid Mech 2011; 11: 99-112.

Toy TJ, Foster GR, Renard KG. Soil erosion: Processes, prediction, measurement, and control. New York: John Wiley & Sons; 2002.

Trepka MJ, Heinrich J, Krause C, Schulz C, Lippold U, Meyer E, et al. The internal burden of lead among children in a smelter town - A small area analysis. Environ Res 1997; 72: 118-30.

Triantafyllou A, Zoras S, Evagelopoulos V. Particulate matter over a seven year period in urban and rural areas within, proximal and far from mining and power station operations in Greece. Environ Monitor Assess 2006; 122: 1-3.

Tursic J, Grgic I, Berner A, Skantar J, Cuhalev I. Measurements of size-segregated emission particles by a sampling system based on the cascade impactor. Environ Sci Technol 2008; 42: 878-83.

UNSO (United Nations Sudano-Sahelian Office). Aridity zones and dryland populations : an assessment of population levels in the World's drylands. New York: Office to Combat Desertification and Drought; 1997.

US Environmental Protection Agency. National Ambient Air Quality Standards (NAAQS) 2011, Washington, D.C.; 2011.

US Environmental Protection Agency. National Ambient Air Quality Standards for Lead. In: Environmental Protection Agency 40 CFR Parts 50, 51, 53 and 58. 73. Federal Register; 2008.

Valentin C, Poesen J, Li Y. Gully erosion: Impacts, factors and control. Catena 2005; 63: 132-53.

Valiulis D, Sakalys J, Plauskaite K. Heavy metal penetration into the human respiratory tract in vilnius. Lithuanian J Phys 2008; 48: 349-55.

102

Vallelonga P, Candelone JP, Van de Velde K, Curran MAJ, Morgan VI, Rosman KJR. Lead, Ba and Bi in Antarctic Law Dome ice corresponding to the 1815 AD Tambora eruption: an assessment of emission sources using Pb isotopes. Earth Planet Sci Lett. 2003; 211: 329. van Alphen M. Atmospheric heavy metal deposition plumes adjacent to a primary leadzinc smelter. Sci Total Environ 1999; 236: 119-34. van Pelt R, Zobeck T. Chemical constituents of fugitive dust. Environ Monitor Assess 2007; 130: 1-3.

Vega E, Mugica V, Reyes E, Sanchez G, Chow JC, Watson JG. Chemical composition of fugitive dust emitters in Mexico City. Atmos Environ 2001; 35: 4033-9.

Volkamer R, Jimenez JL, San Martini F, Dzepina K, Zhang Q, Salcedo D, et al. Secondary organic aerosol formation from anthropogenic air pollution: Rapid and higher than expected. Geophys Res Lett 2006; 33: L17811.

Wang SC, Flagan RC. Scanning electrical mobility spectrometer. Aerosol Sci Technol 1990; 13: 230-40.

Wasserman GA, Liu X, Parvez F, Ahsan H, Factor-Litvak P, Kline J, et al. Water arsenic exposure and intellectual function in 6-year-old children in Araihazar, Bangladesh. Environ Health Persp 2007; 115: 285-9.

Webb N, McGowan H. Approaches to modelling land erodibility by wind. Prog Phys Geo 2009; 33: 587-613.

Weber WJ, McGinley PM, Katz LE. Sorption phenomena in subsurface systems - Concepts, models, and effects on contaminant fate and transport. Water Res 1991; 25: 499-528.

Whicker JJ, Pinder JE, Breshears DD, Eberhart CF. From dust to dose: Effects of forest disturbance on increased inhalation exposure. Sci Total Environ 2006; 368: 2-3.

103

Wichmann HE, Trepka MJ, Heinrich J, Ihme W, Mekel O, Lin JH. From epidemiologic exposure and risk assessment to probabilistic models: Experience with the investigation of health effects of soil contamination in Germany. Int J Toxicol 1997; 16: 391-418.

Wilkening KE, Barrie LA, Engle M. Atmospheric science: trans-Pacific air pollution. Science 2000; 290: 65.

Wolfe SA, Nickling WG. The protective role of sparse vegetation in wind erosion. Progr Phys Geo 1993; 17: 50-68.

Wolnik KA, Fricke FL, Capar SG, Braude GL, Meyer MW, Satzger RD, et al. Elements in major raw agricultural crops in the United States. 2. Other elements in lettuce, peanuts, potatoes, soybeans, sweet corn, and wheat. J Agric Food Chem 1983; 31: 1244.

Woodruff NP, Siddoway FH. A wind erosion equation. Soil Sci Soc Am Proc 1965; 29: 602-8.

Woolf AD, Goldman R, Bellinger DC. Update on the clinical management of childhood lead poisoning. Pediatr Clin North Am 2007; 54: 271-94.

WHO (World Health Organization). Updating and revision of the air quality guideline for Europe. In: EUR/ICP/EHAZ 94 05/PB01, Meeting of the working group "classical" air pollutants; 1995.

WHO (World Health Organization). Health aspects of air pollution with particulate matter, ozone and nitrogen dioxide. Report on a WHO working group; 2003.

Wright J. Making loess-sized quartz silt: data from laboratory simulations and implications for sediment transport pathways and the formation of 'desert' loess deposits associated with the Sahara. Quater Int 2001; 76-77: 7-19.

Wright JP, Dietrich KN, Ris MD, Hornung RW, Wessel SD, Lanphear BP, et al. Association of prenatal and childhood blood lead concentrations with criminal arrests in early adulthood. Plos Med 2008; 5: 732-40.

104

Yáñez L, García-Nieto E, Rojas E, Carrizales L, Mejía J, Calderón J, et al. DNA damage in blood cells from children exposed to arsenic and lead in a mining area. Environ Res 2003; 93: 231-40.

Yin D, Nickovic S, Barbaris B, Chandy B, Sprigg WA. Modeling wind-blown desert dust in the southwestern United States for public health warning: A case study. Atmos Environ 2005; 39: 6243-54.

Yin D, Nickovic S, Sprigg WA. The impact of using different land cover data on windblown desert dust modeling results in the southwestern United States. Atmos Environ 2007; 41: 2214-24.

Yousefi N, Chehregani A, Malayeri B, Lorestani B, Cheraghi M. Investigating the effect of heavy metals on developmental stages of anther and pollen in Chenopodium botrys L. (Chenopodiaceae). Bio Trace Elem Res 2011; 140: 368-76.

Zhou DM, Hao XZ, Wang YJ, Dong YH, Cang L. Copper and Zn uptake by radish and pakchoi as affected by application of livestock and poultry manures. Chemosphere 2005; 59: 167-75.

Zobeck TM, Fryrear DW. Chemical and physical characteristics of windblown sediment, II: chemical characteristics and total soil and nutrient discharge. T ASAE 1986; 29: 1037–41.

Zobeck TM, Sterk G, Funk R, Rajot JL, Stout JE, Van Pelt RS. Measurement and Data Analysis Methods for Field-Scale Wind Erosion Studies and Model Validation. J Brit Geomorphol Res Group. 2003; 28: 1163-88.

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APPENDIX B: METAL AND METALLOID CONTAMINANTS IN ATMOSPHERIC AEROSOLS FROM MINING OPERATIONS

Janae Csavina, 1 Andrea Landázuri, 1 Anna Wonaschütz, 2 Kyle Rine, 2 Paul Rheinheimer, 1 Brian Barbaris, 2 William Conant, 2 A. Eduardo Sáez 1 and Eric A. Betterton 2

[1] Department of Chemical and Environmental Engineering, The University of Arizona, Tucson, AZ 85721 [2] Department of Atmospheric Sciences, The University of Arizona, Tucson, AZ 85721

This article was published in Water, Air and Soil Pollution (Csavina et al. 2011).

B.1 Abstract

Mining operations are potential sources of airborne metal and metalloid contaminants through both direct smelter emissions and wind erosion of mine tailings. The warmer, drier conditions predicted for the Southwestern US by climate models may make contaminated atmospheric dust and aerosols increasingly important, with potential deleterious effects on human health and ecology. Fine particulates such as those resulting from smelting operations may disperse more readily into the environment than coarser tailings dust. Fine particles also penetrate more deeply into the human respiratory system, and may become more bioavailable due to their high specific surface area. In this work, we report the size-resolved chemical characterization of atmospheric aerosols sampled over a period of a year near an active mining and smelting site in Arizona. Aerosols were characterized with a 10-stage (0.054 to 18 m aerodynamic diameter) multiple orifice uniform deposit impactor (MOUDI), a scanning mobility particle sizer (SMPS), and a total suspended particulate (TSP) collector. The MOUDI results show that arsenic and lead concentrations follow a bimodal distribution, with maxima centered at approximately 0.3 and 7.0 m

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diameter. We hypothesize that the sub-micron arsenic and lead are the product of

condensation and coagulation of smelting vapors. In the coarse size, contaminants are

thought to originate as aeolian dust from mine tailings and other sources. Observation

of ultrafine particle number concentration (SMPS) show the highest readings when

the wind comes from the general direction of the smelting operations site.

B.2 Introduction

Mining operations are a potential source of particulate matter containing metal and

metalloid contaminants, such as lead and arsenic. High winds and mining activities such

as grinding, milling and mine tailings management create coarse particles ( ≥ 1 m diameter) through mechanical action while smelting and refining can result in ultrafine ( ≤

0.1 m) and accumulation-mode (0.1 - 1.0 m) particles by condensation of high temperature vapors and subsequent diffusion and coagulation (Jacob 1999). It has been estimated that 40% of the total atmospheric emissions of arsenic arise from smelting operations (Alloway and Ayres 1997).

Particle diameter affects the fate and transport of the aerosols. Small dust particles originating at a certain location easily can be transported thousands of miles before being deposited. For example, Wilkening et al. (2000) reported satellite remote sensing images of trans-Pacific transport of aerosols in April 1998, originating from a massive dust storm in the Gobi desert. Aerosols can affect ecosystem health and biogeochemical cycles. Copper-containing aerosols have been found to exert toxic effects on marine phytoplankton (Paytan et al. 2009). Trace elements, including As, Pb and Cr, have been detected worldwide in cloud, fog, rain and snow, with mining activities in

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some cases cited as possible sources (Schemenauer and Cereceda 1992; Barbaris and

Betterton 1996; Cherif et al. 1998; Rattigan et al. 2002; Mancinelli et al. 2005; Hutchings

et al. 2009; Taylor et al. 2010). Furthermore, metal-containing aerosols can provide a catalytically active for heterogeneous reactions and significantly affect the cycles of atmospheric species (Dickerson et al. 1997).

The effects of urban aerosols on human health are well documented (Ning and

Sioutas 2010, and references therein), although the precise mode of action is not known.

Particle diameter affects aerosol deposition efficiency in the human respiratory system with coarse particles mainly deposited in the upper respiratory tract while fine particles are capable of being transported and deposited deep in the lungs where they can then be transported to the blood stream by macrophages (Park and Wexler 2008; Valiulis et al.

2008; Krombach et al. 1997). Elevated blood lead levels and urinary arsenic have been

observed in children living near nonferrous metal smelters, and this has been partly

attributed to dust inhalation (Baker et al. 1977).

We hypothesize that atmospheric transport of aerosols from mining operations is

an important source of contaminants (such as arsenic and lead) in specific communities

of the arid Southwest, and will become increasingly important with predicted regional

climate change. We have sampled atmospheric aerosols for a year near an active copper

mine and smelter located in Hayden, Arizona. The main objective of our work was to

determine the concentrations of metal and metalloid contaminants as a function of

particle diameter as a means to better understand potential sources, transport and human

toxicity.

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The Hayden site is located approximately 50 miles northeast of Tucson, Arizona.

It is comprised of two towns − Hayden and Winkelman − with a combined population of approximately 1200. The site includes a concentrator, a smelter and tailings facilities. It is located at the confluence of the Gila and San Pedro Rivers and is characterized by complex terrain that gives rise to unusual wind patterns. While not officially a Superfund site, it is currently administered through an Administrative Settlement Agreement and

Order on Consent between EPA, ASARCO (the plant proprietor) and the Arizona

Department of Environmental Quality. In 2005, soil analysis showed that arsenic, lead and copper levels exceeded their respective residential soil remediation levels and in

2008/9, topsoil was removed from over 260 residential properties that had relatively high concentrations of these elements. The Environmental protection agency has reported elevated concentrations of arsenic, lead, copper, chromium and cadmium in atmospheric air samples in Hayden and Winkelman (EPA 2008).

B.3 Materials and Methods

B.3.1 Sampling

The main sampling site was located on the roof of the single-story Hayden High School building in Winkelman, Arizona, approximately 2 km from the mine tailings pile and 1 km from smelting operations, the main smokestack, and slag pile (Figure 1). A meteorological station and an EPA PM 10 monitor are collocated at this site.

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Figure 1. Satelite map of Hayden-Winkelman with locations of mining and sampling operations. Top of the map is North and size of map is approximately 2.7 miles by 4.1 miles. The smokestack is 1000 ft tall. Source: Google Maps.

A ten-stage micro-orifice uniform deposit impactor, MOUDI (M110-R, MSP

Corporation, Minneapolis, Minnesota) (Marple et al. 1991) was used to collect aerosol samples. The MOUDI was operated at an air flow rate of 30 L min −1 for 96-hour sampling periods, which was determined to provide enough sample mass for subsequent analyses. The calibrated cut-points (d 50 -values) for the inlet and 10 stages of the MOUDi sampler are 18, 9.9, 6.2, 3.1, 1.8, 1.0, 0.55, 0.32, 0.18, 0.10 and 0.054 m equivalent aerodynamic diameter. The MOUDI was protected from the weather by a metal box that had a 13-mm wide gap around the perimeter to ensure unimpeded sampling of even the coarsest particles.

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For comparison purposes, total suspended particulates (TSP) were collected on pre-cleaned glass fiber filters (1 m, 102 mm, Pall Corporation) with a portable high- volume air sampler (CF-1002BRL, HI-Q Environmental Products Company, San Diego,

California) operated at a flow rate of 400 L min −1 for 24-hour sampling periods. Samples were collected at three other field sites in southeast Arizona, including at the city of

Green Valley, within one mile of a metal-free mine tailings pile; at the city of Wilcox, near a natural dry lake bed (playa), which is an important natural source of dust; and in

Tucson, on the roof of the Physics and Atmospheric Sciences building of The University of Arizona, a typical urban environment.

Teflon filter substrates (PTFE membrane, 2 m pore, 46.2 mm, Whatman) were used in the MOUDI. The substrates were cleaned with deionized water and with methanol to remove any impurities. Teflon substrates were washed and dried in a particle-free laminar flow cabinet. They were transported to and from the field site with an impactor holder (MSP Corporation) for MOUDI substrates, and sealed envelopes for

TSP filters. Occasionally, aluminum foil substrates (47 mm, MSP Corporation) were used in the MOUDI to facilitate microscopic analysis. A thin coating of heavy-duty silicone spray (MSP Corporation) was applied to prevent particle bounce.

The aerosol number concentration as a function of diameter was measured using a

Scanning Mobility Particle Sizer (SMPS 3936L, TSI Inc.) system (Wang and Flagan

1990). The SMPS consists of an Electrostatic Classifier (TSI 3080) with a DMA 3081 and a butanol-based condensation particle counter (CPC 3772, TSI Inc.). The classifier was operated at a sheath flow rate of 3 L min −1 and a sample flow rate of 0.3 L min −1,

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scanning up for 240 seconds and retracing for 50 seconds, resulting in a collection

diameter range from 14 to 749 nm. The aerosol was collected through a 5.6 m copper

sampling tube (GC grade, Ohio Valley Specialty Chemical, ID 4.83 mm). The

penetration efficiency with respect to laminar diffusion for a sampling tube of this length

is estimated to be 57.2% for 14 nm particles and 81.9 % for 30 nm particles (Hinds

1999). Given the very low relative humidity at this desert location (below 20% for most

of the measurement period), no diffusion drying system was used.

Temperature, relative humidity, wind speed and wind direction were recorded

with 5-second resolution by a weather station and data logger (CR800, Cambell

Scientific, Logan, Utah). Wind speed and direction were analyzed using five-minute

averages with WindRose Pro (Enviroware) software.

B.3.2 Sample Extraction

Substrates were transferred to sealed glass vials and extracted with aqua regia in a

sonicator at 80°C for 60 min (Harper et al. 1983). Fifteen mL of aqua regia (1.03 M

HNO 3 /2.23 M HCl trace metal grade) were used to extract the MOUDI substrates while

40 mL were used for the larger TSP filters. Extract aliquots of 1.2 mL were diluted to 4 mL with deionized water and then refrigerated prior to Inductively-Coupled Plasma Mass

Spectrometry (ICP-MS) or Ion Chromatography (IC) analysis. The relative values of the field blanks were (As) 0.2 (Cd) 13.5, and (Pb) 8.3 expressed as the percentage of the annual mean maximum concentration found for any individual MOUDI size bin. The average relative standard deviation of the field blanks were 1.1, 10.6, 7.2 for As, Cd, and

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Pb, respectively. These values were found from the average of 36 samples that were taken for the year long sampling program. Blanks were subtracted from the field sample concentrations.

B.3.3 Sample Analysis

The ICP (Agilent 7500ce with an Octopole Reaction System) was tuned for robust plasma conditions to reduce matrix effects to 2% or less. Calibration standards are run every 20 samples and are certified AA or ICP standards from Aldrich made with milliQ water, 0.669 M HCl (Fisher, Trace-Metal Grade) and 0.309 M HNO 3 (EMD, Omnitrace) to match MOUDI sample matrix. Additionally, NIST standard reference material (SRM

1643e Trace Elements in Water) is analyzed with each set of data. The separate MOUDI fractions yield size-fractionated mass concentration data for toxic metals and metalloids

(mainly As, Pb, Cd). Size-fractionated concentrations of metal and other species in atmospheric samples have been reported before (e.g. Allen et al. 2001) but, to our knowledge, not in relation to mining operations.

For gravimetric analysis, samples were weighed using EPA Class I equivalent methods on an ultra-microbalance (Mettler Toledo XP2U) to weigh substrates before and after sampling. Mass loading on the substrates was used to convert concentrations from ng species/m 3 of atmospheric air to ppm (mg species/kg dry solids).

Samples were characterized chemically and morphologically at University

Spectroscopy and Imaging Facilities (USIF) of The University of Arizona using a combination of Field-Emission Scanning Electron Microscope (Hitachi S-4800 Type II

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SEM) and Electron Dispersive Spectroscopy (ThermoNORAN NSS EDS). The substrates were coated with a 10 nm layer of platinum (Hummer 6 Sputtering Device).

The SMPS raw counts were processed through the TSI Aerosol Instrument

Manager software, which includes a multiple charge correction and a correction for diffusion losses within the SMPS itself.

B.4 Results and Discussion

The concentrations of As and Pb observed in TSP at the Hayden site are consistently higher than those observed at other sites in southeastern Arizona (Figure 2). Both arsenic and lead are at least 9 and 4 times higher, respectively, in Hayden than at Green Valley,

Wilcox or Tucson, while cadmium is not significantly different. Though the Green Valley site is adjacent to mine tailings, independent analysis has shown that these tailings contain only background levels of As, Pb and Cd. Even though the Hayden concentrations are elevated, lead, at 23 ng/m 3, is below the EPA annual-average standard of 150 ng/m 3 (EPA 2008a). There are no standards for atmospheric arsenic in the US.

The European Union has set a target value of 6 ng/m 3 (annual average), which has been suggested for enforcement in 2012 (EU 2008). The World Health Organization

(Krzyzanowski and Cohen 2008) has set a guideline for arsenic of 0.66 ng/m 3, based on estimates that this concentration represents a 1:1,000,000 lifetime risk level. The EPA

3 Regional Residential Air Screening Level for As (in PM 10 , not TSP) is 0.57 ng/m , which is only slightly exceeded by Wilcox and is exceeded in Hayden by an order of magnitude

(Figure 2).

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100 Green Valley Hayden )

-3 Tucson 10 Wilcox

0.1 Average m (ng Average Concentration

0.01 As Cd Pb

Figure 2. Comparisons of As, Cd and Pb content in atmospheric air (TSP samples). Values are averages over ten 24-hour sampling periods at various sites in Southeast Arizona from July to December 2009. Error bars represent standard deviations over the ten sampling periods.

The MOUDI measurements from December 2008 through November 2009 at the

Hayden site for thirty six 96-hour sampling periods are summarized in Figure 3.

Contaminant concentrations follow a bimodal distribution, with means around 0.3 m

and 7 m diameter. Although there is a wide range of variability, the bimodal nature of

the distribution is generally preserved in all samples (results not shown; see Figure 6 and

discussion below for data from a single sampling period). The total arsenic and lead

concentrations in the size fraction (< 1 m) are 4.3±1.9 and 14.9±7.5 ng/m 3, respectively

and in the size fraction (>1 m) are 1.6±0.8 and 6.2±1.7 ng/m 3, respectively. It is clear

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that the fine particle size (< 1 m) fraction represents the most important contribution of these contaminants to atmospheric air at the site.

3.5 12 )

-3 3.0 10 Pb ) -3 Cd 2.5 As 8

2.0 6 1.5

4 1.0

2 m (ng Concentration Average Pb 0.5 Average As and Cd Concentration (ng m (ng Cd Concentration Average andAs

0.0 0 AF 0.054 0.1 0.18 0.32 0.55 1.0 1.8 3.1 6.2 9.9 18 Cutpoint Diameter ( m)

Figure 3. Annual averaged lead, arsenic and cadmium concentrations from MOUDI observations at the Hayden site for the period December 2008 through November 2009. Data represent average concentrations over thirty six 96-hour sampling periods; AF denotes after filter sample.

To validate the MOUDI data, observed values of PM 9.9 (i.e., the sum of all fractions ≤ 9.9 um diameter as obtained with the MOUDI sampler) were compared with the co-located EPA PM 10 monitor and our TSP collector. Although the size fractions are not exactly coincident, the averaged results show excellent agreement (Figure 4). A t-test for two sample sets assuming unequal variances was performed. All sets proved to accept the null hypothesis with p>0.05 except for the comparison of Cd – TSP. Exact

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results from the statistics are as follows: MOUDI compared to TSP: p = 0.43 (As), 0.008

(Cd), and 0.76 (Pb); MOUDI compared to PM 10 : p=0.17 (As), 0.49 (Cd), and 0.41 (Pb) with all values reported for two-tailed test. Therefore, it can be inferred that the three measurements techniques are not statistically different, except for the TSP and MOUDI measurements of cadmium.

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100 PM <9.9 (MOUDI) PM 10 (Actual) )

-3 TSP (MOUDI) 10 TSP (Actual)

0.1 Average Concentration (ng m Concentration (ng Average

0.01 As Cd Pb

Figure 4. Comparisons of metal and metalloid content of MOUDI samples with PM 10 data from EPA, and TSP observations at the Hayden site. Samples were taken between December 2008 and November 2009 for 36 and 55 sampling periods for the MOUDI and PM 10 monitor, respectively. The TSP concentrations were averaged for 18 sampling periods spanning July 2009 to March 2010. Sampling periods may not have occurred on the exact same days. For MOUDI samples, PM 9.9 represents the average of the summed mass concentrations in the corresponding size range, and TSP represents the average total concentration.

Measurements using the MOUDI over a full calendar year were broken down into a time series by particle diameter to observe possible seasonal changes. Figure 5 shows the monthly averaged concentrations for arsenic, cadmium and lead in different size ranges. These results emphasize that Pb, As and Cd are found predominantly in the fine particle size range. A seasonal dependence is observed for all PM diameters, with lower concentrations obtained during the summer months (May-August) and higher during the

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winter. Precipitation rates were appreciably higher in the summer than for the rest of the year for the area, which might explain the lower concentrations in the summer months. A more likely explanation is the strong convective mixing and dilution that occurs during summer.

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Figure 5. Monthly averages of arsenic, cadmium, and lead PM x measured with the MOUDI sampler at the Hayden site from December 2008 to November 2009. The size ranges are defined as follows: PM >9.9 is the summation of cutpoint diameters 9.9 and 18 m, PM 3.1-9.9 is the summation of cutpoint diameters 3.1 and 6.1 m, PM 1-3.1 is the summation of cutpoint diameters 1 and 1.8 m, and PM <1 is the summation of cutpoint diameters lower than 1 m.

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Metal and metalloid concentrations reported above have been expressed as ng of contaminant per unit m 3 of air (MOUDI data for a representative single sampling period are shown in Figure 6). Gravimetric measurements of the total solid mass collected at each stage of the MOUDI sampler allowed us to calculate intrinsic solids concentrations

(mg contaminant/ kg dry solid) for the contaminants. Figure 7 shows results corresponding to the data in Figure 6. In this particular example, maximum concentrations occur at cutpoint diameters of 0.32 and 18 m. According to Shacklette and Boerngen (1984), Western United States soils have a geometric mean of 5.5 ppm arsenic (range <0.10 to 97 ppm). Surface soils taken from residential and non-residential sites in Hayden and Winkelman before remediation had levels for arsenic and lead of 60

± 124 ppm and 1,939 ± 11,164 ppm, respectively (EPA 2008). Mine tailings from the

Hayden site were found to have 14.0 ± 6.8 ppm and 46.4 ± 33.7 ppm of arsenic and lead, respectively (EPA 2008). The results in Figure 7 show highly enriched concentrations in the range of particle diameters between 0.1 and 0.55 m, when compared to area soils and tailings. This suggests that soil and mine tailings are not the source of these particles.

It is likely that these particles are a consequence of condensation and agglomeration of particles formed from smelting operation emissions. From the single sampling period discussed, a log-normal distribution plot was generated for particle size <1 m for As,

Pb, and Cd (Figure 8). The mean diameter of the distribution was found to be 0.17 m and the geometric standard deviation was 2.68. The near symmetry of the curves indicates a log-normal distribution for the concentration of the respective metals. Figure 9

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shows the wind rose developed for the same sampling period. Relatively high wind speeds were seen in the general direction of the mine tailings (WNW, Figure 1) for this

MOUDI sampling period, which could have produced a higher collection of larger wind- swept particles when compared to the annual average.

3.5 12

As 3.0 10 )

-3 Cd )

2.5 Pb -3 8

2.0 6 1.5

4 1.0 Pb Concentration (ng m Concentration (ng Pb

As and Cd Concentration (ng m (ng Cd Concentration andAs 2 0.5

0.0 0 AF 0.054 0.1 0.18 0.32 0.55 1 1.8 3.1 6.2 9.9 18 Cutpoint Diameter ( m)

Figure 6. Lead, arsenic and cadmium concentrations from MOUDI observations at the Hayden site for a single 96-hour sampling period starting on October 14, 2009.

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1600 3500

As 1400 3000 Cd 1200 Pb 2500 1000 2000 800 1500 600 Pb Concentration (ppm) Pb Concentration 1000 400 As and Cd Concentration (ppm) andAs Cd

200 500

0 0 AF 0.054 0.1 0.18 0.32 0.55 1 1.8 3.1 6.2 9.9 18 Cutpoint Diameter ( m)

Figure 7. Solids concentrations of lead, arsenic and cadmium in MOUDI samples taken at the Hayden site for a 96-hour sampling period starting on October 14, 2009. These concentrations were obtained by dividing the concentrations in Figure 5 for each particle size range by the total mass of particles per unit air volume in the same size range.

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0.25 0.7

0.6 )

-3 0.2 As ) Cd 0.5 -3 Pb 0.15 0.4

0.3 0.1

0.2 dC/dlogDp (ng of m Pb dC/dlogDp 0.05 dC/dlogDp of As & Cd m As (ng Cd of & dC/dlogDp 0.1

0 0 0.01 0.10 1.00 Particle Diameter ( m)

Figure 8. Log-normal plot of lead, arsenic and cadmium concentrations for particle diameters <1 m in MOUDI samples taken at the Hayden site for a 96-hour sampling period starting on October 14, 2009 (Figure 6). From these results, the calculated mean diameter is 0.17 m and the geometric standard deviation is 2.68.

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Figure 9. Wind rose obtained from the weather station co-located with the MOUDI sampler for the 96-hour sampling period starting on October 14, 2009 (corresponding to data in Figures 6 to 8). The results show a relatively high frequency of high wind speeds (> 3 m/s) from the WNW direction, which corresponds with the orientation of the mine tailings with respect to the sampler.

The annual-average (Figure 3) MOUDI coarse (>1 m) and fine (<1 m) size fraction data were compared to Hayden tailings analysis performed by the EPA in terms of arsenic to lead concentration ratios (EPA 2008). The EPA reported tailings ratio was

0.39 ±0.27 with a range of 0.19-0.84. From our measurements, the coarse aerosol ratio was 0.26±0.14 and the fine aerosol ratio was 0.29±0.20. These values suggest that arsenic and lead in aerosol are unlikely to have generated exclusively from mine tailings. It is somewhat surprising that the ratios for the two particle diameter fractions are so similar.

Lead isotope ratios have been use for source apportionment in the past (e.g. Cheng et al.

2010). We will quantify lead isotope ratios for our samples in future work.

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Scanning electron microscopy images from MOUDI samples collected on aluminum foil filters are shown in Figure 10. Utilizing electron dis persive spectroscopy, the spherical particle in Figure 10 (a) was found to contain 21.19± 4.45 % lead by weight, and similar spherical particles were found to contain 10 -20% lead by weight and 0 -10% arsenic by weight. The sphericity of the particle sugges ts that it was formed by gas -to- particle transformation of a high temperature vapor. The particle in Figure 10 (a) has a diameter of about 500 nm, and it was collected on a stage with a cutpoint diameter of

0.32 m. This accumulation range diameter also su ggests a condensation/coagulation mechanism (Seinfeld and Pandis, 1998). Figure 10 (b) shows particles collected on a stage with a cutpoint diameter of 6.1 m. The irregular morphology of these particles suggests that they originated by mechanical action s uch as crushing, grinding or windblown dust.

(a) (b) Figure 10. Scanning Electron Microscope images from MOUDI stages with cutpoint particle diameter of (a) 0.32 m, particle depicted was identified to contain 21.19± 4.45 % Pb by weight by Electron Dispersive Spectroscopy; and (b) 6.1 m.

A preliminary correlation analysis between metals and metalloids for each size range of the MOUDI has been performed to identify the number of possible sources that

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contribute to atmospheric contamination around the site. Tables 1 and 2 present inter- element correlation matrices for particles with diameters <1 m and >1 m, respectively.

The tables report linear correlation coefficients, R, between the concentrations of element pairs. The results show that Al-Sc-Mg and Cd-Pb-As are strongly correlated for all particles. The As, Cd, and Pb inter-dependence in the fine particle size fraction (<1 m), could indicate that these three elements come from the same source, possibly the smelting area. This correlation, however, is also strong in the coarse particle size fraction (>1

m). However, this could be a consequence of coagulation of fine particles into larger particles and/or sedimentation on area soils where fine-sized particles deposit on coarse particles that then become wind-blown dust. It is also suspected that the groups where

Cu is present come from mining activity (i.e. mechanical action) and that the Fe-Mg, Cr-

Fe, Al-Sc, Sc-Fe, and Mg-Al correlation may be of soil origin (e.g. tailings). More specific information that leads to direct source apportionment can be gained by relating size-resolved contaminant concentrations with wind patterns at the site, which we are currently investigating.

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Table 1. Inter-element correlation matrix for particles < 1 m obtained from MOUDI samples for all the sampling periods. Values represent the linear coefficient of correlation (R) between elements in pairs. The shaded fields indicate the strongest correlations (R>0.7). Be Mg Al Sc Fe Cu As Cd Pb Be 0.221 0.194 0.237 0.225 0.281 0.367 0.589 0.539 Mg 0.859 0.845 0.741 0.545 0.082 0.086 0.094 Al 0.887 0.473 0.506 0.023 0.109 0.157 Sc 0.424 0.402 -0.051 0.05 0.103 Fe 0.552 0.304 0.256 0.226 Cu 0.596 0.643 0.686 As 0.893 0.931 Cd 0.931 Pb

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Table 2. Inter-element correlation matrix for particles < 1 m obtained from MOUDI samples for all the sampling periods. Values represent the linear coefficient of correlation (R) between elements in pairs. The shaded fields indicate the strongest correlations (R>0.7). Be Mg Al Sc Fe Cu As Cd Pb Be 0.334 0.232 0.31 0.467 0.374 0.231 0.454 0.432 Mg 0.895 0.916 0.864 0.443 -0.114 -0.116 -0.093 Al 0.895 0.686 0.516 -0.02 -0.01 0.035 Sc 0.774 0.472 -0.141 -0.114 -0.05 Fe 0.507 -0.043 0.006 0.007 Cu 0.513 0.584 0.618 As 0.894 0.936 Cd 0.936 Pb

Aerosol number distributions were measured continuously from March 17 through May 20, 2009, and from September 30 through November 11, 2009 at the

Hayden site, using the SMPS. With 288 5-minute scans per day, a high time resolution was achieved compared to the MOUDI 96-hour sample and the TSP 24-hour sample, which yields the short term evolution of the local aerosol size distribution. Figure 11 shows the average of all size distributions over the entire spring measurement period. For comparison, size distributions from an urban location in the city of Tucson, averaged over a 17-day time period, are shown. The Tucson size distribution reflects the higher number concentrations that would be expected in an urban location. The Hayden size distribution shows lower overall concentrations, owing to the cleaner surroundings, but number concentrations in the ultrafine size range that are relatively high. Since the lifetime of such small particles is short (on the order of an hour for concentrations typically observed at Hayden), this could indicate the influence of a local source (primary or gas-to particle conversion from smelting operations), as opposed to long range

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transport. Episodes of high concentrations of ultrafine particles (nucleation events) within

daily cycles (Figure 12) were observed frequently at the Hayden site. Similar

observations of nucleation events have been made by Stanier et al. (2004 a,b) in

Pittsburgh, PA, and were attributed to sulfuric acid production. Sulfuric acid is a product

of the smelting process in Hayden, which is thought to reach high concentrations in the ambient air.

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Figure 11 . Average SMPS size distribution over one MOUDI measurement period (March 17 through May 20, 2009) compared to an average (February 25 through March 13, 2009) from the city of Tucson.

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Figure 12 . A full day of size distribution data (SMPS measurements for April 18, 2009). Note the relatively high concentrations of 14 – 50 nm particles, starting at 1 pm.

The size resolved number concentrations were averaged by wind direction over the entire spring measurement period, using data from the weather station located at the site (Figure 13). The highest concentration of fine particles can be seen from the NW - N direction, which in relation to the SMPS is the general direction of the smelting operations (Figure 1). The lowest concentrations of fine particles were observed for winds from N to E, an area characterized by open desert, a river valley and little human influence. It is important to point out, however, that the topography of the

Hayden/Winkelman site is complex, with two river valleys, hills and mountains channeling wind patterns. Figure 14 shows two wind roses, one located in Hayden and the other located less than two miles away in Winkelman at the sampling site (Figure 1),

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from identical sampling periods. Note that they have wind patterns that are appreciably

different.

Figure 13. Aerosol number size distributions as a function of wind direction for the measurement period March 17 through May 20, 2009.

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(a) (b)

Figure 14. Wind roses from two weather stations located within two miles of each other for December 2008 through November 2009: (a) Winkelman, where the MOUDI is located, and (b) Hayden, where the smelter is located. Differences show the complex terrain’s effect on wind patterns.

A preliminary comparison of mass distributions between the SMPS and the

MOUDI is shown in Figure 15. The detection ranges of the two instruments had an overlap in the size range from 54 to 320 nm. The SMPS number distributions were converted to volume distributions, averaged in time over the MOUDI sampling period and in size over MOUDI stage diameter ranges. A particle density of 1.5 g/cm 3 was assumed to convert the volume distribution into a mass distribution. It can be seen that the general features of the mass distribution measured by the MOUDI is reproduced by the SMPS data.

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Figure 15. Estimate of mass concentrations from and SMPS volume distributions assuming a density of 1.5 g/cm 3 and using MOUDI gravimetric analysis

B.5 Concluding Remarks

The results presented in this work indicate that atmospheric aerosols in the vicinity of

mining operations contain higher concentrations of arsenic, lead and cadmium than what

would be expected from the natural background. A bimodal distribution with respect to

particle diameter is seen for the three elements, with maximum concentrations occurring

at a cutpoint diameter of 0.32 m. To our knowledge, this is the first time that size- resolved characterization of metals and metalloids has been reported in the vicinity of mining operations. Evidence presented suggests that the fine particle diameter maximum

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in species concentration corresponds to particles produced by vapor condensation and

coagulation, which would be consistent with sources related to smelting operations that include emission of high-temperature vapors. Presence of arsenic, lead and cadmium in the coarse particle size range suggests a local source of windblown dust. The fact that correlation among contaminants is similar in both the fine and coarse particle size ranges might indicate a unique source, which could be the case if windblown dust particles acquire the contaminants by deposition of fine particles.

B.6 Acknowledgements

This work was supported by grant number P42 ES04940 from the National Institute of

Environmental Health Sciences (NIEHS), National Institutes of Health (NIH). The views of authors do not necessarily represent those of the NIEHS, NIH. The authors are grateful

to Ms. Leah Butler of EPA Region 9 and gratefully acknowledge the work by University

Spectroscopy and Imaging Facilities (USIF), Tucson, AZ ( http://usif.arizona.edu/ ).

B.7 References

Allen, A.G., Nemitz, E., Shi, J.P., Harrison, R.M., and Greenwood, J.C. (2001) Size distributions of trace metals in atmospheric aerosols in the United Kingdom. Atmospheric Environment, 35, 4581-4591. Alloway, B.J., & Ayres, D.C. (1997) Chemical Principles of Environmental Pollution. New York: Blackie Academic & Professional. Baker, E.L., Hayes, C.G., Landrigan, P.J., Handke, J.L., Leger, R.T., Housworth, W.J., & Harrington, J.M. (1977) A nationwide survey of heavy metal absorption in children living near primary copper, lead, and zinc smelters, American Journal of Epidemiology, 106, 261-273.

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Barbaris, B., & Betterton, E.A. (1996) Initial snow chemistry survey of the Mogollon Rim in Arizona. Atmospheric Environment, 30, 3093-3103. Cheng, H., & Hu, Y. (2010) Lead (Pb) isotopic fingerprinting and its applications in lead pollution studies in China: A review. Environmental Pollution, 158, 1134-1146. Cherif, S., Millet, M., Sanusi, A., Herckes, P., & Wortham, H. (1998) Protocol for analysis of trace metals and other ions in filtered and unfiltered fogwater. Environmental Pollution, 103, 301-308. Dickerson, R.R., Kondragunta, S., Stenchikov, G., Civerolo, K.L., Doddridge, B.G., & Holben, B.N. (1997) The impact of aerosols on solar ultraviolet radiation and photochemical smog. Science, 278, 827-830. Environmental Protection Agency (EPA) (2008) ASARCO Hayden Plant: Site Overview. http://yosemite.epa.gov/r9/sfund/r9sfdocw.nsf/ce6c60ee7382a473882571af007af70d/3 940634a9aec311e88257478006736ce!OpenDocument . Environmental Protection Agency (EPA) (2008) Lead in Air: Regulatory Actions. http://www.epa.gov/oaqps001/lead/actions.html . European Union (EU) (2008) Air Quality: Existing Legislation. http://ec.europa.eu/environment/air/quality/legislation/existing_leg.htm. Harper, S.L., Walling, J.F., Holland, D.M., & Pranger, L.J. (1983) Simplex optimization of multielement ultrasonic extraction of atmospheric particulates. Analytical Chemistry, 55, 1553-1557. Hinds, W.C. (1999) Aerosol Science and Technology. 2nd edition, New York: John Wiley & Sons. Hutchings, L., Roberts, M.R., & Verheye, H.M. (2009) Marine environmental monitoring programmes in South Africa: a review. South African Journal of Science, 105, 94-102. Jacob, D.J. (1999) Introduction to atmospheric chemistry. Princeton, NJ: Princeton University Press. Krombach, F., Münzing, S., Allmeling, A.M., Gerlach, J.T., Behr, J., & Dörger, M. (1997) Cell size of alveolar macrophages: an interspecies comparison. Environmental Health Perspectives, 105, 1261–1263. Krzyzanowski, M., & Cohen, A. (2008) Update of WHO air quality guidelines. Air Quality, Atmosphere & Health, 1, 7-13. Mancinelli, V., Decesari, S., Facchini, M.C., Fuzzi, S., & Mangani, F. (2005) Partitioning of metals between the aqueous phase and suspended insoluble material in fog droplets. Annali di Chimica, 95, 275-290.

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Marple, V.A., Rubow, K.L., & Behm, S.M. (1991) A microorifice uniform deposit impactor (MOUDI) - description, calibration, and use. Aerosol Science and Technology, 14, 434-446. Ning, Z., & Sioutas, C. (2010) Atmospheric processes influencing aerosols generated by combustion and the inference of their impact on public exposure: a review. Aerosol and Air Quality Research, 10, 43-58. Park, S.S., & Wexler, A.S. (2008) Size-dependent deposition of particles in the human lung at steady-state breathing. Journal of Aerosol Science, 39, 266-276. Paytan, A., Mackey, K.R.M., Chen, Y., Lima, I.D., Doney, S.C., Mahowald, N., Labiosa, R., & Post, A.F. (2009) Toxicity of atmospheric aerosols on marine phytoplankton. Proceedings of the National Academy of Sciences, 106, 4601-4605. Rattigan, O.V., Mirza, M.I., Ghauri, B.M., Khan, A.R., Swami, K., Yang, K., & Husain, L. (2002) Aerosol sulfate and trace elements in urban fog. Energy Fuels, 16, 640-646. Schemenauer, R.S., & Cereceda, P. (1992) Monsoon cloudwater chemistry on the Arabian Peninsula. Atmospheric Environment, 26, 1583-1587. Seinfeld, J.H., & Pandis, S.N. (1998) Atmospheric Chemistry and Physics: From Air Pollution to Climate Change. New York: Wiley. Shacklette, H.T., & Boerngen, J.G. (1984) Element concentrations in soils and other surficial materials of the conterminous United States. U.S. Geological Survey Professional Paper 1270, 105 p. Stanier, C.O., Khlystov, A.Y., & Pandis, S.N. (2004a) Ambient aerosol size distributions and number concentrations measured during the Pittsburgh Air Quality Study (PAQS). Atmospheric Environment, 38 , 3275-3284. Stanier, C.O., Khlystov, A.Y., & Pandis, S.N. (2004b) Nucleation Events during the Pittsburgh Air Quality Study (PAQS): description and relation to key meteorological, gas phase and aerosol parameters. Aerosol Science and Technology, 38, 253-264. Taylor, M.P., Mackay, A.K., Hudson-Edwards, K.A., & Holz, E. (2010) Soil Cd, Cu, Pb and Zn contaminants around Mount Isa city, Queensland, Australia: Potential sources and risks to human health. Applied Geochemistry, 25, 841-855. Valiulis, D., Sakalys, J., & Plauskaite, K. (2008) Heavy metal penetration into the human respiratory tract in Vilnius. Lithuanian Journal of Physics, 48, 349-355. Wang, S.C., & R.C. Flagan (1990) Scanning Electrical Mobility Particle Spectrometer. Aerosol Science and Technology, 13,230-240.

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Wilkening, K.E., Barrie, L.A., & Engle, M. (2000) Atmospheric science: trans-Pacific air pollution. Science, 290, 65-67.

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APPENDIX C: SIZE-RESOLVED AEROSOL CONTAMINANTS ASSOCIATED WITH COPPER AND LEAD SMELTING EMISSIONS IN AUSTRALIA AND ARIZONA: IMPLICATIONS FOR MORE EFFECTIVE EMISSIONS MANAGEMENT AND HUMAN HEALTH RISKS

Janae Csavina, 1 Mark P. Taylor, 2 Omar Félix, 1 A. Eduardo Sáez 1 and Eric A. Betterton 3

1 Department of Chemical and Environmental Engineering, The University of Arizona, Tucson, AZ 85721; 2 Environmental Science, Faculty of Science, Macquarie University, North Ryde, Sydney NSW 2109, Australia, 3 Department of Atmospheric Sciences, The University of Arizona, Tucson, AZ 85721;

This article was submitted to Environmental Science and Technology in August 2012.

C.1 Abstract

Mining operations, including crushing, grinding, smelting, refining, and tailings management, are a significant source of airborne metal and metalloid contaminants such as As, Pb and other potentially toxic elements. Dust and aerosol particles emitted from mining operations can accumulate in surrounding soils, natural waters and vegetation in high concentrations through wind and water transport. Human exposure to the dust can occur through inhalation and, especially in the case of children, incidental dust ingestion, particularly during the early years when children are likely to exhibit pica. However, the physiochemical nature of the emissions and resulting airborne contaminated dust and aerosol is poorly understood. Here we show that size-resolved concentrations of As and

Pb follow a bimodal distribution with the majority of contaminants in the fine size fraction (<1 m) around mining activities that include smelting operations at Mount Isa,

Queensland and Port Pirie, South Australia in Australia and Hayden, Arizona in the

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United States. The sites are highly enriched in both the coarse and fine particle size

fraction with As and Pb when compared to urban samples. A site with contaminated

mine tailings as the only source of contaminants shows enrichment primarily in the

coarse size fraction. This evidence suggests that contaminated fine particles (<1 m) are the result of vapor condensation and coagulation from smelting operations while coarse particles are most likely the result of windblown dust from contaminated mine tailings and fugitive emissions from crushing and grinding activities. Fine particles transport further distances and have higher building penetration efficiencies in the adjoining environment, can be respired to the lungs and thereby have a higher associated dose, are more bioavailable due to higher surface to volume ratio, and are less efficiently removed by air pollution control systems when compared to coarse particles (>3 m).

Determining the chemical composition in dust from mining operations as a function of

particle size is crucial in quantifying the potential deleterious effects on human health and

the environment. Multi-national measurements of the size distribution of contaminants

around mining operations are reported to demonstrate the ubiquitous nature of this

phenomenon so that more effective emissions management and practices that minimize

health risks associated with metal extraction and processing can be developed.

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C.2 Introduction

The role of mining activities in the fate and transport of environmental contaminants is an

important yet under investigated field of study [1]. Dust and aerosol produced by mining

operations often contain elevated levels of metal and metalloid contaminants, including

the toxic elements Pb and As [2-5], which are known to have contributed to negative

ecological and human health effects in surrounding communities, including elevated

children blood Pb levels [6-8]. However, the specific physiochemical nature of these

exposures remains poorly understood. With dust emissions predicted to increase as

climate change intensifies drought in arid regions and human land use increases,

contaminant transport from mining operations is likely to become increasingly important

in the coming decades [9, 10].

Epidemiological and environmental studies have revealed extensive childhood Pb

poisoning within the Australian mining communities of Port Pirie and Mount Isa. This is

largely attributable to the mining and smelting activities and associated dust generation

[3, 6-8, 11]. At Mount Isa, Cu, Zn, Pb and Ag mining and smelting results in the

emission of significant quantities of airborne contaminants, with As and Pb global

emissions for 2009/2010 being 20,000 kg and 120,000 kg, respectively [12]. With a

population of 21,000, Mount Isa’s most recent study reported children aged 1–4 years

had mean blood Pb levels (BLL) of 5 g/dL, with 37% having levels > 6 g/dL and

11.3% having levels > 10 g/dL [11]. Pb and Zn smelting in Port Pirie is also associated with significant atmospheric pollution, with As and Pb TSP measurements in 2009 as

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high as 0.25 g/m 3 and 19.7 g/m 3, respectively, taken 0.4 km from mining activities

[13]. In 2005, 56.5% of children in Port Pirie had BLL >10 g/dL [8]. Further, the

pervasive effect of environmental Pb emissions on human biomarkers of exposure have

shown that the use of the guideline blood Pb level of 10 g/dL is 625 times higher than

the estimated natural BLL (0.016 g/dL) in humans [14].

Many studies have explored the neurotoxic nature of Pb, especially on children who are

more adversely impacted due to their early stage in neurological development and their

higher contaminant dosage at the same concentration when compared to adults [15-17].

Additionally, higher lead exposures have been shown to lower academic performance

and lead to negative social outcomes related to antisocial behavior and criminality [18].

One recent study shows a positive correlation in six US cities between lead-in-air

concentrations and aggravated assault rate [19]. Similarly to Pb, As has also shown

impaired cognitive development in children and may have a synergistic toxic effect with

Pb [20-22]. Arsenic is also a known carcinogen. Childhood Pb and As exposure is a

potentially avoidable environmental health risk factor.

Dust and aerosol generated from mining operations vary in size, which is critical for

physical interactions in the environment and human exposure. An important route of

human exposure is the inhalation of the airborne contaminated particulate. The physical

and chemical properties and size distribution of inhaled aerosols are necessary to

completely assess risks associated with contaminant exposure [23]. The size of the

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particle can predict the efficiency and region of deposition in the respiratory tract [24].

Coarse particles (>2.5 m), such as those resulting from crushing and grinding of ore, deposit in the upper respiratory system and are swallowed and eliminated through the digestive system [25]. In contrast, fine particles (<1 m), such as those originating from smelting operations, are respired deep into the lungs where they may be transported directly to the blood stream and have a higher bioavailability associated with them due to the higher surface to volume ratios [24, 26, 27]. Particle size is also a critical characteristic for transport distance and building penetration within the adjoining environment: fine particles can travel further in the environment with an average residence time of ten days as compared to residence time for coarse particles of seconds to hours [25]. Therefore, determining the chemical composition in dust from mining operations as a function of particle size is crucial in quantifying the potential deleterious effects on human health and the environment.

Csavina et al. [1] report that there is little information available on size fractionated contaminant analysis for dust and aerosols generated from mining operations. Without this information, efforts for mitigation and regulation are not properly assessed. This study reports on the size resolved As and Pb concentrations found in aerosols in the

Australian communities of Mount Isa and Port Pirie mentioned earlier. Additionally, these sites are compared to an ongoing study of dust emissions from mining operations in

Hayden, Arizona USA [2]. Multi-national measurements of the size-fractionated contaminant concentration in the atmosphere around mining operations are reported to

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support more effective emissions management and practices that minimize health risks associated with metal extraction and processing.

C.3 Materials and Methods

Ambient dust and aerosol sampling was carried out in the communities of Port Pirie,

Mount Isa, and Hayden, which are impacted by smelting activities. Samples were also collected in urban settings of Tucson, Arizona and Sydney, New South Wales and where mine tailings with no smelting operations are the primary source of contaminated dust in the communities of Green Valley, Arizona and Iron King, Arizona for comparison purposes. An overview map of these sites can be seen in Figure 1.

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Figure 1. Field sites for size fractioned aerosol sampling: Mount Isa, Port Pirie and Hayden mining sources include smelting activities; Iron King and Green Valley provide only a mine tailings source; and Tucson and Sydney represent urban settings.

Sampling at all sites was performed with a ten-stage micro-orifice uniform deposit impactor, MOUDI (MSP Corporation) [28]. With a flow rate of 30 L min -1, the MOUDI

provides size distribution of dust and aerosol populations at the 50% cut off diameters

(d 50 -values) of 18, 9.9, 6.2, 3.1, 1.8, 1.0, 0.55, 0.32, 0.18, 0.10 and 0.054 m equivalent

aerodynamic diameter. Total sampling run time varied according to distance from and

strength of source. Port Pirie runs were the shortest at 24 hours due to sampling location

at 0.3 km from smelting operations with high historic concentrations of Pb. The MOUDI

was run for 72 hours at Mount Isa due to being 2.8 km from smelting operations. All

other sites were run for 96 hours. At Australian sites, weather data was acquired from

local Australian Environmental Protection Agency’s monitoring site were as at the

Hayden, Arizona site, weather data was acquired from a collocated weather station and

data logger (Cambell Scientific CR800). Teflon filters (PTFE membrane, 2 m pore,

46.2 mm, Whatman) were used as impaction substrates in the MOUDI. Filters were

transferred for Australian field work in sterile petri dishes (Micro Analytix, 800100).

Field blanks were used as controls. Substrates were weighed before and after sampling

using an ultra-microbalance (Mettler Toledo XP2U) according to EPA Class I equivalent

methods. Once gravimetric analysis was complete, filters were extracted in sealed glass

vials with trace metal grade aqua regia and sonication at 80°C for 60 min [29]. Extracted

aliquots were diluted with deionized water according to a dilution factor of 3.33 for

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Inductively-Coupled Plasma Mass Spectrometry (ICP-MS) analysis. The ICP (Agilent

7500ce with an Octopole Reaction System) yielded separate MOUDI sample size fractions in mass concentration data for metals and metalloids. See Csavina et al. [2] for a more detailed description of materials and methods.

C.4 Results and Discussion

An example of the size-fractionated As and Pb aerosol concentrations sampled in Hayden is shown in Figure 2. Csavina et al. [2] has published more extensive results. The mining operations at Hayden include Cu smelting, which occurs approximately 1 km north-northwest of the sampling site. The wind rose insert (Figure 2) is from a weather station co-located with the MOUDI which illustrates winds from the direction of the smelting activities account for roughly 30% of the winds during the sampling period.

The sampling began on February 5, 2011 and ran for 96 hours. Figure 3 illustrates long term sampling of As and Pb over 2 years at Hayden. From Figure 3, it is evident that the single sampling set seen in Figure 2 is qualitatively representative of long term sampling.

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Figure 2. Hayden As and Pb results from a 96-h sample 1 km south-southeast from Cu smelting operations with sampling starting on February 5, 2011; AF denotes after filter sample. Wind rose from co-located weather station.

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4.0 12

3.5 Pb As )

-3 10 ) -3 3.0 8 2.5

2.0 6

1.5 4

Average As Concentration As m (ng Average 1.0

2 ConcentrationPb m (ng Average 0.5

0.0 0 AF 0.054 0.1 0.18 0.32 0.55 1.0 1.8 3.1 6.2 9.9 18 Cutpoint Diameter ( m)

Figure 3. Two year averaged As and Pb concentrations from MOUDI observations at the Hayden site for the period October 2009 through October 2011. Data represent average concentrations of thirty four 96-hour sampling periods; AF denotes after filter sample.

Figure 4 shows MOUDI results from Port Pirie collected on April 17, 2012 for a 24 hour sample. Port Pirie is impacted by Pb smelting operations, which are approximately 0.4 km north of the sampling site. Figure 4 also includes a wind rose generated from weather station data taken approximately 2.8 km south-southeast from smelting operations. Figure 5 illustrates MOUDI results taken on February 21, 2012 from Mount

Isa, QLD AUS where from Cu and Pb smelting operations impact the community. The

72 hour sample was taken 2.8 km northeast of the mining operations, and the wind rose in

Figure 5 was created from a weather station located approximately 1.5 km north- northeast from smelting operations.

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Figure 4. Port Pirie MOUDI results from 24 hour sample 0.4 km south from Pb smelting operations taken on April 17, 2012; AF denotes after filter sample. Wind rose from weather station located approximately 2.8 km south-southeast from smelting operations.

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Figure 5. Mount Isa MOUDI results from 72 hour sample 2.8 km northeast from Cu and Pb smelting operations taken on February 21, 2012; AF denotes after filter sample. Wind rose from weather station located approximately 1.5 km north-northeast from smelting operations

One important comparison from Figures 2 and 4 is that a bimodal distribution is seen

with a higher As concentration in the fine size fraction (<1 m). This is consistent with

Figure 3 for long term sampling in Hayden and with findings from the earlier study in

Hayden, AZ, which included an annual climatology of aerosols [2]. The fine particles are thought to be from smelting operations while coarse particles are from windblown Pb and

As laden mine tailings or fugitive emissions from the crushing and grinding of the ore.

Hayden results in 88% and 86% of the As and Pb, respectively in the fine size fraction.

In Port Pirie, however, 72% of the As is in the fine size fraction while only 37% of the Pb is <1 m. Due to Port Pirie refining for Pb, it makes sense that a higher percentage of Pb

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is in the coarse fraction (>2.5 m), which mirrors the higher concentration of Pb found in the ore. However, it is unclear why Pb and As do not have the same size distribution.

The ore refined at this facility comes from multiple sources, and the difference in sources may be causing the different behaviors of Pb and As. While Mount Isa similarly refines

Pb, the sampling site is 2.4 km further away from the activities when compared to Port

Pirie. Therefore, the majority of the coarse particles have settled out of the atmosphere before reaching the receptor. However, it is important to note that some studies show the

MOUDI to be less efficient at collecting coarse particles than other samplers [30]. While

PM 10 (particulate matter smaller than 10 m) has been shown to provide good agreement

[2], the MOUDI, especially in areas of high contaminant concentrations in the coarse size fraction like Port Pirie, may not provide total ambient contaminant concentrations as those found in total suspended particulate – TSP collection.

Numerous factors influence the contaminant concentration in air around mining operations. The amount of ore processed per time period and the air pollution controls to reduce emissions affect the strength of the source. For example, mine tailings management often includes chemical treatment for dust inhibition. Additionally, many air pollution controls on stack emissions from smelting operations include an electrostatic precipitator or bag house. However, if a large quantity of the contaminants are in the fine size fraction as shown in Figures 2-5, the emission controls have little impact on fine particulate as their efficiency for removal rapidly decreases with particle size [29].

Distance to the source also has important implications for ambient contaminant

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concentrations as well as size distribution of contaminants. Coarse particles have a

residence time in the environment of minutes to hours once released, dependent on

settling velocity, so sites further from the source will have lower concentrations [29]. On

the other hand, the fine size fraction has a residence time of approximately 1-2 weeks

depending on the hydrological cycle [31]. Therefore, distance from the source has less

impact on ambient concentrations of fine particulate when compared to wind direction

and wind speed. High wind speeds above threshold friction velocities of contaminated

soils and mine tailings also influence transport of coarse particulate [32].

Possible correlations between weather patterns and contaminant concentrations were

analyzed from long term observations from MOUDI results at Hayden, AZ USA (Table

1). Source direction for particles >1 m included the wind coming from the general directions of the mine tailings (2 km from receptor) and smelting operations (1 km from receptor) while the particles <1 m were only analyzed for the source of smelting operations. While little correlation is seen from this analysis, it does provide some insight to how factors can be inter-related. For instance, higher correlations for particles

>1 m are seen for wind speed coming from the source direction than when considering wind speed and wind direction separately. For particles <1 m, wind speed and direction has little impact on concentrations. In Mount Isa, an air pollution control technique by the mining company is to “turn down” emissions from the smelter stack when wind direction is positioned for the plume to go over the community [33]. With the majority of the contaminants in the fine size fraction, this analysis indicates that this is a poor

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management practice which makes little sense for the fine particles that have an average residence time in the environment of 1-2 weeks. In addition, when topography is complex such as in Hayden, weather patterns may cause varying direction of the air pollution plume. Temperature is also shown to have little correlation to contaminant concentrations, but the previous climatology analysis of Hayden indicated a possible seasonal variation in contaminant concentrations due to mixing height, which may be inter-related with temperature [2]. Relative humidity can also impact dust generation and coagulation growth of particles, but only slight correlations are seen with opposing impacts on As and Pb associated with particles >1 m [29, 34, 35]. However, the main factor that is missing from this analysis is the strength of the signal due to amount of ore processed which is unknown due to lack of information provided by the impacting mining company.

Table 1. Spearman correlation coefficients ( ρ) for weather conditions versus contaminant concentrations from MOUDI observations in Hayden, AZ USA. Analysis includes 59 sets of data sampled from December 2008 through April 2011.

Wind Speed @ Temperature Relative Humidity Wind Speed Wind Direction Source Direction Arsenic (<1 μm) -0.05 -0.11 -0.27 0.00 -0.16 Lead (<1 μm) -0.02 -0.25 -0.11 -0.02 -0.08 Arsenic (>1 μm) -0.26 0.33 0.05 0.08 0.22 Lead (>1 μm) 0.04 -0.26 0.37 0.35 0.56

Gravimetric analysis presented in Table 2 represents a field site comparison for maximum mass concentration found for any individual MOUDI size bin. Sample sets from Hayden, Port Pirie and Mount Isa correspond to those from Figures 3-5. All other

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sites were collected for 96 hours on the following dates: Iron King - September 21, 2011;

Green Valley - April 3, 2011; Sydney – February 14, 2012; and Tucson – October 14,

2010. From these results, the smelting sites are clearly enriched in Pb and As when compared to urban samples of Sydney and Tucson. Because Mount Isa is the furthest from the mining operations (2.8 km), collected particulate will be more influenced by natural dust and aerosol when compared to the sample from Port Pirie taken 0.4 km from operations. Further, Port Pirie’s operations include Pb smelting, and therefore it is expected that concentrations will be higher for Pb due to higher Pb concentrated ore.

Iron King is a site that has As and Pb laden mine tailings as a predominate source of contaminants and samples were taken approximately 0.2 km from the base of the tailings.

While concentrations were not as high as smelting sites, Iron King is still heavily impacted when compared to the urban samples. Green Valley is also near mine tailings

(2 km from base), but these tailing contain low levels of contaminants due to being from modern mining practices [36]. Levels seen in Green Valley are similar to those seen in

Tucson 40 km away.

Table 2. Maximum mass concentration (ppm) found for any individual MOUDI size bin of As and Pb for field site comparison. Hayden, Port Pirie, and Mount Isa include smelting operations; Iron King, AZ USA has a source of Pb and As contaminated mine tailings, Green Valley, AZ USA has remediated mine tailings; Sydney, NSW AUS and Tucson, AZ USA represent an urban sample.

Hayden Port Pirie Mount Isa Iron King Green Valley Sydney Tucson As (ppm) 8,622 2,471 895 979 50 215 63 Pb (ppm) 13,173 36,399 8,922 724 331 148 302

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A field site comparison for As and Pb concentrations from MOUDI observations is seen in Figure 5. The concentrations were summed over fractions with particles sizes <1 m and >1 m. The concentrations separated according to these size bins illustrates the impact smelting operations have on the fine size fraction in the communities of Hayden,

Port Pirie, and Mount Isa. All contaminants at the smelting sites have higher Pb and As concentrations in the fine size fraction except for Port Pirie where ambient Pb concentrations are also heavily impacted in the coarse size fraction due to the Pb concentrated ore used in the mining operations. With the wind blown As and Pb contaminated mine tailings, it is expected that Iron King will be more heavily impacted in the coarse size fraction as seen in Figure 5. From Figure 5, it is obvious that mining has heavily enriched concentrations of Pb and As compared to urban samples of Tucson and

Sydney with a particularly high impact on the fine size fraction around smelting operations.

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1000.0

Pb As 100.0 ) -3

10.0

1.0

0.1 Contaminant Contaminant Concentration (ngm

0.0 <1 >1 <1 >1 <1 >1 <1 >1 <1 >1 <1 >1 <1 >1 Hayden Port Pirie Mount Isa Iron King Green Tucson Sydney Valley

Figure 5. Field site comparison of MOUDI observations of Pb and As concentrations summed according to two particle size fractions: <1 m and >1 m. Note log scale on y- axis.

From this study, it is evident that smelting operations adversely affect nearby ecology and human health. Mine tailings alone can be a source of contaminants when present at high concentrations, but contaminants reside primarily in the coarse size fraction which will not travel as far in the environment. However, smelting emissions can have a much broader area of impact due to the fine size of the contaminated particles. Further, when inhalation is a route of exposure for contaminated aerosols, particles are respired to the lungs which are thereby transferred to the blood stream via macrophages. The smaller size fraction also has a higher bioavailability due to higher surface to volume ration.

Therefore the fine size fraction has a higher dose for the contaminant uptake when

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compared to the coarse size fraction which is deposited in the upper respiratory tract and expelled through the digestive tract.

C.5 Conclusions

To our knowledge, this study is the first international analysis of the size distribution of metal and metalloids around mining operations for the first time. Ambient particulates collected around mining operations that include smelting resulted in a bimodal distribution for As and Pb with respect to particle diameter. These particles were found to be heavily enriched in As and Pb in both the coarse and fine particle size fraction when compared to urban samples. In contrast, a site with contaminated mine tailings but no smelting activity showed enrichment in the coarse size fraction. A site with low metals content mine tailings showed no enrichment compared to a nearby urban sample.

Numerous factors such as source strength, distance from source, wind direction and speed, temperature and relative humidity have an interdependent effect on ambient contaminant concentrations and size distribution.

C.6 Acknowledgements

This work was supported by grant number P42 ES04940 from the National Institute of

Environmental Health Sciences (NIEHS), National Institutes of Health (NIH). The views of authors do not necessarily represent those of the NIEHS, NIH. The author’s would also like to thank the Endeavour Research Fellowship Program through the Australian

Government’s Department of Industry, Innovation, Science, Research and Tertiary

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Education who supported J. Csavina’s travel and stay in Australia while performing research in Port Pirie, SA and Mount Isa, QLD.

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C.7 References

1. Csavina, J.; Field, J.; Taylor, M. P.; Gao, S.; Landázuri, A.; Betterton, E. A.; Sáez, A. E., A review on the importance of metals and metalloids in atmospheric dust and aerosol from mining operations. Science of The Total Environment 2012, 433 , (0), 58-73.

2. Csavina, J.; Landázuri, A.; Wonaschütz, A.; Rine, K.; Rheinheimer, P.; Barbaris, B.; Conant, W.; Sáez, A. E.; Betterton, E. A., Metal and Metalloid Contaminants in Atmospheric Aerosols from Mining Operations. Water Air and Soil Pollution 2011, 221 , (1-4), 145-157.

3. Taylor, M. P.; Mackay, A. K.; Hudson-Edwards, K. A.; Holz, E., Soil Cd, Cu, Pb and Zn contaminants around Mount Isa city, Queensland, Australia: Potential sources and risks to human health. Appl. Geochem. 2010, 25 , (6), 841-855.

4. Bellinger, D. C., Lead neurotoxicity and socioeconomic status: Conceptual and analytical issues. Neurotoxicology 2008, 29 , (5), 828-832.

5. Benin, A. L.; Sargent, J. D.; Dalton, M.; Roda, S., High concentrations of heavy metals in neighborhoods near ore smelters in northern Mexico. Environ. Health Perspect. 1999, 107 , (4), 279-284.

6. Queensland Health Mount Isa Community Lead Screening Program 2006-2007: A Report into the Results of a Blood-lead Screening Program of 1-4 year Old Children in Mount Isa, Queensland, Environmental Health Services of the Tropical Population Health Network, Northern Area Health Service, Queensland Health. http://www.health.qld.gov.au/ph/documents/tphn/mtisa_leadrpt.pdf (August),

7. Munksgaard, N. C.; Taylor, M. P.; Mackay, A., Recognising and responding to the obvious: the source of lead pollution at Mount Isa and the likely health impacts. Med. J. Aust. 2010, 193 , (3), 131-132.

8. Simon, D.; Lewis, C. Analysis of blood lead levels for 2010. http://www.sahealth.sa.gov.au/wps/wcm/connect/9b121c8046aaf0b999acfb2e504170d4/ Technical+paper+Pt+Pirie+lead+2010-4-PHCC- 20110427.pdf?MOD=AJPERES&CACHEID=9b121c8046aaf0b999acfb2e504170d4 (August),

160

9. IPCC - International Pannel for Climate Change Working Group II Contribution to the Intergovernmental Panel on Climate Change Fourth Assessment Report ; Cambridge University Press: New York, 2007.

10. Breshears, D. D.; Kirchner, T. B.; Whicker, J. J.; Field, J. P.; Allen, C. D., Modeling aeolian transport in response to succession, disturbance and future climate: Dynamic long-term risk assessment for contaminant redistribution. Aeolian Research 2011, In Press, Corrected Proof .

11. Simon, D. L.; Maynard, E. J.; Thomas, K. D., Living in a sea of lead-changes in blood-and hand lead of infants living near a smelter. Journal of Exposure Analysis and Environmental Epidemiology 2007, 17 , (3), 248-259.

12. Department of Sustainability; Environment; Water; Population and Communities National Pollutant Inventory. http://www.npi.gov.au/ (August),

13. South Australia Environmental Protection Agency, Ambient Air Monitoring Data for Port Pirie. In Mitchell, R., Ed. South Australia Air and Noise Branch / Science and Assessment Division, 2012.

14. Flegal, A. R.; Smith, D. R., Lead levels in preindustrial humans. The New England journal of medicine 1992, 326 , (19), 1293-4.

15. Soto-Jimenez, M. F.; Flegal, A. R., Childhood lead poisoning from the smelter in Torreon, Mexico. Environmental Research 2011, 111 , (4), 590-596.

16. Jusko, T. A.; Henderson, C. R.; Lanphear, B. P.; Cory-Slechta, D. A.; Parsons, P. J.; Canfield, R. L., Blood lead concentrations < 10 mu g/dL and child intelligence at 6 years of age. Environ. Health Perspect. 2008, 116 , (2), 243-248.

17. Baghurst, P. A.; McMichael, A. J.; Wigg, N. R.; Vimpani, G. V.; Robertson, E. F.; Roberts, R. J.; Tong, S. L., Environmental exposure to lead and children's intelligence at the age of seven years. The Port Pirie Cohort Study. The New England journal of medicine 1992, 327 , (18), 1279-84.

18. Wright, J. P.; Dietrich, K. N.; Ris, M. D.; Hornung, R. W.; Wessel, S. D.; Lanphear, B. P.; Ho, M.; Rae, M. N., Association of prenatal and childhood blood lead

161

concentrations with criminal arrests in early adulthood. Plos Medicine 2008, 5, (5), 732- 740.

19. Mielke, H. W.; Zahran, S., The urban rise and fall of air lead (Pb) and the latent surge and retreat of societal violence. Environment International 2012, 43 , 48-55.

20. Calderón, J.; Navarro, M.; Jimenez-Capdeville, M.; Santos-Diaz, M.; Golden, A.; Rodriguez-Leyva, I.; Borja-Aburto, V.; Díaz-Barriga, F., Exposure to Arsenic and Lead and Neuropsychological Development in Mexican Children. Environmental Research 2001, 85 , (2), 69-76.

21. Hwang, Y. H.; Bornschein, R. L.; Grote, J.; Menrath, W.; Roda, S., Environmental arsenic exposure of children around a former copper smelter site. Environmental Research 1997, 72 , (1), 72-81.

22. Wright, R. O.; Amarasiriwardena, C.; Woolf, A. D.; Jim, R.; Bellinger, D. C., Neuropsychological correlates of hair arsenic, manganese, and cadmium levels in school- age children residing near a hazardous waste site. Neurotoxicology 2006, 27 , (2), 210-6.

23. Spear, T. M.; Svee, W.; Vincent, J. H.; Stanisich, N., Chemical speciation of lead dust associated with primary lead smelting. Environ. Health Perspect. 1998, 106 , (9), 565-571.

24. Park, S. S.; Wexler, A. S., Size-dependent deposition of particles in the human lung at steady-state breathing. Journal of Aerosol Science 2008, 39 , (3), 266-276.

25. Hinds, W. C., Aerosol Science and Technology 2nd Edition ed.; John Wiley & Sons: New York, 1999; p Medium: X; Size: Pages: 480.

26. Krombach, F.; Munzing, S.; Allmeling, A. M.; Gerlach, J. T.; Behr, J.; Dorger, M., Cell size of alveolar macrophages: An interspecies comparison. Environ. Health Perspect. 1997, 105 , 1261-1263.

27. Valiulis, D.; Sakalys, J.; Plauskaite, K., Heavy metal penetration into the human respiratory tract in vilnius. Lithuanian Journal of Physics 2008, 48 , (4), 349-355.

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28. Marple, V. A.; Rubow, K. L.; Behm, S. M., A microorifice uniform deposit impactor (MOUDI) - description, calibration, and use. Aerosol Science and Technology 1991, 14 , (4), 434-446.

29. Harper, S. L.; Walling, J. F.; Holland, D. M.; Pranger, L. J., Simplex optimization of multielement ultrasonic extraction of atmospheric particulates. Analytical Chemistry 1983, 55 , (9), 1553-1557.

30. Cabada, J. C.; Rees, S.; Takahama, S.; Khlystov, A.; Pandis, S. N.; Davidson, C. I.; Robinson, A. L., Mass size distributions and size resolved chemical composition of fine particulate matter at the Pittsburgh supersite. Atmospheric Environment 2004, 38 , (20), 3127-3141.

31. Seinfeld, J. H.; Pandis, S. N., Atmospheric chemistry and physics : from air pollution to climate change . Wiley: New York, 2006.

32. de Oro, L. A.; Buschiazzo, D. E., Threshold wind velocity as an index of soil susceptibility to wind erosion under variable climatic conditions. Land Degradation & Development 2009, 20 , (1), 14-21.

33. Xstrata Mount Isa Mines’ Community Relations Environmental Initiatives: Mount Isa - Air Quality Control System. http://www.xstratazinc.com/EN/Operations/Mt%20Isa%20Mines/Factsheet%20- %20Air%20Quality%20Control%20system%20-%20Mar%202010%20(lo-res).pdf (July 7),

34. Ravi, S.; D'Odorico, P., A field-scale analysis of the dependence of wind erosion threshold velocity on air humidity. Geophysical Research Letters 2005, 32 , (21).

35. Ravi, S.; D'Odorico, P.; Over, T. M.; Zobeck, T. M., On the effect of air humidity on soil susceptibility to wind erosion: The case of air-dry soils. Geophysical Research Letters 2004, 31 , (9).

36. Betterton, E. A.; Maier, R. M., ASARCO Tailings Analysis. In the concentration of heavy metals and toxic metals (such as arsenic and beryllium) in both the tailings and in the patio dust is low, and comparable to the native soil sample analyzed. ed.; U. Kramer (Director - Pima County Department of Environmental Quality), Ed. University of Arizona: Tucson, Arizona, 2010.

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APPENDIX D: EFFECT OF WIND SPEED AND RELATIVE HUMIDITY ON ATMOERPHIC DUST IN ARID TO SEMI-ARID CLIMATES

Janae Csavina, 1 Jason Field, 2 Omar Félix,1 D. Alba Yadira Corral Avitia,3 A. Eduardo Sáez,1 and Eric A. Betterton 3

1Department of Chemical and Environmental Engineering, The University of Arizona, Tucson, AZ 85721; 2School of Natural Resources and the Environment, The University of Arizona, Tucson, AZ 85721; 3 Departamento de Ciencias Básicas, Universidad Autónoma de Ciudad Juárez, 32538 Juárez, Chihuahua México; 4Department of Atmospheric Sciences, The University of Arizona, Tucson, AZ 85721;

This article was submitted to Aeolian Research in August 2012.

D.1 Abstract

Dust storms cause disturbances to affected communities and deleterious impacts on

human health. Predicting such storms would be helpful to reduce harmful impacts. Dust

generation and dust storm forecasting models have been extensively studied and include

many factors such as soil structure, vegetation cover and atmospheric conditions with

wind speed being the primary variable. Yet, despite these numerous studies, prediction

of dust events remains challenging. Here we show that relative humidity and wind speed

are interdependent determinants in atmospheric dust concentration. Observations of dust

generation in Green Valley, AZ USA finds wind speed and relative humidity compared

separately to PM 10 concentrations show no correlation. However, selecting the data for

high wind speeds, a negative trend is observed between relative humidity and PM 10

concentration. This trend is seen for annual PM 10 data in Green Valley and from dust

generation observations in Juárez, Chihuahua, Mexico indicating relevance to year round

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seasons and other semi-arid regions, respectively. Our results along with a growing number of studies suggest that relative humidity plays an important role in dust generation. Models for dust storm forecasting may be improved by utilizing atmospheric humidity and wind speed as a main drivers for dust generation and suspension.

D.2 Introduction

Dust storms have been shown to have deleterious impacts to human health. Due to near- zero visibility during these events, serious traffic accidents have claimed numerous lives and shut down entire highways for extended periods of time (Novlan et al. 2007). The mere presence of dust in respired air can have negative impacts on the human respiratory and cardiovascular systems (Schwartz 1993; Pope et al. 1995; Peters et al. 1997;

Donaldson et al. 2001; Ghio and Devlin 2001). Additionally, spores and contaminants associated with dust and aerosol can adversely impact human health, causing a range of issues from respiratory infections to toxic metal and metalloid exposure (Low et al. 2006;

Quintero et al. 2010; Csavina et al. 2011; Degobbi et al. 2011).

In arid and semi-arid climates, dust storms are a common nuisance to communities. In El

Paso, TX alone, Novlan et al. (2007) reported that an average of 14.5 significant dust events have occurred annually since 1932. These dust events are predicted to increase in occurrence due to hotter and drier conditions from climate change and therefore are becoming an increasingly studied phenomenon (IPCC - International Pannel for Climate

Change 2007; Breshears et al. 2012).

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Dust events are caused by local and regional aeolian erosion. Wind speed is a primary factor in dust generation with vegetation cover and soil structure playing secondary roles

(Zobeck and Fryrear 1986; Zobeck 1991; Yin et al. 2007). Wind tunnel studies have shown that threshold velocity for aeolian erosion is dependent on atmospheric humidity due to the impact on surface moisture content (Ravi et al. 2004; Ravi et al. 2006; Neuman and Sanderson 2008). Temperature has also been found to have an effect on dust concentrations (Hussein et al. 2006). Yet, despite the many studies on the wind erosion of soils, predictions of dust events are still an significant challenge (Desouza et al. 2010).

During the spring months of March –May, dust storms are a common occurrence in southwestern US and northern Mexico, a part of the semi-arid desert climate. In this study, we examine two sites: Green Valley, AZ USA and Juárez, Chihuahua Mexico.

Dust emissions were sampled at six field locations, ranging in soil and vegetation cover, in the region of Green Valley and two locations in Juárez. In addition, PM x and weather

data from Pima Department of Environmental Quality (PDEQ) were analyzed for longer

term trends. Through analysis of various metrological variables, we show how relative

humidity plays an important role in dust generation.

D.3 Materials and Methods

D.3.1 Green Valley Study

Green Valley, Arizona USA represents a unique region because it contains regional dust

sources from mining operations including ore extraction and mine tailings; a large

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population of elderly people who can be more sensitive to adverse health effects from

particulate inhalation (Donaldson et al. 2001); and the long standing Santa Rita

Experimental Range (source for long-term ecological research of semi-arid grasslands

founded in 1903). Figure 1 shows six sampling locations chosen for the study of a

variety of wind events in the period March – May 2011. The southernmost mine tailings

seen on the map are inactive and low concentration of metals due to being from modern

mining operations (Betterton and Maier 2010). The other three mining areas contain

active mining activities including ore extraction and mine tailings management by

ASARCO and Freeport McMoRan. The dust monitoring sampling locations were chosen

to give a regional perspective on dust concentrations. The five sampling locations

represent a spectrum of vegetation and soil cover in the region. Two of the sites, Pecan

North and Pecan South , are located on the edge of a pecan tree grove and beside a dry river bed. Wastewater , near the city of Green Valley’s wastewater treatment plant, is also

located along the dry river bed. The PDEQ site represents an urban sample for the area and was co-located with equipment from the Pima County Department of Environmental

Quality (PDEQ) where 10-m meteorological observations were also made by PDEQ.

The 10 mile location was chosen to be approximately 10 miles from a mining source.

The Green Valley Fire location was not a part of the dust monitoring for this study, but

2011 annual data from the PDEQ PM 10 monitor and weather data at this station was utilized in data validation.

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Figure 1. Field locations for dust monitoring in Green Valley, AZ USA. Pecan North and Pecan South are located on the edge of a pecan tree grove and beside a dried river bed; Wastewater is located beside a dried river bed; PDEQ represents an urban sample; 10 mile is approximately 10 miles from the source of mining activities; HQ represents the natural background chosen for the region; annual data was taken from Green Valley Fire; and mining activities for the region are labeled.

The Arizona Regional WRF model was used to monitor the forecast for the area. Nine events were captured for dust collection: six and three events were forecasted for windy

(≥ 10 m/s at 10 m) and calm (≤ 5 m/s at 10 m) conditions, respectively. Event time

period was defined by the hours of sample capture between 11:00 and 18:00 local time.

Equipment was set up at each site to collect data or samples for 4 hours during the

forecast event period. Equipment consisted of a Dusttrak Aerosol Monitor (TSI Inc.

DRX 8532), a Kestrel Weather Meter (Nielsen Kellerman 4500), and a Total Suspended

Particulate (TSP) collector (F&J Specialty Products DF-AB-75L-Li). Dusttrak flow rate

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was 3.0 L/min and TSP collector had a flow rate of 60 L/min. Dusttrak measurements were taken with five minute resolution and provide simultaneous real-time mass readings

3 in mg dust/m air for PM 1, PM 2.5 , PM 4, PM 10 , and TSP (<37 m) (Wang et al. 2009).

PM x denotes particle matter concentration in air for all particle sizes less than or equal to diameter x in m. The Dusttrak was housed in an Environmental Enclosure (TSI Inc.

8535) with omni-directional inlet creating the cut-point for TSP to be 37 m.

Meteorological data, including wind speed and direction, relative humidity and temperature were also taken at a five minute resolution with time on all field instruments synchronized before monitoring began. Glass fiber filters (F&J Specialty Products

206447) were used for TSP collection substrates. Filter substrates were transported to and from the field site in sealed petri dishes. Gravimetric analysis was performed on the filters using EPA class I equivalent methods on an ultra-microbalance (Mettler Toledo

XP2U). The sample inlet for the Dusttrak and TSP and the weather vane for the Kestrel were set for collection at a height of 1 m above ground level.

D.3.2 Juárez Study

Two sampling locations in Juárez, Chihuahua, Mexico, were used to monitor PM 10 during the summer of 2008 (May – September). Both locations were in an urban setting with Location A surrounded by paved roads and Location B surrounded by unpaved roads

(Figure 2). Meteorological stations were co-located at Site A and within 2.5 km of Site B and managed by the Department of Civil and Environmental Engineering at Universidad

Autónoma de Ciudad Juárez. Two single-filter samples for the collection of airborne

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particulate matter (Partisol® 2000-FRM, Thermo Scientific) with a PM 10 inlet were used to collect dust samples in both sites. The sampler was operated at a flow rate of 16.7 L min -1 for 24 hour sampling periods. Glass fiber filters were used as substrates (1.5 µm pore, 47 mm diameter, Whatman) for collection of particulate matter.

Figure 2. PM 10 monitoring locations in Juárez, Chihuahua Mexico. Location A is surrounded by paved roads and Location B is surrounded by unpaved roads.

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D.4 Results and discussion

D.4.1 Green Valley location comparison

Average concentrations of PM fractions for the nine wind events are presented in Figure

3. The weather data in the wind rose in Figure 4 were taken from the co-located PDEQ weather station mentioned in Section 2.1. Figure 4 shows a consistent wind direction for the events out of the southwestern quadrant. As previously mentioned, Pecan North and

Pecan South were chosen to be replicates in soil and vegetation cover. In terms of

distance in relation to the mine tailings and the prevailing wind direction, Pecan North

was more heavily influenced by local mining activities with higher concentrations for all

particle sizes. The Headquarters location was chosen due to the lack of influence of mining sources, which resulted in the lowest particulate concentration, as expected. The

PDEQ site is closest in proximity (approximately 3.5 km) to a mine tailings source.

However, the mine tailings are 100 m higher in elevation than the sampler. Through visual observation during high wind events and confirmed by these results, dust essentially passes over the particle monitoring location, and fall out of these particles is occurring at a further distance as seen by these averages.

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0.40 TSP 0.35 PM37 PM10 0.30 PM4 PM2.5 0.25 PM1 ) 3

0.20 (mg/m x

PM 0.15

0.10

0.05

0.00 Pecan North Pecan South Wastewater 10 mile PDEQ Headquarters

Figure 3. An overall average of PM x for the 9 events during March – May 2011 captured from TSP and Dusttrak observations at the Green Valley site.

Figure 4. An overall wind rose for the nine events during March – May 2011 at the Green Valley site.

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D.4.2 Green Valley wind event comparison

Three of the windy events resulted in significantly lower dust concentrations for all sites

(Figure 5(a)) yet both the wind speed and gustiness (frequency >5 m/s) were higher

(Figure 5(b)). On those windy but non-dusty days, the humidity was found to be higher than on windy, dusty days. A summary of these comparisons can be seen in Figure 5.

Wind speed and relative humidity data were acquired from the PDEQ site while frequency of wind speed > 5 m/s was measured by the co-located Kestrel weather stations at each site. The frequency of high winds gives a sense of gustiness (or potential for particle entrainment) for the field locations while wind speed gives a sense for the event’s dust generation potential (Zeng et al. 2010; Cheng et al. 2012). Because relative humidity before and during the event is an important factor for soil moisture content, 24- hour and event relative humidity are compared in Figure 5. Since all the events occurred in the same season, the temperature variation was minimal between and during events, averaging 23.2 ± 2.6 °C. The lack of connection for dust concentrations with temperature may suggest that the difference in relative humidity was a function of the pressure system. The corresponding summary wind rose for “Calm”, “Windy”, and

“Windy Dusty” events can be seen in Figure 6. The data for the wind rose were also taken from the PDEQ weather station. These show that wind direction was fairly consistent for all the events, especially among the six windy events.

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Figure 5. Wind speed, relative humidity and dust concentration compared for wind events separated into “Calm”, “Windy”, and “Windy Dusty” categories each containing three events.

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Figure 6. Wind rose for “Calm”, “Windy”, and “Windy Dusty” categories containing three events each.

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To determine if relative humidity was playing an important role in dust generation,

correlations of PM 10 with wind speed and relative humidity were analyzed and summarized in Table 1. Relative humidity and wind speed data were used from the

Kestrel weather station co-located with the Dusttrak measuring the PM 10 concentrations.

Wind speed and relative humidity compared separately to PM 10 show no significant

correlation. However, when parsing out the data for high wind speeds and binning dust

concentrations according to classes of relative humidity, an increase in correlation is

observed as the selected wind speed is increased. In addition, the negative Spearman

coefficient confirms that lower relative humidity yields higher PM 10 concentration. A

study by Pateraki et al. (2012) in Athens, Greece found E.U. exceedances for PM

fractions were greatest for wind speeds 3.81 m/s < WS ≤ 4.43 m/s. Therefore, 4 m/s was

selected for as a starting point for correlation to high wind speeds. A historic look at dust

events in El Paso, Novlan et al. (2007) also shows a decreasing trend of relative humidity

>15% with frequency of dust storms.

Table 1. Spearman correlation coefficients between PM 10 , (wind speed (WS in m/s) and relative humidity (RH), using data from all nine events and six sites.

Spearman x y correlation (y vs. x)

WS PM 10 0.24 RH PM 10 0.1 RH@ WS >4 PM 10 -0.21 RH@ WS >5 PM 10 -0.36 RH@ WS >6 PM 10 -0.69

RH@ WS >7 PM 10 -0.88

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D.4.3 Green Valley annual analysis

Annual 2011 PM 10 data from PDEQ at the Green Valley Fire Station seen in Figure 1 were analyzed to verify the long term interdependent correlation of wind speed and relative humidity with PM 10 . Temperature was also analyzed for possible correlation to

PM 10 as relative humidity is a function of temperature. A summary of the Spearman correlation analysis can be seen in Table 2. Weak correlation is seen between wind speed and relative humidity versus PM 10 when analyzed independently. However, a stronger correlation is seen when data were parsed out for high wind speeds and binned for classes of relative humidity as done in Section 3.2. Repeating this analysis for temperature yields a lack of correlation.

Table 2. Spearman correlation coefficients between PM10, wind speed (WS in m/s), temperature and relative humidity (RH), using 2011 annual data from Green Valley Fire.

Spearman correlation x y (y vs. x)

WS PM 10 0.14

RH PM 10 -0.24

Temp PM 10 0.08

RH @ WS>4 PM 10 -0.66 Temp @ WS>4 PM -0.0003 10

Figure 7 illustrates the decreasing trend of PM 10 with relative humidity (for relative humidity >10%) as seen in data presented in Section 3.2. Spearman correlation coefficient seen in Figure 7 and Table 2 was calculated using relative humidity >10%.

To further investigate the increasing trend seen for relative humidity <10%, specific

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humidity was calculated using ambient temperature and relative humidity using the local atmospheric pressure of 0.91 atm (Wallace and Hobbs 1977). The analysis preformed in

Figure 7 was redone with specific humidity for the range of relative humidity <10% and the decreasing trend for specific humidity with PM 10 concentration can be seen in Figure

8. Since these data covers an entire year, the trend holds for long term and seasonal variation.

0.06 Spearman correlation = -0.60

0.05 ) 3

0.04

0.03 Concentration(mg/m

10 0.02 PM

0.01

0.00 10 20 30 40 50 60 70 80 90 Relative Humidity (%)

Figure 7. Annual data of relative humidity (@ WS>4 m/s) versus PM 10 concentration selected for wind speeds >4 m/s at Green Valley Fire Station for 2011. Spearman correlation = -0.66.

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0.06

0.05 ) 3 0.04

0.03

0.02 PM10Concentration (mg/m

0.01

0 0 5 10 15 20 25 30 35 40 45 50 Specific Humidity

Figure 8. Annual data of specific humidity (RH<10% and WS>4 m/s) versus PM 10 concentration selected for wind speeds >4 m/s at Green Valley Fire Station for 2011. Spearman correlation = -0.62.

Wind speed and relative humidity have a combined effect on dust concentration, as shown by PM 10 concentration contours in Figure 9. The World Health Organization

3 PM 10 24-hour guideline is <50 g/m (WHO - World Health Organization 1995). The

minimum wind speed necessary to create these concentrations according to the contours

is approximately 7.25 m/s at a relative humidity of 33%. If relative humidity were to

increase to 50% then the minimum wind speed would rise to 9 m/s. These values hold

consistent with a field scale study by Ravi and D’Ordorico (2005) in which wind erosion

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threshold velocities were found to peak at RH 35% with an increasing trend with air humidity (RH<35%) and decreasing trend with humidity (RH>35%).

0.000

8.750

8 17.50

26.25

35.00 6 43.75 52.50

61.25 4 70.00 Wind Speed at 10 m (m/s) 10m Speedat Wind 2

0 20 30 40 50 60 70 80 90 Relative Humidity (%)

3 Figure 9. PM 10 concentration ( g/m ) contours of relative humidity versus wind speed for Green Valley Fire data, RH>20%.

D.4.4 Juárez PM 10 study

The interdependence of PM 10 concentration on wind speed and relative humidity was also observed in Juárez, Chihuahua, Mexico. Similar to Green Valley, Juárez is situated in a semi-arid region that experiences frequent dust storms. Figure 10 shows contour plot of

PM 10 concentrations compared to relative humidity versus wind speed, using data from a field campaign to study the difference between PM 10 concentration of paved (A) and

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unpaved roads (B). As would be expected, higher concentrations of PM 10 are seen for unpaved roads. Both contour plots show a comparable trend of low relative humidity and high wind speeds yielding high PM 10 concentrations. Therefore, this study has implications for other semi-arid climates.

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3 Figure 10. PM 10 concentration ( g/m ) contours compared to relative humidity (%) versus wind speed (km/h) from a study in Juárez, Chihuahua Mexico. Location A monitored PM 10 near paved roads represented in (A) and Location B monitored near unpaved roads represented in (B).

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D.4.5 Supporting Literature

A growing body of research is showing the importance of relative humidity on dust generation (Ravi et al. 2004; Ravi and D'Odorico 2005; Karar and Gupta 2006; Ravi et al.

2006; Shah et al. 2006; Vassilakos et al. 2007; Giri et al. 2008; Neuman and Sanderson

2008). A summary of the findings from these studies can be seen in Table 3. While Ravi

(2004) found a decreasing trend of relative humidity with threshold friction velocity, his later studies found the opposite was true when temperature was relatively constant (Ravi and D'Odorico 2005; Ravi et al. 2006). Ravi (2006) describes the mechanism for moisture-soil interactions: at relative humidity <65%, water adsorption dominates over wet bonding forces while at higher humidity >65%, liquid bridges are formed bonding the soil grains. Neuman and Sanderson (2008) also found increasing threshold friction velocities with relative humidity, describing the mechanism as a consequence of a water film that develops, which depends on the surface roughness; the thickness of the film increases with relative humidity. Neuman and Sanderson’s (2008) finding of the increasing effect of humidity at finer particle size may have interesting implications for mine tailings with the characteristics of fine grain size and unnatural soil structure.

While Karar and Gupta (2006) and Vassilakos et al. (2007) found temperature to be a significant correlation to particulate concentrations, our results do not indicate temperature effect on PM 10 , as seen in Table 2. Our method of binning the relative humidity and temperature when selected for high wind speeds (>4 m/s) reduces the effect of minor observation errors and provides the observed trend. Shah et al. (2006) and Giri et al. (2008) similarly show weak correlation of atmospheric dust concentration with

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temperature. From Table 3, it is apparent that a body of evidence is appearing to show the importance of relative humidity on dust generation.

Table 3. Supporting studies and their findings on the impact of relative humidity on dust generation.

Reference Summary of Findings Wind tunnel tests with no temperature control Ravi et al. 2004 resulted in decreasing threshold velocity with an increase in air humidity. Field study in arid regions of Mojave Dessert (CA) and Canyonlands (UT) USA found for RH <40%, Ravi and D'Odorico 2005 dependency of threshold friction velocity on relative humidity showed an increasing linear correlation. Field study at an industrial site in Cossipore, India in found Spearman correlation coefficients between Karar and Gupta 2006 PM10 and wind speed, temperature, relative humidity and rainfall to be -0.19, -0.64, -0.47, and -0.12, respectively. Wind tunnel study results in increasing threshold Ravi et al. 2006 shear velocity with increasing realtive humidities (<40% and >65%). Field study on metals in total suspended particulate in Islamabad, Pakistan found relative humidity to Shah et al. 2006 have significant influence on metal concentrations while temperature, wind speed and wind direction showed weak relationship on levels. Field study on heavy metals in PM10 in Athens, Greece found relative humidity and temperature to Vassilakos et al. 2007 provide a higher correlation with metals concentrations than wind speed and wind direction.

Field investigation on PM 10 in Nepal found Giri et al. 2008 atmospheric pressure, wind velocity, and humidity to be significant factors influencing concentrations. Wind tunnel tests holding temperature constant found rising relative humidities increases threshold Neuman and Sanderson 2008 friction velocity. Additionall finer particulate is has a steeper increasing slope - more affected by changing humidity.

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D.5 Conclusion

The study of dust generation in Green Valley has implications to dust event predictions.

While dust storm forecasts factor drought conditions in models, wind speed is considered the main driver in dust concentration predictions (i.e. Lu and Shao 2001; Yin et al. 2005).

Here, we show relative humidity and wind speed are near equivalent determinants of dust generation. Results from annual PM 10 data confirm there is no seasonal reliance on relative humidity being a factor in dust concentration. Additionally, results from a study in Juárez, Chihuahua, Mexico confirm the interdependent importance of relative humidity and wind speed in PM 10 concentration in another region. Therefore, relative humidity plays an underappreciated role in dust storms in semi-arid climates. High wind speeds combined with low relative humidity provide the formula for dust generation.

D.6 Additional Data

As part of this study, metals analysis was carried out on all TSP filters and can be seen in

Table 2. This data will not be included in the publication.

Table 2. Metals analysis for each Green Valley site averaged for all wind events.

Be Mg Al Sc Cr Mn Fe Cu As Cd Pb ng/m^3 ng/m^3 ng/m^3 ng/m^3 ng/m^3 ng/m^3 ng/m^3 ng/m^3 ng/m^3 ng/m^3 ng/m^3 PN 0.28 1773.2 10884.5 3.2 12.5 163.9 3653.8 65.4 2.8 1.1 10.1 10mile 0.20 1409.9 12387.7 1.4 6.6 57.7 1484.0 17.8 2.0 1.3 4.3 PS 0.24 1281.7 9738.6 1.4 7.7 108.3 2349.9 75.2 1.9 0.0 5.8 WW 0.21 1149.6 9714.6 1.8 9.0 77.8 1786.5 28.5 2.2 0.5 5.5 PDEQ 0.20 1294.6 9923.6 1.8 7.8 42.7 1108.2 17.1 1.7 0.0 4.0 Head 0.16 1245.7 8032.9 1.7 5.2 31.9 640.2 7.4 1.3 0.5 4.8

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D.7 Acknowledgements

The authors gratefully acknowledge the Pima Department of Environmental Quality for the provision of the PM 10 and weather data used for this publication and Dr. David

Breshears for the use of the field equipment. This work was supported by grant number

P42 ES04940 from the National Institute of Environmental Health Sciences (NIEHS),

National Institutes of Health (NIH). The views of authors do not necessarily represent those of the NIEHS, NIH.

D.8 References

Betterton, E. A. and R. M. Maier (2010). ASARCO Tailings Analysis. U. Kramer (Director - Pima County Department of Environmental Quality). Tucson, Arizona, University of Arizona.

Breshears, D. D., T. B. Kirchner, et al. (2012). "Modeling aeolian transport in response to succession, disturbance and future climate: Dynamic long-term risk assessment for contaminant redistribution." Aeolian Research 3(4): 445- 457.

Cheng, X. L., L. Wu, et al. (2012). "Parameterizations of some important characteristics of turbulent fluctuations and gusty wind disturbances in the atmospheric boundary layer." Journal of Geophysical Research-Atmospheres 117 .

Csavina, J., A. Landázuri, et al. (2011). "Metal and Metalloid Contaminants in Atmospheric Aerosols from Mining Operations." Water Air and Soil Pollution 221 (1-4): 145-157.

Degobbi, C., F. Lopes, et al. (2011). "Correlation of fungi and endotoxin with PM2.5 and meteorological parameters in atmosphere of Sao Paulo, Brazil." Atmospheric Environment 45 (13): 2277-2283.

186

Desouza, N. D., B. Simon, et al. (2010). "Evolutionary characteristics of a dust storm over Oman on 2 February 2008." Meteorology and Atmospheric Physics 114 (3-4): 107-121.

Donaldson, K., V. Stone, et al. (2001). Ambient particle inhalation and the cardiovascular system: Potential mechanisms , Us Dept Health Human Sciences Public Health Science.

Ghio, A. J. and R. B. Devlin (2001). "Inflammatory lung injury after bronchial instillation of air pollution particles." American Journal of Respiratory and Critical Care Medicine 164 (4): 704-708.

Giri, D., M. V. Krishna, et al. (2008). "The influence of meteorological conditions on PM10 concentrations in Kathmandu Valley." International Journal of Environmental Research 2(1): 49-60.

Hussein, T., A. Karppinen, et al. (2006). "Meteorological dependence of size- fractionated number concentrations of urban aerosol particles." Atmospheric Environment 40 (8): 1427-1440.

IPCC - International Pannel for Climate Change (2007). Working Group II Contribution to the Intergovernmental Panel on Climate Change Fourth Assessment Report. Climate Change 2007: Climate Change Impacts, Adaptation and Vulnerability . New York, Cambridge University Press.

Karar, K. and A. K. Gupta (2006). "Seasonal variations and chemical characterization of ambient PM10 at residential and industrial sites of an. urban region of Kolkata (Calcutta), India." Atmospheric Research 81 (1): 36-53.

Low, R. B., L. Bielory, et al. (2006). "The relation of stroke admissions to recent weather, airborne allergens, air pollution, seasons, upper respiratory infections, and asthma incidence, September 11, 2001, and day of the week." Stroke 37 (4): 951-957.

Lu, H. and Y. P. Shao (2001). "Toward quantitative prediction of dust storms: an integrated wind erosion modelling system and its applications." Environmental Modelling & Software 16 (3): 233-249.

187

Neuman, C. M. and S. Sanderson (2008). "Humidity control of particle emissions in aeolian systems." Journal of Geophysical Research-Earth Surface 113 (F2).

Novlan, D. J., M. Hardiman, et al. (2007). "A synoptic climatology of blowing dust events in El Paso, Texas from 1932-2005." Conference on Applied Climatology, American Meteorological Society (J3.12): 13.

Pateraki, S., D. N. Asimakopoulos, et al. (2012). "The role of meteorology on different sized aerosol fractions (PM10, PM2.5, PM2.5-10)." Science of The Total Environment 419 : 124-135.

Peters, A., D. W. Dockery, et al. (1997). "Short-term effects of particulate air pollution on respiratory morbidity in asthmatic children." European Respiratory Journal 10 (4): 872-879.

Pope, C. A., D. V. Bates, et al. (1995). "Health Effects of Particulate Air Pollution: Time for Reassessment?" Environmental Health Perspectives 103 (5): 472- 480.

Quintero, E., F. Rivera-Mariani, et al. (2010). "Analysis of environmental factors and their effects on fungal spores in the atmosphere of a tropical urban area (San Juan, Puerto Rico)." Aerobiologia 26 (2): 113-124.

Ravi, S. and P. D'Odorico (2005). "A field-scale analysis of the dependence of wind erosion threshold velocity on air humidity." Geophysical Research Letters 32 (21).

Ravi, S., P. D'Odorico, et al. (2004). "On the effect of air humidity on soil susceptibility to wind erosion: The case of air-dry soils." Geophysical Research Letters 31 (9).

Ravi, S., T. M. Zobeck, et al. (2006). "On the effect of moisture bonding forces in air- dry soils on threshold friction velocity of wind erosion." Sedimentology 53 (3): 597-609.

Schwartz, J. (1993). "Particulate Air Pollution and Chronic Respiratory Disease." Environmental Research 64 (1): 36-52.

188

Shah, M. H., N. N. Shaheen, et al. (2006). "Characterization, source identification and apportionment of selected metals in TSP in an urban atmosphere." Environmental Monitoring and Assessment 114 (1-3): 573-587.

Vassilakos, C., D. Veros, et al. (2007). "Estimation of selected heavy metals and arsenic in PM10 aerosols in the ambient air of the Greater Athens Area, Greece." Journal of Hazardous Materials 140 (1-2): 389-398.

Wallace, J. M. and P. V. Hobbs (1977). Atmospheric science : an introductory survey . New York, Academic Press.

Wang, X. L., G. Chancellor, et al. (2009). "A Novel Optical Instrument for Estimating Size Segregated Aerosol Mass Concentration in Real Time." Aerosol Science and Technology 43 (9): 939-950.

WHO - World Health Organization (1995). Updating and Revision of the Air Quality Guideline for Europe. EUR/ICP/EHAZ 94 05/PB01 . World Health Organization Regional Office for Europe. Meeting of the Working Group "Classical" Air Pollutants.

Yin, D., S. Nickovic, et al. (2005). "Modeling wind-blown desert dust in the southwestern United States for public health warning: A case study." Atmospheric environment. 39 (33): 6243.

Yin, D., S. Nickovic, et al. (2007). "The impact of using different land cover data on wind-blown desert dust modeling results in the southwestern United States." Atmospheric Environment 41 (10): 2214-2224.

Zeng, Q. C., X. L. Cheng, et al. (2010). "Gustiness and coherent structure of strong winds and their role in dust emission and entrainment." Advances in Atmospheric Sciences 27 (1): 1-13.

Zobeck, T. M. (1991). "Soil properties affecting wind erosion." Journal of Soil and Water Conservation 46 (2): 112-118.

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Zobeck, T. M. and D. W. Fryrear (1986). "Chemical and physical characteristics of windblown sediment, II: chemical characteristics and total soil and nutrient discharge." Transactions ASAE 29 : 1037–1041.

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APPENDIX E: HYGROSCOPIC AND CHEMICAL PROPOERTIES OF AEROSOLS COLLECTED NEAR A COPPER SMELTER: IMPLICATIONS FOR PUBLIC AND ENVIRONMENTAL HEALTH

Armin Sorooshian 1,2 , Janae Csavina 1, Taylor Shingler 1, Stephen Dey 3, Fred J. Brechtel 3, A. Eduardo Sáez 1, Eric A. Betterton 2

1Department of Chemical and Environmental Engineering, University of Arizona, PO BOX 210011, Tucson, Arizona, 85721, USA. 2Department of Atmospheric Sciences, University of Arizona, PO BOX 210081, Tucson, Arizona, 85721, USA. 3Brechtel Manufacturing Inc., 1789 Addison Way, Hayward, California, 94544, USA.

This article was submitted to Environmental Science and Technology in June 2012.

E.1 Abstract

Background: Particulate matter emissions near active copper smelters and mine tailing

sites in the southwestern United States pose a potential threat to nearby environments

owing to toxic species that can be inhaled and deposit in various regions of the body

depending on the composition and size of the particles, which are linked by particle

hygroscopic properties.

Objectives: This study reports the first simultaneous measurements of size-resolved

chemical and hygroscopic properties of particles next to an active copper smelter and

mine tailings site by the towns of Hayden and Winkelman in southern Arizona.

Methods: Size-resolved particulate matter samples collected near an active copper

smelter were examined with inductively coupled plasma mass spectrometry, ion

chromatography, and a hygroscopic tandem differential mobility analyzer.

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Results: Aerosol particles collected at the measurement site are significantly enriched in

metals and metalloids (e.g. arsenic, lead, and cadmium) and water-uptake measurements

of aqueous extracts of collected samples indicate that the particle diameter range of

particles most enriched with these species (0.18-0.55 m) overlaps with the most hygroscopic mode at a relative humidity of 90% (0.10-0.32 m).

Conclusions: Since it is thought that the absolute minimum of single-breath deposition

efficiency of particles in the human respiratory tract occurs at an aerodynamic diameter

near 0.3 m, these results have important implications for where the most contaminant-

laden particles would deposit upon inhalation. These measurements have implications for

public health, microphysical effects of aerosols, and regional impacts owing to the

transport and deposition of contaminated aerosol particles.

E.2 Introduction

The chemical complexity and uncertainties in production mechanisms of

atmospheric aerosol species pose a challenge to assess the impact of aerosol particles on

public health/welfare and for accurate predictions of their interactions with water vapor,

radiation, and clouds. These interactions not only depend on composition, but also

particle size, which also is linked to their composition owing to the process of

hygroscopic water uptake. The southwestern United States is experiencing rapid

population growth, land use change, and drought conditions, which promote natural and

anthropogenic emissions leading to aerosol particles with poorly understood chemical

and hygroscopic properties. Arid and semi-arid regions such as the US Southwest cover

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approximately one-third of the global land area resulting in dust being the most abundant

aerosol type on a mass basis (Andreae and Rosenfeld 2008). The highest nationwide

particulate concentrations of soil dust in the United States are in the Southwest (Malm et

al. 2004). Dust aerosols are of special interest in the Southwest owing to a number of

factors: (i) effects on the development of clouds and precipitation (Rosenfeld et al. 2001;

Rudich et al . 2002; Koehler et al. 2007); (ii) deposition on snowpacks and expedited snow melt as a result of their light-absorbing properties (Painter et al. 2007); (iii) rapidly

diminishing visibility and exacerbating health and safety risks (e.g. traffic accidents)

when in the form of dust storms; and (iv) transport of a suite of allergens, pathogens,

toxic metals, and fungi such as Coccidioides Immitis (Maddy 1965; Kolivras and Comrie

2004; Csavina et al. 2011).

An issue related to public and environmental health in the Southwest is the potential enrichment of trace metals and metalloids in the regional aerosol particles, especially dust, as a result of anthropogenic activity such as smelting and fossil fuel combustion. Among the highest nationwide atmospheric levels of harmful metals and metalloids such as arsenic (As), copper (Cu), and zinc (Zn) are found in southern Arizona and have been suggested to be due to the high density of smelting and mining activity in the region (Dawson and Nash 1980; Malm and Sisler 2000). Mine tailings (i.e. materials left over after the valuable fraction has been extracted from ores) can threaten environmental and public health as a result of transport of toxic compounds into nearby soils, waterways, and the atmosphere (e.g. Alloway 1995; Jung 2001; Navarro et al.

2008; Kwak et al. 2009). Recent measurements made in the vicinity of a smelter in

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Arizona suggested that wind-blown dust particles acquire contaminants such as lead (Pb) and As by deposition of fine particles (i.e. submicrometer) from smelting operations

(Csavina et al. 2011). Earlier work in Arizona also indicated that airborne particles originating from smelting activities are enriched with high levels of metals and metalloids that can settle and contaminate the local topsoil (Dawson and Nash 1979; Germani et al.

1981; Small et al. 1981; Anderson et al. 1988).

A topic that has received little attention is the hygroscopicity of such contaminated aerosol particles. Hygroscopicity is a critical aerosol property that governs the ability of a particle to swell or shrink as a result of variations in relative humidity

(RH), which influences deposition upon inhalation, light interactions, and the efficacy of aerosols at activating into cloud drops. The role of aerosols as cloud condensation nuclei

(CCN) provides a potential explanation for presence of metals and metalloids in wet deposition (Schemenauer and Cereceda 1992; Barbaris and Betterton 1996; Hutchings et al. 2009; Taylor et al. 2010). In this work, the hygroscopicity and composition of size- segregated aerosol particles are examined to understand the nature and potential effects of airborne aerosols adjacent to an active smelter and mine tailings site.

E.3 Methods

Ambient aerosol particles were collected near an active copper smelting site and mine tailings near the towns of Hayden and Winkelman

(http://yosemite.epa.gov/r9/sfund/r9sfdocw.nsf/7508188dd3c99a2a8825742600743735/3

940634a9aec311e88257478006736ce!OpenDocument ) in Arizona (Figure 1). As

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described by Csavina et al. (2011), a 10-stage micro-orifice uniform deposit impactor

(MOUDI; Marple et al. 1991) carried out size-resolved (aerodynamic equivalent Dp,50 cut sizes of 0.054, 0.10, 0.18, 0.32, 0.55, 1.0, 1.8, 3.1, 6.2, 9.9, 18 m) measurements using teflon filter substrates (PTFE membrane, 2 m pore, 46.2 mm, Whatman) on the roof of a

high school building in Winkelman, approximately 2 km from the mine tailings pile and

1 km from the smelter and smokestack. The specific sets of MOUDI samples used for

this study were collected between 9 February 2010 and 25 February 2010, with a

sampling program consisting of weekdays between 07:00-15:30 (Local Time). The

sampling flow rate was 30 L min -1. One half of each filter was analyzed for metal and

metalloid composition using inductively coupled plasma mass spectrometry (ICP-MS).

These filter portions were extracted using aqua regia (1.03 M HNO 3/2.23 M HCl trace-

metal grade) in a sonicator at 80°C for 60 min. Additional details related to the chemical

extraction and analysis procedure are provided by Csavina et al. (2011) . The other halves

of the filters were used for ion chromatography (IC) and water-uptake measurements.

The filter extraction procedure consisted of ultrasonication (15 min) of the filter halves

with 18.2 mega-ohm Milli-Q water. Syringe filters (Acrodisc filter, 25 m) were used to

remove any remaining insoluble matter from the extracts after ultrasonication. IC analysis

was conducted on triplicate samples with a Thermo Scientific Dionex ICS-5000 anion

system, using a 38-min multi-step gradient program with sodium hydroxide eluent (1 mM

from 0 to 8 min, 1 mM to 30 mM from 8 to 28 min, 30 mM to 60 mM from 28 to 38

- - - 2- min). This work reports relative concentrations of inorganic (Cl , NO 2 , NO 3 , SO 4 ) and

organic acid (oxalate, formate, acetate, lactate) species.

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Figure 1. (Left) Map of southern Arizona indicating the location of the measurement site and two other locations (Tucson and Green Valley) that are used as baseline sites for the “Enrichment Factor” analysis. (Right) Close-up of the actual measurement site near the towns of Hayden and Winkelman. The mine tailings are approximately 3 km long.

Portions of the extracted solutions were atomized into a humidified tandem differential mobility analyzer (HTDMA; Brechtel Manufacturing Inc. (BMI) Model

3002) for size-resolved aerosol hygroscopicity measurements. A constant-rate atomizer with controllable liquid supply flow rate and an in-line desiccant dryer (all of stainless steel construction) (BMI Model 9200) were used for re-aerosolization. The generated polydisperse aerosol sample flow from the atomizer was dried to a RH less than 5% prior to being brought to an equilibrium charge distribution in a charge neutralizer. The particles then entered a differential mobility analyzer (DMA; BMI Model 2000C), which was used to select 40 nm diameter dry particles; this diameter was found to coincide with the peak concentration of re-aerosolized particles from the atomizer. The sample flow containing the monodisperse particles was subsequently humidified to a range of pre- selected RHs, chosen to be 50%, 70%, 80%, and 90%. The humidified particles, now grown into a size distribution, then entered a second DMA which is coupled to a mixing-

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type condensation particle counter (BMI Model 1710). The sheath flow in the second

DMA was controlled to the same RH set-point as the humidified sample flow. The

voltage of the second DMA was varied over time to determine particle concentrations as

a function of wet particle diameter and thus the number-size distribution of humidified

particles (Wang and Flagan 1990). The final parameter quantified is termed the

hygroscopic growth factor (GF), which is defined as the ratio of the wet particle diameter

(Dp,wet ) at a defined RH relative to the dry particle diameter (D p,dry ): GF = D p,wet /D p,dr y.

Single values of hygroscopic GFs for each MOUDI filter were determined as the modal diameter of the humidified size distribution measured by the second DMA, divided by the corresponding dry diameter. It is noted that atomizing the extracted MOUDI samples into the HTDMA effectively redistributes the components of the collected aerosols on each

MOUDI size stage in such a way as to normalize the hygroscopic properties of all particles, whether internally or externally mixed, on each stage. It is noted that blank filter and water samples were atomized to confirm the absence of biases associated with the aerosol re-generation process.

To identify transport patterns of air masses originating at the sample site, forward trajectories were computed using the NOAA HYSPLIT model (R. R. Draxler and G. D.

Rolph, Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model,

2003, accessed via NOAA ARL READY Web site, http://www.arl.noaa.gov/ready/hysplit4.html, NOAA Air Resources Laboratory, Silver

Spring, Maryland). One-day forward trajectories were computed daily between 2005-

2010, with the start point being at the MOUDI sampling site (32°59'42.02" N,

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110°46'18.04"W). To complement the trajectory analysis, surface-based measurements of precipitation for a representative site in southern Arizona (Tucson) were obtained from the Arizona Meteorological Network (http://ag.arizona.edu/azmet/ ).

E.4 Results and Discussion

E.4.1 Composition

Size-resolved composition data are summarized in Figures 2 and 3a. In addition to the two-week period of interest, Figure 2 shows averages of the size-resolved trace species levels for representative February data between 2009-2011 to provide for a larger sample size. The size-resolved elemental composition of the particles shows a bimodal behavior (cutpoint Dp ~0.18-0.32 m and D p > 6.2 m) of concentrations of crustal tracers (indicative of soil dust aerosols) including aluminum (Al), iron (Fe), and magnesium (Mg). Aerosol morphology from electron microscopy analysis has shown that the larger size ranges extending above aerodynamic diameters of 1 m (termed “coarse”) are associated with crushing, grinding, and wind-blown dust while the fine particles (Dp <

1 m) likely arise from condensation and coagulation of smelting emissions (Csavina et al. 2011). The other trace elements examined, including As, beryllium (Be), Cd, copper

(Cu), Pb, and scandium (Sc), exhibit the same bimodal distribution, with a number of these species exhibiting higher concentrations in the 0.18-0.55 m range including As,

Cd, and Pb. The rest of the species exhibit higher levels in the coarse size range. The enrichment of trace contaminants in the fine mode is suggestive of relatively fresh smelter emissions while the presence of these species in the coarse mode is indicative of

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either deposition of the fine, smelter-derived particles on the dust aerosols that are

subsequently transported via wind, or of wind-blown dust originating from contaminated soil and tailings.

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Figure 2. (a) Size-resolved aerosol chemical measurements between 9 February 2010 and 25 February 2010, and (b) for the average of five sets of February measurements between 2009-2011 (n = 5) with standard deviation bars provided.

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Figure 3. (a) Size-resolved aerosol water-soluble composition measurements (reported as mass fractions) for the period between 9 February 2010 and 25 February 2010 at Hayden, Arizona. (b) Size-resolved hygroscopic growth factor (GF) measurements after re- aerosolizing a fraction of the same water-extracted samples examined in panel (a) . Thermodynamic GF predictions for ammonium bisulfate (Brechtel and Kreidenweis 2000a, 2000b) and experimental data for laboratory-generated malonic acid (C 3H4O4) aerosols (Sorooshian et al. 2008) are provided at three of the four RHs examined for reference in panel (b) .

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The water-soluble composition of the collected aerosols [i.e. sum of chloride

- - - 2- (Cl ), nitrite (NO 2 ), nitrate (NO 3 ), sulfate (SO 4 ), oxalate, formate, acetate, lactate]

indicates that sulfate mass fractions are highest in the fine particle size range, ranging

between 55-73% amongst the submicrometer MOUDI stages. The main source of sulfate

in the sampling area is expected to be associated with smelter emissions and sulfuric acid

plant emissions. The highest nitrate and nitrite mass fractions are present in the coarse

mode (up to 36% and 9%, respectively, on the 6.2 m MOUDI stage). Previous work has

suggested that owing to reactions of nitric acid (HNO 3) and its precursors with dust,

nitrate is associated with the coarse mode of the regional aerosols (Lee et al. 2008;

Sorooshian et al. 2011). Nitrite can also partition into the aerosol phase as a result of

uptake and reactions of nitrogen oxides on the surface of dust aerosols (Grassian et al.

2001), which could explain its highest mass fractions (8-9%) in the 3.11 m and 6.2 m

MOUDI stages.

Organic acids are ubiquitous in ambient aerosols and are of especial importance owing to their influence on the hygroscopicity of aerosols (e.g. Saxena et al. 1995). A series of low molecular weight organic acids (lactate, acetate, formate, oxalate) accounted for 10 to 36% of the water-soluble mass amongst all the MOUDI stages, with a higher average mass fraction in the coarse sizes (Dp > 1 m) (23% + 10%) as compared to the

fine size range (16% + 9%). Among just the organic acids, acetate exhibited the highest

total mass fraction (11% + 8%), with a greater average contribution to the total organic

acid mass in the coarse range (52% + 7%) as compared to the fine range (30% + 22%).

Formate and lactate exhibited nearly identical average organic acid mass fractions in the

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coarse and fine size ranges (formate ~20%; lactate ~25%), but oxalate exhibited its highest organic acid mass fractions in the fine size range (26% + 13%) as compared to coarse sizes (14% + 21%). Previous work suggests that the presence of such carboxylic acids in coarse particles may be associated with their condensation and adsorption onto pre-existing coarse particle surfaces (Wang et al. 2012), with potential heterogeneous processing (Sullivan et al. 2007). Gas-to-particle conversion is mostly responsible for presence of these low-volatility organic acids in the fine mode, specifically for oxalate, which has been documented to be an important secondary organic aerosol tracer, especially in the aqueous phase for fine particles (Sorooshian et al. 2010).

E.4.2 Enrichment Factors at Sample Site

A common way to quantify the enhancement of specific species in aerosols when comparing different areas or sources is the enrichment factor (EF). This is the ratio of a given element to a reference element at a site of interest divided by the same ratio at a baseline site. The reference element chosen here is the crustal tracer, Sc. EF is therefore calculated as follows:

EF = [C n(sample) /C ref(sample) ]/[B n(baseline) /B ref(baseline) ] (1)

where n = As, Cd, or Pb and ref = Sc. EFs larger than unity would indicate that an element is enriched in the sample aerosols relative to the baseline site chosen. Three baseline sites are used (Figure 1): (i) same location of the normal measurements except

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that the smelter was shut off with similar average frequencies of wind speeds and

directions based on data from two weather stations co-located with the MOUDI sampler

(refer to Supplementary Material); (ii) the city of Green Valley, which is 150 km south of

the measurement site and within 1.5 km of a metal-free mine tailings pile; (iii) on the roof

of the Physics and Atmospheric Sciences building of The University of Arizona, in

Tucson, which represents an urban setting (metropolitan population ~ 1 million; US

Census Bureau 2009) and is 110 km south of the measurement site.

The results in Figure 4 point to significant enhancements in As, Cd, and Pb at the

sample site during periods when the smelter is active, with the largest EFs observed for

As in the smallest size range examined (D p < 1 m). When the baseline chosen was

Hayden without active smelting operations, there was a reduction in all three EFs as a function of size in the coarse range as compared to the other two baseline sites. This suggests that the local coarse aerosol particles (predominantly soil) near the sample site by Hayden and Winkelman are contaminated to a larger extent with these three toxic species relative to the other sites and that active smelting does not significantly alter the background levels of these species for D p > 9.9 m. These results indicate that the

transport of aerosols near the sample site to other nearby locations has the potential to

impact public health owing to the enrichment of As, Cd, and Pb. To put the results of

Figures 2 and 4 in perspective, Pb is currently one of six criteria pollutants that the Clean

Air Act requires the Environmental Protection Agency (EPA) to set National Ambient

Air Quality Standards (NAAQS) for with a current primary standard of 0.15 g m -3 on a

rolling three-month average time basis (http://www.epa.gov/air/lead/standards.html).

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Figure 4 . Size-resolved enrichment factors (Eq. 1) of aerosols sampled adjacent to an active copper smelter and mine tailing site. Error bars represent standard deviations. Three baseline locations are shown for comparison including the same site when the smelter was shut down, Green Valley, and Tucson (sites shown in Figure 1). The data shown in this figure are from between December 2008 and April 2011.

E.4.3 Aerosol Hygroscopicity

Figure 3b summarizes the size resolved sub-saturated hygroscopic GFs of the study aerosols. For comparison to the experimental data, GF predictions for a representative inorganic species (ammonium bisulfate) are provided. These are based on

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a modified Kohler model that includes a parameterization for the solution osmotic

coefficient that approaches the correct thermodynamic limit as the solution becomes

infinitely dilute (Brechtel and Kreidenweis 2000a, 2000b). Representative values for an

organic acid salt (malonic acid, C 3H4O4) are also included from laboratory characterization experiments for this pure test aerosol (Sorooshian et al. 2008). In general, even carbon number organic acids tend to be less hygroscopic than the odd carbon number ones, indicating that malonic acid is relatively more hygroscopic as compared to other organic acids such as succinic and adipic acids, which exhibit GFs near unity for the range of RHs examined in this work (Saxena and Hildemann 1996;

Prenni et al. 2001; Sorooshian et al. 2008).

At a RH of 50%, the hygroscopic GFs of the sample aerosols were within the narrow range of 1.04-1.08, with an increasing range of values at the higher RHs (1.14-

1.25 at 70%; 1.25-1.39 at 80%; 1.43-1.88 at 90% RH). At RHs exceeding 50%, GFs were systematically higher in the fine particles as compared to the coarse fraction and this is most likely due to higher relative amounts of sulfate (55-73%) in the five smallest

MOUDI stages. The reduction in GFs at high RH in the coarse mode is most likely associated with a greater contribution of less soluble material such as organics that are less hygroscopic than the more sulfate-rich fine particles. The measurements lie in between the range of GFs for the two model salts at the high RHs (> 70%), especially in

the fine size range in which chemical composition indicates that the aerosols are mostly

dominated by sulfate-based species; however, at 50% RH, the measured GFs tend to be

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lower than even malonic acid, suggestive of less hygroscopic species having a larger

effect at lower RHs to suppress the overall water-uptake properties.

The results in Figures 2 and 3 are important from a health effects perspective

since particles can be exposed to very humid conditions (> 90%) in the human respiratory

tract (e.g. Byron et al. 1977). The maximum concentrations of As, Cd, and Pb in the 0.18-

0.55 m range, which overlaps with the most hygroscopic mode at a relative humidity of

90% (0.10-0.32 m), are of especial concern from a health perspective as this size is within the range (~0.1-1 m) most effective at penetrating the extrathoracic, conducting,

and pulmonary airways of the human body upon inhalation (Heyder et al. 1986;

Schlesinger 1995). In terms of deposition efficiency in the respiratory tract, it is known

that an absolute minimum of single-breath deposition efficiency of aerosols occurs at an

aerodynamic diameter of 0.3 m (Park and Wexler, 2008). This minimum arises from the

competition between particle residence time and rates of settling/diffusion of particles

onto exposed surfaces. The relatively high GFs that occur at high RH (Figure 3b) suggest

that deposition efficiency of the most contaminant-laden particles would be higher than

expected when just considering dry size alone.

Aerosol particles in the size range most enriched with Pb and As (0.18-0.55 m) exhibit the highest GF at RHs exceeding 50%, which is indicative of a highly hygroscopic aerosol with a greater potential to activate into droplets and participate in cloud formation and subsequent wet deposition. While the coarse aerosol particles deposit via dry deposition more effectively than fine aerosols, they still exhibit significant hygroscopicity, especially in the largest MOUDI stage examined (> 18 m). Dust

207

particles have previously been shown to become more hygroscopic with age as a result of coating by soluble species such as sulfate (e.g. Levin et al. 1996), which allows dust particles to serve as CCN. The results of this study suggest that water-soluble species are associated with aerosols in the coarse mode in the study region and this may be linked to the coating effect mentioned.

E.4.4 Regional Transport

It is of interest to examine the extent to which the contaminated aerosols near the sample site may impact distant areas. It is noted that there are other such active smelters and abandoned mine tailing sites in southern Arizona and these trajectory results are meant to provide a case study example as to how emissions from one such source can influence the greater region. The projected influence of aerosols originating from the study site, as predicted based on 24 hour forward trajectories of air masses from the

HYSPLIT model, is summarized in Figure 5 as a function of time of year. The cities of

Tucson and Phoenix, with populations of over one and four million, respectively (US

Census Bureau, 2009), could be influenced substantially by air parcels released from the study site. One difference among the various months is that the areal extent of forward trajectories is reduced in the summer, most likely due to the absence of prevailing westerly winds during the monsoon season (Sorooshian et al. 2011). Independent of the

HYSPLIT results, analysis of surface precipitation measurements in southern Arizona

(Figure 5) also suggests that wet scavenging would limit the spatial influence of aerosols transported from the measurement site. The seasonal cycle of precipitation in southern

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Arizona is characterized by two modes: the first between November and March and the second and most dominant mode being during the summertime monsoon season that typically occurs between July and August. The other months show similar levels of areal influence with the spring season exhibiting slightly more spatial influence on downwind regions. These results have implications for southern Arizona aerosols (especially soil dust) that are impacted by smelting emissions, which have the ability to transport contaminants to downwind regions, where New Mexico is shown to be the most impacted adjacent state from the point source examined in this work.

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Figure 5. Average forward trajectory frequency plots as a function of time of year using daily data between 2005-2010 from the HYSPLIT model. (The model simulated the release of air parcels from above the surface at the mine tailings site near the towns of Hayden and Winkelman, denoted by the star label in top left panel, and tracked for 24 hours.) Colors indicate the minimum frequency of air parcels at a given point when originating from the source point. Locations of the two metropolitan centers of Tucson and Phoenix are shown in the top left panel. The bottom right panel shows the annual profile of precipitation accumulation averaged over 2000-2009 at a representative location near the source point (Tucson), which independently suggests that wet scavenging of aerosols limits the transport range of particulate matter between July and August as compared to other months.

E.5 Conclusions

Chemical and hygroscopic measurements of size-segregated aerosol samples collected near an active copper smelter and mine tailings in southern Arizona indicate that there is a significant enrichment in metals and metalloids (e.g. arsenic, lead, and cadmium) relative to other reference sites and that the diameter range of particles most enriched

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with these species (0.18-0.55 m) overlaps with the most hygroscopic mode at a relative humidity of 90% (0.10-0.32 m). The coarse fraction of the aerosols exhibits less but still substantial hygroscopic behavior. These measurements have implications for public health (i.e. respiratory deposition), microphysical effects of aerosols (light scattering and drop formation), and regional impacts owing to the transport and deposition of contaminated aerosol particles. Forward trajectories indicate that aerosols from the measurement site may reach highly populated areas in Arizona and adjacent states in less than a day, with the least amount of areal influence during the summer monsoon season owing to the absence of prevailing westerly winds and more efficient wet removal.

E.6 References

Alloway BJ. 1995. The origins of heavy metals in soils. In: Alloway, B.J. (Ed.), Heavy Metals in Soils. Blackie Academic and Professional Publ., New York.

Anderson JR, Aggett FJ, Buseck PR, Germani MS, Shattuck TW. 1988. Chemistry of individual aerosol-particles from Chandler, Arizona, an arid urban-environment. Environ Sci Technol 22, 811-818.

Andreae MO, Rosenfeld D. 2008. Aerosol-cloud-precipitation interactions. Part 1. The nature and sources of cloud-active aerosols. Earth Sci Rev 89, 13-41.

Barbaris B, Betterton EA. 1996. Initial snow chemistry survey of the Mogollon Rim in Arizona. Atmos Environ 30, 3093-3103.

Brechtel FJ, Kreidenweis SM. 2000a. Predicting particle critical supersaturation from hygroscopic growth measurements in the humidified TDMA. part i: theory and sensitivity studies. J Atmos Sci 57:1854–1871.

Brechtel FJ, Kreidenweis SM. 2000b. Predicting particle critical supersaturation from hygroscopic growth measurements in the humidified TDMA. part ii: laboratory and ambient studies. J Atmos Sci 57:1872–1887.

211

Byron PR, Davis SS, Bubb MD, Cooper P. 1977. Pharmaceutical implications of particle growth at high relative humidities. Pestic Sci 8, 521-526.

Csavina J, Landázuri A, Wonaschütz A, Rine K, Rheinheimer P, Barbaris B, et al. 2011. Metal and metalloid contaminants in atmospheric aerosols from mining operations. Water Air Soil Pollut 221, 145-157, doi 10.1007/s11270-011-0777-x.

Dawson JL, Nash TH. 1980. Effects of air pollution from copper smelters on a desert grassland community. Environ Exper Bot 20, 61-72.

Germani MS, Small M, Zoller WH, Moyers JL. 1981. Fractionation of elements during copper smelting. Environ Sci Technol 15(3): 299-305.

Grassian VH. 2001. Heterogeneous uptake and reaction of nitrogen oxides and volatile organic compounds on the surface of atmospheric particles including oxides, carbonates, soot and mineral dust: Implications for the chemical balance of the troposphere. Int Rev Phys Chem 20, 467–548 .

Heyder J, Gebhart J, Rudolf G, Schiller CF, StahlhofenW. 1986. Deposition of particles in the human respiratory-tract in the size range 0.005–15 m. J Aerosol Sci 17, 811–825.

Hutchings JW, Robinson MS, McIlwraith H, Kingston JT, Herckes P. 2009. The chemistry of intercepted clouds in northern Arizona during the North American monsoon season. Water Air Soil Poll 199, 191-202.

Jung MC. 2001. Heavy metal contamination of soils and waters in and around the Imcheon Au–Ag mine. Korea Appl Geochem 16, 1369–1375.

Koehler KA, Kreidenweis SM, DeMott PJ, Prenni AJ, Petters MD. 2007. Potential impact of Owens (dry) Lake dust on warm and cold cloud formation. J Geophys Res 112, D12210, doi:10.1029/2007JD008413.

Kolivras KN, Comrie AC. 2004. Climate and infectious disease in the southwestern United States. Prog Phys Geog 28, 387-398.

Kwak JH, Lenth C, Salb C, Ko EJ, Kim KW, Park K. 2009. Quantitative analysis of arsenic in mine tailing soils using double pulse-laser induced breakdown spectroscopy. Spectrochim Acta Part B 64, 1105-1110.

Lee T, Yu XY, Ayres B, Kreidenweis SM, Malm WC, Collett JL. 2008. Observations of fine and coarse particle nitrate at several rural locations in the United States. Atmos Environ 42, 2720–2732, doi:10.1016/j.atmosenv.2007.05.016.

212

Levin Z, Ganor E, Gladstein V. 1996. The effects of desert particles coated with sulfate on rain formation in the eastern Mediterranean. J Appl Meteorol 35, 1511 – 1523.

Maddy K. 1965. Observations on Coccidioides immitis found growing naturally in soil. Ariz Med 22, 281–288.

Malm WC, Sisler JF. 2000. Spatial patterns of major aerosol species and selected heavy metals in the United States. Fuel Process Technol 65, 473-501.

Malm WC, Schichtel BA, Pitchford ML, Ashbaugh LL, Eldred RA. 2004. Spatial and monthly trends in speciated fine particle concentration in the United States. J Geophys Re. 109, D03306, doi:10.1029/2003JD003739.

Marple VA, Rubow KL, Behm SM. 1991. A microorifice uniform deposit impactor (MOUDI) - Description, calibration, and use. Aerosol Sci Technol 14, 434–446.

Navarro MC, Perez-Sirvent C, Martinez-Sanchez MJ, Vidal J, Tovar PJ, Bech J. 2008. Abandoned mine sites as a source of contamination by heavy metals: a case study in a semi-arid zone. J Geochem Explor 96, 183–193.

Painter TH, Barrett AP, Landry CC, Neff JC, Cassidy MP, Lawrence CR, et al. 2007. Impact of disturbed desert soils on duration of mountain snow cover. Geophys Res Lett 34, L12502, doi:10.1029/2007GL030284.

Park SS, Wexler AS. 2008. Size-dependent deposition of particles in the human lung at steady-state breathing. Aerosol Sci 39, 266-276.

Prenni AJ, DeMott PJ, Kreidenweis SM, Sherman DE, Russell LM, Ming Y. 2001. The effects of low molecular weight dicarboxylic acids on cloud formation. J Phys Chem A 105:11240–11248.

Rosenfeld D, Rudich Y, Lahav R. 2001. Desert dust suppressing precipitation: A possible desertification feedback loop. Proc Natl Acad Sci USA 98, 5975– 5980.

Rudich Y, Khersonsky O, Rosenfeld D. 2002. Treating clouds with a grain of salt. Geophys Res Lett 29, 2060, doi:10.1029/2002GL016055.

Saxena P, Hildemann LM, Mcmurry PH, Seinfeld JH. 1995. Organics alter hygroscopic behavior of atmospheric particles. J Geophys Res 100(D9):18755–18770.

Saxena P, Hildemann LM. 1996. Water-soluble organics in atmospheric particles: A critical reviewof the literature and application of thermodynamics to identify candidate compounds. J Atmos Chem 24:57–109.

213

Schemenauer RS, Cereceda P. 1992. Monsoon cloudwater chemistry on the Arabian Peninsula. Atmos Environ 26, 1583-1587.

Schlesinger RB. 1995. Deposition and clearance of inhaled particles. In Concepts in inhalation toxicology. 191–224.

Small M, Germani MS, Small AM, Zoller WH, Moyers JL. 1981. Airborne plume study of emissions from the processing of copper ores in Southeastern Arizona. Environ Sci Technol 15(3): 293-299.

Sorooshian A, Hersey S, Brechtel FJ, Corless A, Flagan RC, Seinfeld JH. 2008. Rapid size-resolved aerosol hygroscopic growth measurements: differential aerosol sizing and hygroscopicity spectrometer probe (DASH-SP). Aerosol Sci Tech 42, 445–464.

Sorooshian A, Murphy SM, Hersey S, Bahreini R, Jonsson H, Flagan RC, et al. 2010. Constraining the contribution of organic acids and AMS m/z 44 to the organic aerosol budget: On the importance of meteorology, aerosol hygroscopicity, and region. Geophys Res Lett 37, L21807, doi:10.1029/2010GL044951.

Sorooshian A, Wonaschütz A, Jarjour EG, Hashimoto BI, Schichtel BA, Betterton EA. 2011, An aerosol climatology for a rapidly growing arid region (southern Arizona): Major aerosol species and remotely sensed aerosol properties. J Geophys Res 116, D19205, doi:10.1029/2011JD016197.

Sullivan RC, Prather KA. 2007. Investigations of the diurnal cycle and mixing state of oxalic acid in individual particles in Asian aerosol outflow. Environ Sci Technol 41(23): 8062-8069.

Taylor MP, Mackay AK, Hudson-Edwards KA, Holz E. 2010. Soil Cd, Cu, Pb and Zn contaminants around Mount Isa city, Queensland, Australia: Potential sources and risks to human health. Appl Geochem 25, 841-855.

Wang SC, Flagan RC. 1990. Scanning electrical mobility spectrometer. Aerosol Sci Technol 13, 230-240.

Wang G, Kawamura K, Cheng C, Li J, Cao J, Zhang R, et al. 2012. Molecular distribution and stable carbon isotopic composition of dicarboxylic acids, ketocarboxylic acids, and α-dicarbonyls in size-resolved atmospheric particles from Xi’an City, China. Environ Sci Technol in press.

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APPENDIX F: SUPPLEMENTARY MATERIALS

0.45

0.40 As

) 0.35 Cd -3

0.30 Pb

0.25

0.20

0.15

0.10 AverageConcentration (ng m 0.05

0.00 AF 0.054 0.1 0.18 0.32 0.55 1 1.8 3.1 6.2 9.9 18

Cutpoint Diameter ( µm)

Figure 1. Averaged lead, arsenic and cadmium concentrations from MOUDI observations at the Iron King site for the period December 2008 through August 2009. Data represents average concentrations over eighteen 96-hour sampling periods; AF denotes after filter sample.

215

0.45

0.40 )

-3 0.35 As Cd 0.30 Pb 0.25

0.20

0.15

0.10 Average Concentration (ng m Average (ng Concentration

0.05

0.00 AF 0.054 0.1 0.18 0.32 0.55 1 1.8 3.1 6.2 9.9 18 Cutpoint Diameter ( m)

Figure 2. Averaged lead, arsenic and cadmium concentrations from MOUDI observations at the Iron King site for September and October 2011. Data represents average concentrations over three 96-hour sampling periods; AF denotes after filter sample. (Site changed to be closer to acid mine drainage. Mechanical disturbance of mine tailings due to resurfacing occurring during sampling. Scale kept constant for comparison to Figure 1. See below for corrected scale.)

216

3.5

3.0 )

-3 As 2.5 Cd Pb 2.0

1.5

1.0 Averagem Concentration (ng 0.5

0.0 AF 0.054 0.1 0.18 0.32 0.55 1 1.8 3.1 6.2 9.9 18 Cutpoint Diameter ( m)

Figure 3. Averaged lead, arsenic and cadmium concentrations from MOUDI observations at the Iron King site for September and October 2011. Data represents average concentrations over three 96-hour sampling periods; AF denotes after filter sample.

217

16 ) -3 14 12 MOUDI TSP 10 8 6 4 2 Average Concentration (ngm Average 0 As Cd Pb

Figure 4. Averaged TSP lead, arsenic and cadmium concentrations from MOUDI and TSP observations at the Iron King site for the period December 2008 through August 2009.