Anthropogenic and natural levels of arsenic in PM10 in Central and Northern Chile

Atmospheric Environment 36 (2002) 3803–3817

Anthropogenic and natural levels of arsenic in PM10 in Central and Northern Chile L. Gidhagena,c,*, H. Kahelinb, P. Schmidt-Thome! b, C. Johanssonc Swedish Meteorological and Hydrological Institute, 601 76 Norrkoping, Sweden . b Geological Survey of Finland, P.O. Box 96, FIN-02151 Espoo, Finland c ITM Air Pollution Laboratory, Stockholm University, 106 91 Stockholm, Sweden a

Received 17 December 2001; received in revised form 25 March 2002; accepted 8 April 2002

Abstract A few copper and gold smelters in Chile are behind a large fraction of global arsenic emissions, raising concerns for increased concentrations of arsenic in PM10 in Central and Northern Chile. This concern is amplified by the fact that Northern Chile soils and rivers in general are characterized by a high arsenic content. A monitoring and modeling study has been performed to quantify the regional impact of the smelter emissions. Measured atmospheric arsenic concentrations from 2.4 to 30.7 ng m
3 were found at seven rural stations, located tens to hundreds of kilometers away from the nearest smelter. Analyses of topsoil and subsoil samples taken from PM10 monitoring stations revealed levels up to 291 mg kg
1, the highest values found in the northern Atacama desert in Chile. An absolute principal component analysis of selected trace elements in PM10 shows that the regional impact of anthropogenic smelter emissions on airborne arsenic concentrations is more important than the effect of soil dust resuspension. The dominance of the smelter emissions is larger in Central Chile than in the northern parts. The impact of resuspended soil dust on airborne arsenic levels in rural areas was estimated not to exceed 5 ng m
3. The model calculations support the dominant role of anthropogenic emissions and give spatial and temporal variations in atmospheric concentrations consistent with the monitored levels at five of the seven stations. At two of the northernmost stations indications were found of unidentified sources other than the smelters and the resuspended soil dust, contributing to about 5 ng m
3 of total arsenic levels. The study confirms that a strong control or elimination of arsenic emissions from the smelters would lead to arsenic in PM10 levels in Northern and Central Chile comparable to non-polluted areas in other countries. r 2002 Elsevier Science Ltd. All rights reserved. Keywords: Soil resuspension; Smelter emissions; Enrichment factors; Receptor modeling; Regional dispersion modeling

1. Introduction The seven copper and one gold smelters in Central and Northern Chile raise concerns for the exposure to elevated concentrations of airborne arsenic, dispersed on small particles with an aerodynamic diameter o10 mm (PM10). The Chilean Health Ministry tried already in 1994 to introduce a national air quality standard for arsenic in PM10, but this was quickly withdrawn due to *Corresponding author. Fax: +46-11-4958001. E-mail address: [email protected] (L. Gidhagen).

the outstanding economic importance of the mining sector. The latter contributes to approximately 40% of Chilean exports and the possible necessity to close down some of the smelters due to environmental concerns would have had severe economical impacts (O’Ryan and D!ıaz, 2000). This regulation attempt demonstrated the need for a better assessment of airborne arsenic levels and their origin. Inorganic arsenic (As) is extremely toxic and long time exposure may cause serious health effects, such as skin disorders and development of different cancers (Saha et al., 1999). Arsenic enters the body through ingestion, inhalation and skin absorption.

1352-2310/02/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved. PII: S 1 3 5 2 - 2 3 1 0 ( 0 2 ) 0 0 2 8 4 - 4

L. Gidhagen et al. / Atmospheric Environment 36 (2002) 3803–3817

2. Methodology The principal elements used for the determination of anthropogenic and natural arsenic levels in PM10 were sampling of airborne PM10 particles and soil with subsequent chemical analysis, along with regional dispersion modeling. The PM10 sampling took place

PERU Arica

BOLIVIA

Iquique

Pica Quillagua Toconao

Antofagasta

Diego d e Al magro

SOUTH

OCEAN

Vallenar

La Serena

1000 km

PACIFIC

ARGENTINA Viña del Mar Valparaíso

Quillota SANTIAGO

500

The present study focuses on the regional dispersion of anthropogenic arsenic emitted from the smelters and its relative importance compared to airborne arsenic originating from other sources, e.g. soil dust. A monitoring and modeling methodology has been used to yield the spatial distribution of arsenic levels over Central and Northern Chile, separating the effect of direct emissions of anthropogenic origin and the resuspension of soil dust. The results of the study will be used by Chilean authorities to establish a national standard for ambient air concentrations of arsenic. Ambient levels caused by resuspension of soil dust, containing both natural as well as historically deposited anthropogenic arsenic, are not possible to control. In order to create a regulation possible to comply, it is important to determine the baseline arsenic levels, i.e. the levels created by processes such as resuspended soil dust in the absence of today’s anthropogenic emissions. An earlier study of O’Ryan and D!ıaz (2000) presented monitored levels of arsenic in PM10 from some larger Chilean cities as well as registered levels from the monitoring networks operated close to the major copper smelters. However, that study did not allow conclusions concerning the regional influence of the copper smelter emissions, neither was it possible to determine the baseline levels separated from actually measured levels. A later factor analysis study of particle composition in five Chilean cities (Kavouras et al., 2001) revealed that copper smelters constitute important sources of airborne particulate matter in three of the five investigated cities, * del Mar (Fig. 1). Rancagua, Valpara!ıso and Vina Except for copper and gold smelters, there exist other anthropogenic sources of airborne arsenic such as coal combustion and herbicide use (Matschullat, 2000). The copper and gold smelter emissions in Chile are however the only anthropogenic sources of arsenic that create a regional impact. During 1999 the summed arsenic emissions of the eight Chilean smelters was—as reported by the mining companies—6.100 tons. A global estimate for the anthropogenic arsenic emissions sums to 25.450 tons yr
1 (Matschullat, 2000). According to this estimate, the Chilean arsenic emissions during 1999 contributed to 24% of global emissions. For the purpose of regional dispersion of airborne arsenic over Chile, the term anthropogenic arsenic will now onwards be synonymous to arsenic emitted by copper and gold smelters.

Rancagua

Linares 0

3804

Concepción

= Copper/gold smelter Fig. 1. Map over Central and Northern Chile; major cities, monitoring stations and metal smelters are indicated with circles, squares and stars, respectively.

in seven rural locations distributed over Central and Northern Chile (Fig. 1 and Table 1). The monitoring sites were selected to be spatially representative, with free exposure to all wind directions and located at a certain distance from major roads and local industrial activities. The four northernmost stations were located in the desert, with sandy land cover. The fifth station was situated in a transition zone between the desert and cultivated land, while the two southernmost stations were situated in agricultural areas. Persistent westerlies characterize Southern Chile, while the Pacific high determines the wind conditions and the almost absent precipitation prevailing in Northern Chile. Precipitation ! during 1972–1990 was 1283 mm yr
1 in Concepcion (south) and only 2.5 mm yr
1 in the north (Antofagasta). The Central part, around Santiago, is a transition zone with marked seasonal and day-to-day variations. The smaller seasonal variations found in Northern Chile allowed a shorter monitoring period (5 months), while the measurement period for the two southernmost stations covered an entire year. The regional dispersion modeling was used to support the monitoring results in terms of quantifying the anthropogenic impact of the copper smelters on arsenic

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Table 1 Station location, measurement period and number of collected filters for the PM10 monitoring campaign Station name

Long.

Pica Quillagua Toconao Diego de Almagro Vallenar Quillota Linares

69d 69d 68d 70d 70d 71d 71d

20m 32m 00m 03m 45m 15m 33m

Lat. 55s 00s 34s 53s 50s 25s 23s

20d 21d 23d 26d 28d 32d 35d

28m 39m 11m 23m 34m 54m 36m

55s 39s 34s 40s 10s 40s 08s

Altitude

PM10 measurement period

Filter samples

1540 790 2480 750 400 130 150

10 Nov 1999–26 Apr 2000 22 Nov 1999–22 Apr 2000 4 May 2000–7 Oct 2000 18 Nov 1999–22 Apr 2000 8 May 2000–24 Oct 2000 6 Nov 1999–4 Nov 2000 25 Oct 1999–27 Oct 2000

35 39 40 40 38 91 85

levels as well as to provide the spatial distribution of the airborne particle-bound arsenic. The separation of the anthropogenic arsenic of smelter origin from the resuspended dust required chemical analysis of soil samples taken around the PM10 monitoring stations.

smelters. The latter samples were taken by the mining companies themselves as part of their routine air quality monitoring program. They were collected on cellulose filters by using high volume samplers directly impacted by the smelter plumes.

2.1. Soil sampling

2.3. Chemical analysis

The soil sampling was completed in November– December of 1999 and performed along two cross sections, one 5–6 km long following the main local wind direction and another perpendicular to the first. The total amount of samples per station was 12–14 composed of topsoil samples (uppermost 5 cm within a circle of 1 m radius) and one subsoil sample (approximately 0.5 m depth). The soil was sieved to a o2 mm fraction directly at the sample site. The total amount of sieved sample was 1 l (1–2 kg). In the chemical laboratory of Sernageomin (the National Geology and Mining Institute of Chile) in Santiago, the samples were dried at a temperature o401C and half of the sample was sieved to the o63 mm fraction. A maximum of 20–30 g of sieved sample was sent to the Chemical laboratory of the Geological Survey of Finland for elemental analysis.

The PM10 particles collected on PTFE filters were dissolved at room temperature by using a 3:1-mixture of concentrated HNO3 (1.40 g ml
1) and concentrated HF (1.13 g ml
1). Aerosol samples from the vicinity of the smelters, which were collected on cellulose filters, were digested at room temperature with a 3 M HNO3 solution. For elemental analysis of soil samples 0.200 (70.001) g of sample was digested by using a 5:1 mixture of concentrated HF (1.13 g ml
1) and HClO4 (1.68 g ml
1). The PM10-sample solutions were analyzed by using Perkin-Elmer Sciex Elan 6000 inductively coupled plasma-mass spectrometer (ICP-MS). Soil samples and cellulose filter samples were analyzed by using PerkinElmer Sciex Elan 5000 ICP-MS and Jarrell Ash IRIS inductively coupled plasma-atomic emission spectrometer (ICP-AES). Analyzed elements and their detection limits are shown in Table 2. Only elements whose concentration was above the detection limit in 70% of the samples have been used. Chloride, a well-known interfering element in arsenic determination by ICP-MS, was above detection limit in only 1% of all analyzed PM10 samples.

2.2. PM10 monitoring The PM10 monitors used for the rural monitoring program were of the Harvard impactor type with an air flow of 10 l min
1 (Marple et al., 1988). All impactors were equipped with electric switches in order to sample simultaneously from midnight to midnight every fourth day during the periods indicated in Table 1. PM10 particles were collected on 2.0 mm, 37 mm nominal diameter PTFE-membrane filters (Millipore Corporation, USA). Determination of the mass of the collected PM10 particles was done in the Chemical laboratory of Geological Survey of Finland by weighing the unused and exposed filter with accuracy of 10
5 g. In addition to the rural PM10 samples collected especially for the arsenic study, it was also possible to get a few aerosol samples from measurement stations in the vicinity of the

2.4. Meteorological modeling The three-dimensional dynamical meteorological model High Resolution Limited Area Model (HIRLAM) was set up to provide the necessary meteorological input to the model simulations of arsenic dispersion over central and northern parts of Chile. HIRLAM is the result of a co-operation between the meteorological institutes in Denmark, Finland, Iceland, Ireland, the Netherlands, Norway, Spain, Sweden and

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Table 2 Elements determined from PM10, soil and cellulose filter samples and their detection limits Element

Detection limit for PM10 samples (mg)

Detection limit for soil samples (mg kg
1)

Detection limit for cellulose filter samples (mg)

Element

Detection limit for PM10 samples (mg)

Detection limit for soil samples (mg kg
1)

Detection limit for cellulose filter samples (mg)

Ag Al As B Ba Be Bi Br Ca Cd Cl Co Cr Cu Fe K Li Mg

0.001 0.01 0.01 0.05 0.01 0.03 0.002 0.02 2 0.003 100 0.003 0.04 0.005 0.5 1 0.01 0.05

2 50 0.1 n.a. 1 0.01 0.1 10 100 0.05 n.a. 0.2 3 1.5 100 100 0.8 50

0.08 1.2 0.2 n.a. 0.2 0.08 0.08 120 200 0.04 n.a. 0.08 1.6 0.4 200 40 0.4 200

Mn Mo Na Ni P Pb Rb S Sb Se Si Sr Th Ti Tl U V Zn

0.03 0.005 1 0.01 n.a. 0.005 0.001 30 0.003 0.05 20 0.01 0.002 0.05 0.001 0.001 0.005 0.03

10 0.4 150 2 100 10 0.5 50 0.4 n.a n.a 3 0.2 10 0.01 0.04 0.5 5

0.2 0.08 400 1.2 120 0.16 0.04 80 80 n.a. n.a. 0.4 0.08 4 0.02 0.02 0.08 4

France (K.all!en, 1996). It is a hydrostatic grid-point model and the resolutions used for the present study were 11 km in the horizontal and 31 levels in the vertical. The meteorological model domain for the present application extended from southern Peru in the north to Valdivia in Chile in the south, and in west–east direction from the Pacific to the Argentinian Atlantic coast (model configuration listed in Table 3). Since the focus of the present project is on historic periods rather than forecasts, the model was integrated continuously for a number of 1-month periods only changing the lateral boundary conditions. In this manner the HIRLAM model acts as a dynamical downscaling tool to provide a more detailed representation of small-scale meteorological variations using a more coarse resolution forcing on the lateral boundaries (in this case 3 hourly results from the ECMWF global numerical weather prediction model). This approach has been shown to be successful for different kinds of climate applications over Europe (e.g. R.ais.anen et al., 2001). For the present application, a total of 6-month long time series of wind fields were generated by HIRLAM, covering different seasonal conditions during the 12-month long monitoring period (November 1999–October 2000). Discussions of the Chile application of the HIRLAM model are found in Olivares et al. (2002) and Gallardo et al. (2002). The conclusion is that the HIRLAM model is able to describe the seasonal changes in wind patterns, cloudiness and precipitation, as well as the climatic differences between the dry conditions in Northern Chile

and the more humid conditions in the south. Also, the synoptic variations (moving low- and high-pressure systems and fronts) were satisfactorily simulated for the major part of the time. Larger deviations between wind direction measured at surface stations and those predicted by the model were sometimes found, but the deviations were considerably smaller for the comparisons at heights above 100 m (sounding data). The deviations at surface levels are thought to be a result of small-scale topographic effects not resolved on the approximately 11 km horizontal grid used for the HIRLAM runs. The regional dispersion of the smelter emissions—originating from stacks about 100 m high, which for the largest emitters are situated in the Andes some 2000–3000 m a.s.l.—will principally take place above the surface layer where the simulated wind directions agree well with measurements. The ability of the HIRLAM model to simulate the intensive coastal lows and the associated strengthening of the subsidence inversion over Central Chile is shown by Gallardo et al. (2002). 2.5. Regional dispersion model The Swedish Meteorological and Hydrological Institute, has developed a model system for regional dispersion of atmospheric pollutants named Multi-scale Atmospheric Transport and Chemistry model (MATCH). A detailed presentation of this Eulerian transport model can be found in Robertson et al. (1999).

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Table 3 Model configuration Meteorological model HIRLAM

Dispersion model MATCH

Model domain

Long: 75.451W–62.951W Lat: 401S–151S

Horizontal resolution

0.11 (E11  11 km2)

UTM (691W) X=100 000–700 000 (m) Y=6 000 000–8 100 000 (m) 10  10 km2

Vertical resolution Time step (internal) Time step output Boundary input Deposition

126  250 cells Atmospheric column divided into 31 layers (surface layer E60 m) 100 s 3h ECMWF global model 0.51 (E55  55 km2)

MATCH describes the physical and chemical processes that govern emissions, atmospheric transport and dispersion, chemical transformation, wet and dry deposition of pollutants. The MATCH model is currently used in many research and operational applications, e.g. on the European scale for emergency preparedness in case of nuclear accidents (Langner et al., 1998), to study sulfur deposition over Asia (Engardt and Leong, 2001) and over Southern Africa (Zunckel et al., 2000). The model has recently been applied to Central Chile to simulate the dispersion of oxidized sulfur, which is also emitted in large amounts by the copper smelters (Olivares et al., 2002). The MATCH model version used for the present study treats arsenic bound to submicron particles (aerodynamic diameter o1 mm) that are chemically inert but exposed to wet and dry deposition. For model domain and configuration, see Table 3. Emissions from the smelters are treated as elevated point sources. The initial phase of the dispersion process—including dry and wet deposition—is simulated by a Lagrangian particle model. When the particles have traveled a distance that corresponds to the grid model resolution—in this case after 1 h—they are incorporated into the Eulerian grid model. Emission data from eight smelters were available as monthly averages during the monitoring period of 1 yr. The arsenic emissions reported by the smelters summed 500 tons per month in November 1999, but for the following months they were 300 tons per month or lower, this is due to a more strict emission standard coming into force in January 2000. The southernmost smelter was responsible for 47% of total emissions, the second largest 20% and the third 17%. The simulation of the dry deposition process is a major challenge, as the arsenic is expected to be

60  210 cells 15 layers within 0–5 km altitude (surface layer E60 m) 150 s 1h Dry deposition: same over land and sea range: vd ¼ 0:0520:25 cm s
1 Wet deposition: range: L ¼ 0:56 10
4 22:78 10
4 s
1 (mm h
1)
1

dispersed on 0.1–1 mm particles for which there is no effective removal mechanism. Model calculated dry deposition velocities of particles in this size range, verified against wind tunnel measurements, are in the interval 0.005–0.05 cm s
1 (Sehmel, 1980). Later field measurements have found higher deposition velocities. Gallagher et al. (1997) measured particle fluxes above a forest using the eddy correlation technique and obtained deposition velocities of the order of cm s
1 for submicron particles, a clear indication that the use of model calculated dry deposition velocities will lead to an underestimation for rough and vegetated surfaces. However, the arsenic dispersion in Chile takes place mostly in dry and desert-like areas, although cultivated valleys are found, especially in the southern part of the study area. Lamaud et al. (1994) also used the eddy correlation technique to measure dry deposition velocities for submicron particles over a semi-arid area in Niger. The velocities were found to vary during the day, having a midday maximum >0.6 cm s
1, nighttime values of 0.05 cm s
1 during stable conditions and a diurnal mean of 0.4 cm s
1. The surface roughness characterizing the Niger study had only small patches of vegetation, and is more comparable to those found in parts of Northern Chile (although Northern Chile also include large areas without any vegetation at all). Another difference is that wind velocity is likely to be higher in the Sahel area. Altogether this points towards a somewhat smaller dry deposition velocity in Chile, as compared to the Niger results. The large variations found both between, on one hand, the model estimated and wind tunnel verified dry deposition velocities, and on the other hand velocities determined in field experiments, as well as the difficulty in finding experimental values applicable to the very arid conditions in

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3808

Chile, have led to the decision to work with two alternative deposition values. The two dry deposition velocities—0.05 and 0.25 cm s
1—yield a range of the dry deposition process that should cover the uncertainties in the choice of the dry deposition velocity and they are comparable both to model estimated and field experiment determined velocities. Such a range is also in accordance with the degree of simplifications used in the present model application (uniform dry deposition velocity over all land surfaces, no variations due to time of the day and meteorological conditions). The model simulation of the wet deposition process of atmospheric aerosols is based on an empirically determined bulk scavenging coefficient that considers removal due to both in-cloud and below-cloud scavenging. Earlier regional studies using the same dispersion model have used bulk scavenging coefficient values for sulfate that amounts to 2.78  10
4 s
1 (mm h
1)
1 (Zunckel et al., 2000) and 1.0  10
4 s
1 (mm h
1)
1 (Engardt and Leong, 2001). Once again refering to our limited knowledge of the processes that regulate the deposition of arsenic particles, it was decided to run the model for a coefficient interval. Except for the MATCH model standard value for sulfate of 2.78  10
4 s
1 (mm h
1)
1,

also a value five times smaller, 0.56  10
4 s
1 (mm h
1)
1, was used as a lower bound of the bulk scavenging coefficient.

3. Results and discussions 3.1. Monitored elemental levels Averaged PM10 mass concentrations varied between 16.9 and 46.5 mg m
3 (Table 4). The high level registered at the northernmost station Pica was more likely linked to human activities than to natural processes like wind generated dust. There were reports of construction work taking place close to the station in connection with occasional high PM10 peaks. In five of the monitored stations arsenic mean values in PM10 were in the range 2.4–10.4 ng m
3 (Table 4 and Fig. 2). These values are comparable to earlier registrations (4–8 ng m
3) in smaller cities located far away from large smelter emissions (Kavouras et al. 2001). As for an independent reference level, WHO (2000) gives the range 1–10 ng m
3 as typical background levels in rural areas. The highest registered As mean value in the present study, 30.7 ng m
3, was found in Quillota, the station closest

Table 4 Monitored PM10 mean concentrations (mg m
3) and elemental composition (ng m
3) Pica PM10 mass Al As Ba Bi Br Ca Co Cr Cu Fe K Li Mg Mn Na Ni Pb Rb S Sr Ti V Zn

Quillagua

Toconao

D.de Almagro

Vallenar

Quillota

Linares

46.5

16.9

20.5

18.5

32.0

44.4

27.6

2059 10.4 17.4 — 12.0 2343 0.70 6.48 16.8 1274 816 4.12 559 27.7 1320 2.92 10.2 3.56 6434 14.6 95.3 5.28 20.6

649 6.5 5.5 — 9.2 735 — 6.12 20.5 407 365 2.69 350 9.1 895 2.31 5.1 1.34 4086 9.1 32.7 3.07 18.7

931 16.7 8.8 0.49 10.1 1106 — 5.68 13.5 430 578 9.81 378 12.1 569 — 4.2 2.33 — 8.9 38.6 1.46 16.1

770 4.4 5.5 — 9.2 764 0.72 6.23 43.8 576 277 — 338 11.2 1511 2.57 5.4 0.89 4210 4.3 30.0 1.99 14.0

1139 3.9 10.3 — 19.2 1635 0.67 6.44 31.0 1199 582 — 467 25.8 939 — 13.2 2.44 — 5.6 55.3 3.97 14.7

1426 30.7 15.8 1.10 30.9 860 2.07 6.66 73.9 1089 649 — 410 43.4 1652 2.47 58.5 1.97 — 6.3 91.2 4.63 54.4

1186 2.4 6.8 — 9.3 478 — 6.03 4.9 755 407 — 211 19.5 696 1.72 4.6 1.54 — 4.2 71.1 3.37 10.9

Mean values where o70% of the analyzed filter concentrations were above detection limit has been excluded and marked with —.

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Fig. 2. Mean concentrations of As in PM10 (filled), median concentrations of As in topsoil (slanted pattern, max/min values of the 12– 14 samples are also marked) and subsoil As concentrations (horizontal pattern).

to copper smelters—about 30 km to one and 40 km to another—and also located 1 km from a major highway. The elevated As level found in Quillota is similar to the annual mean level reported by Kavouras et al. (2001) * del Mar (33 ng m
3) but lower than in inside Vina Valpara!ıso (73 ng m
3). Those two neighboring cities constitute the largest urban conglomeration in Chile after Santiago (see map in Fig. 1). As will be demonstrated later by the model simulations (Fig. 7), the area * del Mar and Valpara!ıso is to comprising Quillota, Vina different degrees impacted by the emissions from the three southernmost copper smelters. Local dispersion characteristics may explain the different arsenic concentrations registered. Support for an impact of various smelters at different distances from Valpara!ıso can also be found in Kavouras et al. (2001). Also, from the present data set, the mean As level registered in Toconao (16.7 ng m
3) can be considered as slightly raised above typical background levels. Monitored soil concentrations are shown in Fig. 2. Two stations show strongly elevated arsenic levels, in Quillagua both in topsoil and subsoil, in Toconao mainly in the subsoil. The Quillagua station is situated on the lower terrace of the R!ıo Loa, which is characterized by a very high content of natural arsenic as well as strong anthropogenic pollution (Espejo et al., 1999). The soil samples with highest arsenic concentration—some 50–60% higher than the median—were taken in the lower part of the R!ıo Loa valley, indicating that the high arsenic content in the soil is caused by fluvial transport originating both from natural and anthropogenic sources. Despite the high arsenic concentrations in the topsoil of Quillagua, the atmospheric levels are low. An opposite pattern is found in the southern station Quillota, where low arsenic levels in the soil combine with high atmospheric levels. Thus the resuspension of topsoil dust appears to have only limited

importance for the airborne arsenic concentration, at least for the areas covered by this monitoring campaign. Enrichment factors were calculated for the PM10 filter samples, using aluminum as reference element (X stands for different trace elements):     ½XPM10  ½Xtopsoil  EFX ¼ = : ½AlPM10  ½Altopsoil  For the enrichment factor calculation, mean values were used for PM10 concentrations, but median values for topsoil samples in order to avoid too much effect of a few outliers. Table 5a shows a general pattern with enrichment of As, Bi, Br, Cr, Cu, Ni, Pb, S and Zn. Local high natural concentrations of As and Cu in the topsoil lead to certain deviations from this scheme at Quillagua and Toconao, respectively. The EF for As in Quillagua is close to unity (1.5), which indicates that resuspension can be relatively more important at this site. Enrichment factors have also been calculated from filter samples collected within the monitoring networks operating around four of the biggest smelters (Table 5b), showing strong enrichment of As, Bi, Br, Cu, Ni, Pb, S, and Zn; somewhat weaker EFs for Ba, Cr, Na and V. The similar set of elements enriched both within the smelter plumes close to the source as well as at locations tens and hundreds of kilometers away, demonstrates the regional impact of smelter emissions on trace element concentrations in the atmosphere. The time series of trace elements in the PM10 filters were also subject to a Principal Component Analysis (SPSS 10.0) to separate the anthropogenic arsenic fraction from the soil dust fraction. In a first analysis, all elements were used. For six stations—the southernmost station Linares was excluded from the analysis because the As levels were so low—the first factor showed high loading for Al, Ca, Fe, Li, Mn, Rb and Ti, i.e. typical soil elements. The next factors were high in

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3810 Table 5

(a) Enrichment factors for PM10 elements as compared to local soil concentrations

Al As Ba Bi Br Ca Co Cr Cu Fe K Li Mg Mn Na Ni Pb Rb S Sr Ti V Zn

Pica

Quillagua

1.0 6.8 0.9 54.9 35.9 2.2 1.0 3.8 7.7 0.5 1.4 1.9 1.3 0.9 2.3 3.8 10.2 1.2 39.6 1.0 0.4 0.6 4.2

1.0 1.5 0.9 66.8 61.9 0.7 1.8 15.6 24.4 0.9 1.6 0.7 1.3 1.2 2.6 11.4 18.1 1.0 15.8 0.4 0.8 1.6 12.6

Toconao 1.0 18.5 1.2 60.6 68.0 0.0 1.2 7.5 2.6 0.4 2.2 9.0 1.8 0.9 2.0 3.6 8.8 1.6 124.6 1.6 0.4 0.3 6.7

D. de Almagro

Vallenar

Quillota

Linares

1.0 7.3 1.0 24.8 64.7 1.3 2.1 13.8 4.6 0.7 1.4 1.6 1.7 0.9 8.0 8.1 11.6 0.9 191.3 1.1 0.5 0.7 7.9

1.0 8.3 1.4 15.8 103.1 2.9 2.1 7.1 11.3 1.4 1.9 1.5 1.6 1.6 2.9 5.3 18.3 1.5 189.4 1.4 0.8 1.7 5.3

1.0 85.7 1.9 207.1 151.4 1.6 6.0 11.6 25.5 1.2 2.3 1.5 1.9 1.9 4.0 8.9 79.9 1.4 295.4 0.8 1.0 1.7 14.9

1.0 12.0 1.1 58.2 57.2 1.1 1.9 9.7 6.4 1.0 2.3 1.3 1.0 1.2 2.2 5.9 17.1 1.6 334.6 0.7 0.9 1.3 6.5

(b) Enrichment factors for PM10 filters exposed to copper smelter plumes at short distancesa

Al As Ba Bi Br Ca Co Cr Cu Fe K Li Mg Mn Na Ni Pb Rb S Sr Ti V Zn a

Quillagua/Toconao

D. de Almagro

Quillota

Quillota/Linares

1.0 212 99.2 7636 579 1.6 9.7 7.3 1236 1.4 5.0 0.5 0.6 9.2 9.7 22.4 343 7.3 14.6 0.8 0.3 4.2 484

1.0 18294 16.7 16438 3824 8.6 10.9 42.7 1016 4.7 9.9 13.8 4.6 4.3 27.8 156.4 3182 7.9 944 4.3 0.3 36.5 3467

1.0 393 16.9 718 3951 5.3 4.8 24.5 158 2.5 9.1 5.0 13.0 3.7 56.9 86.3 387 3.7 2393 3.1 0.6 15.6 52.2

1.0 29604 4.5 13917 1306 4.1 4.0 42.6 1040 2.2 3.6 2.2 2.7 1.6 8.5 57.5 3283 3.6 21823 1.6 0.4 8.0 390

Soil sample values were taken from the monitoring stations of the present study closest to the smelter (italics).

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Table 6 Results of source apportionment analysis (APCA) Station

Pica

Element

Soil

Smelter 1

Smelter 2

Measureda

APCA

Al Ca Fe As Cu

0.92 0.87 0.94 0.35 0.35 54.6%

0.35 0.43 0.34 0.87 0.88 93.5%

— — — — —

2059 2343 1274 10.4 16.8

1838 2415 1141 13.1 25.3

Al Ca Fe As Pb

0.93 0.96 0.94 0.56 0.15 59.9%

0.35 0.16 0.33 0.75 0.96 94.6%

— — — — —

649 735 407 6.5 5.1

735 515 424 7.2 6.9

Al Ca Li As Bi

0.96 0.97 0.93 0.28 0.11 56.6%

0.16 0.19 0.22 0.93 0.97 94.6%

— — — — —

931 1106 9.8 16.7 0.49

799 873 7.7 19.5 0.47

Al Ca Fe As Pb

0.99 0.97 0.98 0.06 0.26 58.8%

0.11 0.23 0.16 0.91 0.86 91.8%

— — — — —

770 764 576 4.4 5.4

645 571 439 5.3 5.5

Al Ca Ti As Cu

0.93 0.91 0.93 0.40 0.31 56.3%

0.35 0.40 0.36 0.84 0.89 94.7%

— — — — —

1091 1602 52.1 5.6 30.8

1020 1598 49.8 6.3 49.2

Al Ca Fe As Bi Cu Zn

0.98 0.96 0.98 0.07
0.08 0.12 0.15 41.4%


0.07 0.12 0.02 0.83 0.96 0.82 0.26 75.4%

0.08 0.07 0.11 0.49
0.06 0.38 0.94 94.0%

1394 847 1066 32.2 1.2 77 55

977 564 782 52.3 0.9 111 83

Cumulative variance expl.: Quillagua

Cumulative variance expl.: Toconao

Cumulative variance expl.: Diego de Almagro

Cumulative variance expl.: Vallenar

Cumulative. variance expl.: Quillota

Cumulative variance expl.:

Mass (ng m
3)

Factor loadings

a The measured elemental mass may deviate somewhat from Table 3, as the factor analysis requires that all elements are available simultaneously and this could in some cases cause data reduction.

As, Bi, Cu, Mo, Pb, S, V and Zn, elements that according to the enrichment factors of Table 5b characterize smelter emissions. As the principal interest was to separate the smelter contribution from the resuspended dust part, a second analysis was run with only 5–7 trace elements, selecting the ones most representative for smelter emissions and soil dust,

respectively. The resulting factor loadings, after VARIMAX rotation, are displayed in Table 6. In five of six stations—it was possible to explain more than 90% of the variance by just two factors, one identified as soil dust and the other as anthropogenic (smelter emissions). In Quillota two factors were associated with smelter elements, one high in Zn and the other in Bi. A possible

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L. Gidhagen et al. / Atmospheric Environment 36 (2002) 3803–3817

Fig. 3. (a) Separation of soil (filled) and smelter (patterned) contribution to As in PM10, according to APCA calculation (percentual separation of the two sources from APCA, total arsenic mass from measurements). Note that monitored arsenic levels are slightly different from Table 3, as the dataset used for the APCA analysis is somewhat reduced due to the constraint that values for all APCA elements should exist simultaneously. (b) Separation of soil (filled) and smelter (patterned) contribution to As in PM10, according to ‘‘sum-of-oxides’’ calculation. Note that monitored arsenic levels are slightly different from Table 3, as the dataset used for the ‘‘sum-ofoxides’’ analysis is somewhat reduced.

interpretation is that Quillota is exposed to emissions from more than one smelter, with slightly different trace metal profiles in their plumes. Quantitative estimations of the soil dust and smelter contributions were calculated using Absolute Principal Component Analysis (APCA) as described in detail by Swietlicki et al. (1996). Table 6 shows that for most substances the calculated concentrations are similar to the observed, even though, on average for all sites, the calculated arsenic concentration is 25% higher than measured. Fig. 3a shows the soil and smelter contributions to the total arsenic concentrations. In this figure the total measured arsenic concentration was multiplied by the fractions due to soil dust and smelter emissions obtained by the APCA analysis. This shows that the soil contribution to the arsenic levels is in the range of 0.2–3.2 ng m
3. The anthropogenic contribution to airborne arsenic is between 65% and 95% of the total concentration. An alternative method to estimate the contribution of resuspended soil dust to As in PM10 is the ‘‘sum-of-

oxides’’ method (Andrews et al., 2000). The assumption is that the typical crustal elements are present as oxides in airborne particles, e.g. SiO2, Al2O3, Fe2O3, TiO2, CaO and K2O. The mass concentration estimate of resuspended soil dust is obtained by first adding the oxygen mass to the measured elemental mass and then taking the sum of those oxides as the mass contribution to PM10. The arsenic content in soils is known for each monitoring station, thus the locally resuspended arsenic mass can be calculated (Fig. 3b). The calculated arsenic concentrations (0.3–2.3 ng m
3) are somewhat lower than those of the APCA method, but the result confirms the dominating contribution of anthropogenic sources to the total amount of As in PM10. 3.2. Model simulation of arsenic levels Fig. 4 shows the comparison between measured and simulated mean values, based on the periods where both monitor and model results were available (note that the

L. Gidhagen et al. / Atmospheric Environment 36 (2002) 3803–3817

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Fig. 4. Comparison between simulated (lowest model layer) and measured arsenic levels at monitoring stations, for different dry deposition (vd ) and wet deposition (L) rates. Note that monitored arsenic levels in Fig. 4 may deviate sligthly from Table 4, as averages are based on periods where both simulated and measured data were available.

averaged values represent 5 months for the five stations to the north and an entire year for the Quillota and Linares stations). The four simulation results, for which the only difference is the variation in dry and wet deposition rates, express an uncertainty range for the model calculated impact of the smelter emissions. Within the deposition rate intervals given, the results displayed in Fig. 4 point to the dry deposition parameterization as being most important for the airborne arsenic concentration levels. For the stations in the middle and southern part of the simulation domain—Linares, Quillota, Vallenar and Diego de Almagro—the simulated levels are similar to measured ones. The simulated levels as compared to monitored levels were for those four stations in the range 110–144% for the lower dry deposition velocity, and in the range 65–109% for the higher dry deposition velocity. Especially the overestimated concentration levels at Quillota, while using the lower dry deposition velocity, indicate that the higher value of the deposition value is a more realistic value for the vegetated southern parts of the modeling domain. In the remaining three northern stations the simulated levels are smaller than those actually measured, 26–47% of the measured values for the low dry deposition velocity and 18–34% for the high dry deposition velocity. This smaller impact of smelter emissions on arsenic levels at the northern monitoring stations, as compared to the southern stations, is qualitatively consistent with the results of the source apportionment made in the previous section. This means that soil resuspension plays a more important role in the northernmost part of Chile. A more quantitative comparison of measured and simulated concentrations is presented below.

For the two southernmost stations it was possible to verify that the model responds to seasonal variations in a correct manner. As shown in Fig. 5 both measurements and the model calculated concentrations are generally higher during wintertime. There are factors that may explain point by point differences between simulated and measured As levels, e.g. it is important to keep in mind that the air sampling was made every fourth day, allowing only 7–8 daily samples for calculating a monthly average. A more detailed inspection of simulated hourly time series (not shown) shows that the impact of the largest smelter emitter over Quillota is episodic and highly transient, not occurring every day. There is hence also a possibility that the monitored average levels are not fully representative over monthly periods and that higher model levels may not necessarily depend on model errors. More than half of the emitted arsenic is exported outside the model domain (49–77%, depending on deposition rates), most of it across the eastern border (Fig. 6). From a mass balance point of view, the dry and the wet deposition processes and their variation due to different coefficients, are of about the same magnitude. The observation made from Fig. 4, that the arsenic concentrations in, e.g. Quillota are more sensitive to changes in the dry deposition velocity than to changes in the scavenging coefficient, can be explained by the precipitation distribution. For places with more rain, like Quillota, the exact level of the scavenging coefficient is not so crucial, the major part of the column will anyhow be cleaned from arsenic. For other areas with a low precipitation intensity, the exact value of the scavenging coefficient might be relatively more important. The horizontal distribution of the smelter impact is displayed in Fig. 7a and b, for two different dry

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Fig. 5. Comparison between measured and simulated (lowest model layer) monthly arsenic average concentrations at the Quillota station.

Fig. 6. Arsenic budget as calculated by the MATCH model. Period: November 1999–October 2000.

deposition velocities. The 5 ng m
3 isoline may be chosen to represent the extension of a clear anthropogenic impact over natural background levels. An important result from the model simulations is that they demonstrate the important regional impact caused by the smelter emissions, extending over large parts of the Central and Northern Chile. Except for the northernmost part of the model domain, the smelter emissions may explain most of the airborne arsenic measured at the monitoring stations. 3.3. Discussion on as in PM10 levels in Chile The two estimations of baseline levels of As in PM10 based on measurements—the receptor model APCA and the sum-of-oxide estimate—indicated 3.2 ng m
3 as the highest average level of As in PM10 with soil origin. The comparison of airborne As in PM10 and the arsenic content of the soil (Fig. 2) suggests that high levels in the soil do not necessarily imply raised levels in the air, see

e.g. station Quillagua in Fig. 2. For the Chilean authorities one concern has been that anthropogenic arsenic, which has deposited and accumulated in the topsoil of the desert during almost one century, through resuspension contributes to increased arsenic levels in the air. Although the results from the monitoring stations used in this study do not represent more than a few rural areas, they give a clear indication that resuspended soil does not give a regional scale contribution to airborne As levels that compare to the direct impact from present smelter plumes. The stations where the arsenic content in the soil was highest showed either increasing arsenic gradients towards the riverbed (Quillagua) or higher sub than topsoil concentrations (Toconao), a pattern not expected if accumulation of historically deposited topsoil arsenic would be important. In other stations the higher arsenic concentrations were found scattered or, in one case, in alluvial cones. No indications could be found pointing on wind driven resuspension as an effective mechanism of

L. Gidhagen et al. / Atmospheric Environment 36 (2002) 3803–3817

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Fig. 7. Simulated annual mean concentration of As emitted by smelters in PM10 (from lowest model layer) for (a) vd ¼ 0:05 cm s
1, L ¼ 2:78  10
4 s
1 (mm h
1)
1; (b) vd ¼ 0:25 cm s
1, L ¼ 2:78  10
4 s
1 (mm h
1)
1; unit: ng m
3.

accumulating high arsenic concentrations in the topsoil. Note that the conclusion of the small impact of historically emitted arsenic over today’s levels, is viewed in a regional perspective and is probably not valid for wind exposed zones close to each smelter, let us say within 10 km in the predominant wind directions from the smelters. It is also not likely that the upper baseline level upper limit of 3.2 ng m
3, estimated from the rural monitoring data, is valid within larger cities where traffic and other human activities contribute to raised overall PM10 (and therefore also arsenic) levels. The impact on arsenic levels of smelter emissions has been calculated both by a statistical analysis of monitored data (APCA) and by dispersion modeling

with the MATCH model. A comparison of the two estimates is presented in Table 7. The major patterns of the smelters’ impact on arsenic levels, as estimated by the APCA analysis and through the MATCH model simulation, are fairly consistent and show a stronger dominance of the smelters in the southernmost stations. According to the discussion from the model comparison with monitored data in the previous section, the model results in the higher end of the given interval, i.e. those for a low dry deposition velocity and calculated with a low scavenging coefficient can be used for the four northernmost stations. Correspondingly the lower part of the result interval, i.e. those simulated with a high dry deposition velocity, should be used for the three

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Table 7 Smelter contributions to As in PM10 (resuspended soil dust contribution excluded) Station name

Smelters (APCA)

Smelters (MATCH)

Smelters all year (MATCH)

Pica Quillagua Toconao Diego de Almagro Vallenar Quillota Linares

7.7 4.4 13.5 4.3 4.1 30.5 —

1.4–2.8 1.0–2.3 5.4–7.6 3.1–6.0 4.2–8.0 36.4–52.6 1.8–4.7

1.4–2.8 4.0–6.8 4.8–7.5 12.6–19.9 3.2–6.1 36.4–52.6 1.8–4.7

APCA estimate based on 5 months of PM10 measurements at the five northernmost stations, 12 months for the two southernmost. The first MATCH model estimate (second column) is for the monitored period, the last column for a whole year. Simulated values given in a range (different deposition rates), the values most comparable to APCA are marked bold (see text). Unit: ng m
3.

southernmost stations (the simulations with different scavenging coefficient gave basically the same results in the southern model area). The APCA and MATCH model estimates agree reasonably for the southern stations, but APCA indicates a larger smelter impact in Toconano and Pica in the north. It should be noted that the APCA calculation assumes that all monitored arsenic can be explained by two single factors, soil dust and smelter emissions. This means that any other sources must be included in one or both of these two factors. Volcanic active areas are found on many places along the Andes and their exhaust gases could theoretically contribute to arsenic levels in PM10. The fact that the largest absolute difference between the APCA estimate and the MATCH model is found in Toconao, a village situated just beneath the Andes and close to active volcanoes, could point to such a possible impact from volcanic or geyser emissions yielding similar elemental profiles as the smelter emissions, but further investigation is needed to confirm or reject such a hypothesis. The reported construction work close to the Pica station has probably raised the airborne arsenic levels, but how this affects the APCA source apportionment is difficult to say without a more detailed knowledge about the reported activities. The last column of Table 7 shows the MATCH model results for a whole year (actually 6 months spread over a 12-months period). The major difference is that the station Diego de Almagro seems to have had a much stronger impact of smelter emissions during the months when no measurements were made. The distance to the nearest smelter is about 60 km and evidently there are seasonal variations in the principal wind directions that may affect the arsenic levels in Diego de Almagro.

copper or gold smelter—yield average arsenic levels up to 30.7 ng m
3, a level considerably higher than typical levels in non-polluted areas. Simultaneously measured arsenic contents in soils reveal levels up to 291 mg kg
1, which is also substantially higher than for typical nonpolluted conditions. High levels of airborne arsenic are found in the south of the study area and high arsenic content in soils in the north. Consequently, there is no simple relation between arsenic levels in the soil and in the atmosphere. The conclusions of this study are that the emissions from the copper and gold smelters in Chile lead to arsenic levels in PM10 that are tens of ng m
3 higher than natural levels, not only close to the smelters but also on a regional scale. If smelter emissions were eliminated, the source apportionment analysis shows that resuspended soil dust would yield airborne arsenic levels not exceeding 5 ng m
3 in rural areas. The possible accumulation of arsenic in desert topsoils due to deposition of smelter emissions during a century of operation, does not seem to contribute to present, regional-scale, arsenic levels. The model calculations explain the general pattern found by the measured arsenic levels, in which the impact of anthropogenic smelter emissions are higher in the south of the study domain and the arsenic fraction from soil dust is larger in the north. The modeled smelter impact is of comparable magnitude to the source apportionment estimation in five out of seven stations. For two of the northernmost stations there were signs of additional, not identified sources, yielding arsenic contributions of about 5 ng m
3. For one station—Diego de Almagro— the model results indicate a marked seasonal variation in the impact of the nearest smelter, which implies that the monitored 5 months average arsenic level represents an underestimate of the annual average concentration.

4. Summary and conclusions Acknowledgements Measurements of arsenic in PM10 at seven rural stations in Central and Northern Chile—all stations located tens to hundreds of kilometers from the nearest

! Nacional del The study was financed by Comision Medio Ambiente (CONAMA), the environmental

L. Gidhagen et al. / Atmospheric Environment 36 (2002) 3803–3817

agency of Chile. The authors acknowledge Yolanda Silva and Pedro Oyola for their support in the air monitoring campaign, the SERNAGEOMIN laboratory, which collaborated in the soil sampling, as well as Bodil Aarhus and Magnuz Engardt for their modeling support.

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