Health hazard prospecting by modeling wind transfer of metal-bearing dust from mining waste dumps: application to Jebel Ressas Pb-Zn-Cd abandoned ...
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Environ Geochem Health (2014) 36:935–951
DOI 10.1007/s10653-014-9610-y
ORIGINAL PAPER
Health hazard prospecting by modeling wind transfer
of metal-bearing dust from mining waste dumps: application
to Jebel Ressas Pb–Zn–Cd abandoned mining site (Tunisia)
Manel Ghorbel • Marguerite Munoz •
Fabien Solmon
Received: 29 September 2013 / Accepted: 6 March 2014 / Published online: 24 April 2014
Springer Science+Business Media Dordrecht 2014
Abstract This work presents a modeling approach 5.74 and 0.0768 lg/m3 for measured wind speed
to simulate spatial distribution of metal contamination values up to 22 m/s. Preferential areas of contamina-
in aerosols with evaluation of health hazard. This tion were determined in agricultural lands to the NW
approach offers the advantage to be non-intrusive, less from the source dump where Pb and Cd exceeded
expensive than sampling and laboratory analyses. It guidelines up to a distance of 1,200 m. The secondary
was applied to assess the impact of metal-bearing dust spreading directions were SW and E, toward the
from mining wastes on air quality for a nearby village. Health hazard prospecting shown that a major
community and agricultural lands in Jebel Ressas part of the village was exposed to contaminated dust
(Tunisia) locality. Dust emission rates were calculated and that daily hazard quotient (HQ) values reached
using existing parameterization adapted to the con- locally 118 and 158, respectively, for Pb and Cd
tamination source composed of mining wastes. Metal during the study period. However, HQ variations
concentrations were predicted using a Gaussian model in the village are high, both temporally and
(fugitive dust model) with, as input: emission rates, geographically.
dump physical parameters and meteorological data
measured in situ for 30 days in summertime. Metal Keywords Dust Metals Mining wastes
concentration maps were built from calculated PM10 Transfer modeling Air quality Health hazard
particle concentrations. They evidence the areas where
Pb and Cd concentrations exceeded WHO guidelines
(0.5 and 0.005 lg/m3, respectively). Maximum con-
centrations of Pb and Cd in PM10 are, respectively, of
Introduction
M. Ghorbel
Laboratoire de Ressources Minérales Et Environnement Mining activities can generate high quantities of fine-
(RME), Tunis El Manar University, 2020 Tunis, Tunisia grained wastes, which were abandoned in the past and
left exposed to meteoric and wind erosion.
M. Ghorbel (&) M. Munoz
Laboratoire Géosciences Environnement Toulouse (GET), Some studies have addressed dust emission from
UMR 5563, Toulouse, France active mining sites and quarries linked to crushing,
e-mail: ghorbel@get.obs-mip.fr grinding or transport (Chaulya et al. 2003; Sinha and
Banerjee 1997; Sinha 1995; Kakosimos et al. 2011).
F. Solmon
International Center for Theoretical Physics, Strada Such results are used to optimize the material process
Costiera, 34151 Trieste, Italy in order to reduce the negative impact on the areas in
123936 Environ Geochem Health (2014) 36:935–951 the neighborhood. But studies on dust emission and these elements, air quality guidelines of 5 ng/m3 and transfer from abandoned mining wastes are missing 0.5 lg/m3, respectively, for Cd and Pb were estab- except for recent monitoring study of suspending lished (WHO 2000; Baars et al. 2001). particulate matter (Cigagna et al. 2014), and contam- Dust aerosol is a very relevant factor in the ination wind dispersion is generally addressed with a Mediterranean climate and social environments. A descriptive approach of contamination impact on preliminary study on health hazard assessment of surrounding soils. Modeling approach for quantifica- direct dust ingestion has been addressed in Jebel tion of airborne metal concentration and spatial Ressas site as a representative site of Pb–Zn–Cd representation of contamination dispersion is still not mining sites from northern Tunisia (Ghorbel et al. well advanced, neither are addressed the parameters 2010). The results indicated that the population is which influence dust emission from mining wastes. In exposed to Pb and Cd through direct ingestion of fact, wind erosion of mining wastes may be addressed deposited dust in the village transferred from fine- as emission of fugitive dust which is defined as dust grained treatment wastes dumped close to the village that could not be conducted through a confined path. and farming lands. Concentrations of fugitive dust released from a surface For a more complete investigation of exposure to vary greatly depending on the nature and the area of metals around the mining site, air quality has been the sources and on other factors such as surface investigated over an area comprising the mining roughness and weather conditions (Trindade et al. site, the village and the farmlands. The dust 1981; Marticorena and Bergametti 1995; Alfaro and emissions and concentrations of airborne metals Gomes 2001). have been calculated for the PM10 (particulate In Tunisia, numerous Pb–Zn–Cd mining sites have matter of a grain size equal or less than 10 microns) been abandoned with large amounts of mining wastes fraction which is the inhalable particle size usually exposed to wind erosion. They are usually adjacent to taken into consideration by health and environment villages and farmlands. Mediterranean climate pro- organizations (WHO, USEPA). The evaluation was motes spreading of metallic contamination toward air, conducted on a 24-h average concentration basis for soil and habitations, so that population may be 1 month in summer season. In addition, maps of seriously exposed. metal concentrations in inhalable particulate matter Many studies have evidenced the relationship over the studied area were performed and compared between environmental metal contamination and to the air quality guidelines in order to determine people poisoning (Zhuang et al. 2009; Von Braun the location of population potentially at risk and et al. 2002; Malcoe et al. 2002; Lee et al. 2005). Metal- hazard quotients (HQs) have been estimated to contaminated dust can cause various diseases after evaluate the level of hazard for population health exposure of the population in the neighborhood to high over the village. levels of metals through inhalation, ingestion and skin contact. Knowledge about air quality and air pollution is Site presentation fundamental to prevent health risk (Zou et al. 2009). In humans, the pulmonary deposition and absorption of The Jebel Ressas mining site is located 30 km south of inhaled chemicals can have direct consequences for Tunis where Pb exploitation and Zn exploitation were health. But in the literature, health hazard has been conducted during almost 70 years. The Jebel Ressas mainly calculated from metal doses incorporated by village stands at the foot of the Jebel Ressas Mountain consumption of contaminated food crops, drinking where the Pb–Zn extraction zone was located. It is water or soil [lead and blood poisoning is now bordered southwards by the former ore processing established for children due to their hand-to-mouth plant and westwards by the treatment dumps (Fig. 1a). and pica behavior (Carrizales et al. 2006; Malcoe et al. Almost two millions of tons of gravimetry and 2002)]. flotation wastes were generated by the ore treatment Cadmium, lead and their compounds are classified and dumped in three flattop dumps, DI, DII and DIII carcinogens. In addition, various effects are evidenced (Fig. 1b). Next to the western side of the dumps are the for nervous, renal and cardiovascular systems. For farming lands. 123
Environ Geochem Health (2014) 36:935–951 937
Fig. 1 Jebel Ressas site
location
Table 1 Metals content in the mining waste dumps DI, DII, No specific management was conceived to prevent
DIII and surface DIII and reference concentrations in soils for erosion and chemical alteration of mining waste
comparison dumps, and no vegetation has grown on these dumps.
Pb (wt%) Zn (wt%) Cd (mg/kg) The northern dump DIII is the largest one with a
surface of 35,250 m2 and is exposed to the westerly
DI 1.27 5.20 170
and northwesterly prevailing winds which favor
DII 0.09 2.02 110
contaminated dust transfer toward the village.
DIII 2.30 7.11 290
The study area was chosen to assess the dispersion of
DIII surface 2.96 9.27 360
the contamination by wind process around the waste
Reference 0.01 0.03 2.0
dumps. The total surface is of 3.9 9 5 km2. It includes
concentrations
in soils (Baize 1997) the western flank of Ressas Mountain, the waste dumps,
Jebel Ressas village, farmlands and HMA stream.
Methodology
The waste materials have a weak cohesion and fine
grain size dominated by silts and clay. Metal-bearing
In this work, we considered only DIII dump (Fig. 1b)
minerals consisted of carbonates (cerussite, smithson-
as significant dust source in the model for the
ite, hydrozincite), silicates (hemimorphite, willemite),
following reasons:
sulfides (sphalerite and galena) and iron oxyhydroxide
enriched in Pb and Cd (Ghorbel et al. 2010; Ghorbel • its large surface and its high concentrations in Pb
2012). Pb, Zn and Cd concentrations in the waste and Zn and Cd compared to the other two dumps,
dumps and on a representative sample of the surface of • simplification of the modeling operation by reduc-
the dump DIII are given in Table 1. ing the number of sources and parameters.
123938 Environ Geochem Health (2014) 36:935–951
Dust transfer modeling approach the time and the volume that are flagged on the counter
of air volume were noted.
Dust emission and aerosol concentration modeling
Aerosols analysis
An emission model adapted from a desert dust
emission parameterization used in meteorological Pb and Cd in PM10 were analyzed after total acid
and climate model (Marticorena and Bergametti digestion with HF and HNO3. The protocol was
1995; Alfaro et al. 1997) was adapted to the site conducted with four blank filters, one chemistry blank
conditions and used to calculate dust emission rates of and one international standard of urban incinerator ash
the source of contamination. The generated emission (SKO1).
data were then included in a Gaussian dispersion The solutions were analyzed with an ICP-MS
model to obtain estimations airborne dust concentra- Agilent 7500. Isobaric interferences have been cor-
tions. Details about the modeling approach used are rected with automatic calculations and polyatomic
given in the ‘‘Supplementary data’’ part. interference has been corrected by the operator of the
machine.
Spatial distribution of airborne metal concentrations Analysis of blanks shows that contamination from
handling does not exceed 0.3 ppb for Pb while Cd
Pb and Cd mean concentrations over the summer remains below the limit of detection of the machine (0,
month are represented as contour maps to allow 01 ppb).
visualization of the contamination extension and For Pb and Cd, standard analyses show that the
intensity. For this purpose, we used Surfer software. protocol yields are between 92 and 125 %.
Meteorological data Health hazard quotient evaluation
Wind parameters were measured with an Oregon The modeling of air metallic concentrations allows us
Scientific WMR200 weather station installed on the to address health hazard.
roof terrace of the nearest house to DIII dump at a Calculation has been performed for PM10 which is
height of 7.5 m of the ground surface. an inhalable fraction that may remain suspended in the
The station is equipped with a wind vane anemom- atmosphere for weeks and can penetrate deeply in the
eter which measures speeds and gusts of wind in m/s. lungs passages.
The weather data were recorded every minute by a In order to estimate health hazard for Pb and Cd, the
central unit for 30 days between July 13 and August HQ for human health is obtained by dividing metal
12, 2009. At the end of this period, these data were concentrations in airborne PM10 (lg/m3) by the air
retrieved from the central unit to a computer via an quality guideline values for these elements (USEPA
USB cable. 2011).
If HQ [ 1, the exposure level is higher than the
Analytical approach for modeling validation guideline value, then there is a potential risk for the
receptor.
Aerosol sampling
An aerosol sampler was used to collect PM10 on the Results and discussion
roof terrace of the nearest house to DIII dump behind
the meteorological station, to measure the suspended Measured parameters
particle concentrations. The sampler conducts a vol-
ume of air through a particle size separator for PM10. Wind data
These particles are collected on Zefluor Teflon filters.
Filters were replaced every 24 h during the first During the measurement period, wind gusts ranged
7 days of the 1-month study period. During each from 0 to 22 m/s. The rose of hourly mean wind
manipulation, the sampler was stopped, and the date, direction shows that the dominant wind is N (26 % of
123Environ Geochem Health (2014) 36:935–951 939
Fig. 3 Grain size distribution of the mining waste on DIII dump
surface
the study period would be representative of the
summer season.
Temperature
Fig. 2 Rose of hourly mean wind direction in Jebel Ressas site
between July 13th and August 12th, 2009 and wind frequencies
(%) Hourly mean temperatures were calculated from field
measurements. Over the study period, they ranged
between 20 and 47 C.
Table 2 Wind frequencies percentage given by velocity
intervals and directions
Pasquill stability classes and mixing heights
Hourly mean wind N NE E SE S SW W NW
velocity intervals
Information about Pasquill stability class and mixing
V B 1 m/s 20 20 16 21 18 19 5 21 height must be given as input data in FDM model. We
1 \ V B 5 m/s 63 57 52 56 56 47 47 56 obtained them from the Web site of NOAA (2010)
5 \ V B 10 m/s 17 23 32 21 22 34 47 23 (Air Resource Laboratory). These data are specific for
V [ 10 m/s 0 0 0 2 4 0 1 0 our period and locality.
Pasquill stability classes are between A and F and
are noted, respectively, from 1 to 6 with respect to
the total observations) (Fig. 2). SE winds are the format of input files to FDM.
second dominant direction with a frequency of 18 % Mixing height level ranges between 61 and
of the total observations during this summertime. 3,320 m.
Considering their high velocity occurrence (Table 2)
with their high frequency, the SE winds were the most
efficient winds for dust emission during this period. Grain size data
Wind toward the village was less frequent and less
violent. Grain size analysis of the surface sample of DIII dump
Tunis-Carthage is the closest meteorological sta- shows that sand fraction (63–2,000 lm) is dominant
tion to Jebel Ressas site. Meteorological statistics in (85 %). The sand fraction would be carried in saltation
this station between October 2001 and August 2013 and would generate emission flux. PM10 is the particle
show that mean wind speed in summer season fraction of a grain size equal or below 10 lm. It makes
(August, July and June) is 5 m/s and dominant wind 3.5 % of the whole sample (Fig. 3).
is NNE as it is mentioned in Windfinder Web site Emission model calculates emitted particle quanti-
(2013). These results are concordant with our in situ ties for each diameter class previously defined in the
measurements; thus, our derived modeling results over code. So that mean diameter of each class and the
123940 Environ Geochem Health (2014) 36:935–951
Table 3 Grain size distribution of emitted PM10 from the Table 5 PM10 emission flux for the in situ measured wind
surface of DIII dump speed interval and corresponding emission modes
Diameter class [0.1; 1] [1; 2.5] [2.5; 5] [5; 10] Wind speed PM10 emission Emission mode
intervals (lm) (m/s) flux (g/m2/s)
Mean diameter (lm) 0.55 1.75 3.75 7.50
0 0.00 9 10?00 Aerodynamic
Emitted fraction 0.17 0.19 0.19 0.45 entrainment
1 2.13 9 10-10
2 1.71 9 10-09
3 5.76 9 10-09
4 1.36 9 10-08
Table 4 Values of overall aerodynamic roughness (z0g) 5 2.66 9 10-08
rounded to the tenth for the dominant wind directions measured
in Jebel Ressas site 6 4.61 9 10-08
7 7.31 9 10-08
Wind direction Overall aerodynamic
8 1.09 9 10-07
roughness z0g (lm)
9 1.55 9 10-07
SE 260 10 2.45 9 10-05 Saltation
N 240 11 8.71 9 10-04
NW 340 12 5.68 9 10-03
13 5.96 9 10-03
14 4.15 9 10-02
associated emitted fraction were considered as grain 15 2.95 9 10-02
size data for FDM input (Table 3). 16 3.18 9 10-02
17 3.80 9 10-02
Roughness values (z0g) 18 4.69 9 10-02
19 6.03 9 10-02
Surface roughness values for the three directions are 20 2.49 9 10-01
given in Table 4.
21 1.49 9 10-01
For the period from July 13 to August 12, 2009, the
22 1.62 9 10-01
estimation of the emission flux was made with the
roughness of 260 lm, corresponding to the most
efficient wind measured during this period able to
generate dust emission. The most elevated emission rates are noted for days
6, 11, 12, 28 and 30. For these days, wind gusts more
Dust emission rate than 10 m/s were the most frequent over the measure-
ment period. This high PM10 emission rate due to the
PM10 emission flux was calculated for the wind speed fine grain size and weak cohesion of the mining wastes
interval [0; 22 m/s] (Table 5) that was registered would be even higher with the violent winds which
in situ. occur occasionally in this region. In those cases, the
Wind speed and surface roughness are the two influence of the variation in the wind direction on the
principle variables that control emission flux. For low surface roughness would necessary be addressed for
wind speed (\10 m/s in our study case), the particles accurate emission estimation.
are initially mobilized by aerodynamic entrainment. However, the values obtained in this study are
For high wind speed (C10 m/s), saltation is imple- similar to those of Chane Kon et al. (2007), who
mented and emission flux increases rapidly. calculated emission values that reach 3.5 9 10-3 g/
Then, mean daily dust emission rate from the m2/s on the flattop of a mining waste in Mantos
source (dump DIII) was calculated to have a visibil- Blancos (Chile) and where the maximum speed of
ity on daily variations. The flux interval obtained wind during the measurement period was 13 m/s. On
is between 2.46 9 10-6 and 2.50 9 10-3 g/m2/s the other hand, emission rates calculated in this work
(Fig. 4). are significantly lower than the values of Neumann
123Environ Geochem Health (2014) 36:935–951 941
et al. (2009), who measured an interval between 1 and Table 6 Calculated Pb and Cd concentration range in airborne
4 g/m2/s of PM10 with simulations in tunnel of PM10 over Jebel Ressas study site
emission from mining waste. Element (lg/m3) PM10
Pb Cd
Pb and Cd concentrations in airborne PM10
particles Maximum 5.74 7.68 9 10-2
-13
Minimum 3.84 9 10 5.14 9 10-15
Calculated Pb and Cd concentrations in airborne
PM10
Table 7 Daily metal concentrations in PM10 sampled in the
Dust emission rates were integrated in FDM to
village
calculate PM10 airborne concentrations.
Pb and Cd daily concentrations in airborne PM10 Day Pb (lg/m3) Cd (lg/m3)
were calculated on a grid of 2,030 points covering the D1 0.03 0.0002
study area more than 30 days of modeling. Thereby, D2 0.03 0.0004
60,900 concentration values were obtained for each D3 0.012 \0.0002
metal. D4 0.12 0.0016
Then, we calculated the mean metal concentration D5 0.07 0.0008
in each point over the whole period to obtain D6 0.03 0.0004
representative values for summer month. Table 6
D7 0.01 0.0002
gives a summary of the mean metal concentrations
intervals obtained for the considered set of points.
Comparison between modeling and direct analysis
results on the sampling point
Measured Pb and Cd concentrations in airborne
PM10 Pb and Cd calculated and analyzed concentrations
were set up on the same figures (Fig. 5a, b).
Concentrations of metals in collected airborne Both the shapes of the two curves for Pb and Cd and
PM10 on the terrace roof in the Jebel Ressas the orders of magnitude of concentrations obtained
village were converted in lg/m3 with respect to air through direct measurement and modeling are similar,
volume (m3) corresponding to each aerosol sample so modeled values can be validated. Some discrepancy
(Table 7). between the two kinds of values can be explained
Pb concentrations ranged from 0.01 to 0.12 lg/m3, because the model considers that, for every 24 h, the
and Cd concentrations were two orders of magnitude beginning aerosol concentration is zero, which means
lower with a minimum below 0.0002 lg/m3. that the atmosphere is perfectly clean before each 24-h
Fig. 4 Mean daily dust
emission rate for PM10
(particulate matter B10 lm)
from the surface of the waste
dump DIII
123942 Environ Geochem Health (2014) 36:935–951
Fig. 5 Calculated and analyzed a concentrations of Pb in the PM10 fraction in air, b concentrations of Cd in the PM10 fraction in air
calculation. However, in reality, the atmosphere is above 10 m/s was higher and so their efficiency for
never pure and there may be quantities of particles that dust emission and transfer.
remain suspended in the air after a windy day for many Then, agricultural lands and farms are particularly
hours before they eventually settle down. This fact exposed to contamination during the summer season.
explains the underestimation of low concentrations by Maximum concentrations of Pb and Cd in PM10 are,
FDM. Such an agreement between measured and respectively, of 5.74 and 0.0768 lg/m3 (Table 6) in a
predicted FDM data was already shown for dust farm located at few hundreds of meter NW DIII dump
concentrations in air generated by a cement plant and receiving SE wind with a speed up to 22 m/s.
(Abdul-Wahab 2006). Due to the secondary NW winds, the village which
is concentrated in the few tens of meters SE from DIII
Spatial representation of calculated Pb and Cd waste dump is also under the heaviest metals contam-
concentrations in airborne PM10 ination zone shown in Fig. 6.
SW sector is the area likely to be less contaminated
The maps of the spatial distribution of concentrations during the measurement period due to most frequent
in the air were dressed (Fig. 6). This spatial distribu- wind speeds are below 5 m/s, which protect it from
tion uses Pb and Cd monthly mean concentrations contaminated dust.
obtained for the 2,030 receptor points. In fact, season variation in meteorological param-
These maps highlight that metal distributions display eters especially wind direction and speed has an
an eleven branches star centered on the source (DIII important control on air quality. Particularly, because
waste dump) and concentrations decrease with distance the surface roughness value depends on wind direc-
from the source area over several orders of magnitude tion, coupling both parameters in the emission model
pending on the direction. On the study site, minimum Pb would improve the dust emission flux estimation.
and Cd concentrations are in the order of 10-12 and
10-14 lg/m3, respectively, and they are considered as
negligible below 10-4 and 10-6 lg/m3 (several thou- Health hazard prospecting
sands of times lower than the recommended values).
Wind transfer is most important toward NW and Spatial representation
less important toward E and N. Although the prevail-
ing winds are from the N and NW, the transport is In order to obtain an overall vision of the potential
mainly by easterly winds because their frequency of health hazard area from the modeled data obtained on
123Environ Geochem Health (2014) 36:935–951 943
Fig. 6 Spatial distribution of 1 month mean Pb concentrations and mean Cd concentrations in airborne PM10
metal-bearing PM10 in the area of Jebel Ressas for
each of the 2,030 receptor points used in the modeling
process, Pb and Cd mean concentrations calculated for
the considered summer month and exceeding WHO
(2000) air quality guidelines are displayed in Fig. 7.
WHO air quality guidelines are 0.5 lg/m3 for Pb and
5 ng/m3 for Cd.
In the side of agricultural land, a tenth of farms
are concerned by the potential health hazard up to a
distance of about 1,200 m from the mining waste
dumps in the NW direction and of about 700 m
toward W direction. In this area, several farms are
concerned both for exposure of inhabitants to metals
by inhalation and for possible contamination of
locally grown agricultural products which may enter
the food chain and increase the indirect exposure to
metals.
Although the village of Jebel Ressas is not in
the main wind directions, it is also exposed to
contamination by the yearly prevailing NW wind
due to its location close to the waste dumps. Pb
and Cd concentrations in PM10 can exceed guide-
Fig. 7 Spatial representation of areas where Pb and Cd
lines up to a distance of 500 m to the east of DIII
concentrations in airborne PM10 exceeded WHO air quality dump, so that the major area of the village is
guidelines concerned.
123944 Environ Geochem Health (2014) 36:935–951
Table 8 Maximum, minimum and mean concentrations of Pb and Cd in airborne PM10 calculated on the basis of the 30 days study
period individually for each point in the village
Village points PM10 Pb (lg/m3) PM10 Cd (lg/m3)
Max Min Mean Max Min Mean
1 5.82 3.12 9 10-05 5.02 9 10-01 7.79 9 10-02 4.17 9 10-07 6.71 9 10-03
-06 -01 -01 -08
2 8.54 3.11 9 10 9.36 9 10 1.14 9 10 4.17 9 10 1.25 9 10-02
-05 -01 -02 -07
3 1.70 2.28 9 10 2.75 9 10 2.28 9 10 3.05 9 10 3.68 9 10-03
-01 -02
4 4.65 0.00 2.42 9 10 6.22 9 10 0.00 3.24 9 10-03
-01 -02
5 2.91 0.00 1.33 9 10 3.89 9 10 0.00 1.78 9 10-03
6 6.82 9 10-01 0.00 3.19 9 10-02 9.13 9 10-03 0.00 4.27 9 10-04
-06 -01 -08
7 59.0 5.84 9 10 4.83 7.90 9 10 7.82 9 10 6.46 9 10-02
-01 -02
8 5.19 0.00 3.07 9 10 6.94 9 10 0.00 4.11 9 10-03
-01 -02
9 5.15 0.00 3.11 9 10 6.89 9 10 0.00 4.16 9 10-03
-05 -01 -02 -07
10 6.51 1.80 9 10 4.13 9 10 8.71 9 10 2.40 9 10 5.53 9 10-03
11 6.00 0.00 3.47 9 10-01 8.02 9 10-02 0.00 4.64 9 10-03
12 3.03 0.00 2.06 9 10-01 4.06 9 10-02 0.00 2.76 9 10-03
13 3.45 0.00 2.31 9 10-01 4.61 9 10-02 0.00 3.08 9 10-03
-01 -02
14 3.14 0.00 1.98 9 10 4.20 9 10 0.00 2.65 9 10-03
-01 -02
15 3.69 0.00 2.41 9 10 4.94 9 10 0.00 3.22 9 10-03
16 3.78 0.00 2.36 9 10-01 5.06 9 10-02 0.00 3.15 9 10-03
-08 -01 -02 -10
17 3.99 5.07 9 10 2.58 9 10 5.34 9 10 6.79 9 10 3.45 9 10-03
-01
18 37.1 0.00 3.53 4.97 9 10 0.00 4.73 9 10-02
-01 -02
19 2.20 0.00 1.43 9 10 2.95 9 10 0.00 1.91 9 10-03
-01 -02
20 1.75 0.00 1.03 9 10 2.34 9 10 0.00 1.38 9 10-03
-01
21 19.8 0.00 1.03 2.65 9 10 0.00 1.38 9 10-02
22 3.84 0.00 2.57 9 10-01 5.14 9 10-02 0.00 3.43 9 10-03
-01 -01
23 15.6 0.00 6.80 9 10 2.09 9 10 0.00 9.10 9 10-03
-01 -02 -03
24 7.35 9 10 0.00 2.62 9 10 9.84 9 10 0.00 3.50 9 10-04
-02 -04 -04
25 1.68 9 10 0.00 5.59 9 10 2.24 9 10 0.00 7.48 9 10-06
-05 -07 -07
26 1.58 9 10 0.00 6.76 9 10 2.12 9 10 0.00 9.04 9 10-09
27 3.11 9 10-04 0.00 1.04 9 10-05 4.16 9 10-06 0.00 1.39 9 10-07
28 4.60 9 10-03 0.00 1.54 9 10-04 6.16 9 10-05 0.00 2.05 9 10-06
-04 -06 -06
29 1.52 9 10 0.00 5.09 9 10 2.04 9 10 0.00 6.81 9 10-08
-06 -07 -07
30 8.53 9 10 0.00 2.85 9 10 1.14 9 10 0.00 3.81 9 10-09
Estimation of health hazard for the Jebel Ressas In order to prospect more in detail the potential
village population health hazard in the village, we have considered
modeling data for 30 points covering the housing area.
Jebel Ressas village is of several hundreds of people We calculated Pb and Cd concentration in airborne
living during the year in there. Thus, exposure to toxic PM10 in each point for the 30-day period.
metals from mining waste may be qualified as chronic. Table 8 gives maximum, minimum and mean
In this case, health issue in the village should be concentrations of Pb and Cd in airborne PM10
ideally treated at least, on the basis of 1-year data. In individually for each point in the village more than
this modeling approach, we considered one summer the study 30-day period. For Pb, concentrations vary
month to obtain data on the ‘‘worst case’’ knowing that between 0 and 59 lg/m3 with a global mean value of
summer is the driest season with frequent strong wind 0.51 lg/m3. For Cd, concentrations vary between 0
which promotes contaminated dust emission. and 0.79 lg/m3 with a global mean value of
123Environ Geochem Health (2014) 36:935–951 945
Table 9 Maximum, minimum and median values of health HQ related to Pb and Cd in Jebel Ressas village and calculated for each
day on the basis of the whole set of points in the village
Day Health HQ for Pb Health HQ for Cd
Max Min Median Max Min Median
D1 0.22 \10-6 7 9 10-3 0.3 \10-6 9 9 10-3
-6 -6
D2 3.64 \10 0.12 4.88 \10 0.16
D3 0.18 \10-6 1.1 9 10-4 0.23 \10-6 1 9 10-4
-6 -6 -6
D4 0.04 \10 7 9 10 0.05 \10 9 9 10-5
-6 -5 -6
D5 19.74 \10 2.3 9 10 26.42 \10 3 9 10-5
D6 57.91 \10-6 \10-6 77.5 \10-6 \10-6
-6 -6
D7 3.44 \10 0.06 4.6 \10 0.08
D8 2.32 \10-6 7 9 10-3 3.1 \10-6 9 9 10-3
-6 -6
D9 0.13 \10 0.0002 0.17 \10 2 9 10-4
-6 -5 -6
D10 0.38 \10 9 9 10 0.51 \10 1.2 9 10-4
D11 13.3 \10-6 6 9 10-6 17.8 \10-6 8 9 10-6
D12 65.62 \10-6 \10-6 87.82 \10-6 \10-6
D13 0.07 \10-6 0.012 0.1 \10-6 0.016
-6 -5
D14 0.005 \10 7 9 10 0.006 \10-6 9 9 10-5
-6 -5 -6
D15 0.37 \10 2 9 10 0.5 \10 2.2 9 10-5
D16 1.79 \10-6 2 9 10-5 2.39 \10-6 2.4 9 10-5
-6 -6
D17 4.15 \10 1.36 5.55 \10 1.82
D18 3.66 \10-6 1.2 9 10-4 4.89 \10-6 1.6 9 10-4
-6 -5 -6
D19 1.84 \10 3 9 10 2.46 \10 4 9 10-5
-6 -6 -6
D20 3.42 \10 2 9 10 4.57 \10 3 9 10-6
-4 -6 -5 -4 -6
D21 6 9 10 \10 8 9 10 8 9 10 \10 1.1 9 10-4
D22 1.02 \10-6 0.55 1.37 \10-6 0.74
-6
D23 13.02 \10 2.43 17.42 \10-6 3.25
D24 2.85 \10-6 0.22 3.82 \10-6 0.29
D25 1.27 \10-6 0.0021 1.7 \10-6 3 9 10-3
-6 -5 -6
D26 3.4 \10 2 9 10 4.56 \10 3 9 10-5
D27 39.6 \10-6 6 9 10-5 53 \10-6 8 9 10-5
D28 118 \10-6 \10-6 158 \10-6 \10-6
-6 -3 -6
D29 0.09 \10 3 9 10 0.12 \10 4 9 10-3
-6 -6 -6
D30 19.72 \10 \10 26.39 \10 \10-6
HQ values below 0.000001 are designed by \10-6
0.0069 lg/m3. Regarding reference metal concentra- Finally, daily metal concentrations in each point in
tions, both mean values slightly exceed WHO the village could make a very large interval, but a
guidelines. punctual mean value over a given study period gives a
Moreover, in 24 points of the village, Pb and Cd more correct idea on the real concentrations taking
maximum concentrations exceed WHO guidelines. into account the air mixing effect.
If we consider the global air quality during the To prospect the population exposure to contamina-
month through mean metal concentrations in each tion during the study period, a HQ has been calculated
point, six points in the village show mean Pb and Cd for each day for the whole set of points in the village.
concentrations above the guidelines. The HQ defined as the ratio of the potential exposure
123946 Environ Geochem Health (2014) 36:935–951
Fig. 8 Maximum and median values of health HQ due to Pb air contamination (all minimum values are\10-6 and only median values
[10-6 are represented)
to the substance and the level at which no adverse However, median HQ values are low for most of the
effects are expected. If the HQ is calculated to be equal days. Only 5-day display values above 0.1 and only for
or less than 1, then no adverse health effects are the 17th and the 23rd days, HQ exceeds 1 in more than
expected as a result of exposure. If the HQ is greater the half of the considered points.
than 1, then adverse health effects are possible Extremely elevated health HQ is located in points
(USEPA 2011). very close to DIII and exposed to W wind which was
So that HQs related to Pb and Cd are, respectively, the third most efficient wind for contamination
given by: transfer over the study period.
It appears that despite the smallness of the village,
HQðPbÞ there are large variations of the HQ related to the
Calculated Pb concentration in airborne PM10 distance from the source due to the W wind particles
¼
0:5lg=m3 transport capacity.
Contamination of the media generally diminishes
HQðCdÞ with the distance from the contamination source
Calculated Cd concentration in airborne PM10 (Zhuang et al. 2009; Cangialosi et al. 2008), so that
¼ related health risk should also decrease. It is clear that
0:005lg=m3
distance from the source can be a useful, simple proxy
Table 9 gives maximum, minimum and median for assessing exposure and assigning health effects.
health HQ for each of the 30 days of modeling for the But in several papers, the results were opposite to
whole set of points in the village. Only HQ values above the basic assumption of closer proximity equals
10-6 have been reported, considering it as a limit of greater concentration and exposure (Cordier et al.
negligible HQ. Minimum HQ values are all below 10-6. 2004). Our study evidences that although the proxim-
A diagram has been provided for Pb HQ values only ity rule applies, air contamination is mainly related to
because Cd HQ values would have plot almost wind directions and velocities, hence to location
identical although they are slightly higher. relative to the source.
Table 9 and Fig. 8 show large variations in HQs in This study also evidences the variability of health
the village for each day. This fact is mainly related to hazard variable more than 1 month. A more detailed
the daily variation of wind directions which controls study along 1 year on the most exposed locations in
PM10 concentrations. So that, when contamination is the village would be necessary to address the chronic
highly transferred toward a given direction, in other exposure of the population under these conditions.
directions, metal concentrations could be almost zero. In addition, reliable exposure estimation would
Among the 30, 18-day display HQ above 1 require detailed human activity data in order to
(HQ [ 1) in at least two points in the village. establish contact rates previously to any epidemiolog-
Maximum HQ values reach 118 for Pb and 158 for Cd. ical study. Hence, human inhalation models quantify
123Environ Geochem Health (2014) 36:935–951 947
human chemical inhalation from contact with the Finally, modeling allows quantitative prediction
relevant air pollutants (Fryer et al. 2006). and spatial distribution of metal contamination with
evaluation of health hazard. This approach offers the
advantage of being non-intrusive for population and
Conclusion also less time and money consuming than in situ
sampling followed by laboratory analyses. It allows to
This study allowed assessing wind erosion of Jebel target on areas of potential higher hazard.
Ressas wastes dump and then determine the residential However, in order to improve the quality of the
and agricultural areas potentially impacted by mining modeling, it will be necessary to use yearly weather
waste and where health hazard could occur, using an data to obtain more realistic prediction of metals
emission model coupled to a transfer model (FDM). transfer and more precise health hazard prediction. In
Wind transfer of metallic contamination was stud- addition, it will be necessary to carry out a calibration
ied during 1 month in summer season when wind pattern of dust emission and transport in realizing:
erosion is likely most effective.
• Measures of the emission rates on the ground to
The concentrations in the air of PM10 were calcu-
calibrate key parameters,
lated, using meteorological data measured in situ, on a
• Sampling of aerosols with collection duration short
set of receiving points, around the mining waste dump
enough to fit with variability of wind speed and
DIII, corresponding to an area of 19.5 km2.
direction and improve the accuracy of the calculation.
During the study, maximum wind speed is 22 m/s
and the daily mean dust flux reaches 2.50 9 10-3 g/ A difficulty to be overcome will be to develop an
m2/s. This high PM10 emission rate due to the fine aerosol sampling device able to collect a sufficient
grain size and weak cohesion of the mining wastes amount for analysis.
would be even higher with the violent winds which
occur occasionally in this region. Acknowledgments We are grateful for the support provided
by the IRD Ph.D grant and the CMCU program (N09G 1003).
Thus, the comparison with the few existing in the We are indebted to Christiane Cavaré for the quality of her
literature for emission from mining waste does not graphics.
lead to a generalization in particular due to the
diversity of both meteorological and surface physical
Supplementary data: methodology of dust emission
parameters from a site to another.
and aerosol concentration modeling
For the considered period, spatial representation of
PM10 metal concentration results shows the prevail-
Dust emission calculation
ing migration of contamination mostly toward agri-
cultural lands and farms located in the NW of DIII
The dust emission rate is the sum of the direct soil
dump. The secondary spreading directions are SW and
particle aerodynamic entrainment [f (lg/m2/s)] and of
E, in particular toward the village of Jebel Ressas.
the vertical flux [F (lg/m2/s)] resulting from saltation
Moreover, Pb and Cd would exceed air quality
and sand-blasting processes.
guidelines in agricultural land and farms, up to a
The aerodynamic entrainment function (f) gives the
distance of 1,200 m and in the village up to a distance
emission rate of mobilized particles on the source
of 500 m.
surface in the absence of saltation at low wind speed
Modeling and cartography results allow concluding
(Loosmore and Hunt 2000 in Shao 2008).
that health hazard should be expected after inhalation
of Pb and Cd bearing PM10 especially in the village f ¼ 3:6U 3
where the population is concentrated but also in farms
on the main wind direction. where U (m/s) is the friction velocity
Daily and overall HQs are variable but frequently KUðzÞ
very elevated (up to 158 for Cd and up to 118 for Pb) U ¼
ln zz0
which imposes a deeper investigation of the health
situation particularly on some locations close to the K: von Karman’s constant, z: height above the surface,
waste dump. m, z0: roughness height, m, U: wind speed, m/s, F is
123948 Environ Geochem Health (2014) 36:935–951
the emission flux generated by saltation when wind
speed is high enough.
The vertical flux F of particles emitted is first based
on the calculation of the horizontal flux of soil
saltating particles G (g/m2/s) according to Marticorena
and Bergametti (1995):
Z " 2 #
q Ut Ut
G ¼ EC a U 3 1þ 1 dSrel ðdpÞddp
g U U
dp Fig. 9 Schematic drawing of a surface waste ripple exposed to
wind from different directions with parameters of shape used for
E: ratio of erodible to total surface (taken to 1 here), C: aerodynamic roughness calculation
constant of proportionality with a value of 2.61
determined from wind tunnel experiments (White • Plants and stones that are consistently exposed to
1979), qa: air density, g: gravity acceleration (m/s2), the wind. The roughness caused by these elements
dp: soil particles diameter (lm), Srel: relative surface is uniform for all wind directions. Then, it is called
occupied by a soil aggregate bin of diameter dp (m2), isotropic roughness.
U : friction velocity of wind (m/s). It depends on wind • Ripples with curvilinear forms have different
velocity and on aerodynamic roughness z0 of the fronts of exposure to wind according to their
eroded surface. Ut : threshold friction velocity of wind directions (Fig. 9). Thus, the number of fronts
(m/s): minimal wind friction velocity able to mobilize (n) in front of the wind as well as their height
sand particles by saltation (cf Marticorena and (h) and their widths (b) are variable depending on
Bergametti 1995 for more details). the direction of the wind. The effect of such
Initially, this parameterization is designed for desert anisotropic roughness varies with the orientation
dust emissions, and the soil aggregate distribution is of the ripple with respect to the direction of the
related to a given textural class using standard distri- wind. Therefore, wind erosion will not be homo-
bution (e.g., Zakey et al. 2006). Here, the soil aggregate geneous and amounts of emitted dust will vary
distribution has been defined directly from in situ with wind direction. This effect of anisotropic
measurements according to the following method. roughness has been studied to assess the conse-
Samples from DIII surface have been taken in 5 quences of the presence of grooves of labor in the
different points by scraping off the surface on 1–2 cm agricultural land on wind erosion (Saleh et al.
over several hundreds of cm2. The samples were 1997; Armbrust et al. 1964).
homogenized to make one representative sample from On DIII, surface rough elements are spread in a
which 500 g were quartered, got rid of the fraction heterogeneous way. We distinguished between four
more than 2 mm and then analyzed for grain size. different parcels in their aspects of surface roughness
First, dry grain size separation was performed with which was measured for three wind directions:
an AFNOR column: 1,600, 1,250, 630, 400, 355, 250,
200, 150, 125, 80 and 63 lm. • in the N direction which is the dominant wind
Then, grain size distribution of fraction below direction measured in situ during the period
63 lm was determined using a Coulter LS 200 laser between July 13th and August 12th, 2009.
granulometer with a small water volume. Measure- • in the SE direction which is the second dominant
ment range is between 0.393 and 905 lm. direction and for which strong winds were com-
In the G formulation, the roughness length z0 (lm) mon during the period between July 13th and
is a very sensitive parameter. We have thus deter- August 12th, 2009.
mined the roughness from direct measurement of the • in the NW direction which is the annual prevailing
surface of DIII mining waste dump. direction known for the region.
The surface of dump DIII contains erodible and We performed measurements of roughness on
non-erodible or rough elements. There are two types of representative surfaces between 0.25 and 4 m2 accord-
rough elements: ing to the dimensions of the rough elements.
123Environ Geochem Health (2014) 36:935–951 949
For each parcel, we proceeded as follows: Calculation of airborne dust concentrations
1. We determined the number of isotropic ele-
PM10 concentrations (lg/m3) have been calculated
ments (plants, rocks), their widths and their
with the fugitive dust model (FDM, Winges 1992).
heights.
FDM is a Gaussian plume model designed for
2. For each wind direction, we determined the
computing particle concentrations and deposition
number of ripple-fronts exposed to the wind, the
rates from fugitive dust emissions. The model is well
width (b) and the height (h) of each front. k
accepted by USEPA for this purpose. It incorporates
represents the totality (n) of fronts (bh) exposed to
transport, dispersion and deposition of pollutants in
the wind on a surface (s) with k = nbh/s.
the atmosphere, using input data for particulate matter
Pending on the k value, z0 can be calculated (particle diameter, density) and air parameters (wind
using one of the following equations (Marshall velocity, wind direction, temperature) measured in the
1971; Jarvis et al. 1976; Garrat 1977; Raupach et al. site together with flux of emitted PM10 from the
1980; Raupach 1991) where n, b and h are, emission model. For the 30 days, 720 data for hourly
respectively, the number, width and the height of mean emission rate of PM10 were integrated on FDM.
the rough element and s is the measurement surface. Pb and Cd concentrations in PM10 were calculated
So that, z0 = 0.005 h, if k \ 0.1 and z0 = (0.479 using metal content of dump DIII surface.
k - 0.001)h, if k [ 0.1.
The aerodynamic roughness was calculated for the Source and receptors
other wind directions in the same way.
An overall roughness z0g which takes into account The source, DIII waste dump, is assimilated to a
the different types of surfaces was calculated by rectangle of 235 m 9 150 m.
summing the different roughness observed on the We considered 2,000 receptor points obtained with
dump and weighted by the fractions of surface they a 100 9 100 m gridding over a surface of 20 km2. We
occupy. also added 30 points over the village.
From the saltation flux, the aerosol vertical emis-
sion flux is finally calculated according to Alfaro et al.
(1997) approach as:
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