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Radon has been recognised as the main contributor to the natural
radiation dose exposure to human. In addition to natural radon, human
activities like mining have the potential to enhance environmental radon
levels. Gold-mine tailings dams contain traces of 238U and 226Ra, leading
to generation of 222Rn gas in the tailings material due to radioactive decay.
Current methods used to monitor radon from the tailings dams are only
able to provide a close-up of emissions in space and time. These methods
cannot distinguish between tailings radon and background radon as well
as the extent each contributes towards the radon content in the
atmosphere. The only way to determine the increment is through
dispersion modelling. This study develops a technique to accurately
validate radon dispersion modelling that assesses the radon contribution
from Freddies 9 (sometimes referred to as Steyn 9) tailings dam situated
in Odendaalsrus, Free State Province, South Africa. This study was
structured into four parts. The first part dealt with determination of the
radon exhalation rate from the tailings dam. The second part dealt with
measuring the ambient radon concentration in the vicinity of the dam
using Radon Gas Monitors (RGMs). The third part was to measure radon
gas, individual radon daughters, and the F factor at different receptor
points downwind by following the direction of the wind at hourly intervals.
The fourth part involved using the ISCST3 dispersion modelling code to
evaluate radon transport and the effects of local variations around the
tailings dam. Field data were collected during winter months of June to
August in 2016 and 2017. Measurements of the radon exhalation rate, which is the source
term for dispersion modelling from the tailings dam, were performed with
the passive diffusion tube method. Twenty (20) tailings samples were
collected for analysis. The exhalation rate (E) was found to vary from 0.045
Bq/(m2s) to 0.443 Bq/(m2s) with an average value of 0.102 Bq/m2s and a
mean standard deviation of 0.087 Bq/(m2s). The ambient radon concentration values measured with the Radon
Gas Monitors (RGMs), averaged over 116 locations around the tailings dam
distributed over a radius of 2 km from the tailings dam, range from 38
Bq/m3 to 94 Bq/m3, with a mean concentration of 64 ± 11 Bq/m3. These
values are below the 100 Bq/m3 action level stipulated by the National
Nuclear Regulator (NNR) with a slight average increase compared to
background levels of 60 Bq/m3.
Airborne radon concentration at any given location is influenced by
locally exhaled radon and dispersed radon from other locations. As a
means of trying to discriminate between different radon contributors,
radon gas, individual radon daughters, and the F factor were measured at
different receptor points downwind and upwind by following the direction
of the wind at hourly intervals. The AlphaGUARD was used to measure
radon concentration and the Eberline SPA-1A alpha scintillation detector
coupled to Eberline Smart Portable (ESP-2) counter were used to measure
the radon daughters. The Busigin and Phillips three count method was
used to calculate radon daughter concentrations and hence the F factor.
The minimum value of F factor was 0.016 ± 0.012 measured upwind
whereas the maximum value of F factor was 0.502 ± 0.044 measured
downwind. Calculations revealed strong influence by external meteorological
effects on the distribution of radon and radon daughters some distance
from the tailings and background. The F factor, which indicates the “age”
of the gas, and radon gas, increased to their highest values when the wind
was blowing from north-northern-east (NNE). The highest radon daughter
concentrations at various locations were recorded in the mornings.
However, fluctuating and conflicting effects due to different meteorological
conditions on the resultant atmospheric radon concentration, radon
daughters’ concentration and F factor as functions of distance from the
tailings downwind, made the interpretation of results difficult. To further
quantitatively explain these results, an air dispersion model was applied. The USA Environmental Protection Agency's (EPA) Industrial Source
Complex Short Term 3 (ISCST3) dispersion modelling code using a
Gaussian plume model was used to evaluate radon transport and the
effects of local variations around the tailings dam. The tailings was
modelled as point, total emitting surface area (true geometry) and volume
source. The true area geometry was considered as the baseline source
geometry. To improve the accuracy of the model predictions as compared
to traditional approaches, the area source term was corrected to account
for cracks and fissures on the tailings and the geometry of tailings dam
was modelled by taking into account all emitting surfaces as sources.
Compared to the baseline, the model over predicted the flat ground
area source by up to 274 % and under predicted the top level area source
by up to 50%. The volume emission source was over predicted by up to
300% in 60% of the modelling runs and under predicted by 55% in 40% of
the volume model runs. While the top level area source term produced
lower concentrations at near-field ground-level receptors, accounting for
the wakes effect increased the radon concentrations from the top-level area
source of the tailings dam by up to 239%.
From modeling results, the highest concentration predicted by the
model from the true geometry source was found to be 0.843 Bq/m3, which
correspond to the dose of 0.012 mSv/y to the public of due to radon from
the tailings. This value is less than the 1 mSv/y dose constraint stipulated
by the NNR. Model validation from statistical analysis showed a constant trend
for all the scenarios, with minimum variability in the Index of Agreement
(IOA), Normalized Mean Square Error (NMSE) and Fraction of predictions
within a factor of two (FAC2) values. The analysis were based on the model
results over five days of measurements covering both morning and
afternoon. There is an under prediction in the Fractional Bias (FB) and
Geometric Mean bias (MG) on day 1 afternoon. In addition, the model
performed poorly on day 3 afternoon. Further validation of the model was carried out by isolating radon
from different contributors using the “age” of the gas approach and
applying back calculations to identify origin of the radon measured at each
point downwind. As predicted by the model, the origin of the radon source
was traced back to the tailings. |
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