atmosphere

Article
Reducing Mercury Emission Uncertainty from Artisanal and
Small-Scale Gold Mining Using Bootstrap Confidence Intervals:
An Assessment of Emission Reduction Scenarios
Delia Evelina Bruno *,† , Francesco De Simone † , Sergio Cinnirella , Ian Michael Hedgecock ,
Francesco D’Amore and Nicola Pirrone

CNR-Institute of Atmospheric Pollution Research, 87036 Rende, Italy

* Correspondence: delia.bruno@iia.cnr.it
† These authors contributed equally to this work.

Abstract: Atmospheric mercury emission scenarios from artisanal and small-scale gold mining
for 56 tropical and subtropical countries have been elaborated and assessed for their comparative
significance. A multi-step quantitative method that yields narrow and robust confidence intervals for
mercury emission estimates was employed. Firstly, data on gold production for different years, the
ratio of mercury used in the different amalgamation processes, and ancillary input parameters were
retrieved from official and unofficial sources, and their potential for emission reduction examined.
Then, a Monte Carlo method to combine the data and generate mercury emission samples was
used. These samples were processed by a non-parametric re-sampling method (bootstrap) to obtain
robust estimates of mercury emissions, and their 95% confidence intervals, both for the current state
and for the emission scenarios designed in this study. The artisanal and small-scale gold mining
mercury emission (to the atmosphere) estimates agree with those reported in the Global Mercury
Assessment 2018; however, the overall uncertainty is reduced from roughly 100% in the Global
Citation: Bruno, D.E.; De Simone, F.; Mercury Assessment (779.59 tons/y; uncertainty range: 361.07–1197.97) to 27% (1091.93 tons/y;
Cinnirella, S.; Hedgecock I.M.; confidence interval at 95% level of confidence: 964.54–1219.77) in this study. This is a substantial
D’Amore, F.; Pirrone, N. Reducing
outcome since the narrowing of the confidence intervals permits a more meaningful evaluation of
Mercury Emission Uncertainty from
the different emission scenarios investigated, which otherwise, given the broad uncertainty of other
Artisanal and Small-Scale Gold
estimates, would have led only to vague conclusions in a study of this nature.
Mining Using Bootstrap Confidence
Intervals: An Assessment of
Keywords: Hg; ASGM; mining; emission; pollution; atmosphere
Emission Reduction Scenarios.
Atmosphere 2023, 14, 62. https://
doi.org/10.3390/atmos14010062

Academic Editors: Maria de Lurdes

1. Introduction
Dinis, Ana Sofia Silva and João dos
Santos Baptista
Mining, and in particular gold (Au) mining, is one of the oldest activities of mankind [1].
Illegal and unregulated Au mining activities, namely artisanal and small-scale gold mining
Received: 9 November 2022 (ASGM), currently represent one of the most serious environmental issues globally, not
Revised: 22 December 2022 only because the extracted raw material may contain harmful metals, such as As and Pb,
Accepted: 24 December 2022 but also because ASGM commonly uses mercury (Hg) to refine Au due to its cheap price,
Published: 29 December 2022
easy handling and its ability to form an amalgam with Au [2,3].
Au is extracted from a great variety of deposits in different geological contexts: in
very ancient Precambrian metamorphic rocks; in various meso and epizonal deposits; and
Copyright: © 2022 by the authors.
also in much younger alluvial placer deposits. For example, in Southeast Asia, Au is found
Licensee MDPI, Basel, Switzerland. in Cenozoic and Mesozoic deposits [4], while in Africa and Brazil, it is extracted from
This article is an open access article Precambrian quartz veins and also alluvial placer deposits [5].
distributed under the terms and The Au grade differs among lithologies on the basis of petrogenesis, and in some
conditions of the Creative Commons lithologies, depends on the type of weathering. The productivity of a site depends not only
Attribution (CC BY) license (https:// on the grade of the ore, but also on the technology used, which differs from country to
creativecommons.org/licenses/by/ country: greater recovery rates and quantities of Au produced are found in South America
4.0/). where newer technologies are in use, compared to Africa, where less modern and less

Atmosphere 2023, 14, 62. https://doi.org/10.3390/atmos14010062 https://www.mdpi.com/journal/atmosphere

Atmosphere 2023, 14, 62 2 of 17

efficient techniques are more common [6]. Therefore, each combination of ore type and
technique can result in different ratios of Hg used to Au produced [7].
In order to extract Au, one of two (Hg) amalgamation processes are generally used:
concentration amalgamation (CA) or whole ore amalgamation (WOA). While both tech-
niques involve the use of Hg, they are very different in terms of the Hg quantities used.
CA typically concentrates the ore using gravitational separation techniques (such as the
traditional panning method) in order to discard lighter minerals and retain the heavier frac-
tion containing Au. This concentrate is then mixed with Hg to produce the amalgam. Due
to the pre-concentration step, less Hg is required in CA than in WOA. WOA crushes the
ore and without pre-concentration, Hg is added to produce the amalgam. This technique
has a higher Hg:Au ratio and Au recovery is less efficient [8]. Once produced, the Au-Hg
amalgam is then heated to remove Hg, leaving relatively pure Au.
The employment of CA or WOA techniques also has profound implications in terms
of the environmental matrices affected by Hg contamination [9].
During amalgam heating, if a retort is not used to condense the Hg, its vapors are
emitted to the atmosphere and can be inhaled by the operators [10]. Furthermore, the
washing of amalgams can release Hg to soils and waters, exposing indigenous peoples that
live in the neighborhood to high levels of Hg [11].
Hg is a potent neurotoxin and nephrotoxin that can cause chronic and acute ill-
health [12]. Humans and other mammals are mainly exposed to Hg via consumption
of its methylated form (MeHg) that bio-magnifies through the food web [13–15], al-
though gaseous Hg at elevated concentrations can expose people to Hg poisoning via
inhalation [10]. It has been reported that roughly 15 million people, including 4 to 5 mil-
lion women and children, are employed in ASGM activities worldwide (https://www.
planetgold.org/asgm-101) (accessed on 15 January 2021) without any training regarding
the health risks they face [16].
However, the extent of the threat from Hg for human health is not only local, but it
is global [17,18]. Elemental Hg is volatile and can join the global atmospheric Hg pool if
not deposited locally, and therefore affects areas and ecosystems far away from release
points [19,20].
Since the impact of Hg is world wide, Hg production, trade, use and disposal are now
regulated under the Minamata Convention on Mercury(MC) (www.mercuryconvention.org/)
(accessed on 15 January 2021), which was adopted in October 2013 and came into force
in August 2017. Within the convention, a number of approaches are suggested to the
parties to estimate the emissions of Hg from ASGM, and to prepare a national action
plan (NAP) to reduce Hg utilization in order to fulfill the requirements under Article 7
of the MC [7]. Many of these are applicable only at a national level and require direct
interaction between government departments, civil society organizations and other parties,
and workers. Moreover, in many cases, ASGM is poverty-driven and therefore a long-
standing, and often unofficial activity in many developing countries [21]; therefore, ASGM
sometimes escapes official statistics and policy measures under the MC. A number of local
scale studies have focused on quantifying the Hg emissions from ASGM in small areas
using soil, water or air samples [22–24]; however, a global picture of the phenomena is
missing. A better knowledge of the social, environmental, and financial development efforts
in the sector and measurements of Hg used over a cross section of ore-processing techniques
and operators permitted the most recent Global Mercury Assessment (GMA) [25] to reduce
the overall uncertainty of the amount of Hg used for Au production, and therefore to
improve the assessment of Hg emissions from ASGM. Among the anthropogenic sources
of Hg, ASGM is now believed to be the most important, accounting for 838 tons yr−1
(38%) of the emissions to the atmosphere and 1200 tons yr−1 (67%) released to waters
and soils [25]. Four main approaches were used to estimate Hg emissions from ASGM
in the GMA: (1) direct measurements—using a balance to directly weigh amounts of Hg used;
(2) applying a Hg:Au ratio to the quantity of Au produced based on the type of process used (whole
ore amalgamation, concentration amalgamation, and also taking into account of the use of emission
Atmosphere 2023, 14, 62 3 of 17

controls, such as retorts, etc.); (3) interviewing miners and Au merchants who buy or sell Hg; and
(4) using official production data [26].
This multi-faceted approach has, however, many issues. One limitation of the GMA
is that it cannot be updated frequently due to the need to arrange interviews and collect
socio-economic information. In addition, given the not-always-legal nature of ASGM, it
is not surprising that some information is not forthcoming. Moreover, the uncertainty of
the estimates is given by expert consideration and varies across the approaches used [26].
Indeed, the GMA applied four different error ranges to its estimates, based on the approach
used, and on the assumed quality of data. The lowest error range was applied for the
approach using recent quantitative data, namely ±30%, whereas when no quantitative
information was present, the GMA used its widest error range, ±100%. The two remaining
approaches consider error ranges of ±50% when quantitative data were present but signifi-
cantly updated within the past 5 years, and of ±75% when some indication of the quantity
of Hg used was given [27]. Another limitation is the fact that emissions were estimated
using different individual years for the various countries where ASGM is practiced [26].
All these features point to a drawback of the GMA estimates: they lack temporal
and internal consistency, which is a key issue for generating emissions scenarios; see
Mahmoud et al. [28] and references therein. Indeed, for policy considerations, a crucial
aspect is the assessment of a set of plausible alternative states of Hg emissions from
ASGM under different scenarios [17,29], and the issues underscored above clearly hamper
the development of a coherent methodology to calculate alternative emissions scenarios.
For all these reasons, there are few studies dealing with ASGM Hg emission scenarios.
Streets et al. [30] developed a projection of Hg emissions to 2050 under two increasing
emission scenarios, namely A1B and A2, however these are driven by coal combustion in
developing countries and no information is given explicitly for ASGM. Pacyna et al. [31]
projected Hg emissions to 2035 under different scenarios, where ASGM is reduced by 46%
(new policies scenario) and by 76% in the maximum feasible reduction scenario). However,
also in this case, these reductions appear to come from an expert evaluation rather than
from a quantitative and reproducible methodology. In contrast, Telmer and Veiga [16]
qualitatively developed two Hg emissions scenarios, providing two levels of reduction
based on two different Hg recycling schemes.
Tong et al. [32] developed a multi-step procedure, based on bootstrap, to estimate
the uncertainty associated with the national greenhouse gas inventory of Taiwan. The
procedure was applied in a case study involving the carbon stock of Japanese cedar in
Taiwan, whose evaluation depends on a multiplicative equation of parameters characterized
by their own distribution and/or bounds. To overcome the limitations listed above in the
evaluation of Hg from ASGM, in this work, we have applied the multi-step procedure
proposed by Tong et al. [32]. It is based on the non-parametric re-sampling of homogeneous
and comparable data regarding Au production, the scale of ASGM, and the ratio of Hg used
in the different amalgamation processes, collected by scrutinizing official and unofficial
documents for 56 tropical and subtropical countries for different years in the time window
2006–2019. This allowed us to obtain estimates of the Hg emissions from ASGM, along
with robust confidence intervals, greatly reducing the associated uncertainty.

2. Materials and Methods

Through this work, the concept of “releases” is well-described by Kocman et al. [33],
in particular on definition of “remobilization from terrestrial systems”. In this case, the
source of Hg pollution derives from the remobilization of contaminated land and water
management practices, associated with active or abandoned ASGM. Instead, the “emissions”
represent the Hg discharge into the atmosphere, during amalgam burning operations which
can take place at very variable distances from the extraction site.
Atmosphere 2023, 14, 62 4 of 17

2.1. Uncertainty Associated to ASGM Hg Emission Estimates

In order to estimate the emissions and releases of Hg from ASGM, we used the
production of Au as the main driver, exploiting the equation from O’Neill et al. [7]:

Hg
HgUs = ∗ Au ASGMPr (1)
Au
where Au ASGMPr is the national production of Au from ASGM, and Hg:Au represents the
Hg:Au ratio used, which depends mostly on the extraction methods used, namely CA and
WOA, but there are significant differences among ASGM sites.
In general, estimating the Hg emissions from different sources sectors is not a simple
task due to the uncertainties involved. For the ASGM sector, the situation is somewhat
complicated due to the irregular nature of the activities. In the literature, since the amount
of amalgam, and therefore Hg, used is proportional to the quantity of Au produced, the
uncertainty related to the Hg emissions from ASGM in the atmosphere has been considered
roughly equivalent to the uncertainty in the quantity of Au produced [27]. However, this
assumption seems to be too simplistic, for a number of reasons.
The first parameter that influences Hg emissions is the scale of ASGM, Au ASGMPr , in
a given country, which is variable and depends on a number of factors. It is considered
to be on average in the range 20–30% but in some countries, it can be up to 100%; see
Yoshimura et al. [8] and references therein. A major parameter associated with the fraction
of Hg that goes into the atmosphere is the amalgamation technique used; hence, if this
is not known, the uncertainty increases. The Hg:Au ratios reported vary from 3:1 to 5:1
for WOA and 1:1 to 3:1 for CA [34,35]. In a recent study, Hg:Au ratios were proposed that
represent, on average, the characteristic ratios at a continental scale: 1.96 for Africa, 1.23
for Asia and Oceania, and 4.63 for Central and South America [8,36]. However, single
direct measurements at sites employing WOA have shown far higher ratios, including
6.5 in Colombia [37], 15 in Antioquia, Colombia [38], 40–60 in Indonesia [39] and 70
in Burkina Faso [40]. As well as the amalgamation technique used, another source of
uncertainty is the fate of the Hg released, where a fraction goes to the atmosphere and the
remainder to local soils and rivers. Pfeiffer et al. [41] reported that 55% Hg is released to
the atmosphere, while Pfeiffer et al. [42] estimated that 65–83% of the total Hg losses go to
the atmosphere. More recently, CA and WOA were estimated to emit 75% and 20% of the
Hg to the atmosphere, and 25% and 80% to soils and water, respectively [9].

2.2. Multi-Step Bootstrap Procedure to Estimate ASGM Hg Emissions

In this study, we used a multi-step procedure to address the uncertainties mentioned
above in order to give robust estimates of Hg emissions and their associated confidence
intervals. The method follows the work of Tong et al. [32], which was applied to the
national greenhouse gas inventory of Taiwan.
In the first step, (1) the data on Au production for 56 countries belonging to tropical and
sub-tropical region for the period 2006–2019 were collected. The length of the period was
chosen to take in account inter-annual variability, which may arise from poor or incomplete
reporting, fluctuations in the price of Au and changing work opportunities among others.
Data relative to the Hg:Au ratio and the scale of ASGM, Au ASGMPr were also collected. In
the second step, (2) a Monte Carlo method was employed to generate emissions samples of
Hg (Equation (1)) with all the possible combinations of collected parameters, as reported in
Table 1. These sample were then processed (3) by a non-parametric re-sampling method
with replacement (bootstrap [43]) in order to obtain robust estimates of the mean of the Hg
emissions, and their 95% confidence intervals.
Bootstrap is the core of the multi-step procedure presented in this study and has many
advantages in contexts where the underlying distribution of the population is unknown
and/or the number of available samples are limited [43,44]. Among the available methods
to evaluate the confidence intervals of the mean, the percentile approach was used; see [32]
Atmosphere 2023, 14, 62 5 of 17

for a detailed description. To assure the convergence of the mean and of the relative
confidence intervals, N = 10000 re-sample extractions were used.

Table 1. The combination of parameters used in this study for the bootstrap-based estimates of Hg
emissions from ASGM. “Atm” indicates the fraction of Hg emitted into the atmosphere.

CA WOA
Country Region ASGM (%) Ratio Atm. Ratio Atm. Ratio Atm. Reference
Algeria Northern Africa 20–30 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [45–47]
Bolivia South America 90–100 4.63 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [48]
Brazil South America 10–25 4.63 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [49,50]
Burkina Faso Western Africa 10 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [49,51]
Burundi Eastern Africa 20–30 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [45,46,52–54]
Cameroon Middle Africa 20–30 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [53,55]
Chad Middle Africa 20–30 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [53,56]
Chile South America 20–30 4.63 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [53,57]
China Eastern Asia 50–75 1.23 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [58]
Colombia South America 90–100 4.63 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [33,37,49]
Congo Middle Africa 20–30 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [49,59]
Côte d’Ivoire Western Africa 20–30 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [16,60]
Ecuador South America 100 4.63 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [61–63]
Egypt Northern Africa 20–30 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [45,53,64]
El Salvador Central America 20–30 4.63 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [16,65]
Equatorial
Middle Africa 20–30 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [16,45,53]
Guinea
Ethiopia Eastern Africa 20–30 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [16,53,66,67]
Fiji Melanesia 20–30 1.23 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [49,68]
French Guiana South America 100 4.63 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [69]
Ghana Western Africa 25–50 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [16,68,70]
Guinea-Bissau Western Africa 10–25 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [71,72]
Guyana South America 90–100 4.63 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [53,73,74]
Honduras Central America 10 4.63 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [8,45,53,75]
Indonesia South-eastern Asia 25–50 1.23 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [45,53,76]
Kenya Eastern Africa 20–30 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [45,47,53,77,78]
Kyrgyzstan Central Asia 20–30 1.23 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [79]
Lao People South-eastern Asia 20–30 1.23 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [8,53,80]
Liberia Western Africa 20–30 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [47]
Madagascar Eastern Africa 20–30 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [45,47,81]
Mali Western Africa 10–25 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [16]
Mongolia Eastern Asia 25–50 1.23 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [63]
Morocco Northern Africa 20–30 1.23 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [47,77,82]
Mozambique Eastern Africa 100 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [53,83,84]
Myanmar South-eastern Asia 20–30 1.23 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [85,86]
Namibia Southern Africa 20–30 1.23 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [49,87]
Nicaragua Central America 25–50 4.63 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [69,88,89]
Niger Western Africa 20–30 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [53]
Nigeria Western Africa 100 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [90]
Papua New
Melanesia 20–30 1.23 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [8,45,53,85]
Guinea
Perù South America 25–50 4.63 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [45,53,63]
Philippines South-eastern Asia 50–75 1.23 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [8,53]
Rwanda Eastern Africa 20–30 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [49]
Senegal Western Africa 10 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [91,92]
Sierra Leone Western Africa 100 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [45,46,53]
Somalia Eastern Africa 20–30 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [49,93]
South Africa Southern Africa 20–30 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [45,53]
Sri Lanka Southern Asia 20–30 1.23 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [63,77]
Sudan Northern Africa 90–100 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [16,53,94]
Suriname South America 50–75 4.63 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [45,53]
Tajikistan Central Asia 20–30 1.23 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [8,53]
Tanzania Eastern Africa 10–25 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [63]
Togo Western Africa 20–30 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [16,95]
Uganda Eastern Africa 20–30 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [16,53,63,96]
Uzbekistan Central Asia 20–30 1.23 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [53,74]
Venezuela South America 100 4.63 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [8,53,74]
Zimbabwe Eastern Africa 20–30 1.96 0.65–0.83 1.0–3.0 0.75 3.0–5.1 0.2 [86]

The full workflow of the tasks is shown in Figure 1.

Atmosphere 2023, 14, 62 6 of 17

Figure 1. Conceptual workflow of the tasks performed in this study. From an initial database of
56 countries, with more than a decade of observations for the most part of countries, the estimates of
Au produced, Hg used, emitted and released were obtained. On the basis of bootstrap method, the
emissions and releases of Hg were estimated, then six Hg emissions scenarios were devised, and the
corresponding emissions calculated. The comparison with the GMA served also as an out-of-sample
evaluation of the procedure.

2.3. Scenario of Hg Emissions from ASGM

Using the same multi-step procedure, six ASGM Hg emissions scenarios were devel-
oped, using the production of Au as the main driver, and considering different combinations
of Hg extraction and recycling techniques, applied in a variable fraction of ASGM sites
worldwide. In some cases, indeed, the utilization of fume hoods, retorts, and other Hg
recovery procedures meaningfully reduced Hg consumption, with low costs, that were
recovered rapidly due to the decreased Hg purchases. These anecdotal cases reported in
Telmer and Veiga [16] demonstrate that the main impediment of the adoption of these
simple and cheap technologies is their slow spread across the mining communities.
The baseline Scenario 1, “Business As Usual” (Scen Base ), represents the current state,
namely the Au production, the scale of ASGM activity, the ratio of WOA/CA extraction
technologies and the current negligible use of Hg recycling.
Scenario 2, “Reduction of 90% of Hg in 75% of ASGM” (Scen Red90−75 ), was adopted
from an idea of Telmer and Veiga [16] to conceptualize the possible reduction of Hg. They
estimated a plausible reduction of 90% Hg emissions to the atmosphere by the adoption of
fume hoods and retorts in every ASGM site. In this study, we relax somewhat the diffusion
of these technologies, assuming their adoption in 75% of ASGM sites.
Scenario 3, “Reduction of 50% of Hg in 50% of ASGM” (Scen Red50−50 ), is also adopted
from Telmer and Veiga [16], in which they estimated a reduction of 50% in Hg emissions to
the atmosphere from the adoption Hg reactivation or cleaning methods. As for the previous
scenario, in this study, we assume the adoption in 50% of ASGM sites.
Scenario 4, “An increase of 33% of Au extraction activity” (Scen Incr33 ), was developed
to reflect a possible increase of Au extraction if the price of Au rises and/or economic
instability, leading to an increase in Au extraction via ASGM. This scenario assumes that
the CA/WOA ratio remains unchanged.
Scenario 5, “Minimum feasible amount of Hg in CA/WOA” (Scen MinHg ), was devel-
oped to assess the effect of using the minimum feasible amount of Hg for each extraction
technique employed at ASGM sites.
Scenario 6, “Maximum feasible reduction” (Scen MFR ), was developed as the minimum
environmental impact scenario in which each ASGM site adopts retorts and fume hoods
along with the absolute minimum use of Hg by CA. This scenario assumes WOA is no
longer employed anywhere.
The first scenario does not involve any variation with respect to the current conditions,
while scenarios 2 and 3 were proposed by Telmer and Veiga [16] and are quite realistic since
they are based on the diffusion of cheap and easy-to-use techniques. Scenarios 4, 5 and 6
represent hypothetical cases.
Atmosphere 2023, 14, 62 7 of 17

3. Results
Figure 2 shows the level of ASGM for the countries analyzed in this study. The
greatest proportion of ASGM in terms of national total Au production was found to be in
Colombia, Ecuador, French Guiana, Guyana, Mozambique, Nigeria, Sierra Leone, Sudan
and Venezuela, where over 90% of Au production comes from ASGM.

Figure 2. Level of ASGM for each country.

The ASGM Hg emissions estimated employing the multi-step procedure used in this
study are reported in Table 2.

Table 2. Summary of the Hg emissions from ASGM estimated in this study, compared to GMA
estimates. NoASGM indicates that for that country, no Hg emissions from ASGM is assessed
by GMA.

Hg Emissions—Ton/y Uncertainty—Ton/y (%) Hg Releases—Ton/y

Country This Study GMA This Study GMA This Study
Algeria 0.11 [0.09–0.13] NoASGM 0.04 (34%) NO ASGM 0.13 [0.09–0.16]
Bolivia 39.81 [33.31–46.32] 40.50 [28.35–52.65] 13.02 (33%) 24.3 (60%) 34.31 [27.81–40.72]
Brazil 30.10 [25.65–34.56] 49.88 [24.94–74.81] 8.91 (30%) 49.87 (100%) 25.94 [21.51–30.35]
Burkina Faso 5.58 [5.06–6.10] 26.33 [13.16–39.49] 1.04 (19%) 26.33 (100%) 6.31 [5.15–7.49]
Burundi 0.55 [0.44–0.66] 0.23 [0.06–0.39] 0.23 (41%) 0.33 (143%) 0.62 [0.44–0.81]
Cameroon 0.51 [0.47–0.55] 1.13 [0.28–1.97] 0.08 (16%) 1.69 (150%) 0.58 [0.48–0.67]
Chad 0.02 [0.02–0.02] 0.23 [0.06–0.39] 7.18 (36%) 0.33 (143%) 0.02 [0.02–0.03]
Chile 20.60 [18.31–22.92] 1.90 [0.48–3.33] 4.61 (22%) 2.85 (150%) 17.76 [15.34–20.18]
China 280.20 [251.51–308.92] 33.75 [8.44–59.06] 57.41 (20%) 50.62 (150%) 353.25 [283.52–423.02]
Colombia 85.89 [75.94–95.93] 51.04 [25.52–76.56] 20.00 (23%) 51.04 (100%) 74.02 [63.54–84.50]
Congo 10.26 [9.17–11.35] 1.13 [0.28–1.97] 2.18 (21%) 1.69 (150%) 11.59 [9.36–13.86]
Côte d’Ivoire 3.40 [2.90–3.89] 0.23 [0.06–0.39] 0.99 (29%) 0.33 (143%) 3.84 [2.98–4.70]
Ecuador 26.46 [23.33–29.60] 26.35 [13.18–39.53] 6.27 (24%) 26.35 (100%) 22.81 [19.58–26.07]
Egypt 2.65 [2.23–3.07] NoASGM 0.84 (32%) NO ASGM 2.99 [2.26–3.72]
El Salvador 0.10 [0.05–0.15] 0.23 [0.06–0.39] 0.09 (92%) 0.33 (143%) 0.09 [0.04–0.13]
Equatorial Guinea 0.41 [0.37–0.45] 0.23 [0.06–0.39] 0.08 (19%) 0.33 (143%) 0.46 [0.38–0.54]
Ethiopia 0.17 [0.14–0.21] 0.23 [0.06–0.39] 0.06 (36%) 0.33 (143%) 0.20 [0.14–0.25]
Fiji 0.35 [0.32–0.39] NoASGM 0.07 (20%) NO ASGM 0.44 [0.36–0.53]
French Guiana 37.20 [32.55–41.87] 5.63 [2.81–8.44] 9.32 (25%) 5.63 (100%) 32.06 [27.17–36.95]
Ghana 52.60 [46.92–58.30] 41.25 [20.63–61.88] 11.38 (22%) 41.25 (100%) 59.44 [47.83–71.06]
Guinea–Bissau 23.73 [20.80–26.63] 0.23 [0.06–0.39] 5.83 (25%) 0.33 (143%) 26.81 [21.10–32.54]
Guyana 24.52 [21.49–27.55] 11.25 [5.63–16.88] 6.06 (25%) 11.25 (100%) 21.13 [18.09–24.21]
Honduras 0.45 [0.40–0.50] 2.38 [1.19–3.56] 0.10 (22%) 2.37 (100%) 0.39 [0.34–0.44]
Atmosphere 2023, 14, 62 8 of 17

Table 2. Cont.

Hg Emissions—Ton/y Uncertainty—Ton/y (%) Hg Releases—Ton/y

Country This Study GMA This Study GMA This Study
Indonesia 39.41 [34.79–44.12] 124.54 [62.27–186.81] 9.33 (24%) 124.54 (100%) 49.68 [39.38–59.91]
Kenya 0.41 [0.33–0.48] 2.63 [0.66–4.59] 0.15 (36%) 3.93 (149%) 0.46 [0.34–0.58]
Kyrgyzstan 3.23 [2.90–3.56] 3.56 [0.89–6.23] 0.66 (20%) 5.34 (150%) 4.07 [3.26–4.89]
Lao 1.52 [1.36–1.67] 2.25 [1.13–3.38] 0.31 (20%) 2.25 (100%) 1.91 [1.55–2.28]
Liberia 15.99 [14.50–17.49] 2.38 [1.19–3.56] 2.99 (19%) 2.37 (100%) 18.06 [14.72–21.42]
Madagascar 0.13 [0.09–0.18] 1.13 [0.28–1.97] 0.09 (71%) 1.69 (150%) 0.15 [0.08–0.22]
Mali 9.33 [8.32–10.34] 9.38 [4.69–14.06] 2.02 (22%) 9.37 (100%) 10.54 [8.52–12.54]
Mongolia 5.11 [4.42–5.79] 5.46 [2.73–8.19] 1.37 (27%) 5.46 (100%) 6.44 [5.04–7.86]
Morocco 2.50 [2.20–2.79] NoASGM 0.59 (24%) NO ASGM 3.15 [2.46–3.82]
Mozambique 0.29 [0.26–0.33] 3.00 [1.50–4.50] 0.07 (25%) 3 (100%) 0.33 [0.26–0.40]
Myanmar 17.34 [15.60–19.09] 11.25 [2.81–19.69] 3.50 (20%) 16.88 (150%) 21.86 [17.60–26.17]
Namibia 0.05 [0.04–0.05] NoASGM 0.01 (20%) NO ASGM 0.06 [0.05–0.07]
Nicaragua 0.03 [0.02–0.03] 0.70 [0.49–0.91] 0.01 (27%) 0.42 (60%) 0.02 [0.02–0.03]
Niger 0.51 [0.45–0.57] 0.23 [0.06–0.39] 0.12 (23%) 0.33 (143%) 0.57 [0.46–0.69]
Nigeria 12.31 [10.91–13.70] 15.00 [7.50–22.50] 2.79 (23%) 15 (100%) 13.91 [11.04–16.72]
Papua New Guinea 16.91 [15.37–18.46] 3.33 [0.83–5.82] 3.09 (18%) 4.99 (150%) 21.32 [17.45–25.21]
Perù 108.39 [94.80–122.09] 110.36 [55.18–165.54] 27.29 (25%) 110.36 (100%) 93.41 [79.69–107.31]
Philippines 17.98 [16.00–19.96] 23.63 [11.81–35.44] 3.96 (22%) 23.63 (100%) 22.67 [18.16–27.10]
Rwanda 0.02 [0.02–0.02] 0.23 [0.06–0.39] <0.01 (22%) 0.33 (143%) 0.02 [0.02–0.03]
Senegal 2.97 [2.69–3.26] 2.25 [1.58–2.93] 0.57 (19%) 1.35 (60%) 3.36 [2.73–4.00]
Sierra Leone 0.23 [0.20–0.25] 8.25 [4.13–12.38] 0.06 (25%) 8.25 (100%) 0.25 [0.20–0.31]
Somalia 16.32 [14.85–17.77] NoASGM 2.92 (18%) NO ASGM 18.45 [15.08–21.79]
South Africa 44.98 [40.44–49.49] 1.66 [0.42–2.91] 9.05 (20%) 2.49 (150%) 50.83 [41.20–60.32]
Sri Lanka 4.61 [4.05–5.17] NoASGM 1.11 (24%) NO ASGM 5.81 [4.49–7.13]
Sudan 63.93 [55.12–72.85] 62.25 [15.56–108.94] 17.72 (28%) 93.38 (150%) 72.25 [56.19–88.42]
Suriname 29.68 [25.76–33.60] 14.33 [10.03–18.63] 7.84 (26%) 8.6 (60%) 25.58 [21.67–29.51]
Tajikistan 0.87 [0.75–0.98] 3.00 [0.75–5.25] 0.23 (26%) 4.5 (150%) 1.09 [0.86–1.33]
Tanzania 8.97 [8.02–9.92] 26.25 [6.56–45.94] 1.89 (21%) 39.38 (150%) 10.14 [8.24–12.05]
Togo 8.70 [7.78–9.61] 3.00 [0.75–5.25] 1.82 (21%) 4.5 (150%) 9.83 [7.90–11.76]
Uganda 0.49 [0.41–0.57] 3.00 [0.75–5.25] 0.16 (33%) 4.5 (150%) 0.55 [0.41–0.69]
Uzbekistan 0.21 [0.17–0.24] 0.23 [0.06–0.39] 0.07 (34%) 0.24 (104%) 0.26 [0.19–0.33]
Venezuela 8.08 [6.30–9.85] 34.43 [17.21–51.64] 3.55 (44%) 34.43 (100%) 6.96 [5.26–8.66]
Zimbabwe 4.78 [4.15–5.43] 7.75 [3.88–11.63] 1.28 (27%) 7.75 (100%) 5.40 [4.24–6.55]

The five countries that emit most Hg were found to be China, Peru, Colombia, Sudan
and Ghana, with 280, 108, 86, 64 and 53 Tons emitted each year, respectively. Together, these
five countries represent roughly half of the overall estimated Hg emissions from ASGM
calculated in this study. Except for China, the estimated Hg emissions for these countries
agree (within the estimated uncertainty ranges) with those of the GMA.
Hg emissions from China in our study are considerably higher when compared to the
Hg ASGM emissions estimated by GMA. A similar difference was also found recently in the
calculation of the gap between the demand and supply of Hg to the ASGM sector, where
Asia showed an amount of Hg supplied to ASGM significantly lower than the apparent Hg
consumption [36].
Other notable differences between our estimates and those of the GMA are found for
Indonesia (underestimated) and South Africa (overestimated). Similar deviations for these
countries are also seen in the study by Cheng et al. [36].
Overall however, our estimates generally agree with those of the GMA. More im-
portantly, the overall uncertainty is reduced from roughly 100% in the Global Mercury
Assessment (779.59 tons/y; uncertainty range: 361.07–1197.97) to 27% (1091.93 tons/y;
confidence interval: 964.54–1219.77) in this study.
Estimates from this study agree with those of the GMA not only when considering all
the countries, but also when grouped into macro-areas, as illustrated in Figure 3.
Atmosphere 2023, 14, 62 9 of 17

Figure 3. ASGM Hg emissions estimated in this study grouped by macro-area. Estimates from GMA
are also reported for comparison purposes.

Our estimates are generally somewhat higher than those from the GMA, although
for Central and South America and Africa, they agree within the uncertainty range. For
Asia—Melanesia, on the contrary, the estimates do not agree: 387.73 tons/y (confidence
interval: 347.27–428.36) for this study vs. 216.63 tons/y (94.53–338.70) according to the
GMA. The discrepancy (within the uncertainty range) is only a few tons/y and is due
predominantly to the large difference in the China emissions estimates, as described above.
Also for the macro-areas, it is important to underscore how our approach leads to much
narrower uncertainty ranges compared to those of the GMA.
The emission estimates vary greatly between countries even within the same macro-
area. This obviously depends on a number of interconnected factors, including the
abundance of Au veins, the geomorphology of the terrain, and the individual nations
socio-economic conditions. Indeed, the poverty of a country is considered a main driver
of ASGM activity. A number of macro-economic variables are available, such as GDP
(data.worldbank.org/indicator/) (accessed on 15 January 2021) to understand the under-
lying causes of ASGM; however, this is not a simple task due to all the interconnected
factors, and does not follow a simple pattern. A detailed investigation is beyond the scope
this study.

ASGM Hg Emission Scenarios

To further this analysis, we assessed the impact of the six different ASGM Hg emissions
scenarios conceptualized in this study.
Figure 4 presents the total Hg emissions from ASGM for each country in each scenario,
along with the relative confidence intervals (95%), compared with the corresponding data
from the GMA. The detailed Hg emissions by country in each scenario are reported in
Table 3.
Atmosphere 2023, 14, 62 10 of 17

Figure 4. Six emission scenarios of Hg from ASGM compared to GMA. The reduced uncertainty
associated to estimates of this study allows for a critical assessment of the scenarios conceptualized.

The adoption of fume hoods and retort in 75% of ASGM sites, Scen Red90−75 causes
a significant (at 95% level of confidence) reduction of roughly 68% in the Hg emissions,
whereas the adoption of Hg reactivation or cleaning methods in the 50% of ASGM sites
(Scen Red50−50 ) causes a reduction, also significant (at 95% level of confidence) of approxi-
mately 24% in the Hg emissions with respect to current emission estimates.
The other two scenarios that consider a reduction in Hg emissions from ASGM are
somewhat hypothetical. Scen MinHg assumes a complete diffusion of the awareness by the
mining communities regarding the impact of Hg in the environment and therefore minimal
use of Hg regardless of the amalgamation technique used, but without the adoption of any
methods to recycle the Hg employed. This greater awareness would significantly (at 95%
level of confidence) reduce the Hg emissions by half. Scen MFR examines the maximum
feasible reduction of ASGM Hg emissions, assuming the minimal use of Hg in the CA
technique along with the adoption of fume hoods and retorts in every site. In this scenario,
the emissions of Hg from ASGM to the atmosphere would be almost eradicated, being
reduced by over 94%.
In the remaining scenario, Scen Incr33 , the only one that assumes an increase, of 33% in
the Au extraction activity, potentially due to an increase in the price of Au, or to changing
the socio-economic conditions, the ASGM Hg emissions increase (at 95% level of confidence)
proportionally.
The most desirable condition, from an environmental point of view is, of course,
the Scen MFR scenario. However, both this scenario and the Scen MinHg scenario are hy-
pothetical. In contrast, both the Scen Red90−75 and Scen Red50−50 , already proposed by
Telmer and Veiga [16], are, broadly speaking, plausible scenarios that could be imple-
mented, leading to an important and significant reduction in emissions of Hg from ASGM,
and therefore to a consistent reduction in primary anthropogenic Hg emissions.
Atmosphere 2023, 14, 62 11 of 17

Table 3. Summary of the Hg emissions from ASGM estimated in this study in the different Hg emission scenarios envisaged.

Scen Base Scen Red90−75 Scen Red50−50 Scen Incr33 Scen MinHg Scen MFR
Country LB Mean UB LB Mean UB LB Mean UB LB Mean UB LB Mean UB LB Mean UB
Algeria 0.09 0.11 0.13 0.03 0.04 0.04 0.07 0.08 0.10 0.12 0.15 0.17 0.05 0.06 0.07 0.00 0.01 0.01
Bolivia 33.31 39.81 46.32 10.81 12.94 15.06 24.98 29.86 34.69 44.39 52.95 61.63 11.55 14.05 16.55 1.17 1.56 1.95
Brazil 25.65 30.10 34.56 8.33 9.78 11.22 19.23 22.57 25.93 34.14 40.03 45.84 9.11 10.63 12.16 0.95 1.18 1.41
Burkina
5.06 5.58 6.10 1.65 1.81 1.98 3.79 4.19 4.58 6.73 7.43 8.13 2.75 3.01 3.26 0.30 0.33 0.37
Faso
Burundi 0.44 0.55 0.66 0.14 0.18 0.22 0.33 0.41 0.50 0.58 0.73 0.88 0.21 0.30 0.39 0.02 0.03 0.05
Cameroon 0.47 0.51 0.55 0.15 0.17 0.18 0.35 0.38 0.41 0.62 0.68 0.73 0.26 0.27 0.29 0.03 0.03 0.03
Chad 0.02 0.02 0.02 0.01 0.01 0.01 12.39 15.08 17.73 0.02 0.03 0.03 0.01 0.01 0.01 0.00 0.00 0.00
Chile 18.31 20.60 22.92 5.94 6.70 7.45 13.71 15.45 17.20 24.32 27.40 30.49 6.76 7.27 7.79 0.73 0.81 0.88
China 251.51 280.20 308.92 81.85 91.06 100.31 188.79 210.15 231.47 335.37 372.66 410.38 162.85 176.20 189.72 17.68 19.58 21.49
Colombia 75.94 85.89 95.93 24.66 27.91 31.16 56.92 64.42 71.88 101.09 114.23 127.34 28.92 30.32 31.73 3.22 3.37 3.52
Congo 9.17 10.26 11.35 2.98 3.33 3.69 6.90 7.69 8.48 12.20 13.65 15.07 4.88 5.53 6.17 0.52 0.61 0.71
Côte
2.90 3.40 3.89 0.94 1.10 1.26 2.18 2.55 2.92 3.86 4.52 5.17 1.46 1.83 2.19 0.15 0.20 0.26
d’Ivoire
Ecuador 23.33 26.46 29.60 7.61 8.60 9.60 17.53 19.85 22.20 31.08 35.20 39.32 8.82 9.34 9.87 0.97 1.04 1.10
Egypt 2.23 2.65 3.07 0.72 0.86 1.00 1.67 1.99 2.30 2.96 3.52 4.08 1.10 1.43 1.75 0.11 0.16 0.21
El
0.05 0.10 0.15 0.02 0.03 0.05 0.04 0.08 0.11 0.07 0.13 0.20 0.01 0.04 0.06 0.00 0.00 0.01
Salvador
Equatorial
0.37 0.41 0.45 0.12 0.13 0.14 0.28 0.31 0.34 0.49 0.54 0.59 0.20 0.22 0.24 0.02 0.02 0.03
Guinea
Ethiopia 0.14 0.17 0.21 0.05 0.06 0.07 0.11 0.13 0.15 0.19 0.23 0.27 0.07 0.09 0.12 0.01 0.01 0.01
Fiji 0.32 0.35 0.39 0.10 0.11 0.13 0.24 0.26 0.29 0.42 0.47 0.51 0.20 0.22 0.24 0.02 0.02 0.03
French
32.55 37.20 41.87 10.55 12.09 13.62 24.38 27.90 31.42 43.35 49.48 55.77 12.03 13.13 14.24 1.30 1.46 1.61
Guiana
Ghana 46.92 52.60 58.30 15.24 17.09 18.96 35.19 39.45 43.68 62.47 69.96 77.53 24.70 28.33 31.96 2.60 3.15 3.69
Guinea-
20.80 23.73 26.63 6.77 7.71 8.66 15.64 17.79 19.97 27.74 31.56 35.37 10.83 12.78 14.71 1.13 1.42 1.71
Bissau
Guyana 21.49 24.52 27.55 6.98 7.97 8.96 16.12 18.39 20.68 28.58 32.61 36.67 7.76 8.66 9.56 0.83 0.96 1.10
Honduras 0.40 0.45 0.50 0.13 0.15 0.16 0.30 0.34 0.38 0.54 0.60 0.67 0.15 0.16 0.17 0.02 0.02 0.02
Indonesia 34.79 39.41 44.12 11.27 12.81 14.32 26.06 29.56 33.05 46.20 52.41 58.69 21.43 24.78 28.14 2.24 2.75 3.27
Kenya 0.33 0.41 0.48 0.11 0.13 0.16 0.25 0.31 0.36 0.44 0.54 0.64 0.16 0.22 0.28 0.02 0.02 0.03
Kyrgyzstan 2.90 3.23 3.56 0.94 1.05 1.16 2.18 2.42 2.67 3.86 4.30 4.74 1.86 2.03 2.20 0.20 0.23 0.25
Lao 1.36 1.52 1.67 0.44 0.49 0.54 1.02 1.14 1.25 1.81 2.02 2.22 0.87 0.95 1.04 0.09 0.11 0.12
Liberia 14.50 15.99 17.49 4.71 5.20 5.68 10.85 11.99 13.15 19.24 21.26 23.23 7.86 8.61 9.37 0.85 0.96 1.07
Madagascar 0.09 0.13 0.18 0.03 0.04 0.06 0.06 0.10 0.14 0.12 0.18 0.24 0.03 0.07 0.11 0.00 0.01 0.01
Atmosphere 2023, 14, 62 12 of 17

Table 3. Cont.

Scen Base Scen Red90−75 Scen Red50−50 Scen Incr33 Scen MinHg Scen MFR
Country LB Mean UB LB Mean UB LB Mean UB LB Mean UB LB Mean UB LB Mean UB
Mali 8.32 9.33 10.34 2.71 3.03 3.36 6.24 7.00 7.75 11.07 12.41 13.75 4.36 5.02 5.70 0.46 0.56 0.66
Mongolia 4.42 5.11 5.79 1.44 1.66 1.88 3.33 3.83 4.34 5.90 6.80 7.70 2.68 3.21 3.74 0.28 0.36 0.44
Morocco 2.20 2.50 2.79 0.71 0.81 0.91 1.65 1.87 2.10 2.93 3.32 3.71 1.38 1.57 1.76 0.14 0.17 0.20
Mozambique 0.26 0.29 0.33 0.08 0.10 0.11 0.19 0.22 0.25 0.34 0.39 0.44 0.13 0.16 0.18 0.01 0.02 0.02
Myanmar 15.60 17.34 19.09 5.07 5.63 6.20 11.69 13.00 14.33 20.74 23.06 25.37 10.09 10.90 11.72 1.10 1.21 1.32
Namibia 0.04 0.05 0.05 0.01 0.01 0.02 0.03 0.03 0.04 0.06 0.06 0.07 0.03 0.03 0.03 0.00 0.00 0.00
Nicaragua 0.02 0.03 0.03 0.01 0.01 0.01 0.02 0.02 0.02 0.03 0.04 0.04 0.01 0.01 0.01 0.00 0.00 0.00
Niger 0.45 0.51 0.57 0.15 0.16 0.18 0.34 0.38 0.42 0.60 0.67 0.75 0.23 0.27 0.31 0.02 0.03 0.04
Nigeria 10.91 12.31 13.70 3.56 4.00 4.44 8.21 9.23 10.25 14.56 16.37 18.18 5.78 6.63 7.48 0.61 0.74 0.86
Papua
New 15.37 16.91 18.46 4.99 5.50 6.00 11.51 12.68 13.85 20.42 22.49 24.57 9.91 10.64 11.38 1.08 1.18 1.28
Guinea
Perù 94.80 108.39 122.09 30.80 35.23 39.63 71.17 81.29 91.33 126.50 144.16 162.07 34.21 38.26 42.27 3.65 4.25 4.86
Philippines 16.00 17.98 19.96 5.21 5.84 6.49 12.01 13.48 14.95 21.23 23.91 26.57 9.96 11.31 12.65 1.06 1.26 1.46
Rwanda 0.02 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.02 0.02 0.03 0.03 0.01 0.01 0.01 0.00 0.00 0.00
Senegal 2.69 2.97 3.26 0.87 0.97 1.06 2.02 2.23 2.45 3.57 3.95 4.34 1.45 1.60 1.75 0.16 0.18 0.20
Sierra
0.20 0.23 0.25 0.06 0.07 0.08 0.15 0.17 0.19 0.26 0.30 0.34 0.10 0.12 0.14 0.01 0.01 0.02
Leone
Somalia 14.85 16.32 17.77 4.82 5.30 5.79 11.14 12.24 13.35 19.75 21.71 23.67 8.13 8.79 9.44 0.88 0.98 1.07
South
40.44 44.98 49.49 13.13 14.62 16.10 30.33 33.74 37.12 53.73 59.82 65.84 21.48 24.22 26.94 2.28 2.69 3.10
Africa
Sri Lanka 4.05 4.61 5.17 1.32 1.50 1.68 3.05 3.46 3.87 5.40 6.13 6.87 2.60 2.90 3.21 0.28 0.32 0.37
Sudan 55.12 63.93 72.85 17.90 20.78 23.70 41.26 47.95 54.65 73.11 85.03 97.01 27.88 34.43 40.94 2.80 3.83 4.85
Suriname 25.76 29.68 33.60 8.35 9.65 10.92 19.30 22.26 25.25 34.22 39.48 44.76 9.19 10.48 11.78 0.97 1.16 1.36
Tajikistan 0.75 0.87 0.98 0.25 0.28 0.32 0.57 0.65 0.73 1.00 1.15 1.30 0.46 0.54 0.63 0.05 0.06 0.07
Tanzania 8.02 8.97 9.92 2.61 2.92 3.23 6.01 6.73 7.45 10.65 11.94 13.22 4.22 4.83 5.45 0.44 0.54 0.63
Togo 7.78 8.70 9.61 2.53 2.83 3.12 5.84 6.52 7.20 10.34 11.57 12.78 4.16 4.68 5.22 0.44 0.52 0.60
Uganda 0.41 0.49 0.57 0.13 0.16 0.18 0.31 0.37 0.43 0.54 0.65 0.76 0.20 0.26 0.33 0.02 0.03 0.04
Uzbekistan 0.17 0.21 0.24 0.06 0.07 0.08 0.13 0.16 0.18 0.23 0.28 0.32 0.10 0.13 0.16 0.01 0.01 0.02
Venezuela 6.30 8.08 9.85 2.05 2.62 3.20 4.70 6.06 7.41 8.40 10.74 13.06 2.03 2.85 3.67 0.19 0.32 0.44
Zimbabwe 4.15 4.78 5.43 1.35 1.55 1.76 3.12 3.59 4.05 5.53 6.36 7.20 2.11 2.57 3.04 0.22 0.29 0.36
Atmosphere 2023, 14, 62 13 of 17

4. Conclusions
All the 56 countries analyzed in this study, located in the tropical–subtropical zone, are
characterized by three typical environments: desert, Savannah and rain forest. While the
Sahara desert in Africa and the dense rain forest of South America are mostly not heavily
populated, and thus ASGM does not present an immediate threat to large numbers of peo-
ple, Savannah regions in the Indian subcontinent and Southeast Asia are home to more than
a billion people. Although all these areas have significant natural resources, in many cases,
they lack strong legislation to guard against environmental risks [16]. Further, many of these
countries have a high pollution risk with regard to their hydrological resources. Typically,
ASGM sites are situated in some of the most important water resources worldwide.
A multi-step procedure has been employed to estimate ASGM Hg emissions and their
relative confidence intervals for 56 tropical and sub-tropical countries. The procedure,
based on the bootstrap estimate approach, yields estimates that generally agree with those
available from the GMA; however, the uncertainty is greatly reduced, on average, from
100% to 23%. The most noticeable deviations were found for China, and South Africa
(higher estimates in this study), and for Indonesia (a lower estimate in this study). The
same differences were recently found in a study by Cheng et al. [36] and are probably due
to inconsistencies in the officially reported data.
Six ASGM Hg emissions scenarios were investigated to evaluate the impact of a num-
ber of different Hg recycling strategies with varying uptakes among mining communities,
and also of external drivers. Each scenario was developed using the same multi-step
procedure acting on the appropriate (set of) parameters.
The most significant outcome of this study is that all of the ASGM Hg emission
scenarios analyzed in this study are significantly different (at a 95% level of confidence)
from the current Business as Usual Scen Base situation. This is intrinsically due to the
quantitative method employed to estimate the ASGM Hg emissions in each scenario that
greatly reduced the related uncertainties, compared to the GMA estimates. Indeed, using
the approach employed by the GMA experts, no single reduction scenario, except for the
near utopian Scen Bestcase case, could be statistically distinguished from the current situation.
Such an outcome is essential from a policy point of view since it becomes possible to
critically assess the impacts of any ASGM Hg emission scenarios.
The impact of the conceptualized emission scenarios on the ecosystem and human
health are potentially significant, with implications from a policy perspective, not only when
considering the direct costs of Hg emissions abatement from more technological/industrial
sectors, but also the direct saving from the reduced pressure on the health and welfare
systems of countries involved in ASGM.
ASGM represents an important income for people living in underdeveloped countries.
The underlying roots of ASGM activities are due to a number of different and interacting
factors affecting the scale of ASGM, even locally within a given country, including terrain
characteristics, the actual presence of Hg-bearing veins and their depletion over time, and
macroeconomic factors. The accurate analysis of these interactions will be the subject of
future studies to assess the complex impacts of the different drivers, also within the context
of climate change. In this regard, it is important not only to have a robust estimate of the
current ASGM Hg emissions, but also their future projection.

Author Contributions: D.E.B.: conception, methodology, writing, data retrieval, estimate elaboration;
F.D.S.: methodology, writing, data validation, bootstrapping, scenarios; S.C.: methodology, data
validation, writing; I.M.H.: methodology, scenarios, English check; F.D.: data validation; N.P.:
supervision. All authors have read and agreed to the published version of the manuscript.
Funding: The authors would like to acknowledge the contribution received from EU-H2020 projects
which includes E-Shape—Grant Agreement: 820852.
Institutional Review Board Statement: Not applicable.
Data Availability Statement: Data used for this study and results can be provided on request.
Atmosphere 2023, 14, 62 14 of 17

Conflicts of Interest: The authors declare no conflict of interest.

Abbreviations
The following abbreviations are used in this manuscript:
Hg Mercury
ASGM Artisanal Small-scale Gold Mining
Au Gold
CA Concentration Amalgam
WOA Whole Ore Amalgamation
GMA Global Mercury Assessment

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