Figure 5. Seasonal area-averaged of dust column mass
density–PM 2.5 monthly 0.5 × 0.625 deg. [MERRA-2 Model
M2TMNXAER v5.12.4] kg m
−2 over January 1999 to April
2020, Region 1.9899W, 0N, 0E, 5.3212N (Obuasi Goldmines,
Ghana).
Figure 6. Time series, area-averaged of dust column mass
density–PM 2.5 monthly 0.5 × 0.625 deg. [MERRA-2 Model
M2TMNXAER v5.12.4] kg m
−2 for January 1997 to April
2020, Region 0E, 0.9818S, 34.2509E, 0N (Kilimapesa Gold
mine, Kenya).
January 1997 to April 2020 of the Kilimapesa gold
mine. The results show that on February 1999, before
the mine was established, the pollution level of the area
was low as 0.00002706 kg m
−2 . The mine achieved its
highest pollution level of 0.0001898 kg m
−2 in 2016
which accounts for an approximate 601% increase in
the pollution level over 17 years. The pollution levels
were lower for all years for the months of June, August,
and October.
This shows that mining activities have an effect on
the increased level of PM 2.5 over time. This PM 2.5 has
an adverse effect on our health, therefore there is the
need for the regulatory bodies to have a technological approach to provide constant monitoring of these
mining sites in Africa.
4.2 Internet of things for air pollution monitoring
around mining sites
We are in an era where most things have been automated and computerized. All these developments
arise from the advancement of technological practices
which have improved the standard of living. The Internet nowadays has become a worldwide tool which has
been extensively adopted by companies, industries,
institutions, and individuals for smooth and fast work
and business transactions.
Gone are the days where environment protection
agencies embark on on-site environment monitoring
activity, which has been the traditional method of
taking environment pollution readings.
They visit the mining site with a hand-held monitoring device and take readings of the pollution level.
These readings are just a representation of the time
during which they are at the mining site. They will not
be a true picture of the pollution level in the area. The
miners can decide not to perform any active work the
day the Environmental Protection Agency is present
so as not to increase the pollution readings.
Figure 7 is a typical example which shows an Environment Protection Agency taking a site reading of
PM 2.5 at a mining community in Ghana in 2019.
These traditional methods have their limitations. They
require a larger workforce and field personnel for the
on-site readings. Time becomes an important factor
to consider in the traditional approach. It needs more
time and effort to obtain data for processing, analysis,
and usage. It can be costly and it does not give a true
representation of pollution in the area.
With the application of the Internet of things
(IoT), the challenges associated with the traditional
method of environment pollution data collection can
be reduced and eventually eliminated. IoT are normal
objects (“things”) that are endowed with network capabilities (Uckelmann, Harrison & Michahelles 2011).
Figure 7. Personnel from the Environmental Protection
Agency in Ghana taking a manual pollution data from a
mining site.
48
density–PM 2.5 monthly 0.5 × 0.625 deg. [MERRA-2 Model
M2TMNXAER v5.12.4] kg m
−2 over January 1999 to April
2020, Region 1.9899W, 0N, 0E, 5.3212N (Obuasi Goldmines,
Ghana).
Figure 6. Time series, area-averaged of dust column mass
density–PM 2.5 monthly 0.5 × 0.625 deg. [MERRA-2 Model
M2TMNXAER v5.12.4] kg m
−2 for January 1997 to April
2020, Region 0E, 0.9818S, 34.2509E, 0N (Kilimapesa Gold
mine, Kenya).
January 1997 to April 2020 of the Kilimapesa gold
mine. The results show that on February 1999, before
the mine was established, the pollution level of the area
was low as 0.00002706 kg m
−2 . The mine achieved its
highest pollution level of 0.0001898 kg m
−2 in 2016
which accounts for an approximate 601% increase in
the pollution level over 17 years. The pollution levels
were lower for all years for the months of June, August,
and October.
This shows that mining activities have an effect on
the increased level of PM 2.5 over time. This PM 2.5 has
an adverse effect on our health, therefore there is the
need for the regulatory bodies to have a technological approach to provide constant monitoring of these
mining sites in Africa.
4.2 Internet of things for air pollution monitoring
around mining sites
We are in an era where most things have been automated and computerized. All these developments
arise from the advancement of technological practices
which have improved the standard of living. The Internet nowadays has become a worldwide tool which has
been extensively adopted by companies, industries,
institutions, and individuals for smooth and fast work
and business transactions.
Gone are the days where environment protection
agencies embark on on-site environment monitoring
activity, which has been the traditional method of
taking environment pollution readings.
They visit the mining site with a hand-held monitoring device and take readings of the pollution level.
These readings are just a representation of the time
during which they are at the mining site. They will not
be a true picture of the pollution level in the area. The
miners can decide not to perform any active work the
day the Environmental Protection Agency is present
so as not to increase the pollution readings.
Figure 7 is a typical example which shows an Environment Protection Agency taking a site reading of
PM 2.5 at a mining community in Ghana in 2019.
These traditional methods have their limitations. They
require a larger workforce and field personnel for the
on-site readings. Time becomes an important factor
to consider in the traditional approach. It needs more
time and effort to obtain data for processing, analysis,
and usage. It can be costly and it does not give a true
representation of pollution in the area.
With the application of the Internet of things
(IoT), the challenges associated with the traditional
method of environment pollution data collection can
be reduced and eventually eliminated. IoT are normal
objects (“things”) that are endowed with network capabilities (Uckelmann, Harrison & Michahelles 2011).
Figure 7. Personnel from the Environmental Protection
Agency in Ghana taking a manual pollution data from a
mining site.
48