The Impacts of Sand Mining on Water Quality: A South African

Perspective

Asabonga Mngeni

Department of Biological and Environmental Sciences, Faculty of Natural Sciences, Walter Sisulu University, South Africa

Keywords: Sand Mining, River, Water Quality, Upstream, Downstream.

Abstract: Sand mining is a common practice in many rivers and flood plains in South Africa. As a result of population

growth, economic development, and infrastructural development, the demand of sand continues to rise. The

aim of this study was to determine how sand mining affects water quality. The impact of sand mining on

water quality was determined by comparing turbidity, pH, electrical conductivity, and temperature in three

sampling sites (upstream, adjacent, and downstream). In addition, we determined if sand extracted using the

manual method affects the four water parameters the same way they are affected by the tractor-loader-backhoe.

These water parameters were measured in five rivers in the Eastern Cape Province (Tsembeyi, Thina, Gaduka,

Somerville, and Nomhala). The results showed that sand mining affects turbidity, with greater turbidity in the

adjacent sampling sites that the upstream sampling sites. The variation in turbidity can be due to sand extract

suspending solid particles in the water column. On the other hand, pH values in adjacent sampling sites

increased noticeably. The results revealed that the sand extraction method affects electrical conductivity, with

higher electrical conductivity when using the mechanical (tractor-loader-backhoe) method than the manual

method. The difference in electrical conductivity can be explained by the fact that generally turbidity and

electrical conductivity have a positive correlation. Even though other water parameters such as temperature

and pH were not affected by sand mining, results showed that sand mining affects water quality in terms of

turbidity and electrical conductivity.

1 INTRODUCTION

Surface water can be found in the form of streams,

wetlands, ditches, ponds, or lakes near or at sand

mining activities, and Orr and Krumenacher (2015)

argue that water from mining sites is likely to seep

lower into the groundwater. Ground water, on the

other hand, is regarded as a safe source of fresh

drinking water. According to Orr and Krumenacher

(2015), sand mining has a negative impact on surface

water when untreated storm water is released directly

into bodies of water. Furthermore, previous studies

have demonstrated that sand mining alters sediment

deposits in drainage networks, causing turbidity

levels to differ among upstream, adjacent to, and

downstream of sand mining sites (Lekomo et al.

2021; Okeke et al. 2019). This disparity in turbidity

levels could be related to increased riverbed and bank

erosion, which raises suspended particles in the water

at mining sites and downstream (Lekomo et al. 2021).

https://orcid.org/ 0000-0001-5201-7400

The rise in suspended solids has a deleterious

influence on flora and fauna (Kale, 2016). Turbidity,

for example, is a key cause of biological stress

because it is a source of abnormally large amounts of

organic material and nutrients (Kale, 2016).

Furthermore, the reintroduction of harmful

compounds caused by sand mining activities lowers

the oxygen levels in water, and therefore increases the

demand for oxygen amongst animals (Kale, 2016).

Sand mining affects not just turbidity but also

temperature. For example, it has been discovered that

when sand mining activities result in soil erosion and

deforestation, temperature levels rise (Kale, 2016).

Changes in temperature have an impact on

photosynthetic activity, gas diffusion rates, and the

amount of oxygen that can be dissolved in water

(Kale, 2016). According to Kawa et al. (2016)

temperature is essential because it affects water

chemistry, chemical reactions, for example, tend to

accelerate at high temperatures. High water

156

Mngeni, A.

The Impacts of Sand Mining on Water Quality: A South African Perspective.

DOI: 10.5220/0011950900003536

In Proceedings of the 3rd International Symposium on Water, Ecology and Environment (ISWEE 2022), pages 156-161

ISBN: 978-989-758-639-2; ISSN: 2975-9439

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

temperatures can dissolve minerals from rocks,

resulting in increased electrical conductivity (Kale,

2016). Furthermore, temperature influences

biological activities and growth, as well as the types

of organisms that can dwell in water bodies (Kawa et

al. 2016)

According to Kale (2016) water temperature is

likely to be altered by air temperature, quantity of

shadow, and soil erosion, which increases the amount

of sediments in a water body. The solubility of

dissolved oxygen is affected by water temperature; in

cold water, more gases can be dissolved than in warm

water. High water temperatures can boost the

photosynthetic rate of aquatic plants and algae,

resulting in increased plant growth, and algal blooms,

which can lead to eutrophication and severely impact

water biodiversity (Kale, 2016). Furthermore, Kale

(2016) found that extremely cold or extremely warm

temperatures in water raise stress levels in aquatic

creatures.

Because temperature impacts coagulation,

turbidity is similarly tied to temperature. Coagulation

efficiency is affected by temperature, and the optimal

pH for coagulation decreases, as temperature levels

rise (Kale, 2016). Furthermore, corrosion is a

function of the dissolved oxygen content in water, as

oxygen solubility decreases, temperature levels rise.

When compared to the bigger change in corrosion

rates, the change in dissolved oxygen with

temperature is negligible. Warm water carries less

dissolved oxygen than cool water, and aquatic

animals may not be able to survive without it (Kale,

2016).

Enough dissolved oxygen, relatively low organic

content, a pH value around neutral, moderate

temperature, and water free of infectious agents,

poisonous compounds, and mineral debris are all

desirable qualities of water quality (Oluyemi et al.

2010; Singh and Mosley, 2003). Microorganisms that

might cause diseases and chemical substances that are

hazardous to one's health should not be present in

portable water. Previous studies have looked at how

sand mining affects variables like turbidity,

temperature, pH, turbidity, dissolved oxygen, and

electrical conductivity (Mwanzia et al. 2018; Ashraf

et al., 2011; Okeke et al., 2019; Lekomo et al., 2021).

However, sand mining is practiced using a different

method, and there is little that is known about how

different methods affect water quality. As a result, the

aim of this research was to determine the impacts of

sand mining on water quality. The impact of sand

mining on water quality was determined by

comparing turbidity, pH, electrical conductivity, and

temperature in three sampling sites (upstream,

adjacent, and downstream). In addition, the study

determined if sand extracted using the manual method

affects the four water parameters (turbidity, pH,

electrical conductivity, and temperature) the same

way they are affected by the tractor-loader-backhoe

(TLB).

2 MATERIALS AND METHODS

The study was conducted in five sand mining sites

(rivers) which are Somerville (Maclear), Thina

(Mount Frere), Tsembeyi (Lady Frere), Nomhala

(Tsolo), and Gaduka (Mthatha) across the Eastern

Cape Province, South Africa. In all the mining sites

the area of sand extraction is riverbanks. The chosen

mining sites use different methods of extracting sand.

At Somerville and Tsembeyi sand is extracted using

the manual sand extraction methods, such as, spade,

cart and shovel. However, at Thina, Gaduka and

Nomhala the sand is extracted using the tractor-

loader-backhoe (TLB) sand extraction method.

2.1 Sampling Design

This study adopted quantitative research design. Van

der Merwe (1996) defined quantitative research as an

approach to study that aims to test hypotheses,

establish facts, show correlations between variables,

and forecast results. Methods from the natural

sciences are used in quantitative research to assure

objectivity, generalizability, and reliability. Water

samples were collected between May 2018 and

October 2018 which is the dry season. This period

was chosen given that it is the time when sand

extraction is done the most. There is high extraction

of sand during this period because of several reasons,

such as, the new financial year for the government to

issue tenders in the construction industry, low-income

people build their houses when it is dry to avoid

delays and constant demolishing of structures by

heavy rainfall amongst others. A total of 120 samples

were drawn from the five different rivers with each

river having three sampling sites (upstream of mining

sites, adjacent to mining sites and downstream of

mining sites). In each sampling site, (upstream,

adjacent and downstream) there were eight sub-sites,

and a single sample was drawn from each of those

sub-sites, making a total of eight samples in each

sampling site and 24 samples in each river. All the

eight sampling sites were >1 m apart. The upstream

of the mining sites, adjacent to mining sites and

downstream sites were >200 m apart.

The Impacts of Sand Mining on Water Quality: A South African Perspective

157

In each subsite, water parameters, including, pH,

electrical conductivity and temperature were

measured using Hanna Multiprobe parameter

whereas turbidity was measured using Hanna

Turbidity meter.

2.2 Data Analyses

The effect of sand mining on different water

parameters (electrical conductivity, pH, turbidity, and

temperature) was analysed in R using the generalized

linear mixed models (GLMMs) because data were not

normally distributed. The Shapiro-Wilk test was used

to test if data were normally distributed or not. The

lme4 package (Bates et al. 2015) was used when

calculating GLMMs. Analyses were performed for

each of the four water parameters. The Poisson

distribution was the best fit for electrical

conductivity, pH and temperature datasets, the

negative binomial distribution was used for the

turbidity dataset (Bolker et al. 2009). There were two

fixed factors (sampling sites and method of extracting

sand) and one random factor (river) in the models.

The multcomp package (Horthorn et al. 2008) was

used to determine the differences between paired

sampling sites (upstream, adjacent to sampling sites

and downstream) for water parameters that showed

significant differences in the main results.

3 PRESENTATIONS OF

RESULTS

Electrical conductivity, pH and temperature did not

differ among different sampling sites of the river

(Table 1; Figures 1a, b, d). However, there was

significantly higher turbidity in the sampling site

adjacent to sand mining sites compared to the

upstream of sand mining sites (p = 0.002; Figure 1c).

Although not significantly different, the turbidity in

the sampling site adjacent to sand mining sites was

slightly higher than that in the downstream sampling

site (p = 0.052; Figure 1c). Turbidity in the upstream

and downstream sampling sites did not differ (p =

0.55; Figure 1c).

The method of extracting sand affected the electrical

conductivity only, with greater electrical conductivity

when using the tractor-loader-backhoe compared to

the manual method (Table 1; Figure 2a). Even

though, the method of sand extraction did not

significantly affect the pH and the turbidity, the

tractor-loader-backhoe had slightly higher levels of

the pH and turbidity compared to the manual method

(Table 1; Figures 2b, c). The temperature was not

affected by the method of extracting sand, even

though the rivers that used the manual method had

slightly higher temperature, than those that use the

track-loader-backhoe method (Table 1; Figure 2d).

4 DISCUSSIONS

Effect of sand mining on electrical conductivity

focusing on sampling sites.

When electrical conductivity of sampling sites

adjacent to the sand mining site was compared to that

in the upstream and downstream sampling sites, there

were no statistically significant differences. These

results agree with previous studies (Mwanzia et al.

2018; Mwanzia, 2019; Obot et al. 2019; Yen and

Rohasliney, 2013). Similarities in electrical

conductivity across sampling sites could be since

electrical conductivity is a measure of total dissolved

substitution in water, which varies with geological

structure, rainfall, and temperature (Bai et al, 2013;

Yilmaz and Koc, 2014). The fact that the study was

done during dry season, when there was no rainfall,

may explain the observed similarities in electrical

conductivity of upstream, adjacent to sand mining

sites, and downstream sampling sites. However, other

studies discovered that electrical conductivity vary

dramatically from upstream to downstream

(Bhattacharya, 2018; Lekomo et al. 2021; Okeke et

al. 2019). Furthermore, these studies suggested

differences in electrical conductivity between

upstream and downstream sampling sites are since

sand extraction is a source of dissolved ion

precipitation in the water (Bhattacharya, 2018;

Lekomo et al. 2021; Okeke et al. 2019). However, it

is worth noting that the afore mentioned authors

conducted their studies during different seasons,

including the rainy season (Bhattacharya, 2018;

Lekomo et al. 2021; Okeke et al. 2019). As such, the

contrast between our study and these previous studies

could be since our study was conducted during the dry

season, therefore the capacity of rainfall to affect

electrical conductivity was not considered.

Effect of sand mining on pH focusing on sampling

sites

The pH of water is used to determine the amount of

hydrogen present, which is controlled by chemical

reactions and the ion balance (Viera et al. 2020). This

study supports previous studies, which found

similarities of the pH values among the adjacent,

upstream, and downstream sampling sites (Mwanzia,

2019; Mwanzia et al. 2018; Obot et al. 2019; Yen and

Rahasliney, 2013). The identical pH levels between

ISWEE 2022 - International Symposium on Water, Ecology and Environment

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adjacent, upstream, and downstream sampling sites

could be explained by the lack of harmful pollutants.

According to Singh and Gupta (2016) pollution from

diffuse sources is a non-point source of pollution.

Agricultural or storm water runoff, as well as debris

blown into waterways from land, are examples. There

was no obvious contamination (point sources) near

the sand mining sites. Wastewater, also known as

effluent, released legally or illegally by a company,

oil refinery, or water treatment facility, as well as

contamination from leaking septic systems, chemical

and oil spills, and illegal dumping, are all common

point sources of pollution in rivers (Singh and Gupta,

2016). The pH of water in a stream, river, lake, or

underground flow changes depending on the source

of the water, the kind of soil, bedrock, and

contaminants encountered along the route (Kale,

2016). Even though, the study found no statistically

significant differences between the sampling sites,

there is a slight increase in pH values at adjacent

sampling sites (Figure 1b), indicating that sand

mining has the potential of increasing the pH. On the

other hand, the study findings, contradict those of

(Gebreyohannes et al. 2015; Okeke et al. 2019) who

reported significant results among upstream,

adjacent, and downstream sampling sites. The

differences could be explained by the number of

reasons such as oil leak from the TLB used for sand

extraction which possess oil as a determining factor

for pH as alluded by (Singh and Gupta, 2016).

Effect of sand mining on temperature focusing on

sampling sites

Temperature oscillations in natural water bodies are

induced by a range of activities that cause daily and

seasonal changes in surface water. For example,

turbidity usually causes a rise in water temperature

due to heat absorption produced by suspended

particles (Kumar, 2015). Additionally, during

mechanical mining, equipment, such as, tractor-

loader-backhoe (TLB) heats up, resulting in higher

water temperatures. Furthermore, the magnitude and

time spent by TLB on the water column can be linked

to the lack of variance in water temperature. In

addition, previous studies have indicated significant

differences in temperature between upstream and

downstream mining sites and these differences in

temperature were because of heating up of mining

equipment (Okeke et al. 2019; Koehnken and Rintoul,

2018). However, in our study the temperature did not

differ among the three sampling sites (upstream,

adjacent to and downstream of sand mining site),

supporting previous studies (Mwanzia, 2019;

Mwanzia et al. 2018; Obot et al. 2019; Yen and

Rohaslineys, 2013).

Effect of sand mining on turbidity focusing on

sampling sites

The study found that the adjacent sampling sites,

which is near where sand mining is taking place, had

higher turbidity than the upstream sampling site,

which is upstream of sand mining. Increased turbidity

is normally caused by resuspension of sediment,

sedimentation due to stockpiling and dumping of

excess mining material (Ashraf et al. 2011). These

higher levels of turbidity in the adjacent sampling

sites were expected as these sampling sites are very

close to where sand mining occurs. The study

findings are consistent with previous studies, which

found that adjacent sampling site produced

statistically significant turbidity results when

compared to upstream sampling site (Bhattacharya,

2018; Bhattacharya et al. 2019; Koehnken and

Rintoul, 2018; Mwanzia et al. 2018; Okeke et al.

2019; Yen and Rohasliney, 2014).

Although not statistically significant, the turbidity

was slightly greater in the adjacent sampling sites

than in the downstream sampling site. This is most

likely owing to the river's natural desire to clean itself,

causing solid particles to settle, thus the low turbidity

in the downstream sampling sites. Furthermore,

Ashraf et al. (2011) reported that turbidity decreases

with distance downstream. In addition, Okeke et al.

(2019) found that turbidity differs significantly

between adjacent and downstream sampling site.

Even though the downstream sampling site had

slightly higher values than the upstream sampling site

(Figure 1c), the analysis found no significant

differences. The slightly higher turbidity values in

downstream sampling site than upstream sampling

sites could be attributed to sand mining after-effects.

These findings are in contrast with those of Okeke et

al. (2019), who discovered significant differences in

turbidity between upstream and downstream

sampling sites.

Effect of the method of extracting sand on water

quality

According to Padmalal and Maya (2014) mining for

river sand is done both manually and mechanically.

Manual mining is less harmful to the environment,

and the amount of mining is often low. In manual

mining, a simple equipment, such as, spades, shovels,

and carts are utilized, whereas in mechanical mining,

heavy machines, such as, power jet pumps and

tractor-loader-backhoe (TLB) are used. When

The Impacts of Sand Mining on Water Quality: A South African Perspective

159

comparing mechanical (TLB) and manual extraction

methods, the study found that TLB had a significantly

electrical conductivity than manual extraction. This

may be due to the elevated turbidity values in the

adjacent sampling site (Figure 1c). According to Aris

et al. (2014), electrical conductivity has a positive

correlation with turbidity (r = 0.765). It is worth

noting that using TLB in sand extraction raises solid

particles, which raises turbidity. However, the

turbidity together with pH and temperature were not

significantly influenced by the method of sand

extraction methods. Similarities between the manual

and machinery methods are contrary to our

expectations where temperature was expected to be

influenced using TLB given that TLB heats up and

result in higher water temperature. The amount of

time the TLB spends in the water column, the velocity

of the stream, and the amount of water in the rivers

could all explain the lack of higher water

temperatures.

Table 1: Effect of the sand mining on water parameters.

Electrical

Conductivity

χ2 df p χ2 df P

Sampling

sites

0.06 2 0.97 1.04 4 0.59

Method of

extraction

10.09 1 0.001 2.80 1 0.09

Turbidity

2 df P

Temperature

2 df P

Sampling

sites

11.93 2 0.003 0.42 2 0.81

Method of

extraction

1.57 1 0.21 0.15 1 0.69

Figure 1: Effect of sand mining on water parameters (a -

electrical conductivity, b - pH, c - turbidity and d -

temperature) in three sampling sites; upstream of the

mining site, adjacent to the mining site and downstream of

the mining site. Letters above boxplots in turbidity indicate

significant differences (p < 0.05) among sampling sites.

Figure 2: Effect of the method of extracting sand on a)

electrical conductivity, b) pH, c) turbidity and d)

temperature. Letters above and inside boxplots in electrical

conductivity indicate significant differences (p < 0.05)

between the methods.

5 CONCLUSIONS

This study showed that the impact of sand mining is

dependent on the water parameter measured. For

example, sand mining across the sampling sites in the

five rivers in the Eastern Cape Province influenced

water turbidity, while not affecting the electrical

conductivity, pH and temperature. On the other hand,

the electrical conductivity was significantly

influenced by the sand extraction method, while pH,

temperature and turbidity were not affected. If sand

extraction continues without proper control, sand

extraction might lead to irreversible destruction of the

ecosystem and subsequent biodiversity loss. Lastly, it

is important to encourage sustainable resource use,

law enforcement, regulation of sand mining activity

and adopt negative impact minimisation strategies.

ACKNOWLEDGEMENTS

I would also like to thank National Research

Foundation for financing the study.

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