Assessment of Self-Purification Capacity of the Mooi River

Catchment of South Africa

Thabang George Mmutle and Saheed Oke Adeyinka

Department of Civil Engineering, Central University of Technology, President Brand, Bloemfontein, South Africa

Keywords: River Catchment, Self-purification, Dissolved Oxygen, Biochemical Oxygen Demand, Oxygen Deficit,

De-oxygenation, Re-oxygenation, Eutrophication, Water Quality Modelling.

Abstract: Dissolved oxygen is the most essential element in natural water bodies for one of the most important

reasons, namely aquatic life. This content is usually affected by the type and amount of pollution

introduced in natural water bodies. The dissolved oxygen level is usually lowered at any point where a

natural water body such as a river is contaminated (deoxygenation); however, using natural purification

forces, rivers work hard to gain back the amount of oxygen lost in the water due to pollution (re-

oxygenation). This study articulated the self-purification capacity of the Mooi River catchment as a

function of the rate of change of the amount of dissolved oxygen in flowing water to illustrate the

purification strength of a river flow segment between sampling points. This is to subsequently present

the impact of inflowing pollution from different types of adjacent sources and tributary rivers. This was

achieved by conducting measurements of dissolved oxygen and temperature directly from the river,

using an electrolyte dissolved oxygen meter. Respective samples (three-litre samples) were also

collected at every sampling point for a biochemical oxygen demand laboratory analysis taken over five

days. Using the biochemical oxygen demand and oxygen deficit analysis, deoxygenation and re-

oxygenation factors or constants were determined for every flow segment. The mathematical ratio

between the two constants were then used to calculate the self-purification capacity of every segment.

Because the hydraulic dynamics of the river also influence the strength of the river to purify itself, a re-

oxygenation model of hydraulic properties, such as flow velocity hydraulic depth and radius, was

developed and presented by means of a regression analysis. The findings have proven that the river has

the capacity to purify itself along its existing length for both dry and wet seasons. The purification

fluctuations were high during the wet seasons due to the increase in hydraulic flow depth and pollution

by run-off. Oxygen deficiency was very low before the Mooi River confluences with the Vaal River;

therefore, it did not significantly affect the oxygen content of the Vaal River.

1 INTRODUCTION

South africa is listed as one of the most water scarce

countries on the continent (Dube, 2020). To mitigate

this scarcity and implement effective water resource

planning, re-oxygenation coefficient modelling can

be utilised as an integral scientific tool. This model

allows one to predict and monitor pollution loads,

nutrient constituents, and dissolved oxygen recovery

in fresh surface water masses (Ugbebor,

Agunwamba, & amah, 2012). Using a re-

oxygenation coefficient model, this study assesses the

self-purification capacity of the Mooi river catchment

which is situated along the western Gauteng and

North West provinces. This catchment is a vital water

supply for the surrounding population and ecosystem.

The condition of a freshwater mass (ie: a river) at

any point in time, is the result of a balance between

its oxygen resources and the demand made upon them

by the organic polluting matter carried by the stream

(Al-Zboon & Al-Suhaili, 2009). In other words, a

river's capacity to receive and oxidise organic matter

depends upon its oxygen resources. When organic

pollution is discharged into a natural water source,

organic compounds are oxidised by the dissolved

oxygen present in the water.

This process causes a deficiency of dissolved

oxygen (do) in the flowing water and the loss of

oxygen or deoxygenation happens. This deficiency is

typically dismissed by the atmospheric oxygen being

absorbed into the water (re-oxygenation i.e. Gain of

oxygenation). The rate at which the do absorbed into

Mmutle, T. and Adeyinka, S.

Assessment of Self-Puriﬁcation Capacity of the Mooi River Catchment of South Africa.

DOI: 10.5220/0011913200003536

In Proceedings of the 3rd International Symposium on Water, Ecology and Environment (ISWEE 2022), pages 101-105

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)

101

the water can automatically oxidise the organic matter

present in the water is termed the self-purification

rate.

This phenomenon narrates the outcome of the de-

oxygenation and re-oxygenation processes taking

place continuously in a simultaneous manner. Along

with other hydraulics-driven models of re-

oxygenation, Steeter and Phelps' model which is

based on the relation between DO and BOD is used

as a basis for assessment in this study. The model is

given as:

𝐷=



















𝑒







− 𝑒









+ 𝐷



.𝑒







(1)

= initial DO deficit;

D = the DO deficit;

= the BOD degradation constant;

= the atmospheric re-aeration constant;

= the ultimate BOD; and

t = the hydraulic retention time.

As a vital water source, the Mooi River catchment

has been subjected to a lot of pollution over more than

30 years (DWA, Green Drop Progress Report, 2012a;

DWA, Classification of Significant Water Resources

(River, Wetlands, Groundwater and Lakes) in the

Upper, Middle and Lower Vaal Water Management

Areas (WMA) 8, 9, 10, 2012b). Wondersfontein

River, a tributary to the Mooi River, is known through

research to be a much-polluted river containing

contaminants from the gold mining sectors and the

Flip Human Wastewater Treatment Works, thus

influencing the quality of the water in the Mooi River

at the point of their confluence (Barnard, Venter, &

Van Ginke, 2013). Furthermore, with the effluent

from Kokosi Wastewater Treatment Works, the

Loopspruit River subsequently repeats this influence

downstream of the Mooi River. Based on these

influences, this study aims to provide a clearer

perspective on the catchment's ability to purify itself

and continue being useful to its surrounding

population.

2 MATERIALS AND METHODS

The study was conducted on the Mooi River

catchment which is formed by the Mooi River

together with Wondersfontein River and Loopspruit

River as tributaries. The Wondersfontein River

confluxes with the Mooi River's upstream section

from the northeast side, creating one volume of water

mass concentration. The downstream section of the

Mooi River is further joined by the Loopspruit River,

which also influences the quality of the Mooi River.

Along its length lies three storage dams namely;

Klerkskraal, Boskop and Potchefstroom dams. These

dams receive water directly from the Mooi River

(DWA, Green Drop Progress Report, 2012a; DWA,

Classification of Significant Water Resources (River,

Wetlands, Groundwater and Lakes) in the Upper,

Middle and Lower Vaal Water Management Areas

(WMA) 8, 9, 10, 2012b)and their capacities are 8ML,

21ML and 2ML respectively (Annandale & Nealer,

2011).

Fieldwork: 10 sampling points were benchmarked

along the course of the river based on the locations of

interest. The interest was determined by the locations'

closeness to major water points such as dams,

proximity to pollution sources such as wastewater

plants, as well as confluences, etc. The sampling

points are labelled SPL1 to SPL10.

Figure 1.

Table 1 below shows the sampling points and

their descriptions. (Refer to tables 2 and 3 for flow

distances).

Table 1: Sampling points

Sampling point Sampling point description

SPL1 Klerkskraal Dam Outlet

SPL2 Mooi River Brid

SPL3 Mooi River Before Boskop

Dam and Before

Wondersfontein River

Confluence

SPL4 Bosko

Dam Inlet/Outlet

SPL5 Potch Dam Inlet/Outlet

SPL6 Mooi River at Potchefstroom

Cit

Mooi River Mall

SPL7 Mooi River + Loopspruit

River before Potch WWTW

effluent dilution

SPL8 Mooi River + Potch WWTW

effluent

SPL9 Mooi River + Potch WWTW

effluent before Vaal

SPL10 Mooi River + Vaal

confluence

ISWEE 2022 - International Symposium on Water, Ecology and Environment

102

Table 2: Dry season water quality and self-purification indicators.

Sampling

point

Temp.

(℃)

Satu-

ration

(mg/l)

Actual/

in situ DO

(mg/l)

Five-

day

(mg/l)

deficit

(mg/l)

BOD

(mg/l)

Velocity

(m/s)

Accumul

-ated

distance

(km)

Time

(days)

Deoxyge

-na-tion

constant

Reoxy

gena-

-tion

consta

Self-

purific

-ation

factor,

SPL1

21.1 8.880 7.50 6.9 1.380 0.6 0.54 0 0.00 0

SPL2

20.8 8.940 6.60 5.8 2.340 0.8 0.36 12.0 0.39 -0.744 -1.366 1.835

SPL3

20.2 9.060 6.40 5.5 2.660 0.9 0.34 23.6 0.39 -0.299 -0.325 1.088

SPL4

20.5 9.000 7.10 6.4 1.900 0.7 0.26 32.7 0.41 0.618 0.827 1.339

SPL5

22.1 8.690 7.30 6.4 1.390 0.9 0.15 46.4 1.06 -0.238 0.296 -1.244

SPL6

22.4 8.660 6.70 5.7 1.960 1 0.16 49.0 0.19 -0.569 -1.856 3.262

SPL7

21.7 8.760 6.50 5.9 2.260 0.6 0.11 54.8 0.61 0.834 -0.233 -0.279

SPL8

22.3 8.670 5.50 4.3 3.170 1.2 0.1 56.8 0.24 -2.919 -1.425 0.488

SPL9

23.4 8.520 6.40 5.8 2.120 0.6 0.09 110.4 6.89 0.101 0.058 0.580

SPL10

23.9 8.420 6.70 5.9 1.720 0.8 0.83 112.3 0.03 -11.182 8.127 -0.727

Table 3: Wet season water quality and self-purification indicators.

Sampling

point

Temp.

(℃)

Satura

ti-on

(mg/l)

Actual/

in situ DO

(mg/l)

Five-

day

(mg/l)

deficit

(mg/l)

BOD

(mg/l)

Velocity

(m/s)

Accumul

-ated

distance

(km)

Time

(days)

Deoxyge

na-tion

constant

Reoxy

gena-

tion

consta

Self-

purific-

ation

factor, f

SPL1

23.6 8.520 7.4 5.3 1.120 2.1 0.53 0 0.00 0

SPL2

21 8.900 5.2 4.8 3.700 0.4 0.52 12.0 0.27 6.196

−4.46

−0.721

SPL3

21.1 8.850 4.3 3.6 4.550 0.7 0.61 23.6 0.22 -2.551 -0.943 0.370

SPL4

19.5 9.200 7.4 5.4 1.800 2 0.6 32.7 0.18 -5.958 5.263 -0.883

SPL5

23.2 8.560 7 6.1 1.560 0.9 0.17 46.4 0.93 0.857 0.154 0.179

SPL6

23.3 8.540 3.1 1.7 5.440 1.4 0.17 49.0 0.17 -2.535 -7.167 2.827

SPL7

23.2 8.560 2.7 1.8 5.860 0.9 0.2 54.8 0.34 1.312 -0.221 -0.168

SPL8

24.2 8.380 2.6 1.6 5.780 1 0.3 56.8 0.08 -1.331 0.177 -0.130

SPL9

23.8 8.440 4.7 3.9 3.740 0.8 0.35 110.4 1.77 0.126 0.246 1.951

SPL10

26.7 8.030 5.2 4.6 2.830 0.6 0.34 112.3 0.06 4.580 4.439 0.969

Assessment of Self-Puriﬁcation Capacity of the Mooi River Catchment of South Africa

103

Mooi River stretches from the first sampling point

(SPL1) at Klerkskraal Dam to the last sampling point

(SPL10) at the Vaal River confluence. . The analysis

of these results discovered through this study yields a

clear identification and understanding of all the weak

spots along the river length in terms of pollution

subjection and purification strength. This further

allows us to indicate how each river segment reacts to

different types of pollution and other factors affecting

its self-purification. Knowing whether the levels of

these indicators increase or decrease at each segment

enables us to know how the purification is affected by

either pollution or other factors.

2.1 Calculating the De-Oxygenation

) and Re-Oxygenation (k

)

Constants

In addition to the fieldwork, laboratory work was

conducted to determine the BOD using the BOD5

(non-dilution) analysis method, where the DO of a

sample was measured before and after it was

incubated at 20℃ for 5 days. The difference between

the two DOs was taken as the BOD value for each

sample.

The laboratory analysis, which was conducted

using Microsoft Excel, allowed us to develop the re-

oxygenation constant model (k

) for the study area.

The model validation was conducted by running

Microsoft Excel Regression Analysis between k

values obtained using the developed model and the k

values calculated using the actual field data. Before

determining the re-oxygenation rate using the

proposed models, the de-oxygenation constant (k

)

was calculated and the results show that the rate at

which the BOD in the water is decomposed is directly

proportional to the amount of BOD present/remaining

in the water. This means that the de-oxygenation rate

is high when the BOD level is high.

3 RESULTS AND DISCUSSIONS

The re-oxygenation constant (k

) can be calculated by

using the DO deficits of the water on the upstream

sampling points of the catchment determined on the

field together with the DO deficits determined by the

downstream sampling points. The formula is derived

from the rate relationship between the DO deficit and

the rate at which the atmospheric air enters the water

(re-oxygenation). The rate at which the atmospheric

air enters the water is directly proportional to the DO

deficit in the water. In summation, the deoxygenation

coefficient k

is dependent on the pollution or waste

characteristics alone, while the re-oxygenation

coefficient k

, is dependent on factors such as stream

velocity, stream depth and water temperature. Hence,

there is a need to model k

differently and this could

be achieved by data gathering on such parameters as

dissolved oxygen, stream velocity, water depth and

temperature.

For both seasons, the quality of the water

deteriorates by constantly losing oxygen for a flow

distance of about 23,6 km from the first sampling

point at Klerkskraal Dam. This results in high

dissolved oxygen deficit levels. The oxygen deficit

level of the wet season is also affected by the low flow

rate and deep hydraulic depth of the river at this point.

The flow rate is as low as 0,16 m/s with a hydraulic

depth of 0,5 m during the dry season period. The

Reoxygenation rate is less in deep slow-moving

waters due to the insufficiencies of turbulence,

oxygen dilution, and dispersion in water. All this

results in high dissolved oxygen deficit levels. The

oxygen deficit then declines towards the Vaal River

confluence. The flow distance between sampling

points 8 and 10 allows for sufficient re-oxygenation

and recovery from organic matter present in the

water. The highest DO deficit was recorded during

the wet season because of an increase in the hydraulic

depths of the water. High quantities of water in rivers

make it difficult for self-purification to take place

effectively.

4 CONCLUSIONS

The major sources of non-point pollution in the Mooi

River catchment are agricultural activities and urban

runoff during the wet season. The high levels of

phosphorus and nitrogen induced by the excessive use

of agricultural pesticides result in eutrophication.

Furthermore, the Mooi River is a slow flowing river

in some segments, some parts of it are close to

stagnant. At deep hydraulic depths, this results in

slow atmospheric oxygen infusion, algal blooming,

and growth of aquatic plants thus causing rapid

depletion of dissolved oxygen. This affects the

natural self-purification of the catchment.

The quality management system of South Africa

should urgently employ intense purification

modelling of its river systems. This will assist in

identifying pollution sources that are fatal to the

quality of the water in South African water masses.

Furthermore, it will help the water treatment sectors

to identify reliable points of raw water extraction for

portable water treatment, thus reducing the treatment

costs.

ISWEE 2022 - International Symposium on Water, Ecology and Environment

104

Due to South Africa's energy deficiency, reducing

the water treatment intensity means using less

electricity and chemicals during treatment.

Recreational economic activities influenced by

aquatic life such as fish, at places like Boskop Dam

and Potch Dam, have also been affected by the

pollution induced on the Mooi River.

ACKNOWLEDGEMENTS

Central University of Technology Research

Department, Mid-Vaal Water Company, Tshepang

Mmutle and Olebogeng Mmutle.

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Assessment of Self-Puriﬁcation Capacity of the Mooi River Catchment of South Africa

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