ANALYSIS OF THE CHALLENGES POSED BY WATER POLLUTION IN
PROVIDING STANDARD WATER QUALITY: THE CASE OF CHINHOYI
URBAN, ZIMBABWE
Gift MUDOZORI and Samuel KUSANGAYA
Department of Geography & Environmental Sciences, University of Zimbabwe,
P.O. Box MP 167, Mount Pleasant, Harare, Tel. 263 (0)4 303211 ext. 1265, Fax: 263
(0)4 332059, kusangay@arts.uz.ac.zw (Corresponding author)
ABSTRACT
Globally, problems related to water quality are escalating especially in urban areas. In
Zimbabwe, and particularly in Chinhoyi, water pollution is a real challenge. Thus the
study focused on analysing the challenges that water pollution posses on the provision of
standard water quality in Chinhoyi urban. Data collection involved field observations
and measurements, questionnaire interviews and secondary data. Landsat TM satellite
images of the 19th of May 1989, 17th of December 1999 and 30th of September 2000 were
used to determine land use and land cover changes using the Normalised Difference
Vegetation Index (NDVI). It was found out that the excessively high levels of nitrates,
coliform bacteria, iron, and manganese recorded attest to water that is polluted the water
failed to meet the WHO (1982) and standards association of Zimbabwe (SAZ) guidelines
for clean and safe water. Moreover, the consumption of contaminated water increased
the number of reported cases of water borne diseases such as cholera, dysentery and
diarrhoea. This necessitated chlorination in water purification. From the image analysis
it was also found out that more land had been converted to cultivation within and around
the town therefore possibly increasing chances of non-point pollution. It is therefore
recommended that there is need to introduce mechanisms to control the notable levels of
agro-chemical pollution, such as prohibiting urban agriculture along stream banks as
well as to minimise sewage pipe leakage.
Keywords: NDVI, Remote sensing, pollution.
INTODUCTION
Early societies such as Egypt and Kush and Aztec empires were based on the availability
of water. Modern societies with their large settlements and huge demand for water are
also strategically located within the vicinities of reliable and adequate supplies of fresh
water. This is the case with most urban areas in Zimbabwe. However, Zimbabwe is a
1
relatively dry country with an average annual rainfall of about 655mm (Ministry of
Environment and Tourism (MET), (2003). The availability of water varies markedly
across the country, but in all areas it is considered a scarce and limiting resource
(Zimbabwe National Water Authority (ZINWA), 2000). According to Batley (1998), the
greatest constraint to water quality is water pollution, which has intensified as a
consequence of the increased scale and diversity of urbanisation, industrialisation and
agriculture, as in Chinhoyi.
Chinhoyi town has a moderate industrial base with mainly textile, food and beverages,
light manufacturing and dry cleaning factories which use water in various processes. The
town has both sewage and water treatment works which however, can no longer keep up
with the increasing population. This necessitated the construction of the 5000 cubic
metres Water Augmentation Works and the recently completed Biri Dam on the
Manyame River. The town has a population of approximately 49 603 (CSO, 2002)
resident in the high and low-density suburbs of Chikonohono, White City, Mpata,
Hunyani, Cold Stream, Mzari and Orange Groove. It is still expanding with the proposed
development of Ruvimbo high-density suburb and the extension of existing suburbs. It is
against this background that the study was undertaken in order to establish how the
challenges of water pollution affect the provision of standard water quality in Chinhoyi.
Specifically the study sought to identify and examine the sources, nature and possible
effects of pollutants and also to identify the landuse/landcover changes that have occurred
within and around the town of Chinhoyi.
2
MATERIALS AND METHODS
Study Area
Chinhoyi town is located approximately 114 kilometres from the city of Harare covering
an estimated 28 square kilometres (Figure 1). It is the provincial capital city of
Mashonaland West and is under municipal jurisdiction, with the administrative
boundaries flanked by commercial agricultural farms. Its main water source is the
Manyame (Hunyani) River that flows through the town on the eastern side supplying
water for domestic, industrial and agricultural use.
Figure 1: Location of Chinhoyi Town
3
Field observations, measurements, interviews and questionnaires
The areas where the residents and municipal authorities were dumping solid and liquid
waste were observed. Surrounding farms, old and new sewage ponds were also. Water
samples were collected for laboratory analysis at the Chinhoyi Provincial Hospital (CPH)
laboratory for the presence of bacteriological coliform, iron, manganese and nitrate
levels. The results for other parameters for the period 1998 to 2004 were obtained from
the CPH laboratory records.
Semi-structured interviews were administered to the water superintendent; CSC health
officers; CPH senior environmental health officers and Delta beverages personnel. These
interviews provided data on the nature of wastewater and possible pollutants. Information
on the prevalence of water borne diseases was also sought. Questionnaires were
administered to residential units in the town’s suburbs and commercial water users,
providing data on perceived sources and types of pollutants and the measures taken by
residents and local authorities to control pollution. In addition, information on the
incidence of water borne diseases was obtained.
Sampling procedure
A stratified random sample was used in questionnaire surveys with a sample size of 100
residential units in Chinhoyi’s suburbs. The first step was to identify the total number of
residential units in each suburb and then calculate the percentage of units relative to the
4
total household population (Table 1). The next stage was to take a proportionate sample
from each stratum randomly.
Table 1: Number of residential units and questionnaires administered in Chinhoyi’s
suburbs.
Name of Suburb
Number of
Percentage of total
Number of
residential units
residential units
questionnaires
Chikonohono
1210
27.98
27
White city
579
13.39
14
Hunyani
765
17.69
18
Cold Stream
640
14.79
15
Mzari
820
18.96
19
Orange Groove
170
3.93
4
Riverside
95
2.19
2
Hillside
26
0.60
1
Total
4325
100
100
Source: Chinhoyi Municipal Offices (2005)
Landuse landcover change
Landsat TM satellite images of the 19th of May 1989, 17th of December 1999 and 30th
of September 2000 were used to determine land use and land cover changes within the
Manyame Catchment. Landsat images were used because of its ability to measure woody
5
cover on a wide area (Lillesand and Keifer, 2000). Landsat TM has a spatial resolution of
30m by 30m, which is detailed enough to give clear resolution of vegetation cover
(Sabins, 1987; Jensen, 2000). Normalised Difference Vegetation Index (NDVI) was
calculated to give an indication of biomass change. NDVI is the most common vegetation
index (VI) calculated as NIR-R/NIR+R where NIR is the radiance/reflectance in near
infrared wavelengths and R radiance/reflectance in red wavelengths (D’ Arrigo et al.,
1999). NDVI gives estimates of net primary productivity (NPP) and is useful for
assessing extent and condition of vegetation (Kumar and Monteith 1982; Sellers et al.
1996; Rossini and Benedetti, 1993; Mutanga, 2003, 2004a, and 2004b). For instance, in
vegetated areas, the Near Infrared portion of the spectrum is reflected by leaf tissue and
this reflectance is recorded by the sensor. Red light is absorbed by the chlorophyll present
in the leaf tissue, thus reducing the reflectance of red light detected at the sensor. This
contrast of reflectance and absorption by vegetation cover allows us to evaluate the
amount of vegetation present on the surface.
The study area lies within the landsat TM scene P170/R072. The TM imagery was
geometrically corrected to the universal transverse mercator (UTM) grid (zone 35)
projected through a clarke 1880 spheroid to a mean root mean square error (RMS) value
of 10 metres and had an effective ground resolution after resampling following geometric
correction of 30 metres. Relative radiometric correction based on the regression method
was carried out on the NIR and R bands based on the digital numbers (DN) of identified
pseudo-invariant objects (tarred roads, buildings and water bodies). This was meant to
6
reduce error, which might have resulted from atmospheric conditions that may have
occurred at the different dates when the images were acquired.
RESEARCH FINDINGS AND DISCUSSIONS
Water quality analysis
Before the year 2003 the Municipality used to conduct water tests almost on a daily basis
but these are now done irregularly. This scenario was attributed to costs of laboratory
analysis especially the cost of chemicals used, lack of qualified staff and inadequate
facilities. Some data was obtained from the Chinhoyi Provincial Hospital Officials. Some
of the samples were collected when there were leakages and reports of suspected
outbreaks of water borne diseases. Information on bacterial contamination of water was
(Table 2) obtained.
Table 2: Results for faecal bacteriological coliform organisms per 100 ml of treated water
(2000 - 2004).
Date
Site of sample
01-04-04 Provincial Medical
Coliform
Possible Source
Reasons for
results
of pollution
sampling
1
Pipe leakages
Routine
Stores
7
07-09-04 Chinhoyi University
1
Surface runoff
Routine
17-06-03 House No 3298
18+
Pipe leakages
Diarrhoea
Chikonohono
investigation
13-12-02 Chinhoyi University
0
Pipe leakages
Public complaint
05-01-01 CPH main gate tape
2
Pipe leakages
Routine
18-09-00 CPH tape
16
Surface runoff
Bacteriological
examination
25-04-00 House No 3298
28+
Pipe leakages
Suspected
Cholera
outbreak
03-02-00 CPH tape
2
Surface runoff
Suspended matter in
water
Source: CPH laboratory records, 2005.
The suspected cases of pipe leakages occurred at the same time as the incidences of
diseases out breaks, for example the 123 cases of cholera in 2000 and the levels of
pathogenic coliform in raw water for the same period (Figure 2).
8
Cloriform levels
350
320
280
300
250
265
248
200
190
200
180
150
100
50
0
1996
2000
2001
2002
2003
2004
2005
Time (Years)
Level of coliform
Standard coliform level
Figure 2: Recorded and standard coliform bacteria levels.
Source: CPH laboratory records, 2005
Except for the results for the year 2002 and those for January 2005, the levels of coliform
bacteria have always exceeded the S.A.Z (1999) acceptable maximum level of 200
coliform per 100ml. These findings indicate that pathogens which transmit water borne
diseases are an annual threat in the raw water source. Table 3 shows the levels of
parameters that have been recorded for the period 2000-2004.
Table 3: Laboratory results for water quality parameters 2000-2004.
Parameter
WHO / S.A.Z (1996)
2000
2001
2002 2003
2004
pH
9.5
7.8
8.1
8.2
6.8
6.7
Colour NTU
15
0.6
5
10
7
7.5
Chloride
250
7.9
11
5.3
11.5
14.9
Sulphate
250
12.4
10.8
17.5
185.2 208.3
9
Nitrate
50
68.2
57.5
-
64.3
71.3
Fluoride
1.5
0.4
0.7
0.6
0.4
-
Sodium
200
37
215
68.4
254.6 235.1
Iron (Fe)
0.3
0.8
1.2
0.9
1.4
1.7
Manganese
0.5
0.1
0.7
0.1
0.8
1.4
Copper
1
0.225 0.25
0.32
0.3
0.6
TDS
1000
293
530.1 47.4
360.5 728.3
Total hardness 1000
40.3
38.7
31
672.3 978.4
Zinc or boron
0.3
0.1
0.1
0.6
1
0.4
Note: Except for pH and colour all units are milligrams per litre
Source: CPH Laboratory records, 2005.
Most water quality indicators remain within the SAZ or WHO (1996) recommended
guideline value. However, there are other variables that have consistently exceeded the
maximum values, for example, iron, manganese, sodium, and nitrates (Figure 3).
10
2005
2004
2003
2002
2001
2000
Quantity (mg/l)
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
Time (years)
iron
manganese
Figure 3: Annual levels of iron and manganese.
Source: CPH Laboratory records, 2005.
Iron levels measured in raw water have persistently exceeded the WHO or SAZ
recommended standard excessive level of 1.0 milligram per litre. Even the iron levels of
raw water sent to the Government laboratories through CPH were very high at 1.3 mg /L.
Nitrates (mg/l)
The level of iron only fell below the maximum in 2000 and 2002 (Figure 3).
57.5
60
50
50
71.3
64.3
60
50
50
50
50
40
18.5
20
1
0
2000
2001
2002
2003
2004
2005
Time (years)
Recorded nitrate Levels
Standard nitrate level
Figure 4: Annual levels of nitrates (2000-2005) and the standard value (SAZ, 1996).
11
Source: CPH Laboratory records, 2005.
The annual levels of nitrates have been above the standard (maximum) value of 50 mg/l
for the other years (Figure 4) except in 2000 and early 2005. An excessive amount of this
parameter is attributed to the release of nutrients from agro chemical fertiliser
(nitrogenous) application on the local farms and urban agriculture runoff. In the
interviews it was also stated that due to these high levels of pollutants more chlorine has
had to be used in water treatment.
Industrial and municipal water pollution
The major industrial consumers, Cold Storage Company (CSC) and Delta Beverages
meet their own water quality requirements through pre-treatment. They utilise a
combined total of 3 150 000 litres per month in non-chemical additive processes like
washing meat and dilution with other ingredients of sorghum beer. At both plants
additional chlorination is performed in order to meet the European Union (EU) and SAZ
standards. At Delta beverages, approximately 20 000 of the 65 000 litres of water used
daily is discharged as wastewater containing small amounts of caustic soda. Cottpro uses
water directly from the municipal tape and borehole for day to day processes. There is
therefore no addition of chemicals or other potential pollutants and the monthly generated
200 cubic metres is discharged into septic tanks on site. This low contribution of
industries to locally generated water pollution was also supported by the responses to
12
questionnaires. From the questionnaires only 10% identified industrial effluents as
sources of pollutants.
The suburbs of Chikonohono, White City, Gunhill and Cherima located about 1.5
kilometres upstream from the water works discharge their wastewater directly into the
sewer under normal circumstances. However, it emerged from the interviews that there
has been frequent bursting and leakage of sewage pipes. For example, on 15 April and 3
October 2000, a sewer pipe burst discharging about 8600 cubic metres of liquid raw
effluent for 3-4 days before the damage was rectified. The pipe leakage was confirmed by
coliform results of 28+ per 100ml (Table 3) and cases of cholera among the residents.
According to the Water Superintendent, most of the sewage runoff invariably made its
way into the water source, Manyame River. There have also been sewage leakages in
2002, 2003 and 2004.
Agricultural pollution
The farms located along the Manyame River, and around the town, are Portlet, Nicole,
Hunington and Gleensleeves with a total arable area above 2500 hectares under maize
and other crops (Figure 5). In addition, an estimated 350- 450 hectares is utilised for
maize and sugarcane cultivation in small plots as urban agriculture.
13
Figure 5: Farms around Chinhoyi
According to Agriculture Extension Officials, 250- 300 kilograms per hectare of
ammonium nitrate and 250 kg / hectare of compound D fertiliser is used annually
amounting a total of 1 622 500 kg’s applied onto the land. These fertilisers have a known
leachable 15% residual potash and nitrogen in clay soils. During a rainy season with
heavy rainfall for example 1999/2000 and 2003/2004, the proportion leached reached as
high as 22%. Therefore, this resulted in the deposition of approximately 356 950 kg’s of
nitrate residuals in water either directly as surface runoff or indirectly through
underground flow. The deposited residuals from compounds resulted in high levels of
trace heavy metals like zinc, boron and lead from fertilisers, insecticides and pesticides
14
application. These elements accounted for the salty taste, hardness and oily nature of the
raw water that was identified by the questionnaire respondents. Turbidity was within the
recommended maximum in the years 1997 and 1998; however, this was far above the
desirable level of 5 silica units. Turbidity rose to extremely excessive levels in 1999,
2000 and 2004, probably because the heavy rains washed red clay soil from cultivated
arable land upstream. The interviews revealed that the sediment condition of the Piriviri
River (a tributary to Manyame) was suggested as additional evidence for pollution from
agriculture runoff. This sediment laden water contain iron ferric oxides and account for
the high iron levels persistently recorded above the WHO standard maximum of 1.0 mg/
litre acceptable for drinking water.
Water borne diseases
Statistics obtained from CPH’s Health Information office and the Provincial Medical
Directors office indicate the recurrent prevalence of water borne diseases related to the
use of contaminated water (Table 4).
Table 4: Reported cases of water borne diseases in Chinhoyi Urban.
Type of disease
2000
2001
2002
2003
2004
Total
Malaria
4587
5551
5170
5106
6394
26808
Diarrhoea
4123
3503
3420
5057
4035
20139
Dysentery
365
329
335
165
143
1337
15
Bilharzia
218
216
265
152
285
1136
Shigella
18
59
52
21
5
155
Cholera
123
0
0
0
0
123
Total
9434
9658
9178
10 501
10 862
49 698
Source: CPH records, 2005.
Every year (2000-2004) there was a persistent outbreak of dysentery, bilharzia and
diarrhoea (Figure 6), with an average of 4.5% of the population being affected each year
and approximately 40.6% of the town’s population has been affected in the last 5 years.
In 2000, there was an outbreak of cholera which affected 123 people following severe
Number of reported cases
leakages of sewage pipes in Chikonohono and Hunyani suburbs.
400
300
200
100
0
2000
2001
2002
2003
2004
Time (years)
Dysentery
Bilharzia
Shigella
Cholera
Figure 6: Total reported cases of water borne diseases between 2000-2004.
Source: CPH 2005
16
Reported cases increased by 14.41% between 2002 and 2003 as opposed to 2.4% in
2000- 2001. The rapid increases are explained by the 2002-2003 drought which resulted
in minimal to no stagnant water, rendering water borne disease vectors like mosquitoes
unable to breed or transmit diseases. Furthermore, in 2003 direct sewage leakage’s into
the water source (Manyame River) introduced pathogens into the water resulting in out
breaks of diarrhoea, dysentery and shigella. Cases of malaria have been reported each
year affecting about 9.3 - 10.4% of the total population in 2000 - 2004 (Figure 7). This
rose to 13.0% in 2004 mainly as a direct result of intensified rains leading to a substantial
amount of runoff from the upstream suburbs. A local nurse attributed the increased cases
to the existence of numerous stagnant pools of water in and around the town during the
rainy season.
Diarrhoea
40%
Malaria
54%
Dysentery
2%
Bilharzia
2%
Cholera
1%
Shigella
1%
Figure 7: Proportion of the total population affected by specific water borne diseases
(2000-2004).
17
The data on reported cases indicates that there has been a 100% incidence of water borne
diseases between 2000- 2004. This however does not imply that the whole population
was affected but simply shows the recurrence of diseases among residents. Otherwise the
19- 20% prevalence rate of incidence of water borne diseases is more indicative of the
water pollution challenges experienced. Approximately 25% of the respondents to
questionnaires indicated that at least one member of the household had suffered from a
water borne disease after consuming water from the municipal tape. Out of the 25%, 20%
suggested that the person affected by the disease had not used other sources of water in
the town. Only 3% out of the remaining 5% respondents had utilised an open unprotected
source of water without initially taking measures to ensure that the water was safe for
drinking.
Landuse landcover change analysis
Landuse landcover was evaluated using NDVI. This index provides values ranging from
a potential of -1 to 1. As this value increases, so does the amount of vegetation on the
surface. Vegetated areas will generally yield high NDVI values because of their relatively
high near-infrared reflectance and low visible reflectance. In contrast, water, clouds, and
snow have larger visible reflectance than near-infrared reflectance. Thus, these features
yield negative index values. Rock and bare soil areas have similar reflectances in the two
bands and result in vegetation indices near zero. For the study area it was found out that
the mean NDVI for 1989 was –0.044 and rose to 0.015 then dropped to –0.126 by the
18
year 2000 (Table 5 and Figure 8). This implies that overall, in 1999 there was more
vegetation cover than the other two years. It also shows that by the year 2000 vegetation
had declined.
Table 5: NDVI Statistics
1989NDVI 1999NDVI 2000NDVI
Mean
-0.044
0.015
-0.126
Standard Error
0.037
0.033
0.03
Mode
-0.91
-0.62
-0.72
Median
-0.035
0.015
-0.125
Standard Deviation
0.442
0.365
0.305
Sample Variance
0.195
0.133
0.093
Range
1.87
1.26
1.14
Minimum
-0.91
-0.62
-0.72
Maximum
0.96
0.64
0.42
Sum
-6.38
1.88
-13.08
19
Figure 8: Spatial variation of NDVI between 1989 (B), 1999 (C) and 2000 (D)
The NDVI data values were then tested for normality using the Kolmogorov-Smirnov
Test, and was found to be normal (Table 6).
Table 6: Kolmogorov-Smirnov Test
N
Normal Parameters
Mean
NDVI1989
NDVI1999 NDVI2000
144
126
104
-0.0443
0.0149
-0.126
.3653
.3048
.060
.059
Std. Deviation .4419
Most Extreme Differences Absolute
.053
20
Positive
.048
.060
.058
Negative
-.053
-.060
-.059
Kolmogorov-Smirnov Z
.638
.676
.604
Asymp. Sig. (2-tailed)
.810
.751
.858
A: Test distribution is normal.
Since the data were normal, the hypothesis that there is no difference in the NDVI means
between 1989, 1999 and 2000 was then tested using one way analysis of variance (Table
7). One Way ANOVA was used to describe the situation where a continuous response is
being described in terms of a single categorical variable or factor composed of two or
more categories (Thomas and Huggett, 1980). It is a generalisation of Student's t test for
independent samples to situations with more that two groups. When the Analysis of
Variance model is used, the amount of uncertainty that remains is sum of the squared
differences (SS) between each observation and its group's mean. In the output (Table 7)
the Error sum of squares (SSE) appears as the within GROUP sum of squares. The
difference between the Total sum of squares (SST) and the Error sum of squares (SSE) is
the Model Sum of Squares (SSM). Each sum of squares has corresponding degrees of
freedom (df) associated with it. Total df is one less than the number of observations, N-1.
The Model df is the one less than the number of levels The Error df is the difference
between the Total df (N-1) and the Model df (M-1), that is, N-M. The F-Value or F-ratio
is the test statistic used to decide whether the sample means are within sampling
variability of each other. That is, it tests the hypothesis that all the means are equal. F is
21
the ratio of the Model Mean Square to the Error Mean Square. Under the null hypothesis
that the population means are equal and the F statistic follows an F distribution (Shaw
and Wheeler, 1985). The null hypothesis is rejected if the F ratio is large. Results for
ANOVA are given in Table 7 below.
Table 7: One way Analysis of variance (ANOVA)
Groups
Count Sum
Average Variance
NDVI 1989
144
-6.38
-0.04
0.20
NDVI 1999
126
1.88
0.01
0.13
NDVI 2000
104
-13.08 -0.13
0.09
Source of Variation SS
df
MS
F
P-value F crit
Between Groups
1.13
2.00
0.56
3.87
0.02
Within Groups
54.18
371.00 0.15
Total
55.31
373.00
ANOVA
3.02
The p-value was less than the F-value, therefore the null hypothesis that the means are
equal was rejected and it was concluded that there is no evidence to suggest that all
population means were equal. This in turn implied that the mean NDVI for the years
1989, 1999 and 2000 and hence vegetation cover, were different pointing to the fact that
there was significant vegetation cover change in the study area. The decrease in vegetated
22
areas (Table 5) implying that more land was being cleared and field visits indicated that
most land was now under cultivation.
CONCLUSION AND RECOMMENDATIONS
The results revealed that rivers are affected not only by the hydrology and geology of the
area, but also to a very great degree by the human activities along their courses. Thus,
intensive urban and commercial agriculture along the long profile of Manyame river and
its tributaries contributes to pollution by agro chemicals posing a major challenge in the
provision of standard water quality. While efforts taken so far are commendable, more
action and initiatives for water pollution control and abatement need to be adopted such
as banning of urban agriculture. Water quality has to be considered within the spatial
context within which it occurs (Mtetwa et al., 2002). In the current study remote sensing
was used in the evaluation of biomass (vegetation) change to complement measured
water quality parameters. This can be used in predictive and anticipatory management
decisions, especially where changes in water quality are linked to changes in land-use
activities.
REFERENCES
Batley, R (1998) “Urban water in Zimbabwe: performance and capacity analysis” in The
role of government in adjusting economies vol. 33 University of Birmingham,
Birmingham, UK.
23
Central Statistical Office (2002) National Census Report: Preliminary results. CSO,
Harare.
Chenje, M., L. Sola and D. Paleczny. (eds.) (1998), The
State
of
Zimbabwe’s
environment, Government of the republic of Zimbabwe, Ministry of Environment
and Tourism, Harare, Zimbabwe.
Chinhoyi Municipal Offices (2005).
COBBAN, D. A. and SILBERBAUER M. J. (1993) Water quality decision making
facilitated through the development of an interface between a geographic
information system and a water quality database. Proc. 6thS. Afr. Natl. Hydrol.
Symp., Univ. of Natal, Pietermaritzburg, SouthAfrica.
D’Arrigo, R. D. Malmstrom C. M., Jacoby G. C., Los, S. O. And Bunker D. E. (1999),
Correlation between maximum latewood density of annual tree rings and NDVI
based estimates of forest productivity
Environmental Management Act (EMA) (2003). [Chapter 20:27]. Government of
Zimbabwe
Freese, S. D and Nozaic, D.J (2005) “Chlorine: Is it really so bad and what are the
alternatives?” Water SA Special edition 30 (5).
Jensen, John R., (2005). Introductory Digital Image Processing, 3rd Ed., Upper Saddle
River, NJ: Prentice Hall,
Kumar, M., and Monteith, J. L., (1982), Remote sensing of plant growth. In Plants and
the Daylight Spectrum (London: Academic Press).
24
Lillesand, T.M.; Kiefer, R.W. (2000) Remote Sensing and Image Interpretation, John
Wiley & Sons, Inc.
MET (2003) National Environmental Policy (second draft): For discussion and
consultative purpose only. Ministry of Environment and Tourism, Harare.
Mtetwa, S., Kusangaya, S*., Schutte, S.C. (2002) The application of geographical
information systems in the analysis of nutrient loadings from an agro-rural
catchment. Water SA, Volume 29, Number 2, pp 189 - 193.
Mutanga, O., Skidmore, A.K. (2003), Discriminating tropical grass (Cenchrus ciliaris)
canpies grown under different nitrogen treatments using spectroradiometry. In:
ISPRS journal of photogrammetry and remote sensing, International society of
photogrammetry and remote sensing - ISPRS, (2003) 4, pp. 263-272.
Mutanga, O., Skidmore, A.K. (2004a), Hyperspectral band depth analysis for a better
estimation of grass biomass, Cenchrus ciliaris, measured under controlled
laboratory conditions. In: JAG: International journal of applied earth observation
and geoinformation, (2004) Volume 22, pp. 87-96.
Mutanga, O., Skidmore, A.K., Prins, H.H.T.
(2004b) Discriminating sodium
concentration in a mixed grass species environment of the Kruger National Park
using field spectrometry. In: International journal of remote sensing, (2004)
Volume 20, pp. 4191-4201.
Rossini, P., and Benedetti, R., (1993), On the Use of NDVI Profiles as a Tool for
Agricultural Statistics: the Case Study of Wheat Yield Estimate and Forecast in
Emilia Romania. Remote Sensing of Environment, 45:311-326.
25
Sabins, FF, (1987). Remote Sensing Principles and Interpretation. Second Ed.W. H.
Freeman and Company, New York.
Sellers, P. J., Los, S. O., Tucker, C. J., Justice, C. O., Dazlich, D. A., Collatz, G. J., and
Randall, D. A., (1996), A revised land surface parameterisation (SiB-2) for
atmospheric GCMs. Part 2: The generation of global fields of terrestrial
biophysical parameters from satellite data. Journal of Climate, 9, 706–737.
Shaw, G. and Wheeler, D. (1985). Staistical Techniques in Geographical Analysis. John
wiley and sons, UK.
Thomas, R. W. and Huggett, R. J. (1980) Modelling in Geography, a mathematical
approach. Hoper and row, Publishers, London
World Health Organisation (WHO), (1982) Benefits to health of safe and adequate
drinking water and sanitary disposal of human waste. WHO, Geneva.
World Health Organisation (WHO), (2003) Water resources and environmental technical
note G2 lake management. WHO, Geneva.
Zimbabwe National Water Authority (ZINWA) Operational Guidelines for the control of
water pollution in Zimbabwe. ZINWA, Harare.
26