An economic evaluation of water treatment costs in the Umgeni catchment area.
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Date
1996
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Abstract
This study has two objectives: first, to identify the main contaminants responsible
for high water treatment costs in the Umgeni catchment area, and second, to
predict water treatment costs from observed levels of contaminants. Reliable
information about the origin of high water treatment costs is required to inform
both policy and planning decisions. Partial adjustment models of water
treatment costs are estimated using ordinary least squares regression and
principal component analysis.
First a model is estimated for the DV Harris treatment plant, which draws water
from Midmar Dam. This model highlights important policy issues and explains 61
per cent of the variation in chemical treatment costs. Environmental
contaminants have a marked impact on real water treatment costs at the DV
Harris plant. Water treatment costs increase when levels of alkalinity, sodium
and turbidity fall. Conversely, real costs rise with higher levels of dissolved
oxygen and water stability. Paradoxically, clean water - typical of Midmar Dam is expensive to treat. Water treatment costs also rise when concentrations of the
algae, Chiorella, decline.
Second, a model is estimated for the Durban Heights treatment plant, which
draws water from Nagle and Inanda Dams. This model explains 68 per cent of
the variation in chemical treatment costs. Biological contaminants have a
marked impact on real water treatment costs at the Durban Heights plant. Again,
water treatment costs increase when levels of, Chiorella fall. Apparently the
level of Chiorella varies inversely with the level of other, more expensive,
contaminants at both treatment plants. Conversely, real costs rise with higher
levels of total kjeldahl nitrogen, temperature, Anabaena and Microcystis. Water
treatment costs also rise when turbidity and concentrations of silica, suspended
solids and iron increase.
The model predicts actual water treatment costs well (except during occasional
peak cost periods) and provides a useful tool for scenario testing. For example, a
simulation exercise in which turbidity levels were held constant at 6 NTU
(nephelometric turbidity units) indicated an annual saving of R54 531 in water
treatment costs.
Description
Thesis (M.Agric. Mgt.)-University of Natal, Pietermaritzburg, 1996.
Keywords
Water--Purification--KwaZulu-Natal., Water--Purification--Costs., Water treatment plants--KwaZulu-Natal., Water--Pollution--KwaZulu-Natal., Theses--Agricultural economics.