Environmental Engineering Reference
In-Depth Information
kilometre were associated with high levels of ambient SO
2
emissions, being
70 kg/km
3
. As population density increased, SO
2
levels dropped, reaching
their lowest level of 45 kg/km
3
at a population density level of 170 persons
per square kilometre and then started to increase again. The author attrib-
uted the higher SO
2
levels at lower population density to the lack of pres-
sure in countries with spare population to control emissions. However, as
population density increased, more pressure was exerted to control emis-
sions as more people were exposed to pollution. This continued up to the
level where the household use of coal and non-commercial fuels exceeded
the pressure for pollution abatement.
Similarly, as mentioned before, Tuan (1999) tested the impact of popu-
lation density on the EKC trajectory. He hypothesized that the higher the
population density, the lower the turning point of the EKC, and, thus, a
better environmental quality. He found an EKC relationship between per
capita income and CO
2
emissions with a turning point of approximately
$18000. However, with respect to the impact of economic growth and
population rate on CO
2
emissions, Tuan found that their impact varied
according to the level of income and the country's stage of development.
At income levels below $2000 per capita, a highly dense population had a
negative impact on the environment. On the other hand, at income levels
of $2000 per capita or more, a highly dense population had a positive
ef ect on the environment. For instance, at an income level of $1000 per
capita, an increase in the population density of 67 per cent resulted in a
12 per cent increase in CO
2
emissions. However, at an income level of
$10 000 per capita, a similar increase in the population density resulted in
a 22 per cent reduction in CO
2
emissions. The author concluded that at
higher income levels, the higher the population density, the more pressure
was being exerted to control emissions as more people were being exposed
to the pollution.
Vincent (1997) presented an analysis for a single country, Malaysia, in
an attempt to examine the pollution-income relationship. Unlike previous
studies that conducted an analysis on cross-sectional or panel data for a
sample of developed and developing countries, Vincent used a panel data
set for the 13 Malaysian states and he analysed the relationship between
income and a number of air and water pollutants over time (1970s to
1990s). He also measured the impact of population density on the various
pollutants. The pollutants were TSPs, BOD, COD, ammoniacal nitrogen,
pH and suspended solids. The data used were ambient levels measured by
monitoring stations.
He chose Malaysia for several reasons. First, the country had a rich data
set with nearly two decades of readings on ambient air and water pollu-
tion. Second, the country's economy had been one of the fastest growing
