THE FLORIDA STATE UNIVERSITY ARTS AND SCIENCES ...

Among the variety of successful methods used to identify denitrification, the use of dual
isotopes is increasing. Chen and MacQuarrie (2005) have demonstrated that there is a
theoretical relationship between δ 18 O and δ 15 N in
14
−
NO 3 undergoing denitrification. They
concluded that a plot of δ 15 N v/s δ 18 O should yield a slope of 0.51. Comparing their
studies with field data (Bottcher et al., 1990; Aravana & Robertson, 1998; Mengis et al.,
1999; Fukada et al., 2003) indicates that the slopes of the lines plotted do lie close to the
theoretical value of 0.51. While there are a variety of values reported for the slope which
range from 0.48 to 0.67, it is still a useful tool in providing unambiguous evidence of
denitrification. A positively correlated variation in N and O isotopes of
as a signature for denitrification.
1.4. Current methods to estimate the rate of denitrification
−
NO 3 can be used
Among the many factors that affect denitrification, the non-availability of oxygen is
perhaps the most critical factor when the denitrification process is concerned. As oxygen
dynamics in soil are hard to simulate (or to measure), water content is used as a
complementary for the presence of oxygen. The higher the water content, the less oxygen
will be present.
The other main factors that influence denitrification are microbial dynamics, nitrate
concentration, soil temperature and soil acidity (pH), and a variety of additional
parameters as mentioned earlier. Developing a denitrification model that can account for
all of the factors is a major predicament as it leads to an extremely complicated and
difficult model. In addition to the complexity such models usually require several input
factors which can be not only difficult to obtain but also extremely cost intensive. This is
why the majority of denitrification models are simplified models of the real world
situation.
A number of different approaches have been used to develop denitrification sub-models
in N cycling models (Parton et al., 1996),
(1) Microbial growth models,