Environmental Engineering Reference
In-Depth Information
9.7.2 Bioavailability
The concentration in the soil pore water is for neutral organic contaminants cal-
culated from the K OC . The bioavailability of contaminants may be reduced due to
aging (Alexander 2000 ). At contaminated sites the soil pore water concentrations
can be much lower and sorption coefficients can be much higher than equilibrium
partitioning models predict (Ter Laak et al. 2006 ). Cations are attracted by the elec-
trical potential of living cells, but also adsorb to soil organic carbon and to negatively
charged clay particles (Franco and Trapp 2008 ). This reduces their bioavailabil-
ity and, hence, uptake. Also, a depletion of contaminants in soil due to uptake
into plants should be taken into account. In Chapter 16 an extended description
of bioavailability is given.
9.7.3 Soil pH
Soil pH directly affects the speciation of acids and bases, as described by the
Henderson-Hasselbalch law (log [A ]/[HA]
p Ka ). Uptake of anions is
generally lower than of neutral molecules, due to electrical repulsion and slow trans-
fer across membranes. Both weak acids and bases can undergo the ion trap process
(Section 9.5.5 ). This will lead to an accumulation of weak acids from acidic soils,
and of bases from alkaline soils. This conclusion is based on a model prediction and
has not yet been confirmed by experiments. The pH also has an indirect effect on
uptake: many plants do not grow well outside their optimum pH range. Extreme pH
(high or low), will lead to reduced growth, and this may be accompanied by reduced
uptake of contaminants.
=
p H
9.7.4 Uncertainties in Predictions
Sections 9.5 , 9.6 and 9.7 list parameters and variables that influence the uptake of
contaminants into plants. Most likely, this list is far from complete. This may explain
why under some conditions, in some experiments, a high uptake of a contaminant
into a plant may be found, while this may not be the case in the next investiga-
tion, under other conditions. It also explains why data from experiments with plants
often suffer from large scatter. Besides, care must be taken when results obtained
from uptake studies are translated to other crop types, other climates, and other
agricultural practices.
Models may help to design and interpret uptake experiments, in indicating rel-
evant processes and pathways, and hence in translating results to other conditions.
But due to the large number of parameters and their high variability in space and
time, these models can not be expected to give exact results. Some studies tested
the validity of model approaches (Fryer and Collins 2003 ; Legind and Trapp 2009 ;
McKone and Maddalena 2007 ; Rikken et al. 2001 ; Trapp and Schwartz 2000 ).
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