Biology Reference
In-Depth Information
4.1 Introduction
Many waterborne disease outbreaks are caused by the consumption of groundwater contaminated
by pathogenic microorganisms (Goss et al., 1998; Macler and Merkle, 2000; Bhattacharjee et al.,
2002; Close et al., 2006). Pathogenic microorganisms find their way into the sub-surface through
the spreading of sewerage sludge on fields, leakage from waste disposal sites and landfills
(Taylor et al., 2004), or infiltration from cesspits, septic tank infiltration beds, and pit latrines. In
situations where the distance between source of pollution and abstraction point is small, the risk
of abstracting pathogens looms (Foppen and Schijven, 2006).
To predict the presence of pathogens in water, a separate group of microorganisms is usually
used, generally known as fecal indicator organisms. Many microorganisms have been suggested
as microbial indicators of fecal pollution (like enterococci, coliphages and sulphite reducing
clostridial spores; Medema et al., 2003), but one of the most important indicators used
worldwide is
Escherichia coli
.
In a recent work, Schinner et al. (2010) reported different
attachment efficiencies of five waterborne pathogens (Gram negative bacteria:
E. coli
O157:H7
ATCC 700927,
Yersinia enterocolitica
ATCC 23715, Gram positive bacteria:
E. Faecalis
ATCC
29212 and Cynobacteria.
M aeruginosa
UTCC 299 and
Anabaena flosaquae
UTCC 607 ) to
quartz sand indicating that the heterogeneity in transport and attachment behavior observed
among commensal strains (Bolster et al., 2009; Simoni et al., 1998; Albinger et al., 1994) may
not differ from those of pathogenic strains. For long, prediction of microbial transport behavior
in saturated porous has relied on the classical colloid filtration theory (CFT) by Yao et al. (1971).
One of the characteristics of the theory is the use of the sticking efficiency, which is defined as
the ratio of the rate of particles striking and sticking to a collector to the rate of particles striking
a collector, and is mainly determined by electro-chemical forces between the colloid and the
surface of the collector (Foppen and Schijven, 2006). According to the theory, the sticking
efficiency is constant in time and place (Yao et al., 1971; Foppen et al., 2007; Tufenkji and
Elimelech, 2004a). However, recent research results indicate that the sticking efficiency of a
biocolloid population varies due to variable surface properties of individual members of the
population, resulting in differences in affinity for collector surfaces (Albinger et al., 1994;
Baygents et al., 1998.; Simoni et al., 1998; Li et al., 2004; Tufenkji and Elimelech, 2005a; Tong
and Johnson, 2007; Foppen et al., 2007a,b). Based on these findings, an important question is:
What type of distribution describes the variation in sticking efficiencies of a biocolloid
population best? Some workers demonstrated that sticking efficiencies were distributed
according to a power-law (Redman et al., 2001a,b; Tufenkji et al. 2003), while others found a
log-normal distribution (Tufenkji et al., 2003; Tong and Johnson, 2007) or a dual distribution
(Tufenkji and Elimelech, 2004b and 2005b; Foppen et al 2007a,b). However, all studies, aimed
at revealing sticking efficiency distributions, have been conducted for very limited transport
distances (centimeter to decimeter), and can therefore not be considered representative for longer
transport distances, which are so important in microbial risk assessment of groundwater and
therefore quantifying the potential health impacts of pathogenic microorganisms traveling in
aquifers. In those health impact assessments, the minimum value of the sticking efficiency
distribution, and the percentage of individual biocolloids of a total population having such low
sticking efficiency, are crucial parameters, because the minimum value in combination with the
amount of cells largely determine the maximum transported distance, and, hence, the potential
health impact. Foppen and Schijven (2006) indicated that the range of sticking efficiencies of
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