Environmental Engineering Reference
In-Depth Information
Table 6.3 Summary of disease burden estimates for different drinking water contaminants
(Havelaar and Melse 2003 )
Disease burden per 1,000 cases
YLD
DALY a
YLL
Cryptosporidium parvum
1.34
0.13
1.47
Campylobacter spp.
3.2
1.4
4.6
STEC O157
13.8
40.9
54.7
Rotavirus
High-income countries
2.0
12
14
Low-income countries
2.2
480
482
Hepatitis A virus
High-income countries, 15 - 49 yr
5
250
255
Low-income countries
3
74
77
a DALY is the sum of
years of life lost due to death
(YLL) and
years of life lived with
disability
(YLD) (WHO 2014 )
exogenous hazardous events such as extreme rainfall events. When that burden is
corrective, or remedial action is required at the critical control points.
One example of such an action is increased disinfection to counter the effects of the
exogenous (nonnormal) hazardous event.
It should be clear that the implementation of QMRA is nontrivial. For drinking
water treatment plants, the computational requirements for using QMRA would be
huge and the payoff is debatable. For this reason, it may be better to turn to other
risk minimization methods. We offer the following additional points by way of an
assessment of QMRA.
excessive,
6.3.4.3 Critique of QMRA
An assessment of QMRA can be summarized as follows:
1. As the empirical CDFs (cumulative distribution functions) are unknown, it has
been necessary to impose arbitrary CDFs of a known distribution such as the
Poisson distribution, the Beta and Binomial distributions.
2. It is applicable to treatment systems where the known pathogen removal (
π
)is
log reduction.
3. If the removal of pathogens is 4-log or higher, then the focus would shift
entirely to possible ingress or regrowth in the pipeline network.
4. The effectiveness of each process in removing pathogens is variable between
(a) the same processes operated at different treatment plans and (b) over time at
the same plant. Thus, calculating removal performance at any time would be
extremely dif
of
low
cult.
5. Calculating treatment effectiveness depends on the following data:
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