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3540 dx.doi.org/10.1021/es102790d |Environ. Sci. Technol. 2011, 45, 3539–3546
Environmental Science & Technology ARTICLE
Despite rapid technological advancement, the regulatory frame-
work around BW treatment and discharge is still emerging. In
contrast, more mature regulations such as the National Primary
Drinking Water Regulations have, for many years, required the
use of specific test protocols by certified laboratories for validat-
ing treatment efficacy.10 At present, there are no such codified
test procedures designed for validating the effectiveness of BW
treatment systems, either on land-based test beds or aboard
working ships.
The formulation of standardized BW treatment testing pro-
tocols is essential if shipboard BW treatment technologies are to
be widely implemented and discharge standards are to be
enforced. The success of BW regulations for reducing biological
invasions will depend, in large part, on whether (a) approved
treatment systems do in fact reduce organism concentrations to
the specified standards and (b) individual ships are in compliance
with the standards.11,12 This requires the ability to reliably
quantify very few living organisms in large volumes of water.
Enumeration methods are frequently used for quantifying
particles and microorganisms in drinking water. Emelko et al.13
showed that even when using certified sampling and analytical
protocols, enumeration of Cryptosporidium oocysts in drinking
water can yield variable results due to two sources of uncertainty:
(1) sampling error and (2) analytical recovery error. The
sampling and analysis of BW are prone to the same kinds of
error (see Supporting Information for detailed summary of
sampling and recovery errors associated with BW discharge
analyses). In the absence of standardized sampling and analytical
protocols, currently available data are insuffi cient to create a
comprehensive model that quantifies all sources of uncertainty
for BW discharge analysis, as has been possible for drinking
water.13,14
Although we are not yet able to parameterize all potential
sources of error, we present a theoretical model that is designed
specifically to ascertain the baseline sample volumes required to
robustly discern noncompliant zooplankton concentrations un-
der ideal sampling and detection conditions, thereby establishing
a rigorous lower limit (or minimum threshold) for sampling
effort. This is a crucial first step toward establishing robust
sampling procedures for BW regulations that are verifiable and
effective,15,16 as these do not currently exist. Our goal is to
provide formal evaluation and guidance on minimum sampling
effort to verify BW concentrations, since additional error can
never decrease the sampling effort required under the optimized
Poisson model presented (which represents the best case
scenario). As subsequent studies quantify the various sources
of additional error, especially recovery errors, sample volumes
should be adjusted to reflect these measures.
In this study, we focus on IMO and USCG proposed phase 1
standards (hereafter “IMO standard”) and organisms of g50 μm
minimum dimension (hereafter “zooplankton”) to (1) charac-
terize the uncertainty associated with estimating the concentra-
tion of organisms in BW due to the stochastic nature of sampling
BW (i.e., sampling error); and (2) demonstrate, using specific
examples, how various regulatory decisions regarding rates of
both type I (i.e., false positive) and type II (i.e., false negative)
errors17 affect the sample volumes needed to verify organism
concentrations. In particular, we estimate the statistical power to
detect BW concentrations that exceed the current IMO standard
of <10 organisms 3 m 3 using different sample volumes and
regulatory scenarios. As discussed above, we focus only on the
sampling error expected from BW discharges, since sampling
error should represent a significant source of uncertainty, espe-
cially at low concentrations.
’ METHODS
Of primary concern is characterizing the sampling effort
necessary to quantify live zooplankton concentrations in BW
in order to reliably classify BW as noncompliant (g10 3 m 3) or
compliant (<10 3 m 3 ), with high statistical confidence. Impor-
tantly, this sampling effort must be feasible given the realities and
logistic constraints specific to BW treatment system testing and
ship compliance monitoring. Furthermore, BW verification and
compliance testing also require that several decisions be made at
the regulatory level, especially if standardized sampling protocols
are to be developed.
One regulatory decision is how best to handle the inherent
uncertainty associated with sampling discharge concentrations,
even when using the best sampling protocols. There are at least
two philosophies concerning the regulation of BW discharge,
which differ according to where the burden of proof is placed.
The first is based on the presumption of innocence until proven
guilty, which places the burden of proof on the regulator. In this
context, a random sample of ballast discharge may contain >10
zooplankton 3 m 3 and still pass inspection as long as the sample
is not statistically signi ficantly g10 zooplankton 3 m 3. An alter-
native is to place the burden of proof entirely on the regulated
entity, whereby a ship with a measured zooplankton concentra-
tion that is not statistically signifi cantly <10 3 m 3 is presumed
guilty until proven innocent. We use the “presumed innocent”
approach in the examples presented in this paper, however, the
general methods we describe will apply to other approaches.
Given this, treated BW is assumed to have a concentration <10
zooplankton 3 m 3 until proven otherwise, thus the null hypoth-
esis is as follows:
(Ho): Concentration of live zooplankton in treated ballast
water is <10 zooplankton 3 m 3.
At present neither the IMO nor USCG have voiced guidance
on which approach will guide regulatory actions, but the
approach that is used may depend on the setting and the kind
of testing being carried out. For compliance monitoring of
individual ships, the “presumed innocent” approach may be
preferred. Because a high degree of certainty may be desired
for type approval testing of treatment systems (type approval is
the process of testing equipment to ensure that it meets technical,
safety, and regulatory requirements), it may be reasonable for the
burden of proof to be on the manufacturer or ship (i.e.,
“presumed guilty”).
Regardless of the approach, regulators must also define a
standard for how extreme data must be before the null hypothesis
is rejected. In statistical terms, this refers to the type I error rate,
R.17 In the scientific arena, the typical standard is R = 0.05,
however, there is no theoretical reason to assume this should be
the default standard for BW regulation, and in fact, this value is
often debated in scientific literature. In regard to ballast dis-
charge, if the “presumed innocent” approach is used, then larger
R values will result in more ships being falsely accused of
exceeding the limit (i.e., increased false positives). For the
examples in this paper we explore how R values of 0.05 and
0.20 affect sample volume.
Given the statistical framework described above (i.e., R = 0.05
or 0.20 and a “presumed innocent ” approach), we estimated the
likelihood of detecting BW with various concentrations that