Environ Sci Pollut Res (2015) 22:5271–5282
DOI 10.1007/s11356-014-3783-x
RESEARCH ARTICLE
Improvement on species sensitivity distribution methods
for deriving site-specific water quality criteria
Yeyao Wang & Lingsong Zhang & Fansheng Meng &
Yuexi Zhou & Xiaowei Jin & John P. Giesy & Fang Liu
Received: 25 May 2014 / Accepted: 27 October 2014 / Published online: 13 November 2014
# Springer-Verlag Berlin Heidelberg 2014
Abstract Species sensitivity distribution (SSD) is the most
common method used to derive water quality criteria, but
there are still issues to be resolved. Here, issues associated
with application of SSD methods, including species selection,
plotting position, and cutoff point setting, are addressed. A
preliminary improvement to the SSD approach based on poststratified sampling theory is proposed. In the improved method, selection of species is based on biota of a specific basin,
and the whole species in the specific ecosystem are considered. After selecting species to be included and calculating the
cumulative probability, a new method to set the critical threshold for protection of ecosystem-level structure and function is
proposed. The alternative method was applied in a case study
in which a water quality criterion (WQC) was derived for
ammonia in the Songhua River (SHR), China.
Keywords SSD . Water quality criteria . Plotting position .
Threshold . Stratified sampling . Site-specific . Asia .
Ammonia . Nitrogen . Toxicity . Statistics
Responsible editor: Thomas Braunbeck
Electronic supplementary material The online version of this article
(doi:10.1007/s11356-014-3783-x) contains supplementary material,
which is available to authorized users.
Y. Wang : L. Zhang (*) : F. Meng : Y. Zhou
State Key Laboratory of Environmental Criteria and Risk
Assessment, Chinese Research Academy of Environmental
Sciences, Beijing 100012, China
e-mail: zlingsong@163.com
Y. Wang : X. Jin : F. Liu
China National Environmental Monitoring Center, Beijing 100012,
China
J. P. Giesy
Department of Veterinary Biomedical Sciences and Toxicology
Centre, University of Saskatchewan, Saskatoon, Saskatchewan,
Canada
Introduction
Widespread occurrence of toxic substances caused by activities of humans can adversely affect aquatic organisms
(Groombridge and Jenkins 2002). Research on differences in
sensitivities to toxicants among species has become a focus
and shared concern of environmental scientists and managers.
In order to optimize the level of protection at an acceptable
level, some countries with more developed economies have
well-established systems to estimate the maximum limit
which can be accepted by the ecosystem (Jin et al. 2014). Of
which, the species sensitivity distribution (SSD) method is
currently the most commonly used method and has been
adopted by USEPA (1985), ANZECC & ARMCANZ
(2000), RIVM (2007), and CCME (2007) as the official
method to derive water quality criteria (WQC) for protection
of the structure and function of ecosystems.
The SSD method to derive WQC originated almost simultaneously in Europe and in the USA (USEPA 1985; Kooijman
1987). The theoretical basis of SSD is that it is possible to
describe the variability and range of sensitivities among individual taxa with a statistical or empirical distribution function
(Posthuma et al. 2002). In short, the basic assumption of the
SSD concept is that: (1) relative sensitivities of a set of species
can be described by some distribution such as the triangular,
normal, or logistic distribution; (2) the data on sensitivities of
individual species to toxicants that is used to construct SSD
are seen as a random sample from the entire population of
possible sensitivities and are used to estimate descriptive
parameters of the SSD; and (3) when a certain portion of
species are protected, the ecosystem is also protected. Based
on these assumptions, toxicity data are ranked and then a
statistical distribution fitted. Hazardous concentrations (HCs)
can be estimated, which are protective of a given proportion of
the species present within a specified community (Posthuma
et al. 2002; van Straalen and Denneman 1989). Generally, the
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SSD method has proven to be a useful approach to predict the
entire communities (Schroer et al. 2004; Maltby et al. 2005),
but there are still some issues relative to the application of
SSDs (Forbes and Calow 2002). These include, among others,
the selection of species to be included, methods to derive
plotting positions, and what to select as the assessment endpoint, such as the concentration to affect 5 % of species
(HC5). There are polemics particularly around the sufficiency
of the SSD approach to protect ecosystem-level structure and
function (Forbes and Calow 2002).
Sensitivity (tolerances) of individual species is not a
random phenomenon, and it will not change regardless of
the methods used to describe it. Hence, it was deemed
desirable to develop a method to select species more randomly than has been done previously and have a greater
likelihood of accurately describing the entire range of sensitivities within a community or organisms in a particular
ecosystem. To do this, it was necessary to develop a statistical method to estimate the distribution of sensitivities of
all species in an ecosystem (Forbes and Calow 2002).
According to statistical theory, the actual composition of
the sample should be randomly selected (Rice 2011), which
is also consistent with one of the basic assumptions of SSD
methods. Hence, every member of the population will be
selected with uniform probability.
It has also been suggested that the data set should be
statistically and ecologically representative of the community
(Forbes and Calow 2002; Wagner and Løkke 1991). It is
impossible to know the total number of species in a community and nearly impossible to know which species are the
critical species, which, if eliminated, would result in major
changes in the structure and/or function of a community. Rare
species are treated with the same weight as abundant species
(Posthuma et al. 2002). However, by technical means and cost
constraints, species for which toxicological data is available
have generally been selected based on availability or ease of
maintenance in culture rather than by random sampling.
Otherwise, some researchers have given priority to some
specified taxa, trophic levels, and species based on their
experience because they think the specified members are
representative in some respects. In some situation, this strategy might be effective, especially when there are relatively few
toxicity data. It can promote the toxicity test covering different
trophic levels and having greater taxonomic differences, but
this solution violates the random principle and cannot ensure
that there is no bias in estimates.
The dispute about methods of determining plotting position
during probabilistic assessments has long been debated
(Langbein 1960; Benson 1962; Jordaan 2005; Makkonen
2006). The most commonly used methods are the Weibull
and Hazen methods, which have been adopted by USEPA
(1985) and ANZECC & ARMCANZ (2000), RIVM (2007)
as well as CCME (2007) (Eqs. 1 and 2).
Environ Sci Pollut Res (2015) 22:5271–5282
Weibull method:
p¼
i
nþ1
ð1Þ
Hazen method:
p¼
i−0:5
n
ð2Þ
where p is the cumulative probability, i is the rank of the
sample, and n is the sample size.
Theoretically, there is no difference between plots based on
these two methods when the sample size is infinitely large. In
fact, data for many toxicants is lacking, especially for species
endemic to developing countries. Thus, there will be a significant difference between them which will make the results of
calculation different. It is difficult to explain which is more
reasonable for the developer and stakeholder.
In order to eliminate effects of the “tail” of the SSD and get
a more exact criterion value, Van Straalen and Denneman
(1989) have introduced the concept of a “cutoff point” p into
the calculation of criteria. According to their concept, the
choice of a cutoff point can be chosen by the manager, and
the corresponding concentration could be calculated which is
called the HCp. Consequently, this method has become the
official method to derive environmental quality criteria used
by some countries (VROM 1989; ANZECC & ARMCANZ
2000; CCME 2007). However, in practical application, the
most commonly used cutoff point value is the 5th centile,
which indicates the concentration less than which fewer than
5 % of species would be affected. Until now, there were no
clear reasons why a value of 5 % should be chosen. The
practice was, to a large extent, arbitrary (Okkerman et al.
1993; Versteeg et al. 1999). In this case, theoretically, 5 %
species would be affected. In fact, the choice of the HC5
seems to have followed the convention of statistics in which
a type I error (α) of 5 % is accepted (Posthuma et al. 2002).
Otherwise, with toxicity testing of more and more species,
more and more toxicity data are used in the criteria calculation. Thus, the calculation process becomes an interpolation
from extrapolation, and the most sensitive species with sensitivities less than the HC5 would be expected to be affected.
USEPA’s newest WQC for ammonia (USEPA 2013) is an
excellent example of this situation. Compared with the 1999
WQC document (USEPA 1999), the more recently available
toxicity data were used to calculate the WQC, and the acute
and chronic criteria value decreased from 24 to 17 and from
4.5 to 1.9 mg total ammonia nitrogen (TAN)/L, respectively.
The 1999 WQC was based primarily on effects on early life
stages of fishes, whereas the 2013 WQC is based on effects on
more sensitive invertebrate genera, including unionid mussels,
of which, the most sensitive species are Lasmigona subviridis
Environ Sci Pollut Res (2015) 22:5271–5282
and Venustaconcha ellipsiformis (SMAV = 23.41 and
23.12 mg TAN/L respectively). Development of WQC was
based on an implicit assumption that 5 % of species could be
allowed to be adversely affected and still maintain integrity of
an ecosystem. In fact, the 5th centile was chosen because
when the distribution of sensitivities in single species tests
under laboratory conditions, where individuals were exposed
to the maximum and continuous concentration, it was equivalent to the threshold concentrations less than which no adverse effects were observed in multi-species tests
(mesocosms) (Giesy et al. 1999). The aim of this study was
to develop an improved solution based on sample theory and
to improve upon the traditional SSD method. The improved
solution is expected to be more reasonable and convincing
that it is protective of structures and functions of ecosystems.
Improvement on traditional SSD method
Species selection and SSD curve construction
The relationship between sensitivity and/or tolerance of a
species to a toxicant and its natural history such as feeding
guild, morphology, and physiological traits is a concern of
many eco-toxicologists (Forbes and Calow 2002).
Researchers have tried to describe sensitivities of generic
species with specific sets of characteristics so that they could
predict sensitivities of species for which no information on
sensitivity to a particular toxicant existed (Slooff 1983; Vaal
et al. 1997; Zhang et al. 2010; Wang et al. 2014; Zhang et al.
2014). These researchers systematically reported variability
among sensitivities of species to toxicants, and Baird and Van
den Brink (2007) proposed a method to predict the sensitivity
of a species to specific toxicants by use of their unique and
similar traits. It has been determined that the method offers
some promise as a mechanistic alternative to the otherwise
empirical approach to selection of species included in an SSD.
Species that feed on similar foods and have similar physiologies will possibly have similar exposures and responses to
toxicants. In the classification system known as “biotaxy,”
aquatic organisms are divided into different taxa according
to biological variances and phylogenetic relationships.
In statistics, the target population should be defined before
a sampling process is begun. Thus, in assessing the potential
effects of contaminants at the community level of organization, all aquatic species in a specific aquatic ecosystem were
defined as the target population, but in practice, it is difficult
and unreasonable to establish a global aquatic ecosystem scale
WQC. Thus, aquatic ecosystems are usually divided into
different subsystems for management convenience and “basin” is the mostly used scale because of significant difference
in biota. Thus, establishment of a basin scale WQC is necessary because of the difference of species to be protected. In
5273
this assessment, the basin was used as the appropriate scale to
derive a WQC, and all aquatic species in a specific basin can
be identified from a combination of reviews of the literature
and surveys in the field and used to develop the target population to be protected and/or for sampling.
Based on the discussion above, an assumption was made
that the species in the same taxon might have relatively similar
sensitivities to specific toxicants. Thus, taxa can be used as
stratification variables. Toxicity can be defined as the random
selection and result from corresponding strata rather than from
all the species. Based on this assumption, all the screened
toxicity-tested species could be sorted by use of biotaxy into
different strata. Thus, the post-stratified sample method
(Daniel 2011) has been advanced for calculating the cumulative probability in which a weighting coefficient could be used
in order to prevent an overrepresentation (bias) of some strata
(taxonomic groups). The mechanism of the improvement can
be represented by a function.
When the species number in a specific basin is N, it can be
divided into l mutually exclusive, homogeneous strata according to biotaxy, and the species number in each stratum is Ni
(i=1,2,…,l). After retrieving and screening, the number of
screened toxicity-tested species is n, and it also can be sorted
into different strata, and the sample number of each stratum is
ni (i=1,2,…,l). Thus, the sampling fraction of each stratum
can be expressed (Eq. 3).
fi ¼
ni
ði ¼ 1; 2; …; l Þ
Ni
ð3Þ
Equation (4) was used to present the sample set.
8 9 8
s11
X1 > >
>
>
= >
< >
<
s21
X2
¼
X ¼
…
⋅⋅⋅ > >
>
>
; >
: >
:
sl1
Xl
s12
s22
…
sl2
…
…
…
…
9
s1n1 >
>
=
s2n2
…>
>
;
slnl
ð4Þ
in which X stands for the sample set of target population and
Xi(i=1,2,⋅⋅⋅,l) stands for the sample set of each stratum (Ni).
Xi(i=1,2,⋅⋅⋅,l)∈X and sini ði ¼ 1; 2; ⋅⋅⋅; l Þ stand for the sample.
Under ideal conditions, the sampling fraction of each stratum would be the same, but in actuality, it is difficult to obtain
results of toxicity tests for species in all strata, especially
chronic toxicity (Christensen et al. 2003; Jager et al. 2007;
Wu et al. 2013). This limitation results in the number of
elements (screened toxicity test species) sorted into each stratum disproportionally to their representation in the community
and even missing in some strata. Thus, here, discussions of
calculation of cumulative probability in three situations,
which are selected to reduce bias, are presented.
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Environ Sci Pollut Res (2015) 22:5271–5282
(1) Under ideal conditions, the screened toxicity test species
cover all strata and the sampling fraction of each stratum
is the same.
Toxicity values are ranked and assigned a sequence
(Eq. 5).
Ri ¼
1; 2…;
l
X
!
ð5Þ
ni
i¼1
followed assuming that the not included strata contained
more sensitive taxa.
When the number of species in the strata not included
are Nj(j=k+1,k+2,⋅⋅⋅,l), respectively; the total number
of species in the strata not considered can be represented
(Eq. 10).
l
X
Nj
ð10Þ
j¼kþ1
Thus, cumulative probability can be described (Eq. 6).
X
Ri
Pi ¼ X ; i ¼ 1; 2; …;
ni
ni
ð6Þ
(2) When the screened toxicity test species cover all strata,
the sampling fraction of each stratum is different. Then,
the mean value of each stratum was calculated (Eq. 7),
which represents
−
Xi ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
p
nl
si1 si2 … sinl ; ð j ¼ ni ; l ¼ 1; 2; …; l;Þ
ð7Þ
the mean sensitivity of each stratum respectively. Then,
toxicity values are ranked and assigned a sequence
Ri ={1,2,…l}, and a weighted process is introduced into
the cumulative probability calculation (Eq. 8).
l
X
Ri
Pi ¼ l
Ni
i¼1
ð8Þ
N
(3) When the screened toxicity test species does not cover all
strata, the samples can be represented (Eq. 9).
X ¼
The geometric mean value of each stratum (Eq. 7)
represents the sensitivity of the corresponding stratum.
Toxicity values are then ranked and assigned a sequence
number Ri(i=1,2,⋅⋅⋅,k), and weighted values are then
plotted as the cumulative probability (Eq. 11).
!
l
k
X
X
Ni þ
Nj
Ri
i¼1
j¼kþ1
ð11Þ
Pi ¼
N
kþ1
8
>
>
>
>
>
<
X1
X2
…
Xk
>
>
>
X ðkþ1Þ
>
>
: …
Xl
8
s11
>
>
>
>
s21
>
>
< …
¼
sl1
>
>
>
>
>
>
∅
>
>
>
; >
>
: …
∅
9
>
>
>
>
>
=
s12
s22
…
sl2
…
…
…
…
s1i
s2 j
…
slm
9
>
>
>
>
>
>
=
>
>
>
>
>
>
;
ð9Þ
in which Xi(i=1,2,⋅⋅⋅,k) stands for the sample of involved strata respectively, and Xi(i=k+1,k+2,⋅⋅⋅,l) are
an empty set.
Thus, sensitivities of strata not included cannot be
estimated, and this becomes an uncertainty factor for
cumulative probability calculation. Under this condition,
a suggestion to adopt a “conservative method” was
Threshold value for use in regulatory decisions
The threshold of 5 % is somewhat arbitrary and not supported
by any actual detailed analyses other than the fact that it is
often equivalent to the NOAEL from multi-species tests where
data is available, generally for pesticides (Giesy et al. 1999).
Thus, there is no guarantee that this level of protection, based
on the results of toxicity tests on individual species that are
conducted under laboratory conditions, would protect function of a community. The importance of biodiversity for
functions of ecosystems has been demonstrated, and loss of
biodiversity can impair capacities of communities and ecosystems to provide the “ecosystem services” such as providing
food, process organic matter, including contaminants, and
recover from perturbations (Hooper et al. 2005; France and
Duffy 2006; Tilman et al. 2006; Worm et al. 2006).
Ecosystems, while made up of both the physical environment
and a range of individual species, have transcendent, emergent
properties that are greater than the sum of their parts (Giesy
and Odum 1980). Thus, it is difficult to predict what effects
eliminating one of more species would have on the overall
functions of ecosystems. Therefore, theoretically, from the
point of view of conservation of biodiversity, all species
should be protected. At least, criteria should be less than the
threshold for effects on the most sensitive species. Generally,
the number of species in a specific basin is knowable through
historic data retrieving and even field investigation. In this
assessment, the number of species in a specific basin (N)
therefore has a proportion of each taxon of N1 . Thus, the
threshold for effects should be N1 or some value less than N1
rather than 5 %. Thus, theoretically, all species in the basin can
Environ Sci Pollut Res (2015) 22:5271–5282
be considered in setting the threshold for effects or the protective WQC. One limitation of using a probabilistic approach
is that there is no 0 or 100 % on the probabilistic scale. That is
there is no concentration that is less than the value that could
adversely affect a theoretical species. Similarly, there is no
concentration less than which no species would be affected.
Similarly, there is no concentration that is 100 % safe. This
limitation leads to a semantic issue between assessors of risk
and managers. This is particularly true for communication
with the lay public that wants a completely safe environment
with no risk of adverse effects on people or wildlife. For this
reason, the concept of resolution becomes paramount. For
instance, if N was 100, then theoretically, each species would
represent 1/100 or 1 % of the total number of species and the
resolution of the assessment would be 1 %. That is, if the
threshold for effect was set to the concentration equivalent to
the effect concentration for the most sensitive species, there
would be a 1 % chance that a species would be affected, but it
is unknown what the probability for affecting the function of
the ecosystem would be.
Technique flow of the improved SSD methods for deriving
WQC
The flow chart of the steps used to calculate the improved SSD
for use in deriving WQC is given (Fig. 1). The first step is to
retrieve the number of species in a specific basin and collect
all of the toxicity data for all of the relevant species. If there are
some unknown species in the basin, there is no basis for
deriving WQC to protect the missing species because both
the traditional SSD method and the improved method need to
select some of the species on which to conduct toxicity tests,so
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to some extent, the number of species is restricted to all the
known species in the basin. Different biological classifications
should consider the range in sensitivities among species. In
this way, the total universe of species to consider might be
reduced. That is, some species with certain physiological
characteristics might be “exempted” from consideration. For
example, toxicity of ammonia to aquatic plants can usually be
ignored, so only the aquatic animal was considered in deriving
ammonia WQC to aquatic life (USEPA 1999), but, due to their
sensitivities, when the SSD approach is used to derive a WQC
for a metal, aquatic plants and microorganisms should be
considered. Another issue when describing the species (N) in
an environment is that some species might have disappeared
from the basin because of environmental pollution or other
activities of humans. In order to achieve restoration of communities of aquatic organisms, all the species that could
theoretically occur should be considered even if they disappeared from the water system in recent years.
The steps in the proposed analysis include the following:
(1) determining the number of species (N) in a receiving water
to be protected, including possible indirect effects such as
effects on food items; (2) determining for species-identified
toxicity data that exists or can be derived; (3) selecting an
appropriate model to calculate cumulative probability; (4)
constructing the plot position of toxicity data versus accumulating probability and fit the SSD curve; and (5) using 1/N as
the cutoff point to obtain the WQC.
Until now, identification of all of the species in an ecosystem has been impossible, especially for the smaller organisms.
However, now, the use of ecosystem-wide genomics offers the
potential to do exactly that. The authors are currently developing and applying a combination of genomic and informatics
that will allow for the identification and enumeration of all the
species in a particular ecosystem.
Case study of ammonia for the Songhua River, China
Biota of the Songhua River
Fig. 1 Flow chart for development of improved SSD for use in deriving
WQC
The Songhua River (SHR) is in the Northeast China and flows
1434 km from the Changbai Mountains through Jilin and
Heilongjiang provinces. The river drains 557,000 mi 2
(1,440,000 km2) of land and has an annual discharge of
2460 m3/s (87,000 Cu ft/s). Aquatic life is abundant in this
large river, and it supplies a number of ecosystem services, in
particular food products for consumption by humans.
Therefore, it has been decided, for social and economic reasons, that it is important to produce these valued assessment
endpoints.
Recently, because of environmental pollution, overfishing,
and effects on habitat, such as erosion and the associated
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Environ Sci Pollut Res (2015) 22:5271–5282
Table 1 Known aquatic life list native to the Songhua River
Phylum
Number
Class
Number
Order
Number
Family
Number
Chordata
115
Actinopterygii
103
Gasterosteiformes
Esociformes
Salmoniformes
2
1
17
Cypriniformes
66
Perciformes
7
Siluriformes
6
Gadiformes
1
Gasterosteidae
Esocidae
Salmonidae
Osmeridae
Thymallidae
Salangidae
Cyprinidae
Cobitidae
Belontiidae
Channidae
Serranidae
Percidae
Gobiidae
Eleotridae
Siluridae
Bagridae
Lotidae
2
1
11
3
2
1
60
6
1
1
1
1
1
2
2
4
1
Scorpaeniformes
Acipenseriformes
Petromyzontiformes
Caudata
Anura
1
2
3
3
6
Cottidae
Acipenseridae
Petromyzontidae
Hynobiidae
Bufonidae
Microhylidae
Discoglossidae
Ranidae
Hylidae
Cambaridae
Atyoidae
Palaemonidae
Cyclopidae
Canthocamptidae
Diaptomidae
Temoridae
Centropagidae
Leptodoridae
Macrothricidae
1
2
3
3
1
1
1
2
1
1
1
4
20
5
8
2
1
1
6
Polyphemidae
Moinidae
Chydoridae
Sididae
Bosminidae
Daphnidae
Culicidae
Comphidae
Reronareyidae
Libellulidae
Agriidae
Corduliidate
Polycentropidae
1
4
28
6
4
44
17
11
8
21
2
7
3
Arthropoda
243
Cephalaspidemorphi
Amphibia
3
9
Malacostraca
6
Decapoda
6
Maxillopoda
36
Cyclopoida
Harpacticoida
Calanoida
20
5
11
Branchiopoda
94
Cladocera
94
Insecta
107
Diptera
Odonata
17
49
Trichoptera
28
Environ Sci Pollut Res (2015) 22:5271–5282
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Table 1 (continued)
Phylum
Mollusca
Annelida
Number
26
23
Rotifera
16
Total
423
Class
Gastropoda
Number
14
Order
Number
Ephemeroptera
13
Mesogastropoda
5
Sorbeoconcha
Pulmonata
1
8
Bivalvia
12
Veneroida
Eulamellibranchia
1
11
Clitellata
Hirudinea
2
21
Tubificida
Rhynchobdellida
Arhynchobdellida
2
11
7
Rotifera
16
siltation and turbidity, the numbers of individuals of some
valued species have decreased, and some have been extirpated from the SHR and adjacent water bodies. In order to
restore the valued ecological services of the SHR, all of the
values species both currently present and those that were
present historically should be considered. All available
literature since 1960 was collected, and a list of aquatic
organisms was compiled. There were 423 species identified
to occur in the SHR. These belonged to 4 phyla, which
included 115 chordates, 248 arthropods, 26 Molluska and
34 Annelida (Table 1). Relative proportions of species in
different taxa are significantly different; Arthropoda,
Chordata, Annelida, and Mollusca were determined to be
58.6, 27.2, 8.0, and 6.2 %, respectively. According to the
assumption in this study, if each species was given the same
weight in cumulative probability calculation, there may be
a significant bias.
Branchiobdellida
Monogononta
3
16
Family
Number
Hydropsychidae
Rhyacophilidae
Molannidae
6
5
1
Leptoceridae
Limnephilidae
Phryganeidae
Baetidae
Ephemerellidae
Viviparidae
Bithyniidae
Melaniidae
Pleuroceridae
Lymnaeidae
Planorbidae
Corbiculidae
Margaritanidae
Unionodae
Sphaeriidae
Tubificidae
Glossiphoniidae
Haemopidae
2
1
10
2
11
3
1
1
1
7
1
1
3
7
1
2
11
2
Salifidae
Erpobdellidae
Hirudinidae
Branchiobdellidae
Brachionida
Asplancchnidae
Trichocercidae
Synchaetidae
1
3
1
3
10
1
3
2
Toxicity dada retrieving and screening
Toxicity data were retrieved from the ECOTOX database and
other literature. For better comparison and consistency, toxicity
data were further selected based on the following criteria: (1)
Toxicity test should follow ASTM or other certificated standards; (2) same test endpoints, LC50, should be used; (3) the
toxicity data used in the paper were standardized to pH=8 and
25 °C using the method in reference (USEPA 1999); and (4) if
more than one toxicity study was available for the same species
with different endpoints, the minimum value was used. If several
toxicity tests were available for the same species and endpoint,
the geometric mean of these values was used (Table 2).
After retrieving and screening, there were nine species
native to the SHR for which data on acute toxicity of ammonia
was available, and three species native to the SHR for which
data on chronic toxicity of ammonia was available, so only the
5278
Environ Sci Pollut Res (2015) 22:5271–5282
Table 2 Compiled, screened acute toxicity data of ammonia to aquatic life (pH=8 and 25 °C)
Phylum
Class
Order
Family (n)
FMAV
(mg TAN/L)
Species
SMAV
(mg TAN N/L)
Vertebrata
Actinopterygii
Acipenseriformes
Acipenseridae
(2)
19.48
Salmoniformes
Salmonidae
(11)
23.9
Acipenser sinensis
Acipenser brevirostrum
Salmo trutta
Salmo.salar
10.4
36.49
23.75
42.66
Oncorhynchus gorbuschaa
Oncorhynchus mykissa
Oncorhynchus kisutch
Oncorhynchus aguabonita
Oncorhynchus clarki
Oncorhynchus tshawytscha
Prosopium williamsoni
Salvelinus fontinalis
Salvelinus namaycush
Notemigonus crysoleucas
Cyprinus carpioa
Hybognathus amarus
Cyprinella whipplei
Cyprinella spiloptera
Cyprinella lutrensis
Campostoma anomalum
Pimephales promelas
Gobiocypris rarus
42.07
19.3
20.27
26.1
18.37
19.18
12.09
36.39
37.1
14.67
24.74
16.9
18.83
19.51
45.65
26.97
37.07
47.07
Sander vitreus
Etheostoma spectabile
Etheostoma nigrum
Ictalurus punctatus
Cottus bairdi
Gasterosteus aculeatusa
27.52
17.97
16.64
33.14
51.72
Rana pipiens
Pacific regilla
Pacific crucifer
Procambarus clarkii
Pacifastacus leniusculus
22.43
19.49
14.24
21.23
56.49
46.73
328.3
15.23
24.25
21.98
20.64
23.73
25.01
25.64
56.09
68.05
Arthropoda
Cypriniformes
Cyprinidae
(9)
25.69
Perciformes
Percidae
(3)
21.81
Siluriformes
Cottidae
(2)
41.4
Gasterosteiformes
Gasterosteidae
(1)
Ranidae (1)
Hylidae (2)
65.53
Cambaridae
(4)
50.22
Amphibia
Anura
Malacostraca
Decapoda
Branchiopoda
Cladocera
Ephemeroptera
Trichoptera
Mollusca
Lamellibranchia
Eulamellibranchia
22.43
16.66
65.53
Daphnidae
(5)
21.07
Chydoridae (1)
Baetidae (2)
25.01
37.92
Ephemerellidae
(1)
Limnephilidae
(1)
Unionidae (12)
68.05
Orconectes nais
Orconectes immunis
Daphnia pulicaria
Daphnia magnaa
Simocephalus vetulusa
Ceriodaphnia dubia
Ceriodaphnia acanthina
Chydorus sphaericusa
Callibaetis sp.
Callibaetis skokinus
Dorycera grandis
153
Philarctus guaeris
153
7.4
Lasmigona subviridus
3.54
Environ Sci Pollut Res (2015) 22:5271–5282
5279
Table 2 (continued)
Phylum
Class
Order
Pulmonata
Annelida
a
Oligochaeta
Haplotaxida
Family (n)
FMAV
(mg TAN/L)
Corbiculidae
(1)
Lymnaeidae
(1)
Planorbidae (1)
Tubidicidae (2)
Species
SMAV
(mg TAN N/L)
Villosa iris
Lampsilis abrupta
Lampsilis siliquoidea
Lampsilis fasciola
Lampsilis higginsii
Lampsilis cardium
Lampsilis rafinesqueana
Epioblasma capsaeformis
5.04
2.19
5.65
6.21
6.25
7.69
11.65
6.04
6.02
Utterbackia imbecillis
Actinonaias pectorosa
Pyganodon grandis
Corbicula flumineaa
7.16
12.22
21.76
6.02
13.63
Lymnaea stagnalisa
13.63
32.54
29.52
Helisoma trivolvis
Limnodrilus hoffmeisteri
Tubifex tubifex
32.54
26.17
33.3
Screened native species to SHR
acute criteria of ammonia was studied in this paper. The
cumulative probability was calculated according to Eq. 11
due to not all strata (families) were represented. Other researchers have discussed utilization of non-endemic species
to derive WQC (Maltby et al. 2005; Davies et al. 1994; Dyer
et al. 1997; Hose and Van den Brink 2004; Jin et al. 2011), but
currently, due to the paucity of toxicity data, there is no clear
conclusion about the accuracy of this approach. In the assessment, the results of which are presented here; toxicity data for
non-endemic species were used to calculate mean toxicity
values (MTV) (Eq. 7). When no toxicity information was
available for endemic species, based on the assumptions given
above (Species selection and SSD curve construction). For
example, there are seven species of Unionidae that occurred
in the SHR, but there were no toxicity data for any of these
species of clam, so the mean toxicity value of 12 non-endemic
species in the family Unionidae were used (Table 3).
Construction of the SSD and Derivation of the HCp
In the classical taxonomic classification system, species are
classified into seven taxonomic categories which include
kingdom, phylum, class, order, family, genus, and species.
The endemic species of the SHR were classified into 4 phyla,
11 classes, 34 orders, and 91 families (Table 1). According to
stratification sampling theory, within-stratum differences
should be minimized, and between-strata differences should
be maximized. After comparison, the within-stratum differences were great when the stratum was divided into phylum,
class, or order, and there would have been too many strata if
divided by genus, so the stratum was divided by family and
family mean toxicity values (FMTV) which was calculated as
Table 3 Results of ranked FMAV and cumulative probability
Ri
Taxa (family)
Ni
∑Ni
FMAV
(mg TAN/L)
1
2
3
4
5
6
7
8
9
10
11
Untested family
Corbiculidae
Unionodae
Lymnaeidae
Hylidae
Acipenseridae
Daphnidae
Percidae
Ranidae
Salmonidae
Chydoridae
238
1
7
7
1
2
44
1
2
11
28
238
239
246
253
254
256
300
301
303
314
342
–
6.02
7.4
13.63
16.66
19.48
21.07
21.81
22.43
23.9
25.01
0.0565
0.0872
0.1196
0.1501
0.1816
0.2482
0.2846
0.3223
0.3712
0.4447
12
13
14
15
16
17
18
19
20
Cyprinidae
Tubificidae
Planorbidae
Baetidae
Cottidae
Cambaridae
Gasterosteidae
Ephemerellidae
Limnephilidae
60
2
1
2
1
1
2
11
1
402
404
405
407
408
409
411
422
423
25.69
29.52
32.54
37.92
41.4
50.22
65.53
68.05
153
0.5702
0.6208
0.6702
0.7216
0.7716
0.8219
0.8745
0.9478
1.0000
Pi
5280
Environ Sci Pollut Res (2015) 22:5271–5282
Fig. 2 Species sensitivity distribution of acute toxicity data for ammonia
the geometric mean of the SMAVs available for the family
were ranked, then the cumulative probability (Species selection and SSD curve construction) (Table 3). The data were
then fit to the logistic cumulative distribution function by use
of standard regression techniques for the cumulative distribution function of the logistic distribution (Eq. 12; Table 3 and
Fig. 2).
F¼
1þ
a
b
ð12Þ
x
x0
where F is the proportion of species affected, x is ln
(concentration) of FMAV (mg/L), and a, b, x0 are parameters
to be determined.
There were differences among the methods used to fit the
cumulative probability distribution (Table 4). The results of the
first two methods resulted in WQC that were greater than the
toxicity value of the most sensitive species which, in this case,
was 6.02 mg TAN/L. Thus, in some extent, we can say that it
would not provide comprehensive protection to the most known
sensitive species. When the “improved” method was applied,
the result obtained was 5.09 mg TAN/L. The result was adopted
as it provides more comprehensive protection to aquatic life.
The USEPA method uses genus mean toxicity values
(GMTVs) instead of species mean toxicity values (SMTVs)
to calculate HC5 which aim to reduce the bias introduced by
excessive species in some taxon, but the minimum toxicity
value may be covered up by the average processing; thus, the
final HC5 may be higher than the minimum toxicity value
especially when the number of GMTVs is more than 19, and
the same problem also exists in the traditional SSD method. In
order to overcome the problem, a safety factor may be introduced, but the safety factor was chosen by expertise of the
criteria developer, and it was difficult to explain the relationship between SSD curve and ecosystem.
In 2013, the US EPA revised its WQC for ammonia to
replace the value previously recommended in 1999. The acute
and chronic criteria value decreased from 24 to 17 and from
4.5 to 1.9 mg TAN/L (pH = 8, T& = 20°C), respectively
(USEPA 2013; USEPA 1999). People believe the 1999 criteria
which, based on salmonid fish and bluegill sunfish early life
stage toxicity information, can provide comprehensive protection until some more sensitive species, including unionid
mussels and gill-breathing snails, were founded recently. It
was a good example to explain the importance of species
selection which used to derive HC5.
Until now, species toxicity data are very few relative to the
total number (N) of species in specific ecosystem; perhaps,
there are still some more sensitive species that are unclear to
us, so the result of statistical extrapolation cannot avoid being
questioned or criticized when we cannot guarantee that species selection was random. Therefore, the weighted processing in this study based on taxa in a basin may avoid this
situation. The only difficulty is probably there are still some
unknown species in a basin which are not involved in calculation. However, from another angle, neither the traditional
methods nor the improved method can provided accurate and
reliable protection to the species that we do not know that exist
at all. The only thing that we can do is use a conservative
estimation method. The results of the analyses suggest that the
method is feasible and that it delivers output which is (thus)
related via input data choice to the ecosystems of interest.
Conclusions
The distribution of relative sensitivities of aquatic organisms
is an objective natural law, which is fundamental and will not
Table 4 Comparisons of results of various methods to fit toxicological data into the SSD
Method
Distribution model
Cutoff point
Calculated result
(mg TAN/L)
95 %
Toxicity data used
USEPA method
Traditional SSD method
Triangular
Log-logistic
logistic
7.54
7.16
7.64
5.09
–
Improved method
0.05
0.05
0.05
0.0024
Screened native species
Screened native species
All screened species
All screened species
3.18∼6.61
Environ Sci Pollut Res (2015) 22:5271–5282
change with additional knowledge. Currently, due to factors
such as costs, including time, and a lack of methods to culture
and exposure of some endemic species, especially those that
are threatened or endangered, they cannot be collected from
the wild for use in toxicity testing. Therefore, it will always be
difficult to have complete knowledge of the range of sensitivities of organisms and, thus, not possible to have completely
accurate predictions of thresholds that will protect all species.
However, based on sampling theory, it should be possible to
identify a threshold that is likely to be protected of all of the
identified species of concern. Also, sampling method, sample
size, and data analysis are important for the derivation of the
SSD and for conclusions based on them. Here, a method was
introduced that allows construction of a SSD curve that can
more accurately represent site-specific distributions of sensitivities of aquatic organisms and better protect structure and
functions of ecosystems. In principle, this method has a selfadjustment feature. The cumulative probability was calculated
based on site-specific biota which can avoid the bias of
overrepresentation. Furthermore, the threshold was set by
considering all of the species in the specific ecosystem to
provide better ecosystem protection. The FMAV for nonendemic species were also used in WQC that is derived as a
supplement when data for all endemic species were not available. While more effort is needed to increase the power and
precision of this approach, it can provide more representative
site-specific WQC.
Acknowledgments This research was financially supported by the
National Science and Technology Major Project (2014ZX07502-002),
National Natural Science Foundation of China (21307165), and Special
Fund for Environmental Scientific Research in the Public Interest
(201309008). Prof. Giesy was supported by the program of 2012 “High
Level Foreign Experts” (#GDW20123200120) funded by the State Administration of Foreign Experts Affairs, the P.R. of China to Nanjing
University, and the Einstein Professor Program of the Chinese Academy
of Sciences. He was also supported by the Canada Research Chair
program, a Visiting Distinguished Professorship in the Department of
Biology and Chemistry, and State Key Laboratory in Marine Pollution,
City University of Hong Kong.
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Table 1 Acute toxicity of ammonia to aquatic life used in the paper
Phylum
Class
Order
Pulmonata
Mollusca
Family
Species
LC50
(mg TAN/L at
(mg TAN/L at
pH=8 and 25°C)
pH=8 and 25°C)
Arthur et al. 1987
34.3
Arthur et al. 1987
13.63
Williams et al. 1986
11.66
Keller 2000
12.81
Keller 2000
6.712
Ingersoll 2004
5.43
Wang et al. 2007
2.191
2.191
Wang et al. 2007
7.689
7.689
Newton and Bartsch 2007
8.714
Ingersoll 2004
3.893
Mummert et al. 2003
7.049
Wang et al. 2007
5.692
Newton and Bartsch 2007
6.86
Newton and Bartsch 2007
12.95
Ingersoll 2004
10.48
Wang et al. 2007
Helisoma trivolvis
32.54
Lymnaeidae
Lymnaea stagnalis
13.63
Actinonaias pectorosa
12.22
Epioblasma capsaeformis
6.037
Lampsilis abrupta
Lampsilis cardium
Lampsilis fasciola
Reference
30.87
Planorbidae
Lamellibranchia
Eulamellibranchia
SMAV
6.207
Unionidae
Lampsilis higginsii
6.249
Lampsilis rafinesqueana
11.65
Lampsilis siliquoidea
5.646
8.789
Wang et al. 2008
Lasmigona subviridus
3.539
3.36
Black 2001
Pyganodon grandis
21.76
25.13
Scheller 1997
Utterbackia imbecillis
7.164
9.104
Wade et al. 1992
Villosa iris
Planorbidae
Corbiculidae
Vertebrata
Actinopterygii
Gasterosteiformes
Gasterosteidae
Musculium transversum
Corbicula fluminea
Gasterosteus aculeatus
5.036
13.74
6.018
65.53
7.134
Black 2001
8.003
Black 2001
15.46
Black 2001
2.755
Black 2001
6.287
Keller 2000
6.422
Keller 2000
7.76
Keller 2000
2.343
Mummert et al. 2003
6.002
Wang et al. 2007
3.533
Ingersoll 2004
7.07
Scheller 1997
7.81
Scheller 1997
2.858
Wang et al. 2007
10.48
Wang et al. 2007
16.76
Arthur et al. 1987
11.03
Arthur et al. 1987
14.03
Arthur et al. 1987
9.996
Belanger et al. 1991
3.623
Belanger et al. 1991
50.4
Hazel et al. 1971
155.4
Hazel et al. 1971
61.46
Hazel et al. 1971
60.78
Hazel et al. 1971
33.25
Hazel et al. 1971
48.76
Hazel et al. 1971
Oncorhynchus aguabonita
Oncorhynchus clarki
Oncorhynchus gorbuscha
Salmoniformes
26.1
18.37
42.07
Salmonidae
Oncorhynchus kisutch
Oncorhynchus mykiss
20.27
19.3
109.4
Hazel et al. 1971
26.1
Thurston and Russo 1981
21.76
Thurston et al 1978
25.3
Thurston et al 1978
26.13
Thurston et al 1978
30.81
Thurston et al 1978
10.07
Thurston et al 1981a
16.93
Thurston et al 1981a
11.23
Thurston et al 1981a
15.3
Thurston et al 1981a
38.33
Rice and Bailey 1980
46.18
Rice and Bailey 1980
14.02
Buckley 1978
19.1
Robinson-Wilson and Seim 1975
19.66
Robinson-Wilson and Seim 1975
21.4
Robinson-Wilson and Seim 1975
22.29
Robinson-Wilson and Seim 1975
21.63
Robinson-Wilson and Seim 1975
22
Robinson-Wilson and Seim 1975
23.86
Robinson-Wilson and Seim 1975
22.41
Arthur et al. 1987
32.09
Arthur et al. 1987
12.63
Arthur et al. 1987
25.01
Arthur et al. 1987
22.72
Arthur et al. 1987
31.97
Broderius and Smith Jr. 1979
14.99
Calamari et al. 1977
25.17
DeGraeve et al. 1980
31.76
Reinbold and Pescitelli 1982a
26.4
Reinbold and Pescitelli 1982a
33.37
Reinbold and Pescitelli 1982a
27.2
Reinbold and Pescitelli 1982a
26.47
Reinbold and Pescitelli 1982a
48.4
Reinbold and Pescitelli 1982a
12.57
Thurston and Russo 1983
10.22
Thurston and Russo 1983
15.84
Thurston and Russo 1983
11.74
Thurston and Russo 1983
12.4
Thurston and Russo 1983
10.46
Thurston and Russo 1983
14.58
Thurston and Russo 1983
13.28
Thurston and Russo 1983
13.59
Thurston and Russo 1983
15.41
Thurston and Russo 1983
16.77
Thurston and Russo 1983
10.41
Thurston and Russo 1983
15.33
Thurston and Russo 1983
15.53
Thurston and Russo 1983
14.12
Thurston and Russo 1983
15.38
Thurston and Russo 1983
18.48
Thurston and Russo 1983
15.1
Thurston and Russo 1983
18.65
Thurston and Russo 1983
10.16
Thurston and Russo 1983
11.55
Thurston and Russo 1983
14.66
Thurston and Russo 1983
15.74
Thurston and Russo 1983
16.61
Thurston and Russo 1983
17.89
Thurston and Russo 1983
18.95
Thurston and Russo 1983
16.05
Thurston and Russo 1983
19.99
Thurston and Russo 1983
21.52
Thurston and Russo 1983
14.48
Thurston and Russo 1983
20.89
Thurston and Russo 1983
28.54
Thurston and Russo 1983
16.37
Thurston and Russo 1983
29.09
Thurston and Russo 1983
33.14
Thurston and Russo 1983
24.15
Thurston and Russo 1983
24.5
Thurston and Russo 1983
18.25
Thurston and Russo 1983
24.02
Thurston and Russo 1983
24.61
Thurston and Russo 1983
28.77
Thurston and Russo 1983
22.54
Thurston and Russo 1983
23.89
Thurston and Russo 1983
25.43
Thurston and Russo 1983
25.73
Thurston and Russo 1983
25.87
Thurston and Russo 1983
15.96
Thurston and Russo 1983
18.28
Thurston and Russo 1983
22.18
Thurston and Russo 1983
26.95
Thurston and Russo 1983
27.22
Thurston and Russo 1983
13.2
Thurston and Russo 1983
14.91
Thurston and Russo 1983
14.98
Thurston and Russo 1983
15.72
Thurston and Russo 1983
16.61
Thurston and Russo 1983
24.97
Thurston and Russo 1983
26.95
Thurston and Russo 1983
8.85
Thurston and Russo 1983
12.72
Thurston and Russo 1983
15.54
Thurston and Russo 1983
22.87
Thurston and Russo 1983
29.91
Thurston and Russo 1983
16.12
Thurston and Russo 1983
16.61
Thurston and Russo 1983
18.79
Thurston and Russo 1983
29.65
Thurston and Russo 1983
31.03
Thurston and Russo 1983
10.71
Thurston and Russo 1983
17.73
Thurston and Russo 1983
21.43
Thurston and Russo 1983
22.34
Thurston and Russo 1983
23.66
Thurston and Russo 1983
35.06
Thurston and Russo 1983
17.97
Thurston and Russo 1983
21.52
Thurston and Russo 1983
26.01
Thurston and Russo 1983
37.68
Thurston and Russo 1983
26.83
Thurston and Russo 1983
21.94
Thurston and Russo 1983
21.79
Thurston and Russo 1983
11.01
Thurston et al. 1981a
9.405
Thurston et al. 1981a
12.25
Thurston et al. 1981a
6.322
Thurston et al. 1981a
11.92
Thurston et al. 1981a
13.9
Thurston et al. 1981a
17.45
Thurston et al. 1981a
14.88
Thurston et al. 1981a
24.36
Thurston et al. 1981a
20.35
Thurston et al. 1981b
Oncorhynchus tshawytscha
Prosopium williamsoni
Salmo salar
19.18
12.09
42.66
23.44
Thurston et al. 1981b
25.21
Thurston et al. 1981b
27.8
Thurston et al. 1981b
26.65
Thurston et al. 1981b
27.18
Thurston et al. 1981c
18.82
Thurston et al. 1981c
23.78
Thurston et al. 1981c
24.21
Thurston et al. 1981c
18.63
Thurston et al. 1981c
16.18
Thurston et al. 1981c
49.5
Wicks and Randall 2002
7.347
Wicks et al. 2002
46.97
Wicks et al. 2002
25.98
Servizi and Gordon 1990
14.5
Thurston and Meyn 1984
19.53
Thurston and Meyn 1984
18.4
Thurston and Meyn 1984
6.357
Thurston and Meyn 1984
18.94
Thurston and Meyn 1984
14.68
Thurston and Meyn 1984
20.45
Knoph 1992
22.27
Knoph 1992
45.42
Knoph 1992
52.12
Knoph 1992
61.56
Knoph 1992
Salmo trutta
Salvelinus fontinalis
Salvelinus namaycush
Cypriniformes
Cyprinidae
Campostoma anomalum
23.75
36.39
37.1
26.97
75.67
Knoph 1992
88.86
Knoph 1992
89.79
Knoph 1992
28.95
Knoph 1992
36.24
Knoph 1992
38.98
Knoph 1992
41.97
Knoph 1992
62.1
Knoph 1992
69.49
Knoph 1992
54.8
Knoph 1992
57.41
Knoph 1992
23.67
Soderberg and Meade 1992
14.03
Soderberg and Meade 1992
46.4
Soderberg and Meade 1992
27.72
Soderberg and Meade 1992
22.4
Thurston and Meyn 1984
25.03
Thurston and Meyn 1984
23.89
Thurston and Meyn 1984
34.86
Thurston and Meyn 1984
38
Thurston and Meyn 1984
35.5
Soderberg and Meade 1992
43.27
Soderberg and Meade 1992
37.78
Soderberg and Meade 1992
32.62
Soderberg and Meade 1992
26.97
Swigert and Spacie 1983
Cyprinella lutrensis
Cyprinella spiloptera
Cyprinella whipplei
Cyprinus carpio
45.65
19.51
18.83
24.74
43.43
Hazel et al. 1979
47.99
Hazel et al. 1979
16.85
Rosage et al. 1979
21.67
Rosage et al. 1979
20.34
Swigert and Spacie 1983
18.83
Swigert and Spacie 1983
31.18
Hasan and MacIntosh 1986
29.48
Hasan and MacIntosh 1986
16.48
Rao et al. 1975
Hybognathus amarus
16.9
16.9
Buhl 2002
Notemigonus crysoleucas
14.67
14.67
Swigert and Spacie 1983
190.5
Arthur et al. 1987
67.81
Arthur et al. 1987
52.22
Arthur et al. 1987
35.35
Arthur et al. 1987
51.97
DeGraeve et al. 1980
38.74
DeGraeve et al. 1987
40.5
DeGraeve et al. 1987
28.4
DeGraeve et al. 1987
29.01
DeGraeve et al. 1987
26.28
DeGraeve et al. 1987
29.93
DeGraeve et al. 1987
33.9
DeGraeve et al. 1987
24.81
DeGraeve et al. 1987
32.86
Mayes et al. 1986
Pimephales promelas
37.07
23.97
Nimmo et al. 1989
10.74
Nimmo et al. 1989
12.96
Nimmo et al. 1989
22.23
Nimmo et al. 1989
30.1
Nimmo et al. 1989
16.96
Nimmo et al. 1989
24.12
Nimmo et al. 1989
25.93
Nimmo et al. 1989
18.77
Nimmo et al. 1989
45.05
Reinbold and Pescitelli 1982a
20.29
Reinbold and Pescitelli 1982a
50.4
Reinbold and Pescitelli 1982a
23.96
Reinbold and Pescitelli 1982a
36.67
Sparks 1975
27.3
Swigert and Spacie 1983
29.53
Swigert and Spacie 1983
33.38
Thurston et al. 1981c
44.99
Thurston et al. 1981c
44.91
Thurston et al. 1981c
39.49
Thurston et al. 1981c
50.49
Thurston et al. 1981c
34.27
Thurston et al. 1981c
43.55
Thurston et al. 1983
40.88
Thurston et al. 1983
30.74
Thurston et al. 1983
36.4
Thurston et al. 1983
50.36
Thurston et al. 1983
47.72
Thurston et al. 1983
32.53
Thurston et al. 1983
82.04
Thurston et al. 1983
73.06
Thurston et al. 1983
37.78
Thurston et al. 1983
32.44
Thurston et al. 1983
31.67
Thurston et al. 1983
46.25
Thurston et al. 1983
36.95
Thurston et al. 1983
41.65
Thurston et al. 1983
43.79
Thurston et al. 1983
47.74
Thurston et al. 1983
39.45
Thurston et al. 1983
52.14
Thurston et al. 1983
64.34
Thurston et al. 1983
40.7
Thurston et al. 1983
51.65
Thurston et al. 1983
46.53
Thurston et al. 1983
69.38
Thurston et al. 1983
41.22
Thurston et al. 1983
43.05
Thurston et al. 1983
32.53
Thurston et al. 1983
40.07
Thurston et al. 1983
Campostoma anomalum
Catostomus commersoni
26.97
36.68
Catostomidae
Catostomus platyrhynchus
Chasmistes brevirostris
16.15
Deltistes luxatus
13.19
Etheostoma nigrum
Perciformes
31.7
16.64
Percidae
Etheostoma spectabile
17.97
Sander vitreus
27.25
26.97
Arthur et al. 1987
73.6
Arthur et al. 1987
59.94
Arthur et al. 1987
63.1
Arthur et al. 1987
21.61
Nimmo et al. 1989
13.1
Nimmo et al. 1989
41.11
Reinbold and Pescitelli 1982b
38.73
Reinbold and Pescitelli 1982b
15.44
Swigert and Spacie 1983
37.02
Thurston and Meyn 1984
27.23
Thurston and Meyn 1984
31.62
Thurston and Meyn 1984
11.42
Saiki et al. 1999
22.85
Saiki et al. 1999
16.81
Saiki et al. 1999
10.35
Saiki et al. 1999
23.97
Nimmo et al. 1989
24.61
Nimmo et al. 1989
10.18
Nimmo et al. 1989
13.87
Nimmo et al. 1989
16.28
Nimmo et al. 1989
15.63
Nimmo et al. 1989
19.49
Hazel et al. 1979
16.56
Hazel et al. 1979
20.29
Reinbold and Pescitelli 1982c
Cottus bairdi
Siluriformes
Ictalurus punctatus
51.72
33.14
40.12
Arthur et al. 1987
52.33
Arthur et al. 1987
10.91
Arthur et al. 1987
24.07
Mayes et al. 1986
51.72
Thurston and Russo 1981
30.95
Arthur et al. 1987
37.61
Arthur et al. 1987
30.16
Arthur et al. 1987
23.19
Colt and Tchobanoglous 1978
49.7
DeGraeve et al. 1987
41.95
DeGraeve et al. 1987
33.24
DeGraeve et al. 1987
28.32
DeGraeve et al. 1987
32.7
DeGraeve et al. 1987
31.78
DeGraeve et al. 1987
25.25
DeGraeve et al. 1987
15.09
Diamond et al. 1993
29.57
Reinbold and Pescitelli 1982d
29.35
Reinbold and Pescitelli 1982d
51.72
Roseboom and Richey 1977
38.36
Roseboom and Richey 1977
64.58
Sparks 1975
22.74
Swigert and Spacie 1983
32.34
West 1985
49.38
West 1985
Acipenseriformes
Amphibia
Acipenseridae
Acipenser brevirostrum
36.49
Ranidae
Rana pipiens
22.43
Pseudacris crucifer
14.24
Anura
Hylidae
Pseudacris regilla
Annelida
Oligochaeta
Haplotaxida
Tubidicidae
Baetidae
Ephemeroptera
Insecta
Arthropoda
Ephemerellidae
Trichoptera
Malacostraca
Decapoda
Limnephilidae
Cambaridae
19.49
36.49
Fontenot et al. 1998
31.04
Diamond et al. 1993
16.23
Diamond et al. 1993
17.78
Diamond et al. 1993
11.42
Diamond et al. 1993
7.77
Schuytema and Nebeker 1999
11.4
Schuytema and Nebeker 1999
19.45
Schuytema and Nebeker 1999
43.8
Schuytema and Nebeker 1999
37.3
Schuytema and Nebeker 1999
Limnodrilus hoffmeisteri
26.17
26.17
Williams et al. 1986
Tubifex tubifex
33.3
33.3
Stammer 1953
Callibaetis skokianus
56.09
47.26
Arthur et al. 1987
66.56
Arthur et al. 1987
Callibaetis sp.
25.64
25.64
Thurston et al. 1984
70.07
Thurston et al. 1984
54.69
Thurston et al. 1984
82.22
Thurston et al. 1984
158.7
Arthur et al. 1987
147.4
Arthur et al. 1987
210.3
Arthur et al. 1987
270.3
Arthur et al. 1987
Drunella grandis
68.05
Philarctus quaeris
153
Orconectes immunis
238.4
Orconectes nais
46.73
46.73
Evans 1979
Pacifastacus leniusculus
56.49
56.49
Harris et al. 2001
Procambarus clarkii
21.23
17.22
Diamond et al. 1993
Chydoridae
Cladocera
Diamond et al. 1993
Chydorus sphaericus
25.01
25.01
Dekker et al. 2006
Ceriodaphnia acanthina
23.73
23.73
Mount 1982
17.61
Andersen and Buckley 1998
21.71
Andersen and Buckley 1998
19.88
Bailey et al. 2001
24.01
Bailey et al. 2001
26.23
Black 2001
51.45
Black 2001
59.83
Black 2001
18.01
Cowgill and Milazzo 1991
15.06
Manning et al. 1996
23.52
Nimmo et al. 1989
5.494
Nimmo et al. 1989
18.38
Sarda 1994
18.45
Sarda 1994
14.52
Scheller 1997
45.66
Gersich and Hopkins 1986
5.792
Gulyas and Fleit 1990
30.38
Parkhurst et al. 1979,1981
64.46
Reinbold and Pescitelli 1982c
37.28
Russo et al. 1985
13.8
Russo et al. 1985
16.32
Russo et al. 1985
12.46
Russo et al. 1985
Ceriodaphnia dubia
Branchiopoda
26.17
20.64
Daphnidae
Daphnia magna
24.25
Daphnia pulicaria
Simocephalus vetulus
15.23
21.98
10.75
Russo et al. 1985
35.06
Russo et al. 1985
36.4
Russo et al. 1985
38.88
Russo et al. 1985
34.77
Russo et al. 1985
15.23
DeGraeve et al. 1980
29
Arthur et al. 1987
17.64
Arthur et al. 1987
24.15
Mount 1982
18.9
Mount 1982
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Wang N, Ingersoll CG, Hardesty DK, et al. (2007) Contaminant sensitivity of freshwater mussels: Acute toxicity of copper, ammonia, and chlorine to glochidia and
juveniles of freshwater mussels (Unionidae). Environ. Toxicol. Chem. 26(10):2036-2047.
West CW. (1985) Acute toxicity of ammonia to 14 freshwater species. Internal Report. U.S. EPA, Environmental Research Laboratory, Duluth, MN.
Wicks BJ, Randall DJ. (2002) The effect of feeding and fasting on ammonia toxicity in juvenile rainbow trout, Oncorhynchus mykiss. Aquat. Toxicol. 59(1-2): 71-82.
Wicks BJ, Joensen R, Tang Q, et al. (2002) Swimming and ammonia toxicity in salmonids: The effect of sub-lethal ammonia exposure on the swimming
performance of coho salmon and the acute toxicity of ammonia in swimming and resting rainbow trout. Aquat. Toxicol. 59(1-2): 55-69.
Williams KA, Green DWJ, Pascoe D. (1986) Studies on the acute toxicity of pollutants to freshwater macroinvertebrates. 3. Ammonia. Arch. Hydrobiol. 106(1):
61-70.
Table 2 List of fish in Songhua River water system
Phylum
Class
Order
Family
Gasterosteiformes
Gasterosteidae
Esociformes
Esocidae
Genus
Species
Gasterosteus
G. aculeatus
Pungitius
P. sinensis
Esox
E. reicherti
Coregonus
C. ussurinsis
C. chadary
O. keta
Oncorhynchus
O masou
O Gorbuscha
Salmonidae
Salmoniformes
Salvelinus
O. mykiss
Brachynuystax
B. lenok
Actinopterygii
H. taimen
H. ishikawai
Hypomesus
H. olidus
Osmerus
O. mordax
Hypomesus
Chordata
S. pluvius
Oncorhynchus
Hucho
Osmeridae
S. malma
Thymallidae
Thymallus
Salangidae
Protosalanx
Abbottina
Parabramis
H.
transpacificus
nipponesis
T. arcticus
T. arcticus grubei
P. hyalocranius
A. rivularis
A. liaoningensis
P. pekinensis
C. mongolicus
C. alburnus
Culter
C. dabryidabryi
C. oxycephalus
C.
dabryi
shinkainensis
Cypriniformes
Cyprinidae
Ctenopharyngodon
C. idellus
Squaliobarbus
S. curriculus
Megalobrama
Elopichthys
Xenocypris
Phoxinus
M. amblycephalus
M. skolkovil
E. bambusa
X. micro1epis
X. argentea
P. perenurus
P. lagowskii
Phylum
Class
Order
Family
Genus
Species
P. czkanowskii
P. lagowskii
P.
phoxinus
phoxinus
Gnathopogon
Hemibarbus
Carassius
G. mantschuricus
H. labeo
H. maculatus
C. auratus gibelio
H. leucisculus
Hemiculter
H. bleekeri lucidus
H. bleekeri
H. bieekerib
G. soldatovi
G. cynocephalus
Gobio
G. tenuicorpus
G. macrocephalus
G. lingyuanensis
Cyprinus
C.
carpio
haematopterus
Hypophthalmichthys
H. molitrix
Zacco
Z. platypus
Opsariichthys
O. bidens
Pseudorasbora
P. parva
Pseudaspius
P. leptocephalus
Rutilus
R. rutilus lacustris
Rhodeus
R. sericeus
R. fangi
Ladislavia
L. taczanowskj
Mylopharyngodon
M. piceus
Gobiobotinae
G. pappenheimi
Sarcocheilichthys
S. lacustris
Sarcocheilichthys
S. czerskii
T. brandti
Tribolodon
T. hakonsis
Saurogobio
S. dabryi
Paraleucogobio
P. strigatus
Rostrogobio
R. amurensis
Aphyocypris
A. chinensis
Leucisus
L. waleckii
Squalidus
Aristichthys
S. argentatus
S. chankaensis
A. nobilis
Phylum
Class
Order
Family
Genus
Acheilognathus
Cultrichthys
C. erythropterus
C. compressocorpus
Nemachilichthys
N. nudus
Lefua
L. costata
Paramisgurnus
P. dabryanus
Cobitis
C. lutheri
Misgurnus
M. mohoity
Parabotia
P. fasciata
Belontiidae
Macropodus
M. chinensis
Channidae
Channa
C. argus
Percidae
Lucioperca
L. lucioperca
Serranidae
Siniperca
S. chuatsi
Eleotridae
Perccottus
P. glehni
Eleotridae
Hypseleotris
H. swinhonis
Gobiidae
Rhinogobius
R. nagoyae
Cobitidae
Perciformes
Species
A. chankaensis
Pelteobagrus
Bagridae
Siluriformes
P. nitidus
P. fulvidraco
Pseudobagrus
P. ussuriensis
Leiocassis
L. argentivittatus
S. soldatovi
Siluridae
Silurus
Gadiformes
Lotidae
Lota
L. lota
Acipenseriformes
Acipenseridae
Huso
H. dauricus
Acipenser
A. schrenckii
Scorpaeniformes
Cottidae
Mesocottus
M. haitej
S. asotus
L. raissncri
Cephalaspidemorphi
Petromyzontiformes
Petromyzontidae
Lethenteron
L. japonica
L. morii
Table 3 List of chordata in Songhua River water system
Phylum
Class
Order
Caudata
Chordata
Amphibia
anura
Family
Genus
Species
Hynobius
H. leechii
Salamandrella
S. keyserlingii
Onychodactylus
O. fischeri
Bufonidae
Bufo
B. raddei
Microhylidae
Kaloula
K. borealis
Discoglossidae
Bombina
B.orientalis
Ranidae
Rana
Hylidae
Hyla
Hynobiidae
R. chensinensis
R. emeljanovi
H. japonica
Table 4 List of arthropoda in Songhua River water system
Phylum
Class
Malacostraca
Order
Family
Genus
Cambaridae
Cambaroides
C. dauricus
Atyoidae
Neocaridina
N. heteropoda heteropoda
Exopalaemon
E. modestus
Macrobrachium
M. nipponense
Decapoda
Palaemonidae
Palaemonetes
Species
P. sinensis
P. sinensis
A. vernalis
Acanthocyclops
A. viridis
A. bicuspidatus
A. bisetosus
Macrocyclops
M. albidus
Cyclops
C. strenuus
Paracyclops
Ectocyclops
Cyclopoida
Cyclopidae
Thermocyclops
P. fimbriatus
P. affinis
E. phaleratus
T. dybowskii
T. kawamurai
M. rubellus
M. longiramus
Arthropoda
Microcyclops
M. robustus
M. javanus
M. inchoatus
Maxillopoda
M. bicolor
E. serrulatus
Eucyclops
E. macruroides
E. macruroides
Harpacticoida
Canthocamptus
C. carinatus
Bryocamptus
B. vejdovskyi
Canthocamptidae
A. crassa
Attheyella
A. dogieli
A. amurensis
Neutrodiaptomus
Calanoida
Diaptomidae
N.pachypoditus
N. genogibbosus
Tropodiaptomus
T. oryzanus
Mongolodiaptomus
M.birulai
Neodiaptomus
N. schmackeri
Acanthodiaptomus
A. pacificus
Sinodiaptomus
S. chaffanjoni
S. sarsi
Phylum
Class
Order
Family
Genus
Species
Epischura
E. chankensis
Heterocope
H. appendiculata
Centropagidae
Boeckella
B. orientalis
Leptodoridae
Leptodora
L. Kindti
Ilyocryptus
I. sordidus
Lathonura
L. rectirostris
Temoridae
M. rosea
Macrothricidae
Macrothrix
M. hirsuticornis
M. laticornis
M. triserialis
Polyphemidae
Polyphemus
P. pediculus
M. rectirostris
M. micrura
Moinidae
Moina
M. macrocopa
M. chankensis
Acroperus
A. harpae
A. angustatus
Leydigia
L. acanthocercoides
Graptoleberis
G. testudinaria
A. karua
A. intermedia
A. quadrangularis
Branchiopoda
Cladocera
Alona
A. affinis
A. diaphana
A. rectangula
A. guttata
A. costata
Chydoridae
Alonella
A. excisa
Disparalona
D. rostrata
Oxyurella
O. tenuicaudis
Dunhevedia
D. crassa
P. striatus
Pleuroxus
P. trigonellus
P. aduncus
P. hamulatus
C. sphaericus
C. ovalis
Chydorus
C. gibbus
C. barroisi
Pseudochydorus
P. globosus
Peracantha
P. truncata
Eurycercus
E. lamellatus
Phylum
Class
Order
Family
Genus
Species
C. rectirostris
D. brachyurum
Diaphanosoma
Sididae
D. chankensis
D. leuchtenbergianum
D. sarsi
Latonopsis
L. australis
Sida
S. crystallina
B. longirostris
Bosminidae
Bosmina
B. coregoni
B. fatalis
B. deilersi
Scapholeberis
S. mucronata
S. kingi
S. serrulatus
S. vetulus
Simocephalus
S. vetuloides
S. exspinosus
S. serrulatus
C. quadrangula
Daphnidae
Ceriodaphnia
C. hamata
C. laticaudata
D. carinata
D. pulex
D. obtusa
Daphnia
D. longispina
D. hyalina
D. cristata
D. magna
Comphidae
Insecta
Odonata
Anisogomphus
A. maacki
Davidius
D. lunatus
Gomphidia
G. confluens
Nihonogomphus
N. ruptus
Ophiogomphus
O. obscurus
Shaogomphus
S. schmidti
Siebordius
S. albardae
Sinictingogomphus
S. clavatus
Trigomphus
Cercion
Coenagriidae
S. postocularis
Coenagrion
T. citimus
T. succumben
C. plagiosum
C. bifurcatum
C. lanceolatum
Phylum
Class
Order
Family
Genus
Species
Enallagma
E. deserti
Erythromma
E. najas
I. asiatica
Ischnura
I. elegans
I. senegalensis
Deielia
D. phaon
L. dubia orientalis
Leucorrhinia
Libellula
Lyriothemis
L. ijimai
L. basilinea
L. guadrimaculata
L. pachygaster
O. melania
Orthetrum
O. albistylum
O. neglectum
Pantala
Libellulidae
P. flavescens
S. croceolum
S. danae
S. depressiusculum
S. eroticum ardens
S. flaveolum
Sympetrum
S. imitens
S. infscatum
S. pedmontanum
S. risi
S. striolatum imitoides
S. uniforme
Agriidae
Caloptery
C. x atrata
C. x virgo
Cordulia
C. aenea amuresis
Epitheca
E. bimaculata
Epophthalmia
E. elegans
Corduliidate
M. a amphigena
Macromia
M. beijingensis
M. daimoji
M. manchurica
Polycentropidae
Trichoptera
Hydropsychidae
Plectrocnemia
P. kusnezovi
Plectrocnemia
P. wui
Neureclipsis
N. mandjuricus
Hydropsyche
H. nevae
Hydropsychodes
H. tokunagai
Hydroptila
H. chinensis
H. ornithocephala
Phylum
Class
Order
Family
Genus
Species
H. introspinata
Stactobiella
S. biramosa
R. Hokkaidensis
R. Retracia
Rhyacophilidae
Rhyacophila
R. Narvae
R. Lata
R. Yamanakensis
Molannidae
Lepto ceridae
Limnephilidae
Molanna
M. falcata
Setodes
S. argentata
Oecetis
O. nigropunctata
Limnophilus
L. amurensis
A. colorata
Agrypnia
A. czerskyi
A. picta
Oligotricha
Phryganeidae
O. lapponica
P. sinensis
Phryganea
P. japonica
P. bipunctata
S. melaleuca
Sembis
Baetidae
S. phalaenoides
Eubasilissa
E. avalokhita
Cloeon
C. dipterum
Baetis
B. sp.
D. aculea
D. cryptomoria
Drunella
Ephemeroptera
D. lepnevae
D. solida
D. triacantha
Ephemerellidae
D. trispina zeoensis
Cincticostella
C. tshernovae
C. castanea
E. keijoensis
Ephemerella
E. auricilli
E. notofascia
A. alektorovi
A. antuensis
A. cataphylla
Diptera
Culicidae
Aedes
A. chemulpoensis
A. cinereus
A. communis
A. cyprius
A. diantaeus
Phylum
Class
Order
Family
Genus
Species
A. esoensis
A. excrucians
A. flavescens
A. flavopictus
A. galloisi
A. hatorii
A. implicatus
A. koreicoides
A. koreicus
A. leucomelas
A. lineatopennis aureus
A. mercurator
A. nipponicus
A. pullatus
A. punctor
A. sasai
A. sibiricus
A. sticticus
A. koreicus
Anopheles
A. messeae
A. sineroides
A. yatsushiroensis
Armigeres
A. subalbatus
C. hayashii
C. jacksoni
C. modestus
Culex
C. orientalis
C. pipiens pallens
C. rubensis
C. sinensis
C. whitmorei
C. alaskaensis
Culiseta
C. bergrothi
C. nipponica
Toxorhynchites
T. christophi
Tripteroides
T. bambusa
Table 5 List of Mollusca in Songhua River water system
Phylum
Class
Order
Family
Genus
Mollusca
Gastropoda
Mesogastropoda
Viviparidae
Cipangopaludina
Species
C. cahayensis
Phylum
Class
Order
Family
Genus
Species
C. chinensis
Sorbeoconcha
Viviparus
V. chui
Bithyniidae
Parafossarulus
P. striatulus
Pleuroceridae
Semisulcospira
Lymnaea
S. cancellata
S. amurensis
L. stagnalis
R. plicatula
Pulmonata
Lymnaeidae
Radix
Golba pervia
G. truncatula
Planorbidae
Polypylis
P. hemisphaerula
Corbiculidae
Corbicula
C. fluminea
Margaritiana
M. dahurica
Unio
U. douglasiae
Lanceolaria
L. grayana
Margaritanidae
Bivalvia
R. lagotis
R. auricularia
Galba
Veneroida
R. ovata
A. woodiana
Eulamellibranchia
A. woodiana elliptica
Unionodae
Anodonta
A. Welliptica
A. euscaphys
A. arcaeformis
A.arcaeformisflavotincta
Table 6 List of annelida in Songhua River water system
Phylum
Class
Order
Family
Clitellata
Tubificida
Tubificidae
Genus
Species
Spirosperma
S. nikolskyi
Limnodrilus
L.hoffmeisteri
Hemiclepsis
H. marginata
G. complanata
G. heteroclita
G. lata
Glossiphonia
Annelida
Rhynchobdellida
Glossiphoniidae
G. multipapillata
G. weberi
Hirudinea
G. complanata
G. lata
Batracobdella
Helobdella
Arhynchobdellida
Haemopidae
Whitmania
B. paludosa
H. nuda
H. marginata
W. laevis
W. pigra
Phylum
Class
Order
Family
Genus
Salifidae
Barbronia
B. weberi
Dina
D. lineata
Erpobdellidae
Species
E. octoculata
Erpobdella
E. testacea
Hirudinidae
Hirudo
H. nipponia
B. orientalis
Branchiobdellidae
Branchiobdella
B. kobayashii
B. macroperistomium
Table 7 List of rotifera in Songhua River water system
Phylum
Class
Order
Family
Genus
Species
B. angularia
B. budapestiensis
Brachionus
B. calyciflorus
B. capsuliflorus
Brachionida
Rotifera
Rotaria
B. forficula
Argonotholca
A. foliacea
Kellicottia
K. longispina
Euchlanis
E. dilatata
Monogononta
K. quadrala
Keratella
K. valga
Asplancchnidae
Asplanchna
A. priodonala
T. bicristata
Trichocercidae
Trichocerca
T. capucina
T. elongata
P. euryptera
Synchaetidae
Polyarthra
P. trigla