Improvement on species sensitivity distribution methods for deriving ...
Transcript of Improvement on species sensitivity distribution methods for deriving ...
RESEARCH ARTICLE
Improvement on species sensitivity distribution methodsfor 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 mostcommon method used to derive water quality criteria, butthere are still issues to be resolved. Here, issues associatedwith application of SSD methods, including species selection,plotting position, and cutoff point setting, are addressed. Apreliminary improvement to the SSD approach based on post-stratified sampling theory is proposed. In the improved meth-od, selection of species is based on biota of a specific basin,and the whole species in the specific ecosystem are consid-ered. After selecting species to be included and calculating thecumulative probability, a newmethod to set the critical thresh-old for protection of ecosystem-level structure and function isproposed. The alternative method was applied in a case studyin which a water quality criterion (WQC) was derived forammonia in the Songhua River (SHR), China.
Keywords SSD .Water quality criteria . Plotting position .
Threshold . Stratified sampling . Site-specific . Asia .
Ammonia . Nitrogen . Toxicity . Statistics
Introduction
Widespread occurrence of toxic substances caused by activi-ties of humans can adversely affect aquatic organisms(Groombridge and Jenkins 2002). Research on differences insensitivities to toxicants among species has become a focusand shared concern of environmental scientists and managers.In order to optimize the level of protection at an acceptablelevel, some countries with more developed economies havewell-established systems to estimate the maximum limitwhich can be accepted by the ecosystem (Jin et al. 2014). Ofwhich, the species sensitivity distribution (SSD) method iscurrently the most commonly used method and has beenadopted by USEPA (1985), ANZECC & ARMCANZ(2000), RIVM (2007), and CCME (2007) as the officialmethod to derive water quality criteria (WQC) for protectionof the structure and function of ecosystems.
The SSD method to derive WQC originated almost simul-taneously in Europe and in the USA (USEPA 1985; Kooijman1987). The theoretical basis of SSD is that it is possible todescribe the variability and range of sensitivities among indi-vidual taxa with a statistical or empirical distribution function(Posthuma et al. 2002). In short, the basic assumption of theSSD concept is that: (1) relative sensitivities of a set of speciescan be described by some distribution such as the triangular,normal, or logistic distribution; (2) the data on sensitivities ofindividual species to toxicants that is used to construct SSDare seen as a random sample from the entire population ofpossible sensitivities and are used to estimate descriptiveparameters of the SSD; and (3) when a certain portion ofspecies are protected, the ecosystem is also protected. Basedon these assumptions, toxicity data are ranked and then astatistical distribution fitted. Hazardous concentrations (HCs)can be estimated, which are protective of a given proportion ofthe species present within a specified community (Posthumaet al. 2002; van Straalen and Denneman 1989). Generally, the
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. ZhouState Key Laboratory of Environmental Criteria and RiskAssessment, Chinese Research Academy of EnvironmentalSciences, Beijing 100012, Chinae-mail: [email protected]
Y. Wang :X. Jin : F. LiuChina National Environmental Monitoring Center, Beijing 100012,China
J. P. GiesyDepartment of Veterinary Biomedical Sciences and ToxicologyCentre, University of Saskatchewan, Saskatoon, Saskatchewan,Canada
Environ Sci Pollut Res (2015) 22:5271–5282DOI 10.1007/s11356-014-3783-x
SSD method has proven to be a useful approach to predict theentire communities (Schroer et al. 2004; Maltby et al. 2005),but there are still some issues relative to the application ofSSDs (Forbes and Calow 2002). These include, among others,the selection of species to be included, methods to deriveplotting positions, and what to select as the assessment end-point, such as the concentration to affect 5 % of species(HC5). There are polemics particularly around the sufficiencyof the SSD approach to protect ecosystem-level structure andfunction (Forbes and Calow 2002).
Sensitivity (tolerances) of individual species is not arandom phenomenon, and it will not change regardless ofthe methods used to describe it. Hence, it was deemeddesirable to develop a method to select species more ran-domly than has been done previously and have a greaterlikelihood of accurately describing the entire range of sen-sitivities within a community or organisms in a particularecosystem. To do this, it was necessary to develop a statis-tical method to estimate the distribution of sensitivities ofall species in an ecosystem (Forbes and Calow 2002).According to statistical theory, the actual composition ofthe sample should be randomly selected (Rice 2011), whichis also consistent with one of the basic assumptions of SSDmethods. Hence, every member of the population will beselected with uniform probability.
It has also been suggested that the data set should bestatistically and ecologically representative of the community(Forbes and Calow 2002; Wagner and Løkke 1991). It isimpossible to know the total number of species in a commu-nity and nearly impossible to know which species are thecritical species, which, if eliminated, would result in majorchanges in the structure and/or function of a community. Rarespecies are treated with the same weight as abundant species(Posthuma et al. 2002). However, by technical means and costconstraints, species for which toxicological data is availablehave generally been selected based on availability or ease ofmaintenance in culture rather than by random sampling.Otherwise, some researchers have given priority to somespecified taxa, trophic levels, and species based on theirexperience because they think the specified members arerepresentative in some respects. In some situation, this strate-gy might be effective, especially when there are relatively fewtoxicity data. It can promote the toxicity test covering differenttrophic levels and having greater taxonomic differences, butthis solution violates the random principle and cannot ensurethat there is no bias in estimates.
The dispute about methods of determining plotting positionduring probabilistic assessments has long been debated(Langbein 1960; Benson 1962; Jordaan 2005; Makkonen2006). The most commonly used methods are the Weibulland Hazen methods, which have been adopted by USEPA(1985) and ANZECC & ARMCANZ (2000), RIVM (2007)as well as CCME (2007) (Eqs. 1 and 2).
Weibull method:
p ¼ i
nþ 1ð1Þ
Hazen method:
p ¼ i−0:5n
ð2Þ
where p is the cumulative probability, i is the rank of thesample, and n is the sample size.
Theoretically, there is no difference between plots based onthese two methods when the sample size is infinitely large. Infact, data for many toxicants is lacking, especially for speciesendemic to developing countries. Thus, there will be a signif-icant difference between them which will make the results ofcalculation different. It is difficult to explain which is morereasonable for the developer and stakeholder.
In order to eliminate effects of the “tail” of the SSD and geta more exact criterion value, Van Straalen and Denneman(1989) have introduced the concept of a “cutoff point” p intothe calculation of criteria. According to their concept, thechoice of a cutoff point can be chosen by the manager, andthe corresponding concentration could be calculated which iscalled the HCp. Consequently, this method has become theofficial method to derive environmental quality criteria usedby some countries (VROM 1989; ANZECC & ARMCANZ2000; CCME 2007). However, in practical application, themost commonly used cutoff point value is the 5th centile,which indicates the concentration less than which fewer than5 % of species would be affected. Until now, there were noclear reasons why a value of 5 % should be chosen. Thepractice 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 HC5seems to have followed the convention of statistics in whicha 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 calcula-tion. Thus, the calculation process becomes an interpolationfrom extrapolation, and the most sensitive species with sensi-tivities less than the HC5 would be expected to be affected.USEPA’s newest WQC for ammonia (USEPA 2013) is anexcellent example of this situation. Compared with the 1999WQC document (USEPA 1999), the more recently availabletoxicity data were used to calculate the WQC, and the acuteand chronic criteria value decreased from 24 to 17 and from4.5 to 1.9 mg total ammonia nitrogen (TAN)/L, respectively.The 1999 WQC was based primarily on effects on early lifestages of fishes, whereas the 2013WQC is based on effects onmore sensitive invertebrate genera, including unionid mussels,of which, the most sensitive species are Lasmigona subviridis
5272 Environ Sci Pollut Res (2015) 22:5271–5282
and Venustaconcha ellipsiformis (SMAV=23.41 and23.12 mg TAN/L respectively). Development of WQC wasbased on an implicit assumption that 5 % of species could beallowed to be adversely affected and still maintain integrity ofan ecosystem. In fact, the 5th centile was chosen becausewhen the distribution of sensitivities in single species testsunder laboratory conditions, where individuals were exposedto the maximum and continuous concentration, it was equiv-alent to the threshold concentrations less than which no ad-verse effects were observed in multi-species tests(mesocosms) (Giesy et al. 1999). The aim of this study wasto develop an improved solution based on sample theory andto improve upon the traditional SSD method. The improvedsolution is expected to be more reasonable and convincingthat 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 aspecies to a toxicant and its natural history such as feedingguild, morphology, and physiological traits is a concern ofmany eco-toxicologists (Forbes and Calow 2002).Researchers have tried to describe sensitivities of genericspecies with specific sets of characteristics so that they couldpredict sensitivities of species for which no information onsensitivity to a particular toxicant existed (Slooff 1983; Vaalet al. 1997; Zhang et al. 2010; Wang et al. 2014; Zhang et al.2014). These researchers systematically reported variabilityamong sensitivities of species to toxicants, and Baird and Vanden Brink (2007) proposed a method to predict the sensitivityof a species to specific toxicants by use of their unique andsimilar traits. It has been determined that the method offerssome promise as a mechanistic alternative to the otherwiseempirical approach to selection of species included in an SSD.Species that feed on similar foods and have similar physiol-ogies will possibly have similar exposures and responses totoxicants. In the classification system known as “biotaxy,”aquatic organisms are divided into different taxa accordingto biological variances and phylogenetic relationships.
In statistics, the target population should be defined beforea sampling process is begun. Thus, in assessing the potentialeffects of contaminants at the community level of organiza-tion, all aquatic species in a specific aquatic ecosystem weredefined as the target population, but in practice, it is difficultand unreasonable to establish a global aquatic ecosystem scaleWQC. Thus, aquatic ecosystems are usually divided intodifferent subsystems for management convenience and “ba-sin” is the mostly used scale because of significant differencein biota. Thus, establishment of a basin scale WQC is neces-sary because of the difference of species to be protected. In
this assessment, the basin was used as the appropriate scale toderive a WQC, and all aquatic species in a specific basin canbe identified from a combination of reviews of the literatureand surveys in the field and used to develop the target popu-lation to be protected and/or for sampling.
Based on the discussion above, an assumption was madethat the species in the same taxonmight have relatively similarsensitivities to specific toxicants. Thus, taxa can be used asstratification variables. Toxicity can be defined as the randomselection and result from corresponding strata rather than fromall the species. Based on this assumption, all the screenedtoxicity-tested species could be sorted by use of biotaxy intodifferent strata. Thus, the post-stratified sample method(Daniel 2011) has been advanced for calculating the cumula-tive probability in which a weighting coefficient could be usedin order to prevent an overrepresentation (bias) of some strata(taxonomic groups). The mechanism of the improvement canbe represented by a function.
When the species number in a specific basin is N, it can bedivided into lmutually exclusive, homogeneous strata accord-ing to biotaxy, and the species number in each stratum is Ni(i=1,2,…,l). After retrieving and screening, the number ofscreened toxicity-tested species is n, and it also can be sortedinto different strata, and the sample number of each stratum isni (i=1,2,…,l). Thus, the sampling fraction of each stratumcan be expressed (Eq. 3).
f i ¼ niNi
i ¼ 1; 2;…; lð Þ ð3Þ
Equation (4) was used to present the sample set.
X ¼X 1
X 2
⋅⋅⋅X l
8>><>>:
9>>=>>; ¼
s11 s12 … s1n1s21 s22 … s2n2… … … …sl1 sl2 … slnl
8>><>>:
9>>=>>; ð4Þ
in which X stands for the sample set of target population andXi(i=1,2,⋅⋅⋅,l) stands for the sample set of each stratum (Ni).Xi(i=1,2,⋅⋅⋅,l)∈X and sini i ¼ 1; 2; ⋅⋅⋅; lð Þ stand for the sample.
Under ideal conditions, the sampling fraction of each stra-tum would be the same, but in actuality, it is difficult to obtainresults of toxicity tests for species in all strata, especiallychronic toxicity (Christensen et al. 2003; Jager et al. 2007;Wu et al. 2013). This limitation results in the number ofelements (screened toxicity test species) sorted into each stra-tum disproportionally to their representation in the communityand even missing in some strata. Thus, here, discussions ofcalculation of cumulative probability in three situations,which are selected to reduce bias, are presented.
Environ Sci Pollut Res (2015) 22:5271–5282 5273
(1) Under ideal conditions, the screened toxicity test speciescover all strata and the sampling fraction of each stratumis the same.
Toxicity values are ranked and assigned a sequence(Eq. 5).
Ri ¼ 1; 2…;Xi¼1
l
ni
!ð5Þ
Thus, cumulative probability can be described (Eq. 6).
Pi ¼ RiXni; i ¼ 1; 2;…;
Xni 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
X i− ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
si1 � si2 �…� sinlnlp
; j ¼ ni; l ¼ 1; 2;…; l;ð Þð7Þ
the mean sensitivity of each stratum respectively. Then,toxicity values are ranked and assigned a sequenceRi={1,2,…l}, and a weighted process is introduced intothe cumulative probability calculation (Eq. 8).
Pi ¼ Ri
l�
Xi¼1
l
N i
Nð8Þ
(3) When the screened toxicity test species does not cover allstrata, the samples can be represented (Eq. 9).
X ¼
X 1
X 2…X k
X kþ1ð Þ…X l
8>>>>><>>>>>:
9>>>>>=>>>>>;
¼
s11 s12 … s1is21 s22 … s2 j…sl1∅…
…sl2
……
…slm
∅
8>>>>>><>>>>>>:
9>>>>>>=>>>>>>;ð9Þ
in which Xi(i=1,2, ⋅ ⋅⋅,k) stands for the sample of in-volved strata respectively, and Xi(i=k+1,k+2,⋅⋅⋅,l) arean empty set.
Thus, sensitivities of strata not included cannot beestimated, and this becomes an uncertainty factor forcumulative probability calculation. Under this condition,a suggestion to adopt a “conservative method” was
followed assuming that the not included strata containedmore sensitive taxa.
When the number of species in the strata not includedare Nj(j=k+1,k+2,⋅⋅⋅,l), respectively; the total numberof species in the strata not considered can be represented(Eq. 10).
Xj¼kþ1
l
N j ð10Þ
The geometric mean value of each stratum (Eq. 7)represents the sensitivity of the corresponding stratum.Toxicity values are then ranked and assigned a sequencenumber Ri(i=1,2, ⋅ ⋅⋅,k), and weighted values are thenplotted as the cumulative probability (Eq. 11).
Pi ¼ Ri
k þ 1�
Xi¼1
k
N i þXj¼kþ1
l
N j
!
Nð11Þ
Threshold value for use in regulatory decisions
The threshold of 5 % is somewhat arbitrary and not supportedby any actual detailed analyses other than the fact that it isoften equivalent to the NOAEL frommulti-species tests wheredata is available, generally for pesticides (Giesy et al. 1999).Thus, there is no guarantee that this level of protection, basedon the results of toxicity tests on individual species that areconducted under laboratory conditions, would protect func-tion of a community. The importance of biodiversity forfunctions of ecosystems has been demonstrated, and loss ofbiodiversity can impair capacities of communities and ecosys-tems to provide the “ecosystem services” such as providingfood, process organic matter, including contaminants, andrecover from perturbations (Hooper et al. 2005; France andDuffy 2006; Tilman et al. 2006; Worm et al. 2006).Ecosystems, while made up of both the physical environmentand a range of individual species, have transcendent, emergentproperties that are greater than the sum of their parts (Giesyand Odum 1980). Thus, it is difficult to predict what effectseliminating one of more species would have on the overallfunctions of ecosystems. Therefore, theoretically, from thepoint of view of conservation of biodiversity, all speciesshould be protected. At least, criteria should be less than thethreshold for effects on the most sensitive species. Generally,the number of species in a specific basin is knowable throughhistoric data retrieving and even field investigation. In thisassessment, the number of species in a specific basin (N)therefore has a proportion of each taxon of 1
N . Thus, the
threshold for effects should be 1N or some value less than 1
N
rather than 5%. Thus, theoretically, all species in the basin can
5274 Environ Sci Pollut Res (2015) 22:5271–5282
be considered in setting the threshold for effects or the pro-tectiveWQC. One limitation of using a probabilistic approachis that there is no 0 or 100 % on the probabilistic scale. That isthere is no concentration that is less than the value that couldadversely affect a theoretical species. Similarly, there is noconcentration less than which no species would be affected.Similarly, there is no concentration that is 100 % safe. Thislimitation leads to a semantic issue between assessors of riskand managers. This is particularly true for communicationwith the lay public that wants a completely safe environmentwith no risk of adverse effects on people or wildlife. For thisreason, the concept of resolution becomes paramount. Forinstance, if N was 100, then theoretically, each species wouldrepresent 1/100 or 1 % of the total number of species and theresolution of the assessment would be 1 %. That is, if thethreshold for effect was set to the concentration equivalent tothe effect concentration for the most sensitive species, therewould be a 1 % chance that a species would be affected, but itis unknown what the probability for affecting the function ofthe ecosystem would be.
Technique flow of the improved SSD methods for derivingWQC
The flow chart of the steps used to calculate the improved SSDfor use in deriving WQC is given (Fig. 1). The first step is toretrieve the number of species in a specific basin and collectall of the toxicity data for all of the relevant species. If there aresome unknown species in the basin, there is no basis forderiving WQC to protect the missing species because boththe traditional SSD method and the improved method need toselect some of the species onwhich to conduct toxicity tests,so
to some extent, the number of species is restricted to all theknown species in the basin. Different biological classificationsshould consider the range in sensitivities among species. Inthis way, the total universe of species to consider might bereduced. That is, some species with certain physiologicalcharacteristics might be “exempted” from consideration. Forexample, toxicity of ammonia to aquatic plants can usually beignored, so only the aquatic animal was considered in derivingammoniaWQC to aquatic life (USEPA 1999), but, due to theirsensitivities, when the SSD approach is used to derive a WQCfor a metal, aquatic plants and microorganisms should beconsidered. Another issue when describing the species (N) inan environment is that some species might have disappearedfrom the basin because of environmental pollution or otheractivities of humans. In order to achieve restoration of com-munities of aquatic organisms, all the species that couldtheoretically occur should be considered even if they disap-peared 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 waterto be protected, including possible indirect effects such aseffects on food items; (2) determining for species-identifiedtoxicity data that exists or can be derived; (3) selecting anappropriate model to calculate cumulative probability; (4)constructing the plot position of toxicity data versus accumu-lating probability and fit the SSD curve; and (5) using 1/N asthe cutoff point to obtain the WQC.
Until now, identification of all of the species in an ecosys-tem has been impossible, especially for the smaller organisms.However, now, the use of ecosystem-wide genomics offers thepotential to do exactly that. The authors are currently devel-oping and applying a combination of genomic and informaticsthat will allow for the identification and enumeration of all thespecies in a particular ecosystem.
Case study of ammonia for the Songhua River, China
Biota of the Songhua River
The Songhua River (SHR) is in the Northeast China and flows1434 km from the Changbai Mountains through Jilin andHeilongjiang provinces. The river drains 557,000 mi2
(1,440,000 km2) of land and has an annual discharge of2460 m3/s (87,000 Cu ft/s). Aquatic life is abundant in thislarge river, and it supplies a number of ecosystem services, inparticular food products for consumption by humans.Therefore, it has been decided, for social and economic rea-sons, that it is important to produce these valued assessmentendpoints.
Recently, because of environmental pollution, overfishing,and effects on habitat, such as erosion and the associated
Fig. 1 Flow chart for development of improved SSD for use in derivingWQC
Environ Sci Pollut Res (2015) 22:5271–5282 5275
Table 1 Known aquatic life list native to the Songhua River
Phylum Number Class Number Order Number Family Number
Chordata 115 Actinopterygii 103 Gasterosteiformes 2 Gasterosteidae 2
Esociformes 1 Esocidae 1
Salmoniformes 17 Salmonidae 11
Osmeridae 3
Thymallidae 2
Salangidae 1
Cypriniformes 66 Cyprinidae 60
Cobitidae 6
Perciformes 7 Belontiidae 1
Channidae 1
Serranidae 1
Percidae 1
Gobiidae 1
Eleotridae 2
Siluriformes 6 Siluridae 2
Bagridae 4
Gadiformes 1 Lotidae 1
Scorpaeniformes 1 Cottidae 1
Acipenseriformes 2 Acipenseridae 2
Cephalaspidemorphi 3 Petromyzontiformes 3 Petromyzontidae 3
Amphibia 9 Caudata 3 Hynobiidae 3
Anura 6 Bufonidae 1
Microhylidae 1
Discoglossidae 1
Ranidae 2
Hylidae 1
Arthropoda 243 Malacostraca 6 Decapoda 6 Cambaridae 1
Atyoidae 1
Palaemonidae 4
Maxillopoda 36 Cyclopoida 20 Cyclopidae 20
Harpacticoida 5 Canthocamptidae 5
Calanoida 11 Diaptomidae 8
Temoridae 2
Centropagidae 1
Branchiopoda 94 Cladocera 94 Leptodoridae 1
Macrothricidae 6
Polyphemidae 1
Moinidae 4
Chydoridae 28
Sididae 6
Bosminidae 4
Daphnidae 44
Insecta 107 Diptera 17 Culicidae 17
Odonata 49 Comphidae 11
Reronareyidae 8
Libellulidae 21
Agriidae 2
Corduliidate 7
Trichoptera 28 Polycentropidae 3
5276 Environ Sci Pollut Res (2015) 22:5271–5282
siltation and turbidity, the numbers of individuals of somevalued species have decreased, and some have been extir-pated from the SHR and adjacent water bodies. In order torestore the valued ecological services of the SHR, all of thevalues species both currently present and those that werepresent historically should be considered. All availableliterature since 1960 was collected, and a list of aquaticorganisms was compiled. There were 423 species identifiedto occur in the SHR. These belonged to 4 phyla, whichincluded 115 chordates, 248 arthropods, 26 Molluska and34 Annelida (Table 1). Relative proportions of species indifferent taxa are significantly different; Arthropoda,Chordata, Annelida, and Mollusca were determined to be58.6, 27.2, 8.0, and 6.2 %, respectively. According to theassumption in this study, if each species was given the sameweight in cumulative probability calculation, there may bea significant bias.
Toxicity dada retrieving and screening
Toxicity data were retrieved from the ECOTOX database andother literature. For better comparison and consistency, toxicitydata were further selected based on the following criteria: (1)Toxicity test should follow ASTM or other certificated stan-dards; (2) same test endpoints, LC50, should be used; (3) thetoxicity data used in the paper were standardized to pH=8 and25 °C using the method in reference (USEPA 1999); and (4) ifmore than one toxicity study was available for the same specieswith different endpoints, theminimumvaluewas used. If severaltoxicity 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 speciesnative to the SHR for which data on acute toxicity of ammoniawas available, and three species native to the SHR for whichdata on chronic toxicity of ammonia was available, so only the
Table 1 (continued)
Phylum Number Class Number Order Number Family Number
Hydropsychidae 6
Rhyacophilidae 5
Molannidae 1
Leptoceridae 2
Limnephilidae 1
Phryganeidae 10
Ephemeroptera 13 Baetidae 2
Ephemerellidae 11
Mollusca 26 Gastropoda 14 Mesogastropoda 5 Viviparidae 3
Bithyniidae 1
Melaniidae 1
Sorbeoconcha 1 Pleuroceridae 1
Pulmonata 8 Lymnaeidae 7
Planorbidae 1
Bivalvia 12 Veneroida 1 Corbiculidae 1
Eulamellibranchia 11 Margaritanidae 3
Unionodae 7
Sphaeriidae 1
Annelida 23 Clitellata 2 Tubificida 2 Tubificidae 2
Hirudinea 21 Rhynchobdellida 11 Glossiphoniidae 11
Arhynchobdellida 7 Haemopidae 2
Salifidae 1
Erpobdellidae 3
Hirudinidae 1
Branchiobdellida 3 Branchiobdellidae 3
Rotifera 16 Rotifera 16 Monogononta 16 Brachionida 10
Asplancchnidae 1
Trichocercidae 3
Synchaetidae 2
Total 423
Environ Sci Pollut Res (2015) 22:5271–5282 5277
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 Acipenser sinensis 10.4
Acipenser brevirostrum 36.49
Salmoniformes Salmonidae(11)
23.9 Salmo trutta 23.75
Salmo.salar 42.66
Oncorhynchus gorbuschaa 42.07
Oncorhynchus mykissa 19.3
Oncorhynchus kisutch 20.27
Oncorhynchus aguabonita 26.1
Oncorhynchus clarki 18.37
Oncorhynchus tshawytscha 19.18
Prosopium williamsoni 12.09
Salvelinus fontinalis 36.39
Salvelinus namaycush 37.1
Cypriniformes Cyprinidae(9)
25.69 Notemigonus crysoleucas 14.67
Cyprinus carpioa 24.74
Hybognathus amarus 16.9
Cyprinella whipplei 18.83
Cyprinella spiloptera 19.51
Cyprinella lutrensis 45.65
Campostoma anomalum 26.97
Pimephales promelas 37.07
Gobiocypris rarus 47.07
Perciformes Percidae(3)
21.81 Sander vitreus 27.52
Etheostoma spectabile 17.97
Etheostoma nigrum 16.64
Siluriformes Cottidae(2)
41.4 Ictalurus punctatus 33.14
Cottus bairdi 51.72
Gasterosteiformes Gasterosteidae(1)
65.53 Gasterosteus aculeatusa 65.53
Amphibia Anura Ranidae (1) 22.43 Rana pipiens 22.43
Hylidae (2) 16.66 Pacific regilla 19.49
Pacific crucifer 14.24
Arthropoda Malacostraca Decapoda Cambaridae(4)
50.22 Procambarus clarkii 21.23
Pacifastacus leniusculus 56.49
Orconectes nais 46.73
Orconectes immunis 328.3
Branchiopoda Cladocera Daphnidae(5)
21.07 Daphnia pulicaria 15.23
Daphnia magnaa 24.25
Simocephalus vetulusa 21.98
Ceriodaphnia dubia 20.64
Ceriodaphnia acanthina 23.73
Chydoridae (1) 25.01 Chydorus sphaericusa 25.01
Ephemeroptera Baetidae (2) 37.92 Callibaetis sp. 25.64
Callibaetis skokinus 56.09
Ephemerellidae(1)
68.05 Dorycera grandis 68.05
Trichoptera Limnephilidae(1)
153 Philarctus guaeris 153
Mollusca Lamellibranchia Eulamellibranchia Unionidae (12) 7.4 Lasmigona subviridus 3.54
5278 Environ Sci Pollut Res (2015) 22:5271–5282
acute criteria of ammonia was studied in this paper. Thecumulative probability was calculated according to Eq. 11due to not all strata (families) were represented. Other re-searchers have discussed utilization of non-endemic speciesto derive WQC (Maltby et al. 2005; Davies et al. 1994; Dyeret al. 1997; Hose and Van den Brink 2004; Jin et al. 2011), butcurrently, due to the paucity of toxicity data, there is no clearconclusion about the accuracy of this approach. In the assess-ment, the results of which are presented here; toxicity data fornon-endemic species were used to calculate mean toxicityvalues (MTV) (Eq. 7). When no toxicity information wasavailable for endemic species, based on the assumptions givenabove (Species selection and SSD curve construction). Forexample, there are seven species of Unionidae that occurredin the SHR, but there were no toxicity data for any of thesespecies of clam, so the mean toxicity value of 12 non-endemicspecies in the family Unionidae were used (Table 3).
Construction of the SSD and Derivation of the HCp
In the classical taxonomic classification system, species areclassified into seven taxonomic categories which includekingdom, 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 tostratification sampling theory, within-stratum differencesshould be minimized, and between-strata differences shouldbe maximized. After comparison, the within-stratum differ-ences were great when the stratum was divided into phylum,
class, or order, and there would have been too many strata ifdivided by genus, so the stratum was divided by family andfamily mean toxicity values (FMTV) which was calculated as
Table 2 (continued)
Phylum Class Order Family (n) FMAV(mg TAN/L)
Species SMAV(mg TAN N/L)
Villosa iris 5.04
Lampsilis abrupta 2.19
Lampsilis siliquoidea 5.65
Lampsilis fasciola 6.21
Lampsilis higginsii 6.25
Lampsilis cardium 7.69
Lampsilis rafinesqueana 11.65
Epioblasma capsaeformis 6.04
Utterbackia imbecillis 7.16
Actinonaias pectorosa 12.22
Pyganodon grandis 21.76
Corbiculidae(1)
6.02 Corbicula flumineaa 6.02
Pulmonata Lymnaeidae(1)
13.63 Lymnaea stagnalisa 13.63
Planorbidae (1) 32.54 Helisoma trivolvis 32.54
Annelida Oligochaeta Haplotaxida Tubidicidae (2) 29.52 Limnodrilus hoffmeisteri 26.17
Tubifex tubifex 33.3
a Screened native species to SHR
Table 3 Results of ranked FMAVand cumulative probability
Ri Taxa (family) Ni ∑Ni FMAV(mg TAN/L)
Pi
1 Untested family 238 238 –
2 Corbiculidae 1 239 6.02 0.0565
3 Unionodae 7 246 7.4 0.0872
4 Lymnaeidae 7 253 13.63 0.1196
5 Hylidae 1 254 16.66 0.1501
6 Acipenseridae 2 256 19.48 0.1816
7 Daphnidae 44 300 21.07 0.2482
8 Percidae 1 301 21.81 0.2846
9 Ranidae 2 303 22.43 0.3223
10 Salmonidae 11 314 23.9 0.3712
11 Chydoridae 28 342 25.01 0.4447
12 Cyprinidae 60 402 25.69 0.5702
13 Tubificidae 2 404 29.52 0.6208
14 Planorbidae 1 405 32.54 0.6702
15 Baetidae 2 407 37.92 0.7216
16 Cottidae 1 408 41.4 0.7716
17 Cambaridae 1 409 50.22 0.8219
18 Gasterosteidae 2 411 65.53 0.8745
19 Ephemerellidae 11 422 68.05 0.9478
20 Limnephilidae 1 423 153 1.0000
Environ Sci Pollut Res (2015) 22:5271–5282 5279
the geometric mean of the SMAVs available for the familywere ranked, then the cumulative probability (Species selec-tion and SSD curve construction) (Table 3). The data werethen fit to the logistic cumulative distribution function by useof standard regression techniques for the cumulative distribu-tion function of the logistic distribution (Eq. 12; Table 3 andFig. 2).
F ¼ a
1þ xx0
� �b ð12Þ
where F is the proportion of species affected, x is ln(concentration) of FMAV (mg/L), and a, b, x0 are parametersto be determined.
There were differences among the methods used to fit thecumulative probability distribution (Table 4). The results of thefirst two methods resulted in WQC that were greater than thetoxicity value of the most sensitive species which, in this case,was 6.02 mg TAN/L. Thus, in some extent, we can say that itwould not provide comprehensive protection to the most knownsensitive species. When the “improved” method was applied,the result obtained was 5.09 mg TAN/L. The result was adoptedas 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 toxicityvalue may be covered up by the average processing; thus, thefinal HC5 may be higher than the minimum toxicity valueespecially when the number of GMTVs is more than 19, andthe same problem also exists in the traditional SSDmethod. Inorder to overcome the problem, a safety factor may be intro-duced, but the safety factor was chosen by expertise of thecriteria developer, and it was difficult to explain the relation-ship between SSD curve and ecosystem.
In 2013, the US EPA revised its WQC for ammonia toreplace the value previously recommended in 1999. The acuteand chronic criteria value decreased from 24 to 17 and from4.5 to 1.9 mg TAN/L (pH=8, T&=20°C), respectively(USEPA 2013; USEPA 1999). People believe the 1999 criteriawhich, based on salmonid fish and bluegill sunfish early lifestage toxicity information, can provide comprehensive protec-tion until some more sensitive species, including unionidmussels and gill-breathing snails, were founded recently. Itwas a good example to explain the importance of speciesselection which used to derive HC5.
Until now, species toxicity data are very few relative to thetotal number (N) of species in specific ecosystem; perhaps,there are still some more sensitive species that are unclear tous, so the result of statistical extrapolation cannot avoid beingquestioned or criticized when we cannot guarantee that spe-cies selection was random. Therefore, the weighted process-ing in this study based on taxa in a basin may avoid thissituation. The only difficulty is probably there are still someunknown species in a basin which are not involved in calcu-lation. However, from another angle, neither the traditionalmethods nor the improved method can provided accurate andreliable protection to the species that we do not know that existat all. The only thing that we can do is use a conservativeestimation method. The results of the analyses suggest that themethod 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 organismsis 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 Triangular 0.05 7.54 – Screened native species
Traditional SSD method Log-logistic 0.05 7.16 Screened native species
0.05 7.64 All screened species
Improved method logistic 0.0024 5.09 3.18∼6.61 All screened species
Fig. 2 Species sensitivity distribution of acute toxicity data for ammonia
5280 Environ Sci Pollut Res (2015) 22:5271–5282
change with additional knowledge. Currently, due to factorssuch as costs, including time, and a lack of methods to cultureand exposure of some endemic species, especially those thatare threatened or endangered, they cannot be collected fromthe wild for use in toxicity testing. Therefore, it will always bedifficult to have complete knowledge of the range of sensitiv-ities of organisms and, thus, not possible to have completelyaccurate predictions of thresholds that will protect all species.However, based on sampling theory, it should be possible toidentify a threshold that is likely to be protected of all of theidentified species of concern. Also, sampling method, samplesize, and data analysis are important for the derivation of theSSD and for conclusions based on them. Here, a method wasintroduced that allows construction of a SSD curve that canmore accurately represent site-specific distributions of sensi-tivities of aquatic organisms and better protect structure andfunctions of ecosystems. In principle, this method has a self-adjustment feature. The cumulative probability was calculatedbased on site-specific biota which can avoid the bias ofoverrepresentation. Furthermore, the threshold was set byconsidering all of the species in the specific ecosystem toprovide better ecosystem protection. The FMAV for non-endemic species were also used in WQC that is derived as asupplement when data for all endemic species were not avail-able. While more effort is needed to increase the power andprecision of this approach, it can provide more representativesite-specific WQC.
Acknowledgments This research was financially supported by theNational Science and Technology Major Project (2014ZX07502-002),National Natural Science Foundation of China (21307165), and SpecialFund for Environmental Scientific Research in the Public Interest(201309008). Prof. Giesy was supported by the program of 2012 “HighLevel Foreign Experts” (#GDW20123200120) funded by the State Ad-ministration of Foreign Experts Affairs, the P.R. of China to NanjingUniversity, and the Einstein Professor Program of the Chinese Academyof Sciences. He was also supported by the Canada Research Chairprogram, a Visiting Distinguished Professorship in the Department ofBiology and Chemistry, and State Key Laboratory in Marine Pollution,City University of Hong Kong.
References
ANZECC, ARMCANZ (2000) Australian and New Zealand guidelinesfor fresh and marine water quality volume 2 aquatic ecosystems-rationale and background information. Australian and New ZealandEnvironment and Conservation Council. Agriculture and ResourceManagement Council of Australia and New Zealand, Canberra
Baird DJ, Van den Brink PJ (2007) Using biological traits to predictspecies sensitivity to toxic substances. Ecotoxicol Environ Saf 67:296–301
Benson MA (1962) Plotting positions and economics of engineeringplanning. Proc Am Soc Civ Eng Hydraul Div 88(HY6):57–71
CCME (2007) A protocol for the derivation of water quality guidelinesfor the protection of aquatic life. Canadian Council of Ministers ofthe Environment, Winnipeg
Christensen FM, De Bruijn JHM, Hansen BG et al (2003) Assessmenttools under the new European Union chemicals policy. GreenerManag Int 41:5–19
Daniel J (2011) Sampling essentials: practical guidelines for makingsampling choices. SAGE Publications, Inc, Los Angeles
Davies PE, Cook LSJ, Goenarso D (1994) Sublethal responses to pesti-cides of several species of Australian fresh-water fish and crusta-ceans and rainbow trout. Environ Toxicol Chem 13:1341–1354
Dyer SD, Belanger SE, Carr GJ (1997) An initial evaluation of the use ofEur/North American fish species for tropical effects assessments.Chemosphere 35:2767–2781
Forbes VE, Calow P (2002) Species sensitivity distributions revisited: acritical appraisal. Hum Ecol Risk Assess 3:473–492
France KE, Duffy JE (2006) Diversity and dispersal interactively affectpredictability of ecosystem function. Nature 441:1139–1143
Giesy JP, Odum EP (1980) Microcosmology: the theoretical basis.Microcosms in ecological research. DOE CONF 781101. Departmentof Energy Technical Information Center, Oak Ridge, TN, pp 1–13
Giesy JP, Solomon KR, Coats JR et al (1999) Ecological risk assessmentof chlorpyrifos in North American aquatic environments. RevEnviron Contam Toxicol 160:1–129
Groombridge B, Jenkins MD (2002) Global biodiversity: responding tothe change. In: Groombridge B, Jenkins MD (eds) World atlas ofbiodiversity: earth’s living resources in the 21st century. Universityof California Press, Berkeley, CA, pp 195–223
Hooper DU, Chapin FS III, Ewel JJ et al (2005) Effects of biodiversity onecosystem functioning: a consensus of current knowledge. EcolMonogr 75:3–35
Hose GC, Van den Brink PJ (2004) Confirming the species-sensitivitydistribution concept for endosulfan using laboratory, mesocosm, andfield data. Arch Environ Contam Toxicol 47:511–520
Jager T, Posthuma L, de Zwart D et al (2007) Novel view on predictingacute toxicity: decomposing toxicity data in species vulnerabilityand chemical potency. Ecotoxicol Environ Saf 67:311–322
Jin XW, Zha JM, Xu YP et al (2011) Derivation of aquatic predicted no-effect concentration (PNEC) for 2,4-dichlorophenol: comparingnative species data with non-native species data. Chemosphere 84:1506–1511
Jin XW, Wang YY, Giesy JP, Richardson K, Wang ZJ (2014)Development of aquatic life criteria in China: viewpoint on thechallenge. Environ Sci Pollut Res 21:61–66
Jordaan I (2005) Decisions under uncertainty. Cambridge University PressKooijman SALM (1987) A safety factor for LC50 values allowing for
differences in sensitivity among species. Water Res 21:269–276Langbein WB (1960) Plotting positions in frequency analysis. USGS
Water Supply Paper, Washington, pp 48–51, 1543-AMakkonen L (2006) Plotting positions in extreme value analysis. J Appl
Meteorol Climatol 45:334–340Maltby L, Blake N, Brock TCM et al (2005) Insecticide species sensitiv-
ity distributions: importance of test species selection and relevanceto aquatic ecosystems. Environ Toxicol Chem 24:379–388
Okkerman PC, Van de Plassche EJ, Emans HJB et al (1993) Validation ofsome extrapolationmethodswith toxicity data derived frommultiplespecies experiments. Ecotoxicol Environ Saf 25:341–359
Posthuma L, Suter GW II, Traas TP (2002) Environmental and ecologicalrisk assessment: species sensitivity distributions in ecotoxicology.Lewis Publishers, Washington
Rice JA (2011) Mathematical statistics and data analysis third edition.Thomson Brooks/Cole 2007Duxbury, 138
RIVM (2007) Guidance for the derivation of environmental risk limits withinthe framework of “International and national environmental qualitystandards for substances in the Netherlands” Bilthoven the Netherlands
Schroer AFM, Belgers D, Brock TCM, Maund SJ, Van den Brink PJ(2004) Acute toxicity of the pyrethroid insecticide lambda-cyhalothrin to invertebrates of lenthic freshwater ecosystems. ArchEnviron Contam Toxicol 46:324–335
Environ Sci Pollut Res (2015) 22:5271–5282 5281
Slooff W (1983) Benthic macroinvertebrates and water quality assess-ment, some toxicological considerations. Aquat Toxicol 4:73–82
Tilman D, Reich PB, Knops JMH (2006) Biodiversity and ecosystemstability in a decade long grassland experiment. Nature 441:629–632
USEPA (1985) Guidelines for deriving numerical national water qualitycriteria for the protection of aquatic organisms and their uses. Officeof Research and Development Environmental ResearchLaboratories, Duluth, Minnesota
USEPA (1999) Update of ambient water quality criteria for ammonia.Office of Water, Washington
USEPA (2013) Aquatic life ambient water quality criteria for ammonia-freshwater. Office of Water, Washington
Vaal M, van der Wall JT, Hetmens J et al (1997) Pattern analysis of thevariation in the sensitivity of aquatic species to toxicants.Chemosphere 35:1291–1309
van Straalen NM, Denneman CAJ (1989) Ecotoxicological evaluation ofsoil quality criteria. Ecotoxicol Environ Saf 18:241–251
Versteeg DJ, Belanger SE, Carr GJ (1999) Understanding single-speciesand model ecosystem sensitivity: data-based comparison. EnvironToxicol Chem 18:1329–1346
VROM (1989) Premises for risk management. Risk limits in the contextof environmental policy. Ministry of Housing, Spatial Planning andthe Environment (VROM), Second chamber, session 1988–1989,21137, no 5, The Hague, the Netherlands
Wagner C, Løkke H (1991) Estimation of ecotoxicological protectionlevels from NOEC toxicity data. Water Res 25:1237–1242
Wang Z, Jin XY, Wang ZJ (2014) Taxon-specific sensitivity difference ofcopper to aquatic organisms. Asian J Ecotoxicol 9:640–646 (inChinese)
Worm B, Barbier EB, Beaumont N et al (2006) Impacts of biodiversityloss on ocean ecosystem services. Science 314:787–790
Wu FC,MuYS, Chang H et al (2013) Predicting water quality criteria forprotecting aquatic life from physicochemical properties of metals ormetalloids. Environ Sci Technol 47:446–453
Zhang XJ, Qin HW, Su LM et al (2010) Interspecies correlations oftoxicity to eight aquatic organisms: theoretical considerations. SciTotal Environ 408:4549–4555
Zhang LS, Wang YY, Meng FS et al (2014) Study on species selectionmethods in deriving water quality criteria for aquatic life. EnvironSci 35:3959–3969 (in Chinese)
5282 Environ Sci Pollut Res (2015) 22:5271–5282
Table 1 Acute toxicity of ammonia to aquatic life used in the paper
Phylum Class Order Family Species
SMAV
(mg TAN/L at
pH=8 and 25°C)
LC50
(mg TAN/L at
pH=8 and 25°C)
Reference
Mollusca Lamellibranchia
Pulmonata Planorbidae Helisoma trivolvis 32.54
30.87 Arthur et al. 1987
34.3 Arthur et al. 1987
Lymnaeidae Lymnaea stagnalis 13.63 13.63 Williams et al. 1986
Eulamellibranchia Unionidae
Actinonaias pectorosa 12.22 11.66 Keller 2000
12.81 Keller 2000
Epioblasma capsaeformis 6.037 6.712 Ingersoll 2004
5.43 Wang et al. 2007
Lampsilis abrupta 2.191 2.191 Wang et al. 2007
Lampsilis cardium 7.689 7.689 Newton and Bartsch 2007
Lampsilis fasciola 6.207
8.714 Ingersoll 2004
3.893 Mummert et al. 2003
7.049 Wang et al. 2007
Lampsilis higginsii 6.249 5.692 Newton and Bartsch 2007
6.86 Newton and Bartsch 2007
Lampsilis rafinesqueana 11.65 12.95 Ingersoll 2004
10.48 Wang et al. 2007
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
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
Villosa iris 5.036
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
Planorbidae Musculium transversum 13.74
16.76 Arthur et al. 1987
11.03 Arthur et al. 1987
14.03 Arthur et al. 1987
Corbiculidae Corbicula fluminea 6.018 9.996 Belanger et al. 1991
3.623 Belanger et al. 1991
Vertebrata Actinopterygii Gasterosteiformes Gasterosteidae Gasterosteus aculeatus 65.53
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
109.4 Hazel et al. 1971
Salmoniformes Salmonidae
Oncorhynchus aguabonita 26.1 26.1 Thurston and Russo 1981
Oncorhynchus clarki 18.37
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
Oncorhynchus gorbuscha 42.07 38.33 Rice and Bailey 1980
46.18 Rice and Bailey 1980
Oncorhynchus kisutch 20.27
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
Oncorhynchus mykiss 19.3
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
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
Oncorhynchus tshawytscha 19.18
25.98 Servizi and Gordon 1990
14.5 Thurston and Meyn 1984
19.53 Thurston and Meyn 1984
18.4 Thurston and Meyn 1984
Prosopium williamsoni 12.09
6.357 Thurston and Meyn 1984
18.94 Thurston and Meyn 1984
14.68 Thurston and Meyn 1984
Salmo salar 42.66
20.45 Knoph 1992
22.27 Knoph 1992
45.42 Knoph 1992
52.12 Knoph 1992
61.56 Knoph 1992
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
Salmo trutta 23.75
22.4 Thurston and Meyn 1984
25.03 Thurston and Meyn 1984
23.89 Thurston and Meyn 1984
Salvelinus fontinalis 36.39 34.86 Thurston and Meyn 1984
38 Thurston and Meyn 1984
Salvelinus namaycush 37.1
35.5 Soderberg and Meade 1992
43.27 Soderberg and Meade 1992
37.78 Soderberg and Meade 1992
32.62 Soderberg and Meade 1992
Cypriniformes Cyprinidae Campostoma anomalum 26.97 26.97 Swigert and Spacie 1983
Cyprinella lutrensis 45.65 43.43 Hazel et al. 1979
47.99 Hazel et al. 1979
Cyprinella spiloptera 19.51
16.85 Rosage et al. 1979
21.67 Rosage et al. 1979
20.34 Swigert and Spacie 1983
Cyprinella whipplei 18.83 18.83 Swigert and Spacie 1983
Cyprinus carpio 24.74
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
Pimephales promelas 37.07
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
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
Catostomidae
Campostoma anomalum 26.97 26.97 Arthur et al. 1987
Catostomus commersoni 36.68
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
Catostomus platyrhynchus 31.7
37.02 Thurston and Meyn 1984
27.23 Thurston and Meyn 1984
31.62 Thurston and Meyn 1984
Chasmistes brevirostris 16.15 11.42 Saiki et al. 1999
22.85 Saiki et al. 1999
Deltistes luxatus 13.19 16.81 Saiki et al. 1999
10.35 Saiki et al. 1999
Perciformes Percidae
Etheostoma nigrum 16.64
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
Etheostoma spectabile 17.97 19.49 Hazel et al. 1979
16.56 Hazel et al. 1979
Sander vitreus 27.25 20.29 Reinbold and Pescitelli 1982c
40.12 Arthur et al. 1987
52.33 Arthur et al. 1987
10.91 Arthur et al. 1987
24.07 Mayes et al. 1986
Siluriformes
Cottus bairdi 51.72 51.72 Thurston and Russo 1981
Ictalurus punctatus 33.14
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 Acipenseridae Acipenser brevirostrum 36.49 36.49 Fontenot et al. 1998
Amphibia Anura
Ranidae Rana pipiens 22.43 31.04 Diamond et al. 1993
16.23 Diamond et al. 1993
Hylidae
Pseudacris crucifer 14.24 17.78 Diamond et al. 1993
11.42 Diamond et al. 1993
Pseudacris regilla 19.49
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
Annelida Oligochaeta Haplotaxida Tubidicidae Limnodrilus hoffmeisteri 26.17 26.17 Williams et al. 1986
Tubifex tubifex 33.3 33.3 Stammer 1953
Arthropoda
Insecta
Ephemeroptera
Baetidae 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
Ephemerellidae Drunella grandis 68.05
70.07 Thurston et al. 1984
54.69 Thurston et al. 1984
82.22 Thurston et al. 1984
Trichoptera Limnephilidae Philarctus quaeris 153 158.7 Arthur et al. 1987
147.4 Arthur et al. 1987
Malacostraca Decapoda Cambaridae
Orconectes immunis 238.4 210.3 Arthur et al. 1987
270.3 Arthur et al. 1987
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
26.17 Diamond et al. 1993
Branchiopoda Cladocera
Chydoridae Chydorus sphaericus 25.01 25.01 Dekker et al. 2006
Daphnidae
Ceriodaphnia acanthina 23.73 23.73 Mount 1982
Ceriodaphnia dubia 20.64
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
Daphnia magna 24.25
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
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
Daphnia pulicaria 15.23 15.23 DeGraeve et al. 1980
Simocephalus vetulus 21.98
29 Arthur et al. 1987
17.64 Arthur et al. 1987
24.15 Mount 1982
18.9 Mount 1982
Reference
Andersen H, Buckley J. (1998) Acute toxicity of ammonia to Ceriodaphnia dubia and a procedure to improve control survival. Bull. Environ. Contam. Toxicol. 61(1):
116-122.
Arthur JW, West CW, Allen KN, et al. (1987) Seasonal toxicity of ammonia to five fish and nine invertebrates species. Bull. Environ. Contam. Toxicol. 38(2):
324-331.
Bailey HC, Elphick JR, Krassoi R., et al. (2001) Joint acute toxicity of diazinon and ammonia to Ceriodaphnia dubia. Environ. Toxicol. Chem. 20: 2877-2882.
Belanger SE, Cherry DS, Farris JL, et al. (1991) Sensitivity of the Asiatic clam to various biocidal control agents. J. Am. Water Works Assoc. 83(10): 79-87.
Black M. (2001) Water quality standards for North Carolina's endangered mussels. Department of Environmental Health Science, Athens, GA.
Broderius SJ, Smith Jr LL. (1979) Lethal and sublethal effects of binary mixtures of cyanide and hexavalent chromium, zinc, or ammonia to the fathead minnow
(Pimephales promelas) and rainbow trout (Salmo gairdneri). J. Fish. Res. Board Can. 36(2): 164-172.
Buckley JA. (1978) Acute toxicity of un-ionized ammonia to fingerling coho salmon. Prog. Fish Cult. 40(1): 30-32.
Buhl KJ. (2002) The relative toxicity of waterborne inorganic contaminants to the Rio Grande silvery minnow (Hybognathus amarus) and fathead minnow
(Pimephales promelas) in a water quality simulating that in the Rio Grande, Albuquerque, NM. U.S. Fish and Wildlife Service.
Calamari D, Marchetti R, Vailati G. (1977) Effect of prolonged treatments with ammonia on stages of development of Salmo gairdneri. Nuovi. Ann. Ig. Microbiol.
28(5): 333-345.
Colt J, Tchobanoglous G. (1978) Chronic exposure of channel catfish, Ictalurus punctatus, to ammonia: Effects on growth and survival. Aquaculture 15: 353-372.
Cowgill UM, Milazzo DP. (1991) The response of the three brood Ceriodaphnia test to fifteen formulations and pure compounds in common use. Arch. Environ.
Contam. Toxicol. 21(1): 35-40.
DeGraeve GM, Overcast RL, Bergman HL. (1980) Toxicity of underground coal gasification condenser water and selected constituents to aquatic biota. Arch.
Environ. Contam. Toxicol. 9(5): 543-555.
DeGraeve GM, Palmer WD, Moore EL, et al. (1987) The effect of temperature on the acute and chronic toxicity of un-ionized ammonia to fathead minnows and
channel catfish. Battelle, Columbus, OH.
Dekker T, Greve GD, Ter Laak TL, et al. (2006) Development and application of a sediment toxicity test using the benthic cladoceran Chydorus sphaericus. Environ.
Poll. 140: 231-238.
Diamond JM, Mackler DG, Rasnake WJ, et al.(1993) Derivation of site-specific ammonia criteria for an effluent-dominated headwater stream. Environ. Toxicol.
Chem. 12(4): 649-658.
Evans JW. (1979) The construction and use of a continuous-flow bioassay apparatus to determine a preliminary un-ionized ammonia 96-hour LC50 for the crayfish.
M.S. Thesis, University of Kansas, Lawrence, KS.
Fontenot QC, Isely JJ, Tomasso JR. (1998) Acute toxicity of ammonia and nitrite to shortnose sturgeon fingerlings. Prog. Fish Cult. 60: 315-318.
Gersich FM, Hopkins DL. (1986) Site-specific acute and chronic toxicity of ammonia to Daphnia magna Straus. Environ. Toxicol. Chem. 5(5): 443-447.
Gulyas P, Fleit E. (1990) Evaluation of ammonia toxicity on Daphnia magna and some fish species. Aquacult. Hung. 6: 171-183.
Harris RR, Coley S, Collins S, et al. (2001) Ammonia uptake and its effects on ionoregulation in the freshwater crayfish Pacifastacus Leniusculus (Dana). J. Comp.
Physiol. B. 171: 681-693.
Hasan MR, Macintosh DJ. (1986) Acute toxicity of ammonia to common carp fry. Aquaculture 54(1-2): 97-107.
Hazel CR, Thomsen W, Meith SJ. (1971) Sensitivity of striped bass and stickleback to ammonia in relation to temperature and salinity. Calif. Fish Game. 57(3):
138-153.
Hazel RH, Burkhead CE, Huggins DG. (1979) The development of water quality criteria for ammonia and total residual chlorine for the protection of aquatic life in
two Johnson County, Kansas streams. Project completion report for period July 1977 to September 1979. Kansas Water Resources Research Institute, University of
Kansas.
Ingersoll C. (2004) Data call for the national consultation on the Clean Water Act 304(a) aquatic life criteria for ammonia, cyanide, chromium III, and chromium VI.
U.S. Geological Survey. Columbia, MO. (Memorandum to G. Noguchi, U.S. Fish and Wildlife Service, Arlington, VA. July 20.)
Keller AE. (2000) Memorandum to Rob Pepin. Subject: Water quality and toxicity data for unpublished unionid mussels tests.
Knoph MB. (1992) Acute toxicity of ammonia to Atlantic salmon (Salmo salar) parr. Comp. Biochem. Physiol. Comp. Pharmacol. Toxicol. 10(2): 275-282.
Manning TM, Wilson SP, Chapman JC. (1996) Toxicity of chlorine and other chlorinated compounds to some Australian aquatic organisms. Bull. Environ. Contam.
Toxicol. 56(6): 971-976.
Mayes MA, Alexander HC, Hopkins DL, et al. (1986) Acute and chronic toxicity of ammonia to freshwater fish: A site-specific study. Environ. Toxicol. Chem. 5(5):
437-442.
Mount DI. (1982) Ammonia toxicity tests with Ceriodaphnia acanthina and Simocephalus vetulus. U.S. EPA, Duluth, MN. (Letter to R.C. Russo, U.S. EPA, Duluth,
MN.)
Mummert AK, Neves RJ, Newcomb TJ, et al. (2003) Sensitivity of juvenile freshwater mussels (Lampsilis fasciola, Villosa iris) to total and un-ionized ammonia.
Environ. Toxicol. Chem. 22: 2545-2553.
Newton TJ. Bartsch MR. (2007) Lethal and sublethal effects of ammonia to juvenile lampsilis mussels (Unionidae) in sediment and water-only exposures. Environ.
Toxicol. and Chem. 26(10):2057–2065.
Nimmo DWR, Link D, Parrish LP, et al. (1989) Comparison of on-site and laboratory toxicity tests: Derivation of site-specific criteria for un-ionized ammonia in a
Colorado transitional stream. Environ. Toxicol. Chem. 8(12): 1177-1189.
Parkhurst BR, Bradshaw AS, Forte JL, et al. (1979) An evaluation of the acute toxicity to aquatic biota of a coal conversion effluent and its major components. Bull.
Environ. Contam. Toxicol. 23(3): 349-356.
Parkhurst BR, Meyer JS, DeGraeve GM, et al. (1981) Reevaluation of the toxicity of coal conversion process waters. Bull. Environ. Contam. Toxicol. 26(1): 9-15.
Rao TS, Rao MS, Prasad SBSK. (1975) Median tolerance limits of some chemicals to the fresh water fish Cyprinus carpio. Indian J. Environ. Health 17(2): 140-146.
Reinbold KA, Pescitelli SM. (1982a) Effects of cold temperature on toxicity of ammonia to rainbow trout, bluegills and fathead minnows. Project Report, Contract
No. 68-01-5832. Illinois Natural History Survey, Champaign, IL.
Reinbold KA, Pescitelli SM. (1982b) Acute toxicity of ammonia to the white sucker. Final Report to the U.S. EPA, Contract No. 2W-3946 NAEX, Illinois Natural
History Survey, Champaign, IL.
Reinbold KA, Pescitelli SM. (1982c) Effects of exposure to ammonia on sensitive life stages of aquatic organisms. Project Report, Contract No. 68-01-5832, Illinois
Natural History Survey, Champaign, IL.
Reinbold KA, Pescitelli SM. (1982d) Acute toxicity of ammonia to channel catfish. Final report to the U.S. EPA, Contract No. J 2482 NAEX. Illinois Natural History
Survey, Champaign, IL.
Rice SD, Bailey JE. (1980) Survival, size and emergence of pink salmon, Oncorhynchus gorbuscha, alevins after short and long-term exposures to ammonia. Fish.
Bull. 78(3): 641-648.
Robinson-Wilson EF, Seim WK. (1975) The lethal and sublethal effects of a zirconium process effluent on juvenile salmonids. Water Resour. Bull. 11(5): 975-986.
Rosage TF, Schutsky RM, Rapp KM. (1979) Toxicity of un-ionized ammonia to the spotfin shiner (Notropis spilopterus). Proc. Pa. Acad. Sci. 53: 39-42.
Roseboom DP, Richey DL. (1977) Acute toxicity of residual chlorine and ammonia to some native Illinois fishes. Report of Investigations 85. U.S. NTIS PB-170871.
Office of Water Research and Technology, Washington, D.C.
Russo RC, Pilli A, Meyn EL. (1985) Memorandum to N.A. Jaworski. March 1985.
Saiki MK, Monda DP, Bellerud BL. (1999) Lethal levels of selected water quality variables to larval and juvenile Lost River and shortnose suckers. Environ. Pollut.
105(1): 37-44.
Sarda N. (1994) Spatial and temporal heterogeneity in sediments with respect to pore water ammonia and toxicity of ammonia to Ceriodaphnia dubia and Hyalella
azteca. MS Thesis. Wright State University, Dayton, OH.
Scheller JL. (1997) The effect of dieoffs of Asian clams (Corbicula fluminea) on native freshwater mussels (unionidae). Virginia Polytechnic Institute and State
University, Blacksburg, VA.
Schuytema GS, Nebeker AV. (1999) Comparative effects of ammonium and nitrate compounds on Pacific treefrog and African clawed frog embryos. Arch. Envion.
Contam. Toxicol. 36: 200-206.
Servizi J, Gordon R. (1990) Acute lethal toxicity of ammonia and suspended sediment mixtures to chinook salmon (Oncorhynchus tshawytscha). Bull. Environ.
Contam. Toxicol. 44(4):650-656.
Soderberg RW, Meade JW. (1992) Effects of sodium and calcium on acute toxicity of un-ionized ammonia to Atlantic salmon and lake trout. J. Appl. Aquacult. 1(4):
82-92.
Sparks RE (1975) The acute, lethal effects of ammonia on channel catfish (Ictalurus punctatus), bluegills (Lepomis macrochirus) and fathead minnows (Pimephales
promelas). Report to Illinois, Project No. 20.060. Institute for Environmental Quality, Chicago, IL.
Stammer HA. (1953) The effect of hydrogen sulfide and ammonia on characteristic animal forms in the saprobiotic system (Der einfly von schwefelwasserstoff und
ammoniak auf tierische leitformen des sparobiensystems). Vom Wasser. 20: 34-71.
Swigert JP, Spacie A. (1983) Survival and growth of warmwater fishes exposed to ammonia under low-flow conditions. Technical Report 157. Purdue University,
Water Resource Research Center, West Lafayette, IN.
Thurston RV, Chakoumakos C, Russo RC. (1981a) Effect of fluctuating exposures on the acute toxicity of ammonia to rainbow trout (Salmo gairdneri) and cutthroat
trout (S. clarki). Water Res. 15(7): 911-917.
Thurston RV, Luedtke RJ, Russo RC. (1984) Toxicity of ammonia to freshwater insects of three families. Technical Report No. 84-2. Fisheries Bioassay Laboratory,
Montana State University, Bozeman, MT.
Thurston RV, Meyn EL. (1984) Acute toxicity of ammonia to five fish species from the northwest United States. Technical Report No.84-4. Fisheries Bioassay
Laboratory, Montana State University, Bozeman, MT.
Thurston RV, Phillips GR, Russo RC, et al. (1981b) Increased toxicity of ammonia to rainbow trout (Salmo gairdneri) resulting from reduced concentrations of
dissolved oxygen. Can. J. Fish. Aquat. Sci. 38(8): 983-988.
Thurston RV, Russo RC. (1981) Acute toxicity of ammonia to golden trout (Salmo aguabonita) and mottled sculpin (Cottus bairdi). Technical Report No. 81-1.
Fisheries Bioassay Laboratory, Montana State University, Bozeman, MT.
Thurston RV, Russo RC. (1983) Acute toxicity of ammonia to rainbow trout. Trans. Am. Fish. Soc. 112: 696-704.
Thurston RV, Russo RC, Smith CE. (1978) Acute toxicity of ammonia and nitrite to cutthroat trout fry. Trans. Am. Fish. Soc. 107(2): 361-368.
Thurston RV, Russo RC, Vinogradov GA. (1981c) Ammonia toxicity to fishes: Effect of pH on the toxicity of the un-ionized ammonia species. Environ. Sci. Technol.
15(7): 837-840.
Thurston RV, Russo RC, Phillips GR. (1983) Acute toxicity of ammonia to fathead minnows. Trans. Am. Fish. Soc. 112(5): 705-711.
Wade D, Posey J, Simbeck DJ. (1992) Definitive evaluation of Wheeler Reservoir sediments toxicity using juvenile freshwater mussels (Andodonta imbecillis Say).
TVA/WR-92/25. Tennessee Valley Authority, Water Resources Division.
Wang N, Erickson RJ, Ingersoll CG, et al. (2008) Influence of pH on the acute toxicity of ammonia to juvenile freshwater mussels (Fatmucket, Lampsilis
siliquoidea). Environ. Toxicol. Chem. 27:1141-1146.
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 Genus Species
Chordata Actinopterygii
Gasterosteiformes Gasterosteidae Gasterosteus G. aculeatus
Pungitius P. sinensis
Esociformes Esocidae Esox E. reicherti
Salmoniformes
Salmonidae
Coregonus C. ussurinsis
C. chadary
Oncorhynchus
O. keta
O masou
O Gorbuscha
Salvelinus S. malma
S. pluvius
Oncorhynchus O. mykiss
Brachynuystax B. lenok
Hucho H. taimen
H. ishikawai
Osmeridae
Hypomesus H. olidus
Osmerus O. mordax
Hypomesus H. transpacificus
nipponesis
Thymallidae Thymallus T. arcticus
T. arcticus grubei
Salangidae Protosalanx P. hyalocranius
Cypriniformes Cyprinidae
Abbottina A. rivularis
A. liaoningensis
Parabramis P. pekinensis
Culter
C. mongolicus
C. alburnus
C. dabryidabryi
C. oxycephalus
C. dabryi
shinkainensis
Ctenopharyngodon C. idellus
Squaliobarbus S. curriculus
Megalobrama M. amblycephalus
M. skolkovil
Elopichthys E. bambusa
Xenocypris X. micro1epis
X. argentea
Phoxinus P. perenurus
P. lagowskii
Phylum Class Order Family Genus Species
P. czkanowskii
P. lagowskii
P. phoxinus
phoxinus
Gnathopogon G. mantschuricus
Hemibarbus H. labeo
H. maculatus
Carassius C. auratus gibelio
Hemiculter
H. leucisculus
H. bleekeri lucidus
H. bleekeri
H. bieekerib
Gobio
G. soldatovi
G. cynocephalus
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
Tribolodon T. brandti
T. hakonsis
Saurogobio S. dabryi
Paraleucogobio P. strigatus
Rostrogobio R. amurensis
Aphyocypris A. chinensis
Leucisus L. waleckii
Squalidus S. argentatus
S. chankaensis
Aristichthys A. nobilis
Phylum Class Order Family Genus Species
Acheilognathus A. chankaensis
Cultrichthys C. erythropterus
C. compressocorpus
Cobitidae
Nemachilichthys N. nudus
Lefua L. costata
Paramisgurnus P. dabryanus
Cobitis C. lutheri
Misgurnus M. mohoity
Parabotia P. fasciata
Perciformes
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
Siluriformes
Bagridae
Pelteobagrus P. nitidus
P. fulvidraco
Pseudobagrus P. ussuriensis
Leiocassis L. argentivittatus
Siluridae Silurus S. soldatovi
S. asotus
Gadiformes Lotidae Lota L. lota
Acipenseriformes Acipenseridae Huso H. dauricus
Acipenser A. schrenckii
Scorpaeniformes Cottidae Mesocottus M. haitej
Cephalaspidemorphi Petromyzontiformes Petromyzontidae Lethenteron
L. raissncri
L. japonica
L. morii
Table 3 List of chordata in Songhua River water system
Phylum Class Order Family Genus Species
Chordata Amphibia
Caudata Hynobiidae
Hynobius H. leechii
Salamandrella S. keyserlingii
Onychodactylus O. fischeri
anura
Bufonidae Bufo B. raddei
Microhylidae Kaloula K. borealis
Discoglossidae Bombina B.orientalis
Ranidae Rana R. chensinensis
R. emeljanovi
Hylidae Hyla H. japonica
Table 4 List of arthropoda in Songhua River water system
Phylum Class Order Family Genus Species
Arthropoda
Malacostraca Decapoda
Cambaridae Cambaroides C. dauricus
Atyoidae Neocaridina N. heteropoda heteropoda
Palaemonidae
Exopalaemon E. modestus
Macrobrachium M. nipponense
Palaemonetes P. sinensis
P. sinensis
Maxillopoda
Cyclopoida Cyclopidae
Acanthocyclops
A. vernalis
A. viridis
A. bicuspidatus
A. bisetosus
Macrocyclops M. albidus
Cyclops C. strenuus
Paracyclops P. fimbriatus
P. affinis
Ectocyclops E. phaleratus
Thermocyclops T. dybowskii
T. kawamurai
Microcyclops
M. rubellus
M. longiramus
M. robustus
M. javanus
M. inchoatus
M. bicolor
Eucyclops
E. serrulatus
E. macruroides
E. macruroides
Harpacticoida Canthocamptidae
Canthocamptus C. carinatus
Bryocamptus B. vejdovskyi
Attheyella
A. crassa
A. dogieli
A. amurensis
Calanoida Diaptomidae
Neutrodiaptomus 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
Temoridae Epischura E. chankensis
Heterocope H. appendiculata
Centropagidae Boeckella B. orientalis
Branchiopoda Cladocera
Leptodoridae Leptodora L. Kindti
Macrothricidae
Ilyocryptus I. sordidus
Lathonura L. rectirostris
Macrothrix
M. rosea
M. hirsuticornis
M. laticornis
M. triserialis
Polyphemidae Polyphemus P. pediculus
Moinidae Moina
M. rectirostris
M. micrura
M. macrocopa
M. chankensis
Chydoridae
Acroperus A. harpae
A. angustatus
Leydigia L. acanthocercoides
Graptoleberis G. testudinaria
Alona
A. karua
A. intermedia
A. quadrangularis
A. affinis
A. diaphana
A. rectangula
A. guttata
A. costata
Alonella A. excisa
Disparalona D. rostrata
Oxyurella O. tenuicaudis
Dunhevedia D. crassa
Pleuroxus
P. striatus
P. trigonellus
P. aduncus
P. hamulatus
Chydorus
C. sphaericus
C. ovalis
C. gibbus
C. barroisi
Pseudochydorus P. globosus
Peracantha P. truncata
Eurycercus E. lamellatus
Phylum Class Order Family Genus Species
C. rectirostris
Sididae
Diaphanosoma
D. brachyurum
D. chankensis
D. leuchtenbergianum
D. sarsi
Latonopsis L. australis
Sida S. crystallina
Bosminidae Bosmina
B. longirostris
B. coregoni
B. fatalis
B. deilersi
Daphnidae
Scapholeberis S. mucronata
S. kingi
Simocephalus
S. serrulatus
S. vetulus
S. vetuloides
S. exspinosus
S. serrulatus
Ceriodaphnia
C. quadrangula
C. hamata
C. laticaudata
Daphnia
D. carinata
D. pulex
D. obtusa
D. longispina
D. hyalina
D. cristata
D. magna
Insecta Odonata
Comphidae
Anisogomphus A. maacki
Davidius D. lunatus
Gomphidia G. confluens
Nihonogomphus N. ruptus
Ophiogomphus O. obscurus
Shaogomphus S. postocularis
S. schmidti
Siebordius S. albardae
Sinictingogomphus S. clavatus
Trigomphus T. citimus
T. succumben
Coenagriidae
Cercion C. plagiosum
Coenagrion C. bifurcatum
C. lanceolatum
Phylum Class Order Family Genus Species
Enallagma E. deserti
Erythromma E. najas
Ischnura
I. asiatica
I. elegans
I. senegalensis
Libellulidae
Deielia D. phaon
Leucorrhinia L. dubia orientalis
L. ijimai
Libellula L. basilinea
L. guadrimaculata
Lyriothemis L. pachygaster
Orthetrum
O. melania
O. albistylum
O. neglectum
Pantala P. flavescens
Sympetrum
S. croceolum
S. danae
S. depressiusculum
S. eroticum ardens
S. flaveolum
S. imitens
S. infscatum
S. pedmontanum
S. risi
S. striolatum imitoides
S. uniforme
Agriidae Caloptery C. x atrata
C. x virgo
Corduliidate
Cordulia C. aenea amuresis
Epitheca E. bimaculata
Epophthalmia E. elegans
Macromia
M. a amphigena
M. beijingensis
M. daimoji
M. manchurica
Trichoptera
Polycentropidae
Plectrocnemia P. kusnezovi
Plectrocnemia P. wui
Neureclipsis N. mandjuricus
Hydropsychidae
Hydropsyche H. nevae
Hydropsychodes H. tokunagai
Hydroptila H. chinensis
H. ornithocephala
Phylum Class Order Family Genus Species
H. introspinata
Stactobiella S. biramosa
Rhyacophilidae Rhyacophila
R. Hokkaidensis
R. Retracia
R. Narvae
R. Lata
R. Yamanakensis
Molannidae Molanna M. falcata
Lepto ceridae Setodes S. argentata
Oecetis O. nigropunctata
Limnephilidae Limnophilus L. amurensis
Phryganeidae
Agrypnia
A. colorata
A. czerskyi
A. picta
Oligotricha O. lapponica
Phryganea
P. sinensis
P. japonica
P. bipunctata
Sembis S. melaleuca
S. phalaenoides
Eubasilissa E. avalokhita
Ephemeroptera
Baetidae Cloeon C. dipterum
Baetis B. sp.
Ephemerellidae
Drunella
D. aculea
D. cryptomoria
D. lepnevae
D. solida
D. triacantha
D. trispina zeoensis
Cincticostella C. tshernovae
C. castanea
Ephemerella
E. keijoensis
E. auricilli
E. notofascia
Diptera Culicidae Aedes
A. alektorovi
A. antuensis
A. cataphylla
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
Anopheles
A. koreicus
A. messeae
A. sineroides
A. yatsushiroensis
Armigeres A. subalbatus
Culex
C. hayashii
C. jacksoni
C. modestus
C. orientalis
C. pipiens pallens
C. rubensis
C. sinensis
C. whitmorei
Culiseta
C. alaskaensis
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 Species
Mollusca Gastropoda Mesogastropoda Viviparidae Cipangopaludina C. cahayensis
Phylum Class Order Family Genus Species
C. chinensis
Viviparus V. chui
Bithyniidae Parafossarulus P. striatulus
Sorbeoconcha Pleuroceridae Semisulcospira S. cancellata
S. amurensis
Pulmonata Lymnaeidae
Lymnaea L. stagnalis
Radix
R. plicatula
R. ovata
R. lagotis
R. auricularia
Galba Golba pervia
G. truncatula
Planorbidae Polypylis P. hemisphaerula
Bivalvia
Veneroida Corbiculidae Corbicula C. fluminea
Eulamellibranchia
Margaritanidae
Margaritiana M. dahurica
Unio U. douglasiae
Lanceolaria L. grayana
Unionodae Anodonta
A. woodiana
A. woodiana elliptica
A. Welliptica
A. euscaphys
A. arcaeformis
A.arcaeformisflavotincta
Table 6 List of annelida in Songhua River water system
Phylum Class Order Family Genus Species
Annelida
Clitellata Tubificida Tubificidae Spirosperma S. nikolskyi
Limnodrilus L.hoffmeisteri
Hirudinea
Rhynchobdellida Glossiphoniidae
Hemiclepsis H. marginata
Glossiphonia
G. complanata
G. heteroclita
G. lata
G. multipapillata
G. weberi
G. complanata
G. lata
Batracobdella B. paludosa
Helobdella H. nuda
H. marginata
Arhynchobdellida Haemopidae Whitmania W. laevis
W. pigra
Phylum Class Order Family Genus Species
Salifidae Barbronia B. weberi
Erpobdellidae
Dina D. lineata
Erpobdella E. octoculata
E. testacea
Hirudinidae Hirudo H. nipponia
Branchiobdellidae Branchiobdella
B. orientalis
B. kobayashii
B. macroperistomium
Table 7 List of rotifera in Songhua River water system
Phylum Class Order Family Genus Species
Rotifera Rotaria Monogononta
Brachionida
Brachionus
B. angularia
B. budapestiensis
B. calyciflorus
B. capsuliflorus
B. forficula
Argonotholca A. foliacea
Kellicottia K. longispina
Euchlanis E. dilatata
Keratella K. quadrala
K. valga
Asplancchnidae Asplanchna A. priodonala
Trichocercidae Trichocerca
T. bicristata
T. capucina
T. elongata
Synchaetidae Polyarthra P. euryptera
P. trigla