ࡱ> q bjbjt+t+ +AAP]8 8 8 L L L L 8 4 L LH L  ///SLULULULULULUL$zMnOZyL8 //@///yLk7 !H k7k7k7/ 8 SLL L /SLk7xk7>sI^ 8 SL _/L L u5 LHChapter 579: Classification Attainment Evaluation Using Biological Criteria for Rivers and Streams SUMMARY: Criteria to quantify aquatic life standards for Classes AA, A, B and C waters are defined in this chapter. The benthic macroinvertebrate community is used as a surrogate to determine conformance with statutory aquatic life standards, related statutory definitions, and statutory provisions for the implementation of biological water quality criteria, that are provided in fb88s standards for classification of fresh surface waters. Methods described in this chapter will be used to make decisions about classification attainment. 1. Definitions. The following terms are defined for use in this chapter: A. Association value. The probability (0.0-1.0), computed in the final decision model (Section 1(F) below), that a sample from a test community is comparable to statistically defined classification groups (see also Section 1(E)), representing the ecological attributes described in the water quality classification law. B. Classification attainment evaluation. An assessment to determine whether the aquatic life standards of a specified class are achieved. Classification attainment evaluations are performed by the department and reported in the department's biennial assessment report to the Legislature or may be required of an applicant for a waste discharge license or water quality certificate, as defined in this chapter. C. Ecological attribute. A process, function or characteristic of a biological community that is used to evaluate the attainment of the aquatic life standards of a classification and that can be quantified or documented through the use of metrics and indices of the aquatic community structure and function. D. Indeterminate. A term that describes an association value for a class of greater than 0.4 but less than 0.6 so that the conclusion of classification attainment for that class cannot be determined without further information. E. Linear discriminant function. An equation that is a weighted linear combination of predictor variables, derived to best distinguish among a set of classification groups having known statistical properties. F. Linear discriminant model. A set of one or more linear discriminant functions, used in combination, to derive strength of membership of an unknown sample within a water quality classification (e.g. Section 3 (F), the C or Better Model; the B or Better Model; the Class A Model). Membership strength can be converted into an association value for class membership on a 0.0-1.0 scale. G. Quality assurance plan. A plan submitted to, and approved by, the department that addresses study design, sample collection methods, sample processing methods, provisions for a taxonomic reference collection, data management and analysis methods, report preparation, professional oversight of technical staff, and management and security accountability. H. Reference community. Populations of organisms inhabiting a location that is free from known pollution sources or other activities that could bring about a change in the natural state of a community, and is chosen for its similarity to a test community location according to criteria defined in "Methods for Biological Sampling and Analysis of fb88's Rivers and Streams" (DEP LW0387-B2002). I. Taxonomic reference collection. An archived collection of one or more representative specimens of individual taxa identified by a person submitting taxonomic data to the department. Such specimens must be preserved in a separate vial for each taxon, labeled with collection site, collection date, taxonomic name, taxonomist name, and sample tracking identifier (e.g., sample log number). J. Test community. A population of organisms inhabiting a location where classification attainment evaluation is being conducted. 2. Responsibility for sampling A. Assessment. In general, it is the responsibility of the department, or its agents, to conduct sampling for the purpose of making decisions on the attainment of water quality classification (classification attainment evaluation). B. Licensing. Under certain conditions, as listed below, sampling may be required of an applicant for a waste discharge license, water quality certification or other department issued permit. Sampling must be performed by persons who can demonstrate their qualifications and ability to carry out the department's sampling protocol set forth in "Methods for Biological Sampling and Analysis of fb88's Rivers and Streams" (DEP LW0387-B2002). Prior to issuance or reissuance of a waste discharge license or other activities pursuant to 38 M.R.S.A. Chapter 3, Protection and Improvement of Waters and the States responsibilities under the federal Clean Water Act, or issuance of a water quality certification pursuant to Section 401 of the Federal Clean Water Act, an applicant may be required to conduct a classification attainment evaluation. The department may also require monitoring as a condition of any license, permit or certification that it issues. Such monitoring must be conducted according to a plan provided to, and approved by, the department. The decision by the department to require biological monitoring is based on the classification of the water, existing information about the condition of the biological community and other water quality information, past performance of existing controls for point and nonpoint sources of pollution, and the nature, magnitude, and variability of the activity relative to the affected water. 3. Aquatic life classification criteria for fb88 rivers and streams. Methods described in this section are used to make decisions about classification attainment. The models are constructed to sequentially amass evidence concerning the highest level of classification criteria that a test community attains, using quantitative predictor variables defined in Section 3(C). The pertinent question, in terms of the classification attainment, is whether or not a test community is attaining at least its statutory classification. The methods described in this rule may also be used to determine if a given waterbody attains a higher class and therefore may be subject to statutory antidegradation provisions or considered for water quality reclassification. The methods may also be used, where appropriate, for other purposes including assessment of pre-impact baseline conditions or site-specific impact evaluations. A. General provisions for aquatic life standards. Except as otherwise provided in Section 3(G)(3), Professional judgement, of this chapter, all samples of benthic macroinvertebrates that are collected for the purpose of classification attainment evaluation using the linear discriminant model described in the following section, whether collected by the department or by any person submitting data to the department, must be collected, processed and identified in conformance with "Methods for Biological Sampling and Analysis of fb88's Rivers and Streams" (DEP LW0387-B2002). Selection of an appropriate sampling site must also conform to criteria set forth in "Methods for Biological Sampling and Analysis of fb88's Rivers and Streams" (DEP LW0387-B2002). Quantitative analysis of the sample must conform to the requirements set forth in Sections 3(B) through 3(F) of this chapter and must include a quality assurance plan approved by the department, as specified in "Methods for Biological Sampling and Analysis of fb88's Rivers and Streams" (DEP LW0387-B2002). Samples must be identified to the genus level, where practicable. Computation of indices and measures of community structure required for the linear discriminant models must be adjusted to the genus level of taxonomy (see Section 3(C), Variable number 2, Generic Richness). Minimum Provisions. Samples that have been properly collected and analyzed but fail to meet either of the following criteria are unsuitable for further analysis through the linear discriminant models: (1) Total mean abundance (Section 3(C) Variable number 1) must be at least 50 individuals (average per basket/cone/bag); and (2) Generic richness (Section 3(C) Variable number 2) for three replicate basket/cone/bag samplers must be at least 15. Samples not attaining these criteria may be evaluated according to Section 3(G) of this chapter, Professional judgment. B. Aquatic life statistical decision models. The following statistical decision models consist of linear discriminant functions developed to use quantitative ecological attributes of the macroinvertebrate community (see Section 3(C) through 3(E)) to determine the strength of the association of a test community to any of the water quality classes (Appendix 1). The coefficients or weights (see Section 3(F)) are calculated using a linear optimization algorithm to minimize the distance, in multivariate space, between sites within a class, and to maximize the distance between sites between classes. The linear discriminant function has the form: Z = C + W1X1 + W2X2 + ...WnXn Where: Z = discriminant score C = constant Wi = the coefficients or weights (from Section 3(F)) Xi = the predictor variable values (from Section 3(C)) Association values are computed, using variable values from a test sample, for each classification by employing one four-way model and three two-way models. The four-way model uses nine variables pertinent to the evaluation of all classes and provides four initial probabilities that a given site attains one of three classes (AA/A, B, or C), or is in nonattainment (NA) of the minimum criteria for any class. Class AA and Class A have the same aquatic life standards and, therefore, are treated as the same aquatic life class. These probabilities have a possible range from 0.0 to 1.0, and are used, after transformation, as variables in each of the three subsequent final decision models. The final decision models (the three, two-way models) are designed to distinguish between a given class and any higher classes as one group and any lower classes as the other group (e.g., Classes AA/A+B+C vs. NA; Classes AA/A+B vs. Class C+NA; Class AA/A vs. Classes B+C+NA). The equations for the final decision models use the predictor variables relevant to the class being tested (Section 3(F)). The resultant discriminant scores are known as the Mahalonobis Distance where: Mahalonobis Distance = Zt (sample x) = g1 (x,t) + g2 (t) Where: Zt = discriminant score for sample x, class t g1 (x,t) = (x-mt)' S-1 (x-mt) g2 (t) = -2 loge (qt) = 0 (if prior probabilities are equal) Where: x = a vector containing all the values of all the variables for a given linear discriminant function, for a given sample, of class t mt = a vector, as for x, but containing the means of all predictor variables in the given linear discriminant function, for the given sample, of class t S = pooled covariance matrix (the variance of the multivariate observation) qt = value of the prior probability that a given sample is Class A, B, C, or NA. The probability (association value) of a sample x, belonging to a particular class t, is:  EMBED Equation.3  Where: Pt(x) = the probability that sample x belongs to class t (for Classes A, B, C, NA) e = the exponential function -0.5 = a standardization constant from the normal distribution Zt = the discriminant score or Mahalonobis Distance for class t (Classes A, B, C, NA) C. Methods for the calculation of indices and measures used in the linear discriminant models Variables (1) to (30) are as follows. (1) Total mean abundance. Count all individuals in all replicate samplers from a site and divide by the number of replicates to yield the mean number of individuals per sampler. (2) Generic richness. Count the number of different genera found in all replicate samplers from one site. Counting rules for generic richness: (a) Species-level counts. All population counts at the species level are aggregated to the generic level. (b) Family-level counts, no more than one genus. A family level identification that includes no more than one taxon identified to the generic level is counted as a separate taxon in generic richness counts. (c) Family-level counts, more than one genus. A family level identification with more than one taxon identified to generic level is not counted toward generic richness. Counts are divided proportionately among the genera that are present. (d) Phylum, Class, or Order counts. A higher level taxonomic identification (Phylum, Class, Order) is not counted toward generic richness unless it is the only representative. (e) Pupae. Pupae are ignored in all calculations. (3) Plecoptera mean abundance. Count all individuals from the order Plecoptera in all replicate samplers from one site and divide by the number of replicates to yield mean number of Plecopteran individuals per sampler. (4) Ephemeroptera mean abundance. Count all individuals from the order Ephemeroptera in all replicate samplers from one site and divide by the number of replicates to yield the mean number of Ephemeropteran individuals per sampler. (5) ShannonWiener Generic Diversity. Shannon-Wiener generic diversity is computed after adjusting all counts to genus, as described under paragraph (2) above.  EMBED Equation.3  where:  EMBED Equation.3  = ShannonWiener Diversity c = 3.321928 (converts base 10 log to base 2) N = Total abundance of individuals ni = Total abundance of individuals in the ith taxon (6) Hilsenhoff Biotic Index. HBI is calculated using all taxa in the sample that have assigned tolerance values. Tolerance values are provided in Hilsenhoff, William. 1987. An Improved Biotic Index of Organic Stream Pollution, The Great Lakes Entomologist 20:31-39.  EMBED Equation.3  Where: HBI = Hilsenhoff Biotic Index Ni = number of individuals in the ith taxon aI = tolerance value assigned to that taxon N = total number of individuals in sample with tolerance values (7) Relative Chironomidae abundance. Calculate the mean number of individuals of the family Chironomidae, following the counting rules in Variable 4, and divide by total abundance (Variable 1). (8) Relative Diptera richness. Count the number of genera of the Order Diptera, following counting rules in Variable 2, and divide by generic richness (Variable 2). (9) Hydropsyche abundance. Count all the individuals from the genus Hydropsyche in all replicate samplers from a site, and divide by the number of replicates to yield mean number of Hydropsyche individuals per sampler. (10) Probability (A+B+C) from first stage model. The sum of probabilities for Classes A, B, and C from first stage model. (11) Cheumatopsyche abundance. Count all individuals from the genus Cheumatopsyche in all replicate samplers from one site and divide by the number of replicates to yield mean number of Cheumatopsyche individuals per sampler. (12) EPT-Diptera richness ratio. Divide EPT generic richness (Variable 19) by the number of genera from the order Diptera, following counting rules in Variable 2. If the number of genera of Diptera in the sample is 0, a value of 1 is assigned to the denominator. (13) Relative Oligochaeta abundance. Calculate the mean number of individuals of the class Oligochaeta, following counting rules in Variable 4, and divide by total abundance (Variable 1). (14) Probability (A+B) from first stage model. The sum of probabilities for Classes A and B from first stage model. (15) Perlidae mean abundance. Count all individuals from the family Perlidae (Section 3(E)) in all replicate samplers from one site and divide by the number of replicates to yield mean number of Perlidae per sampler. (16) Tanypodinae mean abundance. Count all individuals from the subfamily Tanypodinae (Section 3(E)) in all replicate samplers from one site and divide by the number of replicates to yield mean number of Tanypodinae per sampler. (17) Chironomini mean abundance. Count all individuals from the tribe Chironomini (Section 3(E)) in all replicate samplers from one site and divide by the number of replicates to yield mean number of Chironomini per sampler. (18) Relative Ephemeroptera abundance. Variable 4 divided by Variable 1. (19) EPT generic richness. Count the number of different genera from the order Ephemeroptera (E), Plecoptera (P), and Trichoptera (T) in all replicate samplers, according to counting rules in Variable 2, generic richness. (20) Variable reserved. (21) Sum of mean abundance of Dicrotendipes & Micropsectra & Parachironomus & Helobdella. Sum the abundance of the 4 genera and divide by the number of replicates (as performed in Variable 4). (22) Probability of Class A from first stage model. (23) Relative Plecoptera richness. Count number of genera of Order Plecoptera, following counting rules in Variable 2, and divide by generic richness (Variable 2). (24) Variable reserved. (25) Sum of mean abundance of Cheumatopsyche & Cricotopus & Tanytarsus & Ablabesmyia. Sum the number of individuals in each genus in all replicate samplers and divide by the number of replicates (as performed in Variable 4). (26) Sum of mean abundance of Acroneuria & Stenonema. Sum the number of individuals in each genus in all replicate samplers and divide by the number of replicates (as in Variable 4). (27) Variable reserved. (28) Ratio of EP generic richness. Count the number of different genera from the order Ephemeroptera (E), and Plecoptera (P) in all replicate samplers, following counting rules in Variable 2, and divide by 14 (maximum expected for Class A). (29) Variable reserved. (30) Ratio of Class A indicator taxa. Count the number of Class A indicator taxa as listed in Section 3(D) that are present in the community and divide by 7 (total possible number). D. Indicator taxa for Class A Brachycentrus --- (Trichoptera: Brachycentridae) Serratella -------- (Ephemeroptera: Ephemerellidae) Leucrocuta ------- (Ephemeroptera: Heptageniidae) Glossosoma ------ (Trichoptera: Glossosomatidae) Paragnetina ----- (Plecoptera: Perlidae) Eurylophella ------ (Ephemeroptera: Ephemerellidae) Psilotreta -------- (Trichoptera: Odontoceridae) E. Family functional groups PLECOPTERA Perlidae Acroneuria Agnetina Attaneuria Beloneuria Eccoptura Neoperla Paragnetina Perlesta Perlinella CHIRONOMIDAE Tanypodinae Ablabesmyia Clinotanypus Coelotanypus Conchapelopia Djalmabatista Guttipelopia Hudsonimyia Labrundinia Larsia Meropelopia Natarsia Nilotanypus Paramerina Pentaneura Procladius Psectrotanypus Rheopelopia Tanypus Telopelopia Thienemannimyia Trissopelopia Zavrelimyia Chironomini Pseudochironomus Axarus Chironomus Cladopelma Cryptochironomus Cryptotendipes Demicryptochironomus Dicrotendipes Einfeldia Endochironomus Glyptotendipes Goeldichironomus Harnischia Kiefferulus Lauterborniella Microchironomus Microtendipes Nilothauma Pagastiella Parachironomus Paracladopelma Paralauterborniella Paratendipes Phaenopsectra Polypedilum Robackia Stelechomyia Stenochironomus Stictochironomus Tribelos Xenochironomus F. Model coefficients First Stage Model Coefficients Variable numberTransformationClass AClass BClass CNonattainmentConstant-99.95508-105.70948-112.67581-107.742831nLog (value +0.001) 10.7706111.4698111.8088811.267932-0.38619-0.43340-0.50051-0.488223nLog (value +0.001) 0.239400.03946-0.60923-0.954804nLog (value +0.001)-0.59970-0.55500-0.67722-1.79032521.2273220.9125621.0760219.4654768.016209.1216310.3149210.727467nLog (value +0.001)-11.70298-11.52650-11.49414-11.66371870.7793771.0963772.4651470.225179-0.00535-0.00398-0.001520.00007 Final Classification Models Class C or better model Coefficients Variable numberTransformationClass A-B-CNonattainmentConstant-25.70020-8.5584410Arcsin19.984703.3603211nLog (value +0.001)-0.26001-0.4378112Sq. root5.576725.9273213nLog (value +0.001)-2.33229-1.89945 Class B or better model Coefficients Variable numberTransformationClass A-BClass C-nonattainmentConstant-17.81016-6.9383614Arcsin12.048263.6370715nLog (value +0.001)-1.11091-1.0393416nLog (value +0.001)-0.105820.0197817nLog (value +0.001)0.178130.10825184.03202-1.14508190.874000.6331021nLog (value +0.001)-0.69360-0.53194 Class A model Coefficients Variable numberTransformationClass AClass B-C-nonattainmentConstant-9.59254-4.0855222Arcsin8.343411.52080233.789994.2744725nLog (value +0.001)0.531100.7785126nLog (value +0.001)-0.55838-0.514482812.325299.81592306.94828-0.67475 G. Professional judgment. Where there is documented evidence of conditions that could result in uncharacteristic findings, allowances may be made to account for those situations by adjusting the classification attainment decision through use of professional judgement, as provided in this section, paragraphs 3(G)(1) to 3(G)(3). The department may make adjustments to the classification attainment decision based on analytical, biological, and habitat information or may require that additional monitoring of affected waters be conducted prior to issuing a classification attainment decision. (1) Sampling procedures and minimum provisions conform but other confounding factors exist. When samples of test communities conform to criteria provided in "Methods for Biological Sampling and Analysis of fb88's Rivers and Streams" (DEP LW0387-B2002) and Section 3(A) of this chapter, they are suitable to be analyzed by the linear discriminant models for classification attainment evaluation. These models are not suitable for use in areas of impoundments that thermally stratify or in areas where there is a net annual deposition of fine sediment. Professional judgement may be utilized when conditions are found that are atypical to the derivation of the linear discriminant model, as provided in Section 3(B-F). Factors that may allow adjustments to the model outcome include but are not limited to: habitat factors, including lake outlets from waters classified GPA, unusual naturally-caused substrate character, tidal effects, cataclysmic events, oligotrophic conditions; sampling factors, including disturbed samples, unusual taxa assemblages, and documented human error in sampling; and sample processing factors, including subsample vs. whole sample analysis and documented human error in processing. The following adjustments may be made to correct for these conditions: (a) Raise the finding. On the basis of documented evidence of specific conditions such as those defined above, the department may decide: (i) To raise the classification attainment outcome predicted by the model from nonattainment of any class to indeterminate or to attainment of Class C; or (ii) To raise the classification attainment outcome predicted by the model from attainment in one class to attainment in the next higher class; or (iii) To determine that a sample with an indeterminate outcome for a given class attains that class. (b) Lower the finding. On the basis of documented, substantive evidence that the narrative aquatic life criteria for the assigned class are not met, the department may decide to lower the classification attainment finding. (c) Indeterminate. Where the department cannot make a finding as described in 3(G)(1)(a-b), additional monitoring of the test community may be required before a determination of class attainment can be made. (2) Minimum provisions do not conform. For classification evaluation of test communities that do not conform to criteria provided in Section 3(A) of this chapter, minimum provisions, professional judgement may be used as follows: (a) Determination of nonattainment. Those samples having any of the ecological attributes not attaining the minimum provisions (Section 3(A)), and where there is no evidence of confounding factors that could result in uncharacteristic findings as defined above (Section 3(G)(1)), must be determined to be in nonattainment of the minimum provisions of the aquatic life criteria for any class. (b) Determination of attainment when minimum provisions are not met. Where there is evidence of factors that could result in minimum provisions not being met, professional judgment may be used to make a professional finding of attainment of the aquatic life criteria for any class. Such decisions will be provisional until appropriate resampling is carried out. (3) Standard sampling procedures are not feasible or appropriate. For classification attainment evaluation of test communities that do not conform to criteria provided in "Methods for Biological Sampling and Analysis of fb88's Rivers and Streams" (DEP LW0387-B2002), the department may make an assessment of classification attainment or aquatic life impact in accordance with the following procedures: (a) Approved assessment plan. A quantitative sampling and data analysis plan must be developed in accordance with methods established in the scientific literature on water pollution biology, and the department must approve the plan. (b) Determination of sampling methods. Sampling methods are determined on a site-specific basis, based on habitat conditions of the sampling site, and the season sampled; (i) The preferred method for sampling hard-bottomed substrates is the rock basket/cone/bag method as described in "Methods for Biological Sampling and Analysis of fb88s Rivers and Streams" (DEP LW0387-B2002). (ii) Soft-bottomed substrates will, whenever ecologically appropriate and practical, be sampled by core or dredge of known dimension. (c) Other methods. Other methods may be used where ecologically appropriate and practical. (d) Classification attainment decisions. Classification attainment decisions may be based on a determination of the degree to which the sampled site conforms to the narrative aquatic life classification criteria provided in statutory standards for water quality classification. The decision is based on established principles of water pollution biology and must be fully documented. (e) Site specific impact decisions. Site specific impact decisions may rely on established methods of analysis of comparative data between a test community and an approved reference community. (f) Determination of detrimental impact. A determination of detrimental impact to aquatic life of a test community without an approved reference community may be made if it can be documented, based on established methods of the interpretation of macroinvertebrate data, and based on established principles of water pollution biology, that the community fails to demonstrate the ecological attributes of its designated class as defined by the narrative aquatic life standards in the water quality classification law. 4. Determination of decision results. A waterbodys attainment class is determined by following the process described below, and as shown in Appendices 1 and 2. A. Assess data appropriateness and minimum requirements. The first step is to determine if the sample meets minimum requirements (Section 3(A)) and is appropriate to run through the Linear Discriminant Models (LDM). If the minimum provisions or sampling procedures are not appropriate, then professional judgment may be used to determine the appropriate course of action (Sections 3(G)(2) and 3(G)(3)). B. Determine if sample attains at least Class C. The second step is to use the association value from the C or better LDM (pABC) to determine if the sample meets at least Class C or is in nonattainment of minimum aquatic life criteria. If the association value is equal to or greater than 0.6, the sample attains Class C. If the association value is less than 0.4, the sample does not attain Class C and is determined to be in nonattainment of any classification. If the association value (pABC) is greater than 0.4 and less than 0.6, then professional judgment is used to determine if the sample is (1) Class C, (2) in nonattainment, or (3) indeterminate of Class C (see Section 3(G)(1)). C. Determine if the sample attains at least Class B. For those samples that attain at least Class C, the next step is to use the association value from the B or better LDM (pAB) to determine if the sample is (1) at least Class B with an association value equal to or greater than 0.6, (2) Class C with an association value less than 0.4, or (3) indeterminate of Class B with an association value greater than 0.4 and less than 0.6 (Section 3(G)(1)). D. Determine if the sample attains Class A. For those samples that are at least Class B, the final step is to use the association value from the A LDM (pA) to determine if the sample is: (1) Class A with an association value equal to or greater than 0.6, (2) Class B with an association value less than 0.4, or (3) Indeterminate of Class A with an association value greater than 0.4 and less than 0.6 (Section 3(G)(1)). 5. Application of decision results A. Attainment of statutory classification. A waterbody shall be determined to be in attainment of the designated aquatic life uses and characteristics of its assigned water classification, if the association value, as determined according to Sections 3 and 4 of this chapter, following methods outlined in "Methods for Biological Sampling and Analysis of fb88s Rivers and Streams" (DEP LW0387-B2002), is shown to be equal to or greater than 0.6 for that class or any higher class, or where the provisions for professional judgement determine that the water should be determined to be in attainment of its assigned water classification or any higher classification; and where other standards and criteria pertinent to protecting the aquatic life uses of the classification are determined to be attained (including, but not limited to, support of indigenous fish or other aquatic species, as required in the water quality classification law). B. Reporting. Results of classification attainment evaluation must be reported in the departments biennial water quality assessment report and Section 305(b) of Federal Clean Water Act. Waters that are determined not to attain their statutory classification are listed in the states list of impaired waters as required in Section 303(d) of the Federal Clean Water Act. C. Antidegradation. Waters that attain the aquatic life use standards of a higher classification must be protected in accordance with the applicable statutory provisions for antidegradation. For the purposes of antidegradation, attainment of aquatic life use standards of a higher classification shall be based on classification attainment evaluations of 2 or more consecutive sampling outcomes, from different sampling years, that attain a higher class, and taking into account critical water quality conditions including, but not limited to, conditions of low flow, high water temperature, maximum loading from point source and nonpoint source discharges, and conditions of acute and chronic effluent toxicity. D. Licensing, permitting and water quality certification activities. An activity regulated by the Department may be approved when a classification attainment evaluation finds that the standards of classification of the water body are met and will continue to be met, notwithstanding the approved activity. An activity regulated by the Department may be approved when a classification attainment evaluation finds that the standards of classification of the water body are not met if it can be shown that the activity does not, or a new activity will not, cause or contribute to non-attainment of designated aquatic life uses and other standards of the assigned water classification. If the classification attainment evaluation shows that the standards of classification of the water body are not met, and the applicant is unable to show that the activity does not, or a new activity will not cause or contribute to non-attainment, then a license, permit or water quality certification may only be issued when it can be demonstrated that modifications to the proposed activity, as required by the new license, permit or certification, will provide attainment conditions. If two or more activities can be shown to cause or contribute to non-attainment of the assigned water classification, then a license, permit or water quality certification may only be issued when it can be demonstrated that modifications to the activity, as required in the license, permit or certification, in conjunction with modifications to other sources in the watershed that contribute to the non-attainment, including modifications that are a part of a Department approved total maximum daily load, will provide attainment. The department may require additional monitoring of the affected waters following issuance of any license, permit or water quality certificate. 6. Provisions for transition. All aquatic life samples collected for Classification Attainment Evaluation after January 1, 2000 are subject to the provisions of this chapter. All determinations of classification attainment or nonattainment made by the department for samples collected prior to January 1, 2000 are subject to analytical requirements and provisions described in "fb88 Biological Monitoring and Biocriteria Development Program" (DEP-LW-108) and "Methods for Biological Sampling and Analysis of fb88s Rivers and Streams" (DEP LW0387-B2002). Appendices   AUTHORITY: Title 38 Article 4-A Water Classification Program 464.5 EFFECTIVE DATE: May 27, 2003, filing 2003-162 06-096 DEPARTMENT OF ENVIRONMENTAL PROTECTION Chapter 579: Classification Attainment Evaluation Using Biological Criteria for Rivers and Streams - PAGE 8- 06-096 DEPARTMENT OF ENVIRONMENTAL PROTECTION 1 Discriminant Scores and Association Values are defined in Section 3(B). APPENDIX 1: Process Of Calculating Model Variables And Association Values Using Linear Discriminant Models. First Stage Linear Discriminant Model (LDM) (4-way model: A vs. B vs. C vs. NA) Model calculates Discriminant Score1 using Var1 Var9. Model uses Discriminant Score to calculate the following Association Values1: probability Class AA/A (pA1) probability Class B (pB1) probability Class C (pC1) probability Nonattainment (pNA1) Computer calculates model variables (Var1 Var30) using taxa counts from a sample event using procedures described in Section 3(C). A Second Stage LDM (2-way model: A vs. B, C, or NA) Model calculates Discriminant Score1 using Var22 (pA1) and Var23 Var30. Model uses Discriminant Score to calculate the following Association Values1: probability AA/A (pA) probability B, C, or NA (pBCNA) B or Better Second Stage LDM (2-way model: A or B vs. C or NA) Model calculates Discriminant Score1 using Var14 (pA1+pB1) and Var15 Var21. Model uses Discriminant Score to calculate the following Association Values1: probability B or better (pAB) probability C or NA (pCNA) C or Better Second Stage LDM (2-way model: A, B, or C vs. NA) Model calculates Discriminant Score1 using Var10 (pA1+pB1+pC1) and Var11 Var13. Model uses Discriminant Score to calculate the following Association Values1: probability C or better (pABC) probability NA (pNA) APPENDIX 2: Process For Determining Attainment Class Using Association Values. Indeterminate Section 3.G.1 B Section 3.G.1 A B A Section 3.G.1 pA ( 0.6 pA < 0.4 0.6 > pA ( 0.4 Is the sample Class A? Indeterminate Section 3.G.1 C Section 3.G.1 At least B C At least B Section 3.G.1 pAB ( 0.6 pAB < 0.4 0.6 > pAB ( 0.4 Is the sample Class B or better? Indeterminate Section 3.G.1 NA Section 3.G.1 At least C NA At least C Section 3.G.1 pABC ( 0.6 pABC < 0.4 0.6 > pABC ( 0.4 Is the sample Class C or better? 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