SIST EN 15204:2007

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EUROPEAN STANDARDNORME EUROPÉENNEEUROPÄISCHE NORMEN 15204August 2006ICS 13.060.70 English VersionWater quality - Guidance standard on the enumeration ofphytoplankton using inverted microscopy (Utermöhl technique)Qualité de l'eau - Norme guide pour l'analyse de routine del'abondance et de la composition du phytoplancton parmicroscopie inversée (méthode d'Utermöhl)Wasserbeschaffenheit - Anleitung für die Zählung vonPhytoplankton mittels der Umkehrmikroskopie (Utermöhl-Technik)This European Standard was approved by CEN on 14 July 2006.CEN members are bound to comply with the CEN/CENELEC Internal Regulations which stipulate the conditions for giving this EuropeanStandard the status of a national standard without any alteration. Up-to-date lists and bibliographical references concerning such nationalstandards may be obtained on application to the Central Secretariat or to any CEN member.This European Standard exists in three official versions (English, French, German). A version in any other language made by translationunder the responsibility of a CEN member into its own language and notified to the Central Secretariat has the same status as the officialversions.CEN members are the national standards bodies of Austria, Belgium, Cyprus, Czech Republic, Denmark, Estonia, Finland, France,Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Romania,Slovakia, Slovenia, Spain, Sweden, Switzerland and United Kingdom.EUROPEAN COMMITTEE FOR STANDARDIZATIONCOMITÉ EUROPÉEN DE NORMALISATIONEUROPÄISCHES KOMITEE FÜR NORMUNGManagement Centre: rue de Stassart, 36
B-1050 Brussels© 2006 CENAll rights of exploitation in any form and by any means reservedworldwide for CEN national Members.Ref. No. EN 15204:2006: E

Optical characteristics of inverted microscopes.21 Annex B (informative)
Sample treatment.23 Annex C (informative)
Phytoplankton analysis strategies.27 Annex D (informative)
Identification.30 Annex E (informative)
Use of conventional compound microscopes.31 Annex F (informative)
Statistical procedure.34 Bibliography.40
Figures Figure 1 — Random distribution of particles (note the open spaces).12 Figure 2 — Example of rule for counting cells on the edge of the field. Algae objects crossing both the top and left hand side of grid are not counted whilst those crossing the bottom and right hand side of grid are counted.14 Figure F.1 — Illustration of collecting algal data for a Run-test.35
Tables Table 1 — Settling times for Lugol preserved seawater samples [12].11 Table F.F1 — Maximum allowable variance for Poisson approximation (µ = mean, σσσσ 2 = variance).35 Table F.F2 — Multinomial homogeneity test.36

3.13 preservation process that protects organic substances from decay 3.14 (analysis) protocol specific analytical procedure concerning (sub)sample volume, magnification, number of cells to count, taxonomic level of identification etc. 3.15 repeatability precision under repeatability conditions 3.16 repeatability conditions conditions where independent test/measurement results are obtained with the same method on identical test/measurement items in the same test or measuring facility by the same operator using the same equipment within short intervals of time NOTE This definition should be interpreted as the error occurring between replicate sub-samples from the same sample, counted using the same counting chamber, performed by one analyst using one microscope in a continuous run on one day. 3.17 reproducibility precision under reproducibility conditions
3.18 reproducibility conditions conditions where independent test/measurement results are obtained with the same method on identical test/measurement items in different test or measurement facilities with different operators using different equipment

Table 1 — Settling times for Lugol preserved seawater samples [12] Volume of chamber Height of chamber Settling time ml cm h 2 1 3 10 2 8 25 5 12 50 10 24 100 20 48
 after sedimentation, slide the chamber column aside and place a cover glass on the counting chamber to close it; avoid enclosing air bubbles; these can be eliminated by topping up with water using a small dropper pipette whilst sliding the coverslip back;  gentle moving the counting chamber to the microscope will not affect the settled particles when the chamber is filled completely and a cover glass is used. However, settled algae in an open sedimentation chamber are easily disturbed even when moved carefully. Open chambers should not be used for this reason;  an intense light source (including that from the microscope) might cause settled algae to float again even in a closed counting chamber. It should be checked for each microscopic configuration and procedure whether this phenomenon will indeed affect the analysis. When incomplete sedimentation has been observed or when there are specific sampling or historical indications that buoyant algae like many Cyanobacteria or for instance the lipid containing green alga Botryococcus may be present, the fluid in the upper column of the chamber should be centrifuged at an appropriate speed to estimate whether the amount of buoyant algae is significant. Furthermore, some small Cyanobacteria remain in suspension just above the bottom glass under some circumstances. This can be checked by focusing above the bottom prior to counting. If necessary, gas vesicles of Cyanobacteria can be collapsed, after which a new sub-sample may be prepared. Gas vesicles can be collapsed by putting a sample in a large plastic syringe from which the needle has been removed, leaving an opening of 1 mm or 2 mm in diameter. If the needle end is hit firmly against a wall whilst holding the piston, the sudden increase of pressure inside the syringe will collapse the gas vesicles. Another option is to put a rubber stopper in the opening of a sample bottle and then hit it carefully with a hammer. Collapsing of gas vesicles may sometimes be achieved using acidified Lugol with a slightly higher concentration of glacial acetic acid.

Figure 1 — Random distribution of particles (note the open spaces) 6.7 Algal density For optimum accuracy in identification and counting, the density of settled algae in the counting chamber should be considered and the volume of sub-sample (or dilution) adjusted accordingly. Although the optimum range is subjective, some general guidelines can be given. Firstly, the number of particles should allow for the independent settling of particles. If there are too many particles, agglutination and piling-up of particles (including algae) may occur, resulting in non-random distribution. Secondly, if the number of algae per field is too low, there will be a large random error when counting random fields or transects and hence inefficient counting (many fields or grids to count), but detection levels will be high (algae are not easily overlooked). Conversely, high densities of settled algae will reduce the error and number of fields to count but will reduce the detection accuracy. A chamber/field with a high particle density may result in analyst fatigue, with algae then being overlooked. The ideal density depends on the relative size of the algae (visibility) and the number of non-algae particles (detritus etc.) in combination with the skills of the analyst. Evaluation may be carried out by counting a relevant sample at different densities. 7 Counting procedure 7.1 General The choice of counting strategy depends on the information needed. As microscopic phytoplankton analyses are time consuming and therefore costly, it is important to optimize this strategy (see also Annex C).

Key Y counted N not counted
Figure 2 — Example of rule for counting cells on the edge of the field. Algae objects crossing both the top and left hand side of grid are not counted whilst those crossing the bottom and right hand side of grid are counted Counting a whole chamber is achieved by traversing back and forwards across the chamber, from top to bottom (or vice versa). An eyepiece graticule is used comprising 2 horizontal parallel lines which delimit the transect, preferably with a third vertical line crossing these. Algal objects between the transect lines are counted as they pass the vertical line. Those crossing the top transect line are included, but not those crossing the bottom line as these will be counted on the next transect (or vice versa). The same type of eyepiece can be used for counting transects, taking care to ensure (by validation) that a random distribution of settled algal objects exists and that there are no ‘edge effects’ (more algae settling towards the periphery). NOTE Counting a random number of fields is claimed by some authors to give the most accurate data (smaller coefficient of variation) and to be less time consuming than counting transects (e.g. [25]). When algae are sedimented in more or less concentric-like patterns, as often observed, the algae in the central area of the counting chamber are over-estimated and algae closer to the chamber wall are under-estimated when counting transects. This problem might be anticipated by counting random fields, segments of the chamber or by treating the central and peripheral areas as two or more distinct ‘populations’. In the case of random fields the probability of counting a specific region is proportional to the surface area of that region relative to the total surface area of the counting chamber.

80 fields with just 5 objects. If precision is considered in terms of percentage confidence limits, multiply the above equation by t2 (Student’s t), which yields: 22Dtx=∑ for n-1 degrees of freedom. In this case, a count of 400 objects will result in 95 % confidence limits that lie within 10 % of the mean (t ≈ 2). The estimate of D is only valid when algal objects can be considered to be distributed randomly in the counting chamber. Otherwise it should only be used as a rough approximation. NOTE The precision relates to the type of algal objects counted. If all taxa should be counted and the precision is set for the total number of algal objects, then the precision applies only to the total number of algae, not to individual groups/species. If a single taxon should be counted, the precision should be set for that taxon. 7.3.2.3 Quantitative detection limit The detection limit is an important performance characteristic in phytoplankton surveys. For a single taxon (assuming a random distribution), the detection limit may be determined by Poisson statistics according to: )./()ln(countedtotaldetfVfn⋅−=α (2) where
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