SIST EN 15522-2:2023
(Main)Oil spill identification - Petroleum and petroleum related products - Part 2: Analytical method and interpretation of results based on GC-FID and GC-low resolution-MS analyses
Oil spill identification - Petroleum and petroleum related products - Part 2: Analytical method and interpretation of results based on GC-FID and GC-low resolution-MS analyses
This document specifies a method to identify and compare the compositional characteristics of oil
samples. Specifically, it describes the detailed analytical and data processing methods for identifying the
characteristics of spill samples and establishing their correlation to suspected source oils. Even when
samples or data from suspected sources are not available for comparison, establishing the specific nature
(e.g. refined petroleum, crude oil, waste oil, etc.) of the spilled oil still helps to constrain the possible
source(s).
This methodology is restricted to petroleum related products containing a significant proportion of
hydrocarbon-components with a boiling point above 150 °C. Examples are: crude oils, higher boiling
condensates, diesel oils, residual bunker or heavy fuel oils, lubricants, and mixtures of bilge and sludge
samples, as well as distillate fuels and blends. While the specific analytical methods are perhaps not
appropriate for lower boiling oils (e.g. kerosene, jet fuel, or gasoline), the general concepts described in
this methodology, i.e. statistical comparison of weathering-resistant diagnostic ratios, are applicable in
spills involving these kinds of oils.
Paraffin products (e.g. waxes, etc.) are outside the scope of this method because too many compounds
are removed during the production process [37] to correctly distinguish them from each other. However,
the method can be used to identify the type of product involved.
Although not directly intended for identifying oil recovered from groundwater, vegetation,
wildlife/tissues, soil, or sediment matrices, they are not precluded. However, caution is needed as
extractable compounds can be present in these matrices that alter and/or contribute additional
compounds compared to the source sample. If unrecognized, the contribution from the matrix can lead
to false “non-matches”. It is therefore advisable to analyse background sample(s) of the matrix that
appear unoiled.
When analysing “non-oil” matrices additional sample preparation (e.g. clean-up) is often required prior
to analysis and the extent to which the matrix affects the correlation achieved is to be considered.
Whether the method is applicable for a specific matrix depends upon the oil concentration compared to
the “matrix concentration”. In matrices containing high concentrations of oil, a positive match can still be
concluded. In matrices containing lower concentrations of oil, a false “non-match” or an “inconclusive
match” can result from matrix effects. Evaluation of possible matrix effects is beyond the scope of this
document.
Identifizierung von Ölverschmutzungen - Rohöl und Mineralölerzeugnisse - Teil 2: Analytische Methodik und Interpretation der Ergebnisse, basierend auf GC-FID- und GC-MS-Analysen bei niedriger Auflösung
Dieses Dokument legt ein Verfahren zur Identifizierung und zum Vergleich der Zusammensetzungsmerkmale von Ölproben fest. Insbesondere beschreibt es die ausführlichen Analyse- und Datenverarbeitungsverfahren zur Identifizierung der Merkmale von Proben von Verschmutzungen und zur Ermittlung ihrer Korrelation mit Ölen der mutmaßlichen Quelle. Selbst wenn keine Proben oder Daten von mutmaßlichen Quellen zum Vergleich zur Verfügung stehen, hilft die Feststellung der spezifischen Beschaffenheit (z. B. raffiniertes Mineralöl, Rohöl, Altöl usw.) des freigesetzten Öls dennoch dabei, die mögliche(n) Quelle(n) einzugrenzen.
Diese Methodik ist auf mineralölbezogene Erzeugnisse beschränkt, die einen erheblichen Anteil an Kohlenwasserstoffkomponenten mit einem Siedepunkt über 150 °C enthalten. Beispiele sind: Rohöle, höhersiedende Kondensate, Dieselöle, Rückstände von Bunker oder Schwerölen, Schmiermittel und Mischungen aus Bilgen(wasser)- und Schlammproben sowie Destillat-Kraft- und Brennstoffe und Mischungen. Obwohl die spezifischen Analyseverfahren möglicherweise nicht für niedrigersiedende Öle (z. B. Kerosin, Flugturbinenkraftstoff oder Ottokraftstoff) geeignet sind, sind die in dieser Methodik beschriebenen allgemeinen Konzepte, d. h. der statistische Vergleich alterungsbeständiger diagnostischer Verhältnisse, bei Verschmutzungen unter Einbeziehung dieser Arten von Ölen anwendbar.
Paraffinbasierte Erzeugnisse (z. B. Wachse usw.) fallen nicht in den Anwendungsbereich dieses Verfahrens, weil während des Herstellungsprozesses zu viele Verbindungen entfernt werden [37]. Das Verfahren kann jedoch zur Identifizierung der Art des betreffenden Produkts verwendet werden.
Obwohl sie nicht direkt zur Identifizierung von Öl bestimmt sind, das aus den Matrices Grundwasser, Vegetation, Wildtiere/Gewebe, Böden oder Sediment gewonnen wird, sind sie nicht ausgeschlossen. Es ist jedoch Vorsicht geboten, da in diesen Matrices extrahierbare Verbindungen vorhanden sein können, die im Vergleich zur Probe von der Quelle sich verändern und/oder zusätzliche Verbindungen einbringen können. Wenn dies nicht erkannt wird, kann der Beitrag der Matrix zu falschen "Nicht-Übereinstimmungen" führen. Es ist daher ratsam, Hintergrundprobe(n) der Matrix zu analysieren, die ohne Öl zu sein scheinen.
Bei der Analyse von „Nicht-Öl“-Matrices ist häufig eine zusätzliche Probenvorbereitung (z. B. Reinigung) vor der Analyse erforderlich, und das Ausmaß, in dem die Matrix die erzielte Korrelation beeinflusst, ist zu berücksichtigen. Ob das Verfahren auf eine spezifische Matrix anwendbar ist, hängt von der Ölkonzentration im Vergleich zur „Matrixkonzentration“ ab. In Matrices, die hohe Konzentrationen an Öl enthalten, kann immer noch auf eine Übereinstimmung geschlossen werden. In Matrices, die geringere Konzentrationen an Öl enthalten, kann eine falsche „Nicht-Übereinstimmung“ oder eine „nicht eindeutige Übereinstimmung“ auf Matrixeffekte zurückzuführen sein. Die Bewertung möglicher Matrixeffekte fällt nicht in den Anwendungsbereich dieses Dokuments.
Identification des pollutions pétrolières - Pétrole et produits pétroliers - Partie 2 : Méthode d'analyse et interprétation des résultats sur la base des analyses par CPG DIF et CPG-SM faible résolution
Le présent document spécifie une méthode d'identification et de comparaison des caractéristiques de composition des échantillons de pétrole. Plus spécifiquement, il décrit en détail les méthodes d'analyse et de traitement des données visant à identifier les caractéristiques des échantillons de déversement, et à établir une corrélation avec les pétroles sources potentiels. Même en l'absence d'échantillons ou de données sur les sources potentielles pour effectuer la comparaison, le fait d'établir la nature propre du pétrole déversé (par exemple pétrole raffiné, pétrole brut, huile usée, etc.) aide malgré tout à restreindre la liste des sources potentielles.
Cette méthodologie est limitée aux produits pétroliers contenant une proportion significative d'hydrocarbures avec un point d'ébullition supérieur à 150 °C, par exemple : les pétroles bruts, les condensats à température d'ébullition élevée, les gazoles, les résidus de soute ou de mazouts lourds, les lubrifiants, les mélanges d'eau de cale et de boues, ainsi que les distillats et les mélanges. Si les méthodes d'analyse spécifiques présentées ici ne sont pas forcément adaptées à des pétroles à plus faible température d'ébullition (par exemple kérosène, carburéacteur ou essence), les concepts généraux associés, par exemple la comparaison statistique des ratios de diagnostic résistants aux intempéries, s'appliquent aux déversements impliquant ces types de pétroles.
Les produits à base de paraffine (par exemple les cires, etc.) sont hors du domaine d'application de la présente norme, car un nombre trop important de composés sont retirés au cours du processus de production [37]. Cependant, la méthode peut être utilisée pour identifier le type de produits concerné.
Bien qu'elle ne vise pas directement à identifier le pétrole prélevé dans les eaux souterraines, la végétation, la faune/les tissus, les sols ou les sédiments, ces applications ne sont pas exclues. Cependant, elles nécessitent d'user de prudence, car ces milieux peuvent contenir des composés extractibles susceptibles de s'altérer et/ou de générer des composés supplémentaires par rapport à l'échantillon de la source. Si l'effet du milieu n'est pas pris en compte, cela peut entraîner des « faux négatifs ». Il est donc recommandé d'analyser le ou les échantillons de base du milieu apparemment non contaminé.
Pour analyser des milieux « non pétroliers », des étapes supplémentaires de préparation des échantillons (par exemple nettoyage) sont souvent nécessaires en amont, et le degré auquel le milieu affecte la corrélation obtenue doit être pris en compte. La question de savoir si cette méthode est applicable à un milieu spécifique dépend de la concentration de pétrole par rapport à la « concentration du milieu ». Ainsi, dans les milieux contenant des concentrations élevées de pétrole, une correspondance positive peut malgré tout être obtenue. En revanche, dans des milieux contenant de faibles concentrations de pétrole, on peut obtenir un faux négatif ou une correspondance non conclusive. L'évaluation des effets potentiels du milieu ne fait pas partie du domaine d'application du présent document.
Prepoznavanje razlitij olj - Nafta in sorodni naftni proizvodi - 2. del: Analizne metode in podajanje rezultatov, izhajajočih iz GC-FID in GC-MS nizke ločljivosti
Ta dokument določa metodo za ugotavljanje lastnosti sestave vzorcev olja. Natančneje, opisuje podrobne analizne metode in metode obdelovanja podatkov za prepoznavanje lastnosti vzorcev razlitij in vzpostavlja korelacijo z možnimi viri olja. Tudi če vzorci ali podatki iz možnih virov niso na voljo za primerjavo, lahko opredelitev specifične narave (npr. rafinirana nafta, surova nafta, odpadno olje itd.) razlitega olja pomaga pri omejevanju možnih virov razlitij.
Ta metodologija je omejena na nafto in naftne proizvode z znatnim deležem ogljikovodikovih sestavnih delov z vreliščem nad 150 °C. Primeri vključujejo: surovo olje, kondenzate z višjim vreliščem, dizelska goriva, ostanke goriv iz ladijskih rezervoarjev ali težkih kurilnih olj, maziva ter mešanice vzorcev kaluže in blata, kot tudi destilatna goriva in mešanice. Specifične analizne metode morda niso primerne za olja z nizkim vreliščem (npr. kerozine, goriva za reaktivne letalske motorje ali bencin), vendar se splošni pojmi, opisani v
tej metodologiji, npr. statistična primerjava diagnostičnih razmerij odpornosti na pospešeno staranje, lahko uporabijo pri razlitjih
tovrstnih olj.
Parafinski proizvodi (npr. voski itd.) niso zajeti na področje uporabe te metode, saj je iz njih v postopku proizvodnje [37] odstranjenih preveč spojin, da bi jih lahko pravilno razlikovali.
Vseeno
pa je to metodo mogoče uporabiti za ugotavljanje tipa zadevnega proizvoda.
Čeprav metoda ni neposredno namenjena za ugotavljanje olj, pridobljenih iz podtalnice, vegetacije, prostoživečih živali in rastlin/tkiv, tal ali sedimentov, le-ti niso izključeni. Vseeno pa je potrebna previdnost, saj so spojine, ki jih je mogoče ekstrahirati, lahko prisotne v matricah, ki spremenijo in/ali prispevajo k dodatnim spojinam v primerjavi z izvornim vzorcem. Če se jih ne odkrije, lahko povzročijo
»lažna neujemanja«. Zato je priporočljivo analizirati vzorce ozadja matrice, ki se zdijo brez olja.
Pri analiziranju matric »brez olja« je pogosto pred analizo potreben pripravek dodatnega vzorca (npr. očiščen) in
treba je upoštevati obseg, v katerem matrica vpliva na doseženo korelacijo.
Možnost uporabe metode za določeno matrico je odvisna od koncentracije olja v primerjavi s »koncentracijo matrice«. Pri matricah z visoko koncentracijo olja se lahko še vedno ugotovi pozitivno ujemanje. Pri matricah z nizko koncentracijo olja lahko zaradi učinkov matrice pride do »neujemanja« ali »nedoločnega ujemanja«. Ocena možnih učinkov matrice ni predmet tega dokumenta.
General Information
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Standards Content (Sample)
SLOVENSKI STANDARD
SIST EN 15522-2:2023
01-junij-2023
Nadomešča:
SIST-TP CEN/TR 15522-2:2013
Prepoznavanje razlitij olj - Nafta in sorodni naftni proizvodi - 2. del: Analizne
metode in podajanje rezultatov, izhajajočih iz GC-FID in GC-MS nizke ločljivosti
Oil spill identification - Petroleum and petroleum related products - Part 2: Analytical
method and interpretation of results based on GC-FID and GC-low resolution-MS
analyses
Identifizierung von Ölverschmutzungen - Rohöl und Mineralölerzeugnisse - Teil 2:
Analytische Methodik und Interpretation der Ergebnisse, basierend auf GC-FID- und GC-
MS-Analysen bei niedriger Auflösung
Identification des pollutions pétrolières - Pétrole et produits pétroliers - Partie 2 :
Méthode d'analyse et interprétation des résultats sur la base des analyses par CPG DIF
et CPG-SM faible résolution
Ta slovenski standard je istoveten z: EN 15522-2:2023
ICS:
13.020.40 Onesnaževanje, nadzor nad Pollution, pollution control
onesnaževanjem in and conservation
ohranjanje
13.060.99 Drugi standardi v zvezi s Other standards related to
kakovostjo vode water quality
75.080 Naftni proizvodi na splošno Petroleum products in
general
SIST EN 15522-2:2023 en,fr,de
2003-01.Slovenski inštitut za standardizacijo. Razmnoževanje celote ali delov tega standarda ni dovoljeno.
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SIST EN 15522-2:2023
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SIST EN 15522-2:2023
EN 15522-2
EUROPEAN STANDARD
NORME EUROPÉENNE
March 2023
EUROPÄISCHE NORM
ICS 13.020.40; 75.080 Supersedes CEN/TR 15522-2:2012
English Version
Oil spill identification - Petroleum and petroleum related
products - Part 2: Analytical method and interpretation of
results based on GC-FID and GC-low resolution-MS
analyses
Identification des pollutions pétrolières - Pétrole et Identifizierung von Ölverschmutzungen - Rohöl und
produits pétroliers - Partie 2 : Méthode d'analyse et Mineralölerzeugnisse aus dem Wasser - Teil 2:
interprétation des résultats sur la base des analyses Analytische Methodik und Interpretation der
par CPG DIF et CPG-SM faible résolution Ergebnisse, basierend auf GC-FID- und GC-MS-
Analysen bei niedriger Auflösung
This European Standard was approved by CEN on 25 December 2022.
CEN members are bound to comply with the CEN/CENELEC Internal Regulations which stipulate the conditions for giving this
European Standard the status of a national standard without any alteration. Up-to-date lists and bibliographical references
concerning such national standards may be obtained on application to the CEN-CENELEC Management Centre or to any CEN
member.
This European Standard exists in three official versions (English, French, German). A version in any other language made by
translation under the responsibility of a CEN member into its own language and notified to the CEN-CENELEC Management
Centre has the same status as the official versions.
CEN members are the national standards bodies of Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia,
Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway,
Poland, Portugal, Republic of North Macedonia, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Türkiye and
United Kingdom.
EUROPEAN COMMITTEE FOR STANDARDIZATION
COMITÉ EUROPÉEN DE NORMALISATION
EUROPÄISCHES KOMITEE FÜR NORMUNG
CEN-CENELEC Management Centre: Rue de la Science 23, B-1040 Brussels
© 2023 CEN All rights of exploitation in any form and by any means reserved Ref. No. EN 15522-2:2023 E
worldwide for CEN national Members.
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SIST EN 15522-2:2023
EN 15522-2:2023 (E)
Contents Page
European foreword . 8
Introduction . 10
1 Scope . 12
2 Normative references . 12
3 Terms and definitions . 13
3.1 General. 13
3.2 Sample comparison . 15
3.3 Abbreviations . 15
4 Strategy for the identification of oil spill sources . 16
4.1 General. 16
4.2 Basis for reliable conclusions – Numerical comparisons . 17
5 General lab instructions . 18
5.1 Sampling and sample preparation . 18
5.2 GC-FID and GC-MS analysis . 18
5.3 Conclusions and reporting . 20
6 Sample preparation . 20
6.1 General. 20
6.2 Visual examination and description of samples . 20
6.3 Preparation . 21
6.3.1 Sample storage . 21
6.3.2 Water samples . 21
6.3.3 Oil samples from an Ethylene-tetrafluorethylene (ETFE) net . 22
6.3.4 Thick oil and emulsified oil samples . 22
6.3.5 Tar balls and emulsified lumps . 22
6.3.6 Samples from oiled birds, fish and other animals and vegetation . 23
6.3.7 Sediment . 23
6.4 Sample clean-up . 23
6.4.1 General. 23
6.4.2 Particle removal . 23
6.4.3 Asphaltenes precipitation . 24
6.4.4 Alumina column clean-up of biogenic materials . 24
6.4.5 Silica or Florisil® column clean-up . 25
6.5 Recommended injection concentration . 26
7 Characterization and evaluation of analytical data . 27
7.1 General. 27
7.2 Characterization by GC-FID – Level 1 . 28
7.2.1 General. 28
7.2.2 Evaluation of the influence of weathering on sample comparison . 28
7.2.3 Acyclic isoprenoids ratios – Level 1.2 . 31
7.2.4 Level 1 criteria . 32
7.2.5 Level 1 conclusions . 32
7.3 Characterization by GC-MS – Level 2 . 32
7.3.1 General. 32
2
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SIST EN 15522-2:2023
EN 15522-2:2023 (E)
7.3.2 Visual inspection and overall characterization - Level 2.1 . 32
7.3.3 Treatment of the GC-MS results – Level 2.2 . 33
7.4 Treatment of the results using the MS-PW-plot– Level 2.2 . 33
7.4.1 General . 33
7.4.2 PW-plot calculations . 34
7.4.3 Evaluation of the variability of the analysis and peak integration . 34
7.4.4 Evaluation of weathering . 36
7.5 Treatment of the results using diagnostic ratios – Level 2.2 . 37
7.5.1 General . 37
7.5.2 Diagnostic ratios calculation . 38
7.5.3 Normative diagnostic ratios . 38
7.5.4 Analytical error . 42
7.5.5 Match-criterion for ratios . 43
7.5.6 Criteria for selecting, eliminating and evaluating diagnostic ratios . 44
7.6 Conclusions . 48
8 Reporting . 49
8.1 General . 49
8.2 Internal documentation – technical report . 50
8.3 Identification report – summary report . 51
9 Quality assurance . 51
Annex A (normative) GC-FID analysis . 53
A.1 General . 53
A.2 Analytical standards for GC-FID analyses . 53
A.2.1 N-alkanes . 53
A.2.2 Injection concentration of the standard GC-FID . 54
A.2.3 Storage of frequently used standard solutions . 54
A.3 Suggested instrumental conditions . 54
A.4 Measures to improve and verify the accuracy of the method – GC-FID . 55
A.4.1 Mass discrimination . 55
A.4.2 Column resolution . 56
A.4.3 Linearity . 58
A.4.4 Mid-level concentration . 58
A.4.5 Variance . 59
A.4.6 GC-FID sequence. 59
Annex B (normative) GC-MS analysis . 60
B.1 General . 60
B.2 Analytical standards for GC-MS analyses . 60
B.2.1 General . 60
B.2.2 Crude oil to be used around each sequence . 61
B.2.3 Oil mixture . 61
B.2.4 Analytical standards for PAH homologues . 61
B.2.5 FAMEs . 62
B.2.6 Storage of frequently used standard solutions . 62
3
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SIST EN 15522-2:2023
EN 15522-2:2023 (E)
B.3 Suggested instrumental conditions . 62
B.3.1 GC conditions for the exchange of analytical results . 62
B.3.2 GC-MS conditions for full-scan analysis . 64
B.3.3 MS preparation for selected ion monitoring (SIM) analysis . 65
B.4 Measures to improve and verify the accuracy of the GC-MS method . 66
B.4.1 Relative retention time . 66
B.4.2 Mass discrimination . 66
B.4.3 Peak symmetry and column resolution . 66
B.4.4 Patterns . 67
B.4.5 Linearity . 67
B.4.6 Mid-level concentration . 67
B.4.7 Variance. 68
B.4.8 Sample analysis with GC-MS . 68
Annex C (informative) Precision statement . 69
C.1 General. 69
C.2 Precision of the MS-PW-plot . 69
C.3 Precision of the ratio comparison . 70
C.4 Reproducibility . 71
C.5 The effect of the ratio type on the RSD . 72
®
C.6 Example of a paired ratio calculation in Excel . 73
®
C.7 Calculation of the evaporation line for the MS-PW-plot in Excel . 74
Annex D (normative) Evaluative reporting using match definitions or likelihood ratios . 77
D.1 General. 77
D.2 Match definitions . 77
D.3 Likelihood ratios (LR) . 78
Annex E (normative) List of compounds and compound groups analysed by GC-MS-SIM . 80
E.1 General. 80
E.2 Compounds. 81
E.2.1 General. 81
E.2.2 Compound type . 86
E.3 Normative ratios and informative ratios. . 87
Annex F (informative) Chromatograms and ratios of compounds and compound groups analysed
by GC-MS-SIM . 91
F.1 General. 91
F.2 Alkanes . 91
F.3 Cyclohexanes and polycyclic alkanes . 92
4
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SIST EN 15522-2:2023
EN 15522-2:2023 (E)
F.4 Mono-aromatic and poly-aromatic compounds . 98
F.4.1 Alkyl-benzenes and alkyl-toluenes . 98
F.4.2 PAHs, alkyl-PAHs and S-PAHs . 98
F.4.3 Tri-aromatic steranes . 109
F.5 FAMEs . 110
Annex G (informative) General composition of oils – chemical groups . 114
G.1 General . 114
G.2 Hydrocarbons . 115
G.3 Aliphatic compounds . 115
G.3.1 General . 115
G.3.2 Paraffins . 115
G.3.3 Naphthenes . 115
G.4 Aromatic compounds . 116
G.5 Heteroatomic organic compounds . 116
G.5.1 General . 116
G.5.2 Resins . 116
G.5.3 Asphaltenes . 116
Annex H (informative) Weathering of oils spilled on water and land . 118
H.1 General . 118
H.2 Weathering processes . 118
H.2.1 Weathering of oils spilled on water . 118
H.2.2 Weathering of waterborne oils stranded on land or land based oil spills . 120
H.2.3 Mixing and contamination. 120
H.2.4 Dispersion . 121
H.2.5 In-situ burning . 123
H.3 Evaluation of weathering processes . 124
H.3.1 Evaporation . 124
H.3.2 Dissolution . 127
H.3.3 Photo-oxidation . 129
H.3.4 Biodegradation . 135
H.3.5 Wax redistribution . 138
H.3.6 Mixing . 144
H.3.7 Contamination . 146
H.3.8 In-situ burning . 146
Annex I (informative) Characteristic features of different oil types in oil spill identification . 149
I.1 General . 149
5
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SIST EN 15522-2:2023
EN 15522-2:2023 (E)
I.2 Crude oil . 149
I.2.1 General. 149
I.2.2 Analysis . 150
I.3 Light fuel oil (gas oil, diesel, fuel No 2, biofuels, GTL) . 156
I.3.1 General. 156
I.3.2 Analysis, GC screening . 157
I.3.3 GC-MS analysis . 159
I.3.4 Biofuels .
...
SLOVENSKI STANDARD
oSIST prEN 15522-2:2020
01-december-2020
Prepoznavanje razlitij olj - Nafta in naftni proizvodi v vodi - 2. del: Analizne metode
in podajanje rezultatov, izhajajočih iz GC-FID in GC-MS nizke ločljivosti
Oil spill identification - Waterborne petroleum and petroleum products - Part 2: Analytical
methodology and interpretation of results based on GC-FID and GC-MS low resolution
analyses
Identifizierung von Ölverschmutzungen - Rohöl und Mineralölerzeugnisse aus dem
Wasser - Teil 2: Analytische Methodik und Interpretation der Ergebnisse, basierend auf
GC-FID- und GC-MS-Analysen bei niedriger Auflösung
Ta slovenski standard je istoveten z: prEN 15522-2
ICS:
13.020.40 Onesnaževanje, nadzor nad Pollution, pollution control
onesnaževanjem in and conservation
ohranjanje
13.060.99 Drugi standardi v zvezi s Other standards related to
kakovostjo vode water quality
75.080 Naftni proizvodi na splošno Petroleum products in
general
oSIST prEN 15522-2:2020 en
2003-01.Slovenski inštitut za standardizacijo. Razmnoževanje celote ali delov tega standarda ni dovoljeno.
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oSIST prEN 15522-2:2020
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oSIST prEN 15522-2:2020
DRAFT
EUROPEAN STANDARD
prEN 15522-2
NORME EUROPÉENNE
EUROPÄISCHE NORM
December 2020
ICS 13.020.40; 75.080 Will supersede CEN/TR 15522-2:2012
English Version
Oil spill identification - Waterborne petroleum and
petroleum products - Part 2: Analytical methodology and
interpretation of results based on GC-FID and GC-MS low
resolution analyses
Identifizierung von Ölverschmutzungen - Rohöl und
Mineralölerzeugnisse aus dem Wasser - Teil 2:
Analytische Methodik und Interpretation der
Ergebnisse, basierend auf GC-FID- und GC-MS-
Analysen bei niedriger Auflösung
This draft European Standard is submitted to CEN members for enquiry. It has been drawn up by the Technical Committee
CEN/TC 19.
If this draft becomes a European Standard, CEN members are bound to comply with the CEN/CENELEC Internal Regulations
which stipulate the conditions for giving this European Standard the status of a national standard without any alteration.
This draft European Standard was established by CEN in three official versions (English, French, German). A version in any other
language made by translation under the responsibility of a CEN member into its own language and notified to the CEN-CENELEC
Management Centre has the same status as the official versions.
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Warning : This document is not a European Standard. It is distributed for review and comments. It is subject to change without
notice and shall not be referred to as a European Standard.
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© 2020 CEN All rights of exploitation in any form and by any means reserved Ref. No. prEN 15522-2:2020 E
worldwide for CEN national Members.
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Contents Page
European foreword . 3
Introduction . 4
1 Scope . 6
2 Normative references . 6
3 Terms and definitions . 7
4 Strategy for the identification of oil spill sources . 10
5 General lab instructions . 12
6 Sample preparation . 14
7 Characterisation and evaluation of analytical data . 20
8 Reporting . 41
9 Quality assurance . 43
Annex A (normative) T GC-FID analysis . 44
Annex B (normative) GC-MS analysis . 49
Annex C (informative) Precision statement . 57
Annex D (normative) Evaluative reporting using match definitions or likelihood ratios . 65
Annex E (normative) List of compounds and compound groups analysed by GC-MS-SIM . 68
Annex F (informative) Chromatograms and ratios of compounds and compound groups
analysed by GC-MS-SIM . 79
Annex G (informative) General composition of oils – chemical groups . 99
Annex H (informative) Weathering of oils spilled on water and land . 103
Annex I (informative) Characteristic Features of Different Oil Types in Oil Spill Identification
. 131
Annex J (informative) Example of external documentation – identification report of an oil
spill identification. 165
Annex K (informative) Example of internal documentation – technical report of an oil spill
case . 168
Bibliography . 189
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European foreword
This document (prEN 15522-2:2020) has been prepared by Technical Committee CEN/TC 19 “Gaseous
and liquid fuels, lubricants and related products of petroleum, synthetic and biological origin”, the
secretariat of which is held by NEN.
This document is currently submitted to the CEN Enquiry.
This document will supersede CEN/TR 15522-2:2012.
In comparison with the previous edition, the following technical modifications have been made:
— adding compounds to be analysed in order to include light products in the diesel range;
— adding more information about biodegradation;
— adding a Reporting and a Quality assurance chapter;
— adding Annex C with precision data;
— adding Annex D with likelihood grade conclusions;
— introduction of characterization of FAME in Annex I;
— serious revision of Annexes H, I, J and K, adding new pictures and chromatograms.
This document has been prepared under a mandate given to CEN by the European Commission and the
European Free Trade Association, and supports essential requirements of EU Directive(s).
A list of all parts in a series can be found on the CEN website.
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Introduction
This document describes a forensic method for characterising and identifying the source of oils spills in
the environment as a resulting from accidents or intentional discharges. The method can be used in
support of the legal process as evidence for prosecuting offenders. This method is based on the
experience gained with its former publications over the years.
EN 15522 is composed of two parts that are described by the following CEN documents:
EN 15522-1 – Sampling, describing good sampling practice, detailing sampling equipment, sampling
techniques and the handling of oil samples prior to their arrival at the forensic laboratory;
EN 15522-2 – Analytical Method, which covers the general concepts and laboratory procedures of oil
spill identification, analytical techniques, data processing, data treatment, interpretation/evaluation
and reporting of results.
Oil spill source identification is a complex methodology due to the large variation in sample types and oil
spill situations that can be encountered. Part 1 is a compilation of instructions and experiences from
experts all over the world which will guide the user in sampling, storing and delivering oil samples for
laboratory analysis. Part 2 will guide the reader through the analytical process. It prescribes how to
prepare and analyse oil samples using GC-FID and GC-low-resolution mass spectrometry (GC-MS). Any
chemical difference found between samples is only relevant if this difference is larger than the variability
of the method itself. Good analytical performance and strict quality assurance are therefore essential. In
the Annexes of Part 2, relevant information concerning different types of oil and oil comparison
techniques are presented.
In the usual test method standard, instructions are given how to perform an “analytical” procedure. Oil
spill identification however comprises of an analytical part and an assessment part. The sample
preparation part is described in Chapter 5 and the analytical parts in Annex A for the GC-FID and in
Annexes B, E and F for the GC-MS. The other parts of the document describe tools and instructions how
to assess the analytical data and how to come to a final conclusion. Because each oil case is different in
situation, size, products and weathering, the evaluation part of the method is described as a toolbox. The
Annexes J and K evaluate an oil case and show how the tools may be applied. More examples of specific
oil cases are available as summary reports of the annual round robins organised by Bonn-OSINet [11]
and in literature.
The main purpose of the methodology described in this part of the document is to defensibly identify the
source of an oil spill in the environment by comparing the chemical compositions of samples from spills
with those of suspected sources. The underlying basis for this method is the widely variable nature of oils
with respect to their specific chemical compositions, which allows oils from different sources to be readily
distinguished using the appropriate analytical methods. The method relies upon detailed chemical
characterisation and statistical comparison between samples' (i.e. a spilled oil and a suspected source)
diagnostic features in order to determine whether they “match”. To minimise the danger of “false positive
matches”, good laboratory practices must be maintained. Even so, a “positive match” between a spilled
oil and a suspected source may not be used alone to identify the PRP (potential responsible party), but
this result is often a critical piece of evidence in proving a case within the legal process.
However, in some oil spill identification cases, both the oil spill and suspected source(s) may not
necessarily be unique or homogeneous in nature, e.g. due to the changing/variable nature of oil in the
bilge tanks or due to mixing of oils spilled from several sources in a case of a larger incident. The risk
therefore exists that the chemical composition of the available source samples may not match with that
of the available spill samples. In such cases, oil spill identification methodologies in general will have
limitations and may not necessarily lead to unequivocal conclusions. In other words, the success of this
document in defensibly identifying a spilled oil’s source depends upon the samples available for chemical
study. To minimise the danger for “false positive” and especially “false non-matches”, good sampling
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practice is essential, and particularly the need to obtain appropriate suspect source samples, is crucial
(as described in Part 1: Sampling).
When oil from suspected sources is not available, this document can still be used to characterise the
spilled oil in order to determine the spilled oil type and any specific characteristics. The characterisation
of a spilled oil sample can still be useful for several reasons:
If the source of an oil pollution event is unknown, the investigating authorities should be advised on
the type of oil in order to aid in the identification of a possible source. For example, in the case of a
“mystery” spill, the mere differentiation between pure, unused refined petroleum products (e.g.
diesel fuel versus heavy fuel oil) or versus crude oil or waste oil (e.g. bilge residues, sludge, slops) can
provide potentially valuable information as the possible source(s) for the spill. In such instances, the
type of oil spilled should be identified rapidly, because the chances of identifying and collecting
suspected source oils generally decrease with time;
In some court trials, the differentiation between pure refined products and waste oil may be very
important because it allows conclusions to be drawn regarding the cause of an oil discharge, e.g.
technical failure, accidental discharge, intentional discharge;
In some countries, photos (e.g. taken from an airplane) from a plume behind a ship, combined with
the evidence that the plume contains mineral oil, is enough for a condemnation;
Finally, characterisation of the spilled oil provides a baseline against which future impacts to the
affected area/environment might be compared.
This document is the result of advancements in the field of oil spill identification [e.g. 22, 36, 66, 67 and
78] that have been made since the Nordtest Method [54] was first introduced in 1991. These have
included:
advancements in analytical methodologies;
improved understanding of the specific chemical compositions and diagnostic features of oils;
improved understanding of how an oil’s composition changes in the environment (e.g. due to
weathering);
improvements in the statistical and numerical analysis of chemical data.
These advancements have been made by researchers around the world and documented in a wide range
of peer-reviewed literature. In addition, numerous Proficiency Testing Programs (PTPs; RR-tests) have
been conducted to evaluate and improve upon the methodology. Since 2004, in the framework of Bonn-
OSINET (Bonn-Agreement Oil Spill Identification Network), annual interlaboratory studies have been
organised jointly by RWS-lab (Rijkswaterstaat - Laboratory in the Netherlands) and BSH (Bundesamt für
Seeschifffahrt und Hydrographie in Germany) in which laboratories from around the world participate.
The studies have covered oil spill cases dealing with light fuel oil distillates (diesel oils), bilge water
samples (a mixture of water, gas oils and lubricating oil), crude oils and heavy fuel oils. Findings from
these studies have been discussed at annual meetings by the participating analysts and have been taken
into account for refining the suggested methodology described herein. The final reports of the
interlaboratory studies can be downloaded for free from the Bonn-OSINET part of the Bonn-Agreement
website [11].
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1 Scope
This document describes a method to firstly identify the specific nature of oils spilled in the environment
and secondly compare the chemical composition of samples from spilled oil with those of suspected
sources. Specifically, the document describes the detailed analytical methods and data processing
specifications for identifying the specific nature of oil spills and establishing their correlation to
suspected sources. Even when samples or data from suspected sources are not available for comparison,
establishing the specific nature (e.g. refined petroleum, crude oil, waste oil, etc.) of the spilled oil can still
help to constrain the possible source(s) of the spilled oil.
This methodology is restricted to petroleum related products containing a significant proportion of
hydrocarbon-components with a boiling point above 150 °C. Examples are: crude oils, higher boiling
condensates, diesel oils, residual bunker or heavy fuel oils, lubricants, and mixtures of bilge and sludge
samples, as well as distillate fuels and blends. While the specific analytical methods may not be
appropriate for lower boiling oils (e.g. kerosenes, jet fuels, or gasoline), the general concepts described
in this methodology, i.e. statistical comparison of weathering-resistant diagnostic ratios, can have
applicability in spills involving this kind of oils.
Paraffin as petroleum product (for candles, etc.) is outside the scope of this method, because too many
compounds have been removed during the production process [37]. Still the method can be used to
analyse the type of product involved.
This method is not directly intended for identifying oil spills in matrixes like groundwater, vegetation,
wildlife/tissues, soils, or sediments, and although its application in these matrices is not precluded, it
requires caution. The reason for caution is that the extractable compounds in these matrices may alter
and/or contribute additional compounds compared to the source sample, which if left unrecognised, can
lead to “false non-matches”. It is therefore advisable to analyse background sample(s) from seemingly
uncontaminated matrix. Including these “non-oil” matrices in this oil spill identification method can
require additional sample preparation (e.g. clean-up) in the laboratory prior to analysis and
consideration of the extent to which the matrix can affect the correlation achieved. Evaluating the
possible effects in these matrices is beyond the scope of this document. Whether the method can be used
for this kind of matrices may depend on the oil concentration compared to the “matrix concentration” of
the samples. In matrices containing relatively high concentration of oil, a positive match can still be
concluded. In matrices containing relatively low concentration of spilled oil, a false non-match or an
inconclusive match could be achieved due to matrix effects.
2 Normative references
The following documents are referred to in the text in such a way that some or all of their content
constitutes requirements of this document. For dated references, only the edition cited applies. For
undated references, the latest edition of the referenced document (including any amendments) applies.
EN 15522-1, Oil spill identification –petroleum and petroleum products – Part 1: Sampling
ISO 1998-1:1998, Petroleum industry — Terminology — Part 1: Raw materials and products
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3 Terms and definitions
For the purposes of this document, the terms and definitions given in ISO 1998-1:1998 apply.
ISO and IEC maintain terminological databases for use in standardization at the following addresses:
IEC Electropedia: available at http://www.electropedia.org/
ISO Online browsing platform: available at http://www.iso.org/obp
3.1 General
3.1.1
chain of custody
practice of ensuring security of the sample so that no one has an opportunity to tamper with or otherwise
alter the sample or the results
Note 1 to entry: It includes chronological documentation that records the sequence of sample handling including
sampling, sealing, storage, transfer, analysis and disposal to ensure that only documented sample handlers have
direct access to the samples.
3.1.2
mixing
mixing of sources containing or consisting of petroleum (products) before, during or after the spillage
Note 1 to entry: Can result in a heterogeneous spill composition (see 3.1.4).
3.1.3
contamination
changes in oil composition which take place during/after the spillage in either sample by addition of non-
petroleum compounds from biogenic (e.g. fat from feathers) or anthropogenic sources (e.g. compounds
from plastics)
Note 1 to entry: mixing and contamination are used to differentiate between the addition of petroleum products
(mixing) and non-petroleum products ( contamination)
3.1.4
sample heterogeneity
non-homogenous character of samples caused for example by variable degrees of stirring within a vessel,
tank, pipeline or oil slick originally containing oil(product)s with different compositions
3.1.5
duplicate
two times the injection of the same sample extract within a sequence or within an oil case
3.1.6
replicate
two aliquots of a sample or two samples taken from exact the same sample location and at the same time
Note 1 to entry: Two samples taken from different locations of a larger spill are no replicates, but different samples.
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3.1.7
waterborne oil
petroleum or petroleum product borne by water or available in the water column from marine, estuarial
and aquatic environments
Note 1 to entry: Aquatic environments include lakes and rivers, but exclude groundwater.
3.1.8
weathering
processes that cause changes in oil composition of source and spill samples which can take place after
the spillage, including natural processes like evaporation, dissolution, emulsification, photo oxidation,
biodegradation, wax redistribution and also processes caused by oil response techniques like chemical
dispersion and burning
Note 1 to entry: Sometimes the source is more weathered that the spill. For example when a slop tank contains a
mixture of oil and water and is leaking oil into much colder water, the oil in the slop tank can be biodegraded to a
higher degree than the spilled oil.
3.1.9
bilge
water which may be contaminated by oil resulting from things such as leakage or maintenance work in
machinery spaces. Any liquid entering the bilge system including bilge wells, bilge piping, tank top or
bilge holding tanks is considered oily bilge water
[48]
[SOURCE: MEPC. 187(59) ]
3.1.10
slop (tank)
mixture of water and oil residues from cargo tanks in oil tankers that may contain crude oil, heavy fuel
oil/water emulsions, wax, sediments and other tank residues
3.1.11
sludge
the residual waste oil products generated during the normal operation of a ship such as those from the
purification of fuel or lubricating oil from main or auxiliary machinery, separated waste oil from oil
filtering equipment, waste oil collected in drip trays, and waste hydraulic and lubricating oils
[48]
[SOURCE: MEPC. 187(59) ]
3.1.12
tank washings
tank washing water containing cargo tank residues including oil, wax, sediment and other foreign matter
such as tank cleaning chemicals
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3.2 Sample comparison
3.2.1
PW-plot
graph based on GC-FID or GC-MS data of two samples normalised to a non-weathered compound or group
of compounds and sorted on retention time
Note 1 to entry: The name “PW-plot” is originally a reference to Per Wrang, who introduced the plot in the Nordtest
method [54]. In this document, the name PW-plot is used as an abbreviation of a “Percentage Weathering” plot.
3.2.2
diagnostic ration (DR)
ratio between the peak heights of single compounds or peak areas of compound groups of the same
sample selected by their diversity in chemical composition in petroleum and petroleum products, their
discriminative power and on their known behaviour in weathering processes
3.2.3
likelihood ratio (LR)
ratio of two probabilities
Note 1 to entry: The numerator is the probability of obtaining the evidence when the prosecutors scenario is true.
The dominator is the probability of obtaining the evidence when the defence scenario is true (see Annex D).
3.2.4
critical difference (CD)
14 % of the mean value of a ratio for two different samples
Note 1 to entry: The fixed value of 14 % is based on the maximum allowable RSD of 5 % for the diagnostic ratios
(see 7.5.5).
3.2.5
significant difference
difference between a property or analytical result of two samples that cannot be explained as caused by
weathering, mixing or contamination
Note 1 to entry: Your skin turning brown or red after a while in the sun, doesn’t make you a different person and is
therefore not a significant difference.
3.3 Abbreviations
CD Critical Difference
DR Diagnostic Ratio
FAMEs Fatty Acid Methyl Esters
FID Flame Ionisation Detection
GC Gas Chromatography
HFO Heavy Fuel Oil
HVO Hydrotreated Vegetable Oil
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LCO Light Cycle Oil
LFO Light Fuel Oil
MS Mass Spectrometry
NR Normative Ratio
PTP Proficiency Testing Program
RR Round Robin Test;
RSD Relative Standard Deviation
ULSFO Ultra-Low Sulfur Fuel Oil
VGO Vacuum Gas Oil
4 Strategy for the identification of oil spill sources
4.1 Introduction
Identification of spilled oils in the context of this document implies the comparison of the total chemical
composition of the spilled oil with that of candidate source samples.
NOTE See Annex G for further explanation on composition of oils.
The likeness of a source and spill sample should be tested by analysing the samples with GC-FID and/or
GC-low resolution-MS and by comparing their detailed chemical compositions using a suite of generic
and diagnostic petroleum components. If no or only insignificant differences (i.e. differences being
smaller than the analytical variability limits defined) are observed, a “positive match” should be
concluded. On the other hand, if true differences (i.e. differences not related to changes in the chemical
composition introduced after the spill, e.g. from weathering, contamination or heterogeneity) that are
larger than the variance of the analysis are observed within these diagnostic compounds, it should be
concluded that the samples are a “non-match”. Some investigations can result in conclusions intermediate
to “positive match” and “non-match”, such as “probable match” or “inconclusive” (see D.2). All these
classifications are used in the descriptions and examples of this document.
IMPORTANT - It is practically and technically impossible to measure and compare every chemical in
spilled oil and its prospective source in order to conclude a positive match exists. Therefore, in practice,
two samples are considered a positive match if no statistically significant differences in diagnostic metrics
determined by GC-FID and GC-MS analysis are present that cannot be explained by weathering, mixing
or heterogeneity. This approach, i.e. looking for differences in diagnostic features instead of similarity
among every possible feature, is conceptually more logical and more practically and technically
achievable. As such, only distinct differences between samples can be proved. Therefore, when no
statistically significant differences between samples are observed, a “positive match to a high degree of
scientific certainty” should be concluded.
4.2 Basis for reliable conclusions – Numerical comparisons
The usual practice is to analyse samples qualitatively and then compare the chromatograms and ion
chromatograms visually, such as described in the original Nordtest Method (1991) [54] and the ASTM
methods [3, 4]. The outcome of such qualitative comparisons is subjective and depends on the experience
or bias of the analyst. Because of the high complexity of oils, and the many details that can be compared
in the often very complex chromatograms, qualitative comparisons should always form an integral part
of oil sample comparisons. However, in order to make conclusions more objective, reproducible, and
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therefore, more reliable, this document also requires the use and comparison of quantitative metrics; i.e.
specific peaks or groups of peaks have to be measured and peak ratios have to be calculated and
compared.
These integration values are used in two different ways:
a) Measurements of single compounds normalised to hopane – or, if hopane is not sufficiently
present, to phytane – or, if phytane is also not sufficiently present, to bicyc
...
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