Literature Review of the Indicative Ballast Water Analysis ... · Trafi Publications10-2017 Julkaisun nimi Literature Review of the Indicative Ballast Water Analysis Methods Tekijät

Literature Review of the Indicative Ballast Water Analysis Methods

Okko Outinen & Maiju Lehtiniemi

Trafi Publications Trafin julkaisuja

Trafis publikationer 10/2017

Trafi Publications 10-2017

Title of publication Literature Review of the Indicative Ballast Water Analysis Methods

Author(s) Okko Outinen & Maiju Lehtiniemi, Finnish Environment Institute

Commissioned by, date Finnish Transport Safety Agency, 11 October 2016

Publication series and number

Trafi Research Reports 10/2017

ISSN (online) 2342-0294 ISBN (online) 978-952-311-202-5

Keywords Ballast Water Management Convention, Indicative analysis, Sampling

Contact person Ville-Veikko Intovuori

Language of the report English

Abstract The International Convention for the Control and Management of Ships' Ballast Water and Sediments was adopted by the International Maritime Organization (IMO) in 2004. The Convention will enter into force internationally in September 2017.

The general obligations of the Ballast Water Management Convention include control measures that the Parties to the Convention are required to take to ensure that the ships entering their ports are in compliance with this Convention. In Finland, Trafi is the authority responsible for port state control inspections of ships. The inspection is primarily conducted as a documentary check; however, the authority may always carry out ballast water sampling to verify that the ship is in compliance with the Convention.

The sampling consists of an indicative analysis and a detailed analysis. Indicative anal-ysis refers to indicative sampling of the ballast water pumped out of a vessel. The re-sults indicate whether the ship meets the performance standard laid down in the Bal-last Water Management Convention. If the ship fails to meet the standard, a detailed analysis must be performed in a laboratory. Based on the laboratory results, it is de-cided whether further measures will be taken. The purpose of the study commissioned by Trafi was to find an indicative analysis method for the use of Trafi's port state con-trol inspectors. The Finnish Environment Institute (SYKE) conducted the study for Trafi.

Based on the study, three different methods were found to be best suited for the con-ditions in Finland and the Baltic Sea. The recommended methods are PAM (Pulse am-plitude-modulation fluorometry), ATP (Adenosine triphosphate) and FRR (Fast repeti-tion rate fluorometry). The most important assessment criteria were the reliability and user-friendliness of the method, the time required for obtaining the results as well as the procurement and operating costs of the method.

Date 21 April 2017


Julkaisun nimi Literature Review of the Indicative Ballast Water Analysis Methods

Tekijät Okko Outinen & Maiju Lehtiniemi, Suomen ympäristökeskus

Toimeksiantaja ja asettamispäivämäärä Liikenteen turvallisuusvirasto, 11.10.2016

Julkaisusarjan nimi ja numero

Trafin tutkimuksia 10/2017

ISSN (verkkojulkaisu) 2342-0294 ISBN (verkkojulkaisu) 978-952-311-202-5

Asiasanat Painolastivesiyleissopimus, indikatiivinen analyysi, näytteenotto

Yhteyshenkilö Ville-Veikko Intovuori

Raportin kieli Englanti

Tiivistelmä

Alusten painolastivesien ja sedimenttien valvontaa ja käsittelyä koskeva kansainväli-nen yleissopimus hyväksyttiin Kansainvälisessä merenkulkujärjestössä (IMO) vuonna 2004 ja se on tulossa kansainvälisesti voimaan syyskuussa 2017.

Painolastivesiyleissopimuksen keskeisiin velvoitteisiin kuuluu alusten vaatimustenmu-kaisuuden valvonta, jota jäsenvaltioiden tulee tehdä aluksille niiden vieraillessa jäsen-valtion satamissa. Suomessa vastuuviranomainen alusten satamavaltiotarkastuksissa on Trafi. Tarkastus tehdään ensisijaisesti asiakirjatarkastuksena, mutta viranomaisella on aina mahdollisuus suorittaa painolastivesinäytteenotto vaatimustenmukaisuuden to-dentamiseksi.

Näytteenotto koostuu indikatiivisesta analyysista ja yksityiskohtaisesta analyysista. In-dikatiivisella analyysillä tarkoitetaan suuntaa antavaa näytteenottoa aluksen ulospum-pattavasta painolastivedestä. Tuloksista nähdään täyttääkö alus painolastivesiyleisso-pimuksen mukaisen suorituskykystandardin. Mikäli näin ei ole, tulee suorittaa yksityis-kohtainen analyysi, joka tehdään laboratoriossa. Laboratoriotulosten perusteella pää-tetään ryhdytäänkö mahdollisiin jatkotoimenpiteisiin. Trafin teettämän selvityksen tar-koituksena oli löytää indikatiivinen analyysimenetelmä Trafin satamavaltiotarkastajien käyttöön. Selvityksen Trafille teki Suomen ympäristökeskus (SYKE).

Selvityksen perusteella löytyi kolme erilaista menetelmää, jotka parhaiten soveltuvat Suomen ja Itämeren olosuhteisiin. Suositellut menetelmät olivat PAM- (Pulse ampli-tude-modulation fluorometry), ATP- (Adenosine triphosphate) ja FRR (Fast repetition rate fluorometry) -menetelmät. Tärkeimmät arviointikriteerit olivat menetelmän luotet-tavuus, helppokäyttöisyys, ajallinen kesto tulosten saamiseksi sekä menetelmän han-kinta- ja käyttökustannukset.

Julkaisun päivämäärä 21.4.2017


Publikation Literature Review of the Indicative Ballast Water Analysis Methods

Författare

Okko Outinen & Maiju Lehtiniemi, Finlands miljöcentral

Tillsatt av och datum

Trafiksäkerhetsverket

Publikationsseriens namn och nummer Trafis undersökningsrapporter 10/2017

ISSN (webbpublikation) 2342-0294 ISBN (webbpublikation) 978-952-311-202-5

Ämnesord Barlastvattenkonvention, indikativ analys, provtagning

Kontaktperson Ville-Veikko Intovuori

Raportens språk finska

Sammandrag Internationella sjöfartsorganisationen (IMO) antog 2004 den internationella konvent-ionen om kontroll och hantering av fartygs barlastvatten och sediment. Konventionen kommer att träda i kraft internationellt i september 2017.

Till de viktigaste skyldigheterna enligt barlastvattenkonventionen hör kontroll av att fartygen uppfyller kraven, så medlemsstaterna ska kontrollera de fartyg som besöker deras hamnar. I Finland är Trafi ansvarig myndighet för dessa hamnstatskontroller. Kontrollen görs i första hand som en granskning av dokument, men myndigheten har alltid möjlighet att ta prov av barlastvattnet för att konstatera att kraven uppfylls.

Provtagningen består av en indikativ analys och en detaljerad analys. Med indikativ analys avses en riktgivande provtagning av det barlastvatten som pumpas ut ur farty-get. Resultatet visar huruvida fartyget uppfyller de prestandanormer som föreskrivs i barlastvattenkonventionen. Om så inte är fallet, ska en detaljerad analys utföras i la-boratorium. Laboratorieresultaten avgör huruvida man vidtar eventuella vidare åtgär-der. Syftet med utredningen som Trafi lät göra var att hitta en indikativ analysmetod för Trafis hamnstatsinspektörer. Utredningen utfördes för Trafis räkning av Finlands miljöcentral.

Utredningen fann tre olika metoder som lämpar sig bäst för förhållandena i Finland och Östersjön. De rekommenderade metoderna är PAM (Pulse amplitude-modulation fluorometry), ATP (Adenosine triphosphate) och FRR (Fast repetition rate fluorometry). De viktigaste bedömningskriterierna var att metoden är tillförlitlig, lätt att använda, hur lång tid det tar innan resultaten är färdiga och vilka upphandlings- och driftskost-naderna är.

Publikationsdatum 21.4.2017


FOREWORD The Finnish Transport Safety Agency (Trafi) has commissioned a study on the indicative ballast water analysis methods. The results of the study have been compiled in this report. The study was conducted as a literature review and was based on previous international studies on indicative analysis methods, which were specified with expert interviews. The aim of the study was to find the method best suited for the conditions in Finland and the Baltic Sea. This is the first study of its kind conducted in Finland.

The study was carried out by Assisting Researcher Okko Outinen and Senior Researcher Maiju Lehtiniemi of the Finnish Environment Institute (SYKE). The steering group of the study included Special Adviser Ville-Veikko Intovuori, Chief Adviser Anita Mäkinen and Head of Unit Mirja Ikonen of Trafi, and Okko Outinen and Maiju Lehtiniemi of SYKE.

Helsinki, 10 April 2017

Ville-Veikko Intovuori Special Adviser Finnish Transport Safety Agency (Trafi)


ALKUSANAT

Liikenteen turvallisuusvirasto Trafi teetti tutkimuksen alusten painolastivesien indikatiivi-sista analyysimenetelmistä, jonka tulokset on koottu tähän raporttiin. Tutkimus tehtiin kir-jallisuuskatsauksena, perustuen aikaisempiin indikatiivisista analyysimenetelmistä tehtyihin kansainvälisiin tutkimuksiin ja joita tarkennettiin asiantuntijahaastatteluilla. Tutkimuksen tarkoitus oli löytää Suomen ja Itämeren olosuhteisiin parhaiten soveltuva menetelmä. Ai-kaisemmin vastaavaa tutkimusta Suomessa ei ollut tehty.

Tutkimuksen tekivät Trafille Suomen ympäristökeskuksesta (SYKE) apulaistutkija Okko Outinen ja erikoistutkija Maiju Lehtiniemi. Tutkimuksen ohjausryhmään osallistuivat Tra-fista erityisasiantuntija Ville-Veikko Intovuori, johtava asiantuntija Anita Mäkinen ja yksi-könpäällikkö Mirja Ikonen sekä SYKE:sta Okko Outinen ja Maiju Lehtiniemi.

Helsingissä, 10. huhtikuuta 2017

Ville-Veikko Intovuori erityisasiantuntija Liikenteen turvallisuusvirasto (Trafi)


FÖRORD Trafiksärkerhetsverket (Trafi) har låtit göra en undersökning om indikativa analysmetoder av fartygs barlastvatten. Resultaten av undersökningen har sammanställts i denna rapport. Undersökningen gjordes i form av en litteraturgenomgång som grundade sig på tidigare in-ternationella undersökningar av indikativa analysmetoder och som kompletterades med ex-pertintervjuer. Syftet med undersökningen var att hitta den metod som är mest lämplig med tanke på förhållandena i Finland och Östersjön. Ingen motsvarande undersökning hade tidi-gare gjorts i Finland.

Undersökningen utfördes för Trafis räkning av forskarassistent Okko Outinen och special-forskare Maiju Lehtiniemi vid Finlands miljöcentral. I undersökningens styrgrupp deltog specialsakkunnig Ville-Veikko Intovuori, ledande sakkunnig Anita Mäkinen och enhetschef Mirja Ikonen från Trafi samt Okko Outinen och Maiju Lehtiniemi från Finlands miljöcen-tral.

Helsingfors den 10 april 2017

Ville-Veikko Intovuori specialsakkunnig Trafiksäkerhetsverket (Trafi)


Index

1. Introduction ................................................................................... 1 1.1 Testing compliance for Regulations D1 and D2.............................. 2 1.2 In-tank and in-line sampling for indicative analysis ....................... 4 1.3 Recommended principles for the D2 standard compliance testing .... 5

2. Materials and methods ................................................................... 7 3. Results............................................................................................ 8

3.1 Adenosine triphosphate (ATP) method ......................................... 8 3.1.1 Sampling approach ......................................................... 9 3.1.2 Feasibility .................................................................... 10

3.2 Fluorescein diacetate (FDA) method .......................................... 11 3.2.1 Sampling approach ....................................................... 11 3.2.2 Feasibility .................................................................... 13

3.3 Pulse amplitude-modulation (PAM) method ................................ 14 3.3.1 Sampling approach ....................................................... 15 3.3.2 Feasibility .................................................................... 16

3.4 Microscopy methods ................................................................ 17 3.4.1 Sampling approach and feasibility ................................... 17

3.5 Flow cytometry method ........................................................... 18 3.5.1 Sampling approach ....................................................... 18 3.5.2 Feasibility .................................................................... 20

3.6 Bacterial identification methods ................................................ 21 3.6.1 Detection of bacterial enzymes ....................................... 21 3.6.2 Real-time Polymerase chain reaction (PCR) ...................... 22 3.6.3 Colorimetric bacteria detection ....................................... 22 3.6.4 Other methods for detection of bacteria ........................... 23

3.7 Other potential indicative methods ............................................ 24 3.7.1 Microfluidic lab-on-chip biosensor .................................... 24 3.7.2 Fast repetition rate (FRR) fluorometry ............................. 26 3.7.3 Serial dilution culture-most probable number (SDC-MPN) method

.................................................................................. 27 3.7.4 Laser transmission spectroscopy (LTS) ............................ 28 3.7.5. Analysis techniques for larger zooplankton ....................... 28

3.8 Results overview ..................................................................... 29 4. Discussion .................................................................................... 31

4.1 General issues and uncertainties regarding the sampling methods 31 4.1.1 Minimally reliable methods ............................................. 32 4.1.2 Moderately reliable methods ........................................... 32 4.1.3 Highly reliable methods ................................................. 35

4.2 Accuracy ................................................................................ 35 4.3 Manufacturers ........................................................................ 37 4.4 Study limitations ..................................................................... 39

5. Conclusions and recommendations ............................................... 39 6. Acknowledgements ....................................................................... 40 7. Bibliobraphy ................................................................................. 41 Appendices ........................................................................................ 49

Appendix A: SGS ATP sampling approach ............................................ 49 Appendix B: FDA Pulse counting device ............................................... 52 Appendix C: Indicative methods described in the present study .............. 53 Appendix D: The current target species list for the HELCOM area ............ 54


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1. Introduction

International Maritime Organization (IMO) adopted the International Convention for the Control and Management of Ships Ballast Water and Sediments (BWM Conven-tion) in 2004, to prevent the introductions of non-indigenous species (NIS) with two separate BWM requirements for ships, ballast water exchange standard (Regulation D1) and ballast water performance standard (Regulation D2) (Table 1) (IMO, 2009, David et al., 2013). The transfer of NIS through ship’s ballast water and sediment discharges is widely acknowledged as one of the most recent waterborne threats to natural environment, human health and economy worldwide (Cordell et al., 2009, David and Gollasch, 2015). Regulation D1 requires ships to exchange a minimum of 95% of the ballast water volume at the open sea. Regulation D2 in turn, requires ships to conduct ballast water treatment in order to decrease the amount of dis-charged viable organisms below the agreed limits (IMO, 2009, Albert et al., 2013). The BWM Convention will enter into force 12 months after the date on which at least 30 States, representing at least 35 % of the world’s merchant shipping tonnage have ratified the Convention (IMO, 2009, Albert et al., 2013). Finland ratified the convention on the 8th of September 2016, sufficiently pushing the tonnage percent-age over the required limit. Therefore, as of 8th of September, 2016, 52 States have ratified the BWM Convention representing 35.14 % of the world’s merchant ship-ping tonnage and the BWM Convention will enter into force the 8th of September 2017 (IMO, 2016).

Table 1. Requirements for ballast water exchange and performance standards (IMO, 2009).

Regulation D1 Regulation D2

Sampling efficiency of at least 95% vol-umetric exchange of ballast water. The sampling standard is met, when the vol-

ume of each ballast water tank is pumped through at least 3 times.

Ships conducting ballast water treatment shall discharge:

1. less than 10 viable organisms per cubic me-tre 50 micrometres ( m) in minimum di-

mension,

2. Less than 10 viable organisms per millilitre(ml) < 50 m in minimum dimension and 10

m in minimum dimension and

3. Discharge of the indicator microbes shall notexceed:

a) Toxicogenic Vibrio cholerae with less than 1colony forming unit (cfu) per 100 (ml) or less than

1 cfu per 1 gram of zooplankton samples, b) Escherichia coli less than 250 cfu per 100 ml and

c) Intestinal Enterococci less than 100 cfu per100 ml.

IMO has also provided general recommendations on methods and approaches for compliance monitoring in terms of Regulations D1 and D2 of the BWM Conven-tion. These recommendations are described in detail in the Guidance on ballast water sampling and analysis for trial use in accordance with the BWM Convention and Guidelines (G2, Resolution MEPC.173(58)) (IMO, 2015). Indicative ballast water


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sampling analyses have been recommended as preliminary tests for the determina-tion of potential immediate mitigation measures on whether the ship is compliant or non-compliant to discharge ballast water (MEPC, 2008). Successful ballast water sampling for detailed compliance analysis includes the identification of viable or-ganisms and their populations (IMO, 2015).

The purpose of this literature review was to provide a wide perspective on present indicative analysis methods with special attention drawn to environmental condi-tions and water characteristics in the coastal waters of the Baltic Sea. The purpose of the study was supported with the following objectives;

To present all the existing indicative analysis methods for compliance with Regula-tion D2 of the BWM Convention,

To evaluate the efficiency of each method in terms of lower water salinity and tem-perature and relatively high water turbidity,

To determine and describe the principle of sampling, reliability, applicability, re-quired skills, sampling time and required amount of ballast water, the price and man-ufacturers and previous experiences with the testing for each analysis method and

To compare the advantages and disadvantages of the methods and provide recom-mendations for the most suitable methods available for testing in practice.

1.1 Testing compliance for Regulations D1 and D2

Compliance for the ballast water exchange standard can be simply measured by de-tecting the salinity of the ballast water (Gollasch and David, 2015). If the ship has conducted ballast water exchange at the open sea, the salinity of the ballast water should always be over 30. Water sample for salinity can be collected from the man-hole, air vent, sounding pipe or discharge line and only 50 ml of water is required. General methodological approaches for testing according to Regulation D1 compli-ance are shown in Table 2. Pereira et al. (2016) also suggested that turbidity can be a good indicator for the conduction of ballast water exchange. As ships trading in the Baltic Sea do not have the option for ballast water exchange at open sea, the main focus of this literature review is on testing for Regulation D2 compliance.

Table 2. General methodological approaches for testing Regulation D1 compliance (IMO, 2015).

Indicator Sampling ap-proach

Notifications

Salinity Conductivity meter External elements can affect recorded salinity Salinity Refractometer Varying temperature can alter the readings

Types of organ-isms (Oce-

anic/coastal)

Visual identification Usually expensive, time-consuming and re-quires extensive skills. Can lead to false re-

sults (encysted organisms from previous bal-lasting operations hatch)

Turbidity Turbidity sensors Understanding of turbidity characteristics re-quired

Dissolved or-ganic and inor-ganic constitu-ents (nutrients,

Nutrient sensors Understanding of inorganic or organic constitu-ent characteristics required


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metals, organic matter)

Compliance for the ballast water performance standard can be detected by indicative or detailed analysis (IMO, 2015). Indicative analysis refers to a relatively fast indi-rect or direct measurement of a representative sample from the ballast water tank. The main purpose of indicative sampling is to provide a quick estimation of the as-sociated organism concentration and if the estimation indicates that the ship is non-compliant, detailed analysis samples can be collected for further evaluation of the compliance (David and Gollasch, 2015). Indirect measures can include physical, chemical or biological parameters that require understanding of the method and indi-cate changes in these parameters after potential ballast water treatment, whereas di-rect measures are readily comparable to D2 standard (number of viable organ-isms/volume) (IMO, 2015). A detailed analysis in turn can be defined as a direct measurement of viable organism concentrations in the ballast water. Detailed analy-sis is generally more complex than indicative analysis and tends to provide more precise indications of the quality and quantity of the organism concentrations. The main characteristics of these two analysis methods are represented in Table 3.

Table 3. The primary characteristics of indicative and detailed analysis methods (IMO, 2015).

Indicative analysis Detailed analysis

Purpose To deliver a quick, broad estimation of the concen-

tration of viable organ-isms

To deliver a more accurate, di-rect measurement of the con-centration of viable organisms

Sampling volume Varies and depends on the method

Varies and depends on the method

Representative sampling

Yes, represents organism concentrations of volume of

interest

Yes, represents organism con-centrations of volume of inter-

est Analysis parameters Can be operational (chemical,

physical), and/or indicate per-formance (biological)

Direct counts of organisms (bi-ological)

Time consumption Faster Slower

Skills required Less More

Sampling accuracy Less accurate Better

Confidence with respect to D2

Lower Higher

According to the Regulation D2, organisms of interest can be divided into 3 size classes; viable organisms 50 m, viable organisms 10 m and < 50 m and D-2 bacteria (Enterococci, Escherichia coli and Vibrio cholerae) (IMO, 2009). The main principle for indicative analysis is that it is sufficient to identify the potential compli-ance using only one indicator group of organisms (Gollasch and David, 2015). In general, indicative analysis tools for phytoplankton can be practically utilised on-board with relatively low amount of required ballast water, whereas zooplankton samples might require further analysis in a laboratory. IMO (2015) has compiled the general methodological approaches for indicative analysis sampling (Table 4).


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Table 4. General methodological approaches for indicative analysis methods testing Regulation D2 compliance (IMO, 2015).

Organism group Sampling approach Notifications

Viable organisms 50 m

Visual counts or ste-reo-microscopy

Can be expensive and time-consuming, requires training

Viable organisms 50 m

Visual inspection Most likely limited to only register organ-isms bigger than 1000 micro-metres

(mm) in minimum dimensionViable organisms

10 m and < 50 m

Variable fluorometry Only able to monitor photosynthetic phy-toplankton, underestimating other plank-

tonic organisms in this and other size fractions

Viable organisms 50 m and 10 m and < 50 m

Photometry, nucleic acid, ATP, bulk fluo-

rescein diacetate (FDA), chlorophyll a

Relatively accurate results can be ob-tained. Some organic compounds can in-dicate viability for various periods of time

outside the cell, potentially leading to false results


Flow cytometry Can be very expensive

Enterococci Fluorometric diagnostic kit

Incubation can be time-consuming

Escherichia coli Fluorometric diagnostic kit

Incubation can be time-consuming

Vibrio cholerae (O1 and O139)

Test kits Relatively quick indicative tests available


Pulse counting fluores-cein diacetate (FDA)

Sampling kits most likely larger than the ones for FDA

1.2 In-tank and in-line sampling for indicative analysis

G2 guidelines of the BWM Convention refer to indicative analyses as prior proce-dures testing for compliance (IMO, 2015). However, the guidelines do not strictly describe how the sampling should be conducted from the ballast water tanks or dis-charge line. As there are various types of ships visiting ports on a daily basis, sam-pling equipment and accessibility play key roles in obtaining a sample from the bal-last water (David, 2013).

In-tank sampling has been recommended over in-line sampling by Gollasch and Da-vid (2015), since it can be conducted before the discharge, whereas in-line sampling requires discharge, which in turn can risk the destination areas to outbreaks of NIS. In terms of the alternative sampling points and equipment, water pump sampling via manholes have resulted in more diverse samples than plankton net samples or sam-ples from sounding pipes (David, 2013). However, sometimes manholes can be in-accessible for sampling due to their location or overlaying cargo and the sounding pipes might be the only option for sampling. In addition, in-tank sampling is only appropriate when the ballast water treatment has been conducted during the uptake


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of the ballast water, since if any part of the treatment process occurs during the dis-charge, in-tank sampling is not able to measure the success of the treatment system (IMO, 2009).

According to the representativeness of a sample, in-line sampling is preferred over in-tank sampling. In-line sampling enables the continuous collection of entrained or-ganisms for an integrated sample over most of the ballasting/de-ballasting cycle, whereas the reliability of in-tank sampling can suffer from patchiness of organisms within the ballast water tanks (Wright, 2012). In general, smaller organisms such as bacteria tend to have more homogenous distribution than larger organisms such as zooplankton. Therefore, a representative sample for the smaller organisms (<50 µm) should consist of an integrated low volume sample, preferably collected over all of the ballasting/de-ballasting cycle (Gollasch and David, 2010). In-line sampling pro-vides more reliable and representative samples for compliance monitoring, even though sampling during de-ballasting prevents further sample collection for detailed analysis and enables the transfer of NIS if the ship turns out non-compliant. Over-all, it is stated in the G2 guidelines of the BWM Convention that the samples should be taken from the discharge line and that sampling through manholes, sounding pipes or air pipes is not the recommended approach for Regulation D2 compliance assessments (IMO, 2009). Different sampling options for in-tank and in-line sam-pling are described in Table 5.

Table 5. Sampling options for in-tank sampling in indicative analyses (David, 2013, Gollasch and David, 2015).

Organism group

Sampling point Required equip-ment

Required water vo-

lume (litre)

Number of samp-les

Viable or-ganisms

50 m

Manhole, sound-ing pipe or air vent (In-line)

Plankton net or pump

300 – 500 (net)

100 (pump)

1 integrated sam-ple from the whole water column or from 3 different depths (pump)

Viable or-ganisms 10 m and < 50 m


Pump, water col-umn sampler or

point-source sampler/bucket

5 – 6 1 integrated sam-ple from the whole water column or from 3 different

depths Indicator microbes


Pump, water col-umn sampler or

point-source sampler/bucket

1 1 integrated sam-ple from the whole water column or from 3 different

depths

1.3 Recommended principles for the D2 standard compli-ance testing

According to IMOs’ G2 guidelines, samples are recommended of being concentrated to a manageable size and the sampling process should be undertaken safely and practically (IMO, 2009). As relatively longer sampling times can result in underesti-mation of the present viable organisms, sequential samples of approximately 10 minutes are recommended (David and Gollasch, 2015). Timing of sampling is also important as the organisms are less likely to be homogenously distributed within the ballast water tanks. The uneven distribution of organisms in ballast water tanks can


6

result in potential errors in the compliance determination (Figure 1) (Miller et al., 2011, Frazier et al., 2013, Costa et al., 2015). In order to prevent under- or overesti-mation of organism concentrations, sampling time is recommended to avoid taking sample at the first 5 minutes and the last 5 minutes of the de-ballasting event. The sampling duration of approximately 10 minutes is also advised to be divided to take roughly 0,5 litres every minute, instead of taking the entire required sample volume at once.

As also a single 10 minute sequential sample can under- or overestimate the present organism concentrations, an average of at least 2 random 10 minute sequential sam-ples is recommended (David and Gollasch, 2015). Additional attention during sam-pling should be drawn to ballast water discharge flow rates, as relatively strong flow rates or sheer forces at the valves nearby sampling points can damage organisms and lead to falsified results in terms of organism viability. Therefore the valves at the sampling points should be kept open and the ballast water discharge rate should not exceed 50 litres per minute.

Size classification of organisms can also become problematic during indicative sam-pling (David and Gollasch, 2015). The minimum dimension of an organism is de-signed to include the smallest part of the body and ignore the sizes of spines and an-tennas (IMO, 2009). However, some indicative sampling devices, such as flow cam-eras and flow cytometers can misinterpret the organism sizes and therefore divide organisms into wrong categories, or treat colonies as individuals, specifically for non-spherical objects (David and Gollasch, 2015). Sizes of the colony forming spe-cies should be classified as the minimum dimension of an individual, not the colony.

Even though the sampling of only one size category of organisms should be suffi-cient for the determination of compliance, viable organisms greater or equal to 50

m in minimum dimension tend to exceed the D2 standard limitations more often than smaller viable organisms and microbes (David, 2013). Additionally, different size categories of organisms require different sampling approaches. David and Gol-lasch (2015) and Gollasch et al. (2015) have stated that indicative sampling for or-ganisms greater or equal to 50 µm and regulation D2 bacteria is not as reliable as

Figure 1. Potential sampling outcomes in compliance determination (Jorgensen et al., 2010, Frazier et al., 2013).


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sampling for organisms smaller than 50 µm and greater or equal to 10 µm. Sampling methods for larger organisms usually require larger quantities of water for sampling, as they are present in lower concentrations than smaller organisms and microbes in the ballast tanks. Detection of bacteria in turn, can be too time-consuming, as Regu-lation D2 requires the concentration of bacteria in CFUs (Gollasch et al., 2015) and these bacteria are rarely present even in untreated water (Welschmeyer and Kuo, 2016). Therefore it can be concluded that organisms smaller than 50 µm and greater or equal to 10 µm is the most reliable indicator group for the compliance of the ship, and also easiest to prove (David and Gollasch, 2015, Gollasch et al., 2015).

Overall, a Port State Control (PSC) officer should be able to conduct the sampling with some special training, but without a requirement for academic education in bi-ology or chemistry (David and Gollasch, 2015). Indicative analysis devices should also be portable, or at least the samples should be detectable for compliance in a portable laboratory outside the vessel. Cost-effectiveness of the chosen sampling methods should also be considered, but as a general advice, relatively expensive but accurate and appropriate sampling technology is preferred over cheaper and less ac-curate systems.

2. Materials and methods

The present study was based on existing literature on indicative analysis methods as-sessing compliance status of ships according to the BWM Convention. The cited lit-erature consists mainly of scientific studies provided by Google Scholar and Pub-Med databases, but also of technical and practical reports, publications by govern-mental and international organizations, training manuals and applicable books and reviews. No practical sampling or laboratory work was conducted for this study. Lit-erature on individual indicative methods was searched with the name of the method in association with ballast water sampling, for example “ATP ballast water sam-pling”. Relevant citations within key articles were also used to extend the knowledge around the topic. Overall, 125 references were utilized. In addition, certain key re-searchers and device manufacturers were contacted to gain further information about certain evaluated indicative analysis methods.

The studied methods were compared and evaluated based on their feasibility to in-dicative analysis requirements. As the present study was assigned by the Finnish Transport Safety Agency, feasibility of each method was assessed to serve their in-terests. After consultation with the Agency, it was suggested that indicative analysis sampling devices should cost less than 100,000 €, with a maximum analysis time of 2 hours. Obviously, the fastest devices with the best cost-efficiency and accuracy were preferred over devices that just fitted in to these frames. Additionally, as de-vice’s portability was also considered as a key factor, handheld devices were pre-ferred over portable devices that require larger transportation arrangements.


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3. Results

The most well-known indicative sample analysis methods are shown in Table 6, and they are evaluated in detail in the following chapters. Some methods can analyse only one size category of organisms, whereas others are able to detect multiple size categories. For this reason, the analysis methods will be evaluated individually in-stead of dividing them into different organism size categories

Table 6. Indicative sample analysis methods (Jorgensen et al., 2010, Bradie, 2016, IMO, 2015, David and Gol-lasch, 2015).

Method Description

Adenosine triphosphate (ATP)

Measures luminescence in the presence of luciferase enzyme from seawater extraction. Some test kits give estimation of all bio-logical contamination through the quantification of bioluminescent signal coming from the reaction of the Luminase with intracellular adenosine triphosphate (cATP), the energy carrier of any living

cell.

Fluorescein diacetate (FDA) staining

FDA: Stains living phytoplankton cells for microscopic inspection. Pulse counting FDA: Counts fluorescence pulses over specified

threshold from FDA stained organisms, Bulk FDA: Calculates fluo-rescein production rate and concentration of live cells after incu-

bation, Sytox FDA: stains dead phytoplankton cells.

Pulse amplitude-modulation (PAM) fluorometry (Also

known as variable fluorome-try)

Measures photosynthetic activity and phytoplankton biomass which are considered as indication for viable cells. Analyses living cells based on variable fluorescence (Fv) of chlorophyll of living

algae.

Microscopy Visual inspection, moving organisms can be counted as viable, FDA staining: stained cells can be counted as viable.

Flow cytometry Channels samples to the detector and measures stained organ-isms.

Fluorometric diagnostic kits (Enterococci & Escherichia

coli)

Detects viable bacteria by testing for the presence of key en-zymes or nutrient-indicators. Samples are incubated and exam-

ined for fluorescing wells. The number of these wells refers to the most probable number (MPN) of total bacteria in a sample. Some

of them detect bacteria at 1 cfu/100 ml.

Polymerase chain reaction (PCR) bacterial RNA detec-

tor

Utilizes designed primers to detect group of genes within bacterial RNA, induces the multiplication of RNA with integrated fluorescein

and gives a signal in a fluorometer.

3.1 Adenosine triphosphate (ATP) method

The detection of adenosine triphosphate (ATP) as an indicative measure of cellular biomass has been utilized in the estimation of aquatic organism concentrations al-ready in the 1970s (Hodson et al., 1976). As a molecule produced by all living or-ganisms, ATP presents the amount of total living biomass energy measured in a sample from the ships’ ballast water (Bakalar, 2014). ATP detection is generally based on bioluminescence originated from the firefly’s (Photinus pyralis) luciferin or luciferase complexes (van Slooten et al., 2015). ATP is generally extracted from a sample and reacted with luciferin/luciferase (Karl, 1993). As a measure of metabolic activity, ATP is acknowledged as a relatively good viability indicator for unicellular


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organisms with a tendency for asexual reproduction, even though metabolic activity does not ensure viability (van Slooten et al., 2015).

3.1.1 Sampling approach

The ATP method contains 3 distinctive steps; sample concentration, ATP extraction and determination utilizing a sample swab with liquid-stable reagents and a handheld luminometer (First and Drake, 2014). A water sample is collected and run through a cartridge with a membrane filter using a syringe filter for organism con-centration. This step also purifies the sample by removing dissolved compounds and residual oxidants from interfering with the luminogenic reaction. ATP extraction in-cludes the application of lysis buffers through the filter to lyse cells and extract ATP. The extraction can be diluted if necessary. The dilution is pipetted to the sample swab for the luminometer analysis, which detects relative luminescence units (RLUs). In order to determine the size categories of organism concentrations, water samples can be pre-filtered to remove larger particles (van Slooten et al., 2015). An example from the sampling approach for an ATP analysis is illustrated in Figure 2.The entire sampling protocol for this method is provided in Appendix A.

Figure 2. Sampling approach for the ATP analysis. Redrawn from SGS (2015).


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3.1.2 Feasibility

ATP analysis can be an effective indicator for compliance determination, since it provides the basis for the determination of total viable plankton biomass (Waite et al., 2003). The analysis method enables the evaluation of all organism size catego-ries and the sampling devices are relatively affordable (<10,000$) without substan-tial running costs (van Slooten et al., 2015, Bradie, 2016). As long as the ATP rea-gents can be contained in room temperature, water quality characteristics of the sam-ples, such as low salinity and high turbidity should not influence the outcome of ATP analysis (van Slooten et al., 2015). However, highly saline ballast water in turn, may require additional steps of dilution for the sampling protocol. Relatively high concentrations of total suspended solids (TSS) can however reduce the efficiency of filters (First et al., 2014). Sample contamination risks using the ATP analysis method, such as leaking or damaged filters can be considered as relatively low, alt-hough some delays in the reduction of ATP levels can occur after UV treatment as the affected cells do not die immediately after treatment (van Slooten et al., 2015). The sampling time for ATP analysis is also reasonable, as results can be obtained in less than an hour (SGS, 2015, Bradie, 2016).

However, ATP analyses can be somewhat problematic. Presence of dissolved metals in the water samples can cause underestimations in detected biomass by inhibiting the light production (Sudhaharan and Reddy, 2000). Azam and Hodson (1977) in turn observed that the presence of free ATP in the environment will result in overes-timation of the biomass. The effects of dissolved compounds can be eliminated by applying a filter membrane (First and Drake, 2013), or ATP extraction with boiling tromethamine or activated carbon, although the mentioned extraction techniques are considered being too time-consuming and complex for PSC officers (van Slooten et al., 2015). Sample filtration can be considered highly important, since otherwise it is not possible to differentiate the sampled organism sizes (Bakalar, 2014). Addition-ally, UV treatment for ballast water has been detected to increase the amount of cel-lular ATP in bacteria (Villaverde et al., 1986), which complicates interpretation of the results from UV-radiated samples (First and Drake, 2013).

ATP analyses have been studied in laboratories, as well as during onboard experi-ments, and they have shown relatively interesting and also contrasting results. Wright et al. (2015) studied ATP sampling on organisms above 10 µm in minimum dimension before and after filtration and UV radiation treatment. The ATP analysis in this study revealed great results with relatively short extraction time, as the ATP concentration decreased by 99 % after the (UV+filtration) treatment. Even though the extraction took only 5 minutes, the samples were frozen and analysed further in a laboratory. Therefore it remains unclear, whether the same results would have been obtained in situ. Van Slooten et al. (2015) also reported a strong decrease in ATP levels after UV treatment in a laboratory experiment. In addition, they experienced that the ATP sampling method with syringes and filters was relatively easy to use, portable in a light briefcase and not excessively time-consuming.

First and Drake (2014), in turn, studied the efficiency of UV and chlorine dioxide treatments using ATP analysis on collected seawater samples in a laboratory. The ATP levels did not decline significantly after the UV treatment in this study, even though indicative Pulse Amplitude-modulation (PAM) fluorometry method detected significant decreases after the treatment. However, ATP analysis detected significant decreases after chlorine dioxide treatment, suggesting that appropriate ballast water


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verification methods can also be treatment-specific (First and Drake, 2014). De Lafontaine et al. (2009) noted that ATP analyses can be useful in measuring growth dynamics of viable yeast populations in individual experiments, but also concluded that ATP measurements did not response accurately to yeast cell density in all cases. They used ATP analysis while investigating the efficiency of yeast-based deoxygen-ation treatment method for cold water conditions in a laboratory environment.

Anyhow, ATP analysis has shown promising results after high voltage electricity treatment (Hwang et al., 2010). This laboratory experiment revealed that the ATP levels of zooplankton, phytoplankton and bacteria, all decreased significantly after the electric pulse treatment. Another laboratory experiment by Penru et al. (2012) found significant declines in cellular ATP from seawater samples after UV treat-ment. In fact, one of the most recent studies in ATP method testing for compliance (Welschmeyer and Kuo, 2016) reviewed that ATP devices can be used on-board as portable devices, provide results quickly, indicate results efficiently regardless of the treatment method and can be calibrated to correspond only certain organism size cat-egory.

3.2 Fluorescein diacetate (FDA) method

Fluorescein diacetate (FDA) represents a cell-specific identification method for cel-lular viability (Rotman and Papermaster, 1966). The method estimates living plank-tonic biomass from quantitative and enzymatic transformation of FDA into fluores-cein, its fluorescent product (Welschmeyer and Maurer, 2011). Applications of the FDA method can have some variation, as the fluorescent signal can be inconsistent and FDA does not necessarily stain all organisms (Dorsey et al., 1989, Garvey et al., 2007). Additionally, the permeability of FDA throughout the cell membrane has re-sulted in the development of derivatives (First and Drake, 2013), such as 5-chloro-methylfluorescein diacetate (CMFDA) and carboxyfluorescein diacetate (CFDA), which have the advantage of better cellular retention through their reactive proper-ties in comparison to FDA (Steinberg et al., 2011, Gorokhova et al., 2012).

Another approach for staining is to identify dead cells with nucleic acid stains im-permeable to intact cell membranes, such as the fluorophore SYTOX green and TO-PRO-1 iodide (Gorokhova et al., 2012), and detect the concentration of living organ-isms by subtraction (First and Drake, 2013). Detection of cytoplasmic membrane in-tegrity utilizes probes that fluorescent only when bound to nucleic acids (Berges and Falkowski, 1998). In general, further tests are required in the detection of the total number of dead cells within a sample (First and Drake, 2013). An additional method using FDA staining is FDA pulse counting, which in turn utilizes a detecting ana-lyser to count fluorescence pulses from stained viable organisms (Nakata et al., 2014). The analyser estimates the viable organism concentration from the pulses with a practical threshold.


Even though fluorescein is a non-fluorescein compound, it can be utilized to emit fluorescein through hydrolysis by biological enzyme activity (Rotman and Paper-master, 1966). Green fluorescent emission originates from FDA associated with es-terase activity present in all living organisms (Figure 3) (Welschmeyer and Maurer,


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2011). FDA analyses can be used to quantify numeric counts of viable cells in asso-ciation with an epifluorescent microscope or flow cytometry, or alternatively, as a bulk indicator for viable cell biomass.

Figure 3. Methodological approach for FDA as a marker for viable organisms (Welschmeyer and Maurer, 2011).

FDA analysis includes staining, incubation and counting of samples (Steinberg et al., 2011). A proportion of a sample is mixed with FDA, CFDA or CMFDA into mi-crofuge tubes for sample staining. The incubation step is conducted in the dark at room temperature for 10 – 30 minutes, depending on the specific FDA method used (Steinberg et al., 2011, Gorokhova et al., 2012, van Slooten et al., 2015). The organ-isms in incubated samples can be analysed with an epifluorescent microscope as flu-orescing and non-fluorescing, or mobile and immobile (Steinberg et al., 2011). Al-ternatively, the fluorescence can be analysed with a fluorometer (van Slooten et al., 2015).

As mentioned earlier, staining of dead cells in a sample requires further methods to transform this outcome into viable organism concentrations (First and Drake, 2013). For example, DNA probes permeable to both, dead and living cells can be used to counter-label the cells, of which the membrane permeable-labels can be determined using flow cytometry, epifluorescent microscope or fluorescence measuring plate readers (Peeters et al., 2008, Peperzak and Brussaard, 2011, Steinberg et al., 2012).

FDA Bulk analysis is generally based on a fluorometric analysis of the extracellular bulk liquid containing the suspended cellular material (Welschmeyer and Maurer, 2011). FDA bulk analysis requires filtration of a sample, applying the filter into a cuvette containing reagent buffer and mixing with FDA reagent (Maurer, 2013). The incubation time for bulk FDA analysis is 1 hour, after which the sample is squeezed from the filter into a centrifuge, spinned down and the fluorescence is measured with a spectrofluorometer.

Pulse counting FDA analysis detects fluorescence pulses and estimates the organ-isms viability based on the strength of the pulse (Figure 4) (Nakata et al., 2014). The analysis method requires a mixture of FDA and a sample, which is thereafter run through the pulse counting device. The device counts the organism concentration


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from the sample and shows it on the screen panel on the device. The operability of FDA pulse counting device is represented in Appendix B.

3.2.2 Feasibility

Various applications of FDA analyses have been widely acknowledged as appropri-ate methods to measure organism viability in numerous environmental samples (Adam and Duncan, 2000 described in Akram et al., 2015). FDA analysis enables the sampling of all organism size categories and depending on the elected method, the sampling duration varies between 30 minutes and a few hours and is thus reason-able (Welschmeyer and Maurer, 2011, Bradie, 2016). Bakalar (2014) also estab-lished that FDA devices can be affordably priced, starting from 450 US dollars with minimal running costs. However, the risk of filter damage and leakage is present in FDA analyses that require the use of filters (van Slooten et al., 2015). Additionally, water turbidity greater than 20 NTU can decrease the accuracy of fluorescence de-tection (Bradie, 2016). Granqvist and Mattila (2004) and Engström-Öst and Mattila (2008) reported that water turbidity in the Baltic Sea region can vary between 0.5 and 45 NTU. Anyhow, Waite et al. (2003) reported that generally the most turbid waters in natural conditions range between 10 and 15 NTU.

FDA analysis can however produce inconsistent fluorescence signal and FDA has been observed not being able to stain all organisms (Garvey et al., 2007). Adams et al. (2014) and MacIntyre and Cullen (2016) attempted the utilization of combined FDA and CMFDA stains in organism viability assessment, as derivatives of FDA are impermeable across the cell membrane (First and Drake, 2013). According to their suggestion, even FDA+CMFDA analyses cannot be considered as sufficient vi-ability assessment methods due to relatively significant risk of overestimation of live organisms when fluorescing dead organisms. The overestimation problem has also been reported in association with CFDA analysis on UV-treated seawater, as the cell membrane can be intact within the UV-treated cells even some days after the treat-ment (Tobiesen et al., 2011). Steinberg et al. (2011) reported similar problems with the combined FDA and CMFDA stains assessing heterotrophic and mixotrophic di-noflagellates, but in contrast found the same method successful in viability assess-ment of protists. In general, assessments utilizing any type of staining method have the issue of not being able to make a distinction between living and viable cells

Figure 4. Methodological approach for pulse counting FDA (Nakata et al., 2014).


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(Reavie et al., 2010) and the efficiency of FDA analyses can also depend on the bal-last water treatment method (Akram et al., 2015).

A laboratory experiment by van Slooten et al. (2015) revealed that the conducted FDA analysis was proven both, more time-consuming and less accurate than the conducted ATP analysis. Excessive time consumption of various FDA analyses has been reported also in other studies. On top of the longer sampling duration, Stein-berg et al. (2012) and Carney et al. (2013) reported that fluorescence-based tech-niques tend to struggle with lower sampling volumes and uneven distribution of cells on membrane filters or in counting chambers. In addition, staining of dead cells with nucleic acid stains includes not only the staining of dead cells, but also the re-staining of both, living and dead cells for the subtraction (Steinberg et al., 2012). The process is inevitably vulnerable for measurement errors, as it requires a second-ary count for the cells (First and Drake, 2013) and underestimations of non-viable cells can occur due to viable labelling efficiency of cells with damaged DNA (Leb-aron et al., 1998).

As indicative compliance testing requires quick and portable testing devices, some efforts and improvements have been also applied to FDA analyses. Welschmeyer and Maurer (2011) developed a quicker and more simplified bulk FDA analysis method by combining a series of analysis steps. Even though these sampling proce-dures can be conducted within 30 minutes for one sample, the method was also tested on a full-scale onboard experiment revealing that the completion of the proce-dures took 3 hours, despite leading to relatively promising results on successful bal-last water treatment. Nakata et al. (2014) conducted a pulse counting FDA experi-ment with a portable sampling device obtaining results in 10-30 minutes. They com-pared the measured FDA pulses to organisms counted with a microscope. Even though the study found a highly significant correlation between the counts and pulses without disturbing background fluorescence, the reliability of this device suf-fers from lack of additional studies and underestimation of organisms (Bradie, 2016).

Overall, it has been widely noted that the method of staining dead or living cells ap-pear to have issues with false positive outcomes (Tobiesen et al., 2011, Zetsche and Meysman, 2012, Adams et al., 2014, Wright and Welschmeyer, 2015). Majority of FDA analyses also suffer from relatively long incubation periods and require further onboard studies to establish the approach as a reliable indicative analysis method.

3.3 Pulse amplitude-modulation (PAM) method

PAM fluorometry measures the photochemical efficiency of photosystem II (PS II), fluorometric character of a particle in phytoplankton containing origins of chloro-phyll fluorescence (Schreiber, 2004, Bakalar, 2014). Therefore the methodology in-dicates the viability of phytoplankton regardless of their size, additionally enabling a qualitative or quantitative indication of phytoplankton community through photo-synthetic activity (Schreiber et al., 2007, Gollasch et al., 2015). PAM analyses can be conducted as bulk analysis or a measurement for single cell counts (Villareal, 2004). Somewhat similarly to FDA analysis, PAM method measures selective in-creasing fluorescence signals based on transmitting short, but intensive light pulses (Bakalar, 2014).


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PAM fluorometry refers to the detection of photosynthetic performance parameters utilizing fluorescing saturation pulses to stimulate species sensitive to light (Figure 5) (Heraud and Beardall, 2000). The methodology measures quantum yield of fluo-rescence also known as instantaneous variable or maximum fluorescence, based on minimum (F0) and maximum (FM) fluorescence yields, and photochemical yield (Y), calculated from the following equation (Genty et al., 1989, First and Drake, 2014, Wier et al., 2015);

= 1000 ×

According to this equation where “Y” indicates the photosynthetic efficiency, the values of Y for living and healthy marine phytoplankton generally range between 400 and 600 (First and Drake, 2013, Wier et al., 2015). In addition, low values of Y are an indication of dysfunctional photosynthesis. As the quantum yield of the asso-ciated electron transport is directly proportional to the output of photochemical fluo-rescence quenching (Genty et al., 1989), photosynthetic efficiency can be also deter-mined by using an equation that excludes the multiplication with 1000 (Genty et al., 1989, Bradie, 2016). In this occasion, the typical values of instantaneous fluores-cence range between 0 and 0.8, and values above 0.3 represent viable phytoplankton populations (Villareal, 2004, Stehouwer et al., 2010, First and Lake, 2014, Bradie, 2016).

Figure 5. Typical fluorescence output from a PAM, including device being switched on (LL), activation with 10 second actinic light pulses (AL). SF (Saturation Flashes) represents re-sponses to AL, whereas Fm’ is fluorescence maxima (White and Cricthley, 1999).


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In order to obtain a measurement for the minimum fluorescence (F0), the samples generally need to be pre-adapted to dark. Dark-adaptation period of 10 minutes is considered sufficient (Heraud and Beardall, 2000), but the duration of the dark-adap-tation can vary widely between 10 seconds and 30 minutes depending on the sam-pling device and applied sampling protocol (Heraud and Beardall, 2000, Villareal, 2004, Martinez et al., 2012, First and Drake, 2014, Bradie, 2016). Alternatively, dark-adaptation can be excluded completely to minimize the effect of potential con-tainment artifacts (Villareal, 2004).

The sampling stages include discretionary syringe filtration and adaptation to dark, sample placement into a cuvette and yield measurement by exposing the sample to short sequences of increasing actinic light pulses (Villareal, 2004, Bradie, 2016). The measuring device is usually operating with a cell-counting flow camera or an epifluorescent microscope (Villareal, 2004, Gollasch, 2012). The detection of organ-ism concentration generally requires a conversion from the recorded photochemical yields into cell concentrations, depending on the applied device (Bradie, 2016).

3.3.2 Feasibility

David and Gollasch (2015) concluded that according to their knowledge, PAM fluo-rometry is the best indicative analysis sampling tool with the advantages of quick-ness, portability and ease of use. PAM devices enable the viability results being ob-tained within a matter of minutes, some of them even faster (First and Drake, 2013, Bakalar, 2014, van Slooten et al., 2015). The price range for PAM sampling devices varies roughly between 4,000 and 15,000 US$ with relatively minimal running costs (Bradie, 2016). PAM fluorometers are mainly designed to indicate the presence of phytoplankton in the < 50µm and 10µm size category (Gollasch et al., 2015), but Bradie (2016) reported that some devices are also able to detect organism concentra-tions from the 50 µm category. Additionally Bradie (2016) noted that the accuracy of some PAM fluorometers can decrease at water turbidity greater than 20 NTU. Due to the simplicity and automated nature of PAM sampling technique, the risk for sample contamination using this method can be considered relatively minimal (Ba-kalar, 2014, van Slooten et al., 2015).

Several studies, such as First and Drake (2014), Gollasch et al. (2015), van Slooten et al. (2015) and Casas-Monroy et al. (2016) have also detected limitations while sampling with PAM devices. Understanding of PAM sampling technology is essen-tial, as the methodology is sensitive to dead organisms and vulnerable to systematic errors with treatment systems that eliminate the organisms but do not necessarily re-move the remaining chlorophyll a (First and Drake, 2014). As Casas-Monroy et al. (2016) stated PAM devices are vulnerable to under- or overestimation of certain or-ganism groups. Therefore, it is recommended that additional tests for different or-ganism groups are included along with PAM measurements to confirm the compli-ance status (Gollasch et al., 2015).

Probably one of the most significant limitations associated with PAM fluorometry is that it measures only concentrations of autotrophic organisms via chlorophyll a (van Slooten et al., 2015), whereas the BWM Convention requires the determination of all organisms in the targeted size class (Gollasch et al., 2015). Autotrophs refer to organisms that are able to utilize inorganic materials as a source of energy via photo-synthesis and chemosynthesis (mainly phytoplankton and certain bacteria) (van Slooten et al., 2015). On the contrary, heterotrophs (e.g. ciliates and protozoa) utilize


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organic compounds as a principal source of food and their presence cannot be de-tected using chlorophyll-based methods. In addition, methods testing for the effi-ciency of PS II cannot detect the presence of cyanobacteria, of which fluorescence derives from other pigments than of chlorophyll’s (Sugget et al., 2006).

Despite the limitations of PAM devices, they have been evaluated as one of the most appropriate indicative sampling analysis methods (van Slooten et al., 2015, Casas-Monroy et al., 2016). Shannon et al. (2009) conducted laboratory experiments on treated and untreated artificial seawater samples, successfully detecting variable flu-orescence yield with PAM fluorometry in comparison to decreasing photosynthetic efficiency, altered with photosynthesis inhibiting herbicide 3-(3,4-dichloro-phenyl)-1,1-dimetylurea (DCMU). After the execution of laboratory experiments by van Slooten et al. (2015), Gollasch et al. (2015) and Casas-Monroy et al. (2016), PAM fluorometry has been found superior over ATP and FDA methods in terms of sam-pling duration, ease of use, device portability and occasionally even sampling accu-racy.

Studies by Gollasch et al. (2015) and Bradie (2016) have additionally compared the differences between several PAM devices produced by different manufacturers. BBE 10 cells, Hach BW680, Turner Designs Ballast-Check and Walz Water PAM were all relatively fast, portable and required minimal amount of training without significant differences in these categories. However, Hach BW680 device provided the most consistent results between replicates during the study by Bradie (2016), and was also found easiest to use as a handheld device without any filtration steps (Gol-lasch et al., 2015). Bradie (2016) also revealed that out of these PAM devices, Hach BW680 was the cheapest to purchase.

3.4 Microscopy methods

Microscopic viability analysis for zooplankton can be conducted by stimulating or-ganisms through poking them and detecting motility (David and Gollasch, 2015). The main principle is that moving organisms can be counted as viable. Alternatively, microscopic analysis for phytoplankton can be conducted with FDA staining since movement cannot be considered as a reliable indicator for viability of autotrophs (Bradie, 2016). Staining for epifluorescent microscope analysis can also be con-ducted with CMFDA or Sytox (David and Gollasch, 2015).

3.4.1 Sampling approach and feasibility

Microscopy method is considered as a standard method for ballast water samples, analysing organisms larger or equal to 50 µm or organisms smaller than 50 µm and larger or equal to 10 µm in minimum dimension (Bradie, 2016). The microscopy method approach includes concentration of samples from larger volumes of water into concentrated samples, mixing of samples and pipetting samples for the micro-scopical inspection. In terms of feasibility, microscopy analyses have the advantage of directly referring results to organisms per volume (Wright et al., 2015). However, microscopic inspection requires usually prolonged periods of time, as well as spe-cialized knowledge to identify viable organisms (First and Drake, 2013, Wright et al., 2015). The inspection can take several hours and assessing motility refers to liv-ing organisms, instead of confirming reproductive ability of these organisms, in other words viability (Bradie, 2016).


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The sampling approach for epifluorescent microscope analysis utilizing FDA, CFDA, CMFDA or Sytox stains has been described earlier in section 3.2.1. Any-how, previous studies on epifluorescent microscopy, such as First and Drake (2013), Gollasch et al. (2015), Casas-Monroy et al. (2016) and First et al. (2016) indicate that the approach utilized for direct counts can take several hours and requires not only biological expertise, but also laboratory surroundings (Gollasch et al., 2015). In addition, as cysts do not move or respond to fluorescent stains, microscope tech-niques are most likely unable to detect encysting organisms in a simple or efficient manner (First et al., 2016).

3.5 Flow cytometry method

Flow cytometry is designed to detect the abundance of phytoplankton and bacteria (Gasol and Del Giorgio, 2000, Veldhuis and Kraay, 2000). The methodology utilizes laser beam, which stimulates cells to scatter light when encountering laser and emit fluorescent light after excitation by the laser (Brussaard et al., 2000). Flow cytome-try detects the amount of phytoplankton cells based on red fluorescence signal (>610 nm) originating from chlorophyll a (Veldhuis and Kraay, 2000, Veldhuis et al., 2001), whereas the abundance of bacteria can be determined with additional DNA-specific fluorescent staining (Li et al., 1995, Gasol and Del Giorgio, 2000).


Flow cytometry is an efficient technology for the analysis of individual cell concen-trations from heterogeneous populations (Picot et al., 2012). It is used to enumerate and characterize cells from multicellular organisms and single-celled microbes (Shapiro, 1983, Olsen et al., 2015). Flow cytometer measures scattered light at vari-ous angles and fluorescence emission, as the cells flow through one or several laser beams (Picot et al., 2012). Flow cytometry analysis includes the hydrodynamic fo-cusing for cellular suspension by the fluidic system, detection of excitation source and fluorescence emission by optical collection system as the cells interact with the laser beam, and digitalization of the signal by the electronic system for computer analysis (Figure 6). Forward scatter light (FSC) refers to the size of the cell, side scatter light (SSC) relates to the structure and shape of the cell, whereas specific flu-orescence emission indicates the cell characteristics.


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Figure 6. Flow cytometry analysis approach (Picot et al., 2012).

Flow cytometry assessment can be conducted and utilized in a number of ways (Fig-ure 7) (Lomas et al., 2011). Flow cytometers can be automated devices that include only the appliance of the water sample (Bakalar, 2014), or alternatively, methodo-logical steps like staining and incubation can be conducted by the examiner (Veld-huis and Kraay, 2000, Joachimsthal et al., 2003, Binet and Stauber, 2006). The addi-tional steps include for example viability assessment for phytoplankton utilizing 14C incubation method (Veldhuis et al., 2006), cell counting for bulk FDA samples (Bradie, 2016), direct cell-specific fluorescent analysis (Peperzak and Brussaard, 2011) or utilization of staining methods for DNA content and viability determination (Veldhuis et al., 2001, Lomas et al., 2011).


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Figure 7. Potential variation in flow cytometric assessments (Lomas et al., 2011).

3.5.2 Feasibility

As the purpose of this literature review is to evaluate the most suitable and practical sampling devices for indicative ballast water analyses, only automated and simple flow cytometers will be discussed in this section. Flow cytometry assessment is able to provide an automated, quick and accurate method to sample plankton communi-ties and bacteria (Joachimsthal et al., 2003, Veldhuis et al., 2006, Bakalar, 2011). Bakalar (2014) added that flow cytometry provided the most accurate results in comparison to FDA, ATP, PAM and automated colorimetry analyses in a multidi-mensional projection ranking analysis. Some automated flow cytometry devices


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have the advantage of being able to provide results in a few minutes (Stehouwer et al., 2013).

Some studies have represented estimations that flow cytometric devices can be rela-tively expensive, ranging approximately between 18,000$ and 200,000€ (Stehouwer et al., 2013, Bakalar, 2014). Comprehensive understanding of the applied flow cy-tometer methodology is essential, as several studies, such as Binet and Stauber (2006), Peperzak and Brussaard (2011), Olsen et al. (2015), tend to highlight quick-ness of automated flow cytometer as the advantage of the technology, even though most sampling approaches associated with these studies include sample preparation, incubation or staining procedures that can take significant amount of time up to few days. Flow cytometric analysis has also expressed similar limitations to FDA anal-yses by not being able to make a distinction between dead and living cells (Reavie et al., 2010, Bradie, 2016). Reavie et al. (2010) concluded that flow cytometry cannot be considered as an automated substitute for microscopy for this reason. Olsen et al. (2015) in turn concluded that flow cytometry can differentiate live and dead cells for certain species, but struggle making a distinction between reproductively viable and non-viable cells.

D2 standard of the BWM Convention does not include the detection of organisms <10µm in minimum dimension, even though 90% of all phytoplankton organisms can fall into this category (Olenina et al., 2006, van der Star et al., 2011). This size category additionally includes other organisms, such as micro-zooplankton and pro-tozoa. If this category is to be assessed in the future, flow cytometry can provide a reasonable sampling approach for smaller organisms, as they are usually present in relatively high concentrations.

3.6 Bacterial identification methods

There are several methodological approaches for the identification of bacteria in bal-last water (Gollasch et al., 2012). It is important to emphasize that all methods that measure bacterial abundance in colony forming units (CFU) require a minimum of 4 hours incubation time for the samples, and the D2 standard requirement for bacteria is also expressed as CFUs (IMO, 2009). The following chapters are designed to evaluate the sampling approach and feasibility for each bacterial sampling method, including a few methods that do not measure bacteria in CFUs.

3.6.1 Detection of bacterial enzymes

Bacterial enzyme detection devices are fluorometers that aim to trace bacteria-spe-cific detection enzymes by producing fluorescence from additions of key fluoro-genic substrates, which are hydrolysed by the enzyme (Gollasch et al., 2012). Such devices enable the identification of certain bacteria, including E. coli and Entero-cocci within a matter minutes. They are unsuitable for measuring CFUs, as they can only detect the presence or absence of the targeted bacteria.

Use of most handheld fluorometers requires minimal skills as the sampling approach includes only the addition of a reagent/substrate to the water sample (Gollasch et al., 2012). The device expresses the presence or absence of bacteria on a screen associ-ated with the device. Fluorometers are able to record the readings in approximately 10 to 20 minutes without any incubation. Even though the technology is unable to


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represent results as CFUs, semi-quantitative results can be obtained on whether the sample contains high or low amount of bacterial enzymes. Familiarization to de-tailed device descriptions is advised, as they may require appliance of chemicals and incubation, and the sampling duration for some portable fluorometers can be signifi-cantly longer (Gollasch et al., 2012).

The most probable number (MPN) methodology contains also variations within the method, but the basic idea of the MPN approach is to detect the most probable num-ber of CFUs per 100 ml of E. coli or Enterococci based on number of positively flu-orescing wells (Budnick et al., 1996, Weisberg et al., 2007, Maranda et al., 2013). Enterolert and colilert test kits by IDEXX laboratories Inc. (Westbrook, Maine) uti-lize fluorescing nutrient indicator substrates after the metabolization by the targeted bacteria (Budnick et al., 1996, Cangelosi, 2011). The method includes an incubation time between 18 and 24 hours (Weisberg et al., 2007, Bradie, 2016). Alternatively, IDEXX has also developed a method to quantify heterotrophic plate counts (HPC) in water, which can be altered to MPNs with a 48 hour incubation time (Bradie, 2016). The MPN method has been found more efficient, effortless and accurate than tradi-tional bacteria detection method using membrane filters (Budnick et al., 1996). Even though MPN techniques are the basis of bacterial assessments, their completion re-quires several hours and can be therefore considered unsuitable of being conducted within the turnaround time of a ship in ports (Wright et al., 2015).

3.6.2 Real-time Polymerase chain reaction (PCR)

An alternative approach for the detection of bacterial concentration within a water sample is through genetic nucleic acid priming for E. coli, Enterococci and V. chol-erae (Weisberg et al., 2007, Fykse et al., 2012). PCR is designed to utilize various reagents and schemes of temperature alterations to anneal and denature sequences of nucleic acid for exponential amplification of the targeted genes (Saiki et al., 1985, Weisberg et al., 2007). Real-time PCR is used to amplify DNA sequences, whereas real-time NASBA (Nucleic Acid Sequence Based Amplification) is used to amplify RNA sequences (Weisberg et al., 2007, Fykse et al., 2012).

The method steps include sample filtration, DNA or RNA extraction and the real-time PCR or NASBA. The analysis can be conducted within 7 hours for real-time PCR and within 9 hours for real-time NASBA and they describe the results in 1 CFU/100 ml of V. cholerae (Fykse et al., 2012). The detailed analysis protocol is de-scribed in Fykse et al. (2012). Additionally, quantitative PCR method for the pres-ence of Enterococci have been able to conduct within 2 – 3 hours for recreational waters (Haugland et al., 2005), but it remains unclear whether this methodology could be adjusted to ballast water compliance analyses.

3.6.3 Colorimetric bacteria detection

Colorimetric bacteria detectors are also designed for fast indicative analysis detect-ing the presence or absence of the targeted bacteria without an estimation of the amount of CFUs (Gollasch et al., 2012). The test kits are using known antibodies for the detection of E. coli and V. cholerae (Gollasch et al., 2012), and they can be usu-ally analysed within 15 minutes (Bakalar, 2014).


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The sampling procedures include mixing a few drops of a sample into a test tube containing reagent, transferring the sample into a swab, placing the swab into the sampling device and adding the chase buffer (Gollasch et al., 2012). The test takes approximately 15 minutes to create a distinct colour reaction for positive samples. There are also other similar colorimetric bacteria detection packages that can be done in 15 – 30 minutes and additionally some test kits have further developed a more detailed method with a 6 to 24 hour incubation time to receive more accurate results. This methodology requires further testing to confirm its operability in terms of varying salinities and defining the bacteria detection limits (Gollasch et al., 2012).

3.6.4 Other methods for detection of bacteria

As one of the objectives of the present study was to represent all the existing indica-tive analysis methods for compliance with Regulation D2, the following methods will be featured even though they have been applied to indicative sampling strate-gies relatively sparsely. The sampling approach and feasibility of these methods was evaluated to the most appropriate extent.

The staining of dead and live cells using Sytox and FDA stains for analysis of bacte-rial concentration with flow cytometry has been evaluated earlier under section 3.5. The detection for the concentrations of E. coli and Enterococci can also be done us-ing petri dishes and films (Gollasch et al., 2012). Using petri dishes, the procedures can be conducted by filtering the sample, incubating for 48 hours and then visually assessing the CFUs from the discs. Utilization of petri films requires pipetting 1 ml of the sample into a plate and sealing the plate with a lid. Even though first results can be visually detected after 6 hours, the test for total bacteria count takes 2 to 3 days, whereas the number of colony forming coliform bacteria can be observed after incubation of 1 day. Each petri film product is designed for specific bacteria, includ-ing E. coli and Enterococci petri films (Gollasch et al., 2012).

Alternatively, bacteria can be sampled utilizing microcalorimetry, respirometer or detection of ribosomal ribonucleic acids (rRNA) (Wadsö, 2002, Gollasch et al., 2012, First and Drake, 2013, Bradie, 2016). Isothermal microcalorimeters are able to detect heat generation through metabolism by holding stable sample temperature and measuring heat generation from biological processes indicating the activity or con-centration of organisms (Johnson et al., 2009, Braissant et al., 2010, First and Drake, 2013). The targeted organism concentration and activity is related to the time re-quired to detect metabolic activity. Even though technological development has in-creased the cost-efficiency and portability of microcalorimeters (Braissant et al., 2010), this method is yet to be tested comprehensively on ballast water applications.

Similarly to microcalorimeter, Bactest have designed a portable Speedy Breedy pre-cision respirometer that detects microbial respiration through pressure transients, which refers to gaseous exchanges (Bradie, 2016). A sample is added into a 50 ml closed culture vessel including predetermined nutrient medium, resulting in the growth of micro-organisms. The method is suitable for E. coli and Enterococci and results can be obtained within 12 hours. Hybriscan rRNA detection method in turn can be utilized to analyse the presence of E. coli rRNA with species-specific probes through events of hybridization (Gollasch et al., 2012). The method aims to detect only living cells and allows the determination of non-culturable microorganisms.


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Results can be obtained in approximately 12 to 26 hours, but further research is sug-gested to evaluate whether the method can be applied to the detection of colony forming bacteria.

3.7 Other potential indicative methods

Equally to the previous section (3.6.4), the purpose of this chapter is to represent al-ternative indicative analysis methods that have been discussed in the literature but are not among the most studied, and evaluate their sampling approaches and feasibil-ity. Some methods have similarities or variations to aforementioned methods, whereas some bring completely new approaches to indicative analysis techniques. Additionally, Gollasch et al. (2012) and First and Drake (2013) have briefly pre-sented further methods, such as variable oxygen measurements that can be assessed to test Regulation D2 compliance.

3.7.1 Microfluidic lab-on-chip biosensor

Time-consumption, series of analysis steps required and poor portability of PCR and microarray approaches has led to the development of portable, microfluidic lab-on-chip devices (Vilkner et al., 2004, Senapati et al., 2009). The methodology has the potential to improve reaction efficiency, reduce sampling duration, simplify sam-pling protocol and decrease the quantity of loading samples (Senapati et al., 2009). The approach is based on genetic detection assay with a bead-based microfluidic platform, where probe-operated beads detect fluorescence-tagged target DNA. Alter-natively, lab-on-chip devices can utilize resistive pulse sensors and a laser to detect chlorophyll fluorescence (Song et al., 2012, Wang et al., 2013, Song et al., 2014).

The sampling steps for bead-based microfluidic detection platform include fabrica-tion of microchip using glass slides, filter fabrication within the microchannel, oper-ating the probe on beads, asymmetric PCR and hybridization assay (Senapati et al., 2009). Detailed sampling protocol is described in Senapati et al. (2009). The total detection time is 1 hour, and further, the device was found highly portable and cost-effective. Even though Senapati et al. (2009) promoted the simplicity of the device, the sampling approach contains few fabrication steps, filtration and it requires chem-ical applications, which inevitably increase the risk of error. Additionally, further re-search on this method needs to be done in relation to ballast water sampling to estab-lish the operability of the approach.

Lab-on-chip devices utilizing resistive pulse sensors also require microchip fabrica-tion, use of primers and loading of the sample (Figure 8) (Song et al., 2012, Wang et al., 2013). The method utilizes pre-established correlation curve to reduce the sam-pling time down to 1 – 2 minutes (Song et al., 2012). Song et al. (2012) discovered that the methodology was sufficiently sensitive to detect the difference in phyto-plankton concentrations after electrolysis treatment. The device can act as a suitable replacement for imaging flow cytometers, which tend to be more expensive, time-consuming and less portable (Wang et al., 2013). Furthermore, separate chips can be designed to detect organisms in the preferred size categories (>50 µm, 10 µm and <50 µm or <10 µm) (Song et al., 2012). First and Drake (2013) suggested that lab-on-chip devices could be potentially installed into piping systems in ships to provide real-time data from the ballast water.


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Figure 8. Illustration of the fluorescence detection system (a), microfluidic chip (b) and detection area (c) (Wang et al., 2013).


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3.7.2 Fast repetition rate (FRR) fluorometry

Closely related to PAM fluorometry, FRR fluorometry measures chlorophyll fluo-rescence (Kolber et al., 1998). In comparison to PAM fluorometry using multiple turnovers of PS II from excitation flashes, FRR fluorometry exposes the sample to a high-energy single turnover flash to obtain the maximum fluorescence level (Kromkamp and Forster, 2003). The main differences between PAM and FRR fluo-rometric methods derive from the length of the applied flash pulses. Multiple turno-ver technique utilizes longer flash times, which can lead to overestimation of chloro-phyll due to improved detection for background fluorescence (Figure 9) (Kromkamp and Forster, 2003). Therefore FRR fluorometers are not only more sensitive, but also more suitable for phytoplankton measurements. Additionally, the method enables the detection of cyanobacteria by being able to determine smaller functional cross sections of PS II (Sugget et al., 2001, Sugget et al., 2006).

Figure 9. Multiple turnover technology (blank dots) tends to overestimate the PS II efficiency and electron transport rate in samples in comparison to single turnover technology (black dots) (Kromkamp and Forster, 2003).

Chelsea Technologies Group has developed a portable and quick FastBallast sam-pling device that utilizes applied FRR fluorometry for onboard analyses, which has similar benefits as PAM fluorometers (CTG, 2016). The FastBallast technology aims to provide a detailed analysis from the concentration of organisms in the <50 µm and 10 µm category, either via discrete sample analysis, flow-through analysis or through integrated system in real-time. Additionally, FastBallast technology ena-bles the sampling of larger water volumes and can make a distinction between dif-ferent cell sizes, as well as background fluorescence (CTG, 2016). This technology


27

has huge potential for compliance monitoring in the future, as it claims to combine accuracy of a detailed analysis and simplicity of an indicative analysis method. However, FastBallast technology and FRR fluorometry have not been studied widely in compliance-related ballast water studies to this date. Therefore, the relia-bility of this method requires further confirmation from laboratory and onboard tri-als.

3.7.3 Serial dilution culture-most probable number (SDC-MPN) method

The SDC-MPN method has been applied to phytoplankton enumeration since the 1950s (Knight-Jones, 1951). Throndsens’ (1978) SDC-MPN method is based on di-luting samples into a series of subcultures. The concentration of viable cells can be calculated from the amount of viable cells taking into account the dilution factor (Figure 10). The sampling steps include the dilution of the sample, incubation period of 14 days and fluorometer analysis for the detection of the concentration (Casas-Monroy et al., 2016). Due to the extensive incubation time, this method was not seen as reasonable of being assessed further.

Figure 10. Approach for the SDC-MPN method (Cullen and MacIntyre, 2016).


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3.7.4 Laser transmission spectroscopy (LTS)

In contrast to other light based nanoparticle detection methods measuring light dif-fraction and scattering, LTS technology detects wavelength-dependent light trans-mittance through nanoparticles within a sample (Bohren and Huffman, 2000) sum-marized in Li et al. (2011). The method includes recording of light transmission through a suspension fluid sample containing nanoparticles, measurement for wave-length-dependent light transmission and data analysis and inversion by a computer (Figure 11) (Li et al., 2011). Detailed sampling steps are represented in Li et al. (2011), but similarly to microfluidic lab-on-chip devices, LTS utilizes nanobeads with species-specific tags to detect preferred DNA sequences. Therefore, utilization of LTS technology can be only targeted to individual species, not towards entire or-ganism categories.

Figure 11. LTS approach for DNA detection (Li et al., 2011).

Several studies, such as Li et al. (2011), Li et al. (2010), Egan et al. (2013) and Egan et al. (2015) have tested the LTS technology on invasive species detection from bal-last water. In general, sampling duration for the DNA detection techniques used in these studies varied widely between 1 min and several hours. The methodology was proven sensitive and effective in the detection of the targeted DNA, making clear distinction even to closely related species (Li et al., 2011, Egan et al., 2013, Egan et al., 2015). The devices include also portable, cost-effective and automated versions (Egan et al., 2013), and recent development models aim to obtain accurate results in 10 seconds to confirm the compliance status for ships (Egan et al., 2015).

3.7.5 Analysis techniques for larger zooplankton

Bradie (2016) have represented optical zooplankton analysis (OZA) method to de-tect the concentration of zooplankton larger than 200 µm in minimum dimension within a sample. The sampling approach is based on swimming capability of these organisms, as the device captures successive images from the sample and analyses the results using MATLAB software (Bradie, 2016). Sample is diluted into a bottle and placed into the OZA device. Results from the first sample, as well as replicates can be obtained in approximately 15 – 20 minutes, since the sample must be held still for 15 minutes in the device for the debris to settle down. Bradie (2016) re-ported that even though the OZA method is portable and relatively fast, its develop-ment is not yet ready to estimate the acquisition and running costs of the device, and it highly underestimated organism concentrations in comparison to microscope counts.


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Alternatively, Gollasch (2006) has provided another sampling device to detect zoo-plankton larger than 50 µm in minimum dimension. The device includes a flow me-ter, sampling bag and cod-end filter for sample collection (Gollasch, 2006). After the collection, samples can be analysed with petri dishes and a stereomicroscope. The device is designed to sample relatively large quantities of water within a short time range. Additionally, the device is portable and relatively easy to use. However, as mentioned before, microscopic analysis on zooplankton motility is not the most ap-propriate method to detect compliance as organism motility cannot be considered as a sufficient indicator for viability (Bradie, 2016).

3.8 Results overview

In short, feasibility of a sampling method or device for indicative analysis refers to accuracy and representativeness, sampling duration, ease of use, portability, and cost-efficiency (David and Gollasch, 2015). Furthermore, the most appropriate sam-pling methods would include minimal processing and deliver real-time data for mon-itoring, potentially even directly from the piping system of a vessel (First and Drake, 2013). If real-time analysis methods cannot be achieved, it is essential that the pre-ferred methods require minimal amount of chemicals and reagents used, as these methods require processing in a laboratory environment.

The definition for indicative analysis sampling methods in this study includes meth-ods with maximum analysis time of 2 hours, as this was required by the Finnish Transport Safety Agency. These methods and their feasibilities are evaluated in Ta-ble 7. Table 7 was designed to list the methods that should be discussed further in the discussion. Instead of evaluating the sampling accuracy and representativeness here, it was done for the most reliable methods in the discussion section. All meth-ods described in this study are represented in Appendix C (Table 8).


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Table 7. Feasibilities of the assessed indicative sample analysis methods. Green colour represents the best possible suitability for the assessed factor, whereas orange colour refers to the second best suitability and red colour indicates the poorest suita-bility. Suitability factors are comprehensively described in the discussion. Only the six best methods were ranked into this table and rest of the methods were considered unsuitable indicative analysis methods at the time of the study. Most information in this table has been gathered from the literature and some evaluation has been applied on the ‘skills required’ factor. Device manufacturers include the device manufacturers (e.g. luminometer, fluorometer or epifluorescent microscope), but not neces-sarily manufacturers for all reagents/chemicals. Method ranking is based on our evaluation about the method suitability, num-ber 1 indicating the most suitable method and number 6 the least suitable (Gollasch, 2006, Braissant et al., 2010, Reavie et al., 2010, Welschmeyer and Maurer, 2011, Li et al., 2011, Gollasch et al., 2012, Song et al., 2012, First and Drake, 2013, Stehouwer et al., 2013, Bakalar, 2014, Nakata et al., 2014, David and Gollasch, 2015, Gollasch et al., 2015, van Slooten et al., 2015, Bradie, 2016, CTG, 2016).

Method Duration Method reli-ability

Chemi-cals re-quired

Skills required Portable Cost

Re-quires labora-

tory analysis

Manufacturers Rank

ATP 15 min – 1

hour Highly relia-

ble Yes

Some skills required

Yes <10,000

$ No

Aqua-tools (SGS, Luminul-

tra), Welschmeyer,

Hygiena

2

FDA 30 min – 2

hours Moderately

reliable Yes


Yes 450 – 15,000

$ No

Satake, Turner Designs,

Horiba, Hach, Olympus, SPI Supplies, Sev-

eral manufactur-ers for counting

devices

4

PAM Few minutes Highly relia-

ble No Minimal Yes

4,000 – 15,000

$ No

BBE Moldaenke GmbH, Turner Designs, Heinz Walz GmbH,

Hach

1

Flow cy-tomet-ry

Few minutes – few days

Moderately reliable

No Some skills

required No

18,000 $ –

200,000 €

No

BD Biosciences, Beckman Coul-ter, Inc, FluidIm-aging, Bio-Rad,

Cytobuoy

-

Bacterial fluoro-me-

ters 10 – 20 min

Moderately reliable

Yes Some skills

required Yes

2,000 – 10,000

€ No

IDEXX, Bactest, Triton Marine

Science & Con-sult, Turner de-signs, Vista En-terprises, Inc, DeltaTrak, Inc

6

Microca-lorimetry

Few hours Minimally re-

liable No


Yes N/A N/A N/A -

Colori-metry

15 – 30 min Moderately

reliable Yes


Yes

32,000 –

151,000 $

No Vitek, New Hori-zons Diagnos-

tics, Coleparmer -

Lab-on-chip

1 min – 2 hours

Moderately reliable

Yes Some skills

required Yes N/A No

Chips con-structed by vari-

ous pieces 5

FRR fluo-rometry 2 – 10 minutes Moderately

reliable No Minimal Yes N/A No Chelsea Tech-nologies Group,

Ltd 3

LTS Few min – Few hours

Minimally re-liable

Yes Some skills

required Yes N/A No Devices include

several items -

OZA 10 – 30 min Minimally re-

liable No


Yes N/A No N/A -

Zoo-plank-ton sam-pling bag

30 min Minimally re-

liable No


Yes N/A No Hydrobios -


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4. Discussion

As a PSC officer should be able to conduct the sampling with some training, but without an academic education for biology or chemistry (David and Gollasch, 2015), an indicative method should require minimal skills, and the results should be ob-tained after just placing a sample and pushing a button (Table 7). Reliability of a method refers to not only representativeness and accuracy of a sample, but also how widely has the method been tested and evaluated in the previous studies. These fac-tors ultimately affected our ranking system in Table 7. For example, even though Adenosine TriPhosphate (ATP) method requires an extraction step, it was consid-ered more reliable than FRR method, because ATP has been studied widely in bal-last water related trials. The ranking system was based on personal evaluation of the methods presented in the existing literature, and it should be highlighted that other authorities or experts could have constructed the ranking system differently.

In addition, Finnish Transport Safety Agency noted that UV-treatment is clearly the most recommended method between the Baltic Sea area operators and nearly all Ballast Water treatment systems (BWTS) include also a filtration down to 40 µm or even 20 µm (Stehouwer et al., 2015), although filtration alone is probably an insuffi-cient method to treat ballast water. First and Drake (2014) evaluated that certain in-dicative ballast water analysis methods can be treatment-specific, potentially having variability in the detection of BWTS efficiency between different treatment meth-ods. This attribute of UV-treatment has been taken into consideration in the discus-sion section, even though some methods have been able to overcome issues related to compliance monitoring after UV-treatment. Filtration down to 40 µm in turn, pro-motes the testing of smaller organisms than 40 µm since larger organisms are un-likely to pass the filtration, unless the Port State Control (PSC) authority prefers to test the efficiency of filters.

Several methods in Table 7, such as microcalorimetry, colorimetry, lab-on-chip de-vices, Fast Repetition Rate (FRR) fluorometry, Laser Transmission Spectroscopy (LTS), Optical Zooplankton Analysis (OZA) and zooplankton sampling bag suffer from inadequate amount of reliable studies, or alternatively, have not been applied to ballast water compliance sampling comprehensively.

The main purpose of this section is to determine the most appropriate methods for indicative analysis. Once the most appropriate method or combination of methods has been identified, the following section will include also recommendations on the device manufacturers and device-specific notifications.

4.1 General issues and uncertainties regarding the sampling methods

The previous section revealed that the evaluated indicative analysis methods include all some sort of limitations or uncertainties that can potentially have a negative ef-fect on the accuracy and representativeness of the obtained results. Some of these limitations have been overturned by better technology, but some methods may con-tain relatively high uncertainties within the sampling approach. If these uncertainties or limitations cannot be overturned, the method can be considered as unsuitable for compliance testing, and there is no need to evaluate the method any further.


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4.1.1 Minimally reliable methods

As described in Table 7, the OZA, zooplankton sampling bag, microcalorimetry and LTS methods were all considered as minimally reliable for compliance testing. The OZA method is quite early in the development stages (Bradie, 2016) and has not been used widely for compliance testing. Therefore it cannot be considered as a reli-able indicative analysis method in the present study. In order to obtain results from zooplankton sampling bag method, as well as methods assessing smaller zooplank-ton, microscopic analysis on zooplankton motility is required (Gollasch, 2006) and it has been noted repeatedly in the present study that this requires laboratory environ-ment and organism motility is not an appropriate measure for viability (Bradie, 2016).

The main issue related to microcalorimetry is that sampling duration is depending on heat production from metabolic activity of the targeted organism size category (First and Drake, 2013). For the detection of bacterial concentration, the process of heat production requires several hours for smaller organisms (Wadsö, 2002). The analy-sis time, also known as the size-dependent heat production for larger organisms takes only an hour, but sampling protocol for these organisms requires extensive sample preparation (up to several days) (Johnson et al., 2009). Therefore these meth-ods cannot be recommended for indicative compliance testing.

LTS technology has been studied quite extensively in association with ballast water sampling by Li et al. (2010), Li et al. (2011), Egan et al. (2013) and Egan et al. (2015). The method is able to detect the presence or absence of the targeted species quickly and effectively (Li et al., 2011, Egan et al. 2015). Unfortunately the ap-proach shares the same issue with other species-specific sampling methods by being able to only concentrate on single species. The result represents therefore only the absence or presence of the targeted species and fails to indicate the compliance for all other organisms even in one size category. This disadvantage is difficult to over-turn and LTS approach alone can be considered as insufficient analysis method for compliance determination.

4.1.2 Moderately reliable methods

According to Table 7, moderately reliable methods in this study included Fluores-cein diacetate (FDA), flow cytometry, bacterial fluorometers, colorimetry and lab-on-chip devices. Aforementioned statement “any type of staining method has the is-sue of not being able to differentiate between living and viable cells (Reavie et al., 2010)” has been found particularly valid for variable FDA methods in the present study. Several studies, such as Tobiesen et al. (2011), Steinberg et al. (2011), Adams et al. (2014) and MacIntyre and Cullen (2016) all reported that FDA, carboxyfluo-rescein diacetate (CFDA) and 5-Chloromethylfluorescein diacetate (CMFDA) stains tended to overestimate the viable organism concentrations by staining dead cells. Counting the cell concentrations using an epifluorescent microscope exposes the staining methods for live and dead cells to errors, originating from the prolonged counting processes (First and Drake, 2013). The portable FDA bulk analysis con-ducted by Welschmeyer and Maurer (2011) in turn, suffered from extensive sam-pling duration (3 hours).

The only FDA method that could be potentially recommended for indicative analysis sampling is the FDA pulse counter represented by Nakata et al. (2014). The FDA


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pulse counting method is very simple and Nakata et al. (2014) obtained great results using the device. However, it is difficult to rely on a single study while evaluating the suitability of this method. Bradie (2016) was the other researcher who applied the FDA pulse counting method in her study. She concluded that the device underes-timated the cell counts in comparison to microscopic inspection. The device has po-tential of becoming a reliable indicative analysis tool, but the lack of associated sci-entific studies on its efficiency and reliability does not promote the recommendation of such device. Therefore FDA methods cannot be recommended for further evalua-tion on reliable or suitable indicative analysis methods yet.

Interestingly, even though FRR fluorometry has relatively similar sampling ap-proach with Pulse Amplitude-Modulation (PAM) fluorometry including mutual fea-sibility benefits, such as portability, quickness and simplicity, the methodology has not been assessed or used widely in ballast water trials or studies. Studies by Sugget et al. (2001), Kromkamp and Forster (2003) and Sugget et al. (2006) have reported that FRR approach has sensitivity and accuracy benefits over PAM technology re-garding marine phytoplankton samples. Furthermore, the FastBallast technology uti-lizing applied FRR fluorometry can be a successful compliance monitoring tool in the future if the reported attributes of the device can be scientifically proven. The greatest concern with FRR fluorometry at present relates to lack of ballast water re-lated studies. The technology has been developed almost 20 years ago (Kolber et al., 1998) and if it is indeed superior to PAM fluorometry, which is by far the most rec-ommended indicative analysis method out there, why has the technology not been developed further in D2 compliance monitoring? However, FRR fluorometry cannot be ignored from promising indicative analysis methods, as it has potential character-istics to be an efficient sampling methodology. Further evidence on its applicability on ballast water compliance monitoring is required to confirm the comprehensive suitability of the method.

Flow cytometry method has the benefit of being able to provide accurate results on organism concentrations (Bakalar, 2014) and therefore it is recommended as a pri-mary method for detailed analysis (David and Gollasch, 2015). Even though quick flow cytometers also appear to be available (Stehouwer et al., 2013), it remains un-clear how accurately these devices are able to differentiate viable cells from non-via-ble (Olsen et al., 2015, Bradie, 2016). Stehouwer et al. (2013) and Bakalar (2014) already highlighted relatively expensive prices of flow cytometers, but more im-portant aspect appears to be portability of these devices. After a short exploration on technical specifications of the devices used in Joachimsthal et al. (2003), Peperzak and Brussaard (2011) Stehouwer et al. (2013), Bakalar (2014) and Olsen et al. (2015), it became evident that these devices weigh roughly between 20 and 100+ kilograms, indicating that they are not easily portable. These factors altogether sug-gest that flow cytometers are not the most appropriate devices for indicative analysis sampling.

Method for detection of bacterial enzymes within 2 hours suffers also from rela-tively limited studies associated with ballast water sampling, as mainly Gollasch et al. (2012) and Bradie (2016) have reported about the operability of such devices. Relatively simple, portable and fast devices are indeed available (Gollasch et al., 2012) that are able to provide indicative results. As PAM and ATP methods are una-ble to directly indicate the presence or absence of Regulation D2 bacteria, handheld fluorometers can be recommended if the presence of bacteria explicitly needs to be assessed. However, the literature associated with bacterial fluorometers as indicative


34

ballast water analysis devices was found relatively limited and the operability of these devices is suggested to be tested further. In addition, this methodology cannot be recommended as the only indicative sampling device, since it is designed to only assess the targeted bacterial enzymes and therefore fails to indicate the presence of other organisms within the ballast water.

Detection for bacteria via colorimetry method has been discussed by Gollasch et al. (2012) and Bakalar (2014). Both studies stated that a simple colorimetric assessment for Regulation D2 bacteria can be done within 15 minutes, although Gollasch et al. (2012) noted uncertainties regarding to detection limit for bacteria and operability in varying salinities. After focusing on the details of the devices assessed in these stud-ies, it was revealed that the device evaluated in Bakalar (2014) cannot be considered as portable, weighing approximately 75 kilograms (Vitek, 2016). Similarly, devices evaluated in Gollasch et al. (2012) have been mainly designed to assess stool sam-ples (New Horizons Diagnostics, 2011), without distinct relation to testing of ballast water. Due to the lack of ballast water related studies and applications using color-imetry, the methodology cannot be recommended as a suitable option for indicative analysis.

Similarly to bacterial enzyme detection, lab-on-chip devices are mainly able to ana-lyse only the presence of the DNA-targeted species, even though they can also de-tect chlorophyll fluorescence (Song et al., 2012). However, PAM fluorometry is sig-nificantly faster and easier to use in comparison to lab-on-chip methodology for chlorophyll fluorescence measurements. The actual measurement is simple with lab-on-chip devices, but the chip fabrication increases the complexity of the method. The method suffers from lack of studies executed outside of laboratory, but has pro-vided promising results in studies by Song et al. (2012), Wang et al. (2013) and Song et al. (2014). In short, it would be difficult to recommend lab-on-chip devices over PAM and bacterial fluorometers, but if automated chips can be constructed and installed into ships’ piping system to deliver real-time data, this technology should be definitely developed and supported further (First and Drake, 2013).

Approaches targeting individual species cannot be recommended alone, as there is no universal species always present in ballast water that would indicate the compli-ance status. Same principle applies to detection of D2 bacteria, which can be tested in an indicative manner by using bacterial fluorometers. However, if an extremely harmful species, e.g. a target species is recognized which entry into the country would be aimed to prevent and the sampling is preferred to indicate the presence of this species alone, aforementioned methods targeting individual species can be ap-plied. This kind of case is however not related to the compliance of the vessel and thus does not indicate the ballast water treatment success.

The current target species list for the Baltic Sea area is presented in Appendix D (Table 9) (HELCOM, 2015). Majority of the species in this list belong to the cate-gory greater or equal to 50 µm in minimum dimension, and therefore sampling for these species would target most of the unwanted species. Anyhow, it is essential to understand that if the sampling is targeted to target species, or any other group of species, the process does not apply for compliance monitoring purposes. As pre-sented in the introduction, organisms smaller than 50 µm and greater or equal to 10 µm are the most reliable indicator group for compliance monitoring (David and Gol-lasch, 2015, Gollasch et al., 2015), thus leaving most of the species on the target list outside of the sampling.


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4.1.3 Highly reliable methods

ATP and PAM methods were considered as highly reliable indicative analysis meth-ods in the present study. The essential benefit of the ATP method is the ability to measure and detect all organism categories listed in regulation D2 of the Ballast Wa-ter Management Convention (BWM Convention) (van Slooten et al., 2015, Bradie, 2016). The main issues related to ATP method refer to sampling accuracy, as the presence of inorganic constituents within a sample and potential effects of ultraviolet (UV)-treatment can influence the obtained results (Villaverde et al., 1986, Sudharan and Reddy, 2000). However, more recent studies, such as Penru et al. (2012), Wright et al. (2015) and Welschmeyer and Kuo (2016) have been able to overcome these sampling accuracy related issues, suggesting that current ATP methods can success-fully detect the presence and absence of organisms from ballast water although UV-treatment would have been applied. Therefore ATP method is evaluated also further in the following sections.

PAM fluorometry has been widely recommended as potentially the best indicative analysis sampling method (David and Gollasch, 2015), in terms of cost-efficiency, quickness, portability and technical simplicity (First and Drake, 2013, Bakalar, 2014, van Slooten et al., 2015, Bradie, 2016). PAM method can also be vulnerable to under- or overestimation of organism concentrations (Casas-Monroy et al., 2016) and has limitations in association with measuring only autotroph concentrations (van Slooten et al., 2015). Even though PAM measurements are somewhat limited to de-tect only the presence of phytoplankton, van Slooten et al. (2015) and Bradie (2016) were able to obtain accurate results with PAM devices. Due to the conclusions of these studies, PAM method can also be recommended as a suitable indicative analy-sis method.

Overall, all of the aforementioned methods have their advantages and disadvantages. These methods have been evaluated according to the preferences presented by the Finnish Transport Safety Agency, with the purpose of identifying the most feasible method, but nevertheless, avoiding the feasibility to impact negatively on sampling accuracy and reliability. Based on the feasibility and suitability of the assessed meth-ods, PAM and ATP can be evaluated further in this report as the most appropriate indicative analysis methods.

4.2 Accuracy

Sampling accuracy relates to representativeness and reliability of the evaluated ap-proach. The benefit of ATP method over PAM fluorometry is the ability to evaluate the concentration of all organism size categories including autotrophic and hetero-trophic organisms (van Slooten et al., 2015, Bradie, 2016). PAM fluorometry, in turn, is only able to detect the concentrations of autotrophs through chlorophyll a analysis, which can exclude certain organisms even from the measured <50µm and

10µm size class (Gollasch et al., 2015). In theory, no matter how accurately PAM fluorometry is able to measure and count the presence of viable phytoplankton cells, the outcome cannot be considered as being fully representative of this organism size class. In practice however, if the ballast water is dominated by phytoplankton spe-cies (Bradie, 2016), PAM fluorometry can provide reliable measurements on the compliance status of the ship.


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Even though PAM measurements are somewhat limited to detect only the presence of phytoplankton, van Slooten et al. (2015) and Bradie (2016) were able to obtain accurate results with PAM devices. Furthermore, Gollasch et al. (2015) and Casas-Monroy et al. (2016) were able to occasionally obtain more reliable results using PAM fluorometry than the ATP method. Most PAM devices require calibration in order to transfer the fluorescence values into organisms per volume (Bradie, 2016). This can be relatively problematic, as there is no single cell size in the <50µm and

10µm size class that would provide universal conversion into organism concentra-tions. Therefore device comparison is important, as devices can present different or-ganism concentration estimates with the same fluorescence reading.

ATP extraction can be conducted by using a wide range of buffers, acids and sol-vents (Karl, 1980). The extracting reagent can have a significant impact on the ob-tained results (Welschmeyer and Kuo, 2016), as the ATP method requires extraction of ATP from the cells within a sample (First and Drake, 2014). Extraction tech-niques effect on results, as well as on sampling feasibility, since some extraction methods require laboratory resources (Welschmeyer and Kuo, 2016). Stronger acid extraction and various extraction agents are usually preferred on more complex sam-ples containing various organisms. Welschmeyer and Kuo (2016) studied the effi-ciencies of 3 different ATP extraction techniques on cultured phytoplankton. De-tected ATP concentrations varied significantly between the extraction techniques (Figure 12). However, all of these extraction techniques detected successfully the decreased ATP concentration in samples after UV-treatment in the study.

Figure 12. Efficiencies of 3 different ATP extraction methods (Welschmeyer and Kuo, 2016).


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Interpretation of the results can be somewhat problematic, as for organisms smaller than 50 µm and greater or equal to 10 µm, Aqua-tools (2016) suggests that 500 ng/l of cellular ATP is a critical limit for the compliance of the ship, whereas Welschmeyer and Kuo (2016) express that 26.2 ng/l would be the limit value. Welschmeyer and Kuo (2016) used the same LuminUltra Photon Master luminome-ter that is promoted by Aqua-tools (2016), even though this limit value should be in-dependent of analysis device, as it measures concentration of cellular ATP for the same size class.

Therefore, more research should be targeted on critical ATP concentration limits de-tecting compliance status. It is understandable that compliance limits of ATP have some variation, since the ATP extraction efficiency also varies between the used ex-traction techniques and reagents. Wright et al. (2015), Bradie (2016) and Welschmeyer and Kuo (2016) studied ATP concentrations before and after ballast water treatment. In these studies, the ATP concentrations for the <50 µm and 10 µm category in untreated ballast water varied between 60 and 914 ATP ng/l, whereas after treatment the ATP concentrations varied between 0.07 and 20 ATP ng/l. This finding suggests that when BWTS is working, the ATP concentration in ng/l for this size group should be clearly less than 25. On the contrary, if the same concentrations are above 25, the operability of BWTS can be questioned, as Welschmeyer and Kuo (2016) tested the ATP concentrations from various locations after 4 different treatments using different extraction agents and never recorded a value higher than 11.61 ATP ng/l after treatment.

Even though the ATP method enables the sampling of all organism size categories, it is generally used to estimate the organism concentration in the <50 µm and 10 µm category, since sampling for larger organisms requires significantly larger sam-ple volumes (Miller et al., 2011) and Regulation D2 bacteria are rarely present even in untreated water (Welschmeyer and Kuo, 2016). However, Bradie (2016) used ATP method to detect organisms in the 50 µm category. Her study revealed that the ATP method showed relative correlation to microscopy counts (r= 0.552), and was able to detect significant decrease in organism concentration ( 50 µm) after bal-last water treatment.

4.3 Manufacturers

As mentioned in the previous section, due to the differences in calibration, operabil-ity and device specifications, further research on PAM and ATP devices between manufacturers is needed. The selection between ATP equipment manufacturers was not found widely variable in the present study. The main devices used in ballast wa-ter related studies were aqua-tools 2G ATP kit, Welschmeyer ATP and Sys-temSURE ATP instrument by Hygiena. Aqua-tools developed their device in collab-oration with SGS and LuminUltra (Aqua-tools, 2016). The SystemSURE equipment was primarily designed to detect presence of microbes in municipal waters (Hy-giena, 2016). However, First and Drake (2014) used the Hygiena device in a ballast water study and they detected a significant decrease in ATP after chlorine dioxide treatment but not after UV treatment, possibly due to aforementioned effects of UV treatment potentially increasing the amount of cellular ATP in bacteria (Villaverde et al., 1986). Unfortunately, no further details were found on the Welschmeyer ATP method used in Bradie (2016), which prevented the evaluation of this option. There


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were also various other luminometer and extracting agent manufacturers, but their equipment was not necessarily specified to ballast water compliance monitoring.

Gollasch et al. (2015) and Bradie (2016) compared 4 PAM devices in their studies, which can be identified as the main PAM devices designed for ballast water compli-ance monitoring. Similarly to existing ATP luminometers, there were also other PAM fluorometer manufacturers, but their devices were mainly specified to chloro-phyll recordings from leaves. Manufacturers for the devices studied by Gollasch et al. (2015) and Bradie (2016) were BBE Moldaenke GmbH, Turner Designs, Heinz Walz GmbH and Hach Corporation. Gollasch et al. (2015) reported that the PAM device manufactured by Hach Corporation provided the most consistent results be-tween replicates and was able to match the risk levels correctly with the altered, mi-croscopically inspected phytoplankton counts.

Bradie (2016) obtained relatively different results. All 4 PAM devices correlated highly with microscope counts in the <50µm and 10µm size class with minimal differences (r= 0.82 – 0.87) including significantly high correlations between the de-vices (r= 0.75 – 0.96). Moreover, study by Bradie (2016) revealed that the sensitiv-ity varied between the devices with Hach BW680 being the least sensitive device. Hach BW680 was not able to detect fluorescence signal in any replicates within 17 trials, where organisms were found present by microscopic inspection, whereas other devices were able to record signals from these lower chlorophyll concentra-tions. However, differences between the PAM devices in this size class were rela-tively minimal and correlations with the devices and microscope counts varied re-markably in other organism categories. In terms of feasibility, specifications of these ATP and PAM devices for comparison are presented in Table 10. Certain PAM de-vices, such as Ballast-Check 2 by Turner Designs and Hach BW680 are factory cali-brated and ready for use immediately, but despite some differences in sampling pro-tocols between these devices, none of them was considered difficult to use.

Table 10. Specifications of the ATP and PAM devices compared in the present study. Running costs are presented in brackets. These costs for Hach PAM, Turner Designs PAM and SystemSURE ATP were not available (Gollasch et al., 2015 and Bradie, 2016).

Manufacturer Walz

(PAM)

Turner Designs (PAM)

BBE (PAM) Hach (PAM)

Aqua-tools (ATP)

SystemSURE (ATP)

Weight < 5kg + computer 0.4 kg 5 kg 0.3 kg N/A 0.3 kg

Analysis time 5 min 1 min 2-3 min 1 min 15 – 50 min 1 min

Price 15,000 € (5€/sample)

4,500 – 5,000 $

4,300 – 13,800 €

(1€/sample)

< 5,000

$

7700€ (13.33 –

31.83 €/sample)

2,000 – 2,500 €

Filtration re-quired No No Yes No Yes Yes

Computer use External No Internal No External Optional


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The differences in device feasibility or accuracy are relatively minimal, as can be seen from Table 9 and the associated studies by Gollasch et al. (2015) and Bradie (2016). It makes sense that the most consistent results are obtained by the least sen-sitive device, since it is most likely unable to detect small differences between sam-ple replicates. All of the devices are reasonably priced, can be easily used and oper-ate quickly, although Bradie (2016) presented that the Aqua-tools ATP device in-cludes additional running costs (31.83 € for organisms <50µm and 10µm and 50 µm and 13.33 € for bacteria per sample). PAM fluorometers have variation in the or-ganism count determination (Bradie, 2016), but all devices have been able to detect significant differences after ballast water treatment compared to non-treated waters. As PAM fluorometers are able to detect only the viability of phytoplankton, a com-bination of PAM and ATP tests should deliver a reliable indication of the compli-ance. Particularly, as the statement by Bradie (2016) on the presence of phytoplank-ton being able to indirectly detect the presence of zooplankton explained by lack of predation can be questioned. If phytoplankton is detected absent in ballast water samples, it can be simply because the treatment system has worked, or alternatively, due to predation by zooplankton, or both.

4.4 Study limitations

The outcomes of the present study are primarily limited to existing literature and consultation with other researchers. The device attributes and methodological steps conducted in the previous studies can be evaluated to certain extent, but literature review does not compare directly studies including onboard or lab trials. It is essen-tial to notify that most of the studies cited in this literature review were also limited to studying only one or few indicative methods at a time, or alternatively, the sam-pling devices were tested on ballast water originating from certain areas, where the water was dominated by certain group of species.

Drawn conclusions from comparing individual studies require special caution and further research on compliance monitoring is recommended, preferably including various analysis methods on ballast water originating from various locations. In ad-dition, more attention should be drawn to investigation of methods that are able to provide reliable real-time data on organism viability from ballast water tanks or pipes, which would decrease the burden of PSC officers significantly.

5. Conclusions and recommendations

The main goal of the current study was to present the existing indicative ballast wa-ter analysis methods for compliance monitoring in Finland. Additionally, the present study aimed to identify the best sampling methods and devices in terms of reliabil-ity, accuracy and feasibility. The recommended methods in the present study should therefore be able to combine the advantages of low price, quick analysis time, porta-ble analysis devices and reliable results without requiring an academic education for biology or chemistry (David and Gollasch, 2015).

The outcomes of the present study revealed that indicative ballast water analysis should be conducted using the combination of PAM fluorometry and ATP method. Both of these methods are able to detect the effect of ballast water treatment in a


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simple, quick, portable and relatively cheap manner. Turbid water, a relatively com-mon attribute for Baltic Sea waters (Granqvist and Mattila, 2004) should not cause significant disturbances for the results obtained by PAM and ATP devices (Waite et al., 2003, Aqua-tools, 2016, Bradie, 2016). Utilization of 2 different methods also increases the reliability of sampling, when the presence of different sizes and types of organisms can be detected. The differences between various PAM and ATP de-vices were not considered significant in the present study. It is primarily up to PSC authorities, whether they want to invest on device sensitivity, or prefer relatively cheaper and more portable options.

David and Gollasch (2015) and Gollasch et al. (2015) determined that organisms <50µm and 10µm are the most reliable indicator group for the compliance moni-toring and presence of these organisms can be detected by using both, PAM and ATP method. If PSC authorities prefer focusing the sampling process towards cer-tain harmful species, methods targeting individual species or group of species can be used (e.g. LTS), but due to being unable to detect the presence of other species, the outcomes off these approaches are not representative for the full compliance status of the vessel. Overall, more research on compliance monitoring is suggested, espe-cially using several analysis methods on ballast water loaded from various locations to identify the most efficient and reliable indicative analysis method transparently.

6. Acknowledgements

The present study consisted of searching relevant literature and consulting experts about the existing indicative analysis methods and devices. We would like to thank Finnish Transport Safety Agency, Special Adviser Ville-Veikko Intovuori, Head of Unit Mirja Ikonen and Chief Adviser Dr. Anita Mäkinen for enabling the execution of the present study. Additionally, we want to express our gratitude to experts Stephan Gollasch, Christian Moldaenke, Marcel Veldhuis, Sarah Bailey, Tom Brumett and Stephanie Lavelle on their advises on methodological and technological details.


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Appendices

Appendix A: SGS ATP sampling approach


50


51

(SGS, 2015)


52

Appendix B: FDA Pulse counting device

(Nakata et al., 2014)


53

Appendix C: Indicative methods described in the present study Table 8. Indicative methods explained in the present study (Throndsen, 1978, Bohren and Huffman, 2000, Kromkamp and Forster, 2003, Li et al., 2011, Cangelosi, 2011, Fykse et al., 2012, Gollasch et al., 2012, Wang et al., 2013, Song et al., 2014, Bradie, 2016).

Method Description ATP Measures luminescence in the presence of luciferase en-

zyme from seawater extraction. FDA Measures fluorescence from living or dead cells.

PAM Measures photosynthetic activity and phytoplankton bio-mass. Analyses living cells based on variable fluorescence

of chlorophyll of living algae. Microscopy Visual inspection, moving organisms can be counted as vi-

able. Flow cytometry Channels samples to the detector and measures fluores-

cence of stained DNA or chlorophyll. Bacterial fluorometers Detects presence/absence of bacterial enzymes.

Real-time PCR Method amplifies targeted DNA or RNA and detects pres-ence of targeted species.

Microcalorimetry Detects heat generation through metabolism by holding stable sample temperature and measuring heat generation from biological processes indicating the activity or concen-

tration of organisms. Colorimetry Test kits are using known antibodies for the detection of E.

coli and V. cholerae. IDEXX devices Utilizes fluorescing nutrient indicator substrates after the

metabolization by the targeted bacteria. petridiscs/films Detects CFUs of E. coli and Enterococci after incubation of

48 hours by visual assessment. Speedy Breedy Detects microbial respiration through pressure transients.

Lab-on-chip Detects fluorescence from targeted DNA or chlorophyll.

FRR fluorometry Measures chlorophyll fluorescence based on single turno-ver technology

SDC-MPN Enumerates phytoplankton by diluting samples into a series of subcultures. The concentration of viable cells can be cal-culated from the amount of viable cells and the dilution fac-

tor. LTS Detects wavelength-dependent light transmittance through

nanoparticles within a sample, which refers to presence of targeted DNA sequences.

OZA Detects the concentration of zooplankton larger than 200 µm in minimum dimension from swimming capability of

these organisms by capturing successive images from the samples.

Zooplankton sampling bag Detects zooplankton larger than 50 µm in minimum dimen-sion with a sampling bag, cod-end filter, petri dishes and

stereomicroscope.


54

Appendix D: The current target species list for the HELCOM area Table 9. Current target species list for the HELCOM area including organism size categories in minimum dimension (adult life stage) and salinity tolerances. The tolerance values have some variation and certain species can survive outside of these values for short time periods. Note that the salinity tolerance values for survival are not the same as the salinity range where reproduction is successful. Reproduction is successful in much narrower range of salini-ties. Species marked with red colour are less likely to inhabit Northern Baltic Sea (lower salinity tolerance value above 12), species marked with green have been already detected in certain parts of Finnish coastal waters and species marked with white are potentially adaptable to Northern Baltic Sea. *= tolerance for Dinophysis acuminata, since tolerance for D. sacculus was not found, however species co-occur (De Vooys, 1991, Burkholder et al., 1995, Brown and Luoma, 1995, Khan et al., 1996, Ledesma and O’Connor, 2001, Mann and Harding, 2003, Mingkid et al., 2006, Ashton et al., 2007, Adachi et al., 2008, Locke and Carman, 2009, Herborg et al., 2009, Otani and Yamanishi, 2010, Laabir et al., 2011, Peteiro and Sanchez, 2012, Shah and Surati, 2013, HELCOM, 2015, HEL-COM and OSPAR, 2015, Gollasch et al., 2015, Salgado et al., 2015, Eckford-Soper and Daugbjerg 2016).

Species Size categories for organisms Salinity tolerance Alexandrium catenella Dinophycea (10-50 µm) 10 – 40 (13) Alexandrium ostenfeldii Dinophycea (10-50 µm) 5 – 21 (20)

Arcuatula senhouisa Bivalve (>50 µm) 17 – 35 (10) Asterias amurensis Echinodermata (>50 µm) 18 – 41 (21) Callinectes sapidus Crustacea (>50 µm) 3 – 40 (10)

Caprella mutica Crustacea (>50 µm) 16 – 40+ (2) Cercopagis pengoi Crustacea (>50 µm) 0.5 – 10 (10) Corbicula fluminea Mollusca (>50 µm) 0 – 5 (10)

Coscinodiscus wailesii Bacillario-phyceae (>50 µm) 10 – 40 (8) Crassostrea gigas Mollusca (>50 µm) 5 – 42 (10) Crepidula fornicata Mollusca (>50 µm) 5 – 30 (10) Didemnum vexillum Ascidian (>50 µm) 10 – 33 (11, 15)

Dikerogammarus villosus Crustacea (>50 µm) 0 – 12 (10) Dinophysis sacculus Dinoflagellates (10-50 µm) 15 – 34+ * (1) Dreissena bugensis Mollusca (>50 µm) 0 – 3 (10)

Dreissena polymorpha Mollusca (>50 µm) 0 – 3 (10) Ensis americanus Mollusca (>50 µm) 7 – 32 (7)

Fibrocapsa japonica Raphidophyceae (10-50 µm) 10 – 40+ (12) Ficopomatus enigmaticus Polychaeta (>50 µm) 5 – 40 (10)

Gammarus tigrinus Crustacea (>50 µm) 0 – 30 (10) Gracilaria vermiculophylla Rhodo-phycea (>50 µm) 10 – 40 (10)

Grateloupia turuturu Florideophyceae (>50 µm) 12 – 52 (10) Hemigrapsus sanguineus Crustacea (>50 µm) 24 – 35 (14)

Hemigrapsus takanoi Crustacea (>50 µm) 5 – 35 (17) Hemimysis anomala Crustacea (>50 µm) 0 – 10 (10) Hydroides dianthus Polychaeta (>50 µm) 20 – 30 (18) Karenia mikimotoi Dinophycea (10-50 µm) 18 – 40 (10)

Marenzelleria neglecta Polychaeta (>50 µm) 0.5 – 40 (10) Mytilopsis leucophaeata Mollusca (>50 µm) 0 – 30 (10) Mytilus galloprovincialis Bivalve (>50 µm) 19 – 40+ (5)

Neogobius melanostomus Pisces (>50 µm) 4 – 40 (10) Palaemon elegans Crustacea (>50 µm) 0.5 – 5 (10)

Palaemon macrodactylus Crustacea (>50 µm) 1 – 36 (10) Pfiesteria piscicida Dinophycea (10-50 µm) 0 – 35 (4)

Potamocorbula amurensis Bivalve (>50 µm) 0.1 – 32 (3) Pseudochattonella verruculosa Silicoflagellates (10-50 µm) 11 – 33 (6)

Rangia cuneata Mollusca (>50 µm) 5 – 15 (10) Rapana venosa Mollusca (>50 µm) 7 – 32 (16)

Rhithropanopeus harrisii Crustacea (>50 µm) 0.5 – 30 (10) Styela clava Tunicata (>50 µm) 18 – 40 (10)

Undaria pinnatifida Phaeophyceae (>50 µm) 11 – 34 (19)


55

Table references: 1. Adachi, M., Okamoto, N., Matsubara, M., Nishijima, T., & Suzuki, T. (2008). Occurrence of toxic Dinophysis

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implications for its distribution as a marine non-native species. Marine environmental research, 64(3), 305-312.

3. Brown, C. L., & Luoma, S. N. (1995). Use of the euryhaline bivalve Potamocorbula amurensis as a biosentinelspecies to assess trace metal contamination in San Francisco Bay. Marine Ecology Progress Series, 124,129-142.

4. Burkholder, J. M., Glasgow Jr, H. B., & Hobbs, C. W. (1995). Fish kills linked to a toxic ambush-predator dino-flagellate: distribution and environmental conditions. Marine Ecology Progress Series, 124, 43-61.

5. De Vooys, C. G. N. (1991). Anaerobic metabolism in sublittoral living Mytilus galloprovincialis in the Mediterra-nean—IV. Role of amino acids in adaptation to low salinities during anaerobiosis and aerobiosis. ComparativeBiochemistry and Physiology Part A: Physiology, 100(2), 423-431.

6. Eckford-Soper, L., & Daugbjerg, N. (2016). The ichthyotoxic genus Pseudochattonella (Dictyochophyceae):Distribution, toxicity, enumeration, ecological impact, succession and life history–A review. Harmful Algae, 58,51-58.

7. Gollasch, S., Kerckhof, F., Craeymeersch, J., Goulletquer, P., Jensen, K., Jelmert, A. and Minchin, D.(2015).Alien Species Alert: Ensis directus. Current status of invasions by the marine bivalve Ensis directus. ICES Co-operative Research Report No. 323. 32 pp.

8. Gollasch, S. (2006) DAISIE. Coscinodiscus wailesii. Available from: http://www.europe-aliens.org/pdf/Coscino-discus_wailesii.pdf. Date of access 15/3/2016.

9. HELCOM (2015). List of Target Species currently in use in the HELCOM area. HELCOM, Klaipeda.10. HELCOM & OSPAR (2015). Joint Harmonised Procedure for the Contracting Parties of OSPAR and HELCOM

on the granting of exemptions under International Convention for the Control and Management of Ships’ Bal-last Water and Sediments, Regulation A-4. Available from: http://jointbwmexemptions.org/ballast_water_RA.

11. Herborg, L. M., O’Hara, P., & Therriault, T. W. (2009). Forecasting the potential distribution of the invasivetunicate Didemnum vexillum. Journal of Applied Ecology, 46(1), 64-72.

12. Khan, S., Arakawa, O., & Onoue, Y. (1996). Growth Characteristics of a Neurotoxin-Producing ChloromonadFibrocapsa japonica (Raphidophyceae). Journal of the World Aquaculture Society, 27(3), 247-253.

13. Laabir, M., Jauzein, C., Genovesi, B., Masseret, E., Grzebyk, D., Cecchi, P., Vaquer, A., Perrin, Y. & Collos,Y. (2011). Influence of temperature, salinity and irradiance on the growth and cell yield of the harmful red tidedinoflagellate Alexandrium catenella colonizing Mediterranean waters. Journal of plankton research, 33(10),1550-1563.

14. Ledesma, M. E., & O'Connor, N. J. (2001). Habitat and diet of the non-native crab Hemigrapsus sanguineus insoutheastern New England. Northeastern Naturalist, 8(1), 63-78.

15. Locke, A., & Carman, M. (2009). In situ growth of the colonial ascidian Didemnum vexillum under different en-vironmental conditions. Aquatic Invasions, 4(1), 275-278.

16. Mann, R., & Harding, J. M. (2003). Salinity tolerance of larval Rapana venosa: implications for dispersal andestablishment of an invading predatory gastropod on the North American Atlantic coast. The Biological Bulle-tin, 204(1), 96-103.

17. Mingkid, W. M., Yokota, M., & Watanabe, S. (2006). Salinity tolerance of larvae in the penicillate crab Hemi-grapsus takanoi (Decapoda: Brachyura: Grapsidae). La Mer, 44, 17-21.

18. Otani, M., & Yamanishi, R. (2010). Distribution of the alien species Hydroides dianthus (Verrill, 1873)(Poly-chaeta: Serpulidae) in Osaka Bay, Japan, with comments on the factors limiting its invasion. Plankton andBenthos Research, 5(2), 62-68.

19. Peteiro, C., & Sánchez, N. (2012). Comparing salinity tolerance in early stages of the sporophytes of a non-indigenous kelp (Undaria pinnatifida) and a native kelp (Saccharina latissima). Russian Journal of Marine Biol-ogy, 38(2), 197-200.

20. Salgado, P., Vázquez, J. A., Riobó, P., Franco, J. M., Figueroa, R. I., Kremp, A., & Bravo, I. (2015). A Kineticand Factorial Approach to Study the Effects of Temperature and Salinity on Growth and Toxin Production bythe Dinoflagellate Alexandrium ostenfeldii from the Baltic Sea. PloS one, 10(12), e0143021.

21. Shah, F. and S. Surati (2013). "Asterias amurensis" (On-line), Animal Diversity Web. Available from: http://ani-maldiversity.org/accounts/Asterias_amurensis/

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