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Abbreviations amoA Review A decade of nitrous oxide (N 2 O) monitoring in full-scale wastewater treatment processes: A critical review V. Vasilaki a T.M. Massara b P. Stanchev a F. Fatone c E. Katsou a, b, ∗ [email protected] a Department of Civil & Environmental Engineering, Brunel University London, Uxbridge Campus, Middlesex, UB8 3PH, Uxbridge, UK b Institute of Environment, Health and Societies, Brunel University London, Uxbridge Campus, Middlesex, UB8 3PH, Uxbridge, UK c Department of Science and Engineering of Materials, Environment and City Planning, Faculty of Engineering, Polytechnic University of Marche, Ancona, Italy ∗ Corresponding author. Department of Civil & Environmental Engineering, Brunel University London, Uxbridge Campus, Middlesex, UB8 3PH, Uxbridge, UK. Abstract Direct nitrous oxide (N 2 O) emissions during the biological nitrogen removal (BNR) processes can significantly increase the carbon footprint of wastewater treatment plant (WWTP) operations. Recent onsite measurement of N 2 O emissions at WWTPs have been used as an alternative to the controversial theoretical methods for the N 2 O calculation. The full-scale N 2 O monitoring campaigns help to expand our knowledge on the N 2 O production pathways and the triggering operational conditions of processes. The accurate N 2 O monitoring could help to find better process control solutions to mitigate N 2 O emissions of wastewater treatment systems. However, quantifying the emissions and understanding the long-term behaviour of N 2 O fluxes in WWTPs remains challenging and costly. A review of the recent full-scale N 2 O monitoring campaigns is conducted. The analysis covers the quantification and mitigation of emissions for different process groups, focusing on techniques that have been applied for the identification of dominant N 2 O pathways and triggering operational conditions, techniques using operational data and N 2 O data to identify mitigation measures and mechanistic modelling. The analysis of various studies showed that there are still difficulties in the comparison of N 2 O emissions and the development of emission factor (EF) databases; the N 2 O fluxes reported in literature vary significantly even among groups of similar processes. The results indicated that the duration of the monitoring campaigns can impact the EF range. Most N 2 O monitoring campaigns lasting less than one month, have reported N 2 O EFs less than 0.3% of the N-load, whereas studies lasting over a year have a median EF equal to 1.7% of the N-load. The findings of the current study indicate that complex feature extraction and multivariate data mining methods can efficiently convert wastewater operational and N 2 O data into information, determine complex relationships within the available datasets and boost the long-term understanding of the N 2 O fluxes behavio u r. The acquisition of reliable full-scale N 2 O monitoring data is significant for the calibration and validation of the mechanistic models of -describing the N 2 O emission generation in WWTPs. They can be combined with the multivariate tools to further enhance the interpretation of the complicated full-scale N 2 O emission patterns. Finally, a gap between the identification of effective N 2 O mitigation strategies and their actual implementation within the monitoring and control of WWTPs has been identified. This study concludes that there is a further need for i) long-term N 2 O monitoring studies, ii) development of data-driven methodological approaches for the analysis of WWTP operational and N 2 O data, and iii) better understanding of the trade-offs among N 2 O emissions, energy consumption and system performance to support the optimization of the WWTPs operation. Keywords: Nitrous oxide; Full-scale monitoring campaigns; Emission factors; Production pathways; Triggering operational conditions; Mitigation measures; Multivariate data mining methods; Mechanistic models

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Page 1: O) monitoring in full-scale wastewater treatment processes

AbbreviationsamoA

Review

Adecadeofnitrousoxide(N2O)monitoringinfull-scalewastewatertreatmentprocesses:Acriticalreview

V. Vasilakia

T.M.Massarab

P. Stancheva

F. Fatonec

E. Katsoua,b,∗

[email protected]

aDepartmentofCivil&EnvironmentalEngineering,BrunelUniversityLondon,UxbridgeCampus,Middlesex,UB83PH,Uxbridge,UK

bInstituteofEnvironment,HealthandSocieties,BrunelUniversityLondon,UxbridgeCampus,Middlesex,UB83PH,Uxbridge,UK

cDepartmentofScienceandEngineeringofMaterials,EnvironmentandCityPlanning,FacultyofEngineering,PolytechnicUniversityofMarche,Ancona,Italy

∗Correspondingauthor.DepartmentofCivil&EnvironmentalEngineering,BrunelUniversityLondon,UxbridgeCampus,Middlesex,UB83PH,Uxbridge,UK.

Abstract

Direct nitrous oxide (N2O) emissions during the biological nitrogen removal (BNR) processes can significantly increase the carbon footprint of wastewater treatment plant (WWTP) operations. Recent onsite

measurementofN2OemissionsatWWTPshavebeenusedasanalternativetothecontroversialtheoreticalmethodsfortheN2Ocalculation.Thefull-scaleN2OmonitoringcampaignshelptoexpandourknowledgeontheN2O

productionpathwaysand the triggeringoperationalconditionsofprocesses.TheaccurateN2Omonitoringcouldhelp to findbetterprocesscontrolsolutions tomitigateN2Oemissionsofwastewater treatmentsystems.

However,quantifyingtheemissionsandunderstandingthelong-termbehaviourofN2OfluxesinWWTPsremainschallengingandcostly.

Areviewoftherecentfull-scaleN2Omonitoringcampaignsisconducted.Theanalysiscoversthequantificationandmitigationofemissionsfordifferentprocessgroups,focusingontechniquesthathavebeenapplied

forthe identificationofdominantN2Opathwaysandtriggeringoperationalconditions, techniquesusingoperationaldataandN2Odata to identifymitigationmeasuresandmechanisticmodelling.Theanalysisofvarious

studiesshowedthattherearestilldifficultiesinthecomparisonofN2Oemissionsandthedevelopmentofemissionfactor(EF)databases;theN2Ofluxesreportedinliteraturevarysignificantlyevenamonggroupsofsimilar

processes.TheresultsindicatedthatthedurationofthemonitoringcampaignscanimpacttheEFrange.MostN2Omonitoringcampaignslastinglessthanonemonth,havereportedN2OEFslessthan0.3%oftheN-load,

whereasstudieslastingoverayearhaveamedianEFequalto1.7%oftheN-load.Thefindingsofthecurrentstudyindicatethatcomplexfeatureextractionandmultivariatedataminingmethodscanefficientlyconvert

wastewateroperationalandN2Odataintoinformation,determinecomplexrelationshipswithintheavailabledatasetsandboostthelong-termunderstandingoftheN2Ofluxesbehaviour.Theacquisitionofreliablefull-scale

N2Omonitoringdataissignificantforthecalibrationandvalidationofthemechanisticmodelsof-describingtheN2OemissiongenerationinWWTPs.Theycanbecombinedwiththemultivariatetoolstofurtherenhancethe

interpretationofthecomplicatedfull-scaleN2Oemissionpatterns.Finally,agapbetweentheidentificationofeffectiveN2OmitigationstrategiesandtheiractualimplementationwithinthemonitoringandcontrolofWWTPs

hasbeenidentified.Thisstudyconcludesthatthereisafurtherneedfori)long-termN2Omonitoringstudies,ii)developmentofdata-drivenmethodologicalapproachesfortheanalysisofWWTPoperationalandN2Odata,and

iii) betterunderstandingofthetrade-offsamongN2Oemissions,energyconsumptionandsystemperformancetosupporttheoptimizationoftheWWTPsoperation.

Keywords:Nitrousoxide;Full-scalemonitoringcampaigns;Emissionfactors;Productionpathways;Triggeringoperationalconditions;Mitigationmeasures;Multivariatedataminingmethods;Mechanisticmodels

Page 2: O) monitoring in full-scale wastewater treatment processes

Ammoniamonooxygenase

Anammox

AnoxicAmmoniaOxidation

ANN

ArtificialNeuralNetworks

A/O

Anaerobic-Oxic

A2/O

Anaerobic-Anoxic-Oxic

AOA

AmmoniaOxidizingAchaea

AOB

AmmoniaOxidizingBacteria

AOR

AmmoniaOxidationRate

AS

ActivatedSludge

ASM

ActivatedSludgeModels

BNR

BiologicalNitrogenRemoval

BSM2

BenchmarkSimulationModel2

CAS

ConventionalActivatedSludge

CO2

CarbonDioxide

COD

ChemicalOxygenDemand

Page 3: O) monitoring in full-scale wastewater treatment processes

DO

DissolvedOxygen

EF

EmissionFactor

GC

GasChromatography

GHG

GreenhouseGas

HAO

HydroxylamineOxidoreductase

HRT

HydraulicRetentionTime

ICA

IndependentComponentAnalysis

LCA

Lifecycleassessment

MDS

MultidimensionalScaling

MLE

ModifiedLudzack-Ettinger

MLSS

MixedLiquorSuspendedSolids

N

Nitrogen

N2O

NitrousOxide

N2OR

NitrousOxideReductase

NH2OH

Page 4: O) monitoring in full-scale wastewater treatment processes

Hydroxylamine

NH3

Ammonia

NH4+-N

AmmoniumNitrogen

NO

NitricOxide

NO2−-N

NitriteNitrogen

NO3−-N

NitrateNitrogen

NOB

NitriteOxidizingBacteria

NOR

NitricOxideReductase

nosZ

NitrousOxideReductase

OD

OxidationDitch

PCA

PrincipalComponentAnalysis

PE

PopulationEquivalent

PF

PlugFlow

PLS

PartialLeastSquares

r-A2/O

ReversedA2/O

Page 5: O) monitoring in full-scale wastewater treatment processes

qPCR

Real-timePolymeraseChainReaction

RAS

ReturnActivatedSludge

Redox

Reduction-Oxidation

RT-qPCR

ReverseTranscriptionPolymeraseChainReaction

SBR

SequencingBatchReactor

SP

SitePreference

SRT

SludgeRetentionTime

SVM

SupportVectorMachine

TKN

TotalKjeldahlNitrogen

TN

TotalNitrogen

WWTP

WastewaterTreatmentPlant

1IntroductionWastewatertreatmentplants(WWTPs)producesubstantialanthropogenicnitrousoxide(N2O)amounts(Lawetal.,2012).N2Oemissionsfromwastewatertreatmentprocesseshaveincreasedby44%from1990to2014(US

EPA,2016).Moreover,theyareresponsibleforupto26%ofthegreenhousegas(GHG)emissionsofthewholewatersupplychain(includingdrinkingwatersupply,wastewatercollectionandtreatment,effluentdischarge,sludge

processinganddisposal) (Laneetal.,2015).Therefore, theevaluationof theenvironmental impactofWWTPs isgaining increasingattentionworldwide (Guoetal.,2017;Harris et al., 2015a,bHarrisetal.,2015;Law et al., 2012;

Manninaetal.,2016;Massaraetal.,2017).N2Ohasanapproximately300timeshigherGHGeffectthanCO2(IPCC,2013).Comparedtootherunregulatedcombinedhalocarbonemissions,reducingtheanthropogenicN2Oemissionsin

theatmosphereplaysthemostsignificantroleinpreventingtheozonelayerdepletion(Danieletal.,2010;Portmannetal.,2012).

StricterenvironmentalregulationsarebeingimposedonWWTPs,whiletheneedtocontroltheenergyconsumptioncosts,pushestheplantmanagerstowardsthedeploymentofmoreenergyandcostefficientoperational

strategies(Batstoneetal.,2015;Ghoneimetal.,2016).Theintegrationofadditionalsustainabilitymetrics(e.g.GHGemissions,resourcerecovery)fortheevaluationoftheWWTPperformanceisalsogainingattention(Flores-Alsinaet

Page 6: O) monitoring in full-scale wastewater treatment processes

al.,2014;Cornejoetal.,2016).RecentstudieshaverevealedthatthedirectN2OemissionsofbiologicalprocessesinWWTPscanincreasetheoperationalcarbonfootprintby∼78%(Daelmanetal.,2013a)oreven∼83%(Deslooveret

al.,2011).Moreover,thenewtechnologyadoptioninWWTPsrequirestheconsiderationoftrade-offsbetweendirectandindirectGHGemissionstoensurethatitwillnotresultinincreaseoftheoverallcarbonfootprint.SincetheGHG

emissionshaveamajorcontributiontotheoveralltotalenvironmentalimpactofWWTPs,theyshouldbeconsideredaspartofthedecision-makingprocessfortheimprovementofthetechnologicalandoperationalplantperformance

(Sunetal.,2017bSunetal.,2017a,b;Frutosetal.,2018;Contheetal.,2018).Therefore,researchhasbeenconductedinthepastyearstoidentifyandsuggeststrategiesleadingtoN2Omitigationduringthebiologicalnutrientremoval

(BNR) processes (Desloover et al., 2012;Duan et al., 2017; Law et al., 2012). Different operating parameters, such as dissolved oxygen (DO), pH, ammonium (NH4+) and nitrite (NO2−) concentration, configuration types and

environmentalconditions(e.g.temperature),canaffecttheN2OproductioninWWTPs(Deslooveretal.,2012;Kampschreuretal.,2009b;Massaraetal.,2017).Theexact triggeringoperationalandenvironmentalconditions that

governtheN2Ogenerationarestillunderinvestigationbyresearchersandoperators(Wanetal.,2019).Additionally,theexactmechanismsthatdeterminetheN2Ogenerationarenotfullyunderstood,thushinderingtheestablishment

ofmitigationmeasures(Duanetal.,2017).Consequently,thelong-termdynamicsofN2Oemissionsinfull-scaleWWTPscannotbefullyexplained,evenforconventionalprocesses(e.g.plug-flow(PF)reactors)(Daelmanetal.,2015).

Differentdata,samplingtechniquesandanalyticaltoolshavebeenemployedinexistingfull-scaleN2Ostudies.Theobjectivesofthecurrentreviewpaperareto:i)evaluaterecentfindingsfromN2Omonitoringcampaignsin

termsofN2OEFs,dominantpathwaysandmitigationmeasuresfordifferentgroupsoffull-scaleconfigurationsandreactortypes,ii)examinethediscrepanciesamongthemethodsappliedindifferentmonitoringcampaigns,andiii)

evaluatethedataprovidedbystudiesinvestigatingN2Oemissions inWWTPs.Thecurrentstudycriticallyassesseshowthe findingsof theresearchonN2Oemissionshavebeenextrapolated indifferent full-scaleprocessgroups.

Moreover,thisworkevaluatesthemethodsthathavebeenappliedatfull-scalesystemsfortheN2Oquantification,controlandmitigation.Finally,theresearchgapsthatneedtobeaddressedinfuturestudiesarehighlighted.

2TheimpetusforquantifyingtheN2OemissionsinWWTPs2.1EFbenchmarks

EFquantificationinconventionalandadvancedwastewatertreatmentprocessesisessentialtoassessandreducetheresultingenvironmentalimpact(Foleyetal.,2015).CurrentmethodsforthetheoreticalcalculationofN2O

EFs atWWTPs rely on fixed (Palut andCanziani, 2007) or country-specificEFs and similar over-simplified approaches (SinghandMaurya, 2016). Thesemethods underestimate the actual emissions and are considered unreliable

(CadwalladerandVanBriesen,2017),sincetheyarenotrepresentativefordifferentprocessconfigurations,operationalandenvironmentalconditions.Therefore,amaintargetofthestudiesanalysedinthispaperwasto:i)developana

N2Oemissiondatabasefromdifferentmainstream(Ahnetal.,2010b,2010a)/sidestream(Kampschreuretal.,2008;Weissenbacheretal.,2010)processesandinnovativeconfigurations(Deslooveretal.,2011),andii)identifyN2OEFsfrom

processeslocatedinpreviouslyunreportedregionswithdifferentenvironmentalconditions(Wangetal.,2011).

2.2N2OpathwaysandtriggeringoperationalconditionsThemajorityofthefull-scaleN2OmonitoringcampaignsweredrivenbytheneedtoexpandtheknowledgeontheN2Ogeneration inwastewatertreatmentsystems.Severalstudieshavehighlightedthat it is importantto

understandthedominantN2Ogenerationpathwaysinfull-scaleprocessesandinvestigatetheeffectofspecificoperationalconditionstriggeringspecificenzymaticreactionslinkedwithelevatedEFsinfull-scalebiologicalsystems

(Bollonetal.,2016a;Panetal.,2016;Wangetal.,2016b).

TheN2O production during wastewater treatment involves several microbiological reactions during both autotrophic and heterotrophic processes that require either aerobic or anoxic conditions. Threemain biological

pathways have been identified in BNR systems; hydroxylamine (NH2OH) oxidation, nitrifier denitrification and heterotrophic denitrification (Kampschreur et al., 2009b). The NH2OH oxidation pathway is mainly catalysed by the

autotrophicammoniaoxidizingbacteria(AOB)andammonia-oxidizingarchaea(AOA).However,severalstudieshaveshownthatthecontributionofAOAtoN2Oemissionsinwastewaterisexpectedtobelow(Hooper,1968;Ritchieand

Nicholas,1972;Lawetal.,2012).Carantoetal.(2016)haverecentlyshowedthatN2OcanalsobethemainproductofanaerobicNH2OHoxidationcatalysedbythecytochromeP460inN.europaea.Thelattercanbeconsideredasevidence

ofbiologicalN2OgenerationunderlimitedDOandhighNH3concentrations(i.e.Lawetal.,2012),oraspotentialexplanationforthehighN2Oemissionsobservedduringthetransitionfromaerobictoanoxicconditions.Whiteand

Lehnert(2016)havealsosuggestedthatN2OcanbedirectlyproducedduringtheNH2OHoxidation(mediatedbytheNH2OHoxidoreductase(HAO)enzyme)underaerobicconditions,whereastheNO2−detectedcanresultasby-product

ofthenitricoxide(NO)oxidation.TheAOBcanalsoreduceNO2−toNO(bytheaidofnirk)and,subsequently,NOtoN2O(catalysedbynorB)mainlyunderoxygen-limitingconditionsviatheothernitrification-relatedpathway(nitrifier

denitrification)(PothandFocht,1985).Duringdenitrification,theheterotrophicdenitrifiersareresponsibleforthereductionofNO3−/NO2−tonitrogengas(N2).N2Oisanintermediateofdenitrification(SchulthessandGujer,1996).With

theNOreductase(NOR)ascatalyst,NOisreducedtoN2O(HochsteinandTomlinson,1988).NOcanalsoresultasby-productoftheincompleteNH2OHoxidationandthenserveasasubstrateduringdenitrification(HooperandTerry,

1979).Ifthedenitrificationprocesscontinuesundisturbedly,N2OisreducedtoN2inthefinaldenitrificationstep(catalyzedbytheN2Oreductase(N2OR)).Consequently,theheterotrophicdenitrificationprocesscanacteitherasasink

orasasourceofN2O(RobertsonandTiedje,1987).UnderelevatedNH2OHandNO2−concentrations,abioticyetbiologically-drivenN2OpathwayscanalsoconstituteimportantcontributorstotheN2Oemissions(Soler-Jofraetal.,2016;

Teradaetal.,2017;Harperetal.,2015).Insidenitritationreactorsforexample,theabiotic-bioticpathwayofnitrosationispossible;NO2−canreactwiththebiologicallyproducedNH2OHandformN2Oasend-product(Zhu-Barkeretal.,

Page 7: O) monitoring in full-scale wastewater treatment processes

2015).

BasedontheexistingknowledgeontheN2Oproductionpathways,recentreviewsonN2OemissionsfromwastewatertreatmentprocesseshaveconcludedthatthekeyoperationalvariablesresponsiblefortheN2Ogeneration

includebutarenotlimitedtothefollowing:i)lowDO,NO2−orfreenitrousacid(HNO2)accumulationandchangesintheNH4+concentrationinthenitrifyingzones,ii)limitationoforganicsubstrate(i.e.lowchemicaloxygendemand

toN(COD:N)ratio),aswellas,NO2−accumulationinthedenitrifyingzones,iii)alternationofanoxic/aerobicconditions,andiv)abruptchangesintheprocessesandsystemshocks(Duanetal.,2017;Guoetal.,2017;Lawetal.,2012;

Massaraetal.,2017).

Therefore,N2Oemissionscanoccurbecauseofdiversecontributingfactorsandenzymaticreactions.However,theseparametersandreactionscanoccursimultaneously,dynamicallyandbeyondoperators’controlinfull-scale

systems,whereassmallchanges(e.g.DOchanges)cansignificantlyaffecttheN2Oformation.Previousstudiesonfull-scalemonitoringcampaignsintendedto:i)identifythemostimportantoperatingconditions(e.g.aerationrate,DO,

NO2−concentration,pH,etc.)andcorrelatethemwiththeN2Ogeneration(Brottoetal.,2015;Rodriguez-Caballeroetal.,2014),ii)revealtheeffectsofseasonalvariationsontheN2Oformation(Yanetal.,2014),andiii)identifythekey

pathwaysfortheN2Oproduction(Wangetal.,2016b).

2.3N2OmitigationstrategiesAnotherkeyobjectiveof theN2Omonitoring campaignsperformed in thepast years, particularly significant for theWWTPoperators,was thedevelopmentof operational strategies for theminimizationof the emissions

(Deslooveretal.,2012).Therefore,severalauthorshavesuggestedN2Omitigatingmeasuresbasedonthefindingsoffull-scaleN2Omonitoringcampaigns(i.e.Chenetal.,2016;Mampaeyetal.,2016;Panetal.,2016;Wangetal.,2016b).The

proposedstrategiestocontrolN2Oemissions,areanalysedinthefollowingsections.

3EFestimationusingfull-scaleN2OmonitoringdataThis section emphasizes the need to increase the comparability amongst different studies that report N2O emissions. Moreover, it investigates potential trends in the EFs for certain groups of processes and

summarizessummarises thedata requirements for theEFassessment inWWTPs. Incaseswhere theEFswere reportedwith respect tounitsother than the influent totalnitrogen (TN)or the influentNH4+ content, appropriate

conversionsweremade(wherepossible)(seethesupplementarymaterialA–TableA2).

Fig.1showsthepercentageofpastfull-scaleN2Omonitoringcampaignswithreferencetothetreatmentconfigurationsappliedeachtime.AllprocessesconsideredinFig.1aregiveninthesupplementarymaterialB(Tables

B1andB2).Distinctmainstreamprocessconfigurations include,modifiedLudzack-Ettinger (MLE)reactors,conventionalactivatedsludge (CAS)systems (onlyaerobic reactors),A2/O (anaerobic/anoxic/aerobic)processesandA/O

(anoxic/aerobic)reactors.Additionally,oxidationditch(OD)reactortypesandsequencebatchreactor(SBR)typeshavebeenconsideredasdistinctprocessgroups.Sidestreamprocessesthatincludepartial-nitritationreactors,1-step

and2-steppartial-nitritation-anammoxconfigurations)areconsideredadistinctprocessgroupinFig.1.Theprocessesthatdonotbelongtotheaforementionedprocessgroupsarecategorizedseparately(i.e.Bareseletal.,2016;Mello

etal.,2013;Wangetal.,2016a).Detailsforalltheprocessesareprovidedinthesupplementarymaterial.

Most of the process-focusedmonitoring campaigns include, CAS systems (∼12%), (MLE) configurations (∼12%), A2/O configurations (∼10%), oxidation ditches (ODs) (∼8%) and sidestream partial-nitritation reactors or

anammoxsystems(∼11%)(Fig.1).Overall,thesestudiesrefertoawiderangeofconfigurations(i.e.Aboobakaretal.,2013;Brottoetal.,2015;Castro-Barrosetal.,2015;Sunetal.,2017a,bSunetal.,2017a)thathavebeenmonitored

mainlyforshortperiods(i.e.Ahnetal.,2010b;Bellandietal.,2018;Foleyetal.,2010;Panetal.,2016)withvaryingmethodology(e.g.differentgaseoussamplingandanalyticalmeasurementprotocols)(Daelmanetal.,2015;Renet

al.,2013).

Fig.1Thenumberofmonitoringcampaignsreportedinliteratureperprocessgroup;A/O:Anoxic/oxicreactor,A2/O:anaerobic-anoxic-oxicreactor,CAS:conventionalactivatedsludge,MLE:ModifiedLudzack-Ettingerreactor,OD:oxidationditch,SBR:

sequencingbatchreactor,PNandPN/A:partial-nitritationandpartial-nitritation-anammox(1and2-stage).

alt-text:Fig.1

Page 8: O) monitoring in full-scale wastewater treatment processes

EFcomparabilitylimitationsandbenchmarkingamongstthevariousprocesseswillbediscussedinthefollowingsections.However,asageneralremark,theidentificationofpotentialemissionpatternsandtheEFclassification

forspecificgroupsofprocessesisstillchallenging,mainlyduetodifferencesinmonitoringstrategies,operationalconditionsandlengthofmonitoringperiodsamongtheexistingstudies.Additionally,thereisstilllittlereal-fielddata

regardingN2Oemissionsforseveralconventionalandadvancedbiologicalprocesses(e.g.tricklingfilters,denitrifyingpackedbedreactors,biofilmorhybridpartial-nitritationanammoxsystems,etc.).

3.1EFofsecondaryandsidestreamtreatmentprocessesTheN2Oemissionsofthefull-scalewastewatertreatmentprocessesreportedinpaststudiesvarysignificantly;e.g.rangingfrom0.0025%oftheTN-loadforamainstreamMLEreactor(Spinellietal.,2018)to5.6%oftheTN-

loadforamainstreamaerobic/anoxicsettlingSBRreactor(Sunetal.,2013).Overall,thepotentialofN2Oemissionsfromsidestreamreactors(rangingfrom0.17%to5.1%oftheinfluentN-load–supplementarymaterialB,TableB1)is

considered higher compared to the mainstream BNR processes. The latter is mainly because the nitritation/nitrification occurring during sidestream treatment is linked with higher ammonia oxidation rate (AOR) and NO2−

accumulation(Deslooveretal.,2011;GustavssonandlaCourJansen,2011;Kampschreuretal.,2008).Fig.2showsboxplotsoftheobservedEFs(withrespecttotheinfluentN-load)basedonthetreatmentstep.Thewidthoftheshadedarea

surroundingtheboxplotsrepresentsthedatakerneldensitydistributionoftheEFs.SpecificinformationforthemainstreamandsidestreamtechnologiesincludedinFig.2canbefoundininthesupplementarymaterial(TablesA1,B1

andB2).AverageN2Oemissionsforthestudiedmainstreamprocessesisequalto∼0.87%oftheN-load,whereasthemajorityofthequantifiedEFsarebelow0.27%oftheinfluentN-loadaccordingtoFig.2.Ontheotherhand,Fig.2

showsthatN2OEFsresultingfromthetreatmentoftheanaerobicdigestionsupernatant(sidestreamprocess)arehighlyconcentratedjustbelowthemedian(2%oftheN-load).Onaverage,∼2.1%oftheN-loadisemittedasN2Oin

sidestreamprocesses(supplementarymaterialB,TableB2).Accordingtoalifecycleassessment(LCA)studyquantifyingthedirectGHGemissionsforaWWTPinAustria,thesidestreamDEMONprocesscontributedbyover90%tothe

total directN2O emissions compared to themainstreamBNR (Schaubroeck et al., 2015). However, examples of full-scale sidestream Anammox processes with EFs lower than 1% exist in the literature and demonstrate that the

configurationandefficientoperationalstrategiescanmitigateasignificantpercentageamountoftheN2Oproduced(Jossetal.,2009;Weissenbacheretal.,2010).

Fig.3showstheEFboxplotsofvariousprocessesappliedinWWTPs.Intotal51systemswereconsideredinFig.3(supplementarymaterial,TablesA1andB2).AgeneralremarkisthattheN2OEFforthemajorityofthedifferent

processgroups shown inFig.3 varies from0.01 to2%of theN-load.Discrepancies in the emission loads are observed in themajority of thedifferent processgroups and canbepartially attributed to thedifferent site-specific

operationalcharacteristicsandcontrolparameters.Thisindicatesthatthatapartfromthereactorconfiguration,emissionfluxesdependalsoontheoperational/environmentalconditionsandpreferredenzymaticpathways(Wanetal.,

2019).

Fig.2BoxplotsoftheofthereportedEFswithrespecttothestageofthetreatmentprocesses(i.e.mainstreamorsidestream)usingviolinplotoutlines.Therectanglesrepresenttheinterquartilerange.Themedianisdenotedbytheblackhorizontallinedividingtheboxintwoparts.

Thedotsrepresentthevaluesexceeding1.5timestheinterquartilerange.Theupperandlowerwhiskersstandforvalueshigherorlowertheinterquartilerange,respectively(within1.5timestheinterquartilerangeaboveandbelowthe75thand25thpercentile,respectively).The

violinplotoutlinesshowthekernelprobabilitydensityoftheEFinmainstreamandsidestreamprocesses;thewidthoftheshadedarearepresentstheproportionofthedatalocatedthere.

alt-text:Fig.2

Fig.3BoxplotsvisualizingtheEFrangeforthedifferentgroupsofmainstreamprocesses.Therectanglesrepresenttheinterquartilerange.Themedianisdenotedbytheblackhorizontallinedividingtheboxintwoparts.Thedotsrepresentvaluesexceeding1.5timesthe

Page 9: O) monitoring in full-scale wastewater treatment processes

MainstreamSBRsaregenerallyassociatedwithhigherN2Oemissionscomparedtotheotherprocessgroups.EFsrangebetween2%oftheinfluentTKN-loadforanSBRoperatingunderaeratedfeeding,aerobic,settlingand

decantingsequences(Foleyetal.,2010)and5.6%oftheinfluentTN-loadforanSBRoperatingunderaeratedfeeding,aerobicandanoxicsettlinganddecantingsequences(1 heach).HighN2OfluxesinSBRsareattributedtosudden

changesintheconcentrationsofNH4+andNO2−withinthecycle(comparedtootherconfigurations)ortoaccumulateddissolvedN2Oduringanoxicsettlinganddecantinginthesubsequentaerobicphase(Pijuanetal.,2014).

ODreactortypeshavebeenlinkedwithrelativelylowN2Oemissions(averageequalto0.14%oftheN-load),probablyduetothestrongdilutionofthereactorconcentrations(veryhighrecyclingrates)andlesssensitivityto

systemshocks.OneexceptionisthestudyofDaelmanetal.(2015)whomonitoredacoveredanaerobic/anoxic/oxicplug-flowreactorfollowedbytwoparallelCarrouselreactorsfor1yearandfoundthatthesystemhadEFequalto2.8%

oftheN-load.TheauthorsarguedthatintheCarrouselreactor,thesurfaceaeratorsledtozoneswithlimitedoxygenconcentrationtoallowforcompletenitrification(leadingtonitriteNO2−accumulation),whereastheanoxiczones

werealsolimitedtoallowforcompletedenitrification.

CASsystemsshowninFig.3consistofaerobicreactors(1-stepfeedormultiplestep-feed)withoutdedicatedanoxiczonesfordenitrification.TheyarecharacterisedbyaverageEFequalto0.27%oftheN-load,whereasthe

NH4+removal,rangesbetween38%and53%.PeakloadsandrecirculationoftheanaerobicsupernatantcanberesponsiblefortheN2OfluxesobservedinCASsystems,whereashighaerationrateshavebeenreported,enhancingN2O

stripping(Chenetal.,2016).

MLEconfigurationshaveamedianEFequalto0.857%oftheN-load.MLEprocesseswithhighEFs(upto4%oftheN-load)havebeenreportedbyFoleyetal.(2010).LowEFsinMLEconfigurations(i.e.0.003%,0.065%)have

beenobservedinreactorswithdilutedinfluentconcentrationsduetogroundwaterinfiltration(Bellandietal.,2018),nitrificationefficiencylessthan73%(Ahnetal.,2010b)andlowTNremoval(∼59%)duetoCOD/TN < 1.9(Spinellietal.,

2018).LowEFinMLEreactorsrangingfrom0.003%to0.065%oftheNH4+loadhavebeenalsoreportedinthestudiesofCaivanoetal.(2017)andBellandietal.(2018);however,conversionoftheseEFto%N-loadwasnotpossibleand

werenotincludedinFig.3.

N2OemissionfluxesinA2/Oconfigurations,arerelativelylow,inthemajorityofthestudies,withmedianequalto0.1%oftheN-load.OneexceptionisthestudyofWangetal.(2016b);theymonitoredanA2/Oreactoronceper

monthfor1yearandshowedthattheEFvariedfrom0.1to3.4%oftheN-loadbetweendifferentmonths.TheDOconcentrationandoperatingconditionsvariedsignificantlyinthereactor(i.e.DOrangedfrom0.6to6.8 mg L−1).

LimitedN2Omonitoringstudiesexistinfull-scalesidestreamprocesses.One-stagegranularanammoxreactorshaveanaverageEFof1.1%oftheN-load.Thesametwo-stagesuspendedbiomasspartial-nitritationandanammox

processhasbeenmonitoredintwostudies(Kampschreuretal.,2008;Mampaeyetal.,2016).Inthesestudies,theaverageEFinthepartial-nitritationSHARONreactorwas∼2.8%oftheN-loadandwaselevatedcomparedtofull-scale

one-stageanammoxreactors.N2Ofluxesquantification,inlabandpilot-scalesingle-stagegranularanammoxreactorshaveshownEFsrangingfrom0.1to12.19%ofN-load(Wanetal.,2019).Therefore,morestudiesarerequiredto

establishreliablerangesofEFsinsidestreamprocesses.

DifferencesinthereportedN2Ofluxesarealsoobservedinstudiesthatapplysimilarconfigurationsandoperationalconditions(supplementarymaterialB,TablesB1andB2).Forexample,Kampschreuretal.(2008)andMampaey

etal.(2016)monitoredtheN2Oemissionsinthesametwo-stepSHARON-AnammoxreactorsystemandobservedEFsthatwereequalto1.7%and3.8%oftheN-load,respectively.ApartfromslightlydifferentDOsetpoints(2 mg L−1in

theworkofMampaeyetal.(2016)and2.5 mg L−1intheworkofKampschreuretal.(2008),theoperationalconditions(i.e.temperature,influentN-load,systemtreatmentefficiency,hydraulicretentiontime(HRT),etc.;TableB1)during

thetwomonitoringperiodswerequitesimilar.ThemainidentifieddifferencewastheincreasedanoxicliquidN2Oformationduringtheanaerobicperiodofthepartial-nitritationreactorinthestudyofMampaeyetal.(2016).Intermsof

monitoringprotocols,Kampschreuretal.(2008)collectedgrab-samplesforapproximately3days,whereascontinuousgasmonitoringfor21dayswasconductedbyMampaeyetal. (2016).Moreover,theair infiltrationinthecovered

reactorduetonegativepressurewasnotconsideredinthestudyofKampschreuretal.(2008).

TheaforementionedexamplesemphasizethedifficultyinthecomparisonofGHGemissionsamongvariousstudiesanddevelopmentofEFdatabasesforprocessgroups.ThebenchmarkingofGHGemissionsofdifferentplants

canbehamperedevenifthesamemonitoringprotocolisappliedtomonitorprocessesbelongingtothesamegroup.Therearealsocaseswhereemissionshavebeenmeasuredaccordingtodifferentanalyticalprocedureswithinthe

samesystem;factthataddsfurtherdifficultyinthecomparisons.TherelativelyshortmonitoringperiodsandvaryingmonitoringstrategiescanalsoinfluencethecomparabilityandaccuracyofthereportedEFs.Thiswillbediscussed

indetailinthefollowingsections.

3.2DurationofmonitoringcampaignsandseasonalitySeasonalenvironmentalvariabilities,suchastemperature,caninfluencethebacterialcommunitystructureinWWTPs(Flowersetal.,2013).TheN2OformationandemissionsduringtheBNRareexpectedtohavetemporal

interquartilerange.Theupperandlowerwhiskersrepresentvalueshigherorlowertheinterquartilerange,respectively(within1.5timestheinterquartilerangeaboveandbelowthe75thand25thpercentile,respectively).

alt-text:Fig.3

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variations.TemperaturecansignificantlyaffecttheAOBspecificgrowthrateduringnitrification(VanHulleetal.,2010).ThehighertemperaturealsodecreasestheN2Osolubility,thusintensifyingtheN2Ostrippingtotheatmosphere

(Reinoetal.,2017).Ontheotherhand,Adouanietal.(2015)observedthattheN2Oemissionsincreasedupto13%,40%and82%oftheTN-removedattemperaturesequalto20 °C,10 °Cand5 °C,respectively,inabatchreactorfedwith

syntheticwastewater.ThelatterwasattributedtotheincreasedsensitivityoftheN2Oreductaseactivitiesatlowertemperaturescomparedtootherdenitrificationenzymesand,therefore,toincompletedenitrification.Otherseasonal

variations(e.g.influentloading,wetanddryseason)canalsoimpactontheenzymaticreactionsandaffecttheemissions.Vasilakietal.(2018)observedpeaksofN2Oemissionscoincidingwithprecipitationevents,atlowtemperatures,

inanODduringa15-monthmonitoringcampaign.However,furtherinvestigationisrequiredtounderstandpotentialseasonaleffectsontheN2Oemissions.

Themonitoringperiodsofthefull-scalecampaignsforalltheprocessesconsideredinthisanalysisaresummarizedinsupplementarymaterialB,TablesB1andB2.Fig.4showstheEFformainstreamtechnologiesbasedonthe

lengthofthemonitoringperiod.OnlystudiesthathavereportedEFsintermsoftheinfluentN-loadhavebeenconsidered(supplementarymaterialA-TableA1).Themonitoringcampaignshavebeencategorizedinto3distinctgroups,

basedontheirduration,i)short-termcampaignsperformedinalimitedperiodoftime(lessthan1month),ii)medium-termmonitoringcampaignsthatlastmorelongerthanonemonthbuthavenotcapturedallthetemperatureranges

observedinthesystem,andii) long-termmonitoringcampaignsthat lastat least1year.Bothcontinuousanddiscontinuousmonitoringstudieshavebeenincludedintheanalysis.InthediscontinuousN2Omonitoringstudies, the

gaseousN2Ofluxeshavebeengrab-sampled(i.e.viagas-bags,closedchambersetc.)andsubsequentlyquantifiedusinganalyticalmethodsinthelab(offlinemonitoring)orintermittentlysampled(i.e.withfloatingchambersfor1–2

days/month)butquantifiedcontinuouslyon-site(i.e.viaGHGanalyzers) (onlinemonitoring).Mostdiscontinuousmonitoringcampaignshadmonthlyorbi-monthlysamplingfrequency.Theonlyexception is thestudyof (Ahnetal.,

2010b)Ahnetal.,(2016b)whomonitoredseveralsystemsinonlytwodistinctseasons(warmestandcoldesttemperatures).Discontinuousstudieswithsamplingextendingover1-monthperiod,havebeencategorizedasmedium-termor

long-termbasedonthedurationofthestudy(lessormorethan1year).IncontinuousmonitoringcampaignsN2Ofluxeshavebeencollectedcontinuously(i.e.viachambers)andquantifiedonline,on-site(i.e.viaGHGanalysers).

About30%oftheEFsshowninFig.4arebasedonmonitoringperiodslastinglessthantwodays(Foleyetal.,2010;Filalietal.,2013;Samuelssonetal.,2018).AnnualEFvariationhasbeeninvestigateddiscontinuously(monthlyor

bimonthlysamplingfrequency)inapproximately10%ofthesesystems(i.e.Sunetal.,2015;Wangetal.,2016b),whereaslong-term(≥1year)continuousmonitoringcampaignshavebeenperformedonlyintwooftheexaminedworks

(Daelmanetal.,2015;Kosonenetal.,2016).SBRshavebeenexcludedinordertoavoidfurtherbiasesintheresults,sincethereportedaverageN2OemissionsaresignificantlyhigherthantheaverageEFofothermainstreamN-removal

configurations.Sidestreamtechnologieshavebeenalsoexcludedbecausetheyhavenotbeenmonitoredlong-termtoexamineseasonaleffects.TheaverageEFisequalto0.8%(median0.2%)and0.3%(median0.1%)oftheN-loadfor

thesystemsmonitoredshort-termandmedium-term(butwithoutcapturingthewholespectrumofseasonalityeffects),respectively.ThestudiesinvestigatingseasonaltrendsofN2OemissionsreportedanaverageEFof1.5%(median

1.7%)oftheN-load.

Daelmanetal.(2013b)demonstratedthatshort-termcampaigns,inthesysteminvestigated,arelikelytoproduceunreliableEFestimatesindependentlyofthemonitoringapproach.Additionally,theauthorsfoundthatshort-term

campaignshaveahighprobabilitytounderestimateactualemissions.AccordingtoFig.4,thehighestgaseousN2Oloadsbelongtolong-termcontinuousordiscontinuousmonitoringcampaigns.

Long-termandmedium-termcampaignshavealsoshownahighvariabilityof the reportedN2Oemissions.Amongst theexaminedstudies,Daelmanet al. (2015) implemented the longest continuous real-field campaign that

reinforcedtheexistenceofseasonalemissionvariability.Seasonalityisalsosupportedbythefindingsofseveralotherstudies(Brottoetal.,2015;Sunetal.,2013;Yanetal.,2014).Bollonetal.(2016a)Bollonetal.,(2016b)andBollonetal.

(2016b)Bollonetal.,2016a)studiedasidestreamnitrifyingandpost-denitrifyingbiofiltrationsystem,respectively,byperformingtwomonitoringcampaigns;oneinsummerat22.5 °Candoneinwinterattemperatureslowerthan14 °C.

TheirresultsindicatedasignificantseasonalvariationoftheN2Oformation.TheEFofthenitrifyingfilterswasequalto2.3%oftheNH4+removedduringthesummercampaign,and4.9%oftheNH4+removedduringthewinter

campaign.ThedissolvedN2Oconcentrationinthepost-denitrifyingbiofiltereffluentwasequalto1.3%insummerand0.2%inwinterwithrespecttotheNO3−uptake.

Short-termmonitoringperiodsarelikelytomissunderlyingseasonalvariationsintheN2Oformation(orbeaffectedbyshort-termprocessperturbations),and,consequently,complicatethedirectcross-comparisonsbetween

differentstudiesandtheirfindings.Forinstance,themonitoringofanA2/Oprocessforninemonths(grab-samplestakenoncepermonth)ledtoanaverageEFequalto0.08%oftheinfluentTN(Yanetal.,2014),whereasasimilarA2/O

processmonitoredfortwodays(bytakinggrab-samples)presentedanEFof0.85%oftheinfluentTKN(Foleyetal.,2010).Wangetal.(2016b)showedthattheEFfromaPFA2/Oreactorwascharacterizedbysignificantseasonalityand

Fig.4EFvalueswithrespecttothelengthofthemonitoringperiodformainstreamtreatmenttechnologies.

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variedfrom0.01%to3.5%oftheinfluentTN;withintherangeofEFsreportedinthestudiesbyYanetal.(2014)andFoleyetal.(2010).ItcanbeconcludedthattheEFdifferencesbetweensimilarconfigurationsshowninFigs.2and3,

arestronglyaffectedfrombytheseasonalityoftheemissions.

3.3MonitoringandsamplingmethodsSeveralauthorshavehighlightedthatthesamplingmethodologycaninfluencetheEFquantification(Daelmanetal.,2013b;Aboobakaretal.,2013;Wangetal.,2016a;Kosonenetal.,2016;Hwangetal.,2016).Forinstance,several

sourcesofuncertaintyinchambertechniquessimilartothetechniquesappliedinthewastewatersector,havebeenidentifiedinGHGmonitoringcampaignsofrunningwaters(Ducheminetal.,1999;Lorkeetal.,2015;Matthewsetal.,

2003;Vachonetal.,2010).Adetailedanalysisofthepotentialuncertaintiesrelatedtothedifferentsamplingmethodswasoutofthescopeofthisreview.However,itisimportanttounderlinethatfurtherresearchisneededtorevealthe

influenceofdifferentsamplingmethodsandfacilitatethebenchmarkingofEFvaluesfordifferentgroupsofprocesses.

Thissectionfocusesonthesamplingmethod(continuousanddiscontinuous).ThemaincharacteristicsofthesamplingstrategiesareshowninsupplementarymaterialB,TableB1andB2.

Fig.5 illustrates theboxplotof theaverageEFofmainstreamprocesses forcasesof continuousgaseousmonitoringusingagasanalyzeranalyser versus theboxplot for studieswith intermittent samplingcampaigns.Only

medium-termandlong-termstudieshavebeenconsideredintheanalysis(supplementarymaterialA–TableA1).MainstreamprocessesmonitoreddiscontinuouslyexhibitedanaverageEFof0.44%oftheN-load(medianEFwas0.2%of

theN-load),whereasprocessesmonitoredcontinuouslywithgasanalyzersanalysershadanaverageEFequalto1.2%oftheN-load(medianEFis1.1%).Themajorityoftheprocessmonitoredintermittently(onceortwicepermonth)

havecollectedgrab-samplesofN2O fluxes (supplementarymaterialB,TableB2).Offlinegrab sampling is often characterisedby time limitations; usually the sampling occursduringWWTPoperating times andprovidesdiscrete

measurements(e.g.Wangetal.,2011)thatareunabletocapturethewholespectrumofdiurnalvariabilities(Daelmanetal.,2015;Wangetal.,2016a).Additionally,thetemporalvariabilityofN2Oemissionsishighlydynamic(i.e.Daelman

etal.,2015)andstronglyaffectedbyoperationalconditions.Therefore,low-frequency,long-termsamplingmightnotcaptureadequatelythewholerangeofN2Oemissionsinducedbyshort-termchangesinoperationalconditionsand

pollutant concentrations. Overall, the differences can be attributed to the case-specific nature of the EF, as well as to the restrictions concerning the duration and frequency of the discontinuous campaigns (difficulty in

performingcollectinggrab-samplesforlongerperiods-wholedays,nighttime,weekends,etc.).

3.4TowardsbenchmarkingofEFs:progressandlimitationsTheamountofquantifiedemissionsishighlyaffectedbyavarietyofparameters(e.g.processtype,WWTPcharacteristics,monitoringstrategy,durationofmonitoringcampaign,etc.).Therefore,estimatingtheN2OEFs in

WWTPswitheitherofflineoronlinemonitoringcampaignsremainschallenging.Inthissection,effortshavebeenfocusedontheclassificationofEFsfordifferentgroupsofprocessesbasedontheNH4+removalefficiencyandthe

influentflow-rate.Additionally,anan-N2OmonitoringframeworkforthedevelopmentofcomparableEFsforthewastewatersectorisalsodiscussed.

Fig.6showstheEF(withrespecttotheinfluentN-content)formainstreamprocessesandtheachievedNH4+removalefficiency(%).Thedifferentprocessesarerepresentedbydifferentcolours.Adetailedlistoftheexamined

studiesisprovidedinthesupplementarymaterialB(TableB2).Trianglesshowthatseasonaleffectshavebeeninvestigatedintherespectiveprocess,whereascirclesrepresentshort-termstudiesthathavenotinvestigatedseasonality.

Fig.5BoxplotsoftheaverageEFwithrespecttothemethodofgaseoussamplingformedium-termandlong-termstudies(D:discontinuousmonitoring,C:continuousmonitoring)formainstreamtreatmentprocesses.Therectanglesrepresenttheinterquartilerange.Themedianis

representedbytheblackhorizontallinedividingtheboxintwoparts.Thedotsrepresentvaluesexceeding1.5timestheinterquartilerange.Theupperandlowerwhiskersrepresentvalueshigherorlowertheinterquartilerange,respectively(within1.5timestheinterquartilerange

aboveandbelowthe75thand25thpercentile,respectively).

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ThesizeofthedatapointsrepresentsthesizeoftheWWTP.NospecifictrendwasidentifiedbetweentheobservedEFsandNH4+removalforthedifferentmainstreamprocesses.TheN2Oemissionsformostoftheprocessesinsmaller

WWTPs(influentflow-rate<200,000 m3 d−1)werelessthan0.5%oftheN-load,independentlyontheprocesstypeandnitrificationefficiency.Inaddition,mostoftheprocesseswithN2Oemissionslessthan0.1%oftheN-loadreferred

tostudiesperformingshort-termandmedium-termN2Omonitoringcampaigns.

In-depthcomparisonsrequiremoredetailsonconfigurations,controlstrategiesandoperationalconditions.Currently,therearestillnospecificguidelinestostandardizethereportingofoperational,processandmonitoring

strategyinformationfromexistingstudies.ThesupplementarymaterialBsummarizesprocess-basedinformationfrompastfull-scaleN2Omonitoringcampaigns.Overall,∼70%ofthemainstreamstudieshavereportedtheEFsinterms

ofN-load.Additionally,influentandeffluentNH4+concentrationsareavailablefor∼35%ofthesystemsanalysed.Informationonthewatertemperatureduringthemonitoringcampaignhasnotbeenprovidedforhalfofthestudies.

Limitedstudiesprovidedinformationonthecontrolstrategyofthesystem(i.e.DOset-point)andotheroperationalparametersoftheprocesses(i.e.HRT,SRT).GiventhevariabilityofEFsamongstsimilarprocessgroups(Figs.2–6),

theidentificationofEFpatternsneedstoconsiderprocess-specificoperationalandenvironmentalinformation.

Themonitoring strategies require sampling protocols that are case-specific (e.g. the choice of appropriate sampling locations). Specific protocols for the design ofmonitoring strategies can be found in the study of van

Loosdrecht et al. (2016). Elemental mass balances can also be used to confirm the validity of the measurements (Castro-Barros et al., 2015;Mampaey et al., 2016) independently of the applied monitoring protocol. As shown in

supplementarymaterialB,TableB2,grab-samplingthegaseousfluxesoftenlackstheacquisitionofweekendornight-timesamples,thusfailingtodepictthediurnalvariabilityoftheemissions.Moreover,short-termmonitoringstudies

arefrequentlyunabletoaccuratelycapturethetemporalN2Odynamics(Daelmanetal.,2015;Kosonenetal.,2016).Daelmanetal.(2013b)concludedthattheaccuratequantificationoftheaverageN2Oemissionsrequireslong-termonline

orgrab-samplingmonitoringcampaignsthatconsidertheseasonalvariationsoftemperature.

Theanalysisofhistoricalprocessandplantdatacanbealsouseful,linkingtheemissionswithspecificandreoccurringoperationalandenvironmentalconditions(i.e.e.g.dryvswetweather,temperature)forshort-termand

long-termmonitoringcampaigns.Relationshipsestablishedduringshort-termmonitoringcampaignscanbelinkedwiththeperiodicoperationalandenvironmentalprocessconditionsandcannotbegeneralizedtounderstandthelong-

termN2Odynamics(Vasilakietal.,2018).Thelatterisimportantduetotherestrictionsonthedurationofthemonitoringcampaignsduetoresultingfromtheentailedcosts.Itisalsoessentialtoidentifyandreportprocessperturbations

thatcanaffecttheN2Oemissionsevenonalong-termbasis(Vasilakietal.,2018).

ThecurrentanalysisshowsthatN2Ofluxesmustbereportedtogetherwiththeconfigurationtype,theseasonaloperatingconditions(e.g.pH,temperature,influentTN,effluentTN,PE,wastewatervolume,COD,mixedliquor

suspendedsolids(MLSS),SRT,HRT,recycleratios,etc.).

4N2OmonitoringcampaignsandN2OdynamicsSeveralmethodsthathavebeenappliedinN2OmonitoringcampaignscanincreasetheunderstandingoftheN2Ogenerationinfull-scaleprocesses.Theseinclude:i)techniquesforthetranslationofWWTPoperationalandN2O

dataintoinformation(e.g.graphicalrepresentationofvariables,featureextractiontechniques,multivariateanalysis,etc.),ii)mechanisticmodelssimulatingtheN2Odynamics,andiii)techniquesforunveilingtherelativecontribution

Fig.6EFofthemainstreamtechnologieswithrespecttotheachievedNH4+removal.Thedifferentcoloursrepresentdifferentprocesses,whereasthedifferentshapesdifferentiateshort-term,medium-termandlong-termstudies.Thesizeofthedatapointsdepictsthesizeofthe

WWTPintermsofinfluentflow-rate.(Forinterpretationofthereferencestocolourinthisfigurelegend,thereaderisreferredtotheWebversionofthisarticle.)

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ofdifferentpathwaystotheemissions(e.g.isotopicanalysis,real-timepolymerasechainreaction(qPCR)etc.).

TheeffectofseveralparametersthataresignificantforN2Ogeneration(i.e.DO,COD/N,pH,temperature)hasbeenextensivelyinvestigated(Kampschreuretal.,2009b;Lawetal.,2012;Massaraetal.,2017)basedonlab-

scale, pilot scale and full-scalemainstreamand sidestreamprocesses. This section aims to complement these studies; themain findings of the full-scale technologies are categorized for different processgroups focusingon the

techniquesthathavebeenappliedandtheircontributiontotheunderstandingofthebehaviourofN2Oemissions.

4.1Overviewofthetechniques4.1.1TechniquestranslatingWWTPoperationalandN2Odataintoinformation

Morethan40physical,physico-chemicalandbiochemicalvariables(e.g.temperature, flow-rates,reduction-oxidation(redox)potential,DO,N-compoundsandorganicmatterconcentrations,alkalinity,etc.)canbemonitoredonlinetoevaluate

processperformance(VanrolleghemandLee,2003).Onlinemonitoredvariableswhencombinedwithlaboratoryanalysescanprovideuseful insightintotheN2Opatternsandbehaviour.TechniquesthathavebeenappliedtotranslateWWTPdatainto

informationinfull-scaleN2Omonitoringcampaignsinclude:i)graphicalrepresentationsandsimplefeatureextractionmethodsandii)statisticalanalysisanddataminingmethods.

Inmostofthestudies,theonlineandlaboratorydatautilizationhasbeenlimitedtotheinvestigationandgraphicalrepresentationofthesignificantparameters’profiles(i.e.DO,NO2−,NH4+,aerationflow-rate)incombinationwiththeresponseof

theN2Oemissionbehaviour.Additionally,descriptivestatistics(i.e.centraltendency,dispersion,position,etc.)oftheprocessvariablesandN2Oemissionsiscommonlyanalysedandreported.

CorrelationanalysisandlinearmultivariateregressionmodelsarethemainstatisticaltechniquesthathavebeenusedtorevealtheN2Oemissions’dependencieswithoperationalvariablesinfull-scalesystems(i.e.Brottoetal.,2015;Bollonetal.,

2016a;Aboobakaretal.,2013).Dimensionalityreductiontechniques(e.g.principalcomponentanalysis(PCA),independentcomponentanalysis(ICA)),clustering(e.g.hierarchical,k-means),linearandnon-linearsupervisedlearningtechniques(e.g.partial

leastsquares(PLS),artificialneuralnetworks(ANN))andsupportvectormachines(SVM))arealsopowerfultoolsutilisedtotransformtheWWTPdataintoknowledge(Haimietal.,2013;Corominasetal.,2018).However,advancedinformationextraction

methodshaverarelybeenusedtoanalysedatafromN2Omonitoringcampaigns.Recently,Sunetal.(2017a,b)constructedaback-propagationANNtosimulateN2Oemissionsinananaerobic-oxic(A/O)process;thusdemonstratingthefeasibilityand

simplicityofpredictingN2Oemissionswithdata-drivenmodels.

4.1.2Experimentalstudiesinfull-scalesystems–modificationofoperationalconditionsSeveralN2Omonitoringcampaignsinfull-scalesidestreamprocesseshavetesteddifferentoperationalconditionstoinvestigatetheirimpactontheemissions(Bollonetal.,2016b;Castro-Barrosetal.,2015;Mampaeyetal.,2016).Themajorityof

thestudieshavefocusedoninducingchangesinthedurationandflow-rateofaerationcomparedtothebaselinecontrolstrategyoftheexaminedreactors.

4.1.3TechniquesunveilingtherelativecontributionofdifferentpathwaystotheemissionsIsotopicandmolecularbiologyanalysisareemergingtechniquesthatcanprovideinsightsintotheN2Ogenerationpathways.Molecularbiologymethods(e.g.quantitativereversetranscriptionpolymerasechainreactionRT-qPCR,FISH,etc.)can

quantifythemicrobiologicalstructuredrivingtheN-cycleandthebacterialpopulationabletoreduceN2OatWWTPsundervariousenvironmentalandoperationalconditions(Castellano-Hinojosaetal.,2018;Songetal.,2014).Isotopetechniqueshaveonly

recentlybeenimplementedatfull-scalesystemstodistinguishtherespectivecontributionamongtheN2OpathwaysandincreasetheunderstandingofthepathwaysthatareresponsiblefortheN2Oformation(Townsend-Smalletal.,2011;Tumendelgeret

al.,2014).ArecentcriticalevaluationofnaturalabundanceandlabelledisotopesforN2OstudiescanbefoundinthestudyofDuanetal.(2017).

4.1.4MechanisticmodelsThemechanisticmodelsareapopulartoolforthepredictionoftheN2OgenerationandemissionduringtheBNRinWWTPs.Basedondifferentassumptions,avarietyofone-andmultiple-pathwaymodelshavebeensuggested.Theirstructureis

basedeitheronthewidelyacceptedASMlayout(assuggestedbyHenzeetal.,1987,2000),oronthemorerecentelectroncarrierconceptthatdescribestheN2Oproductionviathemechanismoftherelevantcomplexoxidation-reductionreactionstaking

placeduringwastewatertreatment(e.g.Nietal.,2014).Allmodels,though,considertheeffectofchangingoperationalparameters(e.g.DO,NO2−levels,aerationregime,etc.)ontheN2Ogeneration.Acomprehensiveevaluationofthedifferentmodelling

approaches,underlyingassumptions,kinetics,stoichiometricparameters,calibrationandvalidationproceduresofseveralsingle-pathwayandtwo-pathwayAOBmodels,heterotrophicdenitrificationpathwaymodels,andintegratedN2Omodelsdescribing

theall threemajormicrobiologicalpathways isprovidedbyNiandYuan(2015).Theauthorshaveprovidedguidelinesfortheselectionof themostappropriatemodellingapproachunderdifferentDOandNO2−concentrationsbasedon thestructural

assumptionsofthemodels.ThedebateonthemodelthatbestdescribesanddecouplesthemajorN2Oformationpathwaysisstillongoingwithseveralextensionsandvariationsoftheoriginalapproachesdevelopedrecently(Dingetal.,2017;Domingo-

FélezandSmets,2016;Massaraetal.,2018).

4.2Process-basedinsightsbasedontheappliedtechniques

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Robustdocumentationofthedominantpathwaysamongthedifferentprocessconfigurationsisstillmissing(Maetal.,2017).ThissectiondiscussescorrelationsbetweenN2Oemissionsandoperationalvariablesanddominant

N2Opathwaysthathavebeenidentifiedfordifferentfull-scaleprocessgroups.Table1providesasummaryofthedominantN2Opathwaysthathavebeenreportedfordifferentwastewatertreatmentprocessesbasedonthetechniques

thathavebeenappliedinthemonitoringcampaigns.StudiesthathavenotdiscussedpossibleN2Opathwayshavenotbeenconsidered.

Table1MainfindingsofpaststudiesthatresultintheidentificationofthemostcontributiveN2Oproductionpathway(wherepossible).a:Visualizationofsignificantprofiles&descriptiveanalysis,b:Modified

operationmode,c:Statisticalanalysisanddatamining,d:Mechanisticmodeldevelopment,e:Isotopicanalysis,f:real-timeqPCR.

alt-text:Table1

Source Process NH2OHoxidation Nitrifierdenitrification Heterotrophicdenitrification

Castro-Barrosetal.,2015a,b

One-stagePNAgranularSidestream N2Oemissionselevatedduringshiftsfromlowtohighaeration(NH4+accumulation,highAOR)→mainpathway

NotamainpathwayN2Oemissionselevatedduringshiftsfromlowtohighaeration(NH4+accumulation,highAOR)→potentialcontributor

Mampaeyetal.,2016a,b

One-stageSHARONgranularReactorSidestream Notamainpathway PresenceofNH2OHinanoxicperiods;lowerDO

resultinginincreasedN2OemissionsN2OformationduringanoxicperiodsunderthepresenceofNO2−&smallamountsoforganicsubstrate

Kampschreuretal.,2008a

Two-reactorpartial-nitritation-anammoxprocessSidestream

Excludedduringanoxicconditionsinthenitritationreactor(despitesignificantN2Oformation)

• Anammoxreactor:absenceofO2• Nitritationreactor:emissionsnotaffectedbytheinfluentcomposition(thereforeC/N

ratio)

Notamainpathway

Stenströmetal.,2014a

Nitrification-denitrificationSBR,Sidestream Notdiscussed ConsiderableN2OformationunderDO = 0.5 mg L−1&

NO2−accumulation(>20 mg L−1)

N2Oaccumulationduringdenitrification(underconditionsoflowCOD:NandhighNO2−);quicklystrippedofftotheatmosphereassoonasaerationresumed

Wangetal.,2016ba,c,f A2/Owithplug-flowpattern Notadominantpathway(lowNH4+concentrations)

• CoexistenceofNO2−,NH4+&O2-limitingconditions

• CorrelationbetweenNO2−andN2Oemissions

• StrongresponsesbetweenNOandN2Oemissionsandtherelativeabundanceof

AOB.

Notadominantpathway(nopeaksobservedafteranoxiczones)

Wangetal.,2011a A2/O Notamainpathway

• RapidlyincreasedN2OemissionsduetoDOlimitation(DO < 2.5 mg L−1);maximumN2OemissionatDO = 0.75 mg L−1

• IncreaseinNO2−concentration(from0.2toNotamainpathway

Page 15: O) monitoring in full-scale wastewater treatment processes

0.6 mg L−1)duringnitrificationleadingtoincreaseinN2Ofluxes

Toyodaetal.,2011a,f A2/O

SPIsotopicanalysis:

• ∼50%contributioninthebeginningofaerobictank

SPIsotopicanalysis:

• ∼50%contributioninthebeginningofaerobictank

• Dominantpathwayfrommiddletotheendofaerobictank

N2Owasproducedduringdenitrification

Aboobakaretal.,2013a,c A/Oplug-flowreactor Notdominantpathway ConsidereddominantinzoneswithDO < 1.5 mg L−1

ConsidereddominantinzoneswithdepletedNH4+,DOfluctuations,NO3−availability&lackofNO2−

Sunetal.,2017a,bSunetal.,2017aa

A/OHigherDO:certainNO2−amountpotentiallyutilizedtooxidizeNH3toNH2OH,thusleadingtotheN2Oproduction

Low-DOcondition(i.e.<1 mg L−1)usuallyobservedatthebeginningoftheoxiczone Notamainpathway

Kosonenetal.,2016a,c&Blombergetal.,2018d

A/ObioreactorOnlythisAOBpathwaymodelledduetotheexistingDO&NO2−conditions,N2Oproductionmainlyintheaeratedzones,N2Oconsumptionintheanoxiczones→mainpathway

• IncreasingthenumberofnitrifyingzonesresultinginhigheroverallN2Oemissions

(N2Oproductionpossiblyvianitrifier

denitrification)

• Giventhatanoxic-aerobicvolumecontrolledbytheNH4+concentration,unclearif

increasedemissionscausedbyincreased

NH4+concentrationorincreasednumberof

nitrifyingzones

Notmainpathway

Panetal.(2016)&Nietal.,(2015)a,d

2step-feeding,anoxic/oxic/anoxic/oxicplug-flowreactor

N2OemissionsincreasingwiththeAORincrease(2ndstepoftheplug-flowreactor)

N2OemissionsincreasingwiththeAORincrease(2ndstepoftheplug-flowreactor) Notamainpathway

Rodriguez-Caballeroetal.2014a,b

Anoxic/oxic/shortanoxic/oxicplug-flowreactor Notamainpathway

• N2Opeakswhentransitioningfromanoxictoaerobicconditions;O2limitationconsidered

asenhancingtheactivationofthenitrifier

denitrificationpathway

• N2Oemissionsincreasingwithpotentialshockloads;theAOBlikelytoactivatetheir

denitrificationpathwayaftershockloadsof

toxiccompounds

Notamainpathway

Page 16: O) monitoring in full-scale wastewater treatment processes

Castellano-Hinojosaetal.,2018c,e

Twosequentialbioreactors(anoxicandaerated) Notamainpathway

• StrongpositivecorrelationbetweenAOBabundance&N2Oemission;hence,more

possiblepathwayunderanoxicconditions

• 0.5 < DO < 1 mg L−1:enoughO2providedtotheAOBfortheoxygenationofNH3to

NH2OHbutnotforaerobicrespiration;NO2−

potentiallyusedasalternativeelectron

acceptortocompletenitrification

Notamainpathway

Tumendelgeretal.,2014a,f CAS

SPIsotopicanalysis:

• Upto90%contributionatDO∼2.5 mg L−1

• ∼50%contributionatDO∼1.5 mg L−1

SPIsotopicanalysis:

• DominatedatDO < 1.5 mg L−1

• ∼50%contributionatDO∼1.5 mg L−1Notdiscussed

Daelmanetal.,2015a,c Carrouselreactor

Carrousel:emissionscoincidingwithaeratedperiods(AORgovernedbyDO);therelationshipbetweentheAOR&theN2OproductionusuallyexplainedbyreferringtotheNH2OHpathway;however,notconsidereddominant

• Carrousel:emissionscorrelatedwiththeNO2-concentrationpeaks

• Prevalenceoflow-DOzones

Carrousel:reactorlackingsufficientanoxicspacetoallowthecompletionofdenitrification

Nietal.,2013d ODwithsurfaceaerators

MainpathwaysincehighNH4+concentrationswereobservedwithoutsimultaneousNO2−increaseintheaeratedzones/phases

Notamainpathway Notamainpathway

Nietal.,2013d

Feedingandaeration(90 min)/settling(35 min)/decanting(55 min)SBR

MainpathwaysincehighNH4+concentrationswereobservedwithoutsimultaneousNO2−increaseintheaeratedzones/phases

Notamainpathway Notamainpathway

Sunetal.,2013a,c

Feeding(synchronousaeration)/aeration/settling/decantingSBR(1 heach)

Notamainpathway

• LowDOduringnitrificationsignificantlyaffectingtheN2Oproduction→mainpathway

CorrelationbetweenN2OemissionandinfluentCOD/N→contributor

Rodriguez-Caballeroetal.,2015a,b

Reactionphase(∼130 min)/settling(∼65min)anddecanting(∼65 min)SBR(anoxic/aerobicalternations–3cycletypes)

Notamainpathway

• CertainNO2−accumulationunderaerobicconditions

• N2Ogenerationcontinuingafteraerationstop

N2Ogenerationcontinuingafteraerationstop

• SignificantlinearcorrelationbetweenN2O&• SignificantlinearcorrelationbetweenN2O&NOEFsin

Page 17: O) monitoring in full-scale wastewater treatment processes

Wangetal.,2016aa,c

Full-scalebiologicalaeratedfilter(BAF)forsecondarynitrification

LowinfluentNH3concentration(<6 mg L−1)→notamainpathway

NOEFsindifferentseasons

• Nitrifierdenitrificationsuggestedaspossiblepathwayinaccordancewiththefactthatthe

influentNO2−foundaskeyfactorregarding

theN2O&NOproduction

differentseasons

• Minorpossibilityofheterotrophicdenitrification

contributionduringthe

denitrificationofNOtoN2O

Bollonetal.,2016a,bBollonetal.,2016ba

Nitrifyingbiofiltration(Biostyr®filters)

Notdiscussed Notdiscussed

• RapidincreaseinthenetN2Oproductionrateat

BOD:N < 3

• Intensityincreasedwiththedurationofcarbon-limiting

conditions

Overall,themajorityofthestudiesinvestigatingtheN2Odominantpathwaysinmainstreamfull-scalesystemswithdescriptivestatisticsandvisualinspection(i.e.viaunivariate/bivariategraphs)ofsignificantvariables(e.g.

NH4+,DOconcentrations,influentflow-rateandN2Oemissions)havenotconsideredNH2OHtobeasignificantpathwayforN2Ogeneration,regardlesstheconfiguration(Table1).

Specifically,in1-stepfeedA/OandA2/Oconfigurationsandprocesseswithanoxic/aerobicalternationsandplug-flowpattern,studieshaveobserved:i)spatialN2Opeaksinthetransitionsfromanoxictoaerobiczones(i.e.

Rodriguez-Caballeroetal.,2014;Sunetal.,2017a,bSunetal.,2017a)underlowDOconcentrations(<1 mg L−1),ii)temporalincreaseintheN2OemissionsthatcoincidewithelevationofincreasesintheNO2−andNH4+concentrations(i.e.

peak loads)underO2oxygen-limitingconditions (Wanget al., 2011,2016b), and iii)N2Oemission peaks coincidingwith elevatedNO2−concentrations in theaerobic zones (Sun et al., 2017a,bSun et al., 2017a). Therefore, the nitrifier

denitrificationpathwayhasbeensuggestedtobedominant.ThisisalsosupportedbyWangetal.(2016b)whostudiedtherelativeabundancyoftheAOBandthedenitrifyingbacteriaunderdifferentseasonalconditionsforanA2/O

processwith a plug-flow pattern (DO 0.6–6.8 mg L−1 andN-concentration up to 30 mg L−1) to provide insights on the N2O generation pathways. The authors quantified the expression of functional genes harboring the NH3

monooxygenase(amoA)(i.e.enzymecatalyzingthefirststepofnitrification)fortheAOB,aswellasofthenosZharboringtheN2Oreductase(i.e.enzymecatalyzingthereductionofN2OtoN2)forthedenitrifiersbyRT-qPCR.Wangetal.

(2016b),alsoappliedalsocorrelationanalysis;theN2Oemissions(rangingfrom0.01to3.4%oftheTN-load)weremainlydependentbothontheNO2−concentrationsandaswellasontherelativeAOBabundances,henceindicatingthat

nitrifier-denitrificationwasthedominantpathwayintheaerobiczonesofthereactor.Onthecontrary,theobtainedresultsrevealedthattheemissionswerenotaffectedbytherelativeabundanciesofthedenitrifiers.However,itmust

benotedthatthefunctionalgenelevelsarenotalwaysrepresentativeoftheactivityofthecorrespondingenzyme(Hendersonetal.,2010).Similarly,Castellano-Hinojosaetal.(2018)quantifiedthe16SrRNA,amoAandnosZgenesofthe

totalbacterialandarchaealpopulationinthreefull-scalepredenitrification-nitrificationsystems.Theyemployedmultivariateanalysis(i.e.non-metricmultidimensionalscaling(MDS)andsimilarityanalysisbasedonEuclideandistance,

toidentifytheenvironmentalvariablesthatarebestlinkedtothepatternsofcommunitystructureviaBIO-ENVprocedure)tolinkthebacterialstructurewiththeN2Oemissionsandenvironmental/operationalvariables.Theyfounda

strongpositivecorrelationbetweentheAOBandtheemissionsintheanoxiccompartments,wheretheN2Oreleasewashigher.Therefore,theauthorssuggestednitrifierdenitrificationasthedominantpathway.Ontheotherhand,the

emissionswerenegativelycorrelatedwiththeAOAabundanceandtheN2Oreducers.ItwasconcludedthattheelevatedNO2−concentrations,thelowtemperaturesandshortSRTsmainlyinfluencedtheabundanceofthebacterial

communityencodingthenosZgeneandcontributedtotheN2Oaccumulation.

NH2OHoxidationhasnotbeenconsideredasadominantN2OpathwayinthemajorityofA2/OandA/Oprocessgroups.However,Toyodaetal.(2011),appliedsite-preference(SP)isotopicanalysisinaA2/Oconfigurationand

foundthattheNH2OHoxidationandnitrifierdenitrificationpathwayscontributedalmostequallytotheN2Oformationinthebeginningoftheaerobictank.TheDOconcentrationsinthereactor,though,werenotreported.Additionally,

Blombergetal.(2018)developedanASM3-typeNH2OH-heterotrophicdenitrificationN2OmodelwithaKkLa-basedapproachfortheN2Ostripping(KkLaN2O:masstransfercoefficientforN2O).Themodelwasdevelopedforthefull-scale

undergroundWWTPofViikinmäki(Kosonenetal.,2016)thatisdividedintosixzones(i.e.oneanoxicpre-denitrifyingzone,twoalternatingswitchzones,threeaeratednitrifyingzones).HighDOconcentrationsintheaeratedzones(i.e.

1.5–3.8 mg L−1) and low NO2− concentrations (i.e. 0.1 and 0.7 mg L−1), were observednoted and the model adequately fitted the observed dissolved N2O profiles. However, under the applied stripping modelling, the model

overestimatedtheEF;henceshowingthatthestrippingmodellingapproachmustbeimproved.

Page 18: O) monitoring in full-scale wastewater treatment processes

Nietal.(2015)consideredallN2Oproductionpathwaysinatwo-stepplug-flowtypereactor(anoxic/aerobic/anoxic/aerobic),inanattempttoexplainthedifferencebetweentheEFsofeachstep(1ststep:0.7%ofinfluent-N,2nd

step:3.5%ofinfluent-N)usingrealdataobtainedfromthestudyofPanetal.(2016),forafull-scalestep-feedplug-flowreactor.TheN2Oproductionwasmainlyattributedtotheheterotrophicdenitrificationtakingplaceintheanoxic

zoneofthe2ndstepthatwasreceiving70%lessbiomasscomparedtothe1ststep(Table1).ThismodelhasbeensuccessfullyappliedfortheexplanationoftheobservedEFdifferenceandidentificationofdominantpathwayssinceit(i)

includesallthepossibleproductionpathways,(ii)hasconsideredthedesignandoperatingfeaturesoftheWWTP,and(iii)wascalibrated/validatedusingdatafromtheplantoperation.

Itmustbenotedthat, inananoxic-aerobicplug-flowreactor, theapplicationofzone-basedstepwisemultipleregressionshowedthat theeffectof theN-load,DOandtemperature inontheN2Oemissions variedwithin the

reactor(Abookabaretal.,2013).Therefore,thedominantpathwayscanpotentiallyvaryintheaforementionedstudiesbasedonthelocationofthesamplingbetweenamongthedifferentstudies.

IntheODreactorsN2Ofluxeshavebeenmainly linkedwith: i)strippingofthedissolvedN2Othat isgenerated in theanoxiczones (Sunetal.,2015;Yanetal.,2014)andii)NO2− accumulation in thelow DO -DO zones, thus

indicatingnitrifier denitrification andheterotrophicdenitrification (Daelmanet al., 2015) as dominant pathways. For instance, in anOD reactor, strong positive correlation (Pearson's coefficient)was identified between dailyN2O

emissionsanddailyNO2−peaks(0.7)(487daysmonitoringcampaign)(Daelmanetal.,2015).TheauthorsproposedtouseNO2−peaksasadiagnosticmethodforthepredictionofN2Opeaks.Inthesamesystem,Vasilakietal.(2018)

appliedchangepointdetectiontechniquescombinedwithhierarchicalk-meansclusteringandPCA,torevealtheN2Oemissionpatterns,andgenerationpathwaysandidentifiedchangesintheN2Ofluxes.Thestudyconcludedthatthe

N2Odependencieswithotheroperationalvariables(i.e.NH4+,NO3−,DO)aredynamicandaffectedbytheseasonalvariations.ThepreferredN2Opathwayswerealsofoundtobedependentontimeandoperationalconditions.

Additionally,afull-scaleODwasmodelledbyNietal.(2013)consideringtheNH2OHoxidationpathway(viatheelectroncarriersapproach,andassumingtheNH2OH/NOmodelandnoinhibitionofAOBNOreductionbyDO)and

theheterotrophicdenitrificationpathway(basedonHiattandGrady(2008)andtheelectroncompetitionbetweendenitrificationsteps).EventhoughAlthough,theoperationalcontroloftheODisnotexplicitlydescribed(i.e.aeration/DO

set-points),morethan90%oftheN2Oemissionswereobserved inaeratedzoneswithDO > 2 mg L−1.Themodelwascalibratedusing3-daysdata froman intensivemonitoringcampaignandvalidatedbasedon1-daydatawith

differentinfluentconditions.ThedevelopedmodellinkedthehigherN2OgenerationwiththeNH4-N+concentrationpeaks(upto∼9 mg  L−1inthecalibrationand∼4 mg L−1inthevalidationdataset)withintheaeratedzones(OD)

andsuggestingtheNH2OHoxidationpathwayasdominant.However,inthestudyofNietal.(2013),therespectivecontributionamongthetwoAOBpathwayswasnotexploredwhereasshort-termdatawereusedtovalidatethemodel.

TheAOBdenitrificationmodeldevelopedbyMampaeyetal.(2013)wasappliedusingthesamedatasetfromtheOD,forcalibrationandvalidationpurposes(Spérandioetal.,2016).ThemodelcouldadequatelyfollowthetrendsoftheN2O

behaviouraftercalibrationoftheanoxicreductionfunction(highvalueof0.63wasrequired).NO2−accumulationandtheresultingnitrifierdentificationcontributiontotheemissionscannotbeexcludedinfull-scaleWWTPs.Future

enhancedmodelversionsofmodelsmustconsiderthisfact.

Overall,asshowninsection3,N2OfluxesinCASsystemsaregenerallylow.Tumendelgeretal.(2014)appliedSPisotopicanalysisandobservedthattheNH2OHoxidationpathwaywasresponsibleforupto90%oftheN2O

formationunderhighDO(∼2.5 mg L−1atthemiddleandendoftheaerobictank)inaconventionalASsystem(>44.2%ofNH4+wasnitrifiedandremovedingaseousformprobablyduetounintentionalzoneswithlowDO).Nitrifier

denitrificationandNH2OHoxidationwerealmostequallycontributingtoDOlevelsaround1.5 mgL1,whereasnitrifierdenitrificationdominatedatDOsbelow1.5 mg L−1.

Insidestreamreactors,elevatedN2Oemissionsduringshiftsfromlowtohighaeration(NH4+accumulation,highAOR),havebeenattributedtotheNH2OHpathway(Castro-Barrosetal.,2015).Aerationintensityandprofiles

havebeendeterminedassignificantcontrolparametersfortheN2Ogeneration(Harrisetal.,2015a,bHarrisetal.,2015;Rathnayakeetal.,2015).TheN2Odynamicsunderdifferentaerationintensitiesislikelytodependonthereactor

configuration.Forexample,Mampaeyetal.(2016)andStenströmetal.(2014)observedhigheremissionswhenlowerDOwasappliedinapartialnitritation-anammoxsystemandasidestreamnitrification-denitrificationSBR,respectively.

Ontheotherhand,(Kampschreuretal.,2009a),)couldnotidentifyarelationshipbetweentheN2Oincreaseandthehigheraerationflow-rateduringaprolongedaerationexperimentinasingle-stagenitritation-anammoxreactor.Hence,

theinfluenceoftheaerationregimeontheN2Ogenerationisvariableanddependson,dependingon thereactorconfiguration. InAnammoxreactors,N2Oformationduringanoxicperiodshasbeenmainlyattributedto thenitrifier

denitrification pathway and partially to heterotrophic denitrification (e.g. under conditions of limited substrate provision; Castro-Barros et al., 2015;Mampaey et al., 2016). However, a recent study performed byMa et al. (2017)

demonstratedthatN2OformationviaNH2OHoxidationcanalsooccuratlowDO(∼1 mg L−1)probablycatalysedbycytochromeP460.Thisfindingcontradictspreviousexperimentalandmodel-basedworksaccordingtowhichthe

NH2OHoxidationpathwaydominatessolelyathigherDOconcentrations(e.g.Brottoetal.,2015)andislinkedwiththeAOR(Pengetal.,2014).

StudiesinvestigatingdominantN2Opathwaysforseveralgroupsoffull-scaleprocessesarestillmissing(Table1)andfurtherresearchisrequiredforarobustmappingofthedominantpathwaysindifferentprocessgroups.Asa

generalremark,inmostprocesseswithelevatedN2Oemissions(>0.85%oftheN-load),independentlyoftheconfiguration,elevatedNO2−concentrationswerealsoobserved(Daelmanetal.,2015;Foleyetal.,2010;Rodriguez-Caballeroet

al.,2015;Wangetal.,2016b).

4.3LimitationsandfutureresearchSeveral authorshaveunderlined thedifficulties in determining the respective contribution of eachN2Ogenerationpathwayduring full-scalemonitoring campaigns (Aboobakar et al., 2013;Wang et al., 2016b).None of the

Page 19: O) monitoring in full-scale wastewater treatment processes

techniquesanalyzsed,intheprevioussectionscanbeappliedstandalonetoexplaintheambiguitiessurroundingthemechanismsandoperationalconditionsthatenhancetheN2Oformationduringwastewatertreatment.Thissection,

summarizesthemainlimitationsofthetechniquesappliedtoexplainN2Ofluxesbehaviourinwastewatersystemsanddescribeshowthesetechniques(combined,)canmaximizetheoutcomeoffuturemonitoringcampaigns.

TwomajordrawbackshavebeenidentifiedwhenstudiesrelyexclusivelyonsimpledescriptivestatisticsandgraphicalrepresentationsofoperationalvariablestoextractprovideinsightsontheN2Oemissionsbehaviour.Firstly,

thisapproach,considersindependentlyseveralparametersthataffecttheN2Ogeneration.Thus,itbecomesdifficulttoquantifythecombinedeffectofseveralvariablesinfull-scalesystemsviasimpleunivariateorbi-variategraphical

representations.Forinstance,Castro-Barrosetal.(2015)observedhigherN2Oemissionsandformationinthetransitionfromtheanoxictotheaeratedperiodsinaone-stagegranularpartialnitritation-anammoxreactor.However,this

increasecannotbesolelyattributedtotheDOchange,sincetheAOR,theNH4+andtheNO2−concentrationswerealsoelevatedduringthetransition.Secondly,graphsrepresentingthebehaviourofprocessvariablesinrelationtothe

N2Oemissions usually cover only a limited period (often shorter than themonitoring campaign duration) or visualize average data in themajority of the reported studies. There are limitations in the effective visualization and

dependenciesintheextractioninofinformationfromlong-termtemporalmultivariatedatasets(i.e.overcrowdedandclutteredvisualizationvisualisation)(Shurkhovetskyyetal.,2018).Forexample,Aboobakaretal.(2013)monitoredtheN2O

emissionsofaPFreactorfor56days;theyreportedthediurnalprofileofthenormalizednormalisedaverageemissions,theaverageammoniaNH4+diurnalprofileandtheaveragedailyDOconcentration,-overthroughoutthedurationof

themonitoringcampaign.

The N2O emission behaviour is characterised by temporal variations. Additionally, for a specific process, the dependencies of the emissions with operational variables are also expected to fluctuate under different

environmental/operationalconditionsbasedonthepreferredN2Oproductionpathway.Vasilakietal.(2018)showedthatthedependenciesbetweenN2OemissionsandoperationalvariablesfluctuatedinaCarrouselreactorthatwas

monitored for 15 months. Therefore, employment of advanced visualizationvisualisation techniques capturing the dynamic behaviour of the operational variables from the whole duration of the monitoring campaigns (e.g. data

abstraction,principal component-basedanalysis, clustering-;Shurkhovetskyy et al., 2018;Aigner et al., 2008) can facilitate an accurate and deep understanding of the long-termN2Obehaviour in both sidestream andmainstream

treatmentprocesses.Thereisaninterchangeablelinkamongoperational/environmentalconditions,N2Oproductionpathwaysandemissionrates.Thecombinationofdataminingmethodscanalsobeappliedtoidentifydistinct,and

differentpatternsandoperationalconditions inordertodeterminetherespectivecontributionamongtheN2Oproductionpathways.Itcanrevealthe linksandrelationshipsamongtheconditionstriggeringtheN2Oemissions, the

dominantpathwaysand theemission rates.However, fewdata-drivenmonitoringandcontrol approacheshavebeenvalidated in full-scale applications in thewastewater sector (Haimi et al., 2013). Additionally, there is still little

guidancefortheselectionofthemostappropriatetechniquesforparticularwastewaterapplications(Hadjimichaeletal.,2016).Multivariatestatisticalanalysis techniqueshaveonlyrecentlybeenappliedtotranslatedata fromthe

monitoringcampaignsintousefulinformationregardingtheN2Oproduction(Vasilakietal.,2018).Hence,structuredapproachesanddata-drivenextractiontechniquesneedtobedevelopedtoprocesstheincomingdatafromWWTPs

(Corominasetal.,2018)andacquireinformationconcerningtheN2Oemissionpatterns.

Given thatpreviouslyunreportedpathways (Harrisetal.,2015a,bHarris et al., 2015), oralternativeconditionsunderwhich thealreadyknownpathwaysareactivatedhavebeen recently identified in literature, isotopicand

molecularbiologyanalysiscanbeappliedtosupportprovideinsightsintotheN2Ogenerationpathways.InarecentreviewontheisotopicmethodsfortheidentificationoftherespectivecontributionsofthedifferentN2Opathways,Duan

etal.(2017)concluded,though,thattherearestilluncertaintiesregardingtheaccuracyoftheSPmethods(i.e.notstandardSPsignaturevalues,unknownN2Oproductionpathways,etc.).Therefore,theauthorssuggesttocomplement

theisotopicmethodswithotherapproaches,suchasthemRNA-basedtranscriptionanalysis(Ishiietal.,2014).

MechanisticmodelsconsideringallthepossibleN2Oproductionpathwaysarepowerfultoolstodescribetheoperationoffull-scaleWWTPs,unveilthemostcontributiveN2Oemissionsgenerationpathwaysandguidetowards

mitigationmeasures.However,therearestill,severalchallengesinthepracticalapplication,calibrationandvalidationofmechanisticN2Omodelsinfull-scalesystems.Parameteruncertaintystillplaysasignificantroleinexplicitly

differentiatingthecontributionofdifferentN2Opathwaysviamodellingstudies;forinstance,differentAOBpathwaymodels(i.e.Spérandioetal.,2016)andmodelswithdifferentcontributionsofdenitrificationN2O-producingpathways

(i.e.Domingo-Félezetal.,2017)havebeenadequatelyfittedtodescribetheN2Oemissionsbehaviourinthesamesystems.InclusionofallmajorN2Oproductionpathwaysresultsincomplexandoverparameterizedmodelsimpairing

reliablecalibrationandvalidation.Additionally,short-termcalibrationandvalidationofmodelsunderspecificoperationalconditions(i.e.dryweather)limitstheiraccuracywhenthesystemvariessignificantly(GuoandVanrolleghem,

2014).

Futureresearchcanalsoexplorethepossibilityofcouplingsophisticatedstatisticaltools(e.g.multivariatestatistics,machinelearningalgorithms)withmultiple-pathwaymechanisticmodelsforfull-scaleapplications,and,thus,

tofacilitatethefastandadaptableonline implementationofmodelpredictivecontrolandforecastingdecisionsupporttools.Theco-applicationofmachine learningandmechanisticmodels inserialorparallelconfigurationshave

already been demonstrated in thewastewater sector. For instance, studies have shown that artificial neural network (ANN)models trainedwith the residuals of ASM-typemodels can enhance the prediction and generalization

capabilitiesoftheASMmodelsforhighlydailyvariableinfluentpollutantconcentrationsandflow-ratesthatareusuallyobservedatWWTPs(Fangetal.,2010;KeskitaloandLeiviskä,2015).Additionally,detailedsimulationsofvariables

thatarenotconventionallymonitoredinWWTPs(i.e.NO2−,NO)canbederivedfromthecomputationallyuniversalmechanisticmodelsandusedtogetherwithrawprocessdatatotrainthemachinelearningmodels.Trainedmachine

learningmodelsareveryefficientforonlinemonitoringandprovisionofdecisionsupport,andarewidelyappliedintheindustry(Wangetal.,2018).Forexample,asupportvectormachineregression(SVM)modelwastrainedwiththe

Page 20: O) monitoring in full-scale wastewater treatment processes

simulationresultsofanASM2dmodelforafull-scaleA2/OreactorinthestudyofZhangetal.(2014),todevelopaflexiblemulti-objectiveoptimizationtool.TheSVMmodellinkedtheoperatingparameterdatasetsfromtheASM2d

simulationswithperformanceindexesconsideringcostandpollutantconcentrationaspects.ThedevelopmentoflayerbasedonmachinelearningtechniquescanbeintegratedinauniversalcomplexN2Omodeltofacilitateindividual

processparameterscalibration.MultivariatestatisticsandpatternrecognitionalgorithmscanbeappliedtothevariablesmonitoredonlineinWWTPstodifferentiateoperationalconditions(i.e.basedonseasonalinfluentcomposition

variations,differentprocessratesaffectedbyenvironmentalconditions,systemshocks,etc.)andguidetowardsdifferentcalibrationrequirementswithinthesameprocess.Finally,multivariatestatisticscanbeappliedtoidentifyand

isolatecomplexrelationshipsbetweensystemvariablesandguidetowardsprocess-specificsimplifiedmodellingapproaches.Suchintegrated,practicaltoolscanhelpplantoperatorstodesigneffectivemitigationstrategies.

QuantifyingthecontributionoftheN2Oproductionpathwaysinadditiontothetriggeringmechanismsinbiologicalprocessesremainsachallengeandstillrequiresextensiveresearch(Guoetal.,2017).Asageneralremark,

standardizationofmonitoringandreporting,oflong-termN2Omonitoringcampaigns,alongwithcombinedmultivariateanalysesoftheprovideddataandmechanisticmodeldevelopmentarerequiredtoincreasetheunderstandingand

effectivelycontroltheN2OemissionsatWWTPs.

5MonitoringcampaignsandmitigationstrategiesMitigationmeasureshavebeendevelopedandproposedmainlyasoutcomeofstudiesthattargetedatthe:i)testingofdifferentaeration/feedingcontrolstrategiesinfull-scalesidestreamtechnologies(e.g.Castro-Barrosetal.,

2015;Mampaeyetal.,2016;Rodriguez-Caballeroetal.,2015),ii)developmentofmechanisticmodelssimulatingchangingoperationalconditions(e.g.Nietal.,2015),andiii)establishmentofnon-linearregressionmodels(e.g.ANNs)

topredictthebehaviourofN2Oemissions(e.g.Sunetal.,2017a,bSunetal.,2017a).

5.1Mitigationmeasuresandfull-scalemonitoringcampaignsTable2summarizessummarisesthemainN2Omitigationstrategiesthathavebeenproposedforfull-scalesystemsalongwiththemethodologicalapproachesthatfacilitatedtheidentificationofthesemeasures.AsshowninTable

2,thereisnostandardizedstandardisedmethodologyfortheestablishmentofN2Omitigationstrategiesinfull-scalesystems.

Table2Methodsandmainfindingsofstudiesresultinginmitigationmeasures.

alt-text:Table2

Source Process Method Mainfindings MitigationMeasures

Castro-Barrosetal.(2015) One-stagePNAgranularSidestream

• CalculationofdissolvedN2ObasedonMampaey

etal.(2016)

• Modifiedoperationmode:Prolongedanoxic&

aerationperiods

• Visualizationofsignificantprofiles(i.e.DO)&

descriptiveanalysis

• SmootheraerationtransitionsduringnormalreactoroperationconnectedwithlowerN2O

emissions;comparisonwithexperiments

• ProlongedanoxicperiodsleadingtoincreasedN2Oemissions

• Optimizetheaerationregime

• Ensuresmoothshiftsintheaerationpattern

• Optforshortaerationintervals

• Modifiedoperationmode:prolongedanoxic&

aerationperiods,lower

DOexperiments,shorter

SBRcycles

• Nitritationreactor:N2Oformationhigherduringanoxicperiods

• Splittingtheanoxicperiod:averageanoxicN2O

• Preferablyoperateundershortercycles

• Applycontinuousaerationinnitritation

Page 21: O) monitoring in full-scale wastewater treatment processes

Mampaeyetal.(2016)

One-stageSHARONgranularReactorSidestream

• CalculationofdissolvedN2ObasedonMampaey

etal.(2016)

• Visualizationofsignificantprofiles(i.e.DO)&

descriptiveanalysis

formationratedecreased

• ShortercyclesreducingtheN2OEFby56%attheexpenseofhigherNO3−concentrations

reactor;thisrequiringoptimization

• AlternativelyoperateunderlowerDOsetpoint

Kampschreuretal.,2009a,bKampschreuret.al.,2009a

One-stagePNAgranular

• Modifyoperation:varyingaerationrate

• Visualizationofsignificantprofiles&descriptive

analysis

• Over-aerationsignificantlyimpactingonN2Oemissions

• Ensuresufficientaerationcontrol

Kampschreuretal.(2008)

Two-reactorpartial-nitritation-anammoxprocessSidestream

• Visualizationofsignificantprofiles(N-compounds)&

descriptiveanalysis

• Nitritationreactor:N2Oaccumulationduringthenon-aeratedphase

• Anammoxreactor:NO2−accumulationpotentiallyincreasingN2Oemissions

• Avoidanoxicphasesinnitritationreactor(i.e.smallerreactorstoensuresufficient

HRT)

• Controltheaerationinthenitritationreactor

• Operateaone-reactornitritation-anammoxsystem;potentiallyemittinglessN2Odue

tolimitedNO2−accumulation

Ahnetal.,2010a,b Multipleprocesses(i.e.MLE,step-feedBNR,OD)

• Multiplelinearregressionforseveralprocesses

• InvestigatepossiblelinksbetweenWWTPoperatingconditions&N2Oemissionfluxes

• Aerobiczones:N2Ofluxescorrelatedwithlocation-specificpH,ASmixedliquor

temperature,DO,NH4+&NO2−

concentrations&interactivecombinations

• Anoxiczones:N2Ofluxescorrelatedwithlocation-specificsCOD,pH,ASmixed-liquor

temperature,DO,NO2−&NO3−

concentrations&interactivecombinations

• BNRprocesses:AvoidhighNH4+&NO2−

concentrations,DO&transients

• Aerobicprocesses:avoidincomplete/intermittentnitrification&

over-aeration

• RelyonmoreuniformspatialDOprofilestopromoteSND

• MinimizepeakN-flow(flowequalization)

Page 22: O) monitoring in full-scale wastewater treatment processes

Nietal.(2013)

ODwithsurfaceaerators&Feedingandaeration(90 min)/settling(35 min)/decanting(55 min)SBR

• Mechanisticmodeldevelopment

• Modellingoftwofull-scalemunicipalWWTPs(i.e.anOD&anSBR)

• OD:decreaseintheNH4+concentrationwithoutsimultaneousNO2−increaseinthe

aeratedzones

• SBR:NH4+accumulationleadingtoahighAORduringtheaerobicSBRphases&,finally,tothe

increasedproductionofintermediates(e.g.

NH2OH)

• Usethedevelopedmodeltoaccuratelysimulatetheemissionsfromthesurface

aeratorzoneinODWWTPs,thus

potentiallycorrectingtheN2Oemission

underestimationinfull-scaleWWTPs

wherethefloatingchambermethodisnot

valid

Lietal.,2016 ReversedA2/OandOD• Observations&literature

• N2Ogenerated&emittedmoreinsummerthaninwinter

• Microbialpopulation&aerationstrategyaskeyfactorsofN2Ogeneration&emission

• Avoidincomplete/intermittentnitrification&over-aerationduringtheaerobic

processestoachievelowerN2O

emissions

• ApplyuniformspatialDOprofilestopromoteSNDthatprobablyleadstoless

N2Oemissions

• PerformflowequalizationtocontrolthepeakingfactoroftheinfluentN-loadingto

theAS

• EnsureasufficientlylongSRTtopreventNO2−accumulationduringnitrification

• AvoidtheCODlimitationofthedenitrificationprocessbyminimizingthe

pre-sedimentationoforganiccarboninthe

influent&dosingadditionalorganic

carbon

Panetal.(2016)&Nietal.(2015)

2step-feeding,anoxic/aerobic/anoxic/aerobicplug-flowreactor

• Mechanisticmodeldevelopment

• Step-feedingresultinginincompletedenitrification&affectingtheAORin

nitrification,henceincreasingthetotalN2O

emission

• DecreasetheN2OEFtothelowestvalueof<1%if30%ofthetotalRASreturnsto

the2ndstep

• IncreaseDOavailabilityforbothAOB&

Page 23: O) monitoring in full-scale wastewater treatment processes

Wangetal.(2016b) A2/Owithplug-flowpattern• InvestigationofAOBabundances

• N2Oemittedmainlyfromtheoxiczone,withtheemittinglevelsincreasinggreatlyfromthe

beginningoftheoxiczonetowardsthezone

end

• NO2−accumulationdirectlytriggeringN2Oproduction

• Bothdiurnal&seasonalN2Oemissionlevelsfluctuatingstrongly

• OtherfactorsinfluencingtheN2Oemission:lowDO/temperature

NOB

• ImprovetheAOBlivingconditions

• Applyastep-stageaerationmodewithvaryingaerationintensities(location-

specificemissionpatternsforaplug-flow

process)

• Ensureabettermixingviaahigherhorizontalplug-flowratecombinedwithan

appropriateverticalairflowflux;thelarge

cross-sectionwidthsreducedusing

partitionwallstoelevateflowvelocities

underaconstantA2/Otankworking

volume

Sunetal.(2013)Feeding(synchronousaeration)/aeration/settling/decanting(1 heach)SBR

• Visualizationofsignificantprofiles(DO&N2O)&

descriptiveanalysis

• Multiplelinearregressionanalysistoinvestigate

relationshipofN2O

emissions&

environmentalfactors

• Bimonthlysamplingtoexaminechangesinthe

relationshipbetweenN2O

emissions&environmental

factors(long-term:12

months)

• N2Ofluxfromdifferenttreatmentunits/periodsfollowingadescendingorder:feedingperiod,

aerationperiod,settlingperiod,swirlgrittank,

decantingperiod&wastewaterdistribution

tank

• Feeding&aerationperiodsaccountingfor>99%ofN2Oemissions

• LowDOduringnitrificationmajorlyinfluencingN2Oproduction

• Increasetheaerationrateduringthefeedingperiod&decreaseittoaproper

levelfornitrificationintheaerobicstage

• Supplyexternalcarbonsourceduringdenitrification/changetheoperationalSBR

mode(fromfeedingundersynchronous

aerationtofeedingwithanoxicstirring)to

ensureenoughCODprovision/better

utilizationofinfluentCODfor

denitrification

Rodriguez-CaballeroReactionphase(∼130 min)/settling(∼65min)anddecanting(∼65 min)

• Duringtheexperimentalcampaign,3different

cycleconfigurations

implementedaspartof

thenormalSBR

operation

• N2Oemissionsaccountingfor>60%ofthetotalcarbonfootprintoftheWWTP

• CycleswithlongaeratedphasesshowingthelargestN2Oemissions,withaconsequent

• ApplyintermittentaerationtoreducetheNO2−accumulation

• Adoptacycleconfigurationwithshort

Page 24: O) monitoring in full-scale wastewater treatment processes

etal.(2015) SBR(anoxic/aerobicalternations–3cycletypes)SBR

• Testingofamodifiedcycleconfigurationforthe

N2Omitigation

• Observedissolved&gaseousN2Oprofilesvs

time

increaseinthecarbonfootprint

• TransientNH4+&NO2−concentrations&transitionfromanoxictoaerobicpossibly

involvedintheincreasedN2Oproduction

aeratedperiods

• AllowthesystemtoconsumeN2Othroughdenitrification

Spinellietal.(2018) MLE

• Event-basedsensitivityanalysis

• Box-plotsofdiurnalbehaviourofsignificant

variables&N2O

• LowerCOD:NresultinginhigherN2Oemissionsduetodisturbeddenitrification

• DailyN2Opeaksoccurringunderconditionsofhigheraerationflow-rate(moreintense

stripping)

• Equalizationoftheinfluentflow-rate

Townsend-Smalletal.(2011) MLE

• IsotopiccompositionofN2O

• Bothnitrification&denitrificationcontributingtotheN2OemissionswithinthesameWWTP

• BNRsignificantlyincreasingurbanN2Oemissions

• Applyengineeringprocessesfortheselectionofbacteriacapableofreducing

NO3−withoutreleasingsignificantN2O

amounts

Castellano-Hinojosaetal.(2018)

Twosequentialbioreactors(anoxicandoxic)

• SimultaneouslylinktheabundanceofAOB,AOA&

N2O-reducerswiththe

changesofthe

operational/environmental

variables

• N2OemissionsstronglycorrelatedwithincreasedabundancesofAOB&lowercountsof

N2O-reducers

• UnlikelysignificantcontributionofAOAtoN2Ogenerationsincetheirabundancecorrelated

negativelytoN2Oemissions

• AOBabundancefavouredbyhigherNO3−&NO2−concentrationsintheAS

• AvoidNO2−accumulation,lowtemperatures&excessDOintheanoxic

bioreactorstoenablecomplete

heterotrophicdenitrification&hinder

nitrifierdenitrification

Sunetal.(2017a,b) Biologicaltankwithananoxic&anoxiczoneA/O

• Construction&performanceevaluationof

BP-ANNmodel

• DOhavingasignificantinfluenceontheN2Oproduction

• BP-ANNmodelsuitableforthepredictionofN2OemissionsinotherWWTPswithdifferent

configurations(e.g.A2/O,SBR&nitrification-

anammox),ifinfluent/environmentalparameters

• ApplypropercontrolofDOduringbothnitrification&denitrification

• ApplytheBP-ANNmodelasaconvenient&effectivemethodforthepredictionof

N2OemissionsinanA/OWWTP

Page 25: O) monitoring in full-scale wastewater treatment processes

&N2Oemissiondatacanbeinvestigated

throughfull-,pilot-orlab-scaleexperiments

Blombergetal.(2018)basedonKosonenetal.(2016)

A/Obioreactor• Mechanisticmodeldevelopment

• Modeldescribingthefull-scaleundergroundWWTPofViikinmäki

• AOBpathways:onlyNH2OHoxidationincludedduetothedynamic&relativelyhighDO

concentrations(1.5–3.8 mg L−1)intheaeratedzones&thelowNO2−concentrations(0.1&

0.7 mg L−1)

• N2Oproductionmainlyintheaeratedzones,minorN2Oconsumption&minorstripping

effectintheanoxiczones

• Appliedstrippingmodel:EFoverestimation

• Improvethestrippingmodellingapproach

• Considerthenitrifierdenitrificationcontributioninfuturemodelversions

Chenetal.(2016) CAS

• Fugacitymodel

• Lab-scaleinsituexperiments

• Comparedtootherparameters(e.g.sludgeconcentration/retentiontime),theadjustmentof

theaerationrateeffectivelymitigatedtheGHG

emissionintheASwithoutsignificantly

affectingthetreatedwaterquality

• N2OasmaincontributortothetotalGHGemission(i.e.57–91%oftotalGHGemission)

• LoweringtheaerationrateintheASby75%enableddecreasingthemassfluxofN2Obyup

to53%

• Mostimportantbenefitofchangingtheaerationrate:lowerenergyconsumptionduringthe

WWTPoperation(fractionalcontributionof

pumpingtothetotalemissionfromthe

WWTP = 46–93%withintherangeoftheaerationratetested)

• Reducetheaerationrate

• Addananoxiczone&recirculationtoanon-BNRsystemfornitrification;

Page 26: O) monitoring in full-scale wastewater treatment processes

Ribeiroetal.,2017 ExtendedaerationCAS• Differentaerationratestested

• NitrificationasthemaindrivingforcebehindN2Oemissionpeaks

• Airflow-ratevariationspossiblyinfluencingtheN2Oemissions;highN2Oemissionsunder

conditionsofover-aerationorincomplete

nitrificationalongwithNO2−accumulation

otherwise,highN2Oemissionsexpectedin

caseofincreasedDO

• ControltheDO;dynamicchangesinDOconcentrationsreportedasbeing

responsibleforN2Oemissionpeaksin

SNDBNRsystems

• ControltheTN(denitrification)

• AvoidtheconcurrenceofdecreasedDO&NO2−accumulation

Severalstudieshavemodifiedtheaerationintensity,DOandcycledurationtoinvestigatetheeffectonN2Oemissionswithinfull-scalesystems(Castro-Barrosetal.,2015;Kampschreuretal.,2009a;Mampaeyetal.,2016;Rodriguez-

Caballeroetal.,2015).For instance,Mampaeyetal. (2016)achievedareduction in theN2Oemissionsby56%whenthecycles inaone-stagegranularSHARONreactorwereshortenedby1 h.Rodriguez-Caballero et al. (2015)tested

differentoperationalconditionsinafull-scaleSBR.TheyhavesuggestedanoptimumcontrolstrategyfortheminimizationminimisationofN2Oemissionsbasedontheapplicationofshortaerobic-anoxiccycles(20-minaerobicphaseand

shortdurationofanoxicstage).Therefore,testingdifferentoperationalmodesisregardedasoneofthemosteffectivewaystoidentifymeasuresfortheemissionmitigation(Table2).

Nietal.(2015)developedamechanisticmodelutilizingutilisingthedatafromatwo-stepPFreactor(Panetal.,2016)showingthatthebiomassspecificN-loadingratewasresponsiblefortheelevatedN2Oemissionsobservedin

the2nd stepof theprocess.Differentoperational conditionswere testedwith themodeldemonstrating that lowerN2Oemissions (<1%of theN-load) canbeachieved if 30%of the total returnactivated sludge (RAS) stream is

recirculated to the2nd stepof thePF reactor (Table2).However, it is unknownwhether the suggestedmitigation strategywasactuallydemonstrated in the system.Ahnet al. (2010b) identifieddependenciesbetween theWWTP

operatingconditionsandtheN2Oemissionsviamultiplelinearregression.Accordingtotheirfindings,intermittentaerationorover-aerationmustbeavoidedinaerobicreactors.Castellano-Hinojosaetal.(2018)linkedthepopulationof

AOB,AOAandN2O-reducerswiththechanges intheoperationalandenvironmentalvariables in four4conventionalASsystemswithpre-denitrificationzones.TheyobservedthatN2Oemissionsmainlyoccurreddueto incomplete

denitrification,thusunderliningtheimportanceofensuringthecompletionoftheprocessfortheemissionsmitigation.Overall,themaintechniquesformitigatingtheN2Oemissionsinwastewatertreatmentprocessesinclude:i)the

applicationoftheoptimalaerationintensityandDOconcentration,ii)preventingNH4+concentrationpeaks(e.g.viaequalizationequalisationtanks),iii)theavoidanceofNO2−accumulationthroughpropercontrol,andiv)thesupplyof

additionalcarbonsource(whenrequired)toensurecompletedenitrificationintheanoxicreactors(Table2).

However,studiesapplyingandevaluatingmitigationmeasuresforlong-termapplicationsarestillmissing.Additionally,thereisagapbetweenthedatacomingfromthemonitoringcampaignsandtheirprocessinginorderto

establishamitigationstrategy.Moreover,severalmonitoringcampaignsdonotconcludeonthedevelopmentofmitigationstrategies(Filalietal.,2013;Stenströmetal.,2014;Yanetal.,2014).Thelong-termimplementationandevaluation

oftheproposedmitigationstrategiesisstillanissue.Therefore,theestablishmentofstandardizedstandardisedmethodologicalapproachesfortheidentificationofN2Omitigationstrategiesisrequired.

5.2N2Omitigationstrategies:progressandlimitationsAsshowninsection5.1,statisticaland/ormechanisticmodellingaswellasobservatoryanalysisoftheN2Oemissions’behaviourhavebeencommonlyusedeitherasstandaloneorincombinedanalysesforthedevelopmentof

mitigationstrategies.However,thesuggestedmitigationschemeshavenotyetreachedcommercialapplicationsatfull-scalewastewatertreatmentprocesses.ThereisagapbetweentheidentificationofappropriateN2Omitigation

measures and their integration into the control ofWWTPs. Future studies shall focus on the development, implementation and integration of themitigation strategies into the existing control strategy ofwastewater treatment

processes. Special attention must be paid to trade-offs between GHG emissions, energy consumption, system performance and compliance with the legislative requirements in order to support evidence-based multi-objective

optimizationoptimisationoftheWWTPsoperation.

The investigation of direct GHG emissions at full-scalewastewater systems is important for theminimizationminimisation of the environmental footprint ofWWTPs and the integration of the sustainability dimension into

wastewatertreatmentprocesscontrol.

5.3IntegratingtheN2OemissionmonitoringintotheWWTPoperation

Page 27: O) monitoring in full-scale wastewater treatment processes

Multivariableandmulti-objectiveapproacheshavebeenproposedfortheoptimizationoptimisationoftheWWTPsperformancetoenablethecorrelationofenergyconsumptionoroperationalcostswithsystemperformance(Qiao

andZhang,2018;Zhangetal.,2014).Sweetappleetal.(2014)usedamodifiedversionofthebenchmarksimulationmodel2(BSM2)(Jeppssonetal.,2006)toidentifycontrolstrategiesforthesimultaneousminimizationminimisationofGHG

emissions, operational costsandpollutant loads.TheBSM2modelshavealsobeencombinedwithLCA toevaluate the sustainabilityofdifferentoperating strategies (Flores-Alsinaet al., 2010;Arnell et al., 2017).However, studies

utilizingutilisinglong-termreal-fieldWWTPdataarestillscarce.

Fig.7summarizessummarisestheresearchprioritiesintermsofreal-fieldN2OmonitoringcampaignsthatcanactasfoundationfortheintegrationofN2OemissionsintoWWTPmonitoringandcontrol.Long-termmonitoring

campaignsthatcapturetheseasonalvariability,studiesontheuncertaintiesofthesamplingstrategiesandreportingofoperational,environmentalandsamplingdatacanensuretherobustnessandcomparabilityofthemonitoring

results. The development ofmethodological approaches for the translation ofWWTP data into information can facilitate the understanding of theN2Oemission behaviour and relationship with different operational conditions.

Combinationofmechanisticmodelsand/ordataminingtechniqueswithmethodsfortheN2Oquantificationcanvalidatethemodelsandprovide insights intothedominantpathwaysunderchangingoperationalconditions.Finally,

researchstudiesimplementingandtranslatingtheN2Omitigationmeasuresintocontrolstrategiesareessential.

6ConclusionsAnumberoffull-scaleN2OmonitoringcampaignsinWWTPwerestudied.TheprocesseswereclassifiedsothattherangesofN2OemissionfactorsEFs,dominantN2Opathwaysandtriggeringoperationalconditions,andthe

mitigationmeasuresfordifferentprocessgroupscouldbeset.Thekeyconclusionsofthecurrentrevieware:

• ThereisawiderangeofEFswithinsimilargroupsofwastewatertreatmentprocesses.Theemissionfactorrangesbetween2%and5.6%oftheinfluentN-loadinmainstreamSBR,whileODreactortypes,exhibithavealowEF,∼0.14%oftheN-

load.Long-termcontinuousordiscontinuousmonitoringcampaignsarecharacterisedbyhigherEFscomparedtoshort-termcampaigns.ThestudiesinvestigatingtheseasonalbehaviourofN2Oemissions,haveanaverageEFequalto1.7%of

theN-load,whereasmonitoringcampaignslastinglessthanamonthhaveanaverageEFequaltoof0.7%.Long-termcampaignsshowahighvariabilityofintheN2Oemissions.ThereisnospecificEFcorrelationwiththeNH4+removalinthe

mainstreamprocessesorinspecificgroupsofprocesses.MostoftheprocessesinsmallerWWTPs(i.e.flow-rate<200,000 m3 d−1)hadEFslessthan0.5%oftheN-load,independentlyoftheprocesstypeandnitrificationefficiency.Thisstudy

concludedthatefficientoperationalstrategiescanmitigatethegeneratedN2Ofordifferentconfigurationsandgroupsofprocesses.

• ItisdifficulttocomparetheresultsoftheN2Omonitoringcampaignsbecauseof:i)thedifferencesinthedurationofthemonitoringcampaigns(e.g.short-termcampaignsignoringseasonalvariations),ii)theuncertaintyinthegaseoussampling

methodsandanalyticalmeasurements,andiii)theinsufficientreportingregardingthereactorcontrolstrategy,operationalandenvironmentalconditions,etc.

• SimplefeatureextractionandgraphicalrepresentationsofselectedprocessvariablesandN2OemissionsareemployedtoexplaintheN2Otriggeringmechanisms.GiventhatacombinationofseveralparametersaffectstheN2O generation,

multivariatestatisticalanalysistechniquescanbeausefulalternativeforanalyzinganalysingdataandunderstandingtheN2Oemissionbehaviour.Thecombinedapplicationofmechanisticmodelsandstatisticaltechniquescanleadtobetter

Fig.7MonitoringN2Oemissionsinfull-scalewastewatertreatmentsystems-researchpriorities.

alt-text:Fig.7

Page 28: O) monitoring in full-scale wastewater treatment processes

designofmitigationstrategies.

• Isotopicandmolecularbiologyanalysesareemerging techniques thatcanqualitativelyandquantitativelyassess theN2Ogenerationpathways.Dataminingmethodscanbedeployed to identifypatternsofoperationalconditionsandN2O

emissions.ThiscanbecomplementedwithtechniquesforthedeterminationoftheN2Oproductionpathways.

• Studiestestingandvalidatingthelong-termfull-scaleN2Omitigationmeasuresarestillmissing.

• Futureresearchshouldfocuson:i)long-termN2Omonitoringcampaigns, ii)theuncertaintiesofdifferentsamplingprotocols, iii)theapplicationofdataminingapproaches,machinelearningandmechanisticmodelsforthedevelopmentof

effectiveandadaptivemodelstothatwillbeintegratedintoWWTPoperationandcontrol,andiv)thedevelopment,implementationandintegrationofthemitigationstrategiesintotheexistingcontrolstrategiesofWWTPs.

Declarationofinterests☒Theauthorsdeclarethattheyhavenoknowncompetingfinancialinterestsorpersonalrelationshipsthatcouldhaveappearedtoinfluencetheworkreportedinthispaper.

AcknowledgementsThisresearchwassupportedbytheHorizon2020researchandinnovationprogramSMART-Plant(grantagreementNo690323).

AppendixA.SupplementarydataSupplementarydatatothisarticlecanbefoundonlineathttps://doi.org/10.1016/j.watres.2019.04.022.

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AppendixA.SupplementarydataThefollowingaretheSupplementarydatatothisarticle:

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Multimediacomponent1

alt-text:Multimediacomponent1

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Highlights

• N2Oemissionsbenchmarkinginwastewaterishinderedbynon-standardreporting.

• StructuredmethodologicalapproachesforN2Odataanalysisarestillmissing.

• Mechanisticmodelsmustbecalibratedwithlong-termfull-scaledata.

• Isotopicanalysesshouldbecoupledwithmechanistic/data-drivenmodels.

• StudiesvalidatingN2Omitigationmeasuresarestillmissing.

Graphicalabstract

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