Water pollution from food production: lessons for optimistic and optimal solutions

Water pollution from food production: lessons for optimistic and optimal solutions

Ang Li

, Carolien Kroeze

, Taher Kahil

, Lin Ma

and Maryna Strokal

Foodproductionisasourceofvariouspollutantsinaquatic systems.Forexample,nutrientsarelostfromfertilizedfields, andpathogensfromlivestockproduction.Waterpollution mayimpactsocietyandnature.Large-scalewaterpollution assessments,however,oftenfocusonsinglepollutantsand notonmultiplepollutantssimultaneously.Thisstudydraws lessonsfromairpollutioncontrolforlarge-scalewaterquality assessments,wheremulti-pollutantapproachesaremore common.Tothisend,wepresentaframeworkforfuture waterpollutionassessmentssearchingforoptimisticand optimalsolutions.Wearguethatfuturestudiescouldshift theirfocustobetteraccountforsocietalandeconomic targets.Participatoryapproachescanhelptoensurethe feasibilityoffuturesolutionstoreducewaterpollutionfrom foodproduction.

Addresses

1WaterSystemsandGlobalChangeGroup,WageningenUniversity&

Research,P.O.Box47,6700AAWageningen,TheNetherlands

2KeyLaboratoryofAgriculturalWaterResources,HebeiKeyLaboratory ofSoilEcology,CenterforAgriculturalResourcesResearch,Instituteof GeneticandDevelopmentalBiology,TheChineseAcademyofSciences, 286HuaizhongRoad,Shijiazhuang050021,Hebei,China

3WaterProgram,InternationalInstituteforAppliedSystemsAnalysis (IIASA),Scholssplatz1,A-2361Laxenburg,Austria

Correspondingauthor:Li,Ang(ang.li@wur.nl)

CurrentOpinioninEnvironmentalSustainability2019,40:88–94 ThisreviewcomesfromathemedissueonSystemdynamicsand sustainability

EditedbyHesterBiemans,MarynaStrokal,andPietervanOel

Received:25April2019;Accepted:20September2019

https://doi.org/10.1016/j.cosust.2019.09.007

1877-3435/ã2019TheAuthors.PublishedbyElsevierB.V.Thisisan openaccessarticleundertheCCBYlicense(http://creativecommons.

org/licenses/by/4.0/).

Introduction

Foodproductionisexpectedtointensifyinthecoming years[1,2].Thisisaresultofthegrowingpopulationthat need more food [2]. Intensified food production is, however, a source of multiple pollutants in aquatic systems[3–5].Overuseofchemicals and poor manage- ment strategies in the crop productionsector result in lossesofpesticides[3],heavymetals,pathogens[5],and

nutrients[6–9]inriversfromfertilizedfields.Intensifies livestockproductionisoftenasourceofnutrients[6–9], pathogens[5],andantibioticsinrivers[1].Inmanyworld regions,aquaticsystemsexperiencemulti-pollutantpro- blems[10].Chinaisoneoftheexamples,whereaquatic systems are largely contaminated by pollutants from food production[6–9,11].Multi-pollutant problems are also reported for many rivers of North America and Europe. This holds especially for densely populated areas. In the future, food production may add more pollutants to aquatic systems, impacting society (e.g.

diarrhoea from pathogen contamination) and nature (e.g. harmful algae blooms from excess nutrients).

Theexistingstudiesdiffer in their search forsolutions toreducewaterpollutionfromfoodproduction.Herewe focusontwotypesofanalyses:searchesforoptimisticand foroptimalsolutions.

Optimisticsolutionsshowustowhatextentenvironmental problemscanbesolvedinscenariosreflectingmaximum technical,economic,andsocietalpotentialstosolveenvi- ronmentalproblems.Inscenariossearchingforoptimistic solutions,thefullimplementationofmanagement strat- egiesis oftenassumedto reduce pollution fromhuman activities,for example,food production[6–9].

Optimalsolutionsaccountfortrade-offs,andshowushow environmentaltargetscanbemetinthemostcost-effec- tive,equitable,oracceptableways.Optimizationanalyses typicallyaimtoachievecertaintargetswhilelookingfor the optimal combination of environmental measures [13,14]. Optimization analyses can be combined with participatoryapproach to include stakeholders’interest.

Thisisparticularlyrelevantforsustainabilitytargets,such astheSustainableDevelopmentGoals(SDGs).

Multi-pollutant, large-scale optimization analysis are more commonly applied in air quality control [14–16]

than in water pollution control. Water quality studies often analyze single pollutants and not multiple pollu- tantssimultaneously[3,5–9,11,12].Thisholdsespecially forlarge-scalewaterqualityassessments.

Inthisstudy,we,therefore,drawlessonsfromairpollu- tion control for large-scale water quality assessments, where multi-pollutant approaches are more common.

Wepresentaframeworkforfuturewaterqualityassess- ments searching for optimistic and optimal solutions.

Finally,we provideconcludingremarks.

Lessons fromairpollution controlfor water quality assessments

Inthefollowing,wedrawthreemainlessonsfromexist- ingmodels.Inourdiscussion,werefertotherepresenta- tive modelsthat have beensuccessfully applied for air pollutioncontrolatacontinentalorglobalscaleandtakea multi-pollutant perspective. We use these models as illustrative examplesfor waterquality assessments. We identifyopportunitiesforfurtherdevelopmentofexisting waterqualitymodels.

Lesson1:Integratedmodelsforairpollutioncontrol havebeenmoresuccessfultoolsforinternational decisionmakingthanwaterpollutionmodels

Several integratedmodelsexistfor air pollution control taking a multi-pollutantperspective. RAINS (Regional AirPollutionInformationandSimulation)modelandits extendedversionforgreenhousegasses,GAINS(Green- houseGasandAirPollutionInteractionsandSynergies) are illustrative examples of howintegrated models can successfullybeusedininternationalnegotiationsrelated to environmental problems.RAINSand GAINScanbe usedtoquantifyemissionsandairpollutionimpacts,and to identify least-cost strategies for air pollution control (cost-optimization).RAINSsupportedtheformulationof

‘theEuropeanCommission’s1995AcidificationStrategy’

(http://www.iiasa.ac.at/). RAINS and GAINS played an essential role in international negotiations onthe Con- vention on Long-Range Transboundary Air Pollution (LRTAP, http://www.unece.org/fileadmin//DAM/env/

lrtap/welcome.html). This convention was an interna- tional agreement to deal with air pollution in Europe signed in 1979. The conventionwas extended to eight protocolsonemissionreductiontargetsformultiplepol- lutants intheair.Today, morethan 50countriesin the worldaretakingpartinthisconvention.Theroleofthe models is in providingscientific informationto support negotiations.Thisinformationincludesquantifiedemis- sionsofairpollutants(e.g.sulfurdioxide,nitrogenoxides, ammonia, and volatile organic compound) and green- house gasses(e.g. carbondioxide, methane,and nitrous oxide)fromEuropeancountries,environmental impacts of those emissions, effects of reduction strategies and costsof emissioncontrol[14–16].

ThesuccessoftheRAINSandGAINSmodelsininterna- tionalnegotiationscanbeexplainedbythreemainreasons.

First,thesemodelsintegratedmultiplepollutantsandtheir multipleeffects.Forexample,emissionsofsulfurdioxide, nitrogenoxides,andammoniacauseacidificationofforests andwater.Nitrogenoxidesandammoniaarealsoimportant contributorstoeutrophicationproblems.Second,themod- els considered regional differences in socio-economic development and ecosystem sensitivities. The models contributed to an increased awareness among different stakeholders of the need to develop regional solutions, whileaccountingfortransboundaryemissions.Third,the

models areabletoprovideascientificbasistosupport a dialoguebetweendifferentstakeholders.Modelssupport theidentificationofoptimalsolutions(e.g.cost-effective) forreducingairpollution[14,15].Today,thesemodelsare appliedtomanyworldregions(forChinaandIndia)witha 5-yeartimestepupto2050.

Waterpollution modelsformultiplepollutantshavenot been as widely used as airpollution models in interna- tional negotiations. An important reason is that multi- pollutant models are successful in waterquality assess- ment for the present day, but rather limited for future assessmentsofwaterqualityatthecontinentalorglobal scale.Severalcontinentalandglobalwaterqualitymodels exist for individual groups of water quality parameters (e.g. nutrients). Examples of such models are Global NEWS-2 (NutrientExport fromWaterSheds) for nutri- ents[23,24],IMAGE-GNM(GlobalNutrientModel)for nutrients [25], GloWPa (Global Waterborne Pathogen) for pathogens [5,26], VIC-RBM (Variable Infiltration Capacity – River Basin Model) for water temperature [27], Global TCS (Triclosan) for triclosan [28], global plastic model [29],and the global pesticide model [3].

Somewaterqualitymodelsexistfornationalassessments of water quality. Examples of such models are SPAR- ROW(SPAtiallyReferencedRegressionsOnWatershed attributes) for the United States [30] and MARINA (Modelto AssessRiver Inputsof Nutrientsto seAs)for China[4],withbothmodelsdesignedfornutrientpollu- tion assessment. The WorldQual model accounts for more than one group of pollutants in continental water qualityassessments,butnotforthefuture[12].WorldQ- ual quantifies biochemical oxygen demand, faecal coli- formbacteria,totaldissolvedsolidsandtotalphosphorus (P)inriverreachesforAfrica,LatinAmericaandAsia.A few more models account for multiple pollutants in aquatic systems at the national or continental scale [31].Detailedreviewofexisting,large-scalewaterquality modelsispresentedinStrokaletal.[12].

Lesson 1 highlights the opportunity for existing global andcontinentalwaterqualitymodelstofurtherdevelop toward multi-pollutant assessments. This is needed to explore scenarios in whichwe searchfor optimisticand optimalsolutionsthatcouldsimultaneouslyreducewater pollution ofmultiplepollutants(seeLesson 2below).

Lesson2:Modelscansupportthesearchforoptimistic andoptimalsolutionsformulti-pollutantproblemsin water,byassessingmaximumtechnicalfeasibilityand cost-effectiveness

Modelsareoftenusedasscenariotoolstoanalyzefuture waterquality.Modelsareabletoprojectthefuturewater qualitybyBusinessasUsual(BAU)scenarios.Inscenarios searching for solutions, BAU scenario often used as a baseline scenario, accounting for climate change and socio-economic developments.Climatechangescenarios

exist,forexample,theIPCCSpecificReportonEmissions Scenarios(SRES) [32]or the Representative Concentration Pathways (RCPs) [33]. Scenarios exploring changes in socio-economicdevelopmentinthefutureare,forexam- ple,theMillenniumEcosystemAssessment(MA)scenar- ios[34],or the Shared SocioeconomicPathways(SSPs) [35].

Storylinesoftheclimateandsocio-economicscenariosare incorporatedintowaterqualitymodels(e.g.GlobalNEWS- 2,GloWPA).Thesestorylinesoftenformthebasisofthe alternative scenariosthataimatsearching for optimisticand optimalsolutionstoreducewaterpollution.

Wecanusewaterqualitymodelstoassessthemaximum technicalfeasibilityandcost-effectivenessofsolutionsfor pollution abatement [8,9]. Some of the existing water qualitymodels(e.g.MARINA)areusedtoassessthemaxi- mumtechnicalfeasibilityofsolutionsforreducingeutro- phication problems [1]. Differences in socio-economic development and climate change among subbasins are considered.Forexample,focusingonthemaximumtech- nical potential to avoid coastal eutrophication in 2050, Strokal et al. [8] showedthe possibility to avoid coastal eutrophicationbyimplementingadvancedtechnologies(e.

g.recyclinganimalmanuretoreplacesyntheticfertilizer) aimingatreducinglossesofnutrientstoaquaticsystems.

Similarstudy has also beenconductedfor the pathogens [5].

Scenariosreflectingthemaximumeconomicandsocietal potentialtosolvemulti-pollutantproblemsarelessstudies forlarge-scalewaterqualityassessments.

Useofmodelsforcost-effectivenessanalysesare,however, lesscommonformulti-pollutantwaterqualityassessments [17–20].Thisismorecommonformodelsforairpollution assessmentsaswehighlightedbefore.RAINSandGAINS areabletoexploresolutionswiththemaximumtechnical potential,andidentify theleast-cost strategiestoreduce emissions of multi-pollutants to the atmosphere [14,15]

(see Lesson 1 above). Taking the cost-optimization approachesfromairpollutionmodelasexample,suchas RAINSandGAINS[36],waterqualitymodelscandevelop furtherastoolsforcost-effectivenessanalysesfromamulti- pollutant perspective. Another similar example is the Hydro-Economic Optimization model (ECHO) [13].

ECHOgivesinsightsoncost-effectiveallocationofwater acrossdifferentsectorsforAfricainaspatiallyexplicitway.

A few studies allocate wastewater discharge permits to cities in the most fair way, while considering socio- economicdevelopment[21,22,37,38].Theseinsightsfrom existingoptimizationapproachescanformagoodbasisto developacost-optimizationmodelforwaterqualityassess- mentsatthelargescale.

Lesson3:Toaccountforsocietalfeasibilityinwater pollutionassessmentparticipatoryapproachesmaybe needed

Accounting for the societal feasibility of implementing environmental solutionsis important.This isbecause it

givesusabetterunderstandingofwhethersocietyisprone toacceptcertainmeasuresor not.Thiswillimproveour waterqualityassessments,wheretechnical,economic,and societalaspects areaccounted for.Such assessmentswill facilitatetheformulationofeffectiveenvironmentalpoli- ciestoreducewaterpollutioninthefuture.

Accounting for societal aspects is challenging, but not impossible.Severalwaystodothisexist.Oneistoinvite stakeholderstoco-designsolutionsbasedonexistingsce- narios(e.g.basedonSSPs).Then,effectsofsuchsolutions canbetestedbymodels.Anotherwayistoinvolvestake- holdersinthewholecycleofdevelopingscenarios.Partici- patory approaches can help [39–41]. An exampleis the

‘Story-And-Simulation’approach(SAS).Thisapproachhas beenusedtodevelopscenarios for environmentalproblems [39].Experts(e.g.modellers)togetherwithstakeholders translatequalitativenarrativesintoquantitativescenarios formodels.Thisprocessisiterativeandconsistsofseveral stepsinwhichstakeholdersareinvolved[seeRefs.39–41as example].Participatoryapproachesarepartof theWater Futureand SolutionsInitiative,lunched bytheInterna- tionalInstituteforAppliedSystemsAnalysis(IIASA,http://

www.iiasa.ac.at/).Thisinitiativeisagoodexamplehowto bridgesciencetosocietyandpolicyatdifferentscalesusing variousmodellingtools[42].Thereisaneedto linkthe relevant sustainable development goals (SDGs) to the participatoryapproaches.Forexample,SDG2ZeroHun- ger(foodproduction)andSDG6Cleanwaterandsanita- tion (waterquality) can be used as a scientific basis to supportco-designofsolutionswithstakeholdersduringthe participatoryworkshops.

Frameworkforfuturewater quality assessments

Wepresentaframeworkforfuturewaterqualityassess- ments searching for optimistic and optimal solutions (Figures 1 and 2). Wedesign this framework based on the lessons that we draw for large-scale water quality assessments (Section 2). Our framework provides an illustrative example of how different modelling approaches canbecombined, to explore optimistic and optimal solutions forwater pollution fromfood produc- tionorotherpollutionsources(e.g.humanwaste)takinga multi-pollutantperspective.Theframeworkcoversdri- vers (food production and water pollution controls), pressure (pollutant losses), state (pollutant loads and concentrations), and impact (water pollution impact on natureand society) (Figure 1).For the water pollution impact, various indicators can be integrated into the framework.Forexample.IndicatorsforCoastalEutrophi- cation Potential can be used to reflect the impact of nutrientenrichmentin thecoastal water[43].

Theframeworkallowsfortwotypesofanalyses:exploring optimistic and optimal solutions for water pollution (Figure 1). It focuses on water pollutants from food

production,suchas nutrients,pesticides,andpathogens [3,5,25].Exploringoptimisticfuturescanbedonethrough scenarioanalyses:startingfromstorylines,andoptimistic assumptions about emission control. We can analyze future trends in water pollution, the costs of emission control, and the impacts of pollution on nature and society.Exploringoptimalsolutionstypicallystartsfrom environmentaltargets,andaimsatanalyzingoptimal(e.g.

cost-effective)solutionstoreachthesetargets.Ourframe- work thusfollowsLessons1and2 asformulatedabove.

Existing modelscouldformthebasisoftheframework.

To address the impact of food production on water quality, the framework should be able to quantify the pollutantlossestowatersfromthefoodproductionchain.

It should also include control measures to reduce the

pollutant losses fromthe food productionchain. Italso needs toaccount forthetransport of pollutantsthrough theenvironment,andretentionprocesses.Pollutantsmay be transported byrivers from upstream to downstream andeventuallyenteringtheseas.Duringthetransporta- tion,pollutantscanbelostorretainedintheriversystems.

Examples are nitrogen losses due to denitrification, P retentions insedimentsand retentionsof variouspollu- tants due to river damming. Finally, the framework should account for effects of pollutants in theenviron- ment, on nature and society. Several models exist to quantify pollutant flows from food production to the aquaticsystemsatlargescales[12,44,45].Thesemodels canbeusedtoidentify‘hotspots’ofwaterpollution,and to analyze past and future trends in water pollution [12,46].Theycouldformthebasisoftheframework.

Figure1

the representative Concentration Pathways

The Shared Socioeconomic Pathways

OPTIMISTIC SOLUTIONS OPTIMAL SOLUTIONS

Future Food Production Projections

Water pollution controls

Pollutant losses to aquatic systems

Pollutant flow in aquatic systems

Water pollution Impact Target

(e.g. SDGs)

Cost

Optimistic Optimization

Pollutants

Pollutants

Pollutants

Pollutants

Current Opinion in Environmental Sustainability

Frameworkforfuturewaterqualityassessmentssearchingforoptimisticandoptimalsolutions.ExamplesoftargetsareshowninFigure2.

*Optimisticscenarioanalysis:[6–9].

**Costoptimization:[51–54].

***Multi-indexGinioptimization:[21,22,37,38].

Exploring optimisticsolutions couldstartfrom optimistic storylinesaboutfuturetrendsinsociety,andaboutwhatis technicallyfeasibleintermsofpollutioncontrol(Figure1).

Modelscanthenbeusedforscenarioanalysis,analyzing futuretrendsinwaterpollutionwhileassumingfullimple- mentation ofexisting and futuretechnologiesto reduce waterpollution. Onecouldcomparetheresultswith,for instance,targetsforpollutioncontroldeducedfrompeople, planetor profit boundaries(Figure 2). For China,some examplesexistofmodellingstudiesexploring optimistic scenarios for reducing nutrient pollution by technically feasibleoptionsatbasinor nationalscale[8,9,47].These examplesindicatethatitistechnicallypossibletoreduce pollutionto lowlevelsin thefuture.Sofar,scenarioanalyses searchingforoptimisticsolutionsfocusedmostlyonmeet- ingenvironmentaltargetsbytechnicalsolutions(greenbox inFigure2).Inadditiontooptimismabouttechnologies, onecouldalsoadd optimisticassumptions abouthuman behavior.Forinstance,storylinesmayassumesustainable developmentin society, reflected,for instance, byenvi- ronment-friendly behavior. Insuch futures,farmers and consumerswillbeconcernedabouttheenvironmentand

thusdonotoveruseagrochemicalsincropproductions,and movetovegetariandiets.Optimisticfuturesmay,further- more,assumethatindustryandwastewatertreatmentmay aimforgreendevelopment.

Exploringoptimalsolutionsforwaterpollution,couldstart fromenvironmentaltargets,to bereachedinanoptimal way (Figure 1). Optimal can be interpreted here as economic,technicalorsocialoptimum(Figure2,Section 1).InFigure1,wegiveanexampleofsearchingforcost- effectivesolutions.Cost-optimization hasbeen success- fullyappliedincontrollingtheairpollutioninEuropean countries(seeSection2).Toaccount forpeople,planet and profit simultaneously in optimization analyses, the Gini coefficient could be used (Figure 2). The Gini coefficient reflects equalityof income or wealth within societyaccordingtotheLorenzcurve[48,49].The Gini coefficientcanalsobeusedtoreflecttheequalityinuseof environmental resources, such as allocating the waste discharge permit [21,22,37,38]. Absolute equality in a countryisreachedwhenallpeople haveanequalshare inresources,orineconomy.TheGinicoefficientcanbe usedin optimizationanalysis to searchfor strategies to meettargets (forpeople, planetorprofit)in suchaway thatsocialequalityismaximized.TheGinicoefficientfor pollutantdischargecanbequantifiedforvariousindexes, such as population density or gross domestic product.

Multi-index optimization involves optimization of the equalityin thedischargeofwaterpollution formultiple indexes.Onecouldapplythisapproachinwaterpollution assessment,forinstancetoallocate pollutionrights[22].

Optimisticscenarios and optimization approaches canassist decisionmakersintheirsearchforsolutiontowaterpollu- tion(seeLessons1and2).Toimplementtheframework proposedinFigure1,somehurdleshavetobetakenifwe wanttoapplyitformulti-pollutantproblems.First,existing largescalewaterqualitymodelsrunatdifferentspatialand temporalscales.Theabovementionedglobalandregional waterqualitymodels(Sections1and2)calculatepollutant flowsatscalesof0.5grid(e.g.IMAGE-GNM,GloWPa, VIC-RBM), basinscale(e.g.Global NEWS-2, Triclosan model), or subbasin scale (e.g. MARINA, WorldQual) [3,5,12,25]. Some of them are process-based (e.g.

IMAGE-GNM,VIC-RBM)whileotherstake alumped, parameter-basedmodellingapproach(e.g.GlobalNEWS- 2,MARINA).Mostofthemaresteady-statemodelsthat quantify the annual pollutant flows [details on model reviewsarein Ref.12].Afew modelsquantifyseasonal nutrientflowsfromlandtoseasglobally[50]ornationally [30]. However, pollutioncontroltypicallytakes placeat international,national,orlocalscales(administratescale), andin shorter timeframes. Itis achallenge to integrate biophysicalandadministrativescalesinwaterassessments.

Asecondchallengeishowtoaccountforsocietalfeasibility.

Lesson 3 above calls for participatory approaches.

Figure2

TARGETS

Optimistic

SOLUTIONS are ... feasible

Optimistic

Cost optimization**

Multi-index Gini optimization***

Optimistic*

People Planet Profit

Technically

Economically

Socially

Current Opinion in Environmental Sustainability

Overviewofhowtargets(forpeople,planetandprofit)andsolutions arelinked.ColorsinthecellsfollowthecolorsinFigure1.Sofar, scenarioanalysessearchingforoptimisticsolutionsforwaterquality focusedmostlyonmeetingenvironmentaltargetsbytechnical solutions(thegreenboxinthegraph).Yellowboxesrefersto optimizationanalysesthatcanbeappliedtolarge-scalewaterquality issues,ofwhichsomeexamplescanbefoundintheliterature.Grey cellsindicatetypesofanalysesthatarenotyetwidelyperformed.

Stakeholderscould beinvolved in formulatingstorylines, targetsandinidentifyingoptimisticandoptimalsolutions, while using the modelling framework presented in Figure 1.Thiswillhelptoensure thatoptimisticfutures arerealistic,andthatoptimalsolutionsaccountfor trade-offs.

Ourpresentedframeworkcan1)advancethefieldofwater qualitymodelling;2)helptointegratepeople,planet,and profit-relatedtargetswithtechnical,economic,andsocial solutions; 3) helptolinkwaterandfood securityassess- ments.TheframeworkcanhelptoachievetheSustainable DevelopmentGoals(SDGs)forCleanWaterandSanita- tion(SDG6)andZeroHunger(SDG2)atthesametime.

Forexample,targetsforfoodproduction(relatedtoSDG2) andwaterquality(SDG6)canbeusedasmultiple con- strainsinoptimizationanalyses.Thismayhelptoidentify possiblesynergiesandtrade-offs.

Concludingremarks

In this study, we argue that large-scale water quality assessmentscanlearnfromairpollutioncontrolto iden- tifyoptimisticandoptimalsolutions.Bothoptimistic(e.g.

technically feasible) and optimal (e.g. cost-effective) solutions are needed for effective reduction of future water pollution from food production. We draw three mainlessonsfromairpollutioncontrolfor waterquality assessments, searching for optimistic and optimal solu- tions. These lessons are: 1) Integrated models for air pollution control have been more successful tools for internationaldecisionmakingthanwaterpollutionmod- els; 2)Modelscansupportthesearchforoptimistic and optimal solutions for multiple pollutant problems in water, by assessing maximum technical feasibility and cost-effectiveness;3)Toaccountforsocietalfeasibilityin waterpollutionassessmentparticipatoryapproachesmay beneeded.Next,wepresent aframeworkfor exploring optimistic and optimal solutions for water quality pro- blems.Theframeworkcombinesoptimisticscenariosand optimization approaches with water quality models to explore the optimistic and optimal solutions for water pollution. We show that current water quality studies focus onenvironmental targets and technical solutions.

We argue that future studies could shift their focus to betteraccountforsocietalandeconomictargets.Partici- patoryapproachesmaybeneededtoensurefeasibilityof future solutions to reduce water pollution from food production.

Conflictof intereststatement Nothing declared.

Acknowledgements

Thisworkwaslargelysupportedbyfourprojects:theNationalKeyResearch

&DevelopmentProgramofChinaMOSTproject[grantnumbers 2016YFE0103100];theVeni-NWOproject[projectnumber:5160957841]and KNAW-MOSTproject“SustainableResourceManagementforAdequateand SafeFoodProvision(SURE+)”[grantnumber:PSA-SA-E-01].

References

1. RobertsonGP,VitousekPM:Nitrogeninagriculture:balancing thecostofanessentialresource.AnnRevEnvironResour2009, 34:97-125.

2. SpringmannM,ClarkM,Mason-D’CrozD,WiebeK,BodirskyBL, LassalettaL,deVriesW,VermeulenSJ,HerreroM,CarlsonKM etal.:Optionsforkeepingthefoodsystemwithin

environmentallimits.Nature2018,562:519-525.

3. IppolitoA,KattwinkelM,RasmussenJJ,Scha¨ferRB,FornaroliR, LiessM:Modelingglobaldistributionofagricultural insecticidesinsurfacewaters.EnvironPollut(Barking,Essex:

1987)2015,198:54-60.

4. StrokalM,KroezeC,WangM,BaiZ,MaL:TheMARINAmodel (modeltoassessriverinputsofnutrientstoseAs):model descriptionandresultsforChina.SciTotalEnviron2016, 562:869-888.

5. VermeulenLC,BendersJ,MedemaG,HofstraN:Global cryptosporidiumloadsfromlivestockmanure.EnvironSci Technol2017,51:8663-8671.

6. GuB,LeachAM,MaL,GallowayJN,ChangSX,GeY,ChangJ:

NitrogenfootprintinChina:food,energy,andnonfoodgoods.

EnvironSciTechnol2013,47:9217-9224.

7. MaL,WangF,ZhangW,MaW,VelthofG,QinW,OenemaO, ZhangF:Environmentalassessmentofmanagementoptions fornutrientflowsinthefoodchaininChina.EnvironSciTechnol 2013,47:7260-7268.

8. StrokalM,KroezeC,WangM,MaL:Reducingfutureriverexport ofnutrientstocoastalwatersofChinainoptimisticscenarios.

SciTotalEnviron2017,579(SupplementC):517-528.

9. WangM,MaL,StrokalM,ChuY,KroezeC:Exploringnutrient managementoptionstoincreasenitrogenandphosphorus useefficienciesinfoodproductionofChina.AgricSyst2018, 163:58-72.

10. DiazRJ,RosenbergR:Spreadingdeadzonesandconsequences formarineecosystems.Science2008,321:926-929.

11. LiA,StrokalMM,BaiZ,KroezeCC,MaLL,ZhangFFS:Modelling reducedcoastaleutrophicationwithincreasedcropyieldsin Chineseagriculture.SoilRes2017,55:506-517.

12. StrokalM,SpanierJE,KroezeC,KoelmansAA,Flo¨rkeM, FranssenW,HofstraN,LanganS,TangT,vanVlietMTHetal.:

Globalmulti-pollutantmodellingofwaterquality:scientific challengesandfuturedirections.CurrOpinEnvironSustain 2019,36:116-125.

13. KahilT,ParkinsonS,SatohY,GreveP,BurekP,VeldkampTI, BurtscherR,ByersE,DjilaliN,FischerG:Acontinental-scale hydroeconomicmodelforintegratingwater-energy-land nexussolutions.WaterResourRes2018,54:7511-7533.

14. WagnerF,HeyesC,KlimontZ,Scho¨ppW:TheGAINS OptimizationModule:IdentifyingCost-EffectiveMeasuresfor ImprovingAirQualityandShort-TermClimateForcing.2013.

15. AmannM,CofalaJ,HeyesC,KlimontZ,MechlerR,PoschM, Scho¨ppW:TheRAINSModel.DocumentationoftheModel ApproachPreparedfortheRAINSPeerReview2004.2004.

16. HordijkL,KroezeC:Integratedassessmentmodelsforacid rain.EurJOperRes1997,102:405-417.

17. CoolsJ,BroekxS,VandenbergheV,SelsH,MeynaertsE, VercaemstP,SeuntjensP,VanHulleS,WustenberghsH, BauwensW:Couplingahydrologicalwaterqualitymodeland aneconomicoptimizationmodeltosetupacost-effective emissionreductionscenariofornitrogen.EnvironModellSoftw 2011,26:44-51.

18. CramerM,KoegstT,TraencknerJ:Multi-criterialevaluationof P-removaloptimizationinruralwastewatertreatmentplants forasub-catchmentoftheBalticSea.Ambio2018,47:93-102.

19. JiangY,DinarA,HellegersP:Economicsofsocialtrade-off:

balancingwastewatertreatmentcostandecosystem damage.JEnvironManage2018,211:42-52.

20. IhoA,AhtiainenH,ArtellJ,HeikinheimoO,KauppilaP,KoseniusA- K,LaukkanenM,LindroosM,OinonenS,OllikkaKetal.:Therole offisheriesinoptimaleutrophicationmanagement.Water EconPolicy2017,03:1650031.

21. WangFE,LiYN,YangJ,SunZL:ApplicationofWASPmodeland Ginicoefficientintotalmasscontrolofwaterpollutants:a casestudyinXichengCanal,China.DesalinWaterTreat2016, 57:2903-2916.

22. YuanQ,McIntyreN,WuY,LiuY,LiuY:Towardsgreatersocio- economicequalityinallocationofwastewaterdischarge permitsinChinabasedontheweightedGinicoefficient.

ResourConservRecycl2017,127:196-205.

23. MayorgaE,SeitzingerSP,HarrisonJA,DumontE,BeusenAHW, BouwmanAF,FeketeBM,KroezeC,VanDrechtG:Global nutrientexportfromWaterSheds2(NEWS2):model developmentandimplementation.EnvironModellSoftw2010, 25:837-853.

24. SeitzingerSP,MayorgaE,BouwmanAF,KroezeC,BeusenAHW, BillenG,VanDrechtG,DumontE,FeketeBM,GarnierJ, HarrisonJA:Globalrivernutrientexport:ascenarioanalysisof pastandfuturetrends.GlobBiogeochemCycles2010,24 GB0A08.

25. BeusenAHW,VanBeekLPH,BouwmanAF,Mogollo´nJM, MiddelburgJJ:Couplingglobalmodelsforhydrologyand nutrientloadingtosimulatenitrogenandphosphorus retentioninsurfacewater–descriptionofIMAGE–GNMand analysisofperformance.GeosciModelDev2015,8:4045.

26. VermeulenLC,vanHengelM,KroezeC,MedemaG,SpanierJE, vanVlietMTH,HofstraN:Cryptosporidiumconcentrationsin riversworldwide.WaterRes2019,149:202-214.

27. VanVlietMTH,YearsleyJR,FranssenWHP,LudwigF, HaddelandI,LettenmaierDP,KabatP:Coupleddailystreamflow andwatertemperaturemodellinginlargeriverbasins.Hydrol EarthSystSci2012,16:4303-4321.

28. vanWijnenJ,RagasAMJ,KroezeC:Riverexportoftriclosan fromlandtosea:aglobalmodellingapproach.SciTotalEnviron 2018,621:1280-1288.

29. vanWijnenJ,RagasAMJ,KroezeC:Modellingglobalriver exportofmicroplasticstothemarineenvironment:sources andfuturetrends.SciTotalEnviron2019,673:392-401.

30. SchwarzG,HoosA,BAlexanderR,SmithR:TheSPARROW SurfaceWater-QualityModel:Theory,ApplicationandUser Documentation.2006.

31. EPA,U:BASINS4.5(BetterAssessmentScienceIntegratingPoint

&Non-pointSources)ModelingFramework. ResearchTriangle Park,NorthCarolina:NationalExposureResearchLaboratory;

2019.

32. NakicenovicN,AlcamoJ,GrublerA,RiahiK,RoehrlR,RognerH- H,VictorN:SpecialReportOnEmissionsScenarios(SRES),A SpecialReportofWorkingGroupIIIoftheIntergovernmentalPanel OnClimateChange.CambridgeUniversityPress;2000.

33. vanVuurenDP,EdmondsJ,KainumaM,RiahiK,ThomsonA, HibbardK,HurttGC,KramT,KreyV,LamarqueJ-Fetal.:The representativeconcentrationpathways:anoverview.Clim Change2011,109:5.

34. AlcamoJ,VanVuurenD,CramerW,AlderJ,BennettE, CarpenterS,ChristensenV,FoleyJ,MaerkerM,MasuiT:

Changesinecosystemservicesandtheirdriversacrossthe scenarios.EcosystHumWell-Being2005,2:297-373.

35. O’NeillBC,KrieglerE,RiahiK,EbiKL,HallegatteS,CarterTR, MathurR,vanVuurenDP:Anewscenarioframeworkforclimate changeresearch:theconceptofsharedsocioeconomic pathways.ClimChange2014,122:387-400.

36. BrookeA,KendrickD,MeerausA:GAMS—AUsersGuide.

RedwoodCity,CA:TheScientificPress;1988.

37. SunT,ZhangH,WangY,MengX,WangC:Theapplicationof environmentalGinicoefficient(EGC)inallocating

wastewaterdischargepermit:thecasestudyofwatershed totalmasscontrolinTianjin,China.ResourConservRecycl 2010,54:601-608.

38. ChenB,GuC,WangH,TaoJ,WuY,LiuQ,XuH:Researchon Ginicoefficientapplicationinthedistributionofwater pollution.JFoodAgricEnviron2012,10:833-838.

39. AlcamoJ:ScenariosasToolsforInternationalEnvironmental Assessment.EuropeanEnvironmentAgency;2001.

40. KokK,PeddeS,GrambergerM,HarrisonPA,HolmanIP:New Europeansocio-economicscenariosforclimatechange research:operationalisingconceptstoextendtheshared socio-economicpathways.RegEnvironChange2018:1-12.

41. PeddeS,KokK,OnigkeitJ,BrownC,HolmanI,HarrisonPA:

Bridginguncertaintyconceptsacrossnarrativesand simulationsinenvironmentalscenarios.RegEnvironChange 2019,19:655-666.

42. SatohY,KahilT,ByersE,BurekP,FischerG,TramberendS, GreveP,Flo¨rkeM,EisnerS,HanasakiN:Multi-modelandmulti- scenarioassessmentsofAsianwaterfutures:TheWater FuturesandSolutions(WFaS)initiative.Earth’sFuture2017, 5:823-852.

43. GarnierJ,BeusenA,ThieuV,BillenG,BouwmanL:N:P:Si nutrientexportratiosandecologicalconsequencesincoastal seasevaluatedbytheICEPapproach.GlobBiogeochemCycles 2010,24:GB0A05.

44. TangT,StrokalM,vanVlietMTH,SeuntjensP,BurekP, KroezeC,LanganS,WadaY:Bridgingglobal,basinandlocal- scalewaterqualitymodelingtowardsenhancingwater qualitymanagementworldwide.CurrOpinEnvironSustain 2019,36:39-48.

45. vanVlietMTH,Flo¨rkeM,HarrisonJA,HofstraN,KellerV,LudwigF, SpanierJE,StrokalM,WadaY,WenY,WilliamsRJ:Modelinter- comparisondesignforlarge-scalewaterqualitymodels.Curr OpinEnvironSustain2019,36:59-67.

46. WangM,MaL,StrokalM,MaW,LiuX,KroezeC:Hotspotsfor nitrogenandphosphoruslossesfromfoodproductionin China:acounty-scaleanalysis.EnvironSciTechnol2018, 52:5782-5791.

47. LiA,StrokalM,BaiZH,KroezeC,MaL,ZhangFS:Modelling reducedcoastaleutrophicationwithincreasedcropyieldsin Chineseagriculture.SoilRes2017,55:506-517.

48. RahmanA,SetayeshiS,ZafarghandiMS:Wealthadjustment usingasynergybetweencommunication,cooperation,and one-fifthofwealthvariablesinanartificialsociety.AI&Soc 2009,24:151-164.

49. LambertPJ:SocialwelfareandtheGinicoefficientrevisited.

MathSocSci1985,9:19-26.

50. McCrackinML,HarrisonJA,ComptonJE:Factorsinfluencing exportofdissolvedinorganicnitrogenbymajorrivers:anew, seasonal,spatiallyexplicit,globalmodel.GlobBiogeochem Cycles2014,28:269-285.

51. JiangY,HellegersP:JointpollutioncontrolintheLakeTai Basinandthestabilitiesofthecostallocationschemes.

JEnvironManage2016,184:504-516.

52. PerniA,Martı´nez-PazJM:Aparticipatoryapproachfor selectingcost-effectivemeasuresintheWFDcontext:The MarMenor(SESpain).SciTotalEnviron2013,458–460:303-311.

53. LescotJM,BordenaveP,PetitK,LecciaO:Aspatially- distributedcost-effectivenessanalysisframeworkfor controllingwaterpollution.EnvironModellSoftw2013, 41:107-122.

54. Udı´asA,EfremovR,GalbiatiL,Can˜amo´nI:Simulationand multicriteriaoptimizationmodelingapproachforregional waterrestorationmanagement.AnnOperRes2014, 219:123-140.