Nutrient Recovery from Source-Separated Wastewaters by Integration of Blackwater Treatment with Urban Farming: Characterization of Process and Products

Nutrient recovery from source-separated wastewaters by integration of blackwater treatment with urban farming: Characterization of process and products

Dissertation

zur

Erlangung des Grades

Doktor-Ingenieur

der

Fakultät für Maschinenbau

der Ruhr-Universität Bochum

von

Victor Takazi Katayama

aus São Paulo, Brasilien

Bochum 2018

Dissertation eingereicht am: 12.11.2018

Tag der mündlichen Prüfung: 01.02.2019

Erstgutachter: Prof. Dr.-Ing. Görge Deerberg

Zweitgutachter: Univ.-Prof. Dr.-Ing. Jörg Londong

ABSTRACT

The reuse of treated wastewater in agriculture is one of the most employed approaches for wastewater reclamation. In arid and semi-arid regions facing conditions of water stress, a large share of treated effluents is already used for irrigation of croplands. Although usually driven by the primary goal of recovering water, this wastewater reclamation approach has also been demonstrated to be an effective way to recycle nutrients. Urban farming opens up the possibility of applying this form of direct nutrient recovery to small-scale decentralized wastewater treatment systems. The on-site integration of food production and wastewater treatment shows particular promise in the context of the reclamation of source-separated domestic wastewater streams such as blackwater or urine, which are rich in nutrients and account for the majority of the nitrogen, phosphorus and potassium load in domestic wastewater.

The overarching goal of this thesis is to provide insight into technical aspects that are crucial to determine the effectiveness of this decentralized nutrient recovery approach, and focuses on aspects related both to the processing of the wastewater as well as the agronomic use of the resulting product as a fertilizer. Technical and health risk related aspects regarding the integration of a hydroponic urban farm with an on-site MBR-based blackwater treatment system were assessed.

Key factors were identified as critical for the successful implementation of the system studied here. Firstly, there is the discrepancy between the physicochemical characteristics of blackwater and the composition requirements of a nutrient solution adequate for use in hydroponic cultivation systems. Of highest relevance in this regard is blackwater’s nitrogen content profile, which is characterized by the predominance of ammoniacal nitrogen. This is diametrically opposite to what is considered ideal for hydroponic nutrient solutions, which contain primarily nitrate as nitrogen source. This mismatch is highly consequential for the design of the treatment process. Due to the low alkalinity-to-TAN ratio of blackwater, the full nitrification of its nitrogen load requires the dosing of an external alkalinity source, which itself might compromise the effluent’s quality in terms of its intended use as a nutrient solution.

Another import aspect to be considered is the area requirements. The nutrient recovery of source-separated wastewaters by the direct use of treated effluents in urban farming is fundamentally limited by farming area requirements. In urban areas, where population density is high and available surface area is scarce, the scale-up of such a scheme would face enormous challenges. Finally, the impact of organic micropollutants on the objective and perceived quality of the produce cannot be understated. Such compounds were measured in substantial amounts in biologically treated blackwater, and were taken up in the edible parts. Even though the health risks associated to the micropollutant levels observed in the vegetables produced with BTP effluent are found to be low, it is likely that the presence of those contaminants would have a negative impact in the consumer perception of those products and limit their value if they were taken to market.

Despite of those issues, conceptually the system investigated here represents a very rational solution for the supply of recycled fertilizer for urban farms. In a future where urban farming plays a larger role in the food security of cities, such system would not only supply fertilizer for food production, but, from the wastewater treatment perspective, also act as satellite systems that reduce the nutrient and COD loads to centralized wastewater treatment plants, allowing the existing plant footprints to be reallocated to other purposes, such as quaternary treatment for removal of micropollutants, for instance.

ACKNOWLEDGEMENTS

First and foremost I would like to thank Dennis Schlehuber, Annette Somborn-Schulz, the head of our department, Volkmar Keuter, and my supervisor, Prof. Görge Deerberg, for the support they gave me in the many times I hit the bumps and ruts on the road to the completion of my thesis, which more often than not felt like boulders and craters. Deep gratitude also goes to «Science without Borders», the scholarship program funded by the Brazilian Federal Government, via CAPES («Coordenação de Aperfeiçoamento de Pessoal de Ensino Superior»), which made my stay in Germany possible. The Brazilian people can rest assured that this seed will come to fruition to the benefit of our country.

On the Berlin front, my gratitude goes to Erwin Nolde, Holger Sack, Carsten Beneker and Nora Kaup, the keepers of Roof-Water Farm’s water recycling sanctuary – may the greywater flow and bring you good fortune in the future and beyond. I am deeply indebted to Janine Dinske and her troops, who kept our project’s greenhouse humming through hell and high water (quite literally). Without her work, no lettuce and cucumber would be there to be spoken of. Praise also goes to Christina Senge of the TU Berlin’s Environmental Process Engineering department for all the patience and care in supporting our numerous master’s students during their time in the lab.

I would also like to thank the Prof. Dr. Thorsten Reemtsma, Dr. Monika Möder and Hilke Maas of the Center for Environmental Research – UFZ for their analysis of organic micropollutants in the effluent of our blackwater treatment plant and in the produce grown in our experimental greenhouse. Without your support, one of the key parts of my work would be all loose ends.

Last but not least, my eternal gratitude to the loving Joanna “Moskito” Rabizo for helping me weather the stormy last year of my PhD, when push came to shove and it was either the thesis’ way or the highway.

Victor Takazi Katayama

February 2019

TABLE OF CONTENTS

1 Introduction ...... 1 2 Scope of the thesis ...... 3 3 Literature review ...... 4 3.1 Mineral nutrition of plants ...... 4 3.2 Micropollutants ...... 12 4 Blackwater treatment system ...... 18 4.1 Introduction ...... 18 4.2 Materials and Methods ...... 18 4.2.1 Description of the blackwater treatment system ...... 18 4.2.2 Blackwater characterization ...... 20 4.3 Results and Discussion ...... 23 4.3.1 Characterization of raw blackwater ...... 23 4.3.2 Settling tank ...... 31 4.3.3 Analysis of the pH buffering capacity of settled blackwater ...... 33 4.3.4 MBR operation and effluent quality ...... 36 4.4 Conclusion...... 54 5 Use of blackwater treatment effluent in vegetable production system ...... 55 5.1 Introduction ...... 55 5.2 Material and Methods...... 56 5.3 Results and Discussion ...... 57 5.3.1 Lettuce ...... 58 5.3.2 Cucumber ...... 62 5.4 Conclusion...... 66 6 Micropollutants in BTP effluent and their uptake by crops ...... 67 6.1 Introduction ...... 67 6.2 Material and methods ...... 67 6.3 Results ...... 68 6.3.1 Concentrations in the BTP effluent ...... 68 6.3.2 Micropollutant uptake by lettuce and cucumber ...... 77 6.3.3 Bioconcentration factors ...... 80 6.3.4 Human health risk assessment ...... 89 6.4 Conclusion...... 91 7 Final Remarks ...... 92 8 References ...... 94

LIST OF FIGURES

Figure 1-1 – Circular economy approach applied to food production systems. Source: Steffen et al. (2015) ...... 2

Figure 3-1 – Schematic illustration of ammonium and nitrate transport across plant cell membranes. Source: Marschner and Marschner (2012, S. 353)...... 6

Figure 3-2 – Nutrient availability as function of pH for organic soils. The height of the dark gray bands represent the degree of availability of each nutrient. Source: Taiz and Zeiger (2010)...... 8

Figure 3-3 – Typical dose–response curves for essential and non-essential trace elements in crops. Source: adapted from Alloway (2008)...... 9

Figure 3-4 – Example of crop yield response to salinity. The dotted line represents the response according to the conceptual model that does not account for the salinity caused by the nutrients themselves. Source: Sonneveld et al. (2005)...... 12

Figure 3-5 – Root bioconcentration factors for a variety of organic micropollutants in crops grown hydroponically. Source: Wu et al. (2015)...... 14

Figure 3-6 – Leaf bioconcentration factors for a variety of organic micropollutants in crops grown hydroponically. Source: Wu et al. (2015)...... 14

Figure 3-7 – Pathways for the uptake of dissolved substances from soil (or nutrient) solution via the root system of dicot plants. Source: Miller et al. (2016)...... 16

Figure 4-1 – Schematic description of the blackwater treatment plant. Key for tank labels: B1a – primary settling tank; B1b – settled blackwater storage tank; B10 – 1.0 mm screen; B3 – aeration tank; B4 – membrane tank; B5 – effluent storage tank...... 19

Figure 4-2 – Illustration of the procedure used for the estimation of blackwater flowrates. Shown in the figure is a typical example of the daily fluctuations in the level of the system’s primary settling tank. The ascending segments that followed sludge wastage events (marked in red) were used for linear regression. Dashed lines represent the resulting linear fits...... 21

Figure 4-3 – Monthly variation of measured blackwater flowrates...... 23

Figure 4-4 – Cumulative probability distribution of measured blackwater flowrates...... 24

Figure 4-5 – Periodogram of measured blackwater flowrates...... 24

Figure 4-6 – Measured blackwater flowrates data collapsed by hour of day. The sinusoidal function corresponds to the Fourier transform term associated with the peak frequency identified in the in the periodogram shown in Figure 4-5, of the form: 풇풕 = ퟏퟕ, ퟓퟓ ∙ 풄풐풔ퟐ흅 ∙ ퟎ, ퟎퟒퟏퟕ ∙ 풕 − ퟏ, ퟔퟐ + 풔풊풏ퟐ흅 ∙ ퟎ, ퟎퟒퟏퟕ ∙ 풕 − ퟏ, ퟔퟐ + ퟗퟗ, ퟔퟖ...... 25

iii

Figure 4-7 – Probability plots of total COD, TSS and VSS concentrations measured in raw blackwater. Dashed line corresponds to lognormal distribution fitted to data of the respective variable (crosses). Dot-dashed line corresponds to normal distribution reference line...... 26

Figure 4-8 – Normal probability plots of reference data for per capta loads. Dash-dotted lines represent the linear regression of samples quantiles against corresponding theoretical normal quantiles. The Pearson’s correlation coefficient (ρ) indicate how normal the probability distribution of the data is...... 28

Figure 4-9 – Distribution of COD, TN, TP and TSS concentration values in raw and settled blackwater...... 31

Figure 4-10 – Distribution of N:P ratios in raw and settled blackwater...... 32

Figure 4-11 – Scatter plot of TP and TSS concentrations in raw blackwater. Fitted model: 푻푷 = ퟓ, ퟏퟑ ∙ 푻푺푺 + ퟑퟖ, ퟎ (R² = 0.36, p = 0.01)...... 32

Figure 4-12 – Measured and model titration curves. Measured data points obtained from titration of n = 12 settled blackwater samples. Theoretical reference curve corresponds to that o of a carbonate buffer solution, where pKa1 = 6.35 and pKa2 = 10.33 (25 C). For description of smoothed spline, refer to text...... 34

Figure 4-13 –Buffer intensity curves derived from carbonate model and from smoothed measured titration curves...... 36

Figure 4-14 – Flux and transmembrane pressure at membrane filtration unit over the course of the MBR’s operation...... 38

Figure 4-15 – Permeability of membrane filtration unit over the course of the MBR’s operation. Dotted line indicates the value of 23 L/(m².h.bar), at which the permeability stabilized repeatedly...... 38

Figure 4-16 – Development of MLSS and MLVSS concentrations in the MBR throughout its operational period. Also indicated are the estimates for the steady-state MLSS and MLVSS with respective 95% confidence intervals (shown as dashed lines), based on an input dataset with n=17 entries...... 39

Figure 4-17 – Variation of XVSS/XTSS ratio in the mixed liquor of the MBR over time...... 41

Figure 4-18 – Contour plot of estimated steady-state values of XTSS and XVSS/XTSS ratio, as a function of SRT and fNB. Continuous and dashed lines represent XVSS/XTSS ratio and XTSS levels, respectively...... 42

Figure 4-19 – Contour plot of estimated steady-state values of XTSS and XVSS/XTSS ratio, as a function of SRT and fI. Continuous and dashed lines represent XVSS/XTSS ratio and XTSS levels, respectively...... 44

Figure 4-20 – Contour plot of estimated steady-state values of XTSS and XVSS/XTSS ratio, as a function of fNB and fI. Continuous and dashed lines represent XVSS/XTSS ratio and XTSS levels, respectively...... 44 iv

Figure 4-21 – Bioreactor’s pH and concentration of nitrogen species and potassium in the MBR’s effluent measured during the operation of the blackwater treatment system...... 45

nd Figure 4-22 – Bioreactor KOH dosing rates (QKOH) recorded in the period between the 2 and 29th of October. Indicated in the figure are the confidence intervals for the average dosing rate and theoretical dosing rate...... 48

Figure 4-23 – Comparison of N:P and K:P ratios of raw blackwater, settled blackwater and MBR’s effluent...... 50

Figure 4-24 – Comparison of TN and TP concentrations in raw blackwater, settled blackwater and MBR’s effluent...... 50

Figure 4-25 – Theoretical effluent TAN concentration vs. pH curve for a reactor SRT of 55 days. Model 1 and 2 refer to Equation 4-21 and 4-22, respectively...... 53

Figure 4-26 – MBR effluent pH and KOH dosing rate (top) and effluent N species concentrations (bottom) in the period surrounding the change in setpoint of the reactor’s pH control system...... 54

Figure 5-1 – Ebb-and-flow growing beds used for the hydroponic trials...... 56

Figure 5-2 – Comparison between yield and morphology of lettuce grown in the experimental (BTP effluent as nutrient solution) and control ebb-and-flow beds...... 58

Figure 5-3 – Concentration of TN, NH4-N, NO3-N and NO2-N in the beginning (day 0) and end (day 14) of use of the first batch of BTP effluent in the hydroponic cultivation of lettuce...... 60

Figure 5-4 – Aerial view of the inner courtyard of the apartment complex where the BTP and experimental greenhouse are located. Indicated are the apartment building that generates the blackwater (C), the area occupied by the experimental greenhouse (A) and the cultivated area that would be required for full nutrient recovery (B), see text for more details...... 61

Figure 5-5 – Total mass, number and average weight of fruit harvested from cucumber plants on experimental and control ebb-and-flow beds over the course of the fruiting period...... 62

Figure 5-6 – Pictures of the cucumber plants on the experimental ebb-and-flow bed early in the fruiting period (11.07.2017) when the presentation of interveinal chlorosis was mild (right), and by the time fruit production collapsed (21.08.2017), when most leaves were either severely chlorotic or dead (left)...... 63

Figure 5-7 –Comparison of single-nutrient diagnosis references (Panel B) and early and advanced stage interveinal chlorosis presented by leaves of cucumber plants on the experimental bed (Panel A). References: ① van Roorda Eysinga and Smilde (1981), ② Zorn et al. (2016)...... 64

Figure 6-1 – Variation in measured concentrations of acesulfame and sucralose in the BTP effluent over time...... 71

Figure 6-2 – Variation of concentration of valsartan, metformin, gabapentin, lidocaine and 4- acetoaminopyridine measured in the BTP effluent over time. The values plotted in the graphs are the concentrations of the various compounds normalized by their respective mean...... 73

Figure 6-3 – Variation in concentrations of carbamazepine and two of its metabolites, trans- 10,11-dihydro-10,11-dihydroxy carbamazepine (DiOH-CBZ) and carbamazepine-10,11-epoxide (Ep-CBZ), measured in the BTP effluent over time...... 75

Figure 6-4 – Variation in concentrations of acyclovir measured in the BTP effluent over time...... 75

Figure 6-5 – Concentration of organic micropollutants found in lettuce leaves and average concentrations of the respective compounds measured in the BTP effluent batches during cultivation. Error bars correspond to one standard deviation...... 78

Figure 6-6 – Concentration of organic micropollutants found in cucumber fruit and average concentrations of the respective compounds measured in the BTP effluent batches during cultivation. Error bars correspond to one standard deviation...... 78

Figure 6-7 – Bioconcentration factors for organic compounds found in lettuce leafs and cucumber fruit...... 81

Figure 6-8 – Variation of logBCF with the logarithm of the average concentration of the various organic micropollutants in the BTP effluent batches used for the cultivation of lettuce...... 82

Figure 6-9 – Scatterplots of logBCF versus logDOW (left) and logBCF versus logKOW (right) for organic micropollutants found in lettuce leaves...... 85

Figure 6-10 – Scatterplots of logBCF versus logDOW (left) and logBCF versus logKOW (right) for organic micropollutants found in cucumber fruit...... 85

Figure 6-11 – Scatter plots of logDOW versus BCF and logBCF for organic micropollutants found in cucumber fruit (top) and lettuce (bottom). Dotted lines represent values estimated by Gaussian regression model (see text for more details)...... 86

LIST OF TABLES

Table 3-1 – Classification of nutrients by amount required by plants (macro- or micronutrients) and function. Adapted from Marschner and Marschner (2012, S. 5) and Mengel et al. (2001, S. 3)...... 5

Table 3-2 – Compilation of values found in selected literature for nutrient loads in human excreta...... 10

Table 3-3 – Nutrient solution composition recommended for various vegetable and fruit crops. Source: van der Lugt (2016)...... 11

Table 4-1 – Specifications of membrane filtration unit...... 20

Table 4-2 – List of analytical methods used for the characterization of raw blackwater, settled blackwater and the MBR’s effluent...... 22

Table 4-3 – Summary statistics of measured blackwater flowrates...... 23

Table 4-4 – Summary statistics of raw blackwater composition, and reference values collected from literature...... 26

Table 4-5 – p-values obtained by the two-tailed t-test for the comparison of difference between measured and reference load values...... 29

Table 4-6 – Comparison of COD:N:P ratios calculated from measured and reference per capta loads...... 29

Table 4-7 – Summary statistics of calculated per capta loads and reference values collected from literature...... 30

Table 4-8 – Summary statistics of settled blackwater composition...... 31

Table 4-9 – Parameter values used for the calculation of the steady-state MLSS estimate...... 40

Table 4-10 – Summary statistics of MBR’s effluent composition...... 49

Table 4-11 – Parameter values used for the determination of the steady-state TAN vs. pH curve...... 52

Table 5-1 – Composition of BTP effluent batches used for the cultivation of lettuce and cucumber, and recommended concentrations for nutrient solutions for the respective crops. 57

Table 5-2 – Comparison of reference nutrient uptake rates for lettuce and nutrient application rates used on ebb-and-flow bed operated with BTP effluent...... 59

Table 5-3 – Supportable crop cultivation areas (SCAs) for lettuce grown with BTP effluent as nutrient source...... 61

vii

Table 6-1 – Statistics of micropollutants concentration measured in the 9 BTP effluent batches used as nutrient solution in the hydroponic experiments. Concentration values in units of µg/L...... 69

Table 6-2 – Concentrations of 4-MBTR, Quadrol and BTSA found in a 28-hour composite sample of the graywater treatment effluent used as toilet flush water in the apartment building served by the BTP...... 76

Table 6-3 – pKa, logKOW, logDOW of organic micropollutants found in lettuce leaves and cucumber fruit, and respective α factor values calculated considering the average pH of the BTP effluent batches used for the cultivation of each crop...... 84

Table 6-4 – Values obtained by various authors for parameters of Gaussian TSCF model...... 87

Table 6-5 – Estimated values for parameters of DOW-based BCF Gaussian regression model for cucumber fruit...... 88

Table 6-6 – Threshold consumption rate (TCR) for adults and infants of lettuce and cucumber grown with the BTP effluent, in kg/d [w.w.] For the calculations, a body weight of 60 kg was assumed for adults and 25 kg for infants...... 90

viii

LIST OF ABBREVIATIONS

AOM Ammonia oxidizing microorganisms b.w. Body weight BCF Bioconcentration factor BTP Blackwater treatment plant CDC Critical deficiency concentration CEC Contaminant of emergent concern COD Chemical oxygen demand d.w. Dry weight pH-adjusted octanol-water partition DOW coefficient EC Electroconductivity LCF Leaf concentration factor LCL Lower confidence limit MBR Membrane bioreactor MLSS Mixed liquor suspended solids MLVSS Mixed liquor volatile suspended solids NOM Nitrite-oxidizing microorganisms RCF Root concentration factor SAD Specific aeration demand SCA Supportable crop cultivation area SRT Solids retention time SYD Salinity yield decrease TAN Total ammonia nitrogen TMP Transmembrane pressure TN Total nitrogen TP Total phosphorus TSCF Transpiration stream concentration factor TSS Total suspended solids TTC Threshold of toxicological concern UCL Upper confidence limit VSS Volatile suspended solids w.w. Wet weight WWTP Wastewater treatment plant

LIST OF SYMBOLS

Alk Alkalinity f Volatile fraction in VSS Fraction of VSS concentration that is non- fB biodegradable fd Cell debris fraction Fraction of inorganic suspended solids that fI is non-inert Normalized equivalent fraction of titrant g’ (acid) added J Filtration flux kd Heterotrophic endogenous decay coefficient kdA Autotrophic endogenous decay coefficient

KOW Octanol-water partition coefficient Nap Reference nutrient application rate nbVSS non-biodegradable VSS concentrations

Nx Concentration of nitrogen species “x” p Person Q Flowrate S COD concentration SRT Solids retention time TMP Transmembrane pressure TSS Total suspended solids concentration V Volume VSS Volatile suspended solids concentration

XBIO Biomass concentration

XTSS Mixed liquor suspended solids concentration Mixed liquor volatile suspended solids XVSS concentration Y Heterotrophic yield coefficient

YA Autotrophic yield coefficient α Ionization fraction β Buffering intensity ρ Pearson’s correlation coefficient

1 INTRODUCTION

By 2050, cities are expected to concentrate 66% of the global population. Current empirical evidence points out that urban farming is likely to play a key role in the food supply resilience of those urban systems of the future (Eigenbrod and Gruda, 2015, Jennings et al., 2015, Dubbeling et al., 2016). It makes part of the “City Region Food Systems” planning approach promoted by the Food and Agriculture Administration of the United Nations (FAO), which is seen as vital to the implementation of the Agenda 2030 and the New Urban Agenda (NUA) (Dubbeling et al., 2016). The recognition of its importance culminated in 2015 with the signature of the Milan Urban Food Policy Pact by 138 cities across the world, including megacities such as São Paulo, Mexico City, Tokyo, Chongqing, Beijing and Shanghai.

Although urban farming does not fully replace conventional rural agriculture and its effective application is restricted mostly to horticultural crops, it has been shown that, with the appropriate institutional framework, it can deliver a slew of social, economic and environmental benefits (Jennings et al., 2015). Of utmost relevance is the fact that, as opposed to rural farming, products of urban farming are immensely more likely to be consumed in the same geographical unit where they are produced – the city --, the same place where the generation of wastes that follow that consumption occur. From the environmental perspective, that opens up a host of possibilities to create city-scale resource loops (Figure 1-1) that address one of the primordial sins of current food production systems: the disruption of the global geochemical cycles of nitrogen and phosphorus (Sutton et al., 2013), two of the main inputs of modern agriculture alongside water and pesticides.

That disruption stems from the fact that the advent of synthetic fertilizers led to (a) the dramatic increase of absolute inputs to the environment, and (b) a radical spatial separation between the sources and sinks of those two elements, associated to the separation between the location of farms where fertilizers are ultimately used and the location of its raw material and/or production facilities. A recent coarse estimates indicate that, as a consequence of those disrupted cycles, the input of those elements in the environment are close to (in the case of phosphorus) or beyond (in the case of nitrogen) the planetary boundaries (Foley et al., 2011). Per year, 108 million metric tons of nitrogen and 41 million metric tons of phosphate are used in agricultural applications worldwide. Those amounts are estimated to increase at a rate of 2- 3% per year in the coming years, a trend largely due to the ever-increasing world population.

In the case of P, an additional aggravating aspect is its source. Currently, it is obtained almost entirely from phosphate rock, a non-renewable resource with limited geographic availability (2/3 of the reserves are concentrated in Morocco, China and the USA). The quality of the ore in those reserves is gradually decreasing while at the same time the degree of processing required for the production of fertilizer-grade phosphate increases. These factors have already triggered a regulatory response in Europe: P was recently introduced in the EU list of critical raw materials, and legislation introducing quotas for P-fertilizer derived from secondary sources are currently being prepared both at the European as well as at the German level. Source is also a problem in the case of N. Although it has a seemingly unlimited source in the form of atmospheric nitrogen gas, is also currently directly dependent on non-renewable resources, since the energy and hydrogen required for its fixation in the form of ammonia by chemical synthesis processes is largely derived from natural gas, with all the climate change-related impacts that it implies.

Introduction

Figure 1-1 – Circular economy approach applied to food production systems. Source: Steffen et al. (2015)

Essentially all N and P contained in food consumed in cities ends up in the wastewater collection and treatment systems: in average, western diets result in around 4 kg of nitrogen and 0.7 kg of P per person per year being released into domestic wastewater (DWA, 2008, Friedler et al., 2013, Rose et al., 2015). In the case of a treatment plant serving a population of 100 000 people, this translates into roughly 400 metric tons of N and 66 metric tons of P per year – sufficient for the production of around 200 000 metric tons of tomatoes per year at Dutch yield levels, for example.

New systems and technologies are needed in order to close those urban nutrient loops, and allow for this secondary source of nutrients to be connected back into urban food production systems in a way that is integrated to existing urban infrastructure. In so doing, an urban farming system should emerge that is further integrated in the material flows of the city, contributing to a resilient component for sustainable agriculture and food and nutrition security.

Most of the approaches seeking to recover nutrients from wastewater have so far focused mostly on the recovery of solid products, especially the mineral struvite (MgNH4PO4.6H2O). Although a relatively mature technology with a good track record and several commercial technology providers available, struvite precipitation is yet to reach widespread application, due to several challenges in integrating struvite producers to the fertilizer market and uncertainty about costs, among others (Latimer et al., 2015). With the exception of ammonia recovery by stripping (applicable only to highly concentrated streams, such as manure slurry), nutrient recovery processes focusing on liquid products received considerable less attention (Latimer et al., 2015). This is largely due to the usual underlying assumption of spatially separated points of production and consumption/commercialization of the recovered fertilizer products, under which indeed logistics costs render liquid products with high water content hardly competitive.

Chapter 2

The central proposition of this thesis is that urban farming could change this equation. It allows for the possibility of physical on-site integration of the nutrient recovery process into the food production system, whereby the fertilizer demands of the latter are directly supplied by the former. In this setting, the approach of producing a liquid fertilizer is arguably technically viable, and possibly a very convenient way to perform the recovery of the nutrients.

2 SCOPE OF THE THESIS

This thesis hopes to make a small contribution to the exploration of the new possibilities that urban farming offers for the recovery of nutrients from wastewaters, especially in the context of source-separation of wastewaters. The basis of the work presented here is a pilot-scale nutrient recovery/urban farming system that consists of a biological blackwater treatment unit integrated to a hydroponic plant cultivation system, installed for research purposes at an apartment complex located in a densely populated area in the heart of Berlin. The data used for the analyses presented herein was produced by the project Roof-Water Farm, a research initiative funded by the German Federal Ministry of Education and Research (BMBF) that counted as partners the Department of Urbanism and Habitat of the Technische Universität Berlin, Fraunhofer UMSICHT, the Helmholtz Centre for Environmental Research (UFZ), Terra Urbana GmbH, Nolde & Partner Innovative Wasserkonzepte GmbH, amongst others.

In Chapter 4, the system’s main nutrient source – blackwater – is characterized, along with the biological treatment unit used to process it into the liquid nutrient solution for the hydroponic cultivation unit. The results reported in that chapter put in stark display the challenges involved in designing a purely biological process capable of producing a nutrient solution with physical- chemical characteristics that are appropriate for the application in a soilless cultivation system.

Chapter 5 focuses on the agronomic effectiveness of the liquid nutrient solution produced by the blackwater treatment unit. That chapter reports the results of field trials carried out to determine the agronomic effectiveness of the liquid nutrient solution produced by the blackwater treatment unit in the cultivation of two different crops, lettuce and cucumber. There, the enormous importance of the interaction between the pH of the nutrient solution in modulating the nutrient availability – how it can lead to success or collapse of the crop – is seen in detail.

Finally, Chapter 6 assesses the presence of organic micropollutants in the recycled nutrient solution and their uptake by the produce grown in the hydroponic cultivation unit, along with the health risks that the consumption of those vegetables might present. The results presented in that chapter confirm existent evidence that the uptake of certain organic micropollutants by plants can be substantial, and that this aspect should be of high concern in the context of the integrated urban farming/nutrient recovery approach considered here.

3 LITERATURE REVIEW

3.1 Mineral nutrition of plants

So far, 16 elements were verified as being universally essential for the growth of higher plants: carbon (C), hydrogen (H), oxygen (O), nitrogen (N), phosphorus (P), sulfur (S), potassium (K), calcium (Ca), magnesium (Mg), iron (Fe), manganese (Mn), copper (Cu), zinc (Zn), molybdenum (Mo), boron (B) and chlorine (Cl). Their essentiality is predicated in terms of three criteria first formulated by Arnon and Stout (1939), whereby:

1. The deficiency of the element makes it impossible for the plant to complete its life cycle; 2. The function of the element must not be replaceable by another element; and 3. The element is directly involved in the plant’s metabolism, either as a constituent of an essential metabolite or required for the action of an essential metabolite (as an enzyme cofactor, for example).

Depending on the amount in which they are needed, they can be classified as primary or secondary macronutrients – which are required in largest amount – or micronutrients, of which only tiny amounts are needed for adequate plant growth. Although required in large amounts, carbon, oxygen and hydrogen are usually omitted from the list of macronutrients. Alternatively, they can also be divided in groups based on their role in plant metabolism, as defined by Mengel et al. (2001). An overview of the classification, role and amounts in plant tissue of the essential elements is presented in Table 3-1.

With the exception of carbon and oxygen, all of those elements are acquired by plants primarily in mineral form via their roots. The following sections offer a brief overview of fundamental concepts that govern the uptake of those nutrients – with special focus on the three main macronutrients N, P and K – and therefore important aspects to be considered in the preparation of nutrient solutions for use in soilless farming systems.

Nitrogen

Nitrogen is the element that is taken in highest amounts in mineral form, and is part of key cellular macromolecules such as proteins and nucleic acids. Of all the nutrients required for + plant growth, nitrogen is peculiar in that it can be taken up both as a cation (NH4 ) and an anion - (NO3 ). Regardless of the form, the uptake of nitrogen corresponds to ca. 80% of the total uptake of ions by the root system of plants. Due to their different charges and oxidation states, the transport pathways of ammonium and nitrate in plants are very distinct, and affect the pH of the external medium in diametrically opposed ways, as discussed below.

Chapter 3

Table 3-1 – Classification of nutrients by amount required by plants (macro- or micronutrients) and function. Adapted from Marschner and Marschner (2012, S. 5) and Mengel et al. (2001, S. 3).

Classification Concentration Group Element (macro- or Biochemical Function (mg/kg) micronutrient)

N Macro 1000 Major constituents of organic material. 1 Essential elements of atomic groups that are S Macro 30 involved in enzymatic processes. P Macro 60 Esterification of native alcohol groups in plants. 2 Phosphate esters are involved in energy transfer B Micro 2 reactions.

K Macro 250 Non-specific functions in establishing osmotic Ca Macro 125 potentials. Activation of enzymes. 3 Mg Macro 80 Bridging of reaction partners. Mn Micro 1 Balancing ions. Controlling membrane permeability and electrochemical potentials. Cl Micro 3

Fe Micro 2

Cu Micro 0.1 4 Enable electron transport by valence change. Zn Micro 0.3

Mo Micro 0.001

In order to overcome the electrochemical potential gradient across the plasma membrane of root cells, nitrate requires active transport. As shown in Figure 3-1, nitrate transport is secondary, in symport with two protons via transporters of the NRT1 and NRT2 families. It is therefore an energy-consuming process driven by the proton motive force (PMF), indirectly requiring energy in the form of ATP in a one-to-one ratio to take place. The ATP is consumed by proton pumps, which rectify the reduction in electrical potential across the plasma membrane of the cell caused by the net influx of one charge equivalent per molecule of nitrate transported. More importantly, in terms of the proton balance the transport of nitrate results in the net transfer of protons from the external medium.

+ On the other hand, NH4 , being a cation, is taken up via facilitated diffusive transport along the potential gradient by uniport channels (Figure 3-1). In order to avoid depolarization of the plasma membrane and maintain the electric potential of roots cells at around -120 mV, the + charge influx in the form of the NH4 ion is compensated for by the extrusion of protons by + proton pumps. Moreover, most plants are sensitive to high NH4 concentrations, although the exact reasons for that are not yet fully clear.

Literature review

Figure 3-1 – Schematic illustration of ammonium and nitrate transport across plant cell membranes. Source: Marschner and Marschner (2012, S. 353).

+ Given the sheer amount of nitrogen required for plant growth, the proton balance of NH4 and - NO3 transport can have a significant impact on the pH of the medium external to the root system, particularly in the case poorly buffered systems like nutrient solutions used in hydroponics. Due to the fact that the pH of such nutrient solutions is usually kept in the range of 5.5 to 6.5 and is normally controlled via dosing of acid (for reasons detailed below), N - nutrition in hydroponic systems is provided primarily in the form of NO3 , in order to avoid the + above mentioned acidifying effect of NH4 uptake. However, an improved growth response is typically obtained when nitrogen is provided as a combination of nitrate and ammonium (Raviv and Lieth, 2008, S. 303, Marschner and Marschner, 2012, S. 148).

In blackwater, most of the nitrogen originates from urine. Although the nitrogen content of fresh urine consists almost entirely of urea (DWA, 2008, Rose et al., 2015), it is quickly converted into total ammonia nitrogen and bicarbonate by the enzyme urease as a result of microbiological activity. In practice, the retention time in storage/equalization tanks and plumbing systems is already sufficient to allow for full ureolysis. Feces contributes to the organic fraction of blackwater’s total nitrogen load in the form of undigested dietary proteins and to lesser extent nitrogen compounds in dead and alive bacteria present in the fecal mass. Nitrate is present in negligible amounts.

Phosphorus

Of all three macronutrients, phosphorus is the one that is required in lowest amounts for plant growth, being present in plant tissues in 7.5:5:1 ratio in relation to N and K on a mass basis (Marschner and Marschner, 2012, S. 5). As an element, phosphorus is predominantly assimilated into molecules with a structural role, such as RNA, DNA and phospholipids, as well as molecules involved in energy transfer reactions, such as the enzyme cofactors ATP and NAD.

Plants uptake phosphorus in inorganic form as orthophosphates via transporters from the PHS - family, which transport H2PO4 in symport with two to four protons. Although on a molar basis the uptake of phosphorus involves in average more protons than that of nitrate, its effect on the pH of the solution external to the roots is considerably smaller, given the small P:N uptake ratio. As opposed to nitrogen, essentially all of the phosphorus taken up by plants remains in an oxidized state once within cells, being either stored as orthophosphates in the vacuole (which

Chapter 3 represents around 80-85% of the phosphorus pool in plant tissue), or converted into phosphate ester bonds of structural molecules or pyrophosphates bonds in enzyme cofactors.

In blackwater, in average around 65% percent originates from urine, where it is present as orthophosphate (DWA, 2008). The remainder can be found in feces in the form of calcium phosphates, which stem from the fraction of dietary P and Ca that is not taken up during digestion. Anecdotic evidence indicates that those calcium phosphates are present as amorphous solids (Rose et al., 2015).

Potassium

Potassium is the most abundant cation in the cytoplasm of plant cells, and is required in amounts in the same order of magnitude as those of nitrogen for plant growth. The three main cellular functions of potassium in plants are: as an osmoticum in turgor-driven cellular processes for cell division and plant movement; as a charge equivalent in charge balancing processes that maintain electroneutrality and stable pH within the cytosol, serving as the main counter-ion to mineral anions taken up from the external medium (especially in the case nitrate) and organic anions produced by the biochemical pH-stat system of the cell; finally, potassium also modulates enzyme activity and protein synthesis. The nature of those functions requires that the intracellular concentrations of potassium be tightly regulated in the cytosol, which is maintained at around 100-200 mM by a large variety of different transporters (both active and passive) and ion channels. Potassium is also present in blackwater in dissolved form as a cation, which originates mostly from urine. pH

The pH of the solution external to roots has a large influence on the availability of nutrients, especially in the case of micronutrients. This effect is very well documented for soils, and is mainly associated with (i) ion exchange between soil solution, soil particles and soil-bound organic matter (especially in the case of cations and phosphorus); (ii) aqueous speciation of the nutrients; and (iii) formation of sparingly soluble compounds (Marschner and Marschner, 2012, S. 317). This dependence is illustrated in Figure 3-2 for organic soils, where the relative availability of 12 of the 14 essential nutrients for higher plants grown is depicted.

Similar phenomena should apply both in soilless systems, although the composition of soil solutions is in most cases very different from that of synthetic nutrient solutions. It is expected that adsorption to the substrate could have a role that varies from relevant (when organic substrates like peat are used) to evidently irrelevant in the case of NFT systems (Raviv and Lieth, 2008, S. 210).

As to the two other effects, i.e. aqueous speciation and precipitation, dependence on pH should - be expected for orthophosphate and boron, which are taken up as H2PO4 (pKa=7.21 at 25°C) and boric acid, H3BO4 (pKa=9.27 at 20°C), respectively (Rijck and Schrevens, 1999, Marschner and Marschner, 2012). Phosphorus can also precipitate as various calcium and magnesium phosphates, to an extent which depends on the concentration of carbonates, which compete with phosphates for the formation of calcium/magnesium minerals (Rengel and Marschner, 2005, Raviv and Lieth, 2008, S. 220).

Literature review

Figure 3-2 – Nutrient availability as function of pH for organic soils. The height of the dark gray bands represent the degree of availability of each nutrient. Source: Taiz and Zeiger (2010).

For cationic micronutrients, the formation of insoluble oxides and hydroxides at neutral to alkaline pH ranges is particularly relevant, especially in the case of zinc, manganese and iron – whose Fe3+ oxidation state readily precipitates in the form hydroxides at neutral pH already (Lemanceau et al., 2009, Lindsay and Schwab, 2008, Raviv and Lieth, 2008).

Moreover, by affecting the proton gradient across the plasma membrane of root cells, the pH modulates the activity of secondary transporters that move anions in symport with protons, as is the case of nitrate and phosphate, promoting it when external pH is low and inhibiting it when it is high (Marschner and Marschner, 2012). The opposite applies for the transport of cationic nutrients like potassium, whose uptake relies on the extrusion of protons by proton pumps, as discussed previously.

For the above mentioned reasons, the pH of nutrient solutions in soilless farming systems is usually kept in the range of 5.5 to 6.5, with cationic micronutrients commonly applied as chelates in order to avoid precipitation (Resh, 2013) . Control of the pH is performed by means + of addition of phosphoric acid or nitric acid, or – less frequently – by employing NH4 -based N- nutrition (Resh, 2013). It should be noted, however, that differences between nutrient acquisition strategies of plants and interactions between the nutrients can modulate the influence of pH on the uptake of individual nutrients in soilless systems, as shown by Siraj-Ali et al. (1987), Tyson et al. (2008) and Passos et al. (1999).

Chapter 3

Concentration

The response of higher plants to the provision of essential nutrients is characterized by a “mesa”- shaped yield curve, where three distinct regions can be identified. With increasing concentration of the nutrient in the plant biomass or the external medium, the response goes from the deficiency region – where the growth rate increases proportionally to the availability of the nutrient--, passing through the sufficiency range, where there is a flat response to the increase of the nutrient concentration in the medium, reaching finally the toxic range, where the excessive presence of the nutrient actually impairs the growth rate of the plant.

Figure 3-3 – Typical dose–response curves for essential and non-essential trace elements in crops. Source: adapted from Alloway (2008).

Although informative from a conceptual standpoint, the use of yield curves for the development of nutrient solutions is hindered by the fact that the shape of the curves – in particular the critical deficiency concentration (CDC) (also referred to as lower critical concentration), which marks the transition from the deficiency to the sufficiency range – are affected by a myriad of factors. Besides aspects like plant species, growth stage, the yield curve of an individual nutrient is constrained by the overall nutritional status of the plant as described by Leibig’s law of the minimum, according to which a single limiting nutrient will determine the growth of a plant until all of the nutrients are in the sufficiency range.

For that reason, compositions of nutrient solutions are usually obtained empirically (Smith et al., 1983). Dating from the early works of Hoagland and Aarnon (1950), several compositions for nutrient solutions have been developed, ranging from “universal” solutions, which allow the cultivation of and wide array of plant species, to the current highly optimized crop- and development-stage-specific formulations used in modern commercial hydroponics. A comprehensive listing of historically relevant as well as commonly used solutions can be found in Resh (2013). Some recommend formulations for commercially relevant horticultural species grown hydroponically is presented in Table 3-3.

NPK ratio

Besides concentrations, an important characteristic of fertilizers is the NPK ratio (Marschner and Marschner, 2012). Although the nutrient recovery from blackwater is a topic that has been under investigation in the past 20 years, direct simultaneous measurements of all three macronutrients in raw blackwater are relatively scarce in the literature. That is likely because the concentration of potassium is usually not a relevant effluent quality parameter in domestic

Literature review wastewater treatment, and as a nutrient it has received considerably less attention than nitrogen and phosphorus in terms of efforts for its recovery.

However, per capta nutrient loads in the two main components of blackwater (urine and feces) — for which substantially more data is available – can be used for the estimation of NPK ratios, under the assumption that the contribution to the nutrient load of the water used for flushing is negligible. Based on an extensive literature research, The German Association for Water, Wastewater and Waste (DWA, 2008) has compiled median values for nutrient loads in excreta (the combination of urine and feces). The values, presented in Table 3-2, were obtained from data sources selected in order to produce values representative of German conditions. As secondary reference, in the table are also presented values for the sum of the nutrients loads in urine and feces suggested by Vinnerås et al. (2006)as Swedish design values, and the results of actual measurements of blackwater collected by vacuum toilets obtained by Alp (2010) and Kujawa (2005). The NPK ratios shown on the table were obtained by normalizing the concentrations of all nutrients by the P concentration.

Table 3-2 – Compilation of values found in selected literature for nutrient loads in human excreta.

Source Henze and DWA Parameter Unit Knerr Wendland Hocaoglu Vinneras DWA Ledlin (2008) (2012) (2009) (2010)1 (2006) (2008)2 (2001) ranges N g/(p.d) 8 7 20 12 13 12 1.5-17.5 P g/(p.d) 0.9 0.9 3.7 1.9 1.5 1.5 0.5 - 4.2 K g/(p.d) — — — — 3.7 1.5 0.7-6.1 N:P ratio — 9.2:1 8.4:1 5.3:1 6.3:1 8.3:1 7.9:1 — N:P:K ratio — — — — — 8.3:1:2.5 7.9:1:1 — 1 Screened blackwater 2 Median of values found in literature

It should be pointed out that although those values provide a qualitative insight as to the nutrient balance in blackwater, several factors might theoretically affect the NPK ratio and lead to differences, particularly diet and the point of generation of the blackwater. Regarding the effect of diet, the concentration of N, P and K of an adult human body is kept in homeostatic equilibrium, meaning that those nutrients present in ingested food are eventually released in the form of either urine or feces. Therefore, the NPK profile of blackwater (again under the assumption that in flushing water nutrients are not present in substantial amounts) in principle should mirror that of the diet of the pool of individuals that generated it. Based on an analysis of nutritional data on 180 foodstuffs, Jönsson and Vinnerås (2003) concluded that on an unit milligram protein basis, the P and K content of vegetable foods was respectively 2 and 5.6-fold higher than in protein sources of animal origin. However, recent large-scale nutritional surveys of individuals with dietary habits ranging from vegans to meat-eaters (Allès et al., 2017, Rizzo et al., 2013) indicated that in the overall population those effects are largely damped by the variety of food sources that comprise diets. Although in those studies statistically significant differences could indeed be observed in K and P intake between diets, substantial differences should only be expected in the case of K, and that for individuals with diets in opposite ends of the investigated spectrum, i.e. vegans and meat-eaters.

Chapter 3

As a comparison, NPK concentrations and ratios required by common horticultural crops grown hydroponically are shown in Table 3-3. It can be seen that, although N:P ratios more or less match the requirements of those crops, the nutrient profile of blackwater diverges substantially when it comes to potassium. For application as source material for the production of a liquid fertilizer, this relative K deficiency in blackwater would need to be compensated for with addition of K during treatment. It should be noted that the values in Table 3-3 represent a baseline composition for the nutrient solutions. Particularly for fruiting plants, the recommended NPK ratios might slightly change over the course of the crop cycle (Raviv and Lieth, 2008, Sonneveld and Voogt, op. 2009).

Table 3-3 – Nutrient solution composition recommended for various vegetable and fruit crops. Source: van der Lugt (2016). Crop Sweet Cucumber Eggplant Melon Strawberry Tomato Lettuce Nutrient pepper (Cucumis (Solanum (Cucumis (Fragaria x (Solanum (Lactuca (Capsicum sativus) melogena) melo) ananassa) lycopersicum) sativa) annuum) N (mg/L) 242 238 242 182 238 227 288 P (mg/L) 39 39 39 31 39 47 47 K (mg/L) 313 264 313 188 264 371 371 EC 2.2 2.1 2.2 1.6 2.2 2.2 2.6 (mS/cm) N:P:K 6.2 : 1 : 8 6.1 : 1 : 6.8 6.2 : 1 : 8 5.9 : 1 : 6.1 6.1 : 1 : 6.8 4.8 : 1 : 7.9 6.1 : 1 : 7.9 ratio

Electroconductivity

Another characteristic of nutrient solutions that is highly relevant in agricultural practice is its electroconductivity (EC). The EC provides an indirect measure of the total concentration of ions in solution, and therefore is directly related to the nutrient solution’s salinity. Essentially all crops that are grown commercially as soilless cultures are glycophytes, that is, are sensitive to the salinity of the solution in which the root is immersed. Salinity affects plant growth in three different ways, namely via (i) water stress, (ii) ion toxicity and (iii) nutrient imbalance effects.

The first refers to reduction of the osmotic potential with increasing total ion concentration in the solution external to the root, which diminishes the difference in water potential between solution and root cells, and consequently the capacity of the plant to absorb water. The growth response of crops to EC of the nutrient solution in soilless systems is usually described in terms of a curve similar to the yield curve of individual nutrients (Sonneveld et al., 2005, Shannon and Grieve, 1998). The curve is characterized by a first region at the low end of the EC spectrum where the growth response is positive, reflecting the fact that, as opposed to soil solutions of cultivated fields, the salinity of nutrient solutions is almost entirely due to nutrient ions (Sonneveld and Voogt, op. 2009). Above a critical EC value, denominated salinity threshold value, the yield response remains flat in a narrow EC range and then decreases at a steady rate denominated salinity yield decrease (SYD), as shown in Figure 3-4.

Literature review

The salinity threshold of horticultural crops grown in soilless systems varies from 1.6 to 2.2 (Table 3-3). However, some fruit crops such as tomato – for which improvement of fruit quality is frequently observed at higher salinity values (Magán et al., 2008, Adams, 2015) – are typically grown with nutrient solutions with EC values above their threshold in order to obtain an increased market value for the product, even if this comes at the expense of the total yield (Sonneveld and Voogt, op. 2009, S. 135). The target EC of nutrient solutions for various horticultural crops are presented in Table 3-3.

The two other effects, i.e. ion toxicity and nutrient imbalance, are due to the uptake of sodium and chloride, two ions commonly implicated in the salinity of soil solutions. Ion toxicity refers to excessive uptake of those ions, whereas nutrient imbalance refers to the inhibition that sodium and chloride exert on the uptake of nutrients, in particular potassium.

In order to prevent those two effects, the use of sodium- and chloride-containing salts in the preparation of nutrient solutions is avoided, especially in K and nitrate sources. However, in semi-closed soilless production systems, where the nutrient solution is partially recycled and frequently replenished with raw water to compensate for evapotranspirative losses, the accumulation of sodium and chloride might occur, because those ions are unavoidably present in raw water sources and are taken up by plants to a very limited extent. In such systems, EC is used as an indicator for the state of the nutrient solution, determining when it should be discharged.

3.2 Micropollutants

Of particular concern for production of secondary fertilizer products from blackwater is the presence of the so-called micropollutants. Alternatively referred to as contaminants of emergent concern (CECs) or simply emergent contaminants, the term lacks a formal definition and generally refers to chemical compounds of anthropogenic origin found in the environment in trace amounts, and whose concentrations are not yet subject to regulatory oversight (NRC, 2012).

Chapter 3

A group of micropollutants of particular relevance in the context of blackwater treatment is pharmaceuticals. Together with its metabolites, the majority of pharmaceuticals, such as antibiotics, radiocontrast agents and antidepressants, are excreted predominantly via urine and to a lesser extent in feces (Lienert et al., 2007, Winker et al., 2008), therefore ending up in blackwater. Although direct disposal of unused products in toilets might account for a share of the loads, human excretion is currently seen as the main pathway through which those compounds reach communal wastewaters (Daughton and Ruhoy, 2009), particularly in communities with an aging population (Vatovec et al., 2016).

A limited number of studies measured the concentration of pharmaceuticals and personal care products (PPCPs) in raw blackwater (Butkovskyi et al., 2017, Westhof et al., 2016, Butkovskyi et al., 2015, Graaff et al., 2011). Even accounting for the fact that in all those cases vacuum toilets where used – which require only 4 to 12 times less water for flushing – they indicate that the concentration of pharmaceutical compounds in water-flushed blackwater could be one order of magnitude higher than in combined domestic wastewater. It should be pointed out, however, that the smaller scale of source-separated sanitation systems is likely to lead to a higher dispersion of the expected concentration of any given pharmaceutical compound in blackwater. This stems from the fact that, the lower the size of the population being served, the higher the influence of the health status and habits of any given individual on the total loading to system, an extreme example of which being hospitals (Verlicchi et al., 2012, Santos et al., 2013).

Numerous studies have shown that conventional secondary biological treatment processes are not capable of full elimination of pharmaceutical compounds (Grandclément et al., 2017, Falås et al., 2016). Even though biodegradation or sorption to sludge take place for many of them – in some cases resulting in substantial removal rates –, pharmaceuticals and their metabolites are frequently detected in effluents from centralized wastewater treatment plants in the ppt to low ppb concentration range (Loos et al., 2012). A good example of the increasing concern around the presence of those compounds in the environment is the case of the non-steroid anti- inflammatory drug Diclofenac that, due to the frequency with which it is detected in both WWTP effluents and inland surface waters, was included in the first monitoring watch list of the European Environmental Quality Standards Directive (EQS).

More importantly for the case in question, several of those pharmaceutical compounds were also shown in laboratory and field studies to be taken up and accumulate in plants (McKone and Maddalena, 2007, Miller et al., 2016). The degree to which the compound is taken up is expressed in the form of concentration factors, which represent the ratio of the concentration of the compound in the plant biomass, to that in the medium surrounding the root system (e.g. soil solution in case of plants grown in soil), after equilibrium between those two phases is reached. Concentration factors – also referred to as bioconcentration factors, or BCF – are usually reported for specific tissues, such as roots (root concentration factor, RCF), leaves (LCF) and aerial parts as a whole (transpiration stream concentration factor, or TSCF).

As shown in Figure 3-5 and Figure 3-6, not only do bioconcentration factors of pharmaceutical compounds vary immensely for both root and leaves, but different compounds show varying tendencies to translocate, that is, to be transported away from the root once taken up, as indicated by the BCFleaf/BCFroot ratio.

Literature review

Figure 3-5 – Root bioconcentration factors for a variety of organic micropollutants in crops grown hydroponically. Source: Wu et al. (2015).

Figure 3-6 – Leaf bioconcentration factors for a variety of organic micropollutants in crops grown hydroponically. Source: Wu et al. (2015).

For compounds whose uptake by root cells is passive – according to literature, the case of most xenobiotic compounds of environmental interest (McKone and Maddalena, 2007, Trapp and Legind, 2011) – , their physical-chemical properties strongly affects the favored transport pathway and the degree of translocation the compound will undergo once within the plant (Briggs et al., 1987, Hsu and Kleier, 1996).

In principle, there are three main uptake transport pathways for solutes in general (Taiz and Zeiger, 2010): (i) the apoplast pathway, through which the solute travel from epidermis to

Chapter 3 endodermis exclusively via the apoplast, the space contained by the network formed by cell walls from plant tissue; (ii) the symplast pathway, in which the solute, upon crossing one plasma membrane in the epidermis, is transported only via the plasmodemata connecting the cytoplasm of adjacent cells; and (iii) the transmembrane pathway, where the solute crosses the plasma membranes of the cells in its path, successively entering and exiting them as it travels towards the vascular tissues of the plant.

Although properties like molecular mass, amount of hydrogen bond donors and acceptors, polar surface area and number of rotable bonds (Limmer and Burken, 2013) have been shown to a role in the uptake process, the majority of the models that attempt to predict concentration factors rely solely on the octanol-water partition coefficient (KOW), which provides a measure of the compound’s lipophilicity, and hence its ability to cross cellular membranes. The underlying conceptual assumption is that the uptake process is a purely partitioning phenomenon (Trapp, 2000). In its simplest form, shown in Equation 3-1, the model is reduced to a linear relationship between the logarithms of the concentration factor and KOW.

푙표푔퐵퐶퐹 = 푎 ∙ 푙표푔퐾푂푊 + 푏 [3-1] Although a conceptual interpretation of its parameters do exist (Trapp, 2007), it is mostly considered as being an empirical model, with values for the coefficients obtained by means of linear regression of experimental data (McKone and Maddalena, 2007).

Originally developed for pesticides (Briggs et al., 1982), the KOW model was shown by several studies to also yield fairly good estimates for the RCF of neutral nonionic xenobiotic compounds on a case-by-case basis (McKone and Maddalena, 2007, Doucette et al., 2018). However, a significant limitation is its failure to predict RCFs for ionizable compounds (Briggs et al., 1987, Trapp, 2004, Doucette et al., 2018), which account for a significant share of pharmaceutical compounds frequently detected in raw and treated domestic wastewaters, such as Diclofenac. Those compounds behave like acids or bases and partially dissociate into charged species depending on the pH of the medium and their characteristic dissociation constants. As compared to the neutral parent compound, the ions formed are subjected to additional phenomena that affect their uptake by root tissues and their translocation to aerial parts of the plant (Trapp, 2004). In the case of neutral acidic substances, the negative charge of their conjugated bases causes it to be repelled by the negatively charged root cell walls and/or plasma membrane, which under physiologically normal conditions hold a negative potential in the order of -100 mV. On the other hand, the positive charge of the conjugated acids formed by the dissociation of basic compounds is expected to adsorb to those surfaces, although the extent to which this takes place hasn’t been investigated in depth (Inoue et al., 1998, Trapp, 2004). Even after adjustment of the KOW value to account for the fraction of the compound that is actually in neutral form – and therefore “available” for partitioning into plant material –, single- parameter partition models are mostly unable to explain the uptake by and translocation in plants of those compounds, particularly highly hydrophilic ones (Miller et al., 2016, Doucette et al., 2018).

Literature review

Figure 3-7 – Pathways for the uptake of dissolved substances from soil (or nutrient) solution via the root system of dicot plants. Source: Miller et al. (2016).

The substrate on which the plant is grown is a factor that exerts a substantial influence upon the transport of pharmaceuticals. Soils, in particular their organic fraction, can interact in variety of ways with organic substances and can act as a sink for those compounds. Neutral compounds have a high affinity to soil organic matter, and the sorption onto that fraction in several cases limits their availability to plant uptake (McKone and Maddalena, 2007).

Several studies performed risk assessments for crops irrigated with treated effluents from communal wastewater treatment where traces of pharmaceutical compounds and their metabolites were found, and also where the fields where amended with biosolids prior to cultivation (Malchi et al., 2014, Prosser and Sibley, 2015, Riemenschneider et al., 2016). Although they showed that those practices might indeed result in increased presence of some compounds in plant tissues, the conclusions were that under most conceivable exposure scenarios the long- term health risk presented by the consumption of the edible parts of the product considered in the studies in particular was very small. For instance, in a meta-analysis of published data on plant uptake of pharmaceutical obtained by field experiments conducted under agriculturally realistic conditions, Prosser and Sibley (2015) estimated that an adult would need to consume around 5.5 kg of lettuce in order to reach the acceptable daily intake (ADI) for Diclofenac.

It should be noted, however, that the assessment of such risks is hindered by the scarcity of toxicological data relevant to human chronic exposure to pharmaceutical compounds in sub- therapeutic concentrations (Schwab et al., 2005, Bull et al., 2011, Houeto et al., 2012). In the absence of specific data, the calculation of ADIs is not a trivial affair, requiring substantial expert judgement for the choice of appropriate points of departure (PoDs) from toxicological profiles obtained in studies using animal models, and also for the concomitant determination of values for uncertainty factors (Schwab et al., 2005).

For the purposes of risk screening, the more conservative Threshold of Toxicological Concern (TTC) can be applied, which is used by the European Food Safety Agency in preliminary risk assessment of chemicals in food. The TTC approach relies on the extrapolation of a toxicological reference value for a given compound based on data available for structurally similar compounds. Similarity is assessed in terms of the Cramer Classification Scheme, a series of yes-

Chapter 0 and-no questions which separate chemicals in three different classes, also referred to as Cramer classes (Cramer et al., 1976). As defined by the European Union Reference Laboratory for Alternatives to Animal Testing (2018):

 Class I: contains substances of simple chemical structure with known metabolic pathways and innocuous end products which suggest a low order of oral toxicity;  Class II: contains substances that are of potential intermediate toxicity. They possess structures that are less innocuous than those in Class I but they do not contain structural features that are suggestive of toxicity like those in Class III;  Class III: contains substances with chemical structures that permit no strong initial impression of safety and may even suggest a significant toxicity.

The TTC values are calculated by the division of the 5th percentile of the distribution of no- observed-adverse-effect-levels (NOAELs) of each class by a safety factor of 100 and by a reference body weight of 60 kg. The resulting TTCs are 1.5, 9 and 30 µg/kg-bw/day for Classes I, II and III, respectively (EFSA, 2016). Exposures exceeding those values require a compound specific risk assessment, whereas those inferior are considered safe. Not all substances are liable to be assessed by the approach: metals, metal-containing compouds and polyhalogenated compounds are excluded and always require in-depth risk assessment. Also treated differently are organophosphates and some potentially genotoxic compounds, which have separate TTCs.

In an illustration of the degree of conservativeness of the TTC approach, safety limits for the concentration of carbamazepine in drinking water calculated by Houeto et al. (2012) using TTC as reference were around three orders of magnitude inferior to those derived from ADIs obtained from toxicological data with an uncertainty factor of 900, using otherwise the exact same assumptions for exposure. Nonetheless, field measurements performed by Malchi et al. (2014), Riemenschneider et al. (2016) and Christou et al. (2017) in a variety of crops spanning tubers, leafs and fruits, all irrigated with treated WWTP effluent, have shown that – at least in the conditions of their studies – the concentrations of pharmaceuticals in treated effluents (and/or the uptake by crops) would need to be substantially higher for the respective TTC values to be reached under reasonable exposure scenarios.

4 BLACKWATER TREATMENT SYSTEM

4.1 Introduction

Decentralized systems are increasingly seen as part of wastewater treatment infrastructure of the future, particularly in developing countries where access to centralized wastewater collection and treatment systems is not universal (OECD, 2015, Larsen et al., 2016). Several authors argue that, under certain circumstances, they might be more advantageous than centralized systems due their closeness to wastewater sources. Besides aspects related to their lower dependence on sewer networks for long-distance transport of wastewater, this closeness enables the separate treatment of individual residential wastewater streams (graywater, blackwater, urine), which creates additional opportunities for the recovery of resources from wastewater (Larsen, 2011, Tervahauta et al., 2013, McConville et al., 2014).

Membrane bioreactor (MBR) is a wastewater treatment technology that has received lots of attention in this context. Due to its robustness in terms of the production of particle-free effluents, MBRs are a particularly interesting choice in cases where a reuse of the effluent is intended (Fane and Fane, 2005). In the domain of source-separated wastewaters, aerobic MBRs have been extensively investigated as an alternative for the treatment of graywater (Winward et al., 2008, Li et al., 2009, Jabornig, 2014); its use to the treatment of blackwater, however, has received considerably less attention, with only a handful of studies documenting that sort of application (Atasoy et al., 2007, Boehler et al., 2007, Knerr et al., 2011, Knerr, 2012), a fact that can be attributed to the high concentration of organic matter in blackwater, which, makes anaerobic treatment a more cost-effective choice (Kujawa, 2005). However, for the purposes of converting blackwater into a high quality effluent with characteristics compatible with usage as nutrient solution for hydroponic cultivation systems, a MBR would seem to be a very suitable alternative (Norton-Brandão et al., 2013, Quist-Jensen et al., 2015).

The purpose of this chapter is to report the results of the characterization of the raw blackwater and the effluent that resulted from its treatment, along with a discussion of some aspects of the approach taken for the treatment of the blackwater that are of relevance for the purposes of recovering nutrient in the form of a nutrient solution for hydroponics. A particular focus will be put on the alkalinity requirements for nitrification, which greatly affected the operation of the system and quality of its product.

4.2 Materials and Methods

4.2.1 Description of the blackwater treatment system

The blackwater treatment plant (BTP) investigated in this work is located in the city of Berlin (Germany) and serves a population of approximately 50 persons. The blackwater was produced by 6-liter water flush toilets. The schematic description of the BTP used for the experiments is shown in Figure 4-1. The system consisted of two main treatment units, namely a primary settling unit and a membrane bioreactor (MBR). Raw blackwater flowed directly from the blackwater plumbing line into a settling tank with effective volume of 690 L, which was connected by a gravity overflow to a storage tank with equal capacity, from which the settled

Chapter 4 blackwater was pumped by a centrifugal pump through a 1-mm stainless steel mesh sieve to the MBR. Primary sludge was removed 3 times a day from the tank by an airlift pump that discharged directly into the sewer system. Both tanks were fitted with aeration diffusers, which were activated in order to mix the contents of the tanks prior to the collection of samples, as detailed below. The settling tank was provided with a second overflow that also connected to the local sewerage system, allowing excess blackwater to exit the system when the settled blackwater tank was full.

The MBR consisted of a main aeration tank connected to a secondary tank where the membrane filtration unit was housed, with a combined liquid volume of 1200 L. The two tanks were hydraulically connected by a PVC pipe placed at the tanks’ mid-height and a submersible centrifugal pump with a nominal flow rate of 4.5 m³/h, which was activated for 3 minutes every 15 minutes in order to recirculate the tanks’ contents. The aeration tank was fitted with a fine- bubble diffuser supplied by an air compressor with nominal capacity of 100 L/min.

The specifications of the submerged membrane filtration unit are provided in Table 4-1. It consisted of a flat-sheet ultrafiltration PES membrane module with a molecular weight cut-off of 150 kDa and 6.25 m² of filtration area. Crossflow was effected by a coarse-bubble diffuser installed under the membrane module and supplied with 100 Nm³/min by two air compressors (LA-120, Nitto Kohki GmbH, Germany) connected in parallel. The unit was operated with a filtration/relaxation cycle of 12/4 minutes. Filtration was vacuum-driven by a small centrifugal pump (Syncra 3.0, Sicce S.p.A., Italy) with a maximum of flowrate of 45 L/min. The vacuum generated by the pump was monitored continuously by a pressure transducer (TST 10, Tival Sensors GmbH, Germany) installed on its suction side, from which the transmembrane pressure (TMP) was calculated by subtracting the pressure head generated by the water column above the membrane module. Filtration rates were controlled manually by the adjustment of a ball valve installed on the filtrate side of the filtration unit, and recorded by a conventional low-flow water meter. The MBR’s effluent flow was routed by a three-way actuated valve that directed it either to a 1.0 m³ storage tank or to the sewer.

Blackwater treatment system

The solids retention time (SRT) of the sludge in the MBR was set at a nominal value of approximately 55 days and was controlled hydraulically by the discharge of around 4% of the tanks’ combined liquid volume 3 times a week in alternate days. Oxygen was monitored continuously by a polarographic oxygen sensor (TriOxmatic 700 IQ, Xylem Analytics Germany Sales GmbH, Germany) immersed in the membrane tank. The MBR’s mixed liquor pH was controlled by a self-made PID controller based on an Arduino microcontroller. received pH measurements made by an online pH probe (Inlab 413, Mettler-Toledo GmbH, Germany) installed at a flow cell located on the filtrate line immediately downstream of the membrane unit and adjusted the control output to a peristaltic membrane responsible for the dosing of 50% KOH (CVB Albert Carl GmbH, Germany). The choice of KOH as base was oriented by restrictions to the presence of Na+ and Cl- ions in the effluent associated to its intended use as a hydroponic nutrient solution, which considerably limit the options for alkalinity source.

With the exception of the MBR’s pH control, the operation and monitoring of the whole system was done by a SCADA system based on a WAGO 750 PLC unit (WAGO Kontakttechnik GmbH & Co. KG, Germany).

Table 4-1 – Specifications of membrane filtration unit.

Property Value

Type Flat sheet Surface 6.25 m² Material PES Molecular weight cut-off 150 kDa

4.2.2 Blackwater characterization

Estimation of blackwater flowrates

The blackwater flowrate was estimated from the change in the level of the settling tank, which was measured and logged by the system’s SCADA system. Due to the fact that the overflow to the settled blackwater storage tank was positioned at a similar height to the overflow to the sewer (which defines the settling tank’s maximum level), the settling tank’s level was almost always at full capacity, and substantial level changes occurred almost exclusively during primary sludge wastage events.

The estimation procedure is illustrated in Figure 4-2. It consisted of the identification of the “valleys” in the time series of the settling tank’s level produced by the wastage of sludge, followed by a linear regression on time of the level data collected between that point in time and the time at which the level reached 90% of tank capacity. This level endpoint corresponds to the height of the overflow to the settled blackwater storage tank. The slope of the linear fit was taken as the estimate of the average blackwater inflow rate of the period.

Chapter 4

Raw and settled blackwater characterization

For the characterization of raw and settled blackwater, samples were collected in the period from April to November 2017 on a biweekly basis. Sampling dates were randomized under the constraint that every day of the week should be sampled the same number of times, as recommended by the norm DIN EN ISO 5667-1:2006. Raw and settled blackwater samples were collected simultaneously.

Raw blackwater samples were collected from the settling tank. In order to produce a 24h- composite, the settling tank was first emptied, and the automatic sludge wastage turned off for 24 hours before the scheduled sampling date. Immediately prior to the collection of the samples, the contents of the tank were mixed by the activation of the tank’s aeration diffuser, and 1-L samples were collected in duplicate. Settled blackwater samples were collected directly from the adjoining storage tank as grab samples.

The following parameters were measured in raw blackwater, settled blackwater and MBR effluent samples: total COD (COD), total phosphorus (TP), total nitrogen (TN), ammonium nitrogen (NH4-N), phosphate phosphorus (PO4-P), total suspended solids (TSS), volatile suspended solids (VSS), alkalinity and pH. Potassium (K), calcium (Ca), magnesium (Mg), sulfate (SO4) were additionally measured in raw blackwater and MBR effluent samples.

The respective analytical methods are listed in Table 4-2. For the measurement of COD, TN and TP, samples were first homogenized with an Ultraturrax T25 disperser with S50N dispersing head (IKA-Werke GmbH & CO. KG). Samples for the measurement of NH4-N, PO4-P, Ca, K, Mg and SO4 were filtered with 0.45 µm filters (Sartorius AG, Germany) in pressure filtration cells.

Samples for the measurement of K, Ca, Mg and SO4 were had their pH adjusted to 2 with analytical grade 65% HNO3 immediately prior to storage.

Blackwater treatment system

Table 4-2 – List of analytical methods used for the characterization of raw blackwater, settled blackwater and the MBR’s effluent.

Variable Method

DIN 38409-H41:1980:12 COD DIN 38409-H44:1980:12 TN EN ISO 11905-1:1988-08 TP DIN EN ISO 6878:2004-09

NH4-N DIN 38406-5:1983-10

NO3-N DIN 38405-9:2011-09

NO2-N BVL L 59.11-22:1986-11

PO4-P DIN EN ISO 6878:2004-09 Ca DIN EN ISO 11885:2009-09 K DIN EN ISO 11885:2009-09 Mg DIN EN ISO 11885:2009-09

SO4 DIN EN ISO 10304-1:2009-07 TSS DIN EN 872:2005-04 VSS DIN EN 872:2005-04 Alkalinity DIN 38409-7:2005-12

Estimation of per capta loads to the system

Average daily per capta loads were estimated by matching concentration and flowrate measurements by date. Flowrates were not measured in the 24-hour periods during which composite raw blackwater samples were collected, since the sludge wastage of the settling tank was deactivated in those periods. For that reason, the respective reference flowrates were computed as the average of the flowrates measured on two periods: on the 24 hours preceding the start of the collection of each composite sample, and on the 24 hours following each sampling event.

Data analysis

All data processing and statistical analyses were performed using the Curve Fitting and Statistics and Machine Learning Toolbox of the MATLAB software suite, version R2016a. In particular, calculations of confidence intervals by means of the bootstrap method were performed with the function bootci.

Chapter 4

4.3 Results and Discussion

4.3.1 Characterization of raw blackwater

Flowrates

As shown in Figure 4-3 measured blackwater flowrates displayed significant fluctuation over the experimental period with monthly interquartile ranges ranging from 30 to 79 L/h, as expected given the relatively small population connected to the treatment system. However, on a month-to-month basis median values were comparatively stable at around 95 L/h. The probability distribution of the measured values was strongly non-normal, and shown a very good fit to a gamma distribution with scale and shape parameter values of 5.20 and 0.32, respectively, as can be seen in Figure 4-4. Summary statistics on the measured blackwater flowrate are presented in Table 4-3.

Table 4-3 – Summary statistics of measured blackwater flowrates.

95% CI of mean Unit Mean Std. dev. Max. Min. LCL* UCL* L/h 100 97 103 43 311 18 m³/d 2.4 2.3 2.5 1.0 7.5 0.4

* LCL: lower confidence limit, UCL: upper confidence limit

Figure 4-3 – Monthly variation of measured blackwater flowrates.

Blackwater treatment system

Figure 4-4 – Cumulative probability distribution of measured blackwater flowrates.

Transfer of the measured time-domain flowrate data to the frequency domain by means of a discrete Fourier transform revealed that the only significant discernible periodic behavior in flowrates occurred on a daily basis, as indicated by the peak at frequency of 1 d-1 in the periodogram shown in Figure 4-5. This peak corresponds to a fluctuation with an amplitude of roughly 17 L/h. Hints of this daily fluctuation can be seen when the measured flowrate data are collapsed by hour of the day, as shown in Figure 4-6. Although the goodness of fit of the Fourier transform term in question to the full set of data points is low, its comparison to the averages of the hourly groups suggest that indeed a periodic oscillation might explain some of the daily variance of the flowrate data.

Figure 4-5 – Periodogram of measured blackwater flowrates.

Chapter 4

Figure 4-6 – Measured blackwater flowrates data collapsed by hour of day. The sinusoidal function corresponds to the Fourier transform term associated with the peak frequency identified in the in the periodogram shown in Figure 4-5, of the form: 풇(풕) = ퟏퟕ, ퟓퟓ ∙ {풄풐풔(ퟐ흅 ∙ ퟎ, ퟎퟒퟏퟕ ∙ 풕 − ퟏ, ퟔퟐ) + 풔풊풏(ퟐ흅 ∙ ퟎ, ퟎퟒퟏퟕ ∙ 풕 − ퟏ, ퟔퟐ)} + ퟗퟗ, ퟔퟖ.

It should be noted, however, that the frequency with which flowrates were measured (in average three times a day) makes the observation of cyclic fluctuations with period of less than one day difficult, and those are likely to play a significant role in describing daily patterns in blackwater generation rates. In fact, as pointed out by Mayer and DeOreo (1999) in a comprehensive survey of residential water use in the USA, domestic toilet use (and therefore blackwater generation rates) is likely to display a multimodal profile. In their case, a pronounced peak in toilet use was observed in the morning (at around 8:00) followed by wider, less prominent peaks at the evening (19:00 and 22:00). A similar pattern was observed by Hocaoglu et al. (2010) in the blackwater generation rates of a university dormitory building.

Concentrations

The relatively small amount of samples analyzed was considered too low for Central Limit Theorem to apply (whereby the sampling distribution of the mean asymptotically converges to a normal distribution for large sample sizes). Therefore, the probability distribution of each of the measured parameters was first assessed in order to define the best approach for the computation of confidence intervals for the mean and allow for a better comparison with values available in the literature.

In the cases of K, Ca, Mg and SO4 – where data could not be properly fitted to any parametric distribution – confidence intervals for the mean were obtained by bias-corrected and accelerated (BCa) bootstrapping with 10.000 resamplings. Measurements of the remaining parameters were confirmed to be normally (TN, TP, NH4-N and PO4-P, alkalinity and pH) or log-normally (COD, TSS and VSS) distributed with a 5% significance level by the application of the one-sided Kolmogorov-Smirnov test (Figure 4-7). Confidence intervals for log-normally distributed data were calculated using the method developed by Zou et al. (2009).

The statistics obtained are shown in Table 4-4. A comparative assessment of those values was hindered by the relative scarcity of publicly available studies that investigated the composition

Blackwater treatment system of raw blackwater generated by domestic, gravity-driven, water-flushed systems. Only two studies, conducted by Knerr (2012) and Palmquist and Hanaeus (2005), were found to have focused on blackwater collection systems similar to the one investigated here. The average composition of blackwater reported by both are also given in Table 4-4. The selection of studies for comparison also excluded non-residential cases due to likely effects that the type of use of the building has on the composition of blackwater. Such effects were as pointed out by Knerr (2012), among others, who observed a markedly lower concentration of solids and total COD in blackwater collected in an office building as compared to a residential one – an likely indication of the preference of people to defecate at home.

Figure 4-7 – Probability plots of total COD, TSS and VSS concentrations measured in raw blackwater. Dashed line corresponds to lognormal distribution fitted to data of the respective variable (crosses). Dot- dashed line corresponds to normal distribution reference line.

Table 4-4 – Summary statistics of raw blackwater composition, and reference values collected from literature. 95% CI of mean Reference for mean Std. Palmquist, Parameter Unit n Mean Median Knerr Dev. Hanaeus LCL UCL (2012) (2005) COD mg/L 17 4483 3605 5533 2064 3474 2925 2260 TN mg/L 17 363 338 388 49 371 281 150 TP mg/L 17 55 49 60 11 54 32 43

NH4-N mg/L 15 197 187 206 17 197 201 —

PO4-P mg/L 17 18.1 14.8 21.4 6.4 20.0 23.2 — Ca mg/L 10 64.5 59.8 72.5 10.3 62.8 — 68.6 K mg/L 10 66.8 63.9 71.6 6.2 64.7 — 75.0 Mg mg/L 10 14.6 13.7 16.4 2.0 14.4 — 17.0

SO4 mg/L 9 16.0 15.1 16.4 1.0 16.0 — — TSS mg/L 18 2511 2058 3074 1026 2286 1712 3180 VSS mg/L 18 2340 1904 2902 964 2091 1643 2560 Alkalinity mmol/L 17 23.9 22.0 25.8 3.7 23.9 20.3 — pH -- 17 7.9 7.5 8.3 0.6 8.2 9 8.9

Chapter 4

A proper statistical comparison was possible only with the data obtained by Knerr (2012), who, along with the averages, also reported standard deviations and number of samples analyzed.

With the exception of NH4-N, the averages of all parameters were tested as being significantly different with a two-sided t-test with a maximum p-value of 0.006, under the assumption that the sample variances of each dataset are not equal.

The sources of those differences can only be speculated. However, it is noteworthy that the differences between the averages of TSS and “total” parameters (i.e. COD, TN and TP) concentrations were all positive, whereas those for dissolved parameter where either negative

(PO4-P) or insignificant (NH4-N), which might point out to an eventual bias towards the over- or under- collection of solids in the samples. Another factor that could have played a role in the development of this particular discrepancy pattern is the different type of flushing systems used in the two cases. The toilets used in the apartment building studied by Knerr (2012) had, according to the authors, a dual flushing system with volumes of 9 and 6 L, while in the case studied here the toilets used were of the 6-L single-flush type. The correct use of the dual function (that is, use of the high-volume mode only for flushing of feces) would have as a consequence a higher dilution of solids-rich loads and hence would lead to comparatively lower average measured concentrations for solids, COD, TN and TP.

Although the comparison with the composition reported by Palmquist and Hanaeus (2005) is limited by small number of samples analyzed (n=3) and the absence of information on the exact flushing volume of toilets used in their collection system (reported only as low-volume), it should be noted the average COD, TP and TN concentrations measured by them are below those obtained by Knerr (2012). That indicates that very possibly they are also significantly inferior to the ones measured here.

Per capta loads

Summary statistics for the calculated per capta loads are presented in Table 4-7. As in the case of concentrations, confidence limits for the average per capta loads for each parameter were calculated with the appropriate methods, based on the prior identification of the probability distribution that best matched the respective datasets. The goodness-of-fit of the chosen distributions (shown in Table 4-7) was assessed via the Kolmogorov-Smirnov test with a significance level of 5%. As was the case of their concentrations, the loads of K, Ca, Mg and SO4 could not be fitted to any parametric distribution; the confidence intervals for their means were obtained by bias-corrected and accelerated (BCa) bootstrapping with 10.000 resamplings.

Substantially more reference values for comparison with the COD, nutrients and solids per capta loads calculated here could be found in the literature. As opposed to concentrations, loadings are in principle independent of the type collection system that generated the blackwater, and therefore data obtained from a wider variety of systems (for example those based on vacuum toilets) are suitable for comparison purposes. Favored for the inclusion in the reference datasets were studies that investigated residential settings and produced primary data, i.e. loads were actually measured. Estimated based on diets, such as Otterpohl (2002), were excluded from the inferential statistical analyses. One exception is the dataset provided by the DWA (2008), which is itself the result of a literature research and consists of median values calculated from a wide array of data sources. Also reported in Table 4-7 is the range of load values for each parameter found in that study.

As can be seen in Table 4-7, the load values obtained for COD, TSS, TN and TP were in general higher than the reference values, in some cases substantially so. In particular, the average COD

Blackwater treatment system load (189 g/(p.d)) stands out as higher than all the reference values – around two times the maximum range reported by DWA (2008) for that parameter – with even its lower confidence interval (149 g/(p.d)) being 65% superior to the highest reference value of 90 g/(p.d), reported by Hocaoglu et al. (2010). The average TSS load of 109 g/(p.d) is also considerably higher that all the reference values, with its confidence interval surpassing the upper edge of the range provided by DWA (2008).

In order to assess the statistical significance of those differences, a hypothesis test based on the two-tailed t-test. The t-statistic of the difference between per capta loads measured here and the average of the reference values shown in Table 4-7 for each parameter was calculated, and its p-value determined. The null hypothesis was that for each parameter the measured load values are part of the same population as the reference values, with the underlying assumption that the set of reference values are part of the same normally-distributed population of averages (with unknown mean and variance).

-1.5 -1 -0.5 0 0.5 1 1.5 -1.5 -1 -0.5 0 0.5 1 1.5 2.0 2.001.50 2.0 1.5 COD 1.50 TSS 1.5 ρ = 0.97 1.00 ρ = 0.87 1.0 1.00 1.0 0.50 0.5 0.50 0.5 0.0 0.000.00 0.0 -0.5 -0.50 -0.5 -0.50

sample quantiles sample -1.0 -1.00 -1.0 -1.00 -1.5 -1.50 -1.5 -2.0 -2.00-1.50 -2.0 -1.5 -1 -0.5 0 0.5 1 1.5 -1.5 -1 -0.5 0 0.5 1 1.5 2.0 1.50 2.00 2.0 -1.5 -1 -0.5 0 0.5 1 1.5 1.5 TN 1.50 TP 1.5 1.00 1.0 ρ = 0.90 1.00 ρ = 0.89 1.0 0.50 0.5 0.50 0.5 0.0 0.00 0.00 0.0 -0.5 -0.50 -0.5 -0.50

sample quantiles sample -1.0 -1.00 -1.0 -1.00 -1.5 -1.50 -1.5 -2.0 -1.50 -2.00 -2.0 -1.5 -1 -0.5 0 0.5 1 1.5 -1.5 -1 -0.5 0 0.5 1 1.5 theoretical quantiles theoretical quantiles

Chapter 4

The assumption of normality of distribution was first checked visually by means of normality plots and then verified by means of the Kolmogorov-Smirnov test with a significance level of 5%. The normal probability plots of the distributions are presented in Figure 4-8, where plotting positions were calculated according to Filliben (1975), as suggested by Looney and Gulledge (1985).

As indicated by the p-values in Table 4-5, the average per capta loads of COD and TSS could be considered significantly different to the respective reference average values. The very low p- values (p=0.004 and p=0.003, respectively) suggests that it is highly unlikely that the differences emerged from random fluctuations alone. On the other hand, the null hypothesis would hold for TN and TP even at very high significance levels. It should be pointed out, however, that the statistical power of the tests are considerably limited by the relatively small number of data points in the reference sets.

The overabundance of COD is also clear when the COD:N:P ratios of measured and referenced data are compared, as shown in Table 4-6. With the exception of the data retrieved from the work of Knerr (2012), all other reference datasets yielded considerably lower COD:N and COD:P ratios than the one measured here. Considering that the two main components of blackwater, urine and feces, have very different COD:N:P ratios, and also taking into account the higher than average TSS and VSS per capta loads measured, the rather warped ratios calculated from the measured data would be consistent with an increased presence of feces in the collected blackwater samples. That, in turn, could be due to several factors, from inappropriate sampling approach to differences in usage pattern of the toilets.

Table 4-5 – p-values obtained by the two-tailed t-test for the comparison of difference between measured and reference load values. References Statistics for hyp. test Parameter Unit Mean Mean Std.dev. n df t p-value COD g/(p.d) 189 67 21 5 4 5.83 0.004 TN g/(p.d) 15 12 4 6 5 0.73 0.497 TP g/(p.d) 2.3 2 1 6 5 0.51 0.630 Ca g/(p.d) 2.8 ------K g/(p.d) 2.9 ------Mg g/(p.d) 0.63 ------

SO4-S g/(p.d) 0.23 ------TSS g/(p.d) 109 45 10 5 4 6.62 0.003 VSS g/(p.d) 102 ------

Table 4-6 – Comparison of COD:N:P ratios calculated from measured and reference per capta loads.

Henze and Wendland Hocaoglu DWA Ratio This study Knerr (2012) Ledlin (2009) (2010) (2008) (2001) N:P 6.7 9.2 8.4 5.3 6.3 7.9 COD:N 12 10 5 5 6 4 COD:P 83 90 46 24 39 33 COD:N:P 100:8:1.2 100:10.2:1.1 100:18.4:2.2 100:21.8:4.1 100:16.2:2.6 100:23.8:3

Blackwater treatment system

Table 4-7 – Summary statistics of calculated per capta loads and reference values collected from literature.

95% CI of mean References for mean Std. Henze and DWA Parameter Unit n Mean Median Knerr Wendland Hocaoglu Vinneras DWA Dev. Ledlin (2008) LCL UCL (2012) (2009) (2010)1 (2006) (2008)2 (2001) ranges COD g/(p.d) 16 189 149 251 103 161 80 40 90 74 NA 50 5-93 TN g/(p.d) 16 15 13 17 3.4 16 8 7 20 12 13 12 1.5-17.5 TP g/(p.d) 16 2.3 1.9 2.6 0.6 2.2 0.9 0.9 3.7 1.9 1.5 1.5 0.5 - 4.2 Ca g/(p.d) 9 2.8 2.4 3.1 0.4 2.7 — — — — — — — K g/(p.d) 9 2.9 2.7 3.0 0.2 2.9 — — — — 3.7 1.5 0.7-6.1 Mg g/(p.d) 9 0.63 0.57 0.69 0.08 0.63 — — — — — — —

SO4-S g/(p.d) 8 0.23 0.21 0.25 0.03 0.24 — — — — — 0.2 0.16 - 0.21 TSS g/(p.d) 17 109 88 139 54 88 52 33 37 — 54 51 16-130 VSS g/(p.d) 17 102 81 129 51 81 52 21 33 — — 45 24-118 flowrate L/(p.d) 660 44 42 45 19 42 — — — — — — — 1 screened blackwater

2 median of values reported in the literature

Chapter 4

4.3.2 Settling tank

The raw blackwater flowrates reported in Table 4-3 and the cross-section area of the settling tank (0.48 m²) resulted in the application of an average overflow rate of 4.96 m³/(m².d) to the settling tank over the operational period of the treatment plant, with peak rates reaching 15 m³/(m².d). Those values are considerably lower than the typical design values of 40 m³/(m².d) recommended by Tchobanoglus et al (2003) for settling tanks for primary treatment of domestic wastewater.

The average measured composition of settled blackwater is presented in Table 4-8, together with the average composition of raw blackwater for comparison. As shown in Figure 4-9, the settling step of the treatment removed a substantial fraction of the COD, TSS, TN and TP present in the influent raw blackwater, resulting in median removal rates of 64, 86, 27 and 45, respectively.

Table 4-8 – Summary statistics of settled blackwater composition. 95% CI of mean Parameter Unit N Mean Std. Dev. Median LCL UCL COD mg/L 20 1285 1100 1512 412 1264 TN mg/L 20 270 250 289 42 273 TP mg/L 20 29.8 25.8 33.7 8.3 29.8

NH4-N mg/L 17 174 156 193 36 190

PO4-P mg/L 19 9.3 7.1 11.5 4.6 8.08 TSS g/L 18 0.458 0.274 0.816 0.404 0.330 VSS g/L 17 0.337 0.202 0.635 0.351 0.232 pH — 13 8.44 8.13 8.75 0.5 8.53 Alkalinity mmol/L 20 21.4 19.7 23.0 3.5 22.0

Figure 4-9 – Distribution of COD, TN, TP and TSS concentration values in raw and settled blackwater.

Blackwater treatment system

Removal rates of TN and TP were different, resulting in a marked and significant increase in the N:P ratios of the settled blackwater as compared to the raw influent (Figure 4-10). That the removal of suspended solids affected TP concentrations to a greater extent than TN is consistent with the observation that P loads are more evenly distributed across urine and feces than N loads, which in blackwater originates almost its entirety from urine and are therefore predominantly present in dissolved form (DWA, 2008).

However, a correlation between TP and TSS concentrations in raw blackwater was not immediately discernible in the measured data (Figure 4-11). A meaningful linear relationship between the concentrations of the two parameters could only be identified by the application of a robust regression method, namely the bisquare method, which reduces the influence of outliers by assigning lower weights to data points with higher residuals. Even then, the model obtained was able to account for only 36% of the variance of the TP concentration, as indicated by the R² of the fit to measured data. Inspection of the measurements associated with the three data points on the upper left corner of Figure 4-11 did not reveal any obvious abnormalities that could explain their degree of deviation from the fitted model.

Figure 4-10 – Distribution of N:P ratios in raw and settled blackwater.

Figure 4-11 – Scatter plot of TP and TSS concentrations in raw blackwater. Fitted model: 푻푷 = ퟓ, ퟏퟑ ∙ 푻푺푺 + ퟑퟖ, ퟎ (R² = 0.36, p = 0.01).

Chapter 4

In terms of operational robustness, the settling unit performed immensely better than the primary treatment’s original configuration based on a grinding pump followed by a 1 mm screen, which was plagued by frequent pump blockages caused by various objects discarded in the toilet by residents, chief amongst them the so called “baby wipes”. After the implementation of the settling system, system downtime was reduced dramatically, with only one pump blockage incident registered in the 8-month period between the installation of unit and the conclusion of the experiments.

4.3.3 Analysis of the pH buffering capacity of settled blackwater

As mentioned above, due to intended use of the system’s effluent as a nutrient solution for hydroponic use, one of the primary treatment goals of the system was to achieve full conversion of the ammoniacal nitrogen present in the settled blackwater to nitrate, the nitrogen species whose uptake is generally favored by higher plants.

Nitritation, the first step of biological nitrification, produces 2 protons per mol of ammonia oxidized (Henze et al., 2008). Autotroph organisms that are part of the nitrification chain are particularly sensitive to the pH of their environment (Anthonisen et al., 1976, Shammas, 1986). Knowledge of the pH buffering capacity of a wastewater that is rich in ammonia nitrogen, such as blackwater, is therefore crucial, as it determines to what extent a biological nitrification system will require additional pH control mechanisms in order to keep reactor pH values within an acceptable range and avoid inhibition of the process.

Although alkalinity gives a measure of the overall buffering capacity of a wastewater, nitrifying activated sludge systems are traditionally operated with a pH setpoint of around 7.5, which is far above the pH endpoint of 4.3 used for the titrimetric determination of alkalinity according to standard methods. Consequently, some of the buffering capacity in the wastewater might not be “available” to the treatment system at all.

In order to gain a better understanding of the pH buffering characteristics of settled blackwater, the titration curves of 12 settled blackwater samples were recorded during alkalinity measurements performed as part of the general characterization of settled blackwater. Titrations were performed either manually or by an automatic titrator (848 Titrino plus, Metrohm) according to the norm DIN 38409-7:2005-12:2005 for the measurement of alkalinity.

In order to allow the comparison of their shapes, the titration curves obtained were normalized by the conversion of the volumes of titrant added into adjusted equivalent fractions by the application of Equation 4-1:

푉(푝퐻) − 푉(푝퐻푖) 푔′(푝퐻) = [4-1] 푉(푝퐻푓) − 푉(푝퐻푖)

Where V(pH) is the volume of titrant added at the respective pH value, pHi is the initial reference pH value and pHf is 4.3, the endpoint pH value of titration for the measurement of alkalinity. As initial reference pH value, the minimum initial pH of the set of 12 settled blackwater samples was taken. The resulting normalized titration equivalent fractions for are plotted in Figure 4-12.

The measured titration curves were compared to the theoretical normalized titration curve of a buffer solution containing only carbonate, which is typically the dominant buffering system in

Blackwater treatment system domestic wastewaters (Tchobanoglous et al., 2014). The normalized titration curve of any single- component buffer solution based on a weak diprotic acid (as is the case of carbonate) can be readily obtained if its concentration and dissociation constants K1 and K2 are known, as shown by Equation 4-2 (Stumm and Morgan, 1996): [푯+] + [푶푯−] 품 = ퟐ ∙ 휶 + 휶 + [4-2] 풕 ퟎ ퟏ 푪

Where gt is the theoretical equivalent titrant fraction, C is the total concentration of all species 2- - of the buffer system (CO3 , HCO3 and H2CO3 in the case of the carbonate system), and α0 and

α1 are the ionization fractions of the acid and its conjugated base, given by Equations 4-34-3 and 4-4: 1 훼0 = + + 2 [4-3] 1 + 퐾1⁄[퐻 ] + (퐾1 ∙ 퐾2)⁄[퐻 ] 1 훼1 = + + [4-4] 1 + [퐻 ]⁄퐾1 + 퐾2⁄[퐻 ] In the range of pH values between the equivalence points of the buffer system (approximately 4.2 and 11.2 for a carbonate system with total concentration of 10 mM), the last term of the right side of Equation 4-2 is very small and can be neglected, resulting in an equation that is a function of only the pH and the acid’s dissociation constants. The theoretical adjusted equivalent titrant fraction g’ is then calculated by Equation4-5, using the same initial reference pH value as in the calculation of the empirical g’.

′ 푔푡(푝퐻) − 푔푡(푝퐻푖) 푔푡(푝퐻) = [4-5] 푔푡(푝퐻푓) − 푔푡(푝퐻푖)

Where g’t(pH) are the g’t values at the respective pH values, and pHi and pHf are as defined previously.

Figure 4-12 – Measured and model titration curves. Measured data points obtained from titration of n = 12 settled blackwater samples. Theoretical reference curve corresponds to that of a carbonate buffer o solution, where pKa1 = 6.35 and pKa2 = 10.33 (25 C). For description of smoothed spline, refer to text.

As can be seen in Figure 4-12, the form of the theoretical adjusted titration curve of the carbonate model closely matches the distribution of g’ obtained from measured data in the pH

Chapter 4 values lower than 7. For values above that, the model residuals increase substantially with increasing pH, reaching a difference of almost 0.2 units at a pH value of 8.9, suggesting that another buffer system might be active in that pH range.

A better assessment of how well the carbonate model can describe the buffering capacity profile of settled blackwater can be made in terms of the normalized buffering intensity curve, which is given by Equation 4-6 (Stumm and Morgan, 1996): 푑푔′ 훽(푝퐻) = − [4-6] 푑푝퐻 In order to obtain a smooth buffering intensity curve from the measured data, measured g’ values were first condensed into an empirical curve by fitting a smoothed spline that qualitatively preserved the overall shape of the distribution of the measured data points. The resulting spline, also shown in Figure 4-12, was obtained by using a smoothing parameter p = 0.8056. The first derivative of that empirical curve was then calculated numerically using the centered difference quotient method.

As shown in Figure 4-13, the main peaks in the buffering intensity curves of settled blackwater and the carbonate model are aligned, indicating that indeed the carbonate is likely to be the main buffering system in settled blackwater. However, at the upper end of the pH range – where the buffering intensity of carbonate is weak –, the presence of a prominent secondary peak on the empirical buffer intensity curve confirms that a second buffer system is required to appropriately model the buffering capacity profile of settled blackwater in that range.

The peak’s position at a pH value of around 8.8 provides a strong indication that the buffer in question is ammonia (pKa = 9.25 (25oC) (Rumble, 2017)), which is present in relatively high amounts in settled blackwater, with an average concentration of 14 mM as NH4-N comparable to the total alkalinity value of about 20 mM.

The origins of the small peak at a pH value of 8.2 and the shoulder centered on a pH value of 4.7 are more difficult to be ascertained. No inorganic acids were found in literature that have pKa value(s) in the vicinity of those pH values and could be plausibly present in blackwater in amounts comparable to carbonate. As to organic acids, the complex composition of the organic content of settled wastewater limits the ability for any assertion to be made. It should be pointed out, however, acetic acid has a pKa of 4.76 at 25oC (Rumble, 2017) and is thoroughly conceivable that it could be present in the settled blackwater.

In light of the carbonate model’s incapacity to fully describe the buffering capacity profile of the settled blackwater under consideration here, the estimation of available alkalinity was carried out using the empirical model given by the smoothed spline presented in Figure 4-12. Due to the large size of the spline, composed of 1666 individual cubic-polynomial pieces, the g’ values that the model yields at different pH values are not shown here.

Blackwater treatment system

Figure 4-13 –Buffer intensity curves derived from carbonate model and from smoothed measured titration curves.

Given the empirical g’ curve g’E, the fraction of the total alkalinity that is consumed by bringing the sample’s initial pH down to any given pH value is given by the normalized difference between the respective g’ values; the available alkalinity can then be obtained by multiplying this difference by the actual measured alkalinity value, as shown by Equation 4-7.

By considering a setpoint value of 7.5 (which was used throughout most of the treatment plant’s operational period), the median pH value of the settled blackwater of 8.53, the titrimetric alkalinity measurement’s pH endpoint value of 4.3 and a median measured alkalinity of 22mM, the following estimate for the average available alkalinity in the settled water is obtained:

′ ′ 푔퐸(pH) − 푔퐸(pHi) 퐴푙푘퐴푉 = ( ′ ′ ) ∙ 퐴푙푘푡 [4-7] 푔퐸(pHf) − 푔퐸(pHi) ′ ′ 푔퐸(7.5) − 푔퐸(8.53) 0.1101 − (−0.0826) = ′ ′ ∙ 퐴푙푘푡 = ∙ 22 = 0.1780 ∙ 22 = 3.9 푚푀 푔퐸(4.3) − 푔퐸(8.53) 0.9996 − (−0.0826)

4.3.4 MBR operation and effluent quality

Filtration performance

The flux and transmembrane pressure (TMP) measured at the MBR’s filtration unit over the experimental period are shown Figure 4-14. Based on this data, the membrane’s permeability, shown in Figure 4-15, was calculated by means of Equation 4-8: 퐽 퐾 = [4-8] 푇푀푃 Where K is the permeability, J is the filtration flux, given in L/(m².h), and TMP is the transmembrane pressure, given in bar.

The MBR was initially fitted with a membrane unit that had previously been used for 6 months in preliminary trials in the previous year, and subsequently chemically cleaned in preparation to operational cycle reported here. The membrane’s permeability fell very quickly to ca. 23 L/(m².h.bar) upon the start-up of the MBR.

Chapter 4

In response to the very low permeability, 55 days after the start of the new operational cycle the membrane module was replaced. The new module was operated at the same flux level setpoint as the previous one (3.5 L/(m².h)), which was substantially below the nominal values indicated by the unit’s manufacturer (20 L/(m².h)) and those typically applied in the operation of full- scale MBRs for the treatment of domestic wastewater (WEF, 2012, Judd and Judd, 2011). That was considered acceptable due to the fact that the volume of effluent required for the greenhouse experiments discussed in Chapter 5 – whose supply was the sole purpose of running the treatment system – was extremely low (400 L every two weeks) when compared to the system’s nominal capacity.

It was expected that such a low flux level would considerably limit the membrane’s fouling rate and therefore increase the interval between maintenance stops for the chemical cleaning of membrane, or even render them unnecessary altogether in the course of the 8-month experimental period. However, as can be seen in Figure 4-15, even at such low flux values the rate of membrane fouling was significant. After both the replacement of the membrane module at day 55, and a round of chemical cleaning at day 165, permeability values fell quickly. In a matter of less than 60 days, the permeability returned to the level observed prior to the maintenance interventions, resulting in filtration rates similar or superior to those reported for other pilot-scale MBR systems operated at considerably higher membrane fluxes for the treatment of domestic wastewater (Abegglen, 2008, Patsios and Karabelas, 2011), graywater (Kraume et al., 2010) and blackwater (Boehler et al., 2007, Knerr, 2012).

Such poor membrane filtration performance stands in particularly marked contrast to that obtained by Knerr (2012), who operated a submerged hollow-fiber MBR for the treatment of a blackwater with similar composition to the one used as influent in the system considered here, as discussed previously in Section 4.3.1. In this case, a nominal flux of 2.5 L/(m².h) resulted in a stable membrane permeability of 268 L/(m².h.bar) over an operational period of approximately 8 months.

The high fouling rates are particularly surprising given that the membrane aeration flowrate was set at a value (0.1 Nm³/min), equivalent to a membrane surface specific aeration demand 2 (SADm) of 0.92 m³/(m .h), which is in the upper range of values used in MBRs treating municipal wastewater (Judd and Judd, 2011).

However, also noteworthy is the fact that after an initial high drop following both the replacement and cleaning of the modules used, the permeability values seemed to stabilize at around 23 L/(m².h.bar), as indicated in Figure 4-15. This is intriguing, given that most models that attempt to describe the progression of membrane fouling do not seem to allow for a stabilization of fouling once it starts to occur (Liang et al., 2006, Ng and Kim, 2007, Naessens et al., 2012).

Blackwater treatment system

Figure 4-14 – Flux and transmembrane pressure at membrane filtration unit over the course of the MBR’s operation.

Chapter 4

The only single accounts that could be found in the literature regarding such a fouling stabilization phenomenon were related to gravity-driven MBRs (Jabornig and Podmirseg, 2015, Ding et al., 2016, Wang et al., 2017). In those cases, the stabilization of permeability was attributed to the structure and levels of bioactivity of the biofilm formed on the surface of the filtration membranes under the low hydraulic shear conditions, which are characteristic of the operation of gravity-driven MBRs. Such conditions allow for the development of a “fluffier”, more porous biofilm, whose thickness and porosity are stabilized by the predatory activity of higher microorganisms such as protozoa, and also the sloughing off of excess biofilm (Ding et al., 2016).

In principle, the aeration flowrate applied to the membrane should have caused enough shear to keep such a porous biofilms from developing. However, that hypothesis was not confirmed since the structure of the fouling layer on the membrane was not assessed at any point in the MBR’s operational period.

MLSS and MLVSS development

As shown in Figure 4-16, the development of the mixed liquor suspended solids (MLSS) was marked by extreme fluctuations in concentrations over the bioreactor’s operational period. Those fluctuations are very likely associated with the chronic foaming problems that the system faced throughout its operation. Particularly in the months of July and August, several seemingly random bouts of sustained and intense foaming events occurred that in some occasions led to reactor overflow and substantial loss of MLSS.

In order to provide a reference for comparison with those measured values, an estimate of the theoretical MLSS and MLVSS (mixed liquor volatile suspended solids) concentrations to be expected given the influent and effluent COD, TN and solids concentrations measured, was calculated. The estimation procedure was based on the steady-state solution for the set of

Blackwater treatment system differential equations that comprise the Activated Sludge Model 1 (ASM1) for an activated sludge system with COD removal and nitrification, given by Equations 4-9 and 4-10 (Tchobanoglous et al., 2004).

푌 ∙ (푆 − 푆 ) 푌 ∙ (푁 − 푁 ) 푄 ∙ 푆푅푇 퐼푁퐹 퐸퐹퐹 ∙ (1 + 푓 ∙ 푘 ∙ 푆푅푇) + 퐴 푁퐻4,퐼푁퐹 푁퐻4,퐸퐹퐹 + 푋 = ∙ [푓 ∙ (1 + 푘 ∙ 푆푅푇) 푑 푑 푓 ∙ (1 + 푘 ∙ 푆푅푇) ] [4-9] 푇푆푆 푉 푑 푑퐴 +(푇푆푆퐼푁퐹 − 푉푆푆퐼푁퐹) + 푛푏푉푆푆퐼푁퐹 푌 ∙ (푆 − 푆 ) 푌 ∙ (푁 − 푁 ) 푄 ∙ 푆푅푇 퐼푁퐹 퐸퐹퐹 퐴 푁퐻4.퐼푁퐹 푁퐻4,퐸퐹퐹 ∙ (1 + 푓푑 ∙ 푘푑 ∙ 푆푅푇) + + 푋푉푆푆 = ∙ [ (1 + 푘 ∙ 푆푅푇) (1 + 푘 ∙ 푆푅푇) ] [4-10] 푉 푑 푑퐴 +푛푏푉푆푆퐼푁퐹

Where XTSS and XVSS represent the MLSS and MLVSS concentrations; the variables SINF, NNH4,INF,

TSSINF, VSSINF and nbVSSINF are the COD, ammonia nitrogen, TSS, VSS and non-biodegradable

VSS concentrations in the influent, respectively; SEFF and NT,EFF are the COD and ammonia nitrogen concentrations in the effluent. The parameters of the equations are defined in Table 4-9. The influent’s non-biodegradable VSS concentration can alternatively be expressed in terms of fNB, the fraction of the total VSS that it represents, as shown in Equation 4-11:

푛푏푉푆푆퐼푁퐹 = 푓푁퐵 ∙ 푉푆푆퐼푁퐹 [4-11] Values for the model’s stoichiometric kinetic parameters used for the calculation are listed in Table 4-9. When available, values were taken from Hocaoglu et al. (2010), who characterized sludge obtained from a MBR that ran under similar operating conditions as the system under consideration here and treated blackwater with a similar composition, as mentioned previously.

Table 4-9 – Parameter values used for the calculation of the steady-state MLSS estimate.

Parameter Description Value Unit Source Heterotrophic yield Y 0.47 gVSS/gCOD Hocaoglu et al. (2010) coefficient

Heterotrophic endogenous -1 kd 0.18* d Hocaoglu et al. (2010) decay coefficient

fd Cell debris fraction 0.15 — Henze et al. (2008)

f Volatile fraction in VSS 0.85 — Henze et al. (2008)

YA Autotrophic yield coefficient 0.16 gVSS/gCOD Henze et al. (2008)

Autotrophic endogenous -1 kdA 0.06* d Henze et al. (2008) decay coefficient * Temperature = 20°C

For the calculations, the inflow rate Q and effluent COD concentration Si were considered constant, with the former set at 480 L/d (the median of measured filtration flowrates) and the latter at 207 mg/L (the median of measured effluent COD values). The effluent concentration of NH4-N was considered negligible and set to zero. Additional assumptions were that all the TN in the influent is nitrified (which, as will be discussed previously, is not entirely accurate)

Chapter 4

and that all the VSS present in the influent is entirely biodegradable (fnb=0), an assumption made based on the results obtained by Hocaoglu et al. (2010) on the COD fractionation of mechanically treated blackwater.

The point estimate for the theoretical MLSS and MLVSS concentrations themselves was then obtained by taking the average of the XTSS and XVSS values calculated for 17 settled blackwater samples for which concentrations of all required arguments of Equation X (COD, TN, TSS and VSS) were available. Confidence intervals were obtained by the bootstrap method, with 10.000 resamplings and significance level α=0.05.

The resulting point estimates for XTSS, XVSS and their respective confidence limits are plotted in Figure 4-16. Although MLSS concentrations measured in the bioreactor towards the end of the plant’s operational period are well within the confidence interval yielded by the model, the

MLVSS estimate obtained is off by a significant margin. The rather low XVSS/ XTSS ratio predicted by the steady-state equations primarily reflects the accumulation of the inorganic inert fraction of the solids brought to the reactor by blackwater, which would be expected to be substantial at the high nominal SRT at which the system was operated (Ekama and Wentzel, 2004).

Although the discrepancy between the measured and theoretical values for the XVSS/ XTSS ratio could at first be presumed to stem from the fact that the solids inventory in the bioreactor simply did not reach steady state during the plant’s operational period, analysis of the development of the ratio in the reactor over time suggests otherwise. As shown in Figure 4-17, a statistically significant decrease in the XVSS/ XTSS ratio was indeed observed over the course of the system’s operation. However, the apparent rate at which it occurred (-5.6 × 10-4 per day, p<0.001) was very slow, and could not conceivably be thought of as a sign of convergence towards the value predicted by the steady-state equations. By linear extrapolation, at that rate, circa 765 days would be needed in order for the XVSS/ XTSS ratio to reach the center of the theoretical value’s confidence interval.

Figure 4-17 – Variation of XVSS/XTSS ratio in the mixed liquor of the MBR over time.

Blackwater treatment system

Such unexpectedly high XVSS/ XTSS ratios have been previously observed in other studies dealing with MBRs operated at high SRT treating a variety of wastewaters (Rosenberger et al., 2002, Pollice et al., 2004, Laera et al., 2005, Spérandio and Espinosa, 2008, Lubello et al., 2009), including blackwater (Atasoy et al., 2007, Knerr, 2012). In the context of blackwater treatment,

Knerr (2012) speculated that the high XVSS/ XTSS ratio (between 0.8-0.9) observed in three MBR systems studied by the author could be attributed to the accumulation in the bioreactor of non- biodegradable particulate organic materials that were presumably present in the influent blackwater.

In order to verify if this hypothesis would suffice to explain the XVSS/XTSS levels observed in the case under consideration, the sensibility of the XTSS and XVSS estimates produced by Equations 4-9 and 4-10 to assumptions regarding the degree of biodegradability of the VSS in settled blackwater (as expressed by the factor fNB) was evaluated. As shown in the contour plot presented in Figure 4-18, the analysis indicates that the range of XVSS/XTSS ratios measured here would only be possible if all the VSS in the influent was non-biodegradable. This would be a very unrealistic proposition, which strongly indicates that accumulation of nbVSS in the reactor cannot account alone for the relatively high concentration of VSS in the bioreactor’s mixed liquor.

A second possibility is that one of the fundamental assumptions underlying the mass balance of solids in the ASM models, namely that inorganic solids present in the influent are completely inert and therefore accumulate in the mixed liquor of activated sludge systems, does not apply in the conditions under which the influent settled blackwater was treated in the MBR. Such a phenomenon was previously observed by Pollice et al. (2004), Laera et al. (2005) and Spérandio and Espinosa (2008) in lab-scale MBRs fed with municipal wastewaters and operated with high

SRTs, where higher than expected XVSS/XTSS ratios were demonstrated to be partially attributable to an apparent dissolution of inorganic solids in the mixed liquor.

Chapter 4

To determine if allowing for this possibility would improve XTSS and XVSS estimates, Equation

4-9 was modified by the introduction of the factor fI, a parameter that adjusts the term representing the concentration of inorganic suspended solids in the influent (푇푆푆퐼푁퐹 − 푉푆푆퐼푁퐹) in order to account for its non-inert fraction, as shown in Equation 4-12:

푌 ∙ (푆 − 푆 ) 푌 ∙ (푁 − 푁 ) 푄 ∙ 푆푅푇 퐼푁퐹 퐸퐹퐹 ∙ (1 + 푓 ∙ 푘 ∙ 푆푅푇) + 퐴 푁퐻4,퐼푁퐹 푁퐻4,퐸퐹퐹 + 푋 = ∙ [푓 ∙ (1 + 푘 ∙ 푆푅푇) 푑 푑 푓 ∙ (1 + 푘 ∙ 푆푅푇) ] [4-12] 푇푆푆 푉 푑 푑퐴 +푓퐼 ∙ (푇푆푆퐼푁퐹 − 푉푆푆퐼푁퐹) + 푛푏푉푆푆퐼푁퐹

As can be seen in Figure 4-19, under the assumption that the influent VSS is fully biodegradable

(fNB = 0), Equation 4-12 produces XTSS estimates that allow XVSS/XTSS ratios bounded only by the value of the factor f, the volatile content of biomass (assumed here to be 0.85, as shown in Table 4-9). However, the analysis of that equation also indicates that the particular combination of

XTSS (4.5 – 5.5) and XTSS/XVSS (0.80 – 0.85) levels found in the MBR towards the end its operation would only be reachable at SRTs well upward of 100 days, substantially higher than the nominal SRT with which the system was operated (55 days).

Although the two assumptions discussed above – i.e. the presence in the influent of (i) non- biodegradable volatile suspended solids, and (ii) non-inert inorganic suspended solids – were individually insufficient to improve XTSS and XVSS estimates, their combination is capable of extending the steady-state model’s output space enough to encompass the range of XTSS and

XVSS values observed in the bioreactor. As can be seen in Figure 4-20, appropriate estimates for

XTSS and the XTSS/XVSS ratio are then obtained with fNB values set between 0.2 and 0.3, and fI values between 0.3 and 0.4.

The plausibility of those fNB and fI values is difficult to ascertain, since practically no information is available in the literature regarding the composition of suspended solids in settled blackwater. The only study found on the biodegradability of the volatile fraction of those solids was the aforementioned work conducted by Hocaoglu et al. (2010), who determined that essentially all particulate COD (which can be taken as a proxy measurement of VSS) in a mechanically treated blackwater was biodegradable.

In what regards the composition of the inorganic fraction of settled blackwater solids, no reference could be found at all. However, it should be noted that the range of fI values found to generate appropriate XTSS and XVSS estimates corresponds, incidentally, to the apparent rate of loss of inorganic suspended solids observed by Laera et al. (2005) and Pollice et al. (2004) in the mixed liquor of a MBR operated with complete sludge retention treating municipal wastewater.

In general, the results above should be considered with reservations, since the assumptions underlying the steady-state ASM model was adjusted in a rather ad hoc way; more investigations would be required to assess if they are indeed valid and do not lead to an over-parametrized model. Nevertheless, the results at the very least provide an indication that the use of ASM models for the estimation of solids production in MBRs treating blackwater might not be adequate if the standard assumptions concerning the behavior of the solids in the influent are used.

Blackwater treatment system

Nitrification

In the first 40 days after the system start-up, the pH control system did not function properly, leading to high fluctuations in the pH of the bioreactor, as shown in Figure 4-21. During that period, the mixed liquor pH floated freely, being controlled only by the biological activity in the mixed liquor and the inflow of alkalinity in settled blackwater. Full nitrification was not

Chapter 4

achieved in the period, resulting in consistent high concentrations of NO2-N and NH4-N in the effluent. Once pH control was brought back in operation, pH stabilized, and eventually full nitrification of the influent total nitrogen was reached.

Figure 4-21 – Bioreactor’s pH and concentration of nitrogen species and potassium in the MBR’s effluent measured during the operation of the blackwater treatment system.

With pH control on, pH values in the bioreactor fluctuated between 7.4 and 7.8, with eventual incidents of failure in the control system leading to dips to values as low as 6.0. The rather wide range around the setpoint was due to the suboptimal placement of the system’s pH probe, which was placed at a flow cell immediately downstream of the membrane unit, instead of inside the bioreactor itself. That led to an excessively high dead time in the system’s response to pH changes in the reactor, associated with the 4-minute long relaxation period of the of 16-minute filtration cycle, during which the system was essentially “disconnected” from the internal liquid environment of the bioreactor.

High dosing rates of KOH were required to compensate for the buffering capacity deficit in the influent, resulting in an average added K concentration of 554 mg/L, calculated by subtracting the average concentration of K in the influent (68.3 mg/L) from the average concentration in the effluent at steady-state (622 mg/L). Although high, this value was nonetheless found to be inferior to what would be expected given the estimated alkalinity deficit in settled blackwater, as discussed in the next section.

Blackwater treatment system

Alkalinity requirements and KOH consumption

The alkalinity deficit ∆Alk is defined here as the difference between the alkalinity required to compensate for proton-generating biological processes in the bioreactor (AlkREQ) and the alkalinity available in the influent (AlkAV), as shown by Equation 4-13:

∆퐴푙푘 = 퐴푙푘푅퐸푄 − 퐴푙푘퐴푉 [4-13]

The alkalinity consumed by microbiological activity in a activated sludge process with nitrification and COD removal in steady-state can be calculated by the proton mass balance given by Equation 4-14 (Scearce et al., 1980, Henze, 2000). Besides considering the two protons generated per mol of ammonia nitrogen that undergoes autotrophic nitritation, the model also considers three additional processes in which heterotrophs play a significant role: the ammonification of organic nitrogen (which generates 1 mol of equivalents per mol N); the anabolic uptake of ammonia nitrogen (which consumes 1 mol of equivalents per mol N taken up); and denitrification (that generates 1 mol of equivalents per mol N denitrified):

푁 2 ∙ 푁 푁 푁 퐴푙푘 = − 푂푅퐺 + 푁퐼푇 − 퐷퐸푁 + 퐵퐼푂 [4-14] 푅퐸푄 14 14 14 14

Where Norg, is the concentration of organic nitrogen; NBIO is the concentration of N assimilated by biomass; NNIT is the concentration of ammonia nitrogen nitrified; NDEN is the concentration of N removed by denitrification; and 14 is the molar mass of nitrogen.

In the case under consideration here, the organic nitrogen concentration NORG in Equation 4-14 can be approximated as the difference between the influent concentrations of TN and NH4-N, as the inorganic fraction of N in blackwater very likely consist almost entirely of ammonia nitrogen. Additionally, the concentration of N removed by denitrification can be obtained by a nitrogen mass balance around the bioreactor under the assumption that the only one N input (the influent) and three outputs (effluent, as N2 after denitrification and loss in wasted biomass) exist. The incorporation of those relationships in Equation 4-15 yields:

−(푁푇,퐼푁퐹 − 푁푁퐻4,퐼푁퐹) 2 ∙ (푁푇,퐼푁퐹 − 푁퐵퐼푂) 퐴푙푘푅퐸푄 = + + 14 14 [4-15] (푁 − 푁 − 푁 ) 푁 − 푇,퐼푁퐹 퐵퐼푂 푇,퐸퐹퐹 + 퐵퐼푂 14 14

Where NT,INF and NNH4,INF are the concentrations of TN and ammonia nitrogen in the influent, and NT,EFF is the concentration of TN in the effluent.

Finally, considering that in steady state NBIO is the concentration equivalent of the nitrogen contained in the excess biomass wasted from the reactor, it can be determined by Equation 4-16 if iXB, the nitrogen content of biomass, is known:

𝑖 ∙ 푋 ∙ 푉 푁 = 푋퐵 퐵퐼푂 [4-16] 푏푖표 푄 ∙ 푆푅푇

By noting that the steady-state concentration of biomass XBIO can be directly obtained from

Equation 4-10 by subtracting the term associated with nbVSS from XVSS, the substitution of the resulting relation in Equation 4-17 yields:

Chapter 4

푌 ∙ (푆퐼푁퐹 − 푆퐸퐹퐹) 푌퐴 ∙ (푁푁퐻4,퐼푁퐹 − 푁푁퐻4,퐸퐹퐹) 푁푏푖표 = 𝑖푋퐵 ∙ [ ∙ (1 + 푓푑 ∙ 푘푑 ∙ 푆푅푇) + ] [4-17] (1 + 푘푑 ∙ 푆푅푇) (1 + 푘푑퐴 ∙ 푆푅푇)

Where all the variable and parameters are as defined previously. The combination of Equations 4-13, 4-15 and 4-17 allows the alkalinity deficit in the settled blackwater to be estimated. The procedure followed the same approach employed previously to estimate the steady-state MLSS. The point estimate for the ∆Alk was obtained by taking the average of ∆Alk values calculated for each of 14 settled blackwater samples for which concentrations of all required input variables were available, as indicated by Equation 4-18. Confidence intervals were obtained by the bootstrap method, with 10.000 resamplings and significance level α=0.05.

∑n=14 ∆Alk ∆̂Alk = i i [4-18] n

For the calculation of NBIO, the parameter values listed in Table 4-9 were used. The same basic set of simplifying assumptions that were made in the estimation of the steady-state MLSS concentration were again used, with the addition that NT,EFF and the pH of the influent were considered constant at their median values of 216 mg/L and 8.53.

The resulting estimate of ∆퐴푙푘 = 23.8 ± 1.3 푚푀 indicate that in the conditions under which the biological process was operated, a very high buffer capacity deficit indeed exists. Considering that by using KOH as base one mol of K is added per equivalent dosed, the concentration of K that should theoretically be added is given by:

∆퐶퐾 = ∆퐴푙푘 ∙ 퐸퐾 [4-19]

= (23.8 ± 1.3) ∙ 39 ≈ 929 ± 51 푚푔퐾/퐿

Where ΔCK is the theoretical added K concentration and EK the mass of K per equivalent in KOH, equal to 39 mgK/mmol. Considering the average influent K concentration of 68.3 mg/L, a concentration of 997 mg/L would then be expected to found in the effluent.

Only 62% (622 mg/L) of the expected amount of K was actually accounted for in the effluent. A discrepancy of such magnitude would require that a process (or combination of processes) to either (a) generate roughly half of the required alkalinity in hydroxide equivalents – thereby reducing the alkalinity deficit and KOH dosing requirements —, or (b) remove approximately half of the added K concentration from the mixed liquor’s liquid phase.

In order to assess the plausibility of hypothesis “a”, the KOH dosing rates recorded in the period between the 2nd and 29th of October – during which the bioreactor was fully nitrifying and K concentrations were stable (see Figure 4-21) – were analyzed. It would be expected that if this hypothesis were correct, the measured KOH dosing rate would be inferior to the dosing rate that would be required to compensate for the full alkalinity deficit calculated previously.

The average of the measured dosing rates and its 95% confidence were calculated by bootstrapping with 10.000 resamplings, and then compared with a theoretical estimate derived from the alkalinity deficit estimate calculated previously. This reference dosing rate can be readily obtained by the simple dilution calculation given by Equation 4-20, considering that the equivalence factor of KOH is unity and that the concentration of the KOH feedstock is known.

Blackwater treatment system

∆퐴푙푘 ∙ 푄 ∆퐴푙푘 ∙ 푄 푄퐾푂퐻 = = [4-20] 푓퐸푄 ∙ 퐶퐾푂퐻 13.45

Where QKOH is the theoretical KOH dosing rate, fEQ is the equivalence factor of KOH (equal to

1), and CKOH is the concentration of the KOH feedstock, which in the case considered here was 13.45 M (the molarity of a 50% KOH solution). As in previous calculations, the value of the inflow rate Q was considered constant and equal to 480 L/d, the median of the membrane filtration rate.

The measured KOH dosing rates and their average value, along with the point estimate for the theoretical dosing rate and all associated 95% confidence intervals, are presented in Figure 4-22. There it can be seen that the measured KOH dosing rates were actually significantly higher than the theoretical estimate at a significance level of 0.05, as indicated by the position of the confidence intervals and lack of overlap them. Therefore, the hypothesis that an unknown process in the bioreactor generated alkalinity and led to reduced KOH dosing requirements can be rejected.

nd th Figure 4-22 – Bioreactor KOH dosing rates (QKOH) recorded in the period between the 2 and 29 of October. Indicated in the figure are the confidence intervals for the average dosing rate and theoretical dosing rate.

As to processes that could have removed K from the liquid phase, to best of the author’s knowledge, the only biological process known to have that effect in the context of activated sludge systems is enhanced biological phosphorus removal (EBPR) (Barat et al., 2005, Choi et al., 2011). However, given that approximately 0.25 mols of K are taken up by phosphorus- accumulating organisms (PAOs) per mol of P removed from the liquid phase (Barat et al., 2005), the removal of 381 mg/L of K would require a removal of 1191 mg/L of phosphorus, which is orders of magnitude superior to the concentration of TP in the influent.

Chapter 4

At first, it also seems implausible that some precipitation process might have led to removal of K from the liquid phase since potassium salts are notoriously soluble (Freilich et al., 2014), especially given the bioreactor’s neutral pH and the low ionic strength of the mixed liquor. A literature research revealed no K-containing compound that could possibly be supersaturated in the mixed liquor.

Effluent composition and nutrient recovery efficiencies

The statistics on the composition of the MBR effluent are presented in Table 4-10. For K, NO3-

N and NH4-N, statistics considered only measurements made in samples collected after complete nitrification was reached in the bioreactor, in order to reflect what would be considered the steady-state composition of the MBR’s effluent. This point was reached only towards the end of the system’s operation.

Table 4-10 – Summary statistics of MBR’s effluent composition.

95% CI of mean Parameter Unit n Mean Std. Dev. Median LCL UCL COD mg/L 12 210 117 302 145 207 TN mg/L 15 216 205 227 20 216

NO3-N mg/L 10 222 211 232 14 229 TP mg/L 13 19.9 17.6 22.2 3.8 18.8

PO4-P mg/L 15 18.1 16.7 19.6 2.6 17.6 Ca mg/L 9 81.3 71.9 90.6 12.2 82.0 K mg/L 10 745 631 859 160 752 Mg mg/L 9 13.6 12.8 14.4 1.0 13.5

SO4 mg/L 9 23.6 21.6 25.5 2.5 22.6

As discussed above, the alkalinity available in settled blackwater proved insufficient to buffer the pH changes caused by the nitrification of the entire influent ammonia nitrogen load. That led to high dosage levels of KOH, and resulted in an effluent with an unbalanced NPK ratio when compared to recommended values for hydroponic nutrient solutions for a variety of different crops, as will be discussed in more detail in Chapter 5. In comparison to what was observed in raw blackwater, where the NPK concentrations and ratios indicated a lack of K, the massive KOH dosing requirements led to an effluent with an excess of K – or, conversely, a deficiency of P and N – as can be seen in Figure 4-23. Aggravating this shift were the higher relative process losses that were observed for P over the whole treatment train as compared to N, which caused the N:P ratios to also rise, as indicated in Figure 4-23.

Nutrient losses in the reactor, computed as the differences between median values in settled blackwater and the bioreactor’s effluent, reached 21% for N and 36% for P (Figure 4-24). As calculated in detail in following sections, losses in the form of waste biomass can account for only 15 from a total of 57 mg/L of N lost. The remainder, 42 mg/L, is assumed to have been lost due to denitrification. Although the bioreactor was designed and operated as a fully aerobic system, it is conceivable that the geometry of the tanks and the placement of aeration diffusers could have made the existence of anoxic dead zones in the reactor possible – and therefore the growth of denitrifying microorganisms.

Blackwater treatment system

Figure 4-23 – Comparison of N:P and K:P ratios of raw blackwater, settled blackwater and MBR’s effluent.

Figure 4-24 – Comparison of TN and TP concentrations in raw blackwater, settled blackwater and MBR’s effluent.

As to P, losses due to uptake in biomass of non-phosphorus accumulating organisms can be estimated by considering an average P:N ratio in biomass of 0.16 (Henze, 2000, Henze et al., 2008), which results in an estimated 2.4 mg/L of P lost in excess waste sludge. The remaining

8.6 mg/L of P are unlikely to have been removed by precipitation of struvite (NH4MgPO4·6H2O) and/or calcium phosphates. Comparison between the concentrations of Mg and Ca in raw blackwater and the effluent shows an actual increase for Ca and no meaningful difference for Mg – which would be expected to 6.7 mg/L lower if the 8.6 mg/L of P were precipitated as struvite. That leaves the occurrence of enhanced biological phosphorus removal (EBPR) as the

Chapter 4 most plausible cause for the observed P loss. However, it is unclear how EBPR could have been active to the point of removing almost 1/3 of the P, given that the bioreactor was not designed for it and denitrification – a process that competes with EBPR for readily biodegradable organic substrate – also apparently took place.

The comparison between the composition of raw blackwater and the effluent reveal a median nutrient conservation of 58% for N and 35% for P (Figure 4-24). Here usage is made of the term “conservation” rather than “recovery” because, in the context of the nutrient recovery approach considered here, the blackwater treatment side of the system (and its effluent) cannot be seen separately from the food production side. In that sense, the effluent cannot be regarded as a finished product. Consequently, its nutrient content cannot be considered “recovered” until taken up by the crops cultivated on the adjoining hydroponic system. The significance of this seemingly arbitrary distinction will be made clear in the following chapter, which will discuss how the mismatch between the effluent’s NPK ratio and the ratios recommended for the cultivated crops affects the nutrient recovery potential of the system as a whole.

Nitrification at low pH

One alternative to increase the use of the buffering capacity available in blackwater and reduce the alkalinity deficiency would be to operate the activated sludge process at a lower pH setpoint. The stable operation of bioreactors for the nitrification of wastewaters with high ammoniacal nitrogen content at acidic pH has been demonstrated to be feasible by several authors, such as Hellinga et al. (1999), Udert et al. (2003), Tarre and Green (2004), Jubany Güell (2007) and Shanahan and Semmens (2015), with Fumasoli et al. (2017) reporting sustained activity of ammonia oxidizing microorganisms (AOM) at pH values as low as 4.5 in a moving-bed bioreactor treating human urine.

The exact mechanism through which the pH affects the biological activity of nitrifying organisms is not fully understood (Sötemann et al., 2005). However, from a modelling perspective, it is generally accepted that pH has a direct effect on the maximum growth rate of both AOM and nitrite-oxidizing microorganisms (NOM) (Sötemann et al., 2005, Jubany Güell, 2007, Henze et al., 2008). Besides inhibition-related effects, the pH modulates via acid-base equilibria the availability in solution of the compounds that are thought to be the substrates for those organisms: free ammonia (NH3) and free nitrous acid (HNO2), respectively (Hellinga et al., 1999, van Hulle et al., 2007). In general, it has been observed that AOM are more sensitive to pH changes than NOM, with the maximum growth rate of the former being considerably lower than that of the latter at acidic pH values (Hellinga et al., 1999, Jubany Güell, 2007).

Despite knowledge gaps, two approaches that are frequently used to incorporate the effect of pH in kinetic models of AOM growth are the empirical model developed by Sötemann et al. (2005) (Equation 4-21), and the model developed by Antoniou et al. (1990) based on enzyme kinetics (Equation 4-22):

(푝퐻−7.2) µ푚푎푥퐴,7.2 ∙ 2.35 푝퐻 ≥ 7.2 µ푚푎푥퐴,푝퐻 = { 퐾푚푎푥 − 푝퐻 [4-21] µ푚푎푥퐴,7.2 ∙ 퐾퐼 ∙ 푝퐻 < 7.2 퐾푚푎푥 + 퐾퐼퐼 − 푝퐻

-1 Where µmaxA,pH is the maximum specific growth rate for AOM at the pH of interest , in d ; µmaxA,7.2 -1 is the maximum specific growth rate for AOM at a pH value of 7.2 in d ; and KI, KII and Kmax are empirical model coefficients.

Blackwater treatment system

1 µ = µ ∙ [4-22] 푚푎푥퐴,푝퐻 푚푎푥퐴,표푝푡 10−푝퐾1 10−푝퐻 1 + + 10−푝퐻 10−푝퐾2

-1 Where µmaxA,opt is the optimum maximum specific growth rate for AOM, in d (which can be taken as the value of the parameter µmaxA,7.2 in Equation 4-21); and pK1 and pK2 are empirical model coefficients.

The µmaxA values produced by either one of these models can be readily applied in the steady- state solution of the ASM1 model (Henze et al., 2008, S. 90) to estimate the TAN (total ammonia nitrogen) concentration in the effluent of an completely mixed activated sludge system without nitrogen removal, such as the one under consideration here. When the dependence of the + availability of NH3 on pH due to its acid-base equilibrium with NH4 is also taken in account, as suggested by Fumasoli et al. (2015), the following relationship between effluent TAN and pH results: 1 −푝퐻 퐾 ∙ (푘 + ) 10 푠퐴 푑퐴 푆푅푇 퐶 = ( + 1) ∙ [4-23] 푇퐴푁 푝퐾푎 1 10 µ − (푘 + ) 푚푎푥퐴,푝퐻 푑퐴 푆푅푇

Where CTAN is the effluent TAN concentration, in mg/L; pKa is the logarithm of the acid + dissociation constant of NH4 ; KsA is the half-saturation constant of AOM, in mgNH3/L; and kdA is the endogenous decay rate of AOM, in d-1. Here it should be noted that this expression relies on a simplification of the kinetic models for AOM growth, and does not take into accounts inhibitory effects associated to free ammonia (substrate inhibition) or free nitrous acid.

By applying Equation 4-23 considering the parameter value set given in Table 4-11 and the SRT with which the MBR was operated (55 d), the TAN vs. pH curve shown in Figure 4-25 is obtained. For pH values below 8, the model predicts increasingly higher TAN concentrations with a decrease in pH; at pH values between 6.3 and 6.5 (depending on the model), TAN concentrations approach the average concentration in the effluent, indicating the total collapse of the AOM population (Henze et al., 2008, S. 91).

Table 4-11 – Parameter values used for the determination of the steady-state TAN vs. pH curve.

Parameter Value Unit Source

µmaxA.7.2 1.21 d-1 Jubany Güell (2007) µmaxA.opt

KSA 0.34 mgNH3/L Jubany Güell (2007) -1 kdA 0.2 d Jubany Güell (2007) Kmax 9.5 — Sötemann et al. (2005) KI 1.13 — Sötemann et al. (2005) KII 0.3 — Sötemann et al. (2005)

pK1 6.78 — Antoniou et al. (1990)

pK2 8.69 — Antoniou et al. (1990)

Chapter 4

5 6 7 8 9 10 100 100 90 Model 1 90 80 Model 2 80 70 70 60 60 50 50

(mg/L) 40 40 30 30

20 20 effluent effluent TAN concentration 10 10 0 0 5 6 7 8 9 10 pH Figure 4-25 – Theoretical effluent TAN concentration vs. pH curve for a reactor SRT of 55 days. Model 1 and 2 refer to Equation 4-21 and 4-22, respectively.

In order to assess if the predictions of the model are indeed correct, towards the end of the MBR’s operational period the bioreactor was operated for 20 days at a pH setpoint of 6.0. The transition between setpoints was allowed to occur naturally by the activity of the nitrifying biomass in the reactor. As shown in Figure 4-26, the reduction of the bioreactor’s pH setpoint had an immediate and substantial effect on the consumption rate of KOH, reducing it by 46%, from 1.08 L/d to 0.58 L/d. Despite the change in the pH of the mixed liquor, full nitrification was maintained until the end of the reactor’s operation (Figure 4-26), with the nitrate concentrations not showing any noticeable upward trend.

Due to the short duration of the operation under those conditions (2o days, versus the reactor’s SRT of approximately 55 days), it is not possible to exclude the possibility that the apparent stability of the nitrification process was merely temporary, sustained by the biomass inventory already present in the reactor prior to the change in pH setpoint. This mismatch between the observed reactor behavior and the model’s prediction is nonetheless noteworthy, as it might be an indication that the set of parameter values used here do not reflect the characteristics of the nitrifying sludge investigated here.

Blackwater treatment system

-40 -30 -20 -10 0 10 20 30 12 1.8 1.6 10 1.4 8 1.2 1 6 pH 0.8 4 0.6

pH 0.4 KOH dosing (L/d) rate 2 KOH dosing rate 0.2 mean KOH dosing rate 0 0 -40 -30 -20 -10 0 10 20 30

-40.00 -30.00 -20.00 -10.00 0.00 10.00 20.00 30.00 300 1.4

250 1.2 1 200 0.8

150 N TN : ratio

0.6 - N concentration N concentration (mg/L)

- 100 3 0.4 NO3 NO3-N 50 TN 0.2 NO3-N : TN ratio

TN and and TN NO 0 0 -40 -30 -20 -10 0 10 20 30 time relative to pH setpoint change (d)

Figure 4-26 – MBR effluent pH and KOH dosing rate (top) and effluent N species concentrations (bottom) in the period surrounding the change in setpoint of the reactor’s pH control system.

4.4 Conclusion

The results reported here indicate that one of the most important aspects that need to be taken into account in the design and operation of activated sludge systems for the treatment of blackwater is nitrification. As shown by the characterization of the blackwater investigated in this study, the alkalinity in blackwater can be grossly insufficient to buffer the pH changes that would be caused by the nitrification of its full nitrogen load properly. With only a small fraction of the buffering capacity available in the pH interval between the average pH of blackwater (8.5- 9.0) and the optimal pH for the growth of nitrifying microorganisms (around 7.5-8.0), the dosage of large amounts of an external alkalinity source is required. In the case considered here, where KOH was used for pH control, the compensation of that buffering capacity deficit resulted in the addition of around 500 mg/L of K to the mixed liquor. This aspect is extremely consequential in the context of usage of the effluent as nutrient solution in a hydroponic system, due to impact of the dosage of the alkali has in the salinity of the effluent.

5 USE OF BLACKWATER TREATMENT EFFLUENT IN VEGETABLE PRODUCTION SYSTEM

5.1 Introduction

The reuse of treated wastewater in agriculture is a practice that is arguably one of the most employed forms of wastewater reclamation. It is widespread in arid and semi-arid regions facing conditions of water stress, such as California, Spain and Israel, where a large share of treated effluents is already used for irrigation of croplands (Winpenny, 2010, NRC, 2012, Jaramillo and Restrepo, 2017). Although usually driven by the primary goal of recovering water, this wastewater reclamation approach has also been demonstrated to be an effective way to recycle nutrients (Mortensen et al., 2016).

Urban farming opens up the possibility of applying this form of direct nutrient recovery to small-scale decentralized wastewater treatment systems. The on-site integration of food production and wastewater treatment shows particular promise in the context of the reclamation of source-separated domestic wastewater streams such as blackwater or urine, which are rich in nutrients (Wielemaker et al., 2016) and account for the majority of the nitrogen, phosphorus and potassium load in domestic wastewater (DWA, 2008, Larsen et al., 2013).

Loosely defined as the practice of agriculture in urbanized spaces (van Veenhuizen, 2006), urban farming is a long-standing practice that still plays an important role in the food security of urban populations in some developing countries (Zezza and Tasciotti, 2010, Eigenbrod and Gruda, 2015). It has recently gained renewed attention in developed countries due to its potential to bring about several social and environmental benefits to the urban environment where it takes place (Mok et al., 2014, Clinton et al., 2018). Recent developments in urban farming have focused primarily on highly engineered and intensively managed approaches based on soilless cultivation systems such as hydroponics (Buehler and Junge, 2016), which can be deployed at a wide variety of surfaces, regardless of the availability of farmable soil (including rooftops) and that are capable of supporting higher yields than soil based systems (Raviv and Lieth, 2008). Moreover, hydroponic systems are particularly adequate for the cultivation of crops such as leafy and fruit vegetables, which are frequently favored in urban agriculture (Buehler and Junge, 2016).

Therefore, it can be argued that an integrated urban farming/decentralized wastewater treatment concept that can be successfully implemented in highly urbanized areas will likely employ soilless cultivation on the farming end of the system. To date the application of treated domestic effluents in such cultivation systems has been investigated to a very limited extent. Previous studies that addressed the use of processed urine (Yang et al., 2015), anaerobic digestate (Krishnasamy et al., 2012) and treated domestic wastewater (Maloupa et al., 1999, Oyama et al., 2005, Adrover et al., 2013) indicate that the absence of soil as a chemical buffer makes hydroponic systems particularly unforgiving towards the physicochemical characteristics of effluents, especially the pH and concentration of nutrients.

The purpose of this chapter is to present and discuss the results of the experimental trials where the effluent of the blackwater treatment plant (BTP) described in the previous chapter was used

Use of blackwater treatment effluent in vegetable production system as the nutrient solution of a hydroponic farming system. To the best of the author’s knowledge, the reuse of such an effluent has not yet been investigated in this context. The main objectives of the trials were to determine the agronomic effectiveness of the effluent as a liquid fertilizer, as compared to a nutrient solution prepared with conventional synthetic fertilizers.

5.2 Material and Methods

As already mentioned in the previous chapter, the blackwater treatment plant (BTP) consisted of a settling tank followed by a 1 mm sieve and a 1.2 m³ aerobic membrane bioreactor (MBR) with an ultrafiltration membrane (siClaro FM 611, MARTIN Membrane System AG). The reactor was operated with sludge wastage rates equivalent to a nominal solids retention time (SRT) of 55 days, and treated between 1000 and 480 L of blackwater per day.

The food production part of the system consisted of a non-climatized greenhouse with two 5.6 m² ebb-and-flow growing beds, shown in Figure 5-1. The system was operated from April to August 2017 in two phases. In the first phase (April-May), lettuce (Lactuca sativa var. crispa) was grown. As mentioned in the previous chapter this phase coincided with the MBR’s start-up, when pH of the reactor was not being controlled and full nitrification had not yet been reached. In the second phase (May-August), cucumber (Cucumis sativus) was grown; during this period, the pH of the MBR was controlled by automatic dosing of 50% KOH, with a pH setpoint of 7.5. All crops were grown in rockwool substrate.

Figure 5-1 – Ebb-and-flow growing beds used for the hydroponic trials.

In order to assess the effectiveness of the MBR’s effluent as a nutrient solution, during each crop cycle one bed was supplied with the effluent (referred to as the experimental bed) and the other was operated as a control, being fed with conventional synthetic fertilizer (CANNA Aqua Vega, CANNA Deutschland GmbH) diluted to concentrations levels recommended by van der Lugt (2016) for the respective crops. The effluent flow of the BTP was routed by a three-way actuated valve that directed it either to a 1 m³ storage tank (from where it was pumped to the greenhouse when required) or to the sewer system. In order to collect representative samples, the three-way valve on the effluent line of the MBR was operated in a flow-proportional way, with the collection-to-wastage ratio adjusted so that 1 m³ effluent batches were produced in the two- week intervals between nutrient solution exchange events at the greenhouse.

Chapter 5

On each bed, 100 lettuce and 33 cucumber plants were cultivated. The contents of the nutrient solution tanks of both control and experiment beds (400 L each) were exchanged every two weeks with fresh solutions. The filtrate batches were sampled for characterization immediately before being pumped to the greenhouse. COD, TN, TP, NH4-N, NO3-N, NO2-N, PO4-P were measured with Hach-Lange cuvettes. K, Ca, Mg were measured with ICP-AES, and SO4 with ion chromatography. The overall quality of the BTP effluent as a nutrient solution was measured in terms of the yields of produce of the experimental bed as compared to the control bed.

5.3 Results and Discussion

The median composition of the BTP effluent batches used as nutrient solution in each crop cycle, along with the concentrations recommended in hydroponic nutrient solutions by van der Lugt (2016), are shown in Table 5-1. It can be seen that during the cultivation of lettuce the concentrations of almost all macro- and micronutrients in the BTP effluent were substantially below the recommended levels. NPK ratios were also unbalanced and indicated a relative excess of nitrogen and a marked deficiency of potassium.

Table 5-1 – Composition of BTP effluent batches used for the cultivation of lettuce and cucumber, and recommended concentrations for nutrient solutions for the respective crops.

Crop Cucumber Lettuce Parameter std. recom. std. recom. median mean median mean dev. * dev. * Nutrients (mg/L) Ca 80 83 15 160 82 79 6.5 180 K 522 536 147 313 68 66 2.6 371 Mg 14 14 1 33 13 13 0.4 24 SO4 22 22 1 44 26 26 3.1 64 NH4-N 0.2 1 3 18 83 78 39.2 14 NO2-N 24 54 66 -- 106 104 10.3 -- NO3-N 200 155 88 224 44 41 6.2 224 TN 217 212 20 242 200 200 0.6 238 PO4-P 17 17 2 39 22 21 3.5 47 TP 19 18 2 39 26 25 4.3 47 Nutrient ratios N:P 12.3 12.4 2.2 6 7.6 8.0 1.4 5 K:P 29.1 31.9 12.4 8 2.6 2.7 0.5 8 pH 7.51 7.51 0.05 5.3 7.18 7.32 0.28 5.3 Conductivity 3.18 3.18 0.24 2.2 2.43 2.22 0.40 2.2 (mS/cm) * Reference of recommended values for hydroponic culture: van der Lugt (2016)

Use of blackwater treatment effluent in vegetable production system

5.3.1 Lettuce

Despite the suboptimal composition of the BTP effluent used for the cultivation of lettuce, the mass of produce produced by the experimental bed (23.5 kg) was slightly above that of the control bed (22.5 kg), as shown in Figure 5-2, a difference that is likely to be statistically insignificant. Considering the area of the ebb-and-flow beds (5.6 m²) and that professional climatized greenhouses usually allow 10 harvest of lettuce per year (Barbosa et al., 2015), that value can be extrapolated to a yield of around 420 dt/(ha.year), in line with general estimates for the productivity of hydroponic systems growing that crop (Barbosa et al., 2015) and slightly above the German average of 373.5 dt/(ha.year) for lettuce grown in greenhouses (Destatis, 2018).

25 300 30 23.5 22.5 250 25 20

200 20 15 150 15 10

100 10 average head mass (g) mass head average

total mass harvested (kg) harvested mass total 5

50 5 average head diameter/height (cm) diameter/height head average 0 0 0 mass harvested average lettuce head head head height mass diameter

experiment control

Figure 5-2 – Comparison between yield and morphology of lettuce grown in the experimental (BTP effluent as nutrient solution) and control ebb-and-flow beds.

The discrepancy between the suboptimal nutrient content in the BTP effluent and the high yields obtained from the experimental bed can be explained by the high nutrient application rates that resulted from the frequency of exchange of the nutrient solution. Considering the average TN, TP and K concentrations reported in Table 5-1, and also the total of 1200 L of BTP effluent (3 x 400 L) used over the course of the cultivation period, it was calculated that a total of 43 g/m² of N, 5 g/m² of P and 14 g/m² of K were applied to the experimental ebb-and-flow bed over the course of cultivation period. With the exception of K, those values were considerably above reference values for nutrient uptake rates associated with yield obtained (Table 5-2), meaning that, in term of the gross nutrient requirements for plant growth, the experimental bed was actually over-fertilized. It should be noted that it is possible that the difference between the measured and reference K application rates is not significant, given the variability in reference values reported in the literature (e.g. Feller C. et al. (2011), IPNI (2013), Haifa Chemicals Ltd. (2014b)). Therefore, although initial nutrient concentrations in the BTP effluent were lower than the recommended, the frequent refilling of the nutrient solution tank kept them stable at levels that apparently did not limit their uptake by the plants.

It is particularly surprising that the high nitrite concentrations did not seem to have any apparent negative impact on the growth of the lettuce. Toxic effects of nitrite on the growth of

Chapter 5 a variety of crops are widely reported in the literature (Phipps and Cornforth, 1970, Bancroft et al., 1979, Lee, 1979, Samater et al., 1998, Hoque et al., 2007, Barbouch et al., 2012). For lettuce in particular, Hoque et al. (2007) reported that increasing NO2-N concentrations adversely affected dry matter yield, root growth and number of leaves per plant of Iceberg and Romaine lettuce cultivars, leading to a reduction in biomass production of up to 50% at a NO2-N concentration of 40 mg/L when compared to controls grown with NO3 as nitrogen source.

Table 5-2 – Comparison of reference nutrient uptake rates for lettuce and nutrient application rates used on ebb-and-flow bed operated with BTP effluent.

Application rates (g/m²) Average-to-reference Nutrient median average reference* ratio N 43 43 10 4.3 P 6 5 1.5 3.7 K 14 14 17 0.8 * Reference: IPNI (2013)

Although the exact mechanisms responsible are not yet fully known, nitrite toxicity is commonly attributed not to the nitrite ion itself but to its conjugated acid, HNO2 (Lee, 1979); more importantly, it has been observed that those toxic effects are more pronounced when the pH (in accordance with the assumption that HNO2 is the toxic species) and oxygen concentration in the root medium are low (Bingham et al., 1954, Phipps and Cornforth, 1970, Lee, 1979). Therefore, it is possible that the conditions the plants were exposed to on the ebb- and-flow bed might have contributed to the suppression of the toxic effects of nitrite, considering that the average pH of the effluent batches used during the lettuce crop cycle (7.3) O were substantially higher than the pKa of HNO2 (3.25 at 25 C), and that the relatively high frequency of bed drainage (3 times a day) is likely to have allowed oxygen concentrations in the rockwool substrate to be high.

A second, more remote possibility is that biological nitratation took place in the substrate. Although the oxygen concentrations in the substrate would certainly have allowed that, it is unlikely that nitratation would have taken place at a significant enough rate in the substrate without any inoculation with nitrite-oxidizing bacteria.

Measurements of TN, NH4-N, NO3-N and NO2-N made in the first BTP effluent batch at the beginning (day 0 of the crop cycle) and end (day 14) of its use (Figure 5-3) suggest that the lettuce was able to compensate for the low concentrations of nitrate by using NH4 as its primary nitrogen source, as indicated by the relative high removal of that nitrogen species from the nutrient solution in the period. Although ammonia-based nitrogen nutrition is frequently associated with toxic effects for a wide variety of crops (Mengel et al., 2001, S. 421–423, Marschner and Marschner, 2012, S. 148–149), including lettuce (Hoque et al., 2007), it has been shown that under appropriate hydroponic conditions lettuce can tolerate rather high N NH4-N to TN ratios (Savvas et al., 2006).

Use of blackwater treatment effluent in vegetable production system

220 200 200 180 161 160 140 115 120 100 93 75 80 54 60 Concentration (mg/L) Concentration 34 40 23 20 0 NH4-N NO2-N NO3-N TN

Day 0 Day 14

Figure 5-3 – Concentration of TN, NH4-N, NO3-N and NO2-N in the beginning (day 0) and end (day 14) of use of the first batch of BTP effluent in the hydroponic cultivation of lettuce.

Based on the yield obtained in the pilot-scale trial, estimates of the total farming area that could be supported by the full usage of the BTP’s effluent output were calculated based on the growth cycle length of lettuce (30 days (Feller C. et al., 2011)), the reference nutrient application rates given in Table 5-2 and the average NPK concentration in the BTP’s effluent reported in Table 5-1, as indicated by Equation 5-1:

푄 ∙ 퐶푖 ∙ ∆푡 푆퐶퐴푖 = [5-1] 푁푎푝푖 Where SCA is the supportable crop cultivation area, which corresponds to the maximum cultivation area whose requirements in terms of nutrient i can be met only with the nutrient load in effluent; Q is the volumetric effluent flow rate; Ci is the concentration of nutrient i in the effluent; ∆t is the crop’s growth cycle length; and Napi is the reference application rate of nutrient i.

The values obtained are presented in Table 5-3. At this point, it becomes very clear how the NPK ratio of the effluent is a determinant factor in the overall effectiveness of the nutrient recovery approach discussed here. Due to the previously observed divergence between the NPK ratio of the effluent and the recommended ratio for the cultivation of lettuce, the SCA estimates calculated for each nutrient also diverged, with K being responsible for the limiting value. As consequence of this imbalance, the cultivation of lettuce only with the nutrients contained in the effluent would require that around 81% of the nitrogen and 78% of the phosphorus be wasted in order to guarantee the minimum supply of potassium for the crop throughout it growth cycle, as indicated in Table 5-3.

Also provided in Table 5-3 are the SCA values on a per capta basis, which were calculated by the division of the absolute SCA values by the population equivalent to the baseline BTP effluent flowrate (480 L/d -> 10 persons). This PE value was obtained by considering the total population connected to the BTP (50 persons), adjusted by the share of the average total blackwater flow that was actually treated by the BTP (480 [퐿⁄푑]⁄2.402 [퐿⁄푑] ≅ 0.2).

Chapter 5

Table 5-3 – Supportable crop cultivation areas (SCAs) for lettuce grown with BTP effluent as nutrient source.

Average Average crop SCA (m²) SCA per capta Nutrient availability demand 10 50 (m²/person) (g/d) (g/(m².d)) persons persons N 96.2 0.336 28.6 286 1432 P 12.2 0.049 25.1 251 1255 K 31.9 0.581 5.5 55 275

The per capta SCA provides a more general measure of the area requirements involved in the implementation of an urban farming-based approach to nutrient recovery, and puts on stark display how those requirements represent one of its major limitations. For instance, let us say that, in order to maximize the recovery of N and P, the NPK ratio of the BTP effluent was brought in line with the recommended ratio for lettuce by augmentation with an external K source, so that phosphorus would be the limiting nutrient. In that case, if all the 50 persons of blackwater were to be processed by the BTP and used as hydroponic nutrient solution, an SCA of 1,255 m² would be needed to allow for the complete uptake of the nutrient load by the crop. That, in turn, would require the conversion of a substantial share of the inner courtyard of the apartment complex of which the building that generates the blackwater is part, into greenhouses, as shown in Figure 5-4.

Use of blackwater treatment effluent in vegetable production system

5.3.2 Cucumber

The productivity of cucumber plants on the experimental ebb-and-flow bed (total production of 52 kg of produce) was markedly inferior to the control (125 kg of produce), as shown in Figure 5-5. Comparison between the number of fruit harvested and their average weight over the fruiting period, also presented in Figure 5-5, indicates most of the fruit yield difference between experimental and control bed was due to reduced number of fruit produced by the experimental bed, rather than differences in weight.

Figure 5-5 – Total mass, number and average weight of fruit harvested from cucumber plants on experimental and control ebb-and-flow beds over the course of the fruiting period.

As can be seen in Figure 5-5, after a brief period of stability in the first weeks of July, the output of the experimental bed declined steadily until the cessation of production of new fruit towards the end August. On the other hand, the control bed continued to produce fruit at a more or less constant rate until the end of the experiments. This decline coincided with the marked deterioration in the appearance of the plants on the experimental bed, whose leafs began showing signs of interveinal chlorosis – characterized by the gradual yellowing of tissue between the leaf’s veins caused by the insufficient production of chlorophyll – early in the fruiting period (Figure 5-6). Until then, no apparent abnormality in terms of plant height and number of leaves was observed in plants on the experimental bed when compared to the control. By the time the production of fruit had collapsed, most of the leaves on the plants of the experimental bed were either severely chlorotic or had withered altogether, as can be seen in Figure 5-6.

Chapter 5

The occurrence of interveinal chlorosis provides a strong indication as to the likely cause of the reduced yields, since it is a main symptom of some nutrient deficiency disorders. Although strictly speaking not nutrient-specific, in cucumbers interveinal chlorosis is most prominently associated with the deficiency of Fe, Mg, Mn and Zn (van Roorda Eysinga and Smilde, 1981, Zorn et al., 2016, Haifa Chemicals Ltd., 2014a). Indeed, the visual characteristics of the chlorosis observed on the leaves of cucumber plants on the experimental bed displayed a very close degree of similarity to visual references provided by diagnosis guides available in the literature for the single-nutrient deficiencies of those four nutrients, as shown in Figure 5-7.

A second criterion that allows for the differentiation between Fe deficiency and that of Mg, Mn and Zn is the pattern of onset of interveinal chlorosis – that is, where in the plant the interveinal chlorosis first manifests itself. The deficiency of Fe – whose retranslocation within the plant is more difficult – leads to the development of chlorosis in younger leafs first, whereas the deficiency of Mg, Mn and Zn, who are more readily remobilized, manifests itself first in older leafs (van Roorda Eysinga and Smilde, 1981, Zorn et al., 2016). In this regard, the incidence pattern observed on plants on the experimental bed, where both young and older leaves were simultaneously affected even at an early stage (Figure 5-7), suggests deficiencies of Fe plus any combination of Mg, Mn and Zn was at play. A more precise assessment of the overall nutritional status of the plants was not possible with the available data; the definite identification of the deficient nutrients would have required the direct measurement of nutrient concentrations in plant tissue, which was not carried out as part of this study.

The assessment of the possibility that the presumed nutrient deficiencies were caused by too low concentrations in the BTP effluent is hindered by the fact that, of the four nutrients potentially implicated, only Mg was part of roster of parameters analyzed in BTP effluent samples. However, given that, as micronutrients, only minute amounts of Fe, Mn and Zn are required for adequate plant growth – with recommended concentrations in hydroponic nutrient solutions of 840, 550 and 327 µg/L for the cultivation of cucumber, respectively (van der Lugt, 2016) – it is very unlikely that that was the case.

Use of blackwater treatment effluent in vegetable production system

Chapter 5

In the case of Mg, for which the average concentration in BTP effluent batches used in the experimental bed is known, insufficient nutrient content in the BTP effluent is also unlikely to have been the problem. Comparison with the recommended Mg application rate for cucumber (Haifa Chemicals Ltd., 2014a) indicate that the 59 kg/ha applied to the plants on the experimental bed throughout the crop cycle would be sufficient for a fruit yield of ca. 226 kg/ha, roughly the same yield reached by the control bed. The application rate via BTP effluent was calculated by considering the average Mg concentration reported in Table 5-1; the number of batches used during the cucumber crop cycle (6); a batch volume of 400 L; and a cultivation area of 5.6 m².

It is therefore more probable that the deficiencies were caused by low nutrient availability. One factor that certainly played a role in limiting the availability of Fe, Mn and Zn to the plants is the fact that nitrate was the main nitrogen source available in the BTP effluent during the cultivation of the cucumbers. Due to the way nitrate is transported across the cellular membrane of root cells in higher plants, the uptake of nitrate results in a net uptake of protons from the root medium, and thus in an increase of the rhizosphere pH (Marschner et al., 1986, Haynes, 1990, Hinsinger et al., 2003, Raviv and Lieth, 2008, S. 303–306). When nitrogen nutrition is based solely on nitrate, this pH increase can be substantial, leading to a rhizosphere pH value one to two units higher than that of the bulk nutrient solution (Marschner and Marschner, 2012, S. 353).

Considering that the pH of the fresh BTP batches fed to the experimental bed were already relatively high (average of 7.5, as compared to the recommended value of 5.3 – see Table 5-1), it is likely that the uptake of nitrate by plants drove rhizosphere to pH levels where the uptake of Fe, Mn and Zn could have been inhibited (Welch and Shuman, 1995, Raviv and Lieth, 2008, S. 326, Rengel, 2015). That effect would have been particularly pronounced in the fruiting period, during which the rate of nitrogen uptake in general increases (Raviv and Lieth, 2008, S. 295– 300), which would explain why in the experiments interveinal chlorosis was observed only at that stage of the crop cycle.

While the hypotheses outlined above are more speculative in the case of Mn and Zn, the occurrence of Fe deficiency indirectly caused by nitrate-based nutrition of plants have been extensively studied in the literature (Kosegarten et al., 2004, Jiménez et al., 2007, Nikolic et al., 2007, Alhendawi et al., 2008, Lemanceau et al., 2009, Bloom et al., 2011, Na et al., 2014, Roosta, 2014), and attributed to the inhibition of the activity of enzymes responsible for the reduction of Fe3+ to Fe2+, the species that is believed to be actively transported across the membrane of root cells (Mengel et al., 1994, Kosegarten et al., 2004). Here, it should also be noted that the high concentration of K in the BTP effluent (ca. 70% higher than the recommended values for hydroponic nutrient solutions) could have aggravated any eventual Mg deficiency, since K is widely reported to exert a negative effect on the uptake of Mg by several crops (Kabu and Toop, 1970, Spear et al., 1978, Schwartz and Bar-Yosef, 1983, Brauer, 1994).

Combined, the two hypotheses outlined above would also explain why no nutrient deficiencies were observed in the cultivation of lettuce, during which the composition of the BTP effluent was essentially the same, with the exception of the (i) concentration of K, and (ii) the nitrogen species that were present.

A second factor that might have contributed to the suboptimal performance of cucumber cultivation on the experimental bed is the high salinity of the BTP effluent, as indicated by the high average electric conductivity of the BTP effluent batches used as nutrient solution for that bed presented in Table 5-1. Under conditions of nutrient sufficiency, it would have been

Use of blackwater treatment effluent in vegetable production system expected that osmotic effects would have led to a decrease in yield of around 9%, considering the salinity threshold and salinity yield decrease (SYD) values of 2.5 mS/cm and 13% per mS/cm frequently reported for cucumber (Tanji and Kielen, 2002). It is not clear, however, to what extent the salinity might have interacted with the factors mentioned above to the effect of aggravating nutrient stress.

5.4 Conclusion

The results presented in this chapter indicate that the biological treatment of blackwater in an activated sludge process, when operated under conditions typically recommended for the achievement of full nitrification, produces an effluent with physicochemical characteristics that are not optimal for direct usage as nutrient solution in hydroponics.

The operation of the bioreactor at pH levels that are optimal for the growth of nitrifying microorganisms (7.5 to 8) results in an effluent that, in terms of its application as a nutrient solution in hydroponics, is too alkaline to be compatible with the presence of nitrate as sole nitrogen source for plants. As observed during the cultivation of cucumber, in a nutrient solution with such high initial pH, the excretion of protons by roots during the uptake of nitrate could drive the pH of the solution immediately adjacent to the plant’s roots to levels at which the availability of micronutrients – particularly iron – is reduced, leading to the development of nutrient deficiency syndromes and ultimately the failure of the crop.

On the other hand, the success attained in the lettuce cultivation experiments have demonstrated that the effluent produced by the incomplete nitrification of blackwater during the start-up phase of the treatment plant’s operation – even if undesirable from the conventional wastewater treatment perspective – would be preferable for the purpose of use in hydroponics. Even if the average pH of 7.3 measured in the effluent during that operational phase could also be considered fairly high, the availability of ammonium in its composition as nitrogen source for the plants – whose uptake causes the acidification of the solution, as opposed to nitrate – presumably prevented the establishment of conditions of limited nutrient availability. It should be noted, however, that even though the cultivation of lettuce turned out to be successful, the composition of the effluent used therein deviated considerably from nutrient solution recommendations for that crop.

In summary, the results obtained here provide a proof-of principle that the integration of a hydroponic farming system with an on-site biological blackwater treatment system is technically possible. However, in light of the issues highlighted above, more investigations are required in order to determine alternative process configurations or process operating conditions that would allow the production of an effluent with physicochemical characteristics that more closely match the specifications of conventional nutrient solutions, especially in terms of pH.

6 MICROPOLLUTANTS IN BTP EFFLUENT AND THEIR UPTAKE BY CROPS

6.1 Introduction

As seen in the previous chapter, the effluent of the blackwater treatment plant (BTP) was to some extent successfully applied as nutrient solution in a hydroponic system for the cultivation of lettuce and cucumber. However, in that chapter the discussion focused only on the aspect of crop yields, and not on qualitative aspects regarding the chemical quality of the produce that might come to bear on their fitness for human consumption.

Of particular concern in this regard are the so-called micropollutants. Alternatively referred to as contaminants of emergent concern (CECs) or simply emergent contaminants, the term lacks a formal definition and generally refers to chemical compounds of anthropogenic origin found in the environment in trace amounts, and whose concentrations are not yet subject to full regulatory oversight (National Research Council (U.S.), 2012).

In the context of blackwater treatment, a group of micropollutants of particular relevance is pharmaceuticals such as antibiotics, radiocontrast agents and antidepressants, which, together with its metabolites, are excreted via urine and feces (Lienert, Bürki und Escher, 2007, Winker et al., 2008), and therefore end up in blackwater. Of particular concern in the case under consideration here is that numerous studies have shown that aerobic biological treatment processes are not capable of full elimination of pharmaceutical compounds and other organic micropollutants (Grandclément et al., 2017, Falås et al., 2016) and that several of those compounds are taken up by plants – including lettuce and cucumber plants –, accumulating in their edible tissues (Wu et al., 2015, Miller et al., 2016).

Therefore, the purpose of the work reported in this chapter was to (a) assess the extent to which organic micropollutants were present in the BTP effluent, and (b) evaluate their uptake by the lettuce and cucumber grown in the hydroponic system, in order to determine if that uptake might have had restricted the usage of the effluent as a liquid fertilizer.

6.2 Material and methods

Sampling and analysis of BTP effluent

In order to characterize the micropollutant content of the blackwater treatment plant’s effluent, samples of each effluent batch used in the hydroponic experiments described in the previous chapter were collected and analyzed for organic micropollutants. As described in the Materials and Methods section in the previous chapter, effluent batches were produced by a three-way valve installed on the filtrate line of the plant’s MBR, which routed effluent to a storage tank in a flow-proportional way, with the collection-to-wastage ratio set so that 1 m³ effluent batches were produced every two weeks. The samples were collected from “fresh” batches, i.e. directly from the storage tank, immediately before each batch was transferred to the hydroponic system’s nutrient solution tank for use.

Additionally, in order to characterize the overall exposure to micropollutants that the lettuce and cucumber plants grown with BTP effluent were subjected to throughout their respective

Micropollutants in BTP effluent and their uptake by crops cultivation cycles, samples were periodically collected from the hydroponic system’s nutrient solution tank as the effluent batches were used. In general, samples were collected in the middle (between days 5 and 7) and end (day 14) of the batches’ two-week cycle.

Concentrations of organic micropollutants in liquid samples were measured by liquid chromatography-tandem mass spectrometry (LC-MS/MS), using the method described by Riemenschneider et al (2016). Measurements were performed at the Helmholtz Centre for Environmental Research (UFZ).

Sampling and analysis of plant material

The concentration of organic micropollutants were analyzed only in the edible parts of both lettuce and cucumber. In the case of cucumber, fruit samples were collected at the middle and end of the crop cycle; lettuce samples were collected only at the end of the cultivation cycle, when plants were mature. All samples were rinsed with tap water and ground to fine powder after being freeze-dried. The sample extraction and measurement method are described in detail by Riemenscheneider et al (2017).

Health risk assessment

In order to assess the potential health risk posed by the presence of micropollutants in the vegetables exposed to the BTP effluent, the Threshold of Toxicological Concern (TTC) methodology was employed by use of software Toxtree, v2.6.13 (Ideaconsult Ltd), using the Kroes et al. (2004) decision tree.

The methodology, developed by the European Food Safety Agency as a de minimis risk screening tool for chemicals present in food, provides a conservative estimate as to safe exposure levels via ingestion for a large variety of organic compounds for which specific toxicological data is absent (EFSA, 2016). That estimation is performed based on the structural similarity of the chemical of interest with sets of reference compounds for which toxicological data is available. In the methodology’s current iteration, compounds are separated into 5 classes of varying toxicity (potentially genotoxic compounds; organophosphates; and Cramer Classes I, II and III) via a classification system based on a series of yes-and-no questions that concern the presence of certain structural markers in the molecular makeup of the compound of interest (Kroes et al., 2004). Each class is assigned an exposure threshold (the TTC value) derived from toxicological data available for the reference chemicals of that class; those values are such that lifetime exposure to a chemical at levels below the respective TTC would be extremely unlikely to cause adverse health effects, whereas levels above the TTC warrant an in-depth risk assessment. The TTCs for compounds classified as Cramer Classes I, II and III, are 1.5, 9 and 30 µg/(kg-bw.day) respectively. Organophosphates and some potentially genotoxic compounds have more stringent TTC values: 0.3 and 0.0025 µg/(kg-bw.day), respectively.

6.3 Results

6.3.1 Concentrations in the BTP effluent

Statistics on the micropollutant concentrations measured in the BTP effluent batches used as nutrient fertilizer in the hydroponic experiments are presented in Table 6-1. Most of the compounds found in quantifiable amounts were either pharmaceuticals, metabolites or transformation products thereof, or food additives, comprising 23 of the 37 substances quantified in the BTP effluent batches.

Chapter 6

As the very high standard deviations shown in Table 5-1 indicate, the concentrations of most compounds fluctuated substantially in the period during which samples were collected, with the concentration-time profile of most of the micropollutants found in the BTP effluent characterized by sharp spikes. As will be discussed in more detail in the following paragraphs, in the cases of some pharmaceutical substances, explicit assumptions regarding the health status of the population served by the BTP were able to qualitatively account for those fluctuations; for most of the micropollutants found in the BTP effluent, however, the data available proved not to be sufficient to elucidate the driving processes behind the observed behavior of concentration over time.

Table 6-1 – Statistics of micropollutants concentration measured in the 9 BTP effluent batches used as nutrient solution in the hydroponic experiments. Concentration values in units of µg/L.

function / Compound CAS n** Mean Median Min. Max. description 164265-78- metabolite, Valsartan acid 9 78.3 49.1 8.9 253 5 valsartan artificial Sucralose 56038-13-2 9 43.3 47.2 22.5 60.2 sweetener Acyclovir 59277-89-3 antiviral 9 24.0 17.36 0.76 107 metabolite, DiOH-CBZ 58955-93-4 9 17.0 16.64 8.48 21.7 carbamazepine Metoprolol 51384-51-1 beta-blocker 9 13.6 14.8 2.05 24.6 antidepressant Carbamazepine 298-46-4 9 39.8 12.4 6.28 133 anticonvulsant hypoglycemic Metformin 657-24-9 9 98.6 10.8 0.60 314 agent 4- metabolite, 83-15-8 9 7.64 6.98 0.20 28 Acetoaminoantipyrine antipyrine alpha- metabolite, 56392-16-6 9 5.37 5.66 0.15 13.1 Hydroxymetoprolol metoprolol Gabapentin 60142-96-3 anticonvulsant 9 53.4 4.86 1.91 392 corrosion 4-Methylbenzotriazol 29878-31-7 9 4.80 4.05 2.77 8.12 inhibitor artificial Acesulfam 33665-90-6 9 17.1 3.43 1.48 51.2 sweetener Hydrochlorothiazide 58-93-5 diuretic 9 3.77 3.09 1.74 7.64

Bisoprolol 66722-44-9 beta-blocker 9 2.51 2.94 0.39 4.31 137862-53- Valsartan ARB* 9 5.04 2.82 0.28 15.9 4 Venlafaxine 93413-69-5 antidepressant 9 2.57 2.45 1.17 3.58

Quadrol 102-60-3 adhesive 9 2.23 2.02 0.18 4.53 TP* of BTSA 941-57-1 8 1.67 1.67 0.05 3.84 benzothiazoles metabolite, Oxipurinol 2465-59-0 9 1.82 1.59 0.81 3.47 allopurinol Lidocaine 137-58-6 local anesthetic 9 1.29 1.22 0.24 2.88 486460-32- hypoglycemic Sitagliptin 9 2.59 0.975 0.04 8.55 6 agent

Micropollutants in BTP effluent and their uptake by crops

function / Compound CAS n** Mean Median Min. Max. description antidepressant Lamotrigin 84057-84-1 8 0.92 0.78 0 2.54 anticonvulsant 2- metabolite, 934-34-9 3 0.54 0.72 0.15 0.76 Hydroxybenzothiazole benzothiazoles metabolite, Ep-CBZ 36507-30-9 9 0.69 0.71 0.25 0.94 carbamazepine corrosion Benzotriazol 95-14-7 9 0.73 0.64 0.23 1.37 inhibitor metabolite, Metoprolol acid 56392-14-4 9 0.67 0.58 0.17 1.17 metoprolol Caffeine 58-08-2 stimulant 9 20.3 0.566 0.05 102

Gabapentin-Lactam 64744-50-9 anticonvulsant 6 0.40 0.33 0.18 0.96

DEET 134-62-3 insect repellent 9 0.35 0.31 0.11 0.87 antipyretic, Diclofenac 15307-86-5 9 0.14 0.14 0.04 0.27 analgesic Gabapentin-Lactam 64744-50-9 anticonvulsant 6 0.21 0.13 0.12 0.37

Carbendazim 10605-21-7 biocide 9 0.15 0.08 0.03 0.49

Primidon 125-33-7 anticonvulsant 3 0.06 0.06 0.06 0.07 188425-85- Boscalid fungicide 7 0.05 0.06 0.03 0.09 6 Propranolol 525-66-6 beta-blocker 4 0.07 0.04 0.04 0.14 172960-62- TP* of Metazachlor-ESA 3 0.03 0.03 0.02 0.03 2 metazachlor TP* of Desphenylchloridazon 6339-19-1 3 0.02 0.01 0.01 0.03 chloridazon * TP = transformation product; ARB = Angiotensin II receptor blocker ** n = number of quantifiable detections

Due to the lack of studies available in the literature addressing the presence of micropollutants in effluents of aerobic activated sludge systems treating water-flushed blackwater, it is difficult to assess on a compound-by-compound basis if concentrations measured here are unusual or not for such an effluent. A direct comparison to concentrations reported in micropollutant surveys performed on effluents of centralized wastewater treatment plants (WWTPs) – which are abundant in the literature – is complicated by the dilution that blackwater (which hold most of load of certain micropollutants, such as pharmaceuticals) undergoes in sewage collection systems when mixed with other wastewaters, a dilution that certainly also manifests itself in the micropollutant concentrations measured in the effluent after treatment.

A better insight into the peculiarities of the effluent produced by the BTP in terms of its micropollutant content can be gained by focusing instead on the relative abundance of compounds, as given by the ranking of their concentrations. In those terms, the most prominent feature of the BTP effluent’s micropollutant profile is the very high median concentrations of sucralose, valsartan acid and acyclovir as compared to those of the rest of the quantified micropollutants, which, combined, made up for ca. 50% of the median micropollutant load in the BTP effluent.

Chapter 6

Acesulfame and sucralose are nonnutritive artificial sweeteners widely used in sugar-free tabletop sweeteners and as sugar substitute in a variety of food and beverages, applications that rely on the fact the those compounds provide the organoleptic effect of sugar but are not metabolized, being excreted from the human organism unchanged. Due to their high consumption, high solubility and low biodegradability, those two compounds are regularly found in WWTP effluents in concentrations that are orders of magnitude superior to that of the large majority of pharmaceuticals and other micropollutants – as high as tens of micrograms per liter in the case of acesulfame, and single-digit micrograms per liter in the case of sucralose (Lange et al., 2012, Loos et al., 2012).

For those reasons, the levels at which those two compounds are present in samples could be taken as a benchmark for micropollutant concentrations that are likely to be high. The higher median concentration of sucralose as compared to acesulfame in the BTP effluent might indicate a possible preference on the part of the residents of the apartments served by the BTP for sucralose, which, unlike acesulfame, doesn’t leave a bitter aftertaste (Kamerud and Delwiche, 2007). Another factor that could have played a role in the inversion of the expected concentration ratio for those two compounds is an eventual higher rate of biodegradation of acesulfame in the MBR. Although, as stated previously, acesulfame is regarded as being a recalcitrant substance that resists biodegradation, recent evidence indicates that microbiological communities in activated sludge systems in WWTPs might be gradually developing the ability to catabolize the compound (Kahl et al., 2018). Without measurements in the influent to the MBR, it is not possible to adequately assess if such a removal actually took place in the bioreactor. However, it is interesting to note that the concentration of acesulfame in the BTP effluent exhibited a sharp, almost monotonic decline over the sampling period, from a high of 51 µg/L to as low as 1.5 µg/L, whereas in the case of sucralose concentrations dropped at a much slower rate, as can be seen in Figure 6-1.

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Concentration (µg/L) Concentration 10 10 0 0 0 10 20 30 40 50 60 70 80 90 100 110 120 130 time (days)

Sucralose Acesulfam

Figure 6-1 – Variation in measured concentrations of acesulfame and sucralose in the BTP effluent over time.

Valsartan acid is a transformation product of the biodegradation of several substances in the family of angiotensin receptor II blockers (ARBs) – also referred to as sartans –, a class of pharmaceutical compounds employed for the treatment of hypertension and heart failure

Micropollutants in BTP effluent and their uptake by crops

(Nödler et al., 2013, Schwabe et al., 2017)). The formation of valsartan acid has been observed during activated sludge treatment in WWTPs (Oosterhuis et al., 2013, Bayer et al., 2014, Gurke et al., 2015, Godoy et al., 2015, Estrada-Arriaga et al., 2016), where it appears to be the biodegradation endpoint of at least one ARB, valsartan (Kern et al., 2010, Nödler et al., 2013). Due to its stability, it has been suggested that valsartan acid could serve as a potential estimator of the total influent sartan load in the influent to activated sludge systems (Nödler et al., 2013).

The fact that the median concentration of valsartan acid and sucralose were of the same order of magnitude could be considered an indication that the concentration levels of ARBs in the blackwater were indeed high. That wouldn’t be entirely unsurprising, considering that, in terms of defined daily doses (DDD), sartans were amongst the most prescribed drugs in Germany in 2017 (Schwabe et al., 2017).

Of all the sartans reported as possible parents compounds of valsartan acid – candesartan, valsartan, losartan, irbesartan and olmesartan (Nödler et al., 2013) –, only valsartan was quantifiable in the BTP effluent, with concentrations ranging from 0.28 to 15 µg/L. Curiously, the variation of its concentration over the sampling period displayed a statistically significant positive correlation with that of the antihyperglicemic metformin (Pearson’s correlation coefficient ρ of 0.76, p=0.0184), with the concentration profiles of both compounds characterized by relatively high concentrations in the period between May 16 and June 23, as shown in Figure 6-2. Although this correlation might evidently be a product of pure chance, it could be also associated with presence of individuals afflicted with type 2 diabetes in the population served by the BTP, who at the time were undergoing treatment for diabetic nephropathy, a condition that is frequently managed with ARBs.

The hypothesis that one or more individuals might have been struggling with health complications related to type 2 diabetes is supported by the observation of a spike in the concentration of the anticonvulsant gabapentin in that same time period (Figure 6-2), a medication that is recommend as a first line of treatment for the management of diabetic neuropathic pain (BÄK et al., 2011, Snyder et al., 2016). Two compounds whose concentrations in the BTP effluent also spiked in this timeframe were the local anesthetic lidocaine and 4- acetoaminopyridine, a metabolite of the non-opioid analgesic metamizole. Both lidocaine and metamizole are also frequently employed in the pain management of diabetic neuropathy (BÄK et al., 2011, Snyder et al., 2016), although the claims of effectiveness of lidocaine for that purpose (in the form of patches) are still controversial (BÄK et al., 2011).

Chapter 6

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5 ̅

C 4 C / C 3

0 0 10 20 30 40 50 60 70 80 90 100 110 120 130

Valsartan Metformin Gabapentin

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3.5

2.5 ̅

C 2 C / C 1.5

0.5

0 0 10 20 30 40 50 60 70 80 90 100 110 120 130 time (days)

4-Acetoaminoantipyrin Lidocaine

The presence of the antiviral drug acyclovir in the BTP effluent at the levels observed (median concentration of 17.36 µg/L) was unexpected, since the compound has been shown to be almost completely degraded during biological treatment in activated sludge systems (Prasse et al., 2010, Prasse et al., 2011, Funke et al., 2016). The measured concentration profile of acyclovir was characterized by a sharp concentration peak observed in the BTP effluent batch produced between the 2nd and the 15th of May (Figure 6-4). That would be consistent with the therapeutic regimen in which this compound is employed. As opposed to the other pharmaceuticals found in the BTP effluent, acyclovir is typically used in very short treatment courses (10-14 days) to treat various types of herpes infections (OʼBrien and Campoli-Richards, 1989). In a population as small as the one connected to the BTP, it would be therefore expected that the drug – which is excreted almost entirely unchanged in urine (OʼBrien and Campoli-Richards, 1989) – would be released in blackwater in pulses. However, those same considerations make the long tail

Micropollutants in BTP effluent and their uptake by crops following the peak in the concentration curve harder to explain. No therapeutically relevant usage pattern for acyclovir could be identified that would lead concentrations to remain at the relatively stable level observed in batches 3 to 7, whose production spanned a period of almost 66 days.

The anticonvulsant carbamazepine (CBZ) and its metabolite trans-10,11-dihydro-10,11-dihydroxy carbamazepine (DiOH-CBZ) were also found in relatively high amounts in the BTP effluent: their median concentrations of 12.4 and 16.6 µg/L were the 5th and 3th highest of all compounds. A second CBZ metabolite, carbamazepine-10,11-epoxide (Ep-CBZ), was also consistently in the BTP effluent, although in considerable lower concentrations (median concentration of 0.712 µg/L). Carbamazepine is notorious for its recalcitrance in activated sludge systems, and frequently ranks amongst the pharmaceutical compounds detected with highest concentrations in WWTP effluents (Reemtsma et al., 2006, Radjenović et al., 2009, Loos et al., 2012, Bahlmann et al., 2014, Luo et al., 2014); due to its stability, it has even been suggested as a possible marker for the contribution of treated sewage to surface and groundwater, alongside acesulfame and sucralose (Kahle et al., 2009, Scheurer et al., 2011). DiOH-CBZ and other CBZ metabolites were also shown to be as resistant to biodegradation as their parent compound (Miao et al., 2005, Brezina et al., 2017).

As shown in Figure 6-3, the variation in CBZ concentration across BTP effluent batches was characterized by an initially high concentration level (maximum value of 133 µg/L) followed by a steep downward trend, eventually stabilizing at around 11 µg/L. Concentrations of DiOH-CBZ and Ep-CBZ, on the other hand, remained comparatively stable, with DiOH-CBZ and Ep-CBZ accounting for as little as 13% of the total concentration of measured carbamazepine species in the second BTP effluent batch. This initial parent-metabolite distribution is rather unexpected, since CBZ is reported to be extensively absorbed and metabolized by the human body, with 72% of the oral dose being excreted in urine, primarily in the form of the DiOH-CBZ metabolite – less than 2% of the parent compound is found unchanged (Eichelbaum et al., 1985, Bertilsson and Tomson, 1986). This degree of metabolization is usually reflected in the relative abundance of CBZ and its metabolites in WWTP effluents, where DiOH-CBZ is often the predominant species detected (Miao et al., 2005, Bahlmann et al., 2014, Brezina et al., 2017). It is unclear what might have caused such a skewed carbamazepine parent-metabolite distribution in the BTP effluent. Considering that CBZ concentrations stabilized only 50 days after the start of the sampling campaign, it is unlikely that the high initial concentration levels could have been caused by the disposal of the unconsumed drug in toilets, which would be expected to manifest in the form of a clear concentration pulse.

Besides sucralose and acesulfame, several other non-pharmaceutical compounds were present in the BTP effluent, including benzotriazoles, benzothiazoles and pesticides. Three of those compunds in particular – 4-methylbenzotriazol (4-MBTR), Benzothiazole-2-sulfonic acid (BTSA) and tetrahydroxypropyl ethylenediamine (also referred to be its commercial name, Quadrol) – were consistently found in concentration levels on par with that of acesulfame (Table 6-1). Their presence in the BTP effluent in significant amounts was unexpected, since none of those compounds are reported to be applied as food additives, excipients in pharmaceutical products or in the formulation of toilet cleaning products that might end up being discharged in blackwater.

Chapter 6

date 19.04.2017 09.05.2017 29.05.2017 18.06.2017 08.07.2017 28.07.2017 17.08.2017 160

140

120

100

Concentration (µg/L) Concentration 40

0 0 13 27 50 65 79 93 107 121 time (days)

Carbamazepine DiOH-CBZ Ep-CBZ

Figure 6-3 – Variation in concentrations of carbamazepine and two of its metabolites, trans-10,11- dihydro-10,11-dihydroxy carbamazepine (DiOH-CBZ) and carbamazepine-10,11-epoxide (Ep-CBZ), measured in the BTP effluent over time.

date 19.04.2017 09.05.2017 29.05.2017 18.06.2017 08.07.2017 28.07.2017 17.08.2017 120

100

40 Concentration (µg/L) Concentration 20

0 0 10 20 30 40 50 60 70 80 90 100 110 120 130 time (days)

Figure 6-4 – Variation in concentrations of acyclovir measured in the BTP effluent over time.

Recent studies have shown that human exposure to benzotriazoles and benzothiazoles could lead to their absorption and eventual excretion via urine (Asimakopoulos et al., 2013, Li et al., 2018). However, posterior analysis of the effluent of the graywater treatment plant (GTP) that supplies the water used for flushing toilets in the residences served by the BTP – described in detail by Nolde (2000) – indicates that the loads of those three compounds to the BTP are likely to have their origins in the flushing water itself. Concentrations of 4-MBTR, Quadrol and BTSA measured in a 28-hour composite sample of the GTP effluent collected after the conclusion of the hydroponic experiments, shown in Table 6-2, suggests that indeed very high levels of those

Micropollutants in BTP effluent and their uptake by crops compounds could be present in the flush water. The concentration of 4-MBTR and Quadrol measured in that sample was one order of magnitude higher than median values found in the BTP effluent.

Compound Concentration (µg/L)

4-MBTR 16.75 Quadrol 9.36 Benzotriazole 3.36 BTSA 2.12

The presence of 4-MBTR and Quadrol in the GTP effluent is entirely consistent with the widespread application of 4-MBTR as corrosion inhibitor in the formulation of dishwasher tabs (Vetter and Lorenz, 2013), and of Quadrol as a chelating agent in cosmetics (EWG, 2018). Their presence in those products would be inevitably tied to an eventual release in graywater, as has been shown to be the case for other household chemicals and personal care products (Eriksson et al., 2002, Etchepare and van der Hoek, 2015, Butkovskyi et al., 2017). The same considerations outlined above apply for 1H-benzotriazole, another compound from the benzotriazole group that is also included in dishwasher tabs as a corrosion inhibitor and which was also found in the BTP effluent, albeit in significantly lower concentrations than MBT (median concentration of 638 ng/L).

That those two compounds were still found in the BTP effluent in measurable amounts after passing through two biological treatment systems (with a combined HRT of around 3 days) reflects their reduced biodegradability in activated sludge systems. In the case of 4-MBTR, that recalcitrance has been extensively documented (Reemtsma et al., 2006, Weiss and Reemtsma, 2008, Reemtsma et al., 2010, Huntscha et al., 2014). In the case of Quadrol, no study could be found in the literature that assessed its biodegradability directly; however, it is expected to be low, as is usually the case with complexing agents from the aminoplycarboxylates class with two nitrogen atoms in their structure, such as EDTA (Sýkora et al., 2001, Nörtemann, 2005).

Concerning BTSA, it is not clear what its source in the residences served by the BTP could be, as no household chemical or personal care product could be found that explicitly uses the compound in its formulation. The presence of BTSA in raw and treated municipal wastewaters – where it has been found in fairly high concentrations (Kloepfer et al., 2005, Reemtsma et al., 2006) – is frequently associated with the (bio)degradation of mercaptobenzothiazole (MBT), a commodity chemical with various industrial applications, chief among them as a vulcanization accelerator in the production of rubber (Reemtsma et al., 1995, Wever et al., 2001). Runoff from surfaces impacted by particles produced by the wear of rubber products, especially tires, is frequently implicated as the main input source of MBT to municipal wastewaters (Wever et al., 2001) – a source that is however unlikely to be relevant in the case under consideration here. In the context of domestic wastewaters, the only potential sources of benzothiazoles reported in the literature that might of interest are some textile products – especially those made of polyester – which have been shown to leach measureable amounts of compounds belonging to that family upon washing (Avagyan et al., 2015, Luongo et al., 2016, Liu et al., 2017). To date, however, neither BTSA nor its known parent MBT were found in such leachates.

Chapter 6

Five pesticides, biocides or transformation products thereof were detected at one or more BTP effluent batches, always in very low concentrations: boscalid (fungicide), carbendazim (biocide), terbuthylazine (herbicide), metazachlor-ESA (transformation product of the herbicide metazachlor) and desphenylchloridazon (transformation product of the herbicide chloridazon). Of those, only boscalid and carbendazim were regularly found, with the former being quantifiably present in 7 of the 9 BTP batches, and the latter in all of them. Considering that no pesticide product containing terbuthylazine, metazachlor or chloridazon is available for home use in the German market (BVL, 2018), it is probable that the occasional presence of those compounds or their transformation products in the BTP effluent batches was caused by environmental contamination. As to boscalid, it was applied preventively to both lettuce and cucumber early in the respective growing cycles during the hydroponic experiment in order to control the incidence of powdery mildew, which might have led to the contamination of the nutrient solution tank from which samples of the BTP effluent were collected.

The continuous presence of carbendazim (CDZ) in the BTP effluent, even though in trace amounts (median concentration of 80 ng/L), is intriguing. Primarily used in the European Union as an herbicide until being phased out for agricultural and domestic pest control applications in 2016 (Merel et al., 2018), it is still approved for use as a preservative in films; fiber, leather, rubber and polymerized materials; and construction materials (ECHA, 2018). Even after the discontinuation of its use as an herbicide, CDZ continued to be detected in effluent of WWTPs throughout Europe (Burkhardt et al., 2011, Bollmann et al., 2014, Launay et al., 2016, Merel et al., 2018), a fact frequently attributed to the leaching of CDZ from façades and surfaces treated with paints and plasters containing the compound (Burkhardt et al., 2011). Paper and textile products have been pointed as possible domestic sources of CDZ by Merel et al. (2018), who in leaching tests was able to extract between 11 and 16 ng/g of CDZ from toilet paper, and up to 46 ng/g from a variety of other textile and paper products. However, a rough estimate based on the BTP effluent’s median CDZ concentration of 80 ng/L; the BTP’s baseline effluent flow rate of 480 L/d; the population equivalent associated with the baseline flow rate (10 persons); and a CDZ content of 16 ng/g in toilet paper, indicates that a per capta toilet paper usage rate of ca. 240 g would be required to produce the CDZ mass flows observed in the BPT effluent. That is an unrealistic amount, which clearly shows that, in the case of the residences served by the BTP, other domestic sources are likely to have played a major role in the emission of CDZ.

6.3.2 Micropollutant uptake by lettuce and cucumber

Of the 37 micropollutants detected in quantifiable amounts in the BTP effluent, 19 were found in the lettuce produced by the experimental hydroponic treatment, as shown in in Figure 6-5, with the 4-acetoaminoatipyrine, carbamazepine and metformin being the highest (512, 455 and 308 ng/g on a dry weight basis, respectively). Together, they accounted for almost 60% of the total micropollutant content in leafs of the lettuce harvested. In the case of cucumber, 22 micropollutants were detected in harvested fruit. Carbamazepine was the compound present in highest amount, with a concentration of 146 ng/g (Figure 6-6).

Micropollutants in BTP effluent and their uptake by crops

Lettuce MBR effluent

800 140 700 120 600 100 500 80 400

60 (ug/L) 300 (ng/g [d.w.]) (ng/g 200 40 20

100 effluent in Concentration

Concentration in lettuce leafs lettuce in Concentration 0 0

BTSA

Caffeine

Acyclovir

Lidocaine

Sucralose

DiOH-CBZ

Bisoprolol

Acesulfam

Metformin

Metoprolol

Venlafaxine

Gabapentin

Propranolol

Carbendazim

Clofibricacid

Terbuthylazin

Carbamazepine

Metoprololacid

Hydrochlorothiazide 4-Acetoaminoantipyrine alpha-Hydroxymetoprolol Figure 6-5 – Concentration of organic micropollutants found in lettuce leaves and average concentrations of the respective compounds measured in the BTP effluent batches during cultivation. Error bars correspond to one standard deviation.

Cucumber MBR effluent

300 350 250 300 250 200 200 150

150 (ug/L) 100

100 (ng/g [d.w.]) (ng/g

50 50 Concentration in effluent effluent in Concentration

0 0

Concentration in cucumber fruit cucumber in Concentration

BTSA

DEET

Ep-CBZ

Caffeine

Lidocaine

Sucralose

DiOH-CBZ

Sitagliptin

Bisoprolol

Acesulfam

Metformin

Metoprolol

Venlafaxine

Gabapentin

Carbendazim

Valsartanacid

Carbamazepine

Metoprololacid

Gabapentin-Lactam

Hydrochlorothiazide 4-Acetoaminoantipyrine alpha-Hydroxymetoprolol Figure 6-6 – Concentration of organic micropollutants found in cucumber fruit and average concentrations of the respective compounds measured in the BTP effluent batches during cultivation. Error bars correspond to one standard deviation.

As compared to values measured in other studies where reclaimed wastewater was used to irrigate lettuce and cucumber under real cultivation conditions (Shenker et al., 2011, Calderón- Preciado et al., 2013, Wu et al., 2014, Riemenschneider et al., 2016, Martínez-Piernas et al., 2018),

Chapter 6 the micropollutant concentrations found in the edible part of both crops grown with the BTP effluent can be considered extraordinarily high, with a median micropollutant content of 70 ng/g in lettuce and 6.9 ng/g in cucumber fruit. Although a comprehensive comparison with the findings of those studies is hindered by the lack of overlap in the set of substances detected in plant material in each study, an indication of how high the micropollutant burden in the vegetables produced with the BTP effluent is can be gleaned by considering the concentration of carbamazepine, which was found to be present in plants in all of the mentioned studies. In the case of lettuce, the average carbamazepine concentration of 307 ng/g measured in leafs of that crop here was one to two orders of magnitude superior to those found by Calderón- Preciado et al. (2013), Wu et al. (2014) and Martínez-Piernas et al. (2018), which ranged from 0.04 ng/g to 36 ng/g. Only in the case investigated by Riemenschneider et al. (2016), which concerned an agricultural plot irrigated with water from a river whose flow was dominated by WWTP effluent, were concentration levels (217 ng/g) on par with those reported here. In the case of cucumber fruit, the average concentration of 146 ng/g measured in the produce grown with BTP effluent is also drastically higher than the 0.02 ng/g [d.w.] and 1.0 ng/g [w.w.] measured by Wu et al. (2014) and Shenker et al. (2011), respectively.

The high micropollutant concentrations found in the edible parts of the crops investigated here reflect the substantially higher micropollutant loads in the BTP effluent as compared to the effluents used in the studies used as reference, which were produced by tertiary treatment of municipal wastewater in centralized WWTPs and contained micropollutant concentrations in the single-digit µg/L to ng/L range. To the best of the author’s knowledge, the uptake of micropollutant by plants at exposure levels similar to those observed during the hydroponic experiments reported here have only been investigated in pot- or greenhouse-scale experiments in which the nutrient solution/irrigation water was fortified with external micropollutant sources (e.g. Herklotz et al. (2010), Shenker et al. (2011), Tanoue et al. (2012)) or where crops were grown with high application rates of biosolids (Wu et al., 2010, Wu et al., 2012), under exposure conditions that (until now) were considered by some as agriculturally unrealistic (Malchi et al., 2015).

Even after including those high-exposure studies in the comparison set, the concentrations of carbamazepine found in lettuce leafs and cucumber fruit produced with BTP effluent still rank amongst the highest that could be found in peer-reviewed literature on the topic. In regard to lettuce, similar concentration levels were only found by Bhalsod et al. (2018) in shoots of lettuce irrigated with water containing 50 µg/L of carbamazepine. Higher concentrations were observed only by Wu et al. (2012), who detected 600 ng/g in leafs of plants grown in soil amended with biosolids without any waiting period and additionally spiked with carbamazepine; and Hurtado et al. (2017), who measured a concentration of 724 ng/g [d.w.] in lettuce grown as part of experiments designed to assess the metabolic impact of micropollutant uptake – the highest value found in the literature for this crop.

Regarding cucumber fruit, the carbamazepine concentrations measured here seem to be the highest reported in peer-reviewed literature; in fact, for that compound, they seem to be the highest reported for any fruit vegetable. The second highest are the 55 ng/g [d.w.] measured by Goldstein et al. (2016) in pot trials where plants were irrigated with treated WWTP effluent spiked with carbamazepine to a concentration of 1.95 µg/L.

As to the remaining micropollutants, to the best of the author’s knowledge the presence of several of them in edible parts of lettuce and cucumber have not been reported elsewhere yet. Apart from carbamazepine, studies explicitly addressing the plant uptake by lettuce and/or

Micropollutants in BTP effluent and their uptake by crops cucumber fruit were found only for 4-AAA (Martínez-Piernas et al., 2018), metoprolol (Goldstein et al., 2014, Martínez-Piernas et al., 2018), venlafaxine (Martínez-Piernas et al., 2018), hydrochlorothiazide (Riemenschneider et al., 2016, Martínez-Piernas et al., 2018), caffeine (Calderón-Preciado et al., 2013, Wu et al., 2013, Goldstein et al., 2014, Wu et al., 2014, Riemenschneider et al., 2016, Hurtado et al., 2017, Bhalsod et al., 2018, Martínez-Piernas et al., 2018), acesulfame (Riemenschneider et al., 2016) and the carbamazepine metabolites DiOH-CBZ and Ep-CBZ (Riemenschneider et al., 2016). For all those compounds, the concentrations reported by the respective literature references were substantially inferior to the ones measured here. The only exceptions are DiOH-CBZ, which was found by Riemenschneider et al. (2016) in lettuce leaves in an amount twice as high as the one measured here; and caffeine, which Hurtado et al. (2017) observed to accumulate to concentrations of up to 100 ng/g [d.w.] in lettuce leafs of plants irrigated with a nutrient solution spiked with 50 µg/L of that compound, far above the 7.1 ng/g d.w. found here in plants grown with the BTP effluent.

6.3.3 Bioconcentration factors

More insight into the extent to which the micropollutants present in the BTP effluent were taken up by the edible parts of lettuce and cucumber can be gained by the calculation of the bioconcentration factors (BCFs). The BCF is defined as the ratio of the concentration of a given substance in plant tissue to that found in the liquid medium the plant’s roots are exposed to (soil or nutrient solution), as indicated in Equation 6-1:

퐶푝푙푎푛푡 푡푖푠푠푢푒 퐵퐶퐹 = [6-1] 퐶푙푖푞푢푖푑 푚푒푑푖푢푚

Where the BCF is usually given in units of L/kg. The BCFs obtained for all the compounds found in lettuce leafs and cucumber fruit are presented graphically in Figure 6-7. For their calculation, the value taken as reference for the liquid phase concentration was the average of the concentration values measured in the nutrient solution throughout the respective cultivation periods, including measurements made in each individual BTP effluent batch during their use. It was observed that, in each batch, the concentrations of the micropollutants varied over time, presumably due to the uptake by plants (data not shown here). For that reason, the values shown in Figure 6-5 are different from the averages presented in Table 6-1, which were determined considering only the concentrations of the various compounds in fresh batches, that is, prior to their application in the hydroponic system.

As can be seen in Figure 6-7, carbendazim and lidocaine showed the highest tendency to accumulate in lettuce leaves, with BCF values that were substantially higher than those of the other compounds. No publications were found in the literature addressing the uptake of carbendazim by plants in the context of exposure to contaminated waters or wastewaters; however, studies investigating the agricultural application of the compound, in its previously approved role as a systemic fungicide, by soil drenching have shown that carbendazim, upon being taken up by roots, is quickly translocated to leaves, where it accumulates (Peterson, 1971, Leroux and Gredt, 1975, Sicbaldi et al., 1997). Although those studies have not quantified the extent to which this happens in the form of a BCF value, the high transpiration stream concentration factor (TSCF) reported for carbendazim (Sicbaldi et al., 1997) – a measure of the degree to which the compound is taken up by roots and transported via xylem to shoots – would indicate that a high accumulation in leaves is indeed very likely.

Chapter 6

100

80.8 lettuce 90 cucumber 80

70 57.7 60

50 36.0 29.7

40 24.1

23.7

BCF(L/kg)

21.6

20.0

18.4 16.4

30 15.5

10.8

10.0

9.0

8.9

8.4

7.9

7.8 7.5

20 7.4

5.6

4.6

4.2

3.6

2.4

2.1

1.6

1.3

1.1

1.0

0.9

0.7

0.5

0.3

0.2

0.2 0.1

10 0.0

Carbendazim Propranolol Metoprololacid Lidocaine Venlafaxine Carbamazepine 4-Acetoaminoantipyrine Caffeine Gabapentin Ep-CBZ BTSA DEET alpha-Hydroxymetoprolol Metoprolol Bisoprolol Metformin DiOH-CBZ Acesulfam Hydrochlorothiazide Sucralose Sitagliptin Acyclovir 0 Gabapentin-Lactam

Figure 6-7 – Bioconcentration factors for organic compounds found in lettuce leafs and cucumber fruit.

As to lidocaine, the single study found in the literature that assessed its uptake by plants in a sense that is relevant here – even if in an indirect way– was that conducted by Yokawa et al. (2017), who demonstrated that exposure of the root system of sensitive plant (Mimosa pudica L.) to a 1% lidocaine solution led to a temporary loss of its capacity to fold its leaves in reaction to mechanical stimuli. Although the aspect of bioaccumulation was not even remotely alluded to in that study, at the very least the observation of a physiological response of the plant upon that form of exposure provides a very strong indication that lidocaine could be taken up by plants via roots and translocated to leaves; however, the extent to which that happens is unclear, since the minimum lidocaine concentration required for the compound to exert an observable effect was not determined in that study.

A notable feature of the BCF profile obtained for lettuce leaves is the relatively low value for carbamazepine. When compared to leaf BCFs reported by other studies for lettuce, the value obtained here (3.6 L/kg) ranks amongst the lowest (Calderón-Preciado et al., 2013, Wu et al., 2013, Wu et al., 2014, Hyland et al., 2015b, Riemenschneider et al., 2016, Hurtado et al., 2017, Bhalsod et al., 2018, Martínez-Piernas et al., 2018) – substantially lower than the value of 58 obtained by Wu et al. (2013) in hydroponic experiments with spiked nutrient solution, and the value of 127 observed by Riemenschneider et al. (2016). Based on the findings of a literature review of published leaf BCF values for a wide variety of crops grown both in soil and hydroponically conducted by Wu et al. (2015), it appears that the value obtained here in fact would rank amongst the lowest measured for carbamazepine for any crop. That such a low value was observed in plants cultivated hydroponically is particularly surprising, considering that in general the uptake of micropollutants is facilitated under hydroponic conditions. Soils –

Micropollutants in BTP effluent and their uptake by crops especially their organic fraction – have been shown to hinder the plant uptake of organic pollutants by competing with roots for their sorption (Wu et al., 2015, Miller et al., 2016).

One very speculative explanation for this is that, due to the very high total micropollutant content in the BTP effluent batches, some kind of competitive effect with other compounds limited the root uptake and/or translocation of carbamazepine to the lettuce leaves. This hypothesis would also account for the slight but statistically significant negative Spearman correlation (ρ=-0.52, p=0.025) observed between the average concentration of the various compounds in BTP effluent batches and in the lettuce leaves (Figure 6-8).

2.5

Propranolol 2 DiOH-CBZ Carbamazepine 1.5 Lidocaine Acyclovir Sucralose Gabapentin 1 Metoprolol BTSA Bisoprolol Venlafaxine Caffeine 0.5 Acesulfam

logBCF Carbendazim Metoprolol acid alpha- 0 Hydroxymetoprolol Terbuthylazin -0.5 Clofibric acid

Hydrochlorothiazide -1

-1.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

logCliquid phase

Figure 6-8 – Variation of logBCF with the logarithm of the average concentration of the various organic micropollutants in the BTP effluent batches used for the cultivation of lettuce.

As illustrated in Figure 6-7, in the case of cucumber fruit the BCF profile was quite different from that of lettuce leaves, with some compounds that showed a relatively high tendency to accumulate in lettuce, like 4-AAA and propanolol, having very low BCF values for cucumber fruit. BCF values were in general lower, in accordance with the currently accepted view that most organic micropollutants are translocated primarily in xylem along with water (Trapp, 2004, Dettenmaier et al., 2009), and thus accumulate preferentially in leaf tissues instead of fruit, as previously observed by Shenker et al. (2011) in cucumber and by Wu et al. (2014) across multiple plant species.

The notable exceptions were the anticonvulsant gabapentin-lactam; metoprolol acid, a metabolite of the beta-blocker metoprolol; carbamazepine and one of its metabolites, carbamazepine-10,11-epoxide (Ep-CBZ); and caffeine, which all had substantially higher BAFs for cucumber fruit than for lettuce leaves. The bioaccumulation of the first two – gabapentin- lactam and metoprolol acid – in plant tissues appears not to have been documented in the literature yet, therefore it is unclear if the BCF values obtained for those two compounds in cucumber fruits are indeed exceptional. Since the micropollutant content of cucumber leaves was not measured, it is not possible to determine if those two compounds somehow possess physicochemical properties that allow them to be loaded and transported in phloem particularly

Chapter 6 well, leading them to accumulate primarily in fruits (in which case the ratio of the BCF in cucumber fruits to that in cucumber leaves would also be high); or if their apparent high BCF in cucumber fruit as compared to that in lettuce simply reflect an interspecies difference in the overall uptake of those compounds (in which case the “internal” cucumber BCFfruit-to-BCFleaf ratio would be low). Here it is important to note that differences between species might eventually be relevant, depending on the organic compound in question: some compounds, such as the histamine H1 antagonist diphenhydraminethe; the antibacterial agent triclocarban; the flame retardant tris(2-chloroethyl) phosphate; and the anti-inflammatory agent naproxen have already been observed to accumulate in some fruits in levels similar or superior to those observed in leaf tissues in general (Wu et al., 2012, Wu et al., 2014, Hyland et al., 2015a).

As to carbamazepine and caffeine, the BCF values in cucumber fruit measured here for these two compounds are on the same order of magnitude of those reported by Shenker et al. (2011), Goldstein et al. (2014) and Wu et al. (2014); the fact that they were higher than the respective BCF values in lettuce leaves should rather be attributed to the fact that the latter values were exceptionally low, as discussed previously.

Correlation to physicochemical properties

In order to determine if the degree of uptake of the various compounds could be explained by their physicochemical properties, the bioconcentration factor values obtained were compared to the logarithm of the compounds’ octanol-water partition coefficient (KOW) and pH-adjusted octanol-water partition coefficient (DOW), the logarithms of which are frequently used as predictors of the accumulation of xenobiotic compounds in plants (McKone and Maddalena, 2007, Trapp and Legind, 2011, Doucette et al., 2018).

The logDOW of each compound was calculated according to the procedure described by

Schwarzenbach et al. (2005), whereby the logKOW was adjusted by a factor α, which represents the fraction of the compound that is present as a species capable of effectively partitioning between octanol and water. For compounds that display acid-base behavior (that is, accept and/or donate protons), it is often assumed that only the neutral species undergo partition, in which case the factor α can be determined based only on the acid dissociation constants (pKa) of the substance in question. As shown by Equation 6-2, the exact formula of the α factor varies depending on the presence (and number) of acid and/or base moieties in the molecule:

log 퐷푂푊 = log 퐾푂푊 + log 훼 1 푎푐𝑖푑푠: 1 + ∑ 10푝퐻−푝퐾푎푖

1 훼 = 푏푎푠푒푠: 푝퐾푎 −푝퐻 [6-2] 1 + ∑ 10 푗 1 푎푚푝ℎ𝑖푝푟표푡𝑖푐: { 1 + ∑ 10푝퐾푎푖−푝퐻 + ∑ 10푝퐻−푝퐾푎푗

The pKa and logKOW values of each compound are presented in Table 6-3, along with α factor values calculated considering the average pH of the BTP effluent batches used in the cultivation of each crop (7.3 for lettuce and 7.5 for cucumber). To populate the pKa and KOW list, preference was given to values reported to have been obtained experimentally; when not available (which was the case of several compounds), the median of predicted values obtained from the USEPA Chemistry Dashboard (USEPA, 2018), ContaminantDB (Wishart Research Group) and ChEMBL (Bento et al., 2014) databases was used. In the case of zwitterions, Equation 6-2 does not apply,

Micropollutants in BTP effluent and their uptake by crops since they always have at least one acidic or basic functional group that is protonated and charged since, regardless of the pH of the solution. For that reason, the logDOW for the zwitterionic compounds metoprolol acid and gabapentin were computed using the online cheminformatic tool Chemicalize (ChemAxon).

Lettuce Cucumber pKa (pH=7.3) (pH=7.5) Compound CAS logKOW basic acidic α logDOW α logDOW BTSA 941-57-1 -2.1 -3.5 0.25 1.5·10-11 -10.6 1.0·10-11 -10.8 Epoxy CBZ 36507-30-9 -3.7 15.96 1.51 — — 1.00 1.51 Acesulfame 33665-90-6 — 5.67 -1.33 0.02 -2.99 0.01 -3.16 4-AAA 83-15-8 -1.2 12.52 0.11 1.00 0.11 1.00 0.11 Sucralose 56038-13-2 — 11.91 -0.41 1.00 -0.41 1.00 -0.41 Gabapentin-Lactam 64744-50-9 0.02 — 1.36 — — 1.00 1.36 Hydrochlorothiazide 58-93-5 — 7.9 -0.07 0.79 -0.17 0.72 -0.21 Caffeine 58-08-2 0.52 — -0.07 1.00 -0.07 1.00 -0.07 DEET 134-62-3 0.67 — 2.02 — — 1.00 2.02 Carbendazim 10605-21-7 4.29 — 1.52 1.00 1.52 1.00 1.52 Lidocaine 137-58-6 7.86 — 2.44 0.22 1.79 0.30 1.92 Sitagliptin 486460-32-6 8.78 — 1.50 — — 0.05 0.20 Bisoprolol 66722-44-9 9.27 14.09 1.87 0.01 -0.08 0.02 0.09 Metformin 657-24-9 12.4 — -0.05 8.4·10-6 -5.13 1.2·10-5 -4.95 Metoprolol acid 56392-14-4 9.67 3.54 -0.16 — -1.24* — -1.24* Gabapentin 60142-96-3 10.7 3.68 -1.10 — -1.27* — -1.27* alpha- 56392-16-6 9.67 13.55 0.88 4.5·10-3 -1.47 0.01 -1.30 Hydroxymetoprolol Metoprolol 51384-51-1 9.68 — 1.88 4.4·10-3 -0.48 0.01 -0.31 Venlafaxine 93413-69-5 10.09 — 3.20 1.7·10-3 0.43 2.5·10-3 0.61 Valsartan acid 164265-78-5 — 4 2.28 — — 3.2·10-4 -1.22 DiOH-CBZ 58955-93-4 — 12.07 0.70 1.00 0.70 1.00 0.70 Carbamazepine 298-46-4 — 13.9 2.45 1.00 2.45 1.00 2.45 Acyclovir 59277-89-3 2.27 9.25 -1.56 0.99 -1.57 — — Propranolol 525-66-6 9.42 — 3.48 0.01 1.38 — — * Values obtained directly from cheminformatic tool Chemicalize (ChemAxon)

As shown in Figure 6-9 and Figure 6-10, in both lettuce leaves and cucumber fruit the BCF exhibit a very poor correlation with the logKOW, as has been previously observed for above- ground plant tissues in several other plant uptake studies concerning mixtures of neutral and ionizable organic compounds (Miller et al., 2016).

In terms of the logDOW, one very apparent feature of the distribution of BCF values observed in the hydroponic experiments is that, in both lettuce leaves and cucumber fruit, benzothiazole- 2-sulfonic acid (BTSA) – a very hydrophilic compound present in BTP batches entirely in anionic form (Table 6-3) – reached bioconcentration levels similar to those of some neutral hydrophobic

Chapter 6 compounds, such as carbamazepine. According to prevailing conceptual models for plant uptake of organic compounds, that shouldn’t have occurred: unlike that of neutral compounds, the passive uptake of anionic compounds by roots should be severely hindered by their negative charge, due to the negative potential of root cell membranes (Trapp, 2004). Even once within root cells, the translocation to above-ground tissues of a strong acid such as BTSA is believed to be limited, since it would remain ionized in the various cellular compartments of the plant (which range from 7.5 in the cytosol to 5.5 in vacuoles) and would, therefore, face the same resistance permeating cell membranes in the pathway to the xylem/phloem in order to undergo long-range transport within the plant (Bromilow et al., 1990, Hsu and Kleier, 1996, Trapp, 2004).

2.5 2.5

2.0 2.0

1.5 1.5 BTSA 1.0 1.0

0.5 0.5 logBCF 0.0 0.0

-0.5 -0.5

-1.0 -1.0

-1.5 -1.5 -12 -10 -8 -6 -4 -2 0 2 4 -2 -1 0 1 2 3 4 logD logK OW OW

Figure 6-9 – Scatterplots of logBCF versus logDOW (left) and logBCF versus logKOW (right) for organic micropollutants found in lettuce leaves.

2.0 2.0

1.5 1.5

1.0 BTSA 1.0

0.5 0.5

0.0 0.0 logBCF -0.5 -0.5

-1.0 -1.0

-1.5 -1.5

-2.0 -2.0 -12 -10 -8 -6 -4 -2 0 2 4 -2 -1 0 1 2 3 4 logK logDOW OW

Figure 6-10 – Scatterplots of logBCF versus logDOW (left) and logBCF versus logKOW (right) for organic micropollutants found in cucumber fruit.

Micropollutants in BTP effluent and their uptake by crops

When BTSA is excluded from the analysis, a relationship between logDOW and logBCF values of the various compounds becomes more apparent, as can be seen in Figure 6-11. This relationship is particularly pronounced in the case of cucumber fruits, and is characterized by a positive correlation between the two variables up to a log DOW values of 1.5-2.0, where logBCF values apparently peak. From that point on, the correlation seems to inverse, i.e. the logBCF decreases with increasing logDOW, although more measurements of compounds with logDOW values higher than 2 would have been required to better resolve the descending flank of the relationship.

As compared to other plant organs, the relation between the physicochemical properties of organic compounds and their accumulation in fruits has been considerably less investigated (see review by Doucette et al. (2018)); a comparative assessment of the results obtained for cucumber fruit is therefore difficult. However, it is interesting to note that such concavely shaped relationships have also been observed between lipophilicity and the accumulation of xenobiotic compounds in both xylem lins (Collins et al., 2006, Limmer and Burken, 2013) and

Chapter 6 phloem sap (Tyree et al., 1979, Hsu and Kleier, 1996), with compounds with intermediate lipophilicity (logKOW between 0 and 2.5) generally showing the highest degree of accumulation. This phenomena is often attributed to, on the one hand, the poor cell membrane permeability of hydrophilic compounds (which would hinder them from reaching the plant’s vascular tissue) and, on the other, to the tendency of very lipophilic molecules to partition to the solid phase of plant tissues (which reduce their concentration in the sap) (Collins et al., 2006, Trapp, 2007).

In the case of xylem, the accumulation is usually expressed in terms of the transpiration stream concentration factor (TSCF), defined as the ratio of the concentration of the chemical in the xylem sap (transpiration stream) to the concentration in the external root medium (Equation 6-36-3). Several authors (see Collins et al. (2006) and Doucette et al. (2018)) have observed that the TSCF of a wide variety of neutral organic compounds show an apparent Gaussian dependence on the logKOW (Equation 6-36-3).

푐표푛푐푒푛푡푟푎푡𝑖표푛 𝑖푛 푥푦푙푒푚 (푙표푔퐾 − 푏)2 푇푆퐶퐹 = = 푎 ∙ exp (− 표푤 ) [6-3] 푐표푛푐푒푛푡푟푎푡𝑖표푛 𝑖푛 푟표표푡 푚푒푑𝑖푢푚 2 ∙ 푐2

Where a is the amplitude scale parameter of the Gaussian function, which determines the height of the function’s peak; b is the position parameter, which determines the location of the function’s peak on the independent variable axis; and c is the scale parameter, which determines the spread of the model’s bell-shaped curve. Values for those three parameters have been estimated by various authors – most notably Briggs et al. (1982) – as shown in Table 6-4. For ionizable compounds, this relationship between TSCF and logKOW generally does not hold (Miller et al., 2016), but scattered evidence in the literature indicates that in those cases replacing logKOW by logDOW partially restores the predictive power of the regression model (Tanoue et al., 2012, Wu et al., 2013). Moreover, outside the context of plant uptake models, a similarly shaped relationship between logDOW and PAMPA (parallel artificial membrane permeation assay) permeability was observed for pharmaceutical compounds (Sugano et al., 2010). PAMPA is an assay for estimation of permeability of organic compounds in cellular membranes.

Table 6-4 – Values obtained by various authors for parameters of Gaussian TSCF model.

Value Parameter Briggs et al. Hsu et al. Burken and (1982) (1990) Schnoor (1998) a 0.784 0.700 0.756 b 1.78 3.07 2.50 c 1.10 1.18 1.14

Since translocation in xylem constitute the primary pathway for the movement of non-volatile organic compounds – such as the micropollutants under consideration here – from roots to above-ground parts of plants, the accumulation of those compounds in fruits should be expected to be modulated by their mobility in the xylem to some extent (Trapp, 2007). Indeed, a regression model analogous to the Gaussian model for the TSCF, given by Equation 6-4, could be reasonably fitted to the cucumber fruit’s BCF vs. logDOW data, as shown in Figure 6-11.

(푙표푔퐷 ∙ 푏)2 퐵퐶퐹 = 푎 ∙ exp (− 표푤 ) [6-4] 2 ∙ 푐2

Micropollutants in BTP effluent and their uptake by crops

The estimated values for the model parameters are presented in Table 6-4. Although the goodness of the fit is rather low (R² = 0.28), the model’s fit is statistically significant (p<0.001).

Moreover, it can also be seen in the plot of logBCF vs. logDOW shown in Figure 6-11 that, at least in terms of orders of magnitude, the model seems to be able to account for the particular bioaccumulation behavior of the organic micropollutants observed in cucumber fruit.

Table 6-5 – Estimated values for parameters of DOW-based BCF Gaussian regression model for cucumber fruit.

95% Conf. Interval Parameter Point estimate SE p-value LCL UCL a 17.2 4.2 8.4 25.9 0.0005 b 1.60 0.39 0.78 2.42 0.0006 c 1.28 0.49 0.26 2.30 0.0160

It is also interesting to note that the estimate obtained for the position parameter (95% CI [0.78; 2.42]) seem to be consistent with the values obtained by Briggs et al. (1982), Hsu et al. (1990) and Burken and Schnoor (1998) (Table 6-4), which could be taken as an indication that the translocation of micropollutants to the cucumber fruits was driven predominantly by the xylem. Although the transport of solute to fruits and other plant organs with limited transpiration is carried out predominantly by phloem, stomata remain relatively abundant in the surface of cucumber fruit throughout its maturation period when compared to other fruit (Sui et al., 2017), and therefore the contribution of xylem to the translocation of micropollutants from root to that fruit might be higher than expected. Moreover, the micropollutants found in the cucumber fruits comprise predominantly weak bases and poorly ionizable compounds that, according to the model of phloem mobility of xenobiotics developed by Kleier (Kleier, 1988), should be transported primarily in the xylem due to their particular combination of KOW and pKa values (Kleier and Hsu, 1996). The only exceptions are the weak acids valsartan acid, acesulfame and hydrochlorothiazide, which could accumulate in phloem due to the ion trap effect (Kleier and Hsu, 1996).

In the case of lettuce leaves, the relationship between bioconcentration and logDOW of the compounds is considerably weaker, even after the exclusion of BTSA from the analysis. A trend of increased bioconcentration of compounds with higher logDOW seems slightly apparent, although the positive correlation of those two variables is very weak, as indicated by a low Spearman’s correlation coefficient value (ρ=0.37). Despite the fact that the statistical significance of the correlation is also low in this case (p=0.135), such a positive relationship between the logBCF in lettuce leaves and the logDOW of xenobiotic compounds would be consistent with the observations made by Miller et al. (2016) based on the data obtained by Wu et al. (2013) for compounds with logDOW of 3 or less, such as the ones under consideration here.

Despite the limitations of the regression models discussed above, the fact that the DOW was observed to have a higher predictive power than KOW emphasizes the role that pH can have in shaping the bioaccumulation of ionizable xenobiotic compounds in plants. This fact is of particular relevance to wastewater reclamation systems such as the one considered here, where effluents are applied in a hydroponic plant cultivation system, since the pH of the nutrient solution is a key operational variable in such systems.

Chapter 6

6.3.4 Human health risk assessment

As indicated in Table 6-6, essentially all the organic compounds found in the vegetables produced with BTP effluent were classified as Class III, that is, compounds that possess structural markers associated with a higher toxicity potential, and therefore were assigned a TTC value of 1.5 µg/(kg-bw.d). The only exception was carbamazepine-10,11-epoxide (Ep-CBZ), which was flagged as a potentially genotoxic compound due to the presence of an epoxide moiety in its structure, and consequently had a TTC value of 0.0025 µg/(kg-bw.d) assigned to it.

For the purpose of estimating the threshold consumption rate (TCR) for lettuce and cucumber – the daily consumption of the vegetable that would lead to individual TTC values being exceeded –, the default body weights of 60 and 25 kg for adults and infants were assumed (EFSA, 2016). That assumption yields a TTC of 90 and 37.5 µg/(p.d) for adults and children for Class III compounds, and of 0.15 and 0.0625 µg/(p.d) for genotoxic compounds, respectively. The TCR values, which can be alternatively defined as the vegetable consumption rates required in order for a hazard quotient of unity to be obtained, was calculated by means of Equation 6-5: 퐶 ∙ 푇퐶푅 푇푇퐶 ∙ 푓 퐻푄 = 1 = → 푇퐶푅 = [6-5] 푓 ∙ 푇푇퐶 퐶

Where HQ is the hazard quotient; C is concentration of the compound of interest in the vegetable, in units of ng/g [d.w].; and f is a the wet- to dry-weight conversion factor, equal to 20.3 for lettuce leafs and 47.4 for cucumber fruit.

As can be seen in the Table 6-6, in the case of the majority of compounds, a very high amount of lettuce or cucumber would need to be consumed in order for TTC values to be reached. For instance, in the case of 4-AAA – which in lettuce was the organic micropollutant found in greatest amounts –, the amount of lettuce that an adult would need to consume on a daily basis in order for the TTC to be exceeded (3.6 kg) are close to the average yearly per capita consumption of that vegetable in Germany (5.8 kg) (BMEL, 2018), and around 9 times the daily intake for vegetables (400 g) currently recommended by the German Nutrition Society for adults (DGE, 2015). In the case of Ep-CBZ, the compound’s restrictive TTC results in a threshold for daily consumption rates of cucumber that are also high (1 kg/(p.d)), but already in the domain of events that are unlikely but conceivable.

It should be noted, however, that the TTC methodology considers the risks associated with the exposure to single chemicals; it does not account for eventual additive or synergistic adverse health effects that might arise due to the combined exposure to mixtures, an aspect that could be of relevance here, considering the amount and variety of organic micropollutants found in the vegetables.

Micropollutants in BTP effluent and their uptake by crops

Lettuce Cucumber Compound Cramer Class TCR for TCR for TCR for TCR for adults infants adults infants 4-Acetoaminoantipyrine Class III 3.6 1.5 672 280 Acesulfam Class III 10 4 1164 485 Acyclovir Class III 271 113 — — BTSA Class III 110 46 399 166 Bisoprolol Class III 683 285 575 239 Caffeine Class III 255 106 1154 481 Carbamazepine Class III 5.9 2.47 29 12 Carbendazim Class III 624 260 2399 999 Clofibric acid Class III 417 174 — — DEET Class III — — 1224 510 DiOH-CBZ Class III 26 10.75 110.15 46 Ep-CBZ genotoxic — — 1.2 0.485 Gabapentin Class III 16 6.6 80 33 Gabapentin-Lactam Class III — — 250 104 Hydrochlorothiazide Class III 475 198 1239 516 Lidocaine Class III 22 9.31 163 68 Metformin Class III 4 1.7 291 121 Metoprolol Class III 10 4.23 208 87 Metoprolol acid Class III 491 204 242 101 Propranolol Class III 1290 537 Sitagliptin Class III — — 1522 634 Sucralose Class III 22 9.02 — — Terbuthylazin Class III 4069 1695 — — Valsartan acid Class III — — 2073 864 Venlafaxine Class III 19 8.0 106.44 44 alpha- Class III 24 10.02 875.50 365 Hydroxymetoprolol

In cases such as this, where the mixture is complex, and the toxicological modes of action and possible interactions between the substances of concern are unknown, Meek et al. (2011) suggests that a coarse – but very conservative – estimate of the risk due to the combined exposure to the mixture can also be obtained by using the TTC methodology as a starting point. The procedure, based on Tier 0 of the WHO/IPCS risk assessment framework (Meek et al., 2011), assumes that effects of compounds of the same Cramer class are additive; therefore, for the purposes of the evaluation of exposure, they should be treated as a single substance, with the respective TTC of the class in question as reference.

Following this procedure, TCR values that account for the combined exposure to contaminants in each Cramer class can be calculated by replacing the concentration term in Equation 6-5 with the appropriate sum of concentrations. In the case of lettuce, where all the organic micropollutants that were detected in quantifiable amounts belonged to Class III, that sum is

Chapter 6 equal to circa 2.2 mg/L and corresponds to TCR values of 828 mg.person-1.d-1 for adults and 345 mg.person-1.d-1 for infants. Although substantially lower the TCR values calculated on the basis of the concentration of individual micropollutants, those values can be still considered high when compared to the average daily per capta lettuce consumption in Germany and the recommended daily intake suggested by the DGE. In the case of cucumber, where all the compounds with the exception of Ep-CBZ were classified as Class III, the sum of concentrations is 0.424 mg/L and the combined exposure TCR values obtained for adults and children are 10 kg/(p.d) and 4.2 kg/(p.d), respectively.

In general, those results can be taken as a first indication that it is unlikely that the consumption of the vegetables produced hydroponically with the BTP effluent could represent a health hazard due to contamination with organic micropollutants. Nonetheless, one additional aspect to bear in mind is that the analysis above takes into concern only human exposure to the organic micropollutants via a single pathway – the consumption of the vegetables. A more thorough exposure assessment would be required in order to determine if the consumption of the vegetables produced with the BTP effluent, combined to any additional background exposure, would lead to exposure levels that warrant a more detailed assessment.

6.4 Conclusion

The results reported here provide a first look on the implications that the high presence of organic micropollutants in source-separated wastewaters might have for the decentralized reclamation of wastewater for the purposes of urban farming.

In terms of the micropollutant profile of the effluent of the secondary biological aerobic treatment of blackwater, the results reported above demonstrated that such effluents can contain a wide variety of organic contaminants, especially pharmaceuticals and their metabolites, which can be present in concentrations that can be orders of magnitude higher than those typically observed in the effluents of WWTP treating municipal wastewater.

In small scale systems such as the blackwater treatment plant investigated here, which served a population of only 50 persons, it should be expected that concentrations of pharmaceutical compounds and their metabolites fluctuate substantially over time, given that changes in the health status of any single individual – and the pharmacological treatment used to manage them – can have high impact in terms of the presence and abundance of pharmaceutical compounds and their metabolites in the raw blackwater, and hence the effluent of its biological treatment.

The results obtained here show that organic micropollutants can be extensively taken up by lettuce and cucumber crops grown hydroponically with such effluents, leading to their accumulation in edible parts. Although concentrations in the case studied here were high when compared to those measured in similar studies, risk assessment by the EFSA’s TTC risk screening methodology and by the WHO/IPCS risk assessment framework indicated that human health risks associated to consumption of those vegetables is low.

Final Remarks

7 FINAL REMARKS

In the preceding chapters, technical and health risk related aspects regarding the integration of a hydroponic urban farm with an on-site MBR-based blackwater treatment system were assessed. To the best of the author’s knowledge, an integrated urban-farming/nutrient recovery system such as the one under consideration here has not been previously studied. Although the reclamation of treated effluents of domestic wastewater treatment in agriculture is in itself a very common practice (especially in arid and semi-arid countries), both the use of blackwater as input stream as well as the application of the effluent of biological treatment in an hydroponic system are unique aspects of the system investigated in this study. The amount of prior work regarding the reuse of wastewater or treated effluents in hydroponic systems is particularly scarce – only a handful of peer-reviewed literature were found concerning this topic, among which the work of Oyama et al. (2005), Adrover et al. (2013) and Maloupa et al. (1999), none of which, however, addressing the aspects of nutrient recovery and organic micropollutant uptake. In that sense, the results presented in the previous chapters hopefully will contribute to the understanding of the performance and limitations of this type of wastewater reclamation/nutrient recovery system.

In general, the results obtained during the operation of the integrated urban-farming/nutrient recovery system provide a proof-of-principle that such it is indeed technically feasible. Nonetheless, some key factors (and limitations) were identified as critical for the successful implementation of such an approach for the recovery of nutrients from blackwater. Firstly, there is the discrepancy between the physicochemical characteristics of blackwater and the composition requirements of a nutrient solution adequate for use in hydroponic cultivation systems. Apart from the evident need to remove organic matter and pathogens from raw blackwater, of highest relevance in this regard is blackwater’s nitrogen content profile, which is characterized by the predominance of ammoniacal nitrogen. This is diametrically opposite to what is considered ideal for hydroponic nutrient solutions, which contain primarily nitrate as nitrogen source.

As seen in Chapter 4, this mismatch of nitrogen profiles is highly consequential for the design of the treatment process, particularly the biological nitrification step. Due to the low alkalinity- to-TAN ratio of blackwater, the full nitrification of its nitrogen load requires the dosing of an external alkalinity source, which itself might compromise the effluent’s quality in terms of its intended use as a nutrient solution. The problem is exacerbated if the bioreactor is operated under conditions usually applied in conventional WWTPs to ensure nitrification, i.e. with a pH setpoint value in the neutral to slightly alkaline range, where only a small fraction of the blackwater’s alkalinity is actually available to neutralize the protons produced by the activity of nitrifiers. Moreover, besides limiting the availability of alkalinity, operating the reactor at such “conventional “ pH levels results in an effluent in which the availability of several micronutrients (such as Fe, Mg and Mn) is strongly limited, with serious consequences for plants, as seen in Chapter 5. Fortunately, results from preliminary experiments reported in Chapter 4 indicate that the operation of the biological nitrification process at lower pH levels, which would remediate the issues mentioned above, is possible if an appropriate SRT is chosen for the reactor.

Chapter 7

Another import aspect to be considered is the area requirements. As demonstrated by calculations performed in Chapter 5, the nutrient recovery of source-separated wastewaters by the direct use of treated effluents in urban farming is fundamentally limited by farming area requirements. In the case studied here, around 25 m² of cultivation area per person were estimated to be necessary in order to utilize the full NPK load available in the effluent of the blackwater treatment system. In urban areas, where population density is high and available surface area is scarce, the scale-up of such a scheme would face enormous challenges.

Finally, the impact of organic micropollutants on the objective and perceived quality of the produce cannot be understated. As shown in Chapter 6, such compounds are present in measurable amounts in biologically treated blackwater, and can be taken up in the edible parts of crops in substantial amounts. Even though the health risks associated to the micropollutant levels observed in the vegetables produced with BTP effluent are low, it would not be surprising if the presence of those contaminants would have a negative impact in the consumer perception of those products and limit their value.

Despite of those issues, conceptually the system investigated here represents a very rational solution for the supply of recycled fertilizer for urban farms. In a future where urban farming plays a larger role in the food security of cities, such system would not only supply fertilizer for food production, but from the wastewater treatment perspective act as satellite systems that reduce the nutrient and COD loads to centralized wastewater treatment plants, allowing the existing plant footprints to be reallocated to other purposes, such as quaternary treatment for removal of micropollutants.

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113

CURRICULUM VITAE

Name: Victor Takazi Katayama Date of Birth : 21/02/1984 Place of Birth : São Paulo, Brazil Nationality : Brazilian

EDUCATION

2013 – Present Doctor of Engineering Department of Photonics and Environment Fraunhofer UMSICHT (Oberhausen) Department of Mechanical Engineering Ruhr-Universität Bochum

2010 – 2012 Master of Science Graduate Program in Civil Engineering Polytechnic School University of São Paulo

2003 – 2009 Bachelor of Science in Engineering (5-year Program) Polytechnic School University of São Paulo

1999 – 2001 High School Diploma Palmares School / Visconde de Porto Seguro School São Paulo, Brazil

1991 – 1998 Middle School Diploma Palmares School / Visconde de Porto Seguro School São Paulo, Brazil

WORK EXPERIENCE

01/2019 – Present Staff Scientist Department of Photonics and Environment Fraunhofer UMSICHT (Oberhausen)

11/2012 – 08/2013 Environmental Analyst

ConAm Environmental Consulting São Paulo, Brazil