Journal of Hydrology 603 (2021) 127132

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Journal of Hydrology
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Research papers

Green Roofs for domestic wastewater treatment: Experimental andnumerical analysis of nitrogen turnover
Giuseppe Brunetti a, *, Ioannis-Aristotelis Papagrigoriou a, Jiří Šimůnek b, Christine Stumpp aaUniversity of Natural Resources and Life Sciences, Vienna, Department of Water, Atmosphere and Environment, Institute for Soil Physics and Rural Water Management,
Muthgasse 18, 1190 Vienna, Austriab
University of California, Riverside, Department of Environmental Sciences, CA 92521, USA

A R T I C L E I N F O

A B S T R A C T

This manuscript was handled by Marco Borga ,Editor-in-Chief, with the assistance of Christian
Massari , Associate Editor

The use of Green Roofs (GRs) for domestic wastewater treatment and nitrogen (N) removal is an appealing
opportunity to conjugate hydrological, energetic, and water quality benefits. However, the research in this di­rection has been limited to few experimental studies, while the role of numerical modeling for analysis anddesign has been overlooked. To advance understating of reactive transport processes in GRs and investigate theuse of numerical models, this study presents a comprehensive experimental and numerical investigation of the N
turnover in vegetated and non-vegetated GRs exposed to domestic wastewater injection. A Bayesian framework isused to calibrate a mechanistic numerical model against multiple observations from a controlled laboratory
experiment. A Global Sensitivity Analysis complements the framework to further enlighten the role of multiple
physical processes. Results indicate that GRs have high nitrification capacity, which increases after wastewater
application. The total leached N is 94% of the total injected N in a non-vegetated GR, compared to 67% for avegetated GR. Nitrate leaching is negatively correlated with the root solute uptake capacity of the vegetation,
which may reach up to 32% of the total injected N. Maximization of this factor is desirable and can be achievedby selecting appropriate plant species and reducing water stress periods using a proper irrigation schedule.

1. Introduction
In a world with a growing population with increasing demand forfood and water, current and future generations face multiple social,economic, and environmental challenges. According to the United Na­
tions (2018), the urban population is projected to increase by 2.5 billion
between 2018 and 2050, reaching approximately 68% of the total world
population. If not managed properly, this growth would increase theenvironmental footprint of urban areas by aggravating the alreadyexisting hydrological, energetic, and water quality issues. As reported by
the European Environmental Agency (EEA), between 20% and 25% ofrivers and coastal water bodies are subject to point source pollution fromindustrial facilities, sewage systems, and wastewater treatment plants(WWTPs) EEA, 2018.
Excess nitrogen input into ecosystems has an extensive detrimental
effect on the environment and human health (Hill, 2010; Holmes et al.,2019; World Health Organization, 2011; Yamashita & YamamotoIkemoto, 2014). When untreated, elevated concentrations ofammonia, nitrite, and nitrate in water bodies contribute toeutrophication, leading to hypoxic “dead zones”. Unfortunately, many
conventional WWTPs are not able to completely remove nitrogen spe­
cies from wastewater prior to their disposal into water bodies (Holmes
et al., 2019; Johnston et al., 2019). Although alternative technologies
are highly efficient (e.g., membrane technologies), they are usuallyexpensive and energy-intensive, thus hindering their widespread adop­tion (Matassa et al., 2015; Mehta et al., 2015). Consequently, urbanrivers have become pools of reactive nitrogen and hotspots of regional
pollution (Zhang et al., 2015).
Green infrastructure represents a potential alternative solution to
manage excess nitrogen at the local scale through wastewater recycling
and treatment (Yu et al., 2019). The combination of physical, chemical,
and biological processes occurring in such infrastructure systems, suchas soil water retention, sorption, phytoremediation, and microbial
degradation (Dai et al., 2017; Papaevangelou et al., 2016; Wirasnita
et al., 2018), can be used to provide a preliminary wastewater treatment
diffusively in the urban area (Masi et al., 2017). Considering that roof­
tops may represent up to 50% of impervious surfaces in urban areas andthat domestic wastewater is an important source of nitrogen pollution,* Corresponding author.
E-mail address: giuseppe.brunetti@boku.ac.at (G. Brunetti).
https://doi.org/10.1016/j.jhydrol.2021.127132
Received 4 May 2021; Received in revised form 15 October 2021; Accepted 24 October 2021
Available online 30 October 2021
0022-1694/© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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Journal of Hydrology 603 (2021) 127132

2. Materials and methods

the use of Green Roofs (GRs) for wastewater treatment represents anappealing but unexploited opportunity to conjugate multiple environ­mental benefits (Pradhan et al., 2019). In particular, such systems could
be able to 1) retain, delay, and evapotranspirate stormwater (Baek et al.,2020; Berretta et al., 2014; Brunetti et al., 2016a; Li and Babcock, 2014;
Stovin et al., 2012) 2) reduce the energy consumption of the building by
maximizing the evaporative cooling effect induced by the continuous
feeding of domestic wastewater on the GR (Brunetti et al., 2018; Cas­
tleton et al., 2010; Lazzarin et al., 2005), and 3) diffusively treat
wastewater through a combination of sorption, microbial degradation,and plant’s uptake and detoxification processes.
Despite many potential benefits, research on this topic has been
limited (Vo et al., 2019). Only a few studies have investigated the per­formance of GRs for wastewater treatment, usually with promisingoutcomes. For example, results reported in Zapater-Pereyra et al.
(2016), Zapater-Pereyra et al. (2013) indicated high ammonium, total
nitrogen, and total phosphorous removal efficiencies of a 9-cm deep GR,
mainly due to its high aeration capacity. Similar findings were obtainedby Van et al. (2014), Song et al. (2013), and Thanh et al. (2014).
Furthermore, there is a lack of studies dealing with the development andtesting of numerical models for such systems’ analysis and designs.
Advancement in the understanding of the physicochemical processes in
GRs would clarify their potential benefits and allow for the developmentof numerical models for design and analysis.
Therefore, the main objectives of this study are to experimentallyquantify the nitrogen removal efficiency of GRs treated with waste­water, and numerically describe and investigate the factors driving thenitrogen reactive transport in GRs. To this aim, an experimental
campaign involving the application of wastewater on both vegetatedand non-vegetated GRs under controlled laboratory conditions was
carried out. To characterize water flow and reactive nitrogen transport
in the GRs, the experiment was complemented with tracer applications,subsurface outflow and soil moisture monitoring, and nitrogen con­centration measurements. Observations were combined with a Bayesianinference framework to calibrate the hydrological model HYDRUS(Šimůnek et al., 2016) and inversely estimate soil hydraulic and solutetransport parameters. Finally, the calibrated model is coupled with a
Global Sensitivity analysis to enlighten the role of different physicalprocesses on nitrogen turnover and nutrients leaching from the GR.

2.1. Case study description
2.1.1. Experimental setup
The experimental setup (Fig. 1) included three 15 cm-deep GRs
testbeds assembled in plastic boxes (56×37 cm) and equipped with anirrigation system consisting of multiple drippers. One box served as thecontrol, and its hydraulic behavior was already investigated in Brunetti
et al. (2020a). The boxes were placed on a wooden frame to insulate thebottom and minimize heat transfer from the surrounding environment.
A slope of 1% was set to convey subsurface outflow to a circular hole andthen to a plastic pipe for measurement and sampling. The GRs’
morphology and the irrigation system were identical in all boxes. A 12cm deep granular substrate, consisting of a mixture of volcanic minerals
with a measured porosity and bulk density of 0.66 and 0.9 g/cm3,
respectively, pH equal to 7, initial nitrogen and organic matter contentsof 4 and 8% (Brunetti et al., 2016a), respectively, was used as a growing
medium. A highly permeable geotextile separated the soil substrate fromthe underlining 3 cm-deep drainage layer composed of coarse gravel.
Two testbeds were vegetated, and one was left with a bare soil surface.
Different designs were meant to highlight the role of vegetation on the
coupled water-solute transport. Two plant species frequently used forextensive green roofs were installed (Brunetti et al., 2016a): Lobularia
Maritima and Dianthus Gratianapolitanus. Both plants were transplanted
after a two-week equilibration period, during which only water wasapplied on the testbeds using peristaltic pumps.
Testbeds were equipped with two thermocouples to measure soil
temperature at two different depths and a coaxial impedance dielectricreflectometer (Stevens HydraProbe) at z = − 6 cm to measure soil
moisture and characterize the hydraulic behavior of the substrate. A
substrate-specific calibration curve was developed in the laboratory tolink the measured soil permittivity to the actual volumetric water con­tent (Kargas et al., 2013; Seyfried et al., 2005). An evaporation pan and atipping bucket to measure the evaporation demand and the subsurface
outflow, respectively, completed the monitoring system connected to an
external data logger. The acquisition frequency was set to 1 min.
The assembled testbeds were placed into a climate chamber, where
spring-summer Mediterranean climatic conditions were simulated. In
particular, air temperatures, relative humidity, and day/night alterna­
tions were set to replicate measured data from a weather station insouthern Italy (Fig. 2). No radiation intensity control was available inthe chamber; therefore, only day/night alternation was simulated. The

Fig. 1. A schematic of the experimental setup showing the testbeds’ structure, GRs’ layering, and sensors: (1) a coaxial impedance dielectric reflectometer for themeasurement of the volumetric water content in the middle of the soil substrate (z = − 6 cm), (2) thermocouples to measure the temperature in the substrate (z = − 6cm) and at the bottom of the GRs, (3) a tipping-bucket rain gauge for the measurement of outflow, which is then sampled for tracer and nitrogen analyses, and (4) anevaporation pan to estimate the evaporation demand in the chamber.
2

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Journal of Hydrology 603 (2021) 127132

Fig. 2. (A) Inflow (blue) and illumination (grey) patterns; (B) Measured air temperature (black line) and relative humidity (red line). The orange rectangles indicate
the tracer experiment.

experiment lasted 41 days and encompassed the monitoring of watercontents, subsurface outflow, and the tracer application. Nitrogencompounds in subsurface outflow were analyzed to characterize waterflow and nitrogen turnover in the GRs. Two testbeds were irrigated witha 1:2 primary wastewater effluent (1 part pure water + 1 part waste­water), while pure water was applied to the control box. The effluent
was characterized by negligible amounts of organic nitrogen and ni­
trates and an average ammonium concentration of 25 mg/L, which is inline with other studies (e.g., Do et al., 2019; Tao et al., 2014).

isotopic analysis of deuterated water. Samples were analyzed with laser
spectrometry (L 2140-i, Picarro Inc., Santa Clara, CA, USA). To diminish
the memory effect and attain more precise measurements, the analysis
was set to 11 subsequent applications, while the last three were aver­aged during the post-processing analysis to get the mean sample con­centration. Results given in delta notation were converted into
concentrations.
2.1.3. Characterization of the reactive nitrogen transport
Inorganic nitrogen compounds (ammonium, nitrite, nitrate) weremonitored weekly in the three boxes’ outflow. To ensure the data reli­ability, sampling was done randomly relative to the irrigation cycles twotimes per week with 2 to 3 daily samples. The sampling and laboratory
analysis were done in replicates to minimize errors and avoid outliers.
Plastic vials with 50 mL volume used for sampling were stored in thefridge for a maximum of 3 days due to the unstable nature of nitrogenspecies.
UV-VIS spectrophotometry was used to measure inorganic nitrogenspecies. All samples were filtered through 0.1 μm Savana® Lab Discmembrane filters and stored for up to 2 days at 4 ◦ C without acidpreservation to reduce the potential interference of the dissolved organic
matter. Ammonium and nitrite were measured colorimetrically. The
determination of ammonium is based on the reaction with phenol andhypochlorite to give an indophenol blue in an alkaline medium (Ber­thelot reaction). After the sample preparation, a 30 min reaction time isnecessary before measuring the absorbance at 667 nm. The nitrite
determination is based on the nitrite’s reaction in the acid solution of a
primary amine (Sulphanilamide) to form diazonium salt, which is
coupled to an aromatic amine to produce the red azo dye, whoseabsorbance can be measured at 540 nm.
The second-derivative ultraviolet spectrophotometric method wasused to measure nitrate in the outflow (Ferree and Shannon, 2001). All
samples and standards were treated in the same way: 5 mL of sample (orstandard) was transferred into a glass vial and mixed thoroughly with 1
mL of sulfuric acid (H2SO4) to reduce the pH. Subsequently, each sam­ple’s absorbance (or standard) was measured in the range of 200 and
230 nm in the spectrophotometer, and the maximum second derivativewas calculated. The calibration curve was fitted to the maximum second
derivative of the standard samples and used to calculate outflow nitrateconcentrations.

2.1.2. Transient water flow and tracer experiments
The irrigation schedule was designed to 1) simulate the real oper­
ating conditions of GRs for domestic wastewater treatment, 2) induce analternation of wet and dry soil conditions, and 3) minimize the plantwater stress. In particular, each box was irrigated four times per week for
one hour, during which 3 L of applied solution were distributed on thesoil surface. The amount of applied water was doubled during the tracer
experiments to speed up the tracer’s recovery. As shown in Brunetti et al.
(2018), such an irrigation pattern can significantly enhance the evapo­
rative cooling efficiency of GRs.
Measured volumetric water contents and subsurface outflow wereused to assess the GR’s hydrological behavior, particularly its retention
capacity and unsaturated conditions in the growing medium. The two
tracer experiments were conducted to characterize water flow and so­lute transport at the beginning and the end of the experimental
campaign. Breakthrough concentration curves were reconstructed foreach tracer application by measuring solute concentrations in theoutflow over time. The first tracer experiment was conducted on June
24th, 2019. A known amount of deuterated water (1 mL, 90atom%
deuterated water) and salt (30 g NaCl) was applied to all testbeds
together with a mix of wastewater and pure water in a 1:1 proportion (6L). The same irrigation pattern (6 L per day) was applied in the followingdays until the outflow salt concentration reached the background con­centration. The first tracer experiment lasted 4 irrigation cycles (4 daysand 24 L in each box). The second tracer experiment, also lasting 4
irrigation cycles (days), was conducted using the same procedure 5
weeks later (on July 29th, 2019).
The outflow collection in both experiments was done using 50 mL
plastic vials at equal intervals (15 min) until the outflow was negligible.
The salt concentration was measured using an electrical conductivity(EC) probe. Afterward, an initial salt concentration curve was con­structed for each box. Fifty subsamples for each box were taken for the3

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Journal of Hydrology 603 (2021) 127132

2.2. Modeling theory

focused on the inverse estimation of soil hydraulic and reactive transportparameters of the three testbeds: control (CR), non-vegetated (NV), andvegetated (VG) (Fig. 1. Measured volumetric water contents, subsurface
outflow, tracer breakthrough curve from the first experiment (Fig. 2, andoutflow nitrate concentrations are used for model calibration. Outflow
ammonium concentrations were negligible; therefore, they were notincluded in the analysis. The soil hydraulic parameters (θs, α, n, Ks, l) andthe solute dispersivity (λ) of the control box were already estimated in
Brunetti et al. (2020a) with limited uncertainty. Thus, in the present
study and this scenario, these parameters are set to their median value,
and the analysis is restricted to the estimation of reactive parameters.
Both the root growth and solute and water uptake are neglected in the
NV scenario, with potential evapotranspiration converted into potential
evaporation and actual evaporation depending on the surface moistureavailability. The production of nitrogen from plants’ tissue degradationis considered only for scenarios CR and VG.

2.2.1. Water flow and reactive solute transport
Considering the small dimensions of the testbed (Fig. 1), the trans­
port processes are assumed one-dimensional and strictly vertical.
Variably-saturated water flow is simulated by solving the Richards
equation. Brunetti et al. (2020a) compared multiple hydraulic parame­terizations of the growing medium and demonstrated that the unimodal
van Genuchten-Mualem (VGM) model (van Genuchten, 1980) (param­
eters: θr [L3L− 3], θs [L3L− 3], α [L− 1], n [–], Ks [LT− 1], l [–]) guarantees
the appropriate level of model complexity for the numerical analysis ofGRs. Therefore, this parameterization is used in the present study.
Root water uptake is simulated using the approach of Feddes et al.
(1978). First, the measured potential evapotranspiration demand in thechamber is partitioned into potential evaporation and transpiration
fluxes using the Leaf Area Index (LAI) (Sutanto et al., 2012). LAI is notconstant but varies over time due to plant growth, which is assumed to
be logistic. The calculated potential transpiration flux is then converted
into actual root water uptake by combining the piecewise linear water
stress response model proposed by Feddes (Feddes et al., 1978) (pa­rameters: P0 [L], POpt [L], P2H [L], P2L [L], P3 [L]) and a root density
function that accounts for the root density and growth. A thoroughdescription of the evapotranspiration partitioning and root water uptake
is provided in Brunetti et al. (2019).
The reactive transport of nitrogen in the GR is modeled as asequential reaction chain, in which organic nitrogen (Norg) is first con­verted into ammonium (NH4+) and then directly oxidized to nitrate(NO3–). The intermediate conversion from ammonium to nitrite andfrom nitrite to nitrate is assumed to be instantaneous and thus neglectedin the model. First- (µ) and zero-order (γ) coefficients describe nitrogenturnover and potential production of organic nitrogen by the degrada­tion of plant residues, respectively. The effect of denitrification has been
neglected due to the strongly unsaturated conditions in the substrateduring the experiment. The transport of the kth solute species is
described using the advection-dispersion-reaction equation, assumingthat solutes can exist only in the solid and liquid phases. Linear
adsorption to the solid phase is considered for Norg and NH4+ (Gärdenäs
et al., 2005; Li et al., 2015; Mariano et al., 2016), while 2H and NO3–
exist only in the liquid phase and cannot react to form other compounds.
Unlimited passive uptake of NH4+ and NO3– in the liquid phase isconsidered (Li et al., 2015). The effect of molecular diffusion isneglected.

2.3.2. MULTINEST algorithm
A Bayesian framework is used to calibrate the model and assess its
predictive uncertainty. In particular, the nested sampling estimator
MULTINEST (Feroz et al., 2009) is combined with experimental obser­
vations to inversely estimate the posterior distributions of soil hydraulicand reactive transport parameters for the three scenarios previously
described. For a thorough description of the MULTINEST algorithm,
please refer to Feroz et al. (2009) and Brunetti et al. (2020b).
The main advantage of nested sampling algorithms is their compu­
tational efficiency and the possibility to simultaneously calculate theparameters’ posterior distribution and the marginal likelihood, which
provide a valuable statistical basis for model selection. Based on a pre­liminary sensitivity analysis of the algorithm’s stability, the number oflive points in the MULTINEST was set to 100, and the maximum numberof algorithm iterations was set to 200,000. This threshold value wasnever reached during the analysis, which was performed three times foreach modeling scenario to assess the results’ accuracy. The stopping
criterion is then based on the ratio between the estimated total evidence
Zest and the current evidence Zi:
log(Zi + Zest ) − logZest < 0.1(1)

2.3.3. Likelihood function and prior distributions
Error residuals were assumed to be uncorrelated and normally
distributed with a constant variance, σ 2, thus leading to the following
log-likelihood function ℓ(Ω):

2.2.2. Numerical domain and boundary conditions
The finite-element model HYDRUS-1D (Šimůnek et al., 2016) is used
to solve the Richards and advection-dispersion-reaction equations.
HYDRUS has been used successfully to simulate the hydrological per­formance of multiple Low Impact Development techniques (e.g., Bru­
netti et al., 2016b; Hilten et al., 2008; Palla et al., 2009). As in Brunetti
et al. (2020a), the main modeling assumptions are that the hydrauliceffect of the drainage layer is negligible and that water flow and solutetransport are strictly vertical. The vertical domain is discretized in 100
one-dimensional elements, which are refined near the soil surface. An“atmospheric” boundary condition is specified on the top boundary,while a “seepage face” is used for the bottom boundary. A spin-up periodof 40 days is used to relax the initial conditions. The initial soil nitrogencontent is set to 4.1%, according to laboratory measurements. The plant
growth is assumed logistic and characterized by a growth rate of 0.15 1/
day (Brunetti et al., 2020a). The initial and final LAIs are set to 1.0 and
3.0, respectively. The simulated root depth varies between an initial
measured root depth of 4 cm and a final measured value of 12 cm.

ℓ(Ω) = −

)2
k (
k
k ( )
1 ∑
ln(2π) − ln σ 2 −Hi (Ω) − ̃yi2222σ i=1(2)where k is the number of measurements, and Hi(Ω) and ỹi are the ith
model realization and its corresponding measured value, respectively. In
the Bayesian analysis, the log-likelihood function to be maximized is theaggregated sum of single:ℓ(Ω) = ℓVWC (Ω) + ℓOUT (Ω) + ℓ2H (Ω) + ℓNO−3 (Ω)(3)

where ℓVWC(Ω) is the log-likelihood of volumetric water content,ℓOUT(Ω) is the log-likelihood of subsurface outflow, ℓ2H(Ω) is the loglikelihood of outflow deuterium concentration, and ℓNO3-(Ω) is the
log-likelihood of outflow nitrate concentration. A very low ℓ(Ω) is
attributed to both non-convergent model runs and simulations affectedby high mass balance errors. The standard deviation in eq. (2) is inferredby the Bayesian framework for each measurement type.
Uniform prior distributions are used in the Bayesian analysis.
Parameter bounds are provided in Table 1 and are set based on pre­liminary analyses and a careful literature review (Brunetti et al., 2020a;
Brunetti et al., 2020b; Brunetti et al., 2016a; Hanson et al., 2006; Hillet al., 2019; Sandoval et al., 2017). The residual water content, θr, is set
to 0.0, which is a realistic approximation for coarse-textured soils. A

2.3. Model calibration and uncertainty assessment
2.3.1. Inverse modeling scenarios
The calibration framework encompasses three modeling scenarios
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Journal of Hydrology 603 (2021) 127132

Table 1Model parameters, their bounds, and 5%, 50%, and 95% quantiles of the parameters’ posterior distributions calculated from the Bayesian analysis. The last column
reports the first-order sensitivity indices, S1, calculated using the RBD-FAST method to assess the influence of different factors on the leaching of nitrates from thevegetated GR.
Parameter

Parameter description

Parameter bounds)(
Posterior distributions’ quantiles 50%+45%
− 45%
NV

CR†
3

− 3

θr [cm cm ]θs [cm3cm− 3]

Residual water content
Saturated water content

0(0.38, 0.6)

0.52

α [cm− 1]

VGM shape parameter(0.1, 1.0)

0.57

n [–]

VGM shape parameter(1.1, 2)

1.44

Ks [cm day− 1]

Saturated hydraulic conductivity(1, 6000)

2712

l [–]

Tortuosity(-1, 3)− 0.98P0 [cm]Feddes’ parameter(-20, 0)0

0.42+0.0− 0.0

0.39+0.01− 0.010.22+0.01− 0.01

1.2+0.0− 0.0

1.6+0.01− 0.01

1697+24− 12

347+32− 26

2.97+0.01− 0.01− 0.98+0.02− 0.01

–

− 1

0+1
− 0

–

− 1+2
− 0

0.1

10.9

–
–
–
–
12.9+3.9− 2.8

17.8+5.2
− 4.1

–
–
–
–
0

0.065+0.024− 0.019–
1.18+1.02− 1.01

1.8+0.47− 0.63

–
0.03

–

8
2.33e − 8+4e−− 2e− 87.7+6.2− 6.3

7.06+6.4− 6.1

10.3+3.8
− 4.6

0.003

1.23+1.02− 1.03

1.39+0.86− 1.11

1.3+0.84− 0.91

1.23+1.01− 0.97

1.3+0.91− 1.04

2.1+0.24− 0.430.06–

0.003+0.0− 0.0

0.012+0.0− 0.0–

1.06+0.02− 0.011.56+0.01− 0.01–

POpt [cm]

Feddes’ parameter(-50, − 1)

Feddes’ parameter
Feddes’ parameter
Feddes’ parameter
Bulk density
Dispersivity

− 200
− 800
− 80000.9(0, 25)

KdNorg [cm3 g− 1]µSNorg [day− 1]

Organic nitrogen partition coefficient
Organic nitrogen degradation rate in the solid phase398(0, 2.4)

γNorg [day− 1]

Organic nitrogen production rate in the solid phase(0, 2.4e-6)

KdNH4+ [cm3 g− 1]

Ammonium partition coefficient(0, 15)µLNH4+ [day− 1]

Ammonium degradation rate in the liquid phase(0, 2.4)µSNH4+ [day− 1]

Ammonium degradation rate in the solid phase(0, 2.4)

σVWC [cm3cm− 3]Standard deviation of the volumetric water content measurements(0, 0.02)

σOUT [cm day ]Standard deviation of the subsurface outflow measurements(0, 2.4)–σ2H [g cm− 3]Standard deviation of the deuterium measurements(0, 002)–σNO3- [g cm− 3]Standard deviation of the nitrogen measurements(0, 0.005)

40.001+3.4e−− 2.1e− 4†VG–
0.39+0.0− 0.0

P2H [cm]
P2L [cm]
P3 [cm]ρb [g cm− 3]
λL [cm]− 1

S1 (–)

82.45e − 7+6.1e−− 4.7e− 840.0012+2e−− 2e− 40.005+0.0− 1.9e− 540.0017+1e−− 2e− 440.0036+5e−− 5e− 4

–
0.001
0
0
0
0
0.02

0.69
0

–
–

The soil hydraulic parameters and solute dispersivity for this scenario are set according to Brunetti et al. (2020a).

high value of the partition coefficient is used for the organic nitrogen to
simulate its strong sorption to the solid phase (Brunetti et al., 2020b),
which hinders its transport and occurrence in the liquid phase. Thus,
only the Norg sorbed fraction can react to form NH4+. The upperboundary for the saturated water content is set to be lower than themeasured porosity to account for the effect of air entrapment (Snehota
et al., 2015). Finally, posterior predictive checks are used to “look forsystematic discrepancies between real and simulated data” (Gelman
et al., 2004). Despite the questionable criticism of using the data twice,
we want to emphasize that the posterior predictive checks are afundamental tool for the analyst to assess the model adequacy (Gelman
et al., 2004; Kruschke, 2013).

In the present study, a convergence analysis is used to determine N. Nonconvergent runs are excluded a posteriori from the computation of thesensitivity indices. Parameter bounds are the same as used for theBayesian analysis and reported in Table 1.
3. Results and discussion
3.1. Model calibration and uncertainty assessment
3.1.1. Water flow and solute transport
Results of the model calibration are shown in Fig. 3, which compares
the observations and the posterior predictive checks (grey lines) of
simulated subsurface outflow [(A) and (D) in Fig. 3], tracer break­through curves [(B) and (E) in Fig. 3], and volumetric water contents[(C) and (F) in Fig. 3] for scenarios NV and VG, and outflow nitrateconcentrations [(G), (H), and (I) in Fig. 3] for all scenarios. Model pre­dictions are obtained by random sampling of 100 solutions from theposterior parameters’ distributions, whose quantiles are listed inTable 1.
The fitting quality is satisfactory, and the predictive uncertainty islimited for both scenarios, although the model overestimates measured
subsurface outflow from the non-vegetated testbed. This deviation ac­
cumulates at the beginning of the numerical simulation. Still, it doesn’t
grow significantly over time, suggesting a potential overestimation of
the inflow during the tracer application at the beginning of the experi­mental campaign. Conversely, the quality of the fitting of the volumetricwater content slightly degrades over time, with the model under­
estimating the observations. Inaccuracies in simulated actual evapora­tion can explain this deviation, whose magnitude remains anyway
limited.
Simulated actual evapotranspiration is also partially responsible forthe minor differences between simulated and observed volumetric water

2.3.4. Nitrate leaching from Green Roofs: Global sensitivity analysis
After the model calibration and the uncertainty assessment, a Global
Sensitivity Analysis is performed to identify the most important factors
driving the leaching of nitrates from the GR, which is directly linked tothe system’s environmental impact. To include the vegetation’s effect,scenario VG is used as the basis for the numerical analysis. This choice isintended to provide a statistical basis to enlighten the relative impor­tance of different physicochemical processes on the production andtransport of nitrates in the GR.
The Random Balance Designs Fourier Amplitude Sensitivity Test
(RBD-FAST) (Tarantola et al., 2006; Tissot and Prieur, 2012) is coupledwith HYDRUS. The method combines the accuracy of the classic Fourier
Amplitude Sensitivity Test (Saltelli et al., 1999) with the computational
efficiency of Satterthwaite’s random balance designs (Satterthwaite,
1959) to estimate the first-order effect (S1) in a variance-based context(Saltelli et al., 2006). Higher S1 values are attributed to more influential
parameters. The main advantage of the RBD-FAST method is that the
total number of model runs is reduced down to N instead of d*N (like in
Sobol’ or FAST methods), where d is the number of factors investigated.
5

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Journal of Hydrology 603 (2021) 127132

Fig. 3. Model calibration results. (A) and (D):measured (red lines) and modeled (grey lines) cu­
mulative subsurface outflow for the NV and VG
scenarios, respectively; (B) and (E): measured (bluecircles) and modeled (grey lines) deuterium con­centrations in the outflow for the NV and VG sce­narios, respectively; (C) and (F): measured (blacklines) and modeled (grey lines) volumetric watercontents at z = -6 cm for the NV and VG scenarios,
respectively; (G), (H), and (I): measured (black cir­cles) and modeled/grey lines) nitrate concentrationsin the outflow for the CR, NV, and VG scenarios,
respectively. Model predictions are obtained byrandom sampling of 100 solutions from the poste­rior parameters’ distributions. (G), (H), and (I) havethe same scale to visually appreciate the differencein nitrates leaching between testbeds. RMSE is theroot mean square error calculated from the mediansolution of the posterior parameters distribution.

contents in scenario VG, mainly encountered after irrigation events.
Nevertheless, the fitting quality remains satisfactory, suggesting that thecalibrated model reproduces the hydraulic behavior of the vegetated GR
well. The accurate description of subsurface outflow further confirms
this. In contrast, the tracer breakthrough curve is slightly overestimated,
as reflected in the wider confidence interval for the solute dispersivity(Table 1. This deviaiton can also result from mass differences when
injecting the tracer cocktail or by inhomogeneous distribution of theirrigation water on the soil surface.
The estimated soil hydraulic parameters are characterized by narrowconfidence intervals for both inverse modeling scenarios, NV and VG,
which exhibit similar estimated saturated water contents, θs. These
values are both considerably lower than that reported by Brunetti et al.
(2020a) for the control box. Significant differences also emerge for thesaturated hydraulic conductivity and the tortuosity. Altogether, these
deviations can be partially explained by soil heterogeneity, air entrap­
ment, and nonuniform application of irrigation water on the soil surface.
The estimated shape parameters, α and n, indicate a soil characterized
by a sharp air-entry point and a moderate retention capacity, typical of
GR substrates (Brunetti et al., 2016a; Hill et al., 2019; Sandoval et al.,2017). The calibrated Feddes’ parameters reflect the nearly optimaltranspiration rates of the GR vegetation under wet soil conditions andagree with previous studies (Brunetti et al., 2016a; Kuronuma and

Watanabe, 2017).
3.1.2. Reactive nitrogen transport
Nitrogen turnover is well predicted in all inverse modeling scenarios.
The highest predictive uncertainty is encountered for scenario NV [(H)in Fig. 3], while the scenarios CR [(G) in Fig. 3] and VG [(I) in Fig. 3]
exhibit both narrow prediction bands. This trend was already observed
for water flow and solute transport, suggesting experimental inaccura­cies in the non-vegetated testbed. Nevertheless, the description of thenitrogen reactive transport remains sufficiently accurate also for this
scenario.
Comparing the estimated parameters posterior distributions holds
more interest and clarifies the role of different physical and biologicalprocesses. First, the very low values estimated for γ Norg indicate that therelease of nitrogen from the decomposition of plant residues was limited
during the experiment. Although this was expected due to the limited
duration of the experimental campaign, this process cannot be excludedin the long term and under field operating conditions, where the sea­sonality can trigger changes in the plant status. Interestingly, the esti­
mated degradation rate of organic nitrogen, µSNorg, for the vegetated box
is significantly higher than that calculated for the control box. Thissuggests that wastewater application induces optimal conditions for the
microbial conversion of Norg to NH4+ (Ibekwe et al., 2018). The NH4+6

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Journal of Hydrology 603 (2021) 127132

partition coefficient cannot be inferred for the CR and NV scenarios.
Still, its estimated value for the VG scenario indicates strong sorption ofthis compound to the solid phase, compatible with the high organic
matter content of the substrate (Wang and Alva, 2000). The high tomoderate uncertainty of this estimated parameter is mainly a conse­
quence of the lack of information about the ammonium distribution in
the soil profile.
The NH4+ degradation rate in the liquid phase, µLNH4+, exhibits highuncertainty and cannot be inferred from the observations. Conversely,
the high value estimated for µSNH4+, which, together with µSNorg, in­dicates high nitrification processes in the vegetated testbed. The com­
parison between the estimated standard deviations of nitratemeasurements, σNO3-, confirms experimental inaccuracies for the nonvegetated testbed and might cause the high parameter uncertainty forthis scenario.
Table 2 reports the cumulative N fluxes in the GRs for different
scenarios calculated by averaging the results of 100 numerical simula­tions obtained by random sampling from the posterior parameters’
distributions. The results indicate that the N leaching potential issignificantly higher in the non-vegetated testbed, where it reaches 94%
of the total injected N, compared to the 67% estimated for the vegetatedbox. In the latter scenario, the roots take up 32% of the injected N, while
N input from decomposition plays a minor role. Conversely, this process
is more important for the control scenario, where 63 and 30% of thegenerated N are leached and taken up by roots, respectively. These
percentages are similar to those obtained for the vegetated scenario andsuggest that root solute uptake can mitigate N leaching from GRs.

fertilize traditional GRs to restore nutrient levels and support plants’
growth. Using a numerical model can help optimize fertilization to
avoid excess nutrients leaching (Berndtsson et al., 2006). Finally, thenarrow estimated uncertainty for µSNorg confirms that organic nitro­gen turnover is the dominant process in the release of nitrates fromthe control box.
• The non-vegetated GR releases the highest amount of nitrates, sug­gesting an important role of root solute uptake for the nitrogen
removal from GRs irrigated with domestic wastewater. This uptake isfavored by moderately wet conditions of the soil substrate during theexperiment [(F) in Fig. 3]. Vijayaraghavan et al. (2012) reported
similar differences between vegetated and non-vegetated extensive
GRs. Similarly, Berndtsson et al. (2006) highlighted the role ofvegetation on nitrogen retention in extensive GRs. NO3– leachingfrom the non-vegetated testbed exhibits a decreasing trend [(G) in
Fig. 3], which is again compatible with gradual degradation of theinitially present soil organic nitrogen. This trend is not observed forthe vegetated box and can be partially explained by plant uptake,which hampers the initial accumulation of nitrates. Afterward, theapplied wastewater replenishes the soil nitrogen and induces optimal
nitrification conditions (high µSNH4+ in Table 1, thus maintaining anapproximately constant outflow concentration.
3.2. Global Sensitivity analysis
The previous discussion on the experimental data and model cali­
bration outcomes identified root solute uptake and soil organic com­pounds’ degradation as important processes in nitrogen turnover in theGRs. To further confirm or disprove these findings, a Global Sensitivity
Analysis is performed to determine the factors driving nitrate leachingfrom the vegetated GRs. The convergence analysis indicated that 3000
model executions were sufficient to obtain stable estimations of the firstorder sensitivity indices (reported in the last column of Table 1 for all
investigated parameters. Seven factors out of 15 exhibit an appreciableinfluence (S1 >0.1%) on nitrate leaching, but only five are characterized
by S1 >1%: γ Norg, POpt, µSNH4+, µSNorg, and P0 (in order of importance).
The soil hydraulic and solute non-reactive transport parameters havenegligible influence in the investigated range (Table 1. This may bepartially due to the limited depth (i.e., 12 cm) of the soil substrate,
which masks the effect of transport processes, and the controlled labo­
ratory conditions, which reduce heterogeneity. Similar reasoning can beapplied to the ammonium distribution coefficient, whose retardation
effect on the nitrate transport cannot be fully appreciated in a shallow
soil profile.
As expected, the first-order degradation coefficients, µSNH4+ andµSNorg, have an appreciable impact on nitrate leaching. However, theireffect is topped by γ Norg and POpt, which are the two most influential
factors. These results confirm that root solute uptake and soil organic
matter degradation are important processes in the GR nitrogen balance.
To further clarify their functioning, a local sensitivity analysis is per­formed using the median solution in Table 1 as a reference and changing
alternatively only γ Norg and POpt. Fig. 4 shows the resulting cumulativeroot NO3– uptake and bottom flux as a function of γNorg [(A) and (C) in
Fig. 4] and POpt [(B) and (D) in Fig. 4]. The comparison confirms thebigger influence of γ Norg. High production of organic nitrogen is posi­
tively correlated with an increase in the soil’s nitrate level, which cannot
be completely offset by an increase in root solute uptake. The latter
depends on the plant uptake capacity, which is regulated by the tran­
spiration demand, the vegetation’s characteristics, and the soil volu­
metric water content in the root zone (Brunetti et al., 2021). For
instance, very wet conditions can induce waterlogging effects anddrastically reduce root water and solute uptake. The Feddes’ parameter
POpt is a threshold pressure head value, above which actual transpira­tion drops due to anaerobic conditions. The analysis shows that when
POpt is reduced [(C) in Fig. 4], nitrate uptake declines due to the wet
conditions observed during the experiment [(F) in Fig. 3]. As a3.1.3. Key processes in nitrogen turnover
The comparison between NO3– outflow concentrations and themodel calibration results raises interesting discussion points on the roleof different processes on the nitrogen reactive transport:• The control box (irrigated with tap water) is a source of nitrates. The
average NO3– outflow concentration is 3.6 ± 0.94 mg/L, which isabove the reported threshold concentration of 2.6 mg/L that accel­
erates eutrophication in water bodies (Bartley and Johnson, 2006).
This behavior was already observed by Vijayaraghavan et al. (2012),
who carried out an extensive experimental campaign on the assess­ment of GRs water quality. The authors reported similar concentra­tion values when GRs were irrigated solely with tap water andconcluded that the nitrate release is mainly due to the degradation of
the soil substrate’s organic compounds. This agrees with the resultsof the present study that shows a decreasing trend in nitrate con­centrations [(G) in Fig. 3] caused by the consumption of the initialsoil organic nitrogen, which is not replenished by the degradation ofplant residuals (low γNorg in Table 1. This behavior was also observed
by Monterusso et al. (2004) and suggested the need to periodically
Table 2
Cumulative N fluxes in the GRs for different scenarios (CR, VG, and NV)
calculated by averaging the results of 100 numerical simulations obtained byrandom sampling from the posterior parameters’ distributions. Values inbrackets are the standard deviations. N is the sum of Norg, NH4+, and NO3–.
N balance components
Cumulative N injected (mg/cm2)
Initial N content in the soil (mg/cm2)
Cumulative N generated bydecomposition (mg/cm2)
Final N content in the soil (mg/cm2)
Cumulative root N uptake (mg/cm2)
Cumulative N leached (mg/cm2)

Scenario
CR

VG

NV

–
0.048 (0)0.109
(0.003)0.042(0.001)
0.032(0.001)
0.069(0.005)0.539 (0)0.048 (0)0.011
(0.001)
0.061(0.002)0.171(0.003)0.364(0.003)0.496 (0)0.048 (0)–0.071
(0.004)–0.465(0.012)7

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Journal of Hydrology 603 (2021) 127132

Fig. 4. Sensitivity analysis results. (A) and (C): simulated cumulative root NO3– uptake as a function of γNorg and POpt, respectively; (B) and (D): simulated cu­
mulative bottom NO3– flux as a function of γNorg and POpt, respectively. The plots were obtained by performing numerical simulations with three different values ofγNorg and POpt. In particular, the median solution reported in Table 1 was used as a reference, and only the values of γNorg and Kf were increased 25 and 50 times tohave a fair comparison between the two factors.

consequence, nitrate leaching is higher than in other modelingscenarios.
To summarize, the main findings of the Global Sensitivity Analysis
are:volumetric water contents, subsurface outflow, tracer experiments,
and atmospheric variables in a climate chamber. This allows tonarrow down uncertainties and better understand the physicalfunctioning of the system.

• Organic nitrogen production due to exogenous variables further in­creases nutrient leaching from the GR, reducing its environmental
benefits. Therefore, installing plants characterized by a minimal
potential of tissue decomposition and high resistance and designing
an irrigation system to avoid extended periods of water stress ispreferable. At the same time, fertilization or soil amendments should
be carefully evaluated and potentially excluded when domestic
wastewater is applied. When this is not possible for various reasons,the fertilizer application should be properly designed to minimize
nutrients leaching (Clark and Zheng, 2014) by taking into accountthe in-situ conditions of the GR. The present study demonstrates thatnumerical models are a valuable and recommended tool for this task.
• Root solute uptake enhances the nitrogen removal efficiency fromdomestic wastewater. Thus, the system should be designed tomaximize transpiration by reducing water stress (both due towaterlogging or dry conditions) and supporting plants’ health.
Again, we advocate using a numerical model, which can provide acomprehensive overview of physical processes in the GR.

Still, the study also possesses some limitations and open questions tobe solved in the future. First, the experimental campaign’s duration was
too limited to observe long-term processes such as the alteration of thesoil hydraulic properties due to biofilm formation or wastewater toxicity
effects on the vegetation, which would change both the hydraulicregime and the root uptake capacity. Second, it was impossible to pre­cisely infer certain model parameters (e.g., ammonium partition coef­
ficient) due to both operating conditions and experimental inaccuracies
and limitations. Additional measurements of ammonium concentrationsin the soil profile could potentially mitigate this problem. Finally, theGRs’ morphology, simulated atmospheric conditions, and irrigation
pattern are not representative of a broad class of field-scale operating
conditions, and more tests under variable atmospheric conditions cangive more insights into the physical, chemical, and biological dynamics
of GRs, such as denitrification processes which might occur during thelong-duration and low-intensity rainfall events (Qiu et al., 2020) or due
to water-soluble organic carbon from plant residues during initial
decomposition (Surey et al., 2020). Furthermore, precipitation canaffect the nitrogen turnover by increasing the nitrate leaching potential
and soil water content, which influences the microbial degradation
processes (e.g., Maenhout et al., 2018). However, we want to emphasizethat observations’ information content was sufficient to estimate mul­
tiple parameters with limited uncertainty (Table 1.

4. Strengths and limitations
We consider this study to be innovative in mainly two aspects:• The numerical analysis of nitrogen turnover in GRs irrigated withdomestic wastewater is a novel application in this research field. The
joint use of Bayesian inference techniques and sensitivity analyses
provides a statistical basis for uncertainty assessment and ranking of
the effects of multiple physical processes on nutrients leaching fromthe GRs, respectively.
• The model calibration is performed on a comprehensive dataset froma controlled laboratory experiment on multiple testbeds, which
included not only nitrogen observations but also measurements of

5. Conclusions
This study aimed to expand the current knowledge on the potential
use of Green Roofs for domestic wastewater treatment, with particularemphasis on the nitrogen reactive transport and nitrate leaching. A
Bayesian framework was used to calibrate a mechanistic numericalmodel against multiple observations from a controlled laboratory
experiment. This analysis was complemented by a Global Sensitivity
8

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Journal of Hydrology 603 (2021) 127132

Analysis to further enlighten the role of multiple physical processes.
Results indicate that GRs quickly nitrify wastewater ammonium, andresulting nitrate leaching is negatively correlated with the vegetation’sroot solute uptake capacity. Maximization of this effect is desirable andcould be achieved by selecting appropriate plant species and reducing
water stress periods with a proper irrigation schedule. This would
simultaneously support vegetation’s health and limit the decay anddecomposition of plants’ tissues, which the analysis showed canpotentially influence the nitrogen balance in the GRs. Numerical models
are certainly a valuable tool for these tasks, and to understand anddesign GRs for wastewater treatment.
Based on these results, we recommend extending the experimental
campaign in future studies to observe the long-term behavior of thesystem under different operating conditions. We further recommend
investigating the potential use of GRs for the decentralized removal ofother compounds, such as pharmaceuticals, which show significant
persistence and occurrence in the environment (Brunetti et al., 2022).
GR vegetations could potentially uptake and metabolize many of theseneutral compounds that can easily cross the root membrane (Brunetti
et al., 2021).

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CRediT authorship contribution statement
Giuseppe Brunetti: Methodology, Writing – original draft, Formal
analysis, Software. Ioannis-Aristotelis Papagrigoriou: Conceptuali­zation, Data curation, Investigation. Jiří Šimůnek: Software, Valida­tion, Writing – review & editing. Christine Stumpp: Conceptualization,
Resources, Funding acquisition.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Acknowledgment
We would like to thank Mr. Wolfgang Sokol for setting up theacquisition system and for his help during the experiment.
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