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Surrogate measures for providing high frequencyestimates of total phosphorus concentrations inurban watersheds
Gaetano Viviano, Franco Salerno*, Emanuela Chiara Manfredi,Stefano Polesello, Sara Valsecchi, Gianni Tartari
CNR e Water Research Institute (IRSA), Via del Mulino 19, Brugherio (MB) 20861, Italy
a r t i c l e i n f o
Article history:
Received 28 February 2014
Received in revised form
30 June 2014
Accepted 3 July 2014
Available online 11 July 2014
Keywords:
Surrogate measures
Total phosphorus
Turbidity
Caffeine
Urban water quality
Combined sewer overflows
* Corresponding author. IRSA-CNR, Via Del M2004692.
E-mail address: salerno@irsa.cnr.it (F. Sahttp://dx.doi.org/10.1016/j.watres.2014.07.0090043-1354/© 2014 Elsevier Ltd. All rights rese
a b s t r a c t
Until robust in situ sensors for total phosphorus (TP) are developed, continuouswater quality
measurements have the potential to be used as surrogates for generating high frequency
estimates. Their use haswidespread implications for water qualitymonitoring programmes
considering that TP, in particular, is generally recognised as the limiting factor in the process
of eutrophication. Surrogate measures for TP concentration, such as turbidity, have proved
useful within natural and agricultural contexts, but their predictive capability for urban
watersheds is considered more difficult, due to the different sources of TP, though a strict
relationship with turbidity/suspended matter has been clearly described even for these
environments. In this context, we investigated this still unresolved problem for high fre-
quency estimation of TP concentration in urban environments by monitoring a medium-
sized (71 km2) urban watershed (Lambro River watershed, north Italy) in which we detec-
ted 60 active combined sewer overflows, and an its natural sub-basin for comparison. We
found twodifferent relationships between turbidity andTP concentration in the investigated
urbanwatershed that differently describe the prevalence of TP frompoint sources (domestic
wastewaters) or diffuse origin (surface runoff). In this regard, we first characterise the pre-
vailing sources of TP by using a marker for detecting domestic wastewater contamination
(caffeine), then we describe the mutual relationships amongst the continuously monitored
variables (in our case the occurrence of the First Flush and the clockwise turbidity/discharge
hysteresis). Afterwards we discriminate, by observing variables that are continuously
monitored (in our case, the discharge and the turbidity), amongst the continuous surrogate
records according to their sources. In conclusion, we are able to apply the relevant turbidity/
TP regression equations to each turbidity record and, thus, estimate the respective TP con-
centrationswithhigh frequency. If traditional grab sampling techniqueshadbeenemployed,
the contributions of point sources (up to 34% across 237 monitored days) to the total esti-
mated loads would not have been correctly evaluated, whilst the high frequencymonitoring
is able to catch the dynamics that occur over time scales of a fewhours.We conclude that the
reasonable uncertainty obtained in this study can be achieved in other urban watersheds,
but further studies are required forwatershedsof differing sizes anddegrees of urbanisation.
© 2014 Elsevier Ltd. All rights reserved.
ulino 19, Localit�a Occhiate, 20861 Brugherio (MB), Italy. Tel.: þ39 039 21694221; fax: þ39 039
lerno).
rved.
wat e r r e s e a r c h 6 4 ( 2 0 1 4 ) 2 6 5e2 7 7266
1. Introduction
Phosphorus is generally recognised as the limiting factor in
the process of eutrophication and, therefore, is widely used as
an indicator of the trophic conditions of lakes (OECD, 1982;
Schindler et al., 2008). Restoration efforts that aim to control
inputs from sources of phosphorus into watersheds (Goldman
et al., 1993; Elser et al., 2007; Carraro et al., 2012a) are
considered to be important strategies for decreasing the risk
of cyanobacterial blooms in lakes (Carraro et al., 2012a,b).
Determinations of nutrient loads are typically based on the
collection and analysis of grab samples in combination with
instantaneous estimates of discharge, but the frequency and
regularity of traditional sampling methods may not provide a
good representation of the variations in constituent loading of
P. This is particularly relevant in small watershed and in
systems with flashy hydrology (Scholefield et al., 2005; Jones
et al., 2012). For most water quality monitoring programmes,
the frequency of grab sampling is determined by the balance
between the resolution necessary to estimate accurate loads
and the resource costs of sampling (e.g., Jones et al., 2012).
Several Authors recommend high frequency, continuous
monitoring to reduce the uncertainties in load calculations
that result from infrequent sampling efforts and biased esti-
mation methods (Quilbe et al., 2006; Johnes, 2007; Jones et al.,
2012). A large proportion of annual phosphorus loads may be
exported during short periods of high flows (Grayson et al.,
1996; Demars et al., 2005). Moreover, whilst these research
and monitoring efforts are episodically intensive, they
generally have been too limited in their temporal and spatial
scales to adequately address the factors that influence the
development of detrimental events such as harmful algal
blooms, oxygen depletion and fish kills (Glasgow et al., 2004).
In many situations, turbidity (TURB) has been used as a
surrogate measure for the concentration of suspended solids
as well as constituents such as total phosphorus (TP) thatmay
be associated with suspended matter. The justification for the
choice of this proxy is that: (i) the attenuation of light by a
sample of water should be related to the amount of fine sus-
pended material in the water, and (ii) most of the TP trans-
ported in streams is in a particulate form and attached to fine
suspended material (Grayson et al., 1996; Stubblefield et al.,
2007). However, these surrogate relationships are site-
specific (Grayson et al., 1996; Tomlinson and De Carlo, 2003)
and depend greatly on the source of sediment. Grayson et al.
(1996), Kronvang et al. (1997), Stubblefield et al. (2007) and
Jones et al. (2011) reported significant and high TURB/TP cor-
relations both in natural and agricultural watersheds.
Rasmussen et al. (2008) observed that non-urban sites present
the highest correlations, whilst Miguntanna et al. (2010)
highlighted a direct TURB/TP correlation for an urban water-
shed. Grayson et al. (1996) suggested that some of the outliers
in the TURB/TP relationship could be attributed to “First Flush”
events of urban stormwater. According to these studies, the
TURB/TP relationship for an urban watershed seems more
complex because it is influenced by additive factors (mainly
organic matter of anthropogenic origin) than for a natural
watershed (mainly mineral particles as clay, amorphous ox-
ides as iron oxo-hydroxide, etc.), as described by Oliver et al.
(1999). In summary, whilst in literature TURB has been
found to be a suitable proxy for TP concentration in some
natural and agricultural watersheds, its adequacy for urban
environments still requires further insights. Furthermore,
considering that nearly all European rivers drain populated
areas, the need to understand the contributions from both
diffuse and point source pollution has long been recognised
(e.g., Demars et al., 2005).
The objective of this study is to develop a method for
estimating TP concentrations with a high frequency in ur-
banized watersheds. A completely forested watershed and
one urbanized have been continuously monitored with the
same experimental design to evaluate potential discrepancies
in the behaviour of surrogatemeasures in the estimation of TP
concentration between these two environments. The suit-
ability of potential explanatory variables that have been
continuously monitored as surrogate measures for TP con-
centration in the Lambro River, Northern Italy is evaluated.
We first evaluate the effectiveness of surrogate relationships
for TP concentrations. Then, we investigate the different
sources of particulate matter (using Principal Component
Analysis) and attempt to classify samples according to their
source (using Quadratic Discriminant Analysis and the Sup-
port Vector Machines). We conclude by discussing the appli-
cability of this surrogate method to urban systems and
suggesting potential improvements to our approach.
2. Material and Methods
2.1. Site description
The Lambrone watershed (71 km2) is situated on the southern
edge of the Alps (northern Italy) between the two branches of
Lake Como (Lombardy Region) (Fig. 1). The main watercourse
is the Lambro River, which is also the main tributary of
Pusiano Lake. This is amedium-sized subalpine lake (Buraschi
et al., 2005). The lake TP concentration increased until the
mid-1980s (0.2 mg P/L at the 1984 winter overturn) and has
progressively decreased towards the mesotrophic conditions
(TP concentration 0.039 mg P/L at the 2013 winter overturn)
since the construction of a wastewater treatment plant
(WWTP) in 1985. Despite the improvement in the lake's trophicconditions, several cyanobacterial blooms have caused
ecological problems, particularly those of the filamentous
cyanobacterium Planktothrix rubescens (Carraro et al., 2012a,b).
The precipitation in this area is concentrated in spring and
autumn and tends to increase progressively, from 1500mm/yr
to 2000 mm/yr, from south to north along the altitudinal
gradient of thewatershed (Salerno and Tartari, 2009). The soils
of the Pusiano watershed belong to the following 4 groups in
accordance with the WRB1998 classification system (FAO,
ISRIC, ISSS, 1998): Leptosols, Regosols, Cambisols and Phaeo-
zems (Salerno et al., 2014a,b). The elevation ranges from 282 to
1450 m a.s.l. (above sea level), with a mean slope of 24.2�. Thebasin's corrivation time, indicating the susceptibility of the
basin on a temporal scale to a flash flood response (i.e., the
hydrological response), is 4 h. The mean discharge calculated
over the last 10 years is 1.6 m3/s (Salerno and Tartari, 2009).
Fig. 1 e Map of the locations and features of the two
selected watersheds (the Lambrone eURBAN- and the
Gajum e NATURAL- watersheds): land uses, water quality
monitoring stations, the combined sewer system (CSS)
located under or close the main river channel, and the
active combined sewer overflows (CSOs) are labelled.
wat e r r e s e a r c h 6 4 ( 2 0 1 4 ) 2 6 5e2 7 7 267
This watershed is primarily covered by deciduous forests and
grasslands (87.5%), with only 0.5% of the area used as agri-
cultural land. The remaining portion is urbanized (12%), with
approximately 30,000 people resident in the watershed that
are mainly concentrated along the Lambro River course
(Fig. 1). This densely populated portion of the watershed is
served by a combined sewer system (CSS) that carries the
wastewater to the WWTP treatment plant located outside of
the investigated watershed. During a monitoring field
campaign, along the CSS and the municipal sewer network,
we detected 60 active combined sewer overflows (CSOs) that
exceeded the capacity of the WWTP during rainfall events,
thus overflowing untreated wastewater into the river. This
river outlet is hereafter referred to as the URBAN watershed.
A sub-basin of this watershed, the Gajum, was chosen as a
second study area to evaluate the potential for surrogate re-
lationships in urban vs. natural watersheds. The Gajum
(5.5 km2) is a small sub-basin of an eastern tributary of the
Lambro River (Fig. 1). These two sites were selected for their
distinct characteristics. In fact, the Gajum watershed has a
completely forested land cover, which is primarily deciduous,
and does not contain any anthropic land-use forms. The
elevation ranges from 489 to 1367 m a.s.l. The terrain has an
average slope of 29.2� (markedly higher than that of the
catchment overall), which produces a rapid hydrological
response into the Lambro River. The mean river discharge is
0.15m3/s (2000e2010) (Salerno et al., 2014a,b). This river outlet
is hereafter referred to as the NATURAL watershed.
2.2. Experimental design and chemical analyses
The two sites were instrumented for the collection of high
frequency (time step of 1 h) water qualitymonitoring data. The
equipment includes two In-Situ Inc© Troll9500 multi-
parametric probes for monitoring the water level, tempera-
ture, pH, conductivity (which ranges from 5 to 20,000 mS/cm
25 �C) and TURB (which ranges from 0 to 2830 nephelometric
turbidity units (NTU)). One probe was installed at the URBAN
watershed outlet from 29 April 2010 to 4 September 2013 (1225
days). The second probe was installed at the NATURAL water-
shedoutlet for a shorter period (from4April to 16 June 2011 and
from 21 October 2011 to 20 January 2012e92 days) with the aim
of analysing single events without considering the seasonal
behaviour. The probes were calibrated in the laboratory before
installation (APAT, IRSA-CNR, 2003a) and regularly tested to
ensure their calibration.Dischargeswere calculated fromwater
levels continuously monitored by the probe applying the ve-
locity/level curveasdescribedbySalernoandTartari (2009).The
TP loads were calculated by multiplying the daily discharge by
the daily TP concentration estimated by the surrogate equa-
tions whose development is described below.
In both sites, hourly monitoring was also conducted using
the WS Porti 24T automated refrigerated sampler by Water-
SamGmbH&Co. KG: 15/16 February 2011, 27/28March 2011, 4/
5 Nov 2011 for the URBAN watershed and 31 May/1 June, 8/9
June, 17/18 September 2011 for the NATURAL watershed
(summary statistics of data in Table 1). All river water samples
were collected from the thalweg of the channel as near to the
in situ probes as possible. The automated samplers operate by
pumping water from the river through tubing into sample
bottles held within the main chamber, allowing for the
collection of multiple samples during a storm event. In gen-
eral, the samplers were deployed when precipitation was ex-
pected. Each sample was analysed in the laboratory to
determine the amount of total suspended solids (TSS), the
alkalinity, nutrient concentrations (TN, TP, NeNH4, NeNO3),
the level of caffeine as an anthropogenic marker (e.g., Buerge
et al., 2003; Musolff et al., 2009) and the other main ionic
constituents (K, Ca, SO4, Mg, Na, Cl) (Table 2).
Samples were filtered through 1.2 mm GF/C filters to esti-
mate TSS using the gravimetric method (APHA, AWWA, WEF,
1995). Alkalinity was determined by the potentiometric
method according to Rodier, 1984. TP and TNwere determined
by colourimetricmethods according to Valderrama (1981). The
concentrations of NeNO3, K, Ca, SO4, Mg, Na and Cl were
determined by ion chromatography according to APAT/IRSA-
CNR Methods (APAT, IRSA-CNR, 2003b,c). NeNH4 was ana-
lysed by the colourimetric method as described by Fresenius
et al., 1988. Caffeine was determined by ultra-high
Table 1e Summary statistics of the data used for the correlationmatrix presented in Fig. 2 (total phosphoruseTP, turbiditye TURB, total suspended solids e TSS, and dissolved and particulate total phosphorus e TDP and PP, respectively). Datarefer to the three monitored precipitation events (52 samples for the NATURAL watershed; 76 for the URBAN watershed).
Unit NATURAL watershed URBAN watershed
Mean Median Range Mean Median Range
discharge (Q) (m3/s) 0.17 0.14 0.1e0.4 3.5 1 0.2e12
temperature (t) (�C) 13 13 11e15 10 11 7e13
conductivity (mS/cm 25 �C) 312 316 260e342 350 355 190e408
turbidity (TURB) (NTU) 11 5 0e63 32 20 4e183
pH 8.5 8.5 8.3e8.7 8.5 8.5 8.2e8.8
TSS (mg/L) 9 4.1 4e35 49 13 0e250
TDP (mg/L) 2 2.2 0e5 36 35 14e79
PP (mg/L) 8 3 0e43 66 44 6e366
TP (mg/L) 10 5 3e46 102 80 32e429
wat e r r e s e a r c h 6 4 ( 2 0 1 4 ) 2 6 5e2 7 7268
performance liquid chromatography/triple quadrupole tan-
dem mass spectrometry (TSQ Quantum Access MAX, Thermo
Fisher Scientific Inc., USA). Isotopic dilution with a stable
isotope-labelled internal standard compound (SIL-IS) was
applied in order to compensate for the matrix effects of
samples with high organic matter contents, and an on-line
extraction/purification step was coupled to the analytical
system in order to guarantee a high sample throughput
(Virglino et al., 2008).
2.3. Statistical analyses
2.3.1. Surrogate relationshipsThe variables continuously monitored by the probes
(discharge, temperature, pH, conductivity and TURB) during
the three precipitation events were coupled to concentrations
of the different forms of TP and TSS measured in the relevant
collected samples in order to train the surrogate relationships.
The other variables were analysed in order to investigate and
discriminate the different sources of TP. All of the monitored
high-frequency variables were explored as surrogates by TP
concentration using at first simple and then multiple linear
regressions. We evaluated the predictive capability of the
developed surrogate relationships comparing the correlation
coefficient (r) and the significance. The final selected re-
gressions were tested for checking the normality of residuals.
The evaluation process for the best regression equation was
completed without finding multiple independent variables
that provide a significant contribution (p < 0.05) in the same
equation to estimate TP concentration (e.g., Salerno et al.,
2012). To assess the potential of the surrogate approach, we
show all plots of exploratory against response variables
(Fig. 2). The developed relationships are valid only in the range
of TP concentrations monitored during the three precipitation
events. Moreover we underline the surrogate relationships are
trained here just during precipitation events and not during
base flow conditions because we evaluated, though grab
samples, that this hydrological condition is characterized by
constant values of water quality.
2.3.2. Principal Component AnalysisWe conducted an exploratory analysis of the data collected
during the three precipitation events by using Principal
Component Analysis (PCA) to obtain information about the
relationships amongst variables (e.g., Settle et al., 2007;
Salerno et al., 2014a,b) in case of not simple linearity in the
surrogate relationships. We performed this test using the
“princomp” and the “biplot” functions in the R Project envi-
ronment (Venables and Ripley, 2002).
2.3.3. Uncertainty of estimationsWe evaluated the error associated with estimates of phos-
phorus loads by considering the values of twice the RMSE (root
mean square error) (Guyennon et al., 2013) that were associ-
ated with each TURB/TP regression equation. According to
Kleinbaum and Kupper. (1978) the range of a value plus or
minus twice the RMSE produces the 95% confidence limits for
the prediction of the explanatory variable. The error associ-
ated with the discharge considered here is negligible
(RMSE ¼ 1.5%, Salerno and Tartari, 2009).
2.3.4. Quadratic Discriminant Analysis and support vectormachineWe used Quadratic Discriminant Analysis (QDA) (McLachlan,
1992) and the Support Vector Machines (SVM) technique
(Vapnik and Chervonenkis, 1974; Gokcen and Peng, 2002) to
separate measurements belonging to potential groups identi-
fied with PCA. In fact in case of not simple lineary in the sur-
rogate relationships, if PCAwouldbe able to explain the causes,
it could be necessary to separate measurements in order to
explore new surrogate relationships among smaller, but ho-
mogenous groups of data. QDA is a nearest centroid classifier,
i.e., each class (group) is characterised by its centroid. The
discriminant surface measures the Mahalanobis distance of a
pattern towards the class centre (Mika et al., 1999). A new
observation is evaluated by computing the scaled distance be-
tween its profile and each class centroid. The observation is
then assigned to the class to which it is nearest (Dabney and
Storey, 2007). This test was performed using the “qda” func-
tion in the “MASS” library of the R project (Lachenbruch and
Goldstein, 1979). Whenever QDA is unable to discern groups
because the peripheral data members of each group are far
from their centroids, the Support Vector Machines (SVM)
technique can be applied to the definition of groups. In fact the
SVM technique, in contrast to QDA, is based on the principle of
Structural Risk Minimisation, which is an inductive principle
Table 2 e Summary statistics of the monitored variables observed during three precipitation events in the URBAN watershed (first event: 26 samples; second event: 21samples; third event 29 samples).
Unit 15/16 February 2011 27/28 March 2011 4/5 November 2011
Median Mean s Range Median Mean s Range Median Mean s Range
Precipitation (mm) 1.6 1.4 1.0 0e3.4 0.0 0.3 0.9 0e4.2 1.9 2.5 3.2 0e14.2
Continuous
monitoring
Discharge (m3/s) 1.0 1.0 0.3 0.5e1.1 0.8 0.8 0.1 0.7e1.0 7.8 7.7 3.0 0.2e12.5
Temperature (�C) 7.4 7.7 0.6 7.1e8.9 10.4 10.6 0.8 9.7e12.8 12.0 12.1 0.2 11.8e12.8
Conductivity (mS/cm 25 �C) 357 358 14 331e392 398 395 9 369e407 316 309 37 189e382
Turbidity (TURB) (NTU) 17.2 20.4 7.2 10.6e39.6 4.9 6.7 4.2 4.3e19.6 44.4 59.8 46.3 6.6e183
pH 8.3 8.3 0.1 8.2e8.5 8.4 8.5 0.1 8.4e8.7 8.8 8.7 0.0 8.6e8.8
Grab samples TP (mg/L) 100.5 121.5 68.3 60e359 50.0 81.3 86.5 42e429 67.0 101.1 82.6 32e387.4
TN (mg/L) 2.80 2.95 0.42 2.53e4.23 2.67 2.82 0.48 2.49e4.38 3.35 3.40 0.50 2.79e4.66
NeNH4 (mgN/L) 0.08 0.10 0.07 0.02e0.3 0.07 0.09 0.07 0.01e0.31 0.02 0.04 0.04 0.01e0.15
NeNO3 (mg/L) 2.16 2.18 0.12 1.95e2.45 1.76 1.80 0.14 1.61e2.17 2.50 2.46 0.39 0.93e3.04
TSS (mg/L) 12.4 18.6 14.5 6.8e57.2 2.2 7.4 13.4 0.1e52.7 131.9 146.6 67.2 48.9e264.9
Ca (mg/L) 57.1 56.7 2.3 53.3e61.3 62.0 61.4 2.2 53.9e63.7 52.1 50.9 6.9 27.1e59.6
Mg (mg/L) 7.72 7.78 0.57 6.6e8.95 9.36 9.25 0.39 7.94e9.66 6.15 5.77 1.25 2.9e8.74
Na (mg/L) 4.61 4.66 0.57 3.85e5.76 3.74 3.84 0.22 3.67e4.55 1.70 1.97 0.83 1.45e5.19
K (mg/L) 0.47 0.51 0.08 0.4e0.65 0.49 0.50 0.02 0.48e0.55 0.40 0.42 0.10 0.32e0.73
Alk (meq/L) 3.19 3.20 0.13 3.03e3.52 3.97 3.87 0.22 3.14e4.02 2.95 2.85 0.43 1.58e3.51
SO4 (mg/L) 8.24 8.36 0.61 7.52e 9.85 7.09 7.16 0.25 6.87e7.87 4.76 5.01 1.12 3.51e9.03
Cl (mg/L) 6.50 6.66 0.53 5.83e7.73 4.52 4.56 0.18 4.31e5.11 2.54 2.78 1.08 2.06e7.08
Caffeine (mg/L) 0.529 0.572 0.267 0.252e1.172 0.294 0.485 0.653 0.098e1.162 0.034 0.129 0.262 0.016e1.194
water
research
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(2014)265e277
269
Fig. 2 e Correlations Matrices amongst total phosphorus (TP), explanatory variables (temperature, discharge, conductivity,
pH and turbidity e TURB), total suspended solid (TSS) and dissolved and particulate total phosphorus (TDP and PP,
respectively). (a) NATURAL watershed; (b) URBANwatershed. The asterisks indicate the level of significance of the Pearson'scorrelation coefficient (***, 0.001; **, 0.01; *, 0.05).
wat e r r e s e a r c h 6 4 ( 2 0 1 4 ) 2 6 5e2 7 7270
formodel selection that is used for learning fromfinite training
data sets (Vapnik and Chervonenkis, 1974) and aims to maxi-
mise themargin between the points of two classes by solving a
convex quadratic programming problem (Gokcen and Peng,
2002). This test was performed using the “svm” function of
the “e1071” library in theRproject (Karatzoglouet al., 2006). One
recognised method for judging the performance of any classi-
fication procedure is to calculate the misclassification
probability. The total probability of misclassification (TPM)
represents the probability that one observation belonging to a
first group is misclassified summed to the probability that one
observationof thesecondgroup ismisclassified (e.g., Crouxand
Joossens, 2005). The TPM resultswere used to evaluate theQDA
and SVMperformances in terms of discerning between the two
groups of river water samples. In order to increase the capacity
of these two discriminant analysis to discern groups, we
wat e r r e s e a r c h 6 4 ( 2 0 1 4 ) 2 6 5e2 7 7 271
included some records registered by the multi-parametric
probe whose origin can be attributed with certainty, i.e., we
considered 839hourly records registered either before a rainfall
event or after 24 h after a rainfall event and added 96 hourly
records registered during precipitation events for which grab
water samples shown caffeine values >0.1 mg/L.
3. Results
3.1. Relationships between TP concentrations andpotential explanatory variables
As mentioned above, all of the monitored high-frequency
variables were explored as surrogates for TP concentration.
Moreover, we investigated their relationships with TSS, and
the dissolved (TDP) and particulate (PP ¼ TP � TDP) forms of
phosphorus (Fig. 2). The data discussed below are the mea-
surements collected during the three rain events forwhich the
high-frequency monitoring efforts were coupled with the
automated (hourly collected) water samplings (Table 1).
For the NATURAL watershed (Fig. 2a), TP is strongly
correlated with TURB (r ¼ 0.92, p < 0.001) and had significant
but weaker relationships with discharge and temperature
(r ¼ 0.63, 0.49, respectively). Moreover, we observe that TSS is
strongly correlated with both TURB and TP (r ¼ 0.98, p < 0.001;
r ¼ 0.95, p < 0.001, respectively). The observed tight relation-
ship between TSS and TP is attributed to the fact that most of
the phosphorus transported in streams is bound to suspended
particles (Grayson et al., 1996; Stubblefield et al., 2007). Indeed,
we observe strong correlations for TSS/PP and PP/TP (r ¼ 0.94,
p< 0.001; r¼ 0.99, p< 0.001, respectively; Fig. 2a), and the PP/TP
ratio is very high on average: 80% (PP¼ 8 mg P/L and TP 10 mg P/
L; Table 1). This fraction or PP is comparable with those re-
ported by Johnes (2007) and Jones et al. (2011). The resulting
surrogate equations are reported in Table 3, the cross-
validated correlation coefficient (Aitkin et al., 2009) is 0.92,
p < 0.001 with an RMSE equal to 4 mg P/L (range: 3e46 mg P/L).
In contrast, for the URBAN watershed (Fig. 2b), the corre-
lation coefficients are much weaker than those for the NAT-
URALwatershed for all of the explanatory variablesmeasured.
In particular, the relationships for TURB/TP and discharge/TP
are r¼ 0.37, p < 0.01 and 0.02, p > 0.05, respectively. However, it
is very interesting to note that while there is still a strong
relationship between TSS and TURB (r ¼ 0.95, p < 0.001), the
relationship between TSS and TP is much weaker (r ¼ 0.41,
p < 0.01). In this case, the relationship PP/TP is also very high
(r¼ 0.99, p< 0.001), although the PP/TP ratio (Table 1) is slightly
lower than that of the NATURALwatershed: 65% (PP¼ 66 mg P/
L and TP 102 mg P/L). If we look carefully at the scatter-plots for
Table 3 e Surrogate equations for estimating high frequency T
Site Equation
NATURAL watershed TP ¼ 0.73* TURB þ 1.88
URBAN watershed (non point sources) TP ¼ 1.38* TURB þ 9.51
URBAN watershed (point sources) TP ¼ 4.36* TURB þ 34.4
TURB: turbidity (NTU); TP: total phosphorus (mg/L).a Leave-one-out cross validated correlation coefficient (Aitkin et al., 2009
TURB/TP and TSS/TP for both the NATURAL and URBAN wa-
tersheds, we observe that in the first NATURAL case (Fig. 2a)
the two relationships are biunivocal (one-to-one), whilst in
the URBAN case they exhibit “one-to-two” correspondences,
i.e., each independent variable corresponds to two different
dependent variables (Fig. 2b).
These results raise the question ofwhat the reasons for this
dual behaviour between TSS and TP are in the URBAN water-
shed. To answer this question, we analysed the temporal
behaviour of selected variables for the URBAN watershed
during the three monitored precipitation events for which the
multi-parametric probe data were coupled with hourly auto-
mated water sampling. These results are discussed hereafter.
3.2. River behaviour during three monitoredprecipitation events
Table 2 contains the summary statistics of all of the observed
variables for the URBAN watershed during the three precipi-
tation events that were monitored with high frequency by
coupling the multi-parametric probe data with the hourly,
automated water sampling data.
Fig. 3 depicts the temporal behaviour of some selected vari-
ables (TURB, TP, caffeine, precipitation and discharge) during
these same rain events. The first event (Fig. 3a) was charac-
terised by 27mmof rain in 20 h, with a peak rainfall of 3mm/h.
Consequently, the discharge doubled, and the peaks in TURB,
caffeine and TP (20 NTU, 1.17 mg/L and 359 mg P/L, respectively)
occurred synchronously, one hour after the peak in precipita-
tion. The secondmonitored event (Fig. 3b) had a higher precip-
itation pulse (peak rainfall > 4 mm/h), but a lower total
precipitation (7mmin20h). In thiscase, thedischargeremained
relatively constant throughout theevent, but thepeaks inTURB,
caffeine and TP reached still occurred synchronously (20 NTU,
1.16 mg/L and 429 mg P/L, respectively), 2 h after the peak in
precipitation. The last event (Fig. 3c) was characterised by a
greater amount of rainfall over a period of 20 h (56 mm), with a
peak rate of 14mm/h. This intense event caused an increase in
the river discharge that achieved amaximumvalue of 12.5m3/s
(ten times greater than the other two events). During this
particular storm event, the peaks in caffeine (1.19 mg/L), TP
(387 mg P/L) andTURB (183NTU)were asynchronous. The timing
of peaks in discharge vs. TURB, TP, and caffeine respectively is
not linear and will be described in detail in the paragraph 4.1.
4. Discussion
For the URBANwatershed, there was no simple correlation for
either TURB/TP or TSS/TP, though the relationship for TURB/
P concentrations.
RMSE (mg/L) Range TP (mg/L) rcva p-value
4 3e46 0.92 <0.00132 32e267 0.82 <0.00149 42e429 0.55 <0.001
).
Fig. 3 e Temporal behaviour of some selected variables (turbidity, TP concentration, caffeine, precipitation and discharge -Q-
) monitored in the URBAN watershed during three precipitation events for which the multi-parametric probe measurement
were coupled with the hourly automated collection of water samples. a) 15/16 February 2011; b) 27/28 March 2011; c) 4/5
November 2011. Note that the y-axes have different scales in the three figures.
wat e r r e s e a r c h 6 4 ( 2 0 1 4 ) 2 6 5e2 7 7272
TSSwas similar to that of the NATURALwatershed. Hence, we
attempted to determine whether the “one-to-two” behaviour
observed for the TURB/TP relationship for the URBAN water-
shed (Fig. 2b) represents two distinct groups of data that differ
in the relevant chemical characteristics of the suspended
solids because they are from different sources. In this regard,
we observed that there was a loss of synchrony between the
peaks in TURB and TP during the most intense precipitation
event (Fig. 3c); there is a lag of one hour between the peak in
precipitation and the peak in TP, which is then followed by a
peak in TURB another hour later. This loss of synchrony be-
tween TURB and TP demonstrates that, during this particular
storm event, low TURB levels (and, therefore, low TSS levels,
considering the high TURB/TSS correlation) corresponded to
high TP levels.
This result explains the duality observed in the TURB/TP
relationship at the URBAN watershed, but other questions
remain: What do the two groups of TSS represent? Is it
possible to discriminate between these two groups in order to
improve high frequency estimates of TP for urbanised
watersheds?
4.1. Different sources of suspended solids
The above-mentioned questions can be answered by per-
forming an exploratory analysis of the relationships amongst
the variables measured during the three precipitation events
by Principal Component Analysis (PCA) (Table 2). Fig. 4a shows
that the first Principal Component (PC1) explains 61% of the
variance, while the second Principal Component (PC2)
Fig. 4 e a) PCA of all the variables measured during the
three precipitation events and summarised in Table 2; b)
The relationship for turbidity/caffeine; c) The relationship
for turbidity/TP. The blue and red colours represent group
A (non-point sources) and group B (point sources),
respectively, as identified by PCA. (For interpretation of the
references to colour in this figure legend, the reader is
referred to the web version of this article.)
wat e r r e s e a r c h 6 4 ( 2 0 1 4 ) 2 6 5e2 7 7 273
explains 23%. In the same figure we observe the PC1 and PC2
“scores” (bottom and left axes) and “factor loadings” (top and
right axes). The position of the sample labels (numbers) refers
to the first couple of axes, while the length of the arrows refers
to the second factor loadings. Factor loadings, representing
the weight of variables into each component, are the key to
understand the underlying nature of a particular factor. In our
case PC1 and PC2 cannot be correlated with specific sources
(point or non-point) because every variable has similar factor
loadings in the two components. Nevertheless by plotting
samples in the xy plane described by the two new coordinates
PC1 and PC2 it is possible to classify samples in homogeneous
groups with respect to the new coordinates (PC1 and PC2)
(Fig. 4a).The green ellipse represents the 90% confidence in-
terval for the data: there is only one outlier, which was
removed from further analyses. With respect to the first
component, we are able to clearly distinguish two groups of
samples: group A (blue data), which is characterised by
negative scores (around �5) of PC1, and group B (red data),
with positive scores of PC1 (about þ2).
In PC1, the variables with negative loadings are discharge,
TURB and TSS, whilst conductivity, caffeine, and ionic com-
pounds have positive loadings. Soluble ionic compounds like
NeNH4, Na, Cl, SO4 are found together with caffeine in un-
treated domestic sewages such as those discharged by active
CSOs, while alkalinity, Ca, Mg and conductivity are variables
associated with the background geochemistry of the catch-
ment (Fig. 4a).
The group B has higher caffeine concentrations (>0.1 mg/L)
than the groupA, because sampleswere collected during flush
events. Moreover, these samples are all characterised by
turbidity <37 NTU (Fig. 4b). As mentioned, caffeine is a well-
recognised chemical marker for detecting domestic waste-
water contamination (e.g., Buerge et al., 2003; Musolff et al.,
2009). Therefore, we have identified the sources of the two
groups of samples: group B has a high content of caffeine and
ionic compounds such as sodium and chloride, which are
typical of domestic sewage and can be attributed to the
overflow of CSOs (point sources) into the river. In contrast, the
concentrations of group A samples are mainly attributed to
non point sources such as run off. In fact, group A is charac-
terised by a large range of TURB values, but lower and rela-
tively constant caffeine concentrations (Fig. 4b) which
represent the background concentrations in the basin mainly
due to a residual fraction of untreated domestic sewage in the
watershed. TP is independent from PC1 (factor loading ¼ 0)
which is the main component describing the variance of data
(61%) and this fact can be interpreted considering that the
phosphorus sources are both point and non-point ones.
Fig. 4c besides illustrating the relationship for TURB/TP
discussed above, also highlights the classification of each
sample within the identified groups. The “one-to-two” rela-
tionship is explained by the relative prevalence of point and
non-point sources. The resulting surrogate equations are re-
ported in Table 3, the cross-validated correlation coefficients
are 0.82 (p < 0.001) for the non-point sources and 0.55
(p < 0.001) for the point sources. The RMSEs are equal to
32 mg P/L (range: 32e267 mg P/L) and 49 mg P/L (range:
42e429 mg P/L), respectively. The statistical results for the non-
point sources of the URBAN watershed are comparable with
the correlations reported by Jones et al. (2011) for a prevalent
agricultural watershed with a similar range in TP concentra-
tions (r ¼ 0.70; RMSE ¼ 27 mg P/L; range in TP: 10e200 mg P/L),
whilst the relationship for TURB/TP for point sources has a
weaker predictive capability.
We are now able to explain the temporal behaviour
depicted in Fig. 3. Starting from Fig. 3c, we observed a peak in
caffeine that was in synchrony with the peak in precipitation.
We can deduce that the higher intensity of this rainfall event
caused the rapid overflow of wastewater into the river via
active CSOs. In contrast, the rainfall events presented in
wat e r r e s e a r c h 6 4 ( 2 0 1 4 ) 2 6 5e2 7 7274
Fig. 3a and b, which were characterised by a lower intensity of
precipitation, did not activate the overflows until an hour
later. However, in all the three cases, the overflow spills are
characterised by a modest amount of suspended matter
(reduced levels of turbidity) with high phosphorus content
(most likely organic matter of anthropogenic origin). The
event described by Fig. 3c also differed from the other two in
terms of the lack of synchrony between TP and TURB. In this
case, the large amount of precipitation, after inducing the
activation of the CSOs (detected by the peak in caffeine),
caused an intensive removal of suspended solids from the
basin, but was characterised by a lower TP content than that
observed for the point sources.
A conventional view of changes in turbidity during storm
events is the one that arises from its relationship with
discharge, i.e., the turbidity hysteresis dynamics. In Fig. 5, we
observe the classic dynamics generally found in urban sys-
tems; the suspended sediment concentrations tend to peak on
the rising limb, and then quickly decline (e.g., Walling, 1974;
Asselman, 1999; Wotling and Bouvier, 2002). This type of
clockwise hysteresis is commonly referred to as the First-
Flushings (Camp, 1963, p. 216) or First Flush (e.g., Arthur and
Ashley, 1998; Wotling and Bouvier, 2002). This typical posi-
tive hysteresis loop was observed for all three monitored
events. As shown in Fig. 5, the suspended sediment concen-
trations (turbidity) are much higher on the rising limb than on
the falling limb at equivalent flows (this result is more evident
for the third event, the purple (in web version) curve, which
was longer than the other two). For all three events, the
beginning of this discharge wave is characterised by the de-
livery of suspendedmatter from the CSOs (red (inweb version)
points), but in the third case, the loop contains a wide non-
point phase that is characterised by internal negative cycles.
The positive hysteresis loops of Fig. 5 clearly displays the
grouping individualized though the PCA and, at the same
time, it shows the relationship Q/TURB is far to be linear.
Therefore these two surrogates (Q and TURB) cannot be used
together as explanatory variables in a multiple linear regres-
sion system to predict the TP concentration in the URBAN
watershed. Consequently a discriminant analysis was
conducted.
Fig. 5 e Positive hysteresis loops of the water discharg
4.2. Discriminant analysis
We observed that the TP concentrations in the URBAN
watershed cannot be predicted at high frequency by turbidity
alone, but the analysis of caffeine can distinguish between
point and non-point sources of suspended matter. Unfortu-
nately, caffeine cannot bemonitored continuously and is used
here mainly as an indicator to explain the “one-to-two” cor-
respondence for the TURB/TP relationship, according to the
source. Therefore, the search for a set of continuously moni-
tored variables that can discern the source of the suspended
matter (point or non-point sources) is still required. Below, we
describe two discriminant analyses performed to achieve this
goal. A QDA was conducted between TURB and all other high
frequency variables (discharge, temperature, pH, conductiv-
ity). The lowest misclassification probability (TPM ¼ 40%) was
found between TURB and discharge. In Fig. 6a, the quadratic
red and blue surfaces discern the group B (point sources, red)
samples from the group A (non-point sources, blue) samples,
respectively. A large number of misclassified samples are
concentrated near the discriminant border between the two
surfaces and, in particular, at low values of TURB and
discharge. This error is mainly attributed to the fact that the
QDA technique discerns the two groups simply on the basis of
their centroid. Therefore, we tried to reduce the error by
analysing the same dataset with the SVM technique, which, as
reported above, aims to maximise the margin between the
points of two classes. In Fig. 6b, we can observe that the SVM
technique is better able to correctly distinguish between the
two groups at low values of TURB and discharge. Conse-
quently, the TPM is reduced to 10%.
Extending the discriminant analysis obtained with the
SVM techniques to the entire dataset (237 days), we evaluated
the classification of each monitored hour to either group B
(point sources) or group A (non-point sources). Consequently,
by applying the relevant equation for TURB/TP described in
Table 3, we find that just 9% of the samples had a prevailing
point source origin, but these samples contributed asmuch as
34% (0.66± 0.23 t) of the total phosphorus load (1.96± 1.35 t) for
the entire monitored period. The remaining samples were
mainly from non-point sources (1.30 ± 1.28 t).
e (Q) and turbidity for the three monitored events.
Fig. 6 e a) Quadratic discriminant analysis (QDA) and b)
support vector machines (SVM) analysis between turbidity
and discharge (Q) performed to distinguish between point
and non-point sources (red and blue points and surfaces,
respectively) of TP observed by continuous monitoring.
(For interpretation of the references to colour in this figure
legend, the reader is referred to the web version of this
article.)
wat e r r e s e a r c h 6 4 ( 2 0 1 4 ) 2 6 5e2 7 7 275
4.2.1. Portability and conclusionWe have demonstrated that, during the three precipitation
events monitored, the CSOs are always activated, leading to
a peak in TP concentration that precedes the discharge
peak, namely, the well-described phenomenon of the First
Flush (e.g., Arthur and Ashley, 1998; Wotling and Bouvier,
2002). By using surrogate measures (in our case turbidity
and discharge were selected), we were able to identify re-
cords that have a predominantly point-source origin and to
define the associated TP loads with a reasonable level of
uncertainty (35%, 0.23 t is the uncertainty for an estimated
load of 0.66 t). Furthermore the total load from point
sources that reached the Lambro River during the moni-
tored period (as much as 34%) would not have been evalu-
ated correctly if traditional grab sampling techniques had
been used because these dynamics occur within a few
hours.
Several other authors have recommended employing high
frequency, continuous monitoring programmes to reduce the
uncertainty in load calculations (Quilbe et al., 2006; Johnes,
2007; Jones et al., 2012). This study demonstrates that a
TURB/TP relationship also exists in urban watersheds and
that, although it is not biunivocal, it is exploitable. For this
purpose, we propose: (i) to characterise the prevailing source
of samples using amarker (e.g., caffeine, Fig. 4b) (ii) to describe
the mutual relationships amongst the monitored variables (in
our case, the occurrence of the First Flush, Fig. 5), and (iii) to
classify the records according to their source, using a contin-
uously monitored variable (in our case, discharge and
turbidity, Fig. 6). In this way, it is possible to (iv) apply to each
record the appropriate TURB/TP regression equation, thus
producing high-frequency estimates of TP concentrations.
We conclude this paper by evaluating the portability of this
method. In this specific case, portability refers to the degree to
which the proposed method is replicable in other urban wa-
tersheds. In our case, we developed the method in a water-
shed characterised by a wide number of active CSOs and by a
size (71 km2) at which the sources of TP are distinguishable.
Moreover, the data for our case study present the typical
discharge/turbidity clockwise hysteresis loop of an urban
watershed associated with First Flush events. It is likely that
similar results could be achieved forwatersheds characterised
by these general characteristics. However, this pioneering
technique of using surrogate measures to produce high fre-
quency estimates of total phosphorus concentrations in urban
watersheds requires further testing and development for the
analysis of watersheds with differing sizes and degrees of
urbanisation.
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