Monitoring (micro-)pollutants in wastewater treatment plants: Comparing discharges in wet- and dry-weather

Full text

Monitoring (micro-)pollutants in wastewater treatment plants: Comparing
discharges in wet- and dry-weather
Jessica Ianes
a
, Beatrice Cantoni
a
, Fabio Polesel
b
, Enrico Ulisse Remigi
b
, Luca Vezzaro
c
,
Manuela Antonelli
a,*
a
Politecnico Milano, Department of Civil and Environmental Engineering (DICA) - Environmental Section, Piazza Leonardo da Vinci 32, 20133, Milano, Italy
b
DHI A/S, Agern Alle 5, 2970, Hørsholm, Denmark
c
Technical University of Denmark, Department of Environmental and Resources Engineering (DTU Sustain), Bygningstorvet, Building 115, 2800 Kongens Lyngby
ARTICLE INFO
Keywords:
Bypass
Conventional pollutants
Environmental risk assessment
Micropollutants
Wastewater treatment plant
Wet-weather events
ABSTRACT
Municipal wastewater treatment plants (WWTPs) are crucial for maintaining good quality of surface water,
limiting environmental pollution. However, during wet-weather events, WWTPs become an important point-
source discharge due to the activation of the bypass, which releases a mix of untreated wastewater and storm-
water. This work aims to assess how the WWTP discharges (efuent and bypass) impact on the receiving surface
water body during dry- and wet-weather, monitoring 78 pollutants (7 conventional pollutants, 19 heavy metals,
and 52 micropollutants) in each stream (efuent during dry-weather, efuent and bypass during wet-weather),
including the inuent in dry-weather for comparison. The occurrence, concentration levels and variability, and
environmental risk were addressed, with a specic focus on high-resolution (up to 20-min) sampling of the
bypass, based on the expected relevant temporal dynamicity. A wider range of pollutants occurred in the bypass,
included undetected compounds in the dry-weather inuent. Besides, a greater inter-events variability in bypass
concentrations was observed, but smaller intra-event variability, with only some pollutants exhibiting a distinct
rst-ush effect. To address the challenge of a cost-effective bypass monitoring, we explored the applicability of
readily measurable water quality parameters (total suspended solids and electrical conductivity) as proxies for
micropollutants. Correlations between these parameters and specic pollutant groups suggest a promising path
for further investigation and broader application. The magnitude of the rain event also affected concentration
levels, with event volume clearly affecting pollutants dilution. The environmental risk assessment revealed a
signicantly higher risk associated to bypass discharge compared to the efuent, especially for conventional
pollutants, metals, and terbutryn, highlighting the urgency of improved bypass management strategies. Overall,
this study highlights the contribution of wet-weather discharges from WWTPs, emphasizing the importance of
high-frequency bypass monitoring to capture peak pollutant concentrations and accurately assess the environ-
mental risk.
1. Introduction
Wastewater treatment plants (WWTPs) are essential infrastructure
for safeguarding environmental well-being and public health (Sturm
et al., 2022). They are currently designed to remove conventional pol-
lutants (suspended solids, biodegradable organic matter, nutrients and
microorganisms) (Luo et al., 2014). However, a wide variety of chem-
icals (pesticides, pharmaceuticals, and industrial chemicals) is also
present in wastewater (Golovko et al., 2021), due to their extensive
consumption in urban areas (Petrie, 2021). These chemicals, referred to
as micropollutants due to their low concentration, originate from human
excretion, improper disposal, or rainfall-runoff (Margot et al., 2013).
Micropollutants are often recalcitrant (Cantoni et al., 2024) and can
persist in the environment, thus potentially causing adverse ecological
effects (Styszko et al., 2021). Thus, micropollutants occurrence in
treated wastewater efuent has grown as a concern (Rout et al., 2021),
to the extent that the revision of the Urban Wastewater Treatment
Directive (UWWTD) (European Parliament, 2024) calls for the imple-
mentation of quaternary treatments in WWTPs serving large
catchments.
* Corresponding author.
E-mail address: manuela.antonelli@polimi.it (M. Antonelli).
Contents lists available at ScienceDirect
Environmental Research
journal homepage: www.elsevier.com/locate/envres
https://doi.org/10.1016/j.envres.2024.120132
Received 27 July 2024; Received in revised form 7 October 2024; Accepted 7 October 2024
Environmental Research 263 (2024) 120132
Available online 9 October 2024
0013-9351/© 2024 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (
http://creativecommons.org/licenses/by/4.0/ ).

While advanced treatment processes for micropollutants removal are
being assessed (Mainardis et al., 2024), developed and implemented,
emissions from wet-weather discharges remain largely overlooked
(Botturi et al., 2021; Petrie, 2021). In fact, monitoring efforts have been
primarily focused on assessing pollution levels and compliance with
regulatory standards in WWTP efuents during dry-weather (Aemig
et al., 2021; AL Falahi et al., 2022; Campo et al., 2013; Golovko et al.,
2021; Ramírez-Morales et al., 2020; Tran et al., 2018). However, as
recently highlighted in the UWWTD, monitoring should be extended to
include sewer overows during wet-weather events. During medium/-
large rain events the combined sewer capacity is exceeded, resulting in
the discharge of an untreated mix of wastewater and stormwater into the
receiving water body through combined sewer overows (CSOs). While
several studies monitored CSOs (Becouze-Lareure et al., 2019; Furrer
et al., 2023; Mutzner et al., 2019, 2020; Nickel et al., 2021; Nickel and
Fuchs, 2019; Owolabi et al., 2022; Sojobi and Zayed, 2022), the last
overow of the sewer system, i.e. the WWTP bypass, is often unexplored
(Ianes et al., 2023). The WWTP bypass is essential in protecting the
WWTP from overloads and ooding during wet-weather. However, it
introduces a surge of conventional pollutants and micropollutants into
the environment, undermining the effectiveness of the WWTP, poten-
tially disrupting ecological processes and posing a threat to aquatic
ecosystems (Lee et al., 2022).
This study addresses this crucial knowledge gap by investigating how
the WWTP discharges (efuent and bypass) impact on the receiving
surface water body during dry- and wet-weather, performing a
comprehensive monitoring of a wide range of pollutants across diverse
weather conditions and multiple monitoring periods at a WWTP,
focusing not only on the inuent and efuent, but also on the often-
overlooked bypass, with a risk-based approach. Accordingly, the aims
of the present study were: (a) identify the main sources of each group of
(micro-)pollutants; (b) dene the dynamics of (micro-)pollutants in wet-
weather; (c) assess and compare the environmental risk among dis-
charges and sampling frequencies. To do so, the occurrence, concen-
tration levels, and variabilities of 78 conventional pollutants, heavy
metals, and micropollutants (referred to as (micro-)pollutants) were
assessed in each stream of a municipal WWTP (efuent during dry-
weather, efuent and bypass during wet-weather), including the
inuent in dry-weather for comparison. We also explored the potential
of using readily measurable conventional pollutants as proxy measures
for micropollutants monitoring in the bypass, as a more cost-effective
approach. Collected data were used to assess the environmental risk
due to the bypass compared to the efuent, as supporting tool for a
proper management of wet-weather discharges.
2. Materials and methods
2.1. Study site
This study was carried out in a WWTP (210,000 population equiv-
alent, with 13% from industrial sources; average daily inow of 55,680
m
3
/d) in the outskirt of Milan, serving a 22.5 km
2
urban area and dis-
charging into the Seveso River. About 87% of the sewer system is
combined, with an average catchment imperviousness of 32%. The
WWTP include screening, grit and oil removal, primary sedimentation,
conventional activated sludge biological process, nal sedimentation,
chemical phosphorous removal, cloth ltration, and UV disinfection.
The bypass at the WWTP inlet is activated for ows above 157,440 m
3
/
d.
The average Seveso ow rate upstream the WWTP discharge was
2.01 m
3
/s in 2023, ranging from 0.01 m
3
/s in dry periods to 114 m
3
/s
during rain events. The average WWTP efuent ow rate was 0.68 m
3
/s
(maximum peak of 2.64 m
3
/s in wet-weather), while the maximum
hourly bypass ow rate was 4.91 m
3
/s. During the monitoring cam-
paigns the dilution factor (DF), dened as the sum of the upstream river
ow rate (Liris - ARPA Lombardia, 2024) and the discharge ow rate,
divided by the discharge ow rate, ranged between 1.3 and 3.4 for the
efuent in dry-weather, between 2.0 and 14.8 for the efuent in
wet-weather and between 1.4 and 128.5 for the bypass.
2.2. Monitoring campaigns
Eleven monitoring campaigns were performed from February to
November 2023, using MAXX stationary automatic refrigerated sam-
plers (model SP5A), sampling as follows: four campaigns in dry-weather
conditions (inuent and efuent), seven campaigns in wet-weather
conditions (bypass and efuent). A total of ten rain events were moni-
tored, classied based on the bypass volume: (i) small (S) if V
bypass
≤
10,000 m
3
; (ii) medium (M) if 10,000 <V
bypass
≤25,000 m
3
; (iii) large
(L) if V
bypass
>25,000 m
3
. Table S1 and Fig. S1 provides further details
on the rain events, including the maximum rain intensity and the
Antecedent Dry Period (ADP).
As for the bypass, multiple progressive time-proportional samples
were collected during the same monitoring campaign (up to 12 samples,
depending on the duration of the bypass activation), to describe the
concentration variations throughout the rain event. Each collected
sample was thus representative of a specic period of time Δt and it was
identied with a progressive number (N
sample
). To better describe the
expected temporal variations in the bypass (potential rst-ush), the
sample frequency was higher in the initial phase, and lowered
throughout the event. Specically, the rst 2 h were sampled every 20
min (N
sample
: 1–6); the 3rd, 4th, and 5th hours were sampled every 40
min (N
sample
: 7–9); the 6th, 7th, and 8th hours were sampled every 60
min (N
sample
: 10–12). Based on the sample frequency, a subsample of
150 mL was collected every 2, 4, or 6 min, respectively, for a total of 1.5
L per sample. In total, 60 samples were collected.
Inuent and efuent were monitored by collecting ow-proportional
24-h composite samples. Dry-weather campaigns were conducted after a
dry-weather period of at least 6 days. Efuent samples were collected
after 24 h after the inuent/bypass samples collection, to account for the
WWTP hydraulic retention time.
2.3. Monitored (micro-)pollutants and analytical methods
78 conventional pollutants (7), metals (19), and micropollutants (52)
were analyzed in each sample (see Table S2 for all abbreviations):
electric conductivity at 25 ◦C (EC), total suspended solids (TSS),
chemical oxygen demand (COD), ammonium nitrogen (N-NH
4
), nitrate
nitrogen (N-NO
3
), total phosphorous (TP), Escherichia coli (E. coli), 23
pesticides, 25 PFAS, and 4 other micropollutants previously detected in
Milan groundwater (MEBICAR, 2-Methyl 5-Methylthio 1,3,4-Thiadia-
zole (MMtTD), Dimetridazole (DMZ), and Tris(2-chloroethyl) phos-
phate (TCEP)). Ecotoxicity was also analyzed through immobilization
on Daphnia magna for each inuent and efuent sample, and for three
bypass samples per rain event (the rst, the last, and the central samples
among those collected).
Collected samples were transferred to an accredited laboratory
within one day, and stored at −20 ◦C until analysis, performed within 1
week, with the exception of E. coli and ecotoxicity tests performed
within 1 day. Samples were homogenized by repeated manual shaking
and transferred into amber bottles for the analysis. For metals, the total
concentration was determined through acid digestion in a closed mi-
crowave system followed by mass spectrometry. Pesticides, pharma-
ceuticals, and PFAS samples were settled for at least 24 h and the
supernatant directly injected into an ultrahigh performance liquid
chromatography mass spectrometry system. For the determination of
each analyzed (micro-)pollutant and ecotoxicity test the limits of
quantications (LOQ) and the corresponding analytical standard
methods used, are detailed in Table S2. More details on the analytical
procedure followed for the analysis of micropollutants are reported in
Section S1.
J. Ianes et al.
Environmental Research 263 (2024) 120132
2

2.4. Data processing
Data processing, statistical analyses, and gures were done using R
Studio v4.2.0 and Microsoft Excel. All concentrations data below the
LOQ were substituted by LOQ/2.
2.4.1. Statistical analysis for bypass concentrations
For the bypass samples, the Event Mean Concentration (EMC) for the
i-th (micro-)pollutant was calculated as:
EMCi=
∑
n
Nsample=1
CNsample,iVNsample
∑
n
Nsample=1
VNsample
Eq. 1
where C
Nsample
is the concentration in each sample, V
Nsample
is the volume
bypassed in Δt, and n is the total number of collected samples.
The Coefcient of Variation (CV, interquartile range/median) was
calculated for each (micro-)pollutant to assess the intra- and inter-events
variability of bypass concentrations. The CV representing intra-event
variability was calculated referring to samples of the same event,
while the CV representing inter-events variability was calculated by
considering all samples of the dataset.
The potential occurrence of the rst-ush effect during bypass events
was assessed by calculating the Mass First Flush (MFF) ratio for the i-th
(micro-)pollutant, i.e. the ratio between the percentage of cumulated
mass and volume discharged until k-th sample:
MFFk,i=
∑
k
Nsample=1
CNsample,iVNsample
∑
n
Nsample=1
CNsample,iVNsample
∑
n
Nsample=1
VNsample
∑
k
Nsample=1
VNsample
Eq. 2
This index, usually used to describe the rst-ush in combined sewer
systems (Barco et al., 2008), was adapted here to describe the observed
bypass rst-ush. In fact, the bypass might not capture the highest
concentration peaks typically associated with the rst-ush effect, since
it activates with a certain delay with respect to the beginning of the rain
event, only when the ow rate exceeds a specic threshold. The analysis
of the concentration variability, namely CV and MFF assessment, was
conducted only on (micro-)pollutants with at least 20% detection in the
total number of bypass samples.
Finally, a K-means clustering analysis was performed on all bypass
samples, using EC, TSS, and N
sample
as clustering variables. The elbow
method was used to identify the optimal number of clusters. As the data
were not normally distributed, the Spearman correlation analysis was
used.
2.4.2. Environmental risk assessment
Environmental risk assessment was performed for the j-th discharge
(efuent and bypass) by calculating the Risk Quotient (RQ), as the ratio
between the exposure concentration (measured concentration in each
sample, or the EMC) and the risk threshold (Table S2) set for the i-th
(micro-)pollutant.
For the efuent, the discharge limits (DL) set by the Italian regulation
(D. Lgs. 152/06) were used as risk threshold for conventional pollutants
and metals. The same limits were assumed for WWTP bypass, as no
limits exist in the current legislation. Thus, the RQ was calculated as:
RQi,j=Ci,j
DLi
Eq. 3
Since no efuent limits are available for micropollutants, the lowest
PNECs reported by NORMAN Network (2024) were used, accounting for
the dilution of the receiving water body (DF). As for the bypass, the RQ
was also calculated for each N
sample
collected during time Δt as:
RQi,j,Nsample =Ci,j,Nsample
PNEC
1
DF =Ci,j,Nsample
PNEC
VΔt,j
VΔt,river up +VΔt,j
Eq. 4
To assess the inuence of the bypass sampling frequency on the
environmental risk assessment, a risk ratio was calculated. For each
event, the 95th percentile of concentration derived by different sam-
pling frequencies f (20, 40, 60 min) was obtained (C
95,f
). The risk ratio
was then assessed as the ratio between the RQ calculated with C
95,f
for
each sampling frequency (RQ
95,f
) and the RQ calculated with the EMC
(RQ
EMC
).
3. Results and discussions
3.1. (Micro-)pollutants occurrence
Out of the 78 compounds analyzed, 3/19 metals, 12/23 pesticides,
and 18/25 PFAS were below the LOQ in all samples of all streams
(Fig. 1). A higher number of pollutants was detected in the dry-weather
inuent (28) and the bypass (42), compared to the efuent (27 in dry-
and wet-weather). This could be ascribed to the fact that pollutants that
are washed-off from surfaces and reach the WWTP via stormwater runoff
are likely to show higher frequency of detection or concentrations in the
bypass; contrarily, pollutants mainly originating from human excretion
or groundwater contamination are likely to show a lower frequency of
detection or concentrations during wet-weather.
The bypass stream exhibited the highest detection frequencies and
highest number of detected pollutants, including several compounds not
found in the dry-weather inuent: Tl and As for metals; MET, SMZ, DST-
TBA, TBA, PNT, DST-ATZ, BTZ for pesticides; PFHxA, PFOA, PFBA, 6:2
FTS, cC604, PFHPA for PFAS. Notably, TBA and its transformation
product DST-TBA were detected only once (event 4 in June), and were
present throughout the entire event, indicating a potential increase in
usage during this period.
Moreover, some pollutants (COD, Fe, DST-TBA) were detected in the
efuent exclusively during wet-weather, suggesting a reduction in the
WWTP performance. Conversely, BAM and LM6, typically found in
groundwater in the Milan area (ARPA Lombardia, 2022; Valsecchi et al.,
2017) were detected in all discharges except the bypass. This suggests
wastewater as their main source, with stormwater diluting their con-
centration and leading to no detection.
Fig. 2A–C shows the concentration distributions for the most detec-
ted pollutants across the four streams, supporting a visual comparison of
central tendencies (median) and data variability (interquartile range)
for each pollutant among different streams. An example of the samples
collected during an event is shown in Fig. 2D–F. As expected, conven-
tional pollutants and metals concentrations in the WWTP efuent in dry-
weather were considerably lower compared to the inuent, demon-
strating the WWTP removal effectiveness. Conversely, some micro-
pollutants (DSP-ATZ, DMZ, MMTtD, LM6) exhibited higher efuent
concentrations, indicating limitations in the treatment process for these
types of pollutants, that may be resistant to biodegradation, or accu-
mulate and concentrate in the WWTP.
Efuent wet-weather concentrations do not remarkably differ from
dry-weather concentrations (see Kruskal-Wallis test p-values in
Table S3), except for N-NO
3
(lower), and N-NH
4
(higher), suggesting
that the denitrication process is not as effective as in dry-weather. Also,
micropollutant wet-weather concentrations are generally higher. These
results can be explained by lower hydraulic retention time (Hatoum
et al., 2019), or biomass washout (Fernandez-Fontaina et al., 2012),
resulting in less effective biodegradation and adsorption on activated
sludge, and overall leading to a lower NH
4
and micropollutants removal.
In the bypass, TSS and COD have slightly higher median concen-
trations compared to the dry-weather inuent, while the other con-
ventional pollutants are diluted. Almost all metals (Al, Fe, Zn, Ni, Cu, Pb,
Mn, Cr, V ) show signicantly higher concentrations in the bypass
J. Ianes et al.
Environmental Research 263 (2024) 120132
3

(Table S3), with the exception of Co, with higher concentrations in dry-
weather, suggesting that Co may be released by industrial activities,
such as the production of super alloys, batteries, and catalysts (Santos
et al., 2016), rather than coming from surface runoff. TRB has also
signicantly higher concentrations in the bypass compared to the
dry-weather inuent, while the opposite happens for N-NH
4
, BAM, LM6
Fig. 1. Frequency of detection (concentration >LOQ) of each (micro-)pollutant, divided per pollutant class and stream (BP: WWTP bypass; DW: dry-weather; WW:
wet-weather: IN: WWTP inuent; OUT: WWTP efuent). The frequency of detection for the bypass is referred to the total number of events, not to the total number
of samples.
Fig. 2. (A-C) Measured (micro-)pollutant concentrations, grouped per pollutant class and discharge stream. Bypass boxplots show EMC values (BP: WWTP bypass;
DW: dry-weather; WW: wet-weather: IN: WWTP inuent; OUT: WWTP efuent). Abbreviations for micropollutants are reported in green for pesticides and in purple
for other types. (D-F): Example of measured concentrations and bypass ow rate during event 7.
J. Ianes et al.
Environmental Research 263 (2024) 120132
4

and MEBICAR.
3.2. Dynamics of (micro-)pollutants in wet-weather
Data in Fig. 2A–C shows a higher concentrations variability in bypass
than in dry-weather inuent for all (micro-)pollutants, that can be
explained with a high inter- and intra-event variability. Bypass thus
shows a similar behavior as CSOs (Madoux-Humery et al., 2013; Mutz-
ner et al., 2022; Nickel et al., 2021), being inuenced by factors such as
rainfall intensity and duration, and source seasonality. The 7 monitoring
campaigns cover different rain event sizes and seasons (Fig. S1,
Table S1), allowing the evaluation of a broad range of bypass
characteristics.
Fig. 3 compares the intra- and inter-events variability for each
(micro-)pollutant in terms of CV. The majority of the compounds
showed higher inter-events than intra-event variability, with the highest
CV for COD, TSS, E. coli, Co, Cr, and DSP-ATZ. The opposite was
observed for some metals (Ni, Fe, Pb, V, Mn, B, Tl) and TRB, indicating
higher intra-event variability.
3.2.1. Factors contributing to intra-event variability
The intra-event variability may be attributed to the rst-ush effect
for some (micro-)pollutants that exhibit high concentrations during the
initial stage of a rain event (Li et al., 2022), followed by a decrease while
the bypass continues. Thus, the MFF index was assessed to explore this
hypothesis, since values above 1 suggest the presence of a rst-ush
effect. Calculated MFF values are summarized in Fig. S2, while the
cumulated discharged loads during the progression of the event are
visualized in Fig. 4.
Pollutants displaying MFF>1 in the majority (>50%) of samples
comprises all conventional pollutants, most of the metals, and the
pharmaceutical MEBICAR (Fig. 4A and B); conversely, those charac-
terized by MFF<1 in the majority of samples comprises some metals
(Hg, Cd, Ni, B), pesticides (TRB and DSP-ATZ) and TCEP (Fig. 4C). The
initial peak concentration showed by the rst group is due to the initial
dilution of wastewater or/and the resuspension of sediments accumu-
lated in the network at the beginning of the rain event. Sediment
resuspension also contributes to the increase of metals loads that may
have been discharged by industries and settled during dry-weather.
Wash-off from urban surfaces can be also further contributor to the
metal loads (Müller et al., 2020).
The potential mechanism behind the absence of the rst-ush effect
for the 7 compounds (Fig. 4C) depends on their specic sources. As for
Hg, Cd, and Ni, they can be present in the atmosphere mainly due to
industrial emissions and coal burning, and subjected to wet deposition
(Davis et al., 2001); these metals are detected in Milan’s air (ARPA
Lombardia, 2024). B and DSP-ATZ may originate from their use in green
areas: DSP-ATZ is a transformation product of the herbicide atrazine
(Schollee et al., 2024), while B is an essential micronutrient for plants
(Shireen et al., 2018). TRB and TCEP are used in outdoor long-life ma-
terials with low release rate as biocide and ame retardant, respectively
(ECHA, 2024). Even though the different sources of these 7 compounds,
it seems reasonable that they are released almost constantly throughout
an event.
3.2.2. Factors affecting inter-events variability
All bypass concentrations data were clustered based on TSS, EC, and
N
sample
, being the rst two parameters proxies for the runoff and
wastewater contribution, while the third an indicator for the event
progression. The optimal number of clusters is 3, based on the Elbow
method, with the following average values, while a visualization is
shown in Fig. 5A:
- Cluster 1: TSS =838 mg/L, EC =733
μ
s/cm, N
sample
=3.2
- Cluster 2: TSS =225 mg/L, EC =362
μ
s/cm, N
sample
=2.5
- Cluster 3: TSS =161 mg/L, EC =283
μ
s/cm, N
sample
=7.5
Looking at the distribution of rain events size in the three clusters
(Fig. 5B), we observed that all the small events (type S) belong to cluster
1, while medium-large events mainly belong to cluster 2 with early
samples (1 ≤N
sample
≤5) and cluster 3 with later samples (5 ≤N
sample
≤
12). Three initial samples of medium (M) (N
sample
=2 and 3) and large
(L) (N
sample
=1) events belong to cluster 1 and refer to events (2, 4, 7 of
Table S1) having higher ADP and lower maximum rain intensities with
respect to the other M and L events belonging only to cluster 2 and 3.
Analyzing the standardized concentrations (measured value normalized
to median concentration) in each cluster (Fig. 5C), we found that con-
ventional pollutants and metals in cluster 1 are characterized by the
highest concentrations, which lower in cluster 2 and further in cluster 3.
This means that small events and initial samples of larger events with
high ADP and medium rain intensity are characterized by the highest
concentrations. The inuence of these parameters on the concentrations
in stormwater is largely observed (Brzezi´
nska et al., 2018; Li et al., 2022;
Pochodyła-Ducka et al., 2023). The correlation between higher con-
centrations and ADP is conrmed in these studies, however they report
also positive correlations between rain intensity and concentrations.
Instead, in this study, lower concentrations of conventional pollutants
and metals were observed in larger events, probably due to the dilution
that is already happening at the time of the activation of the bypass. This
means that the moment with highest concentrations related to the
resuspension of solids observed in other studies might have already gone
at the moment of the activation of the bypass, due to the intensity of the
ow.
A different behavior is shown by micropollutants, having similar
median concentrations among the different clusters, but different vari-
ability: while cluster 2 shows the lowest variability, cluster 3 displays
the greatest one, driven by additional ushes that occur towards the
mid-to-end phase of the events.
3.2.3. Conventional pollutants as proxy for micropollutants
The correlation matrix (Fig. 6) provides initial indications for iden-
tifying conventional parameters that might act as proxies for micro-
pollutants in the WWTP bypass. All collected bypass samples across the
monitored events were included in the analysis.
Signicant strong positive correlations were observed between TSS
and the majority of metals (except Ni and B), COD, and TP. Another
group of strongly positively correlated pollutants are those related to the
actual wastewater fraction, i.e., EC, N-NH
4
, TP, and MEBICAR. The
Fig. 3. Inter-events variability vs intra-event-variability (average) for each
(micro-)pollutant, calculated as Coefcient of Variation (CV) to compare the
variability between events and within the same event.
J. Ianes et al.
Environmental Research 263 (2024) 120132
5

principal component analysis also underscores the presence of these two
major groups (Fig. S4). This indicates that TSS and EC could potentially
serve as a proxy for monitoring these (micro-)pollutants during wet-
weather events (Goor´
e Bi et al., 2015b; van Daal et al., 2017): their
measurement in real-time could allow for a more cost-effective approach
compared to analyzing a wide range of individual compounds. For this
purpose, we investigated the relationship of TSS and EC with the most
strongly correlated pollutants (see Fig. S3 for correlations coefcients),
namely V, Fe, Cu, Al, COD (R
2
=0.93, 0.90, 0.87, 0.93, 0.93, respec-
tively) and N-NH
4
, TP, MEBICAR, E. coli (R
2
=0.96, 0.79, 0.76, 0.59,
respectively). We found linear relationships (Fig. S5) with good t for
the rst group correlated with TSS (R
2
equal to 0.83, 0.80, 0.86, 0.79,
0.96 respectively), and less good t for the second group correlated with
EC, where correlations were weaker (R
2
=0.91, 0.70, 0.45, 0.24).
Signicant strong negative correlations were observed between Hg
and pesticides (MET, TBA, DST-TBA, DSP-ATZ), that were rarely
detected: TBA and DST-TBA only during event 4 in June, MET also in
event 2 in March, and DSP-ATZ in event 2 in March and in event 7 in
October. As illustrated before, Hg may be found in stormwater due to
wet deposition. A different behavior was observed for the biocide TRB,
which was always detected, supporting the hypothesis that it is mainly
used as biocide in material protection, rather than as pesticide in green
areas. TRB signicant correlations are all negative and the strongest are
with conventional parameters and metals, as observed also for other
micropollutants. For example, TCEP is characterized by few signicant
correlations, which are however quite strong and negative with all the
conventional pollutants and MEBICAR, conrming that its main source
is stormwater rather than wastewater. Only MEBICAR and PFHxA do not
Fig. 4. Cumulated volumes vs cumulated loads for (A) conventional pollutants and (B) metals and micropollutants showing rst-ush effect; (C) metals and
micropollutants not showing rst-ush effect.
Fig. 5. Clusters composition based on (A) EC, TSS, and N
sample
; (B) event size (S, M, L) and progressive number of sample (N
sample
); (C) sum of standardized
concentrations of conventional pollutants, metals and micropollutants.
J. Ianes et al.
Environmental Research 263 (2024) 120132
6

show the same behavior of negative correlations. This might be due to
the different main sources of these contaminants, suggesting the latter to
derive mainly from wastewater. Another atypical pattern is present for B
and Ni that show very few signicant correlations, but fairly strong
negative correlations with TRB, conrming to be somehow affected by
stormwater, even though the mechanism seems uncertain.
The ecotoxicity of the samples could be evaluated with Daphnia
magna and its correlations. The matrix highlights that strong positive
correlations are present with TP and N-NH
4
, while strong negative
correlations are present with all pesticides. This suggests that samples
with highest concentrations of nutrients, namely those at the beginning
of the event or collected during small rain events, have a higher toxicity,
while at the end of the events when concentrations of pesticides are
higher, but the concentrations of conventional pollutants and metals are
lower, the toxicity is also lower.
Further investigation is necessary to validate these potential proxies,
but the correlation matrix provides a valuable starting point for opti-
mizing bypass monitoring strategies. It is crucial to acknowledge limi-
tations in using this approach, because, as illustrated, not all (micro-)
pollutants exhibit strong correlations with conventional parameters. In
fact, this approach might fail when highly adsorbable pollutants have
different sources with respect to TSS, and poorly adsorbable pollutants
have different sources with respect to wastewater. Further research is
needed to identify appropriate combinations of conventional parameters
that can effectively represent the dynamics of different pollutant groups
during wet-weather events.
3.3. Environmental risk assessment
Environmental risk assessment procedures related to WWTP impacts
are not well harmonized in the literature: the measured environmental
concentrations used in the calculation may correspond to different sta-
tistical measures of the observed concentrations, such as the average
(ˇ
Cesen et al., 2019; Gutierrez et al., 2024), but often the higher con-
centration percentiles are used to assess the risk in the worst scenario
(Beltr´
an de Heredia et al., 2024; Burns et al., 2022; Lee et al., 2021;
Lopez-Herguedas et al., 2022; Vuckovic et al., 2023). Moreover, when
referring to wet-weather events, the EMC is often used as the value to
calculate the risk (de Zwart et al., 2018; Goor´
e Bi et al., 2015a; Iqbal
et al., 2023), without considering possible high peaks during the event.
Also, the dilution of the river should be considered when calculating the
environmental risk using thresholds such as PNECs, while this is often
overlooked (Afonso-Olivares et al., 2017; Díaz-Gardu˜
no et al., 2017;
Hoang et al., 2024). All these aspects were explored and considered in
this study.
3.3.1. Comparison of risks among discharges
Looking at the median values (Fig. 7A), the risk posed by the bypass
is higher than that of the efuent for all the conventional pollutants and
metals. Those with RQ >1, exceeding the discharge limit for WWTP
efuents, are: E. coli, TSS, TP, Al, COD, Fe, Cu, N-NH
4
, Zn.
For some micropollutants (BAM, LM6, MEBICAR, DMZ, MMtTD), the
efuent poses higher risks due to higher concentrations with respect to
the bypass, as seen in Section 3.1. Moreover, due to dilution, also
micropollutants with higher concentrations in the bypass than in the
efuent, can pose higher risks in the efuent than in the bypass, such as
TCEP. Among the micropollutants, only TRB always poses a risk, with
the bypass posing higher risk than the efuent. On average, the efuent
poses higher risks for all other micropollutants compared to the bypass.
However, when looking at the highest percentiles of the risk in the
bypass, due to the high variability in concentration and dilution across
different events, higher RQs can be observed compared to the efuent
for TBA, DST-TBA, MET, TCEP, and PFHxA.
It is evident how the river dilution can be fundamental in correctly
estimating the risk, especially when comparing different discharge
streams.
Moreover, as for the bypass, different results can be obtained if using
concentrations from all collected samples or the EMC: in fact, the me-
dian RQ values result to be always higher for EMC, while the 95th
percentiles of RQ are lower.
3.3.2. Inuence of the sampling frequency
Due to the dynamic nature of pollutant discharge during rain events,
infrequent sampling might miss critical peak concentrations, leading to
underestimations of the risk. For this reason, based on the data collected
during the monitoring campaign, we computed and compared the 95th
percentile of measured concentrations with the 95th percentile of the
concentrations we would have measured in case of a constant sampling
frequency of 40 and 60 min (Fig. 7C). The values were standardized with
respect to the EMC of the corresponding event, which represent a correct
approximation of the average concentration during an event.
The risk estimate through the 95th percentiles (RQ
95,f
) is always
higher than the one obtained using the EMC, independently from the
sampling frequency: the highest values are those related to actual
measured values, decreasing in case of sampling frequency simulations
of 40 and 60 min. Adopting a variable sampling frequency the risk can
be up to almost 4 times the RQ
EMC
, while it is up to 2.5 and up to 2 with
40 min and 60 min frequency, respectively.
These ndings highlight how different sampling frequency and
concentration data elaboration (all data, specic percentiles, EMC) can
lead to different risk estimations.
4. Conclusions and recommendations
This study investigated the occurrence, concentration levels, and
behavior of conventional pollutants, metals, and micropollutants across
different discharge streams of a municipal WWTP, namely inuent and
efuent during dry-weather, and bypass and efuent during wet-
weather. We found that.
- The bypass displayed the occurrence of a wider range of pollutants
compared to the dry-weather inuent, including several ones not
detected otherwise.
- Bypass pollutant concentrations exhibited greater variability and a
rst-ush effect only for some compounds. In fact, some (micro-)
Fig. 6. Correlation matrix performed on all collected samples among all
monitored events. Red color indicates positive correlations, blue color indicates
negative correlations, while white color indicates not signicant (p-val-
ue>0.05) correlations.
J. Ianes et al.
Environmental Research 263 (2024) 120132
7

pollutants exhibited more inter-event variability, while others more
intra-event variability. For practical monitoring purposes, this sug-
gests that the compounds in this last group should be monitored with
high-frequency sampling to effectively capture their variability.
Conversely, for the compounds with higher inter-events variability,
it would be more benecial to plan extensive monitoring campaigns
throughout the year. Moreover, compounds displaying a rst-ush
effect should be monitored more frequently at the start of the
event, whereas the others consistently throughout the event.
- Characteristics related to the rain event (ADP and rainfall intensity)
are very inuential on the resulting concentrations discharged. This
underscores the importance of comprehensive sampling strategies to
accurately capture the variability in pollutant concentrations,
monitoring a range of rain events.
- To address monitoring challenges, we explored the potential of easily
measurable parameters (TSS and EC) as cost-effective proxies for
micropollutant monitoring in the bypass stream. Results suggest
promising correlations between these parameters and specic pol-
lutants groups, warranting further investigation for broader
application.
- Environmental risk assessment revealed a higher risk associated with
the bypass compared to the efuent for conventional pollutants,
metals, and TRB. Furthermore, the sampling frequency for the bypass
monitoring signicantly impacted the risk assessment, underlining
the importance of high-frequency sampling to capture peak con-
centrations and accurately assess environmental risks. For WWTP
operations this translates into prioritizing sampling efforts for pol-
lutants posing the highest risks, focusing on critical time periods or
events when concentrations are likely to be elevated. Moreover,
measures can be implemented either to increment the WWTP ca-
pacity during wet-weather, or to implement treatment strategies
before discharge.
These ndings hold signicant implications for WWTP monitoring
and management strategies. The study underscores the necessity of
including bypass streams into intervention plans to minimize environ-
mental micropollutant contamination. The identication of potential
proxy indicators offers a promising strategy for optimizing bypass
monitoring and reducing associated costs. By rening existing correla-
tions, exploring novel proxies, and calibrating models to site-specic
conditions, WWTPs can signicantly reduce the frequency of tradi-
tional pollutant analysis without compromising data quality. Further-
more, developing predictive models that incorporate factors like rainfall
characteristics and historical pollutant data can provide valuable in-
sights for risk assessment and decision-making. This integrated
approach can help WWTPs achieve more efcient and cost-effective
bypass management while ensuring adequate environmental protection.
Funding
The PhD grant of Jessica Ianes has been funded by the Italian Min-
istry of Research (PON, 2021 PhD Grant DOT1316729, CUP
D45F21003710001).
The research has been funded by PNRR MUR - M4C2 Project “Return:
Natural, man-made and environmental risks” (Project ID: PE-0000005,
CUP D43C22003030002).
Fig. 7. (A) RQs calculated for conventional pollutants and metals; (B) RQs calculated for micropollutants; (C) Ratio between risk calculated with 95th percentile of
concentration obtained at different sampling frequency and the risk obtained with the respective EMC.
J. Ianes et al.
Environmental Research 263 (2024) 120132
8

CRediT authorship contribution statement
Jessica Ianes: Writing – original draft, Visualization, Methodology,
Formal analysis, Conceptualization. Beatrice Cantoni: Writing – review
& editing, Validation, Methodology, Conceptualization. Fabio Polesel:
Writing – review & editing, Validation, Methodology. Enrico Ulisse
Remigi: Writing – review & editing, Validation, Methodology. Luca
Vezzaro: Writing – review & editing, Validation, Methodology. Man-
uela Antonelli: Writing – review & editing, Supervision, Project
administration, Funding acquisition, Conceptualization.
Declaration of competing interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.envres.2024.120132.
Data availability
Data will be made available on request.
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