A New Multi-Index Method for the Eutrophication Assessment in Transitional Waters: Large-Scale Implementation in Italian Lagoons

Abstract

Eutrophication represents one of the most impacting threats for the ecological status and related ecosystem services of transitional waters; hence, its assessment plays a key role in the management of these ecosystems. A new multi-index method for eutrophication assessment, based on the ecological index MaQI (Macrophyte Quality Index), the trophic index TWQI (Transitional Water Quality Index), and physicochemical quality elements (sensu Dir. 2000/60/EC), was developed including both driver and impact indicators. The study presents a large-scale implementation of the method, which included more than 100 Italian lagoon sites, covering a wide variability of lagoon typologies and conditions. Overall, 35% of sites resulted in eutrophic status, 45% in mesotrophic, and 25% in oligotrophic status.

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Citation: Bonometto, A.; Ponis, E.;
Cacciatore, F.; Riccardi, E.; Pigozzi, S.;
Parati, P.; Novello, M.; Ungaro, N.;
Acquavita, A.; Manconi, P.; et al. A
New Multi-Index Method for the
Eutrophication Assessment in
Transitional Waters: Large-Scale
Implementation in Italian Lagoons.
Environments 2022,9, 41.
https://doi.org/10.3390/
environments9040041
Academic Editors: Gotzon
Basterretxea and Veera
Gnaneswar Gude
Received: 7 February 2022
Accepted: 18 March 2022
Published: 24 March 2022
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4.0/).
environments
Article
A New Multi-Index Method for the Eutrophication Assessment
in Transitional Waters: Large-Scale Implementation in
Italian Lagoons
Andrea Bonometto 1, Emanuele Ponis 1, *, Federica Cacciatore 1, Elena Riccardi 2, Silvia Pigozzi 2, Paolo Parati 3,
Marta Novello 3, Nicola Ungaro 4, Alessandro Acquavita 5, Paola Manconi 6, Adriano Sfriso 7,
Gianmarco Giordani 8and Rossella Boscolo Brusà1
1ISPRA, Italian National Institute for Environmental Protection and Research, LocalitàBrondolo 5,
30015 Chioggia, Italy; andrea.bonometto@isprambiente.it (A.B.); federica.cacciatore@isprambiente.it (F.C.);
rossella.boscolo@isprambiente.it (R.B.B.)
2Regional Agency for Environmental Protection in the Emilia-Romagna Region (ARPAE),
Daphne Oceanographic Structure, Viale Amerigo Vespucci, 47042 Cesenatico, Italy; ericcardi@arpae.it (E.R.);
spigozzi@arpae.it (S.P.)
3Environmental Prevention and Protection Agency of the Veneto Region, Via Ospedale Civile,
35121 Padua, Italy; paolo.parati@arpa.veneto.it (P.P.); marta.novello@arpa.veneto.it (M.N.)
4Apulian Regional Agency for the Environmental Prevention and Protection, Corso Trieste, 70126 Bari, Italy;
n.ungaro@arpa.puglia.it
5Regional Agency for Environmental Protection of Friuli Venezia Giulia Region, Via Cairoli,
33057 Palmanova, Italy; alessandro.acquavita@arpa.fvg.it
6Sardinian Environmental Protection Agency, Via Contivecchi, 09122 Cagliari, Italy;
pmanconi@arpa.sardegna.it
7Department of Environmental Sciences, Informatics & Statistics, University Ca’ Foscari Venice, Via Torino,
30123 Mestre, Italy; sfrisoad@unive.it
8Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parco Area
delle Scienze 11/a, 43121 Parma, Italy; giordani@nemo.unipr.it
*Correspondence: emanuele.ponis@isprambiente.it
Abstract:
Eutrophication represents one of the most impacting threats for the ecological status
and related ecosystem services of transitional waters; hence, its assessment plays a key role in the
management of these ecosystems. A new multi-index method for eutrophication assessment, based
on the ecological index MaQI (Macrophyte Quality Index), the trophic index TWQI (Transitional
Water Quality Index), and physicochemical quality elements (sensu Dir. 2000/60/EC), was developed
including both driver and impact indicators. The study presents a large-scale implementation of the
method, which included more than 100 Italian lagoon sites, covering a wide variability of lagoon
typologies and conditions. Overall, 35% of sites resulted in eutrophic status, 45% in mesotrophic, and
25% in oligotrophic status.
Keywords:
transitional waters; eutrophication; ecological indicators; integrated approach; monitoring;
TWQI; MaQI; TWEAM; nutrients
1. Introduction
Eutrophication is widely recognized as one of the most impacting threats for the
ecological status and integrity of transitional waters (TWs) [
1
]. In Europe, in recent decades,
several policies have been adopted in the framework of EU legislation to prevent or
attenuate the impacts of nutrient pollution and its consequences on aquatic ecosystems [
2
].
Above all, the Urban Wastewater Treatment Directive (UWWT, 91/271/EEC) addresses
the major point sources, and the Nitrates Directive (ND, 91/676/EEC) deals with diffuse
pollution of nitrogen from agriculture. Through the assessment of eutrophication risk, both
Environments 2022,9, 41. https://doi.org/10.3390/environments9040041 https://www.mdpi.com/journal/environments

Environments 2022,9, 41 2 of 17
directives require the identification of “sensitive” and “vulnerable” areas, where mandatory
measures (e.g., higher treatment requirements, fertilizer limitations) must be applied.
The monitoring and assessment of eutrophication play a key role in the protection of
aquatic ecosystems. The development of smart and easy-to-use indicators, not requiring
high sampling and analytic efforts [
3
,
4
], but effective in providing reliable detection of the
trophic status and trend, remains an open issue. Indeed, on one hand, these indicators
should be suitable for operational and large-scale applications, but on the other hand,
whereas formally adopted, the results may have huge potential implications in terms of
measures to carry out and of related costs.
Several indicators and indices are available for assessing trophic status in TWs [
5
–
9
],
overcoming the previous approach based on the evaluation of single variables (e.g., nu-
trients or chlorophyll-aconcentration, macroalgae blooms) that was demonstrated to be
not a sufficient diagnostic tool under high spatial and temporal fluctuations [
6
,
10
,
11
]. In
addition, since 2000, the Water Framework Directive (WFD, 2000/60/EC) implementa-
tion stimulated the development of a high number of indices based on the integrity of
aquatic flora and fauna [
12
] that contribute to assessing the direct and indirect response
to eutrophication [
10
] as a deviation from the reference conditions [
1
]. Phytoplankton,
macroalgae, and angiosperms are biological quality elements [
13
–
15
] directly sensitive to
nutrient enrichment, while macroinvertebrates [
16
,
17
] and fish fauna [
18
] are representative
of the indirect effects of eutrophication in relation to oxygen depletion [
2
]. The use of WFD
ecological classification in eutrophication assessments could enhance the quantification of
“an undesirable disturbance of the balance of organisms present in the water and to the quality of
the water concerned” (UWWT, Council Directive 91/271/EEC) that was recognized as an
essential condition for there to be eutrophication [
4
] and was referenced therein, even in
legal pronouncement [
2
], in addition to and in relationship with nutrient enrichment and
accelerated growth of algae.
In the framework of an attempt to provide a unified conceptual framework to un-
derstand eutrophication across different policies, in this paper a multi-index method for
transitional water eutrophication assessment (TWEAM) is proposed. The TWEAM method
gathers a selection of WFD ecological status indicators and the multi-metric Transitional
Water Quality Index [
7
], with the latter including the main causal factors of eutrophication
(N and P concentrations), key primary producers (phytoplankton chlorophyll-a, benthic
phanerogams, and macroalgal cover) and an indicator of eutrophication effects (dissolved
oxygen saturation).
The study presents and discusses a large-scale implementation of the TWEAM method,
applied to more than 100 Italian lagoon sites, covering a wide variability of lagoon typolo-
gies and conditions.
2. Materials and Methods
2.1. Study Area
The transitional water eutrophication assessment method (TWEAM) was tested on
an extensive and heterogeneous dataset, which included most of the Italian transitional
systems (5 Regions, 52 among coastal lagoons, coastal lakes, coastal ponds, and saltworks,
for a total of 126 stations). The dataset collected for this study may be considered repre-
sentative of the geomorphological, hydrobiological, functional, and ecological variability
existing at the Italian scale. Both salinity and tidal regime conditions were well represented
by the dataset (69 stations with salinity <30, 57 stations with salinity >30; 54 non-tidal
stations, 72 tidal stations).
For the purpose of this paper, sites were divided into 3 macro regions: Northern
Adriatic Sea, Apulian, and Sardinian compounds (Figure 1).

Environments 2022,9, 41 3 of 17
Environments 2022, 9, x FOR PEER REVIEW 3 of 17
Figure 1. Study sites. C1: Northern Adriatic Sea compound; C2: Apulian compound; and C3:
Sardinian compound.
2.1.1. Northern Adriatic Sea Compound (C1: 15 Transitional Systems, 79 Sampling Sites)
This compound includes the largest transitional systems found in Italy such as Venice
lagoon, Grado-Marano lagoon, and the transitional water systems of the Po Delta area.
The dataset includes the following systems along the coastal stretch between Trieste (NE)
and Ravenna (S): Grado Marano lagoon (160 km2); Caorle/Baseleghe valleys
(approximately 6 km2); Venice lagoon (549 km2); Po Delta lagoons of Caleri (10.5 km2),
Marinetta (2.5 km2), Vallona (9.1 km2), Barbamarco (7.5 km2), Canarin (9.2 km2),
Scardovari (28 km2), Goro (37 km2), Valle Cantone (6 km2), Valle Nuova (14 km2), Lago
Nazioni (1 km2), Comacchio (118 km2), and Pialassa Baiona (12 km2). The tidal regime is
generally microtidal, with the exception of Comacchio, Valle Nuova, Lago Nazioni, and
Valle Cantone (non-tidal).
2.1.2. Apulian Compound (C2: 8 Transitional Systems, 12 Sampling Sites)
This compound includes 30 non-tidal coastal systems (coastal lagoons, coastal ponds,
and saltworks) for a total area of 220.2 km2 [19]. The dataset used in this study includes
the two main lagoons of the region (Lesina, 51.4 km2 and Varano, 60.5 km2) and the
following transitional systems: Lago Salpi (30 km2 approximately), Torre Guaceto (1.2
Figure 1.
Study sites. C1: Northern Adriatic Sea compound; C2: Apulian compound; and C3:
Sardinian compound.
2.1.1. Northern Adriatic Sea Compound (C1: 15 Transitional Systems, 79 Sampling Sites)
This compound includes the largest transitional systems found in Italy such as Venice
lagoon, Grado-Marano lagoon, and the transitional water systems of the Po Delta area. The
dataset includes the following systems along the coastal stretch between Trieste (NE) and
Ravenna (S): Grado Marano lagoon (160 km
2
); Caorle/Baseleghe valleys (approximately
6 km2
); Venice lagoon (549 km
2
); Po Delta lagoons of Caleri (10.5 km
2
), Marinetta (2.5 km
2
),
Vallona (9.1 km
2
), Barbamarco (7.5 km
2
), Canarin (9.2 km
2
), Scardovari (28 km
2
), Goro
(37 km
2
), Valle Cantone (6 km
2
), Valle Nuova (14 km
2
), Lago Nazioni (1 km
2
), Comacchio
(118 km
2
), and Pialassa Baiona (12 km
2
). The tidal regime is generally microtidal, with the
exception of Comacchio, Valle Nuova, Lago Nazioni, and Valle Cantone (non-tidal).
2.1.2. Apulian Compound (C2: 8 Transitional Systems, 12 Sampling Sites)
This compound includes 30 non-tidal coastal systems (coastal lagoons, coastal ponds,
and saltworks) for a total area of 220.2 km
2
[
19
]. The dataset used in this study includes the
two main lagoons of the region (Lesina, 51.4 km
2
and Varano, 60.5 km
2
) and the following
transitional systems: Lago Salpi (30 km
2
approximately), Torre Guaceto (1.2 km
2
), Punta
della Contessa (2.0 km
2
), Cesine (0.7 km
2
), Baia di Porto Cesareo (2.0 km
2
), and Mar Piccolo
di Taranto (20.7 km2).

Environments 2022,9, 41 4 of 17
2.1.3. Sardinian Compound (C3: 34 Transitional Systems, 35 Sampling Sites)
The Sardinian coastal systems contain over 50 non-tidal transitional ecosystems, gen-
erally of small dimension, which cover a total area of approximately 160 km
2
[
20
]. The
dataset used in this study includes 34 transitional systems of different typologies (coastal
lagoons, coastal ponds, and saltworks) and salinities, located along the entire coastline of
the Sardinia coastline.
2.2. Data Collection and Analyses
Data were collected by the Regional Environmental Protection Agencies (ARPAs) in
the framework of the institutional WFD monitoring activities. The dataset considered in
this study includes data from the period 2014–2016, with the exception of Sardinia, for
which data were collected in the period 2016–2018.
At all sites, physicochemical parameters were collected seasonally (4 times a year) in
each year; macrophyte data were collected twice (spring and autumn) in only one year
over the studied period.
Sampling and laboratory activities were performed according to the national protocols,
described in [
21
]. Water samples were filtered by 0.45
µ
m porosity filters and analyzed
for determination of orthophosphates (P-PO
4
), ammonium (NH
4+
), nitrites (NO
2−
), and
nitrates (NO
3−
) by spectrophotometric analyzers. Oxygen saturation was determined by
portable oximeters. Samples of chlorophyll-awere mostly obtained by filtration of waters
in Whatman GF/F filters (porosity 0.7
µ
m) and then determined in acetone extract by spec-
trofluorometric analyzers. In some sites, it was determined in situ by
portable fluorimeters
.
Macrophyte cover and species identification were determined following [13,21].
2.3. The TWEAM Description
2.3.1. Indices and Metrics Included in the Method
TWEAM is a multi-metric and multi-index method, based on the Macrophyte Quality
Index MaQI [
13
], the physicochemical quality elements supporting good ecological status
(sensu Dir. 2000/60/EC), and the Transitional Water Quality Index TWQI [7].
MaQI is the index adopted by the Italian law in agreement with the WFD requirements
for ecological classification of both macroalgae and aquatic angiosperms in TWs [13]. The
MaQI ecological assessment is based on several metrics: number and percentage of sensitive
macroalgal taxa, relative abundance (wet weight) of Chlorophyta and Rhodophyta, benthic
phanerogams, and macroalgal cover. The frequency of macrophyte monitoring for the
application of MaQI is two seasonal samples every three years for operational monitoring
and every 6 years for surveillance monitoring (Italian Environmental Ministry Decree
(MD 260/2010)).
Dissolved inorganic nitrogen (DIN) and orthophosphates (P-PO
4
) were included
in the TWEAM, as a representative of the physicochemical quality elements to support
biological element classification. According to the MD 260/2010, thresholds for DIN are
defined for two different salinity typologies: <30, including oligohaline, mesohaline, and
polyhaline water bodies; >30 including euhaline and hyperhaline water bodies. Currently,
the threshold for orthophosphates is set only for water bodies with salinity >30 (Table 1).
Table 1.
Thresholds set for physicochemical quality elements supporting biological elements by
national legislation for the implementation of WFD (MD 260/2010).
WB Type (Salinity) Threshold Good/Moderate
salinity < 30 Dissolved inorganic nitrogen
(DIN)
30 µM
salinity > 30 18 µM
salinity > 30 Orthophosphates
(P-PO4)0.48 µM

Environments 2022,9, 41 5 of 17
TWQI is a multimetric index for assessing the trophic status in shallow transitional
water ecosystems that integrates the main causal factors of eutrophication (N and P concen-
trations), the key biological elements (chlorophyll-a, aquatic angiosperm, and macroalgal
cover), and an indicator of the eutrophication effects (dissolved oxygen).
Each variable is transformed with non-linear functions into dimensionless quality
value (QV) ranging from 0 (low quality) to 100 (high quality). The QV was then multiplied
by a weighting factor to take into account the relative contribution of each variable to the
overall water quality value. The final score of the index is calculated as the sum of the
contribution of each variable [7,11], provided in the Supplementary Materials (Table S1).
In this study, the TWQI was calculated by averaging, over the same year, the values of
water quality data (nutrients, chlorophyll-a, and oxygen saturation), sampled quarterly, and
the percentage of angiosperms and macroalgae covers, sampled twice (spring
and autumn
).
2.3.2. Method Calculation
The methodology is based on a three-step evaluation procedure. Phase 1 (Table 2)
consists of a preliminary screening based on the use of the WFD ecological quality status
indicator for macrophytes, i.e., MaQI, and DIN and P-PO
4
concentrations (averaged over
3 years) and thresholds (Table 1).
Table 2.
Application of TWEAM in phase 1 and possible outcomes: integration of nutrients (DIN and
P-PO4) and macrophyte (MaQI) status according to MD 260/2010.
PHASE 1
Physicochemical Elements
Supporting Biological Elements in the Water Column (MD 260/2010)
MaQI Status
Poor/Bad Moderate High/Good
DIN > Threshold
P-PO4> Threshold E1 PHASE 2 PHASE 2
DIN >Threshold P-PO4< Threshold (or n.a.)
or
DIN < Threshold P-PO4> Threshold (or n.a.)
PHASE 2 PHASE 2 PHASE 2
DIN < Threshold
P-PO4< Threshold PHASE 2 PHASE 2 N1
n.a. = not available.
PHASE 1.
Phase 1 allows the classification of the eutrophication status in case of a
match in compliance for nutrients and biological elements.
The possible outcomes of phase 1 are:
E1:
Eutrophic site. Physicochemical supporting elements (DIN and P-PO
4
) indicate a
condition of nutrient enrichment in the water column, and biological sensitive elements
(macrophytes) indicate a significant alteration of the community structure. Both nutrient
concentrations exceed the moderate/good class boundary and the macrophyte ecological
status is bad or poor.
N1:
Not eutrophic site. Both nutrient concentrations are below the moderate/good
class boundary and the macrophyte ecological status is good or high, indicating a negli-
gible probability of alteration of the functioning or the structure of the ecosystem due to
nutrient enrichment.
Current national legislation (MD 260/2010) does not indicate any threshold for P-PO
4
concentration for sites with salinity <30, therefore, the threshold defined for sites with
salinity >30 is also temporarily applied for these sites, assuming that nutrients are usually
higher at low salinities. In the case of P-PO
4
concentrations exceeding this threshold,
phase 2 is requested.
PHASE 2.
In cases of mismatches between nutrients or among nutrients and MaQI
classification (sensu WFD), the site is not clearly attributable to eutrophic or non-eutrophic
conditions and further analysis is required.

Environments 2022,9, 41 6 of 17
Phase 2 (Table 3) integrates TWQI values in the analysis of the eutrophication sta-
tus, using the boundary classification inferred by [
22
]: TWQI score < 40 represents bad
conditions, while 41–50, poor; 51–60, moderate; 61–80, good; and >80, high conditions.
Table 3.
Application of the TWEAM in phase 2 and possible outcomes: integration of nutrients
according to MD 260/2010, MaQI, and TWQI.
PHASE 2
Physicochemical Elements Supporting Biological Elements
in the Water Column (DM 260/2010) TWQI MaQI Status
Poor/Bad Moderate High/Good
DIN > Threshold
P-PO4> Threshold
High/Good N2 N2
Moderate M N2
Poor/Bad
PHASE
E1 E2 M
DIN > Threshold P-PO4< Threshold (or n.a.)
or
DIN < Threshold P-PO4> Threshold (or n.a.)
High/Good M N2 N2
Moderate M M N2
Poor/Bad E2 E2 M
DIN < Threshold
P-PO4< Threshold
High/Good M N2
Moderate M N2
Poor/Bad E2 M
PHASE
N1
n.a. = not available.
The classes identified in this phase are:
E2: Eutrophic site based on the integrated analysis;
N2: Non-eutrophic site based on the integrated analysis;
M:
Mesotrophic site, at risk of eutrophication in the case of the current trend, indicates
a worsening.
The five classes of trophic status can be reduced to three major classes, considering
that both N1 and N2 refer to a non-eutrophic status. The same applies for E1 and E2, which
refer to a eutrophic status (Table 4).
Table 4. Integrated TWEAM outcomes of phase 2.
N1/N2 NON-EUTROPHIC
MMESOTROPHIC
E1/E2 EUTROPHIC
Sites classified in M status need a further phase (phase 3) in order to determine
whether or not the mesotrophic status can be considered a sustainable condition stable over
time, linked to the natural background of high productivity typical of transitional waters
(non-eutrophic), or, on the contrary, there is a latent risk of eutrophication.
Phase 3 is based on a quali-quantitative assessment, including expert judgment, the
analysis of trends, the use of other indicators, the evaluation of trophic status of the
surrounding stations, and the assessment of the implemented measures.
2.4. Statistical Analysis
In order to visualize the variance and the association between parameters and station
typologies, principal component analysis (PCA) was applied to standardized data by using
R software with packages Rcmdr, RcmdrFactoMine, and factorextra [23].
Distributions of stations among station typologies (tidal, salinity) were assessed using
the chi-square test.
3. Results
3.1. TWEAM Metrics
All input data and results are reported in the Supplementary Materials in Tables S1 and S2.
3.1.1. Phase 1: Nutrients and MaQI
Nutrient data used in phase 1 covered a wide range of concentrations (Figure 2a,b).
DIN concentrations (range 4.1–184.3
µ
M) resulted below the good/moderate threshold

Environments 2022,9, 41 7 of 17
(Table 1) in 68.3% of the stations. Concentrations of P-PO
4
(range 0.06–4.88
µ
M) resulted
below the threshold in 73.8% of the stations. A total of 8.7% of the stations resulted in less
than good status, while 17.5% were not classified for P-PO
4
, due to the absence of a specific
threshold for water bodies with salinity <30.
Environments 2022, 9, x FOR PEER REVIEW 7 of 17
Sites classified in M status need a further phase (phase 3) in order to determine
whether or not the mesotrophic status can be considered a sustainable condition stable
over time, linked to the natural background of high productivity typical of transitional
waters (non-eutrophic), or, on the contrary, there is a latent risk of eutrophication.
Phase 3 is based on a quali-quantitative assessment, including expert judgment, the
analysis of trends, the use of other indicators, the evaluation of trophic status of the
surrounding stations, and the assessment of the implemented measures.
2.4. Statistical Analysis
In order to visualize the variance and the association between parameters and station
typologies, principal component analysis (PCA) was applied to standardized data by
using R software with packages Rcmdr, RcmdrFactoMine, and factorextra [23].
Distributions of stations among station typologies (tidal, salinity) were assessed
using the chi-square test.
3. Results
3.1. TWEAM Metrics
All input data and results are reported in the Supplementary Materials in Tables S1
and S2.
3.1.1. Phase 1: Nutrients and MaQI
Nutrient data used in phase 1 covered a wide range of concentrations (Figure 2a,b).
DIN concentrations (range 4.1–184.3 µM) resulted below the good/moderate threshold
(Table 1) in 68.3% of the stations. Concentrations of P-PO
4
(range 0.06–4.88 µM) resulted
below the threshold in 73.8% of the stations. A total of 8.7% of the stations resulted in less
than good status, while 17.5% were not classified for P-PO
4
, due to the absence of a specific
threshold for water bodies with salinity <30.
(a) (b) (c)
Figure 2. Distribution of data used in phase 1. (a) Box plots of DIN concentrations collected over
three years (n = 126). Orange line indicates threshold set by Italian legislation (DM 260/2010) for
water bodies with salinity <30. Blue line indicates the threshold set by Italian legislation (DM
260/2010) for water bodies with salinity >30. (b) Box plots of P-PO
4
concentrations collected over
three years (n = 126). Blue line indicates the threshold set by Italian legislation (DM 260/2010) for
water bodies with salinity >30. (c) Frequency of distribution of MaQI status results of the stations
sampled over one year (n = 126).
Figure 2.
Distribution of data used in phase 1. (
a
) Box plots of DIN concentrations collected over three
years (n = 126). Orange line indicates threshold set by Italian legislation (DM 260/2010) for water
bodies with salinity < 30. Blue line indicates the threshold set by Italian legislation (DM 260/2010)
for water bodies with salinity > 30. (
b
) Box plots of P-PO
4
concentrations collected over three years
(
n = 126
). Blue line indicates the threshold set by Italian legislation (DM 260/2010) for water bodies
with salinity > 30. (
c
) Frequency of distribution of MaQI status results of the stations sampled over
one year (n = 126).
The dataset covers all the ecological quality classes of the macrophyte status attributed
by the MaQI index (Figure 2c), with a prevalence of stations in “poor” (50.8%) and “high”
(23.8%) status.
3.1.2. Phase 2: TWQI
The variables used for the TWQI calculation, with the exception of DO, covered
the whole range 0–100 of the corresponding quality values (QV) defined by the quality
functions of each metric (Figure 3). Overall, the TWQI values cover all five classes of trophic
conditions (Figure 4). “Bad” and “poor” classes were present in 37% of the stations, the
“moderate” class in 27% of the stations, while the remaining 36% of the stations were in a
“good” trophic status.
3.2. TWEAM Application
The application of the TWEAM method to the dataset (Table S2) led to a classification
of 39.7% of stations in a non-eutrophic status (NE1, NE2 classes), 34.9% in a eutrophic
status (E1, E2 classes), and 25.4% in a mesotrophic status.
Considering the geographical compounds (Figures 5and 6), most of the eutrophic
conditions were found in Northern Adriatic lagoons (C1) and, to a lesser extent, in Sardinia
(C3) (41.8% and 31.4% of stations classified in E1/E2 status, respectively). No eutrophic
station was detected in the Apulian sites (C2). The majority of the stations located in C2
and C3 compounds are classified in a non-eutrophic status (66.7% and 60.0%, respectively),
while approximately 30% of the stations of the C1 and C2 compounds and 8.6% of the C3
compound resulted in a mesotrophic condition.

Environments 2022,9, 41 8 of 17
Environments 2022, 9, x FOR PEER REVIEW 8 of 17
The dataset covers all the ecological quality classes of the macrophyte status attributed by the MaQI
index (Figure 2c), with a prevalence of stations in “poor” (50.8%) and “high” (23.8%) status.
3.1.2. Phase 2: TWQI
The variables used for the TWQI calculation, with the exception of DO, covered the
whole range 0–100 of the corresponding quality values (QV) defined by the quality
functions of each metric (Figure 3). Overall, the TWQI values cover all five classes of
trophic conditions (Figure 4). “Bad” and “poor” classes were present in 37% of the
stations, the “moderate” class in 27% of the stations, while the remaining 36% of the
stations were in a “good” trophic status.
Figure 3. Phase 2. Distribution of collected data (blue dots) over the quality values curves (orange
lines) set for the assessment of the metrics composing the TWQI index.
Figure 4. Frequency of distribution of TWQI classes calculated on the dataset (n = 126).
0
5
10
15
20
25
30
35
40
45
N° Stations
TWQI
Figure 3.
Phase 2. Distribution of collected data (blue dots) over the quality values curves (orange
lines) set for the assessment of the metrics composing the TWQI index.
Environments 2022, 9, x FOR PEER REVIEW 8 of 17
The dataset covers all the ecological quality classes of the macrophyte status attributed by the MaQI
index (Figure 2c), with a prevalence of stations in “poor” (50.8%) and “high” (23.8%) status.
3.1.2. Phase 2: TWQI
The variables used for the TWQI calculation, with the exception of DO, covered the
whole range 0–100 of the corresponding quality values (QV) defined by the quality
functions of each metric (Figure 3). Overall, the TWQI values cover all five classes of
trophic conditions (Figure 4). “Bad” and “poor” classes were present in 37% of the
stations, the “moderate” class in 27% of the stations, while the remaining 36% of the
stations were in a “good” trophic status.
Figure 3. Phase 2. Distribution of collected data (blue dots) over the quality values curves (orange
lines) set for the assessment of the metrics composing the TWQI index.
Figure 4. Frequency of distribution of TWQI classes calculated on the dataset (n = 126).
0
5
10
15
20
25
30
35
40
45
N° Stations
TWQI
Figure 4. Frequency of distribution of TWQI classes calculated on the dataset (n = 126).
Similar patterns resulted by aggregating the study stations by tidal regime (microtidal
and non-tidal). A total of 44.4% of the microtidal stations, which represent most of the
Northern Adriatic sites, resulted in eutrophic status, 26.3 in mesotrophic status, and 29.2%
in non-eutrophic status. Conversely, most of the non-tidal stations are in a non-eutrophic
status (53.7%) and only 22.2% resulted in eutrophic status (Figure 7). The difference in
the distribution of TWEAM classes among the two tidal typologies is significant (
χ2
= 9.1,
p< 0.05).

Environments 2022,9, 41 9 of 17
Environments 2022, 9, x FOR PEER REVIEW 9 of 17
3.2. TWEAM Application
The application of the TWEAM method to the dataset (Table S1) led to a classification
of 39.7% of stations in a non-eutrophic status (NE1, NE2 classes), 34.9% in a eutrophic
status (E1, E2 classes), and 25.4% in a mesotrophic status.
Considering the geographical compounds (Figures 5 and 6), most of the eutrophic
conditions were found in Northern Adriatic lagoons (C1) and, to a lesser extent, in
Sardinia (C3) (41.8% and 31.4% of stations classified in E1/E2 status, respectively). No
eutrophic station was detected in the Apulian sites (C2). The majority of the stations
located in C2 and C3 compounds are classified in a non-eutrophic status (66.7% and 60.0%,
respectively), while approximately 30% of the stations of the C1 and C2 compounds and
8.6% of the C3 compound resulted in a mesotrophic condition.
Figure 5. Eutrophic status assessment by the TWEAM method at sampled stations. N1/N2 (green
dots)—not eutrophic; M (yellow dots)—mesotrophic; and E1/E2 (red dots)—eutrophic. C1:
Northern Adriatic compound; C2: Apulian compound; and C3: Sardinian compound.
Figure 5.
Eutrophic status assessment by the TWEAM method at sampled stations. N1/N2 (green
dots)—not eutrophic; M (yellow dots)—mesotrophic; and E1/E2 (red dots)—eutrophic. (
C1
): North-
ern Adriatic compound; (C2): Apulian compound; and (C3): Sardinian compound.
Environments 2022, 9, x FOR PEER REVIEW 10 of 17
Figure 6. TWEAM classification for each compound C1: Northern Adriatic compound; C2: Apulian
compound; and C3: Sardinian compound. Data are expressed as a percentage of stations in each
class.
Similar patterns resulted by aggregating the study stations by tidal regime
(microtidal and non-tidal). A total of 44.4% of the microtidal stations, which represent
most of the Northern Adriatic sites, resulted in eutrophic status, 26.3 in mesotrophic
status, and 29.2% in non-eutrophic status. Conversely, most of the non-tidal stations are
in a non-eutrophic status (53.7%) and only 22.2% resulted in eutrophic status (Figure 7).
The difference in the distribution of TWEAM classes among the two tidal typologies is
significant (χ2 = 9.1, p < 0.05).
Figure 7. TWEAM classification according to the tidal regime of the sampled stations.
Most of the stations with salinity <30 resulted in eutrophic (53.6%) and mesotrophic
status (30.4%), while the non-eutrophic status was found in 15.9% of the sites (Figure 8).
An opposite pattern was found in stations >30 in which the non-eutrophic class prevails
(68.4% of stations) and the eutrophic status was found only in 12.3% of the stations. The
difference in the distribution of TWEAM classes among the two salinity typologies is
significant (χ2 = 38.5 p < 0.0001).
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
C1 C2 C3
Compound
TWEAM classification per geographical compound
NE1/NE2
M
E1/E2
0%
20%
40%
60%
80%
100%
Microtidal Non Tidal
Tidal Regime
TWEAM per tidal typology
NE1/NE2
M
E1/E2
Figure 6.
TWEAM classification for each compound C1: Northern Adriatic compound; C2: Apulian
compound; and C3: Sardinian compound. Data are expressed as a percentage of stations in
each class
.

Environments 2022,9, 41 10 of 17
Environments 2022, 9, x FOR PEER REVIEW 10 of 17
Figure 6. TWEAM classification for each compound C1: Northern Adriatic compound; C2: Apulian
compound; and C3: Sardinian compound. Data are expressed as a percentage of stations in each
class.
Similar patterns resulted by aggregating the study stations by tidal regime
(microtidal and non-tidal). A total of 44.4% of the microtidal stations, which represent
most of the Northern Adriatic sites, resulted in eutrophic status, 26.3 in mesotrophic
status, and 29.2% in non-eutrophic status. Conversely, most of the non-tidal stations are
in a non-eutrophic status (53.7%) and only 22.2% resulted in eutrophic status (Figure 7).
The difference in the distribution of TWEAM classes among the two tidal typologies is
significant (χ2 = 9.1, p < 0.05).
Figure 7. TWEAM classification according to the tidal regime of the sampled stations.
Most of the stations with salinity <30 resulted in eutrophic (53.6%) and mesotrophic
status (30.4%), while the non-eutrophic status was found in 15.9% of the sites (Figure 8).
An opposite pattern was found in stations >30 in which the non-eutrophic class prevails
(68.4% of stations) and the eutrophic status was found only in 12.3% of the stations. The
difference in the distribution of TWEAM classes among the two salinity typologies is
significant (χ2 = 38.5 p < 0.0001).
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
C1 C2 C3
Compound
TWEAM classification per geographical compound
NE1/NE2
M
E1/E2
0%
20%
40%
60%
80%
100%
Microtidal Non Tidal
Tidal Regime
TWEAM per tidal typology
NE1/NE2
M
E1/E2
Figure 7. TWEAM classification according to the tidal regime of the sampled stations.
Most of the stations with salinity < 30 resulted in eutrophic (53.6%) and mesotrophic
status (30.4%), while the non-eutrophic status was found in 15.9% of the sites (Figure 8). An
opposite pattern was found in stations > 30 in which the non-eutrophic class prevails (68.4%
of stations) and the eutrophic status was found only in 12.3% of the stations. The difference
in the distribution of TWEAM classes among the two salinity typologies is significant
(χ2= 38.5 p< 0.0001).
Figure 8. TWEAM classification according to the salinity typology of the sampled stations.
By integrating salinity and tidal regime, the stations were grouped into four main
types (NT > 30; MT > 30; NT < 30; and MT < 30), and a PCA was applied to investigate
the factors that mainly affect the variance of each group (Figure 9). The first (PC1) and
second (PC2) components of the PCA explained together 75.9% of the total variance. PC1
was mainly associated with MaQI, TWQI and, to a minor extent, nutrient concentrations of
phase 1 (DIN, P-PO4). The PC2 was associated to DIN and P-PO4only.

Environments 2022,9, 41 11 of 17
Environments 2022, 9, x FOR PEER REVIEW 11 of 17
Figure 8. TWEAM classification according to the salinity typology of the sampled stations.
By integrating salinity and tidal regime, the stations were grouped into four main
types (NT > 30; MT> 30; NT < 30; and MT < 30), and a PCA was applied to investigate the
factors that mainly affect the variance of each group (Figure 9). The first (PC1) and second
(PC2) components of the PCA explained together 75.9% of the total variance. PC1 was
mainly associated with MaQI, TWQI and, to a minor extent, nutrient concentrations of
phase 1 (DIN, P-PO4). The PC2 was associated to DIN and P-PO4 only.
Figure 9. PCA biplot of the two main components for the whole dataset, including the TWEAM
metrics as variables and TWEAM score as a supplementary quantitative variable. Data are grouped
per salinity (<30, >30) and tidal (MT = microtidal, NT = non-tidal) typologies. For each group,
centroids and the confidence ellipses (95% of samples) are shown.
4. Discussion
Different methods were developed for assessing the trophic status of superficial
water bodies, starting from the influential works by [24] on marine coastal water (TRIX
index) and by [25] on lakes (TSI). The assessment of trophic conditions in transitional
waters is particularly critical because these systems support a high degree of
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
<30 >30
Compound
TWEAM classification per salinity typology
NE1/NE2
M
E1/E2
Figure 9.
PCA biplot of the two main components for the whole dataset, including the TWEAM
metrics as variables and TWEAM score as a supplementary quantitative variable. Data are grouped
per salinity (<30, >30) and tidal (MT = microtidal, NT = non-tidal) typologies. For each group,
centroids and the confidence ellipses (95% of samples) are shown.
4. Discussion
Different methods were developed for assessing the trophic status of superficial water
bodies, starting from the influential works by [
24
] on marine coastal water (TRIX index)
and by [
25
] on lakes (TSI). The assessment of trophic conditions in transitional waters is
particularly critical because these systems support a high degree of anthropogenic activity
and natural productivity and it may be difficult to discriminate among anthropogenic eu-
trophication and a trophic status supported by a natural background [
2
]. Moving from the
state-of-art of the previous multimetric indices assessed for TWs [
5
–
9
], in this paper, a new
method for transitional water eutrophication assessment was presented and widely tested
in Italian lagoons. TWEAM is a screening method based on the most common sampling
data, usually available by water quality monitoring programs. It is a multi-metric and
multi-index method that provides an integrated analysis of drivers, status, and impact indi-
cators of eutrophication. With reference to the previously published TWQI [
7
,
22
], the most
important development is the inclusion of MaQI [
13
], which allows a more robust quantita-
tive evaluation of the impact on most sensitive biological quality elements (sensu WFD) and,
therefore, of the “disturbance of the balance of organisms present in the water” (UWWT Directive)
deriving from nutrient enrichment. Indeed, in transitional waters macrophytes are consid-
ered the most sensitive community to the nutrient enrichment of both the water column and
sediments with fast response to trophic condition
changes [2,26–29]
. Although two metrics
related to macrophytes—macroalgae cover and seagrass cover—were already included in
TWQI, these were representative of the contribution of the primary producers, together
with chlorophyll-aconcentrations as proxy of phytoplankton biomass. The integration
with MaQI enhances the comprehensive analysis of the impact on the benthic macrophyte,
including metrics on macroalgae and aquatic angiosperms species composition.
4.1. Eutrophic Status in Italian Transitional Waters
TWEAM was applied to a consistent and heterogeneous dataset of Italian lagoons,
coastal ponds, and saltworks, covering the geomorphological, hydrobiological, and eco-
logical variability existing at the national scale. The use of such a large dataset (5 regions,
52 transitional systems, and 126 stations) allowed disposal of a large range of values for

Environments 2022,9, 41 12 of 17
each metric composing the method and to have a wide representation of all of the differ-
ent trophic classes. All data were collected in the framework of institutional monitoring
programs, with the minimum frequency stated by WFD. Other studies [
11
] observed that
multimetric indicators are less affected by temporal fluctuations than single environmental
variables, providing a reliable assessment of eutrophication with lower and cost-effective
sampling effort.
The results highlighted the ability of TWEAM to take into account the heterogeneity
of the eutrophic conditions existing in the different compounds. At the national scale,
39.7% of stations were found in a non-eutrophic status, 34.9% in a eutrophic status, and
25.4% in a mesotrophic status. Each single considered geographical compound showed a
proper pattern.
C1: The northwestern Adriatic Sea is a closed shallow basin where various rivers flow,
draining the Po Valley. Among them, the Po River collects most of the civil, industrial, and
agricultural discharges of the most populated and industrialized area of Northern Italy.
The transitional systems of the compound are directly influenced by the freshwater inputs
and by the loads of nutrients coming in [
30
–
32
]. Therefore, it is not surprising that the
frequency of stations classified as eutrophic was quite consistent (>40%) in this compound.
In the two largest lagoons of the area (Grado-Marano and Venice), all of the trophic classes
are well represented, with eutrophic stations mostly located in inner areas, characterized
by low exchanges with the sea inlets and more directly influenced by freshwater inputs. In
the Po delta area, no stations were found in a non-eutrophic status, while eutrophic and
mesotrophic classes were equally represented (50% each). Analyzing the single components
of the TWEAM, these lagoons were generally characterized by higher DIN levels and by a
more degraded status of macrophytes (TWQI and MaQI in poor/bad classes).
C2: In the Apulian compound, eight stations were classified as a non-eutrophic class
and four in the mesotrophic class; the latter exhibited good nutrient status (except one case),
a bad/poor status of MaQI, and a moderate/good class of TWQI. The mesotrophic status
in Mar Piccolo of Taranto is coherent with the reduction in nutrient inputs into the basin
and the shift from relatively eutrophic to moderately oligotrophic conditions observed
by the finding of [
33
]. The non-eutrophic status found in the Lesina and Varano lagoons
is consistent with the recent analysis of anthropogenic pressures and relative impacts
carried out by [
34
]. The variability of trophic status that resulted is mainly driven by MaQI
classification, while most stations showed low nutrient concentrations and high values
of TWQI.
C3: In the Sardinian compound, all the different classes of TWEAM were represented,
with a prevalence of non-eutrophic stations (21 out of 35), followed by eutrophic stations
(11 out of 35), and by mesotrophic stations (3 out 35), coherently, with an estimation of
about one half of the Sardinian lagoons (in terms of areas) in eutrophic or hypertrophic
conditions reported in [
35
]. This is the case of some of the largest lagoons, such as Cabras,
Cagliari (Santa Gilla), Santa Giusta, and S’Ena Arrubia; the latter resulted eutrophic (E2) by
TWEAM (data for Cagliari and Cabras lagoons were not available for this study). The most
relevant parameters in discriminating eutrophic stations were aquatic angiosperm cover,
the high chlorophyll-a content, and orthophosphates concentrations.
4.2. TWEAM Functioning
Regarding the three-step evaluation procedure (Section 2.2), phase 1, based on WFD in-
dicators of pressure (nutrient thresholds) and status (MaQI), allowed the rapid classification
of approximately 25% of stations, of which, approximately 2% were classified in eutrophic
status (DIN and P-PO4 concentrations failed the G/M thresholds and MaQI showed less
than good status), and 23% in a non-eutrophic status (both nutrients under the thresholds
and MaQI in good or high status) (Table S2). In the remaining 75% of investigated sites,
phase 1 did not allow the assessment of eutrophication status due to mis-matching between
DIN and P-PO4 classification (approximately 35%) or between nutrients and MaQI status
(approximately 40%). The mis-matching between nutrients and MaQI classification could

Environments 2022,9, 41 13 of 17
be caused by several factors. Despite the efforts in setting the nutrient thresholds based
on the pressure–response relationships with the most sensitive biological elements [
36
],
the classification system is affected by an intrinsic uncertainty, introduced by the reference
conditions and class boundaries [
37
]. In addition, the presence of pressures on macrophytes
other than nutrient enrichment could impact the agreement between MaQI and DIN or
P-PO4 classification. In these cases, the introduction of TWQI in phase 2 allowed to provide
a more comprehensive and less influenced by class boundaries evaluation, not imposing
thresholds for every single metric, but only class boundaries for the final score.
The results highlighted that the classification based on a single indicator or metric
(e.g., nutrient concentrations) could provide misleading indications. Several stations
resulted in non-eutrophic status, even if exceeding the threshold for nutrients (e.g., stations
FM 401, TEU 201, TEU 401 in Grado-Marano lagoon, FVG Region, C1, and AT_PC01,
AT_PU01, in Apulia Region, C3, in Porto Cesareo and Punta della Contessa water bodies,
respectively) because of the good or high status of MaQI and TWQI. This mainly depended
on the high coverage of aquatic angiosperms, low concentration of chlorophyll-a, and
oxygen saturation being close to 100% (Table S1). This should be carefully taken into account
in the rigid application of the “one out all out” approach proposed by the WFD, where the
exceeding of nutrient thresholds, whose role is to support the pristine status of biological
communities, could lead to a downgrade in the overall ecological status classification.
On the contrary, some stations resulted in eutrophic status even if both DIN and
P-PO4 concentrations were below the G/M threshold (i.e., in good status). In these cases,
MaQI and TWQI resulted in poor/bad status, mainly because of the dominance of oppor-
tunistic macroalgae, absence of aquatic angiosperms (stations ENC1_3 in Venice Lagoon,
Veneto Region, C1, and AT50110-0105 in Stagno di Tortoli, Sardinia Region, C2) and high
concentration of chlorophyll-a (st. AT50110-0105) (Table S1).
The 32% of the stations resulted in mesotrophic status. For these stations, TWEAM
does not automatically provide the assessment of the risk of eutrophication, and further site-
specific analysis is required (Section 2.2). The transitional systems are generally character-
ized by high background productivity. Hence, the identification of a pressure-specific signal
(such as eutrophication) against a highly variable natural background compounded by com-
peting effects of impacts arising from other pressures may be difficult [
1
,
2
]. Mesotrophic
status in transitional waters can represent a sustainable condition stable over time, linked to
a background of high productivity typical of these environments and, therefore, it may not
represent a risk of eutrophication [2], even in the absence of further restoration measures.
4.3. Relationship between Eutrophic Status and WB Types
Coastal lagoons present a wide variability of their hydrological and morphological
conditions (both natural and human modified) [
38
] that control the ecological functioning
of the system and influence the vulnerability of water bodies to nutrient enrichment [
1
].
Freshwater inputs from the watershed directly impact on the nutrient loads and the magni-
tude of water exchanges with the sea, which in microtidal systems, are strictly related to
tide excursion. Moreover, the interaction between freshwater and seawater inflows (as well
as rain, evaporation, and wind-driven forces) controls the salinity patterns of lagoons [
39
],
which could be used as an indicator of the relative contribution of inland and marine
dominance, other than directly influencing the aquatic flora composition [40].
The study stations cover all salinity classes, with a prevalence of polyhaline sta-
tions (69/126) and eu-hyperhaline stations (57/126), while only 12 stations were oligo-
mesohaline. On average, the results confirmed that oligo-meso-polyhaline stations are
generally more sensitive to eutrophication, than eu-hyperhaline (53% and 12% in eutrophic
status, respectively). Differently, microtidal lagoons resulted in worse eutrophication status
(44% eutrophic) than non-tidal (22% eutrophic). Apparently, these results contrast with
the assumption that higher seawater exchange, usually characterizing the tidal waterbody,
reduces the risk of eutrophication by oxygenation and nutrient dilution [
1
]. Actually, 43%
of microtidal stations are in meso-polyhaline waterbodies, and most of them are located

Environments 2022,9, 41 14 of 17
in the Po Delta lagoons, directly influenced by the Po River nutrient loads, or in the in-
ner sub-basin of larger Northern Adriatic lagoons [
38
], characterized by higher residence
time [
41
,
42
] and directly receiving input from their small river basins. Limiting the analysis
to euhaline microtidal stations, only 4/26 resulted in eutrophic status.
In addition, in Mediterranean lagoons, the tidal flow is often a major driver of seawater
exchange and circulation. Indeed, even if classified as “non-tidal”, these lagoons are
often subject to a tidal range just below 50 cm, e.g., in the Southern Adriatic sea (Apulia
compound), and should be more properly defined as “nanotidal”, as suggested by [38].
Despite the above-discussed patterns, in all the above-mentioned groups, a large
variation of trophic status was observed, demonstrating TWEAM’s ability to discriminate
trophic status among stations belonging to similar typology and indicating that, at least for
the Mediterranean basin and, mostly, in the Italian lagoons, salinity and tide excursion do
not prevent, per se, the effectiveness of this method to provide a reliable assessment taking
into account the natural variability of TWs.
Overall, MaQI and TWQI highly contributed to the TWEAM eutrophication assess-
ment, being the factors that mainly explained the PC1 in the PCA analysis. In addition,
in meso-polyhaline stations, the variability of the trophic status is also explained by the
P-PO
4
and DIN concentrations (phase 1), in NT and MT sites, respectively. Differently,
nutrient concentrations (phase 1) seem to have a minor impact on the TWEAM score for
eu-polyhaline microtidal stations (MT > 30), where, generally, the values resulted under
the settled thresholds. However, it must be taken into consideration that nutrient concen-
trations also contribute to the TWQI score, through the quality functions, and therefore,
assessment is not influenced by the thresholds. Hyperhaline non-tidal sites (NT > 30)
showed less clear patterns.
4.4. Further Developments
Further implementation of TWEAM could include the integration of quantitative
indicators for the trend of nutrients and phytoplankton status assessment, in particular
in phase 3 for the evaluation of the eutrophication risk in sites classified as mesotrophic
in phase 2. In the current version of TWEAM, phytoplankton, a key element of primary
production in transitional systems, is only indirectly considered as chlorophyll-acontent in
the determination of TWQI, and no information on biodiversity and abundance are taken
into account. Therefore, inclusion of the Multimetric Phytoplankton Index [
43
], adopted
for Italian lagoon classification, in the TWEAM will be considered. Another issue worthy
of future investigation concerns a site-specific analysis of the TWEAM response to the
different sources of point and non-point pressures, both at metric and variable levels.
5. Conclusions
This study demonstrated the effectiveness of TWEAM to provide a rapid and reliable
quantitative assessment of eutrophication risk in most Italian transitional waters. The
TWEAM approach could be easily adaptable for application to other European TWs,
modifying the type-specific boundaries for nutrients (phase 1). Nutrients and TWQI can be
easily applied, with the former being commonly monitored and regulated in most European
member states and the latter being previously tested and validated in six transitional water
ecosystems. Moreover, the non-linear utility functions and weighting factors for TWQI
calculation were also derived from a wide international literature [
7
]. MaQI is not used
in all member states, but it was intercalibrated with other macrophyte indices used in the
Mediterranean Ecoregion [44]; therefore, it can be directly applicable in that context.

Environments 2022,9, 41 15 of 17
Supplementary Materials:
The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/environments9040041/s1, Table S1. TWQI data input and results;
Table S2. TWEAM data input and results.
Author Contributions:
Conceptualization, A.B., E.P. and R.B.B.; methodology, A.B., E.P., R.B.B., M.N.,
P.P., S.P., E.R. and N.U.; software, A.B. and E.P.; validation, A.B., E.P., F.C., E.R., S.P., P.P., M.N., N.U.,
A.A., P.M. and R.B.B.; investigation, E.R., S.P., P.P., M.N., N.U., A.A. and P.M.; data curation, A.B.,
E.P., R.B.B. and F.C.; writing—original draft preparation, E.P., A.B. and F.C.; writing—review, R.B.B.,
A.S. and G.G. All authors have read and agreed to the published version of the manuscript.
Funding:
This research received no external funding. The work was carried out by the ISPRA and
the Regional Environmental Agencies in the framework of institutional activities of the National
System for Environmental Protection (SNPA) and of the Italian Ministry for Ecological Transition
for the development of the methodologies for the eutrophication assessment in surface waterbodies
(DD 408/2017).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
All data used in this study are included in the Supplementary Materials.
Acknowledgments:
The authors would like to thank the colleagues of the Italian Ministry for
Ecological Transition—Direzione generale USSRI—AT Sogesid—Claudia Vendetti, Silverio Abati,
and Maria Camilla Mignuoli, for their useful input and review during the development of the
TWEAM. We also thank Carla Rita Ferrari (ARPAE), Oriana Blasutto (ARPA FVG), Antonietta Porfido
and Erminia Sgaramella (ARPA Puglia), and Roberto Angius (ARPA Sardegna), for their active
contribution in the finalization and testing of the method. Finally, we thank all colleagues of the
involved Regional Environmental Agencies who contributed to producing the data used in
this paper
.
Conflicts of Interest: The authors declare no conflict of interest.
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