Article
Assessment of the Bulgarian Wastewater Treatment
Plants’ Impact on the Receiving Water Bodies
Galina Yotova 1, Svetlana Lazarova 2, Błażej Kudłak 3, Boika Zlateva 1, Veronika Mihaylova 1,
Monika Wieczerzak 3, Tony Venelinov 2 and Stefan Tsakovski 1,*
1
Sofia University “St. Kliment Ohridski”, Faculty of Chemistry and Pharmacy,
Chair of Analytical Chemistry, 1164 Sofia, Bulgaria; G.Yotova@chem.uni-sofia.bg (G.Y.);
zlateva@chem.uni-sofia.bg (B.Z.); v.mihaylova@chem.uni-sofia.bg (V.M.)
2 University of Architecture, Civil Engineering and Geodesy, Faculty of Hydraulic Engineering,
Chair of Water Supply, Water and Wastewater Treatment, 1046 Sofia, Bulgaria;
ssvetlanalazarova@abv.bg (S.L.); TVenelinov_fhe@uacg.bg (T.V.)
3 Gdańsk University of Technology, Faculty of Chemistry, Department of Analytical Chemistry,
11/12 Naturowicza, 80-952 Gdańsk, Poland; blakudla@pg.edu.pl (B.K.); monwiecz@pg.edu.pl (M.W.)
* Correspondence: tsakovski@gmail.com; Tel.: +359-2-8161426
Academic Editor: Teresa A. P. Rocha-Santos
Received: 15 May 2019; Accepted: 17 June 2019; Published: 18 June 2019
Abstract: Deterioration of water quality is a major problem world widely according to many
international non-governmental organizations (NGO). As one of the European Union (EU) countries,
Bulgaria is also obliged by EU legislation to maintain best practices in assessing surface water quality
and the efficiency of wastewater treatment processes. For these reasons studies were undertaken to
utilize ecotoxicological (Microtox®, Phytotoxkit FTM, Daphtoxkit FTM), instrumental (to determine pH,
electrical conductivity (EC), chemical oxygen demand, total suspended solids (TSS), total nitrogen
(N) and phosphorus (P), chlorides, sulphates, Cr, Co, Cu, Cd, Ba, V, Mn, Fe, Ni, Zn, Se, Pb), as well
as advanced chemometric methods (partial least squares–discriminant analysis (PLS-DA)) in data
evaluation to comprehensively assess wastewater treatment plants' (WWTPs) effluents and surface
waters quality around 21 major Bulgarian cities. The PLS-DA classification model for the
physicochemical parameters gave excellent discrimination between WWTP effluents and surface
waters with 93.65% correct predictions (with significant contribution of EC, TSS, P, N, Cl, Fe, Zn, and
Se). The classification model based on ecotoxicological data identifies the plant test endpoints as
having a greater impact on the classification model efficiency than bacterial, or crustaceans’ endpoints
studied.
Keywords: wastewater treatment plant; surface water quality; biotests; partial least squares–
discriminant analysis
1. Introduction
Water is a vital resource for all human activities, e.g., everyday necessities, agriculture,
manufacturing, transportation. Despite its importance, water is the most poorly managed resource
in the world [1]. According to World Health Organization (WHO), the pollution of water is defined
as any deterioration of the physical, chemical or biological parameters that leads to an adverse impact
on living organisms in the environment or makes the water resource unsuitable for its intended use.
Every time water is used, it acquires contaminants, and its quality decreases. Nearly 80% of the used
water is returned into the environment untreated. This increases freshwater scarcity worldwide, since
the contaminated water may cause human diseases due to the wide variety of viruses, bacteria, and
protozoa, these waters may contain. Apart from these biological contaminants, the wastewater
Molecules 2019, 24, 2274; doi:10.3390/molecules24122274
www.mdpi.com/journal/molecules
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effluents are also polluted with chemicals, e.g., nitrogen, phosphorus, heavy metals [2], and organic
compounds [3,4] among which detergents, pesticides, hydrocarbons, and metabolites. Wastewater
effluents rich in decomposable organic matter are the primary cause of organic pollution. Most heavy
metals present in the water are associated with industrial discharges but are also found in the
wastewater treatment plants’ (WWTP) effluents [2]. Therefore, the management and utilization of
natural resources need to be further improved, and human pollution activities to be reduced. As a
result, several legislation documents and guidelines have been developed—the WHO Guidelines for
the reuse of effluents (developed in 1973, revised in 1989 and 2006) [5], the UN General Assembly’s
Millennium Development Goal for ensuring environment sustainability (adopted 2000) [6], the Water
Framework Directive (WFD, adopted in 2000) [7] and its sub-directives 91/271/EEC concerning urban
wastewater treatment which establishes requirements for discharges from urban WWTPs [8] and
98/83/EC concerning the quality required of drinking water [9].
The WFD focuses on the effectiveness and sustainability of the water environment through an
integrated and coordinated approach to water management [10]. The introduction of the Directive in
2000 had the sole purpose of establishing a framework for the protection of European waters and for
the Member States to reach “good status” for water bodies throughout the European Union (EU).
Being the first European Directive that focused on environmental sustainability [11,12], WFD was
considered as a pilot for future environmental regulations [13], but surface water bodies in “good”
state only increased by 10% from 2009 to 2015 [14].
Surface water quality is directly connected with the development of societies. Political changes
in post-communistic countries result in changes in the water quality due to the transition from the
planned to market economy in 1990. Political and social changes in Bulgaria prior to its accession to
the European Union, necessitated new national regulatory requirements, especially in the field of
environmental preservation and public health. Harmonization of some laws and national regulations,
especially in the field of environment protection, has required state regulations for wastewater
discharge (2002) in water bodies, for drinking water quality (2007), and for surface water quality for
drinking water supply (2002) to fully comply to the EU directives [8,9,15]. At the beginning of
economic changes, pollution decreased due to the transformation of the industry from unprofitable
manufactures and the introduction of environmentally friendly technologies to new not so welldeveloped economic sectors.
The application of multivariate statistical analysis is widely used in environmental pollution
assessment studies of different environmental compartments. The use of multivariate statistical
techniques enables the interpretation of complex data matrices for a better understanding and
assessment of air [16], water [17], soil [18], and sediment quality [19] of the investigated region.
Usually, water quality assessment is based on monitoring of water quality indicators at different
sampling sites in the respective water body during different seasons [20]. Such monitoring programs
generate a large amount of data with a complex structure and “hidden” knowledge concerning water
quality. In many water quality assessment studies, multivariate statistical approaches are applied to
retrieve important information concerning water quality management such as: (i) outlining similarity
groups between water quality indicators and sampling sites [21–25], (ii) identification of factors
(sources) controlling water quality [17,21,23–27], and (iii) revealing of spatial–temporal variations in
the investigated water body [20–22,27,28]. Additionally, multivariate statistical results could be used
for the optimization of water quality monitoring programs by revealing existing patterns of water
quality indicators and sampling sites. Two groups of multivariate statistical techniques for pattern
recognition, unsupervised and supervised, are used in water quality assessment studies. The
unsupervised methods, such as cluster analysis (CA), principal component analysis (PCA), selforganizing maps (SOM), search for similarity in the monitoring data set without a priori information
concerning sample origin. CA identifies groups of similarity between sampling sites with different
water quality indicator profiles [21–23]. In water monitoring studies, PCA is used for the
identification of “hidden” sources controlling water quality [17,21,23,26,27] while application of SOM
enables simultaneous visualization of similarity groups among water quality indicators and
sampling sites [24,25]. An additional advantage of the SOM application in water quality assessment
Molecules 2019, 24, 2274
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is the possibility for inclusion of expert information in the data analysis followed by decision support
techniques, such as Hasse diagram [29–31]. The most widely used supervised pattern recognition
method in water quality studies is discriminant analysis (DA). DA is used to determine the water
quality parameters, which discriminate two or more predefined groups of sampling sites. DA has
been reported as an effective tool to evaluate temporal and spatial changes in water quality
[21,22,27,28]. Further, DA helps in the verification of CA results by identification of the discriminating
quality parameters between identified groups [23,32].
Among traditionally used CA, PCA, and DA, in the study of Singh et al. [17] partial least
squares–discriminant analysis (PLS-DA) has been reported as a potentially valuable statistical tool
for water quality assessment. PLS-DA is a widely used method outside environmental assessment
studies. The method is predominant in metabolomics [33] and also very popular in other fields where
multivariate data need to be evaluated [34]. The PLS-DA method encompasses two steps: (i) PLS
components construction; and (ii) discriminant analysis based on extracted PLS components. The
approach uses dimensionality reduction and diagnostic capabilities of PLS, which may be useful to
represent water quality factors and to outline the importance of water quality parameters. In the
second step, discrimination between groups of sampling sites could be performed based on water
quality factors as each group is characterized by a concentration profile reflecting its own water
quality.
Environmental risk assessment of WWTPs is usually performed by monitoring the quality of
WWTP effluents [35,36] or by taking samples from water body receiving treated wastewaters [17,37].
The first approach is preferred when WWTP effluent quality is compared with a water quality
guideline and/or WWTP efficiency assessment is performed. The second approach is focused on the
impact of WWTP on the surface waters and is more environmentally relevant. Nevertheless,
monitoring of the receiving water body itself without quality information for released treated
wastewaters could lead to biased assessment provoked by different pollution sources.
The choice of water quality indicators for monitoring is another important issue in water quality
assessment. Next to the legislation introduced physicochemical indicators, biotests have proved to be
an effective WWTP assessment tool taking into account the combined effect of environmental
pollutants and holistic impact on the environmental compartments [37,38]. Further increased
consumption of new pharmaceuticals and personal care products lead to the release of new and
emerging pollutants, which could be missed by classical instrumental methods. Thus, for the
adequate estimation of WWTP’s environmental impact, the introduction in a monitoring scheme of
a selected battery of biotests using species from different trophic levels is of particular importance.
The present study aims to assess the impact of the Bulgarian WWTPs on receiving water bodies
by (i) collecting samples from WWTP effluents and water bodies receiving treated wastewaters; (ii)
monitoring a representative set of physicochemical water quality parameters and biotests with
species from different trophic levels; (iii) discriminating water quality factors and parameters
between WWTP effluents and receiving water bodies by PLS-DA. To the best of our knowledge, the
proposed monitoring scheme, and statistical modeling to evaluate WWTPs’ impact on receiving
water bodies are undertaken for the first time in this study.
2. Results
2.1. Sampling and Basic Statistics
Sewage water samples were collected from twenty-one Bulgarian WWTPs receiving urban
wastewaters (refer to Figure 1 for sampling locations) and from the respective receiving bodies.
Samples were taken at three points in case of every WWTP: from WWTP effluents (marked with 0),
from the receiving river in the hydrologic course prior to WWTP outlet (marked with 1) and from the
watercourse after the release of treated wastewaters (marked with 2).
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Figure 1. Sampling: (a) location of the wastewater treatment plants (WWTPs) and (b) sampling
scheme (please refer to Supplementary Material for details on sampling locations and acronyms
used).
Eight physicochemical indicators—pH, electrical conductivity (EC), chemical oxygen demand
(COD), total suspended solids (TSS), total phosphorus (P), total bound nitrogen (N), chlorides (Cl)
and sulfates (SO4)—were determined by spectrophotometric methods using cuvette tests. The
concentrations of twelve potentially toxic elements–Cr, Co, Cu, Cd, Ba, V, Mn, Fe, Ni, Zn, Se, Pb—
were measured using inductively coupled plasma mass spectrometer (ICP-MS).
Additionally, eight ecotoxicity indicators were included in the data set: percentage inhibition of
seed germination (SG)/root growth (RG) of Sorghum saccharatum (SS-SG/SS-RG, respectively),
Lepidium sativum (LS-SG/LS-RG), Sinapis alba (SA-SG/SA-RG), percentage bioluminescence change of
Vibrio fischeri (Microtox) and percentage mortality of Daphnia magna (Daphnia).
Altogether, the obtained data matrix consisted of 63 objects and 28 water quality parameters.
The basic statistics of water quality parameters for WWTP effluents is presented in Table 1.
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Table 1. Basic statistics of water quality parameters in wastewater treatment plant (WWTP) effluents (n =
21), concentration requirements for urban WWTP discharges according to Directive 91/271/EEC and
number of samples exceeding the Directive.
Parameter
Unit
Mean
Min
Max
St. dev.
pH
EC
COD
TSS
P
N
ClSO42Cr
Co
Cu
Cd
Ba
V
Mn
Fe
Ni
Zn
Se
Pb
LS-SG
LS-RG
SA-SG
SA-RG
SS-SG
SS-RG
Daphnia
Microtox
µS/cm
mg/L O2
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
µg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
%
%
%
%
%
%
%
%
8.13
451.7
12.13
3.26
1.14
7.07
42.2
53
0.0026
0.0002
0.0023
0.0113
0.0272
0.0015
0.0329
0.239
0.0028
0.0222
0.0006
0.0010
3.02
−30.59
0.16
−26.15
1.31
14.65
18.10
27.53
7.57
87.30
5.69
0.10
<0.50
1.85
17.4
5
0.0005
0.0001
0.0007
0.0001
0.0131
0.0006
0.0043
0.093
0.0016
0.0056
0.0000
0.0001
0.00
−92.99
−3.45
−69.55
−3.45
−4.71
0.00
−14.36
8.51
1174
23.40
9.40
2.82
14.20
86.6
136
0.0139
0.0005
0.0057
0.0918
0.0560
0.0064
0.1146
0.408
0.0097
0.0618
0.0026
0.0144
6.67
33.83
10.34
27.33
6.90
40.18
46.67
61.81
0.29
275.4
4.27
2.19
0.73
3.41
19.6
37
0.0027
0.0001
0.0015
0.0270
0.0132
0.0013
0.0300
0.085
0.0018
0.0162
0.0006
0.0031
2.27
27.54
4.00
25.56
3.53
12.58
11.76
18.90
Directive
91/271/EEC
125
35/601
1/22
10/152
-
Samples Exceeding (n)
5
1
-
1
The concentrations shown are for more than 10,000 population equivalent (p.e.) and for 2000–10,000
p.e., respectively; 2The concentrations shown are for more than 100,000 p.e. and for 10,000–100,000
p.e., respectively.
As can be seen based on data presented in Table 1, the requirements of Directive 91/271/EEC [8]
for the discharges are met for a vast majority of the samples regarding the controlled parameters COD, TSS, N, and P. For Pazardjik (PAZ) WWTP the concentrations of total nitrogen and total
phosphorus are above the concentration limits set in the Directive. Additionally, four results for total
phosphorus are higher than the concentration limits–the outlets of Pernik (PER), Plovdiv (PDV),
Gabrovo (GAB), and Popovo (POP). These results correspond well with the data obtained from the
mandatory monitoring of the studied WWTPs for the period 2015 to 2017 (see Table S2 in
Supplementary Material). In the detailed 3 years’ mandatory monitoring, problems with samples that
do not comply with the Directive limits are observed for the same WWTPs for the same parameters—
total nitrogen (for PAZ) and total phosphorus (for PAZ, PDV, and POP).
The basic statistics of water quality parameters in surface waters are shown in Table 2.
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Table 2. Basic statistics of water quality parameters in surface waters (n = 42), guide values for surface
water intended for the abstraction of drinking water according to Directive 75/440/EEC and number of
samples exceeding the A1 guide values.
Parameter
pH
EC
COD
TSS
P
N
ClSO42Cr1
Co
Cu
Cd
Ba1
V
Mn
Fe
Ni
Zn
Se1
Pb1
LS-SG
LS-RG
SA-SG
SA-RG
SS-SG
SS-RG
Daphnia
Microtox
Unit
µS/cm
mg/L O2
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
µg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
%
%
%
%
%
%
%
%
1
Mean
8.16
269.5
9.51
10.91
0.38
2.58
13.0
39
0.0021
0.0003
0.0024
0.0199
0.0277
0.0016
0.0508
0.388
0.0031
0.0069
0.0003
0.0013
2.26
−24.82
0.04
−16.72
0.00
13.28
19.37
27.29
Min
7.54
26.90
<5.00
1.80
<0.50
<1.00
1.6
7
0.0003
0.0001
0.0006
0.0001
0.0067
0.0004
0.0072
0.128
0.0012
0.0013
0.0000
0.0001
0.00
-83.91
-3.45
-68.26
-3.45
-9.43
0.00
-16.69
Max
8.53
607.0
59.20
40.30
1.37
9.90
39.4
230
0.0097
0.0006
0.0098
0.2256
0.0531
0.0043
0.1182
0.885
0.0062
0.0209
0.0015
0.0068
10.00
63.89
10.34
27.69
10.34
45.83
40.00
63.41
St. dev.
0.25
162.6
9.19
9.94
0.29
2.01
8.2
38
0.0025
0.0001
0.0019
0.0464
0.0124
0.0011
0.0275
0.186
0.0012
0.0045
0.0003
0.0018
2.85
33.87
3.58
27.61
3.73
14.99
9.75
22.01
Directive 75/440/EEC
А1
А2
А3
6.5–8.5 5.5–9.0 5.5–9.0
1000
1000
1000
30
25
0.4
0.7
0.7
1
2
3
200
200
200
150
150
150
0.05
0.05
0.05
0.02
0.02
0.05
1
1
1
1
0.1
1
1
0.01
0.05
0.1
1
0.1
1
1
0.02
0.5
1
1
0.01
0.01
0.01
0.05
0.05
0.05
-
Samples Exceeding A1 (n)
1
2
6
8
36
18
42
-
The mandatory values, instead of guide values in Directive 75/440/EEC are shown.
Directive 75/440/EEC [15] characterizes the possible drinking water sources as Categories A1:
needing simple physical treatment and disinfection; A2: requiring normal physical, chemical
treatment, and disinfection and A3: with intensive physical, chemical treatment and extended
disinfection. The directive sets the limits for quality requirements of surface waters. As can be noticed,
based on data presented in Table 2, the water quality parameters in the effluent waters of the studied
WWTPs would meet the requirements of the directive if these water bodies were to be used as a
source for drinking water abstraction. Thirteen (out of 20) physicochemical parameters studied (refer
to Table 2) indicate the water in the rivers could possibly be used as category A1 water for drinking
purposes. For nitrogen, the regulations are met only for category A3, for manganese and iron they
meet categories A2 and A3.
2.2. PLS-DA Models
The first PLS-DA classification model was developed for the 20 physicochemical parameters to
discriminate samples divided into two classes: WWTP effluents (21 samples) and surface waters (42
samples). The confusion matrix and area under the curve (AUC) value (Figure 2a) resemble the
excellent prediction model ability [39] with 93.65% correct predictions.
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Figure 2. The partial least squares–discriminant analysis (PLS-DA) model results for WWTP effluents
and surface waters based on physicochemical water quality parameters: (a) Confusion matrix; (b) VIP
(variable importance on projection) scores; (c) Regression vector for WWTP effluents; (d) Regression
vector for surface waters.
With significant contribution for the classification model, the following quality parameters could
be outlined: EC, TSS, P, N, Cl, Fe, Zn, and Se; their VIP (variable importance on projection) score
values are close to or higher than one (Figure 2b). The presented regression vectors for WWTP
effluents (Figure 2c) and surface waters (Figure 2d) resembles the concentration profiles of both
classes. Concerning the abovementioned significant water quality indicators, the WWTP influents are
characterized with higher electrical conductivity and higher concentrations of P, N, Cl, Zn, and Se
while the surface waters possess higher levels of TSS and Fe.
There are four misclassified samples. The effluent of the smallest WWTP in this study Pavel
banya (PBN) is classified as a surface water and three surface waters samples, two in Kyustendil
(KNL) and the one after the WWTP outlet in Stara Zagora (SZG), are classified as WWTP effluents.
The reason for wrong surface waters’ predictions could be found in an unauthorized discharge in the
corresponding river areas.
The second PLS-DA model was carried out using the physicochemical indicators to discriminate
surface water samples. The surface water samples were arranged in two classes, before and after the
WWTP outlet, and each group contains 21 samples. In Figure 3, the results of this analysis are
presented.
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Figure 3. The PLS-DA model results for surface waters before and after WWTP outlet based on
physicochemical water quality parameters: (a) Confusion matrix; (b) VIP scores; (c) Regression
vectors for WWTP effluents; (d) Regression vector for surface waters.
The AUC value (0.81) corresponds to good accuracy of the classification model. In total, 11 out
of 42 samples were misclassified. Two of them (taken before WWTP outlet) are misclassified as
samples taken after the release of treated wastewaters (Figure 3a). The sample taken before WWTP
outlet of Pernik (PER) has high pH, Cl, and Se levels while the surface water sample before WWTP
outlets of Lovech (LOV) possesses elevated pH and Se concentration. The other nine misclassified
samples taken after the WWTP outlet are an indication that the released treated wastewaters do not
substantially affect the surface water quality in the respected received water bodies. Significant water
quality parameters for the classification model are pH, Cl, Mn, Zn, and Se (Figure 3b). The class of
samples taken after the WWTP outlet is characterized with higher values of pH, Cl, Zn, and Se, which
could be considered as an effect of WWTP discharge on the receiving water bodies (Figure 3d).
Finally, the PLS-DA classification model was developed for eight ecotoxicological parameters to
discriminate WWTP effluent samples (21) from the surface waters (42). Аlthough the classification
model is not as reliable (Figure 4a) as the previous one (based on physicochemical parameters), some
important conclusions could be drawn. Тhe ecotoxicological test Phytotoxkit has a more significant
impact on the classification model than Microtox and Daphtoxkit (Figure 4b). The different plant
species used in Phytotoxkit give different responses when exposed to WWTP effluents and surface
water samples (Figure 4c,d). The numbers of the germinated seeds of Sorghum saccharatum (SS-SG)
and Lepidium sativum (LS-SG) decrease in WWTP effluent samples, whereas the root growth of Sinapis
alba (SA-RG) increase.
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Figure 4. The PLS-DA model results for WWTP effluents and surface waters based on ecotoxicological
indicators: (a) Confusion matrix; (b) VIP scores; (c) Regression vector for WWTP effluents; (d)
Regression vector for surface waters.
3. Discussion
The results obtained in the current study are in good agreement with similar European studies
for WWTPs effluents [35] and receiving water bodies subject to WWTP discharge [37]. The water
quality parameters in the effluents indicate problems for WWTPs near Pazardzhik (PZK) and Plovdiv
(PDV). This may be due to the fact that these treatment plants do not use chemical precipitation of
phosphorus and biological nitrogen in the removal facilities. Problems at the outlets of POP, PER,
and GAB may be appearing because of the different sampling regimes between mandatory
monitoring presented in Table S2 (24 h representative sample) and random sampling used in the
current study.
Results for 70% of the studied parameters in the surface waters show good ecological status of
the water bodies, and they can be used as category A1 drinking water sources. For 30% of the
parameters, surface waters need treatment to achieve the limits for category A1. Iron is present at
concentration levels above 0.1 mg/L in all of the samples studied, and content of nitrogen, manganese,
phosphorus, and TSS exceeds the respective limits for A1 category in, respectively, 86%, 43%, 19%.,
and 14% of samples collected.
The excellent discrimination between WWTP effluents and receiving bodies’ surface waters
(Figure 2) is based on two groups of significance for the classification model physicochemical
parameters. The first one consists of TSS and Fe that has higher concentrations in surface waters than
in WWTP effluents. The reason for low concentrations of TSS and Fe in treated wastewaters is the
removal of coarse solids found in raw wastewater. The second group of indicators includes P, N, Cl,
Fe, Zn, and Se, which possess higher values for WWTP effluents group. The main sources of these
elements in wastewaters, excluding toilet loads, are household products used in the bathroom and
laundry [40,41].
Discrimination between surface waters taken before and after WWTP outlets is performed to
assess the impact of WWTPs on receiving water bodies (Figure 3). The misclassification of nine out
of 21 samples taken after the treated wastewater release is an indication that these WWTPs receiving
urban wastewaters do not significantly affect the receiving water bodies. The presence of
physicochemical parameters with significant impact for 73.81% correct model predictions is due to
several reasons. For instance, the higher concentrations of Cl, Zn, and Se in surface waters samples
Molecules 2019, 24, 2274
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taken after the WWTP outlets are caused by the release of treated wastewaters into receiving water
bodies. These water quality indicators could be perceived as a footprint for the WWTPs’ impact on
receiving water bodies. Additionally, WWTP discharge leads to an increase of pH and decrease of
Mn concentration in surface waters taken after the WWTP outlet.
The lower prediction ability of the classification model based on ecotoxicological parameters
(Figure 4) compared to previous PLS-DA models is an indication that some of the used biotests are
not applicable for the discrimination of Bulgarian urban WWTP effluents from the receiving water
bodies. Daphnia and Microtox tests have no significant contribution to the PLS-DA model. This fact
is in agreement with ecotoxicity results for municipality WWTP effluents in Lithuania and Estonia
[38]. It seems that these biotests could be effective for ecotoxicity estimation of industrial WWTP
effluents. The root growth of all plants used in Phytotoxkit (LS-RG, SA-RG, SS-RG) increase in WWTP
effluents as Sinapis alba increase has a significant impact on the classification model. This root growth
increase in WWTP effluents towards surface waters could be explained with the elevated levels of
nutrients (N, P) in treated wastewaters. The other two significant ecotoxicological indicators for
classification model is seed germination of Sorghum saccharatum (SS-SG) and Lepidium sativum (LSSG). The reason for a smaller number of germinated seeds in WWTP effluents than in surface water
samples could be the presence of toxicants in treated wastewaters.
4. Materials and Methods
4.1. Sampling and Sample Preparation
Sixty-three water samples–21 WWTP outlets and 42 surface waters–were collected in August
2018 according to the scheme described in Section 2.1. Water samples were collected in glass bottles
and stored at 4 °C prior to being transported to a laboratory. Fifty milliliters of the sample–intended
for ICP-MS analysis–were filtered with a 25 mm PES sterile syringe filters (0.45 µm) and 1.5 ml of
concentrated nitric acid was added. Two hundred and fifty milliliters of the sample intended for
ecotoxicological analysis was filtered with a 25 mm PES sterile syringe filters (0.2 µm) and frozen.
4.2. Physicochemical Analysis
4.2.1. Spectrophotometric Methods Using Cuvette tests
All steps for sample preparation are described by the producer of the cuvette tests.
The method for the determination of chemical oxygen demand (COD) in water samples using
cuvette tests (LCK 314) is based on the oxidation of the sample with potassium dichromate, sulfuric
acid, silver sulfate, and mercury sulfate [42]. The solution is heated at 148 ± 2 °С with a thermo-reactor
LT 200 (Hach Lange GmbH, Berlin, Germany) for two hours prior to the determination of COD in the
range of 15 to 150 mg/L O2 using a portable spectrophotometer DR 3900 (Hach Lange GmbH, Berlin,
Germany) at 448 nm.
Measurement of total bound nitrogen (N) in water samples with cuvette tests LCK 138 is based
on the oxidation of the organic and inorganic forms of nitrogen with peroxydisulphate to nitrates,
which then react with 2,6-dimethilphenol in sulfuric acid and phosphoric acid media, yielding
nitrophenol [43]. The solution is heated to 100 ± 2 °С (LT 200) for one hour prior to determination of
N in the range 1 to 16 mg/L at 370 nm (DR 3900).
The method for the determination of total phosphorus (P) in water samples using LCK 348 is
based on the interaction of the phosphate ions with molybdate ions and antimony for the formation
of antimonylphosphomolybdate, which is reduced by ascorbic acid to phosphomolybdate blue and
heating it for one hour at 100 ± 2 °С (LT 200) prior to determination of P in the range 1 mg/L to 10
mg/L at 890 nm (DR 3900) [44].
Cuvette tests LCK 311 were used for the determination of chloride (Cl) in the range from 1 to
1000 mg/L. The interaction of the chloride ions with mercury thiocyanate produces a release of
thiocyanate ions for the formation of iron(III)thiocyanate, and subsequent measurement was
performed at 468 nm (DR 3900).
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SulfaVer 4 powder reagent was used for the determination of sulfates (SO4) in water samples.
Sulfate ions in the sample react with barium in the SulfaVer 4 Sulfate Reagent to form insoluble
barium sulfate. The measurement in the range from 2 to 70 mg/L was performed at 450 nm (DR 3900).
Measurements of pH and electrical conductivity (EC) were performed on a combined device
SensIon+ MM734 (Hach Lange GmbH, Berlin, Germany) [45,46].
The determination method for total suspended solids (TSS) in water is based on the airpressured filtration of the sample through glass-fiber filters and subsequent drying of the filter at 105
± 2 °С. The mass of the particles retained onto the filter (1.5 µm) is measured by an analytical balance
(RADWAG AC310/C/2, Radom, Poland) with an accuracy of 0.01 g [47].
4.2.2. ICP-MS
Analysis of the water samples was carried out with an ICP-MS PerkinElmer SCIEX - ELAN DRCe (MDS Inc., Concord, Ontario, Canada). The spectrometer was optimized (RF power, gas flow, lens
voltage) to provide minimal values of the ratios CeO+/Ce+ and Ba2+/Ba+ as well as maximum intensity
of the analytes. External calibration by a multi-element standard solution was performed. The
calibration coefficients for all calibration curves were at least 0.99. The measurement conditions for
ICP-MS are presented in Table 3.
Table 3. Measurement conditions for ICP-MS (Perkin Elmer SCIEX DRC-e).
Instrument
Argon plasma gas
flow
Auxiliary gas flow
Nebulizer gas flow
Lens voltage
ICP RF power
Pulse stage voltage
Dwell time
Acquisition mode
Peak pattern
Sweeps/reading
Reading/replicates
Sample uptake rate
Number of runs
Rinse time
Rinse solution
Operating Conditions
15 L/min
1.20 L/min
0.90 L/min
6.00 V
1100 W
950 V
50 ms
Peak hop
One point per mass at maximum peak
8
1
2 mL/min
6
180 s
3% HNO3 (v/v)
137Ba, 111Cd, 59Co, 52Cr, 63Cu, 57Fe, 55Mn, 60Ni, 208Pb, 78Se, 51V,
Isotopes monitored
66Zn
Single element standard solutions of Ba, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Se, V, Zn (Fluka,
Germany) with initial concentration of 10 µg/mL were mixed and used for calibration after
appropriate dilution to obtain the following concentrations: 0.5, 1.0, 5.0, 10.0, 25.0, and 50.0 ng/mL.
All solutions were prepared with double deionized water (Millipore purification system Synergy,
France). For the acidification of the water samples, ultrapure nitric acid (67–69 % HNO3, Fisher
Chemicals, TraceMetal Grade) was used. The accuracy of the proposed method was checked by
analyzing standard reference material NIST 1640a (Trace Elements in Natural Water). The obtained
values for analytical recovery varied between 95% and 108%, which was considered as satisfactory.
4.3. Ecotoxicological Analysis
To assess the ecotoxicity of the collected samples, a battery of selected biotests was applied. The
selected species belong to different trophic levels in the food chain, as follows: producers: Sorghum
saccharatum, Lepidium sativum, and Sinapis alba (Phytotoxkit F™, MicroBioTests Inc., Belgium);
Molecules 2019, 24, 2274
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consumers: Daphnia magna (Daphtoxkit F™, MicroBioTests Inc., Belgium), and reducers: Vibrio
fischeri—Microtox® (ModernWatern, Cambridge, UK).
The Phytotoxkit F™ biotest measures the change of the seed germination and the growth of the
young roots after several days of exposure of seeds of selected higher plants to polluted samples, in
comparison to a control sample. Originally, this microbiotest is designed to assess the ecotoxicity of
soil samples, but Wieczerzak et al. [48] applied it to liquid samples of both environmental and model
origin. A layer of cotton wool (100% pure cotton) soaked with the water sample (18 mL) was covered
with a filter paper, and 10 seeds of the plant species were placed in the test area. The test was
performed in triplicate for each water sample for each one of the three higher plants, and distilled
water was used as a control sample. After 72 h incubation at 25 °C, the germinated seeds were
counted, images of test plates were taken, and the root growth was measured using the program
Image J (NIH, Bethesda, MD, USA) [49].
The biotest Dapthoxkit FTM magna is a crustacean toxicity screening test for freshwater. The test
kit contains vials with dormant eggs (ephippia) of Daphnia magna. According to the producer’s
procedure (MicroBioTests, Inc., Ghent, Belgium), a Standard Freshwater was prepared as hatching
and dilution medium. On Day 0 the rinsed ephippia were transferred into a hatching Petri dish in
50 mL pre-aerated Standard Freshwater and incubated at 20 to 22 °C under continuous illumination
of minimum 6000 lux for 3 days. The neonates were pre-fed with Spirulina powder 2 h prior to the
toxicity test. Each well of the test plate was filled with 10 mL of the samples or Standard Freshwater
as a control sample (both in triplicates), and 5 neonates were transferred in each well. The number of
dead and immobilized neonates was determined after 48 h incubation in darkness at 20 °C.
The Microtox® biotest utilizes the marine Gram(-) Vibrio fischeri bacteria and their ability to
bioluminescence. The test Reagent consists of lyophilized bacteria which were rehydrated with a
Reconstitution Solution (RS, nontoxic Ultra Pure Water) 20 min prior to the analysis. Osmotic
Adjusting Solution (OAS, nontoxic 22% NaCl) was used to adjust the osmotic pressure of the samples
to approximately 2% NaCl. The Diluent (nontoxic 2% NaCl solution) was utilized as a control sample
and dilution medium. The light emission of the diluted bacteria suspension before and after 30 min
exposure to the samples was measured using the Microtox 500 analyzer, and the bioluminescence
change was calculated. The data were processed using the Microtox Omni Software, according to the
Basic Test Protocol (81.9%).
4.4. PLS-DA
PLS-DA is a special form of Partial least square modeling used to find PLS components which
discriminate the known classes of samples. The separation between different groups is performed by
modeling a relationship between independent input data (X) and output data (Y). Here the input data
(X) consists of water quality indicators and (Y) is categorical variable (i.e., dummy codes +1 and 0)
representing class membership of each sample. For solving of binary classification problems (i.e., two
classes) in this study, the PLS1-DA algorithm was performed [34]. Before the analysis, the
independent input data (water quality indicators) were autoscaled and venetian blinds as crossvalidation procedure was applied. The PLS-DA provides several statistics concerning independent
variables (water quality indicators) and sample classes. The variable importance on projection (VIP)
is a measure of the importance of variables in the prediction model. Water quality indicators with
VIP values higher than 1 are considered to have significant discriminative power in the achieved
classification model. The obtained regression vectors represent the variable profile of known sample
classes. The main parameters assessing the performance of the prediction model are specificity and
sensitivity. Taking the example confusion matrix presented in Table 4 (where Class A is identified as
positive: P, and Class B as negative: N), the Class A sensitivity is calculated as TP/(TP+FN) and
describes the model ability to classify correctly samples belonging to Class A. The specificity is
defined as TN/(FP+TN) and is a measure for the model’s ability to predict membership of samples
belonging to Class B. The receiver operator characteristic (ROC) combined both parameters by
plotting the sensitivity against 1-specificity for different values of discrimination thresholds. The area
under the curve (AUC) is used as the main figure of merit of obtained PLS-DA models. The perfect
Molecules 2019, 24, 2274
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model prediction corresponds to AUC value equal to 1 since values equal to or lower than 0.5 are an
indication for bad classification models [50]. The detailed information on performed PLS-DA models
is presented in Supplementary Material (Table S3).
Table 4. Example confusion matrix.
Actual
Class A
Class B
Class A
TP
FN
Predicted
Class B
FP
TN
All PLS-DA modeling calculations were performed in MATLAB R2018b using PLS Toolbox 8.7
(Eigenvector Research Inc, Manson, WA, USA).
5. Conclusions
The treated wastewaters of the biggest Bulgarian WWTPs show higher electrical conductivity
and higher concentrations of P, N, Cl, Zn, and Se than the receiving surface waters. The WWTPs’
impact on receiving water bodies is characterized by the higher values of pH, Cl, Zn, and Se in surface
water samples taken after the WWTP outlet compared to the samples taken before WWTP discharge.
The significant impact of the plant tests endpoints on discrimination between WWTP effluents and
surface waters proves the potential of such ecotoxicological tests in WWTPs’ impact assessment.
The methodology proposed in the presented study combines original sampling scheme (Figure
1b) and appropriate supervised pattern recognition technique and offers:
• A new way for WWTPs’ impact assessment on receiving water bodies;
• Prioritization of water quality indicators concerning WWTPs’ impact on receiving water
bodies;
• Opportunity for selection of optimal water quality indicator set for assessment of WWTPs’
impact.
Additionally, the used methodology is flexible and could include WWTP influent samples to
assess not only the WWTPs’ impact but their efficiency as well.
Supplementary Materials: The following are available online, Figure S1: Sampling locations of the WWTPs,
Table S1: Sampling locations and acronyms of the WWTPs, Table S2: Number of the samples from the mandatory
monitoring of the studied WWTPs for the period 2015 to 2017 exceeding Directive 91/271/EEC, Table S3: PLSDA models information.
Author Contributions: Conceptualization, B.K., T.V. and S.T.; methodology, G.Y., S.L., B.K., B.Z., V.M., M.W.,
T.V. and S.T.; software, S.T.; validation, G.Y., S.L., B.K., B.Z., V.M., M.W., T.V. and S.T.; formal analysis, G.Y.,
S.L., B.K., B.Z., V.M., M.W. and T.V.; investigation, G.Y., S.L., B.K., B.Z., V.M., M.W., T.V. and S.T.; resources,
G.Y., S.L., B.K., B.Z., V.M., M.W., T.V. and S.T.; data curation, G.Y., T.V. and S.T.; writing—original draft
preparation, G.Y., S.L., B.K., B.Z., V.M., M.W., T.V. and S.T.; writing—review and editing, B.K., T.V. and S.T.;
visualization, G.Y. and S.T.; supervision, B.K., T.V. and S.T.; project administration and funding acquisition, T.V.,
B.K. and S.T.
Funding: This research was funded by the Ministry of Education and Science, Bulgarian National Science Fund,
Grant DN 19/15.
Acknowledgments: The authors gratefully acknowledge the financial support from the Bulgarian National
Science Fund (Grant DN 19/15).
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Chutter, F. Research on the rapid biological assessment of water quality impacts in streams and rivers.
WRC Report No 422/1/98. Water Research Commission: Pretoria, South Africa, 1998.
Molecules 2019, 24, 2274
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
14 of 16
Cantinho, P.; Matos, M.; Trancoso, M.A.; Correia dos Santos, M.M. Behaviour and fate of metals in urban
wastewater treatment plants: A review. Int. J. Environ. Sci. Technol. 2016, 13, 359–386,
DOI:https://doi.org/10.1007/s13762-015-0887-x.
Barreca, S.; Busetto, M.; Vitelli, M.; Colzani, L.; Clerici, L.; Dellavedova, P. Online Solid-Phase Extraction
LC-MS/MS: A Rapid and Valid Method for the Determination of Perfluorinated Compounds at Sub ng·L−1
Level in Natural Water. J. Chem. 2018, 2018, 3780825, DOI:https://doi.org/10.1155/2018/3780825.
Barreca, S. Determination of estrogenic endocrine disruptors in water at sub-ng L−1 levels in compliance
with Decision 2015/495/EU using offline-online solid phase extraction concentration coupled with high
performance liquid chromatography-tandem mass spectrometry. Microchem. J. 2019, 147, 1186–1191,
DOI:10.1016/j.microc.2019.04.030.
Victor, R.; Kotter, R.; O’Brien, G.; Mitropoulos, M.; Panayi, G. WHO Guidelines for the safe use of
wastewater, excreta and greywater - Volume 1: Policy and regulatory aspects. Int. J. Environ. Stud. 2006, 65,
157–176.
The Millennium Development Goals Report; The United Nation, New York, NY, USA, 2015.
European Parliament, Council of the European Union. Directive 2000/60/EC of the European Parliament
and of the Council establishing a framework for Community action in the field of water policy. OJ. L. 2000,
327, 1–73.
Council of the European Union. Council Directive 91/271/EEC concerning urban waste-water treatment.
OJ. L. 1991, 135, 40–52.
Council of the European Union. Council Directive 98/83/EC on the quality of water intended for human
consumption. OJ. L. 1998, 330, 32–54.
Teodosiu, C.; Barjoveanu, G.; Teleman, D. Sustainable water resources management 1. River basin
management and the EC Water Framework Directive. Environ. Eng. Manag. J. 2003, 2, 377–394,
DOI:10.30638/eemj.2003.033.
Johnson, C. Toward post-sovereign environmental governance? Politics, scale, and EU water framework
directive. Water Altern. 2012, 5, 83–97.
Carter, J. Spatial planning, water and the Water Framework Directive: Insights from theory and practice.
Geogr. J. 2007, 173, 330–342, DOI:10.1111/j.1475-4959.2007.00257.x.
Josefsson, H. Achieving ecological objectives, Laws 2012, 1, 39–63, DOI:https://doi.org/10.3390/laws1010039.
Van Rijswick, H.; Backes, C. Ground breaking landmark case on environmental quality standards? J. Eur.
Environ. Plan. Law. 2015, 12, 363–377, DOI:10.1163/18760104-01204008.
Council of the European Union. Council Directive 75/440/EEC concerning the quality required of surface
water intended for the abstraction of drinking water in the Member States. OJ. L. 1975, 194, 26–31.
Junninen, H.; Mønster, J.; Rey, M; Cancelinha, J.; Douglas, K.; Duane, M.; Forcina, V.; Müller, A.; Lagler, F.;
Marelli, L.; et al. Quantifying the impact of residential heating on the urban air quality in a typical European
coal combustion region. Environ. Sci. Technol. 2009, 43, 7964–7970, DOI:10.1021/es8032082.
Singh, K.P.; Malik, A.; Mohan, D.; Sinha, S.; Singh, V.K. Chemometric data analysis of pollutants in
wastewater—a case study. Anal. Chim. Acta. 2005, 532, 15–25, DOI:10.1016/j.aca.2004.10.043.
Einax, J.W.; Soldt, U. Geostatistical and multivariate statistical methods for the assessment of polluted
soils—merits and limitations. Chemometr. Intell. Lab. Syst. 1999, 46, 79–91, DOI: 10.1016/S01697439(98)00152-X.
Barreca, S.; Mazzola, A.; Orecchio, S.; Tuzzolino, N. Polychlorinated Biphenyls in Sediments from Sicilian
Coastal Area (Scoglitti) using Automated Soxhlet, GC-MS, and Principal Component Analysis. Polycycl.
Aromat. Comp. 2014, 34, 237–262, DOI: 10.1080/10406638.2014.886078.
Gurjar, S.K.; Tare, V. Spatial-temporal assessment of water quality and assimilative capacity of river
Ramganga, a tributary of Ganga using multivariate analysis and QUEL2K. J. Clean. Prod. 2019, 222, 550–
564, DOI:https://doi.org/10.1016/j.jclepro.2019.03.064.
Li, T.; Li, S.; Liang, C.; Bush, R.T.; Xiong, L.; Jiang, Y. A comparative assessment of Australia's Lower Lakes
water quality under extreme drought and post-drought conditions using multivariate statistical
techniques. J. Clean. Prod. 2018, 190, 1–11, DOI:https://doi.org/10.1016/j.jclepro.2018.04.121.
Bilgin, A. Evaluation of surface water quality by using Canadian Council of Ministers of the Environment
Water Quality Index (CCME WQI) method and discriminant analysis method: A case study Coruh River
Basin. Environ. Monit. Assess. 2018, 190, 554, DOI:https://doi.org/10.1007/s10661-018-6927-5.
Molecules 2019, 24, 2274
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
15 of 16
Singh, K.P.; Malik, A.; Mohan, D.; Sinha, S. Multivariate statistical techniques for the evaluation of spatial
and temporal variations in water quality of Gomti River (India)—A case study. Water Res. 2004, 38, 3980–
3992, Doi:10.1016/j.watres.2004.06.011.
Astel, A.; Tsakovski, S.; Barbieri, P.; Simeonov, V. Comparison of self-organizing maps classification
approach with cluster and principal components analysis for large environmental data sets. Water Res. 2007,
41, 4566–4578, DOI:10.1016/j.watres.2007.06.030.
Olkowska, E.; Kudłak, B.; Tsakovski, S.; Ruman, M.; Simeonov, V; Polkowska, Z. Assessment of the water
quality of Kłodnica River catchment using self-organizing maps. Sci. Total Environ. 2014, 476–477, 477–484,
DOI:http://dx.doi.org/10.1016/j.scitotenv.2014.01.044.
Acquavita, A.; Aleffi, I.F.; Benci, C.; Bettoso, N.; Crevatin, E.; Milani, L.; Tamberlich, F.; Toniatti, L.; Barbieri,
P.; Licen, S.; et al. Annual characterization of the nutrients and trophic state in a Mediterranean coastal
lagoon: The Marano and Grado Lagoon (northern Adriatic Sea). Reg. Stud. Mar. Sci. 2015, 2, 132–144,
DOI:http://dx.doi.org/10.1016/j.rsma.2015.08.017.
Franklin, J.B.; Sathish, T.; Vinithkumar, N.V.; Kirubagaran, R.; Madeswaran, P. Seawater quality conditions
of the south Andaman Sea (Bay of Bengal, Indian Ocean) in lustrum during 2010s decade. Mar. Pollut. Bull.
2018, 136, 424–434, DOI:https://doi.org/10.1016/j.marpolbul.2018.09.038.
Bostanmaneshrad, F.; Partani, S.; Noori, R.; Nachtnebel, H-P.; Berndtsson, R.; Adamowski, J.F. Relationship
between water quality and macro-scale parameters (land use, erosion, geology, and population density) in
the
Siminehrood
River
Basin.
Sci.
Total
Environ.
2018,
639,
1588–1600.
DOI:
https://doi.org/10.1016/j.scitotenv.2018.05.244.
Voyslavov, T.; Tsakovski, S.; Simeonov, V. Hasse diagram technique as a tool for water quality assessment.
Anal. Chim. Acta. 2013, 770, 29–35, DOI:http://dx.doi.org/10.1016/j.aca.2013.01.063.
Voyslavov, T.; Tsakovski, S.; Simeonov, V. Surface water quality assessment using self-organizing maps
and
Hasse
diagram
technique.
Chemom.
Intell.
Lab.
Syst.
2012,
118,
280–286,
DOI:10.1016/j.chemolab.2012.05.011.
Tsakovski, S.; Astel, A.; Simeonov, V. Assessment of the water quality of a river catchment by chemometric
expertise. J. Chemometrics. 2010; 24, 694–702, DOI:10.1002/cem.1333.
Singh, K.R.; Goswami, A.P.; Kalamdhad, A.S.; Kumar, B. Assessment of surface water quality of Pagladia,
Beki and Kolong river (Assam, India) using multivariate statistical techniques. Intl. J. River Basin Manag.
2019, 2019, 1–10, DOI:10.1080/15715124.2019.1566236.
Gromski, P.S.; Muhamadali, H.; Ellis, D.I.; Xu, Y.; Correa, E.; Turner, M.L.; Goodacre, R. A tutorial review:
Metabolomics and partial least squares-discriminant analysis – a marriage of convenience or a shotgun
wedding. Anal. Chim. Acta. 2015, 879, 10–23, DOI: 10.1016/j.aca.2015.02.012.
Lee, L.C.; Liong, C-Y.; Jemain, A.A. Partial Least Squares-Discriminant Analysis (PLS-DA) for classification
of high-dimensional (HD) data: A review of contemporary practice strategies and knowledge gaps. Analyst
2018, 143, 3526–3539, DOI:10.1039/C8AN00599K.
Platikanov, S.; Rodriguez-Mozaz, S.; Huerta, B.; Barceló, D.; Cros, J.; Batle, M.; Poch, G.; Tauler, R.
Chemometrics quality assessment of wastewater treatment plant effluents using physicochemical
parameters and UV absorption measurements. J. Environ. Manage. 2014, 140, 33–44,
DOI:10.1016/j.jenvman.2014.03.006.
Tobiszewski, M.; Tsakovski, S.; Simeonov, V.; Namiesnik, J. Chlorinated solvents in a petrochemical
wastewater treatment plant: An assessment of their removal using self-organising maps. Chemosphere 2012,
87, 962–968, DOI:10.1016/j.chemosphere.2012.01.057.
Kudłak, B.; Wieczerzak, M.; Yotova, G.; Tsakovski, S.; Simeonov, V.; Namiesnik, J. Environmental risk
assessment of Polish wastewater treatment plant activity. Chemosphere 2016, 160, 181–188,
DOI:10.1016/j.chemosphere.2016.06.086.
Manusadzianas, L.; Balkelyte, L.; Sadauskas, K.; Blinova, I.; Põllumaa, L.; Kahru, A. Ecotoxicological study
of Lithuanian and Estonian wastewaters: Selection of the biotests, and correspondence between toxicity
and chemical-based indices. Aquat. Toxicol. 2003, 63, 27–41, DOI:10.1016/S0166-445X(02)00132-7.
Wang, N.; Zeng, N.N.; Zhu, W. Sensitivity, Specificity, Accuracy, Associated Confidence Interval and ROC
Analysis with Practical SAS Implementations. In proceedings of the Northeast SAS User Group Section of
Health Care and Life Sciences, Baltimore, Maryland, MD, USA, 14–17 November 2010; pp. 1–9.
Tjandraatmadja, G.; Pollard, C.; Sheedy, C.; Gozukara, Y. Sources of contaminants in domestic wastewater:
Nutrients and additional elements from household products. CSIRO Publishing: Canberra, Australia, 2010.
Molecules 2019, 24, 2274
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
16 of 16
European Commission. Pollutants in urban wastewater and sewage sludge; Office for Official Publications of
the European Communities: Luxembourg, 2001; pp. 12–63.
DIN 38409-41:1980-12 - German standard methods for the examination of water, waste water and sludge;
Summary effect and substance characteristics (group H); Determination of the chemical oxygen demand
(COD) in the range above 15 mg / l (H 41). German Institute for Standardisation, 1980. DOI:
10.31030/1209856.
Hach Company. Working Procedure: LCK 138 LATON, 1–16 mg/L Total Nitrogen, TNb DOC312.53.94004.
Available online: https://uk.hach.com/asset-get.download.jsa?id=52788795653 (accessed on 9 June 2019).
Hach Company. Working procedure: LCK348 Phosphate DOC312.53.94020 Available online:
https://uk.hach.com/asset-get.download.jsa?id=25593618013 (accessed on 9 June 2019).
Hach Company. User Manual: 5014 Probe DOC012.98.90299. Available online: https://uk.hach.com/assetget.download.jsa?id=25593610956 (accessed on 9 June 2019).
Hach Company. User Manual: 5070 Probe. DOC012.98.90314. Available online: https://uk.hach.com/assetget.download.jsa?id=25593610971 (accessed on 9 June 2019).
BSI. BS EN 872:2005 - Water Quality—Determination of Suspended Solids—Method by Filtration Through
Glass Fibre Filters. BSI, 2005.
Wieczerzak, M.; Kudłak, B.; Namieśnik, J. Impact of selected drugs and their binary mixtures on the
germination of Sorghum bicolor (sorgo) seeds. Env. Sci. Pol. Res. 2018, 25, 18717–18727, DOI:10.1007/s11356018-2049-4.
Schneider, C.A.; Rasband, W.S.; Eliceiri, K.W. NIH Image to ImageJ: 25 years of image analysis. Nat.
Methods 2012, 9, 671–675, DOI:10.1038/nmeth.2089.
Szymańska E.; Saccenti, E.; Smilde, A.K.; Westerhuis, J.A. Double-check: Validation of diagnostic statistics
for PLS-DA models in metabolomics studies. Metabolomics. 2012, 8, S3–S16, DOI:10.1007/s11306-011-03303.
Sample Availability: Samples are available from the authors.
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons
Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).