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Over the years, increasing demographic pressures have contributed to an exponential increase in contaminants in the water ecosystems, associated with the intensification of agricultural and industrial activities. Some of these contaminants have raised particular concern and have been classified as specific pollutants and priority substances, identified in the Directive 2013/39/EU of the European Parliament [1] and the Agência Portuguesa do Ambiente (APA) [2]. To perform the ecological assessment of aquatic ecosystems, there are two major comprehensive frameworks worldwide, using multiple lines of evidence (LoE), with special emphasis on ecological data (biological communities): the Water Framework Directive (WFD), adopted in Europe through the Directive 2000/06/EC, and the Ecological Risk Assessment (ERA), adopted for example by the US Environmental Protection Agency. WFD and ERA approaches integrate information from different LoE, as the extent of the application of ecotoxicological evidence clarifying cause-effect relationships; the availability of expert judgment in the fine-tuning of sampling practices, strategic analysis, data interpretation, and decision procedures; on the practical meaning of the concept of LoE integration (“one-out, all-out” principle versus integrated risk quantification) [3]. The WFD is an extensive legislative framework for the protection of ground and surface waters in Europe, with defined Environmental Quality standards (EQS) for several parameters [biological, chemical, physicochemical (supporting biological elements), and hydromorphological elements] that must be complied by the different member states, and also advise additional monitoring of substances of national or regional interest [1]. However, it is estimated that a very high number of substances are present in the environment, and for Europe, it is estimated the existence of more than 100,000 compounds [4, 5]. Therefore, the definition of these lists of compounds with environmental concern, although relevant, becomes unreliable, in terms of representation in aquatic ecosystems, since complex mixtures of chemicals occur (e.g., emerging pollutants, metabolites, and transformation products). A list of priority substances, which represent a significant risk to or via the aquatic environment at the EU level, will have to be reexamined by the European Commission (EC) and should not exceed 4 years. In recent years, there has been a growing agreement among authorities and scientists that the tools currently used and proposed by the WFD for water quality assessment require a review to achieve a clearer and future-proof methodology [6, 7]. In this context, ERA is reflected as a complementary alternative for the bioassessment of the quality of freshwater, such as reservoirs. This approach considers some valuable WFD principles and metrics but, at the same time, includes complementary methods, one of which is the incorporation of effects-based tools (e.g., ecotoxicological assays; biomarkers in organisms) for a better assessment of cause-effect relationships; reflecting an effective integration of distinct LoEs (e.g., chemical, ecological and ecotoxicological) [3].
In environmental terms, pollution occurs due to a complex mixture of organic and inorganic compounds that can result in lethal and sub-lethal effects on aquatic organisms, associated with potentially significant losses of habitat and biodiversity. Currently, the assessment of water quality, using organisms as bioindicators of water quality has been widely used, since the biological responses integrate the complex influence of the stressing agents [4, 6, 8, 9, 10], in addition to the complex mixture of compounds that occur in the ecosystem under analysis. According to several studies, the effect-based water quality assessment (e.g. biological responses of organisms to natural waters) has been successful in the identification of ecotoxicological risks in surface waters and the ranking of locations based on these risks, namely for natural water bodies (e.g. rivers, transitional or coastal waters)[5, 6, 11], but also to heavily modified water bodies (e.g. reservoirs)[8, 9, 10]. Bioassays with Daphnia sp. (mostly Daphnia magna) are regularly used in ecotoxicological studies because they have high fertility values, easy to maintain in laboratory conditions, ubiquitous, and important bioindicators for aquatic environments due to their sensitivity to contaminants and position (trophic level: primary consumers) in the aquatic food webs [6, 9, 11].
To answer the research needs outlined above, the present study aimed to apply effect-based approaches (individual and biochemical responses of D. magna to natural waters) in the water quality assessment of Portuguese reservoirs, defining classes of disturbances and ranges of ecotoxicological potential. This work has been divided into two parts with specific objectives. Part 1 of this study was presented in [9], which demonstrated that biochemical parameters (metabolism, oxidative stress, and lipid peroxidation biomarkers) improved the sensitivity of the biomonitoring strategy using bioassays with the standard species D. magna, in the assessment of the ecological quality of water reservoirs, in different sampling periods (Autumn 2018 and Spring, Autumn 2019). The biochemical parameters revealed sensitivity in the evaluation of effects incited by exposure to natural waters from reservoirs, making them useful and reliable in this type of evaluation. According to the results of [9], the biomarker indicative of lipid peroxidation (levels of thiobarbituric acid reactive substances—TBARS) on D. magna represented a consistent tool for evaluation of water quality. This result reflected, in part, the prooxidative state of organisms, food performance, and possible stress scenarios, mainly due to the components of seston (e.g., quantity and quality of phytoplankton) and chemical contamination.
Part 2, the focus of the here-presented study, is intended to evaluate whether effect-based methods can be applied in natural waters quality assessment of reservoirs, by the definition of disturbance classes and ecotoxicological potential values. The integration of physical and chemical and effects-based monitoring approaches can complement and improve the water quality assessment strategies in the future, with the main objective of a nontoxic environment. For this, several ecotoxicological tools were applied, to gain insight into the ecotoxicological potential of the reservoirs understudy and compare the ecological potential with tools proposed by the WFD, as well as with previous studies, in the same areas under study. To this end, the present approach combined the evaluation of the individual (feeding rates) and cellular/molecular responses in D. magna after acute exposure to natural waters [e.g., biochemical biomarkers of oxidative response as activities of catalase (CAT) and glutathione S-transferases (GSTs), the latter also involved in the biotransformation process, lipid peroxidation (LPO measured as TBARS levels), and acetylcholinesterase (AChE) activity, involved in neurotransmission process].
2. Material and methods
2.1 Study areas, water sampling, and physicochemical parameters quantifications
Three reservoirs were selected for conducting this study (Figure 1): Miranda (M) and Pocinho (P) are main course reservoirs and belong to the hydrographic basin of the Douro river; and Aguieira (Ag) which is a northern reservoir that belongs to the Mondego hydrographic basin.
Figure 1.
Map of sampling areas (Miranda, Pocinho and Aguieira reservoirs) with the location of the sampling sites. The different colors represent the 1st level of detail of land occupation according to the land use report (2018).
The purpose for defining these reservoirs and respective sampling sites was based on previous studies of our research group [8, 9, 10, 12, 13, 14], where the water quality was assessed, in the last years, using different indicators and methodologies complementary to the WFD.
Water samples were collected during the spring of 2020, in six sites of the three reservoirs (Figure 1): one site in Miranda, one site in Pocinho, and four sites in Aguieira. The sites were well-defined based on previous works conducted in these reservoirs [8, 9, 10, 12, 13, 14].
In situ, the abiotic parameters pH, dissolved oxygen (mg/L and %), conductivity (μS/cm), and temperature (°C) were measured with a multiparameter probe (Multi 3630 IDS SET F). For conducting chemical analysis [e.g., nutrients, specific pollutants, and priority substances] and bioassays, 5 L of water were collected at each sampling site and transported to the laboratory at 4°C and in the dark. Chemical analyses were carried out within a maximum period of 48 hours after collection. D. magna assays were started within a maximum period of 24 hours, after sample collection.
2.2 Chemical analyses
A set of specific pollutants and priority substances were measured, according to the recommendations defined in the Agência Portuguesa do Ambiente [2] and the Directive 2013/39/EU [1]. Nitrites (NO2−) and nitrates (NO3−) were quantified by liquid chromatography of ions, as dissolved anions [15]. Total Kjeldahl nitrogen (NKj) determination was performed by the Kjeldahl nitrogen method after mineralization with selenium [16]. Calcium and magnesium determinations were effectuated by ion chromatography, as dissolved cations [17]. For the elements, total phosphorus (Ptotal), arsenic, cadmium, copper, mercury, nickel, lead, and zinc, the analysis was performed by the application of inductively coupled plasma mass spectrometry (ICP-MS) [18]. Pesticides and polycyclic aromatic hydrocarbons were not quantified in our study, because, according to previous studies [8, 9, 10, 12, 13, 14] the values of these specific pollutants and priority substances (quantified in autumn of 2018) were below the detection limits of the analytical method, in addition to, that no significant changes in the areas adjacent to the reservoirs were documented during the last years (2019, 2020).
2.3 Biological parameters by WFD—Ecological quality ratio (EQR) for phytoplankton
The phytoplankton community characterization was performed according to the Instituto da água I.P. [19] and Agência Portuguesa do Ambiente (APA) [2] guidelines and briefly described in [9]. For the determination of the ecological potential (EP), the results were expressed in an EQR, determined according to the WFD approach. According to APA [2], the EQS used in the classification of the biological quality (EQR) for Miranda and Pocinho reservoirs was carried out based on the typology “main course”. For main course typology, taking into account the biological elements proposed in the WFD to Portuguese reservoirs, the EP is only classified into two classes: moderate or less, and good or more (Table 1). Aguieira is a northern type of reservoir, and the EP is classified into four classes: Good or more, Moderate, Poor, or Bad (Table 1).
Ecological potential (chemical and physico-chemical elements)
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Biological (Phytoplankton EQR)
North [2] [1.0–0.60] – Good or more [0.6–0.4] – Moderate [0.4–0.2] – Poor [0.2–0] – Bad Main course [2] ≥0.17 – Good or more <0.17 – Moderate or less
0.16
0.12
0.77
0.37
0.33
0.61
Ecological potential (biological)
Moderate or less
Moderate or less
Good or more
Poor
Poor
Good or more
Table 1.
Results of the physical and chemical parameters, and specific pollutants and priority substances concentrations of Portuguese reservoirs. The bold values represent the values outside of the environmental quality standards (EQS). The biological parameter phytoplankton and respective EQS for the main course and north reservoirs are also presented.
Sampling sites: Miranda – M, Pocinho – P and Aguieira – Ag1 to Ag4. Temperature (Temp), Conductivity (Cond), pH, Dissolved oxygen (O2), nitrites (NO2−), nitrates (NO3−), Ammonium (NH4+), Total Kjeldahl nitrogen (NKj), Total phosphorus (Ptotal), Arsenic (As), Zinc (Zn), Copper (Cu), Cadmium (Cd), Mercury (Hg), Nickel (Ni), Lead (Pb), Calcium (Ca), Magnesium (Mg), Iron (Fe), Manganese (Mn), and Ecological Quality Ratio (EQR).
2.4 Water treatments
The water collected in each sampling site of each reservoir was processed in 3 treatments, namely: NF (Non-Filtered water with all components present in the sample); F1 (water filtered through a Whatman GF/C filter with 1.2 μm porosity); and F2 (water filtered through a sterile filter system with a porosity of 0.22 μm) as already defined in previous studies of our group [10, 12].
2.5 Test organisms
2.5.1 Culture maintenance of D. magna
Successive generations of monoclonal cultures of D. magna were continuously kept in controlled laboratory conditions of 16h light and 8h dark photoperiod and temperature of 20 ± 2°C. Cultures were renewed every 2 days and were maintained in synthetic water medium “ASTM hard water” [20], supplemented with a standard organic additive, Ascophyllum nodosum extract [21], to provide essential microelements to Daphnia. D. magna were fed with Raphidocelis subcapitata at a rate of 3.0x105 cells/mL/day. For conducting the bioassays, daphnids with 4 or 5 days, born between the 3rd and 5th broods were used.
2.5.2 D. magna feeding rate assays
D. magna feeding rate assays were conducted according to [22] with some adaptations described in [9]. For each water sample, bioassays were performed on 6-well plates, where each plate corresponded to specific water treatment (NF, F1, or F2). For each water treatment and control (ASTM hard water medium), 5 replicate wells with 5 D. magna individuals, and a blank well (water sample with Raphidocelis subcapitata without daphnids) were performed. The blank well is performed to account for the potential algal growth during the assay period. Mortality was also considered in this study. Feeding rate results were expressed according to [23]. The percent inhibition in feeding rate (% IFR), relatively to the control, was calculated for each water treatment (NF, F1, and F2) as follows:
%IFR=FRc−FRt/FRc×100E1
where:
% IFR: percent inhibition of feeding rate;
FRc: mean value for feeding rate in the control group;
FRt: value for feeding rate for the water treatment.
At the end of the feeding rate assays, pools of organisms from each treatment were preserved for posterior biochemical determinations (oxidative stress, lipid peroxidation, and neurotransmission biomarkers) and stored in microtubes at—80°C until analyses were performed.
2.5.3 Biochemical determinations
For determination of biomarkers of oxidative stress [catalase (CAT) and isoenzymes glutathione S-transferases (GSTs) activities] and levels of lipid peroxidation (LPO) [levels of thiobarbituric acid reactive substances (TBARS)], samples were thawed on ice, and a 1 mL of ice-cold phosphate buffer (50 mM, pH = 7.0 with 0.1% of Triton X-100) was added to each biological sample. Samples were sonicated for 20 s and centrifuged at 14,000 rpm, for 10 min, at 4°C, in a refrigerated centrifuge (Eppendorf 5810R). The supernatant fraction was divided into aliquots and used to perform the biochemical analyses. For the quantification of acetylcholinesterase (AChE) activity, the samples were homogenized with a sonicator, in a volume of 750 μL of ice-cold phosphate buffer (0.1 M phosphate buffer, pH = 7.2), and centrifuged at 6000 rpm for 3 min. The supernatants after centrifugation were collected and used for AChE activity determinations.
All biochemical analyses were adapted to 96-well microplates [9, 24], and spectrophotometric readings were performed in a microplate reader Thermo Scientific, model Multiskan GO (version 1.00.40), with SkanIt Software 3.2.
The total soluble protein concentration of samples was performed according to the Bradford method [25], using a standard of γ-globulin 1 mg/mL. This method is based on the binding of a dye (Bradford’s reagent) to the total soluble proteins, forming a stable complex [24]. This determination permits expressing the enzymatic activities and TBARS levels, as a function of the total soluble protein content of the samples.
CAT is an antioxidant enzyme with peroxidic activity and is responsible for the decomposition of hydrogen peroxide (H2O2) in H2O + O2, where H2O2 consumption occurs with the oxidation of hydrogen donors (phenols, formic acid, and methanol) [24]. The method consists of the monitoring of this decomposition and was performed according to [26]. The enzymatic activity was expressed in nmoles of H2O2 consumed, per minute, per milligram of total soluble protein.
The GSTs activity was quantified according to [27]. GSTs catalyze the conjugation of glutathione in its reduced form (GSH) with the substrate 1-chloro-2,4-dinitrobenzene (CDNB), forming a thioether, whose formation was observed by measuring the increase of the absorbance [24]. Enzyme activities were expressed in mmol of thioether produced, per minute, per milligram of total soluble protein.
LPO was measured through the determination of the levels of TBARS, according to [28], which measures the absorbance of the complex resulting from products of oxidative free radical attack to membrane lipids, with thiobarbituric acid. Results were expressed as millimoles of malondialdehyde (MDA) and MDA-like compounds equivalents, per mg of total soluble protein.
The quantification of the AChE activity was performed by the Ellman method [29]. This enzyme is responsible for the degradation of the synthetic substrate acetylthiocholine into acetate + thiocoline [24], which occurs with the increase in the yellow color produced when thiocoline is complexed with dithiobis nitrobenzoate (DTNB). The enzymatic activity was expressed as nmol of the complex formed, per minute, per milligrams of total soluble protein.
2.6 Water ecotoxicological assessment
At the end of all quantifications of enzyme activities and LPO levels, the percent inhibition in each biochemical determination (% IX), comparatively to the control, was calculated for each water treatment (NF, F1, and F2) as follows:
Xc: mean value for CAT activity, GSTs activity, TBARS levels, or AChE activity in the control group;
Xt: value for CAT activity, GSTs activity, TBARS levels, or AChE activity for the water treatment.
2.7 Statistical analyses
The data from all test variables (the percent inhibition of feeding rate, CAT and GSTs activities, TBARS levels, and AChE activity) were previously analyzed to assure normality and uniformity of variance (Shapiro–Wilk and Levene tests, respectively). All parameters were analyzed by analysis of variance (one-way ANOVA), followed, when significant differences were detected (p < 0.05), by a Tukey test to discriminate differences between treatments (NF, F1, and F2). The data are presented as mean and respective standard errors. The analyses were performed using software SPSS Statistics (version 26) and Sigmaplot (version 11.0).
3. Results and discussion
3.1 General physicochemical characteristics and trace elements concentrations (chemical analysis) in the water samples
Table 1 summarizes the results of physicochemical parameters and chemical analyses including the quantifications of the concentrations of the specific pollutants and priority substances, measured for each site over the sampling period, as recommended by APA [2] and European Parliament and the Council [1]. According to the physicochemical parameters used in the WFD, for Portuguese heavily modified and artificial water bodies, only the pH, O2, NO3- and Ptotal have an environmental quality standard (EQS) values established for a good ecological potential (GEP).
In general, water samples from the three reservoirs were characterized by a basic pH (values range from 8.6 and 9.7, Table 1), with almost values above the EQS (> 9.0, except M and Ag3). The water pH is an important parameter as it can determine the solubility and biological availability of nutrients, but also metals [30]. Dissolved oxygen (%) showed values above the maximum of 120%, in all sites studied. Considering the electrical conductivity, the values ranged between 73 and 438 μS/cm. Higher values of this parameter were registered in the reservoirs belonging to the Douro river basin, Miranda and Pocinho (> 260 μS/cm). In different Aguieira sites, the values showed a low variation between the locations of 73–90 μS/cm. The sites M, P, and Ag3 showed higher contents of nutrients (mainly total phosphorus) when compared with the values of EQS (Table 1). Two types of reservoirs are referred to in the work of [31]: Type 1—lowland “run-of-river” reservoirs located in the main rivers (e.g., Douro), at lower altitudes, had larger catchments, lower residence time, and were higher in mineral content (hardness and conductivity), than Type 2, which are deeper high altitude reservoirs (e.g., Mondego). Considering this distinction, Miranda and Pocinho are reservoirs of Type 1 and Aguieira is Type 2 (for more information see [31]), and in fact, our physicochemical results are supported by these assumptions (Table 1). Higher nutrient concentrations (Ptotal) were observed at Miranda and Pocinho sites than at the Aguieira sites (Table 1). According to [31], Type 1 reservoirs are more nutrient-rich (total phosphate and nitrates due to more extensive agriculture and intensive) than Type 2, corroborating our results. If we consider land occupation (Figure 1) in the area surrounding the sampling site of Miranda, the water pressures are associated with the artificialized territories and forests. For Pocinho, agriculture is highly representative in terms of land occupation. The land occupation in the Mondego river basin area, where the Aguieira reservoir is located, has the surrounding areas mainly represented by forests, agricultural areas, and artificialized territories (Figure 1). Kroll et al. [32] show a solid association between land occupation (urban, agriculture, and forest areas) and nutrients indices in nearby aquatic ecosystems. These findings demystify and support some of the results (e.g., nutrient levels) presented here, as well as work previously developed in these same locations [9].
Concerning the metals, only mercury (Hg) and zinc (Zn) exceeded the EQS (Table 1) defined by the Directive 2013/39/EU of the European Parliament [1] and APA [2], respectively. Hg was present in concentrations above 0.07 μg/L (EQS) at all sites (> 0.63 μg/L). For Zn, concentrations above 7.8 μg/L (EQS) were quantified in all locations of the three reservoirs (> 24.7 μg/L). Several metals such as mercury and zinc (among others) can be highly toxic even in residual quantities [33]. Hg is an important pollutant of water throughout the world, and several human activities are linked to Hg pollution (silver and gold mining, coal combustion, and dental amalgams), and is known to be an inhibitor of enzymes’ activities [9, 30, 34, 35]. The speciation of Zn in water is modulated by pH and dissolved organic matter, which normally binds most of the aqueous zinc [30]. Zn concentrations in natural waters span six orders of magnitude and are strongly influenced by human activities [30]. There are a comprehensive set of proteins that function as transporters, chelators, and molecular sensors for Zn, and are involved in the regulation of Zn uptake by homeostatic processes that are partially understood. However, several studies have proposed theories to explain how zinc compounds affect aquatic animals [30]. However, inter- and intra-specific differences cannot be disregarded, as well as doses and exposure times.
Anthropogenic activities have been found to contribute more to environmental contamination (e.g., water eutrophication which was recognized in the middle and late stages of the twentieth century) due to the everyday manufacturing of materials to meet the demands of the population [36, 37], in its various aspects that include, agriculture, industry, and urban areas. As mentioned by [37] human interference is to a greater extent caused by social and economic pressures, which are associated with the largest changes that occurred in agricultural and forest areas as a result of the extensification of agriculture, deforestation, afforestation, and urbanization. In Europe, these are the trends observed over the last years [38, 39].
3.2 Ecological quality ratio (EQR) for phytoplankton
In general, the phytoplankton EQR (Table 1) shows that the Miranda and Pocinho reservoirs had the worst water quality (moderate or lower), taking into account the defined classes for the main water course typology. The Aguieira reservoir tended to have low water quality, with Ag2 and Ag3 being the most problematic sites with the lowest EQR values recorded. All reservoirs were characterized as eutrophic, especially due to high concentrations of Ptotal recorded over the last few years [8, 9, 10, 12, 13, 14, 31, 35, 40], a condition also observed in the present study (Table 1). The bioavailability of nutrients such as phosphorus favors the overgrowth of phytoplanktonic communities [8, 9, 10, 12, 13, 14], namely, cyanobacteria organisms, that were already associated with poor water quality and recurrently reported blooms was been in these reservoirs [8, 9, 10, 12, 13, 14]. The most prevalent and main group of cyanobacteria detected in all reservoirs was Microcystis. However, Anabaena, Woronichinia, and Pseudanabaena (and others) were also detected, but in a much smaller percentage. The percentage of cyanobacteria detected in Miranda and Pocinho was 54.05, and 57.32%, respectively. Lower percentages of cyanobacteria were observed in the Aguieira sites, namely, 1.07% (Ag1), 4.75% (Ag2), 10.75% (Ag3), and 27.30% (Ag4). However, other phytoplankton indicators are included in the assessment of this reservoir typology (e.g., Algae Group Index (AGI) [8, 10], which is strongly interfering with the final classification of the Ag4 site.
3.3 Water ecotoxicological assessment
The proposal of classes of disturbances (defined by colors) and ecotoxicity results for D. magna, after exposure to treatments of the natural waters of Miranda, Pocinho, and Aguieira reservoirs, are presented in Figures 2–4, respectively. For each figure, the values represent the percentage of inhibition of: A – Feeding rate (% IFR); B – CAT activity (% ICAT); C – GSTs activity (% IGSTs); D – TBARS levels (% ITBARS); E – AChE activity (% IAChE), comparatively to the control. Based on the biological responses under study (percent inhibition of different parameters, previously mentioned), ecotoxicity classes were proposed (Figures 2–4) to achieve an approach to the ecotoxicological potential for each sampling site (Figure 5). Based on the criteria to define the equivalent quality potential, to those presented in the WFD, an estimation of the ecotoxicological potential has been suggested.
Figure 2.
Proposal of classes of disturbances (defined by colors) and ecotoxicity results for D. magna, after exposure to treatments of the natural waters of Pocinho reservoir (NF—Non-filtered water; F1 and F2—Filtered with 1.2 μm and 0.22 μm respectively). The values represent the percentage of inhibition of: A – Feeding rate (% IFR); B – CAT activity (% ICAT); C – GSTs activity (% IGSTs); D – TBARS levels (% ITBARS); E – AChE activity (% IAChE), comparatively to the control. Different letters (a, b, and c) stand for significant differences between treatments, detected by the Tukey test (p < 0.05).
Figure 3.
Proposal of classes of disturbances (defined by colors) and ecotoxicity results for D. magna, after exposure to treatments of the natural waters of Miranda reservoir (NF—Non-filtered water; F1 and F2—Filtered with 1.2 μm and 0.22 μm respectively). The values represent the percentage of inhibition of: A – Feeding rate (% IFR); B – CAT activity (% ICAT); C – GSTs activity (% IGSTs); D – TBARS levels (% ITBARS); E – AChE activity (%IAChE), comparatively to the control. Different letters (a and b) stand for significant differences between treatments, detected by the Tukey test (p < 0.05).
Figure 4.
Proposal of classes of disturbances (defined by colors) and ecotoxicity results for D. magna, after exposure to treatments of the natural waters of Aguieira reservoir (NF—Non-filtered water; F1 and F2—Filtered with 1.2 μm and 0.22 μm respectively). The values represent the percentage of inhibition of: A – Feeding rate (% IFR); B – CAT activity (% ICAT); C – GSTs activity (% IGSTs); D – TBARS levels (% ITBARS); E – AChE activity (% IAChE), comparatively to the control. Different letters (a, b, and c) stand for significant differences between treatments, detected by the Tukey test (p < 0.05).
Figure 5.
Ecotoxicological potential of the sampling sites, according to natural water treatments (NF, F1, and F2), ecotoxicity results (defined in previous Figures 2–4), and ecological potential according to WFD parameters (Table 1).
For the parameters, feeding rate (FR), and TBARS levels (A and D for each Figures 2–4) only two classes of ecotoxicity were defined: non-disturbed (green) and disturbed (yellow). Different aspects of Daphnia biology, as feeding rate is affected by quality (i.e., the number of organic compounds and carbon/nitrogen/phosphate ratio) and quantity of available food [41]. In all reservoirs and sites studied, regarding the parameter of feeding rates (A of Figures 2–4), all reservoirs are characterized as not disturbed, since the feeding rates were positive, especially after filtration treatments. However, we draw particular attention to the Ag3 site, where a significant decrease in the percentage of inhibition of the feeding rate, between NF and F2 was observed. This means that, after the filtrations, seston components were removed, including suspended particles, phyto- and zooplanktonic elements, and bacteria, which could be interfering with the feeding capacity of Daphnia magna. [41] hypothesized that in addition to physical cues (e.g., concentration and physical properties of seston composition in the water), Daphnia detect and uses chemical cues, using their chemosensory system, to locate the most nutritious patches of food in the surrounding environment.
According to [9], lipid peroxidation (LPO) measured as TBARS levels were the most responsive biomarker, in the evaluation of the water quality of the reservoirs under study. If we consider that LPO corresponds to the chain of reactions of oxidative degradation of lipids, resulting in cell damage (e.g., tissue damage), in which free radicals “steal” electrons from the lipids in cell membranes, the distinction of only two classes of ecotoxicities seemed to us to be the most correct and coherent form (D of Figures 2–4). We considered that this biological response, that is, the occurrence or not of oxidative damage, results in the generality of the ability of antioxidant defenses to act to prevent, avoid, or neutralize this oxidative damage by free radicals. Organisms can adapt to increasing free radicals (as reactive oxygen or nitrogen species) production by upregulating antioxidant defenses, such as the activities of antioxidant enzymes (e.g., CAT, GSTs, among others) [42]. Failure of antioxidant defenses to detoxify excess free radicals production can also lead to significant enzyme inactivation, protein degradation, DNA damage, and lipid peroxidation [42]. In particular, LPO is considered to be a major mechanism, leading to impaired cellular function and alterations in physicochemical properties of cell membranes, which in turn disrupt vital functions of D. magna, such as growth, longevity, and reproduction but also feeding behavior. Therefore, an increase in LPO was considered a negative consequence, representing oxidative damage; and a significant decrease will be a positive consequence, that is, the nonoccurrence of oxidative damage, which may be associated with several pathways that avoided, prevented, or neutralized it.
Relatively to the other parameters analyzed (enzymatic activities: B, C, and E of Figures 2–4), based on the biological responses under study (percentage of inhibition), five ecotoxicity classes were proposed, as represented in all figures. Based on the criteria to define the equivalent quality potential, to those presented in the WFD, an estimation of the ecotoxicological potential has been suggested. Classes of ecotoxicity have been defined and to facilitate the analysis of global results, different colors were assigned to each class, according to the ecotoxicity degree of the percent inhibition of the parameter under evaluation. For the present work, we consider the following ranges of values (%) and respective ecotoxicity classes: ≤ − 5 to ≥5 (non disturbed—blue); ≤ − 5 to −30 and ≥ 5 to 30 (slightly disturbed—green); ≤ − 30 to −60 and ≥ 30 to 60 (marginally disturbed—yellow); ≤ − 60 to −90 and ≥ 60 to 90 (moderately disturbed—orange); ≤ − 90 and ≥ 90 (highly disturbed—red). The definition of this range of ecotoxicity classes had as main influences the percentage of effect of 10, 50, and 90% (values with high significance in ecotoxicology), as previously reported by [10]. The range of purposed ranges of ecotoxicity was adjusted, whereby equivalent variations were defined with five ecotoxicity classes, as suggested in the works by [10, 11]. Roig et al. [11] considered an approach to evaluate the ecotoxicological status of rivers (Ebro River watershed, NE Spain), in which the ecotoxicity of pore water has been evaluated in several models organisms, including D. magna. Rodrigues et al. [10] and Roig et al. [11] also proposed five classes of ecotoxicity, based on different endpoints, since they evaluated the effects in several aquatic organisms. Roig et al. [11] for D. magna, this range was demarcated according to the EC50 values and was expressed as % dilution, for pore water assays, from nontoxic (>100) and highly toxic (<10). Rodrigues et al. [10] defined five ecotoxicity classes for R. subcapitata, and this range was defined according to the percent inhibition of yield, from non perturbed (≥ − 10) and highly perturbed (<−90).
Enzymes (e.g., CAT, GSTs, AChE) are proteins that catalyze non-spontaneous chemical reactions in different metabolic pathways, with different physiological functions. CAT is an antioxidant enzymatic defense, GSTs have a dual role in detoxification but also antioxidant defense, and AChE is involved in the neurotransmission process. Enzyme and substrate concentrations influence the reaction rate, altering their activities, which can be significantly inhibited or stimulated [43] after different compound exposure. Antioxidant enzymes (e.g., CAT and GSTs) can be induced by increasing the production of reactive oxygen species (ROS) as a protection mechanism against oxidative stress (adaptation to stress resulting from directly or indirectly generating ROS). In contrast, they can be inhibited when deficiency of the system occurs, inducing a precarious state, making organisms more susceptible to toxic agents (e.g., significant enzyme inactivation or protein degradation by toxicants, or due to the potential attack by excessive concentrations of free radicals) [42, 43]. For example, Hg concentrations (0.08, 0.4, and 2 μg/L) promote perturbations in antioxidant enzymes (e.g. superoxide dismutase; glutathione peroxidase; glutathione reductase; and GSTs) and generate oxidative stress/damage indirectly by binding to antioxidant enzymes containing the thiol group and resulting in depletion of nonenzymatic antioxidant GSH, a scavenger of ROS, for 24 h and 48 h, in neonates and juveniles of D. magna [34]. This study corroborates our work, as the quantified Hg concentrations varied between 0.63 and 1.85 μg/L. However, we cannot neglect the mixture of potential compounds present, as well as their interactions and other features of water. Several factors can alter the catalytic activity of enzymes. Altogether, they reflect the current metabolic situations and trigger changes in the inherent characteristics of the enzyme and its interaction to promote or impede enzymatic reactions. Factors such as pH, temperature, effectors, and inhibitors (e.g., chemical compounds dissolved in water) can modify the enzyme concentration and/or conformation but also the substrate concentrations, influencing the reaction rate, and altering its catalytic activity.
AChE is an enzyme involved in the physiological hydrolytic degradation of the neurotransmitter acetylcholine (ACh), in cholinergic synapses and neuromuscular junctions of most organisms [29], and as such, it is indispensable for the normal functioning of the nervous system (neuromuscular transmission) [44]. This biomarker is used as an indicator of neurotoxicity since it results in severe neurotransmission impairment, which leads to ACh accumulation at synaptic clefts, causing nervous overstimulation and eventually death [45]. Changes in normal neurotransmission may have adverse impacts on key functions, such as food consumption, energy metabolism, growth, and reproduction; ultimately, the impairment of neuronal transmission may result in the death of exposed organisms [46]. The US EPA [47] suggests that a significant AChE activity alteration by 20% or more can be considered a clear toxicological effect of stress exposure. However, we agree that the greater the effect on this biomarker, the worse the final consequence, in terms of the aforementioned sub-individual effect (e.g., food consumption, growth, reproduction, and escape from predators). Another factor affecting AChE activity is allosteric control, which can involve stimulation of enzyme action as well as inhibition. Allosteric stimulation and inhibition allow the production of energy and materials by the cell when they are needed and inhibit production when the supply is adequate [48]. The rate of an enzymatic reaction increases with increased substrate concentration, reaching maximum velocity when all active sites of the enzyme molecules are engaged. The cholinergic system plays a major role in the neurotransmission process, and the simultaneous stimulation of nicotinic and muscarinic receptors by ACh may be necessary to synchronize and balance ionic and metabolic events within cells, which are perturbed [49]. Thus, an increase in AChE activity can be associated with perturbations in several metabolic pathways, which can be mediated by ACh. External factors such as food supply, ambient temperature, and water quality (e.g. contaminants mixture) can also alter the activity of cholinesterases [50]. These factors impair the determination of the “normal” activity of ChE and thus hinder the identification of “abnormal” activity, including that caused by anticholinesterases [50].
3.3.1 Ecotoxicity results from the reservoir and its relationship with WFD parameters (physical and chemical elements, chemical analysis, and biological element)
3.3.1.1 Miranda reservoir
Figure 2 corresponds to the proposal of classes of disturbances (defined by colors) and ecotoxicity results for different parameters quantified in D. magna, after exposure to treatments of the natural waters of Miranda reservoir. Based on the WFD parameters previously discussed, the feeding rates of D. magna exposed to natural waters from Miranda (NF) were not affected (Table 2) by the high presence of cyanobacteria, nor by the levels of total phosphorus, or the concentrations of zinc and mercury, which were recorded above the EQS. Even after the filtration treatments (F1 and F2), these rates were not altered and D. magna did not show disturbances in terms of feeding rates (Figure 2A; Table 2). However, in sub-individual terms (biomarkers), regarding CAT activity, we found that with the application of treatments with filtrations (F1 and F2) there was an increase in oxidative stress (Table 2), which resulted in a worse classification class in F2 treatment (moderately disturbed; Figure 2B). These results are supported by previous work [8, 9], since higher concentrations of phosphorus can promote an overgrowth of phytoplanktonic organisms [9] and, consequently, the blooms of cyanobacteria as Microcystis, which fact confirms our findings, in this work. In turn, these cyanobacterial blooms can result in dangerous levels of toxins such as microcystin-LR toxic to D. magna [9, 51, 52, 53]. Furthermore, it is important to note that the observed variations in CAT activity may still be associated with mercury and zinc levels above the Eq. [9], which may be more bioavailable for D. magna after F2 treatment since only the seston components were removed.
FR
CAT
GSTs
TBARS
AChE
d.f.
F
p
d.f.
F
p
d.f.
F
p
d.f.
F
p
d.f.
F
p
M
2, 13
0.020
0.981
2, 8
10.13
0.012
2, 8
5.554
0.043
2, 8
7.186
0.026
2, 8
7.282
0.025
P
2, 14
3.163
0.079
2, 8
5.803
0.040
2, 8
33.136
0.001
2, 8
47.753
<0.001
2, 8
6.777
0.029
Ag1
2, 14
1.522
0.258
2, 8
4.756
0.048
2, 8
5.659
0.154
2, 8
0.870
0.466
2, 8
1.347
0.329
Ag2
2, 14
0.065
0.938
2, 8
21.056
0.002
2, 8
8.066
0.020
2, 8
87.577
<0.001
2, 8
2.425
0.169
Ag3
2, 14
4.791
0.030
2, 8
2.740
0.143
2, 8
6.310
0.033
2, 8
99.326
<0.001
2, 8
1.054
0.405
Ag4
2, 14
2.889
0.095
2, 8
4.756
0.048
2, 8
2.596
0.154
2, 8
408.549
<0.001
2, 8
3.646
0.092
Table 2.
ANOVA summary table (test differences between natural water treatments – NF, F1, and F2) for the D. magna feeding rate (FR), CAT and GSTs activities, TBARS levels, and AChE activity, for Miranda, Pocinho and Aguieira. For each one, the degrees of freedom (d.f.), F statistics, and associated p-value was shown. Bold values stand for statistically significant differences. Significant values, (after Tukey test, p < 0.05), were represented in the figures with different letters (a, b, c).
Regarding the activity of GSTs, there was an improvement in the ecotoxicological classification, with the application of the F2 treatment (slightly disturbed; Figure 2C). As mentioned in previous studies, changes in the activities of antioxidant enzymes, such as CAT and GSTs, may be associated with the physiological responses of organisms to environmental adaptations, through the influence of phyto and zooplanktonic communities and suspended particles [9]. Chemical analyzes showed very low levels of most quantified contaminants, except for mercury and zinc. On the other hand, as suggested by [54] nutrients seem to be very important in controlling the performance of D. magna, and in fact, this corroborates the results observed in the F2 treatment, in the case of GSTs activity.
The results of the TBARS levels showed significant differences between NF and F2 treatments, although some degree of oxidative damage is still observed (Figure 2D; Table 2). This may indicate that the samples contained some type of disturbing and oxidizing agent, and potentially triggered oxidative stress (previously discussed), with a consequent increase in peroxidative damage (LPO). As mentioned earlier, high amounts of cyanobacteria present in this site, concomitantly with high concentrations of phosphorus and in addition to the high levels of mercury, can be associated with the results of TBARS levels. The accumulation of nutrients (e.g., phosphorus total) in Miranda reservoir can lead to eutrophication causing abnormal growth of the primary producers, which can compromise the quality and balance of the aquatic ecosystem, including the balance between biochemical pathways and physiological functions of organisms, as mentioned by [9, 53].
The results of the AChE activity showed significant differences between NF and F2 in the water treatments, although some degree of neurotoxic alteration was still observed between NF and F2 treatments, an improvement was observed considering the associated ecotoxicological class (Figure 2E; Table 2). AChE activity stimulation and inhibition allow the production of energy and materials by the cell when they are needed and inhibit production when the supply is adequate [48], and in this study seems to have been affected by the seston components, as can be seen from the changes between NF and F2. A direct relationship between the degree of AChE inhibition and toxicity might not always be expected. The reason for such variability can mainly be attributed to biological differences between species that include AChE sequence differences as well as differences in molecule affinities for the AChE-active site.
3.3.1.2 Pocinho reservoir
Figure 3 corresponds to the proposal of classes of disturbances and ecotoxicity results for different parameters quantified in D. magna, after exposure to treatments of the natural waters of Pocinho reservoir. Similar to what was observed for Miranda the feeding rates of D. magna exposed to natural waters from Pocinho (NF) were not significantly affected (Table 2) by the high presence of cyanobacteria (57.32%), nor by the levels of total phosphorus, or the concentrations of zinc and mercury, which were recorded above the EQS (Table 1). Even after the filtration treatments (F1 and F2), these rates were not significantly altered, which shows that in terms of feeding behavior, D. magna did not show significant disturbance in terms of feeding rates, despite apparent differences between treatments (Figure 3A). However, in sub-individual terms (biomarkers), regarding CAT activity, we found that with the application of treatments with filtrations (F1 and F2), an improvement in the ecotoxicological category was observed between the NF and F1 treatments, but the classification from F1 to F2 worsened again (Figure 3B). However, in general, there was an improvement between NF and F2, although not significant (Table 2), but sufficient to decrease the ecotoxicological category from moderately disturbed to marginally disturbed. Thus, at this location, we can see a potential negative interference of the various components of seston in CAT activity, which were removed by the F1 treatment, essentially highlight by the percentage of cyanobacteria. In F2, we may have a potential influence of the high concentration of zinc (80.7 μg/L), compared to the other sites and reservoirs (Table 1). In the absence of other biological communities, zinc bioavailability may be greater and result in increased toxicity to D. magna. Zn toxicity thresholds of D. magna can alter by a factor > 10 as a result of ecological interactions and are highly dependent on Ptotal and pH value, with the lowest Zn thresholds found in higher-P and higher-pH waters [55]. However, only a few cases corroborate this finding in this work, and Pocinho does not fit into this perspective. Furthermore, Fettweis et al. [55] evaluate the effects of 25 to 310 μg/L of Zn and pH 7.3 and 7.8 on 21-d daphnid population size and they concluded that the indirect effects of Zn via producer-consumer relationships can outweigh the direct toxic effects. According to the mentioned work, a higher phytoplankton Zn sensitivity at higher pH, affecting food supply to D. magna, and an increased algal P content at higher Zn, offering a nutritional benefit to daphnids that counteracts direct Zn toxicity under P limitation [55]. These explanations can help to understand what happened between NF for F1 and F2, not only in sub-individual but also individual responses (feeding rate).
Regarding GSTs activity (Figure 3C), a worsening was observed between NF and filtration treatments (F1 and F2). In this case, we can refer to a potential greater bioavailability of metal levels (Zn and Hg or others), in the absence of all seston components, essentially the various biological communities in F2 treatment. These results (Table 2) may indicate a potential interference of GSTs, in the antioxidant defense or detoxification of eventually dissolved compounds, which increased along with the water treatments, from NF to F2.
The levels of TBARS (Figure 3D; Table 2), all previous findings, both in terms of analysis of our results (potential greater bioavailability of dissolved compounds in F2 treatment for Daphnia, which is a filtering organism) and by comparison with other studies [9, 55], are reflected in this parameter, since it obtained a worse classification (disturbed), after F2 treatment, comparatively with NF treatment. In fact, Rodrigues et al. [9] had already selected TBARS levels as a relevant parameter in ecotoxicological assessment studies of water quality with D. magna. Then, since oxidative damage (TBARS levels) is an indicator of LPO as a potential consequence of oxidative stress, we can infer that it may be associated with the functional inefficiency of antioxidant enzymes. However, the changes observed in the enzymatic activities of CAT and GSTs support this fact.
The results regarding the AChE activity show an improvement between NF and F2, wherein in this last treatment we achieved the classification of not disturbed. These results indicate that the seston components (present in NF) could be causing some degree of neurotoxic stress, but when organisms were exposed to F2 treatment, these effects were mitigated. In this case, based on EQS values, the altered WFD parameters (pH, Ptotal, Zn, and Hg), did not show potential toxicity in terms of neurotransmission.
3.3.1.3 Aguieira reservoir
Figure 4 corresponds to the proposal of classes of disturbances (defined by colors) and ecotoxicity results for different parameters quantified in D. magna, after exposure to treatments of the natural waters of Aguieira reservoir. Similar to what was observed for the studied Douro River reservoirs (Miranda and Pocinho), the feeding rate parameter, in all treatments demonstrated a classification of not disturbed for all studied locations (Figure 4A). Apart from the Ag3 site, no significant differences in this parameter, among the treatments were recorded (Table 2). This site presented characteristics that differentiated them from the other Aguieira sites, namely the higher levels of total phosphorus recorded (0.08 mg P/L). Furthermore, higher percentages in terms of cyanobacteria biovolume (10.75%) were observed at this site. In fact, this site was the one that showed the greatest concern in previous studies [8, 10, 12] due to the high nutrient levels, and other metal elements (Zn, Hg), conditions also observed in the present study. In this sense, eutrophication in this reservoir, reported by other authors [9, 10, 12, 31, 40], has been a concern in terms of water quality, and in fact, supports our results. Indeed Ag3 site, with the application of filtration treatments (mainly F2), showed an improvement in feed rates, which reinforces the idea that the presence of several seston components is potential stress inductors for D. magna that can be reflected in food behavior.
Contrary to expectable, and about the antioxidant defense biomarker, catalase activity (Figure 4B; Table 2), a worse classification was observed in the locations Ag1 and Ag4 (in general moderately disturbed), compared to Ag2 and Ag3. In Ag2 and Ag3, a classification of slightly disturbed was observed after filtrations (e.g., F2 treatment). This classification is taking into account the seston components; on the other hand, Ag1 and Ag4 report different scenarios. These sites presented high pH values, and levels of Zn and Hg higher than defined EQS (Table 1). In addition, an ecological potential, taking into account biological EQR, the classification was good or more (Table 1). For this site, we observe that from NF to F2 there was an increase in oxidative stress, possibly associated with greater availability of dissolved compounds (besides quantified) to D. magna, in F2 treatment. In fact, the results of GSTs activity support this potential finding (Figure 4C), but only for Ag1. According to Rostern [30], the pH and concentration of inorganic metal ions (e.g., Zn and Hg) are key factors for toxicity in the physiology and behavior of aquatic organisms. In previous studies, metals such as Zn and mainly Hg are known to be inhibitors of enzymes’ activities and can disrupt antioxidant defenses [9, 30, 34, 35]. However, some care must be considered when comparing sites in the same reservoir, as we only analyze a tiny part of potential dissolved compounds. Moreover, the biological responses observed in the biomarkers, represent the results of an integrated response to complex mixtures. Furthermore, the land occupation also represents an important source of variation of contaminants input, and consequently biomarkers’ response. In addition, land occupation is different in each site and also between reservoirs (Figure 1).
Regarding the TBARS levels (Figure 4D), despite significant variations between treatments (Table 2), in the various sites, the results allowed classifying all treatments as not disturbed. Based on this evidence, we can observe that the antioxidant defenses, despite being altered and indicating potential oxidative stress associated with natural water treatments, in terms of lipid peroxidation, were able to prevent the occurrence of oxidative damage in exposed organisms. In fact, we cannot consider only the antioxidant defenses involved in this work. Other unquantified defenses and metabolic pathways may have acted to neutralize and prevent the occurrence of lipid peroxidation, which could indicate disturbances in cell membranes, which were not observed.
The activity of acetylcholinesterase, despite showing high variations in terms of percentage of inhibition (Figure 4E), was not significant between treatments (Table 2), for organisms exposed to treatments, with water samples from Aguieira site. The nonoccurrence of significant differences may be associated with high differences between replicates for the same treatment. The different toxicity for organisms exposed to the same conditions, but belonging to different replicates, can be considered if we considered the intraspecific variations [56]. In this sequence, the authors refer that the origin of population, animal body size, and pre-exposure history [e.g., organisms from different cultures, different broods (although meeting the criteria of the assay guidelines)] are realistic variables for zooplankton populations that cause different acute toxicities in D. magna [56]. If we consider, for example, the results of the mercury concentrations detected (Table 1), potential neurotoxicity would be expected. Mercury is known to be a neurotoxin that causes structural damage to the brain and inhibits enzymes’ activities needed for normal neurotransmission [30]. Tsui and Wang [56] suggested that acute Hg toxicities were not simply caused by the different Hg body burdens, and several other mechanisms may operate to result in such a varied Hg toxicity (e.g., reduction of Hg uptake, enhancement of intrinsic tolerance, and increase of antioxidant/detoxification activity). The same authors also measured the metal concentrations in water and living D. magna and the results provided useful information to explain whether the apparent tolerance modification was due to a change in metal accumulation and/or to a change in other subtle parameters (e.g., intrinsic tolerance and detoxification activity), which can also alter the neurotoxicity results.
3.3.1.4 Physicochemical and ecological potential vs. ecotoxicological approach
One of the fundamental using biomarkers in ecological risk assessment is based on their potential ability to anticipate effects at higher levels of biological organization. Due to the different sensitivity between ecotoxicological tools evaluated in this work, and the presence of some confounding factors that could play an important role in the final ecotoxicity evaluation, the result of the final ecotoxicological potential has been calculated as the worst classification of all biochemical parameters quantified in D. magna (Figure 5). According to this methodology, CAT and AChE activities parameters were the ones that most contributed to the final ecotoxicological potential. Except for Miranda, for the final NF treatment, in which the yellow color resulted from the parameters GSTs activity and TBARS levels. If we consider the totality of the data, we do not observe an improvement in the ecotoxicological potential, with the filtration treatments, with a few exceptions. The only site where this improvement was evident was Ag2, which is corroborated by previous studies conducted in the same site but using the microalga R. subcapitata [10]. As was done in the previous study, and due to the different sensitivity between WFD parameters (physicochemical, chemical, and biological elements) individually considered, the final ecological potential according to WFD parameters has been calculated. This approach is more in line with the results of the ecotoxicological potential, since considering the contamination classes of ecotoxicity and respective colors, they present greater similarities (Figure 5) with NF treatment (with all components), with the site Ag3 representing an exception. Several reasons were pointed out throughout the manuscript, evidenced by this site, compared to the remaining Aguieira sites reservoir, or even with Miranda and Pocinho. In general, when we considered the sampling points where the ecological potential qualification was bad or poor the similarities between ecologic and ecotoxicological potential were fully agreed upon. The results of the present work allow us to confirm that, when chemical stressors or seston components affect the organism’s homeostasis, an ecotoxicological approach, provided by suitable ecotoxicological tools, could detect these changes with accurate sensitivity. In fact, Rodrigues et al. [9] already demonstrated that feeding bioassays and biomarkers (e.g., antioxidant defense and TBARS levels) proved to be useful and reliable tools in the assessment of water quality. Notice that an accurate battery of ecotoxicological tools is a direct measure of organism functional responses, and they could have more impact on the decision-making process than criteria based on concentrations of chemicals or other physical and chemical parameters, as previously demonstrated by [10, 11].
4. Conclusions
The results of the current case study corroborate that cost-effective and rapid screening short-term ecotoxicological tools, performed with natural waters, using the model organism D. magna, could be useful to complement the determination of the water ecological potential of reservoirs. In the case study of Portuguese reservoirs, ecotoxicological tools evaluated (feeding bioassay and biomarkers) have been performed obtaining good sensitiveness and complementarity between methodologies, in most situations (intra- and inter-reservoirs). Moreover, high coincidences with the ecological potential, recognized following the WFD parameters evaluation, have been found especially when ecosystems’ disturbance due to several stressors was observed (e.g., seston components as some phytoplanktonic organisms as cyanobacteria, presence of organic pollutants, and metals). For future comparative studies, we also suggest the evaluation of the water treatments like those performed in this work, mainly the F2 (water filtered through a sterile filter system with a porosity of 0.22 μm), to evaluate seston quality, as it proved to be an important source of stress for D. magna. This set of biological responses has presented good concordance with the ecological potential of the reservoirs. These results encourage working further on the applicability of cost-effective ecotoxicity tests and early warning tools for the evaluation of water quality and their integration into the current monitoring programs.
Acknowledgments
This work was supported by National Funds (through the FCT—Foundation for Science and Technology) and by the European Regional Development Fund (through COMPETE2020 and PT2020) through the research project ReDEFine (POCI-01-0145-FEDER-029368) and the strategic program UIDB/04423/2020 and UIDP/04423/2020. Sara Rodrigues and Sara Antunes are hired through the Regulamento do Emprego Científico e Tecnológico – RJEC from the Portuguese Foundation for Science and Technology (FCT) program (2020.00464.CEECIND and CEECIND/01756/2017, respectively).
Conflict of interest
The authors declare no conflict of interest.
Ethical approval
All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.
\n',keywords:"water framework directive, lentic ecosystems, model species, bioassays and biomarkers, ecotoxicity classes",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/82525.pdf",chapterXML:"https://mts.intechopen.com/source/xml/82525.xml",downloadPdfUrl:"/chapter/pdf-download/82525",previewPdfUrl:"/chapter/pdf-preview/82525",totalDownloads:5,totalViews:0,totalCrossrefCites:0,dateSubmitted:"June 3rd 2022",dateReviewed:"June 8th 2022",datePrePublished:"July 22nd 2022",datePublished:null,dateFinished:"July 5th 2022",readingETA:"0",abstract:"Several types of stressors come into natural water bodies, degrading their quality, and having harmful effects on aquatic biota. As a result, many attempts have been made to develop complementary techniques to those imposed by the Water Framework Directive (WFD) to improve the water quality assessment strategy in a shorter time and be more faithful to the components and contaminants of the ecosystem. Daphnia magna has been extensively used as a model organism for ecotoxicity testing, and its ecotoxicological responses to several disturbance factors have been being well characterized. From this perspective, this work aimed to evaluate the applicability of the feeding bioassays with D. magna, as well as early distress tools (biochemical biomarkers), in the assessment of water quality of natural waters of reservoirs. Samplings were performed in several sites in three Portuguese reservoirs and were conducted in the spring of 2020. Bioassays and biomarkers results showed sensitivity to different reservoirs since the sites are minimally or moderately impacted. Biological responses can be related to several environmental factors, such as surrounding areas, seston composition, and chemical analysis (WFD), among others not quantified. This set of biological responses has presented good concordance with the ecological potential of the reservoirs.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/82525",risUrl:"/chapter/ris/82525",signatures:"Sara Rodrigues, Ivo Pinto, Sandra Nogueira and Sara C. Antunes",book:{id:"11662",type:"book",title:"Limnology - The Importance of Monitoring and Correlations of Lentic and Lotic Waters",subtitle:null,fullTitle:"Limnology - The Importance of Monitoring and Correlations of Lentic and Lotic Waters",slug:null,publishedDate:null,bookSignature:"Dr. Carmine Massarelli and Dr. Claudia Campanale",coverURL:"https://cdn.intechopen.com/books/images_new/11662.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80356-537-8",printIsbn:"978-1-80356-536-1",pdfIsbn:"978-1-80356-538-5",isAvailableForWebshopOrdering:!0,editors:[{id:"315689",title:"Dr.",name:"Carmine",middleName:null,surname:"Massarelli",slug:"carmine-massarelli",fullName:"Carmine Massarelli"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Material and methods",level:"1"},{id:"sec_2_2",title:"2.1 Study areas, water sampling, and physicochemical parameters quantifications",level:"2"},{id:"sec_3_2",title:"2.2 Chemical analyses",level:"2"},{id:"sec_4_2",title:"2.3 Biological parameters by WFD—Ecological quality ratio (EQR) for phytoplankton",level:"2"},{id:"sec_5_2",title:"2.4 Water treatments",level:"2"},{id:"sec_6_2",title:"2.5 Test organisms",level:"2"},{id:"sec_6_3",title:"2.5.1 Culture maintenance of D. magna",level:"3"},{id:"sec_7_3",title:"2.5.2 D. magna feeding rate assays",level:"3"},{id:"sec_8_3",title:"2.5.3 Biochemical determinations",level:"3"},{id:"sec_10_2",title:"2.6 Water ecotoxicological assessment",level:"2"},{id:"sec_11_2",title:"2.7 Statistical analyses",level:"2"},{id:"sec_13",title:"3. Results and discussion",level:"1"},{id:"sec_13_2",title:"3.1 General physicochemical characteristics and trace elements concentrations (chemical analysis) in the water samples",level:"2"},{id:"sec_14_2",title:"3.2 Ecological quality ratio (EQR) for phytoplankton",level:"2"},{id:"sec_15_2",title:"3.3 Water ecotoxicological assessment",level:"2"},{id:"sec_15_3",title:"Table 2.",level:"3"},{id:"sec_15_4",title:"Table 2.",level:"4"},{id:"sec_16_4",title:"3.3.1.2 Pocinho reservoir",level:"4"},{id:"sec_17_4",title:"3.3.1.3 Aguieira reservoir",level:"4"},{id:"sec_18_4",title:"3.3.1.4 Physicochemical and ecological potential vs. ecotoxicological approach",level:"4"},{id:"sec_22",title:"4. Conclusions",level:"1"},{id:"sec_23",title:"Acknowledgments",level:"1"},{id:"sec_26",title:"Conflict of interest",level:"1"},{id:"sec_23",title:"Ethical approval",level:"1"}],chapterReferences:[{id:"B1",body:'European Parliament and of the Council. 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Dangerous connections: Biochemical and behavioral traits in Daphnia magna and Daphnia longispina exposed to ecologically relevant amounts of paracetamol. Environmental Science and Pollution Research International. 2021;28(29):38792-38808. DOI: 10.1007/s11356-021-13200-5'},{id:"B25",body:'Bradford MM. A rapid and sensitive method for the quantification of microgram quantities of protein utilizing the principle of protein dye binding. Analytical Biochemistry. 1976;72:248-254. DOI: 10.1006/abio.1976.9999'},{id:"B26",body:'Aebi H. Catalase in vitro. Methods in Enzymology. 1984;6:105-121. DOI: 10.1016/s0076-6879(84)05016-3'},{id:"B27",body:'Habig WH, Pabst MJ, Jakoby WB. Glutathione-S-transferases – The first enzymatic step in mercapturic acid formation. The Journal of Biological Chemistry. 1974;249(22):7130-7139. DOI: 10.1016/S0021-9258(19)42083-8'},{id:"B28",body:'Buege JA, Aust SD. Microsomal lipid peroxidation. Methods in Enzymology. 1978;52:302-310. 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In: HEM S, Aglan RF, editors. Heavy Metals. London: IntechOpen; 2018. DOI: 10.5772/intechopen.76082'},{id:"B34",body:'Kim H, Kim J-S, Lee Y-M. Changes in activity and transcription of antioxidant enzymes and heat shock protein 90 in the water flea, Daphnia magna—Exposed to mercury. Toxicology and Environmental Health Sciences. 2017;9(5):300-308'},{id:"B35",body:'Kim H, Yim B, Bae C, Lee YM. Acute toxicity and antioxidant responses in the water flea Daphnia magna to xenobiotics (cadmium, lead, mercury, bisphenolA, and 4-nonylphenol). Toxicology and Environmental Health Sciences. 2017;9:41-49'},{id:"B36",body:'He ZL, Yang XE, Stoffella PJ. Trace elements in agroecosystems and impacts on the environment. Journal of Trace Elements in Medicine and Biology. 2005;19(2–3):125-140. DOI: 10.1016/j.jtemb.2005.02.010'},{id:"B37",body:'Szatten D, Habel M. Effects of land cover changes on sediment and nutrient balance in the catchment with cascade-dammed waters. Remote Sensor Research. 2020;12(20):3414. DOI: 10.3390/rs12203414'},{id:"B38",body:'Feranec J, Jaffrain G, Soukup T, Hazeu G. Determining changes and flows in European landscapes 1990–2000 using CORINE land cover data. Applied Geography. 2010;30:19-35. DOI: 10.1016/j.apgeog.2009.07.003'},{id:"B39",body:'Bordalo AA, Teixeira R, Wiebe WJ. A water quality index applied to an international shared river basin: The case of the Douro river. Environmental Management. 2006;38:910-920. DOI: 10.1007/s00267-004-0037-6'},{id:"B40",body:'Vasconcelos V, Morais J, Vale M. Microcystins and cyanobacteria trends in a 14 year monitoring of a temperate eutrophic reservoir (Aguieira, Portugal). Journal of Environmental Monitoring. 2011;13:668-672. DOI: 10.1039/c0em00671h'},{id:"B41",body:'Lari E, Steinkey D, Steinkey RJ, Pyle GG. Daphnia magna increase feeding activity in the presence of four amino acids. Journal of Plankton Research. 2018;40(5):537-543. DOI: 10.1093/plankt/fby038'},{id:"B42",body:'Barata C, Varo I, Navarro JC, et al. Antioxidant enzyme activities and lipid peroxidation in the freshwater cladoceran Daphnia magna exposed to redox cycling compounds. Comparative Biochemistry and Physiology Part C. 2005;140(2):175-186. DOI: 10.1016/j.cca.2005.01.013'},{id:"B43",body:'Rodrigues S, Antunes SC, Correia AT, Nunes B. Acute and chronic effects of erythromycin exposure on oxidative stress and genotoxicity parameters of Oncorhynchus mykiss. Science Total Environment. 2016;545:591-600. DOI: 10.1016/j.scitotenv.2015.10.138'},{id:"B44",body:'Lionetto MG, Caricato R, Calisi A, et al. Acetylcholinesterase as a biomarker in environmental and occupational medicine: New insights and future perspectives. BioMed Research International. 2013;321213:1-8. DOI: 10.1155/2013/321213'},{id:"B45",body:'Rodrigues S, Antunes SC, Correia AT, Nunes B. Toxicity of erythromycin to Oncorhynchus mykiss at different biochemical levels: Detoxification metabolism, energetic balance, and neurological impairment. Environmental Science and Pollution Research. 2019;26(1):227-239. DOI: 10.1007/s11356-018-3494-9'},{id:"B46",body:'Rhee JS, Kim BM, Jeong CB, et al. Effect of pharmaceuticals exposure on acetylcholinesterase (AChE) activity and on the expression of AChE gene in the monogonont rotifer, Brachionus koreanus. Comparative Biochemistry and Physiology Part C. 2013;158(4):216-224. DOI: 10.1016/j.cbpc.2013.08.005'},{id:"B47",body:'US EPA. SCE policy issues related to the food quality protection act. Office of pesticide programs science policy on the use of cholinesterase inhibition for risk assessment of organophosphate and carbamate pesticides. Federal register. 1998:63'},{id:"B48",body:'Britannica. The Editors of Encyclopaedia. “enzyme”. Encyclopedia Britannica. 2022, Available from: https://www.britannica.com/science/enzyme [Accessed: April 27, 2022]'},{id:"B49",body:'Uberti F, Bardelli C, Morsanuto V, et al. Stimulation of the nonneuronal cholinergic system by highly diluted acetylcholine in keratinocytes. Cells, Tissues, Organs. 2017;203:215-230. DOI: 10.1159/000451023'},{id:"B50",body:'Liu HC, Yuan BQ, Li SN. Developing antibodies from cholinesterase derived from prokaryotic expression and testing their feasibility for detecting immunogen content in Daphnia magna. Journal of Zhejiang University-SCIENCE B. 2016;17(2):110-126'},{id:"B51",body:'Chen W, Song L, Ou D, Gan N. Chronic toxicity and responses of several important enzymes in Daphnia magna on exposure to sublethal microcystin-LR. Environmental Toxicology. 2005;20(3):323-330. DOI: 10.1002/tox.20108'},{id:"B52",body:'Freitas EC, Pinheiro C, Rocha O, Loureiro S. Can mixtures of cyanotoxins represent a risk to the zooplankton? The case study of Daphnia magna Straus exposed to hepatotoxic and neurotoxic cyanobacterial extracts. Harmful Algae. 2014;31:143-152. DOI: 10.1016/j.hal.2013.11.004'},{id:"B53",body:'Wojtal-Frankiewicz A, Bernasińska J, Frankiewicz P, et al. Response of Daphnia’s antioxidant systems to spatial heterogeneity in cyanobacteria concentrations in a lowland reservoir. PLoS One. 2014;9:e112597. DOI: 10.1371/journal.pone.0112597'},{id:"B54",body:'Palma P, Ledo L, Alvarenga P. Ecotoxicological endpoints, are they useful tools to support ecological status assessment in strongly modified water bodies? Science and Total Environment. 2016;541:119-129. DOI: 10.1016/j.scitotenv.2015.09.014'},{id:"B55",body:'Fettweis A, De Schamphelaere K, Smolders E. Zinc toxicity to Daphnia magna in a two-species microcosm can be predicted from single-species test data: The effects of phosphorus supply and pH. Environmental Toxicology and Chemistry. 2018;37(8):2153-2164. 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Faculty of Sciences, Department of Biology, University of Porto, Portugal
CIMAR/CIIMAR, Interdisciplinary Center for Marine and Environmental Research, University of Porto, Portugal
Faculty of Sciences, Department of Biology, University of Porto, Portugal
'},{corresp:null,contributorFullName:"Sara C. Antunes",address:null,affiliation:'
Faculty of Sciences, Department of Biology, University of Porto, Portugal
CIMAR/CIIMAR, Interdisciplinary Center for Marine and Environmental Research, University of Porto, Portugal
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Primary research and original scholarship presented in detail
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FORMATS
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Single or multiple author manuscript
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Edited Book - an edited collection of chapters contributed by various authors
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That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"426586",title:"Dr.",name:"Oladunni A.",middleName:null,surname:"Daramola",slug:"oladunni-a.-daramola",fullName:"Oladunni A. Daramola",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Federal University of Technology",country:{name:"Nigeria"}}},{id:"357014",title:"Prof.",name:"Leon",middleName:null,surname:"Bobrowski",slug:"leon-bobrowski",fullName:"Leon Bobrowski",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Bialystok University of Technology",country:{name:"Poland"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"354126",title:"Dr.",name:"Setiawan",middleName:null,surname:"Hadi",slug:"setiawan-hadi",fullName:"Setiawan Hadi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Padjadjaran University",country:{name:"Indonesia"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"332603",title:"Prof.",name:"Kumar S.",middleName:null,surname:"Ray",slug:"kumar-s.-ray",fullName:"Kumar S. Ray",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Statistical Institute",country:{name:"India"}}},{id:"415409",title:"Prof.",name:"Maghsoud",middleName:null,surname:"Amiri",slug:"maghsoud-amiri",fullName:"Maghsoud Amiri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Allameh Tabataba'i University",country:{name:"Iran"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}}]}},subseries:{item:{id:"14",type:"subseries",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11410,editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",slug:"rosa-maria-martinez-espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",biography:"Dr. Rosa María Martínez-Espinosa has been a Spanish Full Professor since 2020 (Biochemistry and Molecular Biology) and is currently Vice-President of International Relations and Cooperation development and leader of the research group 'Applied Biochemistry” (University of Alicante, Spain). Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,series:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983"},editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",slug:"ana-isabel-flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",slug:"christian-palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",slug:"francisco-javier-martin-romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},onlineFirstChapters:{paginationCount:26,paginationItems:[{id:"83087",title:"Role of Cellular Responses in Periodontal Tissue Destruction",doi:"10.5772/intechopen.106645",signatures:"Nam Cong-Nhat Huynh",slug:"role-of-cellular-responses-in-periodontal-tissue-destruction",totalDownloads:8,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Periodontology - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11566.jpg",subseries:{id:"1",title:"Oral Health"}}},{id:"82654",title:"Atraumatic Restorative Treatment: More than a Minimally Invasive Approach?",doi:"10.5772/intechopen.105623",signatures:"Manal A. 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\r\n\tThe era of antibiotics led us to the illusion that the problem of bacterial infection is over. However, bacterial flexibility and adaptation mechanisms allow them to survive and grow in extreme conditions. The best example is the formation of a sophisticated society of bacteria defined as a biofilm. Understanding the mechanism of bacterial biofilm formation has changed our perception of the development of bacterial infection but successfully eradicating biofilm remains a challenge. Considering the above, it is not surprising that bacteria remain a major public health threat despite the development of many groups of antibiotics. Additionally, increasing prevalence of acquired antibiotic resistance forces us to realize that we are far from controlling the development of bacterial infections. On the other hand, many infections are endogenous and result from an unbalanced relationship between the host and the microorganism. The increasing use of immunosuppressants, such as chemotherapy or organ transplantation, increases the incidence of patients highly susceptible to bacterial infections in the population.
\r\n
\r\n\tThis topic will focus on the current challenges and advantages in the diagnosis and treatment of bacterial infections. We will discuss the host-microbiota relationship, the treatment of chronic infections due to biofilm formation, and the development of new diagnostic tools to rapidly distinguish between colonization and probable infection.
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It will provide significant opportunities and support for scientists, clinical doctors, mycologists, antifungal drug researchers, public health practitioners, and epidemiologists from all over the world to share new research, ideas and solutions to promote the development and progress of medical mycology.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/4.jpg",keywords:"Emerging Fungal Pathogens, Invasive Infections, Epidemiology, Cell Membrane, Fungal Virulence, Diagnosis, Treatment"},{id:"5",title:"Parasitic Infectious Diseases",scope:"Parasitic diseases have evolved alongside their human hosts. In many cases, these diseases have adapted so well that they have developed efficient resilience methods in the human host and can live in the host for years. Others, particularly some blood parasites, can cause very acute diseases and are responsible for millions of deaths yearly. Many parasitic diseases are classified as neglected tropical diseases because they have received minimal funding over recent years and, in many cases, are under-reported despite the critical role they play in morbidity and mortality among human and animal hosts. The current topic, Parasitic Infectious Diseases, in the Infectious Diseases Series aims to publish studies on the systematics, epidemiology, molecular biology, genomics, pathogenesis, genetics, and clinical significance of parasitic diseases from blood borne to intestinal parasites as well as zoonotic parasites. We hope to cover all aspects of parasitic diseases to provide current and relevant research data on these very important diseases. In the current atmosphere of the Coronavirus pandemic, communities around the world, particularly those in different underdeveloped areas, are faced with the growing challenges of the high burden of parasitic diseases. At the same time, they are faced with the Covid-19 pandemic leading to what some authors have called potential syndemics that might worsen the outcome of such infections. Therefore, it is important to conduct studies that examine parasitic infections in the context of the coronavirus pandemic for the benefit of all communities to help foster more informed decisions for the betterment of human and animal health.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",keywords:"Blood Borne Parasites, Intestinal Parasites, Protozoa, Helminths, Arthropods, Water Born Parasites, Epidemiology, Molecular Biology, Systematics, Genomics, Proteomics, Ecology"},{id:"6",title:"Viral Infectious Diseases",scope:"The Viral Infectious Diseases Book Series aims to provide a comprehensive overview of recent research trends and discoveries in various viral infectious diseases emerging around the globe. The emergence of any viral disease is hard to anticipate, which often contributes to death. 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