Tab. 1. List of monitored WWTPs within JDS4<\/h5>\n![\"\"](\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\")
<\/a>\nThe grey water footprint is part of water footprint methodology, focusing on quantifying water consumption throughout the life cycle of a product, process, service, or within an organization. The grey water footprint is defined as a volume of water required to dilute discharged pollution to environmentally safe concentrations according to set environmental limits [4]. It is an environmental indicator that allows comparison of different pollutants by converting them into water volumes needed. The water footprint concept was introduced in 2002 [5], initially containing only quantitative assessments using blue and green water footprints. The expansion of the concept to also include qualitative assessment (grey water footprint) took place between 2005 and 2008 [6]. One of the first studies addressing the grey water footprint of wastewater treatment\u00a0plants is a\u00a0Romanian study from 2011\u00a0[7]. Since then, several studies have been published on the\u00a0grey water footprint of WWTPs, addressing topics such as the\u00a0impact of WWTPs on reducing the\u00a0grey water footprint\u00a0[8\u201311]; quantifying water and carbon footprints of WWTPs\u00a0[9, 12]; and quantifying the\u00a0grey water footprint of industrial wastewater\u00a0[13\u201316]. Several studies also focused on pharmaceuticals, which form one part of CECs, and their grey water footprint\u00a0[17\u201319].<\/p>\n
All three mentioned works dealing with the\u00a0grey water footprint of pharmaceuticals were limited in the\u00a0scope of monitored substances. The\u00a0aim of this study is to use the\u00a0grey water footprint to assess the\u00a0significance of individual CECs detected in wastewater during JDS4. Determining all CECs in wastewater is a\u00a0time-consuming and cost-demanding task. Therefore, for routine monitoring, it is reasonable to select substances with the\u00a0highest grey water footprint.<\/p>\n
DATA AND METHODS<\/h2>\n
The concentrations of the\u00a0detected CECs in the\u00a0form of minimum and maximum values measured in individual matrices were published as supplementary material to an article by Nq et al.\u00a0[3], together with Predicted No Effect Concentration (PNEC) values. PNEC is the\u00a0concentration of a\u00a0chemical substance that indicates the\u00a0threshold at which adverse effects of exposure in the\u00a0ecosystem have not (yet) been observed. These values are not intended to predict the\u00a0upper limit of the\u00a0concentration of a\u00a0chemical substance that has a\u00a0toxic effect\u00a0[20]. In ecotoxicology, PNEC values are often used as a\u00a0tool for assessing environmental risks\u00a0[21], for example by the\u00a0European Chemicals Agency (REACH Regulation (EC) on Registration, Evaluation, Authorisation and Restriction of Chemicals) and other toxicological agencies for assessing environmental risks\u00a0[20]. PNEC value can be used in connection with Predicted Environmental Concentration (PEC) to calculate the\u00a0Risk Characterization Ratio (RCR), also known as the\u00a0Risk Quotient (RQ) or Hazard Quotient (HQ)\u00a0[22]. The\u00a0RCR equals the\u00a0ratio of PEC\/PNEC for a\u00a0specific chemical substance and is a\u00a0deterministic approach for estimating environmental risk at the\u00a0local or regional scale. If PNEC exceeds PEC, it is concluded that the\u00a0chemical substance poses no risk to the\u00a0environment.<\/p>\n
PNEC can be calculated from data on acute toxicity or chronic toxicity for one species, from data on Species Sensitivity Distribution (SSD), or from data obtained from field studies or ecosystem modelling tests\u00a0[20, 23, 24]. Depending on the\u00a0type of data used, an assessment factor is applied, that takes into account the\u00a0reliability of the\u00a0ecotoxicological data used when extrapolating it to the\u00a0entire ecosystem. The\u00a0value of the\u00a0assessment factor depends on the\u00a0uncertainty of the\u00a0available data and ranges from 1 to 1,000\u00a0[20].<\/p>\n
When data from acute toxicity tests are used to calculate PNEC values, the\u00a0quality and relevance of these data must be verified. Ideally, this data should relate to species from multiple trophic levels and\/or taxonomic groups\u00a0[20]. The\u00a0lowest determined concentration causing a\u00a050% effect (L \u2013 lethal, E \u2013 effective, I\u00a0\u2013 inhibitory) \u2013 LC50, EC50, IC50 \u2013 is then divided by the\u00a0assessment factor for calculating PNEC, which is usually 1,000\u00a0[20].<\/p>\n
When using chronic toxicity data to calculate PNEC, the\u00a0No Observed Effect Concentration (NOEC) values are used. NOEC is the\u00a0highest tested concentration at which no statistically significant (p < 0.05) difference in effect was observed in chronic toxicity tests compared to the\u00a0control group. The\u00a0lowest NOEC in the\u00a0set of test data is divided by an assessment factor of 10 to 100, depending on the\u00a0diversity of test organisms and the\u00a0volume of available data. The\u00a0more species or data there are, the\u00a0lower the\u00a0assessment factor is\u00a0[20].<\/p>\n
The Hazardous Concentration for 5 % of species (HC5) can also be used to derive PNEC. HC5 is the concentration at which 5 % of species in the SSD show an effect [10]. A statistical estimate of the SSD value of HC5 can be made from the results of a large number of ecotoxicological tests performed with a single substance using multiple trophic levels of test organisms (fish \u2013 invertebrates\u00a0\u2013 algae). To determine PNEC, the\u00a0HC5 value is then divided by an assessment factor of 1 to 5\u00a0[20]. However, in many cases, there may not be sufficiently large datasets available for determining the\u00a0HC5 value using the\u00a0SSD statistical procedure. In these cases, the\u00a0NOEC value is used for PNEC derivation\u00a0[20].<\/p>\n
When using data on the\u00a0effect of a\u00a0substance from field studies or model tests, the\u00a0value of the\u00a0assessment factor is specific to the\u00a0particular study or experiment\u00a0[20].<\/p>\n
Since most emerging contaminants do not have a\u00a0set maximum permitted concentration in the\u00a0aquatic environment (environmental standard), the\u00a0PNEC value is used in calculating the\u00a0grey water footprint according to the\u00a0Equation 1:<\/p>\n<\/a>\n <\/p>\n
<\/p>\n
<\/p>\n
<\/p>\n
where:<\/p>\n
GWFi\u00a0 \u00a0 \u00a0 \u00a0is\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 grey water footprint of substance i<\/p>\n
Li\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 amount of discharged substance i<\/p>\n
Cmax,i\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 maximum permitted concentration of substance i\u00a0in the\u00a0aquatic environment (environmental\u00a0standard)<\/p>\n
Cnat,i\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0natural concentration of substance i in the aquatic environment; for anthropogenic substances = 0<\/p>\n
Ci\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 concentration of substance i in wastewater<\/p>\n
Q\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 flow rate of discharged wastewater; considering the study\u2019s objective, Q = 1 was assumed<\/p>\n
PNECi\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0concentration of substance i, below which no adverse effect of exposure in the ecosystem is measured<\/p>\n
<\/p>\n
A\u00a0total of 419 CECs found in wastewater during JDS4 were included in the\u00a0analysis. Of these, 311 CECs were detected in treated wastewater discharged from WWTPs, and 306 CECs were detected in wastewater entering WWTPs. Only 198 substances were found both in the\u00a0influents and effluents to\/from WWTPs. The\u00a0largest proportion of detected CECs were pharmaceuticals. With a\u00a0total of 165 substances, they represent 39.4 % of all detected CECs in wastewater (Fig. 1).<\/p>\n<\/a>\n<\/h6>\nFig. 1. Groups of emerging contaminants detected in wastewater within JDS4<\/h6>\n
In the\u00a0next step, values of the\u00a0grey water footprint (GWF) of a\u00a0unit volume were determined according to Equation 1, for the\u00a0minimum and maximummeasured concentrations of each CEC at the\u00a0inflow and outflow to\/from WWTPs. Substances were designated as risky if their maximum GWF value was higher than 0.1 % of the\u00a0maximum GWF value of the\u00a0substance with the\u00a0highest value (at WWTP inflow or outflow). The\u00a0value of 0.1 % was chosen with regard to very high GWF values of the\u00a0substance with the\u00a0highest value at the\u00a0inflow or outflow to\/from WWTP (see Results) \u2013 which statistically represent an outlier value. Another reason that led to the\u00a0choice of such a\u00a0wide range is uncertainties associated with PNEC determination (see Discussion) when the\u00a0assessment factor for different CECs ranges from 1 to 1,000.<\/p>\n
RESULTS<\/h2>\n
Based on the\u00a0procedure described in the\u00a0Data and Methods section, 33\u00a0CECs were selected (Tab. 2<\/em>). In total: 6 substances from the\u00a0Antibiotics group; 1\u00a0substance from the\u00a0Antipsychotics group; 11 substances from the\u00a0Other Pharmaceuticals group; 9 substances from the\u00a0Agricultural chemicals group; and 6 substances from the\u00a0Industrial chemicals group.<\/p>\nTab. 2. Risk CECs detected in wastewater during JDS4<\/h5>\n<\/a>\nIn the columns of maximum concentrations and PNEC, the three highest values are marked in red.<\/p>\n<\/a>\nIn the columns of maximum concentrations and PNEC, the three highest values are marked in red.<\/p>\n
Out of the\u00a033 detected CECs, three substances (Rifaximin, N-Methyldodecylamine, and Orlistat (Na)) were not detected in the\u00a0WWTPs effluents. And conversely, ten substances (17beta-Estradiol, Ciprofloxacin, Fendiline, Metazachlor, Methoprene, Phosphate-2-Ethylhexyl diphenyl (EHDP), Phosphate-Tris(2-ethylhexyl) (TEHP), pp-DDD, pp-DDE, Trenbolone) were not detected in the\u00a0WWTPs influents. The\u00a0criterion of the\u00a0maximum GWF of a\u00a0substance being higher than 0.1\u00a0% of the\u00a0maximum GWF of the\u00a0substance with the\u00a0highest GWF value is met by 13 substances in WWTPs influents (Fig. 2<\/em>) and by 29 substances in WWTPs effluents (Fig. 3<\/em>).<\/p>\n<\/a>\nFig. 2. Maximum and minimum GWF of risk substances at the\u00a0WWTP inflows<\/h6>\n<\/a>\nFig. 3. Maximum and minimum GWF of risk substances at the\u00a0WWTP outflows<\/h6>\n
The highest GWF, in both influent and effluent to\/from WWTPs, was for Telmisartan (used for treating high blood pressure). The\u00a0GWF of Telmisartan in the\u00a0influent of WWTPs is more than 80 times higher than the\u00a0second-highest GWF caused by the\u00a0antibiotic Cloxacillin. In the\u00a0case of WWTP effluents, the\u00a0GWF of Telmisartan is more than 15 times higher than the\u00a0second-highest GWF caused by Galaxolidone (a\u00a0metabolite of the\u00a0synthetic musk Galaxolide), whose maximum measured concentration in discharged wastewater was the\u00a0highest among all monitored substances, almost 12 times higher than of\u00a0Telmisartan. Within the\u00a0JDS4, Galaxolidone was detected in all studied environmental matrices (WWTP influents and effluents, river water, groundwater, and biota) which confirms its high mobility and potentially high ecological risk.<\/p>\n
DISCUSSION<\/h2>\nUncertainties associated with the\u00a0use of PNEC<\/h3>\n
The use of PNEC values instead of maximum permitted concentrations (Cmax) in Equation 1<\/em> leads to some uncertainties in the\u00a0results obtained. The\u00a0first uncertainty lies in the\u00a0representativeness of the\u00a0determination of PNEC values for individual substances. PNECs are based on toxicity and ecotoxicology tests that are performed on specific organism species and under certain conditions. Ecotoxicological data used to determine PNEC can be acquired from various studies that differ in the\u00a0methods and conditions used. These differences can lead to different PNEC values for the\u00a0same substance. For example, in this study, the\u00a0contaminant of most concern is Telmisartan. This is due to a\u00a0combination of high concentrations of this substance in wastewater and concurrently very low PNEC values (55\u00a0ng\/L), which were adopted from the\u00a0source study [3]. However, in other studies, even lower PNEC values for Telmisartan can be found, e.g. 37\u00a0ng\/L [25] or 26\u00a0ng\/L [26]. In contrast, the\u00a0continuously updated ecotoxicological database NORMAN [27] reports the\u00a0last valid value of 49\u00a0\u00b5g\/L (November\u00a027,\u00a02022), i.e. three orders of magnitude higher.<\/p>\nWhen determining PNEC, various factors must be taken into account, such as the\u00a0concentration and exposure of the\u00a0substance in the\u00a0environment. These factors can be difficult to ascertain, potentially leading to uncertainties in PNEC values. PNECs are often determined using models. When using models for predicting the\u00a0behavior of substances in the\u00a0environment, uncertainties may arise as models may not accurately account for all factors affecting the\u00a0behavior of substances in a\u00a0given environment. For emerging contaminants, sufficient toxicological data are not always available for a\u00a0robust PNEC value determination. In such cases, it can be difficult to determine a\u00a0safe level of exposure in the\u00a0environment.<\/p>\n
Another uncertainty lies in the\u00a0unclear interaction between individual substances. PNEC values are determined for individual substances and do not provide information on how these substances may interact with other substances in the\u00a0environment. In ecotoxicology, the\u00a0interactions of CECs are addressed by the\u00a0expression of mixture effects [28\u201330].<\/p>\n
Comparison with other studies<\/h3>\n
The grey water footprint of pharmaceuticals and other CECs in wastewater has so far only been addressed by a\u00a0few studies [17\u201319]. However, the\u00a0aforementioned studies quantified the\u00a0total GWF, while this study focuses on the\u00a0GWF of a\u00a0unit volume of wastewater discharged. A\u00a0direct comparison of values is thus not possible. Nevertheless, it is possible to compare whether substances monitored in previous studies are also significant CECs according to the\u00a0results of this study. Mart\u00ednez-Alcal\u00e1 et al. [19] focused solely on the\u00a0four most common pharmaceuticals (Carbamazepine, Diclofenac, Ketoprofen, and Naproxen). Similar to our study, Mart\u00ednez-Alcal\u00e1 et al. [19] identified Carbamazepine and Diclofenac as more risky\/dangerous\/hazardous contaminants. In the\u00a0study by W\u00f6hler et al. [17], the\u00a0highest GWF was caused by the\u00a0Ethinylestradiol hormone, which was not detected in wastewater during JDS4. The\u00a0main reason for the\u00a0highest GWF of Ethinylestradiol refers to its extremely low PNEC value (0.00001 \u00b5g\/L), used in the\u00a0study by W\u00f6hler et al. [17]. Oxazepam (anti-anxiety and depression medication) was identified as a\u00a0substance with the\u00a0second-highest GWF in the\u00a0Netherlands but was not considered as potentially risky in this study. The\u00a0reason is the\u00a0use of very different PNEC values; in our study, a\u00a0value of 0.37\u00a0\u00b5g\/L was used, while in the\u00a0study by W\u00f6hler et al. [17], a\u00a0value of 0.0019 \u00b5g\/L was used. In contrast, Diclofenac had the\u00a0second-highest GWF in Germany, which corresponds to the\u00a0findings in our study, which also ranks Diclofenac among risky substances in terms of grey water footprint.<\/p>\n
The GWF of a\u00a0unit volume determined according to Equation 1 corresponds to the\u00a0Risk Quotient (RQ) defined as the\u00a0ratio between PEC and PNEC when applied to wastewater. Usually, RQ is applied to water bodies, such as rivers, lakes, and reservoirs. In some cases it has also been applied to wastewater, as in the\u00a0study by Chiffre et al. [31] where the\u00a0highest risk quotients refer to the\u00a0antibiotics Sulfamethoxazole and Ofloxacin. Ofloxacin was not detected in wastewater at monitored WWTPs during JDS4. Sulfamethoxazole was found in wastewater during JDS4, but the\u00a0GWF values (alias risk quotient) were very low, and therefore, it was not identified as potentially risky in this study. The\u00a0difference between these two studies is due to the\u00a0very different PNEC values for Sulfamethoxazole, which are 0.6 \u00b5g\/L (this study) and 0.027 \u00b5g\/L in the\u00a0study by Chiffre et al. [31]. Similarly, large differences in PNEC values can be found for two other substances, Diclofenac and Ciprofloxacin, which were investigated in both compared studies. For the\u00a0other monitored substances, these two studies do not overlap. This highlights the\u00a0great importance of using the\u00a0most reliable PNEC values based on the\u00a0most recent findings, as scientific knowledge in the\u00a0field of PNEC is currently rapidly evolving in relation to the\u00a0attention paid by society to emerging contaminants.<\/p>\n
Another study that dealt with the\u00a0RQ of emerging contaminants in wastewater is a\u00a0relatively recent Egyptian study [32]. In this work, Ampicillin, Diclofenac, and Sulfamethoxazole are identified as substances with the\u00a0highest risk quotient. All these substances were found in wastewater during JDS4, but only Diclofenac was considered as potentially risky. The\u00a0Egyptian study does not provide the\u00a0source of the\u00a0PNEC values used, but comparing the\u00a0amounts of particular substances in discharged wastewater, it is apparent that effluent concentrations were 1\u20133 orders of magnitude higher than the\u00a0maximum concentrations detected in WWTP effluents within JDS4. This implies that the\u00a0amounts of these emerging contaminants discharged via treated wastewater may depend on various factors. One factor is the\u00a0technological equipment of wastewater treatment plants and their ability to remove these substances. Other factors include climatic and operational conditions [33]. Another significant factor is the\u00a0presence of emerging contaminants in WWTPs influents, which is influenced by a\u00a0character of a\u00a0sewerage-drained area, population characteristics, social and healthcare habits, etc. [34]. For example, CEC concentrations in untreated wastewater tend to be higher in the\u00a0Asian region than in Europe or North America [35].<\/p>\n
Screening vs. long-term data<\/h3>\n
Data obtained during JDS4 represent short-term wastewater monitoring. However, the\u00a0variability of CECs in wastewater is subject to seasonal [36, 37] and daily dynamics. Daily dynamics can be suppressed by taking 24-hour composite samples. Seasonal dynamics cannot be captured by the\u00a0screening measurements within JDS4. A\u00a0very interesting insight into the\u00a0CEC seasonal dynamics in wastewater is provided by a\u00a0recently published study of two WWTPs in Ireland [38], where most of the\u00a0monitored CECs showed high variability throughout the\u00a0year. Given that the\u00a0published data do not show a\u00a0clear dependence on the\u00a0season and often fluctuate randomly in individual measurements, it can be assumed that these data also reflect short-term variability caused by a\u00a0range of other factors.<\/p>\n
Grey water footprint of sludge management<\/h3>\n
In this study, we did not address the\u00a0issue of CEC entry into the\u00a0aquatic environment via sludge dewatering and land application, although it is one of the\u00a0significant sources [39\u201341]. Currently, there is no sufficient data to quantify CEC entry from sludge management into the\u00a0aquatic environment.<\/p>\n
CONCLUSION<\/h2>\n
This study focused on the\u00a0significance of particular CECs detected in wastewater within the\u00a0fourth Joint Danube Survey (JDS4). With regard to the\u00a0objectives of the\u00a0study \u2013 determining the\u00a0significance of individual substances \u2013 the\u00a0grey water footprint of a\u00a0unit volume of wastewater was determined (i.e.\u00a0not the\u00a0total grey water footprint). Telmisartan, used to treat high blood pressure, has been tagged as the\u00a0most problematic substance; this is mainly due to relatively high concentrations detected in wastewater and the\u00a0very low PNEC value. Comparing the\u00a0results of this study with other studies highlights the\u00a0main issues that such studies currently have to face. The\u00a0first issue is the\u00a0selection of PNEC values. For particular CECs, very different PNEC values can be found in the\u00a0literature, which can differ by several orders of magnitude. The\u00a0second issue is the\u00a0selectivity of most studies, which usually include only a\u00a0selection of a\u00a0few CECs. From this point of view, JDS4 provided a\u00a0unique dataset, even though it only covered 11 selected WWTPs in the\u00a0Danube river basin. However, the\u00a0available data did not allow an assessment of absolute significance, for which it is necessary to know the\u00a0total amount of particular CECs in the\u00a0wastewater monitored, not just the\u00a0maximum and minimum concentrations.<\/p>\n
The Czech version of this article was peer-reviewed, the English version was translated from\u00a0the Czech original by Environmental Translation Ltd.<\/p>\n","protected":false},"excerpt":{"rendered":"
The Joint Danube Survey (JDS4), organized in 2019, provided a unique dataset on the occurrence of several hundred newly identified contaminants of emerging concern (CEC) in waters of the Danube river basin, including wastewater from selected wastewater treatment plants. In this study, published JDS4 data were used to assess the significance of individual substances identified in wastewater using the grey water footprint approach. Determining all newly identified contaminants is time-consuming and expensive, so it is reasonable to focus on the \u201emost problematic\u201c substances. The advantage of the grey water footprint assessment is conversion of the amount of discharged pollutants into the volume of water needed for dilution to an environmentally \u2018safe level\u2019, allowing comparison of different substances. Based on JDS4 data, out of several hundreds of substances detected, 33 were identified as potentially risky, according to set criteria. However, this list cannot be taken as definitive, as the level of knowledge about the harmfulness of individual substances quickly develops with regard to the risk currently attributed to them. Similarly, the JDS4 dataset reflects a specific data collection methodology, which may not capture all connections related to the impact of the occurrence of new substances on the environment.<\/p>\n","protected":false},"author":8,"featured_media":27781,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[87,90],"tags":[3402,3398,3400,2814,3362,3399,3401,3366,3397],"coauthors":[399,808,1735],"class_list":["post-27921","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-hydrochemistry-radioecology-microbiology","category-waste-management","tag-cec","tag-contaminants-of-emerging-concern","tag-emerging-contaminants","tag-grey-water-footprint","tag-jds4","tag-joint-danube-survey","tag-no-effect-concentration","tag-pnec","tag-predicted-no-effect-concentration"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/posts\/27921","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/comments?post=27921"}],"version-history":[{"count":13,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/posts\/27921\/revisions"}],"predecessor-version":[{"id":32934,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/posts\/27921\/revisions\/32934"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/media\/27781"}],"wp:attachment":[{"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/media?parent=27921"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/categories?post=27921"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/tags?post=27921"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/coauthors?post=27921"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
![\"\"](\"https:\/\/www.vtei.cz\/wp-content\/uploads\/2024\/02\/Ansorge-tab-1-1.jpg\")
<\/a>\n<\/p>\n
<\/p>\n
<\/p>\n
<\/p>\n
where:<\/p>\n
GWFi\u00a0 \u00a0 \u00a0 \u00a0is\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 grey water footprint of substance i<\/p>\n
Li\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 amount of discharged substance i<\/p>\n
Cmax,i\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 maximum permitted concentration of substance i\u00a0in the\u00a0aquatic environment (environmental\u00a0standard)<\/p>\n
Cnat,i\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0natural concentration of substance i in the aquatic environment; for anthropogenic substances = 0<\/p>\n
Ci\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 concentration of substance i in wastewater<\/p>\n
Q\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 flow rate of discharged wastewater; considering the study\u2019s objective, Q = 1 was assumed<\/p>\n
PNECi\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0concentration of substance i, below which no adverse effect of exposure in the ecosystem is measured<\/p>\n
<\/p>\n
A\u00a0total of 419 CECs found in wastewater during JDS4 were included in the\u00a0analysis. Of these, 311 CECs were detected in treated wastewater discharged from WWTPs, and 306 CECs were detected in wastewater entering WWTPs. Only 198 substances were found both in the\u00a0influents and effluents to\/from WWTPs. The\u00a0largest proportion of detected CECs were pharmaceuticals. With a\u00a0total of 165 substances, they represent 39.4 % of all detected CECs in wastewater (Fig. 1).<\/p>\n In the\u00a0next step, values of the\u00a0grey water footprint (GWF) of a\u00a0unit volume were determined according to Equation 1, for the\u00a0minimum and maximummeasured concentrations of each CEC at the\u00a0inflow and outflow to\/from WWTPs. Substances were designated as risky if their maximum GWF value was higher than 0.1 % of the\u00a0maximum GWF value of the\u00a0substance with the\u00a0highest value (at WWTP inflow or outflow). The\u00a0value of 0.1 % was chosen with regard to very high GWF values of the\u00a0substance with the\u00a0highest value at the\u00a0inflow or outflow to\/from WWTP (see Results) \u2013 which statistically represent an outlier value. Another reason that led to the\u00a0choice of such a\u00a0wide range is uncertainties associated with PNEC determination (see Discussion) when the\u00a0assessment factor for different CECs ranges from 1 to 1,000.<\/p>\n Based on the\u00a0procedure described in the\u00a0Data and Methods section, 33\u00a0CECs were selected (Tab. 2<\/em>). In total: 6 substances from the\u00a0Antibiotics group; 1\u00a0substance from the\u00a0Antipsychotics group; 11 substances from the\u00a0Other Pharmaceuticals group; 9 substances from the\u00a0Agricultural chemicals group; and 6 substances from the\u00a0Industrial chemicals group.<\/p>\n In the columns of maximum concentrations and PNEC, the three highest values are marked in red.<\/p>\n In the columns of maximum concentrations and PNEC, the three highest values are marked in red.<\/p>\n Out of the\u00a033 detected CECs, three substances (Rifaximin, N-Methyldodecylamine, and Orlistat (Na)) were not detected in the\u00a0WWTPs effluents. And conversely, ten substances (17beta-Estradiol, Ciprofloxacin, Fendiline, Metazachlor, Methoprene, Phosphate-2-Ethylhexyl diphenyl (EHDP), Phosphate-Tris(2-ethylhexyl) (TEHP), pp-DDD, pp-DDE, Trenbolone) were not detected in the\u00a0WWTPs influents. The\u00a0criterion of the\u00a0maximum GWF of a\u00a0substance being higher than 0.1\u00a0% of the\u00a0maximum GWF of the\u00a0substance with the\u00a0highest GWF value is met by 13 substances in WWTPs influents (Fig. 2<\/em>) and by 29 substances in WWTPs effluents (Fig. 3<\/em>).<\/p>\n The highest GWF, in both influent and effluent to\/from WWTPs, was for Telmisartan (used for treating high blood pressure). The\u00a0GWF of Telmisartan in the\u00a0influent of WWTPs is more than 80 times higher than the\u00a0second-highest GWF caused by the\u00a0antibiotic Cloxacillin. In the\u00a0case of WWTP effluents, the\u00a0GWF of Telmisartan is more than 15 times higher than the\u00a0second-highest GWF caused by Galaxolidone (a\u00a0metabolite of the\u00a0synthetic musk Galaxolide), whose maximum measured concentration in discharged wastewater was the\u00a0highest among all monitored substances, almost 12 times higher than of\u00a0Telmisartan. Within the\u00a0JDS4, Galaxolidone was detected in all studied environmental matrices (WWTP influents and effluents, river water, groundwater, and biota) which confirms its high mobility and potentially high ecological risk.<\/p>\n The use of PNEC values instead of maximum permitted concentrations (Cmax) in Equation 1<\/em> leads to some uncertainties in the\u00a0results obtained. The\u00a0first uncertainty lies in the\u00a0representativeness of the\u00a0determination of PNEC values for individual substances. PNECs are based on toxicity and ecotoxicology tests that are performed on specific organism species and under certain conditions. Ecotoxicological data used to determine PNEC can be acquired from various studies that differ in the\u00a0methods and conditions used. These differences can lead to different PNEC values for the\u00a0same substance. For example, in this study, the\u00a0contaminant of most concern is Telmisartan. This is due to a\u00a0combination of high concentrations of this substance in wastewater and concurrently very low PNEC values (55\u00a0ng\/L), which were adopted from the\u00a0source study [3]. However, in other studies, even lower PNEC values for Telmisartan can be found, e.g. 37\u00a0ng\/L [25] or 26\u00a0ng\/L [26]. In contrast, the\u00a0continuously updated ecotoxicological database NORMAN [27] reports the\u00a0last valid value of 49\u00a0\u00b5g\/L (November\u00a027,\u00a02022), i.e. three orders of magnitude higher.<\/p>\n When determining PNEC, various factors must be taken into account, such as the\u00a0concentration and exposure of the\u00a0substance in the\u00a0environment. These factors can be difficult to ascertain, potentially leading to uncertainties in PNEC values. PNECs are often determined using models. When using models for predicting the\u00a0behavior of substances in the\u00a0environment, uncertainties may arise as models may not accurately account for all factors affecting the\u00a0behavior of substances in a\u00a0given environment. For emerging contaminants, sufficient toxicological data are not always available for a\u00a0robust PNEC value determination. In such cases, it can be difficult to determine a\u00a0safe level of exposure in the\u00a0environment.<\/p>\n Another uncertainty lies in the\u00a0unclear interaction between individual substances. PNEC values are determined for individual substances and do not provide information on how these substances may interact with other substances in the\u00a0environment. In ecotoxicology, the\u00a0interactions of CECs are addressed by the\u00a0expression of mixture effects [28\u201330].<\/p>\n The grey water footprint of pharmaceuticals and other CECs in wastewater has so far only been addressed by a\u00a0few studies [17\u201319]. However, the\u00a0aforementioned studies quantified the\u00a0total GWF, while this study focuses on the\u00a0GWF of a\u00a0unit volume of wastewater discharged. A\u00a0direct comparison of values is thus not possible. Nevertheless, it is possible to compare whether substances monitored in previous studies are also significant CECs according to the\u00a0results of this study. Mart\u00ednez-Alcal\u00e1 et al. [19] focused solely on the\u00a0four most common pharmaceuticals (Carbamazepine, Diclofenac, Ketoprofen, and Naproxen). Similar to our study, Mart\u00ednez-Alcal\u00e1 et al. [19] identified Carbamazepine and Diclofenac as more risky\/dangerous\/hazardous contaminants. In the\u00a0study by W\u00f6hler et al. [17], the\u00a0highest GWF was caused by the\u00a0Ethinylestradiol hormone, which was not detected in wastewater during JDS4. The\u00a0main reason for the\u00a0highest GWF of Ethinylestradiol refers to its extremely low PNEC value (0.00001 \u00b5g\/L), used in the\u00a0study by W\u00f6hler et al. [17]. Oxazepam (anti-anxiety and depression medication) was identified as a\u00a0substance with the\u00a0second-highest GWF in the\u00a0Netherlands but was not considered as potentially risky in this study. The\u00a0reason is the\u00a0use of very different PNEC values; in our study, a\u00a0value of 0.37\u00a0\u00b5g\/L was used, while in the\u00a0study by W\u00f6hler et al. [17], a\u00a0value of 0.0019 \u00b5g\/L was used. In contrast, Diclofenac had the\u00a0second-highest GWF in Germany, which corresponds to the\u00a0findings in our study, which also ranks Diclofenac among risky substances in terms of grey water footprint.<\/p>\n The GWF of a\u00a0unit volume determined according to Equation 1 corresponds to the\u00a0Risk Quotient (RQ) defined as the\u00a0ratio between PEC and PNEC when applied to wastewater. Usually, RQ is applied to water bodies, such as rivers, lakes, and reservoirs. In some cases it has also been applied to wastewater, as in the\u00a0study by Chiffre et al. [31] where the\u00a0highest risk quotients refer to the\u00a0antibiotics Sulfamethoxazole and Ofloxacin. Ofloxacin was not detected in wastewater at monitored WWTPs during JDS4. Sulfamethoxazole was found in wastewater during JDS4, but the\u00a0GWF values (alias risk quotient) were very low, and therefore, it was not identified as potentially risky in this study. The\u00a0difference between these two studies is due to the\u00a0very different PNEC values for Sulfamethoxazole, which are 0.6 \u00b5g\/L (this study) and 0.027 \u00b5g\/L in the\u00a0study by Chiffre et al. [31]. Similarly, large differences in PNEC values can be found for two other substances, Diclofenac and Ciprofloxacin, which were investigated in both compared studies. For the\u00a0other monitored substances, these two studies do not overlap. This highlights the\u00a0great importance of using the\u00a0most reliable PNEC values based on the\u00a0most recent findings, as scientific knowledge in the\u00a0field of PNEC is currently rapidly evolving in relation to the\u00a0attention paid by society to emerging contaminants.<\/p>\n Another study that dealt with the\u00a0RQ of emerging contaminants in wastewater is a\u00a0relatively recent Egyptian study [32]. In this work, Ampicillin, Diclofenac, and Sulfamethoxazole are identified as substances with the\u00a0highest risk quotient. All these substances were found in wastewater during JDS4, but only Diclofenac was considered as potentially risky. The\u00a0Egyptian study does not provide the\u00a0source of the\u00a0PNEC values used, but comparing the\u00a0amounts of particular substances in discharged wastewater, it is apparent that effluent concentrations were 1\u20133 orders of magnitude higher than the\u00a0maximum concentrations detected in WWTP effluents within JDS4. This implies that the\u00a0amounts of these emerging contaminants discharged via treated wastewater may depend on various factors. One factor is the\u00a0technological equipment of wastewater treatment plants and their ability to remove these substances. Other factors include climatic and operational conditions [33]. Another significant factor is the\u00a0presence of emerging contaminants in WWTPs influents, which is influenced by a\u00a0character of a\u00a0sewerage-drained area, population characteristics, social and healthcare habits, etc. [34]. For example, CEC concentrations in untreated wastewater tend to be higher in the\u00a0Asian region than in Europe or North America [35].<\/p>\n Data obtained during JDS4 represent short-term wastewater monitoring. However, the\u00a0variability of CECs in wastewater is subject to seasonal [36, 37] and daily dynamics. Daily dynamics can be suppressed by taking 24-hour composite samples. Seasonal dynamics cannot be captured by the\u00a0screening measurements within JDS4. A\u00a0very interesting insight into the\u00a0CEC seasonal dynamics in wastewater is provided by a\u00a0recently published study of two WWTPs in Ireland [38], where most of the\u00a0monitored CECs showed high variability throughout the\u00a0year. Given that the\u00a0published data do not show a\u00a0clear dependence on the\u00a0season and often fluctuate randomly in individual measurements, it can be assumed that these data also reflect short-term variability caused by a\u00a0range of other factors.<\/p>\n In this study, we did not address the\u00a0issue of CEC entry into the\u00a0aquatic environment via sludge dewatering and land application, although it is one of the\u00a0significant sources [39\u201341]. Currently, there is no sufficient data to quantify CEC entry from sludge management into the\u00a0aquatic environment.<\/p>\n This study focused on the\u00a0significance of particular CECs detected in wastewater within the\u00a0fourth Joint Danube Survey (JDS4). With regard to the\u00a0objectives of the\u00a0study \u2013 determining the\u00a0significance of individual substances \u2013 the\u00a0grey water footprint of a\u00a0unit volume of wastewater was determined (i.e.\u00a0not the\u00a0total grey water footprint). Telmisartan, used to treat high blood pressure, has been tagged as the\u00a0most problematic substance; this is mainly due to relatively high concentrations detected in wastewater and the\u00a0very low PNEC value. Comparing the\u00a0results of this study with other studies highlights the\u00a0main issues that such studies currently have to face. The\u00a0first issue is the\u00a0selection of PNEC values. For particular CECs, very different PNEC values can be found in the\u00a0literature, which can differ by several orders of magnitude. The\u00a0second issue is the\u00a0selectivity of most studies, which usually include only a\u00a0selection of a\u00a0few CECs. From this point of view, JDS4 provided a\u00a0unique dataset, even though it only covered 11 selected WWTPs in the\u00a0Danube river basin. However, the\u00a0available data did not allow an assessment of absolute significance, for which it is necessary to know the\u00a0total amount of particular CECs in the\u00a0wastewater monitored, not just the\u00a0maximum and minimum concentrations.<\/p>\n The Czech version of this article was peer-reviewed, the English version was translated from\u00a0the Czech original by Environmental Translation Ltd.<\/p>\n","protected":false},"excerpt":{"rendered":" The Joint Danube Survey (JDS4), organized in 2019, provided a unique dataset on the occurrence of several hundred newly identified contaminants of emerging concern (CEC) in waters of the Danube river basin, including wastewater from selected wastewater treatment plants. In this study, published JDS4 data were used to assess the significance of individual substances identified in wastewater using the grey water footprint approach. Determining all newly identified contaminants is time-consuming and expensive, so it is reasonable to focus on the \u201emost problematic\u201c substances. The advantage of the grey water footprint assessment is conversion of the amount of discharged pollutants into the volume of water needed for dilution to an environmentally \u2018safe level\u2019, allowing comparison of different substances. Based on JDS4 data, out of several hundreds of substances detected, 33 were identified as potentially risky, according to set criteria. However, this list cannot be taken as definitive, as the level of knowledge about the harmfulness of individual substances quickly develops with regard to the risk currently attributed to them. Similarly, the JDS4 dataset reflects a specific data collection methodology, which may not capture all connections related to the impact of the occurrence of new substances on the environment.<\/p>\n","protected":false},"author":8,"featured_media":27781,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[87,90],"tags":[3402,3398,3400,2814,3362,3399,3401,3366,3397],"coauthors":[399,808,1735],"class_list":["post-27921","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-hydrochemistry-radioecology-microbiology","category-waste-management","tag-cec","tag-contaminants-of-emerging-concern","tag-emerging-contaminants","tag-grey-water-footprint","tag-jds4","tag-joint-danube-survey","tag-no-effect-concentration","tag-pnec","tag-predicted-no-effect-concentration"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/posts\/27921","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/comments?post=27921"}],"version-history":[{"count":13,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/posts\/27921\/revisions"}],"predecessor-version":[{"id":32934,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/posts\/27921\/revisions\/32934"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/media\/27781"}],"wp:attachment":[{"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/media?parent=27921"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/categories?post=27921"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/tags?post=27921"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/coauthors?post=27921"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}<\/a>\n<\/h6>\n
Fig. 1. Groups of emerging contaminants detected in wastewater within JDS4<\/h6>\n
RESULTS<\/h2>\n
Tab. 2. Risk CECs detected in wastewater during JDS4<\/h5>\n
<\/a>\n<\/a>\n<\/a>\nFig. 2. Maximum and minimum GWF of risk substances at the\u00a0WWTP inflows<\/h6>\n
<\/a>\nFig. 3. Maximum and minimum GWF of risk substances at the\u00a0WWTP outflows<\/h6>\n
DISCUSSION<\/h2>\n
Uncertainties associated with the\u00a0use of PNEC<\/h3>\n
Comparison with other studies<\/h3>\n
Screening vs. long-term data<\/h3>\n
Grey water footprint of sludge management<\/h3>\n
CONCLUSION<\/h2>\n