Acta hydrotechnica 35/63 (2022), Ljubljana Open Access Journal ISSN 1581-0267 Odprtodostopna revija 117 UDK/UDC: 502.51:628.32(282)(497.4) Prejeto/Received: 04.05.2023 Izvirni znanstveni članek – Original scientific paper Sprejeto/Accepted: 25.06.2023 DOI: 10.15292/acta.hydro.2022.09 Objavljeno na spletu/Published online: 14.07.2023 GREY WATER FOOTPRINT OF CONTAMINANTS OF EMERGING CONCERN FROM WASTEWATER IN THE SAVA RIVER BASIN SIVI VODNI ODTIS NOVODOBNIH ONESNAŽEVAL IZ ODPADNIH VOD V POREČJU SAVE Libor Ansorge1,*, Lada Stejskalová1, Přemysl Soldán2 1 Výzkumný ústav vodohospodářský T. G. Masaryka, Podbabská 2582/30, Praha, Czech Republic 2 Výzkumný ústav vodohospodářský T. G. Masaryka, Macharova 5, Ostrava, Czech Republic Abstract Water pollution by contaminants of emerging concern (CECs) causes risks to both the environment and human health. We assessed water pollution by CECs in the Sava River basin in two monitoring campaigns carried out in May and July 2017. The grey water footprint (GWF) is a tool that converts the level of pollution by particular substances into the volume of water needed for dilution to a harmless level. Therefore, it can serve as an indicator for comparing various pollutants. The results show that substances that determine the GWF differ in individual locations. The highest value of the GWF was associated with 17β-estradiol, however, found only in one wastewater sample. The study showed that the value of the GWF in individual locations fluctuates and does not depend on the size of the wastewater treatment plant from which the wastewater is discharged. At selected wastewater treatment plants, a sustainability assessment was carried out using the Water Pollution Level indicator. The values in all cases were below the level of 1.0, indicating sustainable discharge; only in two cases did values reach the defined threshold to question the potential of non-sustainable discharge. The study contributes to earlier studies on the GWF and enlarges knowledge regarding the GWF of CECs. Keywords: Contaminants of emerging concern, grey water footprint, micropollutants, Sava River basin, wastewater treatment plant. Izvleček Prisotnost novodobnih onesnaževal (NO) v vodi povzroča tveganje za okolje in zdravje ljudi. Onesnaženost vode z NO v porečju reke Save smo ocenjevali na podlagi dveh vzorčenj v okviru monitoringa, izvedenega maja in julija 2017. Sivi vodni odtis pretvori onesnaženost s posameznimi snovmi v količino vode, ki je potrebna za njihovo razredčenje, na neškodljivo raven. Zato lahko služi kot kazalnik za primerjavo različnih onesnaževal. Rezultati meritev kažejo, da se snovi, ki določajo sivi vodni odtis, razlikujejo od lokacije do lokacije. Najvišja vrednost sivega vodnega odtisa je bila povezana s snovjo 17β-estradiol, ki pa je bila ugotovljena le v enem vzorcu odpadne vode. Študija je pokazala, da se vrednosti sivega vodnega odtisa zelo razlikujejo od lokacije do lokacije in niso odvisne od velikosti čistilnih naprav. Za izbrane čistilne naprave je * Stik / Correspondence: libor.ansorge@vuv.cz © Ansorge L. et al.; This is an open-access article distributed under the terms of the Creative Commons Attribution – NonCommercial – ShareAlike 4.0 Licence. © Ansorge L. et al.; Vsebina tega članka se sme uporabljati v skladu s pogoji licence Creative Commons Priznanje avtorstva – Nekomercialno – Deljenje pod enakimi pogoji 4.0. Ansorge et al.: Grey Water Footprint of Contaminants of Emerging Concern from Wasterwater in Sava River Basin – Sivi vodni odtis novodobnih onesnaževal iz odpadnih vod v porečju Save Acta hydrotechnica 35/63 (2022), 117–128, Ljubljana 118 bila izvedena ocena trajnostnosti z uporabo kazalnika stopnje onesnaženosti vode. Vrednosti so bile v vseh primerih pod ravnjo 1,0, ki pomeni trajnostni izpust onesnaženja, v dveh primerih pa sta vrednosti že spadali v območje negotovosti. Študija prispeva k prejšnjim študijam o sivem vodnem odtisu in povečuje znanje o sivem vodnem odtisu pri tej vrsti onesnaženja. Ključne besede: Novodobna onesnaževala, sivi vodni odtis, mikroonesnaževala, reka Sava, čistilna naprava. 1. Introduction The rapid development of analytical methods in recent years has facilitated the search for new pollutants in aquatic environments. The presence of these pollutants in wastewater, surface water, groundwater, and marine water, as well as in soil and sludge has been well documented (Patel et al., 2019). These substances have been detected in many countries around the world, on all continents, and even in Antarctica (Balakrishna et al., 2023); but especially in North America and Europe (Wilkinson et al., 2022). In the case of contaminants of emerging concern (CECs), it is not clear what effects they can have on the environment. Legal requirements for the regulation of their discharge into surface water bodies have not yet been established. An exception here is for example Switzerland, where the Waters Protection Act was revised in 2014 to further improve wastewater treatment for the removal of CECs. These are mainly active pharmaceutical agents, personal care products, lifestyle compounds (e.g. caffeine), industrial micropollutants, pesticides, etc. CECs appear in the environment as a result of human activity and have the potential to harm ecosystems and humans (Sauvé and Desrosiers, 2014). They can enter the environment in many ways, but one of the most important is considered to be the discharge of wastewater (Astuti et al., 2023; Lapworth et al., 2012; Saidulu et al., 2021). Current wastewater treatment technologies are not designed for CECs removal, therefore some of these substances can more or less pass via wastewater treatment plants (WWTPs) and spread further into the environment. The removal efficiency of CECs at WWTPs varies a considerably, ranging from negative efficiency values (when, due to metabolization processes, some substances are formed during the wastewater treatment process) to very efficient removals approaching 100%. The removal rate depends on many factors (Rapp-Wright et al., 2023). Different wastewater treatment technologies have different removal efficiencies for particular groups of CECs (Samal et al., 2022). Also in the environment, the fate of CECs is influenced by many processes, depending on both i) their physicochemical properties and ii) the extant environmental characteristics. Some CECs are very stable and are detected many kilometers downstream from discharge points. Some CECs (e.g. anti-cancer drugs) can take many months or even years to break down in the environment (Castellano-Hinojosa et al., 2023). This study uses the grey water footprint (GWF) concept to assess water pollution by CECs. The GWF indicates the volume of water needed to assimilate the pollutant load to acceptable concentrations (Hoekstra et al., 2011). Although the water footprint was introduced in 2002 (Hoekstra and Hung, 2002), the GWF was included in the concept couple of years later, in the period of 2005- 2008 (Ansorge and Stejskalová, 2023). The water footprint is an environmental indicator of freshwater use that assesses both direct and indirect water consumption. The GWF of pollution discharged from WWTPs has been studied in several countries around the world, e.g. in Romania (Ene and Teodosiu, 2011; Teodosiu et al., 2016), Spain (Gómez-Llanos et al., 2020, 2018; Morera et al., 2016), the Czech Republic (Ansorge et al., 2020a, 2020b), China (Gu et al., 2016; Li et al., 2016; Qin et al., 2019), Iran (Rezaee and Tabesh, 2022), Canada (Johnson and Mehrvar, 2019), and Turkey (Kalya and Alver, 2022; Yapıcıoğlu, 2020). Most of the studies dealing with the GWF assessment of WWTPs were focused mostly on the standardly monitored pollutants (organic, and nutrient pollution). Research on GWFs in relation to CECs discharged from WWTPs is still incipient; only a few studies have been published dealing with Ansorge et al.: Grey Water Footprint of Contaminants of Emerging Concern from Wasterwater in Sava River Basin – Sivi vodni odtis novodobnih onesnaževal iz odpadnih vod v porečju Save Acta hydrotechnica 35/63 (2022), 117–128, Ljubljana 119 CECs, especially pharmaceuticals. Martínez-Alcalá et al. (2018) studied the GWF of four of the most common pharmaceuticals carbamazepine (CBZ), diclofenac (DF), ketoprofen (KP), and naproxene (NP) in wastewater in southern Spain. Wöhler et al. (2020) modelled the GWF of human and veterinary pharmaceuticals based on the total consumption of these pharmaceuticals in Germany and the Netherlands, and carried out a sustainability assessment in the Vecht River basin. In another study, Wöhler et al. (2021) presented approaches for more detailed modelling of the potential burden of the aquatic environment by veterinary antibiotics (Stejskalová et al., 2022). Researchers dealing with water footprint issues usually find it difficult to obtain funds for in-situ monitoring. However, for the GWF analyses, data sets obtained as part of other legislative or research activities could be used. This creates new specific data sets, which researchers can use for their water footprint studies, such as the GWF dataset of pollution discharged from WWTPs in the Czech Republic (Ansorge et al., 2021). This study aims to determine the GWF of CECs monitored as part of the survey carried out by Slovenian and Croatian researchers who mapped the occurrence of CECs in discharged wastewater from WWTPs in the Sava River basin. According to our best knowledge, apart from the four studies mentioned above, no one has dealt with the issue of the GWF of pharmaceuticals and CECs yet. This study uses monitored data from 6 WWTPs in the Sava River basin, concerning not only pharmaceuticals but also other micropollutants, and thus enlarges our knowledge about the GWF of CECs. 2. Material and methods In May and July 2017, researchers from the Jožef Stefan Institute (Ljubljana, Slovenia) and the Ruđer Bošković Institute (Zagreb, Croatia) carried out two sampling campaigns on wastewater discharges at 6 WWTPs in the Sava River basin (Česen et al., 2019). The Slovenia study monitored the WWTPs in Ljubljana (LJ), Domžale-Kamnik (DK), and Novo Mesto (NM). In Croatia, the WWTPs in Zaprešić (ZP), Zagreb (ZG), and Velika Gorica (VG) were monitored. The monitoring focused on 23 substances, especially on pharmaceuticals and personal care products (PPCPs), lifestyle compounds, and endocrine-disrupting industrial chemicals. The list of monitored substances is presented in Table 1. The mass load of discharged pollution from particular WWTPs was determined from measured concentrations and flow rates at the effluents from monitored WWTPs (Table 2 and 3). Preglednica 1: Novodobna onesnaževala, vključena v monitoring. Table 1: List of monitored contaminants of emerging concern. Abbr. Name CAS BePB Benzyl-paraben 94-18-8 BIS2 2,2′-Methylenediphenol 2467-02-9 BPA Bisphenol A 80-05-7 BPAF Bisphenol AF 1478-61-1 BPB Bisphenol B 77-40-7 BPE Bisphenol E 2081-08-5 BPF Bisphenol F 620-92-8 BPS Bisphenol S 80-09-1 CAF Caffeine 58-08-2 CBZ Carbamazepine 298-46-4 DF Diclofenac as sodium salt 15307-79-6 DFtp1 DF transformation product --- DH-BP 2,4-Dihydroxybenzophenone 131-56-6 E1 Estrone 53-16-7 E2 17β-Estradiol 50-28-2 H-BP 4-Hydroxybenzophenone 1137-42-4 HM-BP Oxybenzone 131-57-7 HPP 4-Cumylphenol 599-64-4 IB Ibuprofen 15687-27-1 KP Ketoprofen 22071-15-4 MEC Mecoprop 93-65-2 MePB Methyl paraben 99-76-3 NP Naproxene 22204-53-1 Ansorge et al.: Grey Water Footprint of Contaminants of Emerging Concern from Wasterwater in Sava River Basin – Sivi vodni odtis novodobnih onesnaževal iz odpadnih vod v porečju Save Acta hydrotechnica 35/63 (2022), 117–128, Ljubljana 120 Preglednica 2: Masna obremenitev [g/dan] novodobnih onesnaževal v maju (N.C. – ni izračunana, ker NO v odpadni vodi niso bila zaznana). Table 2: Mass load [g/day] of contaminants of emerging concern in May (N.C. – not calculated since EC was not detected in wastewater). May LJ DK NM ZP ZG VG BePB 38 N.C. N.C. N.C. N.C. 2.45 BIS2 N.C. 0.639 N.C. N.C. 2.72 0.135 BPA 2.97 3.19 1.46 N.C. N.C. 17.3 BPAF N.C. 0.000659 N.C. N.C. 0.509 0.0224 BPB N.C. N.C. N.C. N.C. N.C. 0.179 BPE N.C. N.C. N.C. N.C. N.C. N.C. BPF N.C. N.C. N.C. N.C. N.C. 0.253 BPS N.C. N.C. N.C. N.C. N.C. 2.67 CAF 43.6 4.14 0.615 331 110 22.5 CBZ 26.1 5.16 1.92 0.575 108 3.95 DF 44.8 8.73 2.73 0.756 41.6 3.91 DFtp1 N.C. N.C. N.C. N.C. N.C. 37.8 DH-BP N.C. N.C. N.C. N.C. N.C. 2.2 E1 23.3 N.C. N.C. N.C. N.C. 13.1 E2 N.C. N.C. N.C. 4.75 N.C. N.C. H-BP 1.23 0.262 0.0708 199 2.7 0.232 HM-BP 0.165 N.C. N.C. N.C. 1.88 N.C. HPP N.C. 0.93 N.C. N.C. N.C. N.C. IB N.C. N.C. N.C. 35.8 N.C. 36.5 KP N.C. N.C. N.C. 10.9 14 16.3 MEC N.C. N.C. 0.0373 N.C. N.C. N.C. MePB 1.35 0.455 N.C. 12.8 3.09 0.272 NP N.C. N.C. N.C. 3.5 21.3 14.5 Preglednica 3: Masna obremenitev [g/dan] novodobnih onesnaževal v juliju (N.C. – ni izračunana, ker NO v odpadni vodi niso bila zaznana). Table 3: Mass load [g/day] of contaminants of emerging concern in July (N.C. – not calculated since EC was not detected in wastewater). July LJ DK NM ZP ZG VG BePB N.C. N.C. N.C. 3.91 N.C. 0.14 BIS2 0.649 0.664 N.C. N.C. 1.16 0.154 BPA N.C. 1.5 9.04 0.585 N.C. 13.9 BPAF N.C. N.C. 0.000209 N.C. N.C. 0.0115 BPB N.C. N.C. N.C. N.C. N.C. N.C. BPE N.C. N.C. N.C. 2.76 N.C. N.C. BPF N.C. 0.0914 0.011 0.675 N.C. 0.349 BPS N.C. N.C. N.C. 0.625 N.C. 2.58 CAF 29.2 8.74 0.598 103 165 144 CBZ 25.3 6.11 2.08 30.8 87.8 3.82 DF 56.8 11.1 2.12 2.24 27.6 4.34 DFtp1 N.C. N.C. N.C. N.C. N.C. 4.63 DH-BP N.C. 0.658 N.C. 1.69 N.C. 3.33 E1 10.9 1.61 N.C. N.C. N.C. 9.64 E2 N.C. N.C. N.C. N.C. N.C. N.C. Ansorge et al.: Grey Water Footprint of Contaminants of Emerging Concern from Wasterwater in Sava River Basin – Sivi vodni odtis novodobnih onesnaževal iz odpadnih vod v porečju Save Acta hydrotechnica 35/63 (2022), 117–128, Ljubljana 121 July LJ DK NM ZP ZG VG H-BP 1.04 N.C. 0.092 N.C. 1.76 0.227 HM-BP 0.741 0.0758 N.C. N.C. 4.09 0.288 HPP N.C. N.C. N.C. N.C. N.C. N.C. IB N.C. N.C. N.C. 25.1 N.C. 36.3 KP 5.91 N.C. N.C. 11.6 23 12.8 MEC N.C. N.C. 0.294 N.C. N.C. N.C. MePB 1.66 N.C. N.C. 5.48 N.C. 0.278 NP N.C. N.C. N.C. 3.63 58.7 12.6 From the mass load of CECs discharged into the receiving water body, the GWF can be calculated according to the Equation: GWF = 𝑚𝑎𝑥{𝐺𝑊𝐹1, 𝐺𝑊𝐹2, … , 𝐺𝑊𝐹𝑛 } (1) The GWF of the WWTP is determined by the substance with the highest value of the GWF. That is determined as the ratio between the mass load of substance i (Li) and the assimilation capacity of the receiving water body (i.e. the difference between the maximum permitted concentration of the substance i in the receiving water body (Cmax,i) and the natural concentration of the substance i in the receiving water body (Cnat,i): GWF𝑖 = 𝐿𝑖 𝐶𝑚𝑎𝑥,𝑖−𝐶𝑛𝑎𝑡,𝑖 [volume/time] (2) For artificial substances that are not present in nature we consider Cnat,i = 0 (Hoekstra et al., 2011). Since CECs do not have the Cmax,i values determined yet, Predicted No Effects Concentration (PNEC) values are used in the GWF calculation. The PNEC indicates the concentration of a chemical substance below which no adverse effects of exposure in the ecosystem were spotted. (Martínez-Alcalá et al., 2018). The PNEC values listed in the NORMAN Ecotoxicology Database were used for this study. The database presents PNEC values agreed upon the basis of pan-European expert consultations (Dulio et al., 2020). PNEC values for diclofenac as sodium salt (CAS 15307-79-6) and transformation products of diclofenac are not listed in the NORMAN Ecotoxicology Database. Therefore, PNEC = 0.05 µg/l was used for both cases, which corresponds with the PNEC of diclofenac (CAS 15307-86-5). The PNEC values used for the GWF calculation are presented in Table 4. Preglednica 4: Vrednosti predvidene koncentracije brez učinka [µg/l]. Table 4: Predicted No Effects Concentration values [µg/l]. Abbr. Lowest PNEC (in freshwater) Last Update BePB 2.94743 26 Mar 2018 BIS2 4.89838 26 Mar 2018 BPA 0.24 27 Nov 2022 BPAF 1.01908 26 Mar 2018 BPB 1.35007 26 Mar 2018 BPE 2.1516 26 Mar 2018 BPF 5.44092 26 Mar 2018 BPS 12.88093 26 Mar 2018 CAF 0.1 03 Oct 2018 CBZ 2 27 Nov 2022 DF 0.05 DFtp1 0.05 DH-BP 1.71313 26 Mar 2018 E1 0.0036 27 Nov 2022 E2 0.0004 27 Nov 2022 H-BP 2.77269 26 Mar 2018 HM- BP 1.5411 26 Mar 2018 HPP 0.78665 26 Mar 2018 IB 0.011 27 Nov 2022 KP 2.09574 26 Mar 2018 MEC 0.1 03 Oct 2018 MePB 5 03 Oct 2018 NP 1.7 27 Nov 2022 For discharges from WWTPs in Ljubljana, Domžale-Kamnik, Zaprešić, and Zagreb, the sustainability assessment was carried out using the Water Pollution Level (WPL). The WPL indicator (Hoekstra et al., 2011) is calculated as the ratio of the GWF to the actual runoff from the river basin (Ract): Ansorge et al.: Grey Water Footprint of Contaminants of Emerging Concern from Wasterwater in Sava River Basin – Sivi vodni odtis novodobnih onesnaževal iz odpadnih vod v porečju Save Acta hydrotechnica 35/63 (2022), 117–128, Ljubljana 122 𝑊𝑃𝐿 = 𝐺𝑊𝐹 𝑅𝑎𝑐𝑡 [---] (3) For the WWTP in Ljubljana, run-off in the Jevnica profile station was used. For the WWTP in Domžale-Kamnik, run-off in the Ljubljana profile station was used. For the WWTP in Zagreb WWTP, run-off in the Oborovo profile station was used. For the WWTP in Zaprešić, run-off in the Jankomir profile station was used. For the WWTPs in Novo Mesto and Velika Gorica, no relevant profiles could be added. Data were provided by the Slovenian Environment Agency (ARSO) and by the Croatian Meteorological and Hydrological Service (Table 5). Considering the uncertainties associated with the GWF calculated using PNEC, an uncertainty interval of ±30% was set (Ansorge et al., 2019): WPL < 70 % ... sustainable discharge 70 % ≤ WPL ≤ 130 % ... potentially non- sustainable discharge WPL > 130 % ... unsustainable discharge Preglednica 5: Vrednosti pretoka [m3/dan]. Table 5: Run-off values [m3/day]. Station on Sava River Run-off 23 May 2017 12 Jul 2017 Jevnica 7 456 320 6 376 320 Ljubljana 4 959 360 4 976 640 Jankomir 14 256 000 8 493 120 Oborovo 14 169 600 8 026 560 3. Results The overview of the GWF values reached in May and July is presented in Tables 6 and 7, respectively. The highest GWF value (11.875 mil. m3/day) was detected in May, at the WWTP in Zaprešić, and was caused by 17β-estradiol hormone, which has the lowest PNEC value among all monitored substances. Estrone (also female hormone) was the determining pollution according to Equation 1, at WWTPs in Ljubljana (in May and July), Velka Gorica (in May), and Domžale-Kamnik (in July). Stimulant caffeine was the determining pollutant at the WWTP in Zagreb (in May and July). Diclofenac (nonsteroidal anti-inflammatory drug, NSAID) was the determining pollutant at WWTPs in Domžale- Kamnik (in May), and in Novo Mesto (in May and July). Another NSAID, ibuprofen, was the determining pollutant at WWTPs in Zaprešić (in July) and Velka Gorica (in July). A comparison of GWF values at individual WWTPs is shown in Figure 1. The Water Pollution Level caused by CECs discharges from monitored WWTPs is presented in Table 8. In all cases, the WPL is <1. However, in the Jevnice profile station (downstream of the WWTP Ljubljana) and Jankomir profile station (downstream of the WWTP Zaprešić), the uncertainty value of ±30% was exceeded in May. Preglednica 8: Stopnja onesnaženosti vode. Table 8: Water Pollution Level. Station on Sava River (WWTP) WPL 23 May 2017 12 Jul 2017 Jevnica (LJ) 0.87 0.47 Ljubljana (DK) 0.04 0.09 Jankomir (ZP) 0.83 0.27 Oborovo (ZG) 0.08 0.21 Slika 1: Primerjava vrednosti sivega vodnega odtisa na posameznih čistilnih napravah. Figure 1: Comparison of grey water footprint values at the individual wastewater treatment plants. 0 20 40 60 80 100 120 May July G re y w at er f o o tp ri n t [ x 1 06 m 3 / d ay ] LJ DK NM ZP ZG VG Ansorge et al.: Grey Water Footprint of Contaminants of Emerging Concern from Wasterwater in Sava River Basin – Sivi vodni odtis novodobnih onesnaževal iz odpadnih vod v porečju Save Acta hydrotechnica 35/63 (2022), 117–128, Ljubljana 123 Preglednica 6: Sivi vodni odtis [m3/dan] novodobnih onesnaževal v maju (N.C. – ni izračunan, ker NO v odpadni vodi niso bila zaznana; najvišja vrednost je označena s krepkim tiskom). Table 6: GWF [m3/day] of contaminants of emerging concern in May (N.C. – not calculated since EC was not detected in wastewater; highest values are in bold). May LJ DK NM ZP ZG VG BePB 12 892 N.C. N.C. N.C. N.C. 831 BIS2 N.C. 130 N.C. N.C. 555 28 BPA 12 375 13 292 6 083 N.C. N.C. 72 083 BPAF N.C. 1 N.C. N.C. 499 22 BPB N.C. N.C. N.C. N.C. N.C. 133 BPE N.C. N.C. N.C. N.C. N.C. N.C. BPF N.C. N.C. N.C. N.C. N.C. 47 BPS N.C. N.C. N.C. N.C. N.C. 207 CAF 436 000 41 400 6 150 3 310 000 1 100 000 225 000 CBZ 13 050 2 580 960 288 54 000 1 975 DF 896 000 174 600 54 600 15 120 832 000 78 200 DFtp1 N.C. N.C. N.C. N.C. N.C. 756 000 DH-BP N.C. N.C. N.C. N.C. N.C. 1 284 E1 6 472 222 N.C. N.C. N.C. N.C. 3 638 889 E2 N.C. N.C. N.C. 11 875 000 N.C. N.C. H-BP 444 94 26 71 771 974 84 HM-BP 107 N.C. N.C. N.C. 1 220 N.C. HPP N.C. 1 182 N.C. N.C. N.C. N.C. IB N.C. N.C. N.C. 3 254 545 N.C. 3 318 182 KP N.C. N.C. N.C. 5 201 6 680 7 778 MEC N.C. N.C. 373 N.C. N.C. N.C. MePB 270 91 N.C. 2 560 618 54 NP N.C. N.C. N.C. 2 059 12 529 8 529 Preglednica 7: Sivi vidni odtis [m3/dan] novodobnih onesnaževal v juliju (N.C. – ni izračunan, ker NO v odpadni vodi niso bila zaznana; najvišja vrednost je označena s krepkim tiskom). Table 7: Grey water footprint [m3/day] of contaminants of emerging concern in July (N.C. – not calculated since EC was not detected in wastewater; highest values are in bold). July LJ DK NM ZP ZG VG BePB N.C. N.C. N.C. 1 327 N.C. 48 BIS2 132 136 N.C. N.C. 237 31 BPA N.C. 6 250 37 667 2 438 N.C. 57 917 BPAF N.C. N.C. 0.21 N.C. N.C. 11 BPB N.C. N.C. N.C. N.C. N.C. N.C. BPE N.C. N.C. N.C. 1 288 N.C. N.C. BPF N.C. 17 2 124 N.C. 64 BPS N.C. N.C. N.C. 49 N.C. 200 CAF 292 000 87 400 5 980 1 030 000 1 650 000 1 440 000 CBZ 12 650 3 055 1 040 15 400 43 900 1 910 DF 1 136 000 222 000 42 400 44 800 552 000 86 800 DFtp1 N.C. N.C. N.C. N.C. N.C. 92 600 DH-BP N.C. 384 N.C. 987 N.C. 1 944 E1 3 027 778 447 222 N.C. N.C. N.C. 2 677 778 E2 N.C. N.C. N.C. N.C. N.C. N.C. Ansorge et al.: Grey Water Footprint of Contaminants of Emerging Concern from Wasterwater in Sava River Basin – Sivi vodni odtis novodobnih onesnaževal iz odpadnih vod v porečju Save Acta hydrotechnica 35/63 (2022), 117–128, Ljubljana 124 July LJ DK NM ZP ZG VG H-BP 375 N.C. 33 N.C. 635 82 HM-BP 481 49 N.C. N.C. 2 654 187 HPP N.C. N.C. N.C. N.C. N.C. N.C. IB N.C. N.C. N.C. 2 281 818 N.C. 3 300 000 KP 2 820 N.C. N.C. 5 535 10 975 6 108 MEC N.C. N.C. 2 940 N.C. N.C. N.C. MePB 332 N.C. N.C. 1 096 N.C. 56 NP N.C. N.C. N.C. 2 135 34 529 7 412 4. Discussion and Conclusion Although different types of CECs were monitored within this study, the determining pollution causing the GWF were (apart from caffeine) only pharmaceuticals. Estrone and 17β-estradiol (both, estrogen steroid female sex hormones) were detected in wastewater 6 times, of which 5 times as determining pollution causing the GWF. Ibuprofen was detected in wastewater 4 times, of which 2 times as determining pollution causing the GWF at a particular WWTP. In the remaining two cases, the decisive contaminant causing the GWF were again estrone or 17β-estradiol. These hormones have set very low PNEC values, compared to other evaluated CECs, about 1-3 orders of magnitude lower. 17β- estradiol has one order of magnitude more stringent PNEC (0.0004 µg/l) than estrone, with the second- lowest PNEC value (0.003 µg/l) among all monitored substances. Ibuprofen has the third- lowest PNEC value (0.011 µg/l) among the monitored substances. Diclofenac and caffeine, which have the fourth- and sixth-lowest PNEC values, were other substances helping the determination of (in some cases) the GWF at particular WWTPs. It can be summarized that diclofenac and caffeine were decisive for the GWF only in cases when the above-mentioned estrogen hormones or ibuprofen (with much lower PNEC values) were not detected in wastewater. This highlights the importance of knowing the environmental impacts of CECs, which are reflected in PNEC values and also in the forthcoming environmental quality standard values. The sustainability assessment using the WPL indicator applied in this study does not reflect the overall status of pollution in the receiving water body. Therefore, even a situation where the discharge is marked as non-risky may actually exceed the available assimilation capacity of the receiving water body in the given profile. To describe the real situation in the watercourse, it is necessary to include in the WPL calculation all sources of all pollutants in the watershed, as envisaged by the Water Footprint Assessment Manual (Hoekstra et al., 2011, p. 87). However, not all the necessary inputs for this study were present. It should be also noted that the calculation of the river basin pollution load in the form of a simple summing up of all GWFs of pollution sources in the watershed, as assumed by the Water Footprint Assessment Manual (Hoekstra et al., 2011, p. 87) neglects the self-purification processes of natural water systems and has other limitations, such as the practical impossibility of summing up all the various pollution sources emitting different pollutants and focusing on just one main pollutant in the river basin (Ansorge et al., 2022). While some CECs degrade relatively quickly in the watershed, other CECs are resistant in the environment. For example, a study focused on the Czech part of the Elbe River basin shows that the amount of pharmaceuticals in individual profiles is in direct proportion to the number of inhabitants living in the river basin upstream the evaluated profile, because the PPCP consumption is, more or less, uniform within the population (Fuksa and Smetanová, 2022). However, the variability of the occurrence of pharmaceuticals in wastewater is high, as demonstrated by this study, as well as many others, from Slovenia (Česen et al., 2018), India (Praveenkumarreddy et al., 2021), South Africa (Mhuka et al., 2020), etc. One of the potential weaknesses of assessing CECs using the GWF may be the cross-interactions of individual substances. Discharged wastewater is a Ansorge et al.: Grey Water Footprint of Contaminants of Emerging Concern from Wasterwater in Sava River Basin – Sivi vodni odtis novodobnih onesnaževal iz odpadnih vod v porečju Save Acta hydrotechnica 35/63 (2022), 117–128, Ljubljana 125 mixture of substances and the GWF assesses them separately. The GWF calculation is based on PNEC values, which represent, from an ecotoxicological point of view, concentrations without toxic effects of the given substance, determined with a certain probability. However, in ecotoxicology, the effects of various mixtures of these substances and the determination of their PNEC are discussed in the form of various model approaches (Coors et al., 2018; Ginebreda et al., 2014). The conclusions of this study can be applied to the Central European region, as according to Hrkal et al. (2023), the homogeneity of PPCP detected in Central European waters testifies both to a similar level of medical care and the health status of the population, as well as to similar consumption habits and lifestyles. Author Contributions Conceptualization L.A.; methodology L.A. and P.S.; validation L.S. and P.S.; data curation L.A. and L.S.; writing—original draft preparation L.A.; writing—review and editing L.A., L.S., and P.S.; funding acquisition L.S. All authors have read and agreed to the published version of the manuscript. Data Availability Statement All data needed to replicate the study are included in this article. 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