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Article

Behaviour of PPCP Substances in a Fluvial Aquifer after Infiltration of Treated Wastewater

by
Zbynek Hrkal
1,2,* and
Frantisek Pastuszek
1,2
1
T.G. Masaryk Water Research Institute, p.r.i. Podbabská 30, 16000 Prague, Czech Republic
2
Department of Hydrogeology, Charles University Faculty of Science, Albertov 6, 12800 Prague, Czech Republic
*
Author to whom correspondence should be addressed.
Appl. Sci. 2023, 13(16), 9348; https://doi.org/10.3390/app13169348
Submission received: 24 July 2023 / Revised: 8 August 2023 / Accepted: 14 August 2023 / Published: 17 August 2023
(This article belongs to the Special Issue Environmental Pollution: Advances in Analysis of Emerging Pollutants and Their Removal Methods)
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Abstract

:
One of the reasons for the still prevailing concerns about the recycling of treated wastewater is the low efficiency of current Waste Water Treatment Plant (WWTP) technologies for removing Pharmaceuticals and Personal Care Products (PPCP) substances, especially pharmaceuticals. The goal of this investigation was to verify the behaviour of these substances after infiltration into the fluvial aquifer. During an experiment, this water (containing 45 PPCP substances) was infiltrated using a 10 m deep well for a period of one month. The whole process of infiltration was intensified by pumping at a well in a distance of 52 m. In the surrounding monitoring wells, 113 PPCP substances were monitored at three-day intervals with a detection limit in the order of tens of ng/L. The results showed that 32 PPCP substances were already present in the fluvial aquifer before the start of the infiltration experiment and thus represent “background” values. These substances are the result of river water seepage. The influence of infiltration was manifested by changes in the chemistry of the monitoring wells 8–12 days after the beginning of the experiment. The experiment demonstrated the high natural attenuation capacity of the fluvial aquifer, which eliminated a wide range of PPCP substances to a level below the detection limit.

1. Introduction, Motivation of the Experiment

In the last decade, Central Europe, including the Czech Republic, has increasingly been faced with the negative effects of drought [1,2,3]. In some regions, long-term drought has caused in extreme cases the drying up of rivers and a long-term drop in groundwater levels [4,5]. The related water management problems are manifested mainly in agriculture, but also in the supply of drinking water to the population [6].
One of the partial solutions is an approach used in Greece, Malta, Portugal, Italy, and Spain, where the share of reused urban effluent ranges between 1 and 12% [7]. Analysis carried out in preparation for the Water Reuse Regulation expected a provision cost for the reclaimed water of less than EUR 0.5 per m3 [8]. The current, relatively restrained attitude towards the reuse of treated waste water in Europe might be gradually mitigated [9]. The European Commission included wastewater in the circular economy action plan and set itself the goal of facilitating water reuse, especially in the field of irrigation [10]. Cleaned wastewater used for irrigation of crops that are not intended for direct human consumption can thus become an important tool for reducing demands on water resources in European agriculture.
The annual production of all WWTPs in the Czech Republic is 23 m3/s, i.e., 738 Million m3/year [11]. These water currently leave the territory of the state without any use in a short period of time. A more meaningful use of this water, for example by its infiltration and subsequent reuse, is prevented by current legislation. Primarily, waste water treatment plants must discharge their production into the receiving water in the river in the Czech Republic. Only in exceptional cases treated wastewater can be infiltrated into an aquifer, never directly into the saturated zone, but always through the soil layer.
One of the main reasons why today’s European public is so cautious about treated wastewater is the fear of discharging PPCPs into the groundwater. Today’s waste water cleaning technologies mostly remove these substances only with problematic efficiency [12,13,14]. The published results suggest that many PPCPs are released into the rivers in nanograms to micrograms per liter concentrations [15,16,17].
The main goal of the project was to provide expert documents that would confirm or, on the contrary, remove objections against the infiltration of treated wastewater into the fluvial quaternary and its possible reuse. The work focused primarily on clarifying the behavior of different types of PPCP substances, the speed of their spread and the possibility of natural attenuation. The tool for solving these questions was an experiment consisting in the infiltration of 2366 m3 of treated wastewater over into a fluvial sand-gravel and monitoring the behavior of 113 monitored PPCP substances at the test site Kojetín in South Moravia (Figure 1).

2. Natural Characteristics of the Experimental Site

The Kojetín area is located 220 km east of the capital of the Czech Republic, Prague. Climatically, the area is slightly warmer and drier than the national average (average annual temperature in Kojetín (1961–2019) is 8.9 °C, in the Czech Republic 7.8 °C; average precipitation in Kojetín is 660 mm, in the Czech Republic 675 mm). Over the past 60 years, the average annual temperature in the Czech Republic has risen by 2 °C. Regarding precipitation, no significant trends have been observed. The nationwide long-term average of precipitation over the last sixty years is stable, only the course of precipitation changes during the year. Increased temperatures, especially in the summer months, result in high evapotranspiration, which is manifested by a decrease in river flows [18,19].
Kojetín experimental site is located on the right bank of the most important watercourse in the eastern part of the Republic, the Morava River. In 2022, only 45% of the long-term monthly flow (which is ca. 47 m3/s) flowed through the Morava River in Kojetín.
Morphologically, the Kojetín area is part of a floodplain with an altitude of around 190 m a.s.l. Geological and hydrogeological conditions are relatively simple. The uppermost zone is represented by flood clays with low permeability, causing the artesian regime of the underlying Quaternary aquifer. This aquifer of fluvial gravel-sands have a thickness of 2.6 to 6 m, and is characterized by an average hydraulic conductivity value of 7.8 × 10−4 m/s and is in close hydraulic connection with the adjacent watercourse of the Morava River. The subsoil of the fluvial Quaternary is represented by Tertiary clays, which form an lower impermeable layer.

3. Experimental Setup and Performance

The area of interest was equipped with observation wells PZ1, PZ5, and CP1 (Figure 2). A well VSK was used for infiltration of treated wastewater from the local WWTP. All wells penetrated the entire thickness of the aquifer. The depth of the wells was between 8 and 10 m, depending on the thickness of the fluvial Quaternary. The casing diameter of pumping/infiltration wells CP1, CP5 and VSK was 425 mm, in the case of monitoring wells PZ1 and PZ13 the diameter was 273 mm. Length of casing again depended on local lithological conditions and was most often in the range of 2–7 m.
Special attention was paid to monitoring the expected clogging of the VSK infiltration well, which was equipped with a casing observation probe Solinst for this purpose. The probe worked on the principle of measuring barometric pressure and was placed in a pipe in the filter material of the well.
The level in the Morava River is maintained by manipulation at the weir at 190.9 m a.s.l. The levels in the wells VSK, CP1, PZ1 in the original artesian regime are at the level of 190.6 m a.s.l.
Czech legislation does not allow the discharge of wastewater into groundwater, but always through the unsaturated zone. Therefore, a container with a volume of 1 m3 filled with fine-grained sand (its granulometry was very similar to the unsaturated zone) was placed between the outlet of the WWTP and the infiltration VSK well.
A slug test was performed before the major infiltration experiment conducted with clean water. The goal was to specify the infiltration rate into the VSK well. The water level measurement took 5.5 h. At one time, the borehole was filled with 212 L of water and the decrease in its level over time was measured. The test was evaluated using the Jacob-Lohman method [20].
The initial amount of infiltrated wastewater was set at 2 L/s and then adjusted so that the level in the VSK well was constant. Due to clogging, the infiltration rate in VSK well gradually decreased from 1.8 L/s to 0.7 L/s.
The infiltration was intensified by pumping from adjacent well CP1. The withdrawal from well CP1 was therefore maintained systematic greater than the infiltration rate. The pumped volume of groundwater from CP1 well ranged from 2.9 L/s to 1.6 L/s. During the experiment, 2366 m3 of treated wastewater was infiltrated into VSK well, and 4332 m3 of groundwater was pumped from CP1 well.
Water samples were taken at scheduled intervals from five monitoring points—the inlet into VSK well with treated wastewater, CP1 well, PZ5 and PZ1 wells, and Morava River for a period of 36 days. A total of 113 PPCP substances were analyzed.

4. Results

4.1. Evaluation of Qualitative Changes

Table 1 provides a complete overview of the occurrence of PPCP substances at the monitoring points in the period before and during the infiltration experiment. For substances that were analyzed in more detail, the mean and median content values are also given.
A comparison of both recipients of PPCPs (i.e., treated wastewater from the WWTP and water in the Morava River) reveals their similarity in terms of the range of detected components. Upstream from the Kojetín site, many other wastewater treatment plants are connected to Morava River. A total of 80 PPCP substances with values above the detection limit appeared in at least one sample on the monitored objects.
The permanent occurrence (that is, they were detected in all samples) exhibited 45 PPCP substances concerning treated wastewater and 32 substances concerning water in the Morava River. At the beginning of the VSK infiltration experiment (7 November 2022), well PZ5 already contained 29 PPCP substances, whilst wells CP1 and PZ1 contained 13 resp. 12 substances. The PZ5 well is located in the proximity of the Morava River (Figure 2). It follows that the river water negatively affects the adjacent fluvial aquifer and a non-negligible set of PPCP substances is “naturally” present in the groundwater unaffected by the infiltration experiment. During floods and due to the dam cascade, influent conditions exist occasionally or permanently in some of areas between the aquifer and the Morava River. This causes the PPCP to transport from the river into fluvial sediments.
The presence of more or less similar PPCP substances both in the river and in the outlet from the WWTP is not surprising; especially in times of low flows, inflows from treatment plants represent a non-negligible part of the flow of watercourses in Europe. This fact is documented by comparing the contents of PPCP substances in some Central European (CE) rivers (see Table 2). The water quality in the Morava River in terms of the range of detected PPCP substances fits very well into this regional context; in absolute values the contents of some substances are even significantly higher (e.g., Oxypurinol, Iopromide, Telmisartan).
Despite the presence of more or less identical substances, large quantitative differences occur between the outlet of WWTP and the Morava River. The highest average concentration in treated wastewater was exhibited by Oxypurinol (13,903 ng/L; Table 1). However, such high difference in content are not always usual. For the other eight substances, average content was higher than 1 µg/L; these were Valsartan acid, Lamotrigine, Diclofenac, Telmisartan, Tramadol, Benzotriazole, Hydrochlorothiazide, and Sucralose. All these substances can also be found in river water, but in lower orders of magnitude—due to the dilution, degradation, or gradual transformation into others. However, such high differences in content are not always a general rule. In Table 1, the group of substances occur where the differences in PPCP content are smaller, or negligible, between the WWTP outlet and river Morava. These are, for example, 4-formylaminoantipyrine, Gabapentin, or Valsartan; the content of Metformin is more or less equal, with Acesulfame even more in the river water than in the WWTP discharge.
The data from Table 1 also shows the significant role of infiltrated water from the Morava River: the adjacent well PZ5 contained a significantly more varied range of PPCP substances before the start of the infiltration experiment than wells CP1 and PZ1 further from the river. The effect of the infiltration of treated wastewater in the VSK well was evidently not manifested in the PZ5 well.

4.2. Cluster Analysis

Changes in groundwater chemistry in wells PZ1 and CP1 due to the infiltration of treated groundwater into the VSK well was confirmed by statistical processing using cluster analysis. Cluster analysis is one of the methods that deal with investigating the similarity of multidimensional objects and classifying them into classes. It allows assessment of the relationships between individual clusters.
In the case of the implemented experiment, 30 chemical analyzes of water for PPCP substances were available, each of these contained a determination of the content of 113 substances (matrix of 30 × 113 elements). Cluster analysis focused on:
  • the number of clusters of PPCP substances—i.e., substances belonging to one set;
  • a measurable degree of difference in the occurrence of pharmaceuticals between individual observation objects.
The graphical output of this analysis is a dendrogram which allows measurable evaluation of the difference in the framework of the entire data matrix. The differences between groundwater, Morava river water, and infiltrated water during the whole experiment are documented in Figure 3. The difference of individual clusters is determined by the so-called inter-cluster distance. The measure expressing the probability of agreement of the created distribution of objects into individual groups is the cophenetics correlation coefficient (CP). The value of cophenetics coefficient was CP = 0.906.
The results showed that the groundwater in wells PZ1, CP1, and PZ5 was significantly different from the output from the WWTP and mostly from the Morava River throughout the experiment. The water in Morava River is significantly more “related” to groundwater than water from the WWTP. As a result of infiltration into the VSK well, the difference between the infiltrated water from the WWTP and groundwater has decreased. This statistically confirmed the change in groundwater quality due to infiltrated water originating from the WWTP.

4.3. Characterization of the Transport Process

The transport of PPCPs from the VSK infiltration well to the PZ1, CP1, or PZ5 boreholes can be demonstrated based on increases in the content of PPCP substances. Temporal changes in content enable the approximate assessment of transport rates. The most obvious markers are potentially those substances which did not occur in monitoring wells at the beginning of the experiment, but appeared later. The focus was on substances that, could be used as tracers due to their physical and chemical properties. Such non-degrading and non-sorbing substances migrate in the rock environment at the same speed as groundwater flows—i.e., without retardation. Oxypurinol and Benzotriazole were supposed to be in this category.
A comparison of both substances reveals that their content are significantly higher in treated wastewater than in the Morava river (Table 3 and Table 4). In well PZ5, the content of both pharmaceuticals were already present before the start of the infiltration experiment and remained more or less stable during the entire test, or there was no fundamental fluctuation. Neither Benzotriazole nor Oxypurinol was present in the CP1 and PZ1 boreholes before the start of the infiltration test. Both substances appeared in these objects with a delay of 8–12 days from the start of the experiment and had an upward trend. The values given correspond to velocities of 6.6–4.4 m/day. The distance between VSK and CP1 wells is 52.5 m. In the context of objectivity, it is necessary to emphasize that the calculated mentioned transport rates are not natural, but influenced by pumping in well CP1.

4.4. Evaluation of Groundwater Flow Velocities and Clogging

All the hydraulic experiments conducted at Kojetín experimental site, including several pumping tests and VSK infiltration, were analysed using MODGLOW-USG software [22]. Inflow tests were carried out in the period 26 October 2021–16 December 2021 (length 52 days). The CP wells and the VSK well are 50 and 80 m from Morava, respectively. After the completion of the pumping of the CP1 and CP2 wells and at the end of the period, short recovery tests were carried out. The maximum withdrawal reached in the final phase of inflow tests, when pumping the VSK well, was 4 L/s. For well CP1, the value of hydraulic conductivity kf was determined in the range of 1.3 × 10−3–1.8 × 10−3 m/s, for well CP2 in the range of 1.3 × 10−3–1.8 × 10−3 m/s and for well VSK in the range of 9.1 × 10−4–1.2 × 10−3 m/s.
The model simulations described the period of inflow tests at wells CP1, CP2 and VSK. Model calibration consisted in optimizing the agreement between measured and simulated groundwater levels. The agreement of the model and measurements was achieved by a combination of manual and semi-automatic tuning of selected hydraulic parameters of the model. The model simulation faithfully affected the observed development of the levels, including the tested wells. The withdrawals were simulated using the Neuman boundary condition (the pumped amount of groundwater was entered) and the groundwater level in the pumped wells was calculated by the model (see example Figure 4).
The transport of non-sorbing, non-reacting PPCPs from VSK well was simulated too. Figure 5 demonstrate the example of simulation of Sucralose content.
The best agreement between measurement and model result was achieved with the aquifer porosity of 15%, which is the expected value when considering a fluvial aquifer. In accordance with the measurement, the model also predicted that the locality of the PZ5 well remained unaffected by transport from the VSK well.
Model calculated breakthrough time (3–5 days) is shorter than observed (8–12 days), even though the model longitudinal dispersivity was reduced to 1 m to minimise dispersion. This suggests that during the initial phase of the transport process, sucralose was degraded. The same process probably took place which caused the initial content of this substance to be zero. We repeated the same experiments and calculations for oxypurinol and benzotriazole with practically identical results.
Clogging of the VSK borehole was manifested by a huge difference in groundwater level response during the pumping and infiltration period. Throughout the proceeding pumping test, the water level in the VSK well decreased by 1 m, when 4 L/s were abstracted. The infiltration experiment was finished with the recharge rate of approximately 0.7 L/s and the water level increased by 4 m. The level difference between the VSK borehole and the casing observation probe increased too. The reason is the position of the observation probe in a pipe in the filter material of the well.
The persistently relatively small level difference between the infiltration well and the casing probe proves that the hydraulic resistance against infiltration arose only at the filter pack/aquifer interface. It is likely that physico-chemical processes caused by the seepage of oxygenated water into the originally anoxic environment, in combination with microbiological processes and sorption to precipitated Fe3+ compounds, contributed significantly to the clogging. It is evident from the obtained data that the artificial recharge of wastewater using the technology of infiltration wells will bring technical problems associated with a decrease in yield.

4.5. Generalization of PPCP Substance Behaviour

The results of the infiltration experiment at the Kojetín site enable PPCP substances to be divided into four groups
  • Substances with random occurrence in very low concentrations and therefore with an undetectable origin.
At the WWTP outlet, the substances of this group often had concentrations below the detection limit, so the infiltration experiment could not affect the concentrations of these substances in the monitoring wells PZ1, CP1 and PZ5. Nevertheless, they were analyzed in low concentrations at irregular intervals in some samples. An example of this group of substances is caffeine, which was detected in well PZ1 in the sample from 20 November and in well CP1 in the sample from 30 November. However, during the entire experiment, caffeine was not detected in the WWTP water samples. A similar result can be observed with other substances such as Bisphenol S, Methylparaben, Paracetamol, Paraxanthine, Propylparaben, and Saccharin. The problem with these substances is their general presence in various components of the environment, including practically all watercourses, so their origin is difficult to unambiguously detect.
  • Substances, the contents of which increased in the Quaternary aquifer after the infiltration experiment.
Substances of this group are characterized by a high concentration in treated wastewater, which was later manifested in the occurrence in wells PZ1 and CP1. It is important that these substances were not present in the wells before the start of the experiment. The resulting content of these substances in the wells remain lower than at the WWTP outlet. Characteristic representatives of this group are oxypurinol, Sucralose, Hydrochlorothiazide, Diclofenac-4-hydroxy, and Benzotriazole. But all the substances which systematically occurred during the infiltration experiment should be included (Table 2). Absence of these substances at the beginning of the experiment reveals that they are degrading at some rate in the groundwater.
  • Substances persistent, apparently without significant WWTP influence
Substances in this group are characterized by a higher concentration in wells than in treated wastewater. They usually occurred in the groundwater from the beginning of the infiltration experiment. The water in the observed wells was therefore more similar to water from the Morava River than to the WWTP. A typical representative is Acesulfame, whose concentrations in the river exceeded twice the average values of the water leaving the WWTP. This group also includes DEET and Sulfamethazine, which are all persistent substances that are not subject to degradation either in the Morava River or in the rock environment of the gravel sands of the experimental site.
  • Substances with a high natural attenuation capacity.
Substances in this group are characterized by concentrations above the detection limit in the water leaving the WWTP, possibly also in the Morava River. Nevertheless, these substances were not detected in the observation wells. It can be assumed that these substances have a good sorption/degradation capability in the rock environment. This is a relatively large group of substances that includes Acebutolol, Atenolol, Azithromycin, Benzotriazole 1-methyl, Bisoprolol, Celiprolol, Cetirizine, Citalopram, Clarithromycin, Climbazole, Clindamycin, Cyclophosphamide, Erythromycin, Fexofenadine, Fluconazole, Irbesartan, Carbamazepine-2-hydr., Carbamazepine-DHH, Carbamazepine-E, Lamotrigine, Iosartan, Metoprolol, Mirtazapine, Naproxen, Naproxen-O-desmeth, Oxcarbazepine, PFOS, Sertraline, Sitagliptin, Sotalol, Sulfamethoxazole, Telmisartan, Tramadol, Trimethoprim, Venlafaxine, Venlafaxine O-desmet, Verapamil, and Warfarin.

5. Conclusions

  • An experiment consisting of the gradual infiltration of 2366 m3 of treated wastewater over a period of 36 days into a fluvial sand-gravel aquifer demonstrated a diverse influence on the quality of groundwater. Of the 113 monitored PPCP substances, 80 were detected at the experimental site, of which 69 were in the WWTP discharge.
  • Groundwater contained a non-negligible range of PPCP substances even before the start of the experiment (PZ1 12 substances, CP1 11 substances and PZ5 17 substances), which represent “background” values. The source of these, mainly pharmaceuticals, is bank infiltration from the Morava River. River water contains a similar range of PPCP substances as WWTP discharge, although mostly in much lower concentrations.
  • The influence of infiltration into the VSK infiltration well was manifested by changes in chemistry about 50 m away with a delay of 8–12 days.
  • Of the monitored PPCP substances present in WWTP discharge, roughly half did not appear in the groundwater at all. It shows the selectively high degree of natural attenuation of the fluvial gravel sand sediments for some substances.
  • On the other hand, a group of substances such Oxypurinol or Benzotriazole, which are often contained in treated wastewater in concentrations of the order of thousands of ng/L, migrated with groundwater, despite the fact that they were not detected as background values. Probably some degree of degradation of these occurred in the aquifer.
  • Due to the lack of long-term medical studies that would document the possible negative impact of the detected substances on human health, the only option is to use the precautionary principle. Until a more efficient wastewater treatment technology is applied, it is clear that, despite the very good attenuation capacity of the rock environment for some substances, the infiltration of treated wastewater remains a problematic issue.
  • However, this depends on the purpose for which the infiltration technology would be used. Both surface and shallow groundwater (in some places) in Central Europe already contain a non-negligible amount of PPCP substances. Therefore, in regions suffering from a lack of water for agricultural purposes, it would be possible potentially to infiltrate WWTP output into groundwater in accordance with current Czech legislation. After purification, these newly generated sources of groundwater could be used for irrigation in the event of a lack of another water source.

Author Contributions

Z.H. and F.P. prepared the manuscript and approved the manuscript, including graphical and statistical interpretations. Z.H. was responsible for the overall coordination of the research and editing of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was completed with support from the project “Water retention in the landscape using artificial recharge as a tool in the fight against drought”. This project has received funding from the Technology Agency of the Czech Republic, SS—Programme of applied research, experimental development and environmental innovation SS01020275.

Data Availability Statement

Data is available through the T.G. Masaryk Water Research Institute, Prague.

Conflicts of Interest

The authors declare no conflict of interest.

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  22. Panday, S.; Langengevin, C.D.; Niswonger, R.G.; Ibaraki, M.; Hughes, J.D. MODFLOW–USG Version 1: An Unstructured Grid Version of MODFLOW for Simulating Groundwater Flow and Tightly Coupled Processes Using a Control Volume Finite-Difference Formulation; U.S. Geological Survey Techniques and Methods: Louisville, KY, USA, 2013; Chapter A45; 66p.
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Figure 1. Location of the experimental site.
Figure 1. Location of the experimental site.
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Figure 2. Location of pumping (CP1), monitoring (PZ1 and PZ5), and infiltration wells (VSK) at the Kojetín experimental site (blue points—wells, yellow point—ground filter)
Figure 2. Location of pumping (CP1), monitoring (PZ1 and PZ5), and infiltration wells (VSK) at the Kojetín experimental site (blue points—wells, yellow point—ground filter)
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Figure 3. Results of cluster analysis documenting the relationships between monitored parameters.
Figure 3. Results of cluster analysis documenting the relationships between monitored parameters.
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Figure 4. Comparison of measured and modeled groundwater levels, pumped borehole CP1. blue line—measured data, red points and line—simulated data.
Figure 4. Comparison of measured and modeled groundwater levels, pumped borehole CP1. blue line—measured data, red points and line—simulated data.
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Figure 5. Measured and modelled content of Sucralose in 2022, relative values.
Figure 5. Measured and modelled content of Sucralose in 2022, relative values.
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Table 1. Information on the presence of PPCP substances at monitoring objects (black fields—values above the detection limit, white fields of values below the detection limit, green period “background” data before the experiment, yellow period data during the experiment).
Table 1. Information on the presence of PPCP substances at monitoring objects (black fields—values above the detection limit, white fields of values below the detection limit, green period “background” data before the experiment, yellow period data during the experiment).
DATE OF SAMPLING20 November 20217 November 202220 November 202224 November 202230 November 20228 December 20222022/12/12AverageMedian20 November 20217 November 202220 November 202224 November 202230 November 20228 December 202212 December 2022AverageMedian20 November 20217 November 202220 November 202224 November 202230 November 20228 December 202212 December 2022AverageMedian7 November 202220 November 202224 November 202230 November 20228 December 202212 December 2022AverageMedian7 November 202220 November 202224 November 202230 November 20228 December 202212 December 2022AverageMedian
ng/L ng/L ng/L ng/L ng/L
NoPPCPWWTP OUTFLOWMORAVA RIVERPZ1CP1PZ5
1Carbamazepine##############27427329404846445535424416191722364550292218132539165628223535344239423837
24-formylaminoantipyr##############289261##############15815124615146746468556174467981439470767380869181938484
3Valsartan acid##############1166128099############190195348878########1571359971####80##174154############217220
4Acesulfame######74######128119##############242220##############214217############177179############179168
5Gabapentin##############690680##############21820924825582######127828541##5951##10272000000
6Lamotrigine##############105092172##########941141090000000 00012044 4938354437384038
7Diclofenac##############1651158085############1391400263357######12757220####32## 2746253331283230
8Telmisartan##############72507060##############4944920000000 000000 3530273332293131
9Valsartan3327502333267338333421272827624935280100111200 00110011 1112131716151414
10Tramadol##############1673138079######95##911091030000000 000000 3831313530303231
11Oxypurinol##############1390313400##############191119600000###### 0####69##491204############395397
12Benzotriazol##############48345620##############563498000062#### 0093##34##12463############156157
13Benzotriazol 5-methy##############47547461############1701530000223328 002134046 ##84967486768685
14Sulfapyridin##############26426914212420253822232201100265256 110223311682417190121316181314
15Hydrochlorothiazide##############155316009296##99######1171040000###### 0068##0## 000000
16Metoprolol##############7947895142675159885459540000000 000000 0220000
17Clarithromycin##34##########3032814224342947665742420000000 000000 000000
18Acebutolol66############1691911212181214191314130000000 000000 000000
19Venlafaxine##############2222252020271922322123210000000 000000 000000
20Sulfamethoxazol##############9681050374771556681575957 000000 000000
21Celiprolol##############2682841213131615161514150000000 000000 000000
22Fexofenadine3159988474413760591218292223341922220000000 000000 000000
23Fluconazole46##83##46236579651821341622251321210000000 000000 000000
24Irbesartan46##86########1091271826342024352125240000000 000000 0001000
25Sotalol##############2891893630495049614546490000000 0000029 000000
26Metformin56############652147##############63555100##240480 0##040##0 0##04200
27Venlafaxine O-desmet##############6425576663##94####8197940000000 000000 000000
28Sucralose##############845096100############18012030000########1453148000####0## 000000
29Bisoprolol##############144138120131013241513130000000 000000 000000
30Diclofenac-4-hydroxy##############7116390035025543521250002466####53240041690## 000000
31Trimetoprim##############222203001301019118100000000 000000 000000
32Atenolol393630273049714036000001010 0000000 000000 000000
33Phenazone6534182329704440340013001411 00000011 0000017 000000
34Furosemide##############78978600000##93 000098#### 0062620## 000000
35Azithromycin##############8096530000000 0000000 000000 000000
36Carbamazepine-E1933232730232526250000000 0000000 000000 000000
37Carbamazepine-DHH2321313441384233340000000 0000000 000000 000000
38Oxcarbazepine5852749281839777810000000 0000000 000000 000000
39Sertraline2025182423232322230000000 0000000 000000 000000
40Carbamazepine-2-hydr.2971465969506355590000000 0000000 000000 000000
41Citalopram69############1181220000000 0000000 000000 000000
42Mirtazapine4864438076385057500000000 0000000 000000 000000
43Benzotriazol 1-methy69########71561361370000000 0000000 000000 000000
44Ketoprofen2026454246378543420000000 00140000 0005000 0150000
45Sulfanilamide50##568169####144810000000 520065##969860650069560## 70669897####10298
46Iopromide5271######0 49194##############5855200000817578 007862093 000000
474-acetamidoantipyrin ########## 760816 ############193180 342543######76744021709827##6855000000
48Cetirizine ########## 387422 4161334454294443 000000 000000 000000
49Sitagliptin ########## 275281 2537374046363737 000000 000000 000000
50Primidone02058717292 607126283829345028332920241921222835242222161816225124202522262336372825
51Climbazole04034424729 33390000000 0000000 000000 000000
52Clindamycin03722535894 363700000120 0000000 000000 000000
53Naproxene-O-desmeth.00492360## 31420000000 0000000 000000 000000
54Iomeprol7462####0##038574##############28924400000#### 000700## 000000
55Verapamil010121628##1211120000000 0000000 000000 000000
56Losartan011211421##20121400191416261613160000000 000000 000000
57Warfarin010101012391081002712003812 0000000 000000 000000
58Erythromycin0011003612 0000000 000000 000000
59Cyclamate00##00450 00##00#### 00##0000 0##0000 0##0000
60DEET01100270## 0000156218 013##393016153416##32##28##6018014026##192825316227
61PFOS0000400 0030000 0000000 000000 000000
62Cyclophosphamide2114003500 0000000 0000000 000000 000000
63Paraxanthine00##0000 00000#### 00##0000 000##00 0##0000
64Ibuprofen0235727000 32##512823##46814600410000 02105200 0960000
65Naproxene0054006957 0000000 0000000 000000 000000
66Saccharin00##00057 710##########14514200##0000 0540##00 0##0000
67Paracetamol0028016016 2101918124162251900180000 0002100 0360000
68Iohexol0000000 7000080##5750570000000 000000 000000
69Ibuprofen-2-hydroxy0000000 909969##66####130990000000 000000 000000
70Ibuprofen-carboxy0000000 28003130596230300000000 000000 000000
71Alfuzosin0000000 381416013371619160000000 000000 000000
72Sulfamethazin0000000 0000000 2428303445302931 36243739284335 26323543424637
73Caffein0000000 00000#### 00##0000 000##00 0##0000
74Diatrizoate0000000 0000000 0000000 000000 000000
75Methylparaben0000000 0000000 00580000 0007600 0##0000
76Bisfenol S00027000 0000000 00470000 0006500 0##0000
77Cotinine00000025 0000000 0000000 0003500 000000
78Ethylparaben0000000 0000000 0000000 000000 0110000
79Propylparaben0000000 0000000 0000000 0002300 0230000
80Amitriptiline0000000 0000000 0000000 0001200 000000
Table 2. PPCPs occurrence in six watercourses in CE region (The colour indicates the frequency of occurrence: red in all rivers; orange—the substance was missing in one of the streams; yellow—the substance was missing in two streams; green—the substance was missing in three rivers [21].
Table 2. PPCPs occurrence in six watercourses in CE region (The colour indicates the frequency of occurrence: red in all rivers; orange—the substance was missing in one of the streams; yellow—the substance was missing in two streams; green—the substance was missing in three rivers [21].
PPCP SUBSTANCESLABEISARJIZERAPOSAVABRYNICAMORAVA
ng/L
Metformin5019447925710421635
DEET26264410106324
Telmisartan195532233610191494
Valsartan169820262137635
Valsartan acid653845441015190
Benzotriazol methyl97160424826152170
Diclofenac207825101010139
Iopromide252525681605585
Irbesartan5341016242525
Paracetamol 5115177625
Gabapentin11982110359 218
Benzotriazol2563482109345 563
Caffeine1411551205050
Iohexol252062525 550
Oxypurinol52637036220425 1911
Acesulfam164662848525 242
Lamotrigine39583855 114
Saccharin7625742525 145
Paraxanthine145501605050
Iomeprol233182123104140 289
Ibuprofen10102610 81
Clarithromycin555 5 42
4-formylaminoantipyr7311453 27 158
Peniciline G555 57
Tramadol372141 49109
Carbamazepine2123235 42
Metoprolol234317 559
PFOS5312 3
Fexofenadine530 55 22
Hydrochlorothiazide257525 25 117
Bisphenol S2525 25
Carbamazepine-DHH 23 5 5
Erythromycin 55 79
Celiprolol10 11 2914
Cotinine101010
Iopamidol 25 941 170
Methylparaben501543
Primidone5517 33
Venlafaxine13245 23
Venlafaxine O-desmethyl2959 5 97
Sulfamethoxazole162317 59
Table 3. Time development of Benzotriazole content at monitored objects.
Table 3. Time development of Benzotriazole content at monitored objects.
CONCENTRATION OF BENZOTRIAZOL IN ng/L
Date of SamplingWWTPMORAVA RIVERCP1 WELLPZ1 WELLPZ5 WELL
7 November 20224390657<20<20159
20 November 20226640459<20<20155
24 November 2022661060693<20173
30 November 2022629047119662124
8 December 20225620838analysis error167168
12 December 20222140498420187155
AVERAGE528258823669156
MEDIAN59555529331157
Table 4. Time development of Oxypurinol content at monitored objects.
Table 4. Time development of Oxypurinol content at monitored objects.
CONCENTRATION OF OXYPURINOL IN ng/L
Date of SamplingWWTPMORAVA RIVERCP1 WELLPZ1 WELLPZ5 WELL
7 November 202210200729<50<50270
20 November 2022134002350<50<50540
24 November 2022171001960338<50438
30 November 2022218002270849299328
8 December 2022147003050analysis error811427
12 December 202212300160016901070366
AVERAGE149171993550363395
MEDIAN140502115379150400
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Hrkal, Z.; Pastuszek, F. Behaviour of PPCP Substances in a Fluvial Aquifer after Infiltration of Treated Wastewater. Appl. Sci. 2023, 13, 9348. https://doi.org/10.3390/app13169348

AMA Style

Hrkal Z, Pastuszek F. Behaviour of PPCP Substances in a Fluvial Aquifer after Infiltration of Treated Wastewater. Applied Sciences. 2023; 13(16):9348. https://doi.org/10.3390/app13169348

Chicago/Turabian Style

Hrkal, Zbynek, and Frantisek Pastuszek. 2023. "Behaviour of PPCP Substances in a Fluvial Aquifer after Infiltration of Treated Wastewater" Applied Sciences 13, no. 16: 9348. https://doi.org/10.3390/app13169348

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