Science of the Total Environment 746 (2020) 141200
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Science of the Total Environment
j ourna l homepage: www.e lsev ie r .com/ locate /sc i totenvOccurrence of pharmaceuticals, hazard assessment and ecotoxicological
evaluation of wastewater treatment plants in Costa RicaDidier Ramírez-Morales a, Mario Masís-Mora a, José R. Montiel-Mora a, Juan Carlos Cambronero-Heinrichs a,b,
Susana Briceño-Guevara a, Carlos E. Rojas-Sánchez c, Michael Méndez-Rivera a, Víctor Arias-Mora a,
Rebeca Tormo-Budowski a, Laura Brenes-Alfaro a, Carlos E. Rodríguez-Rodríguez a,⁎
a Centro de Investigación en Contaminación Ambiental (CICA), Universidad de Costa Rica, 2060 San José, Costa Rica
b Facultad de Microbiología, Universidad de Costa Rica, 2060 San José, Costa Rica
c Sede del Caribe, Universidad de Costa Rica, 70101 Limón, Costa RicaH I G H L I G H T S G R A P H I C A L A B S T R A C T• Occurrence of 70 pharmaceutical com-
pounds in WWTPs in Costa Rica.
• High hazard estimated in every influent
and 96% effluents towards aquatic or-
ganisms.
• Risperidone, lovastatin and diphenhy-
dramine exhibited the highest hazard
in influents/effluents.
• Toxicity of 16.7% effluents towards all
benchmark organisms experimentally
tested.
• Phytotoxicity (inhibition in seed germi-
nation test) particularly critical in
effluents.⁎ Corresponding author.
E-mail address: carlos.rodriguezrodriguez@ucr.ac.cr (C
https://doi.org/10.1016/j.scitotenv.2020.141200
0048-9697/© 2020 Elsevier B.V. All rights reserved.a b s t r a c ta r t i c l e i n f oArticle history:
Received 18 May 2020
Received in revised form 15 July 2020
Accepted 21 July 2020
Available online 26 July 2020
Editor: Paola VerlicchiThe continuous release of pharmaceuticals fromWWTP effluents to freshwater is amatter of concern, due to their
potential effects on non-target organisms. The occurrence of pharmaceuticals in WWTPs and their associated
hazard have been scarcely studied in Latin American countries. This study aimed at monitoring for the first
time the occurrence of 70 pharmaceutical active compounds (PhACs) in WWTPs across Costa Rica; the applica-
tion of the hazard quotient (HQ) approach coupled to ecotoxicological determinations permitted to identify the
hazard posed by specific pharmaceuticals and toxicity of the effluents, respectively. Thirty-three PhACs were
found, with 1,7-dimethylxanthine, caffeine, acetaminophen, ibuprofen, naproxen, ketoprofen and gemfibrozil
being the most frequently detected (influents/effluents). HQ for specific pharmaceuticals revealed 24 com-
pounds with high/medium hazard in influents, while the amount only decreased to 21 in effluents. The top HQ
values were obtained for risperidone, lovastatin, diphenhydramine and fluoxetine (influent/effluent samples),
plus caffeine (influent) and trimethoprim (effluent). Likewise, the estimation of overall hazard inWWTP samples
(sum of individual HQ,∑HQ) demonstrated that every influent and 96% of the effluents presented high hazard
towards aquatic organisms. Ecotoxicological analysis (Daphnia magna, Lactuca sativa andMicrotox test) revealed
that 16.7% of the effluents presented toxicity towards all benchmark organisms; the phytotoxicity was particu-
larly frequent, as inhibition values ≥20% in the germination index for L. sativawere obtained for all the effluents.
The ∑HQ approach estimated the highest hazard in urban wastewater, while the ecotoxicological results
showed the highest toxicity in hospital and landfill wastewater. Likewise, ecotoxicological results and ∑HQKeywords:
WWTP
Pharmaceuticals
Antibiotics
Hazard quotient
Ecotoxicity
Latin America.E. Rodríguez-Rodríguez).
2 D. Ramírez-Morales et al. / Science of the Total Environment 746 (2020) 141200values showed a rather poor correlation; instead, better correlationswere obtained between ecotoxicological pa-
rameters and HQ values for some individual pharmaceuticals such as cephalexin and diphenhydramine. Findings
from this study provide novel information on the occurrence of pharmaceuticals and the performance ofWWTPs
in the tropical region of Central America.
© 2020 Elsevier B.V. All rights reserved.1. Introduction
Pharmaceutical consumption is globally rising, from high-income to
low-income countries (Dimpe and Nomngongo, 2016), with an esti-
mated worldwide increase between 18.6% and 29.3% from 2000 to
2008 (Hoebert et al., 2011). This trend can be ascribed to the growth
of human population and the consequently more extensive use of
these products to improve live standards (Sim et al., 2013; Dimpe and
Nomngongo, 2016; United Nations Population Fund, 2019), along with
factors such as self-medication practices, combined with the release of
prescription pharmaceuticals to the non-prescription category
(Bennadi, 2014), as well as extensive use in other fields, including vet-
erinary and livestock production (Kümmerer, 2009a).
The pharmaceutical active compounds (PhACs) are part of the new-
considered emerging pollutants, a group of anthropogenic produced
substances with extensive use in the context of modern society; to
date, most of them lack environmental legislation and important gaps
on the knowledge of their ecotoxicological effects still exist for many
compounds (Deblonde et al., 2011; Rasheed et al., 2019). By 2016 at
least 631 PhACs have been detected in wastewater, surface water, un-
derground water, drinking water, seawater (aus der Beek et al., 2016)
and even in waters with low anthropogenic influence like those in the
Arctic (Kallenborn et al., 2018) and Antarctic oceans (Emnet et al.,
2015), with concentrations ranging from ng/L to mg/L (aus der Beek
et al., 2016).
Consuming-related practices result in the occurrence of PhACs in
wastewater. When medications are consumed, a fraction of the PhACs
contained in theproduct is excreted as the parental compound, or trans-
formed after metabolization (Nikolaou et al., 2007); therefore, hospital
(Kümmerer, 2009b) and livestock (Sim et al., 2011) wastewater repre-
sent important sources of pharmaceutical residues. Similar is the case of
municipal wastewater, with higher flows but lower concentration; in
addition to excretion, thismatrix also suffers from the additional contri-
bution of medicaments incorrectly discarded into sinks and toilets.
Other PhAC-containing wastewaters include leachates produced in
landfills, which receive medicaments as part of the municipal solid res-
idues (Kümmerer, 2009a).
Most of wastewater treatment processes applied nowadays are pri-
mary or secondary, which are designed to eliminate solid materials and
dissolved organic matter, but they do not effectively remove PhACs;
therefore, important residues remain in the effluent and sludge of
wastewater treatment plants (WWTPs) (Jelić et al., 2012). Several stud-
ies have reported the occurrence of pharmaceuticals inWWTPs, mainly
across Europe (Bueno et al., 2012; Jelić et al., 2012; Loos et al., 2013;
Pereira et al., 2015; Verlicchi et al., 2012), the far East (Sim et al.,
2011), and USA (Subedi and Kannan, 2015), among other regions, at
concentrations ranging from 0.00098 μg/L to 150 μg/L. These
micropollutant residues are finally discharged on surfacewaters, spread
on agriculture fields or end up in landfills (Nikolaou et al., 2007). The
Network of Reference Laboratories, Research Centres and Related Orga-
nizations for Monitoring of Emerging Environmental Substances
(NORMAN Network, 2016), from the European Commission, has en-
listed hundreds of pharmaceutical compounds as future prospects to
be regulated (Geissen et al., 2015).
To the authors' knowledge, there are very few scientific reports on
pharmaceuticals in WWTPs in Latin American countries (Brown et al.,
2006; Pessoa et al., 2014; Causanilles et al., 2017; Botero-Coy et al.,
2018; Rivera-Jaimes et al., 2018). As for 2018, Costa Rica only treated14.4% of its wastewater; the country is in urgent need of improving its
infrastructure in the field of residues treatment. Most operating
WWTPs in the country serve institutions, industries, commercial and
residential complexes, but there is only one large WWTP designed to
treat municipal wastewater, with a maximum capacity for covering up
to 21% of the population with primary treatment; similarly, hospital ef-
fluents are other critical points that lack robust wastewater treatment
programs (Programa Estado de la Nación en Desarrollo Humano
Sostenible, 2018). Moreover, to date, the only work of PhACs in
WWTPs in Costa Rica qualitatively detected acetaminophen, irbesartan,
naproxen, sulfamethoxazole, telmisartan and sucralose, in the influent
and effluent of two WWTPs and their discharging river in the Pacific
coast (Causanilles et al., 2017).
In parallel to pharmaceuticals (and other pollutants) content, the
ecotoxicological properties of WWTP effluents are of relevance to esti-
mate the potential affectation on the ecosystems; nonetheless, these pa-
rameters are commonly overlooked in WWTP studies, in favor of
chemical quantification. Overall, ecotoxicological tests act as a promis-
ing complement to chemical monitoring, as the measured concentra-
tions do not predict toxicity changes for organisms (Bundschuh, 2014).
This work aims to describe the occurrence of PhACs and the poten-
tial hazard of wastewater in WWTPs in Costa Rica. For this purpose,
11 WWTPs used for domestic, landfill and hospital wastewater were
sampled and analyzed for 70 compounds. Two different approaches
were employed to estimate the hazard posed by influent/effluent sam-
ples and the detoxification after theWWTP process, (i) ecotoxicity tests
with three benchmark organisms; and (ii) the hazard quotient (HQ) ap-
proach. The application of the hazard quotient coupled to ecotoxicolog-
ical determinations permitted to identify the risk posed by specific
pharmaceutical compounds, and to estimate the detoxification of
WWTPs in this geographical area. Among the benchmark organisms,
the use of germination tests in lettuce seeds, not commonly employed
to assess phytotoxicity in WWTP effluents, revealed particularly inter-
esting findings regarding the sensitivity of this bioindicator towards
this matrix. Data provides relevant information on the occurrence and
hazard due to pharmaceuticals, and ecotoxicity of WWTPs in tropical
environments.
2. Materials and methods
2.1. Chemicals and materials
The complete list of analytical standards (including purity and ori-
gin) is described in the Supplementary Material. The selection of the
pharmaceuticals was done considering: i) previous experience in
Costa Rica (Spongberg et al., 2011), ii) information about national phar-
maceuticals sales/use provided by health professionals, including both
of human and veterinary use; and iii) literature review of pharmaceuti-
cals of worldwide environmental occurrence (Loos et al., 2013; Robles-
Molina et al., 2014; Boix et al., 2016; Tlili et al., 2016; Paíga et al., 2019,
among others).
Titrisol® buffers, formic acid (98–100%) EMSURE®, ethylenedi-
aminetetraacetic acid disodium salt (EDTA-Na) dihydrate and chro-
matographic grade solvents acetone, acetonitrile, dichloromethane
and methanol, were purchased from Merck (Darmstadt, Germany);
OASIS 200 mg solid phase extraction cartridges HLB with capacity of 6
cm3 were acquired from Waters; 0.45 μm polyamide membrane filters
and 0.45 μm PTFE membrane microfilters were purchased from
D. Ramírez-Morales et al. / Science of the Total Environment 746 (2020) 141200 3Sartorius; the BOD seed inoculum Polyseed® was purchased from
HACH.
2.2. WWTP description and sampling
Themonitoring programcollected samples from11WWTPs in Costa
Rica, covering three different periods (dry season, rainy season and the
transition dry-rainy period) from August 2018 to November 2019. The
distribution and description of the selected WWTPs are shown in
Fig. 1 and Table 1, respectively; seven of the WWTPs are located in the
great metropolitan area in the Central Valley of the country, which in-
cludes the capital city of San José; one is located in the Pacific coast;
and the last three are located in the Caribbean lowlands. The selected
WWTPs serve populations ranging from800 to 850,000 people, with av-
erage flow rates from 5m3/day to 37,500m3/day; they treat water from
domestic, hospital and landfill activities, and mostly apply primary and
secondary treatment.
A total of 62 grab samples corresponding to 32 influents and 30 ef-
fluents, were collected for pharmaceutical analysis in 1 L amber glass
bottles, previously rinsed with deionized water and acetone; additional
1 L and 0.5 L samples collected in HDPE dark bottles were used for the
determination of COD and BOD, and ecotoxicological analysis; respec-
tively. All samples were transported and stored between 0 °C and 4 °C
until the day of analysis. EDTA-Na (0.1%m/v) was added to the samples
for pharmaceutical analysis, and pH was adjusted to 2.50 ± 0.05 with
concentrated HCl or NaOH 10 mol/L.
2.3. Physicochemical parameters
BOD and COD were analyzed within 24-h after sampling, following
the Standard Methods for the Examination of Water and Wastewater
(Baird and Bridgewater, 2017), methods 5210-B and 5220-D,
respectively.Fig. 1. Location of sampled wastewate2.4. Quantification of PhACs: SPE and chromatographic analysis
Sampleswere filtered through 0.45 μmpolyamidemembrane filters.
SPE cartridges were conditioned with 6 mL of methanol followed by 6
mL EDTA 0.1% m/v (pH 2.5). A 200 mL volume of the filtered samples
was passed through the SPE cartridge with vacuum assistance, followed
by 20-min vacuum draining. Samples were eluted from the cartridges
with 6 mL of acetonitrile:methanol (1:1) and 6 mL of methanol:dichlo-
romethane (1:1) with vacuum assistance, and vacuum drained for 5
min. Eluates were concentrated to dryness (nitrogen flow at 28 °C),
reconstituted to 2.00 mL with acetonitrile:water (1:1) with formic
acid (0.1% v/v), and filtered through a 0.45 μm membrane PTFE
microfilter into amber chromatographic vials.
Analyses were performed by UHPLC (1200 Infinity series, Agilent
Technologies, CA, USA) coupled to a triple quadrupole mass spectrome-
ter (6460 series, Agilent Technologies) (Sáenz-Roblero et al., 2020). The
chromatographic separation was performed with an Agilent InfinityLab
Poroshell 120 EC-C18 column (100 mm × 2.1 i.d., particle size 2.7 μm;
Agilent Technologies) and two mobile phases: water with formic acid
(0.1% v/v) (A) andmethanolwith formic acid (0.1% v/v) (B). The follow-
ing gradient was used for the analysis: 3 min at 30% B, then a linear gra-
dient for 15 min until 100% B, 4 min at 100% B, then a drop to 30% B in
0.10 min, and finally 5 min at 30% B (27.10 min total time). The mobile
phase flow rate was 0.3 mL/min, the injection volume 20 μL and the
oven temperature 40 °C. Themass spectrometerwas operated in thedy-
namic multi-reaction method (dMRM); selected parameters for each
analyte are presented in SupplementaryMaterial (Table S1). Conditions
of the mass spectrometer detector are specified in Chin-Pampillo et al.
(2015). Extraction efficiencies of the pharmaceuticals were between
70% and 120%.
To consider the worst-case scenarios, when only a quantifiable
concentration was detected in one of the samples of the influent/effluent
set for a specific PhAC, the complementary influent or effluentr treatment plants in Costa Rica.
4 D. Ramírez-Morales et al. / Science of the Total Environment 746 (2020) 141200
Table 1
Description of sampled WWTPs for this study in Costa Rica.
WWTP Region Served Type of wastewater Discharging point Flow Type of Process
code population treated (m3/d) treatment
WU 1 Central – Cartago 8600 Domestic and River 109 Secondary Activated sludge in facultative conditions and
Industrial biofiltration
WU 2 Central – San José 8241 Domestic and Municipal Sanitary 5 Secondary Activated sludge with extended aeration
Industrial Sewer
WU 3 Central – San José 8241 Domestic and Municipal Sanitary 5 Secondary Activated sludge with extended aeration
Industrial Sewer
WU 4 Central – Alajuela 1794 Domestic and River 15 Tertiary Anaerobic digester with biofiltration by wetland as
Industrial tertiary treatment
WU 5 Central – San José 747 Domestic and River 25 Secondary Activated sludge with extended aeration
Industrial
WU 6 Central – San José 850,000 Domestic and River 37,500 Primary Primary settling and degreasing system
Industrial
WU 7 Central - Heredia 22,000 Domestic and River 725 Secondary Activated sludge with extended aeration
Industrial
WL 8 Pacific Coast - Unknown Landfill lixiviates River Unknown Secondary Ultrafiltration and nanofiltration
Puntarenas
WH 9 Central – San José 100 hospital Hospital Municipal Sanitary 84.5 Secondary Activated sludge with extended aeration
beds Sewer
WU 10 Caribbean lowlands – 1013 Domestic and River 16 Secondary Activated sludge with extended aeration
Turrialba Industrial
WL 11 Caribbean lowlands - 165,000 Landfill lixiviates River 63 Secondary Activated sludge with extended aeration
Limónconcentration was assigned to the non-quantifiable concentration as fol-
lows: i. the LOQ value if LOD b signal b LOQ; ii. the LOD value if signal b
LOD.
2.5. Hazard quotient determination
The hazard quotient (HQ) was estimated according to the European
Commission (2003), Orias and Perrodin (2013) and Lucas et al. (2016).
Table S2 (SupplementaryMaterial) shows the ecotoxicological data col-
lected. Most of the data was obtained from Orias and Perrodin (2013,
2014), who employed the lowest available ecotoxicological concentra-
tion (LAEC), and an appropriate extrapolation factor (EF) defined ac-
cording to the amount and type of data available, to calculate the
predicted no-effect concentration (PNEC) (Eq. (1)). For the PhACs not
included in that review, search in the ECOTOX Knowledgebase
(https://cfpub.epa.gov/ecotox/) from the US-EPA or in other specific lit-
erature was performed, and the calculation of PNEC values followed the
Orias and Perrodin (2013) criteria.
¼ LAECPNEC ð1Þ
EF
The hazard quotient (HQ) is the proportion of the measured envi-
ronmental concentration (MEC) and the PNEC, calculated according to
Eq. (2):
¼ MECHQ ð2Þ
PNEC
HQ values were determined for individual compounds in each sam-
ple to identify the compounds that pose high hazard. The same consid-
erations described in Section 2.4 were taken into account to determine
MEC values,when a noquantifiable concentrationwas obtained for one,
influent or effluent, in the same WWTP sampling. The total hazard
yielded by PhACs in a single samplewas obtained by the sum of individ-
ual HQ values for each compound detected in the sample and expressed
as ∑HQ (Lucas et al., 2016). HQ (or ∑HQ) values ≥1 represent high
hazard, 1 N HQ (or ∑HQ) ≥ 0.1 medium hazard and 0.1 N HQ (or
∑HQ) low hazard (Carazo-Rojas et al., 2018; Lucas et al., 2016).2.6. Ecotoxicological analysis
The immobilization test in D.magnawas performed as described by
Lizano-Fallas et al. (2017). Briefly, ten daphnid neonates (less than24h)
were placed in a glass vial (25mL), exposed to 10mL of samples diluted
inmoderately hard reconstitutedwater (without B12 vitamin complex)
and incubated in the dark at 21± 1 °C for 48 h; each dilution testedwas
performed in triplicate. After the incubation period, immobility of neo-
nates was determined and assumed as equivalent to mortality. The rel-
ative concentration of the sample that resulted in 50% of immobilization
in the daphnids (EC50) was calculated using the “DRC” package for the
software “R” (Ritz et al., 2015).
The Vibrio fischeri bioluminescence inhibition test was based on the
ISO 11348-3 protocol (ISO11348-3:2007, 1998), using the Microtox®
M500 bioassay, as described in Rodríguez-Castillo et al. (2019). The per-
centage of luminescence inhibition was determined by comparing the
response given by a saline control to that given by the respective diluted
sample, after an exposure time of 15 min. The relative concentration of
the sample that causes 50% inhibition was defined as the EC50 value.
Toxicity values were expressed as toxic units (TU) using Eq. (3):
100
TU ¼ ð3Þ
EC50
The phytotoxicity of the samples was determined with seed germi-
nation testswith lettuce (L. sativa var. Georgia) (US-EPA, 1996). Relative
seed germination (SG), relative root elongation (RE) and germination
index (GI) were determined using 10 seeds exposed to samples (5
mL), after 6 d of incubation in darkness at 22 °C. These parameters
were determined by comparison to germination controls obtained by
exposure to distilled water, andwere calculated as described elsewhere
(Lizano-Fallas et al., 2017). Results were expressed as the percentage of
inhibition in the GI.
3. Results and discussion
3.1. Occurrence of pharmaceuticals in WWTPs
A total of 70 PhACs were analyzed on the collected samples, out of
which, 33 compounds were detected at least once, corresponding to a
47% of the total scope of the analytical method (Supplementary
D. Ramírez-Morales et al. / Science of the Total Environment 746 (2020) 141200 5Material, Table S1). These 33 molecules can be divided into the follow-
ing therapeutic groups: 36.4% antibiotics, 21.2% analgesics/NSAIDs
(non-steroidal anti-inflammatory drugs), 15.2% psychiatric drugs, 6.1%
central nervous system (CNS) stimulants, 6.1% lipid lowering drugs,
and 15% others. Similar studies place analgesics/NSAIDs, antibiotics
and psychiatric drugs, along with lipid regulators, as the most common
pharmaceutical groups detected in WWTPs (Jelić et al., 2011; Paíga
et al., 2019); nonetheless, variations of the main compounds may de-
pend on the origin of the residual water, e.g. urban, veterinary, hospital
wastewater, among others (Lucas et al., 2016). The previous values
describe quite well the urban wastewater, as they represented 76%
of the total samples. Nonetheless, some differences were observed
for hospitals and landfills wastewater; in the former, the three
most diverse groups were antibiotics (47.4%), analgesics/NSAIDs
(26.3%) and CNS stimulants (10.5%), while for the latter were analge-
sics/NSAIDs (50.0%), psychiatric drugs (20.0%) and lipid regulators
(20.0%); interestingly no CNS stimulants were found in landfill
wastewater.
Thirty-two PhACs were detected in the influent samples (Fig. 2); the
CNS stimulants 1,7-dimethylxanthine (84.4% of frequency) and caffeine
(81.2%)were the twomost frequent substances, followedby the analge-
sic acetaminophen (75.0%), the NSAIDs naproxen (71.9%), ibuprofen
(71.9%) and ketoprofen (56.2%), and the lipid lowering drug gemfibrozil
(53.1%); the remaining compounds showed frequencies between 3.13%
and 34.4%. In the case of effluent samples, 30 PhACs were detected,
being the most frequent caffeine (53.3%), naproxen (53.3%) and gemfi-
brozil (53.3%), followed by ibuprofen (46.7%), 1,7-dimethylxanthine
(43.3%) and ketoprofen (40.0%); the remaining compounds exhibited
frequencies between 3.3% and 20.0%. Similarly, during the analysis of
156 compounds in the effluents of 90 WWTPs in Europe, Loos et al.100
80
60
40
20
0
P
Fig. 2.Detection frequency of PhACs in influent and effluent samples from selectedWWTPs in C
are included. Letters in parenthesis indicate the therapeutic group for each PhAC: (AB) antibio
alkaloids, (AM) anthelmintics, (BB) β-blockers, (LR) lipid regulators, (PS) psychiatric drugs, (S
Detection frecuency (%)
1,7-Dimethylxanthine (ST)
Caffeine (ST)
Acetaminophen (AI)
Ibuprofen (AI)
Naproxen (AI)
Ketoprofen (AI)
Gemfibrozil (LR)
Lovastatin (LR)
Ciprofloxacin (AB)
Diphenhydramine (AH)
Sulfamethoxazole (AB)
Ofloxacin (AB)
Albendazole (AM)
Carbamazepine (PS)
Diclofenac (AI)(2013) reported higher frequencies for caffeine (93%), naproxen
(66%), gemfibrozil (60%), ibuprofen (57%) and ketoprofen (48%);
even higher results were reviewed by Miège et al. (2009) (compila-
tion of 117 studies on pharmaceuticals removal in WWTPs) for ibu-
profen (97% influent, 93% effluent), naproxen (96% influent, 87%
effluent) and ketoprofen (73% influent, 73% effluent), and lower fre-
quency for gemfibrozil (25% influent, 70% effluent). Other PhACs of
high frequency in effluents presented in Miège et al. (2009) and
Loos et al. (2013), that were less frequently detected in our work
(b20%), included atenolol (100%, β-blocker), carbamazepine (100%,
antiepileptic), codeine (98%, opioid), diphenhydramine (98%, anti-
histaminic), mefenamic acid (100%, NSAID), and risperidone (100%,
antipsychotic).
The average frequency of detection for the quantified com-
pounds was 27.6% in the influents and 16.8% in the effluents. Like-
wise, average frequency of detection for the whole 70 PhACs,
including undetected molecules, was 12.5% in the influents and
7.3% in the effluents.
Fig. 3 shows the concentration of PhACs in the WWTP samples.
Quantified concentrations ranged from 0.07–286.6 μg/L in influent sam-
ples, and 0.10–66.9 μg/L in effluent samples, being caffeine the com-
pound with the highest concentration in both cases. A compilation of
forty works done by Paíga et al. (2019) revealed higher concentration
ranges (influent: 0.00040–3295 μg/L; effluent: 0.00030–232 μg/L),
with the highest concentrations corresponding to salicylic acid (influ-
ent) and caffeine (effluent). The compilation presented by Miège et al.
(2009), also reported a higher range of concentrations in influents
(0.0004–611 μg/L), but lower in effluents (0.0002–33.9 μg/L), being
the highest for naproxen in both cases (although these authors did not
considered caffeine in their study).Influent
Effluent
hAC
osta Rica during the period 2018–2019 (ninfluents = 32; neffluents = 30). Only values N LOQ
tics, (AH) antihistamines, (AI) analgesic and non-steroidal anti-inflammatory drugs, (AK)
P) sympathomimetic drugs and (ST) central nervous system stimulants.
Pseudoephedrine (SP)
Trimethoprim (AB)
Azithromycin (AB)
Cephalexin (AB)
Risperidone (PS)
Atenolol (BB)
Clarithromycin (AB)
Doxycycline (AB)
Enrofloxacin (AB)
Fluoxetine (PS)
Metronidazole (AB)
Clindamycin (AB)
Sertraline (PS)
Mefenamic Acid (AI)
Bacitracin (AB)
Codeine (AK)
Indomethacin (AI)
Lorazepam (PS)
6 D. Ramírez-Morales et al. / Science of the Total Environment 746 (2020) 141200
1000
Influent
Effluent
100
10
1
0.1
0.01
PhAC
Fig. 3. Concentration of PhACs detected in influent and effluent samples from selected WWTPs in Costa Rica during the period 2018–2019.The black line within the box denotes the me-
dian; inferior and superior boundaries of the box indicate the 25th and 75th percentiles respectively; whiskers denote the 10th and 90th percentiles. Black lines alone represent com-
pounds with no more than two detections.
Concentration (µg/L)
Caffeine
Acetaminophen
1,7-Dimethylxanthine
Bacitracin
Doxycycline
Ibuprofen
Pseudoephedrine
Naproxen
Gemfibrozil
Enrofloxacin
Cephalexin
Ciprofloxacin
Ofloxacin
Clarithromycin
Atenolol
Risperidone
Indomethacin
Azithromycin
Ketoprofen
Trimethoprim
Lovastatin
Metronidazole
Sulfamethoxazole
Sertraline
Carbamazepine
Diphenhydramine
Diclofenac
Fluoxetine
Codeine
Albendazole
Clindamycin
Mefenamic Acid
LorazepamThe top three median concentrations in influent samples were 64.0
μg/L for caffeine (mean = 69.9 μg/L), 14.6 μg/L for acetaminophen
(mean = 18.7 μg/L) and 7.26 μg/L for 1,7-dimethylxanthine (mean =
13.96 μg/L), while the top three in effluent samples were 1.08 μg/L for
gemfibrozil (mean = 1.32 μg/L), 0.89 μg/L for caffeine (mean = 6.42
μg/L) and 0.55 μg/L for doxycycline (mean = 0.75 μg/L). In influents,
Verlicchi et al. (2012) reported higher mean concentration for acet-
aminophen (38 μg/L) in a study of over 250 WWTPs, while Sim et al.
(2011) found lower levels of median concentrations for caffeine (15.4
μg/L) and acetaminophen (8.18 μg/L) for 12 WWTPs. For the case of ef-
fluents, lower median/mean values were reported for caffeine, doxycy-
cline and gemfibrozil (Verlicchi et al., 2012; Loos et al., 2013), but higher
values for caffeine are also usual (Sim et al., 2011; Bueno et al., 2012).
Other PhACs with frequent high concentrations in effluents (Sim et al.,
2011; Bueno et al., 2012; Verlicchi et al., 2012; Loos et al., 2013), but
found at low concentrations in this study (b2.2 μg/L), included
mefenamic acid, naproxen and carbamazepine.
Out of the thirty-three molecules detected, three of them presented
a highermedian concentration in the effluent compared to the influent:
carbamazepine, mefenamic acid and lorazepam. This highly persistent
behavior has been previously reported for carbamazepine (Clara et al.,
2004; Leclercq et al., 2009; Paíga et al., 2019); in fact, this is one of the
reasons to consider this compound as an anthropogenic pollution
marker (Clara et al., 2004). It has been proposed that carbamazepine
is excreted in conjugates, i.e. from urine, which suffer from cleavage
due to enzymatic activity once they pass through the WWTP, thus re-
leasing the parental compound, and leading to increased concentrations
in the effluents (Leclercq et al., 2009). Meanwhile, mefenamic acid and
lorazepamhave reported low removal in other studies, mefenamic acid:
35–42% (Bueno et al., 2012; Verlicchi et al., 2012; Margot et al., 2015)
and lorazepam: 29% (Jelić et al., 2011); this low removal in combination
with the variations in thewastewater loads entering theWWTP, and the
use of the grab-sample approach might have led to the higher concen-
trations detected in the effluent. Diclofenac did not present higher
values in the effluent, but only a small decrease of its concentrations
with respect to the influent. This molecule is considered as persistent
and can be found in wastewater as a conjugate; likewise, the WWTP
process breaks the conjugate and releases diclofenac, thus resulting inthe estimation of low removal values for this compound (Archer et al.,
2017).
Regarding the therapeutic groups, antibiotics showed the highest
number of detected molecules (n = 12) and levels up to 3.51 μg/L and
0.95 μg/L (influent/effluent 90th percentiles, respectively); the variety
among this group and the detection frequencies of up to 16.7% in efflu-
ents are worrisome factors in the potential generation of multidrug-
resistant bacteria (Paíga et al., 2019; Kümmerer, 2009b). Analgesics
and NSAIDs followed in number of detected molecules (n = 7); these
substances, usually available without prescription (Paíga et al., 2019),
are more widely used among the population than antibiotics, hence
their frequency (up to 85.0% in influents and 53.3% in effluents) and
concentrations (CnPercentil-90th,influent = 19.71 μg/L and CnPercentil-90th,ef-
fluent = 3.00 μg/L) are higher. The number of psychiatric drugs was
lower (n=5), and presented lower frequency (up to 18.8% in influents
and 20.0% in effluents) and concentrations (CnPercentil-90th,influent = 0.82
μg/L and CnPercentil-90th,effluent=1.03 μg/L) than the twoprevious groups.
The higher values in effluents are due to the increased levels of carba-
mazepine after the water treatment, as previously discussed (Clara
et al., 2004; Leclercq et al., 2009; Hübner et al., 2014). Therapeutic
groups with only two detected molecules included the CNS stimulants,
caffeine and its metabolite 1,7-dimethylxanthine, which represent the
groupwith the highest frequency and concentration; and the lipid low-
ering drugs, lovastatin and gemfibrozil, the latter found in over half of
the samples. The remaining groups were represented by one molecule,
and included alkaloids (codeine), antihistamines (diphenhydramine),
anthelmintics (albendazole), sympathomimetic drugs (pseudoephed-
rine) and β-blockers (atenolol).
The monitoring of PhACs in WWTPs in Latin America has been
scarcely explored in scientific literature. A previous report in Costa
Rica described the qualitative detection of atenolol, caffeine, gemfibro-
zil, naproxen and sulfamethoxazole (Causanilles et al., 2017). Likewise,
atenolol, diclofenac, carbamazepine, gemfibrozil, indomethacin,
naproxen, ofloxacin, sulfamethoxazole and trimethoprim have been
found in WWTP effluents in Mexico, being diclofenac (1,31 μg/L, n =
8), sulfamethoxazole (0.67 μg/L, n = 9) and carbamazepine (0.28 μg/L,
n = 8) the compounds with highest mean concentrations (Brown
et al., 2006; Rivera-Jaimes et al., 2018); values in the present study
D. Ramírez-Morales et al. / Science of the Total Environment 746 (2020) 141200 7were lower for diclofenac (0.19 μg/L, n = 10) and sulfamethoxazole
(0.23 μg/L, n = 11), and higher for carbamazepine (0.70 μg/L, n = 7)
In Colombia, twelve compounds matching with this study were found
at higher concentrations in twoWWTP effluents: the highestmean con-
centrations corresponded to acetaminophen (14.91 μg/L), azithromycin
(3.94 μg/L) and naproxen (1.45 μg/L) (Botero-Coy et al., 2018). Overall,
this work represents the first systematic report of PhACs concentrations
in WWTPs in the region of Central America.3.2. Hazard evaluation in WWTPs
Fig. 4 shows the box plot of HQ values corresponding to detections
per individual compound. Twenty PhACs, corresponding to 60.6% of
the detected compounds in the influent showed high hazard (HQ N 1
for themedian value), and four more, 12.1%, presented medium hazard
(0.1 b HQ b 1 for the median value). The five most hazardous com-
pounds in influents included the psychiatric drugs risperidone
(HQ 3median = 6.0 × 10 ) and fluoxetine (HQmedian = 86), the lipid
regulator lovastatin (HQmedian = 3.9 × 102), the antihistamine diphen-
hydramine (HQ 2median = 2.6 × 10 ) and the CNS stimulant caffeine
(HQmedian = 1.3 × 102). Caffeine achieved its high hazard status from
the high concentrations detected (median concentration = 64.0 μg/L),
ascribed to the extensive coffee consumption (Rodríguez-Gil et al.,
2018). On the other hand, risperidone, fluoxetine, lovastatin and
diphenhydramine presented median concentrations lower than 0.6
μg/L, and their high hazard is due to their high ecotoxicity (PNEC
b0.003 μg/L, Supplementary Material Table S2). High hazard has been
previously demonstrated for caffeine and fluoxetine in influents, as
well as for ciprofloxacin, diclofenac, sertraline and trimethoprim
(Papageorgiou et al., 2016; Paíga et al., 2019).
Most of the pharmaceuticals kept their hazard status in the effluents,
as fifteen PhACs (46.9% of the detected compounds) presented high
hazard, and six (18.8%) exhibited medium hazard. Risperidone
(HQmedian = 1.3 × 103), diphenhydramine (HQmedian = 1.1 × 102), lov-
astatin (HQmedian = 50) and fluoxetine (HQmedian = 34) remained
among the five most hazardous compounds, while trimethoprim was
added (HQmedian = 47) instead of caffeine (HQmedian = 1.8). Risperi-
done, fluoxetine and trimethoprim have been previously reported as
compounds with high hazard in effluents (Verlicchi et al., 2012; Orias
and Perrodin, 2014; Paíga et al., 2019). On the contrary, no HQ informa-
tion was found for lovastatin and diphenhydramine in WWTPs; how-
ever, findings from this work suggest they should be considered in
pharmaceutical prioritization lists in these matrices. Other compounds
that also presented high hazard in effluents, in this and other studies in-
clude: azithromycin, caffeine, clarithromycin, diclofenac, gemfibrozil,
ibuprofen, ofloxacin, and sulfamethoxazole (Verlicchi et al., 2012;
Orias and Perrodin, 2014; Papageorgiou et al., 2016; Paíga et al., 2019).
The monitoring of psychiatric drugs seems critical, as they usually
exhibit high ecotoxicity (Calisto and Esteves, 2009). Risperidone, fluox-
etine and sertraline, are respectively, the first, fourth and thirteenth
molecules with the lowest PNEC (Supplementary Material, Table S2);
remarkably, all the concentrations detected for risperidone and fluoxe-
tine presented high hazard, and the concentrations for sertraline high
and medium hazard, and even though carbamazepine presented me-
dium hazard, its neglectable removal makes this another psychiatric
drug to have in consideration (Leclercq et al., 2009). Besides the high
hazard, diverse sublethal effects have been demonstrated on non-
target organisms for these compounds (Silva et al., 2015); moreover,
synergistic effects have been observed during co-exposure of fluoxetine
with antibiotics (Karine de Sousa et al., 2018), including an increase in
the generation of multiresistant bacteria (Jin et al., 2018), which high-
lights the concern on the presence of these molecules in the environ-
ment. The presence of over 65% of the molecules with HQmedian N 0.1
is an indication of deficiencies in the removal (Verlicchi et al., 2012;
Lucas et al., 2016).Fig. 5 shows the ∑HQ values per sample; this parameter provides
an overall estimation of the intrinsic hazard of the sample, by adding
the individual hazard of all the pharmaceuticals it contains (Lucas
et al., 2016). All the influent samples presented high hazard based on
the pharmaceutical content; the highest hazard values were deter-
mined in urban WWTPs, with a maximum in WU3 (∑HQ =1.0 ×
104). The ∑HQ is particularly critical in the effluents, as it represents
the estimated hazard at the exit of theWWTP, where no further mitiga-
tion strategies are applied. Hence, this is a value to take into account
since 96% of the effluent samples in this study exhibited high hazard.
The maximum value in effluents was found at an urban WWTP (WU1;
∑HQ = 7.7 × 103), whose main toxic component was risperidone,
followed by WU6 (∑HQ = 2.9 × 103), a WWTP that operates solely
with primary treatment, for which lovastatin is the main toxicity con-
tributor. For hospital and landfill WWTPs, the highest effluent hazard
values were ∑HQ = 2.3 × 102 and ∑HQ = 96, respectively, mainly
due to the presence of lovastatin and diphenhydramine. Finally, the
lowest value, which still presented medium hazard, was found in
WU2 (∑HQ= 0.4).
Lucas et al. (2016) reported similar ∑HQ values in effluents from
urban, hospital and veterinary WWTPs, within the range of 19 to 5.6 ×
103, where, contrary to this study, the highest values were obtained in
hospital wastewater. Al Aukidy et al. (2012), on the contrary, reported
lower∑HQ values (5.7 and 13.8) for the effluents of two conventional
activated sludge plants.
3.3. Ecotoxicological analysis of WWTPs
The ecotoxicological analysis of wastewater samples represents a
direct measure of hazard; D.magna and Microtox acute tests were per-
formed due to their sensibility as aquatic organisms, while germination
tests were done to assay phytotoxicity due to the potential use or expo-
sure of WWTP effluents to soil; corresponding results with the three
benchmark organisms are shown in Table 2. Three influents presented
TU values over 20 in tests for D. magna and Microtox, each; contrary
to the findings of HQ, themost toxic samples corresponded to the influ-
ents of landfill (TUD.magna=59.2–60.6, TUMicrotox=32.3–44.6) and hos-
pital wastewater (TUD.magna = 3.3–123.5, TUMicrotox = 3.3–34.6). Most
of the effluents presented no toxicity towards D. magna (83.3%) and
Microtox (75.0%); nonetheless the remaining effluents exhibited toxic-
ity values, with TU N 1 in 12.5% and 16.7% samples for D. magna and
Microtox, respectively. A study conducted by Lanciotti et al. (2004) in
Italy showed a lower percentage of toxic effluents using the same
benchmark organisms, while similar results inmagnitude have been re-
ported for Daphnia sp., with effluent toxicity values ranging from 0.00
TU to 5.32 TU (Ra et al., 2008; Movahedian et al., 2005; and Sánchez-
Meza et al., 2007), and for Microtox, with values from 1.00–15.2 TU
(Araújo et al., 2005).
Similarly, phytotoxicity results in L. sativa also identified the landfill
influent as themost toxic,with GI inhibition of up to 100%. Overall, most
inhibition results for this bioindicator were above 20%, including efflu-
ent samples. The reduction of the ecotoxicity by the WWTP (influent
versus effluent) for D. magna and Microtox were in most cases of
100%; however the removal was quite poor for L. sativa, including sev-
eral results with higher phytotoxicity in the effluents. Nonetheless,
studies reporting WWTP monitoring with seed germination tests
could not be found in scientific literature, and given the results from
this study, this could represent a more critical bioindicator of
ecotoxicity. The high ecotoxicity (and in particular phytotoxicity) in ef-
fluent samples is worrisome, as discharge into water bodies or use in ir-
rigation may translate into adverse affectation to ecosystems.
Wastewater is a complex matrix and its toxicity is a product of nu-
merous components; among them, organicmatter and xenobiotics con-
tent (Araújo et al., 2005; Ra et al., 2008). Fig. 6 shows the relation
between the calculated ecotoxicity in theMicrotox test, and the organic
matter content (as BOD and COD; Fig. S1), obtaining correlation
8 D. Ramírez-Morales et al. / Science of the Total Environment 746 (2020) 141200
10 000
Influent
1000
Effluent
100
10
1
0.1
0.01
0.001
0.0001
PhAC
Fig. 4.Hazard determined as HQ per PhAC detected inWWTPs during the period 2018–2019. The black linewithin the box denotes themedian; inferior and superior boundaries of the box indicate the 25th and 75th percentiles respectively; whiskers
denote the 10th and 90th percentiles. Black lines alone represent compounds with no more than two detections. Values over the green and pink lines indicate medium and high hazard, respectively.
H Q
R i s p e r i d o n e
L o v a s t a t i n e
D i p h e n h y d r a m i n e
C a f f e i n e
F l u o x e t i n e
T r i m e t h o p r i m
O f l o xa c i n e
A z i t h r o m y c i n
C l a r i t h r o m y c i n
D i c l o f e n a c
I b u p r o f e n
D o xy c y c l i n e
A l b e n d a z o l e
Ge m f i b r o z i l
C o d e i n e
A c e t a m i n o p h e n
C i p r o f l o x a c i n e
E n r o f l o xa c i n
S e r t r a l i n e
C e p h a l e x i n
S u l f a m e t h o x a z o l e
N a p r o xe n
K e t o p r o f e n
C a r b a m a z e p i n e
1 , 7 - D i m e t h y l xa n t h i n e
A t e n o l o l
I n d o m e t h a c i n
L o r a z e p a m
B a c i t r a c i n
M e f e n a m i c  A c i d
P s e u d o e p h e d r i n e
M e t r o n i d a z o l e
C l i n d a m y c i n
D. Ramírez-Morales et al. / Science of the Total Environment 746 (2020) 141200 9
100 000
  Influent 
  Effluent 
10 000
1000
100
10
1
0.1
WU 1 WU 2 WU 3 WU 4 WU 5 WU 6 WU 7 WL 8 WH 9 WU 10 WL 11
Sampling site
Fig. 5.Hazard determined as∑HQ per sample duringmonitoring ofWWTPs (three samplings each). Values over the pink line (ΣHQN1) indicate high hazard. Missing bars correspond to
samples that could not be taken.
∑HQcoefficients (r2) higher than 0.6 (comparisonwithD.magna and L. sativa
is shown in Supplementary Material, Figs. S2–S3). The ecotoxicity for
the three bioindicators was also correlated to the pharmaceutical con-
tent, through the ∑HQ approach, (Supplementary Material, Fig. S4),
obtaining very low correlation coefficient (r2 b 0.03). Ra et al. (2008)
presented a similar comparison, achieving a r2 = 0.023, when compar-
ing the effluent toxicity for D. magna with the estimated toxicity for
heavy metals and pesticides. In terms of ecotoxicological prediction,
the organic content showed to be of higher relevance than pharmaceu-
tical occurrence; however, hazard associated to these compounds
should not be underestimated, as correlation coefficients higher than
r2 = 0.2 were found in this study between toxicity for D. magna and
the concentration of some high toxicity pharmaceuticals, as cephalexin
and diphenhydramine (Supplementary Material, Fig. S5)
4. Conclusions
This work represents the first quantitative monitoring of pharma-
ceuticals in WWTPs in Costa Rica and Central America. A screening of
70 active ingredients resulted in the detection of residues for 33
PhACs, mainly from the groups of antibiotics, analgesics/NSAIDs and
psychiatric drugs. A frequency of detection of 12.5%was found in the in-
fluents with a range of concentration of 0.07 μg/L–286.6 μg/L, while in
effluents the detection frequency decreased to 7.3% and concentrations
ranged from 0.10 μg/L–66.9 μg/L.
When compared to ecotoxicological information, these results con-
firm that pharmaceutical concentrations may exceed their PNEC values
even in the effluent of WWTPs, thus making this a hazardous matrix.
Twenty-one of the detected PhACs exhibited medium or high hazard
in effluents; among them, risperidone, lovastatin, diphenhydramine, tri-
methoprim and fluoxetine comprised the most critical compounds,
which should be considered in prioritization lists of pharmaceuticals
in the context of environmental hazard/risk. The∑HQ (based on total
pharmaceutical content) and ecotoxicity results also confirmed the haz-
ard exhibited both in influents and effluents. The ∑HQ estimated the
highest hazard in urban wastewaters, while the ecotoxicity results
showed the highest hazard for hospital and landfill wastewater. Inter-
estingly, considerable phytotoxicity towards L. sativa was frequently
found in the effluents (GI inhibition over 20%), thus remarking the rele-
vance of this parameter to assess the efficiency ofwastewater treatment
processes. Knowledge of the hydrodynamics characteristics of thereceiving water bodies, as well as their monitoring, are key factors re-
quired to properly estimate the real environmental risk these com-
pounds/effluents pose to aquatic ecosystems, to determine whether
their dilution capacity would reduce the potential ecotoxicological
effects.
Overall, the evaluation of PhACs in WWTPs in Costa Rica showed a
situation comparable to that observed in other latitudes. Even though
there is no legislation to regulate the concentration limits of PhACs in
WWTP effluents, this work provides the first input in this geographical
area on this environmental issue, and remarks the necessity to invest in
proper WWTP infrastructure. Future regulation, improvement in phar-
maceuticals management and implementation of treatment technolo-
gies, are all aspects necessary to deal with this increasing concern.
CRediT authorship contribution statement
Didier Ramírez-Morales: Conceptualization, Methodology, Formal
analysis, Investigation, Funding acquisition, Writing - original draft,
Writing - review & editing. Mario Masis-Mora: Investigation, Method-
ology, Formal analysis. José R. Montiel-Mora: Formal analysis, Investi-
gation. Juan Carlos Cambronero-Heinrichs: Conceptualization,
Methodology, Funding acquisition, Writing - review & editing. Susana
Briceño-Guevara: Investigation, Methodology. Carlos E. Rojas-
Sánchez: Resources, Funding acquisition. Michael Méndez-Rivera:
Investigation. Víctor Arias-Mora: Investigation, Writing - review &
editing. Rebeca Tormo-Budowski: Resources, Investigation. Laura
Brenes-Alfaro: Conceptualization, Funding acquisition, Visualization.
Carlos E. Rodríguez-Rodríguez: Conceptualization, Formal analysis,
Project administration, Writing - review & editing, Funding acquisition.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influ-
ence the work reported in this paper.
Acknowledgments
This work was supported by UCREA (project 802-B7-A09) and
Vicerrectoría de Investigación (projects 802-B8-510, 802-B8-144, 802-
B8-145), both at Universidad de Costa Rica, and Ministerio de Ciencia,
10 D. Ramírez-Morales et al. / Science of the Total Environment 746 (2020) 141200
Table 2
Ecotoxicological parameters in influent and effluent samples during sampling campaigns ofWWTPs, according to three benchmark organisms (D.magna, MicrotoxTest and L. sativa). Color
scale is independent for each organism, green colors for lower toxicity values and red colors for higher toxicity values. (For interpretation of the references to color in this table, the reader is
referred to the web version of this article.)
WWTP Monitoring D. magna Microtox test L. sativa
campaign TUInfluent TUEffluent TUInfluent TUEffluent GI inhibition GI inhibition
Influent (%) Effluent (%)
WU 1 I X X X X X X
II 17.99 NT 17.15 NT 81.7 47.8
III 1.58 NT 6.06 0.42 88.6 NT
WU 2 I X X NT NT X X
II 7.54 NT 2.31 1.5 77.7 39.7
III 2.11 NT 15.12 NT 13.8 36.8
WU 3 I X X X X X X
II 9.68 NT NT NT 17.5 16.1
III 1.28 NT 3.96 NT 54.5 25.2
WU 4 I 2.75 1.26 1.95 NT 42.6 NT
II 1.3 0.78 NT NT 67.5 91.4
III NT 1.17 3.01 NT 10.9 31.4
WU 5 I NT NT NT NT 51.2 28
II 3.36 NT NT NT 56.9 23.5
III NT NT NT NT NT 26
WU 6 I 8 NT 4.46 0.008 NT 10.6
II NT NT 1.39 NT 28.3 28.7
III 4.36 NT 9.17 2.35 NT NT
WU 7 I 3.42 NT 1.57 NT 26 22.4
II 0.91 X 4.38 X NT X
III 1.47 NT 7.84 NT 35.9 10.8
WL 8 I X X X X X X
II 59.17 X 44.63 X 100 X
III 60.6 30.4 32.26 7.44 100 100
WH 9 I NT NT 1.1 2.26 NT 28.7
II 3.29 NT 3.26 NT 33.6 21.3
III 123.5 NT 34.6 NT 64 0.1
WU 10 I NT NT 2.32 NT 30.3 32.3
II 1 NT 3.64 NT NT 2.4
III 2.09 NT 1.82 NT 30.2 23.9
NT: non-toxic.
X: Sample not available.Tecnología y Telecomunicaciones de Costa Rica (MICITT, project FI-
197B-17). D. Ramírez-Morales acknowledges Consejo Nacional para
Investigaciones Científicas y Tecnológicas andMICITT for a postgraduateFig. 6. Correlation of ecotoxicity towards Microtox Test with BOD (A) and COscholarship (FI-056B-17). The authors thank Dayana Vega for the elab-
oration of the map, and all the institutions that allowed the monitoring
in the WWTPs.D (B) for selected WWTPs in Costa Rica during the period 2018–2019.
D. Ramírez-Morales et al. / Science of the Total Environment 746 (2020) 141200 11Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.scitotenv.2020.141200.
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