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Field Calibration of Semipermeable Membrane Devices (SPMDs) for Persistent Organic Pollutant Monitoring in a Reservoir

Grupo de Investigación Ingeniar, Facultad de Ciencias Básicas e Ingeniería, Corporación Universitaria Remington, Calle 51 No. 51-27, Medellín 050010, Colombia
Grupo de Investigaciones Biomédicas UniRemington, Corporación Universitaria Remington, Calle 51 No. 51-27, Medellín 050010, Colombia
Grupo de Gestión y Modelación Ambiental—GAIA, Facultad de Ingeniería, Sede de Investigación Universitaria (SIU), Universidad de Antioquia UdeA, Calle 70 No. 52-21, Medellín 050010, Colombia
Author to whom correspondence should be addressed.
Water 2023, 15(7), 1428;
Received: 2 February 2023 / Revised: 18 March 2023 / Accepted: 30 March 2023 / Published: 6 April 2023
(This article belongs to the Section Water Quality and Contamination)


Persistent Organic Pollutants (POPs) in water can reach levels below ppt (ng L−1), representing a challenge for risk assessment using long-term exposure. Therefore, sampling devices to monitor trace levels of organic compounds are suitable due to their sensitivity and low detection limits. In this work, the field calibration and monitoring for POPs, such as Polycyclic Aromatic Hydrocarbons (PAHs) and pesticides, in a drinking water reservoir were carried out using semipermeable membrane devices (SPMDs). The SPMDs were spiked with deuterated PCBs used as PRCs, and their dissipation was measured at 5, 10, 15 and 24 days. The SPMDs were dialyzed in hexane twice and the initial amount (N) and final amount (No) of PRCs in extracts were estimated by GC/MS to calculate the sampling rate (Rs) for target compounds. The PCBs were dissipated more than 30% at 24 days, and Rs were calculated between 27.8 and 72.7 L day−1 for PAHs and 35.8 and 71.3 L day−1 for pesticides. Finally, PAH congeners such as Naphthalene, Acenaphthylene, Acenaphthene, Fluorene, Phenanthrene, Anthracene, Fluoranthene and Pyrene were detected at water concentration (Cw) ranges between 0.5 and 2.7 ng L−1, whilst chlorpyrifos was the only pesticide detected at 19.0 ng L−1 in the reservoir. This calibration showed that PRCs do not dissipate entirely in 24 days and that dissipation modelling may be applied for Cw estimation of POPs in reservoirs.

Graphical Abstract

1. Introduction

Semipermeable membrane devices (SPMDs) have been widely used as passive samplers for the monitoring of non-polar pollutants in water [1]. SPMDs are passive samplers applied in the detection and quantification of a wide range of Persistent Organic Pollutants (POPs), including polycyclic aromatic hydrocarbons (PAHs), flame retardants, organochlorines pesticides, etc., at very low water concentration (Cw) levels. These POPs are included in the Stockholm Convention because of their persistence, toxicity and bioaccumulation [2,3,4].
Non-polar POPs are adsorbed inside SPMDs through lipophilic interactions, which can be extrapolated to the bioaccumulation process due to the similar uptake principle by the biota [1]. Thus, SPMDs are usually applied in bioaccumulation factor estimation.
These membranes include the neutral triglyceride triolein, which mimics the partitioned steps during bioconcentration, providing qualitative and quantitative data for non-polar compounds when Gas Chromatography is coupled with Mass Spectrometry during analytical analyses [5]. Triolein is sealed in a lay-flat, thin-wallet tube of Low-Density Polyethylene (LDPE).
The quantification of POPs in SPMDs is challenging due to the multiple parameters that can affect the process, including fluxes, sampling time, water volume and temperature. Hence, the dissolved levels of POPs in water have been estimated by applying dissipation modelling that follows the kinetics of performance reference compounds (PRCs) which have similar properties to the target compounds [5]. The PRCs are initially spiked in SPMDs, and these substances are dissipated during exposure time in the water. Some deuterated Polychlorinated biphenyls (PCBs) congeners, such as 14, 29 and 50, are selected as PRCs since they do not occur in the environment under natural conditions [6]. The initial amount (No) of PRCs decreases during the exposure time in the field according to Fick’s law [5]. Then, the PRCs’ dissipation can be related to the medium exposition and the flow rate in the water body [7]. The release of PRCs from SPMDs to water is related to the n-octanol-water partition coefficient (log kow). Thus, polar compounds dissipate faster than non-polar ones [8]. However, PRCs should not dissipate completely, allowing one to apply a dissipation modelling which includes the initial and final concentration of these substances. Moreover, field calibration should include studying the dissipation of PRCs to avoid their total loss in the water [9,10].
The use of SPMDs for POP monitoring in Colombian drinking water reservoirs represents a suitable alternative due to their sensitivity and low detection limits. Additionally, field data can be compared with bioaccumulation under natural conditions, which is impossible using conventional methods.
Many reservoir may be affected by antropogenic activities in worldwide [11]. Similarly, La Fe, an important reservoir that supplies drinking water to more than two million people in Medellin, the second largest and most industrialized city in Colombia, is being affected by anthropogenic activities. In a previous contribution, it was shown that pesticides applied during agricultural activities and PAHs from mobile combustion emissions are present in tributaries of the La Fe reservoir [12]. The PAHs represent a risk for human health, wildlife and the environment [13,14,15].
This study focused on the field calibration of SPMDs to estimate the levels of pesticides and PAHs to assess their possible impacts on a Colombian water reservoir’s quality. In addition, we aim to estimate the range of time at which the dissipation was not complete during the SPMDs’ deployment in the drinking water reservoir.

2. Materials and Methods

2.1. Chemicals and Reagents

Organophosphate (OP) Pesticide Mix (Purity > 99.5%), Priority pollutant PAHs in acetonitrile (Sigma-Aldrich, St. Louis, MO, USA) and deuterated PCB 14, PCB 29 and PCB 50 were purchased from Chemservice, West Chester, PA, USA, and used as PRCs. Solvents such as hexane and acetone were supplied by Burdick and Jackson, Morristown, NJ, USA. SPMDs, large canisters and spider holders were all provided by Environmental sampling technologies Laboratory (Est-Lab), St Joseph, MI, USA. The SPMDs used as passive samplers were previously spiked with PCB 14, 29 and 50 at concentrations of 200 ng L−1 by the provider.

2.2. Gas Chromatography /Mass Spectrometric Analysis

A electron-impact and single-quadrupole GC–MS from Thermo Scientific Ultra TRACE GC-ISQ (Waltham, MA, USA) was used for pollutant detection and quantification. Organophosphorus (OP) pesticides (parathion, chlorpyrifos and diazinon), PAHs (Naphthalene, Acenaphthylene, Acenaphthene, Fluorene, Phenanthrene, Anthracene, Fluoranthene, Pyrene) and PCBs (14, 29 and 50) were separated on an Rtx 5sil-MS (30 m × 0.25 mm I.D, 0.25 m film thickness) capillary column from Restek (Bellefonte, PA, USA) using helium gas as a carrier at a flow rate of 1 mL min−1. The column was kept for 1 min at 50 °C; then, the temperature was increased to 320 °C at a rate of 25 °C min−1 and held for 1 min. The injector and interface temperature were maintained at 270 °C, and the source temperature was held at 250 °C. The injection volume was 1 µL. The instrument was operated in SCAN mode. Selection Ion Monitoring (SIM) scanning mode was implemented for target compounds. The linearity of the method was evaluated by five-point calibration curves for organophosphate pesticides and PAHs. The Cw of target POPs was determined using PRC dissipation modelling through a five-point PCB calibration curve. This procedure was carried out in triplicate at low and high levels.

2.3. Study Area and Sampling

The study area is located east of the city of Medellín, Colombia (reservoir). See Figure 1.
The sampling place was located at a bridge crossing of an important tributary for the reservoir. A passive sampler protector tube (PSPT) was fastened to the bridge column to avoid damage to the passive samplers (see Figure 2). The sampling was carried out in a low-rainfall dry season.
In the PSPT (Figure 2a), three large canisters containing the SPMDs were deployed (Figure 2b,c). Each canister was collected and analyzed at 5, 10, 15 and 24 days. All samples were stored in the freezer at −20 °C until the extraction and cleanup.

2.4. Extraction and Cleanup of Passive Samplers

A Standard Operating Procedure (SOP) was carried out for handling the SPMDs; blanks were used to estimate the initial concentration of PRCs and contamination during the handling procedure.
SPMDs were extracted following the procedure reported elsewhere [16]. First, the passive samplers were washed with a soft toothbrush and water to remove fouling and dried with a paper tissue. Then, the samplers were dialyzed in hexane for 24 h twice. After this, the hexane fraction was concentrated in a rotary evaporator. The residue was dissolved in 1 mL of acetone before being centrifuged for 5 min at 2500 RPM, and then the sample was injected in GC/MS. The recovery study was based on the extraction of PRCs previously spiked in the SPMDs by the suppliers, which certified the initial concentration. Therefore, four blanks were extracted to estimate the PRC levels of using dialysis extraction. The % of recovery was calculated according to Equation (1):
R e c o v e r y = i n i t i a l c o n c e n t r a t i o n ( b y s u p p l i e r s ) S P M D s c o n c e n t r a t i o n ( b y A n a l y s i s ) × 100

2.5. PRC Dissipation Modelling for Calibration and Water Concentration

The PRCs’ dissipation was based on the final (N) and initial concentration (No) of deuterated PCBs 14, 28 and 50. The N/No ratio was applied to estimate the release rate constant (ke), which includes the initial and final concentration of these substances [9] (see Equation (2)):
k e = ln N N o t
where t is the field sampling time. ke was then applied to find the sampling rates (Rs) according to Equation (3):
R s = V s k s w k e
where ksw is the SPMDs–water partition coefficient and Vs is the triolein volume in the SPMDs. ksw was determined using the Huckins equation [1]:
l o g   k s w = a 0 + 2.321   l o g   k o w 0.1618   ( l o g   k o w ) 2
where a0 = −2.61 in PCBs, PAHs and 4,4’-DDE and −3.20 in polar pesticides.
Finally, the Cw was estimated using Equation (5):
C w = N V s K S W 1 e x p R s t V s K S W
where Rs is the sampling rate calculated according to Equation (3); this value depends on the dissipation of PRCs [6].

3. Results and Discussion

3.1. GC–MS Identification

Retention times (Rt) for each analyte, the PCBs used as a PRCs and the ions selected as qualifiers (Q1) and quantitative purposes (Q2) are summarized in Table 1. The ions for the SIM method were selected according to fragment ions with higher molecular weight (HMW), higher abundance and high selectivity.

3.2. Quantitative Analysis for Analytes

Quantitative analysis was carried out using PRC dissipation modelling. However, in order to apply the modelling, the masses of different pesticides and PAHs were determined by a calibration curve in hexane following the spiking procedure mentioned above. Correlations (r2) were found to be higher than 0.950 for all compounds at the concentrations tested, implying good linearity in the MS detector’s response. The parameters for quantitative procedures to determine the masses of different analytes and PRCs are shown in Table 2.
The calibration curve showed r2 values higher than 0.980, indicating good linearity for quantification purposes. Additionally, the recoveries after SOPs were between 96% and 103%. The SOP applied here has been previously shown to be adequate for sampling a large range of non-polar compounds using SPMDs [16,17,18].

3.3. The Study of Blanks and SPMD Recoveries

All blanks, including field and laboratory blanks, were extracted according to the SOPs. No blank was found to be contaminated with any target compound studied in this paper. Therefore, all detected compounds during the passive sampling were considered waterborne.
The comparison between the deployment of SPMDs in the reservoir, field blank and laboratory blank is presented in Figure 3.
Finally, the initial average concentration found in the SPMD blanks for PCB-14, PCB-29 and PCB-50 congeners were applied in the dissipation modelling to estimate the analytes’ concentration in the water. The relative standard deviations (% RSD) were 2.1, 1.7 and 4.0 for PCB-14, PCB-29 and PCB-50, respectively, implying a good precision of the sample extraction method, evidenced by the low RSD values.

3.4. Dissipation of PRCs from SPMDs—Calibration Study

Dissipation plots for PCB congeners are shown in Figure 3. The release “half-life” (t50 day−1) for each PCB was calculated by applying one phase decay regression (r2 > 0.97) plot as 4.3, 4.7 and 7.0 for PCB-14, PCB-29 and PCB-50, respectively. According to Huckins, this type of exchange kinetics is constant for a set of conditions and chemicals [5]. A characteristic curvilinear kinetics approaching the equilibrium after 10 days was found for the PRC dissipation modelling (see Figure 4).
Biofouling was observed in the SPMD sampling. However, this did not affect the kinetic dissipation model for PRCs (see Figure 4). Typically, short-term biofouling does not have an important effect on Rs. However, extended periods of biofouling may affect Rs, altering the reservoir’s calculated Cw [19]. Extreme biofouling effects should be considered when extended sampling periods are applied.
On the other hand, the reservoirs show lower water flow velocity, which does not affect the Rs (flow velocities below 10 cm s−1). Additionally, it has been found that both values lack correlation [6]. At low flow velocity values, inertial currents originating from the inflow orifices may be much larger than the calculated linear velocities. Then, the flow at the SPMD–water interface controls the exchange rates in the boundary layer [5]. Field calibration using PRCs allows for the reduction of all kinetic phases into one equation based on the dissipation modelling for these substances (Equation (5)). More lipophilic substances were found to have higher Rs values due to their dependence of log kow (see Equations (3) and (4)). Similarly, higher-molecular-weight compounds showed higher Rs, which may be related to mass-resistant processes [20].
To avoid overestimations of target POP levels in water due to different exchange kinetics (linear, curvilinear and equilibrium), Equation (5) was applied to calculate the Cw of PAHs and pesticides. Target POPs’ Rs values were estimated using Equations (2) and (3). The results are presented in Table 2. Rs values were found to be higher than those reported in the literature, possibly due to the higher flow rates (8 cm s−1) used here compared to previous studies (0.004–0.006 cm s−1) [21]. Higher flow rates may increase the dissipation of PRCs from SPMDs; thus, release rate constants (ke) impact the calculated Rs. For instance, reported Rs values for some PAHs such as ACE, PHE, FLU and PYR are 68.0, 104.9, 125.2 and 130.1 L day−1, respectively, at 90 cm s−1, which are closer to the values presented in Table 2 [22].

3.5. SPMDs Used for Pesticides and PAH Monitoring

PAHs with low molecular weight were detected in SPMDs during the passive sampling deployment. Initial Cw values are presented in Table 3.
In general, Cw was found to increase linearly with sampling time, except for ACEN and FL; this might be related to their lower molecular weight. The linear regression is presented in Figure 5.
Detected PAHs in the reservoir arise from high traffic flow and the combustion of carbon-containing fuels. In addition, the water body is navigated frequently, increasing the probability of contamination from combustion byproducts. The increasing level of PAHs may be related to the continuous input of these substances from the watershed and the linear–curvilinear uptake phase estimated using PCB dissipation modelling.
PYR, FLU, NAPH and PHE represented higher percentages of the total PAHs (more than 80%), which is in good agreement with previous reports [12]. High-molecular-weight (HMW) PAHs, such as benzo[b] fluoranthene (BbF), benzo[k] fluoranthene (BkF) and benzo[a] pyrene (BaP), were also detected in SPMDs, which may be related to the low solubility of these congeners in water. These compounds have been previously detected at low levels using SPMD devices. A previous study in the Great Barrier Reef with an extended SPMD deployment time (50 days) allowed the detection of HMW PAHs > 202 g mol−1 at pg L−1 [23]. Note that the extension of time could increase the sensitivity for HMW PAHs.
In a previous contribution, PAHs’ Cw were found to range between 4.2 and 32.8 ng L−1 in an important tributary for the La FE reservoir, using silicon rubber as a passive sampler [12]. Similarly, PHE presented the highest Cw detected (504–3358 pg L−1). PAHs are mainly produced by fossil fuel combustion, and their levels in water may be initially related to atmospheric deposition. PAHs may reach urban stormwater through road runoff, which in turn reaches the water reservoirs [24,25].
It is important to implement advanced technologies, such as advanced oxidation processes, in water treatment plants in order to remove PAHs, since conventional treatment processes cannot remove POPs entirely [26]. We have reported the presence of PAHs and pollutants such as Levonorgestrel in effluents of wastewater treatment plants using conventional removal processes [12,27]. This reveals that complex fractions containing parent compounds and metabolites may reach natural water, posing several risks to ecosystems, including biota and humans [28]. Among the detected PAHs, PYR poses the most significant ecological risk, followed by ANT, FLU, PHE and NAPH [29]. For instance, ANT, FLU and PYR can induce changes in gestational hormone levels in a placental cell line [30]. These PAHs were found to represent the highest percentage of the total PAHs detected in this study.
For pesticides, an initial SCAN mode was performed in order to identify them in the SPMDs’ deployment during the sampling time. Additionally, calibration curves were included for all target compounds.
CPF was the only pesticide absorbed by SPMDs. This can be related to the fact that CPF has a log kow > 4.6, enhancing its adsorption in this membrane, while the other pesticides have lower log kow values, increasing their affinity with water. To overcome this, other researchers have applied a balanced hydrophilic–lipophilic sorbent-embedded cellulose acetate membrane (HECAM) to increase the range of organophosphorus monitored by passive sampling [31]. In an earlier report, the Cw of legacy pesticides such as dieldrin were found to be between 401 and 1919 pg L−1 in an important tributary using an LDPE polymer membrane deployed for more than 50 days [12]. CPF is widely applied around the reservoir’s watershed due to intensive crop farming such as tree tomato (Solanum betaceum), avocado (Persea Americana) and livestock grazing. In Colombia, CPF comprises about 10% of the total sales of pesticides, which can be associated with its occurrence and detection in water [32]. This substance is not strongly adsorbed by the soil, thus favouring the leaching process [33].
The CPF uptake process was found to fit an exponential curve (r2 = 0.992) in contrast to the PCBs’ dissipation process. While dissipation may show the existence of the elimination process, CPF monitoring exhibits the uptake process (see Figure 6).
The CPF may be adsorbed into SPMDs to reach the equilibrium, and Figure 6 shows a faster uptake in the passive sampler. However, this pesticide may be transformed under natural conditions to 3,5,6-trichloro-2-pyridinol (TCP). A previous report found that CPF is hydrolyzed to TCP at pH values higher than 9.0, showing a half-life close to 12.45 h, indicating the faster transformation and release of TCP in water [34]. TCP’s solubility in water increases, and thus its distribution may be higher. However, this substance is not monitored by SPMDs. Although the environmental effects of CPF and TCP are not entirely understood, CPF has been reported as a breast cancer risk factor at low levels [35]. Additionally, the mixture of CPF/TCP induces acute toxicity in Dapnia pulex [34]. Therefore, no conventional water treatment processes such as AOPs are suitable for CPF/TCP removal or elimination.
Although DZN, PTN and organochlorine (OC) pesticides were considered in the monitoring, they were not detected. OC pesticides were banned 30 years ago in Colombia; however, these pesticides are persistent and can remain in the environment for a long time. In this research, no OC pesticides were detected. However, their metabolites should be considered in future studies. Although some analytes were not detected, the Rs for some of them were estimated by applying the PRC dissipation modelling using the Estimated Water Concentration Calculator from SPMD data using PRCs from USGS (Excel file Version 5.1) [36]. Colombia does not have traditional seasons (i.e., winter/summer); we have dry or rainy seasons. Passive sampling was carried out during the dry season. Although the stational variation was not evaluated in this study, no differences in POPs levels during different seasons (dry or rainy season) have been found previously [12]. However, climatic events such as La Niña or El Niño might affect pollutant concentrations, and the differences in dry or rainy seasons are especially quite pronounced.
Similarly, OCs have been widely detected in USA, China and other countries in water bodies using SPMDs [37,38]. The sensitivity of this method can be improved by deploying several SPMDs. In this paper, only three SPMDs were deployed in the field. Some disadvantages of pollutant monitoring using SPMDs have been reported in the literature. For instance, SPMD preparation involves the addition of triolein inside the polyethylene tube, which increases difficulties in lab handling. In addition, after deployment, the extract purification should include the removal of triolein because traces can increase the background noise, affecting the baseline during chromatographic analysis [39].

4. Conclusions

PRC dissipation was carried out under one-phase decay regression. The dissipation modelling showed that all PRCs were not dissipated completely. The appropriate sampling time was estimated to be between 15 and 30 days. Although biofouling may be present in SPMD membranes, the field calibration showed that biofouling is quantitatively reflected by the dissipation. A directly proportional relation between Rs molecular weight and log kow was found, which can be explained by mass-resistance processes and lipophilic affinity.
Pollutant monitoring after calibration allowed the detection of PAHs at levels higher than 0.5 ng L−1, while the CPF was detected at levels close to 20 ng L−1, showing the anthropic influence on the reservoir via the watershed. These results highlight the impact of the widespread use of pesticides and combustion of fossil fuels by cars on water quality. The use of SPMDs allowed us to infer that bioaccumulation processes might occur in this reservoir because of the similarity of triolein with the organism’s lipidic tissue. Therefore, future studies should include the analysis of autochthonous biota in the reservoir.
Finally, it is recommended to use SPMDs for non-polar pollutants in a reservoir. However, cleaning steps should be performed to remove triolein to avoid background noise during chromatographic analysis.

Author Contributions

Formal analysis, writing—original draft preparation, J.F.N.V. Conceptualization, Writing—review & editing (English editing), J.D.G. Funding acquisition, Data curation, Validation, J.P. Conceptualization, F.J.M. All authors have read and agreed to the published version of the manuscript.


This project was funding by Ministerio de Ciencia y Tecnología MINCIENCIAS under the funding the project entitled “Potencial de bioacumulación de agroquímicos y con taminantes persistentes en una cuenca del oriente antioqueño: Evaluación de un problema de salud (Project code: 136577757707). Additionally, data was supported by Empresas públicas de Medellin-EPM (Project to study the environmental problem in reservoirs in Antioquia-Colombia).

Data Availability Statement

Not applicable.


The authors would like to thank the Ministerio de Ciencia y Tecnología MINCIENCIAS and EPM. Additionally, the authors thank Laboratorio de la vida (Corporación Universitaria Remington) and CORNARE for supporting laboratory experiments. Jazmín Porras would like to thank the Ministerio de Ciencia y Tecnología MINCIENCIAS for the financial support under the project 4000000272.

Conflicts of Interest

The authors declare no conflict of interest concerning the published results.


  1. Huckins, J.N.; Tubergen, M.W.; Manuweera, G.K. Semipermeable membrane devices containing model lipid: A new approach to monitoring the bioavailability of lipophilic contaminants and estimating their bioconcentration potential. Chemosphere 1990, 20, 533–552. [Google Scholar] [CrossRef]
  2. Marrucci, A.; Marras, B.; Campisi, S.S.; Schintu, M. Using SPMDs to monitor the seawater concentrations of PAHs and PCBs in marine protected areas (Western Mediterranean). Mar. Pollut. Bull. 2013, 75, 69–75. [Google Scholar] [CrossRef] [PubMed]
  3. UNEP. The Stockholm Convention. 2019. Available online: (accessed on 10 October 2022).
  4. Richardson, B.J.; Tse, E.S.C.; De Luca-Abbott, S.B.; Martin, M.; Lam, P.K. Uptake and depuration of PAHs and chlorinated pesticides by semipermeable membrane devices (SPMDs) and green-lipped mussels (Perna viridis). Mar. Pollut. Bull. 2005, 51, 975–993. [Google Scholar] [CrossRef] [PubMed]
  5. Huckins, J.; Petty, J.D.; Booij, K. Monitors of Organic Chemicals in the Environment: Semipermeable Membrane Devices; Springer: New York, NY, USA, 2008. [Google Scholar]
  6. Booij, K.; Sleiderink, H.M.; Smedes, F. Calibrating the uptake kinetics of semipermeable membrane devices using exposure standards. Environ. Toxicol. Chem. Int. J. 1998, 17, 1236–1245. [Google Scholar] [CrossRef]
  7. Męczykowska, H.; Kobylis, P.; Stepnowski, P.; Caban, M. Calibration of passive samplers for the monitoring of pharmaceuticals in water-sampling rate variation. Crit. Rev. Anal. Chem. 2017, 47, 204–222. [Google Scholar] [CrossRef]
  8. Chimuka, L.; Cukrowska, E.; Tutu, H. Monitoring of Aquatic Environments Using Passive Samplers. Open Anal. Chem. J. 2008, 2, 1–9. [Google Scholar] [CrossRef][Green Version]
  9. Narváez, V.J.F.; López, C.A.; Molina, P.F.J. Passive sampling in the study of dynamic and environmental impact of pesticides in water. Rev. Fac. Ing. Univ. Antioq. 2013, 68, 147–159. [Google Scholar]
  10. MacKeown, H.; Benedetti, B.; Di Carro, M.; Magi, E. The study of polar emerging contaminants in seawater by passive sampling: A review. Chemosphere 2022, 299, 134448. [Google Scholar] [CrossRef]
  11. Rong, Q.; Cai, Y.; Chen, B.; Shen, Z.; Yang, Z.; Yue, W.; Lin, X. Field management of a drinking water reservoir basin based on the investigation of multiple agricultural nonpoint source pollution indicators in north China. Ecol. Indic. 2018, 92, 113–123. [Google Scholar] [CrossRef]
  12. Onydinma, U.P.; Aljerf, L.; Obike, A.; Onah, O.E.; Caleb, N.J. Evaluation of physicochemical characteristics and health risk of polycyclic aromatic hydrocarbons in borehole waters around automobile workshops in Southeastern Nigeria. Groundw. Sustain. Dev. 2021, 14, 100615. [Google Scholar] [CrossRef]
  13. Ramírez, D.G.; Valderrama, J.F.N.; Tobón, C.A.P.; García, J.J.; Echeverri, J.D.; Sobotka, J.; Vrana, B. Occurrence, sources, and spatial variation of POPs in a mountainous tropical drinking water supply basin by passive sampling. Environ. Pollut. 2023, 318, 120904. [Google Scholar] [CrossRef]
  14. Marin-Palma, D.; González, J.D.; Narváez, J.F.; Porras, J.; Taborda, N.A.; Hernandez, J.C. Physicochemical Characterization and Evaluation of the Cytotoxic Effect of Particulate Matter (PM10). Water Air Soil Pollut. 2023, 234, 138. [Google Scholar] [CrossRef]
  15. Arias-Pérez, R.D.; Taborda, N.A.; Gómez, D.M.; Narvaez, J.F.; Porras, J.; Hernandez, J.C. Inflammatory effects of particulate matter air pollution. Environ. Sci. Pollut. Res. 2020, 27, 42390–42404. [Google Scholar] [CrossRef]
  16. Petty, J.D.; Orazio, C.E.; Huckins, J.N.; Gale, R.W.; Lebo, J.A.; Meadows, J.C.; Cranor, W.L. Considerations involved with the use of semipermeable membrane devices for monitoring environmental contaminants. J. Chromatogr. A 2000, 879, 83–95. [Google Scholar] [CrossRef] [PubMed]
  17. Wang, J.; Bi, Y.; Pfister, G.; Henkelmann, B.; Zhu, K.; Schramm, K.W. Determination of PAH, PCB, and OCP in water from the Three Gorges Reservoir accumulated by semipermeable membrane devices (SPMD). Chemosphere 2009, 75, 1119–1127. [Google Scholar] [CrossRef]
  18. Booij, K.; Zegers, B.N.; Boon, J.P. Levels of some polybrominated diphenyl ether (PBDE) flame retardants along the Dutch coast as derived from their accumulation in SPMDs and blue mussels (Mytilus edulis). Chemosphere 2002, 46, 683–688. [Google Scholar] [CrossRef]
  19. Booij, K.; Van Bommel, R.; Mets, A.; Dekker, R. Little effect of excessive biofouling on the uptake of organic contaminants by semipermeable membrane devices. Chemosphere 2006, 65, 2485–2492. [Google Scholar] [CrossRef]
  20. Valderrama, J.F.N.; Baek, K.; Molina, F.J.; Allan, I.J. Implications of observed PBDE diffusion coefficients in low density polyethylene and silicone rubber. Environ. Sci. Process. Impacts 2016, 18, 87–94. [Google Scholar] [CrossRef]
  21. Sabaliūnas, D.; Södergren, A. Use of semipermeable membrane devices to monitor pollutants in water and assess their effects: A laboratory test and field verification. Environ. Pollut. 1997, 96, 195–205. [Google Scholar] [CrossRef] [PubMed]
  22. Booij, K.; Hofmans, H.E.; Fischer, C.V.; Van Weerlee, E.M. Temperature-dependent uptake rates of non-polar organic compounds by semipermeable membrane devices and low-density polyethylene membranes. Environ. Sci. Technol. 2003, 37, 361–366. [Google Scholar] [CrossRef] [PubMed]
  23. Verweij, F.; Booij, K.; Satumalay, K.; van der Molen, N.; van der Oost, R. Assessment of bioavailable PAH, PCB and OCP concentrations in water, using semipermeable membrane devices (SPMDs), sediments and caged carp. Chemosphere 2004, 54, 1675–1689. [Google Scholar] [CrossRef]
  24. Wicke, D.; Matzinger, A.; Sonnenberg, H.; Caradot, N.; Schubert, R.L.; Dick, R.; Heinzmann, B.; Dünnbier, U.; von Seggern, D.; Rouault, P. Micropollutants in urban stormwater runoff of different land uses. Water 2021, 13, 1312. [Google Scholar] [CrossRef]
  25. Gogou, A.; Bouloubassi, I.; Stephanou, E.G. Marine organic geochemistry of the Eastern Mediterranean: 1. Aliphatic and polyaromatic hydrocarbons in Cretan Sea surficial sediments. Mar. Chem. 2000, 68, 265–282. [Google Scholar] [CrossRef]
  26. Brillas, E. A review on the photoelectro-Fenton process as efficient electrochemical advanced oxidation for wastewater remediation. Treatment with UV light, sunlight, and coupling with conventional and other photo-assisted advanced technologies. Chemosphere 2020, 250, 126198. [Google Scholar] [CrossRef]
  27. Ríos-Sossa, R.; García-Londoño, J.J.; Gil-Ramírez, D.; Patiño, A.C.; Cardona-Maya, W.D.; Quintana-Castillo, J.C.; Narváez-Valderrama, J.F. Assessment of levonorgestrel leaching in a landfill and its effects on placental cell lines and sperm cells. Water 2022, 14, 871. [Google Scholar] [CrossRef]
  28. Bispo, A.; Jourdain, M.J.; Jauzein, M. Toxicity and genotoxicity of industrial soils polluted by polycyclic aromatic hydrocarbons (PAHs). Org. Geochem. 1999, 30, 947–952. [Google Scholar] [CrossRef]
  29. Qin, N.; He, W.; Kong, X.-Z.; Liu, W.-X.; He, Q.-S.; Yang, B.; Ouyang, H.-L.; Wang, Q.-M.; Xu, F.-L. Ecological risk assessment of polycyclic aromatic hydrocarbons (PAHs) in the water from a large Chinese lake based on multiple indicators. Ecol. Indic. 2013, 24, 599–608. [Google Scholar] [CrossRef]
  30. Valderrama, J.F.N.; Gil, V.C.; Alzate, V.; Tavera, E.A.; Noreña, E.; Porras, J.; Quintana-Castillo, J.C.; Ramos-Contreras, C.; Sanchez, J.B. Effects of polycyclic aromatic hydrocarbons on gestational hormone production in a placental cell line: Application of passive dosing to in vitro tests. Ecotoxicol. Environ. Saf. 2022, 245, 114090. [Google Scholar] [CrossRef]
  31. Gao, X.; Xu, Y.; Ma, M.; Rao, K.; Wang, Z. Simultaneous passive sampling of hydrophilic and hydrophobic emerging organic contaminants in water. Ecotoxicol. Environ. Saf. 2019, 178, 25–32. [Google Scholar] [CrossRef] [PubMed]
  32. Colombian Agricultural Institute, ICA Plant Protection Department. Statistics on Commercialization of Chemical Pesticides for Agricultural Use 2012 [Internet]. Boletín técnico Código: [Consultado 2014 Jun 29; citado 2014 Jul 02]. Available online: (accessed on 10 October 2022).
  33. Correa Zuluaga, S.; Ramos Contreras, C.D.; Tangarife Ramírez, J.C.; Narváez, J.F.; LópezCórdoba, C.A.; Molina Pérez, F.J. Potencial de Lixiviação de Chlorpirifos em um Entisol Colômbio. Rev. EIA 2018, 15, 47–58. [Google Scholar] [CrossRef][Green Version]
  34. Ventura, C.; Núñez, M.; Miret, N.; Lamas, D.M.; Randi, A.; Venturino, A.; Rivera, E.; Cocca, C. Differential mechanisms of action are involved in chlorpyrifos effects in estrogen-dependent or-independent breast cancer cells exposed to low or high concentrations of the pesticide. Toxicol. Lett. 2012, 213, 184–193. [Google Scholar] [CrossRef] [PubMed]
  35. Narváez Valderrama, J.F.; Berrío Puerta, J.A.; Correa Zuluaga, S.; Palacio Baena, J.A.; Molina Pérez, F.J. Degradación hidrolítica de clorpirifos y evaluación de la toxicidad del extracto hidrolizado con Daphnia pulex. Rev. Politéc. Colomb. Jaime Isaza Cadavid 2014, 10, 9–15. [Google Scholar]
  36. U.S. Geological Survey. Columbia Environmental Research Center [Update April 2012]. Available online: (accessed on 10 October 2022).
  37. Diedrich, C.J.; Gerig, B.S.; Paterson, G. Spatial Comparison of Persistent Organic Pollutants in the Boardman River Following Impoundment Removal and Channel Restoration. Bull. Environ. Contam. Toxicol. 2022, 109, 977–983. [Google Scholar] [CrossRef] [PubMed]
  38. Wang, J.; Liang, W.; Henkelmann, B.; Pfister, G.; Schramm, K.W. Organochlorine pesticides accumulated by SPMD-based virtual organisms and feral fish in Three Gorges Reservoir, China. Environ. Pollut. 2015, 202, 160–167. [Google Scholar] [CrossRef]
  39. Müller, J.F.; Manomanii, K.; Mortimer, M.R.; McLachlan, M.S. Partitioning of polycyclic aromatic hydrocarbons in the polyethylene/water system. Fresenius’ J. Anal. Chem. 2001, 371, 816–822. [Google Scholar] [CrossRef]
Figure 1. Location of reservoir for drinking water supply (Medellín, Colombia).
Figure 1. Location of reservoir for drinking water supply (Medellín, Colombia).
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Figure 2. (a) The PSPT fastened to the bridge column with a protection grill, including a padlock. (b) Large deployment canisters. (c) SPMDs attached to a spider holder.
Figure 2. (a) The PSPT fastened to the bridge column with a protection grill, including a padlock. (b) Large deployment canisters. (c) SPMDs attached to a spider holder.
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Figure 3. (a) SCAM chromatogram for a typical PCB congener used as a PRC in acetone. (b) SCAN chromatogram of the typical SPMD blank at ten days. (c) SCAN chromatogram of the SPMD deployment during ten days, including PRCs and some target analytes.
Figure 3. (a) SCAM chromatogram for a typical PCB congener used as a PRC in acetone. (b) SCAN chromatogram of the typical SPMD blank at ten days. (c) SCAN chromatogram of the SPMD deployment during ten days, including PRCs and some target analytes.
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Figure 4. Dissipation of PCB congeners from SPMDs used as a passive sampler: PCB-14 (open circles), PCB-29 (open squares) and PCB-50 (filled triangles).
Figure 4. Dissipation of PCB congeners from SPMDs used as a passive sampler: PCB-14 (open circles), PCB-29 (open squares) and PCB-50 (filled triangles).
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Figure 5. Uptake plot for PAHs. Concentration values estimated for target pollutant compounds in a principal tributary of La Fe reservoir. NAPH (✕), ACEN (■), ACE (Δ), FL (▼), PHE (♦), ANT (○), FLU (□) and PYR (▲). The concentration factor was calculated as the ratio between the minimum concentration value found for each target compound and all estimated concentrations. Cw was calculated by multiplying the initial concentration by the concentration factor.
Figure 5. Uptake plot for PAHs. Concentration values estimated for target pollutant compounds in a principal tributary of La Fe reservoir. NAPH (✕), ACEN (■), ACE (Δ), FL (▼), PHE (♦), ANT (○), FLU (□) and PYR (▲). The concentration factor was calculated as the ratio between the minimum concentration value found for each target compound and all estimated concentrations. Cw was calculated by multiplying the initial concentration by the concentration factor.
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Figure 6. CPF SPMDs uptake process.
Figure 6. CPF SPMDs uptake process.
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Table 1. GC–MS SIM mode for identification and quantitation of analytes. N.D., Not detected using SPMDs in SIM mode analysis.
Table 1. GC–MS SIM mode for identification and quantitation of analytes. N.D., Not detected using SPMDs in SIM mode analysis.
Peak No.AnalytesRt (min)Monitor Ions, m/z (Intensity %)
Target IonQ1Q2
1Naphthalene (NAPH),5.81128127129
2Acenaphthylene (ACEN)7.37152151153
3Acenaphthene (ACE)7.54154152153
4Fluorene (FL)7.97166165167
6Diazinon (DZN)8.79137179N.D.
7Phenanthrene (PHE)8.92178176179
8Anthracene (ANT)8.9717889179
11Chlorpyrifos (CPF)9.58314197256
12Parathion (PTN)9.63N.D.N.D.N.D.
13Fluoranthene (FLU)10.20202101203
14Pyrene (PYR)10.44202101203
Table 2. Rs estimated at 24 days of sampling deployment. Molecular weight (MW), Linear regression (LR), correlation (r2). N.D., Not detected using SPMDs in SIM mode analysis.
Table 2. Rs estimated at 24 days of sampling deployment. Molecular weight (MW), Linear regression (LR), correlation (r2). N.D., Not detected using SPMDs in SIM mode analysis.
Peak No.Analyte NamesMWlog kowRs (L day−1)LR (µg mL−1)r2
6DZN (N.D.)304.33.335.80.01–0.500.987
12PTN (N.D.)330.43.840.10.01–0.500.987
Table 3. The Cw estimated for each PAH congener after 5 days of SPMDs’ exposure in the water body of the reservoir.
Table 3. The Cw estimated for each PAH congener after 5 days of SPMDs’ exposure in the water body of the reservoir.
PAH CongenerCw (5 Days Sampling) ng L−1
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Narvaez Valderrama, J.F.; González, J.D.; Porras, J.; Molina, F.J. Field Calibration of Semipermeable Membrane Devices (SPMDs) for Persistent Organic Pollutant Monitoring in a Reservoir. Water 2023, 15, 1428.

AMA Style

Narvaez Valderrama JF, González JD, Porras J, Molina FJ. Field Calibration of Semipermeable Membrane Devices (SPMDs) for Persistent Organic Pollutant Monitoring in a Reservoir. Water. 2023; 15(7):1428.

Chicago/Turabian Style

Narvaez Valderrama, Jhon Fredy, Juan D. González, Jazmín Porras, and Francisco J. Molina. 2023. "Field Calibration of Semipermeable Membrane Devices (SPMDs) for Persistent Organic Pollutant Monitoring in a Reservoir" Water 15, no. 7: 1428.

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