From Prescription to Pollution: Assessing the Ecological Impact and Treatment Technologies for Antidepressant Contaminants
Abstract
1. Introduction
2. Environmental Occurrence and Pathways of Antidepressants
3. Modes of Action of Antidepressants
Bioavailability and Bioaccumulation in Aquatic Organisms
4. Ecotoxicological Effects on Aquatic Biota
4.1. Neurochemical and Molecular Effects
4.2. Impacts on Development, Growth, and Reproduction
4.3. Behavioral Change
4.4. Transgenerational and Epigenetic Effects
4.5. Synthesis: Environmental Relevance and Future Research Directions
5. An Overview of Methods for Treating Water to Remove Antidepressant Residues
5.1. Adsorptive Methods
5.2. Extractive Methods
5.3. Advanced Oxidation Processes
5.3.1. Pharmaceutical Photodegradation Pathways
5.3.2. Photocatalytic Nanomaterials for Drug Removal
5.4. Biological and Advanced Oxidation Processes for Antidepressant Removal
5.5. Comparative Analysis of Different Technologies
6. Gaps and Future Perspectives
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
| OECD | Organization for Economic Cooperation and Development |
| DDD | defined daily dosage |
| ASP | activated sludge |
| MBR | membrane bioreactor |
| MBBR | moving-bed biofilm reactor |
| UF | ultrafiltration |
| NF | nanofiltration |
| RO | reverse osmosis |
| AOPs | advanced oxidation processes |
| UV | ultraviolet |
| GAC | granular activated carbon |
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| Country | Antidepressant | Number of Prescriptions and DDD | Predicted Usage (g/year) | Reference |
|---|---|---|---|---|
| Sweden | Fluoxetine | 276,214 and 20 | 55.2 | [3] |
| Australia | Fluoxetine | 2,039,596 and 20 | 407.9 | [3] |
| Brazil | Fluoxetine | 8,054,380 and 20 | 1610.9 | [49] |
| England | Fluoxetine | 6,713,763 and 20 | 1342.8 | [2] |
| USA | Fluoxetine | 25,578,000 and 20 | 15,346.8 | [1] |
| Sweden | Amitriptyline | 419,437 and 75 | 314.6 | [3] |
| Australia | Amitriptyline | 2,366,938 and 75 | 1775.2 | [3] |
| Brazil | Amitriptyline | 8,265,629 and 75 | 6199.2 | [49] |
| England | Amitriptyline | 13,532,567 and 75 | 10,149.4 | [2] |
| USA | Amitriptyline | 10,265,000 and 75 | 23,096.3 | [1] |
| Sweden | Escitalopram | 603,580 and 10 | 60.4 | [3] |
| Australia | Escitalopram | 4,406,535 and 10 | 440.7 | [3] |
| Brazil | Escitalopram | 10,852,868 and 10 | 1085.3 | [49] |
| England | Escitalopram | 1,100,349 and 10 | 110.0 | [2] |
| USA | Escitalopram | 25,979,000 and 10 | 7793.7 | [1] |
| Sweden | Citalopram | 1,135,550 and 20 | 227.1 | [3] |
| Australia | Citalopram | 1,762,614 and 20 | 352.5 | [3] |
| Brazil | Citalopram | 5,051,155 and 20 | 1010.2 | [49] |
| England | Citalopram | 14,136,645 and 20 | 2827.3 | [2] |
| USA | Citalopram | 22,224,000 and 20 | 13,334.4 | [1] |
| Sweden | Duloxetine | 327,365 and 60 | 1055.8 | [2] |
| Brazil | Duloxetine | 4,460,523 and 60 | 37,863.0 | [1] |
| England | Duloxetine | 1,812,334 and 60 | 1087.4 | [2] |
| USA | Duloxetine | 21,035,000 and 60 | 37,863.0 | [1] |
| Brazil | Nortriptyline | 1,793,719 and 75 | 1345.3 | [49] |
| England | Nortriptyline | 609,775 and 75 | 457.3 | [2] |
| USA | Nortriptyline | 4,347,000 and 75 | 9780.8 | [1] |
| Australia | Desvenlafaxine | 2,266,218 and 50 | 1133.1 | [3] |
| England | Dosulepin | 703,879 and 150 | 1055.8 | [2] |
| England | Clomipramine | 282,652 and 100 | 282.7 | [2] |
| Sweden | Venlafaxine | 542,199 and 100 | 813.3 | [3] |
| Australia | Venlafaxine | 3,097,164 and 100 | 4645.7 | [3] |
| Brazil | Venlafaxine | 5,361,432 and 100 | 8042.1 | [49] |
| England | Venlafaxine | 4,401,567 and 100 | 4401.6 | [2] |
| USA | Venlafaxine | 16,121,000 and 100 | 48,363.0 | [1] |
| England | Imipramine | 150,526 and 100 | 150.5 | [2] |
| Brazil | Paroxetine | 5,320,964 and 20 | 1064.2 | [49] |
| England | Paroxetine | 1,351,979 and 20 | 270.4 | [2] |
| USA | Paroxetine | 11,229,000 and 20 | 6737.4 | [1] |
| Sweden | Sertraline | 1,340,154 and 50 | 670.1 | [3] |
| Australia | Sertraline | 4,255,453 and 50 | 2127.7 | [3] |
| Brazil | Sertraline | 9,636,908 and 50 | 4818.5 | [49] |
| England | Sertraline | 14,815,719 and 50 | 7407.9 | [2] |
| England | Lofepramine | 197,325 and 105 | 207.2 | [2] |
| USA | Sertraline | 38,383,000 and 50 | 57,574.5 | [1] |
| Country | Antidepressant | Wastewater Treatment Plant Effluent (ng L−1) | Surface Water (ng L−1) | Reference |
|---|---|---|---|---|
| Australia | Venlafaxine | - | <0.1–86.2 | [50] |
| Canada | Venlafaxine | - | 0.796–573 | [51] |
| China | Venlafaxine | - | <LOQ–3.03 | [52] |
| Czech Republic | Venlafaxine | - | 11–110 | [53] |
| EU-wide (17 countries) | Venlafaxine | 548 (max) | - | [54] |
| Finland | Venlafaxine | - | 1–15 | [1] |
| Germany | Venlafaxine | - | <LOQ–100 | [42] |
| Portugal | Venlafaxine | - | 13.68–25.80 | [55] |
| Spain | Venlafaxine | 582.7–999.5 | 4.1–177.7 | [56] |
| Sweden | Venlafaxine | - | <0.21–5.6 | [57] |
| UK | Venlafaxine | 21.4–285.1 | 0.9–85.4 | [58] |
| US | Venlafaxine | 188–690 | 73.3–359 | [59] |
| Brazil | Escitalopram | 25–1136 | nd–520 | [49] |
| Turkey | Escitalopram | - | <LOQ–3.08 | [60] |
| Norway | Fluoxetine | nd–1.3 | - | [47] |
| Portugal | Fluoxetine | 68.16–88.46 | [55] | |
| Spain | Fluoxetine | 22.70 (max) | <0.18–10.6 | [61] |
| Sweden | Fluoxetine | <LOQ–94 | <LOQ–32 | [62] |
| Turkey | Fluoxetine | - | <LOQ–2.36 | [60] |
| UK | Fluoxetine | 43.3 (max) | 13.5 (max) | [63] |
| US | Fluoxetine | 104–119 | 5.1–14.4 | [64] |
| Norway | Fluvoxamine | nd–0.8 | - | [47] |
| US | Fluvoxamine | nd–4.6 | nd–0.83 | [59] |
| China | Imipramine | nd–2.1, nd | - | [65] |
| Spain | Imipramine | 30–55 | - | [66] |
| Brazil | Nortriptyline | nd–786 | - | [49] |
| UK | Nortriptyline | 0.9–53.8 | 0.2–19 | [58] |
| Germany | Fluoxetine | - | <LOQ–3.8 | [42] |
| EU-wide (17 countries) | Fluoxetine | 21.5 (max) | - | [54] |
| China | Fluoxetine | - | <LOQ–0.24 | [52] |
| Canada | Fluoxetine | - | 0.76–25.1 | [51] |
| Brazil | Fluoxetine | 25–160 | 25–90 | [49] |
| Australia | Fluoxetine | - | <0.1–36 | [50] |
| Spain | Amitriptyline | 13–20 | nd–0.4 | [67] |
| Portugal | Amitriptyline | - | 1 to 2 | [68] |
| France | Amitriptyline | nd-6.0 | Nd | [69] |
| EU-wide (17 countries) | Amitriptyline | 14.6 (max) | - | [54] |
| Czech Republic | Amitriptyline | - | nd–3.3 | [70] |
| China | Amitriptyline | nd–4.8, nd–0.2 | - | [65] |
| China | Amitriptyline | - | 0.12–0.64 | [52] |
| Canada | Amitriptyline | - | 0.156–54.8 | [51] |
| Brazil | Amitriptyline | nd–80 | 157–196 | [49] |
| Wales | Amitriptyline | <2–357 | <0.5–9 | [71] |
| US | Amitriptyline | 88–119 | - | [72] |
| Drug | Fish Species/Life Stage | Exposure (ng/µg-L or µM) | Observed Effects |
|---|---|---|---|
| Fluoxetine | Fathead Minnow (Pimephales promelas) adult | 100 ng/L–100 µg/L | Changes in eating, predator avoidance, and reproductive behavior; long-term, four-week exposure at ambient levels |
| Fluoxetine | Zebrafish (Danio rerio) embryos | ≥0.88 µg/L (locomotor), ≥6 µg/L (AChE inhibition) | Even close to the environmental maximum, delayed hatching, loss of balance, decreased swimming, and neurotoxicity |
| Fluoxetine/Venlafaxine mixture | Hybrid striped bass | Low µg/L concentrations (additive format) | Slower prey capture, decreased brain serotonin, and a combination that is more effective at low dosages than individual medications |
| Fluoxetine | Cichlid (Cichlasoma dimerus)—adult | Injection: ~2 µg drug/g fish | Testicular foam cells, changed pituitary LH, and no increase in plasma vitellogenin indicate mild endocrine disturbance. |
| Fluoxetine (chronic, multigenerational) | Guppies (Poecilia reticulata) wild/lab over 5 years | Acclimated to ~30 ng/L and 300 ng/L | decreased sperm velocity and body condition; longer gonopodium; and less behavioral flexibility |
| Sertraline | Mosquitofish (Gambusia holbrooki) male & group behavior | Environmental µg/L levels typical | Reduced ability to escape predators; altered foraging and social hostility in group contexts rather than solo tests |
| Citalopram | Stickleback (Gasterosteus aculeatus) | 0.15–1.5 µg/L | decreased stress response, aggressiveness, and eating rate |
| Venlafaxine | Brown trout (Salmo trutta) larvae and juveniles | 10–1000 µg/L | less severe than citalopram at the same dosage; some oxidative stress and mild liver inflammation |
| Mixed SSRIs (fluoxetine, sertraline, venlafaxine) | Mosquitofish circadian behavior | 1, 10, 100 µg/L mixture | At 100 µg/L, venlafaxine alone interfered with rhythms; at 1 and 100 µg/L, the mixture changed diurnal activity, and the dose response was not monotonic |
| Process Category | Technology /Method | Advantages | Disadvantages/Limitations |
|---|---|---|---|
| Physical | Adsorption (such as metal–oxide nanoparticles, biochar, and activated carbon) | High elimination efficiency for many antidepressants (e.g., >80–97%); Flexible adsorbents, a straightforward design, and a comparatively low operating cost [183]; | Moves contaminants to an adsorbent, where they must be disposed of or renewed; performance is influenced by affinity, competing substances, and contact time [184]. |
| Physical | Membrane filtration (UF, NF, RO) | extremely high rejection rates, including trace-level elimination (up to >95%); produces reusable, high-quality permeate. | High energy and running expenses; problems with the disposal of concentrate (brine); membrane fouling and necessary maintenance [19] |
| Biological | Activated sludge (ASP), Membrane bioreactor (MBR), moving-bed biofilm reactor (MBBR) | Broadly used, reasonably priced, and compatible with current plants, MBR/MBBR achieves better removal because of its lengthy SRT and retained biomass. | Many antidepressants, such as carbamazepine and SSRIs, have low biodegradability (less than 55–75%); the effectiveness of removal is largely dependent on the compound’s characteristics and operating conditions [19] |
| Biological | Constructed wetlands/fungal mycoremediation | inexpensive, environmentally benign, and capable of breaking down resistant substances through the action of fungal enzymes or plant microbial synergy | Mostly at the lab or pilot scale, scalability is difficult; slow rates, retention duration, and the requirement for substrate modifications; Less shown performance in complex actual wastewater |
| Chemical (AOPs) | Ozonation, UV, UV/H2O2, Fenton, photo-Fenton, photocatalysis | Extremely high rates of drug breakdown and mineralization (>90%, frequently >95%); total conversion to inorganic end products is feasible; non-selective, broad-spectrum oxidation using OH radicals | High operating and capital costs (catalysts, chemicals, and energy); If partial oxidation occurs, potentially hazardous transformation byproducts may arise; post-biological polishing may be necessary. Radical scavengers (such bicarbonate ions) impair efficient operation. |
| Chemical (AOPs) | Electrochemical oxidation/electro-Fenton | Effectively breaking down resistant medications; can combine with adsorption (like GAC) to improve removal and lessen byproducts | It requires electricity and specific electrodes; it can be expensive. may result in oxidative byproducts such as chlorinated ones, contingent on the chemistry of the water. |
| Positively Ionized Tricyclic Antidepressants | Adsorbents | qm (mmol g−1) | pH | Reference |
|---|---|---|---|---|
| Desipramine | Dowex 50WX4-200 | 3.22 | 6.5 | [194] |
| Clomipramine | Dowex 50WX4-200 | 2.53 | 6.5 | [194] |
| Clomipramine | Carbon sphere | 0.09 | 6.8 | [197] |
| Imipramine | Dowex 50WX4-200 | 3.42 | 6.5 | [194] |
| Imipramine | Carbon sphere | 0.36 | 6.8 | [197] |
| Imipramine | Activated charcoal | 1.17 | 6.8 | [4] |
| Amitriptyline | Dowex 50WX4-200 | 3.76 | 6.5 | [194] |
| Amitriptyline | Ca-montmorillonite | 1.05 | 6–7 | [196] |
| Specific Conditions | Wastewater Treatment Methods | Efficiency Removal (%) | Antidepressant Removed | Reference |
|---|---|---|---|---|
| Enzymatic degradation + fungal adsorption | Biological degradation | 22–98 | sertraline, paroxetine, clomipramine, mianserin, fluoxetine, citalopram, venlafaxine | [247] |
| Activated sludge | Biological degradation | 77–81 | sertraline | [195] |
| Photo-electro-Fenton | Advanced oxidation processes | 95 | amitriptyline | [214] |
| Electro-Fenton | Advanced oxidation processes | 78 | amitriptyline | [214] |
| UVA irradiation | Advanced oxidation processes | 30–40 | doxepine, venlafaxine | [210] |
| Electro-oxidation using BDD anode | Advanced oxidation processes | 76 | amitriptyline | [214] |
| Gamma (γ) radiation | Advanced oxidation processes | 80–100 | sertraline, citalopram | [213] |
| UVB + UVA irradiation | Advanced oxidation processes | 50 | desipramine | [209] |
| UVA + TiO2 + O3 + H2O2 | Advanced oxidation processes | 97 | fluoxetine | [212] |
| H2O2 (PEROXONE) + O3 + UVA | Advanced oxidation processes | 70 | fluoxetine | [212] |
| O3 + UVA + TiO2 | Advanced oxidation processes | 50 | fluoxetine | [212] |
| UVC irradiation | Advanced oxidation processes | 88–100 | amitriptyline, clomipramine | [207] |
| UVC irradiation | Advanced oxidation processes | 92 | trimipramine | [208] |
| Water microemulsion +Brij 30 + Propyl acetate | Extraction | 88–100 | amitriptyline, doxepine, imipramine, clomipramine | [204] |
| Phosphate buffer + phosphonium salt/Tetrabutyl-ammonium | Extraction | 94–100 | amitriptyline | [206] |
| Ethyl acetate | Extraction | 86–95 | sertraline | [202] |
| Molecularly imprinted polymers | Adsorption | 70–100 | sertraline, fluoxetine, citalopram, paroxetine | [195] |
| Molecularly imprinted polymers | Adsorption | 55–90 | bupropion | [195] |
| Diosmectite | Adsorption | 16–31 | amitriptyline, nortriptyline | [4] |
| TiO2 nanoparticles | Adsorption | 7–22 | amitriptyline, nortriptyline | [4] |
| Activated carbon | Adsorption | 17–37 | amitriptyline, nortriptyline | [193] |
| Activated carbon | Adsorption | 37–61 | imipramine | [4] |
| Cation-exchange resins | Adsorption | 57–78 | amitriptyline, clomipramine, imipramine, desipramine | [194] |
| Metric | Adsorption | AOPs | Biological Treatment |
|---|---|---|---|
| Removal efficiency (for psychiatric drugs/trace compounds) | Moderate to high for compounds with good adsorption affinity; can reach >80–95% in favorable lab conditions; lower for hydrophilic or small, ionic molecules. | High for many compounds; often >90% parent compound removal under optimized lab or pilot conditions; mineralization often less complete. | Low to moderate for many psychiatric drugs; some compounds removed well, many recalcitrant ones remain; performance heavily dependent on type of compound and conditions. |
| Energy requirements | Low to moderate: mostly energy for pumping, mixing, possibly regeneration; no high intensity light/radical generation. | High for many types (ozone generation, UV lamps, electrochemical systems etc.); some hybrid or newer AOPs can reduce energy, but still significant. | Low: microbial metabolism, ambient temperature/light (if applicable) predominates; energy mainly for aeration, mixing, pumping. |
| Chemical/reagent costs | Adsorbent material; cost of regeneration or replacement; possible pre-treatment. | High: oxidants (e.g., H2O2, ozone), catalysts, UV lamps, electricity; steady supply; maintenance. | Moderate: nutrients, sometimes external carbon sources, but generally fewer specialized chemicals required. |
| Byproduct risk/toxicity | Low in terms of new chemicals, though removal of parent compound only; risk if desorption or leaching; disposal/regeneration must be considered. | Moderate to High: possible formation of oxidation byproducts, partial transformation products, chlorinated/brominated byproducts (depending on matrix), residual radicals/oxidants; require careful monitoring. | Variable: transformation products may be produced; microbial byproducts (sludge) may accumulate pharmaceuticals; risk in sludge disposal; generally lower risk of “new radical byproducts” but still possible. |
| Scalability/real-world feasibility | Good: can retrofit existing plants with adsorber units; adsorbents relatively well known; GAC, PAC have history; but scale increases cost, logistics of regeneration, footprint are challenges. | Challenging: scaling reactors, infrastructural modifications, ensuring consistent oxidant delivery, safety, handling, capital costs. More feasible as polishing/tertiary treatment rather than primary removal. | Strong: many WWTPs already use biological treatment; improvements (MBRs, longer SRTs, specialized microbes) are possible; nature-based systems (algae, constructed wetlands etc.) promising but require large land, longer times. |
| Operational complexity/monitoring/maintenance | Moderate: maintaining adsorbent performance, avoiding fouling, regular regeneration/disposal, monitoring breakthrough. | High: need to monitor oxidant dosages, byproduct formation, pH, light, etc.; safety issues; maintenance of UV lamps/ozone generators or catalysts; energy systems need overseeing. | Moderate: maintaining microbial health, community composition, handling fluctuations in influent; but generally lower technical sophistication than AOP for certain aspects. |
| Cost per m3 treated (for high removal polishing/trace compound removal) | Moderate to High depending on adsorbent cost/regeneration; on order of some USD or EUR per m3 for high-fidelity trace removal; relatively lower than AOP in many cases. | High: energy + chemical costs make cost per m3 for high removal large; only competitive in specific cases/high value water/regulatory pressure. | Lower: mainstream biological treatment is relatively economical; improvements (e.g., MBRs or longer retention times) add cost but often less than full-scale AOPs. |
| Category | Gaps/Challenges | Future Prospects/Opportunities |
|---|---|---|
| Analytical and Monitoring | Lack of standard methods and quality control for measuring antidepressants and metabolites in water; Poor data on chronic low-dose exposure, mixture interactions, behavioral/developmental endpoints | Development of prioritized sampling frameworks and validated analytical protocols targeting environmentally relevant compounds and mixtures |
| Biological Treatment | Conventional activated sludge systems show poor removal of many antidepressants, including SSRIs and carbamazepine; Limited understanding of how microbial community structure and redox conditions affect biodegradation | Optimization of redox dosing and specialized microbial consortia; bioaugmentation with fungi (e.g., Mucor, Trametes) in MBRs for enhanced degradation [266] |
| Physical Methods (Adsorption, Membranes) | Need for lower-cost, selective adsorbents compatible with real wastewater matrices; Disposal or regeneration of spent adsorbents and handling of concentrated reject streams | Advanced adsorbents (e.g., MOFs, modified biochars) designed for high selectivity and reusability; hybrid approaches (e.g., membrane adsorption AOP) to enhance removal efficiency [267] |
| Chemical/AOPs | High energy and chemical costs; formation of toxic byproducts from partial oxidation; Performance variability depending on water chemistry (e.g., radical scavengers like bicarbonate) | Development of visible-light photocatalysts (e.g., doped TiO2, g-C3N4), fluidized-bed Fenton reactors, sonophotocatalysis to improve efficiency and reduce energy use |
| Hybrid Systems | Few full-scale pilots integrating biological + AOP or adsorption systems; design integration and economics underexplored [267] | Pilot/full-scale implementation of hybrid configurations (e.g., biological + ozonation, adsorption + electrochemical oxidation) optimized for cost-effectiveness and performance [267] |
| Novel/Emerging Tech | Limited real-world application data for mycoremediation, nanoremediation, bioelectrochemical wetlands, permeable reactive barriers | Explore mycoreactors in WWTPs, bioelectrochemical wetlands (BES), nanoadsorbents and PRBs for decentralized or source-control remediation [266] |
| Risk and Regulation | Lack of strong regulatory frameworks addressing pharmaceutical discharge; few tools to assess exposure risk of mixtures [268]; No universal prioritization of antidepressants for monitoring or remediation | Policy development for hospital/mixed-source effluent pre-treatment, standardized risk assessment methods, and prioritization frameworks for tracking key antidepressants [268] |
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Georgin, J.; Schumacher de Oliveira, J.; Dehmani, Y.; El Messaoudi, N.; de Albuquerque Carvalho, M.; Meili, L.; Ramos, C.G.; Franco, D.S.P. From Prescription to Pollution: Assessing the Ecological Impact and Treatment Technologies for Antidepressant Contaminants. Sustainability 2025, 17, 9752. https://doi.org/10.3390/su17219752
Georgin J, Schumacher de Oliveira J, Dehmani Y, El Messaoudi N, de Albuquerque Carvalho M, Meili L, Ramos CG, Franco DSP. From Prescription to Pollution: Assessing the Ecological Impact and Treatment Technologies for Antidepressant Contaminants. Sustainability. 2025; 17(21):9752. https://doi.org/10.3390/su17219752
Chicago/Turabian StyleGeorgin, Jordana, Jivago Schumacher de Oliveira, Younes Dehmani, Noureddine El Messaoudi, Matheus de Albuquerque Carvalho, Lucas Meili, Claudete Gindri Ramos, and Dison S. P. Franco. 2025. "From Prescription to Pollution: Assessing the Ecological Impact and Treatment Technologies for Antidepressant Contaminants" Sustainability 17, no. 21: 9752. https://doi.org/10.3390/su17219752
APA StyleGeorgin, J., Schumacher de Oliveira, J., Dehmani, Y., El Messaoudi, N., de Albuquerque Carvalho, M., Meili, L., Ramos, C. G., & Franco, D. S. P. (2025). From Prescription to Pollution: Assessing the Ecological Impact and Treatment Technologies for Antidepressant Contaminants. Sustainability, 17(21), 9752. https://doi.org/10.3390/su17219752

