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Hardware-software system for rapid toxicity assessment of aquatic environments using P. Caudatum infusoria

https://doi.org/10.30724/1998-9903-2025-27-6-25-37

Abstract

THE PURPOSE. Development of a hardware-software complex (HSC) for rapid toxicity assessment of aquatic environments using Paramecium caudatum as a bioindicator. The primary goal was to create a system capable of registering and analyzing the chemotactic response of test organisms under varying levels of toxic exposure. METHODS. The hardware component includes a high-resolution camera, a specialized flat photometric cuvette, and adjustable LED lighting. The software utilizes computer vision algorithms (OpenCV) for tracking P. caudatum movement and assessing toxicity based on spatiotemporal cell distribution. Experiments were conducted using Lozina-Lozinsky solution as a control and copper sulfate (CuSO₄) solutions at concentrationsranging from 1 mg/L to 0.1 mg/L as test samples. RESULTS. At 1 mg/L CuSO₄, 95% of cells remained localized in the lower cuvette zone (lethality), with a toxicity index corresponding to high hazard (T>0.70). At 0.1 mg/L CuSO₄, 70-75% of the population migrated to the upper zone, similar to the control (T<0.40) The system demonstrated ≤5% error and a 30-minute analysis time. CONCLUSION. The developed HSC enables precise toxicity assessment of CuSO₄- contaminated environments, detecting both critical (1 mg/L) and subthreshold (0.1 mg/L) concentrations. The method’s robustness against imaging artifacts confirms its reliability for ecological monitoring.

About the Author

A. Sokolov
Saint Petersburg Electrotechnical University "LETI" named after V.I. Ulyanov (Lenin)
Россия

Alexey Sokolov – St. Petersburg State Electrotechnical University “LETI” (named after UlyanovLenin)

Saint Petersburg



References

1. United Nations Environment Programme. UNEP Annual Report 2023. 2023. URL: https://wedocs.unep.org/bitstream/handle/20.500.11822/44777/UNEP_Annual_Report_2023.pdf?sequence =19 (accessed: 20.02.2025).

2. Intergovernmental Panel on Climate Change. IPCC. 2021. URL: https://www.ipcc.ch/2021/ (accessed: 20.02.2025).

3. Semenova M.I., Smirnov A.V., Vezhenkova I.V., Kustov T.V., Kovalevskaya A.S. Osobennosti probopodgotovki vodnykh vytyazhek komponentov solnechnykh paneley v tselyakh biotestirovaniya // Izvestiya vysshikh uchebnykh zavedenii. Problemy energetiki. 2022. P. 211–220.

4. Ol’kova A.S. Aktual’nye napravleniya razvitiya metodologii biotestirovaniya vodnykh sred // Voda i ekologiya: problemy i resheniya. 2018. № 2. P. 40–49.

5. Semenova M.I., Smirnov A.V., Vezhenkova I.V., Kustov T.V., Kovalevskaya A.S. Vliyanie rastvorennogo kisloroda v srede na indeksy toksichnosti, poluchaemye razlichnymi metodami biotestirovaniya // Izvestiya vysshikh uchebnykh zavedenii. Problemy energetiki. 2024. № 1. P. 38–49.

6. Brezhneva I.N., Trifonova M.P. Biotestirovanie burovogo shlama na ekotoksichnost’ // Problemy regional’noi ekologii. 2019. № 3.

7. Gorgulenko V.V., Kirillov V.V., Kim G.V., Koveshnikov M.I. Otsenka kachestva donnykh otlozhenii reki Aba metodami bioindikatsii i biotestirovaniya // Vestnik NNGU. 2011. № 2–2.

8. Metodika opredeleniya toksichnosti prob prirodnykh, pit’evykh, khozyaistvenno-pit’evykh, khozyaistvenno-bytovykh stochnykh, ochishchennykh stochnykh, talykh i tekhnologicheskikh vod ekspress-metodom s primeneniem pribora serii “Biotester”. FR.1.39.2015.19242.

9. Zakharov I.S., Zavgorodniy A.V. Biotestovye apparaturnye sredstva i metody kontrolya lokomotsii infuzorii // Izvestiya Yuzhnogo federal’nogo universiteta. Tekhnicheskie nauki. 2008. P. 205– 209.

10. Ol’kova A.S. Protsedura vybora metodov biotestirovaniya v usloviyakh raznykh vidov zagryazneniya // Transformatsiya ekosistem. 2022. № 3 (17).

11. Zavgorodniy A.V. Razrabotka metoda i sredstv kontrolya prostranstvenno-vremennogo raspredeleniya opticheskikh kharakteristik vzvesi infuzorii dlya biotestirovaniya vodnykh sred: avtoref. dis. kand. tekhn. nauk. Saint Petersburg, 2008. 18 p.

12. Ciaparrone G., Luque Sánchez F., Tabik S., Troiano L., Tagliaferri R., Herrera F. Deep Learning in Video Multi-Object Tracking: A Survey // Neurocomputing. 2020. P. 61–88.

13. Esser P., Sutter E., Ommer B. A Variational U-Net for Conditional Appearance and Shape Generation // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. P. 8857–8866.

14. Tian C., Xu Y., Fei L., Yan K. Deep Learning for Image Denoising: A Survey // Advances in Intelligent Systems and Computing. 2019. № 384. P. 61–88.

15. Lukashik D.V. Analiz sovremennykh metodov segmentatsii izobrazhenii // Ekonomika i kachestvo sistem svyazi. 2022. Vol. 24, № 2. P. 57–65.


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For citations:


Sokolov A. Hardware-software system for rapid toxicity assessment of aquatic environments using P. Caudatum infusoria. Power engineering: research, equipment, technology. 2025;27(6):25-37. (In Russ.) https://doi.org/10.30724/1998-9903-2025-27-6-25-37

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ISSN 1998-9903 (Print)
ISSN 2658-5456 (Online)