Wastewater Treatment Technology for Sustainable Tourism: Sunny Beach, Ravda WWTP Case Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Samplings
2.2. Analyzed Parameters and Methods
2.3. Data Analysis
3. Results
3.1. Water Quantity per Overnight
3.2. Hydrochemical and Technological Parameters
3.3. Microscopic Analysis
3.4. Microbiological Parameters
4. Discussion
- Expanding the technological scheme through the enhancement and better control of denitrification. This is planned and currently being implemented during the summer operating mode.
- Increasing the rate of microbial dephosphatation and detoxification through targeted management of the activity and quantity of the microbial segment in the activated sludge (AS) of the genera Pseudomonas and Acinetobacter, as well as regulating the F/M ratio by maintaining an appropriate concentration of organic matter and the ratio of biogenic elements, carbon–nitrogen–phosphorus, optimal for the respective components of the overall treatment process.
- Microscopic monitoring of the floccular structure of the activated sludge is suitable for tracking and controlling potential deformations resulting from inappropriate parameters of the treatment process.
- The integration of a plasma module for the elimination of toxic pollutants and pathogens at the outlet is possible, along with other tested innovative modules such as specialized biofilters with zeolite as a carrier [78].
- Long-term and comprehensive monitoring of the operation of WWTPs that treat water from seasonal tourism provides valuable information on the possibilities for maintaining sustainable water cycle functioning in regions where the main economic sector is mass tourism [79]
- Innovations and the sustainability of water treatment processes in tourist facilities with high economic impact would ensure conditions for the sustainable functioning of water cycles, linked to sustainable water and environmental management in regions with intensive tourism [80].
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Microbiological Parameter | Nutrient Medium | Manufacturer | Incubation |
---|---|---|---|
Aerobic heterotrophs (AeH) | Meat-peptone agar (MPA) | HiMedia | 24 h, 28 °C |
Anaerobic heterotrophs (AnH) | Meat-peptone agar (MPA) | HiMedia | 24 h, 28 °C |
Denitrification bacteria (Dn) | Giltai agar | HiMedia | 7 days, 28 °C |
Pseudomonas sp. | GSP | HiMedia | 24 h, 28 °C |
Acinetobacter sp. | Sellers | HiMedia | 24 h, 28 °C |
El. | Influent 2023 | Effluent 2023 | Influent 2024 | Effluent 2024 | El. | Influent 2023 | Effluent 2023 | Influent 2024 | Effluent 2024 |
---|---|---|---|---|---|---|---|---|---|
Na | 295 ± 9 | 225 ± 7 | 200 ± 4 | 210 ± 5 | Li | 14.3 ± 0.4 | 12.7 ± 0.3 | 10.8 ± 0.3 | 10.1 ± 0.3 |
K | 19.7 ± 0.6 | 18.1 ± 0.5 | 11.7 ± 0.4 | 10.6 ± 0.3 | Mn | 196 ± 6 | 86 ± 3 | 107 ± 3 | 1.7 ± 0.1 |
Ca | 135 ± 4 | 105 ± 4 | 68 ± 2 | 64 ± 1 | Ni | 16.9 ± 0.5 | 14.7 ± 0.4 | 1.2 ± 0.1 | 1.3 ± 0.1 |
Mg | 38 ± 1 | 33 ± 1 | 28.9 ± 0.9 | 28.3 ± 0.8 | Rb | 8.0 ± 0.3 | 7.9 ± 0.2 | 6.6 ± 0.2 | 7.1 ± 0.2 |
Al | 142 ± 4 | 22.6 ± 0.7 | 19.0 ± 0.6 | 0.68 ± 0.02 | Sn | 0.15 ± 0.03 | 0.09 ± 0.01 | 0.18 ± 0.01 | 0.10 ± 0.01 |
As | 6.7 ± 0.2 | 3.4 ± 0.1 | 7.2 ± 0.2 | 4.6 ± 0.2 | Se | 9.7 ± 0.5 | 5.0 ± 0.2 | 8.1 ± 0.4 | 4.4 ± 0.2 |
Ba | 67 ± 2 | 7.3 ± 0.3 | 48 ± 2 | 23 ± 1 | Sr | 993 ± 40 | 730 ± 36 | 865 ± 39 | 634 ± 28 |
Co | 1.32 ± 0.05 | 1.51 ± 0.04 | 0.69 ± 0.02 | 0.43 ± 0.02 | Ti | 383 ± 15 | 316 ± 10 | 339 ± 12 | 45 ± 2 |
Cr | 2.1 ± 0.1 | 1.10 ± 0.04 | 19.1 ± 0.6 | 19.8 ± 0.5 | V | 5.7 ± 0.2 | 4.9 ± 0.2 | 5.3 ± 0.3 | 6.6 ± 0.3 |
Cu | 8.6 ± 0.3 | 2.9 ± 0.1 | 2.1 ± 0.1 | 1.8 ± 0.1 | W | 3.1 ± 0.1 | 2.4 ± 0.1 | 10.0 ± 0.4 | 9.3 ± 0.4 |
Fe | 880 ± 26 | 703 ± 21 | 717 ± 22 | 449 ± 13 | Zn | 18.1 ± 0.5 | 9.9 ± 0.3 | 10.1 ± 0.1 | 14.6 ± 0.3 |
Microbiological Parameter | Month | Phosphorus Release Basin | Denitrification Basin | Nitrification Basin |
---|---|---|---|---|
Denitrification Bacteria | November 2023 | 368,217 ± 58 | 1,840,149 ± 19 | 1,498,674 ± 252 |
April 2024 | 5833 ± 1389 | 7208 ± 974 | 2460 ± 437 | |
Pseudomonas sp. | November 2023 | 46,802 ± 97 | 30,855 ± 4833 | 45,358 ± 928 |
April 2024 | 18,264 ± 5069 | 17,857 ± 5519 | 17,103 ± 5913 | |
Acinetobacter sp. | November 2023 | 464,147 ± 969 | 423,792 ± 26,022 | 814,324 ± 88,859 |
April 2024 | 270,833 ± 4167 | 572,078 ± 60,390 | 16,468 ± 1984 |
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Bogdanova, M.; Yotinov, I.; Topalova, Y.; Lyubomirova, V. Wastewater Treatment Technology for Sustainable Tourism: Sunny Beach, Ravda WWTP Case Study. Water 2025, 17, 7. https://doi.org/10.3390/w17010007
Bogdanova M, Yotinov I, Topalova Y, Lyubomirova V. Wastewater Treatment Technology for Sustainable Tourism: Sunny Beach, Ravda WWTP Case Study. Water. 2025; 17(1):7. https://doi.org/10.3390/w17010007
Chicago/Turabian StyleBogdanova, Magdalena, Ivaylo Yotinov, Yana Topalova, and Valentina Lyubomirova. 2025. "Wastewater Treatment Technology for Sustainable Tourism: Sunny Beach, Ravda WWTP Case Study" Water 17, no. 1: 7. https://doi.org/10.3390/w17010007
APA StyleBogdanova, M., Yotinov, I., Topalova, Y., & Lyubomirova, V. (2025). Wastewater Treatment Technology for Sustainable Tourism: Sunny Beach, Ravda WWTP Case Study. Water, 17(1), 7. https://doi.org/10.3390/w17010007