Applying AQUATOX for the ecological risk assessment coastal of Black Sea at small industries around Samsun, Turkey

  • A. ŞimşekEmail author
  • K. Küçük
  • G. Bakan
Original Paper


The Black Sea receives uncontrolled and irregular fresh water for the production of thermal and hydro energy and the use of coastal areas. Transportation, untreated domestic, industrial and agricultural wastes, fall out into rivers or directly into the sea. In order to discuss and assess further pollution loads and possible management techniques for the coastal pollution problems of Black Sea, different mathematical modeling techniques can be used. Mathematical models are also useful tools to save time and money, as well as to help solve ecological problems more easily and to select an appropriate management alternative for sustainable management. AQUATOX is one of the affinity models for aquatic ecosystems. AQUATOX seeks to determine the fate of various pollutants, such as nutrients and organic chemicals, and their impact on the ecosystem, including fish, invertebrates and aquatic plants. In this study, samples collected from five different points in the organized industrial zone in Samsun province in August and December 2017. pH, conductivity, dissolved oxygen, chemical oxygen demand, total organic carbon, total-N, total-P were carried out in water samples. The model was run for dissolved oxygen, total phosphorus and total nitrogen from the measured parameters, and the results were evaluated by adding to program inputs. The model was run to determine the contribution of contamination in aquatic ecosystems to the assessment of ecological risk. The models are thought to be an essential structure for ecosystems to determine their ecological protection levels.


AQUATOX Black Sea Ecological risk assessment Modeling 



This study was supported by Ondokuzmayıs University PYO.MUH.1901.17.001 scientific research Project.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. APHA–AWWA (2005) Standard methods for the examination of water and wastewater, 17th edn. American Public Health Association/American Water Works Association/Water Environment Federation, Washington, DCGoogle Scholar
  2. Barber MC (2001) Bioaccumulation and aquatic system simulator (BASS) user’s manual, beta test version 2.1. 600/R-01/035. US Environmental Protection Agency, AthensGoogle Scholar
  3. Bartell SM, Lefebvre G, Kaminski G, Carreau M, Campbell KR (1999) An ecological model for assessing ecological risks in Quebec rivers, lakes, and reservoirs. Ecol Model 124:43–67CrossRefGoogle Scholar
  4. Chapra SC, Pelletier GJ, Tao H (2007) QUAL2K: a modeling framework for simulating river and stream water quality, version 2.07: documentation and users manual. Civil and Environmental Engineering Department, Tufts University, MedfordGoogle Scholar
  5. Chen S, Chen B, Fath BD (2013) Ecological risk assessment on the system scale: a review of state-of-the-art models and future perspectives. Ecol Model 250:25–33CrossRefGoogle Scholar
  6. Clough JS (2004) AQUATOX: modeling environmental fate and ecological effects in aquatic ecosystems, vol 3, user’s manual for the BASINS (version 3.1) extension to AQUATOX release 2. EPA-823-R-04-003. US Environmental Protection Agency, Office of Water, Washington, DCGoogle Scholar
  7. DeAngelis DL, Bartell SM, Brenkert AL (1989) Effects of nutrient cycling and food-chain length on resilience. Am Nat 134:778–805CrossRefGoogle Scholar
  8. Di Toro DM (2001) Sediment flux modeling. Wiley, New York, p 624Google Scholar
  9. Di Toro DM, Fitzpatrick JJ, Thomann RV (1983) Water quality analysis simulation program (WASP) and model verification program (MVP)-documentation. Hydroscience, Inc. for USEPA, DuluthGoogle Scholar
  10. Lei B, Huang S, Li T, Qiao M, Wang Z (2008) Prediction of the environmental fate and aquatic ecological impact of nitrobenzene in the Songhua River using the modified AQUATOX model. J Environ Sci 20:769–777CrossRefGoogle Scholar
  11. Li X, Gao Y, Qian H, Wu H (2017) Groundwater vulnerability and contamination risk assessment of the Weining Plain, using a modified DRASTIC model and quantized pollution loading method. Arab J Geosci 10:469CrossRefGoogle Scholar
  12. Martin JL, Ambrose RA, Wool TA (2006) WASP7 benthic algae-model theory and user’s guide. US Environmental Protection Agency, AthensGoogle Scholar
  13. Novotny, V (2006) Review of watershed ecological models, technical report no. 7. Boston, April 2006, p 7Google Scholar
  14. Ouchir N, Ben Aissa L, Boughdiri M, Aydi A (2016) Assessment of heavy metal contamination status in sediments and identification of pollution source in Ichkeul Lake and rivers ecosystem, Northern Tunisia. Arab J Geosci 9:539CrossRefGoogle Scholar
  15. Park RA, Clough JS, Wellman MC (2008) AQUATOX: modeling environmental fate and ecological effects in aquatic ecosystems. Ecol Model 213:1–15CrossRefGoogle Scholar
  16. Park RA, Clough JS, Wellman MC (2013) AQUATOX: modeling environmental fate and ecological effects in aquatic ecosystems. Office of Water of United States Environmental Protection Agency, Washington, DC, pp 302–344Google Scholar
  17. Rykiel EJ Jr (1996) Testing ecological models: the meaning of validation. Ecol Model 90:229–244CrossRefGoogle Scholar
  18. Schol A, Kirchesch V, Bergfeld T, Muller D (1999) Model-based analysis of oxygen budget and biological processes in the regulated rivers Moselle and Saar: modelling the influence of benthic filter feeders on phytoplankton. Hydrobiologia 410:167–176CrossRefGoogle Scholar
  19. Schol A, Kirchesch V, Bergfeld T, Scholl F, Borcherding J, Muller D (2002) Modelling the chlorophyll content of the River Rhine interaction between riverine algal production and population biomass of grazers, rotifers and zebra mussel, Dreissena polymorpha. Int Rev Hydrobiol 87:295–317CrossRefGoogle Scholar
  20. Soylu A (2011) The impact of industrial emissions on local heavy metal content of cattle milks in Samsun, master thesis. Ondokuz Mayıs University, Food Engineering Department, SamsunGoogle Scholar
  21. Traas TP, Stab JA, Kramer PRG, Cofino WP, Aldenberg T (1996) Modeling and risk assessment of tributyltin accumulation in the food web of a shallow freshwater lake. Sci Technol 30:1227–1237CrossRefGoogle Scholar
  22. Traas TP, Janse JH, Aldenberg T, Brock JT (1998) A food web model for fate and direct and indirect effects of Dursban 4E (active ingredient chlorpyrifos) in freshwater microcosms. Aquat Ecol 32:179–190CrossRefGoogle Scholar
  23. Traas TP, Janse JH, Van den Brink PJ, Aldenberg T (2001) A food web model for fate and effects of toxicants and nutrients in aquatic mesocosms. Model description. Report 601516 006. RIVM, BilthovenGoogle Scholar
  24. US Environmental Protection Agency, USEPA (2010) AQUATOX short course. In: SETAC meeting, Portland, 7 Nov 2010Google Scholar
  25. US Environmental Protection Agency, USEPA (2014) Modeling environmental fate and ecological effects in aquatic ecosystems, vol 1: user’s manual, EPA-820-R-14-005. United States Environmental Protection Agency Office of Water, Washington, DCGoogle Scholar
  26. Wool, TA, Ambrose, RB, Martin, JL, Comer, EA (2004) Water quality analysis simulation program (WASP) version 6.0 DRAFT: user’s manual. US Environmental Protection Agency-Region 4, AtlantaGoogle Scholar
  27. Zhang L, Liu J, Li Y, Zhao Y (2013) Applying AQUATOX in determining the ecological risk assessment of polychlorinated biphenyl contamination in Baiyangdian lake, North China. Ecol Model 265:239–249CrossRefGoogle Scholar

Copyright information

© Islamic Azad University (IAU) 2019

Authors and Affiliations

  1. 1.Department of Environmental EngineeringOndokuz Mayıs UniversitySamsunTurkey

Personalised recommendations