Influence of land use on trophic state indexes in northeast Brazilian river basins

  • Olandia Ferreira Lopes
  • Felizardo Adenilson Rocha
  • Lucas Farias de Sousa
  • Daniela Mariano Lopes da Silva
  • Andrique Figueiredo Amorim
  • Ronaldo Lima Gomes
  • André Luiz Sampaio da Silva Junior
  • Raildo Mota de JesusEmail author


Eutrophication is a natural process within the ecological succession of aquatic ecosystems that results from nutrient inputs to water bodies, especially limiting elements such as phosphorus and nitrogen. However, the anthropogenic activities in river basin influence areas accelerate the eutrophication process of water bodies. Eutrophication is a global problem and considered one of the most relevant reasons of aquatic environments’ degradation. In this context, watercourses that make up the Eastern Water Planning and Management Region (RPGA) receive high pollutant contributions due to release of wastewater and agriculture diffuse sources from cities located in influence area. The present study aims to evaluate the land use effect in trophic state of the water bodies in Eastern RPGA basins. The Carlson Trophic State Index in 1977, adjusted by Lamparelli 2004, was used to determine the eutrophication degree of the three river basins (Almada, Cachoeira, and Una) located in the Eastern RPGA. The nutrient and chlorophyll a data were obtained from the Monitoring Program (Monitora) of Environment and Water Resources Institute of Bahia (INEMA), covering the period from 2008 to 2015, at thirteen (13) sampling sites, with quarterly collections. The results showed that, among three basins analyzed, Cachoeira River basin presented the worst values for trophic state index (TSI) due to the high level of anthropization, while best results were found in Una basin. It was verified that land use exerted a significant influence on the water quality of bodies of water evaluated.


Eutrophication Degradation Anthropogenic action Effluents 


Funding information

This study was financially supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) Finance Code 001.


  1. Alvares, C. A., Stape, J. L., Sentelhas, P. C., Gonçalves, J. L. M., & Sparovek, G. (2013). Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, 22(6), 711–728.CrossRefGoogle Scholar
  2. Alves, R. J. V., Cardin, L., & Kropf, M. S. (2007). Angiosperm disjunction “Campos rupestres-restingas”: a re-evaluation. Acta Botanica Brasilica, 21, 675–685.CrossRefGoogle Scholar
  3. APHA. (2005). Standard methods of examination of water and wastewater (20th ed.). Washington: American Public Health Association.Google Scholar
  4. Bose, R., DE, A., Subho, M., Sen, G., & Deb, A. M. (2012). Coastal water pollution in two rivers of the Bengal Delta. Geochemistry International, 50(10), 860–868.CrossRefGoogle Scholar
  5. Brandão, C. S., Silva, L. P., Chaussê, T., & Silva, D. M. L. (2015). Spatio-temporal variability of dissolved loads in rivers in environmental protected areas in Northeast Brazil. Revista Brasileira de Recursos Hídricos, 20(3), 551–559.CrossRefGoogle Scholar
  6. Carlson, R. E. (1977). A trophic state index for lakes. Limnology and Oceanography, 22(2), 261–269.CrossRefGoogle Scholar
  7. Castilla, E. P., Cunha, D. G. F., Lee, F. W. F., Loiselle, S., Ho, K. C., & Hall, C. (2015). Quantification of phytoplankton bloom dynamics by citizen scientists in urban and peri-urban environments. Environmental Monitoring Assessment, 187–690.Google Scholar
  8. CETESB. Companhia Ambiental do Estado de São Paulo. (2015). Qualidade de água. Available in: Accessed August 2015.
  9. Chavez, P., Stuart, C. S., & Jeffrey, A. A. (1991). Comparison of three different methods to merge multiresolution and multispectral data- Landsat TM and SPOT panchromatic. Photogrammetric Engineering and Remote Sensing, 57(3), 265–303.Google Scholar
  10. Cheung, M. Y., Liang, S., & Lee, J. (2013). Toxin-producing cyanobacteria in freshwater: a review of the problems, impact on drinking water safety, and efforts for protecting public health. Journal of Microbiology, 51(1), 1–10.CrossRefGoogle Scholar
  11. Clark, R. N., Swayze, G.A., Wise, R. A., Livo, K. E., Hoefen, T.M., Kokaly, R. F., Sutley, S. J. (2017). USGS digital spectral library splib06a. U.S. Geological. Survey, Digital Data Series, 231. Accessed 22 January 2018.
  12. CPRM. Serviço Geológico do Brasil. (2010). Base Cartográfica do Estado da Bahia - Escala 1:100.000. Blocos C e D. Geologia, Tectônica e Recursos Minerais do Brasil: Sistema de Informações Geográficas - SIG e Mapas. Belo Horizonte.Google Scholar
  13. Da Silva, R. M., Mehli, U., Dos Santos, J. U. M., & De Menezes, M. P. M. (2010). The coastal restinga vegetation of Pará, Brazilian Amazon: a synthesis. Brazilian Journal of Botany, 33(4), 563–573, 2010.CrossRefGoogle Scholar
  14. Ding, J., Jiang, Y., Fu, L., Peng, Q., & Kang, M. (2015). Impacts of land use on surface water quality in a subtropical river basin: a case study of the Dongjiang River basin, southeastern China. Water, 7, 4427–4445.CrossRefGoogle Scholar
  15. GlobCover. (2009). GlobCover Land Cover Maps. Accessed 15 January 2015.
  16. Gomes, R. L., Marques, E. A. G., & Franco, G. B. (2017). The waste disposal suitability of Almada River watershed. Engenharia Sanitária e Ambiental, 22(4), 731–747.CrossRefGoogle Scholar
  17. Halstead, J. A., Kliman, S., Berheide, C. W., Chaucer, A., & Cock-Esteb, A. (2014). Urban stream syndrome in a small, lightly developed watershed: a statistical analysis of water chemistry parameters, land use patterns, and natural sources. Environmental Monitoring and Assessment, 186(6), 3391–3414.CrossRefGoogle Scholar
  18. IBGE. Instituto Brasileiro de Geografia e Estatística. (2010). Available in: Accessed 28 May 2016.
  19. INEMA. Instituto do Meio Ambiente e Recursos Hídricos. (2016). Available in: Accessed 12 November 2016.
  20. Kamjunke, N., Buttner, O., Jager, C. G., Marcus, H., Tümpling, W. V., Halbedel, S., Norf, H., Brauns, M., Baborowsk, M., Wild, R., Borchardt, D., & Weitere, M. (2013). Biogeochemical patterns in a river network along a land use gradient. Environmental Monitoring and Assessment, 185(11), 9221–9236.CrossRefGoogle Scholar
  21. Lamparelli, M.C. Trophic status in São Paulo State water bodies – evaluation of monitoring methodologies. São Paulo (BR): 2004. PhD Thesis – Instituto de Biociências da Universidade de São Paulo.Google Scholar
  22. Lee, G.F., & Jones-Lee, A. (1998). Determination of nutrient limiting maximum algal biomass in waterbodies. Accessed 14 April 2014.
  23. Lourenço, R. W., Landim, P.M.B. (2004). Study on the variability of normalize difference vegetation index/NDVI by indicative kriging. Holos Environment, 4 (1), 38–55.Google Scholar
  24. Lucio, M. Z. T. P. Q. L. (2012). Hydrochemistry of Cachoeira River (Bahia state, Brazil). Acta Limnologica Brasiliensia, 24(2), 181–192.CrossRefGoogle Scholar
  25. Lundberg, C. (2013). Eutrophication, risk management and sustainability: the perceptions of different stakeholders in the northern Baltic Sea. Marine Pollution Bulletin, 66(1–2), 143–150.CrossRefGoogle Scholar
  26. Maia, A. A. D., Carvalho, S. L., & Carvalho, F. T. (2015). Comparison of two indexes of determination of the trophic state in the waters of Baixo São José dos Dourados, São Paulo, Brazil. Engenharia Sanitária e Ambiental, 20(4), 613–622.CrossRefGoogle Scholar
  27. Meneses, B. M., Reis, R., Vale, M. J., & Saraiva, R. (2015). Land use and land cover changes in Zêzere watershed (Portugal) - water quality implications. Science of the Total Environment, 527, 439–447.CrossRefGoogle Scholar
  28. Meng, C., Li, Y., Wang, Y., Yang, W., Jiao, J., Wang, M., Zhang, M., Li, Y., & Wu, J. (2015). TMDL for phosphorus and contributing factors in subtropical watersheds of southern China. Environmental Monitoring and Assessment, 187(8), 514.CrossRefGoogle Scholar
  29. Ouyang, Y. (2012). Estimation of shallow groundwater discharge and nutrient load into a river. (2012). Ecological Engineering, 38(1), 101–104.CrossRefGoogle Scholar
  30. Pilgrim, C. M., Mikhailova, E. A., Post, C. J., & Hains, J. J. (2014). Spatial and temporal analysis of land cover changes and water quality in the Lake Issaqueena watershed, South Carolina. Environmental Monitoring and Assessment, 186(11), 7617–7630.CrossRefGoogle Scholar
  31. Privette, C., & Smink, J. (2017). Assessing the potential impacts of WWTP effluent reductions within the Reedy River watershed. Ecological Engineering, 98, 11–16.CrossRefGoogle Scholar
  32. Santos, S.O. & Souza, A.C. (2014). Panorama do saneamento básico no município de Itabuna (Bahia) de 2000 a 2010. Available in: Accessed 15 October 2016.
  33. SEI. Superintendência de Estudos Econômicos e Sociais da Bahia (2004). Mapas digitalizados do Estado da Bahia: base de dados. Salvador: SEI. (CD-ROM).Google Scholar
  34. Shen, Z.l., & Liu, Q. (2009). Nutrients in the Changjiang River. Environment Monitoring and Assessment, 153(1–4), 27–44.Google Scholar
  35. Shi, P., Zhang, Y., Li, Z., Li, P., & Xu, G. (2017). Influence of land use and land cover patterns on seasonal water quality at multi-spatial scales. Catena, 151, 182–190.CrossRefGoogle Scholar
  36. Silva, K. B., Gomes, R. L., & Rego, N. A. C. (2015a). Social and environmental hydrographics implications of the land use in the plain and coastal boards between Ilhéus and Olivença – BA. (2015). Journal of Hyperspectral Remote Sensing, 5(1), 13–26.Google Scholar
  37. Silva, M. A. M., Souza, M. F. L., & Abreu, P. C. (2015b). Spatial and temporal variation of dissolved inorganic nutrients, and chlorophyll-α in a tropical estuary in northeastern Brazil: dynamics of nutrient removal. Brazilian Journal of Oceanography, 63(1), 1–15.CrossRefGoogle Scholar
  38. Toledo, Jr., A.P., Talarico, M., Chinez, S. J., & Agudo, E. G. A. (1983). A aplicação de modelos simplificadospara a avaliação de processo da eutrofização em lagos e reservatórios tropicais. Accessed 10 Feb 2016.
  39. Tu, J. (2011). Spatially varying relationships between land use and water quality across an urbanization gradient explored by geographically weighted regression. Applied Geography, 31(1), 376–392.CrossRefGoogle Scholar
  40. Vieira, J. M. P., Pinho, J. L. S., Dias, N., Schwanenberg, D., & Van Den Boogaard, H. F. P. (2013). Parameter estimation for eutrophication models in reservoirs. Water Science and Technology, 68(2), 319–327.CrossRefGoogle Scholar
  41. Wan, R., Cai, S., Li, H., Yang, G., Li, Z., & Nie, X. (2014). Inferring land use and land cover impact on stream water quality using a Bayesian hierarchical modeling approach in the Xitiaoxi River watershed, China. Journal of Environmental Management, 133, 1–11.CrossRefGoogle Scholar
  42. Wang, R., Xu, T., Yu, L., Zhu, J., & Li, X. (2013). Effects of land use types on surface water quality across an anthropogenic disturbance gradient in the upper reach of the Hun River, Northeast China. Environmental Monitoring and Assessment, 185(5), 4141–4151.CrossRefGoogle Scholar
  43. Whitehead, P. G., Jin, L., Baulch, H. M., Butterfield, D. A., Oni, S. K., Dillon, P. J., Futter, A. J., Wade, A. J., North, R., O’Connor, E. M., & Jarvie, H. P. (2011). Modelling phosphorus dynamics in multi-branch river systems: a study of the Black River, Lake Simcoe, Ontario, Canada. Science of the Total Environment, 412-413, 315–323.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Olandia Ferreira Lopes
    • 1
    • 2
  • Felizardo Adenilson Rocha
    • 3
  • Lucas Farias de Sousa
    • 1
  • Daniela Mariano Lopes da Silva
    • 1
  • Andrique Figueiredo Amorim
    • 2
  • Ronaldo Lima Gomes
    • 1
  • André Luiz Sampaio da Silva Junior
    • 1
  • Raildo Mota de Jesus
    • 1
    • 4
    Email author
  1. 1.Universidade Estadual de Santa Cruz (UESC)IlhéusBrazil
  2. 2.Instituto Federal de Educação, Ciência e Tecnologia da Bahia (IFBA)JequiéBrazil
  3. 3.Instituto Federal de Educação, Ciência e Tecnologia da Bahia (IFBA)Vitória da ConquistaBrazil
  4. 4.INCT de Energia e AmbienteUniversidade Federal da BahiaSalvadorBrazil

Personalised recommendations