Literature DB >> 14664870

Nutrient chemistry of River Pinios (Thessalia, Greece).

D Bellos1, T Sawidis, I Tsekos.   

Abstract

The impact of human activities with 3-year monitoring on the fluctuation of nutrients along the Pinios River and its tributaries were studied. Their seasonal variations throughout the years 1996-1998 were also presented. High temperatures, from June to August, cause a restriction of the water flow, an enhancement of nutrient concentration with the subsequent increase of eutrophication. High concentrations of nutrients were observed first in winter (wet period), caused by leaching of fertilizers from terrestrial systems after heavy rainfall, later during the warm months due to low water flow of the river, and at last in autumn when plant organisms began to decompose. The intensive algal and macrophyte growth (spring, summer) resulted in severe depletion of nutrients. Organic carbon showed no seasonal trend but its values were high near the estuaries. Nitrate fluxes were high at the initial station (sources) and the Titarisios tributary, whereas nitrites and ammonium were low. In contrary, the Kalentzis tributary with relatively low nitrate values showed increased values of nitrite ammonium or total nitrogen. On the other hand, the Enipeas tributary showed high SO4 values. Phosphates are remarkably present mainly after the city of Larissa, where sewage and industrial discharges occur. None of the nutrients measured in the Pinios River and its tributaries showed a clear seasonal cycle of concentration. Concentrations of nutrients and organic carbon increased as a consequence of anthropogenic inputs, particularly point discharges from sewage treatment plants (i.e. showing distinct, but variable, concentration peaks), as well as diffuse urban and/or agricultural runoff over long areas during storm events. The agricultural management, the urban pollution, mainly from Larissa City, and the climate conditions in the catchment basin (Thessalia Plain) of Pinios River and its tributaries greatly affect the chemical composition of their waters.

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Year:  2004        PMID: 14664870     DOI: 10.1016/S0160-4120(03)00153-3

Source DB:  PubMed          Journal:  Environ Int        ISSN: 0160-4120            Impact factor:   9.621


  5 in total

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5.  Using Non-supervised Artificial Neural Network for Determination of Anthropogenic Disturbance in a River System.

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Journal:  Trop Life Sci Res       Date:  2017-07-31
  5 in total

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