Monitoring and modeling land-based marine pollution


Cebe K., BALAS L.

REGIONAL STUDIES IN MARINE SCIENCE, vol.24, pp.23-39, 2018 (SCI-Expanded) identifier identifier

Abstract

An integrated numerical coastal water quality model was developed to simulate the effects of land-based pollutants. The model was applied to Izmir Inner Bay, Turkey. Water temperature, salinity, conductivity, pH, turbidity, dissolved oxygen, total dissolved solids, ammonium, nitrite, nitrate, total dissolved and inorganic phosphorus, and total dissolved and organic carbon were measured monthly, for one year. Land-based pollutant loadings were estimated using the USEPA's Storm Water Management Model (SWMM, v5.1). The study considered the loads washed off from the surrounding basin of the Izmir Inner Bay, namely, total suspended solids, biological and chemical oxygen demand, inorganic phosphorus, ammonia, nitrite, and nitrate concentrations, from urban and as well as industrial effluents. The coastal circulations and changes in the water quality parameters were simulated by HYDROTAM-3D, a three-dimensional coastal hydrodynamics, transport, and water quality model. The concentrations of water quality parameters, namely, total dissolved phosphorus, ammonia, nitrite, nitrate nitrogen, and dissolved oxygen for Izmir Inner Bay were estimated for the year 2015 and compared with the monthly field measurements conducted at six monitoring stations located near discharge points in the bay. The model successfully simulated the effects of land use on coastal water quality parameters. The results indicated that Izmir Inner Bay coastal waters were hypertrophic in 2015. Best management practices in remediation, street cleaning, good agricultural practices, and increased proportions of wastewater treatment before discharge were considered in the model to reduce the land-based pollution in projections for 2040. The results showed that remediation will reduce the point and distributed source loadings on the sub-basins, and Izmir Inner Bay water quality is expected to improve significantly. (C) 2018 Elsevier B.V. All rights reserved.