2009Unpublished venueRequires access

Effects of seasonal climate change on Chemical Oxygen Demand (COD) concentration in the Anzali Wetland (Iran)

Ahmad Tahershamsi, A. Bakhtiary, Alireza Mousavi, Nasir Toosi

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Abstract

The Anzali Wetland is a distinguished coastal wetland in the southern coast of the Caspian Sea in north of Iran. This wetland is connected to the Caspian Sea through a channel and is supported with freshwater by 10 major rivers. The Anzali Wetland plays a key role as a habitat for many indigenous plants and animal species. It is believed that the ecosystem of the wetland is threatened by inflow of excessive amounts of COD (Chemical Oxygen Demand), T-N (Total Nitrogen) and T-P (Total Phosphorus). In this paper, a two-dimensional depth-averaged model for hydrodynamic and advection-dispersion is used to determine COD distribution in a large area of Anzali Wetland. Forcing actions of rivers input and wind is taken into account. According to available field data, the model is executed for three periods which are dry (23 rd August-1 st September), early rainy (13 th October-25 th October) and mid-rainy (2 nd December-14 th December) seasons. The simulated values are compared with observed ones at four stations. The results indicate that COD concentrations at all four stations were high denoting noticeable organic pollution in the wetland especially in the dry season.

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What this paper is about

The Anzali Wetland is a distinguished coastal wetland in the southern coast of the Caspian Sea in north of Iran. This wetland is connected to the Caspian Sea through a channel and is supported with freshwater by 10 major rivers. The Anzali Wetland plays a key role as a habitat for many indigenous plants and animal species. It is believed that the ecosystem of the wetland is threatened by inflow of excessive amounts of COD (Chemical Oxygen Demand), T-N (Total Nitrogen) and T-P (Total Phosphorus). In this paper, a two-dimensional depth-averaged model for hydrodynamic and advection-dispersion is used to determine COD distribution in a large area of Anzali Wetland. Forcing actions of rivers input and wind is taken into account. According to available field data, the model is executed for three periods which are dry (23 rd August-1 st September), early rainy (13 th October-25 th October) and mid-rainy (2 nd December-14 th December) seasons. The simulated values are compared with observed ones at four stations. The results indicate that COD concentrations at all four stations were high denoting noticeable organic pollution in the wetland especially in the dry season.

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Available abstract

The Anzali Wetland is a distinguished coastal wetland in the southern coast of the Caspian Sea in north of Iran. This wetland is connected to the Caspian Sea through a channel and is supported with freshwater by 10 major rivers. The Anzali Wetland plays a key role as a habitat for many indigenous plants and animal species. It is believed that the ecosystem of the wetland is threatened by inflow of excessive amounts of COD (Chemical Oxygen Demand), T-N (Total Nitrogen) and T-P (Total Phosphorus). In this paper, a two-dimensional depth-averaged model for hydrodynamic and advection-dispersion is used to determine COD distribution in a large area of Anzali Wetland. Forcing actions of rivers input and wind is taken into account. According to available field data, the model is executed for three periods which are dry (23 rd August-1 st September), early rainy (13 th October-25 th October) and mid-rainy (2 nd December-14 th December) seasons. The simulated values are compared with observed ones at four stations. The results indicate that COD concentrations at all four stations were high denoting noticeable organic pollution in the wetland especially in the dry season.

Key concepts: Wetland, Environmental science, Hydrology (agriculture), Wet season, Dry season, Ecosystem, Chemical oxygen demand, Biochemical oxygen demand

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Effects of seasonal climate change on Chemical Oxygen Demand (COD) concentration in the Anzali Wetland (Iran) — Research Paper | ScholarLens