2009Acta Scientiae CircumstantiaeRequires access

Application of a water quality model in pollution load estimation in Dongguan

Yi-bing Su

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Abstract

Lack of pollution data is a problem frequently faced in water environmental planning.To address the problem,a load estimation approach was established based on a mathematical water quality model via pollution source-water quality response relationship.This methodology includes three major steps.The first step is to build the pollution load and water quality response relationship with the mathematical model.Then the optimized target and constraint equations to calculate pollution load are proposed.The last step is to solve the optimization problem,assess the effect of estimation,and determine the pollution load from the sources.Taking Dongguan as a case study,we analyzed water consumption data and pollution status.Then a one-dimensional water quality model was established with Saint-Venant and dilution-diffusion equations.Based on the water quality investigation data in 2005,the pollution loads were assessed by comparing to the water quality modeling results and field monitoring data.In 2005,the domestic and industrial CODCr load was estimated to be 25.2×104 t,of which the domestic pollution load was 60.4%,and the industrial pollution load was 39.6%.The estimated pollution load accurately reflected the water quality change at that time in Dongguan.

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Lack of pollution data is a problem frequently faced in water environmental planning.To address the problem,a load estimation approach was established based on a mathematical water quality model via pollution source-water quality response relationship.This methodology includes three major steps.The first step is to build the pollution load and water quality response relationship with the mathematical model.Then the optimized target and constraint equations to calculate pollution load are proposed.The last step is to solve the optimization problem,assess the effect of estimation,and determine the pollution load from the sources.Taking Dongguan as a case study,we analyzed water consumption data and pollution status.Then a one-dimensional water quality model was established with Saint-Venant and dilution-diffusion equations.Based on the water quality investigation data in 2005,the pollution loads were assessed by comparing to the water quality modeling results and field monitoring data.In 2005,the domestic and industrial CODCr load was estimated to be 25.2×104 t,of which the domestic pollution load was 60.4%,and the industrial pollution load was 39.6%.The estimated pollution load accurately reflected the water quality change at that time in Dongguan.

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Available abstract

Lack of pollution data is a problem frequently faced in water environmental planning.To address the problem,a load estimation approach was established based on a mathematical water quality model via pollution source-water quality response relationship.This methodology includes three major steps.The first step is to build the pollution load and water quality response relationship with the mathematical model.Then the optimized target and constraint equations to calculate pollution load are proposed.The last step is to solve the optimization problem,assess the effect of estimation,and determine the pollution load from the sources.Taking Dongguan as a case study,we analyzed water consumption data and pollution status.Then a one-dimensional water quality model was established with Saint-Venant and dilution-diffusion equations.Based on the water quality investigation data in 2005,the pollution loads were assessed by comparing to the water quality modeling results and field monitoring data.In 2005,the domestic and industrial CODCr load was estimated to be 25.2×104 t,of which the domestic pollution load was 60.4%,and the industrial pollution load was 39.6%.The estimated pollution load accurately reflected the water quality change at that time in Dongguan.

Key concepts: Pollution, Water quality, Environmental science, Total maximum daily load, Water pollution, Computer science, Biology, Chemistry

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