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Model Predicted Water Quality Response to Reductions in Inorganic and Organic Nitrogen Loading

机译:模型预测的水质响应减少无机和有机氮负荷

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As part of a total maximum daily load analysis of North Carolina's Neuse River Estuary, eutrophication modeling was conducted using a modified version of an existing two-dimensional, laterally-averaged model (CE-QUAL-W2). The model simulated conditions in the estuary for a 43-month period that included two episodes of extraordinarily high nutrient and freshwater loading. Phytoplankton blooms with chlorophyll-a concentrations in excess of 40 μg/l were seen in three of the four years simulated, while bottom water anoxia was seen intermittently in each of the four summers. The calibrated model was used to predict the water quality improvement in the estuary associated with a variety of nitrogen load reductions varying from 5% to 30%. For each scenario, an assumption was made as to how much of the reduction would come from dissolved inorganic, dissolved organic, and particulate organic nitrogen. Water quality improvement was quantified by comparing the predicted chlorophyll-a concentrations for the nutrient reduction scenarios to a case without nutrient reduction. Additional cases were run to investigate the extent to which changes in sediment quality, occurring over several years might produce an additional improvement in water quality by reducing sediment oxygen demand and benthically mediated nutrient recycling. As expected, reduced nitrogen loading produced lower water-column nitrogen concentrations and lower chlorophyll-a concentrations. The magnitude of change in chlorophyll-a concentration was surprisingly small, however, as compared to the magnitude of load reduction. The magnitude of change in chlorophyll-a concentration differed according to the apportionment of nitrogen load reduction. Chlorophyll concentrations were most sensitive to inorganic nitrogen reduction and least sensitive to organic nitrogen load reductions. Including sediment denitrification improved the fit between observed and predicted nitrate, chlorophyll-a, and dissolved oxygen concentrations. Denitrification was also found to produce a negative feedback in the system whereby long-term reductions in sediment organic matter concentrations associated with nutrient reduction produced very slightly higher chlorophyll-a concentrations as compared to corresponding cases that did not consider the changes in sediment organic matter concentrations.
机译:作为北卡罗来纳州Neuse河河口的总日最大负荷分析的一部分,使用现有二维横向平均模型(CE-Qual-W2)的改进版本进行了富营养化建模。河口模型仿真条件为43个月的时间,其中包括两发作的营养和淡水载荷。在模拟的四年中三年中,植物植物绽放出叶绿素 - 浓度超过40μg/ L的浓度,而底部水缺氧在四个夏天中的每一个间歇地看到。校准的模型用于预测与各种氮负荷减少相关的浅水的水质改善,从5%变化至30%。对于每种情况,假设是从溶解的无机,溶解的有机和颗粒有机氮的减少量。通过将预测的叶绿素-A浓度与没有营养减少的情况进行比较预测的叶绿素-A浓度来量化水质改善。额外的案件旨在调查沉积物质量变化的程度,在几年内发生可能通过减少沉积物需氧量和脊柱介导的营养回收来产生水质的额外改善。正如预期的那样,降低的氮负载产生了较低的水柱氮浓度和降低叶绿素 - 浓度。然而,与减压幅度相比,叶绿素-A浓度的变化幅度令人惊讶地小。叶绿素-A浓度的变化的大小根据氮负荷降低的分配而不同。叶绿素浓度对无机氮气减少最敏感,对有机氮负荷降低最敏感。包括沉积物脱氮,改善了观察和预测的硝酸盐,叶绿素-A和溶解的氧浓度之间的配合。还发现反硝化在系统中产生负反馈,由此与营养物还原相关的沉积物有机质浓度的长期减少产生非常略高的叶绿素 - 浓度,而不是认为沉积物有机物质浓度的变化的相应情况相比。

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