Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4390299 | Ecological Engineering | 2011 | 6 Pages |
Abstract
Agricultural, industrial, and urban activities are the major sources for eutrophication of surface water ecosystems. Currently, determination of nutrients in surface water is primarily accomplished by manually collecting samples for laboratory analysis, which requires at least 24Â h. In other words, little to no effort has been devoted to monitoring real-time variations of nutrients in surface water ecosystems due to the lack of suitable and/or cost-effective wireless sensors. However, when considering human health or instantaneous outbreaks such as algal blooms, timely water-quality information is very critical. In this study, we developed a new paradigm of a dynamic data-driven application system (DDDAS) for estimating the real-time loads of nitrogen (N) in a surface water ecosystem. This DDDAS consisted of the following components: (1) a Visual Basic (VB) program for downloading US Geological Survey real-time chlorophyll and discharge data from the internet; (2) a STELLA model for evaluating real-time N loads based on the relationship between chlorophyll and N as well as on river discharge; (3) a batch file for linking the VB program and STELLA model; and (4) a Microsoft Windows Scheduled Task wizard for executing the model and displaying outputs on a computer screen at selected schedules. The DDDAS was validated using field measurements with a very good agreement prior to its applications. Results show that the real-time loads of TN (total N) and NOx (nitrate and nitrite) varied from positive to negative with the maximums of 1727Â kg/h TN and 118Â kg/h NOx and the minimums of â2483Â kg/h TN and â168Â kg/h NOx at the selected site. The negative loads occurred because of the back flow of the river in the estuarine environment. Our study suggests that the DDDAS developed in this study was feasible for estimating the real-time variations of TN and NOx in the surface water ecosystem.
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Authors
Y. Ouyang, S.M. Luo, L.H. Cui, Q. Wang, J.E. Zhang,