The purpose of this paper is to find the variation of pollutant concentration field and its impact on water intake of the Fen River entrance into Yellow River. A 2-D water hydrodynamic and quality model was used to simulate flow and variation of the pollutant concentration field. There will be a backwater in tributaries when the water in Yellow River flow into Fen River. The COD concentration decreased significantly at the mouth of the Fen River into the Yellow River. The water quality fit class III and ⅴ at North Zhao water intake in normal year(P=50%) and dry year(P=75%). The pollution area of lower reach is long and narrow in these years because the obstruction of central shoal. The flow path should be approximately 2.3km,2.9km in normal year(P=50%) and dry year(P=75%) to keep COD concentration in class III. Keywords-2-D water hydrodynamic 2-D water quality estuary pollutant transport water intake

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Aiming at the shortcomings of the research on individual identification technology of emitters, which is primarily based on theoretical simulation and lack of verification equipment to conduct external field measurements, an emitter individual identification system based on Automatic Dependent Surveillance–Broadcast is designed. On one hand, the system completes the individual feature extraction of the signal preamble. On the other hand, it realizes decoding of the transmitter’s individual identity information and generates an individual recognition training data set, on which we can train the recognition network to achieve individual signal recognition. For the collected signals, six parameters were extracted as individual features. To reduce the feature dimensions, a Bessel curve fitting method is used for four of the features. The spatial distribution of the Bezier curve control points after fitting is taken as an individual feature. The processed features are classified with multiple classifiers, and the classification results are fused using the improved Dempster–Shafer evidence theory. Field measurements show that the average individual recognition accuracy of the system reaches 88.3%, which essentially meets the requirements.