运用氨氮变化形态进行水质预测分析研究
Research on Water Quality Prediction and Analysis Using Ammonia Nitrogen Change Patterns
DOI: 10.12677/ojns.2026.146079, PDF,   
作者: 汪 浩, 杨 君:台州市生态环境局温岭分局监测站,浙江 台州
关键词: 交接断面;氨氮;溶解氧;变化形态;水质预警;Transfer Section; Ammonia Nitrogen; Dissolved Oxygen; Change Patterns; Water Quality Warning
摘要: 浙江省地表水交接断面水质自动监测站·滨海站是温岭市金清大港入海水质监测断面,也是温岭市重要的出境交接断面之一,目标水质是Ⅲ类。文章分析了该断面两个易超标因子氨氮和溶解氧的变化趋势,发现每一年滨海站氨氮均呈现“V + W”的变化形态,并尝试运用氨氮的变化形态进行水质预警。
Abstract: The Zhejiang Province Surface Water Transfer Section Water Quality Automatic Monitoring Station∙Binhai Station is a water quality monitoring section at the estuary of Jinqing Dagang in Wenling City, as well as one of the important outbound transfer sections in Wenling City. The target water quality is Class III. The article analyzes the trend of changes in two easily exceeding factors, ammonia nitrogen and dissolved oxygen, in this section. It is found that the ammonia nitrogen at Binhai Station shows a “V + W” change pattern every year, and attempts to use the change pattern of ammonia nitrogen for water quality warning.
文章引用:汪浩, 杨君. 运用氨氮变化形态进行水质预测分析研究[J]. 自然科学, 2026, 14(6): 755-761. https://doi.org/10.12677/ojns.2026.146079

参考文献

[1] 杨琴, 谢淑云. BP神经网络在洞庭湖氨氮浓度预测中的应用[J]. 水资源与水工程学报, 2006, 17(1): 65-70.
[2] 纪小红, 潘赢. ISOA-BP组合优化氨氮预测模型[J]. 青海师范大学学报(自然科学版), 2020(4): 25-31.
[3] 潘赢, 贾国庆, 郜伟伟. SOA优化BP 神经网络的水体氨氮预测模型[J]. 佳木斯大学学报(自然科学版), 2020, 38(2): 40-44.
[4] 高平, 宋益春, 彭新建, 毛力. 基于QPSO-GRNN的水质氨氮预测建模[J]. 电脑知识与技术(过刊), 2013, 9(23): 5373-5376.
[5] 童俊. 基于SVR-ARIMA组合模型的金泽水库氨氮预测研究[J]. 微电脑运用, 2020, 36(4): 4-13.
[6] 苏建成. 地表水中温度、溶解氧、氨氮的关系研究[J]. 科技创新与应用, 2020(14): 44-45.