Abstract：
The denitrification process in aquaculture systems plays a crucial role in nitrogen (N) cycle and N budget estimation. Reliable models are needed to rapidly quantify denitrification rates and assess nitrogen losses. This study conducted a comparative analysis of denitrification rates in fish, crabs, and natural ponds in the Taihu region from March to November 2021, covering a complete aquaculture cycle. The results revealed that aquaculture ponds exhibited higher denitrification rates compared to natural ponds. Key variables influencing denitrification rates were Nitrate nitrogen (NO3--N), Suspended particles (SPS), and chlorophyll a (Chla). There was a significant positive correlation between SPS concentration and denitrification rates. However, we observed that the denitrification rate initially rose with increasing Chla concentration, followed by a subsequent decline. To develop parsimonious models for denitrification rates in aquaculture ponds, we constructed five different statistical models to predict denitrification rates, among which the improved quadratic polynomial regression model (SQPR) that incorporated the three key parameters accounted for 80.7% of the variability in denitrification rates. Additionally, a remote sensing model (RSM) utilizing SPS and Chla explained 43.8% of the variability. The RSM model is particularly valuable for rapid estimation in large regions where remote sensing data are the only available source. This study enhances the understanding of denitrification processes in aquaculture systems, introduces a new model for estimating denitrification in aquaculture ponds, and offers valuable insights for environmental management.
摘要：
水产养殖系统中的反硝化过程在氮（N）循环和氮预算估算中起着至关重要的作用。需要可靠的模型来快速量化反硝化速率并评估氮损失。本研究对鱼类的反硝化速率进行了比较分析，螃蟹,和2021年3月至11月太湖地区的天然池塘，涵盖完整的水产养殖周期。结果表明，与天然池塘相比，水产养殖池塘的反硝化速率更高。影响反硝化速率的关键变量为硝态氮(NO3--N),悬浮颗粒(SPS),和叶绿素a（Chla）。SPS浓度与反硝化率呈显著正相关。然而,我们观察到反硝化速率最初随着Chla浓度的增加而上升，随后下降。建立水产养殖池塘反硝化速率的简约模型，我们构建了五种不同的统计模型来预测反硝化速率，其中包含三个关键参数的改进的二次多项式回归模型（SQPR）占反硝化率变异性的80.7％。此外,利用SPS和Chla的遥感模型(RSM)解释了43.8%的变异性。RSM模型对于遥感数据是唯一可用来源的大区域的快速估计特别有价值。这项研究增强了对水产养殖系统中反硝化过程的理解，介绍了一种估算水产养殖池塘反硝化的新模型，并为环境管理提供了宝贵的见解。
