Multivariate statistical analysis was applied to investigate the dependencies and underlying patterns between N₂O emissions and online operational variables (dissolved oxygen and nitrogen component concentrations, temperature and influent flow-rate) during biological nitrogen removal from wastewater. The system under study was a full-scale reactor, for which hourly sensor data were available. The 15-month long monitoring campaign was divided into 10 sub-periods based on the profile of N₂O emissions, using Binary Segmentation. The dependencies between operating variables and N₂O emissions fluctuated according to Spearman's rank correlation. The correlation between N₂O emissions and nitrite concentrations ranged between 0.51 and 0.78. Correlation >0.7 between N₂O emissions and nitrate concentrations was observed at sub-periods with average temperature lower than 12 °C. Hierarchical k-means clustering and principal component analysis linked N₂O emission peaks with precipitation events and ammonium concentrations higher than 2 mg/L, especially in sub-periods characterized by low N₂O fluxes. Additionally, the highest ranges of measured N₂O fluxes belonged to clusters corresponding with NO₃-N concentration less than 1 mg/L in the upstream plug-flow reactor (middle of oxic zone), indicating slow nitrification rates. The results showed that the range of N₂O emissions partially depend on the prior behavior of the system. The principal component analysis validated the findings from the clustering analysis and showed that ammonium, nitrate, nitrite and temperature explained a considerable percentage of the variance in the system for the majority of the sub-periods. The applied statistical methods, linked the different ranges of emissions with the system variables, provided insights on the effect of operating conditions on N₂O emissions in each sub-period and can be integrated into N₂O emissions data processing at wastewater treatment plants.

应用多元统计分析来研究从废水中去除生物氮过程中N ₂ O排放与在线操作变量（溶解的氧和氮成分浓度，温度和进水流速）之间的依赖性和基本模式。所研究的系统是一个大型反应堆，可获得每小时的传感器数据。进行了为期15个月的监测活动，使用二元分割法根据N ₂ O排放量将其分为10个子时期。操作变量与N ₂ O排放之间的依存关系根据Spearman等级相关性而波动。N ₂之间的相关性O排放和亚硝酸盐浓度在0.51到0.78之间。在平均温度低于12°C的亚周期内，观察到N ₂ O排放与硝酸盐浓度之间的相关性> 0.7 。分层k均值聚类和主成分分析将N ₂ O排放峰与降水事件和铵浓度高于2 mg / L关联起来，特别是在以N ₂ O通量低为特征的子时期。另外，在上游活塞流反应器（含氧区的中部）中，所测量的N ₂ O通量的最高范围属于与NO ₃ -N浓度小于1 mg / L相对应的簇，表明硝化速率较慢。结果表明，N ₂的范围O排放部分取决于系统的先验行为。主成分分析验证了聚类分析的结果，并表明铵，硝酸盐，亚硝酸盐和温度可解释大部分子时期系统中相当大的百分比变化。所应用的统计方法将不同的排放范围与系统变量关联在一起，从而提供了操作条件对每个子时期N ₂ O排放量的影响的见解，并且可以集成到废水处理厂的N ₂ O排放量数据处理中。