Faculty of Chemistry, University of Warsaw, 1 Pasteura Street, 02-093, Warsaw, Poland; UNSW Water Research Centre, School of Civil and Environmental Engineering, UNSW, Sydney, Australia; Faculty of Building Services, Hydro and Environmental Engineering, Warsaw University of Technology, Poland.
Faculty of Mechatronics, Institute of Automatic Control and Robotics, Warsaw University of Technology, Poland.
Environ Res. 2022 Nov;214(Pt 2):113871. doi: 10.1016/j.envres.2022.113871. Epub 2022 Jul 14.
Biosolids storage areas are a significant contributor to wastewater treatment plant (WWTPs) odour emissions which can cause sensorial impact to surrounding communities. Most odour impact regulations are based on odour concentration (COD) measurements determined by dynamic olfactometry. Understanding the relationship between odorants concentrations and COD in the biosolids emission is important to identify how the measurement and monitoring can be conducted using analytical rather than sensorial techniques. Some of the odorants are unknown in biosolid emissions, increasing the uncertainty in predicting COD. In this study, emissions from 56 biosolid samples collected from two WWTPs located in Sydney, Australia, were analysed by analytical and sensorial methods, including olfactory detection port (ODP) and dynamic olfactometry. Concentrations of 25 odorants and two ordinal variables represented odour events detected by ODP assessors were linked to COD values. Bayesian Model Averaging and Variable Selection with Bayesian Adaptive Sampling were applied to model the relation between COD and odorants concentrations. Results indicate the usability of the probabilistic methods and nonlinear transformations in modelling the odour concentrations based on odorants concentrations from biosolids emission and the accuracy of a small dataset.
生物固体储存区是污水处理厂（WWTP）气味排放的一个重要贡献者，这些气味排放可能会对周围社区造成感官影响。大多数气味影响法规都是基于通过动态嗅探法测定的气味浓度（COD）测量值。了解生物固体排放物中气味浓度与 COD 之间的关系对于确定如何使用分析而非感官技术进行测量和监测非常重要。一些气味物质在生物固体排放物中是未知的，这增加了预测 COD 的不确定性。在这项研究中，分析了来自澳大利亚悉尼的两个 WWTP 收集的 56 个生物固体样本的排放物，采用了分析和感官方法，包括嗅觉检测端口（ODP）和动态嗅探法。将 25 种气味物质的浓度和两个代表 ODP 评估员检测到的气味事件的有序变量与 COD 值相关联。应用贝叶斯模型平均和贝叶斯自适应采样变量选择来建立 COD 与气味浓度之间的关系模型。结果表明，概率方法和非线性变换在基于生物固体排放物中气味物质浓度建模气味浓度方面的可用性，以及小数据集的准确性。
