Deepnarain Nashia, Kumari Sheena, Ramjith Jordache, Swalaha Feroz Mahomed, Tandoi Valter, Pillay Kriveshin, Bux Faizal
Institute for Water and Wastewater Technology, Durban University of Technology, Durban, South Africa E-mail:
Division of Epidemiology and Biostatistics, School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa.
Water Sci Technol. 2015;72(3):391-405. doi: 10.2166/wst.2015.181.
Biological nutrient removal (BNR) systems across the globe frequently experience bulking and foaming episodes, which present operational challenges such as poor sludge settling due to excessive filamentous bacteria. A full-scale BNR plant treating primarily domestic wastewater was monitored over a period of 1 year to investigate filamentous bacterial growth response under various plant operating parameters. Identification of filamentous bacteria by conventional microscopy and fluorescent in situ hybridisation indicated the dominance of Eikelboom Type021N, Thiothrix spp., Eikelboom Type 1851 and Eikelboom Type 0092. A cumulative logit model (CLM) was applied to elucidate significant relationships between the filamentous bacteria and plant operational parameters. The model could predict the potential abundance of dominant filamentous bacteria in relation to wastewater treatment plant operational parameters. Data obtained from the model corroborated with previous findings on the dominance of most filaments identified, except for Type 0092, which exhibited some unique traits. With further validation, the model could be successfully applied for identifying specific parameters which could contribute towards filamentous bulking, thus, providing a useful tool for regulating specific filamentous growth in full-scale wastewater treatment plants.
全球范围内的生物营养物去除(BNR)系统经常出现污泥膨胀和泡沫问题,这些问题带来了诸如丝状细菌过多导致污泥沉降性能差等运行挑战。对一座主要处理生活污水的全尺寸BNR工厂进行了为期1年的监测,以研究在各种工厂运行参数下丝状细菌的生长反应。通过传统显微镜和荧光原位杂交对丝状细菌进行鉴定,结果表明艾氏021N型、硫丝菌属、艾氏1851型和艾氏0092型占主导地位。应用累积logit模型(CLM)来阐明丝状细菌与工厂运行参数之间的显著关系。该模型可以预测与污水处理厂运行参数相关的优势丝状细菌的潜在丰度。从该模型获得的数据与之前关于大多数已鉴定丝状菌优势地位的研究结果相符,但0092型除外,它表现出一些独特的特征。经过进一步验证,该模型可成功应用于识别可能导致丝状膨胀的特定参数,从而为调节全尺寸污水处理厂中特定丝状菌的生长提供一个有用的工具。