Microbial-based evaluation of foaming events in full-scale wastewater treatment plants by microscopy survey and quantitative image analysis

Abstract

Activated sludge systems are prone to be affected by foaming occurrences causing the sludge to rise in the reactor and affecting the wastewater treatment plant (WWTP) performance. Nonetheless, there is currently a knowledge gap hindering the development of foaming events prediction tools that may be fulfilled by the quantitative monitoring of AS systems biota and sludge characteristics. As such, the present study focuses on the assessment of foaming events in full-scale WWTPs, by quantitative protozoa, metazoa, filamentous bacteria, and sludge characteristics analysis, further used to enlighten the inner relationships between these parameters. In the current study, a conventional activated sludge system (CAS) and an oxidation ditch (OD) were surveyed throughout a period of 2 and 3 months, respectively, regarding their biota and sludge characteristics. The biota community was monitored by microscopic observation, and a new filamentous bacteria index was developed to quantify their occurrence. Sludge characteristics (aggregated and filamentous biomass contents and aggregate size) were determined by quantitative image analysis (QIA). The obtained data was then processed by principal components analysis (PCA), cross-correlation analysis, and decision trees to assess the foaming occurrences, and enlighten the inner relationships. It was found that such events were best assessed by the combined use of the relative abundance of testate amoeba and nocardioform filamentous index, presenting a 92.9 % success rate for overall foaming events, and 87.5 and 100 %, respectively, for persistent and mild events.

Sludge volume index and suspended solids estimation of mature aerobic granular sludge by quantitative image analysis and chemometric tools

“…Given the large amount of data provided by QIA the use of a number of different multivariate statistical techniques, such as cross correlation (CC), partial least squares (PLS), and principal components analysis (PCA) render indispensable to organize the data and extract relevant information. In fact such techniques have already been found useful to correlate operational conditions to sludge characteristics and predict biochemical oxygen demand (BOD 5 ) and removal of trace organic compounds in WWTP [26][27][28].…”

Rheology and Microbiology of Sludge from a Thermophilic Aerobic Membrane Reactor

“…The biological process is based on the activity of different populations of organisms set at growing levels of the detritus food web. Therefore, any tools enabling us to identify and measure the activity of these organisms provide valuable information for improving and strengthening the operation of wastewater treatment plants ( [1][2][3][4] among others).…”

“…Besides, the microscopic observation 2 Journal of Chemistry of floc characteristics and the identification and quantification of filamentous bacteria allow us to highlight disturbances within their community and to foresee the occurrence of specific dysfunctions, for example, sludge foaming, bulking, and solids washout [4,[12][13][14][15]. Ecological features like specific indexes (Sludge Biotic Index (SBI), designed by [1] and Sludge Index (SI), proposed by Grupo de Bioindicación de Sevilla (GBS, Spain) [16][17][18][19]) enable plant managers to tune the operation parameters, such as sludge age, hydraulic retention time, and dissolved oxygen concentration, in order to maintain optimum conditions for the sludge biotic components.…”

Assessment of an aerobic granular sludge system in the presence of pharmaceutically active compounds by quantitative image analysis and chemometric techniques

“…Two previously developed QIA routines were run on the acquired images in Matlab 7.8 (The Mathworks, Natick, MA) for the structural and morphological characterization of the granular and floccular fractions, respectively. A detailed description of these routines can be found elsewhere (Amaral, 2003;Deepnarain et al, 2019;Leal et al, 2016; Mesquita et al, 2016).…”

“…Due to the large amount of information provided by QIA, its combined use with chemometric techniques has become increasingly important in organizing and extracting relevant information from such comprehensive datasets. Thus, different multivariate statistical techniques, including cross-correlation (CC), principal component analysis (PCA), decision trees (DT), partial least squares regression (PLS), and discriminant analysis (DA), among others, were already successfully applied for a number of studies encompassing biological WWT systems monitoring (Amaral, 2003;Deepnarain et al, 2019;Kim et al, 2011;Leal et al, 2016;Mesquita et al, 2016).…”