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

Abstract

In this study, a sequencing batch reactor (SBR) with aerobic granular sludge (AGS) was operated with synthetic wastewater containing environmental relevant concentrations of 17β-estradiol (E2), 17α-ethinylestradiol (EE2) and sulfamethoxazole (SMX). Despite the presence of the studied PhAC, the granular fraction clearly predominated (TSS gran /TSS ranging from 0.82 to 0.98) throughout the monitoring period, presenting aggregates with high organic fraction (VSS/TSS above 0.83) and good settling characteristics (SVI 5 ranging from 15 to 39 mL/ gTSS). A principal component analysis (PCA) with quantitative image analysis (QIA) based data allowed to distinguish the different operational periods, namely with mature granules (CONT), and the E2, EE2, and SMX feeding periods. It further revealed a positive relationship between the biomass density, sludge settling ability, overall and granular biomass contents, granulation properties, granular biomass fraction and large granules fraction and size. Moreover, a discriminant analysis (DA) allowed to successfully discriminate not only the different operational periods, mainly by using the floccular apparent density, granular stratification and contents data, but also the PhAC presence in samples. The filamentous bacteria contents, sludge settling properties, settling properties stability and granular stratification, structure and contents parameters were found to be crucial for that purpose.

Prediction of sludge settleability, density and suspended solids of aerobic granular sludge in the presence of pharmaceutically active compounds by quantitative image analysis and chemometric tools

“…Before the experiments with PhAC, the reactor was operated in absence of PhAC acting as a control (CONT). The experiments with PhAC were conducted operating the SBR in the presence of environmentally relevant concentrations of E2, EE2, and SMX, namely 0.221 mg L − 1 , 0.278 mg L − 1 and 0.290 mg L − 1 , in average, respectively [28], added every week to the system during the experiments. Care was taken to allow the AGS attaining somewhat similar characteristics, mainly in terms of the overall removal performance and granular biomass (fraction and large granules size), in the beginning of the monitoring period for all experiments (CONT, E2, EE2, and SMX).…”

“…The F1-F3 fractions of sludge have been previously used for activated sludge classifications purposes [13], while the G1-G3 size classifications were previously used for anaerobic granular sludge systems' monitoring purposes [16,28]. Detailed information on the employed morphological and structural parameters for the sludge floccular and granular fractions can be found elsewhere [33,34].…”

Monitoring morphological changes from activated sludge to aerobic granular sludge under distinct organic loading rates and increasing minimal imposed sludge settling velocities through quantitative image analysis

“…In these studies, it was possible to assess disturbances in CAS systems, as well as quantify morphological changes during anaerobic granulation and deterioration in an expanded granular sludge blanket reactor (EGSB) reactor using QIA. Recently, the effect of pharmaceutical active compounds on mature aerobic granules was assessed trough QIA (Leal et al, 2021a(Leal et al, , 2021b. In aerobic granulation studies, Beun et al (2002) determined the diameter, aspect ratio and shape factor of granules formed using QIA and reported that the aspect ratio and shape factor stabilized prior to the average diameter.…”

Effects of desloratadine on activated sludge: Behaviour of EPS and sludge properties

“…Quantitative Image Analysis (QIA) has proven to be a suitable tool for monitoring biological treatment systems [18], as well as the structural assessment of biological aggregates in AS, including the identification of AS dysfunctions, namely filamentous bulking and non-filamentous bulking [19]. Furthermore, methodologies of multivariate statistics, such as PCA, have become increasingly important in improving biological processes analysis, mainly in organizing and extracting relevant information from such comprehensive datasets [20].…”