The phototrophic capability of Candidatus Accumulibacter (Accumulibacter), a common polyphosphate accumulating organism (PAO) in enhanced biological phosphorus removal (EBPR) systems, was investigated in this study. Accumulibacter is phylogenetically related to the purple bacteria Rhodocyclus from the family Rhodocyclaceae, which belongs to the class Betaproteobacteria. Rhodocyclus typically exhibits both chemoheterotrophic and phototrophic growth, however, limited studies have evaluated the phototrophic potential of Accumulibacter. To address this gap, short and extended light cycle tests were conducted using a highly enriched Accumulibacter culture (95%) to evaluate its responses to illumination. Results showed that, after an initial period of adaptation to light conditions (approximately 4-5 h), Accumulibacter exhibited complete phosphorus (P) uptake by utilising polyhydroxyalkanoates (PHA), and additionally by consuming glycogen, which contrasted with its typical aerobic metabolism. Mass, energy, and redox balance analyses demonstrated that Accumulibacter needed to employ phototrophic metabolism to meet its energy requirements. Calculations revealed that the light reactions contributed to the generation of, at least more than 67% of the ATP necessary for P uptake and growth. Extended light tests, spanning 21 days with dark/light cycles, suggested that Accumulibacter generated ATP through light during initial operation, however, it likely reverted to conventional anaerobic/aerobic metabolism under dark/light conditions due to microalgal growth in the mixed culture, contributing to oxygen production. In contrast, extended light tests with an enriched Tetrasphaera culture, lacking phototrophic genes in its genome, clearly demonstrated that phototrophic P uptake did not occur. These findings highlight the adaptive metabolic capabilities of Accumulibacter, enabling it to utilise phototrophic pathways for energy generation during oxygen deprivation, which holds the potential to advance phototrophic-EBPR technology development.

Real-time bioprocess monitoring is crucial for efficient operation and effective bioprocess control. Aiming to develop an online monitoring strategy for facilitating optimization, fault detection and decision-making during wastewater treatment in a photo-biological nutrient removal (photo-BNR) process, this study investigated the application of Raman spectroscopy for the quantification of total organic content (TOC), volatile fatty acids (VFAs), carbon dioxide (CO₂), ammonia (NH₃), nitrate (NO₃), phosphate (PO₄), total phosphorus (total P), polyhydroxyalkanoates (PHAs), total carbohydrates, total and volatile suspended solids (TSSs and VSSs, respectively). Specifically, partial least squares (PLS) regression models were developed to predict these parameters based on Raman spectra, and evaluated based on a full cross-validation. Through the optimization of spectral pre-processing, Raman shift regions and latent variables, 8 out of the 11 parameters that were investigated—namely TOC, VFAs, CO₂, NO₃, total P, PHAs, TSSs and VSSs—could be predicted with good quality by the respective Raman-based PLS calibration models, as shown by the high coefficient of determination (R² > 90.0%) and residual prediction deviation (RPD > 5.0), and relatively low root mean square error of cross-validation. This study showed for the first time the high potential of Raman spectroscopy for the online monitoring of TOC, VFAs, CO₂, NO₃, total P, PHAs, TSSs and VSSs in a photo-BNR reactor.