Statistical and microbial analysis of bio-electrochemical sensors used for carbon monitoring at water resource recovery facilities

• Published in: Environmental science water research & technology Vol. 8; no. 1; pp. 252 - 264
• Main Authors: Emaminejad, Seyed Aryan, Morgan, Victoria L, Kumar, Kuldip, Kavathekar, Avanti, Ragush, Colin, Shuai, Weitao, Jia, Zhen, Huffaker, Ray, Wells, George, Cusick, Roland D
• Format: Journal Article
• Language: English
• Published: Cambridge Royal Society of Chemistry 03.10.2022
• Subjects: Abiotic factors; Aeration; Analysis; Automatic control; Biofilms; Biosensors; Carbon; Chemical sensors; Composition; Cycles; Data analysis; Dissolved oxygen; Effluents; Electrical measurement; Electrochemistry; Environmental conditions; Environmental factors; Environmental impact; Fatty acids; Identification; Load fluctuation; Metabolism; Microorganisms; Monitoring; Organic loading; Principal components analysis; Recovery; Resource recovery; rRNA 16S; Sensors; Septic shock; Spectrum analysis; Wastewater; Wastewater composition; Water resources
• ISSN: 2053-1400; 2053-1419
• DOI: 10.1039/d1ew00653c

Water resource recovery facility (WRRF) operations personnel increasingly rely on sensor networks for automated control. Bio-electrochemical sensors (BESs), which leverage electrogenic biofilms to generate an amperometric signal of carbon metabolism, are being developed to monitor changes in wastewater composition and detect toxic shock events. This study presents for the first time, a long-term evaluation of a BES installed in the primary effluent channel of a WRRF for 247 days to quantify its sensitivity to organic load variations and assess the impact of abiotic factors on the BES response signal and biofilm composition, using advanced data analysis and microbial techniques. While the BES signal showed a strong correlation to volatile fatty acid (VFA) concentration, other environmental factors impacted the signal significantly. Principal component analysis identified pH, VFA concentration, and temperature to be the main contributors to the total variance of the signal in the entire dataset. 16S rRNA amplicon sequencing of samples obtained from the anodic biofilm of the biosensor determined Trichococcus and Lactococcus to be the dominant genera in the biofilm. While the precision of in situ carbon monitoring was impacted by environmental conditions, singular spectrum analysis of the biosensor signal identified four underlying cycles in the primary effluent (287, 92, 44, and 7 days). These results aligned closely with cycles identified in dissolved oxygen readings in an aeration basin downstream of the BES. Real-time carbon monitoring of wastewater using bio-electrochemical sensors coupled with advanced data analysis methods provides WRRFs with an opportunity for efficient wastewater quality monitoring and an early warning tool for plant upsets.