Event Date: 
Wednesday, February 25, 2015 - 15:30 - 16:00
University of California Davis

Stress, function and community dynamics in wastewater bioreactors


Biological wastewater treatment plants receive a complex mixture of chemicals and are operated based on principles of general microbial growth kinetics. Regulated effluent criteria determine the extent of treatment required to achieve removal of chemical oxygen demand and nutrients like reduced nitrogen and phophate. Plants are, however, not designed to metabolize specific (micro)pollutants, and the factors influencing the emergence of microbial communities that are tolerant of or have evolved to metabolize and remove toxic compounds are poorly understood. Basic questions in wastewater engineering include ‘What affects the dynamics of wastewater microbial communities?’  and ‘Are communities ever stable and if so does this matter for basic processes like removal of organics and nutrients?’.  
We investigated the impact of defined and sustained chemical stress on wastewater microbial communities and their functions, using the highly toxic and recalcitrant compound 3-chloroaniline (3-CA) as model stressor. Experimental design included replicate bioreactors, sterile synthetic feed, ambient levels of 3-CA, and fixed factors like bioaugmentation and temperature. Process outcomes varied from no removal of 3-CA to complete removal within three weeks. Community changes were dramatic and nitrification was a key function affected by the stressor. Finally, microbial diversity indices based on 16S rRNA gene amplicon sequencing or T-RFLP, combined with influent nutrient concentrations, were used to predict effluent concentrations using support vector regression, a machine learning model. Sensitivity analysis of a preliminary dataset for a full-scale water reclamation plant would suggest that evenness is the most significant input variable for the prediction of soluble COD, nitrate and ammonium concentrations in the effluent. Overall, we show that both detailed analysis of taxonomy and gene expression and general indices of diversity are useful for understanding the link between stable process performance and microbial communities.

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