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Updated: May 4, 2026
09:24
Assembly and Tracking of Microbial Community Development within a Microwell Array Platform
Published on: June 6, 2017
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Identifying Temporal Drivers for Microbial Community Assembly in Wastewater Treatment by Stochastic Physics-Informed
Baoli Wu1,2, Guangqi Liu3, Yuan Yu1
1State Key Laboratory of Urban-Rural Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China.
Environmental Science & Technology
|March 12, 2026
Summary
This study introduces a novel deep learning framework to identify temporal drivers of microbial community assembly in wastewater treatment. It distinguishes between deterministic and stochastic factors influencing bacterial populations for better process control.
Area of Science:
- Environmental microbiology
- Wastewater treatment technologies
- Ecological modeling
Background:
- Microbial community assembly (MCA) is crucial for biological wastewater treatment, influencing pollutant removal via deterministic and stochastic processes.
- Identifying the temporal drivers of MCA remains a significant challenge in optimizing wastewater treatment plants (WWTPs).
Purpose of the Study:
- To develop and validate a novel framework for identifying temporal drivers of MCA in WWTPs.
- To differentiate between deterministic and stochastic influences on microbial dynamics using limited data.
Main Methods:
- Developed a stochastic physics-informed deep learning (SPI-DL) framework integrating generalized Lotka-Volterra (gLV) models and stochastic differential equations (SDEs).
- Employed log-likelihood decoupling (LLD) and SHAP analysis to resolve the contributions of deterministic and stochastic factors over time.
- Applied the framework to study MCA in nitrifying bacteria (ammonia-oxidizing bacteria and nitrite-oxidizing bacteria).
Main Results:
- The SPI-DL+LLD framework successfully identified temporal drivers for MCA in nitrifying bacteria.
- Stochastic variability was primarily linked to flow rate and hydraulic retention time.
- Deterministic succession was associated with specific covariates like dissolved oxygen (DO) for NOB and ammonia/total nitrogen (NH4-N/TN) for AOB.
Conclusions:
- The SPI-DL+LLD framework offers robust representability, predictability, and generalizability for understanding MCA drivers in WWTPs.
- This approach has significant implications for precise process control and the optimization of smart wastewater treatment systems.
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