Identifying causal pathways and risk-decision rules for nitrous oxide emission hot moments in wastewater treatment plants using probabilistic causal machine learning.

Authors

Yangyang Guo, Anlei Wei, Shengbo Yue, Xiaofan Chang, Yifan He, Xiaodan Ji, Jirui Zou, Kangrong Tang, Zixuan Wang

Published in

Bioresource technology. Pages 135637. Aug 15, 2026. Epub Aug 15, 2026.

Abstract

Nitrous oxide (N_2O) emissions from biological wastewater treatment represent a significant challenge for climate-responsible operation due to their intermittency and occurrence as short-lived emission hot moments. Effective mitigation therefore requires accurate prediction and systematic identification of causal pathways and operational risk conditions. This study develops a probabilistic causal machine learning framework based on long-term online monitoring data from a full-scale wastewater treatment plant for low-emission process control and operational management. An optimal predictive model is first established to characterize N_2O dynamics under varying operational regimes. Building upon this foundation, cohort-based SHapley Additive exPlanations are used to identify regime-dependent nonlinear effects of key operational variables, including dissolved oxygen, ammonium, nitrate, and temperature. Linear Non-Gaussian Acyclic Model-based causal discovery is then used to characterize causal pathways associated with N_2O emission hot moments under different operational regimes. Copula-based joint probability analysis is conducted to quantify the likelihood of N_2O emission hot moments under interacting process conditions. Results demonstrate that N_2O emission hot moments are not triggered by single-factor thresholds but emerge from specific combinations of operational states, revealing interaction-dominated and regime-sensitive causal pathways. By converting interpretable machine learning outputs into probabilistic risk-decision rules, the proposed framework provides actionable guidance for adaptive aeration control, substrate load regulation, and proactive emission mitigation. This study establishes a data-driven framework for interpreting and managing N_2O emissions in biological wastewater treatment. Overall, the proposed framework provides practical support for proactive N_2O emission mitigation while maintaining process stability in full-scale wastewater treatment plants.

PMID:
42603571
Bibliographic data and abstract were imported from PubMed on 16 Aug 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.