aceIdentifying causal pathways and risk-decision rules for nitrous oxide emission hot moments in wastewater treatment plants using probabilistic causal machine learning
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…
A probabilistic causal machine learning framework trained on long-term monitoring data from a full-scale wastewater plant can predict N2O hot moments and convert interpretable outputs into risk-decision rules for adaptive aeration and load regulation to reduce emissions.
Evidence
- Peer-reviewedBioresource Technology2026-08-15
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Truvace Impact Record TRV-2026-0802, v1: “Identifying causal pathways and risk-decision rules for nitrous oxide emission hot moments in wastewater treatment plants using probabilistic causal machine learning.” Truvace, 2026-08-17. /record/TRV-2026-0802 (accessed at citation time). sha256 f3cdbae7a3908147…
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