Data-driven versus conventional N2O EF quantification methods in wastewater; how can we quantify reliable annual EFs?

V Vasilaki,S Danishvar,A Mousavi,E Katsou

doi:10.1016/j.compchemeng.2020.106997

Abstract

A long-term N2O dataset from a full-scale biological process was analysed for knowledge discovery. Non-parametric, multivariate timeseries changepoint detection techniques were applied to operational variables (i.e. NH4-N loads) in the system. The majority of changepoints, could be linked with the observed changes of the N2O emissions profile. The results showed that even three-day sampling campaigns between changepoints have a high probability (>80%) to result to an emission factor (EF) quantification with ~10% error. The analysis revealed that support vector machine (SVM) classification models can be trained to detect operational behaviour of the system and the expected range of N2O emission loads. The proposed approach can be applied when long-term online sampling is not feasible (due to budget or equipment limitations) to identify N2O emissions “hotspot” periods and guide towards the identification of operational periods requiring extensive investigation of N2O pathways generation.

Highlights

Nitrous oxide (N2O) emitted during biological nutrients removal, can significantly contribute to the total carbon footprint of Wastewater Treatment Plants (WWTPs)
A recent analysis of N2O emission factors (EF) for over 70 full-scale wastewater treatment processes revealed that the sampling frequency and sampling techniques applied in N2O monitoring campaigns, can significantly affect the quantified EFs (Vasilaki et al, 2019)
This study shows that information hidden in conventional variables monitored in wastewater can be mined to reduce N2O sampling frequency without compromising the quantification of annual N2O EFs and predict the risk of elevated emissions

Summary

Introduction

Nitrous oxide (N2O) emitted during biological nutrients removal, can significantly contribute to the total carbon footprint of Wastewater Treatment Plants (WWTPs). The recent roadmap to carbon neutrality in urban water published by Water and Wastewater Utilities for Climate Mitigation (WaCCliM) project and the International Water Association (IWA) (Ballard et al, 2018), states that direct N2O should be considered for the carbon footprint assessment and reporting. A recent analysis of N2O emission factors (EF) for over 70 full-scale wastewater treatment processes revealed that the sampling frequency and sampling techniques applied in N2O monitoring campaigns, can significantly affect the quantified EFs (Vasilaki et al, 2019). Most of the monitoring campaigns lasting less than one month have reported EFs less than 0.3 % of the N-load. Studies lasting over a year result in a median EF equal to 1.7 %

Methods

Results

Conclusion