Direct and indirect monitoring methods for nitrous oxide emissions in full-scale wastewater treatment plants : A critical review
Copyright © 2024 Elsevier Ltd. All rights reserved..
Mitigation of nitrous oxide (N2O) emissions in full-scale wastewater treatment plant (WWTP) has become an irreversible trend to adapt the climate change. Monitoring of N2O emissions plays a fundamental role in understanding and mitigating N2O emissions. This paper provides a comprehensive review of direct and indirect N2O monitoring methods. The techniques, strengths, limitations, and applicable scenarios of various methods are discussed. We conclude that the floating chamber technique is suitable for capturing and interpreting the spatiotemporal variability of real-time N2O emissions, due to its long-term in-situ monitoring capability and high data acquisition frequency. The monitoring duration, location, and frequency should be emphasized to guarantee the accuracy and comparability of acquired data. Calculation by default emission factors (EFs) is efficient when there is a need for ambiguous historical N2O emission accounts of national-scale or regional-scale WWTPs. Using process-specific EFs is beneficial in promoting mitigation pathways that are primarily focused on low-emission process upgrades. Machine learning models exhibit exemplary performance in the prediction of N2O emissions. Integrating mechanistic models with machine learning models can improve their explanatory power and sharpen their predictive precision. The implementation of the synergy of nutrient removal and N2O mitigation strategies necessitates the calibration and validation of multi-path mechanistic models, supported by long-term continuous direct monitoring campaigns.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:358 |
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| Enthalten in: |
Journal of environmental management - 358(2024) vom: 09. Apr., Seite 120842 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Shang, Zhenxin [VerfasserIn] |
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| Links: |
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| Themen: |
Emission factor |
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| Anmerkungen: |
Date Revised 10.04.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1016/j.jenvman.2024.120842 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM370884183 |
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| 520 | |a Copyright © 2024 Elsevier Ltd. All rights reserved. | ||
| 520 | |a Mitigation of nitrous oxide (N2O) emissions in full-scale wastewater treatment plant (WWTP) has become an irreversible trend to adapt the climate change. Monitoring of N2O emissions plays a fundamental role in understanding and mitigating N2O emissions. This paper provides a comprehensive review of direct and indirect N2O monitoring methods. The techniques, strengths, limitations, and applicable scenarios of various methods are discussed. We conclude that the floating chamber technique is suitable for capturing and interpreting the spatiotemporal variability of real-time N2O emissions, due to its long-term in-situ monitoring capability and high data acquisition frequency. The monitoring duration, location, and frequency should be emphasized to guarantee the accuracy and comparability of acquired data. Calculation by default emission factors (EFs) is efficient when there is a need for ambiguous historical N2O emission accounts of national-scale or regional-scale WWTPs. Using process-specific EFs is beneficial in promoting mitigation pathways that are primarily focused on low-emission process upgrades. Machine learning models exhibit exemplary performance in the prediction of N2O emissions. Integrating mechanistic models with machine learning models can improve their explanatory power and sharpen their predictive precision. The implementation of the synergy of nutrient removal and N2O mitigation strategies necessitates the calibration and validation of multi-path mechanistic models, supported by long-term continuous direct monitoring campaigns | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a Emission factor | |
| 650 | 4 | |a Floating chamber technique | |
| 650 | 4 | |a Machine learning model | |
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| 650 | 4 | |a Nitrous oxide | |
| 700 | 1 | |a Cai, Chen |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Yanli |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Xiangfeng |e verfasserin |4 aut | |
| 700 | 1 | |a Peng, Kaiming |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Ru |e verfasserin |4 aut | |
| 700 | 1 | |a Wei, Zhongqing |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Chenyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Cheng, Shunjian |e verfasserin |4 aut | |
| 700 | 1 | |a Liao, Youxiang |e verfasserin |4 aut | |
| 700 | 1 | |a Hung, Chih-Yu |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Jia |e verfasserin |4 aut | |
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