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  1. Article ; Online: Deciphering carbon emissions in urban sewer networks: Bridging urban sewer networks with city-wide environmental dynamics.

    Chen, Jiaji / Wang, Hongcheng / Yin, Wanxin / Wang, Yuqi / Lv, Jiaqiang / Wang, AiJie

    Water research

    2024  Volume 256, Page(s) 121576

    Abstract: As urbanization accelerates, understanding and managing carbon emissions from urban sewer networks have become crucial for sustainable urban water cycles. This review examines the factors influencing greenhouse gas (GHG) emissions within urban sewage ... ...

    Abstract As urbanization accelerates, understanding and managing carbon emissions from urban sewer networks have become crucial for sustainable urban water cycles. This review examines the factors influencing greenhouse gas (GHG) emissions within urban sewage systems, analyzing the complex effects between water quality, hydrodynamics, and sewer infrastructure on GHG production and emission processes. It reveals significant spatiotemporal heterogeneity in GHG emissions, particularly under long-term scenarios where flow rates and temperatures exhibit strong impacts and correlations. Given the presence of fugitive and dissolved potential GHGs, standardized monitoring and accounting methods are deemed essential. Advanced modeling techniques emerge as crucial tools for large-scale carbon emission prediction and management. The review identifies that traditional definitions and computational frameworks for carbon emission boundaries fail to fully consider the inherent heterogeneity of sewers and the dynamic changes and impacts of multi-source pollution within the sewer system during the urban water cycle. This includes irregular fugitive emissions, the influence of stormwater systems, climate change, geographical features, sewer design, and the impacts of food waste and antibiotics. Key strategies for emission management are discussed, focusing on the need for careful consideration of approaches that might inadvertently increase global emissions, such as ventilation, chemical treatments, and water management practices. The review advocates for an overarching strategy that encompasses a holistic view of carbon emissions, stressing the importance of refined emission boundary definitions, novel accounting practices, and comprehensive management schemes in line with the water treatment sector's move towards carbon neutrality. It champions the adoption of interdisciplinary, technologically advanced solutions to mitigate pollution and reduce carbon emissions, emphasizing the importance of integrating cross-scale issues and other environmentally friendly measures in future research directions.
    Language English
    Publishing date 2024-04-06
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 202613-2
    ISSN 1879-2448 ; 0043-1354
    ISSN (online) 1879-2448
    ISSN 0043-1354
    DOI 10.1016/j.watres.2024.121576
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Leveraging machine learning for prediction of antibiotic resistance genes post thermal hydrolysis-anaerobic digestion in dairy waste.

    Su, Haiyan / Zhu, Tianjiao / Lv, Jiaqiang / Wang, Hongcheng / Zhao, Ji / Xu, Jifei

    Bioresource technology

    2024  Volume 399, Page(s) 130536

    Abstract: Anaerobic digestion holds promise as a method for removing antibiotic resistance genes (ARGs) from dairy waste. However, accurately predicting the efficiency of ARG removal remains a challenge. This study introduces a novel appproach utilizing machine ... ...

    Abstract Anaerobic digestion holds promise as a method for removing antibiotic resistance genes (ARGs) from dairy waste. However, accurately predicting the efficiency of ARG removal remains a challenge. This study introduces a novel appproach utilizing machine learning to forecast changes in ARG abundances following thermal hydrolysis-anaerobic digestion (TH-AD) treatment. Through network analysis and redundancy analyses, key determinants of affect ARG fluctuations were identified, facilitating the development of machine learning models capable of accurately predicting ARG changes during TH-AD processes. The decision tree model demonstrated impressive predictive power, achieving an impessive R
    MeSH term(s) Anti-Bacterial Agents/pharmacology ; Anaerobiosis ; Hydrolysis ; Genes, Bacterial ; Drug Resistance, Microbial/genetics ; Sewage
    Chemical Substances Anti-Bacterial Agents ; Sewage
    Language English
    Publishing date 2024-03-05
    Publishing country England
    Document type Journal Article
    ZDB-ID 1065195-0
    ISSN 1873-2976 ; 0960-8524
    ISSN (online) 1873-2976
    ISSN 0960-8524
    DOI 10.1016/j.biortech.2024.130536
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Enhancing effluent quality prediction in wastewater treatment plants through the integration of factor analysis and machine learning.

    Lv, Jiaqiang / Du, Lili / Lin, Hongyong / Wang, Baogui / Yin, Wanxin / Song, Yunpeng / Chen, Jiaji / Yang, Jixian / Wang, Aijie / Wang, Hongcheng

    Bioresource technology

    2023  Volume 393, Page(s) 130008

    Abstract: Precisely predicting the concentration of nitrogen-based pollutants from the wastewater treatment plants (WWTPs) remains a challenging yet crucial task for optimizing operational adjustments in WWTPs. In this study, an integrated approach using factor ... ...

    Abstract Precisely predicting the concentration of nitrogen-based pollutants from the wastewater treatment plants (WWTPs) remains a challenging yet crucial task for optimizing operational adjustments in WWTPs. In this study, an integrated approach using factor analysis (FA) and machine learning (ML) models was employed to accurately predict effluent total nitrogen (Ntot
    MeSH term(s) Wastewater ; Water Purification ; Nitrates/analysis ; Nitrogen/analysis ; Factor Analysis, Statistical ; Waste Disposal, Fluid
    Chemical Substances Wastewater ; Nitrates ; Nitrogen (N762921K75)
    Language English
    Publishing date 2023-11-18
    Publishing country England
    Document type Journal Article
    ZDB-ID 1065195-0
    ISSN 1873-2976 ; 0960-8524
    ISSN (online) 1873-2976
    ISSN 0960-8524
    DOI 10.1016/j.biortech.2023.130008
    Database MEDical Literature Analysis and Retrieval System OnLINE

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