Methane emissions from sewer networks are an important source of anthropogenic greenhouse gases (GHGs) but are not currently reflected in the national GHG inventory. We found significant CH emissions of approximately 573 [395-831] CH t y from sewer networks in the old residential and commercial areas of Seoul (Gwanak district) using an electric vehicle-based atmospheric GHG monitoring platform. The majority of ethane-to-methane ratios (<0.005) from the observations further suggest that distinctive CH emissions from sewer networks are likely related to microbial activity rather than to simple natural gas leakage. Because over 90% of the sewer network in Seoul is a gravity drain type of combined sewer network, where both wastewater and stormwater flow through the same pipes, resulting in the generation of methane emissions from the microbial activity and the manholes and rain gutters, which are directly connected to the combined sewer networks are major sources of atmospheric methane emissions. This study suggests that appropriate treatment of sewer networks can mitigate missing methane emissions in cities that were not originally included in GHG inventory of South Korea.
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http://dx.doi.org/10.1016/j.envpol.2023.123101 | DOI Listing |
Materials (Basel)
December 2024
Intelligent Water Networks, Melbourne, VIC 3000, Australia.
Around the world, a significant proportion of sewers and sewer maintenance holes are constructed from concrete. Unfortunately, one major problem with concrete sewer infrastructure is corrosion caused by biogenic hydrogen sulphide, which causes major issues for concrete structural integrity. Furthermore, concrete may be significantly corroded and softened but still pass a visual inspection.
View Article and Find Full Text PDFWater Res
December 2024
Department of Chemical Engineering, University of Bath, Claverton Down, Bath BA27AY, UK; SWING - Department of Built Environment, Oslo Metropolitan Uni., St Olavs Plass, Oslo 0130, Norway. Electronic address:
Urban water systems receive and emit antimicrobial chemicals, resistant bacterial strains, and resistance genes (ARGs), thus representing "antimicrobial hotspots". Currently, regional environmental risk assessment (ERA) is carried out using drug consumption data and threshold concentrations derived based on chemical-specific minimum inhibitory concentration values. A legislative proposal by the European Commission released in 2022 addresses the need to include selected ARGs besides the chemical concentration-based ERAs.
View Article and Find Full Text PDFSensors (Basel)
November 2024
Hefei Intelligent Robot Institute, Hefei 230601, China.
Detecting defects in complex urban sewer scenes is crucial for urban underground structure health monitoring. However, most image-based sewer defect detection models are complex, have high resource consumption, and fail to provide detailed damage information. To increase defect detection efficiency, visualize pipelines, and enable deployment on edge devices, this paper proposes a computer vision-based robotic defect detection framework for sewers.
View Article and Find Full Text PDFWater Res X
May 2024
School of Environmental Science & Engineering, Guangdong Provincial Key Lab of Environmental Pollution Control and Remediation Technology, Sun Yat-sen University, Guangzhou, 510275, China.
The frequent occurrence of safety incidents in sewer systems due to the emergency toxicity of hydrogen sulfide (HS) necessitate timely and efficient prediction, early warning and real-time control. However, various factors influencing HS generation and emission leads to a substantial computational burden for the existing dynamic sewer process models and fails to timely control the HS exposure risk. The present study proposed a swift prediction model (SPM) that combined the validated dynamic sewer process model (the biofilm-initiated sewer process model, BISM) with a high-speed machine learning algorithm (MLA), achieving accurately and swiftly predict the dissolved sulfide (DS) concentration and HS concentration in a specific sewer network.
View Article and Find Full Text PDFEnviron Sci Ecotechnol
January 2025
State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China.
Physical, chemical, and biological processes within sewers significantly alter sewage composition during conveyance. This leads to the formation of sulfide and methane-compounds that contribute to sewer corrosion and greenhouse gas emissions. Reliable modeling of these compounds is essential for effective sewer management, but the development of machine learning (ML) models is hindered by differences in data accessibility and sampling frequencies of water quality variables.
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