China's enhanced urban wastewater treatment increases greenhouse gas emissions and regional inequality

Huang, Yujun; Meng, Fanlin; Liu, Shuming; Sun, Siao; Smith, Kate

doi:10.1016/j.watres.2022.119536

Cited by 23 publications

(18 citation statements)

References 31 publications

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“…Notably, in each month of each year, some WWTPs may go out of service, or new WWTPs may come into operation and be recorded in the database. Based on previous studies, electricity consumption for treating 1 m 3 wastewater was used to indicate the ECI (kWh/m 3 ) 2 , while dry solids (DS) of sludge per kilogram of removed COD 6 was used to indicate the SY (kg DS/ kg COD). ECI and SY represent the labels for prediction.…”

Section: Data Sourcementioning

confidence: 99%

“…Additionally, sludge disposal from WWTPs resulted in 11 Mt CO 2 eq emissions in 2019 . Electricity consumption intensity (ECI) and sludge yield (SY) for disposal per functional unit have been identified as the dominant driving forces behind increasing GHG emissions . Conversely, indirect emissions associated with chemical consumption are generally limited to ∼5% , or even negligible. , …”

Section: Introductionmentioning

confidence: 99%

“…9 Electricity consumption intensity 10 (ECI) and sludge yield 11 (SY) for disposal per functional unit have been identified as the dominant driving forces behind increasing GHG emissions. 6 Conversely, indirect emissions associated with chemical consumption are generally limited to ∼5% 6,12 or even negligible. 8,13 Accurately predicting and managing ECI and SY as activity data is crucial for estimating indirect GHG emissions in wastewater treatment policy scenarios 14,15 to support tailormade strategies, 6 minimize uncertainty, and ensure reliability.…”

Section: Introductionmentioning

confidence: 99%

“…28, 29 A key challenge lies in effectively mining the value of observation data with a wide spatial-temporal span but limited recorded parameters, which is a common barrier in scientific domains, 30 to model ECI and SY on a nationwide scale. This is due to the large quantity of WWTP operation data available (such as month-by-month records from more than 5,000 WWTPs in China 6 ), but a limited variety of recorded operation parameters (biomass yield of heterotrophic and autotrophic microorganisms, N content in organic matters, proportion of biodegradable or inert, and soluble or particle organic matters in COD, etc.) at current technical levels.…”

Section: Introductionmentioning

confidence: 99%

“…Over the past decade, China has made remarkable progress in wastewater treatment capacity, effluent standard, and its sludge disposal. , Recently, the 2022 National Eco-Environmental Protection Work Conference in China emphasized the need for integrated planning to reduce pollution and carbon emissions, marking a new stage of developing wastewater treatment plant (WWTP). The operation stage of wastewater treatment is the primary source of greenhouse gas (GHG) emissions, including direct emissions (from biochemical reactions during wastewater collection and treatment, and in treated wastewater discharged to water bodies) and indirect emissions (from sludge disposal, electricity consumption and chemical dosage) . Electricity consumption for WWTP operation in China averaged 0.33 kWh/m 3 , which was 1.6 times that in America in 2018, contributing to 45% of the total 30 Mt CO 2 eq emissions (including biological processes and electricity consumption) .…”

Section: Introductionmentioning

confidence: 99%

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Modeling Indirect Greenhouse Gas Emissions Sources from Urban Wastewater Treatment Plants: Integrating Machine Learning Models to Compensate for Sparse Parameters with Abundant Observations

Huang,

Xie,

et al. 2023

Environ. Sci. Technol.

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Electricity consumption and sludge yield (SY) are important indirect greenhouse gas (GHG) emission sources in wastewater treatment plants (WWTPs). Predicting these byproducts is crucial for tailoring technology-related policy decisions. However, it challenges balancing mass balance models and mechanistic models that respectively have limited intervariable nexus representation and excessive requirements on operational parameters. Herein, we propose integrating two machine learning models, namely, gradient boosting tree (GBT) and deep learning (DL), to precisely pointwise model electricity consumption intensity (ECI) and SY for WWTPs in China. Results indicate that GBT and DL are capable of mining massive data to compensate for the lack of available parameters, providing a comprehensive modeling focusing on operation conditions and designed parameters, respectively. The proposed model reveals that lower ECI and SY were associated with higher treated wastewater volumes, more lenient effluent standards, and newer equipment. Moreover, ECI and SY showed different patterns when influent biochemical oxygen demand is above or below 100 mg/L in the anaerobic-anoxic-oxic process. Therefore, managing ECI and SY requires quantifying the coupling relationships between biochemical reactions instead of isolating each variable. Furthermore, the proposed models demonstrate potential economic-related inequalities resulting from synergizing water pollution and GHG emissions management.

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Section: Data Sourcementioning

confidence: 99%