Spatiotemporal pattern of greenhouse gas emissions in China’s wastewater sector and pathways towards carbon neutrality

Du, Wenjie; Lu, Jiayue; Hu, Yi-Rong; Xiao, Juanxiu; Yang, Cheng; Wu, Jie; Huang, Bao-Cheng; Cui, Shuo; Wang, Yan; Li, Wen‐Wei

doi:10.1038/s44221-022-00021-0

Cited by 67 publications

(25 citation statements)

References 51 publications

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“…As such, integrating spatial and temporal patterns becomes crucial for comprehensive modeling on EC and WWT. 26 The main purpose of this study is to harness machine learning algorithms within both spatial and temporal dimensions to develop EC models. These models serve to distinguish the critical factors related to operational conditions, environmental benefits, and externalities of WWT.…”

Section: Introductionmentioning

confidence: 99%

“…Spatial data often comprises observations with a broad geographical scope but lower accuracy, , while temporal data boasts higher accuracy but may lack regional representativeness. , Spatial observations can shed light on regional properties and technical processes, while temporal data can supply insights into seasonal disparities. As such, integrating spatial and temporal patterns becomes crucial for comprehensive modeling on EC and WWT …”

Section: Introductionmentioning

confidence: 99%

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Spatial and Temporal Modeling on Energy Consumption of Wastewater Treatment Based on Machine Learning Algorithms

Huang,

Yu,

Wang

et al. 2023

ACS EST Water

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To explore the water-energy-carbon nexus of wastewater treatment (WWT), advanced tools such as machine learning play a crucial role. Current research has primarily constructed energy efficiency models, but there exists a lack in considering comprehensive dimensions and comparing pollutant removal types. In this study, we conducted spatial and temporal modeling to predict the energy consumption (EC) of WWT via machine learning approaches. EC (kWh) was the target feature, with the input features covering operational conditions, environmental benefits, and externalities. The optimal spatial model obtained a test R 2 of 0.8224 in ridge regression, while the temporal model achieved a test R 2 of 0.7253 in random forest. Besides, the removal amount (10 3 kg) fit best with EC during the spatial modeling, while the discharge concentration (mg/L) fit best with EC during the temporal modeling. Notably, treatment volume, the removal of chemical oxygen demand, and the removal of ammonia nitrogen emerged as the most significant factors. Given this, our findings suggest optimization implications including scale economy utilization and aeration improvement. The spatial and temporal dimensions also illuminated tailored strategies on influent regulation, technology selection, and effluent standard settings for a specific region and season. Results will provide valuable guidance for existing operation and future design of WWT projects toward energy-saving and carbon neutrality.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Modeling on Energy Consumption of Wastewater Treatment Based on Machine Learning Algorithms

Huang,

Yu,

Wang

et al. 2023

ACS EST Water

View full text Add to dashboard Cite

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“…Studies on wastewater treatment technology policies have focused on their impact on surface water quality control, ,, but discarded the quantification of their impacts on ECI and SY. Similarly, studies on GHG emissions in wastewater treatment often assumed the same ECI and SY in WWTPs with similar technologies, disregarding specific operating conditions. , …”

Section: Introductionmentioning

confidence: 99%

“…Similarly, studies on GHG emissions in wastewater treatment often assumed the same ECI and SY in WWTPs with similar technologies, disregarding specific operating conditions. 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.)…”

Section: Introductionmentioning

confidence: 99%

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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“…In September 2020, the Chinese government announced plans to reach the carbon emission peak by 2030 and achieve carbon neutrality by 2060. However, the CO 2 emission from China’s wastewater treatment reached 53.0 Mt in 2019 . If all contaminants in wastewater were mineralized into CO 2 , the CO 2 emissions would have reached 7797.6 kt, accounting for 14.7% of China’s carbon emissions from wastewater treatment.…”

mentioning

confidence: 99%

Revisiting the Mineralization of Organic Contaminants in Advanced Oxidation Processes

Shi,

Chen,

Xiao

et al. 2023

ACS EST Water

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Spatiotemporal pattern of greenhouse gas emissions in China’s wastewater sector and pathways towards carbon neutrality

Cited by 67 publications

References 51 publications

Spatial and Temporal Modeling on Energy Consumption of Wastewater Treatment Based on Machine Learning Algorithms

Spatial and Temporal Modeling on Energy Consumption of Wastewater Treatment Based on Machine Learning Algorithms

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

Revisiting the Mineralization of Organic Contaminants in Advanced Oxidation Processes

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