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| 1 | Machine learning enabled prediction and process optimization of VFA production from riboflavin-mediated sludge fermentation显示文摘Riboflavin is a redox mediator that promotes volatile fatty acids(VFAs)production from waste activated sludge(WAS)and is a promising method for WAS reuse.However,time-and labor-consuming experiments challenge obtaining optimal operating conditions for maximal VFA production.In this study,three machine learning(ML)models were developed to predict the VFAs production from riboflavin-mediated WAS fermentation systems.Among the three tested ML algorithms,eXtreme Gradient Boosting(XGBoost)presented the best prediction performance and excellent generalization ability,with the highest testing coefficient of determination(R^(2)of 0.93)and lowest root mean square error(RMSE of 0.070).Feature importance analysis and their interactions using the Shepley Additive Explanations(SHAP)method indicated that pH and soluble protein were the top two input features for the modeling.The intrinsic correlations between input features and microbial communities corroborated this deduction.On the optimized ML model,genetic algorithm(GA)and particle swarm optimization(PSO)solved the optimal solution of VFA output,predicting the maximum VFA output as 650 mg COD/g VSS.This study provided a data-driven approach to predict and optimize VFA production from riboflavin-mediated WAS fermentation. | Weishuai Li Jingang Huang Zhuoer Shi Wei Han Ting Lü Yuanyuan Lin Jianfang Meng Xiaobing Xu Pingzhi Hou | 2023 | Frontiers of Environmental Science & Engineering2023，17，11： | 0 |
| 2 | Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method显示文摘Surface ozone(O_(3))is influenced by regional background and local photochemical formation under favorable meteorological conditions.Understanding the contribution of these factors to changes in O_(3)is crucial to address the issue of O_(3)pollution.In this study,we propose a novel integrated method that combines random forest,principal component analysis,and Shapley additive explanations to distinguish observed O_(3)into meteorologically affected ozone(O_(3_MET)),chemically formed from local emissions(O_(3_LC)),and regional background ozone(O_(3_RBG)).Applied to three typical stations in Shanghai during the warm season from 2013 to 2021,the results indicate that O_(3_RBG)in Shanghai was 48.8±0.3 ppb,accounting for 79.6%–89.4%at different sites,with an overall declining trend of 0.018 ppb/yr.O_(3_LC)at urban and regional sites ranged from 5.9–9.0 ppb and 8.9–14.6 ppb,respectively,which were significantly higher than the contributions of 2.5–7.4 ppb at an upwind background site.O_(3_MET)can be categorized into those affecting O_(3)photochemical generation and those changing O_(3)dispersion conditions,with absolute contributions to O_(3)ranging from 13.4–19.0 ppb and 13.1–13.7 ppb,respectively.We found that the O_(3)rebound in 2017,compared to 2013,was primarily influenced by unfavorable O_(3)dispersion conditions and unbalanced emission reductions;while the O_(3)decline in 2021,compared to 2017,was primarily influenced by overall favorable meteorological conditions and further emissions reduction.These findings highlight the challenge of understanding O_(3)change due to meteorology and regional background,emphasizing the need for systematic interpretation of the different components of O_(3). | Jin Xue Fangting Wang Kun Zhang Hehe Zhai Dan Jin Yusen Duan Elly Yaluk Yangjun Wang Ling Huang Yuewu Li Thomas Lei Qingyan Fu Joshua S.Fu Li Li | 2023 | Frontiers of Environmental Science & Engineering2023，17，11： | 0 |
| 3 | Exploring the development of municipal solid waste disposal facilities in Chinese cities:patterns and drivers显示文摘Rapid urbanization in Chinese cities has led to a surge in municipal solid waste(MSW)generation,necessitating the development of high-quality MSW disposal facilities.This study utilizes multi-source data and regression models to examine the status,development pathways,and driving forces of MSW disposal facilities in China.Our findings reveal an inverted U-shaped relationship between the capacities or numbers of MSW disposal facilities and GDP per capita of cities.Historical data show that cities in East and Southeast China preferred incineration,while cities in West and Central China developed landfills more,largely shaped by the imbalance of development levels and endowments among regions in China.The study also identifies mixed and differentiated influences of socio-economic factors on capacity expansion and increases in the number of MSW disposal facilities.The results suggest the need for updated construction guidelines and regulations,as well as enhanced technological and managerial capabilities for MSW infrastructure.These findings can inform policymakers and practitioners in their efforts to promote sustainable waste management practices in China. | Xiao Li Yanan Ren Xuezhao Chen Yang Li Marian R.Chertow | 2023 | Frontiers of Environmental Science & Engineering2023，17，11： | 0 |
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