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Due to the extensive application of sulfonamide antibiotics for disease control on humans and livestock, the discharge of their residuals has caused serious pollution to the water environment. To mitigate this pollution, various types of carbonaceous materials have been used as adsorbents to remove antibiotics from water. In this study, adsorption characteristics of three sulfonamide antibiotics on four carbonaceous materials under complex conditions have been explored in terms of the cycle of adsorption, desorption, and re-adsorption processes, single and interactive effects of multiple influencing factors, kinetics, isotherms, and thermodynamics. Adsorption isotherms of the main substance (sulfamethoxazole) in the co-adsorption system were also analyzed to illustrate the competitive effects of coexisting substances (sulfamerazine and sulfamethazine) and the degree of competition. Results show that initial concentration, pH, and their interactions had significant effects on the adsorption process. The optimal combination of these factors was the initial concentration of 30 mg/L, pH value of 4.0, temperature of 298 K, and ionic strength of 0–0.1 mol/L. The adsorbed amount of SMX was the highest for four materials under the optimal condition (i.e., 91.51 mg/g for PAC, 258.7 mg/g for W-GAC, 184.7 mg/g for 3M-GAC, and 46.14 mg/g for GP). Adsorbents had great potentials to remove sulfonamide antibiotics after desorption, which is valuable for materials' reusability. In addition, the existence of competitive substances would not change the main substance's exothermic or endothermic properties. These results could help reveal the adsorption, desorption, and re-adsorption mechanisms of various carbonaceous materials under complicated situations and provide reference to identify the regeneration conditions of adsorbents as well as determine the levels of influencing factors to remove sulfonamides antibiotics from water.
Bin Luo; Guohe Huang; Yao Yao; ChunJiang An; Peng Zhang; Kai Zhao. Investigation into the influencing factors and adsorption characteristics in the removal of sulfonamide antibiotics by carbonaceous materials. Journal of Cleaner Production 2021, 319, 128692 .
AMA StyleBin Luo, Guohe Huang, Yao Yao, ChunJiang An, Peng Zhang, Kai Zhao. Investigation into the influencing factors and adsorption characteristics in the removal of sulfonamide antibiotics by carbonaceous materials. Journal of Cleaner Production. 2021; 319 ():128692.
Chicago/Turabian StyleBin Luo; Guohe Huang; Yao Yao; ChunJiang An; Peng Zhang; Kai Zhao. 2021. "Investigation into the influencing factors and adsorption characteristics in the removal of sulfonamide antibiotics by carbonaceous materials." Journal of Cleaner Production 319, no. : 128692.
In this study, an integrated framework is proposed for agricultural water and land resources management under uncertain circumstances and energy-water nexus. The framework has two components: the optimized interval solutions generation of unknown market quotation and environment situation through the novel interval fuzzy-expectation programming, and the post-optimization decision-making of the tradeoff between system risk and return through risk-explicit interval programming. An optimization model based on the framework was developed from a real description for agricultural water resources system of Guangdong province under considering various crop, multiple water sources, and the related energy consumption in irrigation and drainage. The impact of energy consumption and risk level control on water resources allocation among different crops and system benefits were analyzed. The results indicated that the preference for surface water resources as the main water sources can effectively reduce regional irrigation energy consumption, and the planting area of each crop would have different variations duo to resources endowment constraints. Risk tradeoff-based results can provide valuable information and additional concern for developing low carbon-oriented agricultural water resources management schemes.
Zhiwei Luo; Yulei Xie; Ling Ji; Yanpeng Cai; Zhifeng Yang; Guohe Huang. Regional agricultural water resources management with respect to fuzzy return and energy constraint under uncertainty: An integrated optimization approach. Journal of Contaminant Hydrology 2021, 242, 103863 .
AMA StyleZhiwei Luo, Yulei Xie, Ling Ji, Yanpeng Cai, Zhifeng Yang, Guohe Huang. Regional agricultural water resources management with respect to fuzzy return and energy constraint under uncertainty: An integrated optimization approach. Journal of Contaminant Hydrology. 2021; 242 ():103863.
Chicago/Turabian StyleZhiwei Luo; Yulei Xie; Ling Ji; Yanpeng Cai; Zhifeng Yang; Guohe Huang. 2021. "Regional agricultural water resources management with respect to fuzzy return and energy constraint under uncertainty: An integrated optimization approach." Journal of Contaminant Hydrology 242, no. : 103863.
Effective management of an urban solid waste system (USWS) is crucial for balancing the tradeoff between economic development and environment protection. A factorial ecological-extended physical input-output model (FE-PIOM) was developed for identifying an optimal urban solid waste path in an USWS. The FE-PIOM integrates physical input-output model (PIOM), ecological network analysis (ENA), and fractional factorial analysis (FFA) into a general framework. The FE-PIOM can analyze waste production flows and ecological relationships among sectors, quantify key factor interactions on USWS performance, and finally provide a sound waste production control path. The FE-PIOM is applied to managing the USWS of Fujian Province in China. The major findings are: (i) waste is mainly generated from primary manufacturing (PM) and advanced manufacturing (AM), accounting for 30% and 38% of the total amount; (ii) AM is the biggest sector that controls the productions of other sectors (weight is from 35% to 50%); (iii) the USWS is mutualistic, where direct consumption coefficients of AM and PM are key factors that have negative effects on solid waste production intensity; (iv) the commodity consumption of AM and PM from other sectors, as well as economic activities of CON, TRA and OTH, should both decrease by 20%, which would be beneficial to the sustainability of the USWS.
Jing Liu; Yongping Li; Gordon Huang; Yujin Yang; Xiaojie Wu. A Factorial Ecological-Extended Physical Input-Output Model for Identifying Optimal Urban Solid Waste Path in Fujian Province, China. Sustainability 2021, 13, 8341 .
AMA StyleJing Liu, Yongping Li, Gordon Huang, Yujin Yang, Xiaojie Wu. A Factorial Ecological-Extended Physical Input-Output Model for Identifying Optimal Urban Solid Waste Path in Fujian Province, China. Sustainability. 2021; 13 (15):8341.
Chicago/Turabian StyleJing Liu; Yongping Li; Gordon Huang; Yujin Yang; Xiaojie Wu. 2021. "A Factorial Ecological-Extended Physical Input-Output Model for Identifying Optimal Urban Solid Waste Path in Fujian Province, China." Sustainability 13, no. 15: 8341.
Virtual water is an important indicator measuring the amount of water needed from the perspective of consumption, which can help decision makers to identify desired system design and optimal management strategy against water resources shortage. In this study, a novel model named as factorial ecologically-extended input-output model (abbreviated as FEIOM) is developed for virtual water management. FEIOM integrates techniques of input-output model (IOM), ecological network analysis (ENA) and factorial analysis (FA) into a general framework. It is effective to evaluate the virtual water flows, reveal ecological inter-connections in virtual water system (VWS), and identify key water consumption sectors that have significant individual and interactive effects on VWS's performance. FEIOM is then applied to identifying optimal virtual water management strategies for Kazakhstan in Central Asia. The main findings are: (i) Kazakhstan is a net importer of virtual water (reaching up to 46.0 × 109 m3), demonstrating that the national economic structure is reasonable, which can abate the national water scarcity and improve its eco-environmental protection; (ii) the virtual water of agricultural sector is net exporter, where vegetables, fruits and nuts occupy 86% of the total agricultural exports; the massive export of water-intensive products further squeezes the water for other users; (iii) the key factors affecting the national VWS are agriculture > primary manufacturing > advanced manufacturing > services. Therefore, from solving water resources shortage and facilitating sustainable development perspectives, Kazakhstan should stimulate the domestic primary manufacturing productions and improve agriculture and advanced manufacturing water-use efficiencies.
X.J. Wu; Y.P. Li; J. Liu; G.H. Huang; Y.K. Ding; J. Sun; H. Zhang. Identifying optimal virtual water management strategy for Kazakhstan: A factorial ecologically-extended input-output model. Journal of Environmental Management 2021, 297, 113303 .
AMA StyleX.J. Wu, Y.P. Li, J. Liu, G.H. Huang, Y.K. Ding, J. Sun, H. Zhang. Identifying optimal virtual water management strategy for Kazakhstan: A factorial ecologically-extended input-output model. Journal of Environmental Management. 2021; 297 ():113303.
Chicago/Turabian StyleX.J. Wu; Y.P. Li; J. Liu; G.H. Huang; Y.K. Ding; J. Sun; H. Zhang. 2021. "Identifying optimal virtual water management strategy for Kazakhstan: A factorial ecologically-extended input-output model." Journal of Environmental Management 297, no. : 113303.
Research on the overall social, economic and environmental (SEE) impacts of water resources projects (WRP) has gradually attracted attention. However, the WRP-related indirect impacts as linked through various supply chains from a number of disaggregated sectors and their interactive relationship are rarely studied. Here in this study, under a general disaggregated input-output (IO) framework, a distributive Three Gorges Project input-output (DTGIO) model is developed to investigate the composite effects of Three Gorges Project (TGP) based on a disaggregated TGP-IO table for the Yangtze River economic belt (YREB). DTGIO model could facilitate exploring i) the interactions among key TGP-related effects from production and consumption sides; ii) the impacts of various TGP-induced changes on specific SEE sectors; iii) the detailed TGP water flow paths as induced by the final demand of each province in YREB. It is discovered that the TGP has the greatest impact on agriculture. Although the construction of the TGP submerged a large amount of land and adversely affected agriculture in the reservoir area, its effective flood control function can reduce flooding in the “land of fish and rice” and create favorable conditions for agricultural development. The connection between the TGP and the inner provinces of YREB is mainly realized by the hydropower generation sector. For the economic benefits of the TGP, the economic benefits of the investment of various sectors of the TGP are diversified, which should be adjusted according to the government's objectives. For the ecological effects of the TGP, the interaction of sectors (especially the irrigation service sector and hydropower generation sector on consumption side, and the hydropower generation sector and water supply sector on production side) should be valued and a sense of joint management for the relevant sectors of TGP should be established.
Mengyu Zhai; Guohe Huang; Jianyong Li; Xiaojie Pan; Shuai Su. Development of a distributive Three Gorges Project input-output model to investigate the disaggregated sectoral effects of Three Gorges Project. Science of The Total Environment 2021, 797, 148817 .
AMA StyleMengyu Zhai, Guohe Huang, Jianyong Li, Xiaojie Pan, Shuai Su. Development of a distributive Three Gorges Project input-output model to investigate the disaggregated sectoral effects of Three Gorges Project. Science of The Total Environment. 2021; 797 ():148817.
Chicago/Turabian StyleMengyu Zhai; Guohe Huang; Jianyong Li; Xiaojie Pan; Shuai Su. 2021. "Development of a distributive Three Gorges Project input-output model to investigate the disaggregated sectoral effects of Three Gorges Project." Science of The Total Environment 797, no. : 148817.
Assessing the impacts of climate change on hydrologic regimes through hydrologic modeling is challenged by data uncertainty, predictor-selection uncertainty, and model uncertainty as well as their interrelationships. In this study, a Multi-level factorial ensemble data-driven hydrological model (MFEDHM) is developed to quantify the interactive, individual, and integrative impacts of multiple boundary conditions (e.g., climate conditions) on hydrological processes, and revealing the spatial heterogeneity of these impacts under various uncertainties and non-predictor impacts. In the MFEDHM, multi-level factorial analysis is integrated with ensemble prediction (i.e., Bayesian Model Averaging) and data-driven hydrological model. The MFEDHM is applied to quantitatively analyze the rainfall-runoff relationships of 16 catchments over China. Results reveal that the multilevel factorial analysis can accurately reveal both individual and interactive impacts of climate variables on hydrologic processes, and the impacts of non-climatic factors. As the most important finding of this study, climate-change impacts on hydrology show significant spatial heterogeneities over China. For instance, contemporaneous climatic conditions dominate (57%-64%) runoff changes and variations in Southern China, while precedent climate conditions pose significant impacts (20%-67%) on runoffs in Northern China; the overall influence of non-predictor factors (anthropogenic) on runoffs may decrease by 0.07% for the catchment-area increment of 10000 km2 and ranges from 4% to 27% over China. The development of the MFEDHM can enhance the reliability of ensemble hydrologic prediction, and provide scientific support for climate-change impacts assessment and adaptation under complexities.
Feng Wang; Guohe Huang; Guanhui Cheng; Yongping Li. Multi-level factorial analysis for ensemble data-driven hydrological prediction. Advances in Water Resources 2021, 153, 103948 .
AMA StyleFeng Wang, Guohe Huang, Guanhui Cheng, Yongping Li. Multi-level factorial analysis for ensemble data-driven hydrological prediction. Advances in Water Resources. 2021; 153 ():103948.
Chicago/Turabian StyleFeng Wang; Guohe Huang; Guanhui Cheng; Yongping Li. 2021. "Multi-level factorial analysis for ensemble data-driven hydrological prediction." Advances in Water Resources 153, no. : 103948.
Facing the conflicts of climate change, energy consumption, carbon emission, and economic development, it is essential to investigate the impacts of the carbon tax policy implemented in specific regions. A CGE-based multi-dimensional carbon policy (CMDCP) model is developed to i) explore the inter-provincial interdependences by interfering with the economic policies of a single province, and ii) quantify interactive relationships among various components including climate, energy, carbon economy and tax. Integrated approach of computable general equilibrium model and input-output analysis is applied to a series of segmented carbon tax schemes for Guangdong IC modeling and China IE modeling. It is found that when the carbon tax rate is 100 yuan/ton, the GDP of Guangdong will fall by less than 0.5% under three scenario types. At the same time, they could bring 1.3, 1.2 and 1.6 million tons of emission reductions. Levying the carbon tax based on the difference in carbon emission volume is most beneficial for emission intensity reduction. For China, the impact of the segmented carbon tax in specific province has a slight impact on the entire supply chain emissions. It is suggested that a carbon tax of 10–40 yuan/ton could be adopted by Guangdong. Moreover, Guangdong could consider implementing the stepped carbon tax for it can effectively avoid the lack of flexibility of traditional carbon tax policy.
Mengyu Zhai; Guohe Huang; Lirong Liu; Zhengquan Guo; Shuai Su. Segmented carbon tax may significantly affect the regional and national economy and environment-a CGE-based analysis for Guangdong Province. Energy 2021, 231, 120958 .
AMA StyleMengyu Zhai, Guohe Huang, Lirong Liu, Zhengquan Guo, Shuai Su. Segmented carbon tax may significantly affect the regional and national economy and environment-a CGE-based analysis for Guangdong Province. Energy. 2021; 231 ():120958.
Chicago/Turabian StyleMengyu Zhai; Guohe Huang; Lirong Liu; Zhengquan Guo; Shuai Su. 2021. "Segmented carbon tax may significantly affect the regional and national economy and environment-a CGE-based analysis for Guangdong Province." Energy 231, no. : 120958.
Surface functional groups and the resultant changes in adsorption performance can be changed by long-term effects on pyrogenic organic matters with TBBPA, causing impacts to native vegetations.
Jian Shen; Guohe Huang; ChunJiang An; Yao Yao; Peng Zhang; Xiaying Xin; Scott Rosendahl. Long-term effects of TBBPA-contaminated pyrogenic organic matter under abiotic aging: insights on immobilization capacity, surface functionality correlation, and phytotoxicity to Thinopyrum ponticum. Environmental Science: Nano 2021, 1 .
AMA StyleJian Shen, Guohe Huang, ChunJiang An, Yao Yao, Peng Zhang, Xiaying Xin, Scott Rosendahl. Long-term effects of TBBPA-contaminated pyrogenic organic matter under abiotic aging: insights on immobilization capacity, surface functionality correlation, and phytotoxicity to Thinopyrum ponticum. Environmental Science: Nano. 2021; ():1.
Chicago/Turabian StyleJian Shen; Guohe Huang; ChunJiang An; Yao Yao; Peng Zhang; Xiaying Xin; Scott Rosendahl. 2021. "Long-term effects of TBBPA-contaminated pyrogenic organic matter under abiotic aging: insights on immobilization capacity, surface functionality correlation, and phytotoxicity to Thinopyrum ponticum." Environmental Science: Nano , no. : 1.
Ceramic filters are a point-of-use (POU) technology applied for water purification in developing regions. Nano-CeO2 modified ceramic filter water purifier (CeO2–CFP) is designed to provide clean potable water and address drinking water safety issues in remote areas. To assess the impact of the entire life cycle of CeO2–CFP on the water environment, a life cycle assessment (LCA)-based water footprint framework was established. The context of remote areas was used to exemplify the calculation of the developed model under different technical scenarios. The production of CeO2–CFP in the high-tech scenario exhibited excellent environmental performance and water resource cost-effectiveness was found to be only 1.59. Raw materials (71.41%) and staff consumption (82.54%) represented the largest share of water footprint in the high-tech and low-tech scenarios, respectively. Sensitivity analysis was proceeded to identify the critical factors affecting the water footprint of CeO2–CFP system and the interactions of these significant factors were investigated. A results-based analysis was carried out in consideration of environmental, social, and economic aspects, and some recommendations for reducing water footprint of CeO2–CFP were formulated.
Xiaohan Yang; Guohe Huang; Peng Zhang; ChunJiang An; Yao Yao; Yongping Li; Siyuan Zhou. Life cycle-based water footprint analysis of ceramic filter for point-of-use water purification in remote areas. Science of The Total Environment 2021, 786, 147424 .
AMA StyleXiaohan Yang, Guohe Huang, Peng Zhang, ChunJiang An, Yao Yao, Yongping Li, Siyuan Zhou. Life cycle-based water footprint analysis of ceramic filter for point-of-use water purification in remote areas. Science of The Total Environment. 2021; 786 ():147424.
Chicago/Turabian StyleXiaohan Yang; Guohe Huang; Peng Zhang; ChunJiang An; Yao Yao; Yongping Li; Siyuan Zhou. 2021. "Life cycle-based water footprint analysis of ceramic filter for point-of-use water purification in remote areas." Science of The Total Environment 786, no. : 147424.
Selecting an appropriate wind farm location must be specific to a particular administrative region, which involves restrictions balance and trade-offs. Multi-criteria decision making (MCDM) and GIS are widely used in wind energy planning, but have failed to achieve the selection of an optimal location and make it difficult to establish a set of independent factors. Fuzzy measurement is an effective method to evaluate intermediate synthesis and calculates the factor weight through fuzzy integrals. In this paper, optimal wind farm location is analyzed through coupling Grid GIS technique with λ fuzzy measure. Dalian City is selected as the study area for proving the feasibility of the proposed method. Typography, meteorological, transmission facilities, biological passage, and infrastructure are taken into the index system. All the indexes are specialized into victor grid cells which are taken as the base wind farm location alternative unit. The results indicate that the Grid GIS based λ fuzzy measure and Choquet fuzzy integral method could effectively deal with the special optimization problem and reflect optimal wind farm locations.
Liang Cui; Ye Xu; Ling Xu; Guohe Huang. Wind Farm Location Special Optimization Based on Grid GIS and Choquet Fuzzy Integral Method in Dalian City, China. Energies 2021, 14, 2454 .
AMA StyleLiang Cui, Ye Xu, Ling Xu, Guohe Huang. Wind Farm Location Special Optimization Based on Grid GIS and Choquet Fuzzy Integral Method in Dalian City, China. Energies. 2021; 14 (9):2454.
Chicago/Turabian StyleLiang Cui; Ye Xu; Ling Xu; Guohe Huang. 2021. "Wind Farm Location Special Optimization Based on Grid GIS and Choquet Fuzzy Integral Method in Dalian City, China." Energies 14, no. 9: 2454.
In this study, a C-vine copula-based quantile regression (CVQR) model is proposed for forecasting monthly streamflow. The CVQR model integrates techniques for vine copulas and quantile regression into a framework that can effectively establish relationships between the multidimensional response-independent variables as well as capture the upper tail or asymmetric dependence (i.e., upper extreme values). The CVQR model is applied to the Xiangxi River basin that is located in the Three Gorges Reservoir area in China for monthly streamflow forecasting. Multiple linear regression (MLR) and artificial neural network (ANN) are also compared to illustrate the applicability of CVQR. The results show that the CVQR model performs best in the calibration period for monthly streamflow prediction. The results also indicate that MLR has the worst effects in extreme quantile (flood events) and confidence interval predictions. Moreover, the performance of ANN tends to be overestimated in the process of peak prediction. Notably, CVQR is the most effective at capturing upper tail dependences among the hydrometeorological variables (i.e., floods). These findings are very helpful to decision-makers in hydrological process identification and water resource management practices.
Huawei Li; Guohe Huang; Yongping Li; Jie Sun; Pangpang Gao. A C-Vine Copula-Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China. Sustainability 2021, 13, 4627 .
AMA StyleHuawei Li, Guohe Huang, Yongping Li, Jie Sun, Pangpang Gao. A C-Vine Copula-Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China. Sustainability. 2021; 13 (9):4627.
Chicago/Turabian StyleHuawei Li; Guohe Huang; Yongping Li; Jie Sun; Pangpang Gao. 2021. "A C-Vine Copula-Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China." Sustainability 13, no. 9: 4627.
With climate change, understanding and assessing the impact of climate variations on non-stationary changes of streamflow is of importance in the hydrologic and atmospheric sciences. In this study, tempo-spatial and scaling effects in the impacts of 18 climate variations on nonstationary streamflow for 279 watersheds across Canada are explored. Specifically, the change point and trends of streamflow are examined through Pettitt's test and Mann-Kendall test. Spatial patterns of correlations between the climate variations and flow rates over Canada, especially their non-stationarity, are investigated at seasonal and decadal scales. The patterns are also quantified by seven spatial classification algorithms under method uncertainty. A series of findings regarding the impacts are revealed. For instance, nonstationary changes of streamflow exist for approximately 9% of Canadian watersheds and most of them are located in Prairie Provinces and the eastern coast. The Atlantic Multidecadal Oscillation, Niño 12, Niño 3, Niño 4, and Niño 3.4 pose significant impacts on Canadian streamflow, which vary with watersheds and seasons. The impacts are closely associated with human activities, e.g., significant impacts of climate variations on populated-area streamflow over Canada. Different climatic variations have different time-varying effects on streamflow. All watersheds have obvious clustering characteristics and four spatial patterns are identified, which is insensitive with classification algorithm. These findings are conducive to understanding the hydrological impacts of atmospheric circulation and enhancing the reliability of hydrological prediction.
F. Wang; G.H. Huang; G.H. Cheng; Y.P. Li. Impacts of climate variations on non-stationarity of streamflow over Canada. Environmental Research 2021, 197, 111118 .
AMA StyleF. Wang, G.H. Huang, G.H. Cheng, Y.P. Li. Impacts of climate variations on non-stationarity of streamflow over Canada. Environmental Research. 2021; 197 ():111118.
Chicago/Turabian StyleF. Wang; G.H. Huang; G.H. Cheng; Y.P. Li. 2021. "Impacts of climate variations on non-stationarity of streamflow over Canada." Environmental Research 197, no. : 111118.
In this study, a two-stage factorial-analysis-based input-output model (TFA-IOM) is advanced for virtual water assessment, which integrates techniques of factorial analysis (FA) and ecological network analysis (ENA) into input-output model (IOM). TFA-IOM can not only identify the crucial water transaction sectors and the associated integral utility relationships, but also investigate the individual and interactive effects of multiple factors on virtual water metabolic network (VWMN) through measuring water consumptions of different sectors. The developed TFA-IOM is applied to Kyrgyzstan in Central Asia to quantify its virtual water and identify its metabolic network, where agricultural and animal husbandry sectors are the main water consumers. Our major findings are: (i) Kyrgyzstan is a country relying on net virtual water import (reaching up to 3,242 × 106 m3); (ii) WHT (wheat) is the main virtual water supplier (with 349 × 106 m3 virtual water to others); (iii) WHT, VGF (vegetables, fruit, and nuts), PFB (plant-based fibers), CTL (bovine cattle) and RMK (raw milk) are the main sectors affecting the VWMN (e.g., utility relationship and integral virtual water recycling index). From a long-term and sustainable development point view, stimulating Kyrgyzstan’s domestic WHT and RMK productions and improving VGF, PFB and CTL water-use efficiencies can facilitate reducing net virtual water import and promoting the mutualism degree of the national VWMN.
H. Zhang; Y.P. Li; J. Sun; J. Liu; G.H. Huang; Y.K. Ding; X.J. Wu. A two-stage factorial-analysis-based input-output model for virtual-water quantification and metabolic-network identification in Kyrgyzstan. Journal of Cleaner Production 2021, 301, 126960 .
AMA StyleH. Zhang, Y.P. Li, J. Sun, J. Liu, G.H. Huang, Y.K. Ding, X.J. Wu. A two-stage factorial-analysis-based input-output model for virtual-water quantification and metabolic-network identification in Kyrgyzstan. Journal of Cleaner Production. 2021; 301 ():126960.
Chicago/Turabian StyleH. Zhang; Y.P. Li; J. Sun; J. Liu; G.H. Huang; Y.K. Ding; X.J. Wu. 2021. "A two-stage factorial-analysis-based input-output model for virtual-water quantification and metabolic-network identification in Kyrgyzstan." Journal of Cleaner Production 301, no. : 126960.
In this study, a multi-scenario factorial analysis and multi-regional input-output (MFA-MRIO) model is developed, which is capable of evaluating carbon dioxide (CO2) emission and simulating CO2 emission reduction path, as well as disclosing individual and interactive effects of multi-factor, multi-sector and multi-city for urban agglomeration. A case study of Jing-Jin-Ji region that is one of the most strategic core regions for China’s economic development is conducted to prove the applicability of the MFA-MRIO model. Multiple scenarios based on direct CO2 reduction and final demand mitigation on various industries are examined. The major findings are: (i) for the whole region in the future, metallurgical industry, electric heating industry, and transportation would be the main CO2 emission sectors; (ii) among all CO2 emission transfers, CO2 flow from Hebei to Beijing would be the highest, especially for metallurgical industry; (iii) for the whole region in the future, the annual growth rate of CO2 emission from the tertiary industry would be higher than that of the secondary industry; (iv) with a high GDP growth rate, loose direct CO2 reduction policy on sectors would also achieve effective CO2 mitigation; (v) with a high GDP growth rate, appropriate final demand reduction policy on high-carbon industries in Tianjin and Hebei would have a positive effect on carbon intensity reduction. These findings can provide desired decision support for CO2 mitigation of urban agglomeration within a multi-sector and multi-city context.
P.P. Wang; Y.P. Li; G.H. Huang; S.G. Wang; C. Suo; Y. Ma. A multi-scenario factorial analysis and multi-regional input-output model for analyzing CO2 emission reduction path in Jing-Jin-Ji region. Journal of Cleaner Production 2021, 300, 126782 .
AMA StyleP.P. Wang, Y.P. Li, G.H. Huang, S.G. Wang, C. Suo, Y. Ma. A multi-scenario factorial analysis and multi-regional input-output model for analyzing CO2 emission reduction path in Jing-Jin-Ji region. Journal of Cleaner Production. 2021; 300 ():126782.
Chicago/Turabian StyleP.P. Wang; Y.P. Li; G.H. Huang; S.G. Wang; C. Suo; Y. Ma. 2021. "A multi-scenario factorial analysis and multi-regional input-output model for analyzing CO2 emission reduction path in Jing-Jin-Ji region." Journal of Cleaner Production 300, no. : 126782.
Land-use and climate changes have impacts on hydrological processes for river basin. In this study, a multi-scenario ensemble streamflow forecast (MESF) method is developed for analyzing the streamflow variation under considering climate and land-use changes, through incorporating CA-Markov model, global climate model (GCM) and Soil and Water Assessment Tool (SWAT) model within a general framework. The advantages of MESF are as follows: (i) it can simultaneously assess the impacts of land-use and climate changes on streamflow; (ii) it can obtain the possible trend and the range of future streamflows through ensemble forecast under multiple scenarios; (iii) based on analysis of streamflow processes under extreme scenarios, it can examine the effects of key factors on streamflow. The MESF method is applied to the upper reaches of the Amu Darya River Basin in Central Asia. Totally 72 scenarios, under different land-use patterns, GCMs and Representative Concentration Pathways (RCPs), are analyzed. Ensemble forecast results reveal that (i) during 2021–2050, the average annual precipitation and the average annual temperature would both increase, but the mean annual streamflow would decrease; (ii) compared to the impact of land-use change, climate change has more obvious effects on the streamflow (with contribution of 78.8%–98.7%); (iii) among all factors of land-use change, glacier melting triggered by climate warming is the most prominent factor; (iv) the peak flow in one year would have a tendency to shift from summer to spring due to the rising temperature and the speeding up snow melt.
Z.P. Xu; Y.P. Li; G.H. Huang; S.G. Wang; Y.R. Liu. A multi-scenario ensemble streamflow forecast method for Amu Darya River Basin under considering climate and land-use changes. Journal of Hydrology 2021, 598, 126276 .
AMA StyleZ.P. Xu, Y.P. Li, G.H. Huang, S.G. Wang, Y.R. Liu. A multi-scenario ensemble streamflow forecast method for Amu Darya River Basin under considering climate and land-use changes. Journal of Hydrology. 2021; 598 ():126276.
Chicago/Turabian StyleZ.P. Xu; Y.P. Li; G.H. Huang; S.G. Wang; Y.R. Liu. 2021. "A multi-scenario ensemble streamflow forecast method for Amu Darya River Basin under considering climate and land-use changes." Journal of Hydrology 598, no. : 126276.
Water pollution accidents occur in drinking water sources may pollute water environment, destroy water ecological balance, and threaten the drinking water safety for residents. Based on water quality hydrodynamic mathematical equations and Digital Elevation Model, a new water pollution accident prediction program for drinking water sources in Three Gorges Reservoir under different hydrological conditions was developed. Moreover, this new program was firstly used to forecast and analyze flow field changes and concentration reduction of a hazardous chemical pollution accident in Huangjuedu drinking water source. By comparing the calculated values and the measured ones of February 10 to February 19, 2016, the maximum relative error was less than 10%, which indicated that the new program was feasible and reasonable. For pollution accident prediction in the drinking water source, the results showed that the maximum transverse velocity had stabilized as 2.070 m/s, 2.411 m/s and 2.717 m/s since 20 min after the accident responding to discharge flow of Xiangjiaba with 1800 m3/s, 2700 m3/s,and 3600 m3/s, respectively. The maximum vertical one was 1.372 m/s, 1.598 m/s and 1.872 m/s respectively since 20 min after accident under the corresponding conditions. Furthermore, the water quality of the drinking water source met the standard 23 min after the accident under the condition that the discharge flow of Xiangjiaba was 1800 m3/s. For conditions that discharge flows were 2700 m3/s and 3600 m3/s, the corresponding times to meet the standard were 21 min and 20 min after the accident, respectively. Thence, relatively large dispatch flows would help reduce the impacts of pollution accidents and accelerate water quality improvement of drinking water sources. This study provides a new and useful tool for predicting the diffusion and migration of pollutants in drinking water sources of Three Gorges Reservoir. And it also gives a practical reference for decision makers to forcast hazardous chemical pollution accidents in drinking water sources.
Aifeng Zhai; Baodeng Hou; Xiaowen Ding; Guohe Huang. Hazardous chemical accident prediction for drinking water sources in Three Gorges Reservoir. Journal of Cleaner Production 2021, 296, 126529 .
AMA StyleAifeng Zhai, Baodeng Hou, Xiaowen Ding, Guohe Huang. Hazardous chemical accident prediction for drinking water sources in Three Gorges Reservoir. Journal of Cleaner Production. 2021; 296 ():126529.
Chicago/Turabian StyleAifeng Zhai; Baodeng Hou; Xiaowen Ding; Guohe Huang. 2021. "Hazardous chemical accident prediction for drinking water sources in Three Gorges Reservoir." Journal of Cleaner Production 296, no. : 126529.
Chen Lu; Guohe Huang; Xiuquan Wang; Lirong Liu. Ensemble projection of city-level temperature extremes with stepwise cluster analysis. Climate Dynamics 2021, 56, 3313 -3335.
AMA StyleChen Lu, Guohe Huang, Xiuquan Wang, Lirong Liu. Ensemble projection of city-level temperature extremes with stepwise cluster analysis. Climate Dynamics. 2021; 56 (9-10):3313-3335.
Chicago/Turabian StyleChen Lu; Guohe Huang; Xiuquan Wang; Lirong Liu. 2021. "Ensemble projection of city-level temperature extremes with stepwise cluster analysis." Climate Dynamics 56, no. 9-10: 3313-3335.
Frequent drought events under climate change are endangering food security and sustainable agricultural development. Quantitative assessment of crop yield anomalies under drought conditions is essential for effective water resources management and adaptative drought risk mitigation strategies. In this study, a copula‐based bivariate probabilistic framework model is developed to assess the impacts of drought events on crop yield, where the correlation of crop yield anomalies and standardized precipitation evapotranspiration index (SPEI) at multi‐month lags are quantified. This model has advantages in quantifying the impacts of drought scales on crop yield through the joint probability of the corresponding series. Then, the model is applied to Xinjiang Province in northwestern China, an arid region with extensive agricultural activities. SPEI and yield anomalies of wheat, maize and cotton during 1984‐2015 are firstly identified, and then copula‐based framework is constructed to quantify probabilistic crop yield losses caused by drought events. Finally, crop water and irrigation requirements of the crops under different drought severity conditions are compared and analyzed. Our findings are (i) wheat and maize yield anomalies are vulnerable to long drought time‐scales, with exceedance probabilities of required yield anomalies returning to normal are 19.3% for wheat and 21.1% for maize, while cotton is susceptible to short drought time‐scales, with exceedance probability of 42.3% for drought recovery; (ii) response of wheat crop yield anomalies to the SPEI at 1‐, 3‐, 6‐ and 12‐month scales is the most sensitive, followed by maize, and cotton is the least; (iii) under extreme drought conditions, the probability of cotton yield reduction is lower than that of wheat and maize. Results may enhance our understanding of the impacts of drought scales on crops during the growing season, thus providing general guidance for rational irrigation management of crops and incentives for irrigation to mitigate drought risk, ultimately promoting sustainable agricultural development.
Huawei Li; Yongping Li; Guohe Huang; Jie Sun. Probabilistic assessment of crop yield loss to drought time‐scales in Xinjiang, China. International Journal of Climatology 2021, 41, 4077 -4094.
AMA StyleHuawei Li, Yongping Li, Guohe Huang, Jie Sun. Probabilistic assessment of crop yield loss to drought time‐scales in Xinjiang, China. International Journal of Climatology. 2021; 41 (8):4077-4094.
Chicago/Turabian StyleHuawei Li; Yongping Li; Guohe Huang; Jie Sun. 2021. "Probabilistic assessment of crop yield loss to drought time‐scales in Xinjiang, China." International Journal of Climatology 41, no. 8: 4077-4094.
This study introduced a clustered polynomial chaos expansion (CPCE) model to reveal random propagation and dynamic sensitivity of uncertainty parameters in hydrologic prediction. In the CPCE model, the random characteristics of the streamflow simulations resulting from parameter uncertainties are characterized through the polynomial chaos expansion (PCE) model based on the probabilistic collocation method. At the same time, a multivariate discrete non-functional relationship between PCE coefficients and hydrological model inputs is established based on stepwise cluster analysis. Therefore, compared with traditional PCE method, the developed CPCE model cannot only reflect uncertainty propagation in stochastic hydrological simulation, but also have the capability of random forecasting. Moreover, the dynamic sensitivities of model parameters are investigated through the multilevel factorial analyses. The developed approach was applied for streamflow forecasting for the Ruihe watershed, China. Results showed that with effective quantification for the random characteristics of hydrological processes, the CPCE model can directly predict runoff series and generate the associated probability distributions at different time periods. The dynamic sensitivity analysis indicates that the maximum soil moisture capacity within the catchment plays a key role in the accuracy of the low-flow forecasting, while the degree of spatial variability in soil moisture capacities has a remarkable impact on the accuracy of the high-flow forecasting in the studied watershed.
F. Wang; G.H. Huang; Y. Fan; Y.P. Li. Development of clustered polynomial chaos expansion model for stochastic hydrological prediction. Journal of Hydrology 2021, 595, 126022 .
AMA StyleF. Wang, G.H. Huang, Y. Fan, Y.P. Li. Development of clustered polynomial chaos expansion model for stochastic hydrological prediction. Journal of Hydrology. 2021; 595 ():126022.
Chicago/Turabian StyleF. Wang; G.H. Huang; Y. Fan; Y.P. Li. 2021. "Development of clustered polynomial chaos expansion model for stochastic hydrological prediction." Journal of Hydrology 595, no. : 126022.
Carbon emission reduction and carbon sink growth are essential to realize climate change mitigation. In this study, a fractional multi-stage simulation-optimization energy model is developed to tackle multiple uncertainties in regional energy systems and reflect system efficiency under conflicting objectives. Specially, simulation method is used for projecting energy demand and associated carbon emissions through integrating support-vector-regression, Monte Carlo simulation and stochastic impacts by regression on population, affluence, and technology tool into a general framework. Meanwhile, multiple complexities in terms of multi-region, multi-stage, and conflicting objectives are addressed through optimization techniques of fractional programming and multi-stage stochastic programming. To illustrate the applicability and superiority of the developed model, it is employed to the energy system and carbon emission management in the Pearl River Delta urban agglomeration. The major findings in the research include: electricity demand would grow by 26.6% from 2020 to 2035. The rate of renewable energy generation per unit cost under economic-environmental objectives would be 18.7% higher than that under single economic objective. Carbon emissions can be reduced under scenarios of climate change mitigation and socioeconomic development pathway. Meanwhile, forest carbon sink can be an effective alternative to mitigate carbon emissions.
R. Cao; G.H. Huang; J.P. Chen; Y.P. Li. A fractional multi-stage simulation-optimization energy model for carbon emission management of urban agglomeration. Science of The Total Environment 2021, 774, 144963 .
AMA StyleR. Cao, G.H. Huang, J.P. Chen, Y.P. Li. A fractional multi-stage simulation-optimization energy model for carbon emission management of urban agglomeration. Science of The Total Environment. 2021; 774 ():144963.
Chicago/Turabian StyleR. Cao; G.H. Huang; J.P. Chen; Y.P. Li. 2021. "A fractional multi-stage simulation-optimization energy model for carbon emission management of urban agglomeration." Science of The Total Environment 774, no. : 144963.